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title: Effectiveness of Individual Oral Health Care Training in Hospitalized Inpatients
in Geriatric Wards
authors:
- Stephanie Viebranz
- Marco Dederichs
- Anja Kwetkat
- Ina Manuela Schüler
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001549
doi: 10.3390/ijerph20054275
license: CC BY 4.0
---
# Effectiveness of Individual Oral Health Care Training in Hospitalized Inpatients in Geriatric Wards
## Abstract
Objective: To investigate the effectiveness of individual oral health care training (IndOHCT) on dental plaque removal and denture cleaning in hospitalized geriatric inpatients. Background: The literature reveals neglect of hygiene and oral care in people aged over 65 years, especially in persons in need of care. Hospitalized geriatric inpatients have poorer dental health than those non-hospitalized. Furthermore, the existing literature reporting on oral healthcare training interventions for hospitalized geriatric inpatients is scarce. Materials and Methods: This pre-post-controlled intervention study dichotomized 90 hospitalized geriatric inpatients into an intervention group (IG) and a control group (CG). Inpatients in the IG received IndOHCT. Oral hygiene was assessed using the Turesky modified Quigley–Hein index (TmQHI) and the denture hygiene index (DHI), at baseline (T0), at a second examination (T1a), and after supervised autonomous tooth brushing and denture cleaning (T1b). The influence of the Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Barthel Index (BI) scores on oral hygiene was examined. Results: There was no significant plaque reduction on teeth or dentures between T0 and T1a in either group. Between T1a and T1b, plaque reduction on the teeth was more effective in the IG than in the CG ($p \leq 0.001$). Inpatients with 1–9 remaining teeth removed significantly more dental plaque than inpatients with 10 or more remaining teeth. Inpatients with lower MMSE scores ($$p \leq 0.021$$) and higher age ($$p \leq 0.044$$) reached higher plaque reduction on dentures. Conclusions: IndOHCT improved oral and denture hygiene in geriatric inpatients by enabling them to clean their teeth and dentures more effectively.
## 1. Introduction
The increased proportion of older people in the general population is expected to increase the need for care in the coming centuries [1]. General diseases occur more frequently in older age, further increasing the need for medical and general care. Hospitalized geriatric inpatients carry a double burden of disease. In addition to age-related disabilities, they suffer from multimorbidity and acute illnesses [2].
Due to the prioritization of acute medical care during hospitalization, little focus is given to oral hygiene and oral health care in the hospital setting [3]. The majority of older people only have complaint-oriented dentist visits [4,5]. Due to the high hospitalization rate of older people, it is possible to reach seniors who otherwise might only visit the dentist for dental pain [6,7].
Oral health has a significant impact on general health [8]. It is associated with the prevalence of systemic diseases, such as renal insufficiency [9], diabetes [10], atherosclerosis [11], and cardiovascular diseases [12]. Furthermore, good oral hygiene can significantly reduce the risk of respiratory infections [13,14]. Poor oral health is also associated with higher frailty [15,16,17] and in-hospital mortality in inpatients [18].
In addition, malnutrition is one of the major problems in the treatment of geriatric inpatients. This is caused by impaired chewing function, which is associated with the consumption of soft and unhealthy food [19]. Oral health and hygiene should be assessed regularly [20] to detect oral problems early. Timely executed preventive and curative treatment reduces the risk of complications and general health deterioration. Individualized and intensive preventive oral care appears to be effective [21,22] in maintaining oral health.
The literature reveals neglect of hygiene and oral care in people aged over 65 years [23], especially in persons in need of care [5]. Since cognitive and fine motor skills decrease with older age [24], it becomes more difficult to perform effective oral hygiene autonomously. Effective oral hygiene is a challenge for inpatients and care providers in geriatric settings [25]. Social and medical changes, lack of dexterity, decreased salivary flow, poor oral hygiene, and wearing dentures are risk factors for increased susceptibility to dental caries [26].
Neglected oral hygiene significantly impairs quality of life. Several studies have described the association of dental caries, periodontal disease, and inadequate dentures with quality-of-life deterioration in older people [27,28,29,30].
Studies focusing on oral hygiene in geriatric inpatients are limited [31,32,33]. Hospitalized geriatric inpatients have poorer dental health than the non-hospitalized [34,35,36]. Hospitalized geriatric inpatients have lower numbers of natural teeth and are more frequently edentulous [31,37].
The aim of the present case-controlled intervention study was to assess the oral health and oral hygiene status of geriatric inpatients and to evaluate the efficacy of dental and denture cleaning training interventions on plaque reduction. Three hypotheses were tested: [1] individualized oral health care training improves the oral hygiene status of geriatric inpatients, [2] age, sex, and oral health status of geriatric inpatients have no influence on plaque removal on teeth and dentures, and [3] the geriatric assessment tests—Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Barthel Index (BI) have no influence on plaque reduction on teeth and dentures.
## 2. Materials and Methods
This case-controlled intervention study was conducted between 2012 and 2015 at the Department of Geriatric Medicine, Jena University Hospital, Germany. The study protocol was approved by the Ethics Committee of Jena University Hospital, Germany (3277-$\frac{10}{11}$), and the study was registered with the German Registry of Clinical Trials (DRKS 00004742).
## 2.1. Study Sample
The inclusion criteria for participation in this study were [1] hospitalization at the Clinic of Geriatric Medicine, Jena University Hospital, [2] a mini-mental state examination (MMSE) score ≥ 24 points, and [3] provision of informed consent. Inpatients aged ≥ 80 years are generally classified as geriatric. Inpatients older than 60 years are also hospitalized in geriatric wards if, in addition to multimorbidity, they also suffer from typical geriatric syndromes, such as immobility, fall-related injuries, incontinence, dementia, depression, and malnutrition. Sample size calculation was performed prior to inpatient enrolment. To achieve a statistical power of $80\%$ for proving the superiority of the intervention group (IG) compared with the control group (CG) in plaque reduction, 43 inpatients were required in each group, 32 of whom needed to be dentate.
Of the 2772 geriatric inpatients hospitalized during the study period, 1211 met the inclusion criteria. Of these eligible inpatients, only 165 consented to participate.
Thirty-three and forty-two inpatients dropped out in the IG and CG, respectively, due to death, transfer to other hospitals, withdrawal of consent, quarantine, deteriorated general condition, or lack of willingness to continue participating in the study. The assignment to both groups was sequential. First, inpatients were assigned to the IG until the required number of dentate and edentulous inpatients was reached. Inpatients satisfying the matching criteria for sex and dentition status (dentate/edentulous) were then enrolled in the CG. Finally, data from 45 inpatients, 32 of whom were dentate, were included in each group. All inpatients provided written informed consent to participate and for access to their medical records. The inpatient enrolment procedure is illustrated in Figure 1.
## 2.2. Oral Examinations
Prior to the clinical study, the dental examiners (I.S., B.B., and K.A.) were calibrated by theoretical and practical training. The calibration was performed on six inpatients in a setting comparable to that of the study. Inter-examiner reproducibility was assessed using kappa statistics (k): 0.7 for K.A. vs. B.B. and I.S. vs. K.A. and 0.9 for B.B. vs. I.S.
The first oral examination (baseline; T0) was performed shortly after inpatient admission to the hospital. The examination was conducted by a dentist and an assistant for documentation. The inpatients were examined while sitting or lying on the hospital bed. Complete dental status was assessed using an illuminated mirror (DenLite; Miltex Inc., York, PA, USA) and a standard probe after drying the teeth with cotton rolls. Dental caries was diagnosed according to the WHO standard [38] using the DMFT index. Gingival and periodontal health was evaluated using the periodontal screening index (PSI) [39], using a standard ball-end probe with a light probing force. The highest score for the entire dentition was recorded. Dental plaque was assessed by the Turesky modified Quigley–Hein index (TmQHI) [40] after staining all the teeth with Mira-2-Ton© solution (Miradent Hager and Werken GmbH & Co. KG, Duisburg, Germany). Dentures were only briefly rinsed under running water to remove food residue without brushing or plaque staining. Plaque distribution was evaluated using the denture hygiene index (DHI) [41].
The second (T1a) and third (T1b) assessment was performed in one session shortly before the inpatients’ dismissal from the hospital. The mean time between T0 and T1a was 12.4 ± 3.6 days. At T1a, TmQHI and DHI were recorded. The inpatients were examined for the presence of dental and denture plaque. Subsequently, the inpatient performed oral health care autonomously while the examiner exclusively observed the inpatient. No interventions or assistance were given. Afterwards, at T1b, the plaque parameters TmQHI and DHI were recorded again.
## 2.3. Intervention
After the first oral examination at T0, inpatients in the IG received a one-time individual oral health care training (IndOHCT) from the examiner. This individualized training included oral health information, motivational arguments, and practical exercises of tooth brushing and denture cleaning. The information provided included the inpatient’s oral health and oral hygiene status as well as the etiology and consequences of dental plaque, gingivitis, and caries. The important role of oral hygiene in maintaining good oral and general health, especially in older inpatients with multimorbidity, was explained. Practical training began with staining all teeth with a plaque revealing solution (Miradent Hager and Werken GmbH & Co. KG, Duisburg, Germany) and followed by observing the inpatients’ tooth brushing and denture cleaning as they usually do. Patients were encouraged to use their own tooth and denture cleaning utensils and were not provided with standardized oral hygiene products. The intention was to observe how patients were able to use their usual toothbrushing utensils to identify any motor impairments in their use and to provide guidance and individualized tips on how to use the products more effectively to improve plaque removal. Consequently, oral hygiene products were recommended individually according to the functional and cognitive status of the patient and their use was intensively trained [42]. Insufficiently cleaned tooth surfaces and segments of the dentures were detected collaboratively. For these surfaces and segments, alternative brushing techniques were explored, tested, and training given. Functional limitations of the inpatients, such as reduced manual dexterity, shoulder mobility, and visual acuity, were taken into consideration. If necessary, silicone handles were attached to toothbrushes to compensate for weak grip strength. Proper lighting and the use of personal glasses were recommended. Inpatients were trained to hold the dentures in a safe manner, clean the mucosal surface of the dentures, and direct brushing along the tooth axis. If considered helpful, the use of special tools, such as single tuft brushes, interdental brushes, triple-headed toothbrushes, or denture brushes was demonstrated, and training given. Practical training was continued until the inpatients mastered the techniques. This IndOHCT was conducted in a collaborative, patient-centered, and motivational manner to empower the inpatients to perform oral hygiene at their individual optimum levels.
Inpatients in the CG did not receive IndOHCT during the study period. To remedy this disadvantage, similar training was provided after the completion of data collection in this group.
The intervention lasted between 6.5 and 18 min. It lasted longer in subjects with residual dentition than in edentulous subjects and those without dentures.
During hospitalization, all the inpatients performed self-controlled oral hygiene. No oral hygiene-related instructions were given to the medical staff.
## 2.4. Collection of Geriatric Assessment Data
Geriatric assessment data were collected from medical records. As part of the daily routine, the MMSE by Folstein [43], Geriatric Depression Scale (GDS) by Yesavage [44,45], and Barthel Index (BI) [46] of all inpatients were assessed by the medical staff of the Department of Geriatric Medicine, with standardized and validated instruments.
The BI assesses basic everyday functions in inpatients with neurological or musculoskeletal impairment [46], evaluating the current degree of independence in self-sufficiency. A higher score indicates greater inpatient independence. In order to focus on competencies relevant to oral hygiene, Barthel’s criteria for personal hygiene (0–5 points), eating (0–5–10 points), and dressing (0–5–10 points) were included in further analyses. A maximum of 25 points was achievable, and fewer points indicated higher deficits.
The German version of the GDS was used to detect depressed mood and signs of depressive disorders. The scores range from 0 to 15. A score of 0–4 is within the normal range, 5–9 indicates mild depression, and ≥10 indicates moderate to severe depression [44]. We investigated whether depressive mood (GDS), cognitive impairment (MMSE), and limitations of independence (BI) influenced the ability to perform good oral hygiene.
## 2.5. Statistical Data Analysis
Data were collected using Excel 2016 (Microsoft Corporation, Redmond, WA, USA) and statistically analyzed using IBM SPSS Statistics for Windows, version 22.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were used to calculate numbers, mean values, and standard deviations. The t-test for the analysis of mean values and Fisher’s test for categorical data were applied. The effect size (ES; Cohen’s d) was calculated to demonstrate the effect of IndOHCT on plaque reduction. The ES was categorized as small (≤0.4), medium (0.5–0.7), or large (≥0.8) [47]. The level of significance was set at p ≤ 0.05, and no correction for multiple analyses was applied.
## 3.1. Study Sample at Baseline
Inpatient characteristics of the IG and CG at baseline are presented in Table 1. The inpatients were aged between 63 and 93 years (mean age, 81.1 ± 7.1 years). The inpatients were grouped by age according to the age categories of the population-based representative fifth German oral health study [5]. The subgroup of inpatients aged < 75 years comprised only $17.8\%$ of the study sample.
One fifth were male. In the IG, 13 inpatients had complete dentures in the mandible and maxilla. Another 6 inpatients were partially edentulous in the maxilla and had single dentures and 18 inpatients had removable partial dentures. In the mandible, another 3 inpatients had single dentures and 18 inpatients in the IG wore removable partial dentures.
There were also 13 edentulous inpatients in the CG. All wore dentures in the maxilla, but one of these patients lost his single denture of the mandible before hospitalization. Furthermore, another 6 inpatients had single dentures in the maxilla and 17 patients had removable partial dentures. In the mandible, another 19 patients of the CG had removable partial dentures. On average, all inpatients possessed 8.4 ± 8.4 and dentate inpatients 11.8 ± 7.6 remaining teeth.
There was no significant difference in oral health parameters between the groups at baseline, except the DHI and TmQHI.
The plaque values at baseline are shown in Table 2. At T0, the mean overall DHI in the IG (9.2 ± 5.7) was slightly lower than in the CG (11.1 ± 5.2); however, the difference was not statistically significant.
In younger inpatients, the DHI scores were significantly higher in the CG ($$p \leq 0.047$$) than in the IG. Inpatients with partial dentures in the maxilla ($$p \leq 0.007$$) had significantly higher DHI scores. At baseline, inpatients with mandibular partial dentures had significantly higher TmQHI scores in the IG ($$p \leq 0.031$$) than in the CG. No significant differences in DHI scores were detected between the complete denture wearers in either group.
## 3.2. Evaluation of oral Hygiene Status between the Beginning (T0) and End (T1a) of Hospitalization
Differences in plaque (TmQHI and DHI) reduction between T0 and T1a are presented in Table 3. No significant plaque reduction on the teeth or dentures was observed between T0 and T1a.
Age had a high ES on the DHI. Younger inpatients removed more plaque than older inpatients; however, the difference was not statistically significant (ES = 1.3; $$p \leq 0.133$$).
Sex, number of natural teeth, and PSI score had no significant influence on plaque reduction on teeth and dentures.
The BI total score had no significant effect on the DHI or TmQHI scores. Nevertheless, inpatients with high scores in the reduced BI scores removed significantly more plaque on dentures than those with lower scores (ES = 0.8; $$p \leq 0.029$$).
GDS and MMSE scores had no influence on the DHI score. None of the geriatric assessment tests were significantly associated with the TmQHI score.
## 3.3. Evaluation of Oral Hygiene Status before and after Autonomous Tooth Brushing and Denture Cleaning (T1a-T1b)
Table 4 demonstrates the teeth and denture plaque differences between T1a and T1b. Regardless of whether they received IndOHCT, both groups had reduced plaque on the teeth and dentures.
Inpatients in the IG showed more effective plaque reduction than the controls. Plaque reduction on dentures did not reach statistical significance in the direct comparison between the two groups ($$p \leq 0.093$$). Regardless of group membership, inpatients aged ≥ 75 years reduced significantly ($$p \leq 0.044$$) more denture plaque than younger inpatients, with a high ES (0.7).
In women, plaque reduction on teeth was more effective in the IG than in the CG ($p \leq 0.001$). Plaque reduction also improved in males; however, there was no significant difference between the groups.
Inpatients in the IG with 1–9 natural teeth removed more plaque than those in the CG ($p \leq 0.001$) and those with 10–28 natural teeth (ES = 0.8; $$p \leq 0.026$$). The number of natural teeth had no influence on the DHI score.
The PSI score demonstrated a significant effect on plaque reduction on the teeth (ES = 0.9; $$p \leq 0.02$$). In the IG, inpatients with gingivitis and periodontitis removed more plaque from the teeth than periodontally healthy inpatients. However, the PSI score had no effect on denture hygiene.
In the IG, plaque removal was significantly ($p \leq 0.001$) more effective in inpatients with higher GDS scores than in those with lower GDS scores, with a medium ES (0.6). In contrast, the CG showed the opposite effect. Inpatients with higher GDS scores reduced plaque on the teeth less effectively. However, these findings were not statistically significant. The GDS score had no influence on the DHI score.
The MMSE score significantly influenced denture hygiene in the entire study sample between T1a and T1b, with a medium ES (−0.5; $$p \leq 0.021$$). Inpatients with higher MMSE scores had significantly fewer plaques than those with lower scores (ES = −0.5; $$p \leq 0.021$$). The MMSE score had no influence on the TmQHI score. Between T1a and T1b, the BI score had no influence on the TmQHI and DHI scores.
## 4. Discussion
Our findings confirm that geriatric inpatients have a poor oral health status [9,48,49,50]. Oral cleanliness is required for maintaining or improving oral health. The oral health and oral hygiene status of hospitalized older inpatients are often poor [9,51]. Individual oral health care training could be effective, especially in short-term outcomes [52]. Nevertheless, studies on the effect of training to improve oral and denture hygiene in hospitalized older inpatients, even over a short period of time, are scarce [53]. Gibney et al. demonstrated an improvement in oral hygiene with a seven-day intervention under the guidance of an oral health therapist or trained nurse. There was a significantly higher proportion of inpatients (35–$37\%$) whose oral cleanliness improved from unhealthy to healthy after the seven-day intervention than during the pre-intervention phase ($17\%$). The TmQHI of the inpatients in the IG of the present study also improved by $35.5\%$ between T1a and T1b, consistent with the findings of Gibney et al.
## 4.1. Evaluation of Oral Hygiene Status between the Beginning (T0) and end (T1a) of Hospitalization
Despite extensive IndOHCT in the IG at baseline, there were no significant differences between the IG and CG in plaque reduction on teeth and dentures. It can be speculated that inpatient self-motivation to maintain good oral hygiene was low. One possible reason for the stagnation of plaque scores between T0 and T1a could be that more focus was being placed on the acute inpatients’ general diseases, and not on oral hygiene. Consequently, dental and prosthetic care received little attention. Presumably, inpatients focus to increase their BI score by mobility training with the aim to become fit faster so that they can leave the hospital sooner. They invest their energy into mobility training by walking and climbing stairs [54]. Therefore, oral hygiene becomes less of a focus for the inpatients. Moreover, these efforts further reduce the motivation for oral hygiene.
Younger geriatric inpatients removed denture plaque more effectively than older inpatients but without statistical significance. At older ages, fine motor skills decrease, impairing practical competencies, such as oral hygiene, which was confirmed by Grönbeck Lindén et al. and Curreri et al. [ 55,56]. Elderly inpatients are at a risk of poor oral hygiene. Only approximately $33.4\%$ of older people aged over 85 years are still fully capable of performing sufficient oral hygiene [5].
Sex, number of natural teeth, PSI score, and geriatric assessment tests had no significant influence on the TmQHI score between T0 and T1a. Nevertheless, the findings revealed a possible relationship between geriatric assessment and the DHI score. Activities of daily living measured by the total BI score had no significant effect on the DHI or TmQHI scores. However, the reduced sub-scores revealed a significant and high effect. High scores on the eating, personal hygiene, and dressing criteria were useful in predicting high denture cleaning efficacy. These findings are consistent with those of other studies where high BI scores were associated with good dental and denture hygiene [57].
## 4.2. Evaluation of Oral Hygiene Status before and after Autonomous Tooth Brushing and Denture Cleaning (T1a-T1b)
Between T1a and T1b, geriatric inpatients in both groups effectively removed plaque on the teeth and dentures. This suggests that the mere presence of a quietly observing caregiver may lead to improved plaque removal. Supervision clearly places the focus at that moment on the performance of oral hygiene. The more effective plaque removal on natural teeth in the IG could be explained by the received IndOHCT. Inpatients were able to perform more effective cleaning techniques that they were taught using their personal toothbrushing products and for which training was given. Geriatric inpatients who received IndOHCT achieved increased plaque reduction, achieving a significantly lower TmQHI score. Thus, oral and denture hygiene is improvable through supervision and implementation of IndOHCT. These findings are consistent with those of other studies [21,58]. Nakre and Harikiran summarized the effectiveness of a wide range of oral education programs on plaque reduction. The programs were more effective in short-term studies. Almas et al. investigated plaque reduction in chronically ill inpatients who received oral hygiene instructions and were re-evaluated after a seven-day period. There was more than $47\%$ reduction in plaque scores. These findings are approximately consistent with those of the present study, which observed a $35.5\%$ plaque reduction on natural teeth and $58.5\%$ improvement in the DHI score in the IG.
Inpatients with 1–9 residual teeth benefited significantly more from IndOHCT, succeeding in removing more dental plaque than those with more residual teeth. Single or gap-bounded teeth are usually more difficult to clean than complete dentition, especially on approximal surfaces. A dentition with gaps requires the learning of new techniques whereas for an almost complete dentition, lifelong routine techniques can be maintained. Therefore, during IndOHCT, special attention was given to inpatients with reduced numbers of teeth to exercise individualized cleaning techniques.
Geriatric inpatients with healthy periodontal tissues or gingivitis also benefited more from IndOHCT than those with periodontitis. Presumably, inpatients with periodontitis would not have practiced effective oral hygiene for a long time. They were invited to learn fundamentally new techniques that could be perceived as very difficult. In addition, pain, loose teeth, and bleeding may have hampered the brushing process. In contrast, inpatients who already had good oral hygiene only had to change their habits slightly. Often, only small changes in brushing techniques were sufficient to improve plaque reduction.
The MMSE score demonstrated a medium effect on denture hygiene. Inpatients with lower MMSE scores removed more plaque on dentures than did those with higher MMSE scores. They almost halved the DHI scores of the baseline examinations. Due to the very limited inclusion criteria, which excluded inpatients with an MMSE score < 24 from the study, conclusions regarding the relationship between MMSE and plaque scores cannot be reliably drawn. Mental disorders are a risk indicator for poor oral health. Tooth decay, edentulism, and poor denture and oral hygiene are more prevalent in older adults with cognitive impairment than in those who are cognitively healthy [59].
Nevertheless, programs to improve oral health should also be implemented in hospitals. Many multimorbid individuals or older people requiring care take advantage of dental assistance irregularly and complaint-based [60]. As many studies, including the present study, show, older people in need of care sometimes have desolate dental status. Only $22.5\%$ of the elderly (aged ≥ 75 years) in Germany who are in need of care perform oral hygiene without limitations, while $42.6\%$ are no longer able to perform adequate oral hygiene [5]. Hospitalization may be the only chance for medical staff to routinely examine the oral cavity and detect oral health neglect early.
Comprehensive medical and dental preventive strategies for older people aiming to increase their quality of life should include individualized oral healthcare training. This study clearly revealed that the oral hygiene of geriatric inpatients can be improved by individualized supervised hands-on training on teeth and denture cleaning. Thus, both hypotheses could be partially confirmed: [1] First, the findings confirmed the effectiveness of IndOHCT in geriatric inpatients. IndOHCT improved the oral hygiene status of geriatric inpatients, and this is in line with the findings of other studies, showing that IndOHCT programs can be successful [61]. Nevertheless, the present study showed that the intervention alone did not improve oral hygiene. Only additional observation during tooth and denture care could lead to significant plaque reduction. Thus, supervision is another important element for improving oral hygiene in geriatric inpatients. [ 2] Second, age, sex, and the oral and geriatric health status of geriatric inpatients play a minor role in the older person’s capacity to remove plaque on teeth and dentures.
In comparison with the present study, an intervention study by Frenkel et al. also clearly demonstrated that oral health education of non-dental staff can improve the oral health of institutionalized elders [62]. Dental care awareness needs to be increased in geriatric wards. Literature reveals that preventing and early detection of oral disease can improve the general well-being and conserve financial resources [63]. Education of nursing staff is crucial for improving oral health in geriatric inpatients [22,64,65]. However, it is not only the nursing staff that should be better trained in oral hygiene. Supervision in performing oral hygiene can also be performed by untrained staff and relatives, which could be a cost-effective possibility. Therefore, they should be integrated into intervention programs to ensure improved oral hygiene, even after hospitalization [66]. In addition, there is often a lack of oral hygiene products in geriatric wards or they are from low quality, which can hinder the execution of IndOHCT by nursing stuff. Further research is necessary to find strategies that will address the problem on a population level and that would be cost-effective, realistic, and sustainable.
## 4.3. Strengths and Limitations of the Study
The present study contributes to the evidence that IndOHCT is effective for enhancing tooth and denture brushing skills in geriatric inpatients during hospitalization. Furthermore, the narrow selection of geriatric inpatients with precise group assignment according to dentition status, wearing of prostheses, and sex reduced selection bias and fostered good comparability between groups. Bias due to various general diseases and associated general conditions could not be avoided. Furthermore, caries experience in the study sample was consistent with the national average (older patients in need of care, 25.4 vs. 24.6 DMFT) [5]. Nevertheless, comparability with the fifth German oral health study must be considered with caution, since the present study also included younger elders and those without care needs. Only $28.9\%$ of the inpatients required daily care; however, all had multimorbidity and acute medical conditions. In contrast to the fifth German oral health study, the geriatric inpatients in this study had a lower prevalence of edentulism ($28.9\%$ vs. $53.7\%$) and had more natural teeth on average (8.4 vs. 5.9). Moreover, periodontal disease was less common in the present study ($57.8\%$ vs. $90\%$).
It is well known that learning new skills can be inhibited by existing habits [67]. Strategies to enhance behavior changes in oral health should therefore be based on behavioral theory [68]. In this study, the InOHCT empowered the patients to enhance their tooth-brushing skills by intensive hands-on training in individual denture and toothbrushing techniques and theoretical face-to-face instruction on the importance of good oral hygiene.
This study had some limitations. First, it was conducted in a hospital setting. An examination at the inpatient’s bed does not provide comfort or the ability to position the inpatient to sufficiently inspect the dorsal areas of the oral cavity. Plaque might have been overlooked in areas that were difficult to inspect. In addition, the study was conducted exclusively at a single site; therefore, generalization and transfer to other sites is limited.
Second, only inpatients with an MMSE score of at least 24 points were included in the study. Therefore, the weak relationship between cognitive impairment and oral health might have selection bias. To investigate the ability to learn effective oral hygiene, inpatients with moderate and severe cognitive impairment should be included in further investigations.
Third, the influence of medication was not considered, which could lead to dry mouth and poorer plaque values [69].
Fourth, the study covered an average hospitalization period of 12.4 days; therefore, it is unclear whether the effect of IndOHCT is sustained after hospitalization. The literature reveals that short-time observations failed to have essential long-term effects [69]. Long-term studies would provide more reliable data regarding the effectiveness and sustainability of IndOHCT.
Therefore, due to the limited number of studies, further research on the effectiveness of tooth and denture cleaning training in geriatric wards is of particular interest, as well as long-term follow-up studies evaluating the sustained success of IndOHCT in geriatric inpatients.
## 5. Conclusions
Within the limitations of the present study, it was revealed that sex, age, and oral and geriatric health status had only a marginal influence on plaque removal on teeth and dentures of geriatric inpatients. However, it was demonstrated that IndOHCT has a positive effect on the competence of geriatric inpatients to remove plaque from teeth and dentures over a short-term observation period. Therefore, it is recommended to provide IndOHCT to geriatric inpatients during hospitalization.
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|
---
title: The Effect of a 12-Week Physical Functional Training-Based Physical Education
Intervention on Students’ Physical Fitness—A Quasi-Experimental Study
authors:
- Hailing Li
- Jadeera Phaik Geok Cheong
- Bahar Hussain
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001550
doi: 10.3390/ijerph20053926
license: CC BY 4.0
---
# The Effect of a 12-Week Physical Functional Training-Based Physical Education Intervention on Students’ Physical Fitness—A Quasi-Experimental Study
## Abstract
Children have received much attention in recent years, as many studies have shown that their physical fitness level is on the decline. Physical education, as a compulsory curriculum, can play a monumental role in contributing to students’ participation in physical activities and the enhancement of their physical fitness. The aim of this study is to examine the effects of a 12-week physical functional training intervention program on students’ physical fitness. A total of 180 primary school students (7–12 years) were invited to participate in this study, 90 of whom participated in physical education classes that included 10 min of physical functional training, and the remaining 90 were in a control group that participated in traditional physical education classes. After 12 weeks, the 50-m sprint ($F = 18.05$, $p \leq 0.001$, ηp2 = 0.09), timed rope skipping ($F = 27.87$, $p \leq 0.001$, ηp2 = 0.14), agility T-test ($F = 26.01$, $p \leq 0.001$, ηp2 = 0.13), and standing long jump ($F = 16.43$, $p \leq 0.001$, ηp2 = 0.08) were all improved, but not the sit-and-reach ($F = 0.70$, $$p \leq 0.405$$). The results showed that physical education incorporating physical functional training can effectively promote some parameters of students’ physical fitness, while at the same time providing a new and alternative idea for improving students’ physical fitness in physical education.
## 1. Introduction
The essential foundation for a person to achieve good health is established during childhood, and this groundwork will subsequently determine health in adulthood [1,2]. A powerful marker of health in children is physical fitness (PF) [3], and this indicator appears to be growing in significance in their everyday lives [4]. Not only has PF been reported to be essential for performing school activities and meeting home responsibilities, but it has also been proclaimed to provide adequate energy for sports and alternative leisure activities [5]. There is evidence that children with low PF levels are associated with negatively impacting health outcomes, such as obesity, heart disease, impaired skeletal health, and poor quality of life [6].
Physical education (PE) is regarded as an ideal intervention point for promoting students’ health and PF because it involved almost all children [7]. Dobbins et al. [ 8] noted that PE-based interventions could ensure $100\%$ of students were exposed to the intervention, which could benefit a large number of children across a wide range of demographic groups. Additionally, Errisuriz et al. [ 9] suggested that even minor PE modifications could improve fitness, and that the key was to discover a PE-based intervention that could be executed successfully. However, some studies have highlighted that there were barriers that prevented PE from playing a vital role in promoting students’ physical health and fitness, such as the scope, quantity, and quality of PE classes [10,11,12,13,14]. Ji and Li [15] also pointed out that PE in China had become a “safety class”, “discipline class”, and a “military class” which overemphasized the uniformity of movements and in which students often did not even sweat throughout the duration of the class. Such PE would not benefit public health and could make the students’ physique even worse [16]. In response to the shortcomings of traditional PE classes in China, a physical education and health curriculum model was proposed in 2015, which emphasized that each PE class must include 10 min of fitness training using diversified, enjoyable, and compensatory methods and means [17]. This model was mainly aimed at traditional PE classes that did not have a specific time allocated for PF exercises [16]. Many types of training methods have been suggested to improve PF such as school-based, high-intensity interval training [18], integrated neuromuscular exercise [19], game-based training [20], and sports training [21]. Moreover, functional training (FT) had also been advocated as a method to improve PF [22,23,24,25]. FT is a training concept and method system that focused on the basic posture and movement patterns, integrated various qualities to optimize the most basic movement abilities of the human body, and systematically optimized the links such as movement pattern, spinal strength, kinetic chain, recovery, and regeneration, to improve athletic ability [26].
FT is a relatively novel form of fitness [27], which originated in sports medicine, then was used in the coaching of sports, and was finally adopted in gymnasiums [28]. Nowadays, FT has become a fitness hot topic, ranking among the top 20 worldwide fitness trends based on the American Society of Sports Medicine (ACSM) global fitness trend survey since 2007 [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43]. One of the reasons for its popularity is due to its health benefits; FT was designed to enhance the ability of exercisers to meet the demands of performing a wide range of activities of daily living at home, work, or play without undue risk of injury or fatigue [44]. Another reason was related to the performance benefit, as Boyle [45] noted that FT could help train speed, strength, and power for improved performance. Furthermore, FT required little space, little equipment, and little time, adding to its popularity [46]. In 2011, China introduced FT when preparing for the London Olympics [47]. To highlight the importance of FT in sports and distinguish it from medical institutions’ FT, the word “physical” was added before “functional training”, and physical functional training (PFT) became a widely used term to replace FT in China [48]. The PFT included pillar preparation, movement preparation, plyometrics, movement skills, strength and power, energy system development, and regeneration and recovery [26]. PFT had the characteristic of “separation and combination” in the application, so each PFT section could be designed and arranged flexibly, based on different stages of training and tasks, as needed [49].
With the deepening research on PFT in sports [25,50], more researchers began to transplant PFT to school PE. Through a systematic review of the research on PFT from 2009 to 2019, Kang, et al. [ 51] pointed out that researchers focused on PFT theoretical research from 2009–2012, applied research integrating PFT with PE from 2012–2014, and after 2014—with the enrichment and depth of PFT research topics—researchers focused on the application of PFT in school PE to improve students’ PF. However, these studies mainly involved teenagers and college students, with less attention on children [24,51]. Therefore, this research aimed to integrate PFT, an innovative PF training method, into PE and evaluate the impact of a 12-week PFT-based PE intervention on primary school students’ PF. The PFT intervention was designed to take up only 10 min of a regular PE lesson. It was hypothesized that the PF of the participants who underwent the PFT intervention would be improved after 12 weeks. Additionally, it was also hypothesized that the PF performance of the participants of the PFT group would be better than the participants of the control group at the end of the 12-week program.
## 2.1. Study Design
This study used a 12-week quasi-experimental design in which groups of participants were assigned to an intervention or control condition in a primary school in China. The intervention group participated in a 10-min PFT intervention program which was included in the PE class. The control group remained in the traditional PE class without the PFT intervention.
## 2.2. Participants
According to the PE and Health Curriculum Standards for Compulsory Education (2011 Edition) [52], the learning levels of primary school students were divided into three levels based on the characteristics of students’ psychosomatic development, which were first and second grades as level one, third and fourth grades as level two, and fifth and sixth grades as level three. Consequently, in this study, students from second grade, third grade, and sixth grade were selected to represent students from all three levels. Two classes from the selected grades were randomly chosen as the experimental class (EC) and control class (CC), respectively, with 30 students in each class. A total of 180 male and female students between the ages of 7 and 12 (8.97 ± 1.84 years) participated in the study.
All students read the participant information form, and their parents or guardian signed the informed consent form. This study was conducted according to the procedures approved by the University of Malaya Research Ethics Committee (UM.TNC2/UMREC—667, 19 November 2019).
## 2.3. Measurements
To evaluate the impact of PFT on students of different grades and levels, this study selected the mandatory PF indicators for all students based on the 2014 revised Chinese National Student Physical Fitness Standard (CNSPFS) battery [53] and were as follows: height and weight, 50-m sprint, sit-and-reach, and timed rope skipping. At the same time, two additional indicators of agility T-test and standing long jump were selected to evaluate agility and power, according to the PF test guidelines [54]. All measurements were taken before and after the 12-week intervention, in the same order.
## 2.3.1. Height and Weight Test
Participants’ height and weight were measured by using a portable instrument (GMCS-IV; Jianmin, Beijing, China) to reflect their anthropometric characteristics. Testing was performed with the subject standing on the bottom plate of the equipment barefoot, with the head upright, the torso naturally straight, the upper limbs naturally drooping, and the heels close together. The toes were 60 degrees apart, and two to three seconds later, the measurement result appeared on the LCD [55]. The unit of measurement for height was in meters (m) and weight in kilograms (kg).
## 2.3.2. 50-m Sprint Test
The 50-m straight racetrack, a starting flag, a whistle, and a stopwatch were used in this test, which was employed to assess speed. Before the test, the participants were in a ready position, standing with one foot in front of the other and the front foot behind the starting line. After the participants were prepared, the starter gave the instructions “set” then blew a whistle and waved the starting flag. The participants ran to the finish line as fast as possible while the finish line timer started timing, and the timekeeper stopped timing at the same time when the participant ran across the finish line. Each participant was allowed two trials. The best time was taken and recorded in seconds (s) to two decimal places.
## 2.3.3. Sit-and-Reach Test
The sit-and-reach test was carried out by a seat-forward flexion tester (GMCS-IV; Jianmin, Beijing, China) to assess flexibility. During the test, the participant sat on a flat surface with legs straight and flat against the test longitudinal plate, approximately 10~15 cm apart. The upper body was bent forward, with the palms down and hands side by side, reaching forward along the measuring line as far as possible. Participants took the test twice, and the best result was recorded in centimeters (cm) to one decimal point.
## 2.3.4. Timed Rope Skipping Test
The rope-skipping test was conducted by using a rope and a stopwatch to assess strength, muscle endurance, and coordination. During the test, participants were required to skip continuously for one minute with their feet together. The tester timed, counted, and recorded the number of times the rope was skipped.
## 2.3.5. Agility T-test
A stopwatch, measuring tape, and four cones were used in this test to assess agility. Figure 1 shows the layout for the agility T-test. The participant began at cone 1, the same starting position for each trial. On the go command, the participant ran and touched cone 2, then cone 3. After touching cone 3, the participant shuffled sideways and touched cone 4. Next, the student shuffled back, touched cone 2, then ran back to the end line. Timing started on the command and stopped as the participant passed the end line. Each participant had two chances to take the best score in seconds (s).
## 2.3.6. Standing Long Jump Test
The test was conducted by using a tape measure to assess power. During the test, with feet slightly apart, the participant stood behind a line drawn on the ground. A two-foot takeoff and landing were used, with forwarding force provided by swinging the arms and bending the knees. The participant attempted to jump as far as possible, landing on both feet without falling back. The test outcome was measured from the start line to the closest point of contact (back of the heel) after landing. Two jumps were allowed, and the best was taken in cm.
## 2.4. Intervention Program
The program included three stages, starting with two weeks of the basic stage, which was mainly used to learn the basic movement pattern, then moving on to five weeks of advanced stage Ⅰ, and another five weeks of advanced stage Ⅱ.
The basic stage focused on teaching the basic movement patterns to develop PF based on mastering basic movement patterns. Advanced stage Ⅰ comprised of PFT modules using the medicine ball, agility ladder, pad, or cone to develop the participants’ PF. Advanced stage Ⅱ was mainly based on the same PFT modules of stage Ⅰ but with an increase in the training load. In terms of arranging the training load, it was generally to overcome the self-weight and light load. The change of load from advanced stage Ⅰ to advanced stage Ⅱ was realized through the following forms: [1] the change of training route, from unidirectional to multidirectional change, and [2] the distance and repeat times. The exercise components and a detailed arrangement of the intervention are presented in Table 1.
## 2.5. Procedures
First, in this study, a team of research assistants comprised of three primary school teachers from the experimental school was trained in data collection and intervention implementation.
Then, the teachers organized the participants to perform the height and weight test, followed by the 50-m sprint, sit-and-reach, timed rope skipping, agility T-test, and standing long jump test for the baseline assessment. During the tests, PE teachers first put forward some safety considerations to the participants. After the introduction, they used 10 min to organize the students to warm up, including jogging and muscle stretching before taking the baseline tests. In the testing process, each student had two opportunities for each test, and the best score was recorded.
Next, participants were required to attend three PE sessions per week for 12 weeks. The EC took part in the PE class that was incorporated with 10 min PFT program while the CC participated in the traditional PE classes that had no mandatory requirements for PF training [16] and were mostly comprised of games activities (see Table 1 for example of games activities).
Finally, all participants were tested again by using the same format as the baseline.
## 2.6. Statistical Analysis
SPSS 25.0 software (IBM SPSS Statistics for Windows, Version 25.0. IBM Corp.: Armonk, NY, USA) was used to process and analyze the PF test results of children. The normality distribution of data was checked by using the Shapiro–Wilk test for all measurements. Based on the distribution results, the independent sample T-test (parametric) or the Mann–Whitney U test (nonparametric) was used to compare the test scores between the EC and CC prior to the start of the experiment. The paired sample T-test (parametric) or Wilcoxon signed-rank test (nonparametric) was used to compare the score changes between baseline and posttest, for the EC and CC, respectively. Cohen’s d was used to describe effect sizes for the parametric test according to the following conventions: small (0.20 to 0.49), medium (0.50 to 0.79), and large (0.80 and above) (Cohen, 1988). Pearson’s r was used to describe effect sizes for the nonparametric tests according to the following conventions: small (0.10 to 0.29), medium (0.30 to 0.49), and large (0.50 and over) [56,57].
Analysis of covariance (ANCOVA) was conducted to determine significant differences between the posttest scores of EC and CC. Height, weight, and baseline scores of each measurement variable were entered as covariates. Quade’s rank-transformed analysis of covariance (nonparametric ANCOVA) as an alternative method was used if the data did not meet the assumption for ANCOVA [58,59]. Effect sizes for statistically significant outcomes were reported as partial eta squared (ηP2), with small, medium, and large effect sizes classed as 0.01, 0.06, and 0.14, respectively [56].
## 3.1. Comparison of Baseline Characteristics
An overview of the anthropometric characteristics of participants is shown in Table 2. There were no significant differences between the EC and CC at baseline for all measures in all grades ($p \leq 0.05$) (Table 3).
## 3.2. Effect of Intervention
After 12 weeks of PE classes, within-group comparisons were made between participants in both the experimental and control classes at each grade level (see Table 4). For the second grade, the EC showed significant improvement in the 50-m sprint, timed jump rope, agility T-test, and standing long jump after the experiment ($p \leq 0.001$), whereas scores for the sit-and-reach ($$p \leq 0.187$$) were not significant. The CC showed significant improvements in the 50-m sprint, timed rope skipping, and standing long jump after the experiment ($p \leq 0.001$), whereas the scores for the sit-and-reach ($$p \leq 0.073$$) and agility T-test ($$p \leq 0.670$$) were not significant. In third grade, there was a significant increase in all indicators in both the EC and CC ($p \leq 0.001$). In the sixth grade, there was a significant increase in the posttest values compared to the baseline of all indicators in the EC ($p \leq 0.001$), whereas in the CC, there was a nonsignificant increase in the timed rope skipping ($$p \leq 0.483$$) and standing long jump ($$p \leq 0.171$$) and a significant increase in the other indicators ($p \leq 0.05$). Although the results varied by grade level, overall, participants in both EC and CC made significant improvements in PF scores after 12 weeks of PE classes ($p \leq 0.05$).
The results of the comparison between the experimental and control class groups are shown in Table 5. Overall, the differences in the postintervention indicators between the students in EC and CC were highly significant, except for the sit-and-reach ($$p \leq 0.405$$). The specific results for each grade were as follows. In the second grade, EC was significantly better than CC in the 50-m sprint, timed rope skipping, and agility T-test, but the differences in sit-and-reach ($$p \leq 0.680$$) and standing long jump ($$p \leq 0.079$$) were not statistically significant. In the third grade, the 50-m sprint, timed rope skipping, and agility T-test scores of EC were significantly better than CC, whereas the differences in sit-and-reach ($$p \leq 0.120$$) and standing long jump ($$p \leq 0.244$$) between the two groups were not statistically significant. In the sixth grade the 50-m sprint, timed rope skipping, and standing long jump scores of EC were significantly better than CC, whereas the differences in sit-and-reach ($$p \leq 0.980$$) and agility T-test ($$p \leq 0.222$$) indicators between the two groups were not statistically significant.
## 4. Discussion
The purpose of this study was to evaluate the impact of a 12-week PFT-based PE intervention on primary school students’ PF. It was hypothesized that the PF of the participants who underwent the PFT intervention would be improved after 12 weeks. In addition, it was hypothesized that the PF of the participants of the PFT group would be better than the participants of the control group at the end of the 12-week intervention.
When the baseline scores were compared with the post-test scores, the results revealed that the PF of the EC students who participated in the PFT intervention had improved after 12 weeks, in line with our hypothesis. At the same time, students of the CC had also significantly improved across time. It appeared that the traditional PE class, which comprised mostly of games activities, was able to improve students’ PF after 12 weeks, regardless of whether there was a 10-min PFT component included in the class or not. This is a positive finding for PE in schools—the current classes were somewhat beneficial to the students. This finding was supported by Cocca, et al. [ 60], Cocca, et al. [ 61], and Petrušič, et al. [ 62] who also found that PE classes, including games, could improve the PF of students.
When baseline data were entered as covariates, the results of this study showed that there was a significant difference in the scores of the EC over the CC in all PF variables except for the sit-and-reach test, which also supported our hypothesis that participants of the EC would display better PF performance compared to the CC. The results of the study suggested that PFT could provide a novel exercise method for PE modules to improve students’ PF.
In this study, the largest differences between the groups were in the 50-m sprint, which evaluated speed, and the timed rope skipping, which assessed muscle strength and coordination. The EC at each level was significantly better than the CC. This was consistent with previous studies showing that PFT could improve muscle strength and speed. Yildiz, Pinar, and Gelen [24] implemented an eight-week FT versus traditional training program in preteen tennis players (9.6 ± 0.7 years) and reported that FT was more effective than traditional training in both strength and speed. Tomljanović, Spasić, Gabrilo, Uljević, and Foretić [27] similarly proved that a five-week functional training program for males aged 23 to 25 could improve speed and strength performance. Limited literature is available to compare the combined effect of PFT on coordination. Nevertheless, Li et al. [ 63] pointed out that PFT emphasized the integration of nerve-muscle functions and strengthens the efficient control of nerves over muscles in multiple dimensions, all-around range, and speed in a wide range, which helped speed, agility, and coordination gain better performance.
Next, the agility T-test showed significant differences between the EC and CC at levels one and two. In the third level, although both the EC and CC improved over time, there was no significant difference between the two classes. The positive changes in measured agility might be related to enhanced lower-extremity reflexes and proprioception and improved postural control in subjects through 12 weeks of training [27]. Meanwhile, the insignificant results of the students in level three might be related to the students’ 50-m × 8 shuttle run practice, which also promoted the development of the CC students’ agility in the corresponding teaching and practice.
In addition, the standing long jump test, which evaluated power, showed no significant differences between the groups in levels one and two but revealed a significant difference in level three. This may be related to the motor coordination ability that affected explosive power [27]. Low-level students are not as good as high-level students in postural control and muscle coordination during movement practice. The stimulation generated during movement practice might not be enough to stimulate the neuromuscular system to burst intensity [64]. Therefore, students in the lower levels could not benefit from PFT until they reached a later age, at which point motor coordination was better developed.
Finally, there was no significant difference between EC and CC in all three levels of the sit-and-reach test for assessing flexibility. According to previous research [23,24,65], PFT interventions could significantly improve the flexibility of participants. It was possible that the inconsistency of the results with other studies could be because the PFT program of this study did not include dynamic stretching and static stretching exercises, which were often arranged in warm-up and cool-down modules of training programs [26,66]. Because this study mainly focused on the main model of the PE class, the stretching module was not included in the PFT program.
In summary, the highlight of this study was that primary school students’ PF, such as speed, coordination, strength, and agility, was superior after 10 min of PFT in each PE class, which was in line with the previous studies that had found that PFT could improve PF [22,45,67,68]. It was possible that PFT emphasized the neural involvement in the training process [63,69,70] to affect the entire neuromuscular system [69,71]. In addition, according to a previous study, PFT also strengthened the body’s stretch reflex, which increased the reflexivity of muscle activity through the rapid pulling of the muscle shuttle to promote muscle force and power output [26]. However, it was also found that students at the lower levels were less effective than those at the higher levels in terms of power generation, which could be related to the quality of the movement performed. PFT focused on the quality of the movement rather than the load and quantity of the movement [26]. Hence, in lower-level students who had weaker limb control, the quality of their movement could have been affected, and consequently their performance was worse than the upper-level students.
There are also limitations in this study. First, the participants were all primary school students, which had a limited cognitive level, the quality of the movement completion was affected to a certain extent in the process from understanding the movement to implementing it. Secondly, in the selection of movements in the program, some simple and easy-to-implement movements were selected, which reduced the intensity of the exercise to a certain extent. Finally, all the students came from one class at each level. This was to accommodate the timetable, as all of the PE lessons were not conducted for all of the students at the same time. As such, we chose the participants from one class in each level based on the available slot given by the PE teacher.
## 5. Conclusions
The study results identified that the 12-week PE effectively improved the PF level of students both in EC and CC. However, the PFT integrated into PE produced more positive effects on students’ PF than traditional PE, such as speed, agility, and coordination, which revealed that PFT could be an acceptable and effective type of exercise for school children to improve their PF. The targeted selection of PFT exercises which were designed to be incorporated into the existing PE modules could be adopted by PE teachers to develop students’ PF.
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|
---
title: 'Learning Designers as Expert Evaluators of Usability: Understanding Their
Potential Contribution to Improving the Universality of Interface Design for Health
Resources'
authors:
- Amanda Adams
- Lauren Miller-Lewis
- Jennifer Tieman
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001568
doi: 10.3390/ijerph20054608
license: CC BY 4.0
---
# Learning Designers as Expert Evaluators of Usability: Understanding Their Potential Contribution to Improving the Universality of Interface Design for Health Resources
## Abstract
User-based evaluation by end users is an essential step in designing useful interfaces. Inspection methods can offer an alternate approach when end-user recruitment is problematic. A Learning Designers’ usability scholarship could offer usability evaluation expertise adjunct to multidisciplinary teams in academic settings. The feasibility of Learning Designers as ‘expert evaluators’ is assessed within this study. Two groups, healthcare professionals and Learning Designers, applied a hybrid evaluation method to generate usability feedback from a palliative care toolkit prototype. Expert data were compared to end-user errors detected from usability testing. Interface errors were categorised, meta-aggregated and severity calculated. The analysis found that reviewers detected $$n = 333$$ errors, with $$n = 167$$ uniquely occurring within the interface. Learning Designers identified errors at greater frequencies ($60.66\%$ total interface errors, mean (M) = 28.86 per expert) than other evaluator groups (healthcare professionals $23.12\%$, $M = 19.25$ and end users $16.22\%$, $M = 9.0$). Patterns in severity and error types were also observed between reviewer groups. The findings suggest that Learning Designers are skilled in detecting interface errors, which benefits developers assessing usability when access to end users is limited. Whilst not offering rich narrative feedback generated by user-based evaluations, Learning Designers complement healthcare professionals’ content-specific knowledge as a ‘composite expert reviewer’ with the ability to generate meaningful feedback to shape digital health interfaces.
## 1. Introduction
For developers of online health resources, some inherent difficulties are identifying and recruiting representatives of the intended audience to participate in usability evaluations [1], compared to products for general or commercial public consumption. For information resources serving complex health-subject domains with complicating factors, including multidisciplinary team interventions [2], patients with multimorbidity or sensitive care areas [3], recruitment becomes increasingly exigent. Strategies are also required to overcome ethical, privacy, and communication barriers to accessing, identifying, and recruiting patients or carers within healthcare settings, services, and systems [4].
Palliative care is one such domain. Palliative care is provided to patients who have been diagnosed with a non-curable life-limiting condition, illness, or disease, as a family-centred care model supporting the quality of life of the dying person until death, and provides help for carers and families during the illness and bereavement [5,6]. Therefore, the need for online palliative care information transcends cultural and social boundaries across the socioeconomic divide, is not limited to gender or age, and can support geographically isolated communities where health services are limited. This diversity in user characteristics, backgrounds, and experience demands the application of user-based usability evaluation methods during the development period to generate feedback to modify the interface to ensure that all users can find and understand the information provided to assist with decision-making for loved ones at the end of their lives.
For developers applying a user-centred approach, accessing patients receiving palliative care or their carers could influence the likelihood of usability being evaluated. Involving patients and carers in the process is challenging on three fronts: [1] identifying potential volunteers from within the community [7], specialist palliative care services or from acute care settings [8]; [2] gatekeeping protecting participation due to their perceived ‘vulnerability’ by healthcare professionals [9,10,11]; and [3] availability to be involved due to time constraints [12] or caring commitments [7].
Demographically, carers are likely to be engaged with the online environment [13] and representative of a heterogenous participant group. Carers have diverse backgrounds, ethnicities, previous experiences, digital competency, and health literacy. User interface design for digital palliative care resources is required to support end users with wide-ranging literacy levels, knowledge, information needs, and technical abilities. Consequently, involvement in evaluations can support development teams in understanding the relationship between elements of their interface design and end-user interactions to optimise the end-user experience for all users regardless of their needs, abilities, or requirements.
Where representative carers or family members as end users may not be available to participate in evaluations [14], alternative methods could be deployed as a proxy for these user-based evaluation activities [15]. Access to available experts and the selection of appropriate expert-based usability evaluation methodologies (UEMs) depends on the setting, team structure and experience (or maturity) in developing online health information resources. For commercial entities with mature development structures, teams are highly experienced in user research, with usability experts having implicit product knowledge. Subject-matter experts are adjunct to the design process and offer insights into the intended audience’s lived experiences.
For digital health resources developed from research activities, multidisciplinary development teams within academic settings are likely to be non-specialists in designing and developing online or digital health products. Subject-matter experts and academic development teams will likely adopt a collaborative approach to supplement their deficiencies in the product development cycle through contracted partnerships with individuals offering expertise in areas including technical development, design, evaluation, or marketing and promotion. As content specialists, healthcare professionals and clinicians are vested in translating their research outcomes into a meaningful experience for their patients, carers and family members. However, the involvement of usability experts is unlikely due to a lack of funds to support this expertise, along with the recognition of the need to engage in formative assessment to optimise interface design for end users.
In higher education institutions, Learning Designers consult with teaching academics to guide development, incorporate learning pedagogy, and construct measures or instruments assessing the effectiveness of materials to scaffold learners’ knowledge [16]. In an evaluation setting, understanding features contributing to end-user acceptance and the functionality of the interface design enhances the competency to identify issues or errors that contribute to interface usability levels [17]. Learning Designers, therefore, could be a source of usability evaluation expertise available to academic multidisciplinary research teams in academic settings whose projects are underfunded or under pressure, and who are inexperienced in designing and developing digital health resources for patients, carers, and families.
## 1.1. Expert and Composite Usability Methods as Alternatives to User-Based Evaluations
In contexts where representative end users may not be available to participate in evaluations [14], alternative UEMs can be deployed as a proxy for user-based activities [15]. These inspection methods involve experts applying theoretical knowledge to explore the interface from the perspective of a surrogate end user [18]. Expert feedback generated is explored to inform the reiteration of the user interface instead of usability data generated from contextualised to real lived experiences or experiential learnings of the representative end user. For multidisciplinary teams, usability evaluations of user interfaces are most likely to be conducted as expert peer reviews rather than cognitive walkthroughs or heuristic evaluations. Access to double experts, evaluators with both usability and medical or health domain knowledge is limited, and upskilling existing healthcare professionals to be competent usability experts is impossible due to time constraints or limited interest.
As usability novices [19], healthcare professionals are typically involved in a hybrid inspection approach to generate feedback on the interface by undertaking content peer review (to ensure reliability, accuracy, and quality of information) [20], whilst providing additional narratives on perceived usability errors as a surrogate end user. Considerations of interface design elements, including information structure, visual aesthetics, functionality (navigation and interactivity) and understandability of content, are assessed from the healthcare professionals’ real-world experiences of the needs and requirements of their patients, carers, or families [21]. Whether healthcare professionals with a humanistic perspective to understand patients’ or carers’ interactions based on practise experience [20] are adequate to identify most usability problems impacting end users is pertinent.
## 1.2. Heuristic Evaluations, Cognitive Walkthroughs, Compared to Expert Review
Heuristic evaluation and cognitive walkthroughs are other inspection UEMs where experts detect potential usability issues during sessions. Heuristic evaluations require experts to assess the interface’s usability against a set of guidelines or principles [22,23]. Cognitive walkthroughs invite experts to undertake activities within the interface, to behave like a user relative to their cognitive model of the resource, informed by objectives or needs and knowledge [22,24]. Research indicates that development teams may need to consider whether end-user experience levels should influence the choice between heuristic evaluations and cognitive walkthroughs [25].
Expert peer review also considers end users’ knowledge and awareness of the subject domain and specific content-based errors detected by domain specialists based on the understanding and context of the objective and application of information provided within the resource [26]. All three methodologies require experts to have subject domain knowledge and an awareness of how end users will behave within the interface. Only the heuristic evaluation requires participant evaluators to be double experts who are either experienced or trained by resource developers in assessing usability and are specialists in content [22,27].
## 1.3. Expert UEM versus Gold Standard ‘Usability Testing’ Method
For all development teams, usability testing is considered the ‘gold standard’ [28], a method crucial to strategically reiterate the interface centred on the needs and technical abilities of the intended audience [29]. Usability testing is an empirical UEM assessing the efficiency, effectiveness, and satisfaction of interface interactions as a reflection of cognitive processes driving end-user interactive behaviour [30]. Feedback to inform reiteration of the user interface is generated from the end user’s ability to identify usability errors relating to an individual user’s characteristics, context, and use environment [30].
Pragmatically, the likelihood of multidisciplinary academic development teams conducting usability testing depends on funding, time available, or the availability or experience of project staff to recruit, conduct, interpret and report outcomes within constrained processes. Moreover, unlike commercial developers with a specific audience, the generalist nature of health resources forces additional complications upon developers to discern range and prioritise selection, accounting for a diverse audience’s knowledge, skills, and abilities.
In comparison, expert-based usability inspection methods can offer a high rate of return for a relatively small investment of time and money [25,31] in comparison [30] whilst outsourcing usability expertise to specialists. Expert inspectors identify between 30–$60\%$ of errors [32]; on average, $49\%$ of common errors are shared between methodologies. However, double experts do not have the ability to emulate errors associated with critical end-user behaviours [20], resulting in a high frequency of false positives identified, non-veritable errors for end users and missing errors, severely impacting end-user interactions with the resource [32,33].
## 1.4. Learning Designers as Double Composite Experts for Usability Evaluations
The difficulties experienced by multidisciplinary development teams underpinned by usability evaluation inexperience and practical constraints surrounding development processes could be offset by the involvement of an ‘in-house’ composite double expert. For example, accessing Learning Designers already employed within higher education settings could offer development teams the ability to examine interface flaws objectively, comparing content to domain knowledge to judge quality, reliability, and accuracy. Further offering feedback as a subjective assessment of the interface for technical errors associated with operational or functional aspects that decrease usability and impede end-user interactions. How this feedback compares to the quality or efficacy of end-user-derived usability feedback is unknown.
As the literature has not previously reported recruitment, participation, or outcomes from usability evaluations of digital health information resources undertaken by Learning Designers during development, this current study will explore the following research questions: RSQ1. How does usability feedback differ between Learning Designers and healthcare professionals when completing an expert review of a palliative care resource?
RSQ2. Is there a role in usability evaluations of digital health interfaces for Learning Designers: as a sole expert evaluator group, in combination with healthcare professionals, or a tripate with healthcare professionals and end users?
## 2.1. Study Description
Traditional usability practise requires developers to recruit three to five heuristic double experts [34] with domain content knowledge and experience or training in usability. Given the scarcity of experts in the health domain with usability expertise, recruiting representatives from both reviewer groups could balance the identification of both content-based and usability errors whilst countering any perceived weaknesses in each group’s ability to detect interface issues, especially in scenarios where end users are deficient in the process. Therefore, the primary objective of this study is to investigate the feasibility of involving subject-matter experts (healthcare professionals) and digital experts (Learning Designers) to explore their availability for recruitment and the appropriateness of their feedback to reiterate a palliative care resource interface through error identification. A novel hybrid approach to evaluation was utilised, combining expert peer review and the cognitive walkthrough evaluation process to generate feedback from the expert evaluators.
## 2.2. Prototype Development and Overall Evaluation Approach
Comprehensive usability evaluation was undertaken on an early prototype of the Australian Carer Toolkit for Advanced Disease (known here as the CarerHelp Toolkit or ‘the Toolkit’). The Australian Government Department of Health funded the Toolkit project. The CarerHelp *Toolkit is* an online resource designed to support family carers of relatives or friends with the advanced disease living at home within the community. This resource aims to increase family carers’ knowledge and confidence to support the provision of end-of-life caregiving at home through evidence-based information, educational activities to build skills or knowledge, and access to how-to guides in the form of vignettes, interactive activities, and videos.
Usability evaluation approaches from the Web Development Model for Healthcare Consumers (WDHMC) [20] were applied to the prototype. This tripate included user-based (usability testing), expert-based (expert peer review) and content-based evaluation methodologies deployed during the development phase. With the current study focusing on outcomes from expert peer review and errors identified by carers within the usability testing process, the primary objective is to explore the feasibility of involving Learning Designers in usability evaluations of digital health resources.
## 2.3. The Palliative Care Website Prototype
Usability evaluations were conducted on a prototype of the CarerHelp Toolkit; information was structured across two levels consisting of four sections: Carer Pathways, Carer Voice, Carer Library and About CarerHelp. User-based usability testing was undertaken on an earlier version of the prototype accessed by experts due to the timing of evaluation within the development cycle. These prototypes were similar, although the earlier version had fewer interactive features and limited visual representations within the interface compared to the later version. Screengrabs are provided in Figure 1 (Homepage) and Figure 2 (internal webpage of the prototype version) of the Toolkit prototype as evaluated by the expert evaluator groups.
The reiterated post-release version of the CarerHelp *Toolkit is* freely available online at the URL (accessed on 10 January 2023): https://www.carerhelp.com.au/.
This study received ethical approval from the Flinders University Social and Behavioural Research Committee (Project Number 8347).
## 2.4. Expert Review Methodology
Two groups of experts, healthcare professionals (HCP) working in palliative care as subject-matter experts and Learning Designers (LD) as practising designers of digital education (having interface design, usability awareness and interaction experience—described as ‘digital expertise’), were invited to participate as reviewers within this study. Suitability for the study was limited to professionals currently practising and there were no limitations placed on levels or years of experience. Instead, inclusion criteria focused on availability to be involved around regular working hours, access to a device with Internet connectivity, and the willingness to share feedback in written and verbal formats (using online conferencing software). Between 4 and 8 reviewers were sought for each review group to ensure reviewer diversity (areas of expertise and individual characteristics). As this was an exploratory study, a smaller sample for each group was considered acceptable, especially given the difficulties in identifying and recruiting representatives from both professions (palliative care clinicians and Learning Designers). Expert reviewers could not participate in the study due to a lack of flexibility in their work schedules rather than disinterest.
## 2.4.1. Evaluator Group—Palliative Care Healthcare Professionals
Local, state and national palliative care organisations/services were approached to identify potential HCPs who had previous experience or were currently supporting the palliative care needs of patients and their carers living within a community setting. Palliative care specialists, general practitioners, nurses, and allied health professionals were invited to participate in the usability sessions through their services. These included Southern Adelaide Palliative Service, Palliative Care Queensland and the Australian and New Zealand Society of Palliative Medicine. Four HCPs who were actively involved in palliative care practise consented to review the Toolkit. Participants included two directors of palliative care services (also providing care as a general practitioner and nurse), a social worker and nurse practitioner.
## 2.4.2. Evaluator Group—Digital Experts: Learning Designers (LDs)
The professional organisation for online designers (learning, educational and instructional) working within the private and higher education sector, the Australasian Society for Computers in Learning in Tertiary Education (ASCILTE), assisted by widely promoting the study to their membership. Seven LDs from across Australian higher education institutions self-nominated and consented to be an expert reviewer.
## 2.5. Expert Review Protocol
Before commencing the review process, participants provided informed consent after the researcher had explained the research protocol and perceived risks involved. Each expert was then asked for descriptions of their professional credentials (professional title, professional practise setting and post-qualification years of experience (working as HCP or LD) and to self-assess their level of technical ability using the Internet by responding to the following question: I am:An avoider of everything onlineA novice or learner or beginnerMostly confident—having intermediate skillsAn expert who is confident in finding and using online information Evaluators completed the review process in two stages; the first was a digital document providing a structure and guiding interaction with the prototype (refer to Supplementary File S1). Professionals were asked to comment and record their thoughts on content, navigation, interface features, interactive activities, or widgets, including what they determined necessary for the end user. All expert reviewers were invited to provide (as much or as little) feedback as they liked and were not limited to the guiding questions or statements within the feedback document. Although some activities embedded within the Toolkit were out of the scope of the review.
Once the review document was completed and returned to the researcher, participants undertook the second stage of the review process by remotely debriefing their findings during a 30 min online interview session and providing an opportunity to explain their written feedback. In addition, the functionality of the conference software [35] demonstrated visual issues critical to function, incorrect or non-sensical in the context of the content, information flow and navigation across and within pages of the Toolkit.
## 2.6. User-Based Evaluation Methodology
User-based feedback on issues and errors within the prototype was generated through formal usability testing methodology, with participants representing the intended audience of the CarerHelp Toolkit.
## Usability Testing Participants
Family or primary carers of patients with palliative care needs living at home within the community were invited to participate in the study. Carers were sought from the wider community and specialist palliative care services within the southern areas of Adelaide, South Australia. A cohort of active and bereaved carers (6–8 months post-death) was identified by the Network Facilitator at the Laurel Hospice Caregiver Network (Southern Palliative Care Service, Adelaide). The researcher contacted carers who were interested and eligible to participate, and the study protocol was explained. Perceived risks were clarified, and formal consent to participate in usability testing was provided. A sample size of six was calculated by applying the probabilistic model of problem discovery [36] to identify interface errors occurring $50\%$ of the time at a level of error discovery of $98\%$ [37,38].
All participants customised the device and peripherals, reflecting their natural Internet interaction environment experienced at home (for example, using desktop or mobile, mouse or trackpad and screen augmentation). All online interactions were documented and digitally recorded using conference software to ensure that all audio commentary, facial expressions, and accompanying cursor movements were captured for post-session analysis.
## 2.7. Usability Testing Session Protocol
The following section will not comprehensively describe the usability testing protocol or methodology. Instead, detailed explanations of scenario development, task descriptors, interface satisfaction assessment, and outcome performance measures are described in the formal usability report informing reiteration of the interface (refer to Supplementary File S2).
Before beginning the session, the researcher ensured that each carer was aware of the Toolkit content and any perceived risks and emphasised that the session could be stopped at any time if individuals were experiencing distress. After providing formal consent, carers completed questionnaires describing their online behaviours, self-rating their technical ability and level of health literacy.
Each of the six participants then completed eight scenario-based tasks within the CarerHelp Toolkit interface using the Concurrent Think Aloud technique, where individuals verbally describe their thoughts or feelings when completing each task. Participants completed an identical set of tasks during the sessions; each task had a completion limit of 3 min. Completion was when the target information was located within the allowed time. Failures were registered if the target was not found within time, if the incorrect target was identified or if the participant abandoned the task.
## 2.8. Data Analysis
Methods undertaken within this study follow traditional formative usability evaluation processes requiring small groups of evaluators to provide reiterative feedback to improve interface design. Therefore, it is acknowledged that this study is under-powered and unlikely to detect statistically significant differences between evaluator groups due to the small sample sizes. However, trends in descriptive usability error detection patterns and exploration of types or severity of usability errors can assist in understanding the potential roles LDs can play in optimising digital interface designs for palliative care resources (and health interfaces more widely).
## 2.8.1. Expert Evaluator Feedback
After deidentifying data, a qualitative meta-summary of content findings was generated from the written feedback document and other narratives from the debrief interviews from both reviewer groups. Quantitative logic was then applied to aggregate error types between participants, and this provided a process to assess error frequency and identify problems, missing resources or content, and opportunities or suggestions for interface improvements. Types of errors describe problems experienced by evaluators relating to specific functional aspects of user interaction at the user interface level.
Further analysis of content-specific errors identified within written information included frequency-based analysis of the types of content errors detected by reviewers by applying a modified coding schema to accommodate the interface’s online environment and technological aspects to error data as described in Table 1 (as compared to Sayoran’s original schema [39] for revision of written text).
## 2.8.2. Meta-Aggregation of Usability Errors across Review Groups
All types of errors from all three reviewer groups (USER (carers), HCP and LD) were collated into a single list, with a bottom-up approach to meta-aggregation adopted to collate the differences from summarising types of usability errors detected within the Toolkit interface across all evaluators.
Aggregation provided an opportunity to compare commonalities or differences in error types identified by specific reviewers, further providing the capacity to highlight interface problems discovered exclusively by a single reviewer group or perceived (shared) across more than one reviewer group. Error types were grouped into classes categorised by approaches, development considerations, content level or structural elements of the user interface. These are considered groups of errors relating to global aspects of user interface design.
Classes of usability error included:Content-specificDesign or content constructionInformation flowNavigationEmbedded resource or activityPedagogy or educational strategiesMinor typographical or grammatical errorsMajor typographical or grammatical issues The severity of interface errors is then assessed on three factors [40] influencing usability:Frequency of the occurrence within the interfaceImpact of the error (if it occurs) on users to overcomeThe persistence of the error within the interface continuously affects evaluator interactions In considering these factors, each error was assessed and, using Nielsen’s Severity Rating scale [40], received one of the following severity ratings: 0 = I do not agree that this is a usability problem at all
1 = Cosmetic problem only: need not be fixed unless extra time is available on the project 2 = Minor usability problem: fixing this should be given low priority 3 = Major usability problem: important to fix, so should be given high priority 4 = Usability catastrophe: imperative to fix this before a product can be released
In rating interface issues, errors persistently having a significant impact on evaluators received the highest severity rating.
## 3.1.1. HCP Demographics
Of the four HCPs recruited for the study, two were from Palliative Care Queensland and one from the Australian and New Zealand Society of Palliative Medicine and Southern Adelaide Palliative Care Service. The HCPs had a minimum of seven years specialising in palliative care (range 7–20 years, median = 12 years). Three of the four self-rated themselves as having expert technical skills, with one self-rated their ability as being of intermediate level. Characteristics of the HCPs are presented in Table 2.
## 3.1.2. LD Demographics
Seven Learning Designers were recruited in total, all members of the ASCILTE organisation. All participants were employed within the university sector and held positions in institutions within five different states of Australia. All LDs were self-assessed as experts in using technology whose combined experience spanned 24 years post-qualification (range 3–27 years, median 12 years). In addition, two participants were managing academic units; although they had extensive experience as educational technologists, the other five LDs actively practised the design of educational materials. Characteristics of participants working in higher education as professional designers of digital learning experiences working are summarised in Table 3.
## 3.2. RSQ1: Comparative Analysis of Usability Error Data from Expert Evaluators
The differences between feedback generated through expert review UEM by subject-matter (HCP) and LD with digital expertise were examined. The types, frequency and severity of usability errors detected by each evaluator group were examined to explore areas of strengths or weaknesses when detecting problems within the interface. Additionally, content-specific and types of qualitative descriptions of errors were compared between groups to supplement the analysis of usability problems detected by each set of evaluators.
## 3.2.1. Analysis of Frequency and Usability Error Types Detected by Expert Reviewers (LD versus HCP)
In total, both expert evaluator review groups identified 279 errors within the Toolkit prototype interface (all data are presented in Table A1—Appendix A). LDs found 202 ($72.40\%$) errors, with each designer detecting a mean (M) = 28.26 errors, compared with HCPs, who identified 77 ($27.60\%$) errors at an average of 19.25 errors per reviewer. It was acknowledged that due to the small numbers of participants, there was difficulty in ascertaining existing statistically significant differences between error identification and years of experience for each evaluator. However, there were trends in the types of errors identified, frequency and severity of errors which can be observed within the data collected.
For HCPs, years of experience did not reflect an increase in the number of errors detected; however, data suggested an inverse trend where newer HCPs were more adept at identifying more errors within the interface (MHCP 6–10 years = 22 errors, $57.1\%$ total: MHCP 11–15 years = 1 error, $22.1\%$: MHCP 16–20 years = 16 errors, $20.8\%$). Those HCPs who had been practising palliative care for 6–10 years identified the highest number of errors with a medium–low severity compared with other cohorts. In addition, $79.2\%$ of interface errors were detected by HCPs who were self-rated experts with technology compared to intermediate-skilled HCP reviewers ($20.8\%$).
For LDs, years of experience designing online positively influenced error detection frequency. Although LDs with greater than 16 years of experience identified an equivalent number of errors as those with less experience (104 errors, $51.49\%$ versus 98 errors, $48.51\%$), LDs with increased practical experience on average detected 52 errors compared with $M = 19.6$ errors for LD with less than 16 years designing experience. LDs also identified a higher frequency of errors rated highly severe than errors found by HCPs, with $71.4\%$ of the most severe errors detected by LDs with between 11–20 years of experience. Errors with the highest frequency were proportional to LD and HCP reviewer groups when calculated as a percentage of total errors identified. Specific content errors constituted over $50\%$ of total errors detected by HCP and $38.1\%$ for LD. The frequency of navigation issues was comparable between 14–$15\%$ of total errors per group, and LD identified a greater proportion of errors impacting information flow ($18.3\%$ total errors) than the HCP reviewer group ($11.7\%$ total errors).
## 3.2.2. Comparison of Content-Specific Errors Identified by HCP and LD Reviewers
Analysis of the qualitative feedback was undertaken by the primary researcher (AA) and categorised the content based on the modified Sayoran’s schema provided in Table 1. Categorisation of the feedback provided by the eleven expert reviewers identified a total of 120 content specific errors categorised into eight error types. Results are displayed in Figure 3. A detailed summary of feedback types and example descriptions are presented in Table A2—Appendix A).
Whilst LDs detected a greater frequency of errors than HCPs overall (77 errors, $64.2\%$ versus 43 errors, $35.8\%$ respectively), average errors per reviewer were similar across groups (MLD = 11.0, SD = 5.9 versus MHCP = 10.8, SD = 3.1). Types of content errors with the greatest frequency within the interface by experts explicitly referenced issues within embedded resources or learning activities ($18.3\%$, [5] in Table A2), for example: descriptions of errors requiring rephrasing with examples of revisions statements ($18.3\%$) such as: an explicit reference to problems within the interface ($16.7\%$), including: Or:
HCPs and LDs detected equivalent numbers of error descriptions with a provision of evaluation statements and assertions on the learnings from the text. LDs identified a greater frequency of errors describing grammatical or spelling errors (3 HCP:8 LD), were more likely to provide suggestions on applying strategies to content (4 HCP:9 LD) and were more forthcoming with the provision of alternate text through revision statements compared with healthcare professionals (1 HCP:21 LD). Unsurprisingly, HCPs identified specific issues or errors within the written content of the Toolkit webpages and were skilled at detecting content mistakes in-text to provide feedback based on statements of their knowledge of palliative care.
## 3.3. RSQ2: The Acuity of Expert Usability Error Data Was Compared to End-User Usability Error Data Generated from Formal Testing of the Prototype Undertaken with Primary Carers as Representatives from the End-User Group
Analysis examined the frequency and types of errors identified by each evaluator group. The role of LDs as evaluators in designing digital palliative care resources was explored through comparisons of LD usability data across evaluator groups, both as an independent reviewer group and in combination with the other evaluators. Shared (mutually inclusive) and exclusive errors were mapped to understand the influence that different evaluators have on usability data collected as a pragmatic reflection of the availability of these groups to participate in evaluations during the development process undertaken within academic development settings.
## Evaluator Group Comparisons between LDs, HCPs, and USERs (Carers)
Similarities and differences in the error type and frequency of detection by reviewer groups involved within the Toolkit usability evaluation approaches were explored by meta-aggregation of HCP, LD, and USER data to characterise error occurrences within the interface. Errors were categorised by reviewer, type, level of exclusivity or inclusivity and whether the errors are unique or co-existing across the reviewer group. Unique errors are discernible occurrences identified by single or multiple reviewers whose accretion decreases total interface error counts compared to the overall error number.
Seventeen reviewers identified a total of 333 errors that did not occur exclusively for any single reviewer group, and these are co-existing within the interface and identified by all three reviewer cohorts. Further analysis found 167 unique errors occurring once within the interface (error data summarised in Figure 4, data presented in Table A3—Appendix A).
The HCP reviewer group identified similar frequencies of co-existing and uniquely occurring errors ($23.12\%$ and $25.15\%$, respectively), whilst the USER group detected $10.78\%$ of unique errors, although only $16.22\%$ of the overall errors within the interface. LDs identified the greatest number of errors overall and unique errors at the highest frequency within the interface (202, $60.66\%$ and 107, $64.07\%$, respectively) compared to the other reviewer groups. LDs also detected higher frequency on average per reviewer in both inclusive (MLD = 28.86: MHCPs = 19.25: MUSER = 9.0 errors) and uniquely occurring issues (MLD = 15.29: MHCP = 10.0: MUSER = 3.0 errors). Differences in the rate of error identification between the USER and expert reviewers widened when combing error counts into a single EXPERT cohort (HCP + LD). The EXPERT group attributed 279 errors ($83.78\%$ of the total) and 133 uniquely occurring errors ($88.10\%$ total) at an average of 12.09 errors per expert.
Exclusive and mutually inclusive errors were identified across and between reviewer groups (data are presented in Table A4—Appendix A), and the total overall error count remained constant ($$n = 333$$). Further data consolidation into combinations of reviewer groups where each error instance is assigned a single identifying group decreased the overall unique error count from $$n = 167$$ to $$n = 143$.$ Similarly, the analysis found that LDs identified over $50\%$ of the total unique errors compared with HCPs ($16.08\%$) and $5.59\%$ for the USER review group. Consequently, review groups in combination with LDs were more likely to identify an increased proportion of unique errors compared to other HCP or USER group combinations (uniquely exclusive and mutually errors are mapped between and across evaluator groups in Figure 5, and Figure 6 displays overall frequency of errors identified).
This pattern is analogous to counts of overall co-existing errors. LD was also more likely to identify errors or issues with site or platform performance and accessibility for end users. Five distinct accessibility issues were detected by just the LD reviewers and the LD + USER group. Differences in error detection rates between groups were analysed using independent t-tests (homogeneity of variance as assessed by Levene’s test for equality of variances and where failed, t-test for equality of means was conducted using the Welch–Satterthwaite method), significance was indicated when $p \leq 0.05$ at $95\%$ confidence level. LDs identified, on average, a significantly greater number of overall errors within the interface than all other reviewer groups except for the HCP + LD review group: HCP (t(11.361) = −2.460, $$p \leq 0.031$$), USER (t(10.521) = 2.983, $$p \leq 0.031$$), HCP + USER (t(13.25) = 2.545, $$p \leq 0.024$$), LD + USER (t[20] = 2.144, $$p \leq 0.044$$) and HCP + LD + USER (t[20] = 2.747, $$p \leq 0.012$$).
LDs could also identify a significantly greater number of unique errors within the interface than all other reviewer groups: HCP (t(12.043) = −2.204, $$p \leq 0.048$$), USER (t(10.707) = 2.864, $$p \leq 0.016$$), HCP + LD (t(11.402) = 2.392, $$p \leq 0.035$$), HCP + USER (t(10.555) = 2.573, $$p \leq 0.01$$) and LD + USER (t(10.662) = 2.788, $$p \leq 0.018$$) and HCP + LD + USER (t(10.032) = 3.191, $$p \leq 0.01$$). Significant differences were also observed between the average number of overall errors detected by the HCP reviewer group compared to HCP + USER (t(10.55) = 2.573, $$p \leq 0.027$$) and HCP + LD + USER groups (t(10.309) = 2.712, $$p \leq 0.021$$). There was only a single error type that all three reviewer groups identified. An error relating to grammatical or spelling errors specific to content within the pages or activities appeared in fourteen instances across the interface and was mutually inclusive to all reviewers participating in the evaluation process.
## 4. Discussion
For digital health or medical information resource developers, there are inherent difficulties in undertaking inquiry-based usability evaluations that generate crucial expert or user-based feedback to inform interface reiterations. Factors such as identification, access, and availability of suitable experts or representative end-user recruitment influence evaluations’ likelihood of integration into typical development processes. The involvement of double experts who are equally knowledgeable of clinical subject matter and skilled in usability to evaluate digital health interfaces heuristically is appealing to development teams as it can alleviate the need for end users [41,42] to participate in the process. However, the ability to find and afford to engage these experts in development processes is limited in academic settings.
Access to LDs, a potentially rich source of technically skilled professionals with background knowledge of user-centred design and an understanding of usability evaluation approaches, would be advantageous, especially for inexperienced, under-resourced development teams. Universities engage LDs to work across interaction, visual and education design. Alignment between designing educational materials, Toolkit instructional components [43] and evaluation practise could ideally position LDs to assist in developing digital health products or resources in multidisciplinary settings. LDs understand features contributing to end-user acceptance and functionality of the interface design enhances the team’s ability to identify issues and resolve these that contribute to levels of usability, especially if end users are unavailable. Researchers are increasingly adopting usability evaluations within their typical designing approaches when creating within the digital environment, especially with the emergence of highly immersive technologies. These include virtual, augmented, or mixed reality systems and the gamification of educational resources or interactive platforms [44]. Involvement and the types of feedback generated by LDs involved in usability evaluations as experts in their own right are less clear within the literature.
In this current study, there was an opportunity to explore the potential value to multidisciplinary teams in having access to ‘composite heuristic experts’ to improve interface designs for diverse audiences by assessing a palliative care prototype. Inviting LDs to be involved in evaluations provided a unique opportunity to compare types, frequency and severity of errors identified between evaluator groups through a hybrid UEM process (modified cognitive walkthrough/expert review) to assess usability within an academic setting. The interface of the palliative care digital Toolkit was assessed by HCPs (as subject experts), LDs (interaction experts), and representatives end users (carers). The potential role in evaluation and suitability as a double heuristic expert was explored by analysing the types, frequency and severity of usability errors identified by LDs, then comparing these to those detected by subject-matter experts and users. Areas of overlap and exclusion were mapped to understand the composition of evaluators and end users required to be involved to optimally shape the interface design for a resource supporting a diverse audience. Firstly, it is important to highlight the continuing difficulties in recruiting end users and evaluators, including LDs, to participate in usability evaluations.
## 4.1. Ease of Recruitment of Experts and End Users
Difficulties in recruiting representative end users from health-specific domains were a primary driver in exploring the relative ease of identifying experts involved in the process compared to end users, for this study being carers providing palliative care within the community. Although approaches were similar, expert reviewers were equally challenging to recruit, not through lack of interest or willingness to participate but rather an availability constrained by time due to work commitments. Just as the unmoderated and remote review increased opportunities for participation in both groups of experts (as geographically distanced members were technically able to negotiate various platforms and software required for activity completion), this method could also limit participation given the diversity in user characteristics impacting interactive behaviours, especially within HCPs. Interestingly, HCPs with lower technical or digital competency levels should be encouraged to participate as a reviewer for developers to understand some of the difficulties or barriers their users could face when interacting with their interface.
Conversely, end users were extremely difficult to recruit. Promotion through carer peak body organisations electronic communication channels failed to identify any potential carers interested in participating in the study from within the local carer community. A gate-keeper advocate provided access to interested carers from the local palliative care community service. No fewer than twelve active and recently bereaved primary carers were approached from within the carer network to satisfy the usability quota of six participants. Those who did not want to be involved acknowledged that their willingness to participate was tempered by anxiety and fear of reliving painful or sad experiences rekindled by viewing the Toolkit content. These feelings are offset by the need to assist developers in improving the resource to help carers have all the missing information that could have been of value during their own experience. Active carers expressed that time and availability due to their caring duties were also reasons to consider participating in the study. The difficulties in accessing suitable participants within vulnerable populations, including from within the palliative care domain, subsequently influenced the ability to recruit without impacting the timeliness of the development process. Once recruited, both evaluators and end users were pleased to be involved in the evaluation process, offering valuable feedback on the usability of different aspects of the Toolkit prototype interface. Finally, the analysis of verbal feedback demonstrated different approaches undertaken by healthcare professionals and LDs when providing expert feedback on the errors or problems encountered within the interface.
## 4.2. The Types of Feedback Provided
Unsurprisingly, subject-matter experts (HCPs) were more likely to identify content-specific errors provided as statements of their palliative care knowledge in supporting carers’ needs in their professional practise. HCPs view the structure of sentences, text organisation and the relationship to how the text relates to a specific purpose or audience by applying process knowledge (competencies, motivations, and strategies as the reader) with metacognition to review text for issues [45] to ensure reliability, accuracy, and quality of information [20].
Learning Designers could also identify content-based errors, although their feedback offered alternatives or rephrased content as revision statements described in their review documentation. Narratives offered an insight into lay understandings of palliative care that were less detailed than the commentary offered by HCPs and should be privileged. This information potentially improves interface reiterations creating understandable content similarly levelled at an audience with a limited understanding of palliative care as part of their lived experience. Feedback provided represented a comparison of reviewers pre-existing knowledge to the overall text from a generalist viewpoint compared to the HCP process, who approached the review process through a sequential, step-by-step method to formulating feedback as directed by their domain knowledge [39].
Qualitative descriptions offering valuable experience-based context to perceiving why errors are considered a problem for a reviewer is but one facet of understanding barriers to interface usability. The identification of explicit errors can highlight critical interface areas impeding end-user interaction. For example, the similarities and differences in the types, frequency and severity of errors detected by each group were explored to map strengths and perhaps weaknesses when comparing LDs to HCPs and end users.
## 4.3. Error Identification
Meta-aggregation and applying quantitative logic to the analysis highlighted errors in the interface that were shared or discrete to reviewer groups. Experts, on average, were equally skilled at identifying high-frequency content errors; however, as reviewers were evaluating the Toolkit interface, there was a trend for LDs to be more sensitive to errors affecting the user experience of the information. Errors were more likely associated with information flow between and within pages, navigation devices or scripted hyperlink text, and interactions between the site and the user. Typical usability error types were more pronounced within the interface for LDs; HCPs identified a sub-set of these error types, perhaps reflecting common issues that they, as typical users, had previously experienced during their interactions with online technologies. HCPs and LDs identified examples of all categorised error types. As a reflection of their usability knowledge and professional practise, LDs detected four discrete error types that can improve interactions for users who face barriers to using or accessing health information, including visual representations, utility, error recovery, and accessibility.
As a single reviewer group, LDs demonstrated the ability to detect errors at a greater frequency than HCPs and carers whilst, on average, having improved efficiency in identifying errors per evaluator. Rates of error identification across the interface of the CarerHelp prototype indicated that LDs detected similar error quotients as heuristic double expert evaluators [46] and, in some cases, in greater percentages [42,47,48]. This pattern was not observed for HCPs, identifying a relatively low error rate [48] across the interface compared to double heuristic experts. It was negated when combined as an ‘expert’ group identifying over $80\%$ of all errors within the prototype when rates compared to carers, a pattern observed in health [49,50], non-health focused interfaces [27,51] and other research studies [52]. Outcomes from the data analysis suggested that LDs and USERs are more likely to identify similar errors, having identified a higher number of shared error types than the frequency of shared errors within the HCP and USER groups. Additionally, findings indicate the commonality between LDs and USERs in how the information within the user interface is perceived, understood, or comprehended within the context of being a non-specialist in the palliative care domain. It is essential to acknowledge that USERs identified equivalent error types as the expert reviewers, although at a lower frequency.
Study observations and qualitative narratives suggest LDs are well suited to review online health toolkits due to technical skills and awareness of building within online platforms or programs, integrating activities to create interactive experiences, and understanding interface features contributing to functionality, including navigation.
Conceptually, online health toolkit interfaces [43,53] are like those produced by LDs in typical practise within higher education settings. Educational online course materials and toolkits share the requirements for developers to recognise the needs and abilities of learners through interface design. LDs and toolkit developers are observed adopting strategies to translate knowledge through instructional components or interactive features to support learning objectives.
## 4.4. Potential Role of Learning Designers in Usability Evaluations
The combination of HCPs and LDs could effectively generate usability feedback where heuristic double experts are absent or, in many cases, unavailable. Both reviewer groups balanced the types of feedback provided to developers and the variety of error types identified within the interface. The data indicated that each group complemented the other, especially in covering the deficits in identifying errors that are unsighted or not recognised by one group tend to be detected by the other. LDs bring their expertise, knowledge of interaction design and, although usability evaluations are not a standard component of their everyday practise, an awareness of usability principles and their personal ‘baggage’ [54]. An individual’s baggage is linked to life experiences, socio-cultural characteristics shaping interactions online, and the ability to contextualise information to previous lived experiences or empathise with other people’s perceived situations. For this palliative care resource, LDs demonstrated similar interactions with the interface information as the carers, identifying co-existing errors within the interface and offering revisions to content contextualised to the carer. Participating LDs expressed both a personal or shared experience of palliative care/caring for a loved one (voiced from the shared perspective of a family member, friend, or colleague), articulated empathy and voiced a personal connection to someone they know could be caring soon. HCPs were defined by their relationship to the Toolkit content or knowledge and were not forthcoming with a personal perspective. Instead, like their feedback on the usefulness of the Toolkit post-release [55], their feedback was solely from a professional perspective and experience of caring for palliative patients and their carers.
The necessary recruitment of representatives for both review groups to participate in the process was equally problematic. Reviewers, regardless of the profession, were enthusiastic about being involved and whilst remote facilitation enabled involvement geographically, difficulties identifying suitable and available professionals were further compounded by complex development processes.
## 4.5. Can Expert Evaluators Replace End Users in the Development of Digital Health Resources?
For development teams designing and building online health information resources, these two groups of experts could replace users within the evaluation process, as experts are more adept at identifying errors across both skills, technical [26] and rule-based interactions than users [47]. The data also highlighted the dangers of employing expert-only reviewers to undertake usability evaluations in place of end users. Unlike heuristic evaluations [42], USERs and experts have identified similar problems within the Toolkit interface during expert peer-review processes, adding complexities for development teams to recognise the difference between complementary and contradictory errors. Unlike complementary errors or errors detected similarly by more than one reviewer group, error contradiction between groups recognises the presence of an error whilst the other does not perceive the same. For developers, error ‘false alarms’ [32] increase concerns about reiterative decisions shaping interfaces without the end-user voice. Contradictive errors complicate decision-making by knowing which are not veritable within the interface, increasing the risk of resolving for one group and creating a new interface error for another. End users (carers in this study context) can offer developers a voice with lived experience of being a carer providing palliative care even with the levels of extreme difficulty experienced in recruiting carers and having the lowest error detection rates overall.
Qualitative narratives provide a powerful mechanism to reiterate the interface, improve the content, and shape interface functionality. This narrative also adds weight to identifying veritable errors, an alternate perspective to expert opinions, and improving usability and experience within the interface.
## 4.6. Study Limitations
It is important to recognise the exploratory nature of this study as this research approach follows user-centred design principles involving methodologies to generate formative data to inform the reiteration of the interface. Pragmatically, the small evaluator sample sizes reflect typical usability evaluation practise. It is acknowledged that on analysis of usability data, small, subtle, or nuanced differences in the between-group comparisons are unlikely to be detected as the sample size was small and statistically underpowered [32,37]. It is important to recognise that this is not an unusual outcome from feasibility or proof-of-concept studies, where small samples indicate the potential value and validate investment in further research. This study was conducted during the development of the CarerHelp Toolkit; access to stable prototypes was aligned to provide timely feedback to the project group to inform reiteration of the interface. As timelines for the two evaluation methodologies did not align, the two reviewer groups, carers (USER) and experts (LD + HCP), did not review the exact version of the prototype. The USER group utilised an earlier version of the Toolkit, whilst both LDs and HCPs evaluated a similar but later version. This version had identical content, although it featured additional embedded interactives and unavailable resources at usability testing with carers on the earlier version. Whilst this limitation could have resulted in an increasing trend between error frequency and quantity of content between the two usability sessions, many of the interactive features were out of the scope of the expert review process. Nonetheless, it is essential to note that the core of the CarerHelp Toolkit remained constant and was evaluated by all three reviewer groups.
## 4.7. Future Research
Outcomes from this study suggest that there is potential value in LDs and HCPs being involved as composite double experts to generate valuable feedback in the development and design of palliative care digital resource.
Further investigation is required to understand the comparative differences or similarities in the type, frequency and severity of the usability errors identified by composite experts and those detected by trained, heuristic double experts. Areas of commonality between the two evaluator groups could assist multidisciplinary development teams in deciding whether investing time and money to either access or train in-house heuristic experts is worthwhile. This is especially so given the low investment and high return of utilising HCPs and LDs to generate valuable feedback to supplement end-user narratives of the lived experience.
Future research could investigate the types of usability information that might support healthcare professionals to become heuristic double experts. In addition, there is scope to develop, implement and evaluate education or practise guidance that could support healthcare professionals to rapidly expand and develop their existing technical abilities to include scholarship in user-centred design principles and an understanding of the need to consider end-user experience in interface design. The question of composite expert diversity is also an important area to explore, especially given HCPs are both diverse in their abilities, backgrounds, and experiences and in their specialty areas. For example, types of feedback generated by different HCPs such as specialist palliative care physicians and nurses compared to those of generalised specialties who are caring for patient’s clinical and psycho-social symptoms at the end of life. Findings could support the wider application of this approach to develop generalised health online resources or m-health applications supporting health promotion, monitoring, or encouraging behaviour change in the wider population.
## 5. Conclusions
Recruiting end users is difficult, and for multidisciplinary teams creating digital resources for carers of palliative care patients is further complicated by the perceived sensitivity of the subject domain, design inexperience, and working within an increasingly complex development environment. Within a palliative care context, usability evaluations involving LDs as expert evaluators were explored to understand their potential to support HCPs in shaping health interface designs when end users are difficult to recruit. Comparative analysis of content-specific errors found that HCPs were more likely to offer feedback by applying process knowledge to their understanding of the palliative care interface information and carers as end users. LDs identified similar content errors; however, they could offer narratives from their perspective of someone who has experienced death or a loved one dying and were equally adept at identifying high error frequencies. Importantly, LDs were increasingly sensitive to errors impacting end users who face barriers to accessing and using digital health information.
Our research suggests that through their professional aptitude as digital interaction designers and ability to reflect on their lived experiences, LDs have and can provide a unique and valuable perspective as evaluators during usability evaluations. However, feedback generated from carers as end users was highly contextualised to their needs and reflected their experiences as patients, carers or families interacting with healthcare, information, or systems. For developers of digital health interfaces, the involvement of experts cannot replace the insights of the lived experience and the influence of diversity of user characteristics on the types of feedback generated by end-user participation. Recruiting end users for usability evaluations remains a crucial step in creating meaningful and useful interactive experiences whose designs can support our communities’ health information needs.
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|
---
title: 'Empowerment and Knowledge as Determinants for Quality of Life: A Contribution
to a Better Type 2 Diabetes Self-Management'
authors:
- Pedro L. Ferreira
- Carminda Morais
- Rui Pimenta
- Inês Ribeiro
- Isabel Amorim
- Sandra Maria Alves
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001584
doi: 10.3390/ijerph20054544
license: CC BY 4.0
---
# Empowerment and Knowledge as Determinants for Quality of Life: A Contribution to a Better Type 2 Diabetes Self-Management
## Abstract
The purpose of this study was to assess how knowledge and empowerment impact the quality of life (QoL) of a person with type 2 diabetes, leading to better communication and disease management. We conducted a descriptive and observational study of individuals with type 2 diabetes. The Diabetes Empowerment Scale-Short Form (DES-SF), Diabetes Knowledge Test (DKT), and EQ-5D-5L were used, in addition to sociodemographic and clinical characteristics. Evaluating the variability in the DES-SF and DKT in relation to the EQ-5D-5L and identifying possible sociodemographic and clinical determinants were conducted using univariate analyses followed by a multiple linear regression model to test whether the factors significantly predicted QoL. A total of 763 individuals were included in the final sample. Patients aged 65 years or older had lower QoL scores, as well as patients who lived alone, had less than 12 years of education, and experienced complications. The insulin-treated group showed higher scores in DKT than the non-insulin-treated group. It was also found that being male, being under 65 years of age, having no complications present, and having higher levels of knowledge and empowerment predicted higher QoL. Our results show that DKT and DES are still determinants of QoL, even after adjusting for sociodemographic and clinical characteristics. Therefore, literacy and empowerment are important for the improvement of the QoL of people with diabetes, by enabling them to manage their health conditions. New clinical practices focused on education, increasing patients’ knowledge, and empowerment may contribute to better health outcomes.
## 1. Introduction
Diabetes mellitus (DM) is a metabolic disease characterized by hyperglycemia resulting from defects in insulin secretion and/or its action [1,2,3]. The incidence and prevalence of diabetes present geographically variable distributions [4,5] and are estimated to be continuously increasing across all continents, involving significant, non-negligible costs [4,6].
According to the Organization for Economic Co-operation and Development (OECD), in 2019, the estimated standardized prevalence of diagnosed diabetes in the European population aged 15 years or more was over $7\%$. Compared to this European figure, Portugal showed a rate of $9\%$, one of the highest prevalence rates in Europe [5]. However, between 2009 and 2019, due to an evidently better performance in primary health care, the age and sex-standardized rates of avoidable hospital admissions for adults with diabetes was 56 per 100,000 inhabitants, the fourth-best European score.
Type 2 DM, like some other chronic non-communicable diseases, is asymptomatic, representing an increased risk of long-term complications. In this context, disease management is a constant challenge. In fact, coping with a chronic disease, such as diabetes, involves dealing with specific physical aspects of the disease. Dietary self-regulation (diet, food selection, proportions), monitoring blood glucose levels, physical exercise, drug therapy (dose, frequency, timing), foot care, and stress control are fundamental aspects of type 2 diabetes self-care management [4,7,8]. This involves psychosocial changes and limitations, as well as associated concerns, such as adherence to therapeutic regimes and uncertainty about the future, resulting in a series of losses leading to changes in independence and decreased well-being and QoL [9].
The prevention of long-term complications is also associated with metabolic control [4,9]. However, effective diabetic management cannot be achieved only under the supervision of health professionals [4,6]. The individual with diabetes plays a central role in the effectiveness and self-management of his/her healthcare process.
The management of diabetes implies an adequate level of knowledge in order to develop critical awareness about the necessity of changes regarding the therapeutic process [10], thus promoting its effective adoption [4,9]. It requires not only the acquisition and deepening of specific knowledge in the area of diabetes but also the ability of people to set their own goals and objectives.
To ensure that people with diabetes and their families acquire “skills for health”, in the terms recommended by the World Health Organization (WHO), it is necessary to include them in the center of the care management process [11]. This implies the rupture of paternalistic healthcare models, where people are seen as mere passive recipients [12]. There is an urgent need to (re)orient clinical practices based on collaborative approaches, to empower people with diabetes to control lifestyle habits and health behaviors and make more appropriate decisions for their condition [13].
In this context, clinical practices must assume an interactive and multidirectional approach based on the values, principles, culture, and individual experiences of people with type 2 diabetes and their families. More than adapting people to their disease, it is important to take people as care partners, to enhance their endogenous (such as locus of control, motivation, and personality characteristics) and exogenous resources (like access to health information and equipment and economic aspects), and to cope with the management of this chronic and complex disease [13,14].
Thus, collaborative psychotherapeutic and motivational approaches oriented to self-management form the basis of empowerment [6].
The identification of the level of knowledge about type 2 DM (including diet, lifestyle, therapeutic management, and complications [15]), empowerment, and self-management are central concepts in this research.
Empowerment is considered a social process in which people take ownership of their own lives through interactions with others, producing critical thinking about reality and promoting the construction of social and personal capacities [16]. In line with the WHO, empowerment is characterized as the personal and collective ability to contribute to the construction of policies and services more oriented to their needs and potentialities [11,17]. Self-management is defined as a person’s strategies for controlling the disease, promoting health, and living well with the disease [18].
Several studies revealed that empowerment can be associated with an improvement in the clinical and non-clinical outcomes of people with type 2 diabetes, such as HbA1c, behavioral changes, health literacy, psychological status, self-care, and control [19,20,21,22,23]. Therefore, to increase empowerment among these patients, it is important that they have sufficient knowledge about the disease and their health status [23]. A systematic review of the factors associated with glycemic control showed that patients’ knowledge regarding diet enhanced their dietary self-regulation and led to informed decision-making adapted to their health condition [24]. Possessing knowledge about the disease has been found to be conducive to the successful self-management of type 2 diabetes through the development of activities, skills, and self-care attitudes [21].
Therefore, when aiming for effective metabolic control, QoL, and subjective well-being [25], these conceptions confirm the possibilities of combining personal and collective strategies [6] involving people with diabetes, their families, the community environment, and health professionals.
In this sense, it is important to proceed with the research supporting clinical practice on the QoL of people with type 2 diabetes and its relationship with empowerment and knowledge, with a focus on more person- and family-centered care models.
Based on these assumptions, this study aimed to assess how knowledge and empowerment impacted the QoL of a person with type 2 diabetes, with the purpose of contributing to an improvement in self-management processes. It also aimed to explore possible sociodemographic and clinical factors determining QoL.
## 2.1. Study Design
We carried out a descriptive and observational study in four health institutions in the North Region of Portugal. The research project was approved by the Ethical Committee of the Northern Regional Health Authority ($\frac{62}{2018}$) responsible for all health units where data were collected.
## 2.2. Sample
The population under study was composed of individuals with type 2 diabetes, followed in external consultations of four health institutions. We estimated a total of 85,820 elements, which was the estimated total number of patients with diabetes covered by the four institutions. The sample size was based on a confidence level of $95\%$ and a maximum sampling error of $4\%$. We obtained a sample of 763 persons who met the following inclusion criteria: a diagnosis of type 2 diabetes over one year, enrolled in multidisciplinary consultation for at least three months, and older than 18 years of age. The recruitment strategy was sequential. Each participant was guaranteed anonymity and confidentiality. Free prior informed written consent was always requested. The questionnaires were self-completed and privacy was safeguarded through the use of separate rooms. The rate of participation was around $90\%$.
## 2.3. Measurement Instruments
To collect the necessary information for this study, we applied the Portuguese versions of the following measurement instruments: the Diabetes Empowerment Scale-Short Form (DES-SF), the Diabetes Knowledge Test (DKT), and the EQ-5D-5L. In addition, we also collected some sociodemographic variables (sex, age, education, and living alone) and clinical characteristics of the participants (body mass index—BMI, glycated hemoglobin—HbA1c, treatment with insulin, and time of diabetes diagnosis). The following sections provide brief descriptions of each questionnaire.
## 2.3.1. Diabetes Empowerment Scale-Short Form (DES-SF)
It was developed by Anderson et al. in 2000 and aimed to measure psychological self-efficacy in people with diabetes. The original DES consists of 37 items divided into 8 dimensions: assessing the need for change; developing a plan; overcoming barriers; asking for support; supporting oneself; coping with emotion; motivation; and making appropriate choices in diabetes care, according to priorities and circumstances. The number of items was reduced to 28, encompassing three subscales: (i) assessment of dissatisfaction and readiness to change; (ii) psychosocial management of diabetes; and (iii) goal setting and achievement [26].
To create the DES-SF, the authors choose the item with the highest correlation in each domain of the original scale, resulting in a questionnaire composed of eight items [26]. This measurement instrument presents five response options, from 1 (totally disagree) to 5 (totally agree). The final score was calculated using the average of the scores of the 8 items, in which higher scores indicated better psychosocial self-efficacy. In a sample of 229 participants, the DES-SF proved to have good reliability, with a Cronbach’s alpha of 0.84. Content validity was verified with questionnaire scores and HbA1c levels varied positively after a six-week educational program based on patients’ problems [27]. In this study, we calculated the final score by converting it into a scale from 0 to 100.
In Portugal, Aveiro et al. [ 2015] used the DES-SF in a sample of the Portuguese population that was composed of 81 people with type 2 diabetes, and this measure demonstrated good internal consistency (Cronbach’s alpha of 0.87) and stability over time (test-retest correlation coefficient of 0.33). The construct validity showed statistically significant differences in the correlation between DES-SF scores and HbA1c levels (r = −0.114) [28].
## 2.3.2. Diabetes Knowledge Test (DKT)
This measurement instrument was developed by the Michigan Diabetes Research Training Center in the 1990s, and it was created to assess the overall knowledge that a person with diabetes has about the disease. It is composed of two subscales: the first is composed of 14 items and is appropriate for adults with type 1 or type 2 diabetes; the second is composed of nine items specifically for people with diabetes treated with insulin. The questionnaire takes about 15 min to complete [29]. The scoring of this instrument is performed according to the number of right answers.
Fitzgerald et al. [ 1998] verified the reliability of this questionnaire, which showed Cronbach’s alpha values greater than 0.70 in both subscales. The authors formulated several hypotheses to assess construct validity and concluded that there were statistically significantly higher DKT scores among people with type 1 diabetes, people with a higher level of education, and people who had participated in a diabetes therapy education program [29]. This instrument has been widely used in studies in Portugal, and an association between knowledge about the disease and the ability to control type 2 diabetes has already been demonstrated [15,30].
## 2.3.3. EQ-5D-5L
This instrument was developed in 2011 by the EuroQol group, with the purpose of creating an instrument capable of measuring health-related quality of life. It is composed of five dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression), and it offers five response options, ranging from 1 (no problems) to 5 (extreme problems), representing five levels of severity for each dimension [31]. Through a mathematical algorithm, it is possible to convert the response values into a personal quality of life index, ranging from 0 (death) to 1 (perfect health), and allow for the calculation of health-related quality of life that corresponds with the person’s health status [32]. At the end of the questionnaire, there is a visual analog scale (EQ-VAS), where the person identifies his/her current health status, from 0 (worst imaginable health status) to 100 (best imaginable health status) [33].
In 2013, the Portuguese version of the EQ-5D was adapted and validated. This questionnaire proved to be well accepted and reliable, with a Cronbach’s alpha of 0.71 across the five dimensions and an intraclass correlation coefficient of 0.86 for the EQ-VAS. This instrument was also revealed to be feasible, having demonstrated construct and criterion validity with the 36-item Short-Form Health Survey [33]. In 2019, Ferreira et al. calculated the EQ-5D-5L health state preferences of the Portuguese population. The societal value set varied between −0.603 and 1 [34].
## 2.4. Statistical Analysis
Descriptive analyses were conducted to characterize the sample and as a complement to inferential statistics. The Spearman coefficient was used to obtain correlations between the DES-SF, DKT, and EQ-5D-5L (after Kolmogorov–Smirnov normality tests were applied). Cronbach’s alpha coefficients were calculated to assess the reliability of the instruments. The observed Cronbach’s alpha coefficients for the instruments were 0.80 for the DES-SF, 0.81 for the DKT, and 0.81 for the EQ-5D-5L, indicating good internal consistency [35].
The variability of the DES-SF and DKT on the EQ-5D-5L, and the identification of possible sociodemographic and clinical determinants, were evaluated using univariate analyses via independent t-tests, followed by a linear regression model using the backward method of variable selection (with a p-value > 0.1 as a threshold for variable elimination). Residual analyses were conducted to assess normality, linearity, and homoscedasticity. In addition, to further evaluate homoscedasticity, the Breusch–Pagan test was used as described in Gujarati [36], and collinearity was evaluated using the variance inflation factor (VIF) and tolerance statistics. The sociodemographic variables selected for entry into the model were: sex, age (under 65; 65 or above), education (under 12 years of education; 12 years of education or above), and living arrangements (either living alone or not). As for the clinical variables, the amount of time after diagnosis (3 years or less; more than 3 years); HbA1c (under 7; 7 or above); BMI (under 30; 30 or above); the care setting (primary care; hospital external consultations); having undertaken insulin treatment (no; yes); and the presence of associated complications (no; yes), as a proxy for severity, were included.
Data were analyzed using IBM SPSS (Statistical Package for the Social Sciences) version 28, and missing data were handled using the pairwise approach.
## 3.1. Sample
A total of 763 persons were included in the final sample, where $51.5\%$ were female. The youngest person was 24 and the oldest was 92 years of age, with a mean age of 66 ± 11.9 years. Table 1 shows the information regarding the sociodemographic and clinical characteristics of the observed sample. The majority of our sample was comprised of older individuals, individuals with lower education levels, and those living with someone else.
In addition, it was observed that the most frequently associated pathology reported was hypertension ($65.0\%$), followed by dyslipidemia ($39.6\%$). Most of the reported complications caused by diabetes included retinopathy ($19.3\%$) and arteriopathy ($12.8\%$).
Table 2 presents the descriptive statistics for the DES-SF, DKT, and EQ-5D-5L; results for the DKT are separated by insulin treatment status, as explained in the methodology section, and the difference between the groups was statistically significant (|t| = 6.020, p-value < 0.001).
## 3.2. Determinants of Quality of Life
Spearman coefficients between EQ-5D-5L and both DES-SF and DKT were, respectively, 0.334 and 0.211; these are small values, but both were associated with a p-value < 0.001. The value for the correlation between DES-SF and DKT was 0.052 (p-value = 0.086).
The inferential analysis, shown in Table 3, showed that female patients had lower EQ-5D-5L scores, with a mean ± standard deviation of 0.60 ± 0.28 versus 0.67 ± 0.29 for male patients (p-value = 0.001).
Patients who were 65 years or older had lower EQ-5D-5L scores compared to patients aged less than 65 years of age: 0.59 ± 0.30 versus 0.72 ± 0.26 (p-value < 0.001), respectively. As for living arrangements, patients living alone had lower EQ-5D-5L scores compared to those living with company: 0.59 ± 0.30 versus 0.65 ± 0.29 (p-value = 0.039). When comparing patients with different levels of education, patients with lower levels of education (i.e., under 12 years of education), had lower EQ-5D-5L scores compared to patients with 12 years of education or above (0.60 ± 0.29 vs. 0.78 ± 0.24; p-value < 0.001). Finally, patients with associated complications had lower scores compared to the ones with no complications (0.55 ± 0.29 vs. 0.70 ± 0.28; p-value < 0.001). All other sociodemographic and clinical values did not present significant values in univariate analysis. In addition, no significant associations were found between sex and sociodemographic and clinical characteristics.
The results for the linear regression model using the backward method are presented in Table 4 and show that sex, the presence of complications, age, and levels of HbA1c can be used to explain the EQ-5D-5L. The model showed no signs of violation regarding the linearity and normality of the residuals. The VIF factors ranged from 1.012 to 1.078, and the tolerance statistics ranged from 0.928 to 0.988. The Breusch–Pagan test for heteroscedasticity was not significant.
By analyzing the results for the EQ-5D-5L, the sociodemographic and clinical predictors involved in the model, it was observed that males presented higher EQ-5Q-5L scores, as did the younger age group and the group with no complications. The value of EQ-5D-5L increased as the DES increased, and similar results were observed in the analysis of the DKT variable.
## 4. Discussion
Studying QoL, as a fundamental feature of human life mediated by the value system, culture, goals, and people’s expectations [37], is essential for the implementation of interventions targeted at the specificities of each community.
The sample under study was mostly composed of women, although the prevalence of diabetes in *Portugal is* higher among males [38]. The present sample’s composition may be due to men being less likely to seek health surveillance appointments. In this sense, it is urgent that health professionals optimize health monitoring consultations of different scopes, paying particular attention to the underreporting of diabetes and, consequently, its complications [38,39], especially among men. Other research reflects some diversity regarding gender [40,41] in close articulation with the social construction of gender and its reflection on health.
Similar to other studies, the sample was also characterized by having a high mean age (65.94 ± 11.86 years) [37], as well as a high number of people living alone and a high number of people with low academic achievement levels [40,41]. Thus, these people’s specific vulnerability to age-related comorbidities is enhanced by the high mean time to diagnosis, given the linear relationship between the duration of the disease course and the incidence of complications [39]. Considering diabetes is a condition that can be controlled but not cured, the essential aim of healthcare, particularly in older patients, is to delay its impairment in the remaining years of life and contribute to improvements in their QoL [40].
In this study, the results of the assessment of empowerment were moderate and the levels of knowledge were slightly lower among people undergoing insulin therapy, but much lower among people utilizing other therapeutic approaches. Some publications [42,43] have highlighted the relevance of knowledge about the disease in developing and improving the level of empowerment within people with diabetes, with a view toward changing behaviors, self-management, and self-control, as well as the improvement of their psychological status. Health services should devote greater attention to this finding because although chronic diseases, such as diabetes, require surveillance and monitoring by specialized health professionals, the effectiveness of their management especially depends on patients and their families [43].
Regarding QoL, the results obtained showed modest levels. QoL tends to be lower in people with chronic diseases [44], with diabetes being considered one of the most significant contributors to worse QoL levels [45]. Similar results have been evidenced in other studies, seeming to indicate, in general, that diabetes represents a psychological burden with the ability to negatively affect people in the emotional, physical, and social domains [41]. The disease challenges and the demands that treatment brings to the patients’ daily lives are identified as the main reasons for the decrease in perceived QoL [2,46].
Therefore, there is an emerging need, in addition to other indicators for monitoring the effectiveness of therapeutic treatments, to implement the systematic monitoring of QoL as a measure not only of the health status of people with diabetes but also of the implemented intervention [2]. It is about bringing in the voices of people with diabetes and looking at the impact of the disease and its treatment on physical, social, and mental well-being [13,47].
The data concerning the QoL assessment of people with type 2 diabetes and its association with sociodemographic factors revealed variability [37]. This study found statistically significant relationships between the QoL of people with type 2 diabetes and sociodemographic and clinical variables. In line with other studies, women, people aged 65 years or older, those with lower academic levels, and those who self-reported disease complications perceived worse QoL [37,41]. Statistically significant differences were observed related to BMI, HbA1c, time of diagnosis, setting of care, and insulin treatment. These results differ from other studies in which worse perceived QoL appeared to be associated with inverse scores of HbA1c [48], BMI [49,50], treatment using insulin [51] and the time of diagnosis, having considered 10 years as a cut-off point [9]. The fact that diabetes is a slow-evolving and silent disease, along with cultural aspects, may, at least in part, explain the relationships between perceptions of QoL and the clinical variables.
To identify statically significant factors associated with QoL in patients with type 2 diabetes, we built a multiple linear regression model. Thus, being younger, male, and not having diabetes complications were predictors of better perceived QoL.
The inverse relationship between age and QoL found in this study is corroborated by other research using similar statistical techniques [52]. However, other studies present contrary results, in which sociodemographic factors, such as age, gender, and education were not predictors of QoL [53]. In that study, the authors applied a specific instrument to measure QoL in people with type 2 diabetes, the AsianDQoL. This instrument has five domains: energy, memory, diet, sex, and finance, and the dimensions are not similar to those of the EQ-5D-5L, which could explain the contrary results.
A higher number of diabetes complications as a significant predictor of worse QoL is in line with other findings [1,52,54,55]. Other studies valuing complications focus on specific types of complications rather than the number, reporting that their severity is associated with worse QoL [53]. Donald et al. [ 2013] report that worse QoL related to diabetes complications is also associated with mental health conditions, such as depression and anxiety, and these relationships persisted after adjustment for sex, age, duration of diabetes, treatment regimen, and other clinical and sociodemographic variables [54].
Another result that should be highlighted is related to the observed BMI, which was substantially high in this study. A high BMI is usually significantly associated with lower perceived QoL [45], which was not observed in this study, even after adjusting for other covariates. This result contrasts with the study conducted by Timar et al. [ 2016], which found a significant association between obesity and QoL [51].
Despite the significant advances in the therapeutic approaches to diabetes mellitus, studies still point to the persistence of modifiable social determinants, gaps in glycaemic control, and the prevention of complications. Studies focusing on the knowledge and self-management skills of people with diabetes as determinants for achieving their goals, particularly regarding QoL are, however, less frequent [56].
Several studies have shown that empowerment can reduce HbA1c levels [43], trigger behavioral changes, and increase health knowledge, self-management and control [6], QoL, motivation, and resilience [10]. The published research has revealed the need for new empowerment practices that take the potential and constraints of the patients and their families as a starting point [57], as well as the beliefs of health professionals [42].
Empowerment programs should anticipate comprehensive and multifaced approaches [42] that are socially and culturally appropriate for the populations to function as partners in care [14]. Additionally, people with diabetes are adults—a substantial portion of whom are at advanced ages—whose lifestyles have been acquired and repeated over many years. Thus, behavioral changes are intended to be stable; imply complex psychological processes, in which there is a need to intentionally and consciously replace spontaneous behaviors with others that are healthier; and are therapeutically more effective. There is also the relevance of social interaction contexts, where inappropriate behaviors are often reinforced by family and friends and repercussions on health are not immediately visible.
Approaches imminently oriented toward exclusively somatic goals and negotiating with people without considering cognitive specificities, such as memory and learning, as well as their social, familiar, affective, and academic contexts have proven to be ineffective.
Therefore, the role of healthcare professionals should focus on facilitating conditions that allow for the acquisition of in-depth knowledge about the disease and the strengthening of motivation and resilience so that people with type 2 diabetes can make decisions and successfully take responsibility for managing their disease.
It is urgent that the current models of care organization, which view individuals from an isolated perspective, be progressively replaced by other, collaborative models. These models look at people from all settings where they are found and use interactive strategies with intergenerational, peer, political, and community involvement. These models create new perspectives and opportunities to combat social and gender inequalities more effectively and are capable of positively influencing the modifiable social determinants of chronic diseases, particularly diabetes.
As a possible limitation, we may address that our sample was selected in an outpatient setting, so it does not include inpatients. Patients with more severe health conditions, including nephropathies, neuropathies, and amputations, are less prevalent in our sample. Another study, with a sample involving people with diabetes who are hospitalized or at home, could provide the inclusion of these complications in the statistical model that explains QoL. Additionally, this study did not address the impact of socioeconomic status on QoL, even though it is a determinant of health which determines diabetes. However, in Portugal, the impact may be mitigated because, for this chronic disease, the NHS/government supports about $100\%$ of the expenses for insulin and antidiabetic drugs. Thus, even with the increase in costs due to diabetes in total health expenditure, the percentage of the financial burden for users has remained constant over time [58].
This study noted a high mean age of the participants (66 years), and more than $80\%$ of them reported less than 12 years of education, which may reflect a lower level of health literacy. Future research with a younger or more educated sample may be relevant in order to compare results. It will also be interesting to apply other instruments to identify the perceptions, beliefs, and barriers of participants, as well as to measure self-efficacy, motivation, cultural safety, and others, with the aim of finding other determinants of QoL and supporting better strategies for the empowerment chronic disease self-management.
## 5. Conclusions
*The* general purpose of this study was to better understand the population of individuals with type 2 diabetes, particularly with regard to its more specific characteristics, namely knowledge and social impacts, in order to be able to build more specific and guided intervention programs for patients and their integration into the community.
The literature review indicates that diabetes is one of the chronic diseases with a higher impact on perceived QoL. In this study, QoL levels were moderate. Higher perceptions of QoL were evident in males, those of a younger age, those without complications, and those with greater knowledge of diabetes; these led to the empowerment of relevant predictors for a better perception of QoL.
Those with lower educational levels had lower perceptions of QoL; however, this effect was no longer observed when the model was adjusted for other factors. The values obtained for BMI, due to underlying health implications, are also worrying, despite apparently not having influenced perceptions of QoL.
This requires the adoption of systematic, diversified, and more effective prevention and treatment measures, such as those more culturally, socially, and affectively adjusted to the endogenous and exogenous resources of people with type 2 diabetes and their families. In addition, it requires a more proactive attitude from health systems regarding the postponement of healthcare among men.
In a new approach, the role of healthcare professionals should be to provide knowledge for people with diabetes to reflect on their disease and, mainly, improve communication processes so that informed decisions can be made in developing a self-management plan that fits each person realistically, clinically, socially, and psychologically.
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---
title: Mental Health Conditions– and Substance Use—Associated Emergency Department
Visits during the COVID-19 Pandemic in Nevada, USA
authors:
- Zahra Mojtahedi
- Ying Guo
- Pearl Kim
- Parsa Khawari
- Hailey Ephrem
- Jay J. Shen
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001596
doi: 10.3390/ijerph20054389
license: CC BY 4.0
---
# Mental Health Conditions– and Substance Use—Associated Emergency Department Visits during the COVID-19 Pandemic in Nevada, USA
## Abstract
Background—Mental health conditions and substance use are linked. During the COVID-19 pandemic, mental health conditions and substance use increased, while emergency department (ED) visits decreased in the U.S. There is limited information regarding how the pandemic has affected ED visits for patients with mental health conditions and substance use. Objectives—This study examined the changes in ED visits associated with more common and serious mental health conditions (suicidal ideation, suicide attempts, and schizophrenia) and more commonly used substances (opioids, cannabis, alcohol, and cigarettes) in Nevada during the COVID-19 pandemic in 2020 and 2021 compared with the pre-pandemic period. Methods—The Nevada State ED database from 2018 to 2021 was used ($$n = 4$$,185,416 ED visits). The 10th Revision of the International Classification of Diseases identified suicidal ideation, suicide attempts, schizophrenia, and the use of opioids, cannabis, alcohol, and cigarette smoking. Seven multivariable logistic regression models were developed for each of the conditions after adjusting for age, gender, race/ethnicity, and payer source. The reference year was set as 2018. Results—During both of the pandemic years (2020 and 2021), particularly in 2020, the odds of ED visits associated with suicidal ideation, suicide attempts, schizophrenia, cigarette smoking, and alcohol use were all significantly higher than those in 2018. Conclusions—Our findings indicate the impact of the pandemic on mental health- and substance use-associated ED visits and provide empirical evidence for policymakers to direct and develop decisive public health initiatives aimed at addressing mental health and substance use-associated health service utilization, especially during the early stages of large-scale public health emergencies, such as the COVID-19 pandemic.
## 1. Introduction
Mental health and substance use issues are intertwined, and both reportedly increased in the United States and globally during the COVID-19 pandemic [1,2,3,4,5]. Nevada has distinct characteristics in terms of mental health and substance use, with rates that are typically higher than the national average [6]. Nevada ranks 44th out of 51 states in the USA regarding the prevalence of mental illnesses [7]. Nevada is also one of the worst-performing states for access to mental health care services (39 out of 51 states) [8]. The pandemic had a significant economic impact on Nevada, a world tourism center. More than $90\%$ of those employed in the hospitality industry lost their jobs during the lockdown in 2020, raising concerns about the mental health of its residents during the pandemic [9]. The pandemic struck mental health and substance use not only because of the disease itself, but also because of lockdowns, isolation, the economic downturn, and job losses [10]. The COVID-19 pandemic also reduced emergency department (ED) visits in the US, with the lowest-acuity ED visits having the most dramatic reduction, indicating that patients who did not require as much immediate medical attention were more likely to avoid going to EDs during the pandemic [11]; however, some conditions experienced proportionally smaller ED decreases, particularly those involving mental health and substance use [12,13]. The limited access to mental health care in Nevada [8] might push more patients with mental health-related conditions to EDs during crises, while the state also has limited ED resources compared with the national average [14]. Analyzing ED visits that have increased proportionally during the pandemic will prompt policies for caring for vulnerable patients during a public health emergency or crisis in order to prevent them from ending up in the ED, and Nevada statistics can provide insightful information on this matter.
The COVID-19 pandemic had a wave pattern. Depending on the wave and the state governors’ decisions, different policies were in place across the USA during the COVID-19 pandemic in 2020 and 2021 [15]. For example, in Nevada, COVID-19-related regulations were stricter in 2020 than in 2021 [16]. The COVID-19 pandemic literature on ED visits associated with mental health conditions and substance use tended to focus on the period early in the pandemic in 2020 [15,17,18,19,20,21,22,23]. Ridout and colleagues, in a study conducted early in the pandemic in 2020, found that a significant number of youth, especially women with no prior psychiatric history in North California, were admitted to the ED for suicide-associated issues [21]. A cross-sectional study of ED visits on children (5–17 years old) with a primary mental health diagnosis in the *Chicago area* found that visits for suicide or self-injury increased by $6.69\%$ during the pandemic [22]. Venkatesh and colleagues, similar to Pines and colleagues, found that substance use-associated ED visits proportionally increased during the pandemic [15,17]. In a study inclusive of the Southern States, Petal and colleagues found an upsurge in opioid overdoses: $10.3\%$ more opioid-associated deaths occurred from January to October 2020 than in 2019 [24]. Although these studies offer insightful information on the pandemic’s early stages and specific facilities, they have limited potential for generalization to other facilities and the rest of the pandemic period.
Mental health conditions and substance use are linked, and substance use can be regarded as a mental health condition that also affects behavior [25]. For example, the rates of cigarette smoking are approximately two- to four-fold higher in patients with a psychiatric disorder [5]. Some mental health conditions and substance use might be of particular concern due to their high prevalence and/or serious outcomes, as well as their reported increases during the pandemic [26,27,28]. Suicidal ideation, suicide attempts, and schizophrenia are all common and serious conditions that rose during the COVID-19 pandemic [27,28]. Uncontrolled schizophrenia has been associated with an increase in suicide attempts, and suicide is the tenth leading cause of death in the US [29,30]. Opioids, cannabis, alcohol, and cigarettes are commonly used substances in the US [27], and their use reportedly increased during the pandemic [12,31].
By determining the prevalence of certain conditions among ED patients during the pandemic, policymakers would be able to construct vital public health interventions intended to target a subset of the population with a higher possibility of ED visits during a crisis. This higher possibility could be due to the rising prevalence of these conditions in the general population or dwindling non-ED-facility options during lockdowns in the pandemic. Another explanation would be that these subsets, relative to the general population, might exhibit less behavioral caution in terms of ED visits during the pandemic. It is worth mentioning that between 13.7 and $27.1\%$ of all ED visits in the USA could be unnecessary or treated at alternative sites [32]. Prior studies on mental health conditions and substance use associated with ED visits were conducted early in the pandemic, either in general (not on individual conditions) [20] or on just one condition [21]. The aim of this study was to compare potential changes in ED visits associated with common and/or serious mental health conditions (suicidal ideation, suicide attempts, and schizophrenia) and more commonly used substances (opioids, cannabis, alcohol, and cigarettes) during the COVID-19 pandemic in 2020 and 2021 as opposed to the pre-pandemic years. With this approach, this study attempted to examine whether the earlier effect of the pandemic would differ from the later effect on mental health and substance use among ED visits.
## 2.1. Data
The Nevada State Emergency Department Databases (SEDDN) containing all ED visits in 2018 and 2019 (two years before the pandemic), as well as 2020 and 2021 (two years since the pandemic), were used. The SEDDN contains rich information on all non-federal acute community hospitals in Nevada [26]. All ED visits associated with opioids, cannabis, cigarette smoking, alcohol use, suicidal ideation, suicide attempts, and schizophrenia were identified using the International Classification of Diseases, 10th Edition (ICD-10). These codes are listed in Supplemental Table S1 and have been used in prior publications [26,33]. The University of Nevada, Las Vegas, institutional review board deemed this study exempt because the SEDDN database provides administrative data after complete de-identification [26]. For data analysis, a total of 4,185,416 ED visits (2018–2021) were included in this study. The demographics of the study population, as well as the seven variables’ frequencies from 2018 to 2021, are indicated in Table 1.
## 2.2. Measures and Data Analysis
Seven dichotomous dependent variables were studied here as follows: three common and serious mental health conditions, including suicidal ideation, suicide attempts, and schizophrenia, and four commonly used substances, including cigarette smoking, alcohol drinking, opioid use, and cannabis use. Age, gender, race/ethnicity, and payer source have been previously associated with these dependent variables [26] and were, therefore, included as independent variables in the regression model. In order to control for time and detect a potential trend, year was included as a dummy variable in all seven regression analyses, as used by other prior studies [34].
The patients’ age groups (<12, 12–17, 18–24, 25–34, 35–44 (reference), 45–54, 55–64, and ≥65), gender, payer source (Medicare, Medicaid, uninsured, other insurance, and private insurance (reference)), race/ethnicity (Black, Hispanic, Asian/Pacific Islander, White (reference), and others), and time (years 2018 (reference), 2019, 2020, and 2021) were the independent variables in each analysis [26].
Multiple visits from the same patient would be considered distinct ED visits because the data had been deidentified. As a result, the ED visits served as the unit of analysis [26]. To account for variations within hospitals due to the clustering effect, we utilized the generalized linear model for multivariable analysis and treated hospital as the random effect while estimating the fixed effect of the independent variables of individual hospital discharges [26]. All statistical analyses were conducted using SAS software version 9.4 (SAS Institute Inc.; Cary, NC, USA). p-values of <0.05 (2-tailed) were considered statistically significant.
## 3. Results
The numbers of ED visits were 1,107,950 ($26.5\%$), 1,153,000 ($27.5\%$), 924,887 (22.1), and 999,579 ($23.2\%$) from 2018 to 2021, respectively, with a total number of 4,185,416 (Table 1). In all of these four years, more than $50\%$ of ED visits were by women. Medicaid was the most prevalent payer source, covering more than $35\%$ of ED visits. The proportion of White people who visited an ED decreased from $54.0\%$ to $48.8\%$, whereas it increased for Black, Hispanic, and Asian people. Among all ED visits, the percentage of suicidal ideation was 1.69 in 2018, peaked at 1.96 in 2020, and decreased to 1.89 in 2023; the percentage of suicide attempts was 0.11 in 2018, peaked at 0.13 in 2020, and decreased to 0.12 in 2021; the percentage of schizophrenia was 1.09 in 2018, peaked at 1.87 in 2020, and decreased to 1.48 in 2021; the percentage of opioid use was 0.67 in 2018 and it peaked at 0.70 in 2020; the percentage of cannabis use was 1.26 in 2018 and peaked at 1.48 in 2020; the percentage of alcohol drinking was 3.33 in 2018 and peaked at 4.00 in 2020; and the percentage of smoking was 7.43 in 2018 and peaked at 9.67 in 2020 (Table 1). Generally, the rates of these conditions were higher in 2018 than in 2019 (Table 1). Therefore, 2018 was set as the reference year.
Table 2 indicates the factors associated with the mental health conditions of suicidal ideation, suicide attempts, and schizophrenia among ED visits in Nevada from 2018 to 2021. The odds of suicidal ideation-, suicide attempt-, and schizophrenia-associated ED visits were significantly higher during both years of the pandemic (2020 and 2021) compared with 2018. The adjusted odds of suicidal ideation-associated ED visits were $11\%$ ($95\%$ CI = 1.04–1.19) and $9\%$ ($95\%$ CI = 1.02–1.17) higher in 2020 and 2021, respectively, than those in 2018. The odds of suicide attempt-associated ED visits were $20\%$ ($95\%$ CI = 1.09–1.33) and $16\%$ ($95\%$ CI = 1.05–1.27) higher in 2020 and 2021, respectively, than those in 2018. The odds of schizophrenia-associated ED visits were $60\%$ ($95\%$ CI = 1.47–1.75) and $28\%$ ($95\%$ CI= 1.17–1.40) higher in 2020 and 2021, respectively, than those in 2018. The odds of suicidal ideation and schizophrenia were significantly $8\%$ lower ($95\%$ CI = 0.86–0.99) and $23\%$ higher in 2019 ($95\%$ CI = 1.12–1.34) than those in 2018, respectively. Other factors were also related to the odds of these mental health conditions for ED visits. ED visits associated with these three mental health conditions were significantly less likely to be female (suicidal ideation: OR = 0.43, $95\%$ CI = 0.41–0.46; suicide attempts: OR = 0.927, $95\%$ CI = 0.865–0.99; schizophrenia: OR = 0.33, $95\%$ CI = 0.31–0.35). The age group of 12–17 years had significantly higher odds of suicidal ideation- (OR = 1.45, $95\%$ CI = 1.32–1.59) and suicide attempt- (OR = 4.86, $95\%$ CI = 4.32–5.46) associated ED visits compared with the control age group of 35–44 years. However, the control group had higher odds of schizophrenia-associated ED visits compared with the other five age groups (Table 2). Compared with the White race, the three other races (Black, Hispanic, and Asian) had significantly lower odds of suicidal ideation-, suicide attempt-, and schizophrenia-associated ED visits (Table 2), except for schizophrenia-associated ED visits for Black people, who had higher odds of schizophrenia-associated ED visits compared with White people (OR = 1.41, $95\%$ CI = 1.32–1.52). Compared with private health insurance, both Medicaid and Medicare were significantly associated with higher odds of all three types of mental health-associated ED visits (Table 2).
Table 3 indicates the factors associated with the use of opioids, cannabis, alcohol, and cigarette smoking among ED visits in Nevada from 2018 to 2021. Opioid- and cannabis-associated ED visits had significantly lower odds in 2019 and 2021 compared with those in 2018. Cannabis-associated ED visits had significantly $11\%$ higher odds in 2020 compared with 2018 ($95\%$ CI = 1.06–1.16). Cigarette smoking- and alcohol-drinking-associated ED visits had higher odds in 2020 and 2021 compared with 2018, and the highest odds were for smoking-associated ED visits in 2020 (OR = 1.27, $95\%$ CI = 1.22–1.32). Women compared with men had lower odds of all these four conditions-associated ED visits (Table 3). Compared with the 35–44 age group, the 25–34 age group and the six other age groups had significantly higher and lower odds of opioid-associated ED visits, respectively. Cannabis-associated ED visits had higher odds in the 18–24- and 25–34-year age groups compared with the 35–44-year age group. None of the age groups had significantly higher odds of smoking-associated ED visits compared with the 35–44-year age group. Alcohol-drinking-associated ED visits had significantly higher odds in the age groups of 45–54 and 55–64 years compared with the age group of 35–44 years. Compared with the White race, the Black, Hispanic, and Asian races had significantly lower odds of opioid-, smoking-, and drinking-associated ED visits. Regarding cannabis-associated ED visits, only the Black race had significantly higher odds, while the other two races, i.e., Hispanic and Asian, had significantly lower odds compared with the White race. ED visits covered by Medicare and Medicaid, as well as those of the uninsured, had higher odds of opioid, cannabis, cigarette smoking, and alcohol use compared with ED visits covered by private insurance (Table 3).
## 4. Discussion
Here, we examined certain mental health and substance use conditions among ED visits in Nevada between 2018 and 2021 using multivariable analysis. We investigated 2020 and 2021 separately in order to comprehend how the various time points during the pandemic impacted ED visits associated with these conditions. We found that ED visits increased from 2018 to 2019, decreased in 2020, and increased again in 2021, but not to the pre-pandemic level. This trend is also consistent with the national trend in ED visits [35]. Generally, COVID-19-related lockdowns in Nevada were laxer in 2021 than they were in 2020 [16]. Despite the fact that there were more COVID-19 cases in 2021 than in 2020 [16], our findings suggest that the pandemic effects were possibly stronger in 2020 than those in 2021, but there are more details to our findings.
Most previous studies on mental health conditions-associated ED visits during the COVID-19 pandemic were limited to 2020 [20]. A study on one million non-COVID-19 ED visits in Missouri, USA, in 2020 found that the proportion of mental health conditions among all ED visits increased. They did not mention what mental health conditions were included in their study [20]. Using national data, Holland and colleagues found that suicide attempt-associated ED visits increased in 2020 compared with the pre-pandemic period, but they did not study those rates in 2021 [35]. We found that the odds of suicidal ideation-, suicide attempt-, and schizophrenia-associated ED visits significantly increased in 2020 and 2021 compared with 2018, with the highest odds in 2020, indicating a stronger earlier impact of the pandemic than that in 2021. Regarding suicide, *Nevada is* now ranked 12th in the US and is no longer among the top 10 states. With 642 fatalities and a rate of 19.8 suicides per 100,000 people, Nevada came in seventh place in 2019 [36]. Nevada’s Office of Suicide Prevention reports that, while suicide rates nationwide have increased, they have remained stable or even decreased in Nevada [36]. Neither suicidal ideation- nor suicide attempt-associated ED visits significantly increased in 2019 compared with 2018, indicating that their increase during the pandemic may not have been due to their possible gradual increase in Nevada. However, schizophrenia-associated ED visits significantly increased in 2019 compared with 2018. In our study, the odds ratio for schizophrenia in 2020 was the highest (Table 2). More research is needed to determine whether this highest odds ratio is related to the pandemic or its gradual increase. It has been reported that the frequency of schizophrenia among ED visits increased in the early pandemic, which might have been due to the increased need for emergency care for schizophrenia patients [13] or an increase in the incidence of the disease.
The COVID-19 pandemic has been associated with increased alcohol consumption and cigarette smoking [10]. We found that the odds of alcohol and cigarette smoking use among ED visits significantly increased in 2020, and 2021 compared with 2018, with the highest odds in 2020, another indication of the strong early impact of the pandemic. Consistently, data on ED visits in the Washington, DC/Baltimore, and Maryland areas in 2019 and 2020 revealed that a higher percentage of patients reported alcohol drinking during the pandemic [12]. Another study in Ontario, Canada, found that the proportion of all-cause ED visits due to alcohol increased by $11.4\%$ [37]. A study from Minnesota, USA, indicated a significant increase in ED visits among smokers [38]. Alcohol use and cigarette smoking have been consistently reported to have increased among ED visits during the COVID-19 pandemic [37,38], which could be a result of their increased rates during the pandemic [10] and/or possible increased emergency conditions among its users.
Opioids and cannabis had been subject to new rules and legislation at the federal and state levels prior to the pandemic [26]. Beginning in 2010, there was a decrease in opioid-associated ED visits, which coincided with federal initiatives calling for more prudent opioid prescription [26]. Policies regarding cannabis consumption mainly depend on the State authorities [26]. Nevada legalized cannabis for both medical and recreational use in 2001 and 2016 [26,39,40]. The legal use of medical and recreational cannabis went into effect in 2013 and 2017, respectively, while stricter opioid prescription laws went into effect in Nevada in 2018 [39,40]. We found that opioid-associated ED visits did not significantly increase during the pandemic compared with those in 2018, and even significantly decreased in 2021 and 2019 compared with 2018, which might be related to the strict opioid prescription laws in Nevada [39,40]. It was supposed to be the case that opioid use in all three years of 2019, 2020, and 2021 should have been lower than that in 2018. The lack of a difference between 2020 and 2018, however, may indicate that the strong early impact of the pandemic offset the strict opioid prescription laws starting in 2018 [39,40]. Using national data, Holland and colleagues found that opioid overdose-associated ED visits increased in 2020 compared with the pre-pandemic period, but they did not study those rates in 2021 [35]. Patel and colleagues investigated patients presenting to the ED with opioid overdoses at the University of Alabama in 2019 and 2020. They reported an increase in opioid overdose visits in 2020 compared with 2019 [24]. The differences between our results and those of others might be due to the use of different inclusion criteria. Our inclusion criteria were opioid use, but in other studies, their inclusion criteria were opioid overdoses [24,35]. Deaths due to opioid overdoses decreased in Nevada from 2018 to 2019, but increased from 2019 to 2020 according to a report by the Centers for Disease Control and Prevention [41].
Less information is available regarding cannabis-associated ED visits. The proportion of cannabis-associated ED visits was previously found to increase among children in 2019 compared with 2020 [2]. We found that cannabis-associated ED visits significantly increased in 2020 and decreased in 2021 compared with 2018. Nevada statistics indicate that cannabis sales increased from 2018 to 2021 [42]. Therefore, the decrease in cannabis-associated ED visits may not be due to lower consumption. More behavioral research is needed to investigate the underlying causes of this decrease and whether it will persist in the future. According to recent data, recreational cannabis legalization may be a harm-reduction strategy to combat the opioid epidemic and has been associated with reduced opioid-related ED visits, particularly among men and adults between the ages of 25 and 44 [43]. Future studies will determine whether the lack of a significant increase in opioid-related ED visits in Nevada can be attributed to the harm-reduction effects of recreational cannabis legalization.
Our study has some limitations. We identified the seven mental health and substance-use conditions using ICD codes. Any coding error could have resulted in the misclassification of ED visits. There are also other common and serious mental health and substance-use conditions. Notably, uncontrolled depression, anxiety disorders, bipolar disorders, and other mental health conditions can all contribute to suicide and have been exacerbated by the COVID-19 pandemic [12,13,17,18,19]. We were not able to accommodate all of these important conditions in our study, which aimed to investigate each condition separately, rather than combining them. Furthermore, we were unable to account for all potential confounding factors. For example, opioid use has been subject to strict federal and state regulations [26], but we did not account for this in our regression model. We analyzed the odds of certain conditions over the year. The increase in odds indicates that the proportion of ED visits for one condition versus others is higher in a given year, but it does not necessarily imply that the number of that specific type of ED visit has increased over the year. In our analysis, however, the observed increases in odds were often accompanied by corresponding increases in numbers (Table 1).
Our findings add to the literature as we analyzed longer periods during the COVID-19 pandemic and covered mental health- and substance use-associated ED visits in 2020 and 2021, whereas previous studies mainly focused on 2020 [35], from which both the early impact and the later impact of the pandemic were examined. Our findings indicate a stronger early impact than a later one. We also specifically investigated serious mental health conditions, rather than less serious mood disorders [20]. Further, we specifically looked at different mental health and substance use conditions separately, which is very important for making informed decisions. Policymakers need to know what mental health conditions or substances need more attention.
## 5. Conclusions
In conclusion, among the seven common mental health conditions and substance use-associated ED visits in Nevada, suicidal ideation, suicide attempts, schizophrenia, cigarette smoking, and alcohol drinking had significantly higher odds in both 2020 and 2021 compared with the pre-pandemic period. Cannabis-associated ED visits had significantly higher odds only in 2020 compared with the pre-pandemic period, but opioid-associated ED visits did not have significantly higher odds during the pandemic compared with the pre-pandemic period. It was observed that the pandemic had stronger early effects on mental health and the use of substances, and the effects may have decreased as the pandemic continued. This information can help policymakers to better comprehend how the outbreak affected society both stateside and nationally, make informed decisions, and develop effective policy measures and programs to prepare an effective response to large-scale public health emergencies, such as the COVID-19 pandemic, at their early stages.
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|
---
title: 'Trends in Health Behavior of Polish Women in 1986–2021: The Importance of
Socioeconomic Status'
authors:
- Monika Lopuszanska-Dawid
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001600
doi: 10.3390/ijerph20053964
license: CC BY 4.0
---
# Trends in Health Behavior of Polish Women in 1986–2021: The Importance of Socioeconomic Status
## Abstract
In the last 35 years Poland has undergone a series of fundamental economic, social, and biological transformations. With the transition from a centrally planned to a free-market economy, a period of economic and social transformation, Poland’s accession to the European Union, and the COVID-19 coronavirus pandemic, living conditions in the country have seen dramatic changes. The aim of this study was to assess whether there were changes in the basic health behaviors of Polish women, and if so, in what directions and with what strength, and whether there were differences in these changes depending on the socioeconomic status. Information on basic lifestyle factors (drinking alcohol, smoking, coffee drinking, and physical activity) and socioeconomic status (level of education, Gini coefficient, Gender Inequality Index, women total employment, employed women being in managerial positions, women among scientists) of 5806 women aged 40–50 years were analyzed. During the 1986–2021 period, based on the same methodology, team of technicians and research tools, six birth cohorts of women were examined in 1986, 1991, 1996, 2006, 2019 and 2021. Highly statistically significant changes were found in the frequencies of declared health behaviors from 1986–2021, according to the order of significance in coffee and alcohol consumption, physical activity levels, and smoking and smoking intensity. In subsequent cohorts, there were fewer and fewer women who did not drink coffee and alcohol, while more drank more than two cups of coffee a day and drank alcohol more often than 2× a week. Furthermore, they were more likely to be physically active, and slightly fewer were smokers. The lifestyles of the women were less likely to depend on their socio-economic status than the cohorts. In 1991 and 1996, there was a marked intensification of unhealthy behavior. Changes in the analyzed health behaviors may have been caused by adaptation to the high level of psychosocial stress observed during the transition of the 1986–2021 period and may result in changes in the biological condition and quality and length of life of Polish women. Research on social differences in health behavior provides an opportunity to analyze the biological effects of changes in the living environment.
## 1. Introduction
In the last 35 years, Poland, like other countries in Central and Eastern Europe, has undergone a series of fundamental economic, social, and biological transformations. They started with the massive economic and social transformation from a centrally-planned economy typical of communist countries to a free-market economy, followed by Poland’s accession to the European Union, and the COVID-19 coronavirus pandemic. During this period, the polish population was subjected to a kind of unique and unplanned biological and historical “experiment”. Both phenomena have been associated with fundamental social changes, leading to dramatic changes in everyday life and significant economic consequences. These phenomena have brought a heightened sense of chronic insecurity about the health and lives of most people in Poland. In the last years of the period studied, there has been an unprecedented phenomenon with a significant impact on daily life: the worldwide COVID-19 pandemic associated with an increased sense of threat to health and life and social isolation.
As indicated by the findings of various studies, an unstable living environment (e.g., the social and systematic transformation of the late 1980s and early 1990s, the pandemic period) is a strong factor in psychosocial stress and a challenge that different social groups have tried to meet in different ways [1,2,3,4,5]. Numerous studies have demonstrated that broadly understood mental comfort has a direct effect on life expectancy, and the discomfort significantly increases the risk of premature death [6,7,8]. Changes in the broadly understood living environment of the Polish population had a clear impact on the lifestyles of Poles and on their health status. It is proven that high levels of stress can contribute to a number of unhealthy behaviours and habits, e.g., excessive consumption of high-calorie foods, abuse of alcohol and medicines, smoking, drug use, deterioration of sleep quality, avoidance of physical activity or general apathy [9,10]; although results in this regard remain partial and inconsistent. Diversified skills in coping effectively with stressful situations and accumulating the negative effects of chronic stress can lead to health differences between groups or social strata. With the differentiated ability to adapt to the new reality, both of individuals and entire social groups, the effects of psychosocial stress of the period 1986–2021 could make the biological effects of perceived psychosocial stress temporally and socially differentiated, thus perhaps manifesting gradients in lifestyle and biological condition [11].
Studies have also demonstrated that many biological features of the body are highly dependent on the position of the individual on the scale of social prestige, e.g., educational level or social position [4,12,13]. The lower the position of individuals on the educational scale, the worse the broadly defined biological condition. Studies of the Polish population have shown that people with lower education, for example, have less favorable biological parameters of bone mineralization [14], lower body height [4,11], are more obese [11,15], age faster [16,17,18], and have higher mortality rates [19].
The significant biological differences between the representatives of different education categories are mainly associated with different levels of health awareness, and different lifestyles and models of healthy and unhealthy behavior [20,21]. Furthermore, there are temporal secular trends in human biological traits that reflect changes in the living conditions of given birth cohorts [4,11,19].
Living conditions over the past 35 years have been fraught with stressors that may have affected women’s lifestyles. Therefore, it is undoubtedly interesting to attempt to assess changes over time in the basic elements of the lifestyle of Polish women and to determine which basic factors, including time (successive years of the study, cohorts) or educational level (being a good measure of social standing) are more responsible for the lifestyle. Assessing lifestyle changes, their strength and direction, and learning about the strength of the impact of the analyzed factors on women’s lifestyle can be of great practical importance, mainly by identifying groups at risk of deterioration of the biological condition and shorter healthy life expectancy, i.e., groups with lower capacity to adapt to a changing environment.
## 2.1. Material
The material for the analysis consisted of data on 5806 adult Polish women aged 40–50 years of the Caucasian race, living in Polish cities with over 500,000 inhabitants. Research data of the participants were presented by year of examination: 1: 1986, 2: 1991, 3: 1996, 4: 2006, 5: 2019 and 6: 2021. The numbers of women in subsequent groups are respectively: 1: $$n = 2016$$; 2: $$n = 1181$$; 3: $$n = 1203$$; 4: $$n = 642$$; 5: $$n = 498$$; 6: $$n = 266$$, details are given in Table 1 (the number of respondents in each year of the study in accordance with the Power test at a minimum level of 0.8). The choice of women in the fifth decade of life is dictated by the fact that people in this age group are usually characterized, on the one hand, by a kind of stability in life in professional, economic and private terms, and on the other hand, it is a period of relatively rapid changes in body systems associated with entering menopause. In addition, this is an exceptionally sensitive decade in which cardiovascular diseases frequently manifest themselves. Moreover, studies of middle-aged women can provide knowledge about factors which impact longevity [22,23].
The data were collected within the framework of several research projects (1986–2021 years) financed from various non-statutory grant funds (e.g., KBN—Committee for Scientific Research, MNiSW-Ministry of Science and Higher Education grants, MEiN—Ministry of Education and Science). Research in the following years (1986, 1991, …) was carried out as part of research projects that varied thematically. The scientific goals of these projects were not focused on health behavior analysis. For the purposes of this study, the same basic questions and answers were selected from the raw data for each project. Thus, based on scientific projects carried out in a certain time sequence in the past, while maintaining consistency in research questions, it is possible to analyze trends in changes in health behaviors over 35 years. The years assigned to each cohort (1986, 1991, etc.) represent the middle year of each research project.
The average number of people participating in Polish examinations, is about $25\%$ [6,16]. The participants included in the analysis were not selected in any way in terms of any parameters. The study group was ethnically homogeneous, without linguistic or cultural minorities. The women participating in the study did not suffer from severe chronic diseases at the time of the study or in the past. No pathologies were also found in the women in the physical examination. About $1\%$ of the questionnaires were rejected from the final database due to the incompleteness of the answers provided to all questions. Interventionary studies involving animals or humans, and other studies that require ethical approval, must list the authority that provided approval and the corresponding ethical approval code.
## Ethical Approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All persons were orally informed about the aims of the projects and all testing procedures and all gave their informed consent prior to their inclusion in the study. At any time, the subjects could withdraw without giving any reason.
The grant applications to the KBN and the MNiSW were reviewed and approved by a board of experts in many fields (including ethical standards). Before the study began the board took into account the observance of ethical standards in this human research. Only projects that received the highest criteria for substantive and ethical evaluation received funding. The most recent studies have been approved of the Senate Ethics Committee for Scientific Research of the Józef Piłsudski University of Physical Education in Warsaw (SKE 01-$\frac{5}{2020}$, 20 November 2020).
## 2.2.1. Collecting Data
The research was based on the same methodology, team of technicians and research tools. Surveys were conducted at each time point using a supervised survey method (paper and pencil). The participants completed questionnaires on health behavior independently in the presence of a person providing clarification, if necessary. The face-to-face interview method was not used and significantly reduced the risk of manipulating the data, for example, to gain the social approval of the interviewer [24]. Any ambiguity in the interpretation of the questions was clarified by interviewers trained for the purpose.
Survey data on basic socioeconomic status included information on the age (in years) and education of the women surveyed (data collected directly from respondents). In Poland, the educational level is still an excellent measure of the position on the social scale [4,11]. Three categories of the educational level of the women surveyed were used: [1] tertiary education, [2] secondary education, and [3] vocational education and lower. In addition, in order to assess the social and economic status of women, which well describes the situation of women on the labor market and in the economy in Poland in specific years, objective and key indicators characterizing the position of women in Polish society at a specific time were included in the analysis. The main indicators included were: Gini coefficient, Gender Inequality Index, women total employment (in %), employed women being in managerial positions (in %), women among scientists (in %) (data extracted indirectly from relevant international databases) [25].
The research questionnaire included questions on selected basic lifestyle elements of the women surveyed. In all the surveys (1–6), the same short questions were asked and the participants had the same short answers to choose from. Lifestyle was assessed based on the basic elements such as alcohol consumption, cigarette smoking, and its intensity, coffee consumption, and the level of leisure-time physical activity. Based on the answers to the question about alcohol consumption, the women surveyed were divided into three categories: [1] non-drinkers (drinking exceptionally rarely or never), [2] occasional drinkers (up to 1–2 a month), [3] frequent drinkers (more than two times a month). Responses about cigarette smoking were divided into three categories: [1] never, [2] quit smoking (at least 1 year ago), [3] smoker. Furthermore, cigarette smokers also declared how many cigarettes they smoked on average per day, with three categories to choose from: [1] less than 5 cigarettes per day, [2] 5–20 cigarettes per day, and [3] more than 20 cigarettes per day. Coffee drinking frequency was assessed using three categories: [1] “I don’t drink coffee”, [2] “I drink one or two cups a day”, and [3] “I drink three or more cups a day”. The question concerning physical activity included three categories distinguished by the regularity and intensity of the declared activity: [1] physically active (regularly doing any physical activity), [2] exercising irregularly, and [3] physically inactive.
## 2.2.2. Statistical Analysis
A non-parametric chi-squared test χ2 was used to assess the significance of differences in the frequencies of specific education categories and health behavior between years (cohorts), and within educationally homogeneous groups between years of the study. The level of significance was set at α = 0.05. All significance levels are given to the nearest 0.0001. Logistic regression analysis was used to assess the risk of individual unhealthy behaviors for subsequent study years and educational levels. Odds Ratio (OR), $95\%$ confidence intervals ($95\%$CI), McFadden’s R-squared values of determination, Hosmer-Lemeshow test for goodness of fit for logistic regression models and levels of statistical significance (p) have been given. The STATISTICA 12.0 and 13.5 packages were used for analyses [26].
## 3. Results
Frequencies for data collected directly from respondents: educational level, lifestyle parameters for each study year, and chi-squared test values for the significance of changes over time were presented in Table 1. Changes in the educational structure of the women surveyed during the period studied were large and highly statistically significant (χ2 = 856.21; $$p \leq 0.0001$$). In subsequent cohorts, there was a noticeable increase in the percentage of women with tertiary education. There was an increase between the 1986 and 2021 cohorts in the percentage of the most educated women, from $23.8\%$ and $31.9\%$ in 2006 to $63.5\%$ in 2021. At the same time, the percentage of women with primary education decreased significantly, from $34.7\%$ in 1986 to $3.7\%$ in 2021. *In* general, the largest group throughout the period studied was women with secondary education (about $37.01\%$ on average).
The variation in the intensity of drinking alcohol during the study period was highly statistically significant (χ2 = 1808.02; $$p \leq 0.0001$$). In all cohorts, the vast majority of women reported only occasional drinking, and the percentage of these participants ranged from $50.99\%$ in 1986 through $78.51\%$ in 2006 to $73.31\%$ in 2021. At the same time, there was a very marked decline in the number of abstinent women, from ca. $42.21\%$ in 1986 and $7.48\%$ in 2006 to $11.28\%$ in 2021. In contrast, the percentage of women reporting frequent drinking increased nearly 2.5-fold, from $6.80\%$ in 1986 to $15.41\%$ in the latest cohort, with cumulative percentages of heavy drinkers in 1991 and 1996 (respectively, $59.95\%$ and $36.49\%$) (Table 1).
The change in smoking frequency in subsequent cohorts showed significant variation (χ2 = 354.65; $$p \leq 0.0001$$). Despite the high fractions of female smokers in each study cohort (average for surveys 1–6: $35.95\%$), a positive slight decrease was observed in the fraction of female smokers, especially after the 1990s. Across cohorts 1–6, the percentage of women giving up their smoking habit nearly doubled from $11.36\%$ in 1986 by $21.03\%$ in 2006 and $23.68\%$ in 2021. The fraction of non-smokers ranged from $34.41\%$ to $68.27\%$. The largest numbers of women declaring to smoke cigarettes, $47.47\%$ and $41.16\%$, were found for the 1996 and 1991 cohorts, respectively. Furthermore, the fewest women (just $5.33\%$) declared quitting smoking in 1991 (Table 1).
Analysis of the intensity of cigarette smoking by female smokers indicated a worrying trend: the number of cigarettes smoked a day has clearly increased. Between 1986 and 2021, the fraction of women claiming to smoke more than five cigarettes a day increased from nearly $50\%$ in 1986 to around $80\%$ in almost all subsequent cohorts (except 2019, with $57\%$). The number of women smoking more than 20 cigarettes a day increased significantly in that period, from $14.25\%$ in 1986, a peak of $30.24\%$ in 1991 to about 21–$26\%$ in 1996–2019, and $15.79\%$ in the latest survey. The most unfavorable pattern of cigarette smoking was observed in 1991, when as many as $83.74\%$ of women smoked more than 5 cigarettes a day, of which $30.24\%$ smoked very heavily (more than 20 cigarettes a day) (Table 1).
The analysis of changes in the amount of coffee drunk by the women studied over the period studied showed the greatest significance of differences in frequencies compared to other lifestyle elements (χ2 = 2439.36; $$p \leq 0.0001$$). Throughout the period, on average, about $40\%$ of women drank 1–2 cups of coffee a day. There was a marked decrease in the percentage of women not drinking coffee, from $47.67\%$ in 1986 to just $2.91\%$ in 1996 and $11.27\%$ in 2021. At the same time, the percentage of women drinking more than two cups of coffee a day has increased significantly in recent years, from $5.85\%$ at the beginning of the period studied, $76.56\%$ in 1996, and $18.05\%$ in 2021, with an average of about $35.14\%$ for the entire period 1–6 (Table 1).
The frequency distribution of the different levels of physical activity of the women surveyed changed significantly between 1986 and 2021 (χ2 = 1681.55; $$p \leq 0.0001$$). These findings demonstrated a low level of leisure-time physical activity of women but they also indicated a positive trend of the gradually increasing percentage of women who were physically active on regular basis (Table 1).
The percentage of women regularly involved in some forms of physical activity increased significantly, especially since the late 1990s. In the surveys conducted in 1996, 2006, 2019 and 2021, the percentage of physically active women at least doubled compared to 1986, and, in 2019, reached $69.48\%$. The percentage of women reporting low physical activity, passive leisure-time activities or irregular physical activity, dropped from nearly $77\%$ in 1986 to about $57\%$ in 2021. A disturbing phenomenon is the doubling of the number of women who spend their leisure time completely passively, rising from $15.77\%$ in 1986 to $31.62\%$ in 2006, with the number declining again to around $16\%$ in the latest cohort.
Table 2 presents the frequencies of categories of individual health behaviors in successive cohorts within homogeneous educational groups (data collected directly from respondents), the significance of differences in these frequencies, and the significance of differences in the frequencies of categories of behavior regardless of the year of the study but depending on the educational level. For all lifestyle elements, statistically significant differences were found in the reported categories of behavior between educational groups (results presented in the last column of Table 2). The largest differences were observed for alcohol drinking (χ2 = 358.47; $$p \leq 0.0001$$), followed by physical activity (χ2 = 213.00; $$p \leq 0.0001$$), coffee drinking (χ2 = 144.14; $$p \leq 0.0001$$), cigarette smoking, and the number of cigarettes smoked (respectively χ2 = 110.34, $$p \leq 0.0001$$ and χ2 = 17.61, $$p \leq 0.0015$$). Differences in the frequencies of health behavior categories between successive surveys (1–6) were found to be significantly greater than differences between educational groups. For example, for alcohol drinking, the differences in frequencies in the 1986–2021 period in subsequent behavioral categories of tertiary or vocational education are about 2 times greater than the differences based on educational level (respectively χ2 = 833.44 and χ2 = 633.79 relative to χ2 = 358.47). An analogous direction of the relationship was found for all health behaviors. The largest relative differences were noted for coffee drinking, for which the importance of time (cohorts 1–6) is about 6–7 times more strongly a differentiating factor in coffee drinking habit than educational level (e.g., χ2 = 1073.07 for tertiary education in 1986–2021 vs. χ2 = 144.14 for the importance of the level of education, regardless of the year of the study). Therefore, despite statistically significant differences in individual categories of health behavior, it is the cohort that is by far the stronger differentiating factor of health habits than education (Table 2).
Table 3 illustrates the results of logistic regression analysis, indicating the odds ratio (OR) for the prevalence of negative health behaviors (unhealthy behaviors) in successive years of the study (with 1986 as the reference group) and in individual educational levels (tertiary education as the reference group) (analysis including data collected directly from respondents). The risk of heavy alcohol consumption by the women surveyed during the period studied increased markedly in 1991 (more than 18 times) (OR = 18.3546), followed by a double decline in 1991–2021 (respectively for years 2019 and 2021, OR = 1.7028, OR = 2.0486). As the educational level decreased, the risk of heavy alcohol consumption decreased.
Compared to 1986, the risk of smoking cigarettes was $32\%$ higher in 1991 (OR = 1.3153) and about $79\%$ higher in 1996 (OR = 1.7900). In subsequent cohorts, the risk of smoking was lower than in 1986, with the lowest OR recorded for 2019 (OR = 0.4922). As the level of education decreased, the risk of smoking increased. For women with vocational education, it was about $41\%$ higher than for women with tertiary education (OR = 1.4056).
The risk of the intensity of smoking more than 20 cigarettes a day for women in 1991 was almost 3 times higher than in 1986 (OR = 2.6970) and about 2 times higher in 1996 and 2006 (respectively OR = 1.8437 and OR = 2.1607).
The habit of drinking more than two cups of coffee a day was about 20 times more frequent in 1991 than in 1986 (OR = 19.9733), and about 60 times more frequent in 1996 (OR = 60.3562). In the subsequent years of the study, the risk of consuming this amount of coffee decreased to about 3 times that of the baseline year (OR = 2.8215). Women with secondary and vocational education had a lower risk of consuming more than two coffees per day (respectively OR = 0.3509 and OR = 6353).
With subsequent cohorts, the risk of being physically inactive at first significantly increased almost 8-fold (1986: OR = 7.8062) and decreased in 2019 to OR = 0.25 and then in 2021, and slightly increased to the level recorded in 1986 (OR = 0.9002). As the educational level decreased, the risk of being physically inactive decreased slightly (e.g., for vocational OR = 0.7449).
The values of all χ2 tests, McFadden R-square coefficients of determination oscillating from 0.2858 to 0.9153, and the values of all Hosmer-Lemeshow tests for logistic analysis indicated goodness-of-fit for the analyses performed in the study (value p from 0.0070 to 0.0001) (Table 3).
Table 4 presents the results of a logistic analysis in which, in addition to data obtained directly from the respondents, five indicators characterizing the general situation of women in Polish society and the labor market in specific years were included in the variables determining health behavior. The inclusion of indirectly acquired variables in the logistic analysis increased the goodness-of-fit of the models in most cases, which can be seen in the higher values of the Hosmer-Lemeshow test (e.g., for smoking, the test value increased from 27.3277; 0.0001 to 61.4640; 0.0001). The exception is the effect of the determinant variables on the risk of unhealthy alcohol consumption, for which the goodness of fit of the model decreased (from 79.1956; 0.0001 to 16.0403; 0.0248). With regard to almost all of the baseline health behaviours analysed, the cohort (and therefore the importance of change over time) remained an exceptionally strong predictor of health behavior change, reaching the absolute highest OR values for the risk of unhealthy alcohol consumption, smoking, coffee consumption or the risk of being physically inactive. The second most important predictor of health behavior was found to be the indicator of employed women being in managerial positions. It is a significant factor for, in turn, drinking alcohol, smoking cigarettes and drinking coffee. As the frequency of women being in managerial positions increases, the frequency of unhealthy health behaviours increases (e.g., in the case of frequent alcohol consumption OR = 25.6732; $$p \leq 0.0001$$). The Gender Inequality Index and, further down, the Gini coefficient also reach significance (Table 4). *In* general, as the number of female employees and the number of female scientists increases, the risk of anti-health behavior decreases.
## 4. Discussion
An undeniably positive effect of the ongoing economic and social changes in the 35 years of 1986 to 2021, especially the beginning of the period, was the rapidly increasing access to consumer goods in the form of both domestic and imported products to meet basic needs and durable goods [27]. A number of source data indicate that access to goods and their flexible prices have caused significant changes in lifestyle patterns, often in a health-promoting direction. Noticeable directional changes were observed in basic lifestyle elements that are highly relevant to health, with different educational groups seeming to respond somewhat differently to the changing reality. In the 1990s, marked changes were noted in increased unhealthy behavior, intensifying alcohol consumption, cigarette smoking, and increased physical inactivity. These changes may have had a significant impact on changes in the biological and functional status and the quality of life of Polish women. Of fundamental importance in the health behavior of women in Poland appears to be their situation in the labor market and the economy.
## 4.1. Alcohol Drinking
A clear trend of a highly significant increase in alcohol consumption was observed during the period studied, especially the years of economic transition in Poland and other Central and Eastern European countries [21,28,29]. The annual consumption of all alcoholic beverages in Poland in units of pure alcohol per capita aged 15 years or older, declined from 9.5 L in 1988 to 7.7 L in 2001, followed by a rise to 8.8 L in 2006, and 9.8 L in 2019, 9.6 L in 2020, and 9.7 L in 2021 [30]. The fluctuation in alcohol consumption between 1986 and 2021 was accompanied by a marked change in drinking patterns. In the late 1980s and early 1990s, hard liquor (e.g., vodka) was mainly consumed, while consumption of spirits declined and consumption of lower-alcohol beverages, such as wine and beer, increased in subsequent years [31]. Between 1986 and 2021, the amount of beer consumed in Poland almost tripled (from 1.9 L to 5.2 L of pure alcohol) [31]. The amount of wine consumed between 1986 and 1995 declined from 1.9 L to 0.9 L of pure alcohol, only to rise to 1.4 L in the next five years, before dropping to 1.1 L in 2006 [31] and just 0.8 L in 2020. However, since the early 2000s, there has been a trend of successively increasing the consumption of spirits again. Between 2001 and 2021, their consumption increased from 1.7 L to 3.7 L [32]. The observed temporal trends in changes in the quantity and quality of alcohol consumed depended on access to a particular type of alcohol. The onset of the free economy in Poland and the opening of the market to foreign goods have resulted in an influx of new alcoholic stimulants that are attractive to Poles. Increased alcohol consumption in Poland immediately after the privatization of the state-owned enterprises manifested itself in increased hospitalizations for alcohol psychosis or alcohol dependence, admissions to psychiatric hospitals for alcohol-related disorders, increased mortality rates from liver cirrhosis, and increased traffic accidents due to driving under the influence of alcohol [33].
The results of a study of alcohol consumption patterns in Poland [34] showed that nearly three-quarters of women consumed alcohol. Polish women drink much less alcohol on average than men and experience related problems much less often. Men drink alcohol almost three times more often than women (average 106 days per year vs. 37 days). The difference is mainly attributable to men’s more frequent beer drinking (an average of 98 days per year for men and 21 days per year for women) and more than twice as frequent spirits drinking (20 days) as for women (9 days). In the case of wine, it is women who drink almost twice as often as men (18 days vs. 10 days). Women drink almost four times less pure alcohol per year than men (the average is 2.2 L for women, and 8.1 L for men). An increase in alcohol consumption was also noted in the women surveyed. Among the respondents, the percentage of women who did not drink alcohol was found to have decreased from $42.21\%$ in 1986 to $11.28\%$ in 2021, with an increase in the number of women reporting frequent drinking. This is in line with the decreasing difference in drinking between men and women found in other European countries, mainly due to an increase in women’s consumption of alcohol, probably lower-alcohol beverages (mainly wine), especially in the group aged over 40 years [35]. Similar trends have been observed in Russia, Lithuania, and Estonia [36].
## 4.2. Tobacco Smoking
Another highly unhealthy lifestyle element affecting the health of the Polish female population is smoking. *In* general, the women surveyed reported quitting cigarette smoking more frequently from cohort to cohort, but a particularly negative aspect of cigarette smoking by the women surveyed is that smokers, in successive cohorts, reported smoking more and more cigarettes a day. Female smokers tended to smoke 5 to 20 cigarettes over time compared to smoking fewer cigarettes. The unfavorable trends in smoking patterns among Polish women are confirmed by WHO data [30] and other surveys [11,21]. The reduction in the frequency of cigarette smoking among men and the increase in the number of men declaring quitting smoking observed between 1984 and 1999 was not reflected in women [21,37]. Women, regardless of their education, were less likely to quit smoking than men, and there was an additional increase of more than $18\%$ of smokers among women with a lower level of education.
The phenomenon of women’s intensification of their smoking habit and women’s higher biological sensitivity to the toxic effects of cigarette smoke appears to be responsible for the increase in women’s deaths from lung and other tobacco-related cancers, and the reduction in men’s excess mortality rates compared to women [19,38,39,40]. Studies in thirty-six European countries have shown that male mortality due to lung cancer is trending downward [41]. Furthermore, mortality rates of women from the same cause continue to rise in many countries, with mortality rates due to lung cancer continuing to rise in high-risk countries in Eastern Europe (Hungary, Poland and the Czech Republic), and Northern Europe (Denmark, Iceland and Great Britain).
Research results indicate that smoking of cigarettes may be strongly associated with the acceleration of aging processes of the female reproductive system and increasing the risk of earlier menopause [23]. Chmara-Pawlińska and Szwed indicate that non-smoking women have menopause on average two years later than women who are habitual smokers [23]. Moreover, the number of cigarettes smoked every day affected the age of menopause, accelerating its occurrence in women who smoked more than five cigarettes per day. Thus, the differentiated degree of intensification of smoking by Polish women seems to have a modifying effect on the length of the female reproductive period in the last 35 years.
Social motivations for smoking cigarettes and addiction to smoking as a habit rather than nicotine addiction make women more likely to relapse, more likely to smoke irregularly and to smoke very large amounts of cigarettes under stress. The smoking habit helps reduce stress and negative emotions, to which women are quite susceptible, and the main barrier to women quitting this habit is a lack of faith and skill in successfully quitting and a fear of gaining weight. Furthermore, the associations demonstrated between certain personality traits and the likelihood to smoke cigarettes confirmed that open-minded and sociable people with high ambitions for success, e.g., professional success, are highly likely to become addicted to cigarette smoking [42]. Furthermore, a disturbing trend can be observed; despite most European women quitting smoking, the percentage of Polish female smokers is amongst the highest [31].
When considering the problems of smoking addiction, it is important to be aware that in addition to its direct effects in the form of increased mortality rates from tobacco-related cancers, it also affects the increased risk of death from cardiovascular diseases [43]. According to the Centers for Disease Control and *Prevention data* [44], smoking puts women’s health at greater risk than men’s. Cigarette smoking shortens lives by an average of 14.5 years in women and by 13.2 years in men. Furthermore, female smokers have twice the risk of having a heart attack and developing bowel cancer, even though they smoke less than men. Female smoking is associated with more dangerous health consequences, as estrogen exacerbates the carcinogenic effects of cigarette smoke components and accelerates the growth of pathological cells in the lungs. Female smokers, more than male smokers, are at risk of chronic respiratory diseases, chronic obstructive pulmonary disease, osteoporosis, and cervical and breast cancer. Taking oral contraceptives also increases the risk of heart attack and stroke. *In* general, smoking cigarettes not only shortens the length of life but also a disease-free life. The etiology of smoking addiction, which is different from that of men, and the increasing percentage of female smokers show the importance of this problem.
Negative trends in cigarette smoking by women reached their peak in the initial shock period of the transition, in which the largest number, 41.16–$47.47\%$ of women reported smoking cigarettes, with the smallest number of them ($5.3\%$) quitting the habit. Unfavorable trends in cigarette smoking, combined with other adverse health behaviors, especially during a difficult period of rapid economic transformation in Europe, worsen the quality of life long before death, further increasing the risk of cardiovascular disease, the leading cause of mortality, morbidity, and hospitalizations in women across Europe [44,45].
## 4.3. Coffee Consumption
During the period studied, the fraction of women who did not drink coffee decreased markedly, while the percentage of women who drank more than two cups of coffee a day increased from the baseline $5.85\%$ to $76.56\%$ in 1996 and $18.05\%$ at the end of the period. A study of 8821 Polish men and women conducted as part of the HAPIEE project found that heavy coffee drinkers had lower relative body weight, smaller waist circumference, lower systolic and diastolic blood pressure and triglyceride levels, and higher levels of the HDL fraction of cholesterol compared to those drinking less than one cup a day [46]. It has also been proven that high coffee consumption among women (more than two cups a day) reduces the risk of metabolic syndrome by $31\%$ compared to women who drink at most one cup of coffee a day [47]. These researchers found metabolic syndrome in $30.2\%$ of non-coffee drinkers and in $23.7\%$ of those who drank more than two cups of coffee a day. More and more studies confirm the health-promoting effects of coffee on the human body, although the results are still sometimes debatable [47,48,49]. A number of studies have confirmed its beneficial effects on, for example, glucose levels, insulin metabolism [50,51], BMI and waist circumference [52], or the potentially decreased risk of cardiovascular disease and cancer, especially in women [48,53]. The exact mechanism behind the health-promoting effects of drinking coffee on the human body is subject to debate but the main role is attributed to polyphenols, antioxidants, and anti-inflammatory and anti-diabetic drugs. Coffee contains significant amounts of vitamins and minerals, such as ascorbic acid (vitamin C), B vitamins, riboflavin, niacin, folic and pantothenic acids, magnesium, potassium, manganese, fluorides, chlorogenic acid, caffeine, diterpenes, trigonelline, and melanoidins. The rich composition of biologically active substances has a protective effect on the cardiovascular system and counteracts metabolic disorders [48,49,51]. The changes in coffee consumption patterns observed among the women surveyed therefore appear to be highly health-promoting.
## 4.4. Physical Activity
Human biological status is significantly dependent on the level of physical activity, especially its recreational form. The effects of physical exercise, including involvement in competitive sports, on female health and life expectancy have long been of interest to a number of researchers [54,55]. Numerous research results, including those obtained for the Polish population, have shown a significant relationship between the level of physical activity and a number of functional indicators, such as cardiovascular fitness, lipid profile, and homocysteine concentration [56,57]. The women surveyed were not very physically active in all cohorts, but there was a positive increasing trend in the percentage of women who were regularly physically active from $23.26\%$ in 1986 to $42.48\%$ in 2021. Unfortunately, over the period studied, the percentage of inactive women multiplied in several cohorts, from $15.77\%$ at baseline to a dramatic $62.15\%$ in 1991 and $16.54\%$ in 2021. As indicated by the results of a survey of the physical activity of Poles conducted by CBOS [58], despite the fact that men and women appreciate the importance of physical exercise and recreational physical activity and being active outdoors, as many as $39\%$ declared that they had rested passively and did not practice sports or perform any exercises for physical fitness. However, there has been a positive trend of a slow decrease over time in the number of people who were completely inactive from $74\%$ in 1997 to $59\%$ in 2003 and $39\%$ in 2018 [59,60].
Other results from studies of the Polish population highlight clear social gradients in the level of recreational physical activity [21,58,60]. As the educational level decreased, the tendency toward inactive leisure activities increased. The CBOS results [58] show that among the physically active respondents, there are twice as many people with tertiary education compared to those least educated ($83\%$ vs. $42\%$). In a study conducted between 1984 and 1999, an increase in the frequency of regular physical activity was noted only among women with tertiary education [21]. The increase in the percentage of women declaring active leisure activities, especially women in their 40s and 50s, is likely due to increased health consciousness and also better care of their appearance. In light of the strong links between physical activity levels and perceived emotional states such as anxiety or depression proven in the literature, the trend of an increasing percentage of women who are active is favorable from the standpoint of the mental status of adult women [61].
The changes in basic health behaviors observed in the Polish population over the analyzed period were clearly reflected in healthy changes in the values of the basic somatic physique characteristics of the women studied, i.e., an increase in average body height, a decrease in relative body weight and body fat percentage, or decreasing body circumference, healthier measures of cardiovascular fitness, and biochemical blood results [4,11]. Changes in women’s health and dietary habits may also have reduced the incidence of diet-related conditions, i.e., obesity, hyperglycemia, hypercholesterolemia, and contributed to the stabilization of systolic and diastolic blood pressure and heart rate [4,11].
## 4.5. Psychosocial Stress and the Importance of Socioeconomic Status
With the generally positive trends in changes in health behavior, the sharp changes observed during the first shock years of the systemic transformation in Poland are noteworthy. High levels of psychosocial stress can induce a range of unhealthy behaviors and habits [9,10]. Different skills in coping effectively with stressful situations and accumulating the negative effects of chronic stress can lead to health differences between groups or social strata [8,16,62]. The social changes in the period studied (1986–2021), especially its first stage, related to the transition from a centrally-planned to a free market economy, proved to be so dramatic that they were associated with a great and sudden stress experienced by the entire society, including women aged 40 to 50 years. Both the direct and indirect effects of intense stress, through constant stimulation of the hypothalamic-pituitary-adrenal axis and the development of a stressful lifestyle, resulted in dramatic adverse health-related changes in the women studied. Not surprisingly, many parameters describing the biological status of the women surveyed showed their breakdown during this period [11,63]. The level of psychosocial stress experienced and differences in the level of knowledge and health awareness regarding the importance of a healthy lifestyle or the need for regular preventive examinations are considered among the key reasons for the social gradients in biological measures of health status. Some researchers have demonstrated associations between educational levels and selected lifestyle elements, including the frequency of harmful smoking habits, alcohol abuse, or levels of involvement in recreational physical activity [21,64,65]. It is common that the prevalence of risky unhealthy behaviors, such as cigarette smoking, alcohol abuse, and the prevalence of sedentary lifestyles, increase as the educational level decreases. These relationships tend to be particularly pronounced in men [66], while among women, the association of educational level with the self-reported frequency of healthy and unhealthy behaviors are not so straightforward. An example of this is the increasing percentage of women with secondary education who regularly and heavily smoke cigarettes and consume alcohol observed over the past two decades in this and previous studies [21]. More frequent use of cigarettes by these women may be a mechanism to reduce the high levels of stress resulting from the need to find the balance between meeting high demands at work, professional ambitions, and the demands of family life, and controlling appetite and protecting against unwanted weight gain [67,68]. Some studies have shown the educational level to be the most important socioeconomic factor, associated with almost all parameters of biological condition, and the significant health risks throughout the study period. Among Polish women, regular social gradients were found in most parameters of biological fitness. The different level of sensitivity of different social groups to the same external stimuli in the form of social and political changes in Poland has been confirmed by other analyses of the Polish population, such as the study of secular trends in the body height of children and adolescents or the maturation of girls [69,70]. They have indicated that certain social subgroups, such as those at the top of the social ladder, experience more dynamic changes in body height compared to lower subgroups. Therefore, the magnitude of the changes associated with Poland’s political transformation at the turn of the 21st century or the pandemic crisis may clearly differ from one social group to another.
## 4.6. COVID-19
Highly stressful situations, such as the shock period of socioeconomic transition or the COVID-19 pandemic, have negatively affected well-being, mental health, and lifestyles [71,72]. Stress is conducive to the consumption of more alcohol [73,74], increased nicotine addiction [75], snacking between meals [76], and disrupted sleep patterns [77], which can result in weight changes [78,79], increased risk of cardiovascular disease, and premature deaths. Research results in this field are still inconclusive. Some researchers point to trends in improved healthy behaviors during the pandemic, such as an increase in the number of people quitting nicotine addiction [80] or a reduction in alcohol consumption [81].
Lockdowns announced in many countries and quarantines due to COVID-19 have significantly affected the level of physical activity, inducing a change to a more sedentary lifestyle [82]. Due to the social isolation and the extensive restrictions imposed by national governments (e.g., closure of sports facilities, prohibition of access to forests in Poland), it has been quite a challenge to meet international recommendations for levels of physical activity. With the closure of sports facilities and the inability to perform their favorite physical activities, such as team games, gym workouts, and swimming, some of the respondents began to train at home and in outdoor settings [83] in an attempt to stay motivated to practice sports [84]. Unfortunately, significant decreases in physical activity have been recorded in most countries [85], which was also confirmed in the present study. In Polish women, the percentage of physically inactive respondents in 2021 increased by as much as about $400\%$ compared to 2019 (from $5.42\%$ in 2019 to $16.54\%$ in 2021), while the fraction of those regularly active decreased from $69.48\%$ to $42.48\%$.
## 4.7. Social Situation of Polish Women
Poland still remains a country with uneven social development of individual population groups, a particular example of which are social differences based on gender. The situation of women in the labor market and in Polish social life has improved slightly in recent years. According to the UNDP Reports on Human Development [2022] [25,86] the Gender Inequality Index in Poland (GII—a composite measure of gender inequality in three dimensions: reproductive health, empowerment and the labor market, a low GII value indicates low inequalities between women and men) since the early 1990s is successively decreasing (1991: 0.278; 1996: 0.217; 2006: 0.159; 2019: 0.111; 2021: 0.109). However, women still face difficulties in advancing in their careers and obtaining salaries commensurate with their qualifications. Gender inequalities in the labor market still exist, making it difficult for women to achieve professional success. Positive trends are also observed in other indicators, e.g., employment of women in high positions or in the parliament (from $12.7\%$ in 1991 to $27.5\%$ in 2021). However, this is still not enough to ensure gender equality in this area. Moreover, the percentage of women working in science in Poland has gradually increased since the 1980s. Nevertheless, there are still some challenges related to gender inequality in science, such as a lower representation of women in senior scientific positions, fewer women receiving scientific awards and less funding for research conducted by women. According to Eurostat data, in 2020, $33.6\%$ of women aged 25–64 in Poland had a university degree [25]. It can be seen that this percentage has been steadily increasing in recent years, suggesting that more and more women in Poland are gaining tertiary education. In 2010, the percentage was $21.1\%$ and in 2015 it was $26.7\%$. Nevertheless, the values of these indicators are still less favorable than the average for the European Union [86]. The improving socio-economic situation of women, increases the comfort of social functioning, but also imposes high psychological and physical costs. The direct physical cost of the high pace of life, professional, financial and family success in women is a change in basic health behavior towards masculinization. By adopting a lifestyle typical of men, with a high incidence of hazardous behavior, women reduce their psychosocial stress levels [25,87].
## 4.8. Limitation Study
The collected research material is large, but it does not constitute a strictly random sample of the female population of big cities, as it only contains information about people who volunteered and wanted to participate in the implemented grant projects. Therefore, it can be expected that if the answers were given by randomly selected women, the results would be less positive. In addition, a minor drawback of the research material may be the inequality in the number of samples from subsequent years of research (although each sample meets the requirements of the minimum sample size for the analysis). Furthermore, it would be advisable to extend the catalog of socio-economic factors with other factors, for example, professional or family situation, which would probably significantly enrich the obtained results. Another limitation of this study may be the differences between the cohorts in the relationship between the level of knowledge, skills, competences or awareness and the level of education.
## 5. Conclusions
The observed directional changes in the basic components of the lifestyle of Polish women between 1986 and 2021, resulting from changes in environmental conditions, including the level of psychosocial stress, led to variations in the biological status of Polish women [11]. The masculinization of women’s lifestyles observed in Poland, similar to the urban areas of developed countries, resulting from the increasing occupation of high-ranking positions at work and the need to balance work and family life, is associated with a high psychophysical costs. Competition at work, prolonged stress, the fast pace of life, and aspirations to achieve professional, financial, and family success are offset by changes in women’s lifestyles, which in turn are reflected in the change in the structure of causes of death over the past quarter century [11,87,88].
The behavioral implications of the intense political, economic, social, and biological changes between 1986 and 2021 indicate a slight improvement in the lifestyles of economically active women in the fifth decade of life. In addition to individual relevance, information on trends in changes in women’s basic health behaviors should have a significant impact on national health policies and the design of appropriate educational models to promote healthy lifestyles among women. New prevention programs should take into account both the vulnerability and the varying adaptability of different social groups to changes in environmental conditions. In light of the increasing life expectancy, the results obtained in the present study allow a conservative prediction for extending the number of years without chronic diseases and increasing the quality of life of adult women.
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|
---
title: 'Smoking Bans and Circulatory System Disease Mortality Reduction in Macao (China):
Using GRA Models'
authors:
- Xinxin Peng
- Xiaolei Tang
- Jing Hua Zhang
- Yijun Chen
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001606
doi: 10.3390/ijerph20054516
license: CC BY 4.0
---
# Smoking Bans and Circulatory System Disease Mortality Reduction in Macao (China): Using GRA Models
## Abstract
This study evaluates the association between smoking rates and mortality from circulatory system diseases (CSD) after implementing a series of smoking bans in Macao (China). [ 1] Background: Macao phased in strict total smoking bans since 2012. During the past decade, smoking rates among Macao women have dropped by half. CSD mortalities in Macao also show a declining trend. [ 2] Method: Grey relational analysis (GRA) models were adopted to rank the importance of some key factors, such as income per capita, physician density, and smoking rates. Additionally, regressions were performed with the bootstrapping method. [ 3] Results: Overall, smoking rate was ranked as the most important factor affecting CSD mortality among the Macao population. It consistently remains the primary factor among Macao’s female population. Each year, on average 5 CSD-caused deaths were avoided among every 100,000 women, equivalent to about $11.45\%$ of the mean annual CSD mortality. [ 4] Conclusions: After the implementation of smoking bans in Macao, the decrease in smoking rate among women plays a primary role in the reduction in CSD mortality. To avoid excess CSD mortality due to smoking, Macao needs to continue to promote smoking cessation among the male population.
## 1. Introduction
Tobacco smoking has been well established as an independent, modifiable risk factor for premature mortality of several medical causes such as coronary, cerebral, and peripheral arterial diseases [1,2]. Cardiovascular diseases have been the leading cause of death worldwide associated with smoking [3,4]. While heavy smoking is equally hazardous to both genders, women smokers have been found to be at greater risk of smoking-related cardiovascular diseases, such as coronary heart disease [3,5], ischemic heart disease [6], and acute myocardial infarction (AMI) [7,8,9].
The WHO (World Health Organization) Framework Convention on Tobacco Control (WHO FCTC) has been recognized as the most powerful tool to counter tobacco’s negative impacts. Smoking bans are supported by WHO on the grounds that they improve health outcomes by lowering exposure to second-hand smoking (SHS) [10] or third-hand smoke (THS) [11] and potentially reducing the number of smokers. Smoking cessation by the individual is an effective measure to reduce cardiovascular diseases, also helping to reduce the economic burden of healthcare [12,13,14,15,16,17,18]. Overall, women smoke less than men, but it is noteworthy that despite significant tobacco control efforts, women’s smoking rates have barely changed and in some countries have even increased [19]. Around the world, increasingly young women (under 25 years old) are smoking tobacco [19]. Targeting tobacco smoking was especially predicted to reduce premature cardiovascular disease mortality among women in high-income Asia-Pacific and Western Europe regions [20].
## 1.1. Tobacco Smoking in China
China is the country with the greatest tobacco consumption in the world [18]. In 2018, the smoking rate among the population over the age of 15 in China was $26.6\%$, of which the male smoking rate was $50.5\%$. The size of the smoker population in China exceeds 300 million. It is estimated that more than 1 million people in China lose their lives due to tobacco smoking every year and the mortality may increase to 2 million per year by 2030 if no effective action is taken [21].
Some major central cities and provincial capital cities in China have taken strict actions in recent years to adopt smoke-free laws or implement comprehensive tobacco control actions [15,17,22]. Smoking is prohibited in all indoor workplaces, indoor public places, and public transportation in 9 of the 21 Chinese cities that have enacted smoke-free laws [22].
## 1.2. Health System and Smoking Bans in Macao (China)
The Macao Special Administrative Region of China (“Macao”) is located on the Pearl River Delta on the southeast coast of mainland China. With a total population of about 671,900 [23], Macao has the world’s highest population density of 20,620 persons per square kilometer [24]. With a life expectancy of 84.98 years [25], people over the age of 65 account for $12.1\%$ of the population [24].
Macao’s health system is a hybrid one, in which a public health provider has a key role and some private ones play supplementary roles [26]. Circulatory system diseases are the second leading cause of death in Macao, accounting for about $23.9\%$ of total deaths in 2021 [27].
Macao has grown into a major international resort city and a top destination for gambling tourism, accounting for $60\%$ of the local GDP and $70\%$ of local tax revenue. In 2019, the total number of workers in the six tourism satellite industries (gaming, retail trade, food and beverage, hotel, passenger transportation, and travel agency services) was approximately 203,000, accounting for nearly half of Macao’s working population [28]. In particular, more than twenty percent of Macao’s working population is in the gaming industry, which is infamous for heavy smoking and poor indoor air quality. It was estimated that, when there were no smoking bans, each year, 20 percent of local deaths were caused by smoking [29].
Despite strong resistance from the gaming industry [30], during the past decade, the Macao government has put significant efforts into establishing a smoke-free local environment through a variety of approaches, including legislation, law enforcement, health education, and smoking cessation aid. Smoking bans were phased in from 2012 to ensure that indoor air quality meets safety standards, protecting the health of residents and visitors alike. A partial ban with casinos as the exception in January of 2012, a full smoking ban allowing smoking lounges in local casinos in October 2014 [30], and later a blanket smoking ban without smoking lounges after 2018 were implemented in Macao [31]. Virtually all forms of tobacco advertising and promotion through any medium are prohibited.
The Macao government has demonstrated firm determination and action to enforce these smoking bans. From 2018 to 2020, tobacco control law enforcement officers of Macao inspected a total of 859,000 locations, and the total case number of prosecutions for smoking ban offenses reached 13,300 [32]. The maximum fine for smoking offenses was raised from USD 75 equivalent to USD 188. The Health Bureau of Macao provides free clinical services for smoking cessation. In August 2022, an amendment to the smoking ban was passed in Macao, prohibiting the manufacturing, transporting, distribution, importing, or exporting of e-cigarettes in and out of Macao. The retail sale, advertising, or promotion of e-cigarettes is prohibited.
Figure 1 below displays the time trend patterns of CSD mortality and smoking rate among male and female residents in Macao over the past 20 years, respectively. The overall smoking rate among the Macao population aged 15 years and above decreased from $33.7\%$ in 2011 (upon the implementation of the smoking ban) to $11.2\%$ in 2020 [33].
As displayed in Panel A of Figure 1, neither the curve of male smoking rates nor that of the male CSD mortality rates have a clear tendency. In Panel B of Figure 1, the curve of female CSD mortality rates demonstrates an observable declining tendency and is associated with an apparently declining trend in smoking rates.
The literature providing empirical evidence of the beneficial impacts of a smoking ban on circulatory system diseases is rich, especially cardiovascular diseases [4,14,15,16,17,34,35,36]. However, only a small amount of the literature examines empirical health evidence regarding smoke-free policies in China [15,17]. Currently, there has been no empirical study examining the health outcome of smoking bans in Macao during the past decade. Meanwhile, due to confounding factors such as health technology advancements in the treatment of underlying diseases, promotion of healthy lifestyles, and preventive medicine, smoking bans’ effects on health outcomes in major cities or nationwide may not be significant in empirical studies [37].
## 1.3. Aims of this Study
Applying grey relational analysis (GRA) models, this study aimed to assess the contribution of smoking bans to the decline of circulatory system diseases in Macao (China). GRA assumes a non-functional sequence model and does not generate results in conflict with qualitative analysis; its advantages include being computationally simple and not requiring large amounts of data or data normalization [38,39]. This method can be flexibly applied to various fields [38,40,41].
The findings of this study may provide an empirical evaluation of smoking bans in Macao and policy suggestions for the next stage. The empirical evidence on the positive effects of smoking bans on health outcomes will serve as a policy reference for promoting a comprehensive smoke-free policy, even among the tourism and hospitality sectors in China and other Asian nations.
## 2. Research Methods
A series of grey relational analysis (GRA) models were applied in this study to examine the role of smoking rate in reducing CSD mortalities. Ordinary least-squared (OLS) regression analysis was performed to estimate the size of the association. Due to the small observation numbers in this sample ($$n = 40$$), a bootstrapping method (with a repetition of 1000 times) was adopted to generate robust standard errors.
## 2.1. Grey Relational Analysis (GRA)
Also known as Deng’s grey incidence analysis, GRA models are based on grey system theory [40,42,43,44,45]. Grey system theory is based on the recognition and realization that all-natural and social systems are intrinsically uncertain and subject to a variety of uncertainties and noises, which are caused by internal or external disturbances as well as the limitations of human knowledge and perception. Discrepancies in the system or the available data is one of the defining qualities of an uncertain system [45]. Generally, imperfection or inaccuracy of information can be categorized into three types based on its origin (namely conceptual, level of perspective, and prediction inaccuracies). For instance, phrases, such as “large”, “small”, “fat”, “thin”, “good”, “bad”, “young”, and “beautiful”, are commonly used, but they are subjective and lack a precise meaning [45]. A system with imperfect information is represented as being midway between a white system (with perfect information) and a black system (with zero information) [45].
Based on the intuition introduced above, GRA analyzes the degrees of geometric curve similarity between data sequences. If the curve similarity is higher, the data sequences are judged to be of higher relevance, and vice versa [42,45]. In this way, a GRA model reflects the interactions between the factors examined based on the correlation coefficient of points. Specifically, Deng’s GRA mode follows the computational steps below [40,42,43,44,45,46,47].
Step 1 is to construct the reference sequence x0 (the dependent variable) and the comparison sequence xi ($i = 1$, 2, 3…, n), which serves as the independent variables [48].
Step 2 is to calculate γi (k), given observation number k ($k = 1$, 2… m), according to Equations [1]–[3]. γi (k) is also called a grey relational coefficient. [ 1]γi(x0(k),xi(k))=minmin|x0′(k)−xi′(k)|+εmaxmax|x0′(k)−xi′(k)||x0′(k)−xi′(k)|+εmaxmax|x0′(k)−xi′(k)| where [2]xi′(k)=xi(k)xi¯(k), [3]xi¯(k)=1n∑$k = 1$nxi(k) $i = 1$, 2…, n; $k = 1$, 2…, m; (k indicates observation time or observation number).
In Equation [1], ε has a value between 0 and 1, and the middle value is often assumed to be 0.5 [42,49]. ε is also called the resolution coefficient.
Step 3 uses the results of γi (k) from Step 2 to calculate βi (k), the grey relational degree, as in Equation [4]. βi (k) is also called Deng’s degree of grey incidence. [ 4]βi(k)=1n∑$k = 1$Nγi(k) The correlation degree is interpreted as a ranking order. The parameters for the GRA models range from 0–1, with it being regarded as strongly associated if it is close to 1 and frailly associated if it deviates from 1 [42,49,50]. The higher the correlation degree is, the higher the ranking is [47]. In this way, the grey relational degree reflects the inter-influences among the factors analyzed [43,45,48]. To justify the comparison of coefficients in GRA, each variable of the original data is standardized, removing the units of measurement before calculating the correlation degree.
Javed et al. [ 42,49,51] and Liu et al. [ 45] further developed and refined GRA models with computing details, including several types of “grey relational degree” numbers (or “degree of grey occurrence”). While absolute GRA utilizes the specific point grey and analyzes the correlations between factors, Relative GRA utilizes the integral visual angle [45]. Calculating the average value of absolute GRA and relative GRA, the SDGRA reflects the line of similar degree compared to the proximity of the pilot’s rate of change. It is a comprehensive indicator of sequence relationships [45]. Further, the SSGRA model was developed by calculating the average value of Deng’s GRA and absolute GRA. SSGRA has the advantage of reflecting “overall closeness between two sequences based on particular points and integral perspectives” [49].
In summary, Deng’s GRA model is typically regarded as the baseline model, while SDGRA and SSGRA models’ estimations are preferred to absolute GRA and relative GRA models [49].
## 2.2. Advantages of GRA Models
When compared to traditional statistical inference models, GRA models have several advantages for decision making [51,52]. First, GRA can effectively provide meaningful inference even with missing, insufficient, or incomplete data or with uncertainties and incomplete information [51,53].
Second, unlike statistical and probability theory models, GRA models do not need a normal distribution assumption or a big sample size [45,54]. With only a small amount of data, GRA can reliably identify key factors based on the relationship between the reference series and the comparability series data [50]. The dataset available in this study consists solely of aggregated annual disease mortality rates and smoking rates from the past 20 years, which are insufficient for performing traditional regression analysis directly. Despite their data limitations, GRA models can yield meaningful analysis results.
Third, in many decision or policy making scenarios, the order of relationship closeness determined by the grey relation degree is frequently more appropriate to use than the precise numerical values of the estimated coefficients. [ 51,53]. In this study, it was meaningful to obtain ranking information about the importance of smoking rates among multiple confounding factors, which might also contribute to the reduction in CSD mortality.
In addition to engineering [55], management [52], and environmental science [56], GRA models have been adopted in healthcare management studies to evaluate patient satisfaction [42,49], healthcare service quality [57,58], performance [59], efficiency [60,61], healthcare resource allocations [62,63], etc.
## 2.3. Ordinary Least Squared (OLS) Regression Analysis with Bootstrapping
OLS regression analysis was performed based on the relevant variables identified using GRA models. The Ramsey regression equation specification error test (RESET) was performed as a diagnostic test of regression specification error for potentially omitted variables.
To address the issue of a small sample size, we adopted the bootstrap method to generate a robust standard error. The bootstrapping method is valid for a small sample because it is a nonparametric approach for evaluating the distribution of statistics-based on random resampling [64]. Unlike a traditional parametric approach, the bootstrapping method does not depend upon strong distributional assumptions of a sample (such as i.i.d. or normal distribution). Instead, it estimates the asymptotic covariance matrix by random sampling from the empirical distribution [65].
## 2.4. Data Analysis
The baseline model of GRA in this study was Deng’s GRA model, in which the arithmetic mean is taken as the initial point [54]. SDGRA and SSGRA models were the main models. Absolute and Relative GRA were performed only for reference.
The three explanatory variables included income per capita, physician density, and smoking rate, including observations from 2000 to 2020. Explanatory variables with observations lagged one year were examined as a robustness check. Extra robustness checks included rate of alcohol use and rate of overweight from 2000 to 2015 as explanatory variables.
The Gray Level Correlation Software 7.0.1 (Grey System Research Institute, Nanjing, China) (available at http://igss.nuaa.edu.cn, accessed on 18 December 2022) was adopted to perform Deng’s, absolute, relative, and SDGRA models. The SSGRA model was computed using Microsoft Excel (Version 2002).
The Stata 14 statistical package (Stata Corp LP, College Station, TX, USA) was used to perform OLS regression analysis.
## 3.1. Data Sources and Ethical Declaration of the Data
Annual data on income per capita and physician density from the year 2001 to 2020 were obtained from the Statistics and Census Service of Macao. Annual data on resident smoking rates were collected from Macao Health Bureau and Macao Sports Bureau.
Residents’ alcohol consumption rates and obesity data were obtained from the Macao Citizen Physical Fitness Monitoring Report (2001, 2005, 2010, and 2015), which was sponsored and published by the Sports Bureau of the Macau Government. The original data of alcohol consumption rates and obesity rates comprised discrete points within the years 2001, 2005, 2010, and 2015 from four waves of the survey.
Using only the publicly available statistics data disclosed by government departments, this study did not collect any person’s data. There were no experimental designs used in this study, nor were any patients or survey respondents involved. Therefore, this study did not require extra ethics approval.
## 3.2. Dependent Variables
The mortality rate of circulatory system disease (CSD) (ICD10: I00–I99) [66] from 2001 to 2020 was analyzed in this study. CSD in the ICD10 includes: acute rheumatic fever (I00–I02); chronic rheumatic heart diseases (I05–I09); hypertensive diseases (I10–I15); ischemic heart diseases (I20–I25); pulmonary heart disease and diseases of pulmonary circulation (I26–I28); other forms of heart disease (I30–I52); cerebrovascular diseases (I60–I69); diseases of arteries, arterioles, and capillaries (I70–I79); diseases of veins, lymphatic vessels and lymph nodes, not elsewhere classified (I80–I89); and other and unspecified disorders of the circulatory system (I95–I99).
In Macao’s population, cardiovascular and cerebrovascular diseases account for more than $90\%$ of the deaths in the category of CSD [27]. Rates of the total population, male citizens, and female citizens over the studied period were analyzed separately.
## 4.1. Descriptive Statistics of the Data
Table 1 below reports the descriptive characteristics of key variables analyzed in this study. As reported in Panel A, during the studied period, CSD mortality in Macao was 86.9 per 100,000 people, with similar levels among both male and female populations. The density of physicians was about 2.4 per 1000 people in Macao.
While the population smoking rate was about $15\%$ on average during the study’s period, the smoking rate was as high as $33\%$ among men, in contrast to about $2\%$ among women in Macao.
As reported in Panel B of Table 1, the rate of alcohol use among male residents in Macao during the studied period is $46.2\%$, which is about 3.4 times the female rate. While the male rate of alcohol use in Macao had only a moderate increase of about 2.68 percentage points, the female rate of alcohol use in Macao increased from $10.2\%$ in 2001 to $16.96\%$ in 2015. In contrast to the stable overweight and obesity rate among women aged 40 and over, the rate among men increased by about 15.6 percentage points from $29.1\%$ in 2001 to $44.7\%$ in 2015.
## 4.2. Results of GRA Models
GRA models were applied to analyze the data, ranking the importance of the relevant factors of CSD mortality in Macao from 2001 to 2020. As reported in Table 2, the baseline model results show that, for the male population in Macao, physician density and smoking rates are ranked as the most important determinants. The results are consistent among all GRA models. For the female population, physician density and smoking rates are also important determinants, while Deng’s GRA and SDGRA models show that the women’s smoking rate is ranked as the most important one. The ranking pattern of the total population largely is a mixture.
Considering the time-lag effects of physician density (primary care) and smoking rates, we further lagged the explanatory variables for a one-year period and performed the same analysis. As reported in Table 3, the GRA ranks of relevant male CSD mortality factors are consistent with the baseline mode. As for the female population, all models, except the relative GRA model, consistently estimated smoking rate as no. 1. In particular, Deng’s GRA, SDGRA, and SSGRA models were found to be the most comprehensive and effective evaluation models.
For robustness checking, GRA tests were expanded by adding two extra variables: the rate of alcohol use and the rate of overweight and obesity among people aged 40 and older from 2001 to 2015. As reported in Table 4, the GRA models suggest that, for the male population, rate of alcohol use and physician densities are the leading factors relevant to CSD mortalities during the study period. For female CSD mortalities, smoking rate is consistently rated as the most significant relevant factor, followed by the risk factor of overweight and obesity (Aged >40).
## 4.3. Regression Analysis of CSD Mortality in Macao
Based on the GRA results, we also performed regression analysis on CSD mortalities and relevant factors as shown in Table 5. The dependent variable was the annual CSD mortalities, which included 40 observations from the male and female population. Smoking rate and physician density were included as two explanatory variables because these two factors are rated by GRA methods as the most important factors. A dummy variable of “female after smoking ban” was generated to indicate the female observations after the implementation of the smoking ban in 2012 (included). The estimated coefficient of this variable captured the excess changes in female mortality after the implementation of the smoking ban.
Column [1] reports the regression analysis results of the baseline model. While physician density has a highly significant negative association with CSD mortality, the coefficient of smoking rate is insignificant. This may be mainly due to the insignificant effects of smoking rates among men.
Column [2] is the full model, including the variable of interest “female after smoking ban”. As reported in Column [2], while an increase in physician density (1 per 1000 people) is significantly associated with a reduction of about 8.95 CSD deaths per 100,000 people, the CSD mortality in Macao after the implementation of the smoking ban in 2012 has an extra annual reduction of 5 deaths for every 100,000 women. In addition, the R-squared is 0.283, which is the highest one among the three specifications tested.
Column [3] reports results without including the smoking rate in the regression, and the results are robust.
The bottom line of Table 5 reports the results of the Ramsey RESET test, which indicates no evidence of omitted variables in all three specifications.
## 5. Discussion
Applying GRA models, this study examined the key factors associated with CSD mortalities in Macao (China) from 2001 to 2020. The findings of this study based on GRA models indicate that smoking rate is consistently the most important factor associated with women’s CSD mortality in Macao, while physician density was ranked as the second most important factor. In contrast, for men’s CSD mortality in Macao, the physician density was estimated as the most important factor, while smoking rate has a secondary role.
These findings suggest that women in Macao may have obtained substantial health benefits from smoking bans, while men had few. Two major reasons for this difference may be considered. First, women may have directly benefited from their own smoking cessation encouraged and supported by public health policies, as evidenced by a significantly lower smoking rate following the smoking bans [18]. Second, after full smoking bans in the local community, women usually have extra health benefits from less exposure to secondhand smoke (SHS) [16]. In particular, decreased exposure to SHS among nonsmokers may result in a decrease in myocardial infarctions [14].
This study’s estimate of the smoking ban’s health outcome effect size is comparable to those that have been reported for mainland China. This study estimates that, owing to the smoking bans in Macao since 2012, on average each year about 5 CSD deaths were avoided among every 100,000 women, equivalent to about $11.45\%$ of the mean annual CSD mortality (43.66 per 100,000 people), or about 16.8 CSD deaths among the Macao female population. A study of Beijing’s 2015 tobacco control policy package estimated that the associated drop in hospital admissions for cardiovascular diseases was overall more than $10\%$ [75]. In another study, Zheng et al. estimated reductions in hospital admissions were about $5.4\%$ of AMI and $5.6\%$ of stroke cases [76]. Additionally, the increasing trend in stroke admission events was reduced by $15.3\%$ [76]. Research studying Tianjin (China) found the mortality rate from AMI decreased by $16\%$ per year, while the mortality rate of stroke among those under 35 decreased by $2\%$ annually after the implementation of smoke-free legislation [17].
Meanwhile, the findings of this study indicate that Macao’s smoking bans did not successfully achieve their health goals among the male population. The full smoking ban in Macao’s casinos was expected to help many casino workers quit smoking. According to a casino employee survey in 2008 (before the implementation of the initial smoking ban in Macao), more than half of the respondents ($$n = 315$$, men = 165, $52.4\%$) reported that they would try to quit smoking if smoking was outlawed at work [77]. Nevertheless, despite the eventual implementation of the smoking ban, the men’s smoking rate in Macao did not significantly decline and the associated mortality could not be avoided.
In addition, the findings of this study also reveal that alcohol using may be among the leading risk factors for CSD mortality among men in Macao. This is a similar health concern among men in mainland China [78], and most Chinese people are unaware of the rigorous evidence-based public health warning that zero consumption is the only safe alcohol usage level.
This study has several limitations. First, the analysis of this study is largely limited by the availability of the data. An overall reduction in female mortality and smoking rates was observed, but no information is available regarding associated societal health inequalities, such as the differences between lower and higher SES groups. Second, the GRA method has a limitation in that GRA models with different estimation approaches may sometimes produce different analysis results. The ranking orders predicted by GRA models lack precise numerical values, such as the number of deaths avoided, for further policy impact analysis. To address concerns about the gender gap in smoking cessation, future research should focus on identifying male-specific smoking cessation barriers in the context of complete smoking bans. Whereas internal barriers, such as stress and cravings, emerged to be more prominent in women, external barriers, such as the widespread availability of cigarettes and the social aspects of smoking, were more prevalent in men [79].
## 6. Conclusions
Applying GRA models, this study found that smoking rate is rated as the most important factor associated with women’s CSD mortality in Macao between the years of 2001 and 2020. Women’s CSD mortality decreased annually by $11.45\%$ as a result of smoking cessation, but there were no comparable results for men.
The findings of this study have significant public health policy implications for Macao. Although smoking bans have been successfully implemented in the city’s casinos, Macao nevertheless needs to focus on reducing the male smoking rate and promoting a healthy lifestyle. It is recommended that mainland China and other emerging economies with high smoking rates adopt total smoking bans and strong legal enforcement. Additional steps, including health education and cessation support services, should be taken to further realize smoking rate reduction in high-risk population groups.
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|
---
title: Lacrimal Gland Epithelial Cells Shape Immune Responses through the Modulation
of Inflammasomes and Lipid Metabolism
authors:
- Vanessa Delcroix
- Olivier Mauduit
- Menglu Yang
- Amrita Srivastava
- Takeshi Umazume
- Cintia S. de Paiva
- Valery I. Shestopalov
- Darlene A. Dartt
- Helen P. Makarenkova
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001612
doi: 10.3390/ijms24054309
license: CC BY 4.0
---
# Lacrimal Gland Epithelial Cells Shape Immune Responses through the Modulation of Inflammasomes and Lipid Metabolism
## Abstract
Lacrimal gland inflammation triggers dry eye disease through impaired tear secretion by the epithelium. As aberrant inflammasome activation occurs in autoimmune disorders including Sjögren’s syndrome, we analyzed the inflammasome pathway during acute and chronic inflammation and investigated its potential regulators. Bacterial infection was mimicked by the intraglandular injection of lipopolysaccharide (LPS) and nigericin, known to activate the NLRP3 inflammasome. Acute injury of the lacrimal gland was induced by interleukin (IL)-1α injection. Chronic inflammation was studied using two Sjögren’s syndrome models: diseased NOD.H2b compared to healthy BALBc mice and Thrombospondin-1-null (TSP-1-/-) compared to TSP-1WT C57BL/6J mice. Inflammasome activation was investigated by immunostaining using the R26ASC-citrine reporter mouse, by Western blotting, and by RNAseq. LPS/Nigericin, IL-1α and chronic inflammation induced inflammasomes in lacrimal gland epithelial cells. Acute and chronic inflammation of the lacrimal gland upregulated multiple inflammasome sensors, caspases $\frac{1}{4}$, and interleukins Il1b and Il18. We also found increased IL-1β maturation in Sjögren’s syndrome models compared with healthy control lacrimal glands. Using RNA-seq data of regenerating lacrimal glands, we found that lipogenic genes were upregulated during the resolution of inflammation following acute injury. In chronically inflamed NOD.H2b lacrimal glands, an altered lipid metabolism was associated with disease progression: genes for cholesterol metabolism were upregulated, while genes involved in mitochondrial metabolism and fatty acid synthesis were downregulated, including peroxisome proliferator-activated receptor alpha (PPARα)/sterol regulatory element-binding 1 (SREBP-1)-dependent signaling. We conclude that epithelial cells can promote immune responses by forming inflammasomes, and that sustained inflammasome activation, together with an altered lipid metabolism, are key players of Sjögren’s syndrome-like pathogenesis in the NOD.H2b mouse lacrimal gland by promoting epithelial dysfunction and inflammation.
## 1. Introduction
Dry eye disease affects millions of adults worldwide and can be divided into two major types: evaporative dry eye and aqueous-deficient dry eye (ADDE) [1,2]. ADDE is characterized by a reduced secretion or an altered composition of fluid from the lacrimal gland (LG) [3,4], the exocrine tubuloacinar gland responsible for secreting the aqueous layer of the tear film [5,6,7]. ADDE induces eye irritation and pain, and may lead to severe ocular surface disorders [8,9]. The leading cause of ADDE is the chronic inflammation of the LG triggered by aging or auto-immune diseases such as Sjögren’s syndrome (SS).
Dry eye disease involves inflammatory mechanisms and the production of several tear cytokines, including interleukins (IL)-1α and IL-1β, which correlate with clinical severity [10]. IL-1α/β are potent proinflammatory cytokines that function as key danger signals during infection or tissue damage. IL-1α/β binding to their receptor IL-1R1 promotes the transcription of genes involved in acute and chronic inflammation. In mice, reversible ADDE can be experimentally induced by a single injection of IL-1α into the LG [11,12]. Several studies demonstrated that IL-1α induces acute LG inflammation and destruction within the first two days after the injury [11,12,13,14]. The resolution of inflammation was noted on the third day after injury and was followed by cell proliferation and complete regeneration within 5–7 days [12,13].
Inflammasomes are large intracellular multiprotein complexes that play a central role in innate immunity [15,16]. The largest class of inflammasomes contain an apoptosis-associated speck-like protein containing a CARD (ASC, encoded by the gene Pycard) and pathogen/danger sensors, which recruit and activate the pro-caspase 1 (pro-CASP1). Each type of inflammasome is characterized by a particular sensor or receptor: PYRIN, the nucleotide-binding domain (NOD), leucine-rich repeat (LRR)-containing protein (NLR) family (e.g., NLRP$\frac{1}{3}$/6, NLRC4), or the pyrin and HIN domain-containing protein (PYHIN) family (e.g., AIM2 and IFI204, the murine homolog of human IFI16). Canonical inflammasomes cleave IL-1β and IL-18 precursors to generate the mature cytokines. Activated CASP1 and the non-canonical inflammasome formed by CASP$\frac{4}{11}$ can also cleave the pore-forming protein gasdermin D (GSDMD), which mediates interleukin secretion [17]. GSDMD is required for pyroptosis, an immunogenic form of cell death which enables the massive release of active IL-1α and IL-1β from dying cells [15,18]. Inflammasomes can be activated by a multitude of infectious and sterile stimuli, including microbiome-derived signals and host-derived signals, and were found in different cell types [19]. We previously showed that acute LG inflammation triggers the upregulation of inflammasome-related molecules: Casp4, Nlrp3, the purinergic receptors P2RX7 and P2RY2, the Pannexin-1 (Panx1) membrane channel glycoprotein—a key regulator of inflammasome assembling—, and numerous proinflammatory factors including IL-1β and IL-18 [14].
Whilst inflammasome signaling is critical for the initiation of a fast innate immune response to tissue damage or invading pathogens, aberrant inflammasome activation contributes to various pathologies, including autoimmune disorders, cardiometabolic diseases, cancer, and neurodegenerative diseases [20,21]. It has been shown that NLRP1, NLRP3, NLRC4, and AIM2 inflammasomes play a significant role in shaping immune responses and regulating the homeostasis of intestine and ocular surface immunity in several inflammatory diseases [22,23,24,25,26,27]. In human patients suffering from SS dry eye, conjunctival impression cytology demonstrated an upregulation of Nlrp3 and Casp1 [25]. Baldini and coauthors [28] showed that in the salivary glands of SS patients, the increased expression of NLRP3, CASP1, and P2RX7 was a marker of disease and correlated with the focus score evaluating the number of immune cell infiltrates in gland sections. Moreover, AIM2 inflammasomes are activated in the salivary epithelium of primary Sjögren’s syndrome (pSS) patients and correlate with the expression levels of type I interferon (IFN) signature genes [29]. IFNs are major regulators of the innate immune response and contribute to the activation of canonical and non-canonical inflammasomes [30], partly by inducing the guanylate-binding proteins (GBPs) that are dynamin-like GTPases [31,32]. Among them, GBP1 and GBP2 are upregulated in the biopsies of pSS salivary glands [33] and the latter was proposed as a biomarker for SS in saliva [34] and salivary glands [35]. Although the implication of inflammasome modulators in chronically inflamed LG remains unknown, their therapeutic potential is supported by our observation that the inhibition of Panx1 or Casp4 reduced the inflammation of LGs from thrombospondin-1-null (TSP-1-/-) mice, a model for SS, and improved the epithelium repair through the increased engraftment of progenitor cells [14].
In this study, we investigated inflammasome formation during acute and chronic LG inflammation using the R26ASC-citrine mouse. This reporter mouse forms fluorescent ASC specks when inflammasomes are activated [36]. We show that LG epithelial cells can form inflammasomes upon sensing danger signals and inflammation. To identify inflammasome types involved in acute and chronic inflammation, we analyzed RNA-sequencing (RNA-seq) data from a previously published study on acute injury [13] and performed RNA-seq of LGs from NOD.H2b mice [37]—a pSS mouse model. Our results indicate a strong activation of multiple inflammasome complexes during acute and chronic inflammation. Finally, we analyzed the RNA-seq data of the LGs during acute and chronic inflammation in terms of the biological pathways to identify candidate mechanisms that promote the resolution of inflammation. We demonstrated that lipid biosynthesis is activated during the resolution of inflammation/regeneration after acute injury, but that genes for fatty acid and cholesterol synthesis are, respectively, down- and upregulated during chronic inflammation. Moreover, chronic inflammation also downregulates the genes involved in the tricarboxylic acid (TCA) cycle and the β-oxidation of fatty acids in the mitochondria. Combined, our results show that during chronic inflammatory disease, LG epithelial cells have reduced lipid biosynthesis, accumulated cholesterol, and showed mitochondrial dysfunction. Altogether, these alterations likely induce cell damage, sustain inflammasome activation, and impair LG regeneration and function.
## 2.1. Mice
For the in vivo detection of the activated inflammasome complex, we employed the transgenic mouse expressing a mouse ASC-citrine fusion protein in the Rosa26 (R26) locus (R26ASC-citrine, kind gift of Dr. Golenbock) [36]. R26ASC-citrine mice were bred and maintained on the C57BL/6J background.
B6.129S2-Thbs1tm1Hyn/J mice (TSP-1-/-, RRID:IMSR_JAX:006141) were originally purchased from Jackson Laboratory (Sacramento, CA, USA) and were bred and maintained on the C57BL/6J background. For immunoblotting, TSP-1-/- mice were compared to age-matched wild-type (WT) C57BL/6J mice. For immunofluorescence, we crossed R26ASC-citrine x TSP-1-/- mice to obtain TSP-1-/-:R26ASC-citrine mice that were compared to age-matched R26ASC-citrine mice. The NOD.B10Sn-H2b/J mice (NOD.H2b, RRID:IMSR_JAX:002591) and their BALB/cJ controls (BALBc, RRID:IMSR_JAX:000651) mice were purchased from Jackson Laboratory (Sacramento, CA, USA).
The mice were housed under standard conditions of temperature and humidity, with a 12 h light/dark cycle and free access to food and water. All experiments were performed in compliance with the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research and the Guidelines for the Care and Use of Laboratory Animals, published by the US and National Institutes of Health (NIH Publication No. 85-23, revised 1996), and were pre-approved by TSRI Animal Care and Use Committee.
## 2.2. Induction of Inflammasome Formation with Lipopolysaccharide and Nigericin
R26ASC-citrine mice were primed by intraglandular injection of lipopolysaccharide (LPS, 1 µg/mL). After 3 h, inflammasome activation was induced by the injection of nigericin (10 µM). Six hours after the last injection, the mice were sacrificed and LGs were dissected out and processed for frozen section preparation.
## 2.3. LG Acute Injury
For acute injury experiments, the R26ASC-citrine and WT C57BL/6J females were used. LG inflammation in these mice was induced by the intraglandular injection of IL-1α, as previously described [11]. Briefly, 12 female mice (10 to 12 weeks old) were anesthetized, and the exorbital LG was injected with either saline (vehicle) or IL-1α (1 μg; PeproTech, Cranbury, NJ, USA) in a total volume of 2 μL using a Hamilton glass syringe (#300329, World Precision Instruments, Inc., Sarasota, FL, USA) and NanoFil 35G needle (#NF35BV-2, World Precision Instruments, Inc., Sarasota, FL, USA). The LGs from uninjected mice were used as additional controls. The LGs were harvested 6 and 12 h after injection and processed for immunohistochemistry and RNA extraction.
## 2.4. Frozen Section Preparation and Immunostaining
The dissected LGs were fixed with $2\%$ paraformaldehyde in PBS (pH 7.4) for 45 min and frozen in 2-methylbutane cooled by liquid nitrogen, and 10-μm cryosections were prepared using Hacker/Bright OTF5000-LS004 Cryostat. The sections were blocked with $1\%$ bovine serum albumin in Tris-buffered saline containing $0.05\%$ Tween 20. The following primary antibodies were used for immunostaining overnight at 4 °C: mouse monoclonal α-smooth muscle actin antibody ($\frac{1}{200}$, clone 1A4; #A2547, RRID:AB_476701, Millipore-Sigma, Rocksville, MD, USA) was used to label the myoepithelial cells (MECs) and pericytes (contractile cells around the endothelial cells), rat monoclonal CD31 antibody ($\frac{1}{100}$, #553370, RRID:AB_394816, BD Biosciences, Franklin Lakes, NJ, USA) was specific to blood vessels, mouse monoclonal E-Cadherin antibody ($\frac{1}{200}$, #610182, RRID:AB_397581, BD Biosciences) labeled the epithelial cells, and the rabbit polyclonal AIM2 antibody ($\frac{1}{100}$, #63660, RRID:AB_2890193, Cell Signaling Technology, Danvers, MA, USA) was used to detect AIM2-inflammasomes.
Appropriate fluorochrome-conjugated secondary antibodies were obtained from Invitrogen (Waltham, MA, USA) and nuclei were counterstained with DAPI. The formation of inflammasome complexes following bacterial and sterile stimuli in WT mice and during chronic inflammation in TSP-1-/- mice was detected using the ASC-citrine fusion protein that is constitutively and ubiquitously expressed in R26ASC-citrine mice and forms fluorescent specks upon inflammation. Images were taken using a LSM 880 laser scanning confocal microscope (Zeiss, Oberkochen, Germany) at the microscopy core of the Scripps Research Institute (La Jolla, CA, USA). Three different fields were analyzed per animal. Inflammasomes were counted using Imaris Spots software, which allows for the detection and counting of small particles. The number of specks detected in the inflamed LGs was adjusted by subtracting the number of specks found in their respective controls.
## 2.5. Western Blotting Analysis
For protein extraction, the dissected LGs were rinsed in cold PBS and transferred into 2 mL tubes pre-filled with 2.8 mm ceramic beads (#19-628, Omni, Inc., Kennesaw, GA, USA) containing 500 µL of ice-cold RIPA buffer without detergents (50 mM Tris-HCl + 150 mM NaCl + 1 mM EGTA) and supplemented with protease/phosphatase inhibitor cocktail (#5872, Cell Signaling, Danvers, MA, USA). The tissue was homogenized with the Omni Bead Ruptor 4 (# 25-010, Omni, Inc., Kennesaw, GA, USA; 2 cycles: Speed 5, 40 s each) and lysate was kept on ice. An appropriate volume of complete RIPA buffer supplemented with each detergent five-times concentrated was added to the sample (final concentration: $1\%$ Nonidet P-40 + $0.5\%$ sodium deoxycholate + $0.1\%$ SDS + 1 mM EDTA) before incubation on ice for at least 30 min. Then, lysate was centrifuged (15 min, 15,000× g, 4 °C) and the supernatant was collected for protein quantitation using Pierce BCA protein Assay kit (#23225, Thermo Fisher Scientific, Waltham, MA, USA). After denaturation (with NuPAGE LDS + β-mercaptoethanol at 70 °C for 10 min), 30 µg total protein from each sample was separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (NuPAGE Novex Bis Tris gels, ThermoFisher Scientific, Waltham, MA, USA) and transferred to polyvinylidene difluoride membranes using the iBlot2 system (ThermoFisher Scientific, Waltham, MA, USA). The transfer membranes were stained for total protein with the No-Stain Protein Labeling Reagent (#A44449, ThermoFisher Scientific, Waltham, MA, USA), according to the manufacturer’s instructions. The membranes were then blocked with TBS + $0.1\%$ Tween + $5\%$ milk for 1 h at RT before incubation with the appropriate primary antibody at 4 °C overnight: anti-GSDMDC ($\frac{1}{1000}$, #sc-393656, RRID:AB_2728694, Santa Cruz Biotechnology, Dallas, TX, USA,), anti-CASP1 ($\frac{1}{1000}$, #22915-1-AP, RRID:AB_2876874, ProteinTech, San Diego, CA, USA), or anti-IL-1β ($\frac{1}{1000}$, #A16288, RRID:AB_2769945, Abclonal, Woburn, MA, USA). After 3 washes, the blots were incubated for 1 h with the appropriate horseradish peroxidase-linked secondary antibody and processed for chemiluminescence. The signal was detected using the iBright (ThermoFisher Scientific, Waltham, MA, USA) or ChemiDoc MP (Bio-Rad, Hercules, CA, USA) imaging systems. The quantification of the relative protein abundance was performed using Image Lab software (v6.1, Bio-Rad, Hercules, CA, USA). Background-adjusted band volumes were corrected with the normalization factor (calculated using total protein stain for BALBc/NOD.H2b samples and β-actin for TSP-1-/- samples) and normalized to a reference volume corresponding to the average of the biological replicates in the control condition.
## 2.6. qRT-PCR
The total RNA was isolated using Trizol and converted into cDNA using RT2 First Strand Kit (#330404, Qiagen, Germantown, MD, USA). *The* gene expression was assessed using the SYBR Green kit, according to the manufacturer’s instructions (Applied Biosystems, Waltham, MA, USA) using a 7300 Real-Time PCR System (Applied Biosystems, Waltham, MA, USA). The sequences for qRT-PCR primers are listed in Table 1. qPCR data were analyzed using the comparative Ct (ΔΔCt) method. Actb and Gapdh were used as internal reference genes.
## 2.7. RNA-Sequencing (RNA-seq) Data Analysis
For acute injury experiments, we used the previously published data available from the Gene Expression Omnibus (GEO) database (Accession number: GSE99093) [13]. To study chronic inflammation, we mined our RNA-seq data of BALBc and NOD.H2b male LGs processed at 2, 4, and 6 months of age, deposited under GSE210332. Data were analyzed using ROSALIND®® software (https://rosalind.bio/ accessed on 19 February 2023) developed by ROSALIND, Inc. (San Diego, CA, USA). As previously described, ROSALIND uses DESeq2 R library to normalize read counts and calculate fold-changes along with the corresponding p-values [37].
For all projects, differentially expressed genes (DEGs) were selected based on a fold-change (FC) cut-off equal to log2(FC) ≈ ±0.585 (corresponding to FC = 1.5) and p-adj < 0.05. On the figures, the statistical significance of log2(FC) compared to the respective control (uninjured LG for acute injury; age-matched BALBc for NOD.H2b LGs) is shown with: * p-value adj. < 0.05; ** p-value adj. < 0.01; *** p-value adj. < 0.001.
For acute injury data, a pathway enrichment analysis was conducted using Metascape [38] using default parameters (min. overlap = 3, p-value cut-off = 0.01, min. enrichment = 1.5) and Gene Prioritization by Evidence Counting (GPEC), an algorithm identifying the subset of input genes that are more likely to be true hits, and by interrogating WikiPathways and Gene Ontology Biological Process databases. The dendrogram was created with the web-based tool Clustergrammer (https://maayanlab.cloud/clustergrammer/ accessed on 25 May 2022) using complete-linkage clustering with Euclidian distances.
## 2.8. Statistical Analysis
Prism9 v9.1.2 (GraphPad software Inc, La Jolla, CA, USA) was used to plot the results and test their statistical significance. First, a Shapiro-Wilk normality test was performed to evaluate if the data follow a normal distribution. If data passed the normality test, the statistical significance between the two conditions was assessed with an unpaired t-test and the results were represented as mean ± standard deviation (SD). To compare more than two groups, a one-way ANOVA was used. Otherwise, non-parametric tests were used (Mann-Whitney test for two groups, and Kruskal-Wallis otherwise) and the plots showed the median ± interquartile range (IQR), as specified in the legends. The significant differences are represented as * if p value $p \leq 0.05$, ** if $p \leq 0.01$, and *** if $p \leq 0.001.$
## 3.1. Epithelial Cells Can Sense Microbial/Sterile Inflammatory Stimuli
In contrast to non-canonical inflammasomes formed by CASP4, most types of canonical inflammasomes require the assembly of the adaptor protein ASC for the activation of the inflammasome cascade leading to IL-1β/IL-18 maturation, and eventually release through the GSDMD pores, also leading to cell death (Figure 1A). Thus, for the in vivo detection of the canonical inflammasome complexes, we used a transgenic mouse constitutively expressing the ASC-citrine fusion protein (R26ASC-citrine) (see enlarged micrograph in Figure 1A) [36]. To test whether inflammasomes could be formed in the LG, we first mimicked a bacterial infection in the R26ASC-citrine reporter mouse, as previously described [36]. In brief, LGs were injected with lipopolysaccharide (LPS, typical pathogen-associated molecular pattern, or PAMP), which constitutes the priming signal inducing Il1b transcription, and with nigericin, a pore-forming bacterial toxin which activates the NLRP3 inflammasome (Figure 1A) [39]. An anti-α-SMA antibody was used to identify MECs that surround the secretory units formed by acinar cells and the pericytes wrapped around blood vessels (blood vessels were labeled by antibody to CD31). Therefore, the tubular structures negative for CD31 and α-SMA staining were identified as ducts. In control LGs, we observed just a few ASC specks (Figure 1B and Figure S1A). By contrast, numerous ASC specks were formed 6 h after LPS-nigericin stimulation, compared to the vehicle-injected LGs (Figure S1C). Many of the ASC specks were found in the LG epithelium (Figure 1C and Figure S1B).
To test whether LG cells could also form inflammasomes upon sterile stimuli, we induced acute injury by injecting IL-1α in the LG on one side of the R26ASC-citrine mouse and injected the other LG with a saline (vehicle control) (Figure 1D,E). Similar to LPS-nigericin stimulation, ASC specks were detected 6 h after IL-1α-injection (Figure 1E). They were primarily formed in acinar cells and MECs (Figure 1E,F and Figure S1D). At 12 h after injury, ASC complexes were also detected within ducts and infiltrating immune cells (Figure 1G). The proportion of cells forming inflammasomes and the diameter of ASC specks significantly increased over time in injured LGs (Figure 1H,I). Following inflammasome formation (Figure 1J), we also observed higher cell death rates with characteristic nuclear fragmentation in IL-1α-injected LGs (Figure 1K,L).
Altogether, these results show that LG epithelial cells form inflammasomes in response to microbial or sterile pro-inflammatory stimuli and may eventually undergo pyroptosis prior to immune infiltration.
## 3.2. Inflammasome Activation during Chronic Inflammation
We then tested whether inflammasomes are activated in the LG epithelial cells during chronic inflammation. First, we analyzed NOD.H2b males, which develop robust lymphocytic infiltrates in the LG at 4–6 months of age [37] but do not have autoimmune diabetes [40,41]. To determine whether inflammasomes are active, we performed Western blotting to study the proteolytic maturation of downstream targets in the LGs of 6-month-old (6M) NOD.H2b and control BALBc males (Figure 2A,B and Figure S1E). Although there was no significant difference in the abundance of the full length CASP1 (Pro-CASP1, p46) between the LGs of NOD.H2b and BALBc mice, it was detected at a slightly higher molecular weight in NOD.H2b mice (Figure 2A). This suggests a post-translational modification (ubiquitination, phosphorylation) that could modulate the inflammasome activity [42]. Moreover, in NOD.H2b LGs, we detected an increased amount of the cleaved form of CASP1 p33, which forms the active species on the inflammasome hub with the p10 subunit of CASP1 (Figure 2A). This complex is able to process many different substrates [43]. Consistent with inflammasome/CASP1 activation, we found that both the precursor (31 kDa) and the mature form of IL-1β (17 kDa) were significantly increased in the diseased LGs, and that the cleaved form of GSDMD was approximately five times more abundant in NOD.H2b as compared to BALBc LGs (Figure 2B).
To determine if the activation of inflammasome signaling is not specific to NOD.H2b mice, we also analyzed the TSP-1-null (TSP-1-/-) mouse—another model of pSS [44,45]. *We* generated the TSP-1-/-:R26ASC-citrine mice and used the R26ASC-citrine mice as controls. While the control mice had only a few ASC specks in the LG, we found significantly more ASC specks in epithelial cells of the TSP-1-/-:R26ASC-citrine mice at 2M and 6M, respectively (Figure 2C–F). The number of ASC specks increased with disease progression in the LGs of TSP-1-/-:R26ASC-citrine mice (Figure 2G) and correlated with the increased abundance of pro-IL-1β and IL-1β (Figure 2H).
In summation, these results show that inflammasome complexes are constitutively formed in the LG of two murine pSS models of different background strains. Inflammasome activation gradually increases with the progression of the disease, suggesting that it could play a significant role in the pathogenesis through the secretion of inflammatory cytokines.
## 3.3. Acute Injury and Chronic Inflammation Upregulate Several Types of Inflammasomes
Key components of the inflammasome machinery are highly expressed during inflammation in different tissues [24,46,47,48]. To identify the signaling pathways promoting their transcription, we analyzed a previously published RNA-seq data (GSE99093) obtained for LG acute injury/regeneration in BALBc mice [13]. Similar to our experiments, the LGs of these mice were injured by the intraglandular injection of IL-1α and the bulk RNA-seq was performed at days 0 (uninjected control), 1, 2, 3, 4, 5, 7, and 14 after injury.
Consistent with our previous study [14], we found a strong induction of Il1b and Il18 transcription on days 1 and 2 (Figure 3A, Table S1). Therefore, we investigated the expression of all genes listed in the Gene Ontology Biological Process entitled “Interleukin-1 production” (GO:0032612) that were significantly enriched on days 1 and 2 after injury (Figure 3B, Table S1). Thus, the gene expression heatmap demonstrates that LG samples from day 1 and 2 after injury cluster together, showing an upregulation of many genes of this pathway, while data from day 3 after injury display a significant decrease in the set of genes involved in inflammasome activation and interleukin processing (Figure 3B). In agreement with the original study [13], uninjected (day 0) and saline-injected controls (day “14S”) were highly similar (Figure 3B) and, thus, we retained the uninjected LGs as controls to study the gene expression changes. The main cluster of genes upregulated during the inflammatory phase (lower part of the heatmap) included the genes of the toll-like receptor (TLR)-myeloid differentiation primary response 88 (MyD88)-nuclear factor kappa B (NFκB) axis that promotes the transcription of Il1b and inflammasome components [49,50], and its partners such as F2rl1 that acts synergistically with TLR2 and TLR4 [51,52], both upregulated in this dataset (see selected genes shown in frame on Figure 3B, Table S1). The inflammasome sensors Ifi204 (murine ortholog of human IFI16 [49]), Nlrp3, Aim2, Mefv (encoding PYRIN), and Naip5 (that forms hetero-oligomeric inflammasomes with NLRC4 upon recognition of bacterial fragments [53]) were significantly upregulated on days 1 and/or 2 after acute injury, and decreased to basal levels on day 3 (Figure 3B,C, Table S1). By contrast, there was no significant change in the expression of Nlrp6, Nlrc4, and Nlrp1b (Figure 3C). Nlrp1a was not expressed at any time (Figure 3C), which was expected, as the BALBc strain lacks Nlrp1a expression [54]. Casp1 and Casp4 were significantly increased during the first two days after injury, while the expression of both caspases returned to basal levels on day 3 after injury (Figure 3B and Figure S2A, Table S1). There was a modest increase in the Casp4 mRNA level at days 4 and 5 but, from day 7, its expression level was not different from the uninjected controls. In addition, we detected the upregulation of the NLRP3-activator Gbp5, and the mediators of pyroptosis Panx1 and Gsdmd (Figure 3B, Table S1).
The results obtained by qRT-PCR performed in our lab after acute injury of LG in C57BL/6J mice were highly similar to the RNA-seq data from BALBc mice (Figure S2B). This demonstrates that the experimental model of LG acute injury provides mechanistically robust and reproducible results, even when different mouse strains are used. Therefore, these findings suggest that several types of canonical inflammasomes (NLRP3, AIM2, IFI204, PYRIN, NAIP5/NLRC4, and possibly non-canonical inflammasomes) are transiently activated by IL-1α-injection.
We also analyzed the transcriptomic changes leading to inflammasome priming and activation during the development of chronic inflammation. To do this, we mined our previously published RNAseq data of LGs from 2M, 4M, and 6M NOD.H2b (diseased) and BALBc (control) males (GSE210332) [37]. In this study, we showed that, although there is a major shift in the gene expression between 2M and 4M/6M mice, 2M NOD.H2b LG already features many alterations at the transcriptomic level compared to BALBc controls. We also found that B and T cell infiltrates appeared as early as 2M (early stage of the disease) in NOD.H2b males—although not to the same extent as 6M males (clinical stage).
Consistent with our previous observations, the expression of Il1b and Il18 was significantly increased with the disease progression (Figure 4A) and genes for the “Interleukin-1 production” (GO:0032612) pathway were significantly enriched in the list of differentially expressed genes (DEGs) between all diseased and control animals. Our data show a set of genes that are upregulated in NOD.H2b mice (Figure 4B, Table S2). Among these genes, we found several pro-inflammatory factors, including TLRs, Tnf, and Nod2, as well as Ifng, (coding for IFN-γ) that participate in NF-κB activation (Figure 4B and Figure S3A, Table S2). IFN-γ not only promotes the transcription of inflammasome components, but also their assembly through the activation of GBPs [55,56]. In NOD.H2b mice, the expression of genes for inflammasome sensors Nlrp3, Nlrc4, Naip5, Ifi204, Aim2, and Mefv were significantly increased from 2 months of age, while no significant changes were detected for Nlrp1b and Nlrp6 mRNAs (Figure 4C, Table S2). Although Nlrp1a is not expressed in BALBc mice, we noticed that its mRNA expression significantly increased with age in NOD.H2b LGs. Importantly, the upregulation of inflammasome sensors was associated with the increased transcription of Casp1 and Casp4 (Figure 4B and Figure S3B), along with Gsdmd and Panx1 (Figure 4B, Table S2). While most of inflammasome-related genes were significantly upregulated at 2M compared to the healthy controls, the expression level of some of them (including Aim2, Nlrc4, Nlrp1a, Casp1, Panx1, and Gsdmd) increased further in NOD.H2b LGs at 4M (Figure S3C). By contrast, none of the genes from the “Interleukin-1 production” pathway passed our thresholds when comparing 4M and 6M NOD.H2b LGs. No DEGs were found for the 2M/4M and 4M/6M comparisons in the control BALBc LGs. This shows that, in NOD.H2b LGs, the activation of the inflammasome pathway amplifies up to 4M, the age at which the chronic inflammation is established [37].
Therefore, during chronic inflammation, several transcriptional activators of inflammasome components are upregulated from the early stages of the disease. Compared to acute injury, which leads to the upregulation of Nlrp3, Aim2, Ifi204, Mefv, and Naip5, chronic inflammation also increased Nlcr4 expression, suggesting that most of the identified sensors can be involved in both acute and chronic inflammation. By immunostaining, we confirmed an increased signal for AIM2 expression in NOD.H2b compared to BALBc LGs (Figure S3D,E). In NOD.H2b LGs, AIM2 complexes were found in the perinuclear areas of epithelial cells, and eventually in other compartments, such as blood vessels (Figure S3F). The constitutive activation of inflammasomes in NOD.H2b LGs could be also facilitated by the upregulation of the gene encoding SYK (spleen-associated tyrosine kinase) (Figure S3G), which induces ASC phosphorylation and oligomerization that is essential for the assembly of NLRP3 inflammasome and CASP1 activation [57]. Finally, the upregulation of Casp4 and members of IFN-γ signaling suggests the activation of the non-canonical inflammasomes.
Taken together, these results demonstrate that the expression/activation of multiple inflammasome complexes in the LG is transient during acute inflammation but is sustained during chronic inflammation. Both acute and chronic inflammasome activities likely lead to epithelial cell damage, the induction of adaptive immune response, and the formation of lymphocytic infiltrates. Therefore, we investigated whether molecular pathways promoting the resolution of inflammation and inflammasome inhibition after acute injury are altered during chronic inflammation.
## 3.4. The Resolution of Inflammation and LG Regeneration following Acute Injury Is Concomitant with the Activation of Lipid Metabolism
An analysis of the transcriptome of regenerating LGs determined day 3 as the critical time point for the switch between inflammatory and regenerative processes [13]. This time point is characterized by a significant decrease in the CD45+ (immune) cells in the LG, particularly neutrophils and monocytes as previously reported [13], and a decrease in the inflammasome components and a reduction in Il1b and Il18 expression down to basal levels. Therefore, we hypothesized that the genes involved in the resolution of inflammation and inhibiting the inflammasome signaling are upregulated on day 3 after LG injury.
We identified 337 differentially expressed genes (DEGs) on day 3 after the injury (relative to the uninjured control, log2(FC) cutoff = ±log2(1.5) and p-adj < 0.05), including 219 upregulated genes. An analysis of these 219 upregulated genes with Metascape demonstrated that most of the top 30 significant biological pathways were related to the activity of immune cells and inflammatory responses (Figure 5A). The protein-protein interaction enrichment analysis carried out with these 219 genes (Figure S4) identified 6 densely connected network components related to: [1] phagocytosis and tumor necrosis factor (TNF) production, [2] mitosis, [3] sterol biosynthesis, [4] leukocyte/myeloid activation, [5] inflammatory response, and [6] memory. The heatmap comparing the enrichment p-values between the first three days after injury revealed that the inflammatory pathways belong to the primary response highly activated on days 1 and 2. Indeed, these pathways become much less significant on day 3, meaning that many of these genes were not upregulated anymore (Figure 5A). Hierarchical clustering showed a group of pathways related to the metabolism and localization of lipids that were significantly enriched on day 3, but not significant on days 1 and 2. Nonetheless, some of the genes that were involved in sterol biosynthesis (Acat2), inert fat breakdown (Lipa), and lipid transport/signaling (Abcg1, Apobec1, Apoe, Lrp1, Ldlr, Pltp, Ptafr) were maximally expressed on day 2—except for Ldlr, whose expression level reached a peak on day 3 (Figure S5).
To exclude the genes belonging to the primary inflammatory response initiated on day 1 and to identify the mechanism(s) specifically promoting the switch to LG repair on day 3 after the injury, we selected genes specifically upregulated on day 3 after the LG injury (53 genes) and genes upregulated on day 2 with the expression level maintained or increased from day 2 to day 3 (13 genes) (Figure S6). Based on these criteria, we generated a list of 66 genes (Table S3) and performed gene set enrichment analysis with Metascape to interrogate several ontology sources (Gene Ontology, KEGG Pathway, Reactome, WikiPathways) (Figure 5B). The enriched ontology terms were grouped by similarity into clusters and named after the most significant pathway. Most of the resulting biological processes were interconnected and related to the biosynthesis and metabolism of lipids (Figure 5C). The most significant cluster with 18 differentially activated genes was “Cholesterol metabolism with Bloch and Kandutsch-Russell pathways” (Figure 5B). Among these genes, we identified Srebf1 reaching its highest level on day 3 after IL-1α injury (Figure 6A), and its inhibitors Insig1 and Prkaa2 (coding for a subunit of AMPK) (Figure S7A) [58,59]. Srebf1 encodes the transcription factor sterol regulatory element-binding protein 1 (SREBP-1), a master regulator of lipid homeostasis (Figure 6B). The translocation of SREBP-1 into the nucleus activates the transcription of genes regulating the biosynthesis and uptake of fatty acids and cholesterol [59] (Figure 6B). Consistent with SREBP-1 activation, many enzymes of the pathways processing acetyl-CoA for the biosynthesis of fatty acids and cholesterol reached their highest expression level on day 3 after injury (Figure S7B,C), including the rate-limiting enzymes acetyl-CoA carboxylase beta (ACACB) and squalene epoxidase (SQLE) (Figure 6A,B). Similarly, the gene encoding Acyl-CoA synthetase short-chain family member 2 (ACSS2) that catalyzes the formation of acetyl-CoA and the activation of fatty acids into fatty acyl-CoA (Figure 6B) was upregulated only on day 3 (Figure 6A). We also found an upregulation of the predicted lipase gene Gm8978 and other lipid-related genes: Aldh3b2, coding for the aldehyde dehydrogenase 3 family member B2 (ALDH3B2) protein that removes toxic aldehydes from lipid droplets, and Dgkg, encoding diacylglycerol kinase gamma (DGKγ), which produces phosphatidic acid (PA) by phosphorylating the second messenger diacylglycerol (DAG) (Figure S7D).
Taken together, these results confirmed that the inflammatory pathways significantly enriched among the 219 upregulated DEGs outlined the primary immune response that occurred within days 1 and 2. Furthermore, in contrast to cholesterol conversion and transport pathways that increased quickly after injury, most of the genes involved in lipid biosynthesis were upregulated only on day 3, suggesting that this transcriptional program may specifically control the resolution of inflammation and gland regeneration. The activation of lipid metabolism most likely promotes the synthesis of cell membranes (that is composed of triglycerides, phospholipids, cholesterol), adenosine triphosphate (ATP) production (through β-oxidation and the use of acetyl-CoA by the TCA cycle), and the generation of second messengers (i.e., PA and DAG) regulating various cell processes (Figure 6A), and may also repress the inflammasome pathway.
## 3.5. Lipid Metabolism Is Altered in Chronically Inflamed LG of NOD.H2b Mice
We hypothesized that the mechanism(s) promoting the resolution of inflammation after acute injury might be altered in NOD.H2b mice. We have recently reported that the downregulation of genes involved in fatty acid biosynthesis, TCA cycle, and fatty acid β-oxidation was associated with disease progression in NOD.H2b LGs [37]. Since these pathways are interconnected and participate in lipid homeostasis (Figure 6B), we analyzed the genes of relevant pathways that were significantly enriched in the meta-analysis of BALBc/NOD.H2b comparisons, according to Gene Ontology (p-elim < 0.05) and WikiPathways (p-adj < 0.05) (Table S4).
Among these genes, we found key regulators of lipid metabolism downregulated in the LG of NOD.H2b mice as early as 2 months of age (Table S4). Most altered were the genes coding for peroxisome proliferator-activated receptor alpha (PPARα, encoded by Ppara, Figure 7A) and its partner retinoid X receptor alpha (RXRα, Rxra) that regulate fatty acid transport, oxidation ketogenesis, and promote SREBP-1 activity [60,61]. There was also a downregulation of Agt coding for the angiotensin precursor ANGT that promotes the transcription of lipogenic genes, such as Srebf1. Srebf1 itself was reduced (Figure 7A) along with its synergic partner ChREBP (encoded by Mlxipl, Table S4) that, together with SREBP-1, activates the transcription of genes involved in fatty acid biosynthesis.
Consistent with the downregulation of the PPARα-dependent transcriptional program, the expression levels of enzymes involved in mitochondrial fatty acid β-oxidation were also reduced in the LGs of the NOD.H2b mice (Table S4, Figure 7B). Similarly, we noted the downregulation of enzymes mediating ketone catabolism such as Oxct1 (Table S4). Altogether, this suggests a reduced mitochondrial pool of acetyl-CoA available for the TCA cycle. The decreased TCA cycle activity most likely reduces ATP production and also the amount of citrate that can be used for acetyl-CoA synthesis in the cytosol (Figure 6A). We also found the downregulation of enzymes catalyzing the synthesis of acetyl-CoA from citrate, acetate, or pyruvate (Table S4), including ACSS2 (Figure 7C), which also activates fatty acids by the reaction with acetyl-CoA to form fatty acyl-CoA (Figure 6A).
Since acetyl-CoA is the primary substrate for de novo lipid biogenesis, we hypothesized that this process is altered in chronically inflamed LGs. In addition to reduced Srebf1 levels, many enzymes for fatty acid biosynthesis such as Acacb were indeed downregulated in diseased glands, thus suggesting a reduced generation of free fatty acids and activated acyl-CoA (Figure 7C,D and Figure 8, Table S4). The number of significant DEGs in NOD.H2b LGs and the extent of the alterations in their expression level compared to their respective BALBc controls increased with age, especially between 2M and 4M (Figure 7C,D, Table S4). By contrast, the enzymes catalyzing the elongation of long-chain fatty acids (LCFAs) into very long-chain fatty acids (VLCFAs) (Elovl5, Elovl6, Hacd4) were upregulated in diseased LGs (Figure 7D, Table S4). VLCFAs serve as precursors for eicosanoids such as inflammatory prostaglandins, thus suggesting that eicosanoid metabolism was also altered in NOD.H2b mice.
In addition, many enzymes of the pathways leading to cholesterol synthesis were upregulated during chronic inflammation, including the rate-limiting enzymes 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGCR) and SQLE (Figure 7E,F, Table S4). We also noticed the downregulation of Cyp27a1 and Cyp46a, which convert free cholesterol into secreted metabolites. Finally, there was a significant increase in the expression level of Soat1 and Soat2 catalyzing the formation of cholesterol esters (Figure 7E,F, Table S4). Of note, the expression level of Srebf2, which preferentially promotes cholesterol biosynthesis, was not altered by chronic inflammation (Figure S8A). This supports our observation that fatty acids, but not cholesterol synthesis, are reduced during chronic inflammation.
Some of the genes altered during chronic inflammation were in the list of lipogenic genes upregulated on day 3 after injury so we analyzed the expression of the entire gene set activated at the beginning of the regenerative phase after acute injury (Table S4, Figure S8B). As expected, we found a significant downregulation of enzymes responsible for acetyl-CoA and fatty acid biosynthesis contrasting with the upregulation of genes related to cholesterol biosynthesis, transport, and lipases.
All these observations indicate an alteration of mitochondrial metabolism—with decreased acetyl-CoA and ATP production—and of de novo lipid biosynthesis using these metabolites, due to the downregulation of PPARα/SREBP-1-dependent transcriptional programs (Figure 8). It is possible that enzymes involved in eicosanoid metabolism might promote the synthesis of pro-inflammatory lipids over anti-inflammatory metabolites (Table S4, Figure 8). Considering the overall upregulation of enzymes of the mevalonate and lanosterol pathways (Figure 8), the chronically inflamed glands may accumulate cholesterol, as previously reported in NOD mice [62]. Together with the impaired mitochondrial and fatty acid metabolism, this may induce epithelial cell stress and promote inflammasome activation in the LGs of NOD.H2b mice. Few of these alterations were significantly aggravated between 2M and 4M/6M NOD.H2b mice (Table S4). Only Acacb was further downregulated at each time-point, while it was not affected in BALBc mice. Altogether, this suggests that changes in lipid metabolism are one of the earliest mechanisms of disease development.
## 4. Discussion
In this study, we showed that several types of inflammasomes could be activated in LG epithelial cells during acute and chronic inflammation. This suggests that, similar to corneal epithelial cells [63], LG epithelial cells function as sentinel cells [64]. We also found that inflammasome activation precedes epithelial cell death after IL-1α-induced acute injury and discovered increased GSDMD cleavage in chronically inflamed LGs of NOD.H2b mice. Studies in the salivary gland suggest that inflammasome activation and pyroptosis exacerbate inflammation through immune cell infiltrations in SS patients and promote salivary gland dysfunction [28,29]. To our knowledge, there is no such report about pyroptotic events in the lacrimal gland. However, topical administration of anakinra (IL-1 receptor antagonist) to the cornea improved the ocular surface integrity and tear secretion in Aire-deficient mice with SS-like disease [65], suggesting that secretion of IL-1β by epithelial LG cells during chronic inflammation could participate in corneal damage.
During acute and chronic inflammation, various types of inflammasome sensors are upregulated in the LG, thereby illustrating the complexity of the innate inflammatory response in this organ. Surprisingly, both acute and chronic inflammation activated similar inflammasome sensors: [1] AIM2 and IFI204 that are activated by DNA released from damaged mitochondria or dead cells; [2] NLRP3 that is activated by a wide variety of stimuli, including oxidative stress and stress-induced lipid signaling; [3] PYRIN, whose loss-of-function mutation causes the monogenic autoinflammatory disease familial Mediterranean fever [66]; and [4] NLRC4 that, if mutated, causes constitutive CASP1 cleavage in cells leading to severe autoinflammatory syndromes in humans [67]. In the LG, Nlrc4 was upregulated only by chronic inflammation but the transcription of its partner NAIP5 was increased during both acute and chronic inflammation. Our data also show that several signaling pathways essential for NF-κB/inflammasome activation, including TLR/MyD88, were upregulated by acute and chronic inflammation. MyD88-deficiency was shown to significantly dampen disease development in NOD.H2b mice [68], thus supporting a critical role in SS pathogenesis for this signaling pathway. In our study, we also noted the robust upregulation of GBPs and SYK that were recently described as modulators of inflammasome signaling [69]. SYK controls the activation of AIM2 and NLRP3 inflammasomes by phosphorylating ASC [70], thereby promoting its oligomerization and the recruitment of pro-CASP1 [71]. GBPs are part of the interferon signature that is involved in the pathogenesis of SS [72] and may play a role in SS and other autoimmune diseases by regulating inflammasome activation. Taken together, we propose that the sustained activation of inflammasome pathways observed in NOD.H2b and TSP-1-/- mice contribute to LG chronic inflammation.
In the search for molecular suppressors of inflammasome signaling following IL-1α injection, we discovered that Srebf1 and the genes involved in lipid biosynthesis/transport were upregulated at the resolution of inflammation. As for now, the link between lipid metabolism and inflammasome activity was mostly studied in immune cells. During the acute phase of inflammation, macrophages accumulate cholesterol, which activates inflammasomes through TLR signaling [73]; for example, by forming TLR4-inflammarafts that upregulate Il1b as shown in microglia [74]. Oishi and co-authors elegantly showed that in macrophages, SREBP-1 is first inhibited and then induced by TLR4/MyD88 at the later stages of the inflammatory response to promote the synthesis of anti-inflammatory fatty acids that promote the resolution of inflammation [75]. Similarly, the SREBP-dependent lipogenic program is induced by the activation of CASP1 by NLRP3 and NLRC4 inflammasomes in vitro [76]. Whether similar crosstalk between SREBP-1 signaling and inflammasomes occurs in LG epithelial cells remains to be determined.
By contrast, we showed that reduced mitochondrial metabolism (fatty β-oxidation, TCA cycle) and fatty acid metabolism were associated with disease progression in NOD.H2b mice and mainly affected LG epithelium [37]. NOD.H2b LGs might display similar mitochondrial alterations as diabetic NOD mice [77] and the mitochondrial damage itself could enhance inflammasome signaling [78]. A decrease in acetyl-CoA pools may also have a profound effect on gene expression through protein acetylation [79,80] and impair de novo lipid biosynthesis. Our data indeed showed the downregulation of PPARα/SREBP-1 signaling and of downstream genes involved in fatty acid metabolism, while genes promoting the generation and transport of cholesterol were upregulated at the early stages of the disease. Thus, the activation of cholesterol biosynthesis could be SREBP-1-independent and/or higher free cholesterol levels would exert negative feedback on Srebf1 expression. We also found the downregulation of Cyp27a1 and Cyp47a1 catalyzing the conversion of cholesterol into the secreted form 25-Hydroxycholesterol that reduces Il1b transcription and CASP1 activation [81].
In agreement with our study, Wu et al. [ 2009] showed altered lipid homeostasis in the LGs of diabetic NOD males [62]. In this model, cholesterol ester accumulation preceded lymphocytic infiltration and was not a consequence of dacryoadenitis. Altered lipid homeostasis is not restricted to mouse models of SS, since fat deposition in the LG is a feature of SS patients [82]. We previously reported large cytoplasmic vacuoles in LG acinar cells of NOD.H2b mice, suggesting they accumulate lipid droplets [37]. The activation of the lanosterol/mevalonate pathway could be a compensatory mechanism aimed at increasing other non-steroid products, for example, to rescue mitochondrial function [83]. The resulting cholesterol accumulation in epithelial cells might in turn downregulate SREBP-1-dependent lipid biosynthesis, promote lipotoxic damage, and activate inflammasome signaling.
Consistent with our findings, PPARα is also downregulated in experimental mouse models of LG inflammation induced by high-fat diets [84] or obstructive sleep apnea [85] that lead to lipid accumulation and dry eye symptoms. The latter can be alleviated by fenofibrate [84,85], an FDA-approved PPARα activator. Fenofibrate is a hypolipidemic drug that is used to treat the symptoms of high cholesterol and triglycerides in human. Recently, Guo and co-authors showed that fenofibrate improved tear production and corneal surface state and reduced lymphocytic infiltrates in the LGs of NOD/ShiLtJ mice through the modulation of Th17/Treg cell differentiation [86]. Thus, promoting fatty acid β-oxidation and biosynthesis through the activation of PPARα/SREBP-1 signaling could inhibit pro-inflammatory pathways in the LGs of NOD.H2b mice and promote regenerative processes.
The crosstalk between epithelial cells and lymphocytes plays a key role in SS development [87,88]. In fact, anti-inflammatory drugs such as Rituximab (B-cell depleting agent) and Anakinra (anti-IL-1) had only transient or no effects on SS patients [87,89]. We thus believe that anti-inflammatory molecules, combined with drugs that restore epithelial cell homeostasis, may lead to better outcomes. Therefore, one possible avenue of research is the combination of fenofibrate with iguratimod (anti-rheumatic drug inhibiting TNF-α, IL-1, IL-6, BAFF-R, CD38 signaling) or necrosulfonamide (inhibitor of GSDMD-pore formation) to decrease inflammation, reduce deleterious effects of inflammasome activation, and durably improve the epithelial function in SS.
## 5. Conclusions
In summary, our work shows that, in addition to secreting antimicrobial and immunoregulatory factors into the tear fluid, the lacrimal gland epithelium plays a pivotal role in the innate immune response by activating inflammasome signaling in response to exogenous or endogenous stimuli. The dysregulation of this protective defense mechanism during chronic inflammation is associated with an imbalance between the metabolic pathways producing fatty acids and cholesterol. These alterations contribute to pSS pathogenesis and thus represent a new avenue for therapeutics development.
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|
---
title: 'The Effects of Workplace Stressors on Dietary Patterns among Workers at a
Private Hospital in Recôncavo of Bahia, Brazil: A Longitudinal Study before and
during the COVID-19 Pandemic'
authors:
- Lorene Gonçalves Coelho
- Priscila Ribas de Farias Costa
- Luana de Oliveira Leite
- Karin Eleonora Sávio de Oliveira
- Rita de Cássia Coelho de Almeida Akutsu
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001619
doi: 10.3390/ijerph20054606
license: CC BY 4.0
---
# The Effects of Workplace Stressors on Dietary Patterns among Workers at a Private Hospital in Recôncavo of Bahia, Brazil: A Longitudinal Study before and during the COVID-19 Pandemic
## Abstract
Working in a hospital environment is known for presenting unhealthy features that affect the workers’ health—features which have currently been intensified due to the COVID-19 pandemic. Hence, this longitudinal study aimed to ascertain the level of job stress before and during the COVID-19 pandemic, how this changed, and its association with the dietary patterns of hospital workers. Data on sociodemographic, occupational, lifestyle, health, anthropometric, dietetic, and occupational stress were collected before and during the pandemic from 218 workers at a private hospital in the Recôncavo of Bahia, Brazil. McNemar’s chi-square test was used for comparison purposes, Exploratory Factor Analysis to identify dietary patterns, and Generalized Estimating Equations to evaluate the interested associations. During the pandemic, participants reported increased occupational stress, shift work, and weekly workloads, compared with before the pandemic. Additionally, three dietary patterns were identified before and during the pandemic. No association was observed between changes in occupational stress and dietary pattens. However, COVID-19 infection was related to changes in pattern A (0.647, IC$95\%$0.044;1.241, $$p \leq 0.036$$) and the amount of shift work related to changes in pattern B, (0.612, IC$95\%$0.016;1.207, $$p \leq 0.044$$). These findings support calls to strengthen labour policies to ensure adequate working conditions for hospital workers in the pandemic context.
## 1. Introduction
Work is an essential part of human life, since it provides their means of subsistence, dignifying them as beings who live in society. At the same time, it can submit individuals to work environments that are harmful to their health [1]. Unsafe work environments can predict physical and mental strain and occupational stress, as evidenced by Tao et al. [ 2] in their study with geological investigators, and by Alrawad et al. [ 3] in their work with mineworkers.
Working in a hospital environment is also known to present unhealthy characteristics which affect workers’ health, and this has intensified due to the COVID-19 pandemic. As a result, studies reporting an increase in psychological distress and the occupational stress levels of these individuals have been recurrent in the literature [4,5].
Before the COVID-19 pandemic, Ribeiro et al. [ 6] found that $27.4\%$ of workers of a hospital in southern Brazil were exposed to intermediate and high levels of work stress, and Fang et al. [ 7] identified that $20.5\%$ of nurses in a university hospital in southern Taiwan were exposed to high psychological demands in the work process. During the pandemic, Saind and El-Shafei [8] found that $75.2\%$ of nurses in a COVID-19 triage hospital in Sharkia Governorate, Egypt, had a high level of occupational stress, and Magnavita et al. [ 9] reported that $71.1\%$ of physicians in a hub hospital in Latium, Italy, were also submitted to high levels of stress at work.
The increase in stress levels among hospital workers during the pandemic can be related to the emergence of situations that they had rarely experienced before, such as increased stress in patient care, a feeling of high risk in work performance, concern for their health, with the health of family members, and with self-isolation [10]. Furthermore, with the increased number of hospitalizations due to COVID-19, there were changes in the structure and organization of work in hospitals, imposing an even more harmful work environment on workers [5].
In turn, it is known that occupational stress, i.e., high psychological demands at work, is associated with changes in workers’ lifestyles and health [11,12,13,14], especially with regards to food. Coelho et al. [ 10], in an integrative review, demonstrated the occurrence of negative changes in nurses’ eating habits because of work. Nuhu et al. [ 12] also found changes in the food consumption of nurses from two hospitals in Ghana; those with high levels of stress at work had low caloric intake. Additionally, Islam et al. [ 14] found that health professionals at a field hospital against COVID-19 in Gazipur, Bangladesh, had their meals irregularly.
Thus, it is essential to consider changes in the behaviour and food consumption of workers in stressful situations due to the well-known association between food and nutrition and non-communicable chronic diseases, making them important factors to maintain and promote good health [12,13,14]. In addition, for workers, when food consumption is inadequate, there may still be a reduction in work capacity and productivity, which makes the process a perverse cycle where the reduction in productive capacity compromises income and, consequently, the ability to provide good food [1].
With regards to ways of evaluating food consumption, this has been performed while only considering the isolated consumption of nutrients or foods for several years. However, food and their nutrients are consumed together, and interact with each other, having been established in the literature that their real effects can only be observed when the entire eating habit is considered [12,15].
Therefore, the number of studies using the assessment of dietary patterns has increased in epidemiological studies, since foods are analysed synergistically and simultaneously in this approach, considering complex combinations between nutrients, which facilitates the description and knowledge of the effect of food on the health and disease process [13,14,15].
There is a lack of studies specifically on occupational stress and dietary patterns, which seek to understand this relationship within a theoretical framework, reinforcing the need for further investigations, for a better understanding of the phenomenon of eating at the interface of occupational stress, and over time, limiting themselves to evidences from cross-sectional studies that do not allow understanding causality relationships, or that deal with food as secondary feeding or the isolated consumption of nutrients. Thus, the need to deepen investigations is reinforced, adopting a robust study design, such as a prospective cohort, for better understanding the phenomenon of food in the interface of occupational stress, which justifies the accomplishment of the present work. Accordingly, we hypothesized that high levels of occupational stress contribute to changes in the dietary patterns of hospital workers.
Thus, the objective of this study is to verify the level of occupational stress before and during the COVID-19 pandemic, and its change and association with the dietary pattern of workers at a hospital in the Recôncavo of Bahia, Brazil.
## 2.1. Study Design and Sample
This is a longitudinal study that used baseline and the first follow-up data from one of the hospitals in the cohort “Evaluation of Food and Nutrition Services in three hospitals in the health network of Salvador, Bahia”. Only one of the study hospitals was included in this study, as the other sites withdrew consent to participate during the COVID-19 pandemic. The hospital in question is in the town of Santo Antônio de Jesus, Bahia, and had a staff of 371 workers in 2019. Initially, all 371 workers were invited to participate in the study; however, according to the inclusion and exclusion criteria described below, as well as the losses that occurred during the study, the final sample included 218 workers from different sectors of the hospital, as described in other publications [16,17].
## 2.2. Eligibility Criteria
Workers of both sexes, aged over 18 (Brazilian majority), who agreed to participate in the research by signing a free and informed consent form were eligible. Individuals with problems that compromised taking anthropometric measurements were not included: those who had recent abdominal surgeries and suffer from abdominal lesions, tumours, hepatomegaly, splenomegaly, ascites, and amputees; as well as pregnant women, or those who had given birth in the last six months, due to changes in body composition characteristics at these stages of life [18].
## 2.3. Data Collection
Data collection was performed by a team of nutritionists trained in research protocol. Sociodemographic, occupational, lifestyle, health, anthropometric, and occupational stress variables were collected between May and October 2019 (before the pandemic—baseline), and between October and November 2020 (during the pandemic—first follow-up), considering the same instruments, techniques, and procedures in both evaluation periods.
## 2.3.1. Sociodemographic, Occupational, Lifestyle, and Health Variables
The variables in question were collected through a structured questionnaire. Gender, age, skin colour/ethnicity [self-reported], marital status, education, and income were the sociodemographic variables. Occupational variables included occupation [health professional, or other], how long they had worked at the hospital [months], weekly workload, and shift work. With regards to lifestyle, the variables of smoking and alcohol consumption habits, and level of physical activity were evaluated through the reduced and validated version of the International Physical Activity Questionnaire, with workers classified as having low (<600 metabolic equivalents (MET)—minutes/week), moderate (600 to 3000 MET-minutes/week), and high levels of physical activity (≥3000 MET-minutes/week) [19]. In relation to health, the variables of family history for non-communicable chronic diseases, perception of one’s own health, and self-reported contamination/infection by COVID-19 were considered.
## 2.3.2. Anthropometric Variables
Weight, height, and waist circumference (WC) formed the anthropometric variables. Weight was measured using a portable digital scale with bioimpedance on a platform (Full Body Sensor—Body Composition Monitor and Scale, model HBF-516, OMRON® brand). Respondents were weighed following techniques described in the literature [20]. Height was measured using a portable stadiometer (Alturaexata®). The technique used is recommended by the World Health Organization (WHO) [20]. The Body Mass Index (BMI) was calculated from weight and height measurements, represented by the Kg/m2 ratio [20]. The cut-off point used to classify the nutritional status of workers, according to the BMI, was that proposed by the WHO [21]. The WC was measured using a flexible, inelastic measuring tape, following WHO recommendations [20]. This measure was used to predict the risk of metabolic and cardiovascular complications in workers, while considering the cut-off points proposed by the WHO [22].
## 2.3.3. Dietary Variables
The Brazilian Longitudinal Study of Adult Health—(ELSA-Brasil) food frequency questionnaire (FFQ) was used to obtain an estimate of usual food consumption in the twelve months prior to the study evaluation periods [23].
The ELSA-Brasil FFQ presents a list of foods made up of 114 items and is structured in three sections: [1] food/preparation, [2] measures of consumption portions, and [3] consumption frequency, with eight categories: more than 3 times/day, 2–3 times/day, once/day, 5–6 times/week, 2–4 times/week, once/week, 1–3 times/month, and never/almost never [23].
We also highlight that the 114 items on the FFQ are categorized into the following food groups: “breads, cereals, and tubers”, “fruits”, “vegetables and legumes”, “eggs, meat, milk, and dairy products”, “pasta and other preparations”, “candy”, and “beverages” [23]. However, for the purposes of the analysis of this study, the foods in the “breads, cereals, and tubers”, “vegetables and legumes”, “eggs, meat, milk, and dairy products”, and “beverages” groups were reorganized into “breads and cereals” and “tubers”, “vegetables”, “legumes”, “oilseeds”, “eggs”, “meat”, “milk and dairy products”, “fats”, “beverages”, and “sugary drinks”, respectively, according to Food-Based Dietary Guidelines for the Brazilian Population [24].
In addition, data from FFQ consumption frequencies (daily, weekly, and monthly) were converted into daily consumption portions, to use a time unit in the analyses, as proposed by Coelho [25].
## 2.3.4. Occupational Stress Variables
The instrument used to assess occupational stress was the JCQ in its reduced version, translated and validated for the Brazilian population. The JCQ consists of 17 questions, divided into the following dimensions: [1] demand, [2] control, and [3] social support, with the response options presented on a Likert scale (1–4) [26].
The “demand” dimension comprises five questions that address pace, workload, time, conflicting demands, and work effort. There are six questions for the “control” dimension, related to learning, skill, creativity, repetitiveness, responsibility, and decision-making. The “social support” dimension has six questions about interpersonal relationships [26].
To classify occupational stress, we used the Demand–Control Model, which makes the theoretical assumption that the coexistence of great psychological demands and low control in the work process generate job strain, which results in increased stress at work [26]. Following this, participants were classified as having “high occupational stress” if they report above the median score in the “demand” dimension and below the median score in the “control” dimension of the JCQ, and “low occupational stress” otherwise [27].
## 2.4. Identification of Dietary Patterns
Identification of workers’ dietary patterns was carried out through factor analysis of the principal components at both points in time of the study (before and during the COVID-19 pandemic) and considering the 14 food groups described above.
This type of analysis reduces by one factor (dietary pattern) the food groups that are correlated with each other, but that are independent and do not contribute to other patterns in the analysis, indicating the factor loading of the correlation between the food group and its respective factor [28].
To verify the applicability of the data to the factor analysis, the Kaiser–Meyer–Olkin (KMO) test and the Bartlett sphericity test were used, considering acceptable values above 0.60 and $p \leq 0.05$, respectively. The KMO assesses the factor model adequacy through partial correlations and their respective weights, as the closer to 1, the higher the factor model adjustment, and values lower than 0.60 are not accepted [29,30]. Bartlett’s sphericity test considers there are no correlations between the data, i.e., the correlation matrix generated in the analysis is an identity matrix. Therefore, the factor model is suitable when it produces a correlation matrix that differs from the identity matrix, which is indicated by p-values ≤ 0.05 [29,30].
After verifying the adequacy of the factor analysis for the data set, each food group had its commonality assessed. The commonality reflects the level of connection between the variable (group) and the factor (pattern). It can vary from 0 to 1; the closer to 1, the higher the connection between them. In this study, variables with commonality values > 0.30 were considered as representative of the factor [29,30].
As for the number of factors selection, the criterion of eigenvalues or Kaiser’s criterion was used. As the eigenvalue is influenced by the total number of components of the analysis, generally high in studies on food consumption, some authors use a criterion of eigenvalues greater than 1 as a cut-off point to determine the total number of factors to be retained in the analysis [31]. Thus, this cut-off point was adopted since it allows better interpretability of dietary patterns and retains a smaller number of factors with the highest percentages of variance; being more representative of the food of the studied workers.
Finally, to improve the interpretability of food groups belonging to each dietary pattern and to obtain unrelated patterns, we use the Varimax orthogonal rotation method. At the end of the analysis, the rotated matrix was evaluated, and considering the sample size of this study, the variables that presented a factor loading > 0.30 in the retained factors characterized the dietary patterns [29,30].
The analyses were performed using STATA for MAC statistical software (Version 17.0, Stata Corp LP, College Station, TX, USA).
## 2.5. Identification of Variables
The dietary patterns identified through the factor analysis were named A, B, and C, and they were the outcome variables of this study. Their measurements were taken at the baseline, and after a minimum interval of twelve months’ follow-up, to assess changes over time. For integration into statistical models, the patterns were considered in their categorical form, adopting the 50th percentile (P50) (<P50 [0] and >P50 [1]) as the cut-off point.
Occupational stress, also assessed at the beginning and after a minimum twelve-month interval, was considered the main exposure in this study. Integration into statistical models took place in a categorical form: “absence or low levels of stress at work” [0] and “high levels of stress at work” [1]. Similarly, other occupational characteristics considered stressors at work were considered as additional exposures: shift work, no [0] and yes [1], weekly workload, <44 [0] and >44 h, and infection by COVID-19, no [0] and yes [1].
The covariates of the study included: age (years), sex (women, men), educational level (<high school, >college), income (<3 minimum wages (MW), 3–5 MW, >5 MW), occupation (health professional, other), smoking status (current/ex-smoker, non-smoker), alcohol consumption (yes, no), physical activity level (low, medium, high), health self-perception (excellent/good, regular/bad), and nutritional status according to body mass index (underweight/normal range, overweight/obese) and waist circumference (low risk, increased/high risk).
## 2.6. Statistical Analyses
Descriptive statistical analysis expressed the categorical variables as absolute and relative frequencies, and the continuous variables as mean and standard deviation. Data normality was checked by the Shapiro–Wilk test. McNemar’s chi-square or Wilcoxon tests were used to compare the prevalence of occupational stress before and during the COVID-19 pandemic. Pearson’s chi-squared test and Student’s t-test were used to verify the distribution of dietary patterns according to the covariates of the study.
In order to assess the influence of occupational stress and additional exposures (shift work, weekly workload, and COVID-19 infection) on changes in dietary patterns A, B, and C before and during the pandemic, Generalized Estimating Equation models (GEE) were constructed. These are appropriate for categorical response variables and repeated measures, reflecting the relationship between outcomes and exposures, considering the correlation and interdependence between measures at each moment in time [32]. The GEE is able to produce more efficient and less biased estimates of correlated (repeated) data, since it considers the intra- and inter-individual correlation structure [32]. The matrix chosen for this study was the correlation matrix.
Quasi-likelihood criterion (QIC), under the corrected independence model, was used to fit the models to the data, which is an adaptation of Akaike’s information criterion (AIC) method for GEE analyses. The QIC is calculated by comparing the quasi-likelihood of the independence model with the complete model. The lower the QIC, the better the model fits [33,34].
A model was built for each outcome variable (dietary patterns A, B, and C)—inserted into a categorical and time-variant form—depending on the main exposure variable (occupational stress) and additional exposures (shift work, working hours, weekly workload, and COVID-19 infection)—also categorized. Initially, univariate analysis was performed, and those with a p value lower than $20\%$ were selected. These variables, together with those which showed potential for confounding in the bivariate analysis, were included in the model. Potential confounding variables were those associated with both exposure and outcome, expressed as a change of $10\%$, or more, in the association measure, compared with the reduced model measure [35]. Interaction terms were tested—built based on the literature and the data structure of the study—to assess the existence of modification of the effect of exposure variables on the outcome variable, using the maximum likelihood-ratio test (log likelihood-ratio test), evaluating the significance of the interaction term in the multivariate model. The variables which presented a significance of less than $5\%$ remained in the final model.
The analyses were performed using STATA for MAC statistical software (Version 17.0, Stata Corp LP, College Station).
## 2.7. Ethical Aspects
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the School of Nutrition at the Federal University of Bahia Ethics Committee for ethical pertinence [36], under number 4,316,252. Written informed consent was obtained from all subjects.
In addition, in compliance with ethical assumptions, all workers who presented significant changes in the indicators evaluated were referred to local health services and remained in the study.
## 3. Results
At the baseline, the workers’ mean age was 32.60 (8.30). The average length of hospital work experience was 45.96 (35.72) months. In all, $41.70\%$ of the workers were health professionals, while the remainder occupied other positions, such as administrator, cleaner, telephonist, and labourer, etc. With regards to educational level, $54.60\%$ of the participants attended high school, and $45.40\%$ subsequently took college or university courses. Most of the workers ($52.30\%$) were married or had a common-law partner, and $42.20\%$ were single. Further worker characteristics at the baseline are reported in Table 1.
With regards to the prevalence of work stress (primary exposure) among workers, and other occupational characteristics (additional exposures) before and during the COVID-19 pandemic, there was a $107\%$ (14.20 versus $29.40\%$) increase in the high level of occupational stress, $26\%$ (32.10 versus $39.40\%$) in the number of individuals working shifts, and $32\%$ (22.90 versus $30.30\%$) in those working more than 44 h a week. All these differences were highly significant (McNemar’s chi-square test $p \leq 0.001$, $$p \leq 0.001$$ and $$p \leq 0.02$$, respectively).
In addition to the stress levels and occupational characteristics mentioned above, the contamination of workers by COVID-19 was also investigated, since it is considered a new stressor in the hospital work environment. In total, $67.40\%$ ($$n = 147$$) of the individuals in the sample reported having tested positive for COVID-19 during the pandemic.
As for dietary patterns, three were identified in both assessment periods, i.e., before and during the COVID-19 pandemic. The three pre-pandemic patterns explained $45.51\%$ of the sample variance and those during the pandemic explained $44.47\%$.
The first pattern identified before the pandemic (A) was characterized by the basic food groups for the Brazilian population (bread and cereals, and legumes), as well as pasta and other preparations, meat, milk and dairy products, fruits, candy, and fats, being responsible for $18.00\%$ of the total variance. During the pandemic, the first pattern (A) was responsible for $17.20\%$ of the variance. Tubers, eggs, vegetables, fruits, and oilseeds were the food groups with the positive, high factor loadings (>0.30) which characterized this pattern (Table 2).
The second pattern prior to the pandemic (B) was also characterized by the tubers, eggs, vegetables, fruits, and oilseeds groups. With regards to the second pattern during the pandemic (B), this was formed by bread and cereals, meat, fats, pasta and other preparations, candy, and sugary drinks groups (Table 2). The variance attributed to each of these patterns was $15.95\%$ and $14.71\%$, respectively.
The third and final pattern (C) explained 11.56 and $12.56\%$ of the total variance before and during the pandemic. In these two evaluation points in time, the characteristic groups were bread and cereals, legumes and beverages; and bread and cereals, milk and dairy products, fats and beverages, respectively (Table 2).
Distribution of the workers’ sociodemographic, lifestyle, and health characteristics, that is, the covariates of the study, according to dietary patterns A, B, and C are described in Table 3. Pattern A was associated with the workers’ income ($$p \leq 0.042$$), and pattern B to educational level ($$p \leq 0.009$$), while pattern C had both characteristics ($p \leq 0.001$ and $$p \leq 0.009$$, respectively).
With regards to the influence of occupational stress on changes in dietary patterns before and during the pandemic, no significant results were observed, according to the raw GEE models (Table 4). However, when considering additional exposures, a positive and significant association was confirmed between COVID-19 infection and changes in dietary pattern A (0.684, $95\%$CI0.079; 1.290, QIC 303677, $$p \leq 0.027$$), and between shift work and changes in pattern B (0.650, $95\%$CI0.053; 1.248, QIC 302894, $$p \leq 0.033$$) (Table 4).
These results were adjusted, considering the covariates of the study: the model between COVID-19 infection and changes in pattern A was adjusted for income, physical activity level, and nutritional status, according to the WC (0.631, $95\%$CI0.030; 1.231, QIC 302501, $$p \leq 0.031$$), with only the covariate nutritional status according to the WC (0.647, $95\%$CI0.044; 1.241, QIC 302952, $$p \leq 0.036$$) remaining as an adjustment in the final model. Adjustment of the model for shift work and changes in dietary pattern B was carried out through the covariates of educational level and income (0.611, $95\%$CI0.015; 1.206, QIC 30148, $$p \leq 0.044$$), with adjustment of the final model only including the educational level (0.612, $95\%$CI0.016; 1.207, QIC 300462, $$p \leq 0.044$$). In the four scenarios presented, statistical significance was maintained between exposures and outcomes.
## 4. Discussion
The results of this study revealed a significant increase in the high level of stress at work, as well as the number of individuals working shifts and more than 44 h per week during the COVID-19 pandemic. In addition, three dietary patterns were identified at both points in time of the study, with no association between these and occupational stress. Thus, our hypothesis was rejected. However, with regards to additional exposures, it was found that COVID-19 contamination was associated with pattern A, and shift work with pattern B.
With regards to the changes identified in the participants’ occupational characteristics, they reflect alterations in the structure and organization of work in hospitals. These are due to the increase in hospital admissions, on account of COVID-19, which has imposed on workers a work environment which is even more harmful to their health [5]. In addition, the high percentage of contamination of workers by COVID-19 ($67.40\%$) is consistent with another reality of which these professionals had minimal experience. This is related to increased stress in patient care, the feeling of high risk in work performance, and concern for their own health [10].
Other studies have also demonstrated the effects of the pandemic on hospital workers’ health. According to Zhou et al. [ 5], symptoms of depression, anxiety, insomnia, and somatization are more severe in health teams than in the general population. There is also an increase in the level of occupational stress: Arafa et al. [ 37], when studying hospital workers in Egypt and Saudi Arabia, found that $55.9\%$ had work stress, with $36.6\%$ experiencing mild to moderate and $19.3\%$ high to very high stress.
Due to this increasingly worrying scenario, another important factor to be evaluated is changes to these workers’ lifestyles, especially with regards to food, since they may result from occupational stress and may increase the risk of developing chronic, non-communicable diseases [38,39,40].
It is known that the relationship between food and stress occurs due to the considerable overlap of the physiological systems involved with food consumption and response to stress [41]. Due to this close relationship, stress can be associated with both an increase and decrease in food consumption [41,42]. At least temporarily, stress can also lead to other biological and behavioural changes, such as slower gastric emptying and increased preference for foods high in sugars and fats as a tool to manage temperament, tension, and stress [41].
Thus, investigation into food, through dietary patterns, is relevant, especially when considering changes in occupational factors imposed by the COVID-19 pandemic. In this study, three dietary patterns were identified before and during the pandemic, which accounted for 45.51 and $44.47\%$ of the total sample variance, respectively.
Initially, pattern A was related to the bread and cereals, fruits, legumes, meat, milk and dairy products, fats, pasta and other preparations, and sweets groups, reflecting the traditional diet of Brazilian people, which is mostly composed of rice, beans, and meat of some kind [43]. During the pandemic, the food groups related to this pattern were tubers, fruits, vegetables, oilseeds, and eggs, indicating improvements in the quality of their diet, since these are considered indicators of healthy eating [24,44]. These changes were not associated with alterations in occupational stress levels; however, they were associated with the contamination of workers by COVID-19. Steele et al. [ 44], in their cohort study with 10,116 Brazilian adults from all regions of the country, found similar results; that is, a significant increase in the consumption of vegetables, fruits, and legumes during the pandemic.
These authors also explain that the COVID-19 pandemic may influence food in two ways: harmful or beneficial. The beneficial aspect, as seen in this study, refers to the improvement in diet through increased consumption of healthy indicators, which may arise from a possible concern by individuals to consume healthy foods, as an alternative to strengthening the immune system and the body’s defence against the coronavirus [44].
With regards to pattern B, the associated food groups before the pandemic were tubers, fruits, vegetables, oilseeds, and eggs; while during the pandemic, this was bread and cereals, meat, fats, pasta and other preparations, sweets, and sugary drinks. Unlike pattern A, changes in pattern B indicate a deterioration in the quality of food during the pandemic, indicated by the presence of unhealthy indicators, i.e., a source of sugars and fats [24]. However, these changes were not related to changes in occupational stress levels but to an increase in shift work.
The association between the change in pattern B and the increase in shift work verified in this study is in line with the literature, which indicates that shift work, defined as non-day, irregular, and/or rotational work, is associated with changes in workers’ lifestyles and, therefore, to future health problems [45,46]. Among these changes are those in eating behaviour, such as difficulty in maintaining a healthy diet, and/or increased consumption of high-calorie foods, rich in sugars and fats [45,47].
Farías et al. [ 48], in their study with health professionals from a hospital in Santiago, Chile, obtained similar results: lower diet quality index and vegetable consumption score, as well as lower frequency of meals, and higher omission of main meals. Furthermore, according to the systematic review carried out by Souza et al. [ 49], shift workers tend to present changes in meal patterns, skipping meals, and consuming food at unconventional times, increasing the consumption of unhealthy foods, especially those rich in saturated fats, and intake of sugary drinks.
In view of this, changes in the behaviour and dietary patterns of hospital workers, especially those related to pattern B, are a matter of concern, representing a serious risk to the health of these individuals, especially in the current context of a pandemic. The findings of this study, and others in the literature, indicate the need to establish strategies for better organization of routines and work in hospitals, to minimize the impacts of shift work and occupational stress, and to provide greater flexibility for workers to carry out their daily activities.
## 5. Conclusions
The present study aimed to verify the level of occupational stress, as well as the presence of workplace stressors, their changes, and associations with the dietary pattern of hospital workers, over time and comparing two points in time, before and during the COVID-19 pandemic.
Hence, it was possible to obtain a better and more comprehensive understanding of the work, food, and health conditions of workers in the hospital environment, which was provided in the innovative design of this work, effectively reflecting the impacts of the pandemic.
## 5.1. Research Limitations
The main limitations of this study refer to convenience sampling and self-reporting of contamination by COVID-19 by hospital workers. The first limitation is justified by the fact that the study was carried out during the pandemic, which made it difficult to conduct face-to-face interviews, due to high work demands, the turnover of professionals, and compliance with safety protocols. Despite this, the originality and innovative nature of this study are highlighted, comparing information before and during the pandemic and reflecting the changes imposed by the context of the pandemic. With regards to the self-reporting of contamination by COVID-19, it is believed that the impact of this measure on the results of this study may be minimized. The sample is composed of hospital workers, who by nature and workplace are assumed to hold greater and more accurate information about their health status and diagnosis of the disease than the general population.
## 5.2. Future Research
The COVID-19 pandemic has significantly changed the functional and lifestyle characteristics of the workers studied, resulting in an increase in the levels of stress and occupational stressors, as well as changes in these individuals’ dietary patterns, especially with regards to patterns A and B.
These findings are important for broadening the discussion regarding the health surveillance of these individuals in this current health crisis, both individually and collectively. They also constitute an important source of information to formulate corrective and preventive measures appropriate to the reality of hospital workers, with the objective of including not only healthy eating and living habits into their routines, but also non-invasive interventions related to stress at work, minimizing and/or preventing the risk of harm in later stages of life.
Finally, given the importance of professionals in the hospital sector in coping with the pandemic, further studies must be carried out to assess the post-pandemic context, that is, the implications left by the pandemic on their occupational, social, and health conditions, with emphasis on longitudinal assessments and representative samples.
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|
---
title: Impact of Contextual-Level Social Determinants of Health on Newer Antidiabetic
Drug Adoption in Patients with Type 2 Diabetes
authors:
- Yujia Li
- Hui Hu
- Yi Zheng
- William Troy Donahoo
- Yi Guo
- Jie Xu
- Wei-Han Chen
- Ning Liu
- Elisabeth A. Shenkman
- Jiang Bian
- Jingchuan Guo
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001625
doi: 10.3390/ijerph20054036
license: CC BY 4.0
---
# Impact of Contextual-Level Social Determinants of Health on Newer Antidiabetic Drug Adoption in Patients with Type 2 Diabetes
## Abstract
Background: We aimed to investigate the association between contextual-level social determinants of health (SDoH) and the use of novel antidiabetic drugs (ADD), including sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a) for patients with type 2 diabetes (T2D), and whether the association varies across racial and ethnic groups. Methods: Using electronic health records from the OneFlorida+ network, we assembled a cohort of T2D patients who initiated a second-line ADD in 2015–2020. A set of 81 contextual-level SDoH documenting social and built environment were spatiotemporally linked to individuals based on their residential histories. We assessed the association between the contextual-level SDoH and initiation of SGTL2i/GLP1a and determined their effects across racial groups, adjusting for clinical factors. Results: Of 28,874 individuals, $61\%$ were women, and the mean age was 58 (±15) years. Two contextual-level SDoH factors identified as significantly associated with SGLT2i/GLP1a use were neighborhood deprivation index (odds ratio [OR] 0.87, $95\%$ confidence interval [CI] 0.81–0.94) and the percent of vacant addresses in the neighborhood (OR 0.91, $95\%$ CI 0.85–0.98). Patients living in such neighborhoods are less likely to be prescribed with newer ADD. There was no interaction between race-ethnicity and SDoH on the use of newer ADD. However, in the overall cohort, the non-Hispanic Black individuals were less likely to use newer ADD than the non-Hispanic White individuals (OR 0.82, $95\%$ CI 0.76–0.88). Conclusion: *Using a* data-driven approach, we identified the key contextual-level SDoH factors associated with not following evidence-based treatment of T2D. Further investigations are needed to examine the mechanisms underlying these associations.
## 1. Introduction
More than 100,000 individuals die from diabetes each year in the United States (US) [1]. Of these deaths, $60\%$ are attributed to concurrent cardiovascular disease (CVD), with myocardial infarction being the most common cause [2]. Among the antidiabetic drugs (ADD) currently available on the US market, two relatively novel agents, sodium-glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1a), are associated with significant reductions in blood glucose levels and have been found particularly effective in reducing the risk of CVD in individuals with type 2 diabetes (T2D) [3]. In addition, these novel antidiabetic agents have been shown to associate with weight loss, reduced risk of hypoglycemia and cardiorenal protection, favorable benefits that are of great importance to patients with T2D [3]. The American Diabetes Association (ADA) recommends SGLT2i and GLP1a for patients with T2D who have CVD, heart failure, chronic kidney disease, or an increased risk of these conditions, regardless of their glycemic status [4,5].
However, the utilization of SGLT2i and GLP1a in real-world T2D patient populations is relatively low in the US compared to other ADD [6,7], especially among historically marginalized communities, such as racial and ethnic minority groups and individuals experiencing socioeconomic disadvantages. Data from commercial insurance and Medicare, for example, showed that Black patients were 10–$20\%$ less likely to receive newer ADD than White patients [7,8,9,10]. While such disparity can be explained overall by racial disparity as a distal cause, its proximal cause—the underlying mechanism whereby racial and ethnic groups have initiated SGLT2i/GLP1a—remains largely unknown.
In the past, research and clinical approaches centered on the individual-level have led improvements in self-management outcomes and reduction in cardiovascular risk among patients with T2D [11]. More recently, researchers have acknowledged the need to consider external factors, namely the social determinants of health (SDoH) to achieve the goal of sustainable improvement in diabetes outcomes [12]. SDoH refer to the various social, economic, and environmental factors, including access to healthcare, education, employment, housing, and social support that have an impact on people’s health, well-being, and quality of life [13]. Contextual-level SDoH refers to the broader social and built factors within community or region that influence health outcomes, and are increasingly recognized as a vital source of information to develop healthcare policies designed to improve population health management and value-based care [14,15]. Previous studies have demonstrated the association of contextual-level SDoH with geographic variation and diabetes risk [16]. However, minimal data exist on the extent to which contextual-level SDoH (e.g., residential segregation, food environment, and neighborhood walkability) may impact healthcare use, including initiating evidence-based treatment in T2D care [8]. A Dutch study published in 2012 examined the association of regional-level aging composition and socioeconomic status with spatial variation in ADD use but without a comprehensive evaluation of multiple contextual-level SDoH [17].
Given that race and ethnicity are social constructs [18], contextual-level SDoH can play important roles in the development of racial and ethnic disparities across geographic regions [19]. Therefore, understanding how contextual-level SDoH impact the adoption of these outcome-improving therapies in millions of Americans with T2D is imperative. Accordingly, this study aimed to examine the association between patients’ contextual-level SDoH and their initiation of the newer ADD, and how such associations may vary across racial and ethnic groups. With such empirical evidence, the racial disparity in SGLT2i/GLP1a utilization can be better understood, and relevant policymaking can be better guided.
## 2.1. Data Source and Study Population
This is a retrospective cohort study using data from the OneFlorida+ network, containing large collections of electronic health records (EHR) covering more than 19 million patients from Florida (~16.8 million), Georgia (~2.1 million), and Alabama (~9.1 thousand) [20]. We assembled a cohort of adults (i.e., aged ≥ 18) identified as having at least one inpatient or outpatient T2D diagnosis (using ICD-9 codes 250.x0 or 250.x2, or ICD-10 code E11) and ≥1 ADD prescription. The algorithm used to identify T2D has been validated in OneFlorida+ with a positive predictive value (PPV) > 94 [21] and is preferred over using only diagnosis codes, which can lead to misclassification error [22]. Among the T2D cohort, we identified individuals who initiated SGLT2i or GLP1a, or another second-line ADD (i.e., dipeptidyl-peptidase-4 inhibitors, sulfonylureas, thiazolidinediones, and basal insulin) in 2015–2020. The index date was the day of the first prescription of a second line ADD, defined as no use of the drug in three prior years. We restricted the study cohort by only including those individuals who had ≥ 2 inpatient or outpatient encounters per year in OneFlorida+ in the three years prior to the index date to obtain complete information for modelling.
## 2.2. Study Outcome and Covariates
The outcome was the initiation of a newer ADD (i.e., SGLT2i or GLP1a) versus another second-line drug. We collected baseline demographic and clinical information on or within the 3-year period prior to the index date, including age, sex, race-ethnicity (non-Hispanic White [NHW], non-Hispanic Black [NHB], Hispanic, and other), rurality (defined using linkage to rural–urban continuum codes [RUCC] based on patients’ residencies’ Federal Information Processing System [FIPS] county code and classified the rurality into three levels by the US Department of Agriculture’s (USDA) Economic Research Service: RUCC ≤ 3 as metropolitan; 3 < RUCC ≤ 7 as urban; and 7 < RUCC ≤ 9 as rural), primary payer (Medicare, Medicaid, private insurance, no insurance, and other), diabetes complications and comorbidities (such as cardiovascular disease and chronic kidney disease), co-medications (i.e., use of another ADD, antihypertensives, statins, and antidepressants), clinical presentation (most recent blood pressure and body mass index [BMI], identified in four categories: ≤25, 25–30, 30–100 kg/m2, or missing), and lab values (most recent hemoglobin A1c [HbA1c], identified in four categories: ≤7, 7–10, 10–21 mmHg, or missing). Clinical data were extracted from de-identified EHR records in the OneFlorida+ network.
## 2.3. Contextual-Level SDoH
We obtained data on built and social environment measures from six well-validated sources with different spatiotemporal scales, characterizing food access, walkability, vacant land, neighborhood disadvantage, social capital, crime and safety. All measures were spatiotemporally linked to each individual considering residential mobility during the study period. Area-weighted averages were first calculated according to a 250 m buffer around the centroid of each 9-digit ZIP code. Time-weighted averages were then calculated, accounting for each individual’s residential history.
Table 1 summarizes the contextual-level data sources and the corresponding spatiotemporal scales. A total of 43 food access measures at census tract level in 2015 and 2019 were obtained from USDA’s Food Access Research Atlas [23]. Walkability was assessed using the National Walkability Index developed by the US Environmental Protection Agency (EPA) [24], which assesses walkability on a scale from 1 to 20 for each census block group, with 1 indicating the least walkable and 20 the most walkable. Vacant land measures at the census-tract level from 2015 to 2019 were obtained from the US Department of Housing and Urban Development aggregated with US Postal Service administrative data [25] and a total of 18 measures that were available across all years were included. The neighborhood deprivation index (NDI), a socioeconomic status measure, was obtained at the census block group level based on data from the 2015 to 2019 American Community Survey (ACS). It yields information on the income, education, employment, and housing quality of a neighborhood and allows ranking by socioeconomic disadvantage [26]. In addition, ten social capital measures were constructed using the Census Business *Pattern data* based on the North American Industry Classification System (NAICS) codes [27] at the 5-digit ZIP code tabulation area (ZCTA5) level. Furthermore, eight county-level annual measures of crime and safety were obtained from the Uniform Crime Reporting Program from 2015 to 2019 [28]. A total of 81 SDoH measures were included in the analyses.
## 2.4. Statistical Analysis
We conducted normalization transformations for all continuous contextual-level SDoH variables using the bestNormalize package in R, which implements several transformation methods, including log, square root, exponential, arcsinh, box cox, and Yeo-Johnson transformations [29]. The best transformation was determined based on Pearson P statistics. All continuous variables were also z-score standardized (mean = 0 and standard deviation = 1). All contextual-level SDoH factors and covariates of interest described above had missing values for <$2\%$ of the participants; *Missing data* for all contextual-level SDoH factors were imputed using the chained equations method of the MICE package in R. A variable was considered a predictor in the imputation model if its proportion of non-missing values among counties with missing values in the variable to be imputed was larger than $40\%$ and they were correlated (i.e., with the absolute correlation value > 0.4) with the variable to be imputed or the probability of the variable being missing. We imputed a single dataset given the minimal impacts of the imputation procedure due to the large sample size and a small fraction of missing data. Missing information on BMI and HbA1c were not imputed and maintained as a separate category.
We used a two-phase approach to identify key contextual-level associated with initiation of SGTL2i/GLP1a versus other second-line ADD [30,31]. In Phase 1, we randomly split the data into a $50\%$ discovery set and a $50\%$ replication set. We considered all the 83 contextual-level SDoH for associations with newer ADD initiation after accounting for multiple comparisons. We built multivariable logistic regression models for each contextual-level factor after adjusting for demographics, urbanicity, diabetes complications, co-medications, clinical presentation, and primary payer. To account for the multiple testing, the Benjamin-Hochberg procedure was used to control the false discovery rate (FDR) at $5\%$ [32]. A variable was considered significant if it had an FDR-adjusted p-value (or q-value) < 0.05 in both the discovery and the replication sets. A correlation heatmap was generated to show the pairwise Pearson correlations of the variables retained from Phase 1. Variables from highly correlated pairs (with the absolute value of correlation coefficients > 0.6) were removed to avoid collinearity between variables [33].
In Phase 2, we used a multivariable logistic regression model including all significant variables identified from Phase 1 as well as all the demographic and clinical information, including age, sex, primary payer, BMI, HbA1c, type of residence, cardiovascular disease, chronic kidney disease, use of insulin and non-insulin antidiabetic medications to estimate the effect sizes. Adjusted odds ratios (aOR) and $95\%$ confidence intervals (CI) were reported.
For the key contextual-level SDoH identified using the two-phase approach, we dichotomized them using the 80th percentile from the key variables as the cutoff. A Higher numeric value in NDI and percent of vacant addresses indicates a neighborhood that is more disadvantaged in socioeconomic profile and has a larger vacancy in addresses. Therefore, we defined neighborhoods with the top 20th percentile in NDI as more deprived neighborhoods, and neighborhoods with the top 20th percentile in percent of vacant addresses as neighborhoods with more occupancy. We applied multilevel logistic regression and adjusted for demographic and clinical characteristics to determine the effect variation by race-ethnicity of key contextual-level SDoH in association with newer ADD initiation.
Analyses were performed using the R statistical software (version 3.6.1; R Development Core Team) and SAS 9.4 (Cary, North Carolina). The study was approved by the Institutional Review Board at the University of Florida (IRB202102283).
## 3.1. Descriptive Analysis
Our final analysis comprised 28,874 patients in the cohort. Table 2 highlights the demographic and clinical characteristics of the study population by race and ethnicity. Overall, the mean age was 58 (±15) years, and $61\%$ were women. The majority of the patients were enrolled in public insurance programs such as Medicare ($37\%$) and Medicaid ($35\%$). Compared with NHW patients, NHB patients were younger (54.6 vs. 58.5 years, $p \leq 0.01$) and more likely to be covered by Medicaid ($41\%$ vs. $28\%$, $p \leq 0.01$), while Hispanics and patients of other races were older (mean age of Hispanics: 61 years, other race/ethnicity: 60 years), and more likely to be women. Of our cohort, 11,649 patients ($40\%$) had initiated the newer ADD (i.e., SGLT2i or GLP1a). NHW and patients of other races/ethnicities were more likely to have initiated a newer ADD versus another second-line ADD compared to NHB (NHW and other race/ethnicity: both $44\%$, NHB: $38\%$, Hispanics: $35\%$, $p \leq 0.01$)
## 3.2. Selection of Contextual-Level SDoH
Figure 1 is a volcano plot summarizing the results from Phase 1. After accounting for multiple comparisons using the Benjamin Hochberg procedure, a total of 20 and 11 variables were significantly associated with novel ADD use in the discovery and replication sets, respectively. Among them, ten variables from three categories were significant in both the discovery and replication sets, including the NDI, percentage of low food access (percentage without vehicle access living a half-mile from supply, a food access measure variable), and eight variables documenting the vacant housing in the neighborhood. All ten variables were associated with a lower likelihood of initiating newer ADD (with OR < 1, Figure 1). We observed high correlations among the eight variables documenting vacant land measures (all pairwise correlation coefficients > 0.6, Appendix A, Figure A1). Therefore, we kept only one variable, the percent of vacant addresses in the Phase 2 analysis, as this variable is a more comprehensive measure than the others in the category.
In Phase 2 analysis, the NDI, percentage without vehicle access living a half mile from supply, and percent of vacant addresses, were simultaneously included in a multivariable logistic regression model after adjusting for baseline demographic and clinical information. Two variables—NDI and percent of vacant addresses—remained statistically significant in the multivariable model. Therefore, our two-phase approach identified two contextual-level SDoH that were significantly associated with a lower likelihood of newer ADD initiation, which are neighborhoods with a higher degree of deprivation and neighborhoods with more vacant housing (Table 3).
## 3.3. Association of Contextual-Level SDoH and New ADD Initiation across Racial and Ethnic Groups
Table 4 shows the results from multivariable logistic regression of binary key contextual-level SDoH variables in association with the novel ADD initiation in the overall cohort and in each racial-ethnic subgroup. In the overall cohort, NHB were significantly less likely to use newer ADD than NHW (aOR 0.82, $95\%$ CI: 0.76–0.88, $p \leq 0.01$) after adjusting for all the covariates listed above. Patients living in a more deprived neighborhood were associated with a significantly lower likelihood of initiating a newer ADD than the remaining patients (aOR 0.87, $95\%$ CI: 0.81–0.94, $p \leq 0.01$). Patients living in a neighborhood with more occupancy were less likely to initiate a newer ADD (aOR: 0.91, $95\%$ CI: 0.87–0.95, $p \leq 0.01$) than their counterparts. We observed similar trends in racial and ethnic subgroups, and no significant interaction of race/ethnicity and contextual-level SDoH was detected.
## 4. Discussion
SDoH are not only experienced by individuals but also exert influence at the community level. Community-level information about the neighborhoods in which individuals live, learn, work, and play is recognized as the community’s vital signs [18], conveying contextual-level social deprivation and impacting health risks. Our study is unique in linking a set of contextual-level factors documenting social and built environments to extensive collections of EHR data via individuals’ residential histories in a cohort of real-world patients with T2D. Using a data-driven approach, we determined the key contextual-level SDoH factors associated with evidence-based treatment for T2D. After accounting for multiple testing and high correlations among the exposures, two contextual-level SDoH variables characterizing the neighborhood deprivation and vacant housing were identified as being significantly associated with individuals’ limited initiation of newer ADD known to improve cardiorenal outcomes of T2D. These results provide evidence supporting a spatially explicit data-driven approach in developing interventions to address disparities in initiation of T2D treatment.
Increasing evidence has demonstrated an association between neighborhood factors and diabetes outcomes. For example, a more disadvantaged socioeconomic status, poorer food access and built environment (e.g., walkability, recreational facilities), and less social cohesion are associated with the risk of T2D [34,35,36,37]. Additionally, lower neighborhood socioeconomic status was significantly associated with worsening physical and mental health status and poor glycemic control among patients with diabetes [38,39]. However, very few studies have examined whether contextual-level SDoH may influence healthcare quality, such as the initiation of evidence-based treatment. A study conducted using claims data found that contextual-level SDoH, such as poor food access, weak social support, and lack of a healthy built environment, were significantly associated with non-adherence to antihypertensive medication [40]. A randomized trial that enrolled 749 Mexican–American patients at a university-affiliated clinic showed that patients who lived in neighborhoods with greater deprivation were much less likely to adhere to their ADD protocols than those living in neighborhoods in the next higher quartile on the deprivation index [41]. In a US-based study examining the association between neighborhood social environment factors and adherence to oral antidiabetic medications, residents living in neighborhoods with high sociability were more likely to adhere to ADD regimens than their counterparts in less sociable surroundings [42].
The current study found that the NDI, an index documenting neighborhood deprivation, was significantly associated with newer ADD initiation. NDI is a composite indicator of contextual-level socioeconomic disadvantages in four areas beyond the strictly specified healthcare setting: income, housing quality, employment, and education. Previous studies have documented the association between neighborhood deprivation, attributed to income, employment, and education, and the quality of diabetes care, reporting that patients living in more deprived neighborhoods were significantly less likely to obtain high-quality diabetes care [7,8,10]. At an individual level, a lack of income and unemployment can create barriers to accessing high-quality diabetes care, while a lack of education has been linked to poor health literacy [43]. At the contextual level, the role of political context could also shape socioeconomic factors, and this interplay could result in unequal resource distribution and structural inequalities in the neighborhoods that perpetuate health disparities [44]. Therefore, individuals with a low socioeconomic profile at the contextual level may face barriers to the use of novel ADD treatment.
The consequences of vacant housing can extend far beyond just an empty space. Vacant land usually is an indicator of population out-migration and disinvestment. In addition to the increased risk of violence and crime [45], vacant housing often leads to a reduction in business and employment, therefore resulting in a lack of community resources, as well as access to essential facilities such as food, medical and social support services [46,47], further exacerbating health disparities in these communities. This lack of resources can have far-reaching consequences on the health and well-being of individuals residing in these areas. Previous studies have shown that empty lots are associated with higher levels of chronic stress and fewer social interactions, and thus resulting in unfavorable health outcomes [25]. In our study, individuals living in a neighborhood with more vacant addresses had lower access to the newer ADD, which could be explained by the lack of access to high-quality diabetes care. It is essential to address the issue of vacant housing and provide necessary resources and support to such disadvantaged communities. Developing innovative strategies, such as mobile medical clinics, have been effective in serving the requirements of medically vulnerable populations, such as the urban poor [48] and populations without stable housing [49], for whom accessibility to fixed healthcare is limited due to the lack of facilities and meager financial resources. MMCs could improve access to care by overcoming geographic and social restrictions, such as neighborhoods with many vacant addresses, which traditional, permanent healthcare facilities must avoid, thus addressing health inequities and mitigating social obstacles to healthcare.
Despite having a disproportionately higher risk of cardiovascular disease, patients from racial and ethnic minority groups have a lower probability of initiating guideline-based therapies that improve their outcomes, including the uptake of new ADD [9,50]. It is suggested that differences might be driven by the disadvantages in insurance coverage and poor socioeconomic status among these racial and ethnic subgroups, and it has been acknowledged in several studies that Medicare Advantage enrollees are less likely to initiate newer ADD than commercial insured patients [7,8,9]. However, in our study, the racial and ethnic disparities in new ADD use persisted after adjusting not only for insurance, but also for NDI, a proxy to socioeconomic status. This represents that such disparity was not driven solely by insurance factors and socioeconomic status. However, we did not identify a significant interaction between race/ethnicity and key contextual-level SDoH in association with initiating newer ADD. While it is possible that the interaction lies elsewhere and was not captured using the two-phase method presented in this study, our findings highlight the structural–environmental factors that drive inequities in the use of evidence-based treatment, independent of race and ethnicity.
Our study has several limitations. First, our study does not exclude patients with gestational diabetes, and there is a possibility of misclassification for individuals with pregnancy and gestational diabetes but not diagnosed by physicians. Second, the two-phase approach we used did not consider non-linear associations and potential interactions. Generalize additive model could be considered in future work to account for the non-linear relationships among key contextual-level SDoH in association with the study outcome. Additionally, Bayesian kernel machine regression and Bayesian multiple index models can capture the complex interrelations among contextual-level SDoH. Third, although many contextual-level SDoH have been included to characterize the social and built environment, this list is not exhaustive. Continuing efforts are needed to improve the measurement of the contextual-level SDoH further. Fourth, our study cohort was constructed using EHR data, and we cannot completely preclude the prevalence of users of second-line ADD. However, we extended our baseline to three years and restricted individuals who had at least two encounters per year to capture prescription and medical information, which largely eliminated the cases of prevalent users. In addition, regarding the association between individual-level factors and newer ADD initiation, our results were consistent with prior studies using claims data [50], suggesting the validity of the current study’s findings. Finally, participants included in this study were limited to those who received care at one or more sites included in the OneFlorida+ Clinical Research Network. Thus, our results may not be generalizable to those who did not receive healthcare at one of these facilities.
## 5. Conclusions
In a cohort of T2D patients from a statewide network of EHR, we identified two key contextual-level SDoH factors associated with limited use of new ADD: individuals living in neighborhoods with a higher deprivation index and more vacant addresses were less likely to initiate newer ADD compared with those living in less deprived and more fully occupied neighborhoods. Although the specific mechanisms underlying these associations require further investigation, our findings have contributed to the growing body of evidence of the neighborhood-level factors, their interplay with race across various spatial contexts, and their circumstances on evidence-based healthcare. It is crucial to gain a comprehensive understanding of these complex factors to develop effective strategies for addressing health equities and promoting evidence-based treatment in T2D care.
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|
---
title: Factors Influencing the Level of Depression and Anxiety of Community-Dwelling
Patients with Schizophrenia in China during the COVID-19 Pandemic
authors:
- Shanshan Chen
- Xiaohua Sun
- Qisha Zhu
- Yuan Zhao
- Jinsong Tang
- Haidong Song
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001627
doi: 10.3390/ijerph20054376
license: CC BY 4.0
---
# Factors Influencing the Level of Depression and Anxiety of Community-Dwelling Patients with Schizophrenia in China during the COVID-19 Pandemic
## Abstract
The coronavirus disease 2019 (COVID-19) poses a huge challenge to global public health. People with schizophrenia living in communities urgently need effective interventions to help them adjust to life and work, but they have not received enough attention. This study aims to assess the prevalence of anxiety and depression symptoms in community-dwelling patients with schizophrenia in China during the epidemic and to explore the possible influencing factors. Methods: *Using a* cross-sectional survey, we collected 15,165 questionnaires. Assessments included demographic information, concern about COVID-19-related information, sleep status, anxiety and depressive symptoms, and accompanying illnesses. The 7-item Generalized Anxiety Disorder (GAD-7) and the 9-item Patient Health Questionnaire (PHQ-9) were used to evaluate depression and anxiety levels. Group comparison was conducted by t-test, ANOVA, or chi-square test wherever suitable, with Bonferroni pairwise correction. Multivariate logistic regression was performed to identify predictors for anxiety and depression. Results: $16.9\%$ of patients had at least moderate anxiety, and $34.9\%$ had at least moderate depression. T-test showed that females scored higher on GAD-7 and PHQ-9 than males, and patients without accompanying long-standing diseases, who were not concerned about COVID-19, had lower GAD-7 and PHQ-9 scores. ANOVA showed that participants aged from 30 to 39, with higher education scored higher on GAD-7, and patients with better sleep, and having less concern about COVID-19, had lower GAD-7 and PHQ-9 scores. Regression analysis indicated that participant ages of 30–39 and 40–49 positively predicted anxiety, whereas patient ages of 30–39 years positively predicted depression. Patients with poor sleep, accompanying diseases, and concern about the COVID-19 pandemic were more likely to experience anxiety and depression. Conclusion: During the pandemic, Chinese community-dwelling patients with schizophrenia had high rates of anxiety and depression. These patients deserve clinical attention and psychological intervention, especially those with risk factors.
## 1. Introduction
The discovery and rapid spread of the coronavirus disease 2019 (COVID-19) brought huge challenges to the public health and medical communities around the world [1]. The health effects of this virus are worrisome, including death, a strained healthcare system and economic uncertainty. Likewise, the epidemic may have devastating psychological and social effects [2]. Numerous studies assessed the mental health of the general population during the COVID-19 pandemic [3,4], but there was insufficient research on the emotional impact of schizophrenia patients during the epidemic.
It was reported that patients with schizophrenia had a significantly increased risk of contracting COVID-19 compared to the normal population [5]. High-risk factors included failure to properly recognize self-protection needs and to adhere to preventive behaviors due to impaired cognitive function [6], difficulties in evaluating health information, limitations in access to healthcare [7], and being easily influenced by the ongoing media coverage of the epidemic [8]. Compared with inpatients, community-dwelling patients with schizophrenia lack full-time management of medical care, so community and family support need to play a better role [9]. Therefore, it is necessary to assess the mental health burden of patients with schizophrenia during the COVID-19 pandemic and provide timely community and family support.
Several studies conducted in China in 2020 showed that hospitalized schizophrenic patients with suspected COVID-19 in isolation wards experienced sleep disturbances, significantly higher scores on depression and anxiety scales, and increased stress compared to general hospitalized schizophrenic patients [10,11]. In addition, hospitalized patients with schizophrenia with suspected COVID-19 were reassessed as having significantly increased anxiety symptoms after 10–14 days of isolation [11].
Although community-dwelling patients with schizophrenia did not have as narrow and limited social network connections as long-term hospitalized patients [12], they were relatively underreported during the epidemic. A study shows that many community schizophrenia patients, despite being stable, still have some psychiatric symptoms that affect their lives to some extent [12,13]. An outpatient health study showed that only about $25\%$ of patients with schizophrenia achieved functional remission during the 3-year follow-up period [14]. Community-dwelling patients with schizophrenia are in urgent need of effective intervention methods to help them adjust to life and work during a pandemic. Therefore, we intended to explore the psychological burden of community-dwelling patients with schizophrenia during the pandemic. One study showed that community-dwelling patients with schizophrenia or bipolar disorder experienced more severe anxiety and depressive symptoms during the urban lockdown compared to community healthy controls [15]. However, the report did not analyze anxiety and depressive symptoms separately in patients with schizophrenia during the outbreak. A Spanish study showed that compared to the control group, community-dwelling patients with schizophrenia ($$n = 42$$) experienced significantly higher scores in Hospital Anxiety and Depression Scale Anxiety (HADS-A) and Hospital Anxiety and Depression Scale Depression (HADS-D) during the COVID-19 pandemic [16]. Moreover, $40.8\%$ of community-dwelling patients with schizophrenia ($$n = 76$$) reported depression and $32.9\%$ reported anxiety [17]. Overall, current research on depression and anxiety in community-dwelling schizophrenics during the epidemic period is insufficient, and inadequate sample sizes are a shortcoming.
Given the evolving and unpredictable duration of COVID-19, using a cross-sectional, web-based and large sample study, the first objective of this study was to examine the mental health burden of community-dwelling patients with schizophrenia during the COVID-19 outbreak, and the second objective was to analyze potential influencing factors. This study assessed the impact of the COVID-19 pandemic crisis on the mental health of community-dwelling patients with schizophrenia. A study of community-dwelling patients with schizophrenia would help provide effective psychological screening and interventions. We hypothesized that community-dwelling patients with schizophrenia during the COVID-19 pandemic had varying degrees of anxiety and depressive symptoms, and that middle age, poor sleep, concerns about epidemic information, and concomitant other long-standing illnesses would have an impact on depression and anxiety.
This article is divided into five parts. The first part introduces the background of the study and the research hypothesis, the second part describes the details of the research methodology. The third part contains the results of this study and the corresponding explanations. The fourth part discusses the results of this study. The last part contains the conclusion, shortcomings and directions for future research. Figure 1 shows the theoretical framework.
## 2.1. Sleep Status
In our study, sleep status refers to the sleep level of the subjects during the past 12 months. If the sleep status was good most of the year, a better status is recorded, and if the sleep status was bad most of the year, the status “poor” is chosen. Poor sleep includes sleep problems such as difficulty falling asleep or staying asleep. Schizophrenia is commonly accompanied by sleep disturbances [18]. There is an important relationship between sleep disorder and depression [19]. People with persistent insomnia have higher levels of severe depression, general anxiety, and panic [20]. A small foreign sample study showed that community patients with schizophrenia or bipolar disorder reported more anxiety and depression and experienced more sleep disturbances compared to normal individuals during the COVID-19 epidemic and embargo, but the study did not analyze the underlying factors influencing depressed and anxious mood and the grouping did not distinguish patients with schizophrenia from those with bipolar disorder [15].
## 2.2. Accompanying Long-Standing Diseases
A long-standing disease is a group of non-communicable diseases that have a long history of onset and cannot be cured once they develop. The common ones include coronary heart disease, stroke, hypertension, malignant neoplasm, diabetes, chronic respiratory diseases, etc. The risk of schizophrenia patients suffering from diabetes [21], and hypertension [22] is higher than that of the general population. There is a complex association between depression and chronic diseases [23]; for example, diabetes increases the risk of depressive symptoms [24], and depressive symptoms are associated with hypertension [25]. However, there are no studies related to mood and chronic illness in large samples of community schizophrenics, and certainly even fewer investigations during the COVID-19 epidemic.
## 2.3. Concern about COVID-19 and the Degree of Concern about the COVID-19 Epidemic
We explained the level of concern about the epidemic when we collected the questionnaire. Less attention refers to the occasional check on outbreak-related information. General attention refers to a moderate level of attention. More attention means that you are often proactive in getting information and keeping tabs on the progress of the outbreak. There is a lot of information about the epidemic, including inaccurate information. When people pay more attention to negative information, there will be more adverse emotional reactions [26]. However, similar studies conducted on patients with schizophrenia in the community are not currently found.
## 3.1. Research Approach
In this study, the research method we used was a questionnaire survey. This is a low-cost method, commonly used for a wide range of surveys [27,28].
## 3.2. Questionnaire Development
This study consists of 2 parts. The first part contains demographic data and some other information, including sleep status, and other accompanying long-standing diseases, and concerns about COVID-19. We defined “ long-term diseases “ and explained “ sleep status” and “concerns about COVID-19” above, in the literature review. The options all have 2–3 simple categories. The second part of the questionnaire, which is the main part of our study, contains the Depression Scale and the Anxiety Scale, which are widely used scales for measuring depression and anxiety worldwide [29]. The research questionnaire is in Supplementary Materials File S1. In addition, a pilot test was conducted to ensure that participants understood the purpose and content of the questionnaire before it was widely distributed. In the pilot test, the questionnaire was reviewed by three subject matter experts and this provided adoptable recommendations [30].
## 3.3.1. Anxiety Scale
The 7-item Generalized Anxiety Disorder (GAD-7) was adopted to assess the severity of self-reported anxiety [31]. It is composed of 7 items to evaluate how often over the past two weeks the patient has suffered from various issues, such as “difficulty in relaxing” or “excessive worry”. Response categories are “not at all”, “several days”, “more than one week”, and “nearly every day”, scored as 0, 1, 2, and 3, respectively. The total score of the GAD-7 is calculated by summing each item score. The total score ranges from 0 to 21, with a score of 5 indicating that the patient has anxiety. A score of 5, 10 or 15 represents the threshold for “mild”, “moderate” or “severe” anxiety, respectively. Studies showed that the scale has good internal consistency (Cronbach’s α = 0.92), and the test-retest reliability coefficient was 0.83. When the decomposition value was 10 points, the sensitivity was $89\%$ and the specificity was $82\%$ [32]. In this study, we conducted a reliability analysis yielding Cronbach’s α = 0.893. In the validity analysis, KMO = 0.915 and Bartlett’s Test of Sphericity <0.001. The cumulative sum of squares of the total variance of GAD-7 item 1 to item 7 was $61.940\%$, $71.599\%$, $78.494\%$, $84.781\%$, $90.333\%$, $95.505\%$, and $100.000\%$, respectively. The standard loadings for items 1 to item 7 of the GAD-7 were 0.706, 0.805, 0.800, 0.827, 0.806, 0.783, and 0.775, respectively, all of which were above 0.7.
## 3.3.2. Depression Scale
The 9-item Patient Health Questionnaire (PHQ-9), also with good reliability and validity, was employed to screen depressive disorder and measure the severity of symptoms [33]. PHQ-9 is made up of 9 items to evaluate how often over the past two weeks the patient has suffered from nine issues, including depressed mood and anhedonia. Response categories are “not at all”, “several days”, “more than one week”, and “nearly every day”, scored as 0, 1, 2, and 3, respectively. The total score ranges from 0 to 27, with a score of 5 indicating that the patient has depression. A score of 5, 10, 15 or 20 represents the threshold for “mild”, “moderate”, “severe” or “extremely severe” depression, respectively. Nine items of PHQ-9 include anhedonia, depressed mood, sleep disturbance, fatigue, appetite changes, low self-esteem, concentration problems, psychomotor disturbances, and suicidal ideation. In this study, we conducted a reliability analysis yielding Cronbach’s α = 0.897. In the validity analysis, KMO = 0.926 and Bartlett’s Test of Sphericity <0.001. The cumulative sum of squares of the total variance for PHQ-9 item 1 to item 9 was $56.065\%$, $66.292\%$, $73.043\%$, $78.714\%$, $83.856\%$, $88.506\%$, $92.671\%$, 96.552, and $100.000\%$, respectively. The standard loadings for items 1 to 9 of PHQ-9 were 0.789, 0.799, 0.718, 0.788, 0.753, 0.776, 0.764, 0.754, and 0.571, respectively, and almost all were above 0.7 except the last item.
## 3.4. Sampling and Data Collection
This cross-sectional questionnaire was conducted in a prefecture-level city in Hangzhou, China from April 7 to May 10, 2020. According to the local area division, the city has 13 districts, counties (cities) and 2 functional areas. The researchers attempted to contact all schizophrenia patients registered with the local health system and eventually obtained 15,165 questionnaires from schizophrenia patients who were eligible for enrollment, of whom 2047 subjects were not successfully enrolled because of their unstable condition.
We designed the content of the questionnaire. Patients with schizophrenia living in the communities under their respective management were contacted by physicians engaged in the prevention and treatment of mental illness in each district, county, and prefecture through telephone, home visits, and outpatient clinics, and the questionnaires were completed online by the physicians after obtaining patient information. Prior to the start of the study, local physicians were trained with detailed information on the questionnaire items and quality control of data collection. During the survey process, physicians were responsible for explaining the purpose of the study, presenting the questionnaire content, and ensuring that participants fully understood it.
Inclusion criteria were: living in China, being able to communicate normally and meeting the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) diagnosis of schizophrenia, and keeping the disease stable over the course of a year. Exclusion criteria were: in the acute phase of schizophrenia, accompanying serious medical illness. The 15,165 subjects included 7010 male participants ($46.2\%$) and 8155 female participants ($53.8\%$). Age ranged from 10 to 96 years old, and the mean age was 55.4 years ± 13.9 S.D.
## 3.5. Ethical Approval
The study complied with the ethical standards of the Declaration of Helsinki and was approved by the Ethics Committee of Hangzhou Seventh People’s Hospital (No. 2020035). All participants provided written informed consent. Subjects who communicated face-to-face signed a paper informed consent form, and those who communicated by phone signed an electronic informed consent form through an online platform. Because the participants in this study included minors, informed consent was obtained from their parents and/or legal guardians.
## 3.6. Statistical Analysis
Data analyses were conducted using IBM SPSS Statistics software version 19.0 (IBM Corporation, Armonk, NY, USA). Categorical data were described by the case numbers (percentage) and quantitative data as mean score ± S.D. Group comparison was conducted by t-test, ANOVA, or chi-square test wherever suitable, and pairwise comparison was conducted by Bonferroni test. In order to look for the score differences of PHQ-9 and GAD-7, patients with schizophrenia were grouped into six sub-groups by demographic and clinical status, namely sex, age, marital status, educational level, sleep status, and other accompanying long-standing diseases. Multivariate logistic regression models were performed to identify independent predictors for anxiety and depression, respectively. GAD-7 and PHQ-9 scores were first transformed into binary variables with the threshold of moderate anxiety or depression and then served as dependent variables in the respective regression models. Anxiety was divided into mild anxiety and moderate and above anxiety; moderate and above anxiety was defined as a patient with a score of >9. Depression was divided into mild depression and moderate and above depression; moderate and above depression was defined as a patient with a score of >9. Sex, age, marital status, educational level, sleep status, other accompanying long-standing diseases, concern about COVID-19, and the degree of concern about COVID-19 served as independent variables. Statistical significance was set at a two-sided p-value < 0.05.
## 4. Results
The completed questionnaire data were entered into an SPSS file. Since the management of schizophrenia in the community is systematically standardized, with specific community physicians managing regular patients over time, and maintaining follow-up visits and follow-up, subjects who agreed to be enrolled provided valid and complete data. There were no missing values and no outliers were found in this study. We performed validation factor analysis to test the reliability and validity of the depression scale and anxiety scale in this study. This study wanted to explore the potential influences that affect patients with schizophrenia presenting moderate and higher levels of depression and anxiety, consistent with exploring the relationship between dichotomous dependent variables and independent variables, so a dichotomous logistic regression model was used for analysis. Previous studies have shown that roughly $80\%$ of patients with schizophrenia have reported depression [34]. Although there was an increase in the proportion in the present findings, patients have been receiving regular attention to mood changes from community healthcare providers during routine management of the illness, and mild depression has less impact compared to moderate and higher depression. However, some studies have shown that patients with moderate depression are at significantly increased risk for suicide [35] and that major depressive disorder is a significant factor in suicide in patients with schizophrenia [36]. In addition, some studies have shown that the worsening of anxiety symptoms is a strong predictor of medication switching during treatment, suggesting that severe anxiety enhances the adverse effects of treatment management [37]. Therefore, we prefer to explore the potential influences affecting moderate and higher levels of anxiety and depression, which certainly does not make us neglect to focus on patients with mild depression and anxiety.
## 4.1. General Distribution of Patient’s Anxiety or Depression
Among 15,165 cases of patients with schizophrenia, the mean score of GAD-7 and PHQ-9 were 8.1 ± 2.1 and 10.4 ± 2.7, respectively, and the prevalence of anxiety and depression was $100\%$. The proportion with mild anxiety was $83.2\%$, moderate anxiety accounted for $15.7\%$, and $1.2\%$ had severe anxiety. Meanwhile, $65.1\%$ of participants had minimal symptoms of depression, the moderate depression rate was $25.7\%$, the proportion with severe depression was $8.2\%$, and $1.0\%$ had extremely serious depression. See Table 1 for details.
## 4.2. Subgroup Analyses of Questionnaire Scores
Regarding the GAD-7 score, group comparison showed that female patients were more anxious than men (t = −2.03, $$p \leq 0.042$$). Patients in the 30–39 age group were more anxious than other age groups ($F = 2.84$, $$p \leq 0.014$$). Patients with university degrees or above had the highest anxiety ($F = 4.08$, $$p \leq 0.007$$). Patients with other accompanying long-standing diseases were more anxious than those without other diseases ($t = 4.18$, $p \leq 0.001$). Patients with poor sleep ($F = 158.87$, $p \leq 0.001$) had high levels of anxiety. Similarly, PHQ-9 results showed that female patients were more depressed than men (t = −3.27, $$p \leq 0.001$$). Patients with poor sleep ($F = 284.00$, $p \leq 0.001$) had higher levels of depression. Patients with other accompanying long-standing diseases were more depressed than those without other diseases ($t = 6.86$, $p \leq 0.001$). See Table 2 for details.
## 4.3. Concern about the COVID-19 Pandemic
T-tests showed that patients who were concerned about COVID-19 scored higher on GAD-7 ($t = 8.17$, $p \leq 0.001$) and PHQ-9 ($t = 2.29$, $$p \leq 0.022$$), respectively, compared to those who were not concerned about COVID-19. ANOVA showed those with general concern about COVID-19 scored higher on GAD-7 ($F = 93.19$, $p \leq 0.001$) and PHQ-9 ($F = 95.30$, $p \leq 0.001$), respectively, than those with less or more concern about COVID-19. Detailed data is presented in Table 3.
## 4.4. Multivariate Logistic Regression Models for Anxiety and Depression among Community-Dwelling Patients with Schizophrenia
The details about the multivariate analyses of predictors with logistic regression models for anxiety and depression are shown in Table 4. Our study showed that people in the 30–39 (OR:1.14; $95\%$ CI [0.39, 3.4]; $$p \leq 0.811$$) and 40–49 (OR:1.16; $95\%$ CI [1.03, 1.32]; $$p \leq 0.018$$) age groups, with other accompanying long-standing diseases (OR:1.15; $95\%$ CI [1.03, 1.29]; $$p \leq 0.013$$), who were concerned about the COVID-19 pandemic (OR:1.44; $95\%$ CI [1.26, 1.65]; $p \leq 0.001$), were more likely to experience anxiety. Good sleep (OR:0.23; $95\%$ CI [0.19, 0.28]; $p \leq 0.001$) can reduce the risk of anxiety. Meanwhile, patients aged 30–39 (OR:1.23; $95\%$ CI [1.09, 1.38]; $$p \leq 0.001$$), with other accompanying long-standing diseases (OR:1.29; $95\%$ CI [1.18, 1.42]; $p \leq 0.001$), who were concerned about the COVID-19 pandemic (OR:1.49; $95\%$ CI [1.34, 1.64]; $p \leq 0.001$) were more likely to experience depression. Good sleep (OR:0.26; $95\%$ CI [0.22, 0.31]; $p \leq 0.001$) can reduce the risk of depression. Other independent variables are non-significant predictors in the logistic regression model for anxiety and depression (Table 4). Interestingly, we performed regression analysis separately for males and females and found that male patients with a partner (OR:0.88; $95\%$ CI [0.79, 0.97]; $$p \leq 0.013$$) had a lower risk of depression (Table 5), and the remaining results did not change much. Detailed data is shown in Table 4 and Table 5.
## 5. Discussion
In the current study, all community-dwelling patients with schizophrenia had different degrees of anxiety and depression during the epidemic period. Moderate to severe anxiety accounted for $16.9\%$, and the percentage of moderate and above depression was $34.9\%$, which partly meets our first hypothesis. Regression analysis showed that ages from 30 to 39, poor sleep, other accompanying long-standing diseases, and concern about the COVID-19 pandemic are potential factors of depression and anxiety, which is consistent with our second hypothesis.
15,165 cases of community-dwelling patients with schizophrenia in this survey all had mild to severe anxiety and depression, which is supported by previous research. Compared to the control group, community-dwelling patients with schizophrenia experienced significantly higher scores in HADS-A and HADS-D during the COVID-19 pandemic [16]. Moreover, $40.8\%$ of community-dwelling patients with schizophrenia ($$n = 76$$) reported depression and $32.9\%$ reported anxiety [17].
In this study, the GAD-7 and PHQ-9 scores of schizophrenia patients in the 30–39 age group were higher than those of other age groups. Compared with patients with schizophrenia who were older than 60 years, patients aged from 30 to 39 and from 40 to 49 were more likely to experience anxiety, and compared with patients older than 60 years old, patients aged 30–39 scored higher in depression. Previous studies showed that during the COVID-19 pandemic, people aged 30–49 had higher scores on epidemic knowledge and paid more attention to epidemic information, which may increase the risk of depression and anxiety [38]. As we know, the unemployment rate of schizophrenic patients is high, ranging from $80\%$ to $90\%$, resulting in limited economic income [39]. Middle-aged patients undergo a period of shouldering societal and familial responsibility, although their physiological function is gradually declining. During the epidemic, due to limited social activities [40], the financial resources of patients may be greatly affected, which may lead to greater stress among community schizophrenic patients, resulting in more obvious symptoms of anxiety and depression.
We found that patients with a partner had less serious depression symptoms than those without any partner. Studies have shown that schizophrenia is usually associated with severe damage in many areas of life, including intimacy and social adjustment [41]. Patients with schizophrenia, especially men, are less likely to get married than others [42,43]. Several studies showed that being unmarried is a sociodemographic risk factor for schizophrenia [42,44], and for patients with schizophrenia, being single itself may present a risk of adverse outcomes [45]. Previous studies showed that married patients with schizophrenia or schizoaffective disorder evaluate their quality of life higher than single subjects, and have fewer suicidal ideations than divorced, widowed, or separated subjects [46]. When the COVID-19 pandemic occurred, the situation changed. The epidemic posed a serious threat to patients’ children and families. The consequences of these difficulties may be long-term, partly because environmental risks penetrate the structure and process of the family system [47], while the patients’ partners can take more risks together, so as to reduce the pressure of the patient. Moreover, male schizophrenia patients who were currently in marital status had the least disease-related symptoms [48].
The results of this study revealed that schizophrenic patients with poor sleep had more severe anxiety and depression symptoms, which was similar to previous studies [49]. Approximately $90\%$ of people diagnosed with depression [50] and approximately $70\%$ of patients experiencing anxiety [51] self-reported lack of sleep. Substantial evidence suggests that sleep disturbance is a prodromal symptom of recurrent depressive episodes [52,53]. In addition, depression, anxiety, fear, etc., are more likely to cause sleep problems [54].
We found patients with schizophrenia accompanied by other long-standing diseases were at higher risk of anxiety and depression. A Turkish study showed that the general population with chronic diseases will be more seriously affected by depression and anxiety symptoms, and that other accompanying chronic diseases was a risk factor for anxiety. It may be that patients with schizophrenia are more sensitive and aware of how their body feels [55].
The GAD-7 and PHQ-9 scores of community-dwelling patients with schizophrenia who were concerned about COVID-19 were higher than those who did not express concern. Besides, the group comparison above showed that the anxiety and depression symptoms of patients with general concern about the epidemic were more serious than those with less or more concern. For the first result, this may be because patients who did not express concern about COVID-19 at all could not understand the severity of the epidemic, and were less worried about the health problems of the epidemic, which led to insignificant anxiety and depression symptoms. For those who were occasionally concerned about the epidemic, aware of the methods of controlling infectious diseases including risk communication, hygiene habits, social distancing, and believing that vaccines were safe and effective [56], their anxiety and depression were naturally reduced. Patients who were moderately concerned but not thoroughly concerned about the outbreak experienced the most severe symptoms of depression and anxiety, probably because they were not ignorant, but overwhelmed by seeing more information on COVID-19 [40]. Some of the information or relevant knowledge they obtained may have been superficial, especially in uncertain periods. Conspiracy theories and rumors were particularly popular during the pandemic. For example, some people saw many rumors but did not further obtain rumor refutation information. Social media may be double-sided as an information source under the influence of some people [56]. Since the information obtained by patients was too much but not in-depth enough and true information could not be distinguished from false, patients were more likely to feel a lack of control, producing more serious anxiety and depression.
## 6.1. Conclusions
This was the first study to investigate the psychological burden of community-dwelling patients with schizophrenia with a large sample during the COVID-19 epidemic. The study showed that anxiety and depression symptoms of different degrees generally appeared in community schizophrenia during the epidemic period, which confirmed that the epidemic had a certain impact on the mental health of patients. In addition, we found that in the current study, the 30–39 age group, sleeping poorly, having other accompanying long-standing diseases, and concern about COVID-19 were risk factors for anxiety or depression. Besides, patients with general concerns about COVID-19 had more severe symptoms of depression and anxiety. Living with a partner was a protective factor for depression in male patients.
As our findings showed, patients with schizophrenia had obvious psychological stress responses such as anxiety and depression during the epidemic. Previous research has shown that pandemics can lead to unemployment and family poverty, separation of family members, and social isolation [56]. At the same time, the number of patients being screened for safety reasons decreases, and individuals with psychiatric symptoms may have difficulty accessing medical assistance [55]. Therefore, we need to focus on the mental health of patients in a timely and adequate manner. These patients are at risk of experiencing more severe consequences after stimulation, such as worsening of psychiatric symptoms and disease relapse [57]. Community grassroots medical staff and members of care and rescue groups need to focus on follow-up and emotional counseling for patients with the above characteristics. For example, patients with the above characteristics should be surveyed with a higher frequency of questionnaires to understand their emotional state in time. In serious cases, medication and psychological intervention should be carried out. In addition, community workers need to guide patients to correctly understand COVID-19 and reduce unnecessary concerns. Moreover, primary medical staff need to pay more attention to the sleep status of schizophrenia patients in the community, including drug adjustment and behavioral intervention. Finally, it is important to actively treat other concomitant diseases of patients with schizophrenia. Of course, this mental health program should not increase the burden on healthcare providers or the risk of spreading the infection to others [56].
## 6.2. Limitations and Future Research
This survey also has certain limitations. First, due to the special period of epidemic prevention and control, the patient’s mental health questionnaire survey was delivered by home visits, telephone calls, outpatient clinics, etc. Different delivery forms may cause misunderstandings and inaccuracy of the results. Although physicians had unified the standards for questionnaire interviews, different transmission forms may still cause misunderstandings and inaccurate results. In this study, telephone or face-to-face interviews were used. Face-to-face communication may help patients understand better than phone conversations, and the results obtained would be relatively more accurate. Second, in order to obtain patient information conveniently, all results were derived from self-report scales; although physicians tried their best to explain the items, this could still lead to biases in patients’ recall. Third, this study was mainly based on the evaluation of patients by local physicians practicing mental illness prevention and the results may be affected by their subjective evaluation. Finally, we did not evaluate the depression or anxiety symptoms of these patients before the outbreak, which leads to a lack of longitudinal comparison. In the future, we hope to carry out longitudinal research and a more detailed assessment of the emotion and cognition of patients with schizophrenia in the community to help improve their community and family support.
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|
---
title: Ambient Environmental Ozone and Variation of Fractional Exhaled Nitric Oxide
(FeNO) in Hairdressers and Healthcare Workers
authors:
- Tonje Trulssen Hildre
- Hilde Heiro
- Ingvill Sandven
- Bato Hammarström
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001628
doi: 10.3390/ijerph20054271
license: CC BY 4.0
---
# Ambient Environmental Ozone and Variation of Fractional Exhaled Nitric Oxide (FeNO) in Hairdressers and Healthcare Workers
## Abstract
Fractional exhaled nitric oxide (FeNO) is a breath-related biomarker of eosinophilic asthma. The aim of this study was to investigate FeNO variations due to environmental or occupational exposures in respiratory healthy subjects. Overall, 14 hairdressers and 15 healthcare workers in Oslo were followed for 5 workdays. We registered the levels of FeNO after commuting and arriving at the workspace and after ≥3 h of work, in addition to symptoms of cold, commuting method, and hair treatments that were performed. Both short- and intermediate-term effects after exposure were evaluated. Environmental assessment of daily average levels of air quality particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3) indicated a covariation in ozone and FeNO in which a 35–$50\%$ decrease in ozone was followed by a near $20\%$ decrease in FeNO with a 24-h latency. Pedestrians had significantly increased FeNO readings. Symptoms of cold were associated with a significant increase in FeNO readings. We did not find any FeNO increase of statistical significance after occupational chemical exposure to hair treatments. The findings may be of clinical, environmental and occupational importance.
## 1. Introduction
Nitric oxide (NO) was first detected as an intracellular messenger in various cells, such as in platelets, the nervous system, and vasculature, where it is known for its relaxing activity related to the endothelium and vasodilation and as an effector molecule in immunological reactions [1]. It was later measured in exhaled breath as fractional exhaled NO (FeNO) and was shown to be increased in patients with asthma [2,3]. Additionally, NO has been implicated in several inflammatory diseases, obesity, diabetes, and heart disease [4]. NO is regulated by nitric oxide synthase, and three major isoforms have been identified, of which inducible nitric oxide synthase (iNOS or NOS2) is associated with immunoregulation by cytokines and other stimuli in both the innate and the adaptive immune system [5,6]. Activated macrophages and other innate immune cells generate NO as a pro-inflammatory response to various pathogens [7]. More recent research has shown that iNOS is regulated on the epigenetic level by DNA methylation after environmental and occupational exposures [8,9,10]. In asthma, iNOS has predominantly been associated with the regulation of T-cell function and differentiation [11]. High FeNO values are associated with allergic/eosinophilic inflammation, also known as Type 2 inflammation [12]. FeNO can be used as an indicator of inhaled corticosteroid response and has been present in clinical use for several years as an evaluation tool for asthma control [13]. Diurnal variations of FeNO have been recorded in the airways of healthy subjects from roughly 5 to 20 ppb (parts per billion), in controlled asthmatics from 20 to 40 ppb, and in uncontrolled asthmatics from 20 to 70 ppb [14].
Previously, FeNO was found to be significantly elevated in a cohort of welders at levels that were normally associated with Type 2 inflammation (median 43.5 ppb) [15]. Welders are exposed to respiratory irritants (particulate matter, gases and smoke). The occupational hazards in a hairdressing salon are complex and include many respiratory irritants and allergens [16]. To our knowledge, there are no previous studies investigating FeNO variations after exposure in hair salons.
Increased air levels of particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3) exacerbate asthma in children and adults and are associated with the onset of childhood asthma [17]. Normal and increased levels of FeNO are difficult to interpret in both the diagnosis and treatment of asthma [18]. FeNO variations require further explanation with respect to their role in identifying and treating respiratory diseases and environmental and occupational exposure.
The aim of the study was to investigate short- and intermediate-term FeNO variations after environmental and occupational exposures in hairdressers and healthcare workers (HCWs).
## 2.1. Study Design
Non-smoking subjects that were aged 18 years or older and scheduled for 5 working days each week were included in this study. The exclusion criteria were active smoking and physician-diagnosed respiratory diseases. The study was set up as an observational study of hairdressers and HCWs. A total of 15 hairdressers working at six hair salons in downtown Oslo covering an area of about one square kilometer (~0.4 square miles) were recruited via invitation. However, one hairdresser was considered to be an outlier (FeNO > 60 ppb) due to a probable respiratory disease. A total of 15 HCWs working at outpatient clinics or as technical assistants were recruited from Oslo University Hospital at two different locations 3–6 km (1.9–3.7 miles) from the downtown area. The HCWs were both an occupational control, with respect to the hairdressers, and an environmentally exposed group, as they experienced similar exposures as the hairdressers by living in or close to Oslo. The study was performed over two consecutive work weeks due to logistics and to minimize sensor variations by using the same FeNO testing instrument sensor. A questionnaire was included in the case report form, in which daily questions were asked and noted by the investigators at all sampling times. Daily questions included symptoms, mode of commuting and traveling time, exposure to smoke or vaping fumes, and the type and number of hair treatments that were performed. Most hairdressers started their workday 09:00–11:00 a.m., whereas HCWs started their workday 08:00–09:00 a.m. Due to sampling logistics, a few hairdressers started their day before the first sampling; however, the samples were taken within the first 60 min of work, and the occupational exposures in these instances were regarded as small. The daily sampling of the hairdressers followed their usual work week from Monday to Saturday with one day off. Only four hairdressers worked Saturday. All of the HCWs worked Monday to Friday.
A network of air pollution detectors is easily accessible in Norway that provides information on per-minute, average hour, and average daily levels of PM2.5, PM10, NO2, SO2, and ozone (www.nilu.no (accessed on 29 December 2022)).
## 2.2. FeNO Measurement and NIOX VERO© Repeatability
A portable NIOX VERO® device was used to measure the level of FeNO in ppb. It had a disposable mouthpiece with a filter. A fresh pre-calibrated sensor that included 300 measurements was used for the whole study and during the repeatability tests. FeNO was measured after exhalation, followed by inhalation through the mouthpiece and a NO-scrubber for NO-free air supply, and lastly, by exhalation trough the mouthpiece with a respiratory rate of 50 mL/s (±5 mL/s) for 10 s. There was one unexpectedly high within-day change (~20 to 40 ppb) of measurement that was repeated with a fresh mouthpiece, with only one ppb change. To test for instrument repeatability, four subjects in the HCW group were selected. They performed five additional continuous FeNO measurements with fresh mouthpieces (the measurements are shown in Supplementary Materials S1). The coefficient of variation (CV) was calculated as within-subject SD/within-subject mean, as described previously [19]. Variation was estimated to be $8.5\%$ ± $3.5\%$. The manufacturer of NIOX VERO© provides the following precision data: <3 ppb of measured value for values <30 ppb, <$10\%$ of measured value for values ≥30 ppb.
## 2.3. Statistical Analysis
FeNO, as the primary end-point, was tested as a continuous variable, in addition to sampling and commuting time. Normality distribution tests (Kolmogorov–Smirnov and Shapiro–Wilk) showed both normality and non-normality for FeNO and other key variables. We applied both parametric tests with Student’s t-test unpaired and paired with Levene’s test for equality of variances and a non-parametric test with Mann–Whitney and histogram distribution. The statistical analyses were performed by using SPSS v28 and Graphpad Prism 9.2.0.
## 2.4. Ethics
All of the participants provided informed consent, and we can confirm that all of the research was performed in accordance with relevant guidelines/regulations. The study was approved by the regional ethics committee (case no. 480861), the Hospital Data protection officer (case no. 22–16786), and registered at www.clinicaltrials.gov (accessed on 29 December 2022) (Identifier: NCT05507944).
## 3.1. Demographics
The HCWs were significantly older than the hairdressers (45.9 vs. 33.4 years $$p \leq 0.001$$) (Table 1). Otherwise, there were no significant demographic differences between the two groups.
## 3.2. Diurnal Variation of FeNO
Figure 1a,b shows the diurnal variations in FeNO during week 38 (19–24 September 2022) and week 39 (26–30 September 2022) among hairdressers and HCWs. No short- or intermediate-term increase in FeNO was detected during the weeks.
## 3.3. Air Quality Levels
Figure 2a shows the corresponding average daily air quality levels. The data values are available in Supplementary Materials S2. A decrease in the ozone levels during both weeks corresponds with a decrease in FeNO S (measured after commuting and arriving at the workspace) but not in FeNO E (measured after ≥3 h at work), with a 24-h latency. In Figure 2b, the decrease in ozone and FeNO S in ppb is emphasized. When ozone decreased by $48\%$ or 9.38 ppb (19.73 to 10.35 ppb) in week 38 and $34\%$ or 9.33 ppb (27.75 to 18.42 ppb) in week 39, FeNO S decreased by $19\%$ or 3.25 ppb (17.25 to 14.00 ppb) and $20\%$ or 3.75 ppb (18.46 to 14.71 ppb), respectively. The FeNO values are available in Supplementary Materials S3. When correcting for symptoms of cold, as shown in Supplementary Materials S3, FeNO S decreased by $26\%$ or 4.89 ppb (19.09 to 14.20 ppb) and $12\%$ or 1.98 ppb (16.25 to 14.27 ppb), respectively.
## 3.4. FeNO Measurements, Sampling and Commuting Time
Among hairdressers and HCWs, there was no significant daily increase in FeNO (Table 2). The distribution of the data and normality tests are available in Supplementary Materials S4 and S5.
## 3.5. Symptoms of Respiratory Infections
There were significantly increased FeNO S and FeNO E measurements among the participants that reported cold symptoms ($p \leq 0.001$) (Figure 3). No participants reported fever or shortness of breath.
## 3.6. FeNO, Commuting and Hair Treatments
Figure 4a shows the FeNO measurements related to commuting by car, public transport, bicycle, and as pedestrians. The data values are available in Supplement Materials S6. There was a significant increase in FeNO S and FeNO E among those who reported commuting as pedestrians ($$p \leq 0.026$$ and $$p \leq 0.040$$). Figure 4b shows the FeNO measurements in relation to the hair treatments that were performed by hairdressers, including bleaching, dyeing, the use of hair spray and other treatments (mostly nails). The data values are available in Supplementary Materials S6. Among those who reported that they had performed bleaching and dyeing, there was a significant decrease in FeNO S ($$p \leq 0.007$$ and $$p \leq 0.009$$) and FeNO E ($$p \leq 0.014$$ and $$p \leq 0.007$$).
## 4. Discussion
We did not detect any short- or intermediate-term increases in FeNO corresponding to occupational exposures among the hairdressers. All of the hair salons had good ventilation systems. Hairdressers performing bleaching and dyeing had the lowest FeNO levels. The cause is unclear, although the non-exposed groups had higher than normal levels of FeNO. As expected, symptoms of cold significantly increased FeNO. FeNO was slightly increased among those who commuted as pedestrians for 5 min or more. They may also have been more exposed to air pollution than other commuters. However, FeNO did not increase among those who bicycled, although this was a small group. Interestingly there were large decreases in FeNO after commuting on different weekdays for both hairdressers (week 38, Wednesday to Thursday) and HCWs (week 39, Thursday to Friday). When compared with air pollution levels, large decreases were also present for ozone during both weeks, although one day before the decrease in FeNO. The decrease in ozone was 35–$50\%$, whereas the decrease in FeNO was close to $20\%$, which is above our repeatability analysis and the NIOX VERO© manufacturer precision data.
Several studies have investigated FeNO and occupational exposures over the last 20 years with varying results, showing elevated values in studies focusing on spray painters, underground tunnel workers and welders, among others [15,20,21]. Daily increases in FeNO, up to $40\%$, in shoe and leather makers were found in one study [22]. Our findings may explain unexpected variations in FeNO measurements. Welding, spray-painting, and shoemaking, which produce ozone or volatile organic compounds (VOCs), an ozone precursor, increase FeNO levels among the exposed workers. These studies share large differences in baseline values of FeNO, ranging from 6 ppb to 25 ppb. A recent systematic review of occupational asthma and FeNO noted that different threshold levels of FeNO made drawing conclusions difficult [23]. Occupational studies have focused on particle matter exposures and chemicals without accounting for environmental effects. It is common in occupational medicine and hygiene to expect exposure in the workplace to be several times, if not magnitudes higher, than ambient environmental levels. For example, in the study related to welders [15], the median of PM2.5 was 604 µg/m3, and the highest air pollution level in Oslo during the two weeks in our study of PM2.5 was 10 µg/m3. The other markers of air pollution during our study were relatively low, and they did not correlate with the FeNO measurements, although NO2 showed some inverse correlation with ozone.
Ozone is different in this regard, as ambient environmental and occupational levels can be in the same range. Ground-level ozone is produced through chemical reactions between solar radiation, nitrogen oxide pollution (NOx) and VOCs [24]. Air pollution levels of ozone can reach more than 100 ppb in polluted areas, whereas welding in occupational settings can reach close to 200 ppb [25]. The Occupational Safety and Health Administration (OSHA) standard for ozone is 100 ppb averaged over eight hours, whereas The World Health Organization (WHO) sets an eight-hours environmental limit of 50 ppb [26,27]. Indoor levels of ozone in offices are about $10\%$ of outdoor levels and may, by emission from printers and photocopiers, increase to 30–$40\%$ of outdoor levels [28].
Ozone and FeNO covariation were only present in the FeNO sampling after commuting and not after ≥3 h of work, which may indicate, although hypothetically, that other forms of exposure, such as chemical, biological, or physical, possibly have short-term effects depending on agent and dose.
A study on twins concluded that environmental contributions accounted for $40\%$ of FeNO variations; the remaining was related to genetics [29]. A community-based population study comparing FeNO measurements and air pollution exposure showed a positive association of ozone in non-asthmatics with a five-day average air pollution of ozone [30]. In a longitudinal study of an elderly population, 12 weeks of weekly FeNO measurements showed a positive correlation between FeNO and five-day average ozone air pollution [31]. However, the effect of exposure to 300 ppb ozone on healthy volunteers for 75 min did not show any increased FeNO at 6 and 24 h post-exposure [32]. Studies on daily variations of FeNO in healthy subjects have found small within-day and between-day changes [19,33]. We have not found any studies in environmental or occupational settings that have performed diurnal FeNO measurements concerning variations.
Epidemiological studies of airway disease and exposure to ozone have not been consistent, possibly because ozone is a secondary air pollutant that is confounded by NOx. However, there is evidence that supports the correlation between exposure to ozone and childhood-onset asthma [34]. A recent, large case–control study showed that exposure to ozone was the only air pollution that was associated with asthma exacerbation requiring hospitalization [35].
A biological mechanism of the pathological effects of ozone is suggested in a mouse model of rhinitis [36]. Lymphoid cell-sufficient mice that were exposed to 500 ppb ozone for 4 h daily up to 9 days developed nasal Type 2 immunity and eosinophilic rhinitis with mucous cell metaplasia, whereas lymphoid cell-deficient mice did not. A marked influx of neutrophils was detected 2 h post-exposure but less after 24 h, and eosinophils dominated after 4 and 9 days. Several animal and human studies have supported an airway remodeling effect by long-time ambient ozone exposure [37,38].
The strengths of our study are that it used the diurnal measurements of workers that were exposed to airway irritants during a whole work week, both before and after occupational exposure, in addition to the consideration of environmental exposures during two work weeks. The limitations are a relatively low number of workers in each occupational group and that it is a relatively short longitudinal study concerning more subtle environmental effects. In addition, except for hair treatments, commuting method, and time, we obtained no data related to indoor and outdoor exposures during leisure time.
## 5. Conclusions
We did not find any short- or intermediate-term increases in FeNO in hairdressers related to work exposure. There was a decrease among workers that were exposed to bleach and dyeing, although the significance of this is unclear. There were variations in FeNO among both hairdressers and HCWs, which may be attributed to environmental ambient ozone levels. These are intriguing findings of clinical, environmental, and occupational importance, which need to be followed up by larger diurnal cohort studies in both respiratory healthy and non-healthy individuals.
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|
---
title: Polymorphic Variants of Genes Encoding Angiogenesis-Related Factors in Infertile
Women with Recurrent Implantation Failure
authors:
- Aleksandra E. Mrozikiewicz
- Grażyna Kurzawińska
- Marcin Ożarowski
- Michał Walczak
- Katarzyna Ożegowska
- Piotr Jędrzejczak
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001634
doi: 10.3390/ijms24054267
license: CC BY 4.0
---
# Polymorphic Variants of Genes Encoding Angiogenesis-Related Factors in Infertile Women with Recurrent Implantation Failure
## Abstract
Recurrent implantation failure (RIF) is a global health issue affecting a significant number of infertile women who undergo in vitro fertilization (IVF) cycles. Extensive vasculogenesis and angiogenesis occur in both maternal and fetal placental tissues, and vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) family molecules and their receptors are potent angiogenic mediators in the placenta. Five single nucleotide polymorphisms (SNPs) in the genes encoding angiogenesis-related factors were selected and genotyped in 247 women who had undergone the ART procedure and 120 healthy controls. Genotyping was conducted by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). A variant of the kinase insertion domain receptor (KDR) gene (rs2071559) was associated with an increased risk of infertility after adjusting for age and BMI (OR = 0.64; $95\%$ CI: 0.45–0.91, $$p \leq 0.013$$ in a log-additive model). Vascular endothelial growth factor A (VEGFA) rs699947 was associated with an increased risk of recurrent implantation failures under a dominant (OR = 2.34; $95\%$ CI: 1.11–4.94, padj. = 0.022) and a log-additive model (OR = 0.65; $95\%$ CI 0.43–0.99, padj. = 0.038). Variants of the KDR gene (rs1870377, rs2071559) in the whole group were in linkage equilibrium (D’ = 0.25, r2 = 0.025). Gene–gene interaction analysis showed the strongest interactions between the KDR gene SNPs rs2071559–rs1870377 ($$p \leq 0.004$$) and KDR rs1870377–VEGFA rs699947 ($$p \leq 0.030$$). Our study revealed that the KDR gene rs2071559 variant may be associated with infertility and rs699947 VEGFA with an increased risk of recurrent implantation failures in infertile ART treated Polish women.
## 1. Introduction
Recurrent implantation failure (RIF) is the condition in which the embryo fails to implant after at least three transfers in three consecutive in vitro fertilization (IVF) cycles. Currently, RIF is considered one of the main challenges of reproductive medicine and concerns about $15\%$ of women treated for infertility [1]. Moreover, it was estimated that $5\%$ of women suffer from recurrent pregnancy loss, $75\%$ of cases of which were observed to be due to RIF [1]. The described risk factors of RIF include advanced maternal age, BMI, tobacco, alcohol intake, and endometriosis. The reasons for RIF could also be divided into embryo factors (genetic abnormalities) and uterine factors (anatomical abnormalities; immunological factors; biomolecular factors; glycodelin-A; infection). However, the influence of the male factor (sperm quality) and female factors (low quality of gametes; thrombophilia; inherited and acquired; other genetic polymorphisms such as miRNA, HLA–G, p53, VEGF; vitamin D deficiency; alterations in vaginal microbiota) on the occurrence of RIF has also been observed [2,3,4]. Unfortunately, the complex details of the processes that occur in women with RIF remain unclear to date. Moreover, recurrent pregnancy loss (RPL) is multifactorial and many cases remain unexplained. Recurrent implantation failures and recurrent miscarriages has partially overlapping causes, and the association of genetic variants with RPL is more frequently studied [5,6,7]. In both RIF and RPL, the problem is pregnancy loss, but women with RIF also have difficulty getting pregnant. Therefore, despite the similarities, the causes of RIF and RPL may differ.
Implantation is essential for embryo survival and successful reproduction. This process requires the competent blastocyst, receptive endometrium, and the synchronized dialogue between maternal and embryonic tissues. The delicate balance between these factors is very important for the embryo adhesion and attachment to the endometrium and the formation of fetal–mother contact [8,9,10]. In the next phase, the embryo invades the endometrium and blood cells arise from the mesoderm. A normal pregnancy requires the development of the complex vascular network of both the mother and the fetus to meet the increasing oxygen and metabolic demands of the developing embryo. The placenta is a unique vascular organ that receives blood supplies from both the maternal and the fetal systems and thus has two separate blood circulatory systems [11].
Blood vessels form in two ways: vasculogenesis, whereby vessels arise from blood islands, and angiogenesis (branching and nonbranching), which entails sprouting from existing vessels [12]. Extensive angiogenesis occurs in both the maternal and fetal placental tissues [13]. The embryonic vasculature is formed by the segregation, migration and assembly of mesodermal angioblasts, a process called vasculogenesis. In the complex process of angiogenesis, the activity of many growth factors and their receptors on various pathways plays a key role. The most potent angiogenic factors to promote vasculogenesis and angiogenesis in the placenta include vascular endothelial growth factors (VEGFs) and their receptors (VEGFRs), FGF family molecules, the angiopoietin system, and many others [11]. Angiogenesis is a multi-stage process, during which significant changes occur in the environment surrounding the cells. Growth factors increase vascular permeability, stimulate specific proteases (collagenases and plasminogen activators) to proteolytic degradation of the extracellular matrix (ECM) and cause proliferation of endothelial cells. The final stage is followed by chemotactic migration of endothelial cells and invasion of the ECM, formation of the lumen and functional maturation of the endothelium [14].
In humans, the VEGF family is composed of multiple isoforms encoded by five genes (VEGFA, VEGFB, VEGFC, VEGFD, and placental growth factor—PIGF). These ligands bind to VEGFRs belong to the type IV receptor tyrosine kinase (RTK) family and include VEGFR1 (FLT1 gene), VEGFR2 (KDR gene) and VEGFR3 (FLT4 gene). VEGFR1 and VEGFR2 regulate angiogenesis and vascular permeability, and VEGFR3 mainly regulates lymphangiogenesis [15]. VEGF also interacts with heparan sulfate proteoglycans (HSP), and neuropilin 1 and 2 co-receptors (NRP1 and NRP2). Moreover, growth factors are dimers and can form both homo and heterodimers [16].
VEGFA (usually called VEGF), first described by Senger et al. [ 17], is one of the most studied growth factors. It is a highly specific vascular endothelial cell mitogen and also the strongest pro-angiogenic factor in the VEGF family. VEGFA binds with high affinity to two VEGF receptor tyrosine kinases (VEGFR1, VEGFR2) and with lower affinity to co-receptors NRP1 and NRP2 [16,18,19]. There is a correlation between altered VEGF expression and reproductive failure, including recurrent implantation failure and recurrent miscarriage (RM) [20].
The VEGFA gene is highly polymorphic, especially in the promoter, 5′-untranslated and 3′-untranslated regions. Some of these variants—rs699947 (−2578C > A), rs1570360 (−1154G > A), rs2010963 (−634C > G) and rs3025039 (+936C > T)—have been associated with variable VEGF protein expression and serum VEGFA levels [21]. Several publications have reported an association of VEGFA gene variants rs833061 (−460T > C), rs25648 (−7C > T) and mainly rs1570360 (−1154G > A) with recurrent implantation failures [22,23,24,25].
As well as the expression of angiogenic factors during embryonic implantation, also the expression of their receptors has been demonstrated. In the placenta, the activity of VEGFR1 and VFGFR2 receptors was observed [26,27]. Tyrosine kinase 1 (FLT1) is the VEGFA and placental growth factor receptor and is expressed in the trophoblasts of the placenta throughout gestation. A soluble form of VEGFR1 called sFlt-1 is markedly increased during the last two months of preeclamptic pregnancy compared with normotensive pregnant controls [28]. The rs722503 polymorphism is located in intron 10 of the FLT1 gene and can alter the regulatory motif for binding of nuclear factor-κB (NF-κB). NF-κB is a transcription factor that can participate in both activation and repression of transcription and is associated with angiogenesis and cell proliferation [29,30,31]. In addition, multiple-SNP analysis by Wujcicka et al. [ 32] showed that the TT variants for CSF2 (rs25881) and FLT1 (rs722503) polymorphisms were associated with an approximately two-fold increase in the prelabor rupture of membranes (PROM) risk when corrected for APTT and PLT parameters and pregnancy.
The kinase insertion domain receptor (KDR), also known as vascular endothelial growth factor receptor 2 (VEGFR2), plays an important role in embryonic development. VEGF-activated receptor stimulates endothelial cell proliferation and is crucial for the development of the embryonic vascular system and hematopoietic system [33,34]. Studies show that the minor allele G of the rs2071559 polymorphism, located in the promoter region, may lead to a decrease in VEGFR2 transcriptional activity, while the minor allele T of the rs1870377 (Gln472His) polymorphism has been associated with reduced VEGFR2 binding affinity [35,36].
Basic fibroblast growth factor 2 (FGF2) is the prototype member of a family of structurally related fibroblast growth factors (FGFs). Growing evidence suggests that fibroblast growth factor/FGF receptor (FGF/FGFR) signaling has crucial roles in a multitude of processes during embryonic development and adult homeostasis by regulating cellular lineage commitment, differentiation, proliferation, and apoptosis of various types of cells. Fibroblast growth factor 2 (FGF2) has a particular role in the formation of endothelial precursors, angioblasts, and their assembly into the initial pattern of the vasculature early during embryonic development [37,38,39]. The rs308395 polymorphism within the FGF2 gene promoter may influence transcription factor binding, and thus FGF2 expression [40].
Considering the above-mentioned interesting insights, we tested the hypothesis that single nucleotide polymorphisms (SNPs) in genes encoding the angiogenesis pathway predispose to infertility and recurrent implantation failure. We evaluated the association of five polymorphic variants in VEFGA (rs699947), FLT1 (rs722503), KDR (rs2071559, rs1870377) and FGF2 (rs308395) genes with infertility and recurrent implantation failure among Polish women.
## 2.1. Baseline Characteristics of Study Subjects and Control Groups
There was no significant difference in maternal age between cases and controls (33.11 ± 3.51 vs. 32.50 ± 3.60 years, $$p \leq 0.123$$). Body mass index was significantly higher in the cases than in the control group (23.36 ± 4.17 vs. 20.71 ± 1.79, $p \leq 0.001$). Over a quarter ($25.9\%$) of the women in the study group had a BMI above 25. In the cases, the median AMH before ART treatment level was 21.00 pmol/L (IQR 11.17–30.79).
Of the total 247 infertile women who underwent an ART treatment cycle, $70.9\%$ had a maximum of two prior failed embryo transfers and $29.1\%$ had at least three prior failed embryo transfers (RIF patients). In 89 cases, the indication for the ART procedure was the male factor, in 119 cases idiopathic infertility and in 39 cases the female factor (oviduct + ovulatory). In the study group, 95 ($38.5\%$) women did not become pregnant. One hundred and twenty women ($48.6\%$) achieved one, $11.3\%$ two, and four women ($1.6\%$) achieved three pregnancies. From the whole number of 188 pregnancies obtained after in vitro fertilization, in 55 cases ($29.3\%$) fresh embryo transfer was performed and in 133 cases ($70.7\%$) frozen embryo transfer was performed. Detailed patient characteristics are summarized in Table 1.
## 2.2. Association Studies
As a first step the frequencies of genotypes and alleles of selected VEGFA, FLT1, KDR and FGF2 polymorphisms were analyzed. The genotype distribution of these SNPs in controls were in accordance with the Hardy–*Weinberg equilibrium* ($p \leq 0.05$). Differences in SNP allele frequency distribution between the cases and the healthy controls were analyzed using the chi2 test and odds ratios (ORs). A statistically significant difference was observed only for KDR rs2071559. Compared with the A allele, the G allele of rs2071559 was more frequent in infertile women ($0.55\%$ vs. $0.47\%$ in controls, OR = 1.378, $95\%$ CI 1.011–1.877, $$p \leq 0.042$$ (Table 2).
Multiple logistic regression analysis with adjustment for age and BMI was performed in codominant, dominant, recessive, over-dominant and log-additive models. The genotype distribution of these SNPs is shown in Table 3. Based on the data, KDR rs2071559 was associated with an increased risk of infertility in crude analysis under a log-additive model (major allele homozygotes vs. heterozygotes vs. minor allele homozygotes); $$p \leq 0.034.$$ After adjusting for age, BMI was significantly associated under a codominant model ($$p \leq 0.04190$$), a recessive model (AA + AG vs. GG: OR = 1.89; $95\%$ CI 1.07–3.34, $$p \leq 0.025$$) and a log-additive model (OR = 0.64; $95\%$ CI 0.45–0.91, $$p \leq 0.013$$). The results indicated that rs2071559 might have a significant association with infertility in our population. For other analyzed polymorphisms, no statistically significant difference was observed (all $p \leq 0.05$) (Table 3).
## 2.3. Stratification Analysis
In order to investigate the possible impact of the analyzed SNPs on the occurrence of recurrent implantation failures, we divided 247 infertile cases into two subgroups: women with RIF ($$n = 72$$) and those with less than 3 previous failed embryo transfers ($$n = 175$$). Clinical characteristics are shown in Table 4. Comparing the groups separated in this way, we observed that the patients with RIF were statistically significantly older (mean ± SD: 34.1 ± 3.7 vs. 32.7 ± 3.4 years, $$p \leq 0.005$$). However, we did not observe any differences in BMI means and serum AMH level medians between groups. In both groups, the indications for the ART procedure and the type of embryos used were similar ($$p \leq 0.763$$ and $$p \leq 0.6985$$, respectively). As many as $58.3\%$ of women with recurrent implantation failures never became pregnant. In the RIF group, 41 pregnancies were achieved, whereas in the group without RIF there were 147 (Table 4).
A comparison of pregnancy outcomes was also made between fresh embryos ($$n = 55$$) and frozen embryos ($$n = 133$$) after IVF treatment. Pregnancy ended with childbirth in $85.5\%$ of mothers from the fresh and in $88.7\%$ from the frozen embryo transfer. Women who underwent frozen blastocyst transfer more often gave birth by caesarean section ($65.3\%$ vs. $44.7\%$, $$p \leq 0.015$$). The average birth weight of infants was slightly lower in the fresh embryo transfer group and was 3315.1 ± 512.6 g compared to 3458.1 ± 412.8 g in the frozen group ($$p \leq 0.0940$$). There were no statistically significant differences between the groups in gestational age, placenta weight and Apgar score (Table 5).
Next, we evaluated the possible associations between studied polymorphic variants and recurrent implantation failures. Our data indicated no significant difference in the genotype frequencies of studied FLT1, KDR and FGF2 gene polymorphisms between RIF and NO-RIF women.
However, comparing subgroups, we observed a statistically significant difference between them for the VEGFA rs699947 variant. In the codominant model, the genotype frequency was: CC–$27.4\%$ and $13.9\%$, CA–$50.9\%$ and $58.3\%$, AA–$21.7\%$ and $27.8\%$ in women without and with RIF, respectively ($$p \leq 0.070$$, padj. = 0.052). This SNP was associated with an increased risk of recurrent implantation failures under a dominant (OR = 2.34; $95\%$ CI 1.11–4.94, $$p \leq 0.023$$, padj. = 0.022) and a log-additive model (OR = 0.65; $95\%$ CI 0.43–0.99, $$p \leq 0.040$$, padj. = 0.038) (Table 6).
## 2.4. Haplotype and Gene–Gene Interaction Analysis
*To* generate a linkage disequilibrium (LD) map, polymorphisms of the KDR gene (rs1870377, rs2071559) and FGF2 rs308395 located on the same chromosome 4 were selected. An LD plot was constructed using combined genotype data from both groups of cases and controls (plot 1A), only cases (plot 1B) and only for controls (plot 1C) using the program HaploView, version 4.1. The LD analysis showed that rs1870377 and rs2071559 (distance between 19392 bp) in the whole group (cases and controls) were in linkage equilibrium (D’ = 0.25, r2 = 0.025, LOD = 1.74); thus, haplotype analysis was not conducted. We only observed weak LD between examined KDR gene polymorphisms in infertile cases (D’ = 0.395, r2 = 0.053, LOD = 2.31). The results are shown in Figure 1.
To search for gene–gene interactions, we used multifactor dimensionality reduction (MDR 3.0.2). Analysis of the dataset of infertile cases and controls revealed synergistic interactions between KDR rs2071559 and KDR rs1870377 (IG = $1.86\%$) and KDR rs1870377 and VEGFA rs699947 (IG = $1.13\%$) (Figure 2).
*These* gene relationships were confirmed in the SNPassoc package. The analysis showed the strongest interaction between the KDR gene rs2071559–rs1870377 ($$p \leq 0.004$$) and rs1870377–rs699947 ($$p \leq 0.030$$). The interaction between the polymorphism of the KDR rs2071559 gene and VEGFA rs699947 was not statistically significant ($$p \leq 0.372$$).
Statistical power for infertility susceptibility analysis was calculated by a Genetic Association Study (GAS) Power Calculator [41] using the following parameters. Numbers of cases and controls and allele frequencies are presented in Table 4 and Table 5. Infertility prevalence is 10–$15\%$ on average in the European populations [42]. Under an additive model, the power of our study to detect an association at a significance level of 0.05 was $10\%$ (average for all tested SNPs) for a genotype relative risk (GRR) equal to 1.1 and $0.69\%$ for a GRR 1.5.
## 3. Discussion
The proper development and function of the placenta are crucial not only for the survival and development of the fetus in utero. The placenta, being the first fetal organ to develop and to function normally, must be highly vascularized [13,43]. An appropriate course of angiogenesis is necessary for a successful pregnancy, and the correct uteroplacental circulation is crucial in the process of implantation and embryo development. Disruption of these processes can lead to various undesirable consequences in pregnancy, such as recurrent pregnancy loss, including recurrent miscarriage and recurrent implantation failure. Some of the most important genes involved in angiogenesis are from the vascular endothelial growth factor family. The best characterized family member is VEGFA, an important factor that regulates angiogenesis, with several isoforms, and that participates in multiple physiological pathways. Several polymorphisms have been reported in the promoter region of the VEGFA gene, including −2578C > A (rs699947) and −1154G > A (rs1570360), which are associated with altered VEGF secretion (Peach et al., 2018; Almawi et al., 2013). Several studies have been conducted in different populations to investigate the association between VEGFA gene polymorphisms and RIF, with conflicting results [22,23,24,25,44]. Most research between recurrent implantation failure and VEGFA gene polymorphisms has paid attention to the −1154G > A (rs1570360) variant. Although studies have been conducted in different populations, there is a noticeable relationship between RIF and the frequency of the minor −1154A allele. Turienzo et al. [ 22] reported that the rs1570360 polymorphism in the dominant model (GG vs. GA/AA) is associated with an increased risk of implantation failure (OR = 1.842, CI $95\%$ 1.002–3.422). Goodman et al. [ 25] found that homozygosity of the VEGFA −1154AA gene was significantly higher among women experiencing recurrent implantation failure compared with fertile control women ($19\%$ vs. $5\%$, $$p \leq 0.02$$) and may serve as a susceptibility factor affecting the chances of recurrent implantation failure [25]. In addition, Vagnini et al. [ 23] found an association between this variant and RIF in Brazilian women (OR = 2.12 $95\%$ CI: 1.16–3.87, $$p \leq 0.01$$ in the dominant model). In a meta-analysis of three case–control studies comprising 305 RIF cases and 378 controls, Zeng et al. [ 45] confirmed the association of (−1154G > A) polymorphism and RIF under the allele (OR 1.39, $95\%$ CI 1.08–1.78, $$p \leq 0.01$$) and dominant genetic model (OR 1.56, $95\%$ CI 1.10–2.20, $$p \leq 0.01$$). Other polymorphic variants of the VEGF gene may also be associated with the occurrence of recurrent implantation failure. In 119 Korean women with RIF and 236 controls, the VEGF rs833061 (−460T > C), rs25648 (−7C > T) and rs3025020 (−583C > T) genetic polymorphisms were analyzed. The rs833061 C and rs25648 T VEGF alleles were associated with a higher risk of RIF (OR = 1.813, $$p \leq 0.009$$ and OR = 2.213, $$p \leq 0.005$$, respectively) [24]. Another study found that the VEGF rs2010963 (+405G > C in the 5′-untranslated region) CC genotype may predispose to recurrent implantation failure after intracytoplasmic sperm injection—embryo transfer (ICSI-ET) [46]. In this study, we observed a statistically significant difference for VEGFA −2578C > A polymorphism between women without and with RIF. This variant was associated with an increased risk of recurrent implantation failures under a dominant (OR = 2.34; $95\%$ CI: 1.11–4.94, $$p \leq 0.023$$, padj. = 0.022) and a log-additive model (OR = 0.65; $95\%$ CI: 0.43–0.99, $$p \leq 0.040$$, padj. = 0.038). Although the polymorphism rs699947 selected in our work is very often studied in connection with various diseases, we found only one study that investigated the occurrence of RIF in Korean females. In the 116 women with RIF and 218 controls, the VEGF −2578C > A, −1154G > A, −634C > G and 936C > T genetic variants were determined. The VEGF -2578AA genotype was associated with an increased prevalence (≥4) of RIF (AOR = 2.77; $95\%$ CI: 1.10–7.02; $$p \leq 0.031$$). The results of this research indicated that the VEGFA -2578AA genotype, −634G allele and −2578A/−1154A/−634G/936C haplotype could be a genetic marker of RIF. Interestingly, in this study, no statistically significant difference was observed between the RIF and the control women for the −1154G > A polymorphism [44].
The influence of FLT1 gene polymorphisms is often studied in preeclampsia [29,30,47]. Soluble FLT1 (sFLT1), which is encoded by an alternatively spliced transcript of FLT1, is an antagonist of VEGF and PIGF. Levels of sFLT1 in maternal blood have been found to be elevated in PE patients. In white women, FLT1 rs722503, FLT4 rs307826, and VEGFC rs7664413 were significantly associated with preeclampsia [47]. Several studies have found circulating levels of sFLT1 to be raised in women with threatened abortion and RM [48,49]. However, little is known about the role of FLT1 and its polymorphic variants in RIF. In a study by Bansal et al. [ 50], serum levels of VEGFA and its receptor FLT1 were compared with levels of NK cells, activated NK cells, and NK cytotoxicity in 62 women with re-implantation failure (RIF) and 72 healthy controls. VEGFA levels were found to be significantly elevated in women with RIF compared to healthy controls, but there was no difference in FLT1 levels between the groups. In our study, the FLT1 gene rs722503 polymorphism was not associated with infertility or RIF in the population of Polish women. Genetic variants of the second VEGF receptor, encoded by the KDR gene, are a frequent subject of association studies with recurrent miscarriages. Rah et al. [ 51] reported that the kinase insert domain-containing receptor gene (−604T > C) rs2071559 polymorphism was associated with recurrent pregnancy loss in Korean women. In the present study, this variant was associated with an increased risk of infertility (after adjusting for age and BMI, rs2071559 was significantly associated under a codominant [$$p \leq 0.042$$], a recessive [$$p \leq 0.0245$$] and a log-additive model [$$p \leq 0.013$$]). For the second analyzed KDR polymorphism (rs1870377), no statistically significant difference was observed. However, in gene–gene interaction analysis, this variant was in strong interaction with VEGFA rs699947 ($$p \leq 0.030$$).
Fibroblast growth factor 2 (FGF2) belongs to the FGF superfamily, comprising at least 22 members in humans. It is a pleiotropic signaling molecule involved in many biological processes including angiogenesis, embryonic development and wound healing. FGF2 is widely used in stem cell research as an agent of self-renewal (proliferation) and differentiation in vitro [52]. Several polymorphisms in the FGF2 gene have been identified, of which rs2922979 (intron), rs308395 (promoter) rs1476217 (3′-UTR), rs308397 (promoter), and rs3747676 (3′-UTR) are the most investigated. The rs308395 variant selected for this study was previously studied in connection with the development of high myopia, diabetic retinopathy, multiple myeloma, risk of cleft lip or in the process of restenosis in patients with stable coronary artery disease treated with a metal stent [53,54,55,56,57]. We did not observe an association of this SNP with infertility or recurrent implantation failures in the studied population of Polish women.
Our results show that the maternal body mass index was significantly higher in the infertile women than in the control group (23.36 ± 4.17 vs. 20.71 ± 1.79, $p \leq 0.001$). More than a quarter ($25.9\%$) of women undergoing ART therapy were obese, which may indicate the importance of BMI in infertility. However, we did not observe differences in BMI means between the RIF groups and women with less than three previous failed embryo transfers. Recently, two interesting studies on this topic have been published. In the first, Nogales et al. [ 2021], in a multicenter study with 2832 patients undergoing pre-implantation genetic testing for aneuploidies (PGT-A), investigated which factors, excluding embryo aneuploidies, are associated with miscarriage in patients who have undergone a single euploid blastocyst transfer. One of the main findings was a significant relationship between body mass index (BMI) and miscarriage rates ($13.4\%$ in underweight women, $12.1\%$ in normal weight, $14.5\%$ in overweight, and $19.2\%$ in obese women, odds ratio (OD) 1.04; $95\%$ CI, 1.01–1.07, $$p \leq 0.006$$). However, in the second, Canadian study, gestational carriers (healthy women with proven fertility and a good obstetric history, who chose to carry a baby not genetically related to them for intended parents) were matched by BMI to infertile patients treated during the same years provided they had undergone a cycle completed to a transfer. The results of this study showed that BMI was not statistically or clinically predictive of ART outcomes or of pregnancy outcomes, among gestational carriers. It is possible that BMI alone may not be a major factor in determining the outcome of infertility treatment; other metabolic and endocrine factors may be at play [58].
The studies of the Forkhead transcription factors family (FOX) conducted in recent years are also interesting. They play an important role in regulating the expression of genes involved in cell growth, proliferation and differentiation. Studies of human endothelial cells and gene knockout mouse models have revealed the role of FOXO proteins in regulating endothelial cell angiogenic activity and blood vessel formation [59,60]. Study in loss-of-function mouse models revealed that FOXO1 significantly downregulated arterial gene expression in the mouse yolk sac prior to the onset of blood flow in early embryonic development and downregulated Kdr transcripts without affecting the overall identity, survival, or proliferation of endothelial cells [61]. Another member of the FOX family, FOXP3, has been reported to inhibit breast cancer angiogenesis by downregulating VEGF expression [62]. FOXP3 gene variants and haplotypes are associated with altered incidence of RPL [5,6].
Normal angiogenesis enables the development of the placenta and a successful pregnancy. It is tightly regulated by a balance of pro- and anti-angiogenic factors that are the subject of much research. There are suggestion that infertile women with RIF could benefit from the use of platelet-rich plasma (PRP) containing growth factors (PDGF, EGF, TGFβ, VEGF, HGF, FGF2) [63].
Moreover, miRNAs are abundantly expressed in the human placenta, and miRNA dysregulation is associated with recurrent pregnancy loss and the pathogenesis of repeated implantation failures. Recently published studies indicate that miR-16 regulates angiogenesis and placental development by targeting VEGF expression and is involved in the pathogenesis of RSA [64]. In a study, Wang et al. [ 65], differentially analyzed the raw data deposited in microarray datasets, to screen DE-mRNAs, DE-miRNAs, and DE-circRNAs, respectively. The kinase insertion domain receptor (KDR) gene was identified by the protein–protein interaction network as one of six hub genes and was downregulated in RIF endometrial tissue samples compared to fertile control samples. In addition, three miRNAs (hsa-miR-424-5p, hsa-miR-195-5p and hsamiR-29b-3p) targeting KDR mRNA were differentially expressed in RIFs [65].
The improvement of conditions for successful implantation in patients with RIF includes the variety of strategies. It is well known that one of the important causes of RIF is the poor oocytes quality. Some interesting studies shown that the oocytes quality could be improved by myo-inositol supplementation, a compound known for its multiple role in the induction of ovulation [66]. In the case of chronic anovulation, the other form of this compound, d-chiro-inositol, was shown to modulate the activity of aromatase by reducing gene expression, inducing in this way the ovulation [67]. Some considerations focus on enhancing the implantation rate by using the embryo culture supernatant to endometrial cavity before embryo transfer [68]. Another reason of fertilization failure caused by the male factor is the cryptic sperm defects in apparently normal spermatozoa. Some studies focused on these problems indicate the necessity to conduct routine tests to detect sperm defects [69]. It is also very important to determine the role of genetic causes connected with infertility, which is suspected in at least about half of all cases. *The* genes involved in meiosis, DNA repair, ovarian development, steroidogenesis, folliculogenesis, and spermatogenesis could play pivotal role in fertilization failure mechanisms. On the other hand, the presence of autoimmune antibodies remains to play the role in infertile processes. Thus, cell and gene therapies could be very helpful for infertile couples to improve their autoimmune conditions and, in this way also, the oocyte maturation and embryo development [70]. Interesting also is the use of artificial intelligence algorithms for enhancing diagnosis of the RIF and ART outcome (pregnancy rate, live birth rate). The computerised analysis systems include ultrasound monitoring of folliculogenesis, endometrial receptivity, embryo selection based on quality and viability, prediction of post implantation embryo development, and oocyte and semen analysis. Through the implementation of different computer algorithms, it is possible to analyse the biological and clinical predispositions in infertile couples [71]. Relatively new are the insights of psychological variables involved in the risk condition of medically-assisted reproduction. The studies focus on depression and anxiety levels according to the number of ART attempts and, on the other hand, they assess the impact of ART on the quality of life and family interactions in couples undergoing ART procedures. These considerations could enhance mental wellbeing in infertile couples [72].
## 4.1. Patient Selection
Our study population included 247 infertile women who underwent an ART treatment cycle and were recruited into the study. All women were enrolled in the Department of Infertility and Reproductive Endocrinology of Poznan University of Medical Sciences, Poznan, Poland between January 2017 and December 2022.
Recurrent implantation failure was defined as the absence of pregnancy after three cycles of IVF using good quality embryos. All women included in the study had their own good quality embryos available for transfer. Each patient in the study group had a regular menstrual cycle and an optimal basal serum follicle stimulating hormone (FSH) level measured on the third day of the last cycle. None of the patients had been taking hormone therapy within the last three months.
The exclusion criteria for the study group were as follows: an abnormal karyotype of parents and any identified fetal genetic abnormalities, systemic connective tissue disorder, antiphospholipid antibody syndrome, hereditary thrombophilia, positive antinuclear antibodies, endocrine dysfunction (luteal insufficiency, hyperprolactinemia, thyroid diseases), and alternative reason for subfertility such as infectious and anatomical causes.
All women in the study group received luteal phase support and underwent ICSI to increase the chance of conception. Fresh or frozen embryo transfer was always performed on the fifth day, by two people (minimum 15 years of experience in the same clinic). Preimplantation Genetic Screening and Diagnosis (PGS/PGD) methods were not performed due to lack of medical indications.
One hundred and twenty age-matched, healthy women with at least two uncomplicated pregnancies ending in the live birth of a healthy full-term newborn were selected for the control group. All women from the control group without evidence of reproductive difficulty had naturally conceived pregnancies. All subjects from the control group had regular menstrual cycles, no evidence of autoimmunity and no past history of pregnancy loss or immunological and endocrinological diseases.
Patients and controls were of Polish origin, from the same geographical area. All patients were informed about the purpose of the study and gave their written consent to participation. The study was approved by the Ethics Committee of the Poznan University of Medical Sciences (no. $\frac{1159}{19}$, date: 5 December 2019).
All procedures performed in this study were in accordance with the ethical standards of our university and with the Helsinki Declaration.
## 4.2. Sample Collection for Genetic Testing and DNA Extraction
The genomic DNA sample was stored in S-Monovette EDTA-coated tubes (Sarstedt, Nümbrecht, Germany) and extracted from peripheral blood leukocytes using the QIAamp DNA Mini Kit according to the manufacturer’s instructions (Qiagen GmbH, Hilden, Germany). DNA concentration and quality were determined spectrophotometrically using a NanoDrop 2000c spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Isolated DNA was stored at −80 °C until analysis. All participants signed informed consent for genetic testing, in which the study management was described.
## 4.3. DNA Amplification and Genotyping
Five SNPs, localized in the genes encoding angiogenesis-related factors, were selected according to the SNP database (dbSNP) of the National Center for Biotechnology Information (NCBI) [41] (http://www.ncbi.nlm.nih.gov/projects/SNP, accessed on 22 February 2022) and the 1000 Genomes *Project data* (http://www.internationalgenome.org/, accessed on 22 February 2022), based on minor allele frequency (MAF) of at least $5\%$ in European populations. Basic information about the tested variants is presented in Table 7.
Genotyping was performed in the Molecular Biology Laboratory of Poznan University of Medical Science by polymerase chain reaction and restriction fragment length polymorphism (PCR-RFLP).
The primers and restriction enzymes used for the RFLP reactions were from previously published research and are presented in Table 2 [29,40,73,74]. Products were analyzed by electrophoresis on $2\%$ agarose gel with Midori Green Advanced DNA Stain (Nippon Genetics, Düren, Germany). Positive and negative controls were included in each reaction and for quality control, $10\%$ of the samples were randomly genotyped twice by different individuals, and the reproducibility was $100\%$. SNP characteristics, primer sequences, and details of the PCR-RFLP assays are presented in Table 8.
## 4.4. Anti-Müllerian Hormone Analyses
Blood samples were drawn from an antecubital vein between 8 a.m. and 10 a.m. after an 8 h fast into serum vacuum tubes (Becton, Dickinson and Company Franklin Lakes, NJ, USA). After the blood had clotted at room temperature for 15–30 min, the samples were centrifuged at 1000–2000× g for 10 min and stored at −80 °C until analyses were conducted. The serum anti-Müllerian hormone (AMH) levels for the infertile cases were measured on the cobas Modular E170 immunoanalyzer (Roche Diagnostics International Ltd., Rotkreuz, Switzerland) using the Elecsys AMH Plus (measuring range: 0.07–164 pmol/L).
## 4.5. Statistical Analysis
All statistical analyses were conducted in the R statistical software version 4.1.2 [75]. For continuous variables, normality was checked by the Shapiro–Wilk test. Normally distributed continuous variables were expressed as mean ± standard deviation (SD) and in the absence of normal distribution as median and interquartile range (IQR). Bivariate analyses were conducted with the t-test or the Mann–Whitney test for ordinal scales, and the chi-square test or Fisher’s exact test for nominal scales. Genotype frequency distributions and the Hardy–*Weinberg equilibrium* (HWE) were evaluated using the SNPassoc package [76]. Genotype distributions are shown as numbers and percentages (%).The associations between infertility and the SNP variants were evaluated by odds ratios (ORs), adjusted odds ratios (AORs), and $95\%$ confidence intervals ($95\%$ CIs) from logistic regression. Linkage disequilibrium (LD) among the selected SNPs was calculated using Haploview v.4.2 software [77]. Interaction analyses were performed using the open source MDR software [78]. GAS (Genetic Association Study Power Calculator) was used to perform power calculations [79]. A p value less than 0.05 was considered significant.
## 5. Conclusions
We conducted a case–control study to investigate the relationship between genetic variation in four genes of the angiogenesis pathway with infertility and RIF in Polish females. *The* genetic variants selected by us have been the subject of many studies before, but not in connection with RIF. We found only one article regarding the importance of rs699947 of the VEGFA gene in RIF Korean women. The strength of our study is that the study population consisted of a homogenous population, which minimized other possible confounding genetic variables. Another one of the strengths of our study is the careful selection of the control group. In order to test the influence of genetic variants not only on RIF but also on infertility, we selected as controls the mothers of at least two children who became pregnant without assisted reproduction methods and did not have any miscarriages.
Since maternal age and BMI are some of the major factors contributing to implantation failure, patients in the infertile and control groups were age-matched. Unfortunately, the body mass index was significantly higher in the subjects than in the control group, but we did not observe differences in mean BMI between the RIF groups and women with less than three previous failed embryo transfers. After dividing the study group, we showed that patients with RIF were statistically significantly older than infertile NO-RIF women. Because confusion is a major issue and accounts for many discrepancies between published studies, we adjusted for maternal age and BMI in the statistical analysis of the results.
This study has several potential limitations which should be acknowledged. Embryo implantation is a very complex process dependent on many factors; therefore, it is unlikely that only single nucleotide polymorphism explains the entire susceptibility to infertility and RIF. Therefore, we performed a gene–gene interaction analysis. A combination of polymorphisms of several genes is more effective in predicting disease susceptibility. For complex analysis, it could also consider the environmental data influenced to infertility and RIF. Our study focused only on maternal genetic variants, although angiogenesis occurs in both maternal and fetal placental tissues and its genetic polymorphisms may have influenced RIF. Furthermore, the sample size of the current study was relatively small, thus, the present findings need to be confirmed in future studies with a large sample size.
Due to the biological complexity and multifactorial nature of many common diseases, single genetic variants still show poor discriminatory power for diagnosis. However, understanding the molecular mechanisms of infertility and RIF by identifying new genetic variants may be the key to developing new therapeutic strategies in the future. Molecular pharmacology is the basis of new drug development and, currently, VEGFR inhibitors have been widely used in the treatment of various tumors. However, current VEGFR inhibitors are limited to a certain extent due to limited clinical efficacy and potential toxicity, which hinder their clinical application [80]. Understanding the molecular mechanisms and function genes encoding angiogenesis-related factors in health and disease is fundamental to the development of new ways to target VEGFs and their receptors.
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|
---
title: 'Mental Health and the COVID-19 Pandemic: Observational Evidence from Malaysia'
authors:
- Eugenie Sin Sing Tan
- Shaun Ashley Fung Xian Chin
- Manimeyapan S. Palaniappan Sathapan
- Astrid Disimond Dewi
- Farahnaz Amini
- Normina Ahmad Bustami
- Pui Yee Tan
- Yu Bin Ho
- Chung Keat Tan
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001641
doi: 10.3390/ijerph20054046
license: CC BY 4.0
---
# Mental Health and the COVID-19 Pandemic: Observational Evidence from Malaysia
## Abstract
The interplay of physical, social, and economic factors during the pandemic adversely affected the mental health of healthy people and exacerbated pre-existing mental disorders. This study aimed to determine the impact of the COVID-19 pandemic on the mental health of the general population in Malaysia. A cross-sectional study involving 1246 participants was conducted. A validated questionnaire consisting of the level of knowledge and practice of precautionary behaviors, the Depression, Anxiety, and Stress Scales (DASS), and the World Health Organization Quality of Life—Brief Version (WHOQOL-BREF) was used as an instrument to assess the impacts of the COVID-19 pandemic. Results revealed that most participants possessed a high level of knowledge about COVID-19 and practiced wearing face masks daily as a precautionary measure. The average DASS scores were beyond the mild to moderate cut-off point for all three domains. The present study found that prolonged lockdowns had significantly impacted ($p \leq 0.05$), the mental health of the general population in Malaysia, reducing quality of life during the pandemic. Employment status, financial instability, and low annual incomes appeared to be risk factors ($p \leq 0.05$) contributing to mental distress, while older age played a protective role ($p \leq 0.05$). This is the first large-scale study in Malaysia to assess the impacts of the COVID-19 pandemic on the general population.
## 1. Introduction
Global health is threatened by the ongoing outbreak of the respiratory disease named Coronavirus Disease 2019 (COVID-19) [1]. The disease is caused by a single, positive-strand RNA virus known as SARS-CoV-2, which was initially reported in Wuhan, Hubei Province, China [2]. Transmission of COVID-19 occurs mainly through respiratory droplets, and its estimated basic reproduction number (R0) ranges from 1.5 to 3.5 [3]. Its relatively high infectivity, long incubation period, long viral shedding period, and steadfast spreading to almost all continents led the World Health Organization to declare a pandemic on 12 March 2020 [2]. As of 8 July 2022, WHO reported more than 550 million confirmed COVID-19 cases, including more than 6 million mortalities [4].
Malaysia is the third-highest country for the number of COVID-19 cases and the fourth-highest country for the number of COVID-19 deaths within the Southeast Asian region [5,6]. The Malaysian government implemented a series of quarantine policies to halt the transmission of COVID-19. In the year 2020, there were four phases of Movement Control Order (MCO) from 18 March to 12 May 2020, two phases of Conditional Movement Control Order (CMCO) from 13 May 2020 to 9 June 2020, and three phases of Recovery Movement Control Order from 10 June 2020 to 31 March 2021 [7,8,9]. In mid-2021, Malaysia declared yet another nationwide Full Movement Control Order (FMCO) from 1 June to 28 June amid a surge of daily COVID-19 cases to 8000 [10].
Pandemics were associated with various psychosocial stressors involving oneself and loved ones. People experienced significant disruptions to their daily routines, such as financial incomes [11], restricted outdoor activities [12], sleep cycles [13], dietary patterns [14], and health behaviors [15]. The anxiety of the population heightened due to the uncertain prognosis of COVID-19, the imposition of unfamiliar public health measures [16], severe shortages of medicine and food [17], the loss of finances [18], and conflicting messages from authorities [19]. Those undergoing quarantine experienced stress, irritability, panic, depression, insomnia, fear, confusion, anger, frustration, boredom, and stigmatism [20,21,22,23,24]. Inadvertently, health systems prioritize screenings and control of disease transmissions ahead of managing the mental health and well-being of the population [25,26,27].
The interplay of physical, social, and economic factors during the pandemic adversely affected the mental health of previously healthy people and exacerbated mental conditions for those with pre-existing disorders [28,29]. Phobic anxiety, panic buying, doom scrolling, travelling against movement restriction orders, absconding from treatment facilities, and binge-watching were associated with impairment of self-control, mental exhaustion, sleep, and mood disturbances [30,31,32,33]. Recent studies reported increased addictive disorders during the COVID-19 quarantine, such as internet addiction, online gambling, pornography, alcoholism, or drug misuse among the general population [34,35,36]. Home isolation restricted family members to their residences, aggravated household conflicts, and increased domestic violence and child maltreatment [37,38,39,40]. Meanwhile, survivors of COVID-19 experienced post-traumatic stress disorder (PTSD) with disproportionately elevated symptoms among those requiring inpatient admission, ventilation support, and treatment for pre-existing mental disorders [41,42].
Although movement control orders were necessary to curb the transmission of COVID-19, their prolonged and repetitive impositions were detrimental. These hostile experiences caused the country to endure financial stress [43,44], social disorders [45], and emotional disorders [46], which inevitably spiked cases of suicide attempts and depression [47]. Notwithstanding the severe mental impacts on Malaysians, studies have remained limited to healthcare professionals and university students, thus, neglecting the true implications of COVID-19 on the entire population [48,49,50]. With that, this study aims to determine the interplay of associations between COVID-19 knowledge, precautionary measures, mental health, and quality of life among Malaysians. It is hypothesized that the COVID-19 pandemic has caused negative impacts on mental health as well as quality of life among Malaysians. These findings are pertinent for the timely intervention of dysfunctional processes and maladaptive lifestyles that may result in the onset of psychiatric conditions [51].
## 2.1. Study Design
This cross-sectional study was conducted from 1 January 2021 to 31 December 2021. The study was conducted in full compliance with the principles outlined in the Declaration of Helsinki and Malaysia’s Good Clinical Practice [52]. Participant recruitment was done via convenient sampling, and the survey was conducted online using Google Forms. The inclusion criteria were: [1] being aged 18 and above; [2] residing in Malaysia for more than 12 months; and [3] being willing to give informed consent. On the other hand, exclusion criteria were: [1] underlying mental illness; [2] active infection with COVID-19; and [3] healthcare workers. The eligibility of each participant was confirmed according to the protocol checklist, and their written informed consent was obtained. The study was approved by the principal investigator’s institutional ethics committee (UCSI University, Malaysia, approval code IEC-2020-FMHS-046).
## 2.2. Knowledge about COVID-19
A validated questionnaire developed by Zhong and colleagues was modified slightly for use in assessing participants’ understanding of COVID-19 [53]. The questionnaire consisted of twelve questions: four on clinical presentations, three on transmission routes, and five on prevention and control. These questions were provided with three options, namely “Yes”, “No”, and “I don’t know”. A correct answer was given 1 point, and an incorrect/not knowing answer was given 0 points. Total knowledge scores ranged from 0 to 12, with 0 to 4 points denoting low levels of knowledge, 5 to 8 points denoting moderate levels of knowledge, and 9 to 12 points denoting high levels of knowledge. The questionnaire was validated by the National Health Commission of the People’s Republic of China, indicating acceptable reliability with a Cronbach’s alpha coefficient of 0.71 [53].
## 2.3. Precautionary Behaviors
A modified version of the validated questionnaire developed by Leung and his colleagues assessed participants’ precautionary behaviors [54]. The original questionnaire was designed to assess the overall well-being and practices during SARS outbreaks in Hong Kong. In this study, only the precautionary measures section, which consists of seven questions, was used.
## 2.4. Depression, Anxiety and Stress Scales (DASS)
The validated Depression, Anxiety, and Stress Scales (DASS) were used to assess self-perceived mental distress [55]. DASS-21 is a self-report questionnaire that contains twenty-one questions, seven per subscale of depression, anxiety, and stress. Participants rated each question on a scale of 0 (did not apply to me at all) to 3 (applied to me very much or most of the time). Sum scores were computed by summing up scores within the same subscale and multiplying them by a factor of 2. The cut-off scores for the depression, anxiety, and stress subscales were 21, 15, and 26, respectively; thus, scores above these denoted high severity of mental distress [56]. DASS was previously validated for Malaysians with a Cronbach’s alpha coefficient of at least 0.74, indicating acceptable reliability [57].
## 2.5. World Health Organization Quality of Life—Brief Version (WHOQOL-BREF)
The validated World Health Organization Quality of Life—Brief Version (WHOQOL-BREF) was adopted to assess the quality of life amid the COVID-19 pandemic [58]. A WHOQOL-BREF assessment is a short-form questionnaire that determines the meaning of different aspects of life to the participants and their satisfaction with their experiences concerning those aspects of life. It is a self-perceived questionnaire consisting of four domains, namely physical health (seven items), psychological status (six items), social relationships (three items), and environmental conditions (eight items). Participants were asked to rate all the items on a Likert scale of 1 to 5 (1 = very poor, 2 = poor, 3 = neither poor nor good, 4 = good, and 5 = very good; 1 = very dissatisfied, 2 = dissatisfied, 3 = neither satisfied nor dissatisfied, 4 = satisfied, and 5 = very satisfied; 1 = not at all, 2 = a little, 3 = a moderate amount, 4 = very much, and 5 = an extreme amount; 1 = not at all, 2 = a little, 3 = moderately, 4 = mostly, and 5 = completely; 1 = not at all, 2 = a little, 3 = a moderate amount, 4 = very much, and 5 = extremely; or 1 = never, 2 = seldom, 3 = quite often, 4 = very often, and 5 = always). Items with negative scoring were reversed when summing up the total domain score. After that, it was converted to a transformed score within the range of 4 to 20. Domain scores were scaled positively, with a higher score denoting better QoL. WHOQOL-BREF was previously validated for Malaysians with a Cronbach’s alpha coefficient of 0.88, indicating good reliability [59].
## 2.6. Statistical Analysis
Categorical data were expressed in frequency and percentage, while continuous data were presented as the mean ± SD for normally distributed data or the median (interquartile range) for non-normally distributed data. Where appropriate, the association relationship was analyzed using an independent samples t-test, one-way analysis of variance (ANOVA), or a Chi-square test. Correlation analyses (Pearson’s) were performed to assess the predicted relationships between demographic, DASS, and WHOQOL-BREF outcome measures. Pearson coefficients) range from +1 to −1, with +1 representing a positive correlation, −1 representing a negative correlation, and 0 representing no relationship. Results are considered significant if $p \leq 0.05.$ *Statistical analysis* was performed using SPSS 26.0 (IBM Corp., New York, NY, USA) for macOS.
## 3.1. Characteristics of Participants
Of the 1246 participants who enrolled in this study, the majority ($$n = 506$$, $40.6\%$) were below or at the age of 30 at the time of study entry. Female participants ($$n = 675$$, $54.2\%$) were slightly more numerous than their male counterparts. The highest educational levels were at the pre-university and graduate levels, with $32.7\%$ and $35.0\%$, respectively. Annual incomes observed a normal distribution, with the majority ($$n = 629$$, $50.5\%$) earning USD 10,000 to USD 20,000 in a year. Financial struggles were similar between groups. Most participants ($$n = 350$$, $28.1\%$) do not have any dependents living with them, followed by having two dependents ($$n = 279$$, $22.4\%$), and lastly having three, one, and more than three dependents with $18.9\%$, $16.3\%$, and $14.3\%$, respectively. Meanwhile, some participants ($$n = 150$$, or $12.0\%$) suffered from chronic diseases. A history of being positive for COVID-19 or being a close contact was similar between groups. The factors analyzed are normally distributed, with no significant difference between categorical variables except employment status and chronic diseases (Table 1).
## 3.2. Level of Knowledge, Precautionary Behavior, Depression, Anxiety, and Stress Scales (DASS), and Quality of Life (WHOQOL-BREF) of Participants
Most participants ($$n = 1097$$, $88.0\%$) showed a high level of knowledge about infectious diseases, and none had a low level of knowledge. Precautionary measures were similar for nearly all assessed behaviors, except for face mask-wearing, which was practiced by $81.5\%$ of participants. The means (SD) of depression, anxiety, and stress were 13.7 (8.9), 13.0 (8.6), and 14.6 (8.5), respectively. With regards to severity, $69.7\%$ had depressive symptomatology ($13.1\%$ were severe and $7.9\%$ were extremely severe), $72.6\%$ had anxiety symptoms ($11.5\%$ were severe and $24.3\%$ were extremely severe), and $42.6\%$ had stress symptoms ($11.4\%$ were wsevere and 1.4ere % extremely severe). Meanwhile, the means (SD) of physical health and psychological status were 13.0 (2.6) and 12.9 (2.6), respectively, and those for social relationships and environmental conditions were 13.5 (3.2) and 13.4 (2.4), respectively (Table 2).
## 3.3. Analysis of Association
Age, educational level, employment status, and annual incomes were found to be significantly ($p \leq 0.05$) associated with all DASS symptoms and QOL domains, with higher impacts on the groups of 31 to 40 years old and 41 to 50 years old (similarly high), secondary educational level, part-timer, and annual income group of less than USD 10,000. Gender was significantly ($p \leq 0.05$) associated with depression, anxiety, social relationships, and environmental conditions, which impacted male participants mainly. Financial struggle was significantly ($p \leq 0.05$) associated with anxiety and all QOL domains. Participants with one dependent were also significantly ($p \leq 0.05$) associated with all DASS symptoms and QOL domains, except for the environmental condition domain. A history of chronic diseases was significantly ($p \leq 0.05$) associated with depression, anxiety, and social relationships. In contrast, the history of being positive for COVID-19 positive or being a close contact was significantly ($p \leq 0.05$) associated with anxiety and stress. In addition, results indicated that participants with a moderate level of knowledge were significantly ($p \leq 0.05$) more impacted in terms of stress, physical health, and environmental conditions (Table 3).
## 3.4. Correlation of Coefficients
Table 4 shows the Pearson correlation coefficient matrix of the observed variables. Age was inversely correlated with knowledge (r = −0.070, $p \leq 0.05$), depression (r = −0.116, $p \leq 0.001$), anxiety (r = −0.083, $p \leq 0.01$), and stress (r = −0.081, $p \leq 0.01$), and directly correlated with physical health ($r = 0.102$, $p \leq 0.001$), psychological status ($r = 0.089$, $p \leq 0.01$), social relationships ($r = 0.068$, $p \leq 0.01$), and environmental conditions ($r = 0.063$, $p \leq 0.05$). The level of knowledge was found to significantly correlate ($p \leq 0.05$) with anxiety ($r = 0.064$) directly and environmental conditions (r = −0.073) inversely.
Depression showed a strong direct correlation with anxiety ($r = 0.756$, $p \leq 0.001$) and stress ($r = 0.748$, $p \leq 0.001$), and a moderate inverse correlation with physical health (r = −0.505, $p \leq 0.001$), psychological status (r = −0.493, $p \leq 0.001$), social relationships (r = −0.431, $p \leq 0.001$), and environmental conditions (r = −0.419, $p \leq 0.001$). Anxiety showed a strong direct correlation with stress ($r = 0.740$, $p \leq 0.001$) and a moderate inverse correlation with physical health (r = −0.471, $p \leq 0.001$), psychological status (r = −0.438, $p \leq 0.001$), social relationships (r = −0.405, $p \leq 0.001$), and environmental conditions (r = −0.459, $p \leq 0.001$). Stress showed a moderate inverse correlation with physical health (r = −0.476, $p \leq 0.001$), psychological status (r = −0.475, $p \leq 0.001$), social relationships (r = −0.409, $p \leq 0.001$), and environmental conditions (r = −0.438, $p \leq 0.001$).
Meanwhile, the physical health domain of WHOQOL-BREF showed a moderate direct correlation with psychological status ($r = 0.568$, $p \leq 0.001$), social relationships ($r = 0.409$, $p \leq 0.001$), and environmental conditions ($r = 0.557$, $p \leq 0.001$). Psychological status showed a moderate direct correlation with social relationships ($r = 0.403$, $p \leq 0.001$) and environmental conditions ($r = 0.524$, $p \leq 0.001$). Lastly, social relationships were moderately and directly correlated with environmental conditions ($r = 0.489$, $p \leq 0.001$).
## 4. Discussion
The widespread morbidity and mortality associated with the COVID-19 pandemic have profoundly affected every individual’s life since the declaration of the novel coronavirus disease 2019 as an international public health emergency in January 2020 [60]. In order to limit the spread of COVID-19 and curb the drastic increase in mortality, the World Health Organization [4] recommended the implementation of Public Health and Social Measures (PHSM), such as imposing lockdown by country, state, or district on a global scale [61]. Pursuant to that, Malaysia has declared two total nationwide lockdowns during the long pandemic [62]. Prolonged lockdowns have caused inevitable changes to the usual activities, livelihoods, and routines of people, eventually leading to deteriorated mental health and increased self-harm or suicidal behavior [63]. Recent studies have pointed out that self-isolation, quarantine, spatial distancing, misleading social media content, and social and economic discord are major contributing factors to anxiety, stress, helplessness, loneliness, and depression [64,65]. Quality of life was simultaneously impacted in the general population and in post-COVID-19 patients [66,67]. Malaysia was ranked fourth in the Event Scale-Revised (IES-R) and Depression, Anxiety, and Stress Scale (DASS-21) to be impacted by the COVID-19 pandemic among seven middle-income countries in Asia [68].
The knowledge level of participants about COVID-19 was assessed using a questionnaire developed during the first outbreak in Wuhan, China [53]. The questionnaire was adopted in this study with further grouping into low, moderate, and high levels of knowledge. Results revealed that most participants ($$n = 1097$$, $88.0\%$) possessed a high level of knowledge after approximately two years of battling COVID-19. This finding is supported by a recent study that highlighted the direct proportional relationship between time of media exposure and perceived knowledge among the general public [69]. Prolonged lockdown periods in Malaysia have led to high dependency on various online sources to acquire updated information about the pandemic [70]. Notwithstanding the high level of COVID-19 knowledge among our participants in this study, only half were practicing precautionary measures such as covering their mouth when coughing or sneezing, using serving utensils, practicing good hygiene, or social distancing. These lax precautionary measures could be attributed to the central government’s lack of firm, persistent, and consistent enforcement. Although social distancing was strongly imposed during the beginning of the pandemic, it lacked endurance and was promptly eased following the decline in COVID-19 positive cases, increased occupancy in intensive care units (ICU), and decreased R0 value. Eventually, the public will lose sight of the need for social distancing and preventive measures. Similar observations were reported in India following its first wave of COVID-19 cases [71]. Second, high mask-wearing compliance could reduce adherence to social distancing, as indicated by our results. This observation can be attributed to a mechanism termed risk compensation behavior, in which individuals embrace higher risk when their safety is presumed [72].
Our results indicated the participants’ average scores for depression, anxiety, and stress to be 13.7, 13.0, and 14.6, respectively; these values were higher compared to the data reported in the most recent study [68]. The sudden hike in DASS scoring is most likely due to the prolonged lockdown implemented in 2021. Quarantine and isolation at extended lengths were deemed highly effective countermeasures for the transmission of COVID-19, but they inevitably impacted individuals’ mental health, especially their emotional well-being [73]. Growing evidence supports the negative impacts of quarantine in causing psychological distress in the form of anxiety, depression, worry, anger, confusion, and post-traumatic stress symptoms [47,73,74]. Apart from the long lockdown period, our data illustrated the potential attributions by age, gender, educational level, employment status, annual income, number of dependents, medical background of chronic illnesses, and history of being COVID-19 positive or in close contact. This is consistent with the previous findings reported in Asian countries [68,75,76,77]. Although individuals of older age (60+ years) were thought to have a greater risk of contracting and dying from COVID-19, a study has shown that this group possessed better emotional well-being, which acted as a buffer against the negative psychological impacts of COVID-19 [78]. Contrastingly, individuals younger than 50 were reported to have a more evident association with adverse mental health. This suggested that stress arising from financial insecurity is an essential risk factor for psychological morbidity, especially for those working adults between 31 and 50 years old, as observed in our study [79,80]. The faltering economy and reduction in business activities during the pandemic had a detrimental effect on workers with low income and unstable employment statuses [75]. A recent model suggested that unemployment caused by the pandemic could result in an additional 9570 suicides per year worldwide [81].
Quality of life was defined as an individual’s perception of their life status in the context of the culture and value system in which they live and concerning their goals, expectations, standards, and concerns [82]. WHOQOL was employed as a multidimensional tool to assess QoL in different aspects of life and was validated to be a useful assessment tool even in different cultural populations [83]. The average scores were 13.0, 12.9, 13.5, and 13.4 for the respective physical, psychological, social, and environmental domains. Although this scale has no cut-off score, our reported values were generally lower than previous studies focusing on specific groups (students, healthcare workers) or a specific timeframe (the first lockdown) during the beginning of the pandemic in Malaysia [84,85,86]. The predictors of QoL were age, educational level, employment status, annual income, and financial struggles for all four domains. Meanwhile, gender was accountable for social and environmental domains, and chronic disease was for social domains. Like mental health, older age appeared to be a protective factor, even though the elderly were classified as a high-risk population during the pandemic. This could be attributed to their financial stability [87], optimism, or reduced fear of death [88]. Our findings were in line with previous studies reporting older age to exhibit a similar or even better well-being status than before the pandemic [87,89,90,91,92]. As highlighted in the earlier study, older people may have better psychological strengths acquired from their life-challenging experiences, equipping them with skills to deal with adversity [93]. Apart from age and financial stability, chronic diseases were also reported to be one significant variable in determining QoL [94]. Some studies have shown that QoL is lower among patients with specific chronic non-communicable diseases (NCD) such as diabetes, hypertension, and cardiovascular disease [95,96]. Due to the fear of COVID-19 infection, populations with chronic diseases often refrain from social interactions, thus lowering their QoL in the social domain [97].
Correlation analysis revealed that age correlated negatively with knowledge, depression, anxiety, and stress, while it correlated positively with all four domains of WHOQOL. This is consistent with our speculation that information about COVID-19 was mainly acquired through social media. Older people were particularly hesitant to utilize digital services due to their refusal to learn new technologies [98]. The digital competency gap between younger and older adults is reasonably large, especially in developing countries [99]. Nonetheless, minimizing the use of social media in acquiring COVID-19 information is beneficial for reducing the symptoms of depression and anxiety [100,101]. The unverified and contradictory information on social media often caused more confusion than consolidating a consistent effort against the pandemic [101]. This study also explained the negative correlation between age and mental distress. The better QoL presented in the older population could potentially be attributed to their greater tolerance to COVID-19 risk, better sleep quality, higher optimism, and better relaxation during the pandemic [102]. The traits of depression, anxiety, and stress showed moderate negative correlations with all four domains of WHOQOL in this study. These findings concurred with previous studies reporting mental distress as a useful predictor for QoL outcomes during the pandemic [103,104,105,106]. One study highlighted that anxiety could be useful to encourage the practice of precautionary measures, but it may disrupt daily work and family life if improperly managed.
Although the pandemic is ending, previous frequent and prolonged lockdowns have caused inevitable changes for everyone. This present study indicated that prolonged lockdowns had profoundly impacted the mental health of the general population in Malaysia, reducing their quality of life during the pandemic. Employment status, financial instability, and low annual incomes appeared to be the risk factors contributing to mental distress, while older age played a protective role in contrast. To our best knowledge, this is the first large-scale study in Malaysia to assess the mental health and quality of life of the public during the pandemic. Our findings shed light on the impact of lockdowns and pandemics in the long run. Preventive measures or intervention programs such as community mental health support programs, awareness and educational campaigns, or suicide prevention programs should be implemented soonest to prevent the exacerbation of pre-existing mental conditions due to the pandemic. The primary limitation of this study is its inability to establish temporal links between outcomes and factors; the base rates of mental health symptoms compared to other time points cannot be inferred through a cross-sectional study. A longitudinal study is recommended to determine long-term mental implications involving all potential risk factors highlighted in this study.
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|
---
title: 'Protective Role of Social Networks for the Well-Being of Persons with Disabilities:
Results from a State-Wide Cross-Sectional Survey in Kerala, India'
authors:
- Saju Madavanakadu Devassy
- Lorane Scaria
- Shilpa V. Yohannan
- Sunirose Ishnassery Pathrose
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001646
doi: 10.3390/ijerph20054213
license: CC BY 4.0
---
# Protective Role of Social Networks for the Well-Being of Persons with Disabilities: Results from a State-Wide Cross-Sectional Survey in Kerala, India
## Abstract
The current study presents the findings from a cross-sectional survey on social factors associated with the well-being of persons with disabilities (PWDs) in Kerala, India. We conducted a community-based survey across three geographical zones, North, Central, and South of Kerala state, between April and September 2021. We randomly selected two districts from each zone using a stratified sample method, followed by one local self-government from each of these six districts. Community health professionals identified individuals with disabilities, and researchers collected information on their social networks, service accessibility, well-being, and mental health. Overall, 244 ($54.2\%$) participants had a physical disability, while 107 ($23.78\%$) had an intellectual disability. The mean well-being score was 12.9 (S.$D = 4.9$, range = 5–20). Overall, 216 ($48\%$) had poor social networks, 247 ($55\%$) had issues regarding service accessibility, and 147 ($33\%$) had depressive symptoms. Among the PWDs with issues with service access, $55\%$ had limited social networks. A regression analysis revealed that social networks ($b = 2.30$, $$p \leq 0.000$$) and service accessibility (b = −2.09, $$p \leq 0.000$$) were associated with well-being. Social networks are more important than financial assistance because they facilitate better access to psycho-socioeconomic resources, a prerequisite for well-being.
## 1.1. People with Disabilities in Kerala, India
Disabilities and related complications, irrespective of their types, pose severe challenges across the globe. Globally, more than $15\%$ of people live with a disability, and the prevalence is significantly higher among people from low- and middle-income countries than in other developed countries [1]. According to the disability census of Kerala, there are 793,937 people with disabilities in Kerala, which accounts for $2.32\%$ of the total population [2], where the national average is $2.21\%$ [3]. Among the different types of disabilities in Kerala, locomotor disability is the most common type, accounting for $31\%$ of the total PWDs, followed by multiple disabilities accounting for $17\%$, and mental illness ($12\%$). Vision and hearing impairment accounts for $7.8\%$ and $7.6\%$, respectively. Further, $46.63\%$ of PWDs in Kerala are living below the poverty line [2]. In low- and middle-income countries like India, the rapid increase in disability incidence and severity has not been accompanied by planned initiatives to enhance their well-being and overall health [4]. Due to various systemic barriers, the meager welfare services and programs already available are only accessed by a small proportion of people [5,6].
## 1.2. Social Networks of People with Disabilities
People with disabilities generally experience low levels of social integration and inclusion compared to the general population [7,8] for various reasons, such as functional limitations [9], social stigma, and discrimination [10]. People with disabilities have fewer social contacts and are less likely to begin relationships in everyday life [11], further leading to poorer employment opportunities and health outcomes [12]. Moreover, a study on different social networks among people in Kerala showed that $37.6\%$ of PWDs had a private restricted network type rather than a locally integrated one [13]. All of these reasons clubbed together can cause an increased risk of social isolation among this already vulnerable group [14,15]. Further, people with disabilities have been found to have higher odds of depression and anxiety levels [13], and the personal and health characteristics of PWDs have been found to be mediated by social cohesion in Kerala [16].
In countries like India, where resource scarcity weakens social security nets, family members and neighbors should play a crucial role in the care and support of PWDs [17]. Neighborhood connectivity has the potential to provide knowledge from network members about locally accessible formal and informal resources, effective interventions, health behaviors, and employment opportunities [18]. PWDs create their networks based on employment, routine activities, family connections [19,20], and neighborhood interactions [21,22]. In unequal societies with weak safety nets, this networking is vital for learning about available resources, preventing the loss of existing services, lobbying for additional welfare measures, ensuring greater access to resources locally [23], and creating more growth opportunities. The existing evidence shows that more cohesive societies cooperate in providing welfare services to meet the needs of PWDs, mainly through resource mobilizations at the societal level [24]. Moreover, PWDs feel identified with a group or neighborhood that accepts and is compassionate towards them, which increases their social status [24], and, consequently, their mental health [25].
Family and neighborhood are the best sources of support for PWDs, given the scarcity of social support measures and the overall collectivist nature of Indian societies. However, there is a dearth of evidence about the specific social factors associated with the well-being of those with disabilities. We assume that developing a sense of connectedness and inclusion would play a pivotal role in enhancing their well-being which would moderate the negative impact of disabilities. The findings of this study will help practitioners and policymakers in India to devise strategies focused on strengthening social networking and neighbourhood connectivity to enhance the well-being of these people.
## 2.1. Design
We conducted a cross-sectional, community-based study of PWDs across three geographical zones—North, Central, and South of Kerala state, India—between April and September 2021. Kasaragod, Wayanad, Kannur, Kozhikode, and Malappuram districts make up the Northern zone. The Central zone consists of four districts: Palakkad, Thrissur, Ernakulam, and Idukki. The Southern zone includes Trivandrum, Kollam, Pathanamthitta, Alappuzha, and Kottayam districts. We randomly selected six districts from these three zones (two from each) using a stratified sampling method, followed by selecting one local self-government (LSG) body from each of these six districts. The local self-government bodies are administrative divisions within each district that function as sub-units of each district. The LSGs include municipalities or corporations (sub-units in urban areas) and panchayats (rural areas). We randomly selected two units from urban areas (one corporation and one municipality). Four Panchayats (more panchayats were included to ensure better representation. ( The Kerala state has 941 grama panchayats, 87 municipalities, and 6 corporations). Accredited Social Health Activists (ASHAs), who have an advantage due to their domicile, helped identify people with disabilities. After listing the names of the PWDs who had been identified, researchers made home visits until they had 75 consenting PWDs (or, in the case of children or those with severe disabilities, their carers) from each selected local self-government. Figure 1 describes the participant recruitment procedures of the current study.
## 2.2. Participant Recruitment
We recruited PWDs and their caregivers from the community through a multistage recruitment procedure. The researchers included the PWDs residing in the targeted location who consented to participate. We included people within the four major disability categories, including physical disability, intellectual disability, multiple disabilities, and other forms of disabilities. A random number technique was employed to identify 75 PWDs from each district and recruit a total of 450 participants for the current study.
## 2.3.1. Outcome Variable
The primary outcome measure of well-being was measured by the WHO Well-Being Index [21], which is a set of five questions measured on a Likert scale with response options of “all of the time” [5], “most of the time [4], “more than half of the time” [3], “less than half of the time” [2], “some of the time” [1] and “at no time” [0]. The scores ranged between 0 and 25, and a higher score indicated better well-being. The tool has been validated and found to have good reliability coefficients [26].
## 2.3.2. Exposure Variables
Sociodemographic variables, mental health, well-being, and access to services were the major exposure variables measured in the current study. Sociodemographic variables included age, gender, education, marital status, employment, the color of the ration card, the type(s) of disability, and the percentage level of disability. Age was ascertained in years and was later grouped into four categories: children (0–18 years), young adults (19–39 years), middle adulthood (40–59 years), and elderly (above 60 years). Education was measured in five categories: not literate, literate but did not complete primary education, completed primary education (10th grade), completed secondary education (12th grade), and completed tertiary and above (graduation, diploma, or post-graduation). Marital status was ascertained in four categories: currently married, never married, widowed, and divorced/separated. Occupational details were measured as “employed”, “unemployed”, “student”, or “completely dependent”. A ration card is an official document, issued by the state government, that describes the eligibility to purchase subsidized food grains from the government distribution system. The colors, coded as yellow, pink, blue, and white, describe the socio-economic status of each household. The yellow and pink cards are for households below the poverty line, while the blue and white cardholders fall above the poverty line. The types of disabilities were categorized into four areas: physical disability, including locomotor disability, vision, hearing, and speech impairment; intellectual disability, including mental retardation and autism; multiple disabilities; and other forms of disabilities, which included disabilities due to a chronic neurological condition, Parkinson’s, or mental illness. The percentage of disability is ascertained from the disability certificate issued by the Government of India.
Mental health was measured using the DASS 21 (Depression, Anxiety, and Stress) Scale [27]. It includes 21 self-reported questions rated on a four-point scale (0–3), with “0” denoting “did not apply to me at all” and “3” meaning “applied to me very much, or most of the time”. The DASS 21 is a reliable and valid tool to measure mental health among adults [28].
Access to services was measured using a set of self-reported questions based on accessibility in four major areas: family income/employment, essential services, health care, and mental health care. Accessibility was rated on a four-point scale (1–4), with “1” denoting “as much as I need”, “2” representing “most times”, “3” indicating “sometimes,” and “4” meaning “not at all”. We also asked self-reported questions about barriers to accessing care in four major areas: awareness, absence of services, lack of support, and transportation, to which the participants replied using binary response options of “yes” [1], denoting the presence of the barrier, and “no” [0], indicating an absence.
Social networks were measured using a set of self-reported questions about the level of contact and support received from families, friends, and neighbors. The questions were measured on a four-point Likert scale (0–3), with 0 denoting “at no time”, 1 denoting “sometimes,” 2 denoting “most times”, and 3 denoting “at all times”. Based on median scores, they were classified as people with poor social networks and people with adequate social networks for analysis purposes.
## 2.4. Data Analysis
We performed descriptive statistics to profile the PWDs concerning their geographical locations and other demographic variables. We calculated frequencies and percentages through two-way tables to find differences between the subgroups of interest. Further, Chi-square tests were used to determine the statistical difference between the variables. Linear regression was performed to identify the various factors associated with well-being among people with disability. The level of statistical significance was set at $p \leq 0.05.$ All statistical analyses were performed in IBM SPSS 26 package (New York, NY, USA) and STATA (StataCorp LLC Version 15, Lakeway Drive, TX, USA).
## 2.5. Ethical Considerations
We obtained ethical committee approval from the institution’s Institutional Review Board (Ref. No. – RCSS/IEC/$\frac{002}{2021}$, dated 15 January 2021). We obtained informed written consent from participants and their caregivers before inclusion. We also explained the voluntary nature of participation and the right to withdraw at any data collection stage.
## 3.1. Demographic Characteristics
The study included data from 450 respondents (Table 1), the majority of whom were males ($62\%$). More than $65\%$ of the respondents were in the early/or middle adulthood stage, and $12.9\%$ were elderly. Overall, $72\%$ of the respondents had completed primary education, while $3\%$ were uneducated/illiterate. Further, $63\%$ of the respondents were unmarried, $54\%$ were unemployed/entirely dependent on family members, and $71\%$ were below the poverty line. Of the types of disability, $54.2\%$ had a physical disability, which included a multitude of disabilities related to vision, hearing, speech, or locomotor functioning, and $24\%$ of the population had intellectual disabilities.
The mean well-being score for the study population was 12.9 (±4.9). There was no significant difference in well-being scores within the demographic variables studied. However, the scores were slightly higher for children, females, people who completed secondary or tertiary education, and people with less than $40\%$ disability. Summative scores for depression, anxiety, and stress, measured by the DASS scale in the current study group, were 6.62 (6.3), 9.3 (8.7), and 8.2 (7.7), respectively. Further, 147 ($32.7\%$) of PWDs had mild or above depression, 93 ($20.7\%$) had mild or above anxiety, and 278 ($61.8\%$) had mild or above stress.
Demographically, PWDs without formal education existed at the highest rates in the northern zone of Kerala, while unemployment among PWDs was the highest in the southern zone. The summative scores of well-being were the highest among PWDs in the south zone (mean = 14.2), followed by the north (mean = 13) and the central (mean = 11.7) zones. Mental illness, in terms of depression, anxiety, and stress, was the highest among PWDs in the central zone. Social support from neighbors and family members was comparatively higher in the southern zone than in others.
## 3.2. Service Accessibility
We studied access to income/employment, food, medical health care, and mental health care to study the service accessibility among PWDs. In the current population, there were many ($40\%$) who could not access income-generating employment or medical services ($25\%$). In contrast, most had access to essential services ($94\%$), and $86\%$ had access to mental health treatment. Service access in all areas was comparatively higher among males. Furthermore, access to income and essential services was relatively higher among PWDs residing in the southern parts of Kerala. In comparison, access to treatment was better in the northern parts compared to other zones (Table 2).
Of the 450 participants, 203 ($45.11\%$) PWDs had no issue accessing services. However, among 247 people with service access issues, 149 ($33.11\%$) had trouble accessing one service, 64 ($14.22\%$) had issues with two services, 27 ($6\%$) PWDs with three services, and 7 ($1.56\%$) PWDs with all the services listed. Among the 247 PWDs with service access issues, $55\%$ had limited social networks. However, among people with adequate service access, $60\%$ had adequate social networks.
Table 3 describes the subgroup analysis of the accessibility variables with social networks and the types of disabilities. Inadequacies in accessing employment, essential services, medical care, and mental health care were more prevalent in people without adequate support from their families and neighborhoods. Overall, $62\%$ of respondents having inadequate employment (statistically significant at $$p \leq 0.000$$), $59\%$ of respondents having insufficient access to food/other essential services, $52\%$ of respondents having inadequate medical health care, and $54\%$ of respondents having poor access to mental health care had lower social network scores.
Table 4 presents the results of a linear regression analysis conducted to understand the association between social networks and well-being among the respondents. In the current study, people with adequate support were found to have 2.3 times higher scores for well-being compared to people with poorer social networks. The inability to access services and the presence of depression, anxiety, and stress symptoms were negatively associated with well-being in the current population.
## 4. Discussion
The current study aimed at identifying the role of social networks and other social factors in improving the well-being of people with disabilities. Demographically, PWDs with locomotor disabilities were the most common type, and the northern zone of Kerala had the largest percentage of PWDs without a formal education. In contrast, the southern zone had the highest rate of PWDs who were unemployed. Study results point to a comparatively lower number of people accessing services in the central geographical zone of Kerala. Although more of the PWDs in the southern zone were unemployed, those in the northern zone had less schooling. The center zone, which performed well in both of these areas, had poorer levels of well-being and a greater demand for mental health services, especially due to limited access to disability services. This can be explained by the fact that these zones are home to a predominantly urban population with lower neighborhood connectivity and linkages [29]. The current study findings suggest that poor neighborhood connectedness leads to limited access to information, and thereby, to services. This finding is in line with another study conducted in South India [30].
Furthermore, the study findings stressed the importance of family and neighborhood support networks for better well-being and protection against adverse health outcomes in PWDs [31]. People with adequate support networks in the study had higher scores for overall well-being, and this is consistent with studies elsewhere [32,33,34]. This is all the more critical in unequal and stratified, resource-poor societies like India, which is characterized by inadequate safety nets and lean spending on social welfare, such as health care, education, and unemployment insurance [4].
Tapping into local neighborhoods’ physical, social, and service facilities depends on the neighborhood’s culturally defined friendliness/helpfulness and support patterns. There is sufficient evidence to prove that people living in supportive communities require fewer mental health services [35] due to better well-being. The supportive neighborhood disseminates knowledge about self-care and promotes access to locally available services, amenities, and affective support [36]. Through an amalgamation of collective efficacy, social support, and the prevalence of local organizations and voluntary associations, this social connectedness improves access among PWDs [37]. The affective or cognitive closeness with others makes it easier for people to communicate their concerns and gain knowledge about resources [38], especially regarding the non-governmental and volunteer organizations that can address their needs [39]. The participants’ enhanced ability to obtain resources from their networks significantly increases their well-being. Due to their social disconnect, people with disabilities are frequently deprived of opportunities for inclusion, which has an impact on their well-being.
If social networking is created with cultural sensitivity, and in accordance with the current community ecosystem, it can enhance inclusion, social functioning, and resource linkages. The advancement of technology would be another way to increase connectedness and enlarge the borders of the neighborhood. A few digital networking models are worth experimenting with in order to enhance their social inclusion, learn about the resources available, and also advocate for legislation to promote access and social inclusion [40]. This study challenges the current focus of policymakers and practitioners, who emphasize financial support alone as a means of enhancing the well-being of PWDs. Social networks can potentially address the problems with inclusion, accessibility, and emotional requirements, indicating that PWDs are moving up the ladder of Maslow’s hierarchy of needs. This upward trend could be linked to the general economic progress of caring families in conjunction with the nation’s development. The findings encourage policymakers to undergo a paradigm shift in the target areas of intervention strategies. It should be firmly founded in defense of the rights, dignity, and self-worth of people with disabilities from a psycho-socioeconomic perspective as opposed to only an economic one. The current gaps in the care of PWDs could be filled by co-creating social networks, simplifying the linking pathways, and devising customized interventions.
The current study has its limitations as well. Firstly, this being a cross-sectional study, the observed associations cannot be interpreted as causal inferences. The study only included PWDs identified through community health workers and included only the known cases, which can limit the generalizability of the findings. Disability, a complex multidimensional phenomenon, cannot be fully measured quantitatively, which might be another limitation of the current study. However, the study’s findings encourage researchers to investigate PWDs lived experiences, particularly in light of the nation’s evolving psychosocial and economic environment.
## 5. Conclusions
Social networks and support are particularly crucial, as even the already existing formal and informal services and resources for these groups are embedded within the systems of society. The lack of a formal networking platform through which to meet each other in an empathetic environment is a significant barrier to accessing various resources. The well-being of people with disabilities would be improved by developing supportive neighborhood communities and including PWDs through participatory approaches. Social support and social networking take precedence over financial support because they give people a sense of belonging to a community and make it easier for them to obtain information about formal and informal services, eventually enhancing their well-being. The evidence of social networks and connectivity in enhancing the well-being of PWDs compels researchers to devise strategies to scale up the networking by utilizing technological advancements, such as mHealth, social media, and geospatial resource navigation facilities to detect, register, monitor, and link them, further facilitating customized service access to the respective community-dwelling PWDs.
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|
---
title: 'Dietary Advanced Glycation End Products and Risk of Overall and Cause-Specific
Mortality: Results from the Golestan Cohort Study'
authors:
- Elham Hosseini
- Zeinab Mokhtari
- Hossein Poustchi
- Masoud Khoshnia
- Sanford M. Dawsey
- Paolo Boffetta
- Christian C. Abnet
- Farin Kamangar
- Arash Etemadi
- Akram Pourshams
- Maryam Sharafkhah
- Paul Brennan
- Reza Malekzadeh
- Azita Hekmatdoost
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001649
doi: 10.3390/ijerph20053788
license: CC BY 4.0
---
# Dietary Advanced Glycation End Products and Risk of Overall and Cause-Specific Mortality: Results from the Golestan Cohort Study
## Abstract
Controversy exists regarding the association of dietary advanced glycation end products (dAGEs) with the risk of disease outcomes and mortality. We aimed to examine, prospectively, the association between dAGEs intake and the risk of overall and cause-specific mortality in the Golestan Cohort Study. The cohort was conducted between 2004 and 2008 in Golestan Province (Iran) recruiting 50,045 participants aged 40–75 years. Assessment of dietary intake over the last year was performed at baseline using a 116-item food frequency questionnaire. The dAGEs values for each individual were calculated based on published databases of AGE values of various food items. The main outcome was overall mortality at the time of follow-up (13.5 years). Hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for overall and cause-specific mortality were estimated according to the dAGEs quintiles. During 656, 532 person-years of follow-up, 5406 deaths in men and 4722 deaths in women were reported. Participants at the highest quintile of dAGE had a lower risk of overall mortality (HR: 0.89, $95\%$ CI: 0.84, 0.95), CVD mortality (HR: 0.89, $95\%$ CI: 0.84, 0.95), and death from other causes (HR: 0.89, $95\%$ CI: 0.84, 0.95) compared to those in the first quintile after adjusting for confounders. We found no association of dAGEs with risk of mortality from cancer (all), respiratory and infectious diseases, and injuries. Our findings do not confirm a positive association between dAGEs and the risk of mortality in Iranian adults. There is still no agreement among studies investigating dAGEs and their health-related aspects. So, further high-quality studies are required to clarify this association.
## 1. Introduction
Advanced glycation end products (AGEs) are a diverse group of compounds formed as the end products of spontaneous glycation of amino groups of amino acids through the non-enzymatic Millard reaction [1]. During the heat processing of foods, the Millard reaction occurs when the carbonyl group of reducing sugars interacts with the amino acid of peptides or proteins, resulting in the reversible formation of Schiff base compounds that can promptly undergo molecular rearrangements to so-called Amadori products [2]. The Amadori products are pertinent precursors for AGEs as they can rearrange into AGEs [2]. The Schiff base compounds or the Amadori product precursors can also be degraded into reactive dicarbonyls such as methylglyoxal, glyoxal, and 3-deoxyglucosone. These reactive dicarbonyls can react with a free or bound amino acid and form AGEs [2]. If excessive amounts of AGEs reach tissue and circulation, they become pathogenic [1]. This can occur by consuming a diet containing animal-source foods and cooking processes, in particular roasting, grilling, boiling, and frying, resulting in a further formation of AGEs in foods [3]. Diets with high AGEs content have been associated with cardiovascular diseases (CVD) and metabolic dysfunction [4,5,6].
Similar non-enzymatic reactions, as described above, occur during the normal glycation process of the cell in human tissues to form AGEs, but at lower rates due to the lower physiological temperature [7]. Additional endogenous AGE formation pathways include glycolysis and the polyol pathway. In glycolysis, glyceraldehyde 3-phosphate produced through the general metabolism of glucose or fructose can spontaneously decompose to the reactive dicarbonyl compound methylglyoxal, resulting in AGEs formation [7]. The polyol pathway is active under hyperglycemic conditions and requires glucose conversion to sorbitol and sorbitol conversion to fructose, promoting the accumulation of dicarbonyl compounds and AGEs [7]. Moreover, lipid peroxidation of polyunsaturated fatty acids in cell membranes can also lead to increased dicarbonyl production and subsequent AGE formation [7]. The role of endogenous AGEs in various diseases and conditions, including diabetes and its microvascular complications, neurodegenerative disorders, some cancers, bone diseases, and oxidative stress conditions and chronic inflammation, has been explored [1,8].
Two major mechanisms are attributed to the pathologic effect of AGEs: Firstly, they may conjoin proteins and directly change their structure and consequently their features and function. Secondly, AGEs bind to a specific receptor assigned as the receptor for AGEs (RAGE), which is a multi-ligand receptor and therefore binding of AGE ligands to the receptor can result in stimulation of the proinflammatory transcription factor nuclear factor-kappaB, inducing oxidative stress and inflammatory conditions [9].
The possible effect of dietary AGEs (dAGEs) on human health was previously ignored because it was believed that dietary AGEs are only slightly absorbed [3]. However, experimental studies with diets rich in AGEs have indicated a positive correlation between dAGEs and the body’s AGE pool [10]. A higher intake of dAGEs increased the chance of general and abdominal obesity as the main risk factors for several chronic diseases [11,12]. In a prospective cohort study, higher dAGEs intake increased the risk of breast cancer in postmenopausal women [13]. Consumption of dAGEs promoted the growth of breast and prostate tumor models by forming a tumor-promoting stromal microenvironment [14]. Although some studies have investigated the association of dAGEs and chronic disease mortality in healthy populations, as well as adults with co-morbidities, little is known about the ability of dAGEs for predicting all and cause-specific mortality in a general adult population. To our knowledge, no study has investigated the association of dAGEs intake with the risk of overall mortality in Iran. Therefore, we aimed to examine, prospectively, the association between dAGEs and risk of overall mortality in an Iranian population. The association of dAGEs with the risk of CVD and cancer mortality was also investigated.
## 2.1. Background
We examined data from the Golestan Cohort Study (GCS), a population-based cohort of the general population in the Golestan Province, in Northeast Iran. The design of the GCS has been previously described elsewhere [15]. In summary, the cohort aimed to investigate the incidence of oesophageal squamous cell carcinoma. The study was conducted between 2004 and 2008 in Golestan Province, recruiting 50,045 participants aged 40–75 years, from Gonbad city and 326 rural areas ($20\%$ and $80\%$ from urban and rural areas, respectively). Each participant was provided with an informed consent form before enrollment. Participants were excluded if they had an inaccurate assessment of energy intake, were diagnosed with cancer before the study, missing or inconclusive information on the food frequency questionnaire (FFQ) and/or the general questionnaire (containing information on socio-demographic and socio-economic status, history of diabetes and hypertension, smoking, alcohol drinking, opium use, and anthropometrics), and extreme values of body mass index (BMI). In total, 48,632 individuals were included in our analyses (27,975 women and 20,657 men) (Figure 1). The Institutional Review Boards of the Digestive Disease Research Center (DDRC) of Tehran University of Medical Sciences, the US National Cancer Institute (NCI), and the World Health Organization International Agency for Research on Cancer (IARC) approved the study.
## 2.2. Dietary Assessment
The FFQ from the GCS was used to assess the usual frequency and portion size of dietary intake of 116 food items over the past 12 months. The questionnaire was found reliable and valid [16]. Data on usual portion size, consumption frequency, and servings consumed each time was obtained for each food item at recruitment. Consumption frequency of each food item was questioned according to a daily, weekly, or monthly basis and converted into daily intakes; portion sizes were then converted into grams using household gauges [17,18]. Nutritionist V software and the Iranian Food Composition Table [19] were used to assess daily dietary intake. To estimate the dAGEs intake of different foods including fruits, vegetables, dairies, cereals, meats (white and red meat) and processed meats (sausage, hamburger, salted fish, and smoked fish), and fats, published databases of AGE values of various food items were used to calculate a weighted mean value of dAGE in each FFQ line item [3,20]. We used published databases of the AGE content of commonly consumed foods because there is no information available on AGE values in the Iranian Food Composition Table [19]. In these databases, the AGEs content of 549 food items was measured using a validated immunoassay method [3,20] and data were available for Nε-carboxymethyllysine (CML) as the most-studied AGE in literature. We defined the CML values in kilo-Unit (kU) per 100-g solid food or 100 milliliters of liquid for 84 food items. For each food item, the individual AGE value was calculated by multiplying the assigned CML value by the frequency and portion size (gram value of the respective food item) reported by the individual. The total dAGE value for each participant was then calculated as the sum of the individual AGE values of various food items included in the FFQ. Food items with no similar food available in the databases were considered missing (32 food items) [11]. Because AGEs values were not available for all kinds of fruits, vegetables, and legumes, the mean values of comparable fruits, vegetables, and legumes were considered [11,12].
## 2.3. Measurement of Potential Confounding Variables
All participants were interviewed by instructed clinicians and/or non-clinicians, and data on lifestyle and demographics were obtained using a pre-defined questionnaire. Anthropometrics including weight, height, BMI, and waist-to-hip ratio (WHR) were taken based on the World Health Organization guidelines [15,21]. Physical activity was expressed in the metabolic equivalent of task per minute per week and grouped into tertiles [22]. Wealth score was a proxy of socioeconomic status and was estimated for each participant based on house ownership, structure, size and appliances, family size, etc. [ 23]. Data on wealth scores were then categorized into quartiles. Other potential confounders included age, gender, cigarette smoking, opium use, alcohol drinking, and history of diabetes and hypertension.
## 2.4. Follow-Up and Cause of Death Ascertainment
Follow-up strategies of this cohort study have been detailed elsewhere [15]. In summary, follow-ups were performed every 12 months. The vital status of the participants was obtained through phone calls or home visits by the study group. The overall success rate at the time of follow-up (13.5 years) was $98.9\%$ ($\frac{517}{50}$,045 lost to follow-up). The main outcome was all-cause mortality. Any death report was affirmed by a clinician visit and a complete validated verbal autopsy questionnaire [24]. Moreover, two external internists separately investigated all information regarding the verbal autopsy and medical records and recognized the cause of death. In case of any disagreement between the two specialists, a third, more proficient internist considered all data and made the ultimate decision [15]. For the analyses, major causes of death among the participants were assessed as the secondary outcomes. Analyses were performed only on subjects with affirmed death.
## 2.5. Statistical Analysis
Total dAGE values were categorized into quintiles and the characteristics of participants were compared across the quintiles of dAGE. Analysis of variance (ANOVA) statistical analysis and the χ2 test for continuous and categorical variables were used to compare the characteristics of participants across the quintiles of dAGE. Cox proportional hazard models with follow-up duration as the timescale and dAGE quintiles as the exposure, with the lowest category as the reference, were used to assess the associations between dAGE and risk of overall and cause-specific mortality. In the Cox models, age and multivariate-adjusted hazard ratios (HRs) and $95\%$ confidence intervals (CIs) were provided for each outcome. In the multivariate models, the HRs were adjusted for confounding variables, including age, gender, energy intake, physical activity, pack-years of cigarette smoking, BMI, alcohol drinking, opium use, and history of diabetes and hypertension. The length of follow-up for each participant was considered from the recruitment date to the study until the date of death, lost to follow-up, or the reference follow-up date (30 July 2018), whichever arose first. All the statistical analyses were carried out in SPSS (version 18; SPSS Inc., Chicago, IL, USA) and $p \leq 0.05$ was regarded as significant.
## 3. Results
In total, 48,632 participants were included in our analysis, of which $57.5\%$ were women and $79.7\%$ were inhabitants of rural areas. The average age (standard deviation (SD)) of participants at baseline was 52 (8.9) years. During 13.5 years (3.4) of follow-up, 10,128 deaths were documented ($46.6\%$ women). The main causes of death were cardiovascular diseases [3762], gastrointestinal cancer [966], other cancers [815], respiratory diseases [648], infectious diseases [418], injuries [402], and other causes [1527].
As presented in Table 1, participants at the highest quintile of dAGE values were younger, had higher BMI and WHR, and were more likely to smoke compared with those at the lowest quintile. There were also more alcohol drinkers, and fewer reports of a history of diabetes and hypertension among the participants at the highest quintile of dAGE. Moreover, compared with those at the lowest quintile, participants at the highest quintile of dAGE had higher wealth scores and more energy intake. Calculated total dAGE values for all food items in FFQs ranged from 67.6 to 21,995.9, and the mean dAGE value (SD) of all participants was 7066.7 (2916.8). Participants with higher dAGEs tended to consume more fruits, vegetables, dairy, cereals, meats, and fats (Table 1).
Table 2 presents HRs for all-cause mortality, according to the dAGE quintiles. Participants at the highest quintile of dAGE had a lower risk (age-adjusted) of all-cause mortality (HR: 0.86, $95\%$ CI: 0.81, 0.92) compared to those in the first quintile. Further adjustment for other confounding variables including energy intake, physical activity, smoking, BMI, alcohol drinking, opium usage, and history of diabetes and hypertension did not change the results (Table 2).
Table 3 presents HRs for cause-specific mortality, according to the dAGE quintiles. Participants at the highest quintile of dAGE had a lower risk of CVD mortality (HR: 0.88, $95\%$ CI: 0.79, 0.98) compared to those in the first quintile, and the decreased risk was more evident in women. Adjusted HRs indicated no association of dietary dAGE intake with risk of mortality from all cancer (HR: 0.89, $95\%$ CI: 0.76, 1.03), gastrointestinal (HR: 0.89, $95\%$ CI: 0.72, 1.09), and other cancers (HR: 0.89, $95\%$ CI: 0.71, 1.11). These findings did not differ in sex-specific analyses. Participants at the highest quintile of dAGE had a lower risk of death from other causes (than all cancers, respiratory and infectious diseases, and injuries) (HR: 0.79, $95\%$ CI: 0.67, 0.92) compared to those in the first quintile, and the decreased risk was more evident in men. No association was observed between dAGE quintiles and death from infectious and respiratory diseases and injuries (Table 3).
## 4. Discussion
Examining longitudinal data from the GCS, we did not find dAGEs to be associated with an increased risk of overall and cause-specific mortality. We observed that a higher intake of dAGEs was associated with a reduced risk of overall mortality, CVD mortality, and death from other causes. A gender-specific analysis showed that the highest versus lowest quintiles of dAGEs in men were in association with a $12\%$ and $24\%$ reduced risk of overall mortality and death from other causes, respectively. Compared to the lowest quintile, women at the highest quintile of dAGEs had $9\%$ and $19\%$ lower risk of overall and CVD mortality, respectively.
The findings of the present study are in agreement with the recent study of Nagata et al. [ 25], who showed that a higher intake of CML, a major AGEs product, was inversely associated with the risk of total mortality in Japanese adults. Furthermore, no association was found between dietary intake of AGEs and total and colorectal cancer mortality among colorectal cancer patients in the EPIC (European Prospective Investigation into Cancer and Nutrition) study [26]. Similar findings have been reported when examining the association of serum AGEs and all-cause and CVD mortality [27]. Dissimilarly, adolescents with the highest dAGE intake were more likely to have metabolic syndrome when compared to the lowest quartile of dAGE intake [28]. In a large prospective cohort during the period of 12.8-year follow-up, higher dAGE intake was associated with increased risk of breast cancer in postmenopausal women [13]. Moreover, higher dAGEs have been related to the increased risk of all-cause, and CVD and breast cancer mortality in postmenopausal women diagnosed with invasive breast cancer [29]. In another study, during the follow-up of 10.5 years, men but not women in the fifth quintile of dAGE intake had higher risk of pancreatic cancer [30]. In the course of 13-year follow-up, no significant association was revealed between higher CML intake and the total cancer risk in male and female participants [31]. Yet, CML intake at the highest quartile was associated with the increased risk of liver cancer, while it was associated with the decreased risk of male stomach cancer [31]. One explanation for the contradictory results is the inconsistency of the AGE content of foods or diets used in different studies due to different cooking processes. Besides, population characteristics per se might affect the association as well. The majority of studies were performed on subjects with preexisting medical conditions which could affect the results when compared to healthy adults or the general population.
Controversy exists regarding the toxicity of AGEs in the body. In observational studies, higher dAGEs have been associated with intermediate outcomes such as oxidative stress and inflammation in type 2 DM patients [4]. In subjects with cardio-metabolic diseases such as overweight, obesity, or prediabetes, an AGE-restricted diet reduced some inflammatory markers and improved insulin sensitivity [5]. However, a meta-analysis of clinical trials did not support the effect of AGE-restricted diets on the inflammatory profile of healthy individuals and those with diabetes or renal failure [32]. On the other hand, a positive association of dAGEs with chronic disease outcomes such as breast cancer [13], obesity [12], and chronic kidney disease [33] has been shown. The toxicity effect might originate from the studies in which the dietary content of AGEs is a significant contributor to the excess serum AGEs levels [34]. This toxicity, however, has been debated in the literature [35].
Studies examining the association of dAGEs and total and/or cause-specific mortality are rare, and thus there is no conclusive evidence suggesting dietary AGEs to be detrimental to human health [36]. A major part of the AGE content of foods absorbed is rapidly excreted by kidneys, resulting in insignificant plasma levels of these metabolites [37]. Due to the very rapid excretion of CML from the body, the probability of any effect on body proteins has been considered to be low, and therefore should have only limited consequences in some organs such as the liver and kidneys [37]. Therefore, the effect of dAGEs on human health still needs further elucidation.
Our results showed that higher dAGE values were less protective in men, regarding the association of dAGEs and risk of CVD mortality, compared to female participants. This could be explained by some additional CVD risk factors such as age above 50, smoking, alcohol drinking, and opium use being more frequent in men. On the contrary, compared to the lowest quintile of dAGE, men with higher dAGE values had a lower risk of total mortality and death from respiratory diseases, probably due to the lower BMI and WHR and more physical activity compared to women.
Our study has several strengths including the longitudinal design, the large sample size representing the general population, and a high rate of follow-up. Additionally, we performed our analyses by adjusting for the most relevant confounders. There are some limitations as well. The first was dAGEs values considered for each food item from the beginning. Since there is no AGE value available for any food item in Iranian food composition tables, we used the most commonly studied AGE databases based on diets common in a Northeastern Metropolitan US area [3,20], which might not represent the Iranian foods estimated in this study. Moreover, even for similar food items, the AGE content of food measured in literature might differ from the AGE content of food items in the FFQ used in the present study due to different cooking processes and could, therefore, affect the results. Secondly, we considered the same AGE values for some similar food items such as fruits, legumes, and vegetables for which no respective AGE values were available in the literature. Additionally, some characteristics of subjects might have changed since baseline measurement and would therefore affect the analyses.
In conclusion, our findings indicated an inverse association between dAGEs intake and the risk of overall and cause-specific mortality. Although it has been shown that dietary AGEs are associated with an increased risk of diseases, our findings did not confirm a positive association between dAGEs and mortality in Iranian adults. There is still no agreement among studies investigating dAGEs and their health-related aspects. Evidence has either debated against the adverse effects of dAGEs or revealed a protective effect of an AGE-restricted diet on different health conditions for some specific dAGEs due to antioxidant activity. Yet, studies on healthy subjects are limited and current evidence is indecisive. So, further high-quality studies are required to clarify the impact of dietary AGEs on disease and mortality risk.
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|
---
title: 1α,25(OH)2D3 Promotes the Autophagy of Porcine Ovarian Granulosa Cells as a
Protective Mechanism against ROS through the BNIP3/PINK1 Pathway
authors:
- Shiyou Wang
- Qichun Yao
- Fan Zhao
- Wenfei Cui
- Christopher A. Price
- Yifan Wang
- Jing Lv
- Hong Tang
- Zhongliang Jiang
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001661
doi: 10.3390/ijms24054364
license: CC BY 4.0
---
# 1α,25(OH)2D3 Promotes the Autophagy of Porcine Ovarian Granulosa Cells as a Protective Mechanism against ROS through the BNIP3/PINK1 Pathway
## Abstract
Vitamin D (VD) is one of the important nutrients required by livestock; however, VD deficiency is reported to be widespread. Earlier studies have suggested a potential role for VD in reproduction. Studies on the correlation between VD and sow reproduction are limited. The aim of the current study was aimed to determine the role of 1,25-dihydroxy vitamin D3 (1α,25(OH)2D3) on porcine ovarian granulosa cells (PGCs) in vitro to provide a theoretical basis for improving the reproductive efficiency of sows. We used chloroquine (autophagy inhibitor) and reactive oxygen species (ROS) scavenger N-acetylcysteine in conjunction with 1α,25(OH)2D3 to explore the effect on PGCs. The results showed that 10 nM of 1α,25(OH)2D3 increased PGC viability and ROS content. In addition, 1α,25(OH)2D3 induces PGC autophagy according to the gene transcription and protein expression levels of LC3, ATG7, BECN1, and SQSTM1 and promotes the generation of autophagosomes. 1α,25(OH)2D3-induced autophagy affects the synthesis of E2 and P4 in PGCs. We investigated the relationship between ROS and autophagy, and the results showed that 1α,25(OH)2D3-induced ROS promoted PGC autophagy. The ROS-BNIP3-PINK1 pathway was involved in PGC autophagy induced by 1α,25(OH)2D3. In conclusion, this study suggests that 1α,25(OH)2D3 promotes PGC autophagy as a protective mechanism against ROS via the BNIP3/PINK1 pathway.
## 1. Introduction
Ovarian granulosa cells (GCs) play a pivotal role in follicle growth and atresia. Autophagy is a process of self-phagocytosis widely present in almost all eukaryotes and is one of the degradation pathways of redundant or abnormal cellular components. The molecular pathways that regulate autophagy are highly conserved. As an important autophagy-related protein, ATG7 is associated with autophagosome formation. BECN1 (Beclin1) forms a complex with the class III phosphoinositol 3-kinase molecule Vps34, which initiates and promotes autophagy. Microtubule-associated protein 1 light-chain 3 (LC3) is essential for the formation and maturation of autophagosomes. SQSTM1(P62) protein functions as a selective autophagy receptor for the degradation of substrates. In the ovary, GC autophagy affects follicle development. Autophagy occurs in GCs of porcine follicles [1]. Previous studies showed that autophagy is the leading cause of follicular atresia in neonatal mice [2], and autophagy-related genes and proteins are continuously expressed during cytogenesis. ATG7 is expressed in oocytes, and LC3 exists in GCs [3]. Autophagy is induced specifically in GCs during folliculogenesis. The LC3 protein is expressed mainly in GCs during all developmental stages [4]. Autophagy is closely related to the growth, proliferation, and apoptosis of GCs [5]. Inadequate autophagy of GCs leads to reduced progesterone synthesis [6] and disruption of GC differentiation [7]. Our previous studies showed that mir-21-3p regulates autophagy in bovine granulosa cells through the PI3K/AKT signaling pathway [8]. Both FSH [9] and ERβ [10] can induce autophagy in bovine ovarian granulosa cells via AKT/mTOR pathway. The underlying mechanism of GC autophagy remains to be determined, and the relationship between GC autophagy and follicle development remains unclear.
Vitamin D (VD) is a steroid derivative with a wide range of biological properties, and its main active form is 1,25-dihydroxy vitamin D3 (1α,25(OH)2D3). Studies conducted over the past 20 years have found that VD plays a vital role in maintaining the regular female reproductive system. Vitamin D and its clinical implications regarding the developmental competence and fertilization of oocytes has prompted researchers’ attention. A recent study revealed that in the case of VD deficiency and hypocalcemia at the same time, a significant reduction in oocyte retrieval after ovarian stimulation was observed, and the generated oocytes showed a poor maturation ability [11]. Moreover, VD signaling leads to an increased production of steroid hormones in granulosa cells, which are crucial for oocyte maturation and pregnancy, too. Vitamin D is essential for female gametes and their micro-environment [12]. Vitamin D might improve follicular development and subsequently oocyte quality. The biological effects of VD are usually achieved through the vitamin D receptor (VDR), which is found in the female reproductive system, including the uterus, endometrium, ovary, and placenta [13]. Vitamin D stimulates the production of estrogen, progesterone, and IGF-binding protein 1 in human ovarian cells [14]. To date, most of the studies on VD in follicle development have focused on humans. The effect of VD on porcine follicle development has been less reported. The classic function of VD is to maintain musculoskeletal health by maintaining calcium homeostasis. Meanwhile, it has been shown that VD can affect the equilibrium state of oxidation/reduction in C2C12 cells [12], resulting in a balance of ROS production and elimination. Vitamin D affects the oxidative capacity of cells by regulating the activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPX) [15]. Vitamin D’s effects on the redox status of porcine ovarian granulosa cells has not been reported. In addition, VD plays an essential role in the autophagy induced by primary monocytes and macrophages [16]. Vitamin D was reported to regulate autophagy via calcium ions [17], the PI3K/AKT/mTOR pathway [18], inflammatory factors [19], antimicrobial peptide [16], etc. In recent years, increasing evidence has demonstrated the critical role of GC autophagy in follicle development and atresia [20]; however, the effect of VD on the autophagy of GCs remains unclear. In the current study, we assumed that 1α,25(OH)2D3 regulates the PGC autophagy and affects the redox status and function of PGCs. To test this hypothesis, we examined the effects of 1α,25(OH)2D3 on intracellular ROS content and autophagy in PGCs and evaluated the effects of 1α,25(OH)2D3 on 17β-estradiol (E2) and progesterone (P4) secretion in PGCs.
## 2.1. Effects of 1α,25(OH)2D3 on the Viability of PGCs
We first investigated the effects of doses and times of 1α,25(OH)2D3 treatment on PGC viability and the expression of cell-cycle-related genes in PGCs (Figure 1). The proliferation of PGCs (treatment with 10 nM 1α,25(OH)2D3) was observed under a light microscope (Figure 1A). The results indicated that the viability was significantly increased in the groups of 10, 100, and 200 nM of 1α,25(OH)2D3 treatments for 12 h, 24 h, and 36 h in comparison to the control group, respectively (Figure 1B). The PGCs with the treatment of 10 nM of 1α,25(OH)2D3 for 24 h were selected for further studies. Then, quantitative real-time PCR was used to detect the expression of cell-cycle-related genes. The results showed that the gene expression of VDR, CDK1 and CCNB1 was increased, while the expression of P21 was decreased in 1α,25(OH)2D3 treatment compared with the control (Figure 1C–F). These results suggest that 1α,25(OH)2D3 promotes the viability of PGCs.
## 2.2. 1α,25(OH)2D3 Increases Intracellular ROS in PGCs
To study the role of 1α,25(OH)2D3 on intracellular ROS in PGCs, PGCs were treated with 1α,25(OH)2D3, and the intracellular ROS was determined by DCFH-DA staining and observed under a fluorescence microscope (Figure 2A). The results indicated that 1α,25(OH)2D3 significantly increased the ROS content (Figure 2B). Similar results were confirmed by flow cytometry (Figure 2C,D). The results of relative gene expression in PGCs showed that 1α,25(OH)2D3 significantly down-regulated the expression of SOD1 and GSH-PX-1 genes in PGCs (Figure 2E,F); however, 1α,25(OH)2D3 did not change the CAT gene relative expression gene in PGCs (Figure 2G). Our results showed that VD3 significantly decreased enzyme activities of SOD (Figure 2H) and GPX (Figure 2I) in PGCs. Together, these results indicated that 1α,25(OH)2D3 increased intracellular ROS content in PGCs.
## 2.3. Mitochondria Status in the 1α,25(OH)2D3-Treated GCs
Mitochondria are the primary source of ROS in cells. To study the relationship between 1α,25(OH)2D3 and ROS, Mito-Tracker was used to label the PGC mitochondria in this experiment (Figure 3A). The results showed that 1α,25(OH)2D3 significantly increased the abundance of mitochondria in PGCs (Figure 3B). Moreover, 1α,25(OH)2D3 increased considerably the relative expression of the ND1 gene, a mitochondrial DNA (mtDNA, Figure 3C). In contrast, the current results showed that 1α,25(OH)2D3 did not change the mitochondrial membrane potential of PGCs in this experiment (Figure 3D). These results suggest the mitochondria status in the 1α,25(OH)2D3-treated GCs.
## 2.4. 1α,25(OH)2D3 Induces PGC Autophagy
The cumulative data suggest that mitochondria play an important role in activating autophagy. To study the effects of 1α,25(OH)2D3 on PGC autophagy, MDC was used to label the autophagic vacuoles in PGCs treated by 1α,25(OH)2D3 and chloroquine (an inhibitor of autophagy) in this experiment, and the fluorescence of autophagic vacuoles in PGCs was detected by fluorescence microscope (Figure 4A). The current results showed that the treatments of 1α,25(OH)2D3, chloroquine significantly increased the number of autophagic vacuoles (Figure 4B). The relative expression of ATG7, Beclin1, and LC3 genes were significantly up-regulated in PGCs treated with 1α,25(OH)2D3, chloroquine, and 1α,25(OH)2D3 with chloroquine, respectively (Figure 4C–E). The treatment of 1α,25(OH)2D3 down-regulated the P62 mRNA expression, while chloroquine significantly up-regulated P62 mRNA expression in the PGCs compared with that of the control group; however, the treatment of 1α,25(OH)2D3 with chloroquine did not change the P62 mRNA expression in comparison to the control group (Figure 4F). Figure 4G showed the expression of autophagy proteins, and the results showed that the expression pattern of the P62 protein was similar to that of its gene in PGCs (Figure 4H). Moreover, 1α,25(OH)2D3 significantly increased the LC3II/LC3I level compared with that of the control group, while the treatments of chloroquine and 1α,25(OH)2D3 with chloroquine decreased the LC3II/LC3I level, respectively (Figure 4I). In addition, the LC3II/LC3I levels in the cells treated with chloroquine were lower than the treatment of 1α,25(OH)2D3 with chloroquine (Figure 4I). These results suggest that 1α,25(OH)2D3 induces PGC autophagy.
## 2.5. The Effects of 1α,25(OH)2D3 on Steroid Production of PGCs through Autophagy
To investigate the effects of 1α,25(OH)2D3 on steroid production, the PGCs were treated by 1α,25(OH)2D3, chloroquine and 1α,25(OH)2D3 with chloroquine together, and the contents of E2 and P4 were measured in this experiment. The results showed that compared with the control group, the concentration of E2 in PGC medium treated with 1α,25(OH)2D3 was significantly increased. In contrast, that in PGCs treated with chloroquine was significantly decreased (Figure 5A). Furthermore, the co-treatment of 1α,25(OH)2D3 and chloroquine did not change the E2 concentration in PGCs compared to the control group (Figure 5A). Although 1α,25(OH)2D3 increased the concentration of P4 in PGCs, both the treatments of chloroquine and 1α,25(OH)2D3 with chloroquine did not change P4 production in PGCs compared to the control group (Figure 5B). The treatments of 1α,25(OH)2D3 and 1α,25(OH)2D3 with chloroquine significantly up-regulated the relative expression of ESR1, CYP19A1, PGR, and STAR genes in PGCs in comparison to the control group; nevertheless, chloroquine did not change the expression of these genes (Figure 5C–F). The STAR protein level was identified by Western blotting (Figure 5G), and the results showed that 1α,25(OH)2D3 significantly increased the STAR level in PGCs in comparison to the control group; however, the treatments of chloroquine or 1α,25(OH)2D3 with chloroquine did not change the STAR level in PGCs (Figure 5H). These results suggest that the steroid production in PGCs was affected by 1α,25(OH)2D3-induced autophagy.
## 2.6. 1α,25(OH)2D3-Induced ROS Promotes Autophagy in PGCs
To explore the effects of 1α,25(OH)2D3-induced ROS on PGC autophagy, ROS scavenger N-acetylcysteine (NAC) was used to treat the PGCs for 24 h, followed by 1α,25(OH)2D3 treatment. The results indicated that NAC significantly decreased the viability of PGCs with or without 1α,25(OH)2D3 treatment (Figure 6A). As Figure 6B shown, ROS content was significantly reduced in NAC-treated PGCs, and the cells co-treated with 1α,25(OH)2D3 and NAC (4, 8 mM). Based on the results above, 4 mM NAC was used for the following experiments. DCFH-DA staining was used to detect the ROS, and similar results as those above were observed in PGCs treated with 4 mM NAC (Figure 6C,D). Moreover, 4 mM of NAC significantly decreased the number of autophagic vacuoles induced by 1α,25(OH)2D3 treatment in PGCs (Figure 6E,F). To confirm the results of MDC staining, the expression of autophagy-related genes in PGCs was measured. NAC down-regulated the 1α,25(OH)2D3-stimulated expression of ATG7, Beclin1, and LC3 (Figure 6G–I) and up-regulated the P62 mRNA expression (Figure 6J). In PGCs treated with NAC in the presence/absence of 1α,25(OH)2D3, LC3, and P62 protein expression patterns were observed to be similar to their gene expression patterns (Figure 6K–M). Together, we demonstrated that 1α,25(OH)2D3-induced ROS promotes autophagy in PGCs.
## 2.7. 1α,25(OH)2D3 Induces Mitophagy in PGCs through the ROS-BNIP3-PINK1 Signaling Pathway
To study the orientation of 1α,25(OH)2D3-induced autophagy, PGCs were treated with 1α,25(OH)2D3, chloroquine, and NAC. RT-qPCR was used to measure gene expression, and Western blotting was used to determine the protein levels in this experiment. Both 1α,25(OH)2D3 and chloroquine up-regulated the relative expression of BNIP3 (the marker of mitophagy) and PINK1 gene in PGCs compared with that of the control (Figure 7A,B). Furthermore, the protein levels of BNIP3 and PINK1 were increased in the treatments of 1α,25(OH)2D3, chloroquine, and 1α,25(OH)2D3 with chloroquine, which was similar to the relative expression of genes (Figure 7C–E). Compared with the treatment of 1α,25(OH)2D3, the relative expression of BNIP3 and PINK1 genes were significantly decreased in treatments of PGCs of NAC with 1α,25(OH)2D3 and NAC alone (Figure 7F,G). Moreover, the protein levels of BNIP3 and PINK1 were similar to the expression patterns of their genes in the treatments of 1α,25(OH)2D3, 1α,25(OH)2D3 with NAC, and NAC in PGCs (Figure 7H–J). We found that 1α,25(OH)2D3 induces mitophagy in PGCs through the ROS-BNIP3-PINK1 signaling pathway.
## 3. Discussion
1,25-dihydroxy vitamin D3 is a lipid-soluble secosteroid hormone established to play a wide range of biological functions [21]. More and more studies have shown that VD plays a vital role in life processes, including reproduction [22]. Breeding sows are the foundation of pig farm production, and their fecundity plays a crucial role in the benefit of the pig farms. Studies on the correlation between VD and sow reproduction are limited. Although studies have revealed the role of 1α,25(OH)2D3 on autophagy via the PI3K/AKT/mTOR pathway, the mechanism of 1α,25(OH)2D3 in the autophagy of ovarian granulosa cells remains unclear. Here, we demonstrate that [1] 1α,25(OH)2D3 increased porcine ovarian granulosa cell viability and the ROS content by increasing the number of mitochondria and decreasing the activities of superoxide dismutase and glutathione peroxidase; [2] porcine ovarian granulosa cell autophagy is regulated by 1α,25(OH)2D3 and affected the synthesis of E2 and P4; and [3] the ROS-BNIP3-PINK1 pathway was involved in porcine ovarian granulosa cell autophagy induced by 1α,25(OH)2D3.
The present results showed that 1α,25(OH)2D3 increased the VDR mRNA expression and promoted the proliferation of PGCs. This finding suggests that VD may play an important role in follicle development, which was supported by studies of VDR expression in goats [23] and mice [13]. Cell proliferation is controlled by the balance between cyclin-dependent kinases (CDKs) and its inhibitor (CKI). The expression of CDK1 and CCNB1 promotes the increase of cell number [24], and CDK function is tightly regulated by CKIs such as P21, which is related to cell cycle proliferation [25]. Our results showed that 1α,25(OH)2D3 up-regulates the expression of CDK1 and CCNB1 genes, while the expression of P21 was down-regulated in PGCs. The mechanism by which 1α,25(OH)2D3 regulates GC proliferation remains incompletely understood. The mechanism by which 1α,25(OH)2D3 regulates the cell cycle process needs to be further studied.
In this study, the results of fluorescence microscope and flow cytometry analysis in PGCs indicated that 1α,25(OH)2D3 increased the content of ROS with down-regulation of SOD1 and GSH-PX-1 genes and the reduction of SOD and GSH enzyme activities. Generally, VD has antioxidant abilities through the reduction of ROS production to decrease oxidative stress [15]; however, VD also induces ROS production as a byproduct in reproductive tissues accompanied by steroidogenesis [26]. Antioxidant enzymes can eliminate excessive ROS production and protect the redox homeostasis in cells. Although VD deficiency has been associated with increased SOD enzyme activity in patients with chronic low back pain [27], a study showed that SOD enzyme activity was lower in rats deficient in VD. The absence of vitamin D leads to decreased SOD activity in vivo and in vitro. Vitamin D deficiency led to an increase in activities of the glutathione-dependent enzymes and a decrease in SOD and catalase enzymes in rat muscle [28]. These studies suggest that vitamin D supplementation is associated with changes in antioxidant enzyme activity. The current data indicates that 1α,25(OH)2D3 treatment significantly decreased SOD and GPX enzyme activities in PGCs.
Intracellular ROS mainly originate from mitochondria. Our results showed that 1α,25(OH)2D3 treatment increased the expression of the ND1 gene, a mitochondrial metabolism-related gene, and the abundance of mitochondria in PGCs. Previous studies have demonstrated that VD is related to mitochondrial density [29] and the direct role of VDR in regulating mitochondrial respiration in skeletal muscle in vitro [30]. Mitochondrial abundance and mitochondrial DNA (mtDNA) copy number determine the metabolic activity of mitochondria [31]. The integrity of mtDNA and the activation of transcription and translation processes are essential for the induction of mitochondrial activity [32]. The mitochondrial mRNA transcription (such as mt-ND1~mt-ND6, CoxI~CoxIII) and their translation processes are activated with the increase of mitochondrial activity in serum-stimulated HeLa cells [33]. Meanwhile, the present results indicated that 1α,25(OH)2D3 did not affect the PGC mitochondrial membrane potential (MMP), which reflects the mitochondria functional status and is thought to be correlated with the cell differentiation status, tumorigenicity, and malignancy [34]. Mitochondrial fusion requires an intact MMP. The dissipation of MMP results in the rapid fragmentation of mitochondrial filaments, reforming interconnected mitochondria upon the withdrawal of MMP inhibitors [35]. The present results showed that 1α,25(OH)2D3 increased mitochondrial activity without negatively affecting mitochondrial function.
Our results demonstrate that 1α,25(OH)2D3 is responsible for autophagy in PGCs. Autophagy is influenced by various factors and environmental stimuli, including oxidative stress [36], starvation, and epigenetic regulation [37]. It has been reported that VD can regulate autophagy in different degrees, including induction, maturation, and degradation [38]. A particular concentration of 1α,25(OH)2D3 can induce autophagy in primary monocytes and macrophages [16]. Meanwhile, the association between vitamin D and autophagy has also been reported in immunity [39] and cancer [40]. 1α,25(OH)2D3 plays a protective role in acute myocardial infarction through autophagy induced by the PI3K/AKT/mTOR pathway [18]. Active vitamin D attenuates osteoarthritis by activating autophagy in chondrocytes through the AMPK-mTOR signaling pathway [41]. In addition, 1α,25(OH)2D3 can reduce cell dysfunction and intracellular oxidative stress by lowering excessive autophagy in cells [42]. These studies suggest that vitamin D can maintain cellular homeostasis by promoting or inhibiting autophagy. The current results showed that 1α,25(OH)2D3 promotes autophagy in PGCs, and the elevation of PGC autophagosome was confirmed by MDC staining.
GCs are one of the primary cell types in the follicle, and steroidogenesis is an essential physiological process affecting follicle maturation and ovulation. Here, we found that 1α,25(OH)2D3 promoted the secretion of E2 and P4 in PGCs. The current results showed that 1α,25(OH)2D3 stimulates the expression of ESR1, PGR, CYP19A1, and STAR mRNA in PGCs and increases the concentration of E2 and P4 in PGCs. The bilateral role between hormone secretion and autophagy has been confirmed in GCs of bovine [9] and goose follicles [43], which showed that hormone secretion induces autophagy; on the contrary, autophagy promotes hormone secretion in granulosa cells. In the present study, the results of co-treatments of 1α,25(OH)2D3 and chloroquine on PGCs reveal the weak expression of ESR1, CYP19A1, PGR, and STAR mRNA and lower STAR protein level in PGCs compared with that of 1α,25(OH)2D3 alone. These results suggest that 1α,25(OH)2D3-induced autophagy in PGCs promotes steroid hormone synthesis by regulating steroid synthesis enzymes. No reports exist about a possible role of autophagy in steroid hormone synthesis in GCs, but autophagy is implicated in the development and regression of ovarian cells. Autophagy is involved in the death of rat luteum cells through apoptosis, which is most evident in corpus luteum regression [44]. The accumulation of autophagosomes induces apoptosis of granulosa cells [3]. In addition, a link between steroid hormones and autophagy has been reported in farm animals. E2 and P4 increased autophagy in bovine mammary epithelial cells in vitro [45]. E2 and P4 may regulate mammary gland development, proliferation, and apoptosis of mammary epithelial cells in dairy cows by inducing autophagy [46].
Oxidative stress is one of the impact factors for cellular autophagy, and mitochondria are the primary source of intracellular ROS. In this study, the role of ROS in the autophagy of PGCs was investigated, and the results revealed that 1α,25(OH)2D3-induced ROS promotes the PGC autophagy. Moreover, 1α,25(OH)2D3 increases autophagosomes and LC3 protein levels in PGCs, while the inhibitor of ROS reverses this effect. Recent studies have shown ROS can initiate the formation of autophagosomes and autophagic degradation [47], and autophagy, in contrast, serves to reduce oxidative damage and ROS levels by removing protein aggregates and organelles such as mitochondria [48]. *In* general, there is a relative balance of ROS produced by mitochondria in the cell. Once the balance is disrupted, the cell gets rid of the excess mitochondria. Our results showed that the account of mitochondria was increased, which is the primary reason for the ROS increase in PGCs treated with 1α,25(OH)2D3. Together, these results indicated that 1α,25(OH)2D3-induced ROS promotes autophagy in PGCs.
Accumulating evidence suggests that ROS can induce autophagy through various mechanisms. Here, we detected the gene expression and protein level of BNIP3 and PINK1 in PGCs treated with 1α,25(OH)2D3; the results showed that 1α,25(OH)2D3-induced PGC autophagy is mainly mitophagy caused by ROS. It has been confirmed that various mechanisms were involved in ROS-induced autophagy, such as ROS–NRF2–P62 [49], ROS–HIF1–BNIP3/NIX [50], and ROS–TIGAR [51]. BNIP3 is a receptor for mitophagy, an autophagy process that eliminates excess or damaged mitochondria. Previous studies have shown that BNIP3 plays a vital role in PINK1 localization to the outer mitochondrial membrane and proteolysis [52]. Our results showed that 1α,25(OH)2D3 could induce the expression of BNIP3 and PINK1 mRNA and protein levels, and NAC could reverse the effect of 1α,25(OH)2D3 by reducing intracellular ROS. It has been reported that BNIP3 promotes autophagic cell death in response to hypoxia; however, Bellot et al. identified autophagy induced by BNIP3 in response to hypoxia as a mechanism to promote tumor cell survival [53]. Although autophagy activation is critical, autophagy is not always beneficial for cell survival or death. The results of this study indicate that 1α,25(OH)2D3 promotes cell survival and activates PGC autophagy, which is related to 1α,25(OH)2D3-induced ROS. These results suggest that 1α,25(OH)2D3-induced ROS induces PGC mitophagy via the BNIP3-PINK1 pathway.
The reproductive performance of sows is an essential factor affecting the economic benefits of the pig industry. Improving the reproductive performance of sows is also one of the goals pursued by breeders and pork producers. The number of ovulations in sows depends on the number of follicles initially collected and the number of terminal atresias. Autophagy and apoptosis of GCs in follicles are closely related to follicular atresia [4]. Follicle growth and development is a complex biological process which is regulated by many factors, including various steroid hormones, metabolic enzymes, and local growth factors [54]. The close relationship between the changes in steroid hormones and synthetic enzymes and GC autophagy is rarely reported. Based on this, the effects of 1α,25(OH)2D3 on the synthesis of E2 and P4, proliferation, and autophagy were investigated in PGCs, and the specific mechanisms were explored to provide a theoretical basis for improving the reproductive efficiency of animals. These results provide new insights into the ability of 1α,25(OH)2D3 to regulate the biological function of GCs and follicular development, which may have reference significance for the study of the reproductive performance of pigs.
## 4.1. Cell Culture
Granulosa cells were isolated and cultured using the method described by Jiang et al. [ 55]. Porcine ovaries were collected from local slaughterhouses from commercial pigs aged about 1 year, independent of the stage of the estrus cycle. About 20–30 ovaries were collected and transported to the laboratory in saline with penicillin (100 IU/mL) (Gibco-BRL, Gaithersburg, MD, USA) and streptomycin (100 mg/mL) (Gibco-BRL) within 1 h. The ovaries were washed twice with $75\%$ alcohol and then 2–3 times with saline buffer ($0.9\%$, PH < 7) (37 °C). For each replicate, at least 20 ovaries were collected to obtain sufficient GCs from follicles. Medium-sized follicles (3–5 mm) were selected, and a 10 mL syringe was used to aspirate follicular fluid with GCs. Then, the cell suspension was filtered through a 40 μm cell filter, and the mixture was centrifuged at 800× g for 5 min to remove the follicular fluid. Granulosa cell pellets were resuspended in DMEM/F12 (Gibco-BRL) medium. Cell viability was determined by trypan blue exclusion (Solarbio Technology Co., LTD, Beijing, China). For cell culture, cells were diluted with DMEM/F12 and seeded in tissue culture plates at a specific density (5 × 103 cells/well for 96-Well, 1 × 106 cells/well for 24-well). DMEM/F12 of diluted cells contained the following substances: 4 ng/mL sodium selenite, 10 mM sodium bicarbonate, $0.1\%$ bovine serum albumin (BSA), 100 U/mL of penicillin, 100 μg/mL streptomycin, 1 mmol/L non-essential amino acid mix, 2.5 μg/mL transferrin, 10 ng/mL bovine insulin, 10−7 M androstenedione, and 1 ng/mL bovine FSH (Bioniche Inc., Belleville, ON, Canada). The cells were cultured at 37 °C in $5\%$ CO2 and $95\%$ air. After 24 h, the medium was replaced, and then the treatment was carried out. At least three independent replicates were performed.
## 4.2. Cell Viability Assay
The CCK-8 (Beyotime Biological Technology Co Ltd., Shanghai, China) proliferation assay was used to evaluate the cell viability of PGCs. The cells were seeded at a specific density (5 × 103 cells/well) into 96-well plates, and then the cells were treated with 1α,25(OH)2D3 (1 nM, 10 nM, 100 nM, 200 nM; 12 h, 24 h, 36 h, 48 h) (Solarbio), chloroquine (10 μM, 24 h) (Sigma-Aldrich, St. Louis, MO, USA), or N-acetylcysteine (2 mM, 4 mM, 8 mM; 24 h) (Beyotime) at different doses and times. After various treatments, 10 μL of CCK-8 solution was added to each well and placed at 37 °C for 3 h. The treated wells had cells, culture medium, CCK-8 solution, and drugs. The control (untreated) wells had cells, culture medium, and CCK-8 solution. The blank wells had culture medium and CCK-8 solution. The absorbance of each well was measured at a wavelength of 450 nm. For each group of 4–6 wells, the average of their optical density (OD) was calculated as follows: cell viability (%) = [treated wells OD − blank wells OD] / [control wells OD − blank wells OD] × $100\%$. At least three independent replicates were performed.
## 4.3. DNA and RNA Extraction and Quantitative Real-Time PCR
Approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3, chloroquine (10 μM), and NAC (4 mM) for 24 h. DNA was extracted using Universal Genomic DNA Purification Mini Spin Kit (Beyotime). RNA was extracted using SimplyP Total RNA Extraction Kit (Hangzhou Bioer Technology Co Ltd., Hangzhou, China) according to the manufacturer’s protocol. Briefly, cells were collected, and 300 μL RIPA was added. Then, the binding solution was added, the mixture was transferred to the purification column and centrifuged at 12,000× g for 30 s, and the liquid in the tube was discarded. After that, washing liquid was added and centrifuged at 12,000× g for 30 s. The RNA purification column was transferred to the RNA eluent tube, and 40 μL eluent was added and centrifuged at the highest speed (14,000–16,000 g) for 30 s. The purified RNA was obtained. The process of DNA extraction is similar. Related solvents were added successively and the purified DNA was extracted by centrifugation. The concentration of DNA and RNA was measured by microvolume spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The A260/A280 ratio ranges from 1.9 to 2.0. RNA (500 ng) was reverse transcribed into cDNA according to the FastKing cDNA First Strand Synthesis Kit instructions (Tiangen Biochemical Technology Co Ltd., Beijing, China). Then, the mRNA expression level of genes was measured using ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China) in a StepOnePlus real-time PCR System (Applied Biosystems, Foster, CA, USA). The reaction volume was 20 μL: 10 μL 2×ChamQ SYBR qPCR Master Mix, 0.4 μL F-primer (10 μM), 0.4 μL R-primer (10 μM), 5 μL template DNA/cDNA, 4.2 μL ddH2O. The primer sequences are shown in Table 1. The common thermal cycling parameters of RT-PCR are as follows: pre-denaturation: 95 °C for 3 min. Cycle reaction: 95 °C for 10 s; 60 °C for 30 s; 40 cycles. Melting-curve: 95 °C for 15 s; 60 °C for 1 min; 95 °C for 15 s. Melting-curve analyses were performed to verify product identity. *Target* gene expression was quantified relative to GADPH expression. The 2−∆∆Ct method was used to calculate the relative gene expression. All samples were run in triplicate.
## 4.4. Western Blot Analysis
Approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3, chloroquine (10 μM), and NAC (4 mM) for 24 h. Protein was extracted with RIPA buffer (Beyotime) following the manufacturer’s protocol and quantified using the bicinchoninic acid (BCA) protein assay kit (Beyotime). Briefly, after the PGCs were treated, the medium was removed and the cells were washed twice with PBS. RIPA buffer was treated with the cells for 30 min (on ice). The supernatant was collected by centrifugation at 12,000 r/min. The BCA working solution was prepared, the protein standard concentration was 0.5mg/mL, and the protein standard was added to the 96-well plate according to the following amounts: 0, 1, 2, 4, 8, 12, 16, 20 μL. Appropriate volume protein samples were added to standard wells (refill to 20 μL per well). BCA working solution (200 μL) was added to each well for 30 min at 37 °C. The wavelength between A562–595 nm was determined, and the protein concentration was calculated according to the standard curve. The proteins were adjusted to the same concentration by sample buffer. Cytosolic protein (20 μg) was subjected to $12\%$ SDS-PAGE and transferred to polyvinylidene difluoride (PVDF) membranes (Beyotime) in a Bio-Rad wet Blot Transfer *Cell apparatus* (transfer buffer: 39 mM glycine, 48 mM Tris-base, $1\%$ SDS, $20\%$ methanol, pH 8.3). The obtained membranes were blocked in QuickBlockTm Western’s blocking buffer (Beyotime) for 1 h at room temperature. Membranes were washed in TBST (150 mM NaCl, 2 mM KCl, 25 mM Tris, $0.05\%$ Tween20, pH 7.4) and incubated with primary antibodies: β-actin (ACTB) (Mouse polyclonal to ACTB, 1:5000 dilution) (Proteintech Group, Inc., Chicago, IL, USA), LC3 (Rabbit polyclonal to LC3I/II, 1:2000 dilution) (Abcam, Cambridge, UK), BNIP3 (Rabbit polyclonal to BNIP3, 1:1000 dilution) (Abcam), PINK1 (Rabbit polyclonal to PINK1, 1:1000 dilution) (Cell Signaling Technology, Danvers, MA, USA), STAR (Rabbit polyclonal to STAR, 1:500 dilution) (Cell Signaling Technology), P62 (Rabbit polyclonal to P62, 1:10,000 dilution) (Servicebio, Wuhan, China) in QuickBlock™ Primary Antibody Dilution Buffer (Beyotime) overnight at 4 ℃. Membranes were then washed and labeled for 2 h at room temperature with anti-rabbit HRP-conjugated IgG goat (1:4000 dilution) or anti-mouse HRP-conjugated IgG (1:4000 dilution) (Sungene Biotechnology, Tianjin, China) diluted in QuickBlock™ Secondary Antibody Dilution Buffer (Beyotime). Finally, membranes were washed in TBST, and the protein bands were visualized with a chemical luminous imaging system (Millipore, Billerica, MA, USA).
## 4.5. Analysis of Steroid Hormone Production
Approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 in the presence/absence of chloroquine (10 μM) for 24 h. E2 and P4 in the medium were measured by the specific ELISA kit (Ruixin Biological Technology Co., Ltd., Quanzhou, China) following the manufacturer’s protocol. Briefly, medium in the co-culture system was collected by centrifuging at 1000 g for 10 min at 4 °C, and the liquid supernatant was used for steroid assays. Each sample was measured 5 times and averaged. The inter- and intra-assay CVs of E2 were $5.6\%$ and $3.4\%$, and that of P4 were $6.9\%$ and $7.9\%$. The minimum detected concentrations of E2 and P4 were 4.8 pg/mL and 1.45 ng/mL, respectively.
## 4.6. Measurement of Reactive Oxygen Species
ROS generation was detected by DCFH-DA (Beyotime). Fluorescence intensity was measured by a fluorescence microplate reader, fluorescence microscope, or flow cytometry.
Fluorescence microplate reader: approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 in the presence/absence of N-acetylcysteine (2 mM, 4 mM, 8 mM) for 24 h. After treatments, the cells were co-cultured with 10 μM DCFH-DA for 30 min, the residual DCFH-DA was removed, and the cells were washed with PBS 3 times. The intracellular fluorescence was read by a fluorescence microplate reader at an excitation/emission wavelength of $\frac{488}{525}$ nm. Each sample was measured 3 times and averaged.
Fluorescence microscope: approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 in the presence/absence of N-acetylcysteine (4 mM) for 24 h. Then the cells were stained with DCFH-DA as described above. Cells were observed under a fluorescence microscope, and fluorescence images were obtained. LED intensity, integration time and camera gain were fixed during taking pictures (Olympus Corporation, Tokyo, Japan). Image J software was used to process the images, and the mean fluorescence values of different groups were calculated.
Flow cytometry: approximately 1 × 106 cells/well were seeded into 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 for 24 h. The cells were stained with DCFH-DA as described above. After staining, cells in each group were collected (about 1 × 106 cells/mL), and ROS was detected by flow cytometry (BD FACSAria™ III) (Becton Dickinson, Franklin Lakes, NJ, USA) within 30 min. The excitation light was 488 nm, and the emission light was 525 nm. Fluorescence was detected by the FL1 channel. Samples were acquired on a flow cytometer using a stop condition of 10,000 events on the gate of interest. Using the flow cytometry software, dot plots of FSC (on the X-axis) and SSC (on the Y-axis) were opened, and a gate was drawn around the cells of interest. In the experiment, untreated normal cells were set as the control group and the gate position was developed according to the two-parameter scatter plot of the control group. Data were analyzed using the FlowJO software.
## 4.7. Detection of Mitochondrial Abundance
Approximately 1 × 106 cells/well were seeded into 24-well plates. After the cells were attached, they were treated with 10 nM 1α,25(OH)2D3 for 24 h. Cells were stained with Mito-Tracker Green (Beyotime) according to the manufacturer’s instructions. After removing the cell culture medium, the cells were incubated with prepared Mito-Tracker Green working solution for 30 min at 37 °C. Then the cells were washed with PBS 3 times. The cells were treated with an anti-fluorescence quenched sealing solution and then observed under a fluorescence microscope (Olympus Corporation). LED intensity, integration time, and camera gain were fixed during picture-taking. Image J software was used to process the images, and the mean fluorescence values of different groups were calculated.
## 4.8. Mitochondrial Membrane Potential Detection
Approximately 1 × 106 cells/well were seeded into 24-well plates. After the cells were attached, they were treated with 10 nM 1α,25(OH)2D3 for 24 h. Then, the cells were treated with a mitochondrial membrane potential detection kit (JC-1) (Solarbio) following the manufacturer’s protocol. Briefly, JC-1 staining working solution was added to each well, thoroughly mixed, and then the cells were incubated for 20 min at 37 °C in a cell incubator. The cells were washed 2–3 times with PBS. After staining, cells in each group were collected (about 1 × 106 cells/mL), and the JC-1 signal was visualized by flow cytometry (excitation: 488 nm; emission: 530 nm) (BD FACSAria™ III) within 30 min. Green fluorescence was detected through FL1 channel, and red fluorescence was detected through FL2 channel. Samples were acquired on a flow cytometer using a stop condition of 10,000 events on the gate of interest. Using the flow cytometry software, dot plots of FSC (on the X-axis) and SSC (on the Y-axis) were opened, and a gate was drawn around the cells of interest. In the experiment, untreated normal cells were set as the control group, and the gate position was developed according to the two-parameter scatter plot of the control group. Data were analyzed using the FlowJO software.
## 4.9. Double Staining with MDC and DAPI
Approximately 1 × 106 cells/well were seeded in 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3, chloroquine (10 μM), and NAC (4 mM) for 24 h. MDC (monodansylcadaverine) was used as a tracer of autophagic vesicles. The autophagosomes are marked as clear green dots under the fluorescence microscope. After treatment, the cells were treated with MDC (0.05 mM) (Kaiji Biotechnology Co., Ltd., Nanjing, China) and DAPI (1 μg/mL) (4′,6-diamidino-2-phenylindole) (Solarbio) following the manufacturer’s protocol. Briefly, the cells were grown with MDC and DAPI at 37 °C for 15 min and fixed immediately with paraformaldehyde ($4\%$) in PBS for 20 min, then observed under a fluorescence microscope (Olympus Corporation). LED intensity, integration time, and camera gain were fixed while taking pictures. Image J software was used to process the images, a total of 200 cells in each sample were analyzed, and the percentage of cells with green spots indicates the percentage of autophagy.
## 4.10. Measurement of Superoxide Dismutase and Glutathione Peroxidase
Approximately 1 × 106 cells/well were seeded in 24-well plates. After being attached, PGCs were treated with 10 nM 1α,25(OH)2D3 for 24 h. The activity of intracellular SOD and GPX were measured in PGCs using kits (Nanjing Jiancheng Bioengineering Research Institute Co., Ltd., Nanjing, China) for scientific research following the manufacturer’s protocol. Briefly, after the cells were washed twice with PBS, the cells were carefully scraped off with a cell scraper, and the cell mixture was centrifuged at 1000× g for 10 min, and then the supernatant was discarded. Protein concentration was determined with the BCA assay kit (Beyotime). The results were detected by a visible spectrophotometer (550 nm wavelength) (Thermo Fisher Scientific). Each sample was measured 5 times, and the average of the results was taken. The inter- and intra- assay CVs averaged $7.8\%$ and $6.5\%$, respectively.
## 4.11. Statistical Analysis
Independent t-tests were used to evaluate the significance of the results between groups. Statistical significance was determined by ANOVA followed by post hoc tests. The Tukey–Kramer HSD test was used to analyze the differences between the means (GraphPad Prism version 9.0, GraphPad Software Inc., San Diego, CA, USA). p-values < 0.05 were considered statistically significant. All data are presented as the mean ± SM of 3 or more repeated observations from at least 3 independent experiments.
## 5. Conclusions
In summary, these results demonstrate for the first time that 1α,25(OH)2D3 induces mitophagy through the ROS-BNIP3-PINK1 signaling pathway, which promotes the proliferation and maintains the function of PGCs. Our results provide important information for determining the role of 1α,25(OH)2D3 during ovarian follicular development.
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|
---
title: Clinical Characteristics and Predictors of Long-Term Prognosis of Acute Peripheral
Arterial Ischemia Patients Treated Surgically
authors:
- Piotr Myrcha
- Mariusz Kozak
- Jakub Myrcha
- Mirosław Ząbek
- João Rocha-Neves
- Jerzy Głowiński
- Włodzimierz Hendiger
- Witold Woźniak
- Izabela Taranta
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001670
doi: 10.3390/ijerph20053877
license: CC BY 4.0
---
# Clinical Characteristics and Predictors of Long-Term Prognosis of Acute Peripheral Arterial Ischemia Patients Treated Surgically
## Abstract
Background: Acute peripheral arterial ischemia is a rapidly developing loss of perfusion, resulting in ischemic clinical manifestations. This study aimed to assess the incidence of cardiovascular mortality in patients with acute peripheral arterial ischemia and either atrial fibrillation (AF) or sinus rhythm (SR). Methods: This observational study involved patients with acute peripheral ischemia treated surgically. Patients were followed-up to assess cardiovascular mortality and its predictors. Results: The study group included 200 patients with acute peripheral arterial ischemia and either AF ($$n = 67$$) or SR ($$n = 133$$). No cardiovascular mortality differences between the AF and SR groups were observed. AF patients who died of cardiovascular causes had a higher prevalence of peripheral arterial disease ($58.3\%$ vs. $31.6\%$, $$p \leq 0.048$$) and hypercholesterolemia ($31.2\%$ vs. $5.3\%$, $$p \leq 0.028$$) than those who did not die of such causes. Patients with SR who died of cardiovascular causes more frequently had a GFR <60 mL/min/1.73 m2 ($47.8\%$ vs. $25.0\%$, $$p \leq 0.03$$) and were older than those with SR who did not die of such causes. The multivariable analysis shows that hyperlipidemia reduced the risk of cardiovascular mortality in patients with AF, whereas in patients with SR, an age of ≥75 years was the predisposing factor for such mortality. Conclusions: Cardiovascular mortality of patients with acute ischemia did not differ between patients with AF and SR. Hyperlipidemia reduced the risk of cardiovascular mortality in patients with AF, whereas in patients with SR, an age of ≥75 years was a predisposing factor for such mortality.
## 1. Introduction
Acute peripheral arterial ischemia is defined as a rapidly developing loss of perfusion, resulting in variable ischemic clinical manifestations and potential necrosis of the involved organ or extremities. This disease is associated with high morbidity and mortality [1]. The incidence of acute limb ischemia is ~1.5 cases per 10,000 persons per year [2]. Diagnostic errors and delays in treatment may lead to the loss of limbs, related to the lack of sufficient time for new blood vessel growth to compensate for the loss of perfusion, or even loss of life [3]. According to previous studies, 15–$20\%$ of patients die within the first year after acute lower extremity ischemia, and most of these deaths occur in the peri-operative period. Apart from higher in-hospital mortality, patients with acute arterial ischemia experience adverse events, such as the following: congestive heart failure exacerbation, myocardial infarction, deterioration in renal function, and respiratory complications [4]. There are various causes leading to the occurrence of acute limb ischemia, including the following: arterial embolism ($46\%$), in situ thrombosis ($24\%$), complex factors ($20\%$), and stent- or graft-related thrombosis ($10\%$) [5]. Atrial fibrillation (AF), a sustained cardiac arrhythmia, is the most common cause of embolism and a risk factor for peripheral arterial occlusion [6]. Thromboembolic complications of AF frequently cause morbidity and mortality [7]. Embolism-associated limb ischemia was demonstrated to be related to a higher mortality risk when compared to the occlusion of an artery with local thrombosis in atherosclerotic etiology [8]. Moreover, the mortality risk in patients with AF-related peripheral embolic complications was greater than in those with myocardial infarct-related embolism [9].
The aim of the study is to assess the incidence of cardiovascular mortality in patients with acute peripheral arterial ischemia and either AF or sinus rhythm (SR) and to attempt to identify the predisposing factors of cardiovascular mortality in these two groups of patients during long-term follow-up.
## 2.1. Study Design and Participants
This is a retrospective observational study involving 200 consecutive patients with acute peripheral ischemia and either AF ($$n = 67$$) or SR ($$n = 133$$), who were admitted to the Department of Vascular Surgery between January 2014 and November 2018. The median follow-up was 21 IQR (7–37) months.
A complete medical history, including information of prior treatment, was obtained from all participants. The diagnosis of acute arterial ischemia was based on the subjective and physical examination and, depending on the area of ischemia, on the different imaging examinations [10]. All patients underwent a duplex ultrasound examination (DUS). Computed tomography angiography (CTA) was performed in $78\%$ of cases of acute limb ischemia and $92\%$ cases of mesenteric ischemia.
AF was diagnosed as defined by the European Society of Cardiology [11]. The risk of thromboembolic complications was assessed with the use of the CHADS2 and CHA2DS2-VASc scales at hospital admission. CHADS2 and CHA2DS2-VASc scores did not include the thromboembolic event that resulted in hospitalization. The CHADS2 score was calculated for each patient in accordance with the following guidelines: congestive heart failure, hypertension, diabetes mellitus, and age ≥75 years were counted as 1 point each; a history of stroke or transient ischemic attack counted as 2 points [12]. The CHA2DS2-VASc score was also calculated for each patient by current clinical guidelines. This score ranges from 0 to 9 points and includes the following clinical characteristics: congestive heart failure or left ventricular dysfunction (1 point), hypertension (1 point), age ≥75 years (2 points), diabetes mellitus (1 point), prior stroke/transient ischemic attack (TIA) or thromboembolism (2 points each), vascular disease (1 point), age 65–74 years (1 point), and sex category (female; 1 point) [13]. The sum of all factors gives the individual patient’s risk score. In addition, the estimated glomerular filtration rate (eGFR) was calculated using the simplified four-variable Modification of Diet in Renal Disease (MDRD) formula: eGFR = 186 × (serum creatinine)−1.154 × (age)−0.203 × 0.742 if female] [14].
The study was approved by the university Bioethics Committee (no. $\frac{111}{2020}$) and was conducted according to the principles of the Declaration of Helsinki. The university Ethics Committee waived the requirement of obtaining informed consent from the patients.
## 2.2. Surgical Treatment
All patients were treated surgically using open thrombectomy/embolectomy, mechanical thrombectomy (MTH), direct catheter thrombolysis (DCT), or primary amputation. The type of treatment depended on the cause and the depth of ischemia and the patient’s general condition. Revascularisation was not performed in the case of advanced intestinal and limb ischemia/necrosis. In patients with major comorbidities, who experienced significant improvement in their clinical state after conservative treatment, the surgical treatment was postponed for elective preparation. The condition of peripheral circulation was critical in deciding on the treatment method. Due to the lack of peripheral flow in most patients, the implementation of DCT was performed. The restoration of peripheral blood flow was an introduction to other procedures in case of significant stenosis; the treatment was discontinued if proper circulation was restored.
All patients treated with MTH and DCT underwent arteriography. Unfractioned Heparin (UFH) was administered intravenously in all patients with acute ischemia; it was infused with an infusion pump to prolong APTT 2.5–3 times. The infusion was preceded by a bolus of 5000–10,000 IU heparin. In most cases, limb open embolectomy/thrombectomy was performed under epidural or spinal anesthesia. In patients in whom this type of anesthesia was contraindicated, local anesthesia was used along with sedation. The clots were removed using a Fogarty’s catheter, proximally and peripherally, until an acceptable inflow and outflow were obtained. Endarterectomy was performed in the presence of massive atherosclerotic plaques at the site of the artery incision. Arteriotomy was closed with a primary suture or resorting to patch angioplasty in case of a small diameter of the artery. In the absence of a good inflow or outflow, patients were eligible for bypass. In the case of suspected subfascial edema, fasciotomy was performed.
Open visceral thrombectomy/embolectomy was performed by laparotomy, either trans- or retroperitoneal in nature. Patch angioplasty, transposition, or bypass were performed, depending on the etiology of the occlusion. In the case of intestinal necrosis, its resection was performed within the limits of visually healthy tissue. Patients were always qualified for a “second look” within 24–48 h.
## 2.3. Endovascular Treatment
Using WinPepi® V11.65, the required sample for a survival test was computed with a $90\%$ statistical power (β) and a 0.05 significance level [15]. Although bigger event rate disparities are stated, the sample was calculated at 147, with a hazard ratio of 1.6 (1.3 to 1.9) across groups [16,17]. A total estimated sample of 154 was collected with an expected loss-to-follow-up rate of $5\%$.
DCT and MTH were the first-choice endovascular methods used to treat acute ischemia in all areas [18]. Access via the common femoral or left radial artery was used. Percutaneous transluminal angioplasty (PTA) and stenting were performed. At the time of DCT infusion, Alteplase (Actilyse-Boehringer-Ingelheim®) 1 mg/h was administered (5 mg bolus). UFH was administered simultaneously to the sheath (500 IU/h). The fibrinogen and APTT levels were set four times a day. DCT was terminated earlier if the fibrinogen level fell below 150 mg/dL [19,20]. Control arteriography was performed before sheath removal. During mechanical thrombectomy, AngioJet (Boston Scientific, Marlborough, MA, USA) and Rotarex (Straub Medical, Vilters-Wangs, Switzerland) systems were used with different catheter diameters, depending on the size of the artery. If the procedure’s effectiveness was insufficient, DCT or PTA/stent was performed. The Spider embolic protection system (Medtronic) was used during some procedures on the arteries of the lower limbs. After the surgery, UFH was administered intravenously using an infusion pump with the target of prolonging APTT 2.5–3 times. In the case of simultaneous occlusion of the celiac trunk and superior mesenteric artery, we tried to open both arteries. DCT was used carefully because of the known mechanism of endogenous thrombolysis occurring during intestinal ischemia [21].
## 2.4. Study Endpoint
The study endpoint was cardiovascular mortality during long-term observation.
## 2.5. Statistical Analysis
Categorical data are expressed as numbers of patients and percentages. The Chi-squared test or Fisher’s exact test were used to compare proportions. Numeric variables are presented as medians and quartiles and compared using the Mann–Whitney U test, because their distribution was not normal (assessed by the Shapiro–Wilk test, graphical curve analysis and kurtosis). In the context of survival analysis, the endpoint was defined as cardiovascular death. The follow-up was calculated as the number of days from surgery to death (cardiovascular or not) or to the end of the study (for live patients). Survival curves for AF and SR groups were created by the Kaplan–Meier method.
Patients’ characteristics and type of surgery were assessed in univariable Cox proportional hazards regression models to evaluate the relationship with cardiovascular death. The regressive predictive model was created by resorting to regression analysis and dimension reduction by the method of backward feature elimination. Variables with clinical relevance included in the multivariate analysis were associated with the group including cardiovascular and non-cardiovascular death in the univariate analysis, with statistical significance $p \leq 0.1.$ Some multivariable Cox proportional hazards models are also presented. Hazard ratios (HR) in univariable and multivariable Cox models were estimated, along with $95\%$ confidence intervals. A stratified analysis was conducted for SR and AF patients. Cox proportional hazards regression models were not created for categorical variables with less than five patients in any category. Statistical tests were two-tailed, and p-values < 0.05 were considered significant. All statistical analyses were performed using the R software package version 3.6.2.
## 3.1. Characteristics of the Study Group
In the present study of 200 patients, 67 ($33.5\%$) had AF and 133 ($66.5\%$) had SR. Patients with AF were statistically significantly older (78.0 vs. 70.0, $$p \leq 0.003$$) and women represented the majority ($62.7\%$ vs. $39.1\%$, $$p \leq 0.002$$) in this group, in comparison to the group with SR. The incidence of comorbidities did not differ significantly between groups; only ischemic heart disease was more prevalent in the group with AF ($55.2\%$ vs. $35.3\%$, $$p \leq 0.007$$). Patients with AF had statistically lower eGFR than patients with SR (64.3 vs. 73.7, $$p \leq 0.03$$). In this group of patients, the results of CHADS2 and CHA2DS2-VASc were also higher than in patients with SR. In patients with AF, embolic occlusion was more frequent, while in patients with SR, the occlusion was associated with thrombotic material. Thromboembolic material was observed in both groups, predominantly in the lower limbs ($74.6\%$ vs. $84.2\%$, $$p \leq 0.10$$). The clinical characteristics of patients with AF and SR are presented in Table 1.
## 3.2. Incidence of Mortality in Patients with Acute Peripheral Arterial Ischemia and Atrial Fibrillation or Sinus Rhythm
The median follow-up in the group with AF was 20.9 (IQR: 7.4, 34.3) months, and in the group with SR it was 22.6 (IQR: 7.4, 40.3) months, $$p \leq 0.45.$$ There were no differences in all-cause mortality between the AF group and SR group ($43.3\%$ vs. $31.6\%$, $$p \leq 0.10$$). Cardiovascular mortality was similar in patients with AF and SR ($28.4\%$ vs. $18.8\%$, $$p \leq 0.12$$) (Table 2).
The analysis of Kaplan–Meier curves shows that in the initial period after surgery, the chances of survival were similar in both groups (Figure 1).
## 3.3. Factors Predisposing to Cardiovascular Mortality
In the group with AF, in patients who died of cardiovascular causes, the prevalence of PAD and hypercholesterolemia was lower than in those who did not die of such causes (PAD $31.6\%$ vs. $58.3\%$, $$p \leq 0.048$$; hypercholesterolemia $5.3\%$ vs. $31.2\%$, $$p \leq 0.03$$) (Table 3).
The comparison of patients with SR who died of cardiovascular causes and those with SR who did not die of such causes revealed that the first group more frequently had GFR < 60 mL/min/1.73 m2 ($47.8\%$ vs. $25.0\%$, $$p \leq 0.03$$), and they tended to be older (age >75 years: $60.0\%$ vs. $33.3\%$, $$p \leq 0.04$$) (Table 4).
In this study, the CHA2DS2-VASc score was similar in patients who died of cardiovascular causes and in those who did not die of such causes, both in the AF and in the SR group.
The multivariable analysis showed that the presence of hyperlipidemia reduced the risk of cardiovascular mortality in patients with AF, whereas in the case of patients with SR, an age of ≥75 years was the factor predisposing one for such mortality (Table 5).
## 4. Discussion
AF increases the risk of thromboembolic episodes, which are often responsible for high morbidity and mortality in this group of patients [7,22,23]. The study of Barreto et al. [ 24], comprising patients with peripheral arterial embolism, confirmed the role of AF in the pathogenesis of acute limb ischemia. In our study, the estimated glomerular filtration rate (eGFR) of AF patients was significantly lower than in SR patients. Moreover, they had higher CHA2DS2-VASc scoring in comparison to patients with sinus rhythms. Despite this, only $35.8\%$ of patients in the AF group were receiving oral anticoagulants, and even fewer were treated with antiplatelet agents ($13.4\%$) before hospital admission. In turn, SR patients were significantly more often administered antiplatelet treatment (APT) ($40.6\%$), which is in accordance with current recommendations. Howard et al. [ 5] demonstrated that premorbid levels of anticoagulation in patients suffering from acute events of cardioembolic origin, as well as known AF, are deficient. However, the vast majority of patients with a high thromboembolism risk (CHA2DS2-VASc scores ≥ 2) had no contraindications to anticoagulation. Additionally, Ralevic et al. [ 25], in a prospective observational study of consecutive patients with lower limb amputation, found that despite a high prevalence of AF, patients often did not receive the recommended oral anticoagulation therapy. In this study, the occlusion in patients with AF and SR was mainly localized to the lower extremities. The higher prevalence of acute lower limb ischemia has also been indicated in other studies. Ischemia affecting upper extremities is relatively uncommon, accounting for less than $5\%$ of all cases of limb ischemia [26,27]. In this study, acute peripheral arterial ischemia in AF patients was primarily caused by an embolus ($65.7\%$), and in SR patients by a thrombus ($55.6\%$). This observation was confirmed by Mutirangura et al. [ 28], who revealed that AF was more prevalent in patients with acute arterial embolism than acute arterial thrombosis.
Systematic reviews and meta-analyses have undoubtedly indicated the association of AF with an increased risk of mortality in patients with coronary artery disease [29,30]. However, the prognostic implication of AF in acute peripheral arterial ischemia has not been extensively studied. We did not observe statistically significant differences in mortality between patients with AF and patients with SR, who were operated on due to acute ischemia. Cardiovascular mortality was slightly higher in patients with AF compared to those with SR ($28.4\%$ vs. $18.8\%$); however, this difference failed to reach statistical significance. A similar trend was observed in the study of Ralevic et al. al. [ 25], who demonstrated that lower limb amputation, cardiovascular death, as well as adverse cardiovascular events were more common in patients with AF during follow-up compared with patients without AF. Lorentzen et al. [ 31] showed that AF enhanced the risk of mortality, decreased patients’ quality of life, and increased the number of hospitalizations. Moreover, according to Vohra et al. [ 32], in patients with AF-related peripheral embolic complications, the mortality risk was higher compared to individuals with embolism associated with myocardial infarction. Data from the Reduction of Atherothrombosis for Continued Health Registry [33] demonstrated that AF was an independent predictor of long-term CV events in patients with symptomatic peripheral arterial disease (PAD) [34]. We can only suspect that the lack of statistically significant differences in mortality between AF and SR patients in our study is associated with the relatively small number of AF patients, as well as with the introduction of appropriate treatment, because, as mentioned above, before the hospitalization, many patients were not receiving the best medical treatment.
In this study, we also observed that the CHA2DS2-VASc scale was not a predictor of cardiovascular mortality in patients with AF and SR. *In* general, the CHA2DS2-VASc score can be used to assess the risk of stroke in patients with atrial fibrillation. However, published results show considerable variability in relation to the mortality of AF patients and the correlation with the CHA2DS2-VASc score, especially regarding patient history, drug treatment, and clinical status [35,36,37]. In an observational retrospective cohort study (CONSORT compliant), the predictive value of CHA2DS2-VASc was confirmed in relation to overall all-cause mortality [36]. Patients with higher risk scores had a survival rate of $79.1\%$, while medium-risk and low-risk patients had survival rates of $95.6\%$ and $100\%$, respectively. According to Potpara et al. [ 38], the CHA2DS2-VASc score is a reliable predictor of 30-day unfavorable outcomes of patients with acute ischemic stroke. Its sensitivity and specificity for unfavorable short-term functional outcomes is greater in comparison to other scores, including the CHADS2 and HAS-BLED ($93.5\%$ vs. $92.4\%$ vs. $71.7\%$ and $77.0\%$ vs. $61.5\%$ vs. $69.6\%$, respectively; all $p \leq 0.05$). Despite the CHA2DS2-VASc score differing significantly between the AF and SR groups in our study, it did not correlate with cardiovascular mortality, probably due to the fact that many more patients with AF received appropriate treatment before the hospitalization, which might have influenced their outcomes. The presence of AF may be a primary driver of the administration of therapy for stroke prevention, which decreases mortality rate. Jackson et al. [ 35] confirmed that systemic oral anticoagulant treatment (OAC) was associated with lower rates of all-cause mortality, cardiovascular death, and first stroke/TIA among patients with CHA2DS2-VASc score ≥ 2. Also, in most of studies, this scale was used to assess all-cause death, not cardiovascular mortality.
A univariable analysis of factors modulating the risk of cardiovascular mortality in our population of patients with acute peripheral arterial ischemia and AF demonstrated that PAD and hypercholesterolemia (obesity paradox) reduced the risk of cardiovascular mortality. According to numerous studies, PAD and AF share similar epidemiologic patterns and risk factors, and their presence is related to increased morbidity and mortality [39]. A sub-analysis performed with the use of data from the Reduction of Atherothrombosis for Continued Health Registry demonstrated that the combined presence of AF and PAD significantly increased the rates of cardiovascular (CV) death [33]. Additionally, Lin et al. [ 40] found that the coexistence of AF and PAD considerably enhanced the risk for all major adverse outcomes, and it was associated with at least a two-fold higher risk of CV death than in patients with AF or PAD only. The reduction in cardiovascular mortality related to the presence of PAD in AF patients in this study may be associated with the fact that PAD patients were probably previously intensively treated with antihypertensive, lipid-lowering, and antiplatelet drugs. Indeed, $94.7\%$ of patients in this group used antiplatelet drugs. It is also possible that patients with an earlier diagnosis of PAD introduced dietary changes and ceased smoking, thus decreasing their cardiovascular risk. The importance of hypercholesterolemia as a factor in reducing cardiovascular mortality in the group with AF was also confirmed in a multivariable analysis, in which it decreased the risk of death by $87\%$. Again, such a phenomenon could be associated with the fact that patients with a history of hyperlipidemia were treated with statins and other lipid lowering drugs, which reduced their cardiovascular mortality. Additionally, Clua-Espuny et al. [ 41] found that mortality among AF patients was significantly lower for those treated with statins. The obesity paradox in atrial fibrillation patients, particularly for all-cause and cardiovascular death outcomes, has been extensively described [42,43].
In the case of patients with SR, the univariable analysis revealed a correlation between cardiovascular mortality and age. The risk of cardiovascular mortality increased 1.6 times with every ten years. Additionally, in a retrospective review of patients with acute limb ischemia, the risk of mortality increased with age and renal failure, but also with the female gender, cancer, in situ thrombosis or embolic etiology, cardiac events, and hemorrhagic events [44]. Eliason et al. [ 45] indicated that in patients with acute lower extremity ischemia, an age of less than 63 years was an independent variable associated with a decreased risk of in-hospital mortality. Finally, the analysis of the National Audit of Thrombolysis for Acute Leg Ischemia (NATALI) database confirmed that the mortality of patients who had undergone intra-arterial thrombolysis to treat acute leg ischemia was higher in women and older patients, and in patients with native vessel occlusion, emboli, or a history of ischemic heart disease [46]. The relationship between higher mortality and advanced age may be due to the fact that the prevalence of comorbidities increases with advancing age in many populations. The impact of age was also confirmed in our multivariate analysis.
We also observed that in patients with SR and eGFR < 60 mL/min, the risk of cardiovascular death was 2.48-fold higher when compared to those with higher eGFR. In turn, Kuoppala et al. [ 47] demonstrated that renal insufficiency was among the independent factors associated with in-hospital mortality after thrombolysis. Moreover, they indicated that, among other reasons, renal insufficiency and an age ≥80 years were associated with mortality during follow-up. They suggested that the administration of a contrast agent during angiography may be partly responsible for such a negative relationship in this subgroup of patients. Renal impairment is also more frequent and aggravated in patients with CAD and vascular complications, which can also explain the association between lower GFR and cardiovascular mortality. Maithel et al. [ 48] confirmed the relationship between renal insufficiency and poorer outcomes in patients after open vascular surgery. Additionally, in patients with AF, renal dysfunction proved to be a strong, independent predictor of left atrial appendage thrombus formation [49]. This study has demonstrated that acute peripheral arterial ischemia continues to be associated with high mortality despite advances in endovascular-based therapies and improved critical care.
The choice of the treatment method in patients with acute limb ischemia is difficult. There are no strict criteria defining the risk of reperfusion syndrome after revascularization. Studies on the preoperative inflammatory biomarkers’ neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio are encouraging. Increased preoperative values of these factors may be indicators of a poor outcome and the need for primary amputation [50].
A significant limitation of the study is the small size of the study group. Patients were treated and followed in a single tertiary care center with a high volume, which might affect the external validity of the results. Other limitations involve the lack of data on non-anticoagulant/antiplatelet and other therapy before hospitalization, the lack of detailed information on the post-surgery period, and the lack of data on ischemic events during the follow-up period.
## 5. Conclusions
The mortality of patients operated on due to acute ischemia did not differ significantly between the group of patients with AF and those with SR. Moreover, the CHA2DS2-VASc scale proved not to be a good predictor of cardiovascular mortality in patients with AF and SR. The presence of hyperlipidemia reduced the risk of cardiovascular mortality in patients with AF, whereas in the case of patients with SR, an age of ≥75 years was a factor predisposing one to such mortality.
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|
---
title: Psychometric Testing of the CEECCA Questionnaire to Assess Ability to Communicate
among Individuals with Aphasia
authors:
- Willian-Jesús Martín-Dorta
- Alfonso-Miguel García-Hernández
- Jonathan Delgado-Hernández
- Estela Sainz-Fregel
- Raquel-Candelaria Miranda-Martín
- Alejandra Suárez-Pérez
- Alejandra Jiménez-Álvarez
- Elena Martín-Felipe
- Pedro-Ruymán Brito-Brito
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001674
doi: 10.3390/ijerph20053935
license: CC BY 4.0
---
# Psychometric Testing of the CEECCA Questionnaire to Assess Ability to Communicate among Individuals with Aphasia
## Abstract
[1] Background: The CEECCA questionnaire assesses the ability to communicate among individuals with aphasia. It was designed using the NANDA-I and NOC standardised nursing languages (SNLs), reaching high content validity index and representativeness index values. The questionnaire was pilot-tested, demonstrating its feasibility for use by nurses in any healthcare setting. This study aims to identify the psychometric properties of this instrument. [ 2] Methods: 47 individuals with aphasia were recruited from primary and specialist care facilities. The instrument was tested for construct validity and criterion validity, reliability, internal consistency, and responsiveness. The NANDA-I and NOC SNLs and the Boston test were used for criterion validity testing. [ 3] Results: five language dimensions explained $78.6\%$ of the total variance. Convergent criterion validity tests showed concordances of up to $94\%$ (Cohen’s κ: 0.9; $p \leq 0.001$) using the Boston test, concordances of up to $81\%$ using DCs of NANDA-I diagnoses (Cohen’s κ: 0.6; $p \leq 0.001$), and concordances of up to $96\%$ (Cohen’s κ: 0.9; $p \leq 0.001$) using NOC indicators. The internal consistency (Cronbach’s alpha) was 0.98. Reliability tests revealed test–retest concordances of 76–$100\%$ ($p \leq 0.001$). [ 4] Conclusions: the CEECCA is an easy-to-use, valid, and reliable instrument to assess the ability to communicate among individuals with aphasia.
## 1. Introduction
Aphasia is a multimodal cognitive disorder caused by acquired brain damage that impacts spoken language, listening comprehension, reading, writing, and other cognitive processes dependent on the activity of the left cerebral hemisphere, which are essential for the proper functioning of language [1,2]. Cerebrovascular accidents (CVAs) are the most common cause of aphasia [3]. Multicentre studies show an incidence of aphasia ranging from $18\%$ to $38\%$ [4,5,6,7].
The use of language is one of the most distinctive attributes of the human species, enabling us to become social beings and partake in a particular culture [8]. Through language, we interact with those around us and ourselves, and we learn and organise our thoughts. Language represents a basic feature of our personality. Given the various functions that language has for human beings, its impairment or loss has a significant impact on the quality of life of individuals with aphasia, making it difficult for them to carry out certain activities of daily living and negatively affecting their physical, mental, emotional, familial, and social wellbeing [9,10,11]. The consequences of aphasia can be exacerbated by inappropriate communication strategies in the healthcare setting [12]. It has been shown that when nurses interact with individuals with aphasia, patients are given little opportunity to start or maintain a conversation, and conversations revolve around the professional’s goals rather than the patient’s needs and demands [13]. This asymmetrical communication leads to a loss of autonomy and makes it difficult for the patient with aphasia to participate in their own care, resulting in feelings of frustration, helplessness, fear, anger, and/or resignation [14].
The complexity of aphasia requires a multidisciplinary and interdisciplinary approach, in which nurses have a fundamental role to play as care providers. Assessment, the first step in the nursing process, is key to ensuring an accurate diagnosis and a care plan tailored to the needs of the individual with aphasia [15]. We believe that this assessment should also facilitate the search for strategies to achieve the most symmetrical and effective nurse–patient communicative interaction possible. The growing production of nursing studies in the past two decades reflects the interest of researchers in caring for patients with language and communication disorders [16,17]. However, surprisingly few studies have been devoted to the assessment of the communicative abilities of aphasia patients. Most aphasia assessment tests have emerged from disciplines such as neurology, psychology, linguistics, and speech therapy. Exploratory modalities include screening tests designed to determine the presence or absence of aphasia; assessment batteries, commonly extensive tests constructed from multiple subtests usually requiring specific knowledge for the interpretation of results; and tests to assess one specific component of language in particular, such as naming or language comprehension [18,19,20]. The characteristics of these assessment tools make them unsuitable for daily use by nurses [19].
The CEECCA, *Cuestionario para* la Evaluación Enfermera de las Capacidades Comunicativas en la Afasia (Nursing Assessment of Ability to Communicate among Patients with Aphasia questionnaire), is based on the NANDA-I nursing diagnoses classification (NANDA-I), the Nursing Outcomes Classification (NOC), and the pictograms of the ARASAAC, Centro Aragonés para la Comunicación Aumentativa y Alternativa (Aragonese Centre for Alternative and Augmentative Communication). The CEECCA is intended to assess the communicative abilities among patients with aphasia based on areas of interest for care. It consists of 43 items assessing 14 specific areas of language, corresponding to five global dimensions likely to be affected in aphasia: “Verbal expression”, “Written expression”, “Expression through pictograms”, “Auditory comprehension”, and “Reading comprehension”. Its design includes the defining characteristics (DCs) of the two communication-related NANDA-I nursing diagnoses: “Impaired verbal communication” [00051] and “Readiness for enhanced communication” [00157], as well as the NOC outcome indicators “Communication” [0902], “Communication: expressive” [0903], “Communication: receptive” [0904], and “Information processing” [0907]. In an initial study [21], the design and construction process of the CEECCA and the results of the content validity tests were published. In these tests, the CEECCA obtained high content validity and representativeness scores. This study demonstrated the utility of the NANDA-I and NOC classifications for the construction of instruments to improve the accuracy of nursing diagnosis and the measurement of outcome indicators in specific care settings. After this preliminary study, it was necessary to determine the remaining psychometric properties regarding the validity and reliability of the CEECCA instrument in a sample of individuals with aphasia.
The research hypothesis of this study was that the CEECCA is a valid and reliable instrument for nurses to use to assess the ability to communicate among individuals with aphasia, including dimensions of interest for care.
The objectives of this study were to carry out the necessary validity and reliability tests to obtain the psychometric properties of the CEECCA (criterion validity, construct validity, internal consistency, and reliability) and to describe the profile of the patients with aphasia in the validation sample based on their sociodemographic and clinical variables.
## 2. Materials and Methods
A study of design and validation of a health questionnaire was proposed. The protocol used was based on the proposals put forward by Carvajal et al. [ 22] and Ramada-Rodilla et al. [ 23] for validating health measurement instruments already used in other studies on the design and validation of assessment instruments based on the NANDA-I and NOC classifications [24,25]. Once the design, construction, and content validity phases of the CEECCA were completed [21], the instrument was tested on a sample of individuals with aphasia to calculate the remaining psychometric properties. Data collection process and statistical tests used to determine the validity and reliability of the questionnaire are described below.
## 2.1. Data Collection
This stage consisted of four phases. ( A)Selecting the members of the research team. Three nurses were selected using a convenience sampling method: two from the Primary Care Management Board of the Tenerife Healthcare Area and one from the Management Board of the Nuestra Señora de La Candelaria University Hospital. The instruction phase for the professionals comprised five joint explanatory meetings of approximately 60 min each. Their content focused on explaining the objectives and methodology of the study, the instructions for administering the CEECCA and the proxy instruments, as well as other methodological and ethical considerations. Each collaborator was given a field notebook with all the documents required for the administration of the tests and data collection.(B)Selecting the sample. Participants were selected using a convenience sampling method in various settings: The University Hospital of the Canary Islands (HUC), primary healthcare facilities in the Tenerife Healthcare Area, as well as private rehabilitation centres and associations. Inclusion criteria were persons aged 18 and over, with an active diagnosis of aphasia registered in the clinical record as a consequence of acquired brain damage, with aphasic symptoms detected by The Boston test for diagnosing aphasia (Spanish adaptation, second edition) [26], with Spanish as their mother tongue, and who agreed to participate in the study. The exclusion criteria were patients with a low level of consciousness (in a vegetative state and/or minimally conscious), a personal history of neurological or neurodegenerative disease prior to the brain injury that caused the aphasia, a psychiatric–psychological history of communication disorder prior to the brain damage, a cognitive level preventing them from taking the test, pre-morbid reading and writing disability, severe visual or hearing impairment that hinders the correct administration of the instrument, behavioural problems that impede collaboration with the researchers, or a history of alcoholism and/or other drug abuse.(C)Administering the proxy instruments. The three proxy instruments used in the validation phase of the CEECCA questionnaire had previously been used in the design and construction phase of the questionnaire, enabling consistency to be maintained between the two processes: The Boston test for diagnosing aphasia (Spanish adaptation, second edition) [26]; the selected indicators of the four communication-related Nursing Outcomes Classification (NOC) outcomes [27]; and the selected defining characteristics (DCs) of the 2012–2014 NANDA-I nursing diagnosis “Impaired verbal communication” [28]. The standardised nursing classifications used were the latest revisions available at the time of administration.
Firstly, a speech therapist administered the activities selected from the Boston test for the diagnosis of aphasia:Conversational speech. Descriptive speech. Visual confrontation naming. Writing mechanics. Written confrontation naming. Auditory discrimination of words. Auditory comprehension of commands. Reading comprehension. Matching pictures and words. Reading comprehension. Reading sentences and paragraphs.
Additionally, this test corroborated the diagnosis of aphasia registered in the clinical record.
During this first visit, clinical and sociodemographic data were collected and informed consent was obtained.
At an interval of one to three days, a nurse assessed the selected communication-related NOC outcome indicators and determined the presence or absence of the DCs of the NANDA-I diagnosis “Impaired verbal communication”. ( D)A nurse administered the CEECCA to each subject in the sample. The CEECCA was administered at the primary healthcare facilities in the Tenerife Healthcare Area, at the HUC rehabilitation units, in the rehabilitation departments of the collaborating centres and associations, and in the participants’ own homes. The CEECCA was administered once more by another nurse under the same conditions at an interval of one to seven days. Alternatively, one of the nurses who previously administered the questionnaire repeated the process four weeks later.
## 2.2. Data Analysis
The results obtained from the administration of the CEECCA questionnaire and the proxy instruments, and the data on clinical and sociodemographic variables, were entered as they became available into an SPSS v.25.0 database for further refinement and processing. The data processing plan involved four phases.
## 2.2.1. Sample Size
The necessary sample size was calculated by taking as a reference the sample sizes used to validate other instruments included in a 2017 systematic review aimed at identifying and evaluating the psychometric properties of screening-type tests for diagnosing post-stroke aphasia [29]. The review included nine studies [30,31,32,33,34,35,36,37,38]. The samples of aphasia patients, with whom these tests were validated, had an average of 42 participants. Taking this data as a reference and considering the difficulty in recruiting participants with this clinical and psychosocial profile, a sample of around 50 participants was deemed necessary to estimate the correlation coefficients for analysing the convergent criterion validity and reliability coefficients (Cohen’s κ) of the questionnaire through non-random concordance estimates of at least 0.30 while maintaining $95\%$ confidence levels. The sample was described by expressing nominal variables as absolute and relative frequencies and by expressing quantitative variables as the median (minimum–maximum).
## 2.2.2. Validity Tests
Validity tests were carried out using a principal component analysis, following the Kaiser–Meyer–Olkin (KMO) sampling adequacy measure and Bartlett’s test of sphericity, confirming the dimensions that make up the questionnaire, using a varimax rotation to check that the component items of the questionnaire load towards the areas that theoretically make up its dimensions. The analysis was performed using the scores obtained from each of the subjects in the sample in the first administration of the CEECCA.
Concordances between the first administration of the CEECCA questionnaire and the three proxy instruments were estimated using Cohen’s κ corrected for random chance effects. Each area of the CEECCA was compared with the selected areas of the Boston test, as shown in Table 1.
To obtain the concordance between the two instruments, the results of each area of the CEECCA were used as dichotomous variables (i.e., functional/dysfunctional) in accordance with the qualitative rules designed for this purpose. It was agreed to select the 60th and 70th percentiles from the percentile table summarising the results of the Boston test. Two concordance tests were performed with this proxy instrument, with results equal to or above the selected percentiles being considered functional, while results below them were considered dysfunctional.
Regarding the NANDA-I classification [28], Cohen’s κ concordance degrees were estimated between the results of each area of the CEECCA (in terms of functionality/dysfunctionality) and the presence of DCs of the NANDA-I diagnosis “Impaired verbal communication” relating to that particular area (Table 2).
The degree of concordance between the results of each area in the CEECCA and the results of the evaluation through the four communication-related NOC outcome indicators [27] was also calculated (Table 3). For this proxy instrument, two concordance tests were performed: an initial test, in which scores from 3 to 5 on the Likert scale inclusive, and assessing each NOC outcome indicator, were set as the criterion for functionality; and a second test, with scores of 4 and 5 considered functional. This conversion allowed the NOC indicator scores to be reformulated into dichotomous variables. Cohen’s κ statistic was again used.
Additionally, non-parametric correlations were calculated between the CEECCA total scores and the total scores of the selected subtests in the Boston test, the total number of DCs of the NANDA-I diagnostic label, and the total score obtained in the evaluation of the NOC outcome indicators for the sample. The Spearman–Brown rho statistic was used for this calculation.
## 2.2.3. Inter-Observer Reliability, Intra-Observer Reliability, and Internal Consistency
The degree of concordance between nurses (with an interval between one and seven days) and of individual nurses (with a four-week interval) was calculated for the results of each area in the CEECCA. These estimates were made using Cohen’s κ concordance statistic corrected for random chance effects. As a supplementary reliability analysis, internal consistency tests were carried out by calculating Cronbach’s alpha and the correlation between each CEECCA item with the other component items of the instrument. This was calculated using the Spearman–Brown rho statistic.
## 2.2.4. Responsiveness
The responsiveness of the questionnaire was tested on a sample subject who underwent a two-week, 20-hour intensive speech therapy rehabilitation programme. The two-hour sessions were held over a period of five days. The intervention consisted of conversation therapy supplemented with activities of increasing difficulty that focused on the subject’s affected processes. The intervention was conducted by a speech therapist with expertise in this type of intervention. The CEECCA was administered two days before the intervention and again the day after the end of the intervention. In addition, the selected subtests of the Boston test were administered before and after the intervention to check for changes using a benchmark instrument.
## 3.1. Sample Description
The sample consisted 47 subjects diagnosed with aphasia, with 16 females ($34\%$) and 31 males ($66\%$) recruited from 20 May 2019 to 18 February 2020 (9 months and 5 days). Their median age was 65 years (41–94 years). All participants were recruited on the island of Tenerife, in the Canary Islands, Spain. Thirty-four percent of the subjects were recruited in primary care consultations, $44.7\%$ in specialised care consultations, and $21.3\%$ in other associations or rehabilitation centres. Regarding their level of education, $25.5\%$ could read and write, $34\%$ had completed primary education, $12.8\%$ had completed secondary education, $17\%$ had a technical or vocational training degree, and $10.6\%$ had a university-level education. Chronic health problems were present in $93.6\%$ of the sample. The most prevalent comorbidities were high blood pressure ($72.3\%$), dyslipidaemia ($57.4\%$), depression ($36.2\%$), urinary incontinence ($27.7\%$), atrial fibrillation ($23.4\%$), obesity ($21.3\%$), constipation ($21.3\%$), epilepsy ($19.1\%$), type 2 diabetes mellitus ($17\%$), anxiety ($12.8\%$), faecal incontinence ($10.6\%$), dysphagia ($10.6\%$), and insomnia ($10.6\%$). Aetiological factors leading to aphasia included ischaemic stroke ($59.6\%$), haemorrhagic stroke ($17\%$), neurodegenerative disease ($10.6\%$), traumatic brain injury (TBI) ($6.4\%$), central nervous system infection ($2.1\%$), and brain tumour ($2.1\%$). The types of aphasia in their clinical records included anomic aphasia ($25.5\%$), mixed transcortical aphasia ($19.1\%$), global aphasia ($17\%$), primary progressive aphasia ($12.8\%$), motor aphasia ($4.3\%$), transcortical motor aphasia ($4.3\%$), anomic motor aphasia ($4.3\%$), semantic variant primary progressive aphasia ($4.3\%$), and transcortical sensory aphasia ($2.1\%$). One participant in the sample ($2.1\%$) could not be assigned any of the established aphasic syndromes. Chronic aphasia with a course of more than 12 months was present in $76.5\%$ of the sample. The majority of the sample was right-handed ($97.9\%$), with only one participant being left-handed.
## 3.2. Administration of the CEECCA
The mean duration of the first administration of the questionnaire was 16 min (9–32), the second administration was 15 min (5–37), and the third administration was 15 min (8–37).
After the first administration of the CEECCA, the language area with the highest percentage of dysfunctionality was “Naming actions in writing” ($72.3\%$), followed by “Verbal expression: descriptive speech”, “Naming objects verbally”, and “Auditory comprehension of sentences” (each with $57.4\%$). The areas with the lowest percentages of dysfunctionality were “Expressing actions through pictograms” ($14.9\%$) and “Auditory comprehension of words” ($17.0\%$). The remaining areas displayed percentages of dysfunctionality between $44.7\%$ and $29.8\%$.
## 3.3. Construct Validity
Barlett’s test of sphericity provided a result of 903 ($p \leq 0.001$), and the Kaiser–Meyer–Olkin (KMO) statistic was 0.30. The five theoretical dimensions explained $78.6\%$ of the total variance. Table 4 shows the rotated component matrix describing the grouping of the items in the five dimensions.
Given the strong correspondence between the theoretical locations of the component items of the CEECCA and the statistical locations resulting from the factor analysis, the decision was made not to make any changes to the initial structure of the instrument.
## 3.4. Convergent Criterion Validity
Table 5 shows Cohen’s κ correlation coefficients comparing each area of the CEECCA with the selected areas of the Boston test [26], and taking the 70th and 60th percentiles as references.
Table 6 shows the results of the convergent criterion validity tests comparing each CEECCA area with the selected DCs of the NANDA-I diagnosis “Impaired verbal communication” [28].
Table 7 shows the results of the convergent criterion validity tests, comparing each area of the CEECCA with the selected indicators of the four NOC outcomes related to communication [27].
The non-parametric correlations between the CEECCA score and the scores of each of the proxy instruments for the whole sample are shown below (Table 8).
## 3.5. Reliability through Internal Consistency
The internal consistency value (Cronbach’s alpha) was 0.98. The intensity of the strength of the inter-item correlation is represented using different colours in Figure 1 [40].
## 3.6.1. Inter-Nurse Reliability in Administering the CEECCA
Table 9 shows the inter-nurse reliability results in terms of functionality and dysfunctionality for each area of the questionnaire in the first two administrations, with a time interval of one to seven days.
## 3.6.2. Intra-Nurse Reliability When Administering the CEECCA
The reliability results in terms of functionality and dysfunctionality for each area of the CEECCA when the same nurse administered the questionnaire at baseline and at one month are shown below. Table 10 shows the results for nurse (a) and Table 11 shows the results for nurse (b).
## 3.7. Responsiveness
The CEECCA areas that exhibited the greatest changes after the intervention (i.e., from a dysfunctional to a functional outcome) were “Descriptive speech”, “Naming objects verbally”, “Writing name and surname(s)”, “Naming objects in writing”, “Naming actions in writing”, and “Auditory comprehension of sentences”. The areas that did not change in terms of functionality but obtained better scores after the intervention were “Conversational speech” and “Naming actions verbally”. The areas that remained unchanged after the intervention were “Expressing actions through pictograms”, “Expressing emotions through pictograms”, “Auditory comprehension of words”, “Auditory comprehension of verbal commands”, “Reading comprehension of words”, and “Reading comprehension of sentences”. No item in the CEECCA areas displayed poorer scores after the intervention. The Boston test subtests obtained better scores, with only the scores on the subtest “Conversational speech” remaining unchanged.
The resulting CEECCA questionnaire is available in Supplementary Materials.
## 4. Discussion
The psychometric tests carried out on the assessment instrument derived from this study have yielded satisfactory results, providing a valid, reliable tool for nurses to assess the main dimensions of language in individuals with aphasia in a simple way and adapted to their daily work. The CEECCA is an instrument whose design [21] and validation processes incorporate aspects of NANDA-I nursing diagnoses and NOC outcome criteria. This allows consistency to be maintained throughout the nursing process as applied in clinical practice [41].
A potential limitation of this study is the sample size used to validate this instrument. In our opinion, the time constraints and operational limitations of the study, together with the difficulty in recruiting participants with this clinical and neuropsychological profile, have prevented a larger sample size from being recruited. Other nursing assessment instruments based on the NANDA-I and NOC classifications have been validated using larger sample sizes [24,25,42]. However, sample sizes are notably smaller in several validation studies of screening-type instruments for the diagnosis of aphasia [30,31,32,33,36,37,38]. These studies do not discuss the reasons for using such a limited sample of subjects with aphasia; however, the frequency of this phenomenon suggests that other studies have also encountered difficulties in recruiting subjects fitting this profile. Similarly, limited samples of patients with aphasia were reported in other studies not devoted to the design and validation of language assessment instruments. For instance, in 2020 a systematic review [43] on the use of transcranial direct current stimulation (tDCS) and a speech therapy intervention in patients with aphasia illustrates this point. This review included 35 studies, with a mean sample size of 14 participants and only one study with more than 40 participants. Some studies mentioned the challenge of obtaining informed consent from individuals with language and communication disorders, resulting in a systematic exclusion of people with aphasia from the samples of many studies [44,45]. A Cochrane review assessing the effectiveness of different strategies in improving the care provided to post-stroke patients and their families [46] revealed that, of the 14 reviewed studies, only one included patients with aphasia, and ten studies considered the presence of aphasia as an exclusion criterion. The authors believe that, in future CEECCA reviews, a larger sample size for validation should be a priority, along with a longer research period.
The calculation of the construct validity of the questionnaire began with carrying out the sampling adequacy tests that warranted the performance of a factor analysis. Bartlett’s test of sphericity, with statistical significance being $p \leq 0.05$, indicated that the variables that made up the test were correlated and, therefore, a factor analysis could be performed. However, the Kaiser–Meyer–Olkin (KMO) statistic provided a result of 0.30, suggesting that the data fitted a factor model poorly [47,48], which was mainly due to the limited sample size. As a result, the calculation of the total variance explained with five dimensions gave a result of $78.6\%$, a high value that points to the possibility of reducing the dimensions of the questionnaire, as three dimensions explained $72.0\%$ of the total variance. However, it was decided not to reduce the number of dimensions and to explore the statistical locations of the items. The rationale for this decision was in the interest of maintaining a questionnaire structure and design that would allow the diagnostic labels of dysfunctionality to be established for the dimensions derived from the selection process based on the NANDA-I and NOC classifications and screening instruments for the diagnosis of aphasia. The rotated component matrix distributed the items into their different factors, maintaining a similar and coherent structure to the one proposed in theory. Despite this, most of the items assessing the “Auditory comprehension” dimension, especially the “Auditory comprehension of words” area, shared a factor with the items assessing the “Expression through pictograms” dimension. In this regard, the CEECCA uses a multiple-choice auditory word recognition test to assess these areas. The two tasks necessarily involve the same processes; therefore, it was expected that a patient with dysfunctional auditory comprehension of words assessed using the CEECCA will perform relatively poorly in the area of “Expression through pictograms”.
Convergent criterion validity tests using the Boston Diagnostic Aphasia Examination (second edition) as a proxy instrument showed concordance percentages representing moderate to strong correlations for most of the areas compared using both the 60th and 70th percentile as references [39,49]. The areas in the CEECCA questionnaire with the strongest correlation were “Conversational speech”, “Descriptive speech”, “Naming objects verbally”, “Naming actions in writing”, and “Auditory comprehension of verbal commands”, with total concordance percentages of up to $93.7\%$. The area with the lowest kappa value was “Auditory comprehension of words”, with a total percentage of less than $60\%$ and a weak correlation for both the 60th percentile and the 70th percentile. These results may be explained by the different methods used by the two tools for assessing this area. While the CEECCA assesses this area using an auditory discrimination test with five very familiar words with high levels of agreement in terms of naming, the Boston test assesses this area using 36 words from six semantic categories, with different levels of phonemic complexity, lexical frequency, and imaginability. The Boston test will, therefore, be able to identify problems in the discrimination of less familiar words and will be more sensitive in detecting impairment in the recognition of words belonging to a particular semantic class. However, the comprehensive assessment of the patient’s performance in all language areas of the Boston test makes it an instrument that requires a long administration time (between one and a half to two hours) [35] and specific knowledge on the part of the assessor in order to make a proper evaluation of the patient [50]. In turn, the CEECCA seeks opportunities for communicative interaction in each language area through a simple assessment process that does not require a long administration time. To this end, it was necessary to limit the number of items and prioritise the interest in detecting functionality/dysfunctionality in patients with more severe communication disorders, even knowing the loss of sensitivity that the tool would experience in identifying dysfunctionality in milder or more selective communicative disorders. Another aspect to consider is that the Boston test was administered by speech therapists with experience in the care of individuals with aphasia, while the CEECCA was administered by nurses without specific knowledge in speech rehabilitation. Even so, the percentages of total concordance between the two tests ranged between $93.7\%$ ($p \leq 0.001$) and $55.3\%$ ($p \leq 0.001$) for the 70th percentile, and between $91.5\%$ ($p \leq 0.001$) and $57.4\%$ ($p \leq 0.001$) for the 60th percentile. These data were supported by the degree of correlation between the Boston test subtest total scores and total performance on the CEECCA questionnaire as measured using the Spearman–Brown correlation coefficient, with a coefficient of 0.96 ($p \leq 0.001$) indicating a strong positive association [51].
Convergent criterion validity tests that used the presence or absence of the DCs of the NANDA-I diagnosis “Impaired verbal communication” as correlation variables indicated κ values suggesting weak to moderate concordance strengths [39,49]. On this point, it is important to mention that the NANDA-I classification is not a diagnostic tool, and, therefore, it may be questionable to perform a criterion validity test using it. However, we believe that it is interesting to consider the possibility that the diagnostic labels proposed by the CEECCA serve as sublevels of specificity of the diagnostic labelling proposed by the NANDA-I. In this test, it was observed that the DCs that were more specifically related to the area of language assessed had higher concordance strengths. For example, the language area “Naming objects verbally” related to seven DCs of the NANDA-I diagnosis. In this case, the percentages of total concordance with the DCs “Difficulty forming words” and “Slurred speech” were higher than with the DC “Difficulty expressing thoughts verbally”, which refers to a manifestation not necessarily related to a verbal naming problem. When calculating the strength of the correlation between the CEECCA total scores and the total scores for the DCs of the NANDA-I diagnosis present in the sample, the results indicated a strong negative correlation of −0.85 ($p \leq 0.001$). This negative correlation was due to the assignment of a value between zero (the poorest possible response) and four (or three) (the best possible CEECCA response), so that a lower CEECCA score for the whole sample was correlated with a higher number of DCs present in the sample.
The κ values obtained in the convergent criterion validity tests using the selected indicators of the four communication-related NOC outcomes indicated moderate to strong correlations [39,49]. When a score of 1 or 2 on the Likert scale of the NOC taxonomy was considered dysfunctional, $82\%$ of concordances observed were moderate to very strong; when taking a score of 1, 2, or 3 as dysfunctional, the percentage of moderate to very strong concordances dropped to $60\%$. The Spearman–Brown correlation coefficient showed a strong positive correlation (0.91) between the CEECCA scores and the NOC indicator assessment scores for the whole sample. A study on the psychometric properties of an instrument (CoNOCidietDiabetes) [25], whose design and validation used the NOC classification, also obtained better levels of correlation between the total scores of the two instruments (rs = 0.72; p-value = 0.001) than between the individual correlations for each NOC indicator, where $41\%$ of correlations were rated as weak and only $9.1\%$ were rated as moderate. Other studies on the design and validation of instruments based on the NOC indicators obtained values similar to those obtained in this study when comparing the results for the whole sample using conceptually similar instruments as a reference. For instance, a 2015 study [52] evaluating the psychometric properties of an instrument reported that the Spanish version of a pain level scale based on the NOC outcome “Pain level” showed a strong correlation (rs = −0.81; p-value < 0.001) with the numerical pain rating scale.
*In* general, convergent criterion validity tests for the CEECCA questionnaire appeared to show adequate levels, which were even higher than those obtained in other design and validation studies based on the NANDA-I and NOC classifications with larger sample sizes [25,42,52].
The internal consistency of the questionnaire was high, with a Cronbach’s alpha value of 0.98 and a predominance of moderate and strong inter-item correlations. Although such a high value may suggest item redundancy, Cronbach’s alpha value does not increase when an item is removed from the questionnaire. This, together with our interest in maintaining a structure that would allow diagnostic labels of dysfunctionality to be established for each area of the CEECCA, meant that a reduction in the number of items was not considered.
Inter-nurse reliability when administering the CEECCA (i.e., when two different nurses administer the instrument in an interval of one to seven days) showed concordance percentages above $90\%$, with κ values above 0.75 ($p \leq 0.001$).
Intra-nurse reliability, both when it was the same nurse in the first and third administration (nurse a) and in the second and third administration (nurse b), showed concordance percentages above $80\%$ in twelve of the fourteen areas of the questionnaire. The areas with the lowest levels of concordance were the areas corresponding to comprehension-related dimensions, especially the “Auditory comprehension of sentences and verbal commands” and “Expression through pictograms” dimensions. Goodglass and Kaplan [26] pointed out that test–retest reliability should be interpreted with caution due to the high fluctuations in the performance of patients with aphasia; however, they also note that, when aphasia becomes chronic, variability in language performance is markedly reduced. On the other hand, a time interval of four weeks does not seem to be long enough to explain this change as an effect of the progress of the disorder itself, or to explain a significant change because of rehabilitation if the patient was receiving it. In the reviewed literature, there is no clear consensus on the most appropriate time interval for conducting an intra-rater reliability test on subjects with aphasia. An interval of 20 to 40 days has been used to calculate intra-rater reliability in other similar studies with subject samples without aphasia [53,54,55]. For the Community Integration Questionnaire Adjusted for People with Aphasia, only inter-rater reliability testing was performed, not intra-rater reliability testing [56]. None of these test–retest reliability calculations were performed for other instruments such as the Frenchay Aphasia Screening Test [30] or the Ullevaal Aphasia Screening (UAS) test [32].
The fact that a high percentage of the sample ($66.2\%$) was receiving speech therapy rehabilitation at the time of assessment could be considered as a change variable for the results at one month; however, in more than $76\%$ of the sample, aphasia had been present for more than 15 months. This reduces the likelihood of relevant changes caused by rehabilitation in a 4-week interval. In addition, none of the rehabilitation interventions received during this stage underwent changes in their characteristics or intensity.
To test the responsiveness of the CEECCA, a specific intervention was introduced that modifies the intensity and characteristics of the speech therapy rehabilitation. The intervention consisted of an intensive therapy based on conversation therapy, supplemented with activities of increasing difficulty focusing on the affected processes, lasting 20 h and spread over ten sessions. Although the available evidence is not yet sufficient to determine, categorically, at what time intervals and intensity levels positive results occur, some authors suggest that a minimum of two hours per day, for a period of two to three weeks, can be considered intensive treatment [57]. The intensity of rehabilitation treatment is considered a relevant variable for its success [20]. A number of systematic reviews assessing the effects of speech and communication therapy in patients with post-stroke aphasia [58,59] highlight the positive relationship between high-intensity rehabilitation treatments and improved outcomes in functional communication and writing. In recent years, a growing number of studies point in the same direction, concluding that high-intensity rehabilitative interventions improve learning and brain plasticity and strengthen synaptic contacts between neurons [60,61,62], even among patients with chronic aphasia [63,64]. The subject assessed in this test was diagnosed with motor aphasia with a course of more than four years. According to the pre-intervention assessment, the areas relating to the verbal and written expression dimensions exhibited the greatest degree of dysfunctionality. After the intervention, these same areas showed the greatest improvements when compared to their baseline levels, according to both the CEECCA and the Boston test.
## 5. Conclusions
The preliminary results obtained suggest that the CEECCA is a valid, reliable instrument for the nursing assessment of the ability of individuals with aphasia to communicate, including dimensions of interest for their care. Using the GRAQoL Index, which assesses the psychometric properties of health measurement instruments through the fulfilment of set criteria, the CEECCA questionnaire obtained a final score of $75\%$, with an A grade of recommendation and above average results when compared to other health instruments [65]. The CEECCA can be administered at any stage of aphasia and in any healthcare setting. This instrument favours nurse–patient communication by indicating which dimensions and areas of language are functional in order to maintain a communicative exchange.
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---
title: Effect of Different Intensities of Aerobic Exercise Combined with Resistance
Exercise on Body Fat, Lipid Profiles, and Adipokines in Middle-Aged Women with Obesity
authors:
- Du-Hwan Oh
- Jang-Kyu Lee
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001681
doi: 10.3390/ijerph20053991
license: CC BY 4.0
---
# Effect of Different Intensities of Aerobic Exercise Combined with Resistance Exercise on Body Fat, Lipid Profiles, and Adipokines in Middle-Aged Women with Obesity
## Abstract
We aimed to investigate the effect of different intensities of aerobic exercise (VO2max: $50\%$ vs. $80\%$) on body weight, body fat percentage, lipid profiles, and adipokines in obese middle-aged women after 8 weeks of combined aerobic and resistance exercise. The participants included 16 women aged >40 years with a body fat percentage of ≥$30\%$; they were randomly assigned to the resistance and either moderate (RME, $50\%$ VO2max, 200 kcal [$$n = 8$$]) or vigorous aerobic exercise groups (RVE, $80\%$ VO2max, 200 kcal [$$n = 8$$]), respectively. After 8 weeks of exercise, we observed that body weight and body fat percentage decreased significantly in both groups ($p \leq 0.01$). The total cholesterol ($p \leq 0.01$) and LDL ($p \leq 0.05$) levels decreased significantly in the RME group, while triglyceride levels decreased significantly in both groups ($p \leq 0.01$). The HDL levels tended to increase only slightly in both groups. The adiponectin levels decreased significantly in the RVE group ($p \leq 0.05$), and the leptin levels decreased significantly in both groups ($p \leq 0.05$). To prevent and treat obesity in middle-aged women, combined exercise (aerobic and resistance) is deemed effective; additionally, aerobic exercise of moderate intensity during combined exercise could be more effective than that of vigorous intensity.
## 1. Introduction
According to the World Health Organization (WHO), obesity is defined as “abnormal or excessive fat accumulation that presents a risk to health.” Obesity became a global health problem by the end of the 20th century [1]. Obesity refers to the excessive accumulation of fat in the human body due to excessive caloric intake, irregular lifestyle, and lack of physical activity and is a known cause of various adult diseases, such as diabetes, high blood pressure, hyperlipidemia, and cardiovascular disease [2]. Obesity leads to changes in blood lipid profiles, and there is a direct relationship between chronically elevated cholesterol levels (dyslipidemia) and an increased risk of cardiovascular diseases [3,4].
A major breakthrough in the perception of adipose tissue as an endocrine organ was the discovery of adipokines, which are biologically activated substances; leptin was the first such discovery [5,6]. Adiponectin, an adipokine, enhances insulin sensitivity and promotes anti-inflammatory and antifibrotic activities [7]; however, studies show that adiponectin is reduced in patients with obesity and coronary artery disease, suggesting its crucial role in obesity-associated cardiovascular diseases [7,8]. Leptin—an important hormone in the prevention and treatment of obesity—regulates many physiological processes, such as appetite suppression, energy consumption, and non-shivering thermogenesis [9,10]. The circulating levels of leptin are highly proportional to the amount of adipose tissue [11]. Women with a large relative fat mass tend to exhibit two-fold higher leptin levels in circulation when compared with those of men with similar body weight [12]; therefore, the risk of chronic diseases, which is associated with high circulating levels of leptin, is higher in women than in men.
The WHO recommends exercise, which is the most effective modality for the prevention and treatment of obesity [1]; additionally, regular exercise reduces body fat, improves lipid profiles, and changes adipokine levels [13]. In middle-aged women, reduced physical activity may result in lower estrogen secretion and increasing fat mass and central adiposity; these conditions are linked to the development of morbidities, such as type 2 diabetes, hypertension, atherosclerosis, dyslipidemia, and metabolic syndrome [14]. Physical inactivity and lower hormone secretion in middle-aged women can lead to decreased lean mass, muscle strength, and bone mass; in turn, this may cause musculoskeletal diseases, such as sarcopenia, impaired balance and movement, and increasing falls, consequently decreasing the quality of life [15]. Exercise programs for middle-aged women should therefore include resistance exercises that increase lean mass and muscle strength, and combined exercise—aerobic plus resistance exercises—could alter body fat, lipid profiles, and adipokine levels [14]. In previous studies, combined exercises decreased body weight and body fat [16], improved lipid profiles [14,17], and induced positive changes in adiponectin and leptin levels [14,18].
The most effective exercises for the prevention and treatment of obesity should consider some important factors, such as intensity, volume, frequency, and type of exercise [19]. However, the effects of different aerobic exercise intensities with the same volume and frequency have not yet been elucidated. Therefore, the purpose of this study was to investigate the effects of performing combined resistance and aerobic exercise of different intensities ($50\%$ VO2max vs. $80\%$ VO2max) on body fat, lipid profiles, and adipokines in obese middle-aged women after 8 weeks of exercise. This premise upholds that all participants perform the same amount of exercise (daily energy expenditure of 400 kcal per day by the American College of Sports Medicine’s [ACSMs] recommendation; aerobic exercise—200 kcal and resistance exercise—200 kcal) [20].
## 2.1. Subjects
This study was approved by the Research Ethics Committee of Dongguk University (DGU IRB 20200033-1). We included 16 middle-aged women (age > 40 years) with obesity (>$30\%$ body fat) and without any previous diagnosis of metabolic disease or other health problems. The participants did not perform any regular physical activity or exercise. The participants were informed of the procedures and signed a document of informed consent before participating. They were instructed to maintain their typical diet pattern throughout the study, and compliance with this instruction was assessed using food questionnaires (1-day recall). The typical diet was based on the daily recommended calorie intake of 2000 kcal for Korean women and comprised foods commonly consumed by Koreans; expert feedback on the diet was provided. The participants were randomly assigned to the resistance and moderate aerobic exercise (RME, $50\%$ VO2max + total body resistance exercise [TRX], $$n = 8$$) and resistance and vigorous aerobic exercise (RVE, $80\%$ VO2max + TRX, $$n = 8$$) groups according to the intensity of exercise. The physical characteristics of the participants are presented in Table 1.
## 2.2. Body Fat Measurement
The body fat was measured by a certified expert in three regions (triceps, front of thigh, and iliac crest) using the skinfold thickness method, and all processes were conducted according to the International Society for Advanced Kinanthropometry. After measurement, body fat was estimated using the formula by Siri [21] and Jackson et al. [ 22].
## 2.3. Blood Samples and Analysis
Blood samples were obtained from the antecubital vein after a 12-h fast (both before and after 8 weeks of exercise) and collected into vacutainer tubes with EDTA under the same conditions and time periods. The collected blood was centrifuged at 3000 rpm for 10 min and stored in a deep freezer at −70 °C. The total cholesterol levels and the respective fractions were analyzed using enzymatic colorimetric assays (Modular Analytics Co., Manchester, UK).
## 2.4. VO2max Measurement
To estimate VO2max, a 1-mile (1609 m) walk was performed by the participants wearing a heart rate monitor (Polar Electro, Kempele, Finland); the rating of perceived exertion was checked every minute to adjust the exercise duration and speed during the test. After the test, the VO2max per body weight was estimated based on the exercise time and heart rate with the following formula [23]:VO2max (ml/min/kg) = 132.853 − (0.1692 × body mass in kg) − (0.3877 × age) + (6.315 × sex) − (3.2649 × time in min) − (0.1565 × HR)[1] Sex: man = 1, woman = 0; HR: Heart rate immediately after the end of walking.
## 2.5. Exercise Program
The exercise program used in this study is shown in Table 2 and was performed five times a week by each group. Energy consumption was measured using the Polar heart rate monitor to measure 400 kcal from when the target intensity was reached (daily energy expenditure of 400 kcal per day by the ACSMs recommendation) [20]. The exercise intensity and rating of perceived exertion were continuously supervised, and the exercise speed was adjusted until the end of the exercise. After a 15-min warm-up under expert supervision, the participants ran on a treadmill at $50\%$ VO2max in the RME group and $80\%$ VO2max in the RVE group to reach a 200-kcal expenditure. The aerobic exercise was directly supervised by an expert so that the exercise intensity remained fixed for each group. The average times of RME and RVE were 45–48 min and 30–33 min, respectively. As the aerobic component was based on caloric expenditure measured through the heart rate response, the time duration of sessions was individualized to each participant. Thereafter, total body resistance exercise (TRX) was performed. The TRX exercises comprised the use of resistance bands to perform various upper body, lower body, and abdominal exercises (Table 2). Before starting the program, a detailed explanation of the movements was given to the participants, and an expert supervised all exercises. TRX was performed at 60–$70\%$ of the HRmax in both groups, for a further 200-kcal expenditure [20]. There were no modifications made to the exercises.
## 2.6. Statistical Analysis
All data analyses in this study were conducted using IBM SPSS Statistics ver. 22.0 (IBM, Armonk, New York, NY, USA). The means and standard errors of all measurements were calculated. The sample size for this study was calculated using the G-Power program (University of Dusseldorf, Dusseldorf, Germany). We set the effect size at 0.2, power at 0.9, number of groups at 2, and number of measurements at 2 for two-way analysis of variance (ANOVA). As the study included a human intervention process, specifically during the COVID-19 pandemic, it was particularly difficult to recruit participants and maintain the study, hence the small sample size with a reduced Z power. A two-way ANOVA was used to determine the effects of interactions between group (RME vs. RVE) and time (pre- vs. post-) on the measured variables. When there were significant interaction effects, the post-hoc was analyzed using LSD, and the level of significance was set at α = 0.05.
## 3.1. Body Weight and Body Fat
After eight weeks of exercise, there were no significant interaction effects between group and time on body weight and body fat percentage. In the main effect test, although body weight ($p \leq 0.01$, RME; 64.58 ± 13.69 vs. 61.53 ± 14.18, RVE; 66.95 ± 10.87 vs. 63.64 ± 9.51) and body fat percentage ($p \leq 0.01$, RME; 34.98 ± 3.39 vs. 28.73 ± 4.75, RVE; 35.23 ± 4.25 vs. 28.15 ± 4.86) significantly decreased after exercise in both groups, there was no difference between the groups (Figure 1 and Figure 2).
## 3.2. Lipid Profiles
After eight weeks of exercise, there were no significant interaction effects between group and time for total cholesterol (TC), triglyceride (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL). The main effect test results for TC ($p \leq 0.01$) and LDL ($p \leq 0.05$) significantly decreased after exercise in the RME group, and TG ($p \leq 0.01$) significantly decreased after exercise in both groups. Although HDL demonstrated an increasing trend, it was not statistically significant. There were no differences between the groups for any of the variables (Table 3, Figure 3, Figure 4, Figure 5 and Figure 6).
## 3.3. Adipokines
Adiponectin demonstrated a significant interaction effect between group and time ($p \leq 0.05$); however, there was no interaction effect on the leptin concentration in the blood. After 8 weeks of exercise, adiponectin levels significantly decreased in the RVE group ($p \leq 0.05$), and leptin levels significantly decreased in both groups ($p \leq 0.05$) (Table 4, Figure 7 and Figure 8). The fasting glucose level did not reach statistical significance after exercise, despite exhibiting a decreasing trend.
## 4. Discussion
This study compared the effects of different aerobic exercise intensities for combined exercises ($50\%$ VO2max + TRX vs. $80\%$ VO2max + TRX) on body fat, lipid profiles, and adipokines in middle-aged women. This study revealed important findings regarding the difference in aerobic exercise intensity when combined with resistance exercise and indicated that moderate-intensity aerobic exercise had positive effects on more variables when combined with resistance exercise (TC, TG, LDL, adiponectin, and leptin).
Changes in body weight and body fat are important factors related to the treatment of health problems and diseases [20]. Regular exercise has a positive effect on changes in body weight and body fat [13]. The results of this study are consistent with those of previous studies, which showed that a combination of aerobic and resistance exercise—which increases lipid oxidation, fat-free mass, and resting metabolic rate—significantly reduces weight and body fat [16,24]. Thus, combined exercise may be a more efficient exercise program for decreasing body weight and body fat in obese middle-aged women, regardless of the exercise intensity. However, there was a discrepancy between the expected calorie consumption by the exercise program and the amount of body fat loss. Our findings suggest that adding healthy lifestyle interventions, such as exercise, may motivate participants to choose healthier dietary options, which may further impact calorie expenditure and subsequent weight loss.
Aerobic exercises—such as walking, jogging, and running—are traditionally performed to alter blood lipid profiles, with various results being reported according to exercise intensity [25,26]. O’Donovan et al. [ 25] controlled the exercise volume to directly assess the impact of aerobic exercise intensity. In their study, participants of the moderate- ($60\%$ VO2max) and high-intensity ($80\%$ VO2max) exercise groups completed three 400 kcal sessions weekly for 24 weeks. It was reported that TC and LDL levels only significantly decreased in the high-intensity group ($p \leq 0.05$). Kraus et al. [ 26] reported that LDL and TG levels significantly decreased, while HDL levels significantly increased, following high-intensity aerobic exercise ($p \leq 05$); however, previous studies have reported that moderate-intensity aerobic exercise also significantly decreases blood lipid profiles [27,28]. These results suggest that aerobic exercise is a factor in lipid reduction, regardless of exercise intensity. Although there is limited data on the effects of combined aerobic and resistance exercise, several researchers have suggested that some combined exercises can effectively lower blood lipid profiles and increase HDL. Some previous studies have reported that TC [29], TG [17,30], and LDL [29,30] levels decreased significantly, while HDL [19,31] levels increased significantly after combined exercise (moderate aerobic exercise plus TRX). In this study, the TC levels in the blood decreased significantly in the RME group ($p \leq 0.01$), while the TG levels decreased significantly in both groups ($p \leq 0.01$). The LDL levels decreased significantly in the RME group after exercise ($p \leq 0.05$), and although it did not reach statistical significance, the HDL levels tended to increase slightly in both groups. These results demonstrate that moderate-intensity exercise may activate fat metabolism more effectively and may lead to additional physiological effects, such as increased muscle strength and lean body mass, owing to TRX, which was included in the combined exercise regimen [32].
Although most adipokines secreted by adipocytes have a positive correlation with obesity, adiponectin is negatively correlated with obesity: the levels of adiponectin in the blood decrease with increasing obesity [33]. A decrease in the adiponectin levels in the blood was reported to play an important role in the development of atherosclerosis as it reduced the inhibitory effect on atheromatous production [34]. Additionally, a decrease in adiponectin levels has been reported in patients with obesity [35], type 2 diabetes [36], and cardiovascular disease [34,37].
Adiponectin is closely related to various chronic diseases and is known to be significantly affected by exercise and reductions in body weight and body fat. Lim et al. [ 38] reported that the levels of adiponectin increased with a decrease in body weight and body fat percentage after combined exercise. However, Hara et al. [ 39] reported no change in adiponectin levels despite a decrease in body fat after combined exercise, while Paulo et al. [ 24] and Langleite et al. [ 40] reported a decrease in adiponectin levels. Although both groups in our study exhibited significantly reduced body weight and body fat after 8 weeks of exercise, the levels of adiponectin decreased significantly in the RVE group, while there were no changes in the RME group. The adiponectin level decreased or exhibited no change because this level is inversely correlated to the adiponectin receptor, which is expressed in the muscle and adipose tissue after exercise [40,41]. Another possible reason is that the increase in catecholamines secreted during exercise and the decreased fasting glucose level after exercise suppressed the gene expression of adiponectin [42,43]. Additionally, there are differences in the degree of weight loss compared to that reported in previous studies. In this study, there was a significant decrease in body weight and body fat; however, the two types of aerobic exercise intensities for combined exercise did not have a positive effect on the increase in blood adiponectin. Therefore, the effect of aerobic exercise intensity combined with resistance training on adiponectin levels remains unclear.
The levels of leptin in the blood increase in proportion to the amount of body fat [11], and it has been reported that blood leptin levels are approximately twice as high in women as those in men with the same body fat (%) [12]. In addition, the risk of various chronic diseases is higher in women. Previous studies reported that leptin levels decreased with a decrease in body weight and body fat after aerobic exercise [44,45]. However, other studies have reported no change in the levels of leptin despite a decrease in body weight and body fat after resistance exercise [46,47] and combined exercise [38]. In the present study, there was a significant reduction in weight and body fat percentage along with leptin levels in both groups after 8 weeks of exercise; this finding is consistent with the results of several previous studies. These results indicate that a decrease in body fat due to exercise is accompanied by a decrease in leptin levels in the blood; this phenomenon is thought to occur due to changes in the fat mass stored in the body through an improved balance of energy and fat metabolism [48]. Additionally, regarding catecholamine changes during exercise, an increase in the activity of norepinephrine induces a decrease in the levels of leptin in the blood by improving the use of fatty acids and reducing leptin resistance [49]. In this present study, effects were observed, but no differences existed between the two exercise programs. Additional studies are needed to examine how the difference in exercise intensity affects other obesity-related factors, such as gut hormones and other adipokines. Although we attempted to manage them, we could not tightly control all individual activities and diets. The limitations of our study include the small sample size, control of calorie intake, and diet composition.
## 5. Conclusions
The purpose of this study was to investigate the effects of different aerobic exercise intensities for combined exercise ($50\%$ VO2max vs. $80\%$ VO2max with TRX) on body weight, body fat, lipid profiles, and adipokines. We reported that weight and body fat decreased significantly in both groups ($p \leq 0.01$), TC levels in the blood decreased significantly in the RME group ($p \leq 0.01$), and TG levels decreased significantly in both groups ($p \leq 0.01$) after exercise. The LDL levels decreased significantly in the RME group ($p \leq 0.05$), while HDL levels tended to increase slightly in both groups after exercise. The adiponectin levels decreased significantly in the RVE group ($p \leq 0.05$), and the leptin levels decreased significantly in both groups ($p \leq 0.05$) after exercise. An interesting finding of this study is that the effect of combined exercise was similar to that of aerobic exercise alone for improvements in body fat, lipid profiles, and adipokine levels. Although, additional physiological effects can be expected due to the resistance exercise component of the combined exercise.
In conclusion, combined aerobic plus resistance exercises could be effective in preventing and treating obesity in middle-aged women. Additionally, aerobic exercise of moderate intensity during combined exercise could be more effective than that of vigorous intensity.
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|
---
title: 'Walk Score and Neighborhood Walkability: A Case Study of Daegu, South Korea'
authors:
- Eun Jung Kim
- Suin Jin
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001684
doi: 10.3390/ijerph20054246
license: CC BY 4.0
---
# Walk Score and Neighborhood Walkability: A Case Study of Daegu, South Korea
## Abstract
Walking is a popular physical activity that helps prevent obesity and cardiovascular diseases. The Walk Score, which measures neighborhood walkability, considers access to nine amenities using a geographic information system but does not deal with pedestrian perception. This study aims to [1] examine the correlation between access to each amenity, an individual component of the Walk Score, and perceived neighborhood walkability and [2] investigate the correlation with the perceived neighborhood walkability by adding variables of pedestrian perception to the existing Walk Score components. This study conducted a survey with 371 respondents in Daegu, South Korea, between 12 October and 8 November 2022. A multiple regression model was used to examine the correlations. The results showed no association between perceived neighborhood walkability and the individual component of the Walk Score. As variables of environmental perception, the fewer hills or stairs, the more alternative walking routes, the better separation between road and pedestrians, and the richer the green space, the more people perceived their neighborhood as walkable. This study found that the perception of the built environment had a more substantial influence on perceived neighborhood walkability than the accessibility to amenities. It proved that the Walk Score should include pedestrian perception and quantitative measurement.
## 1. Introduction
Walking is an inexpensive and popular physical activity that helps prevent obesity and cardiovascular disease [1,2,3,4]. The advantages of walking have increased interest in a walkable environment [5,6]. The built environment plays a crucial role in determining the quality of walking, and a well-designed built environment positively affects walkability [7,8,9]. Therefore, it is essential to understand how the characteristics of the built environment are related to walking.
Various indices have been developed to measure the built environment regarding walkability [1,10,11,12]. The Walk Score—a user-friendly and convenient index—is widely used in many studies [11,13]. The index enables comparing environmental walkability among countries and is used globally [14]. Walk Score comparison between cities in Western countries and Seoul in South Korea enables intriguing research; such comparison can draw policy implications from environmental walkability differences between countries [15].
Kim et al. measured the Walk Score in Seoul and confirmed some significant correlation with pedestrian satisfaction [16]. However, this study points out that although the Walk *Score is* efficient as a walkability index, it is limited in its application to the Asian context. The built environment in Korea has dense and mixed land use, and amenities are oversupplied in most cities with these characteristics [17,18,19,20]. Consequently, people have easy access to amenities by walking. In this context, Korean cities can receive a high Walk Score, but explaining Korea’s walkability only with accessibility to the destinations, which constitutes the Walk Score, can cause distorted results [21]. Therefore, it is necessary to consider the additional variables required to develop the Walk Score as a walkability index suitable for the Korean context.
The Walk *Score is* a quantitative indicator calculated by nine types of access to amenities and two types of pedestrian friendliness [22]. However, it has shortcomings in that it does not include the pedestrians’ perception of the built environment [7]. Bereitschaft said evaluating walkability only based on the Walk Score has low accuracy since it does not consider people’s perception of the neighborhood environment, even though it has an advantage in understanding the overall neighborhood walkability [11]. Tuckel and Milczarski found that the Walk Score was associated with walking for transport but not with walking for leisure purposes. They did not examine the association between the Walk Score and the perceived neighborhood walkability, and therefore, it will be necessary to examine the relationship between them [6]. Perception of the built environment in which people live determines their walking attitude and willingness to walk, and from this perspective, it is essential to understand citizens’ perception of the built environment that affects walking [1,23,24,25].
Therefore, this study aims to [1] find out how much the nine amenity elements constituting the Walk Score are related to the perceived neighborhood walkability of the residents and [2] add variables measuring people’s awareness of the built environmental walkability to the Walk Score and examine the correlation between the perceived neighborhood walkability.
## 2.1. Concept of the Walk Score
The Walk *Score is* a free public web-based tool for measuring local walkability and is serviced in the USA, Canada, Australia, and New Zealand. The Walk *Score is* determined by its accessibility to nine amenities (grocery, restaurants, shopping, coffee shops, banks, parks, schools, books, and entertainment) for each address [22]. When the facility is within 0.25 miles, it obtains a full score using a distance decay function. The score decreases as the distance increases, providing a score for the amenity within up to 1.5 miles [26].
Based on this, scores are generated according to the network distance from the address to the destination and the facility’s weight. Each amenity obtains a different weight, and the considered number of facilities differs (Table 1). The weights and the number of facilities were determined based on previous studies of walkability. Then, it multiplies 6.67 by each amenity’s weight and aggregates the numbers to obtain a normalized score from 0 to 100. Meanwhile, penalties based on average block length and intersection density, considered pedestrian friendliness, deduct scores by 1–$10\%$ [22]. The Walk *Score is* calculated in this method, and the scores are divided into five tiers: 0–24 points are “car-dependent”, 25–49 points are “somewhat car-dependent”, 50–69 points are “somewhat walkable”, 70–89 points are “very walkable”, and 90–100 points are “walker’s paradise” [26].
## 2.2. Built Environmental Factors and Walking
Several studies have found a significant association between the built environment and walking [24,27,28,29,30,31,32,33,34,35,36,37,38,39]. This study classified built environmental factors that affect walking into four categories: convenience, connectivity, safety, and comfort. First, on the convenience of the built environment, Kim et al. and Lee et al. investigated the factors affecting pedestrian volume and satisfaction in Seoul and found that wider sidewalks increase pedestrian volume and satisfaction, while steep roads negatively affect them [24,33]. Herrmann-Lunecke et al. examined how pedestrians’ perception of the built environment affects the walking experience in Santiago, Chile [30]. This study showed that pedestrians are happier when the sidewalk is wider, while narrow sidewalks invoke anger and fear. Similarly, Zumelzu Scheel et al. investigated pedestrians’ perceptions of the built environment in southern Chile and confirmed that wider sidewalks in good condition promote walking [39]. These studies show that convenience is related to the ease and efficiency of walking and making people willing to walk. Therefore, factors that measure convenience may include the presence of various facilities, sidewalk width, sidewalk condition, hills and stairs, and pedestrian shelters.
Second, on connectivity, Adkins et al. analyzed how urban design characteristics affect the perception of walking environment attractiveness [27]. This study showed that walking environment attractiveness increases with better pedestrian connectivity. Liao et al. examined factors influencing walking time for people in Taiwan [34]. The study showed that if the roads are well connected, people are likelier to walk more than 150 min weekly. Furthermore, Ferrari et al. examined the relationship between the perceived built environment of neighborhoods and walking and cycling in eight Latin American countries [29]. As a result, the study found that people were more likely to choose walking for transport when there were more alternative routes in the neighborhood. Meanwhile, Nag et al. identified that well-connected pedestrian road without obstacles promotes pedestrian satisfaction [35]. Connectivity is a factor evaluating whether walking is uninterrupted and whether the road network is well connected. Specifically, more alternative routes, better sidewalk connections, and fewer pedestrian obstacles can be the factors of connectivity and will further increase pedestrian walking.
Third, Yu et al., Ariffin and Zahari, and Oyeyemi et al. studied the safety factor of the built environment [28,36,37]. Yu et al. investigated how the elements of perceived neighborhood walkability are related to well-being and loneliness among older adults in Hong Kong [37]. The study found that traffic safety is significantly associated with well-being, which decreases when pedestrians face difficulties with walking due to heavy traffic. Meanwhile, Ariffin and Zahari examined the built environmental factors that can promote walking behavior in Malaysia [28]. This study showed that reducing the risk of crime motivates people to walk, emphasizing the importance of safety awareness to encourage walking. Oyeyemi et al. investigated the relationship between older adults’ sedentary time and attributes of the neighborhood environment in Nigeria [36]. The results showed that lacking safety from crime is likely to increase older adults’ sedentary time. The safety category measures whether pedestrians can walk safely from traffic collisions and crime. Accordingly, variables such as street segregation, crosswalk and traffic lights, and traffic volume can be used as the safety factors from traffic collisions, and security facilities (CCTV, streetlights, etc.) may be employed as safety factors from crime.
Fourth, Zhang et al. investigated the relationship between older adults’ frequency and duration of the walking trip and the built environment in the Zhongshan Metropolitan area, China [38]. The study found that older adults were more encouraged to walk when the percentage of green space land use was higher. Lee et al. examined the correlation between the neighborhood environment variable and neighborhood satisfaction [31]. The result showed that perceived aesthetics, such as trees in the neighborhood, no trash, attractive architecture, and natural scenery, positively correlated with neighborhood satisfaction. In another study, Lee et al. examined the factors affecting pedestrian satisfaction according to land use and road type [32]. Results showed that green space positively influenced pedestrian satisfaction, and clean streets in the commercial district also increased satisfaction. The comfort factor is how pleasant the built environment is to walk, and it measures pedestrians’ perception of green space, noise levels, etc. More green spaces and natural scenery, cleaner streets, less odor and smoke, and lower noise levels can be comfort factors that promote pedestrian walking.
As we reviewed above, the built environmental factors that affect walking were summarized into four categories: convenience, connectivity, safety, and comfort. According to the previous studies examined, the variables of walkable built environments that can be investigated in the survey questionnaire of this study were selected as follows: [1] convenience: various facilities, sidewalk width, sidewalk conditions, hills and stairs, and pedestrian shelters, [2] connectivity: multiple alternative routes, sidewalk connection, and pedestrian obstacles, [3] safety: pedestrian segregation, crosswalk and traffic lights, traffic volume, and security facilities, [4] comfort: green spaces, natural scenery, street cleanness, odor and smoke, and noise level. We will use 17 items from four categories as the independent variables of the built environmental perceptions in this study.
## 3.1. Research Area and Data Collection
This study covered the city of Daegu, located in the southeastern part of South Korea (Figure 1). Daegu is 883.7 km2 wide and had a population of 2,385,412 as of 2021 [40]. This study used survey data from the larger project (Healthy Walking Project) and was conducted from 12 October to 8 November 2022. This study was approved by the institutional review board of the research team and surveyed individuals aged 18 or older to examine the awareness of neighborhood walkability in the built environment. The questionnaire was distributed to a total of 487 people, and 371 valid responses were used for analysis in this study.
## 3.2.1. Dependent Variable: Perceived Neighborhood Walkability
The dependent variable of this study, the perceived walkability of the neighborhood, was collected through a survey. The respondents evaluated the degree of the walkability of their neighborhood between 0 and 100 points to the question “How good is the walkability of your neighborhood?”. The average of the perceived neighborhood walkability was 76.10 (SD = 17.57).
## 3.2.2. Independent Variables
This study used the weighted accessibility values to each amenity comprising the Walk Score as independent variables. As shown in Table 1, grocery and restaurants have a weight of 3, shopping and coffee shop have a weight of 2, and the remaining five amenities have a weight of 1. For this reason, the amenity accessibility values with the weight are slightly different for each amenity in Table 2. Meanwhile, data on five amenities, grocery, restaurants, shopping, coffee shop, and entertainment, were collected from D-Data Hub [41]. Data on banks were obtained from the Financial Supervisory Service of Korea by requesting location data. Data on parks and schools were collected from Road Name Address [42], address-based industry support services, and data on Books from BigData MarketC [43]. Moreover, the average block length and intersection density related to pedestrian friendliness are factors deducting scores. According to the Walk Score criteria, the average block length is less than 120 m, and the intersection density is higher than 200 at the locations of all respondents in this study. Therefore, when calculating the average block length and intersection density within the 400 m network buffer based on the respondents’ address in this study, there was no deduction at all points, and this study did not need to consider the two pedestrian friendliness factors. Accessibility with the weights of the individual amenities constituting the Walk Score was calculated using ArcGIS 10.5 (Esri, Redlands, CA, USA). Figure 2 shows the locations of nine amenities, which are the Walk Score components, within walking distance from the respondent’s home.
The perception variable that evaluates neighborhood walkability in the built environment was selected based on the literature review, and data were collected through the survey. It consists of 17 items under four categories (Table 2). All the perception variables were measured with a 5-point Likert scale, from strongly disagree [1], disagree [2], neutral [3], agree [4], and strongly agree [5].
## 3.2.3. Control Variables
As control variables, several individual characteristics of the respondents, such as gender, age, and weekly minutes of walking, were used, and the data were collected through the survey. For weekly minutes of walking, the number of walking days per week is multiplied by the average walking time (as minutes). In the descriptive statistics, $37.5\%$ of the respondents were men, and $62.5\%$ were women. The average age of respondents was 34.80 (SD = 14.49), and the weekly minutes of walking was 161.62 (SD = 115.74).
## 3.3. Statistical Analysis
This study conducted a regression analysis to investigate the relationship among perceived neighborhood walkability, the Walk Score, and built environment awareness. This study used SPSS 27 (IBM Corporation, Armonk, NY, USA) software for the analysis.
## 4. Results
Table 3 shows the results of multiple regression analysis for the perceived neighborhood walkability. Model 1 considered only the Walk Score’s nine amenities, and Model 2 included additional variables that measure the perception of the built environment. The results are as follows. First, in Model 1, the perceived neighborhood walkability was higher when the school had better accessibility, and the other eight amenities did not significantly associate with the perceived neighborhood walkability. In this context, Model 2 found that only access to banks was a significant variable related to the perceived neighborhood walkability among amenities consisting of the Walk Score. These results show that the accessibility to amenities is not significantly correlated to perceived neighborhood walkability. In other words, the Walk Score alone cannot explain the degree to which the pedestrians feel good about walking. It suggests that additional variables are required along with accessibility to amenities.
Second, this study examined the variables measuring the perception of the built environment in Model 2. Excluding the items with multicollinearity problems, 6 out of 17 variables were analyzed. The analysis confirmed that the perceived neighborhood walkability decreases as people feel uncomfortable with steep roads and stairs, similar to previous studies [24,44,45]. In addition, multiple alternative routes correlated statistically with perceived neighborhood walkability at 0.001 level. It means pedestrians think the walkability is higher when more alternative routes are available. This result is similar to a previous study showing that alternative routes to access the destination are likely to promote walking [29]. Meanwhile, pedestrians generally thought their neighborhood was more walkable with clear segregation between pedestrians and vehicles, and previous studies also confirmed this finding [27,28,30,38,46,47,48]. In the case of green spaces, there was a statistically significant correlation with the perceived neighborhood walkability at the significance level of 0.001. Pedestrians think the walkability is higher when they feel the neighborhood has visually rich green spaces, and this is similar to previous studies that proved that green spaces positively influence walking [24,27,49,50]. Traffic volume, odor, and smoke on the road did not show a statistically significant association with the perceived neighborhood walkability.
Third, the respondents’ gender, age, and weekly minutes of walking did not have a statistically significant correlation with neighborhood walkability. Xiao et al. found that women and older adults are more likely to walk [51]. In addition, a similar study found that the average number of walks per week positively affected emotional health but did not show significant results related to physical activity [52]. Although previous studies have shown that individual characteristics are significantly associated with walking, this study shows an insignificant association between individual characteristics and perceived neighborhood walkability.
## 5. Discussion
Several studies prove that the built environment has a significant relationship with walking, and interest in creating a walkable environment has increased accordingly. It used the Walk Score walkability index to understand neighborhood walkability. However, Walk Score has limitations as a walkability index in dense countries such as South Korea. Moreover, the Walk Score misses a qualitative indicator to measure the perception of the built environment. Previous studies argued that if the perception of the built environment from a qualitative perspective is considered along with quantitative walkability indicators, it will better measure neighborhood walkability [6]. Therefore, this study attempted to examine whether the accessibility to the nine amenities used in the Walk Score relates to the perceived neighborhood walkability. Furthermore, this study aimed to determine the correlation with the perceived neighborhood walkability, including the variables measuring the perception of the built environment.
The discussed contents based on the research results are as follows. First, it was confirmed that the Walk Score alone, which mainly considers accessibility to the destination, does not estimate the perceived neighborhood walkability. Model 1, which examined the relationship between accessibility to amenities and perceived neighborhood walkability, showed 0.027 explanatory power. Model 2, including variables measuring the perception of the built environment, showed a relatively high explanatory power of 0.335. The increase in explanatory power in Model 2 is due to the inclusion of perception variables. It also showed that the accessibility to most destinations in the Walk Score could not explain the perceived neighborhood walkability. Therefore, the Walk Score, which focuses on amenities, does not reflect neighborhood walkability and requires additional qualitative variables.
Second, this study proved that the perception variables of walkability in the built environment in a highly dense city are significantly related to perceived neighborhood walkability. Among the perception of the built environment variables, hills and stairs hindered the perceived neighborhood walkability. Hills and stairs are the elements that disturb walking, causing detours and risk of falls. Therefore, hills and stairs can be used to evaluate convenience and safety. In particular, since older people are more vulnerable to slopes and stairs, they can be considered when evaluating walkability by age group. Third, the diversity of alternative routes increased the perceived neighborhood walkability. Pedestrians perceived it as more pedestrian-friendly when more options were given to reach their destination because they could walk the preferred route. Similarly, previous studies have also argued that areas with good connectivity provide more routes [53]. Therefore, the alternative route is expected to be a significant variable when evaluating street connectivity in the future. Fourth, pedestrian segregation increased the perceived neighborhood walkability. The sidewalk is the most basic pedestrian infrastructure and is crucial for pedestrians deciding on the walking route [49,54]. Pedestrian segregation should be considered for walkability since it promotes pedestrians’ psychological safety. Accordingly, perceptions of pedestrian segregation can be included in the safety aspect. Fifth, green spaces bring pleasantness and enhance perceived neighborhood walkability. Pedestrians walk longer and are more satisfied when walking a route with abundant green spaces and well-managed [27,55]. Moreover, green spaces can be an indicator of identifying a pedestrian-friendly environment as it is an essential factor in determining health. Therefore, not only the accessibility to the park constituting the Walk Score but also the perceived green space can be considered for the evaluation item of the pedestrian friendliness index. Moreover, since green spaces can be identified using the Normalized Difference Vegetation Index (NDVI) and Google Street View, it will be more widely applicable if they are quantitatively evaluated in future research. These results proved that the perception variable of the built environment significantly affected the individual’s perceived walkability. Therefore, it is necessary to introduce additional qualitative variables and quantitatively measured built environments to evaluate neighborhood walkability in the future.
This study’s limitations and future research direction are as follows. First, it is necessary to verify whether the results of this study can be applied to cities other than Daegu and such research in the future will be efficient in understanding walkability in South Korea. This study was conducted in a large city, Daegu, but future research on small and medium-sized cities will be effective. Second, though this study used the Walk Score only, future research can expect higher policy applicability by using other qualitative and quantitative indicators such as the Walkability Index, Pedestrian Index of the Environment, and the Neighborhood Destination Accessibility Index, including the Walk Score to examine correlation with perception of the built environment. Lastly, this study was conducted on all adult groups aged 18 or older, but it is necessary to consider especially for the elderly who have mobility difficulties. This is because the level of perceived neighborhood walkability will vary depending on age. It can be an important future study to examine the built environmental factors that affect the neighborhood walkability of the elderly with restrictions on walking [56]. Furthermore, as attempted in Hirsch and colleagues’ study, it will be possible to examine the Walk Score and the mobility of older adults [57].
Despite these limitations, this study reviewed whether the Walk Score could explain the individual’s perceived neighborhood walkability in Daegu with the high density of the built environment. It further examined the correlation between the perceived neighborhood walkability and the perception of the built environment in addition to the Walk Score. This study found that the perception of the built environment had a more substantial influence on perceived neighborhood walkability than the accessibility to amenities. It proved that the Walk Score should include pedestrian perception and quantitative measurement. It is also meaningful in that it provided evidence that the perception that people experience should be accompanied by the quantitatively measured Walk Score.
## 6. Conclusions
Promoting walking is suggested as one of the physical activities for health in public health, transportation engineering, and urban planning. This study determines whether the accessibility to the destination, which consists of the Walk Score, is related to the perceived neighborhood walkability. We added the variables of perception of the built environment to investigate the relationship between the perceived neighborhood. As a result, the Walk Score alone could not explain the perceived neighborhood walkability. Moreover, it confirmed a correlation between the perception of the built environment and perceived neighborhood walkability. Therefore, this study argues that more qualitative factors should be included and suggests additional perceptions of the built environment must be considered in Walk Score. This study is expected to contribute to developing the Walk Score to the next level.
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|
---
title: 'Risk of Subsequent Preeclampsia by Maternal Country of Birth: A Norwegian
Population-Based Study'
authors:
- Karolina S. Mæland
- Nils-Halvdan Morken
- Erica Schytt
- Vigdis Aasheim
- Roy M. Nilsen
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001690
doi: 10.3390/ijerph20054109
license: CC BY 4.0
---
# Risk of Subsequent Preeclampsia by Maternal Country of Birth: A Norwegian Population-Based Study
## Abstract
In this nationwide population-based study, we investigated the associations of preeclampsia in the first pregnancy with the risk of preeclampsia in the second pregnancy, by maternal country of birth using data from the Medical Birth Registry of Norway and Statistics Norway (1990–2016). The study population included 101,066 immigrant and 544,071 non-immigrant women. Maternal country of birth was categorized according to the seven super-regions of the Global Burden of Disease study (GBD). The associations between preeclampsia in the first pregnancy with preeclampsia in the second pregnancy were estimated using log-binomial regression models, using no preeclampsia in the first pregnancy as the reference. The associations were reported as adjusted risk ratios (RR) with $95\%$ confidence intervals (CI), adjusted for chronic hypertension, year of first childbirth, and maternal age at first birth. Compared to those without preeclampsia in the first pregnancy, women with preeclampsia in the first pregnancy were associated with a considerably increased risk of preeclampsia in the second pregnancy in both immigrant ($$n = 250$$; $13.4\%$ vs. $1.0\%$; adjusted RR 12.9 [$95\%$ CI: 11.2, 14.9]) and non-immigrant women ($$n = 2876$$; $14.6\%$ vs. $1.5\%$; adjusted RR 9.5 [$95\%$ CI: 9.1, 10.0]). Immigrant women from Latin America and the Caribbean appeared to have the highest adjusted RR, followed by immigrant women from North Africa and the Middle East. A likelihood ratio test showed that the variation in adjusted RR across all immigrant and non-immigrant groups was statistically significant ($$p \leq 0.006$$). Our results suggest that the association between preeclampsia in the first pregnancy and preeclampsia in the second pregnancy might be increased in some groups of immigrant women compared with non-immigrant women in Norway.
## 1. Introduction
Preeclampsia is a pregnancy complication affecting 3 to $5\%$ of women globally [1,2]. It is a leading cause of perinatal morbidity and mortality [2] as well as a risk factor for adverse long-term maternal health consequences including cerebrovascular and cardiovascular diseases [3,4]. Although the exact cause of preeclampsia is unknown, its risk strongly increases with higher maternal age, body mass index, interpregnancy weight change, gestational diabetes, and chronic hypertension [5,6]. Recent research has further highlighted an increased risk of preeclampsia in women with COVID-19 infection in early pregnancy [7]. Additionally, a genetic predisposition appears to increase the risk; women experiencing preeclampsia in a first pregnancy have a significantly increased risk of preeclampsia in a second pregnancy compared with those who do not develop the condition in the first pregnancy [8,9].
Previous studies of preeclampsia suggest that immigrant women overall have a lower risk of preeclampsia than women in the host population in the receiving countries [10,11,12,13]. This has been largely explained by the healthy migrant effect, in that women migrating from one country have better health at arrival than the general population in the receiving country [10,14]. However, more recent studies using maternal country of birth as the exposure show a more nuanced picture, with a higher risk of preeclampsia in refugees and women from low-income countries [13,15]. Thus, to better understand the variation in preeclampsia risk across immigrant groups in receiving countries, alternative hypotheses should be investigated.
In Norway, antenatal care services are offered free of charge and the use of interpreters is statutory [16,17]. However, previous studies suggest that subgroups of immigrant women giving birth in receiving countries may not receive intelligible information and recommendations given during pregnancy and childbirth [18,19]. They also report a low usage of interpreters in maternity care and difficulties navigating the healthcare system to gain information and receive appropriate care during pregnancy [18,19]. Due to such structural barriers to access healthcare, immigrant women may receive poorer quality of care during pregnancy compared with non-immigrants. It is therefore conceivable that some subgroups of immigrants may also be susceptible to complications and health problems during pregnancy.
As part of the postpartum follow-up program in Norway, all women with preeclampsia in a pregnancy should be informed of the high recurrence risk of preeclampsia in a subsequent pregnancy [20]. They should further be advised to avoid general risk factors for preeclampsia such as high interpregnancy weight gain [5]. However, if structural barriers reduce access to healthcare, this information may not be given or correctly understood, reducing the possibility to prevent preeclampsia in a subsequent pregnancy. If this information is not communicated in a tailored and intelligible manner in maternity care for immigrant women, we might expect a higher risk of recurrent preeclampsia in some immigrant groups compared with non-immigrant women.
To test this hypothesis and to identify the subgroups of immigrant women susceptible for preeclampsia, we examined the association of preeclampsia in a first pregnancy with the risk of preeclampsia in the second pregnancy across seven maternal regions of birth as defined by the Global Burden of Disease study (GBD).
## 2.1. Study Design
This population-based registry study used individual-linked data from the Medical Birth Registry of Norway (MBRN) and Statistics Norway. The linkage of data and identification of all pregnancies to the same woman was enabled through the national identity number assigned to all Norwegian residents. The MBRN comprises mandatory, standardized notification of all live- and stillbirths from 16 weeks of gestation (12 weeks since 2002) in Norway since 1967 [21]. The data include information on maternal health before and during pregnancy, and information on maternal and infant health during pregnancy, labor, and birth [21]. Statistics Norway collects, processes, and distributes official statistics in Norway [22]. Data comprise sociodemographic and migration-related factors about all individuals who are or have been a resident in Norway since 1990 [23].
## 2.2. Study Sample
We analyzed all women with first and subsequent births from 1990 to 2016 ($$n = 661$$,098 women with 1,322,870 pregnancies). In particular, women giving birth before 1990 or having their first child outside of Norway during the study period (i.e., women registered as multiparous at the first registered pregnancy in the MBRN) were not included in the initial source population. Furthermore, we focused our analyses only on women categorized as immigrant women (foreign-born with two foreign-born parents) and non-immigrant women (Norwegian-born with at least one Norwegian-born parent). Foreign-born women with one foreign-born parent and those born in Norway to two foreign-born parents (second generation immigrants) were not analyzed as these represented smaller heterogeneous groups. After performing these exclusions, our study sample contained 645,137 women with 1,291,947 pregnancies (Figure 1).
## 2.3. Preeclampsia
Preeclampsia was based on coding according to the International Statistical Classification of Disease and Related Health Problems, 8th (1990–98) and 10th revisions (1999 onwards). This coding corresponds with the criteria given by the Norwegian Society of Gynecology and Obstetrics, i.e., an increase in blood pressure (≥$\frac{140}{90}$ mmHg) combined with proteinuria (≥300 mg in a 24 h urine collection) after 20 weeks of gestation [20,24]. The diagnosis was recorded in the MBRN by open text (1990–1998) or by checkbox (from 1999 onwards). Validation studies covering two periods (1967 to 2005 and 1999 to 2010) [25,26] indicate that the registration of preeclampsia correlates well with medical records.
## 2.4. Region of Birth
Maternal country of birth was obtained from Statistics Norway. Due to the small numbers of preeclampsia in both the first and second pregnancies in the study population, we categorized maternal country of birth (immigrant women only) according to the seven super-regions defined by the GBD study [27,28] as follows: (i) Central Europe, Eastern Europe, and Central Asia; (ii) high income; (iii) Latin America and the Caribbean; (iv) North Africa and the Middle East; (v) South Asia; (vi) Southeast Asia, East Asia, and Oceania; and (vii) Sub-Saharan Africa. The high income regions contained women from the following countries: Southern Latin America, Western Europe, North America, Australasia, and high income Asia Pacific [28].
## 2.5. Other Variables
The MBRN also provided information on maternal age at birth (in years), year of childbirth, parity, and interpregnancy interval (in months). The interpregnancy interval was calculated as the time between the birth of a first child to an estimated conception of a second child (time of birth minus gestational age) to the same woman [29]. Length of residence (immigrants only) was calculated as the difference between year of childbirth of the first child (data from the MBRN) and year of official residence permit in Norway for the mother (data from Statistics Norway).
## 2.6. Statistical Analyses
All analyses were performed in Stata IC version 16 (Stata Statistical Software, College Station, TX, USA), using women as the study unit of analysis. Women with multi-fetal pregnancies were counted only once.
The analyses were organized in two parts (see Figure 1). First, we described absolute preeclampsia risk in first pregnancy and absolute recurrence risk in subsequent pregnancies up to the fourth pregnancy in the source population ($$n = 1$$,291,947 pregnancies). We additionally calculated the numbers for each subsequent pregnancy in these analyses. All calculations were performed separately for immigrants and non-immigrants overall and the results were visualized in a tree diagram using the approach by Hernández-Díaz et al. [ 8].
In the second part and the main analysis, we compared the risk of preeclampsia in the second pregnancy given preeclampsia status in the first pregnancy for women with at least two pregnancies and for each of the seven maternal GBD regions of birth ($$n = 1$$,102,559 pregnancies). Investigations of preeclampsia risk beyond the second pregnancy were not performed due to limited preeclampsia numbers for several immigrant groups of higher parities. The associations were estimated using log-binomial regression models and reported as crude and adjusted risk ratios (RRs) with $95\%$ confidence intervals (CIs), adjusted for chronic hypertension, year of first childbirth, and maternal age at first birth.
Finally, to investigate if the RR of preeclampsia in a second pregnancy after preeclampsia in the first pregnancy differed across the seven GBD regions, a likelihood ratio test was performed by comparing the log-likelihood for a model with and without an interaction term (preeclampsia in first pregnancy × GBD super-regions). A significant interaction term would indicate different effect estimates across groups.
In the sensitivity analyses, we excluded women with multi-fetal pregnancies and HELLP syndrome (hemolysis, elevated liver enzymes, and low platelet count). We also performed additional adjustments for education, interpregnancy interval, and length of residence (immigrants only) to account for other possible background differences between groups. We further adjusted for maternal body mass index for the years available (2008–2016) for immigrant and non-immigrant women overall. The results remained essentially the same.
## 2.7. Ethics and Public Involvement
This is an observational study approved by the Southeast Regional Committees for Medical and Health Research Ethics in Norway; reference number: $\frac{2014}{1278}$/REK Southeast Norway. Data were used under license for this study.
This study used standardized surveillance data. Patients were not involved in the development of the research question, outcome measures, design, or conduct of the study.
## 3. Results
The overall risk of preeclampsia in the study was $3\%$ ($5\%$ in the first pregnancy and $2\%$ in later pregnancies). The risk of preeclampsia in the first pregnancy for immigrants and non-immigrants was $2.9\%$ ($$n = 2965$$) and $4.8\%$ ($$n = 26$$,125), respectively.
Table 1 shows the relevant background characteristics in the sample of women with at least one subsequent pregnancy. Among immigrants, women from high income regions represented the largest group ($$n = 13$$,508 women) while the smallest group comprised women from Latin America and the Caribbean ($$n = 1445$$ women).
Figure 2 presents the risks of preeclampsia for up to four subsequent pregnancies in immigrant (Figure 2A) and non-immigrant (Figure 2B) women. Among those with preeclampsia in the first pregnancy, the risk of preeclampsia in the second pregnancy was $13.4\%$ ($$n = 250$$) for immigrants and $14.6\%$ ($$n = 2876$$) for non-immigrants. For women with a third pregnancy, the risk of preeclampsia in all three subsequent pregnancies was $21.3\%$ for immigrants and $28.7\%$ for non-immigrants (Figure 2).
The mean maternal age at first birth ranged from 24.9 [SD 3.9] to 29.9 [SD 4.4] years in immigrant women from South Asia and the high-income regions, respectively. Among women with two or more pregnancies, mean parity ranged from 2.2 [SD 0.5] in immigrant women from Latin America and the Caribbean to 2.8 [SD 1.1] in immigrant women from Sub-Saharan Africa. The mean interpregnancy interval between the first and second pregnancy ranged from 24 months [SD 22.6] in Sub-Saharan immigrants to 35 months [SD 29.4] in women from Latin America and the Caribbean.
Table 2 shows the crude and adjusted RR for preeclampsia in the second pregnancy for women with preeclampsia in the first pregnancy compared with women without preeclampsia in the first pregnancy. Immigrant women from Latin America and the Caribbean had the highest RR of preeclampsia in the second pregnancy (adjusted RR 17.4 [$95\%$ CI 8.1–37.4]), followed by immigrant women from North Africa and the Middle East (adjusted RR 14.9 [$95\%$ CI 10.5–21.3]). The lowest RR of preeclampsia in the second pregnancy was found in non-immigrant women (adjusted RR 9.5 [$95\%$ CI 9.1–10.0]). The difference in RR across regions of birth was statistically significant by the likelihood ratio test in both crude ($$p \leq 0.004$$) and adjusted ($$p \leq 0.006$$) regression models.
In immigrant women, those with preeclampsia in the first pregnancy were more likely to proceed with a second pregnancy compared with those who did not develop preeclampsia in the first pregnancy (Figure 2; $63\%$ and $56\%$, respectively), but no apparent group difference was seen for later pregnancies. For non-immigrant women, the likelihood of a second pregnancy was almost similar for those with and without preeclampsia in the first pregnancy (Figure 2; $76\%$ and $73\%$, respectively), but fewer women with previous preeclampsia had a third pregnancy ($29\%$ and $34\%$).
When excluding women with multi-fetal pregnancies ($$n = 26$$,086) and women with HELLP syndrome ($$n = 683$$), the results in Table 2 remained essentially the same. Furthermore, additional adjustment for education, interpregnancy interval, and length of residence (immigrants only) did not affect the results notably.
## 4. Discussion
In this study, we found that all women who experienced preeclampsia in the first pregnancy had a substantially increased risk of preeclampsia in the second pregnancy compared with women without preeclampsia in the first pregnancy, irrespective of the country of birth. We further showed that this association was stronger for immigrant women overall as well as for certain subgroups of immigrant women compared with non-immigrant women.
Our finding of a stronger association with preeclampsia in immigrant women compared with non-immigrant women may support our predefined hypothesis of the current study. The importance of follow-up and tailored information is crucial to reduce the subsequent risk of pathology in pregnancy [30]. All women developing preeclampsia in Norway should be carefully informed about the recurrence risk before entering a subsequent pregnancy [20]. They should also be advised not to gain interpregnancy weight as this increases the risk of recurrent preeclampsia [5]. Moreover, women with a history of preeclampsia should be advised to control their blood pressure early in a subsequent pregnancy [20]. This information is essential to increase the awareness of possible lifestyle adjustments and for the early detection of preeclampsia in subsequent pregnancies. However, due to possible structural communication barriers between immigrant women and the healthcare system [18,19], we hypothesized that immigrant women with preeclampsia in a first pregnancy to a lesser extent than non-immigrants receive or acquire sufficient preventive information on recurrent preeclampsia in a subsequent pregnancy. If our hypothesis is true, we therefore would expect a higher risk of subsequent preeclampsia in some immigrant groups compared with others.
To our knowledge, this is the first study to compare the RR for preeclampsia in a subsequent pregnancy between immigrant and non-immigrant women. Being the first study, the discussion of our results in comparison to previous studies is therefore challenging. However, in light of our hypothesis, it may be more interesting to compare our results with results from countries that immigrant women in Norway frequently migrate from. If the RR of subsequent preeclampsia in immigrant women in a receiving country is higher compared with the RR of data from a woman’s country of birth, our hypothesis of poorer communication in receiving countries may be supported. For example, in a hospital-based study in Tanzania, the RR of preeclampsia in a second pregnancy was reported to be 9-fold for women with a history of preeclampsia compared with those without a history [31]. In our study, we found that immigrant women from the Sub-Saharan African region overall had an almost 11-fold increased risk of preeclampsia in a second pregnancy. A higher RR in immigrant women compared with non-immigrant women may support our hypothesis of poorer communication between immigrant women and healthcare providers.
Although our results could support the communication barrier hypothesis, findings should be discussed in light of the large RR and their CIs. When comparing the RR across GBD regions, the RR varied from 10 to 18. However, the CI for these effect estimates largely overlapped the RR of non-immigrant women (see Table 2), except for immigrant women from North Africa and the Middle East (RR 15) as well as immigrant women from high income countries (RR 14). Further, when analyzing immigrants overall, we found that the RR for subsequent preeclampsia for immigrants and non-immigrants was 13 and 10, respectively. Despite the higher RR for preeclampsia in immigrants compared with that of non-immigrants, the RRs are large and the difference in RR between the groups is relatively small. We therefore should be careful to firmly conclude that immigrant women with preeclampsia in a first pregnancy are susceptible to a higher risk of preeclampsia in a second pregnancy compared with non-immigrant women.
Because our study did not directly measure the hypothesized communication barriers, we cannot be entirely certain that the difference in the RR between immigrants and non-immigrants is truly caused by poorer communication between immigrants and healthcare providers. There might be other potential mechanisms for the observed differences, including a genetic susceptibility for increased preeclampsia in some immigrant groups that we were not able to control for in our analyses. Further, the complexity of migration should not be underestimated [32,33] and the stressors related to the process of migration, i.e., unsafe migration routes, could have had an impact on our results. However, despite not accounting for these mechanisms, we would expect that the RR for some immigrant groups was lower than that found for non-immigrants. Instead, our results showed a consistently higher RR for all studied GBD groups, which may strengthen the hypothesis of communication barriers in immigrant women compared with Norwegian-born women.
Consistent with previous studies [11,13,15], we found that the overall risk of preeclampsia (the proportion of preeclampsia across all parities) was lower in immigrant than in non-immigrant women ($3\%$ vs. $5\%$). The lower overall risk of preeclampsia in immigrant women compared with non-immigrants has mainly been explained by the healthy immigrant effect [12], in that women moving to another country are healthier than the general population in the receiving country [34]. In this study, focusing on the preeclampsia risk in the second pregnancy given preeclampsia status in the first pregnancy, it appears that immigrants do not have a lower RR for preeclampsia in a second pregnancy. A plausible explanation for the diverging results of overall and subsequent risk of preeclampsia may relate to the genetic aspect of preeclampsia. Those who develop preeclampsia in a first pregnancy are at a genetically high risk of developing the condition in a subsequent pregnancy for both immigrant and non-immigrant women, irrespective of the healthy migrant effect.
Awareness of the risk of subsequent preeclampsia and preventive measures to reduce this risk in the second pregnancy is crucial for women with preeclampsia in the first pregnancy. Tailored information on the importance of follow-up during pregnancy to obtain the best compliance in maternity care is hence crucial for immigrant women. The main strengths of this study include the national population-based design, the standardized collection of data, and the large sample size. The large sample size and the long timespan of the study enabled a detailed analysis on the risk and subsequent risk for both immigrants and non-immigrants over time. By using the unique personal identification number, all pregnancies to the same woman were identified and enabled an accurate calculation of risk and subsequent risk up to a fourth pregnancy. Previous validation studies of preeclampsia diagnosis in the MBRN [25,26] have reported that the diagnosis correlates well with medical records, adding further strength to our study.
This study has some limitations. Because of the low number of recurrent preeclampsia cases in most countries, we grouped our study sample into broad GBD regions. This may have led to an underestimation or overestimation of the risk of preeclampsia for immigrant women from a specific country, which may further reduce generalizability to specific immigrant groups.
## 5. Conclusions
In this national population-based study of women with two or more pregnancies, both immigrant and non-immigrant women with preeclampsia in a first pregnancy had a substantially increased risk of preeclampsia in a second pregnancy compared with those without preeclampsia in a first pregnancy. The variation between GBD regions overall was not that strong; however, immigrant women from some GBD regions appeared to have a higher risk of preeclampsia in a second pregnancy than non-immigrants. Close follow-up for all women with a history of preeclampsia is important for early detection and possible treatment of the condition in a subsequent pregnancy.
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|
---
title: Lifestyle Score and Risk of Hypertension in the Airwave Health Monitoring Study
of British Police Force Employees
authors:
- Ghadeer S. Aljuraiban
- Rachel Gibson
- Doris S. M. Chan
- Paul Elliott
- Queenie Chan
- Linda M. Oude Griep
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001706
doi: 10.3390/ijerph20054029
license: CC BY 4.0
---
# Lifestyle Score and Risk of Hypertension in the Airwave Health Monitoring Study of British Police Force Employees
## Abstract
Background: Evidence suggest that promoting a combination of healthy lifestyle behaviors instead of exclusively focusing on a single behavior may have a greater impact on blood pressure (BP). We aimed to evaluate lifestyle factors and their impact on the risk of hypertension and BP. Methods: We analyzed cross-sectional health-screening data from the Airwave Health Monitoring Study of 40,462 British police force staff. A basic lifestyle-score including waist-circumference, smoking and serum total cholesterol was calculated, with a greater value indicating a better lifestyle. Individual/combined scores of other lifestyle factors (sleep duration, physical activity, alcohol intake, and diet quality) were also developed. Results: A 1-point higher basic lifestyle-score was associated with a lower systolic BP (SBP; −2.05 mmHg, $95\%$CI: −2.15, −1.95); diastolic BP (DBP; −1.98 mmHg, $95\%$CI: −2.05, −1.91) and was inversely associated with risk of hypertension. Combined scores of other factors showed attenuated but significant associations with the addition of sleep, physical activity, and diet quality to the basic lifestyle-score; however, alcohol intake did not further attenuate results. Conclusions: Modifiable intermediary factors have a stronger contribution to BP, namely, waist-circumference and cholesterol levels and factors that may directly influence them, such as diet, physical activity and sleep. Observed findings suggest that alcohol is a confounder in the BP–lifestyle score relation.
## 1. Introduction
Cardiovascular disease (CVD) is a leading cause of death worldwide, with cardiovascular incidents accounting for almost $85\%$ of total CVD mortality [1]. Hypertension, or high blood pressure (BP), is a major risk factor for cardiovascular morbidity [2] identified as the greatest single preventable cause of mortality worldwide [3].
Hypertension is highly influenced by well-established behavioral lifestyle risk factors, such as smoking and unhealthy diets, and other intermediary factors such as hyperlipidemia and central adiposity [4]. Promoting a healthy lifestyle is an effective approach for improving high BP; however, most studies supporting hypertension prevention recommendations assessed only the single effects of, for example, physical activity (PA) [5] or other lifestyle factors [6], and only a few investigated lifestyle factors concurrently, adding weight to the concept that multiple factors can exert a greater effect when considered together [7,8,9,10]. However, most available scoring systems, such as the QRISK1 and QRISK2 scores [11], used in the National Institute for Health and Care Excellence (NICE) guidelines (including obesity, smoking, serum cholesterol, and other factors [12]) and the American Heart Association’s Life’s Simple 7 (comprising seven modifiable behavioral factors—smoking, body mass index (BMI), PA, diet, cholesterol, BP, and fasting blood glucose [13,14]) have been established to reduce the risk of CVD. Whether these scores apply to the risk of hypertension is yet to be investigated. Further, risk factors included in previous studies were limited to either young adults or only a few risk factors at a time, thus not capturing the multitude of other lifestyle factors that may lower the risk of hypertension further, e.g., sleep [5,15,16,17,18,19,20,21].
Therefore, combining lifestyle factors instead of exclusively focusing on each may significantly impact BP [22] and can be more far-reaching since individual lifestyle recommendations showed differential effects in specific subgroups [23]. In light of this, to promote targeted interventions and identify which lifestyle factors have greater impact on BP/hypertension, the current study aimed to evaluate: a basic lifestyle-score (including available factors from the QRISK2 score [11]); individual lifestyle factors and their combined scores; and the inclusion of individual lifestyle factors to the basic-score. Cross-sectional data from the Airwave Health Monitoring Study, the first large cohort investigating the health of the police workforce in Great Britain [24], comprising a major resource for biomedical research with 42,112 enrolled by the end of 2012 [24] were used. Uniquely, this cohort allows for the consideration of job strain and working patterns specific to the police force, which could impact the achievement and maintenance of a healthy lifestyle [24], and will help in evaluating healthy lifestyle-factors in a population faced with unique occupational challenges.
## 2.1. Study Design
The study design and recruitment details have been published previously [24]. In brief, the study launched in 2004 and a total of 53,114 members of the police force were enrolled by end of 2015. All participants provided written informed consent, and the study ethics were approved by the National Health Service Multi-Site Research Ethics Committee (MREC/13/NW/0588). For this analysis, participants who attended health-screening measurements between 2007 and 2015 were included. Those diagnosed with diabetes or CVD and those with missing data of key variables required for this analysis, e.g., BP, PA, sleep duration, waist-circumference, smoking, and biochemical data were excluded ($$n = 12$$,652). The final sample included ($$n = 40$$,462) adults (25,382 men and 15,080 women).
## 2.2. Clinic Visit
Participants were invited for health screening at study clinics, where, following a standard protocol, trained staff conducted clinical examinations and average measurements were used in the analyses. Non-fasting venous blood samples were collected on-site and transported to the study-laboratory to assess levels of serum total and HDL cholesterol (IL650-analyser Instrumentation Laboratory, Bedford, MA, USA). All laboratory equipment were quality assured and controlled. Weight and height were measured twice with participants wearing light clothes, without shoes or socks using a Marsden H226 portable stadiometer and weighing scale. Waist-circumference was measured twice between the lower rib and the iliac crest in the mid-axillary line using a Wessex-finger/joint measure tape. BP was measured three times, 30 s apart, after participants were seated and relaxed (Omron HEM 705-CP, OMRON Corp., Kyoto, Japan). Hypertension was defined as having a systolic BP (SBP) ≥ 140 mmHg and a diastolic BP (DBP) ≥ 90 mmHg [25], or self-reported diagnosis or the intake of anti-hypertensive medication.
## 2.3. Socio-Demographic and Lifestyle Data
Participants completed a self-administrated electronic questionnaire providing socio-demographic and lifestyle data (e.g., age, sex, and education-level). Job strain was measured using the Karasek Job Content Questionnaire [26] which uses the quadrant approach [27] to categorize participants under high (low control, high demand), active/passive (high control, high demand)/(low control, low demand), and low strain (high control, low demand). Physical activity (PA) was assessed using the short version of the International PA Questionnaire [28]. The questionnaire asks participants to report the frequency and duration of domain-specific activities and energy expenditure in metabolic equivalent minutes/week, and based on this data, intensity of activities (high, moderate, or low) are assigned [28].
## 2.4. Dietary Data
A subsample of participants ($$n = 8546$$) completed 7-day food diaries to report their dietary intake. Photographs and common household measures developed by Nelson et al. were provided [29] for better portion-size estimation. Details on cooking methods and brand names were included. For quality-control, trained nutritionists/dietitians followed a study-specific operational manual to code the diaries and match food/drink items recorded to a UK Nutritional database code and a portion-size [30]. For nutrient-analysis, Dietplan software (version 6.7; Forestfield Software Ltd., Horsham, UK) based on the UK nutrient-database of McCance and Widdowson [31] was used.
## 2.5. Nutrient-Rich Food 9.3 Index-Score
Diet-quality was assessed using the Nutrient-Rich Food 9.3 (NRF9.3) index-score [32], reported to be highly correlated with the Healthy Eating Index, a measure of diet quality-score established by the US Dietary Guidelines [33]. For the NRF9.3 index-score calculation, the sum of the percentage of daily nutrient values of nine nutrients to encourage (protein, dietary fiber, vitamins A, C, E, calcium, iron, potassium, and magnesium) minus the sum percentage of maximum recommended values for three nutrients to restrict (saturated fat, added sugar, and sodium) per 100 kcal was computed. A higher NRF9.3 index-score reflects higher-nutrient quality per 100 kcal.
## 2.6. Lifestyle-Score
A basic lifestyle-score including available factors from the QRISK2 score [11]; waist-circumference, smoking and serum cholesterol (Table 1) was calculated. For the basic lifestyle-score, participants were stratified into three mutually exclusive categories: poor (0–3 points), intermediate (4 points), and ideal (5–6 points).
Additionally, other individual lifestyle factors likely to be on the causal pathway for the risk of hypertension (sleep duration, PA, alcohol intake, and diet quality) and their combined scores were calculated. Participants were also stratified into three mutually exclusive categories: poor, intermediate, and ideal. Each lifestyle factor was defined as poor, intermediate, and ideal, following the 2020 Impact Goals definitions [14]. Ethnic/gender-specific cut-offs for waist-circumference [12,34] were used. For PA, the American Heart Association guide for assessing PA was applied [35]. For sleep, the American Academy of Sleep Medicine and Sleep Research Society [36] guidelines were applied to identify poor (≤5 or ≥9 h), intermediate (6 h), and ideal (7–8 h) amounts of sleep. For diet quality, participants were classified based on published cut-offs of a similar UK sample population [37] into poor (NRF9.3 < 15), intermediate (NRF9.3 16–25), and ideal (NRF9.3 > 25) diet quality.
To evaluate the impact of these lifestyle factors on the basic lifestyle-score relative to BP/hypertension, additional scores were calculated by adding one factor at a time to the basic lifestyle risk-score, defined as follows: a basic lifestyle-score + sleep duration, a basic lifestyle-score + sleep duration + PA, a basic lifestyle-score + sleep duration + PA + alcohol intake, and a basic lifestyle-score + sleep duration + PA + alcohol intake + diet quality (in a subsample $$n = 8546$$).
## 2.7. Statistical Analysis
To calculate scores, ideal levels were given 2 points, intermediate 1 point, and poor 0 points. The sum of points for each lifestyle factor was used to calculate the cumulative score, with the lowest possible score being zero (poor levels of all factors) and the highest for all seven factors being 14 (ideal levels of all factors).
Baseline characteristics of participants were presented according to levels of the basic lifestyle-score (ideal (5–6 points), moderate (4 points), and low (0–3 points)) using a linear age, sex, and employment country-adjusted model to assess the linearity of the investigated relations.
Associations of lifestyle factors with BP were evaluated using multivariate linear-regression models adjusted for age, sex, and employment country. Subsequently, two sequential multivariate linear regression models adjusted for potential confounders were used to determine associations with BP for each 1-point higher basic lifestyle-score. Further, individual lifestyle factors and their combined scores were investigated in relation to BP. Finally, the relative impact of each lifestyle factor on the basic lifestyle-score was assessed by adding one factor at a time to the basic lifestyle-score. Logistic regression analysis was applied to estimate the odds of hypertension per total and levels of the lifestyle-scores. Stratified analyses and interaction terms were applied, detecting no evidence of the potential effect modification by age, sex, and BMI. Despite no evidence of effect modification, and given that the average age of participants was relatively young (mean = 40.4 (SD = 8.9) y), participants were stratified by age (≤30, 30 to ≤40, 40 to ≤50, >50 y) and the linear regression analysis was repeated to gain more insight into the relation with BP.
To investigate whether the main findings were independent of characteristics such as self-reported diagnosis of hypertension, antihypertensive drug use, and prevalent major chronic diseases (e.g., diabetes), the multivariate linear regression analyses were repeated in a sub-cohort of participants with characteristics that might bias the association between the basic lifestyle-score and BP. A sub-cohort of participants was identified with a self-reported diagnosis of hypertension and users of antihypertensive drugs and with prevalent cardiovascular diseases and diabetes mellitus from the foregoing cohort ($$n = 5686$$). Additionally, a sub-cohort excluding energy mis-reporters from 8546 participants who completed the dietary data was defined using the Goldberg equation ($$n = 7567$$) [38]. The SAS version 9.3 (SAS Institute, Cary, NC, USA) was used to perform the statistical analysis; p values < 0.05 were considered statistically significant.
## 3.1. Demographic and Lifestyle Characteristics of the Sample
The sample included 40,462 participants with an average age (mean (SD)) of 40.5 (8.9) years. Overall, $95\%$ of the participants were White and $63\%$ were men (Table 2). When participants were stratified by the basic lifestyle-score, about $30\%$ had poor, $26\%$ intermediate, and $44\%$ ideal lifestyle-score.
## 3.2. Association between the Basic Lifestyle-Score and BP/Hypertension
A 1-point higher basic lifestyle-score was associated with SBP/DBP differences of −2.05/−1.98 mmHg (Model 2; Table 3).
Logistic regression analyses showed a significant relationship between the basic lifestyle-score and the odds of hypertension (OR per 1 point increase = 0.72 ($95\%$CI: 0.70, 0.74)) (Model 2, Figure 1A and Table S1).
Across levels of the basic lifestyle-score, the odds of having hypertension decreased with scoring higher for the basic lifestyle-score, with ORs being 0.49 ($95\%$ CI: 0.46, 0.54) for intermediate level and 0.34 ($95\%$ CI: 0.32, 0.37) for the ideal level compared with the poor level (Model 2, Figure 2A and Table S1). Age-stratified multivariate regression analysis showed that the association between the basic lifestyle-score and BP was stronger in the older age groups (40 to ≤50 and >50 years), (SBP: −2.46 ($95\%$CI: −2.62, −2.29); DBP: −2.25 ($95\%$CI: −2.36, −2.14)) and (SBP: −2.34 ($95\%$CI: −2.70, −1.98); DBP: −1.72 ($95\%$CI: −1.93, −1.52)) compared to the younger age groups (Table S2).
## 3.3. Association of Individual Lifestyle Factors and Their Combined Scores with BP/Hypertension
A 1-point higher waist-circumference-score was associated with −3.63 mmHg lower SBP ($95\%$ CI: −3.80, −3.47) and a −3.53 mmHg lower DBP ($95\%$ CI: −3.64, −3.42). Similarly, smoking, cholesterol, sleep duration, PA, alcohol intake, and the NRF9.3 index-score were associated with lower SBP and/or DBP (Model 2; Table 3).
Logistic regression analyses only showed significant associations between waist-circumference, smoking, cholesterol, sleep duration, and PA scores, and the odds of hypertension (Model 2, Figure 1A and Table S1). Across levels of each individual lifestyle factor, the odds of having hypertension decreased with scoring higher for waist-circumference, cholesterol, sleep duration (only for ideal vs. poor level), and PA (Model 2, Figure 2A and Table S1).
When lifestyle-score factors (sleep duration + PA + alcohol intake + diet quality) were combined, the association attenuated with −0.18/−0.62 mmHg lower SBP/DBP (Model 2; Table 3).
Significant associations were observed between combined lifestyle-score factors and the odds of hypertension (Model 2, Figure 1B and Table S1). Across the levels of combined lifestyle-score factors, the odds of having hypertension decreased with scoring higher for combined lifestyle-score factors with OR being 0.80 ($95\%$CI: 0.69, 0.92) for the ideal level compared with the poor level (Model 2, Figure 2B and Table S1).
Age-stratified analysis showed comparable results of individual score relations to BP, with a trend of stronger associations between waist-circumference-score and BP in older compared to younger participants (Table S2). However, relations between the combined individual scores and BP attenuated and were no longer statistically significant in age-stratified analysis (Table S2).
## 3.4. Association of Inclusion of Individual Lifestyle Factors to the Basic Score with BP/Hypertension
The relative impact of each lifestyle factor on the basic lifestyle-score showed that the association with SBP and DBP attenuated when adding sleep duration, PA and diet quality, but remained statistically significant (Model 2, Table 3). However, the addition of alcohol intake to the basic lifestyle-score only slightly altered the results (SBP −1.91 ($95\%$ CI: −2.00, −1.81; DBP −1.85 ($95\%$ CI: −1.92, −1.79)) mmHg.
When alcohol was added to the basic lifestyle-score + sleep + PA, it did not further attenuate the results (SBP −1.07 ($95\%$ CI: −1.14, −1.00; DBP −1.27 ($95\%$ CI: −1.32, −1.22)) mmHg (Model 2, Table 3). Thus, in model 3, sleep + PA + diet quality was added to the basic model and adjusted for alcohol intake; however, the results remained the same.
The relationship with the odds of hypertension also attenuated but remained significant when all other lifestyle components (sleep duration, PA, alcohol intake, and diet quality) were added to the basic lifestyle-score (Model 2, Figure 1A and Table S1). Associations prevailed across the levels of lifestyle-score factors included in the basic score (Model 2, Figure 2A and Table S1).
For age-stratified analysis, lifestyle factors included in the basic lifestyle-score showed a stronger trend in the relation with BP among older (>50 y) compared to younger adults (≤30 y) (Table S2).
## 3.5. Association of Basic Lifestyle-Score with BP in Sub-Cohorts
The regression analyses were repeated using model 2 in the sub-cohorts that excluded the participants with characteristics that might bias the associations with BP (e.g., self-reported diagnosis of hypertension, antihypertensive drug use) (Table S3), and found that the results prevailed and remained statistically significant.
## 4. Discussion
The present large cohort study evaluated cross-sectional associations of lifestyle-scores in relation to BP/hypertension, reporting a 2.0 mmHg lower SBP (an epidemiologically significant difference at the population level [39]) and a $30\%$ lower risk of hypertension for each 1-point higher adherence to a basic lifestyle-score (including waist-circumference, smoking and serum cholesterol). When lifestyle factors were considered individually, only waist-circumference, low serum cholesterol level, and low alcohol intake contributed to a lower SBP and/or DBP and the risk of hypertension, which can be explained by a healthy waist-circumference and low serum cholesterol. Although significance of the associations prevailed, associations attenuated with the addition of sleep duration, PA, and diet quality. Although evaluated in a smaller subsample, a lifestyle-score including sleep duration, PA and diet quality did not show comparable BP-lowering benefits as the basic lifestyle-score. Significantly lower BP was observed with healthier lifestyle-scores in young adults (≤30 y), with a larger mean difference in BP in the older age group (>50 y) compared to younger age groups.
The relationships between lifestyle factors and BP found here are not surprising given that they were chosen a priori based on the existing literature demonstrating their relationship with BP [4]. It is likely that some lifestyle variables have a stronger contribution to lowering BP than others, namely, more objective ones including waist-circumference and cholesterol levels. Furthermore, when alcohol intake was added to the basic lifestyle-score, it did not further attenuate the results, suggesting that alcohol is a confounder in the relationship, given its relationship with both BP (the outcome) [40], and waist-circumference [41], smoking [42] and serum cholesterol [43] (the exposures). On the other hand, when other factors such as PA, diet, sleep duration, and smoking were added to the basic lifestyle-score, the association with BP attenuated, suggesting that these factors may act as mediators in the association of the basic lifestyle-score with BP. The relationship between these factors and cholesterol or waist-circumference has been well-established [44,45,46,47,48,49]. For example, the attenuation observed when diet and PA were added to the basic lifestyle-score may be attributed to their significant and direct impact on weight and serum cholesterol levels. This suggests that interventions focused on healthier diets and increased PA are important and have the potential to reduce BP and the risk of hypertension [44,50]. Even in young adults, <30 y, lifestyle-scores were related to lower BP, supporting findings that maintaining healthy behaviors from an early age can have favorable impacts on BP and a reduction in hypertension risk [51].
The scores evaluated as part of this work demonstrated a significant relationship with the odds of hypertension. Furthermore, the scores are also suggestive of the magnitude of risk with a more ideal lifestyle being associated with a lower risk of hypertension than an intermediate lifestyle, thus, demonstrating the potential value of the score for assessing hypertension risk. Importantly, although the addition of sleep, PA, and diet attenuated the association of the basic lifestyle-score with SBP/hypertension, the lifestyle-score including only sleep, PA and diet (although in smaller subsample) did not show a lower BP/hypertension comparable to the basic lifestyle-score. This suggests that the basic-score cannot be merely replaced by the lifestyle-score including sleep, PA, and diet in this population.
The present study fills a gap in evaluating the combined impact of several lifestyle factors on BP/hypertension and uses several validated measures for assessing lifestyle data including the International PA Questionnaire [28] and the NRF9.3 [32]. The study used cross-sectional data and therefore a temporal relationship between hypertension and lifestyle factors cannot be established. As with any interview-based data collection, some variables used in the lifestyle-score were subject to misreporting or recall bias. Another consideration is that the Airwave Health Monitoring Study recruits from a distinctive population—those working in the police force [24]. As such, it provides a novel opportunity to study a population with unique occupational challenges. However, the generalizability of the research conducted in this cohort may be limited with the study population being predominantly male with a small proportion of staff from ethnic minorities. It is unknown how well the results can be generalized to the UK population at-large, nor to populations outside of the UK, although underlying biological pathologies are likely to be similar in other groups. Future work can aim to validate and assess the reliability of this tool in the current and other cohorts.
## 5. Conclusions
Given the pervasiveness of hypertension and its contribution to mortality worldwide [3], identifying which lifestyle behaviors impact hypertension risk the most is valuable. This study highlights the value of objective factors including waist-circumference and cholesterol levels that suggested a stronger contribution to BP than others. The combined impact of lifestyle behaviors suggests that alcohol is a confounder in the BP–lifestyle score relationship, and suggests that factors influencing weight, such as diet and PA, may be important in managing the risk of hypertension. Strategies to adopt healthy behaviors may be useful to lower BP and manage hypertension risk by clinicians, researchers, and members of the public, even in young adulthood.
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|
---
title: 'Association between Mothers’ Emotional Problems and Autistic Children’s Behavioral
Problems: The Moderating Effect of Parenting Style'
authors:
- Xiujin Lin
- Lizi Lin
- Xin Wang
- Xiuhong Li
- Muqing Cao
- Jin Jing
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001708
doi: 10.3390/ijerph20054593
license: CC BY 4.0
---
# Association between Mothers’ Emotional Problems and Autistic Children’s Behavioral Problems: The Moderating Effect of Parenting Style
## Abstract
Mothers’ emotional problems are associated with autistic children’s behavioral problems. We aim to test whether parenting styles moderate associations between mothers’ mood symptoms and autistic children’s behavioral problems. A sample of 80 mother–autistic child dyads were enrolled at three rehabilitation facilities in Guangzhou, China. The Social Communication Questionnaire (SCQ) and the Strengths and Difficulties Questionnaire (SDQ) were used to collect the autistic symptoms and behavioral problems of the children. Mothers’ depression and anxiety symptoms were measured using the Patient Health Questionnaire 9 (PHQ-9) and the General Anxiety Disorder 7-item (GAD-7) scale, respectively, and parenting styles were measured using the Parental Behavior Inventory (PBI). Our results show that mothers’ anxiety symptoms were negatively associated with their children’s prosocial behavior scores (β = −0.26, $p \leq 0.05$) but positively related to their social interaction scores (β = 0.31, $p \leq 0.05$). Supportive/engaged parenting styles positively moderated the effects of mothers’ anxiety symptoms on their prosocial behavior score (β = 0.23, $$p \leq 0.026$$), whereas hostile/coercive parenting styles had a negative moderation (β = −0.23, $$p \leq 0.03$$). Moreover, hostile/coercive parenting styles positivity moderated the effects of mothers’ anxiety symptoms on social interaction problems (β= 0.24, $p \leq 0.05$). The findings highlight, where mothers adopted a hostile/coercive parenting style while experiencing high anxiety, their autistic child may have more serious behavioral problems.
## 1. Introduction
Children with autism spectrum disorder (ASD) may exhibit persistent deficits in social communication and social interaction as well as restricted and repetitive patterns of behavior [1]. Regardless of the core symptoms, behavioral problems are also commonly observed but difficult to manage among children with ASD [2]. Thus, parenting children with ASD is challenging [3,4,5] and mood symptoms and disorders are widely reported in parents of autistic children [6,7,8]. According to a previous study, mothers with stable and positive moods are supportive of functional improvements in their children with ASD; thus, providing support for mood and mood-related problems in mothers of children with ASD is beneficial for the child and the mother [9].
We reviewed previous studies regarding parents’ emotional and child behavioral problems, research in the general population has identified well-established connections between parents’ mental health difficulties and the symptoms and behavioral problems of their children [10,11,12]. A number of research studies have reported on the relationship between mothers’ mood symptoms and behavioral problems in children with ASD [13], and those with autistic symptoms [8,14]. Furthermore, children with ASD would have a better prognosis, including social interaction, attention problems, and hyperactivity/inattention symptomatology, when parents had healthy and positive emotional states [9,15,16]. Previous research had limitations, such as a lack of focus on the possible moderating variables that could explain the relationship between anxiety and depression symptoms in mothers and the behavioral problems and symptoms of their children with ASD. Thus, understanding the role of mothers’ depression and anxiety symptoms is important in the development and maintenance of behavioral difficulties in children with ASD.
Research has established that parenting has a critical influence on a child’s development, such as their social development [17] and self-esteem [18], and is also related to the quality of the parent-child relationship [19]. A series of studies has shown that parenting styles are associated with the behavioral problems of children with ASD, such as externalizing problems [20] or internalizing behavioral problems [21]. In addition, parenting styles may affect the intervention and rehabilitation of ASD, for example, a positive parenting style predicts better social competence in children with ASD [22,23]. However, mothers with anxiety symptoms have a more negative parenting style, such as intrusive involvement in anxious children compared to children with typical development (TD) [24]. Hentges et al. found that mothers’ depression has an indirect effect on internalizing problems in children with TD via hostile parenting [25]. Thus, understanding how parenting style interacts with mothers’ mood problems and autistic children’s behavioral problems will help with the development of targeted interventions, as well as understanding whether parenting style may worsen or protect against these effects.
In the current study, we recruited 2–12-year-old autistic children and their mothers to examine the possible moderating influence of parenting style in the association between the mothers’ mood problems and their autistic children’s behavioral problems. Based on existing research, the current study holds several hypotheses: [1] Mothers with higher levels of anxiety and depression will have autistic children with more behavioral problems; [2] Parenting styles will moderate the association between mothers’ moods and behavioral problems in autistic children. More specifically, those with hostile/coercive parenting styles and high levels of anxiety and depression are associated with more behavioral problems in their autistic children. To the best of our knowledge, this study is the first to consider the effects of mothers’ moods and parenting styles on their autistic children’s behavioral problems.
## 2.1. Participants and Procedures
Data were from an ongoing study that started in September 2020 in Guangzhou, China. Children from ages 2 to 12 years were recruited from three special schools affiliated with the Guangzhou Disabled Persons Federation. The inclusion was restricted to children diagnosed with ASD by a child psychiatrist (according to DSM-V). Children with other neurodevelopmental abnormalities, such as epilepsy and cerebral palsy, and physically handicapped were excluded. A total of 110 parent–child (with ASD) dyads agreed to participate in the study. After excluding all questionnaires submitted by fathers, we were left with 80 mother–child dyads for the final analysis. Written informed consent from the mothers had been obtained before the questionnaire and behavior evaluations were completed. The mothers were asked to finish a structured questionnaire (including anxiety, depression symptoms, parenting styles, child behavioral problems, and demographic information, described below), with a uniform guide. Autistic children were assessed for cognition by licensed researchers who had been in standardized training. This study was approved by the Ethics Committee of the School of Public Health at Sun Yat-sen University.
## 2.2. Scales in the Questionnaire
Mothers’ anxiety: The General Anxiety Disorder 7-item (GAD-7) scale, a brief, seven-item self-report scale designed to assess generalized anxiety in mothers was used to evaluate anxiety symptoms [26]. Each of the seven items is scored from 0 (not at all) to 3 (nearly every day). The total GAD-7 scale score ranges from 0 to 21. A higher score indicates greater symptoms on the GAD-7. The current study used the recommended mild-to-severe cut-off scores for anxiety (GAD-7 ≥ 5) to classify subjects with or without a history of anxiety. The Cronbach’s α among Chinese was 0.898 [27].
Mothers’ depression: Symptoms of depression were evaluated by Patient Health Questionnaire 9 (PHQ-9), a nine-item self-report scale designed to assess symptoms of depression in mothers [28]. Each of the nine items can be scored from 0 (not at all) to 3 (nearly every day), and the total scale score ranges from 0 to 27. A higher score indicates greater symptoms on the PHQ-9. The current study used the recommended mild-to-severe cut-off scores for anxiety (PHQ-9 ≥ 5) to classify subjects with or without a history of anxiety. The Cronbach’s α among Chinese was 0.85 [29].
Parenting style: We used the Parental Behavior Inventory (PBI) to evaluate the mothers’ parenting styles. The PBI was designed by Love-Joy [30]; it is a parent’s self-evaluation of their parenting behavior with preschool and junior school children. It is a 20-item self-rated questionnaire including support/participation, and hostility/coercion parenting styles. The Cronbach’s α of support/participation and hostility/coercion were 0.807 and 0.652, respectively in Chinese [31].
Children’s behavioral problems: We used the Strengths and Difficulties Questionnaire (SDQ) to evaluate the behavioral problems of children. Mothers were asked to complete the extended version of the SDQ for children with ASD. The SDQ is a 25-item questionnaire that represents a problem of hyperactivity/inattention (SDQ-HA, 5 items), emotional symptoms (SDQ-ES, 5 items), peer problems (SDQ-PP, 5 items), conduct problems (SDQ-CP, 5 items), and prosocial behavior (SDQ-PB, 5 items); it is designed to assess the behavioral and emotional problems in children and adolescents [32]. Each of the 25 items is rated as being not true [0], somewhat true [1], or certainly true [2], and each of the SDQ subscales consists of five items, thereby yielding scores between 0 and 10. The hyperactivity/inattention, emotional symptoms, peer problems, and conduct problems subscales produce a score for total difficulties, which can range between 0 and 40. A higher score indicates more deficient functioning. For the strength score of the prosocial subscale, a higher score indicates better functioning. The current study used the recommended cut-off scores for each subscale (SDQ-HA ≥ 7, SDQ-ES ≥ 7, SDQ-PB ≤ 4, SDQ-PP ≥ 6, and SDQ-CP ≥ 5) to classify children with ASD as with or without a history of behavioral problems. The SDQ has good reliability and structural validity in Chinese individuals [33].
Autistic behaviors: The Social Communication Questionnaire (SCQ) was used to evaluate the core symptoms of autism. The SCQ scale is a 40-item scale for parents or caregivers designed as a brief screening measure of ASD [34]. The items are based on those with the most discriminative diagnostic efficacy in ADI-R. The SCQ is mainly divided into three areas, namely, the social interaction domain (S), the communication domain (C), and the restricted, repetitive, and stereotyped patterns of behavior domain (R). All items were answered by “Yes” or “No” (0 = no abnormal behavior, 1 = abnormal behavior). A higher score indicates greater symptoms on that subscale. Cronbach’s α coefficient for the present study was 0.89.
## Demographic Information
Baseline characteristics were recorded using written questionnaires, including the mother’s age, education, ethnicity, age and gender of the child with ASD, family income, and the number of family members.
## 2.3. Statistical Analysis
The study was designed to answer another question, but the collected data were used here to address the current questions. SPSS v23.0 statistical software was used to conduct statistical analysis. The descriptive statistics for continuous variables were presented as the mean (M) and standard deviation (SD), and the count data were described by prevalence (%). Pearson’s correlations were conducted on maternal anxiety and depression symptoms, parenting style, and behavioral problems in children with ASD. Multiple linear regression was used to determine if parenting style moderated the associations between mothers’ anxiety or depression symptoms and behavioral problems in children with ASD. We added mothers’ anxiety or depression symptoms and the parenting style in the first step, and the interaction of mothers’ anxiety or depression symptoms and the parenting style in the second step. All regression analyses included children’s gender and age, family income, and maternal education as covariates in the third step. Standardized regression coefficients presented all betas, and the significant level was $p \leq 0.05.$ A simple slope analysis was conducted using the Process 2.16 macro plug-in of SPSS 23.0.
## 3.1. Demographic Information
Table 1 outlines the sample demographic information for the children included in the analysis. In children with ASD ($87.5\%$ boys), 53 ($66.3\%$) children were under 6 years old. As for the mothers, $69.3\%$ were more than 35 years old, $72.5\%$ have low education (less than 9 years), and $76.3\%$ have a family income of less than 8000 yuan per month.
## 3.2. Prevalence of Behavioral Problems in Children with Autism and Mothers’ Emotional Problems
The prevalence of abnormal SDQ-HA, SDQ-ES, SDQ-PB, SDQ-PP, and SDQ-CP behavioral problems (shown in Supplementary Table S1) among children with ASD was 53 ($66.3\%$), 5 ($6.3\%$), 62 ($77.5\%$), 71 ($88.8\%$), and 13 ($16.3\%$), respectively. In addition, the prevalence of depression and anxiety symptoms in mothers with children with ASD was 31 ($38.8\%$) and 38 ($37.5\%$), respectively. The mean and standard deviation of the score of the supportive/engaged parenting style was 33.03 ± 7.74, and that of the hostile/coercive parenting style was 17.91 ± 6.49.
## 3.3. Correlation Analysis
In the correlation analysis between the mothers’ mood symptoms and children’s symptoms (Table 2), the mothers’ depression along with anxiety symptoms were positively associated with the children’s hyperactivity score ($r = 0.28$, 0.29 for depression and anxiety, respectively; $p \leq 0.05$) and negatively associated with the prosocial behavior score (r = −0.27 −0.26 for depression and anxiety, respectively; $p \leq 0.05$). In addition, mothers’ depression symptom was related to a higher conduct problems score ($r = 0.25$, $p \leq 0.05$). As for the parenting style, supportive/engaged was associated with lower SCQ scores in social (r = −0.31, $p \leq 0.05$), repetitive (r = −0.33, $p \leq 0.05$), and SCQ total score (r = −0.33, $p \leq 0.05$); however, hostile/coercive was associated with a higher score in the SDQ total score ($r = 0.24$, $p \leq 0.05$) and communicating domain ($r = 0.24$, $p \leq 0.05$).
## 3.4. Relationship between Mothers’ Anxiety Symptoms and Children’s Prosocial Behaviors Measured by SDQ Moderated by Parenting Style
After adjusting for the children’s gender and age, family income, and mothers’ education, multiple linear regression analysis showed that mothers’ anxiety symptoms were negatively associated with children’s prosocial behavior (β = −0.26, $p \leq 0.05$); and supportive/engaged parenting style had a marginally positive relationship to children’s prosocial behavior (β = 0.21, $$p \leq 0.051$$). In addition, a supportive/engaged parenting style positively moderated the effect of mothers’ anxiety symptoms on children’s prosocial behavior (β = 0.23, $$p \leq 0.026$$); conversely, a hostile/coercive parenting style negatively moderated the effect of mothers’ anxiety on children’s prosocial behavior (β = −0.23, $$p \leq 0.031$$, see Table 3). The negative results of mothers’ depression and children’s behavioral problems collected in the SDQ are shown in Supplementary Table S2.
We used a simple slope analysis taking into account the effect of the interaction between mothers’ anxiety symptoms and supportive/engaged parenting style on children’s prosocial behavior. As an example, we set the two special values of mothers’ anxiety symptoms and supportive/engaged parenting style as a standard deviation above and below the average. In addition, we calculated the simple slope of maternal anxiety symptoms on children’s prosocial behavior when a supportive/engaged parenting style was high/low. The results showed that mothers’ anxiety symptoms were negatively associated with children’s prosocial behavior when the supportive/engaged domain was low (b = −0.282, $p \leq 0.01$), whereas there was no significant association when the supportive/engaged domain was high (b = −0.212, $p \leq 0.05$, see Figure 1a).
For high levels of hostile/coercive parenting styles, mothers’ anxiety symptoms were negatively associated with children’s prosocial behavior (b = −0.300, $p \leq 0.01$), but had no association when the hostile/coercive domain was low (b = −0.063, $p \leq 0.05$, see Figure 1b).
## 3.5. Effects of Mothers’ Anxiety Symptoms on Children’s Social Interaction Moderated by Parenting Style and Measured by SCQ
After adjusting for children’s gender and age, family income, and mothers’ education, multiple linear regression analysis showed that hostile/coercive parenting styles positively moderated the effect of mothers’ depression on children’s social interaction (β = 0.24 for hostile/coercive; $p \leq 0.05$; see Table 4). Supportive/engaged marginally moderated the effect of mothers’ depression on children’s social interaction (β = 0.20; $$p \leq 0.052$$; see Table 4). The results of mothers’ depression and children’s other behavioral problems by SCQ are shown in Supplementary Table S3.
The simple slope showed that mothers’ anxiety symptoms were positively associated with children’s social interaction with a high hostile/coercive parenting style ($b = 0.423$, $p \leq 0.01$) but not with a low hostile/coercive parenting style ($b = 0.114$, $p \leq 0.05$, see Figure 2).
## 4. Discussion
Parenting a child with autism is difficult; emotional problems, including depression and anxiety, have been widely reported among parents of autistic children [8,35]. By using a cross-sectional study, we tested if parenting styles moderated the association between mothers’ emotional symptoms and autistic children’s behavioral problems and social communication. The main findings of this study confirmed that [1] mothers’ anxiety and depression symptoms are positively associated with the severity of behavioral problems among children with ASD; and [2] moreover, when parenting style is low supportive/engaged, or high hostile/coercive, mothers’ anxiety symptoms are associated with a decrease in children’s prosocial behavior or an increase in social interaction problems.
Our findings indicated that mothers’ anxiety was associated with less prosocial behavior or more social interaction problems in autistic children [8,14]. We proposed several mechanisms to explain why a negative parenting style may worsen the situation. [ 1] More anxiety symptoms can cause mothers to adopt negative parenting strategies, such as aggressive behavior and violence [36], thereby eroding children’s self-esteem and their ability to regulate their own emotions [37] and increasing children’s behavioral problems. On the contrary, a supportive parenting style will provide children with a positive parent–child interaction, which the child may use when interacting with others, thereby benefitting their peer relationships and social belonging, having enough social support when encountering problems, and decreasing the rate of problem behaviors [38,39,40,41]. [ 2] Research on children with TD showed that negative parenting behaviors, such as being hostile/coercive, can function as a risk factor during children’s behavioral problem development; whereas positive parenting behaviors can be a protective factor [25,42]; children with ASD may share the same mechanism. [ 3] Mothers’ anxiety symptoms lead to intrusive, hostile, and neglectful behaviors, as well as less involvement in parenting their children [43,44], which may cause problematic parent–child interactions, thereby finally decreasing the prosocial behaviors of children with ASD [45].
A negative correlation between mothers’ depression symptoms and children’s prosocial behavior [46,47] is found among TD, which is similar to our findings in autistic children. This finding may be attributed to the incapability of depressed mothers to respond to their children’s needs, which limits the whole family’s initiative to seek proper intervention for the children [48]. Conversely, mothers’ internal physiological changes can be inherited by their children, which can increase the emotional and behavioral problems of autistic children [49]. However, the relationship between mothers’ depression symptoms and children’s behavioral problems is not moderated by parenting style in the current study, which can be attributed to the limited sample size.
The present study found that mothers of children with ASD had a comparable rate of depression and anxiety symptoms ($38.8\%$ and $37.5\%$, respectively) with previous research [50,51,52], which confirmed the elevated risk of depression among mothers of autistic children. We also compared our rate with the rate from other regions of China [8,53]. We found that anxiety and depression symptoms among mothers with children with ASD were more prevalent in the present study. In addition, our study addressed the most salient problem behaviors in autistic children, which were hyperactivity/inattention, prosocial behavior, and peer problems; this was similar to the previous research [54,55]. Our current findings corroborate and further attest that children with ASD often exhibit co-occurring behavioral problems. According to Coplan et al., pragmatic language is a possible pathway in the development of behavioral problems, as it plays an important role in children’s communication with peers, especially in the school-age period. Children solve problems and achieve social goals through adequate language skills, which, for autistic children, are lacking [56]. Thus, limited language skills may make them feel insecure about engaging in peer relationships and cause more problematic behaviors [57]. Moreover, previous studies suggested that other features of autism such as sensory difficulties [58] or resistance to change [59] also caused more problem behaviors.
Our results also confirmed that a hostile/coercive parenting style was positively related to hyperactivity and the total behavioral problem level of autistic children (see Supplementary Table S2), which was reported by Maljaars et al. [ 21]. The coercion theory [42] proposes that in a coercive cycle, aversive child behaviors reciprocally influence parenting behaviors, which results in the negative reinforcement of undesirable behaviors in children and parents [60]. Conversely, a positive parenting style is associated with the prosocial behaviors of children with TD [61,62]. This finding is in line with ours (see Supplementary Table S3), which also supported the coercion theory.
This study has a few limitations. First, in view of the cross-sectional design, the causal relationship was not addressed, and the negative finding of an interaction effect between parenting style and depressive mood could be attributed to the relatively small sample size. We recommend a longitudinal study with a larger sample size to clarify the potential mechanism of the association. Second, our study did not consider the role of fathers; the parenting style and emotional symptoms of fathers should be considered in future studies. Third, as there was no normative or clinical comparison group, a more robust design should be considered. Fourth, a limitation of our study was that we used maternal reports of children’s behavioral problems with a single informant (i.e., the mother). Future studies should include multiple informants, such as teachers and fathers to further explore these associations.
## 5. Conclusions
In this research, we confirmed a high rate of anxiety and depression symptoms among autistic children’s mothers, as well as behavioral problems of autistic children. High levels of anxiety and depression in mothers are linked to more behavioral problems in their autistic children. Negative parenting styles, such as low supportive/engaged or high hostile/coercive, further enhance the association between mothers’ mood problems and less prosocial behaviors, and more serious social interaction problems among these children. So far most parenting programs aimed at parents of children with ASD have focused on improving communication in children; studies addressing parenting strategies are limited [63]. Thus, we propose that parents may need more support in coping with emotional problems and improving their parenting skills to decrease the problem behavior of autistic children.
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|
---
title: 'mtR_find: A Parallel Processing Tool to Identify and Annotate RNAs Derived
from the Mitochondrial Genome'
authors:
- Asan M. S. H. Mohideen
- Steinar D. Johansen
- Igor Babiak
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001721
doi: 10.3390/ijms24054373
license: CC BY 4.0
---
# mtR_find: A Parallel Processing Tool to Identify and Annotate RNAs Derived from the Mitochondrial Genome
## Abstract
RNAs originating from mitochondrial genomes are abundant in transcriptomic datasets produced by high-throughput sequencing technologies, primarily in short-read outputs. Specific features of mitochondrial small RNAs (mt-sRNAs), such as non-templated additions, presence of length variants, sequence variants, and other modifications, necessitate the need for the development of an appropriate tool for their effective identification and annotation. We have developed mtR_find, a tool to detect and annotate mitochondrial RNAs, including mt-sRNAs and mitochondria-derived long non-coding RNAs (mt-lncRNA). mtR_find uses a novel method to compute the count of RNA sequences from adapter-trimmed reads. When analyzing the published datasets with mtR_find, we identified mt-sRNAs significantly associated with the health conditions, such as hepatocellular carcinoma and obesity, and we discovered novel mt-sRNAs. Furthermore, we identified mt-lncRNAs in early development in mice. These examples show the immediate impact of miR_find in extracting a novel biological information from the existing sequencing datasets. For benchmarking, the tool has been tested on a simulated dataset and the results were concordant. For accurate annotation of mitochondria-derived RNA, particularly mt-sRNA, we developed an appropriate nomenclature. mtR_find encompasses the mt-ncRNA transcriptomes in unpreceded resolution and simplicity, allowing re-analysis of the existing transcriptomic databases and the use of mt-ncRNAs as diagnostic or prognostic markers in the field of medicine.
## 1. Introduction
Mitochondria are organelles present within all eukaryotic cells, performing oxidative phosphorylation [1] and apoptosis processes [2], among others. Metazoan mitochondria possess their own genomes, which are relatively small (usually 15–20 kb) and contain 14 to about 40 genes, typically 37 in vertebrates [3]. Owing to the multiple cellular copies of mitochondrial DNA, the abundance of mitochondrial transcripts can range from 5 to $30\%$ (depending on the cell type) of the total cellular RNA [4,5]. Mitochondrial non-coding RNAs (mt-ncRNAs) are referred as those encoded in the mitochondrial genome, although nuclear genome-encoded non-coding RNAs (ncRNAs) can be present in mitochondria [6]. Both mitochondrial small non-coding RNA (mt-sRNA) and long non-coding RNA (mt-lncRNA) have been identified both inside mitochondria and in other cellular compartments, and some of their implicated gene regulatory functions have been proposed [4,5,7,8,9]. Despite the growing evidence of regulatory functions of mt-ncRNAs, no appropriate bioinformatic tools to identify them are available up to date.
There are tools such as MITOS [10] or DOGMA [11] to annotate mitochondrial genome, but these tools cannot identify and quantify mt-ncRNAs. Although DOGMA can annotate nucleotide sequences to the mitochondrial genome, the tool requires the entire mitochondrial genome sequence as input and does not work with mt-ncRNAs, which are much shorter. The current analysis of the high-throughput sequencing data relies on the use of tools designed for the nuclear genomic RNA. These tools, as well as DOGMA, cannot identify mt-sRNAs effectively for mt-sRNAs, and frequently have non-templated additions, as well as sequence and length variants [12]. Tools such as tDRmapper [13], SPORTS [14], or MINTmap [15] can be used to analyze mitochondrial tRNA derived fragments (mt-tRFs). However, there is no tool to simultaneously analyze all small RNAs (sRNA) mapping to the mitochondrial genome.
Most tools designed for small RNA data analysis deploy a three-step procedure with some minor modifications [16]. This includes: [1] read count generation, [2] mapping the unique set of sequences to a reference FASTA, and [3] parsing the mapped output files. Read count generation is the most time-consuming step, but it can be significantly reduced by parallelizing the processes on all the available CPU cores. We have developed mtR_find, a bioinformatic tool for identification, annotation and analysis of mtRNA in new or existing transcriptomic datasets produced in any type of sequencing technology. mtR_find uses PYTHON’s multiprocessing functionality that helps to parallelize the analysis of multiple sequencing files for read count generation, thereby massively reducing the data processing time. Along with the tool, we propose a nomenclature to encompass the mt-RNA specificity. The tool allows retrieving the important biological information from the existing datasets in a high-throughput mode in an unpreceded efficiency.
## 2.1. Performance
The total read counts for the three datasets were: 332.3 million (dataset-1, sRNA-seq of liver samples from malignant tumor tissue of HCC patients and non-malignant tissue from uninfected individuals), 318.2 million (dataset-2, sRNA-seq of semen samples from lean versus obese men), and 93.4 million (dataset-3 (RNA-seq of mouse oocytes); Supplementary File S1). The sRNA datasets were analyzed through parallel processing by mtR_find, and the total execution time for datasets 1 and 2 was 3 min 44 s and 2 min 38 s, respectively. For comparison, the total execution time using MINTmap for dataset-1 and dataset-2 was 48 min 2 s and 34 min 7 s, respectively. The mt-lncRNA analysis was not performed using the parallel processing due to pickling limitations in PYTHON multiprocessing module [17]), and the total execution time was 11 min 29 s. The duration of serial execution of datasets 1 and 2 was 9 min 9 s and 11 min 40 s, respectively. Consequently, the serial execution took ~2.75 times longer than the parallel execution, indicating the efficiency of parallel execution. Besides parallel execution, there are other differences in the way the tool handles mt-sRNAs and mt-lncRNAs. The tool does not consider sequences longer than 50 nt for mt-sRNA computation and shorter than 50 nt for mt-lncRNA. For mt-sRNA, every single sequence is considered unique by the tool. For mt-lncRNA, the tool outputs the unique sequence count and, in addition, the counts of lncRNA sequences with same 5′ end but variable 3′ end are summed together. In addition to mt-lncRNAs that are longer than 200 nt, mt-lncRNA option of mtR_find also identifies ncRNAs that are 50–200 nt long, which are categorized as mid-size or intermediate RNAs. In order to study only lncRNAs that are longer than 200 nt, users can use the “—filter 200” argument as a command line option while running mtR_find.
## 2.2. Read Statistics
Datasets-1, -2, and -3 had, respectively, 36,136, 93,128, and 9222 unique sequences with a total read count greater than 200 (Supplementary Files S2–S4). The numbers of sequences that mapped to the mitochondrial genome were 2120 (constituting $1.2\%$ of total reads), 8899 ($4.4\%$), and 178 ($1.4\%$), respectively (Supplementary Files S5–S7). Out of these, reads mapping to heavy strand composed $71.5\%$, $67.4\%$, and $43.5\%$ of the total mitochondria-derived sequences respectively, while the remaining reads mapped to the light strand (Supplementary File S8, Figures S1–S3).
## 2.3. Length Distribution and Annotation of mt-ncRNAs
We found a diverse size range (Supplementary File S8, Figure S4) and gene origins (Supplementary File S8, Figure S5) of mitochondrial non-coding RNAs in the datasets examined. Datasets-1 and -2 were enriched in mt-sRNAs in the size range of 31–32 nt and 27 nt, respectively, while the mt-lncRNAs in the dataset-3 were in the size range of 87 to 141 nt. Most of mt-lncRNAs in the dataset-3 had length variants (Supplementary File S7). The majority of them belonged to three genes, namely, ATP6, ATP8, and CytB (Supplementary File S8, Figure S5C).
## 2.4. Differential Expression of mt-ncRNAs
There were differences in number of reads mapping to mitochondrial genes between the subject and control groups in both the dataset-1 and dataset-2 (Supplementary File S8, Figure S5). PCA for mt-sRNAs (Supplementary File S8, Figures S6 and S7) and the heatmap of top 50 highly variable read sequences (Figure 1) showed clustering of two different groups consistent with the subject and controls, although there was a small variability within groups resulting from biological replicates.
Differential expression (DE) analysis of mt-ncRNAs was performed on the data from dataset-1 (chronic hepatitis C-associated cancer vs. non-cancer liver samples; chronic hepatitis B-associated cancer vs. non-cancer liver samples; chronic hepatitis C-associated cancer vs. uninfected cancer liver tissue samples; and chronic hepatitis B-associated cancer vs. uninfected cancer liver tissue samples, Table 1) and dataset-2 (semen from obese vs. lean subjects). In the dataset-1, there was a significant reduction ($p \leq 0.005$) in the relative abundance of tRNA half (tRH) mapping to tRNA genes of nuclear genome origin, namely, tRFs from tRNAGly and tRNAVal in cancer tissue when compared to non-cancer liver tissue [18]. We observed a similar trend for DE mitochondrial tRHs. For example, when looking to chronic hepatitis C-associated cancer vs. non-cancer liver tissue samples comparison, 13 out of 354 DE tRFs were tRHs and 10 of them were significantly downregulated in the cancer cells (Supplementary File S9). Five of these ten mitochondrial tRHs originated from tRNAVal. In the dataset-2, 75 DE mt-sRNAs (39 up- and 36 down-regulated in semen samples from obese vs. lean individuals) were identified, all of them originating from the mitochondrial large subunit rRNA (Supplementary File S10). The majority of them existed as length variants and all of them clustered at a region with sequence start site between 2690 and 2706 in the mitochondrial large subunit (mtLSU) rRNA gene, with 2704 and 2705 being the two most common sequence start sites.
## 2.5. Novel Mitochondrial tRFs and Non-Coding RNAs Detected by mtR_find
The DE mt-tRFs (783 unique mt-tRFs) from the dataset-1 were compared with tRFs downloaded from MINTbase, an extensive database of 28,824 nuclear and mitochondrial tRFs obtained from 12,023 cancer datasets using MINTmap tool [19]. There were 365 ($46.6\%$) tRFs not found in MINTbase, including 214 tRFs-5, 42 tRFs-3, 43 i-tRFs-3, 56 i-tRFs-5, 8 tRNA-half-5, and 2 tRNA-half-3 (Supplementary File S11). All these novel tRFs had normalized reads per million (RPM) value greater than one (Supplementary File S11), a cut-off value in MINTbase.
## 2.6. Performance of the Tool with Simulated Data Set
There were 16 simulated sequences of mt-lncRNA, including 7 from the heavy strand, 5 from the light strand, and 4 antisense to heavy strand genes with substitutions and grouped as light strand transcripts. The simulation gave results concordant with the mtR_find (Supplementary File S8, Table S1). The CSV files from both the simulation and mtR_find analyses were loaded as data frames using PYTHON pandas module, element-wise comparison was performed between the two data frames, and the results were similar (Supplementary File S12).
## 3. Discussion
mtR_find is the first small RNA tool to incorporate parallel processing by reading multiple input files simultaneously and processing them at the same time. The mtR_find tool performs much better when compared to published small RNA tools such as MINTmap [15]. Results from testing mtR_find on the simulated dataset shows that the sensitivity of mtR_find is high. The read count algorithm of mtR_find can be used for developing tools for the analysis of other sRNA types by replacing the reference and modifying the annotation criteria. Even though the parallel processing significantly reduces the execution time, it has to be noted that the execution time is CPU-dependent. Furthermore, if the number of CPU is not commensurate with the available RAM, the script might run into memory errors. In such a case, a user has to lower the CPU count manually by using the command line parameters to circumvent the issue. The execution time of mtR_find is much lower than MINTmap and also includes the time to download both the GTF file and the mitochondrial genome. If these files are provided manually as input files, then the execution time will be further reduced. Moreover, mtR_find identified 365 tRFs that are not present in MINTbase v2.0. Due to the presence of overlapping reading frames in several mitochondrial genes, mt-sRNA sequence start and end sites of ±3 were used for annotating the mt-sRNAs in our tool; indeed, 266 out of the 365 sequences had sequence start site or end site at ±3 nt from the gene start or end boundary, respectively (Supplementary File S11). And, 42 out of these 266 mt-sRNAs, had sequence start or end site either before or after the 5′ and 3′ end of tRNA gene boundary, respectively. Hence, mtR_find is highly sensitive in capturing all mtsRNAs from the mitochondrial genome.
mtR_find identified features in the test datasets that had not been identified before. mtR_find identified reads mapping to the light strand in the range of 28.5–$56.5\%$. This result is discrepant with the previous studies on mt-sRNAs, where it has been shown that the number of reads from the light strand constituted approximately 3–$5\%$ of all the mitochondrial reads [4,12]. Notably, we found a considerable number of reads mapping to the light strand in an anti-sense orientation to the heavy strand genes. Small RNAs derived from a nuclear genome are classified based on their biogenesis pathways, and the length of small RNAs acts as a proxy indicator for biogenesis. For example, tRNA half (tRH), miRNAs, and piRNAs are typically 32–34 nt, 21–22 nt, and 26–31 nt in length, respectively, in most studied species [20]. A quick review of the findings from the original studies (dastasets-1 and -2; [18,21]) revealed that these datasets were enriched in tRHs and piRNAs of nuclear genome origin, respectively. Interestingly, we found that a majority of mt-sRNAs in the dataset-1 were tRH of 31–32 nt length, and this frequency of mitochondrial tRH was strikingly similar to that of nuclear tRH [18], suggesting a similar biogenesis pathway. In the case of dataset-2, majority of mt-sRNAs of 27 nt size mapped to mt-rRNA. Although the size range is indicative of piRNA biogenesis, there is only a single study showing the localization of PIWI proteins as well as piRNAs mapping uniquely to the mitochondrial genome [22]. We found the sequence start sites of these putative 29 mitochondrial piRNAs [22] either exactly overlapped or were in the proximity of ±3 nt of sequence start sites of 27-nt mt-sRNAs from the dataset-2. However, it is not known whether these mt-sRNAs are processed through a particular biogenesis pathway with a defined biological function. Except for tRFs, no curated database exists for mitochondria-derived sRNAs or ncRNAs. Therefore, all the remaining differentially expressed mt-RNAs from datasets 1 and 2, have been not catalogued before. In case of mt-lncRNAs in dataset-3, the majority of sRNAs were derived from ATP6, ATP8, and CytB. lncCytB is among the most abundant mitochondrial lncRNAs in HeLa cells [23] and its abnormal trafficking has been demonstrated in human hepatocellular carcinoma cells [24]. To our knowledge, other mt-lncRNAs found in mouse oocytes and 1-cell embryos (dataset-3) have no functional annotations yet.
mtsRNAs identified in datset-1 and daaset-2 might have biological implications. The abundance of tRH of nuclear genome origin is positively correlated (Spearman’s rho = 0.67–0.87) with angiogenin mRNA/protein abundance in non-cancer liver tissue [18]. Differences in the expression of nuclear genome-derived tRFs produced through enzymatic cleavage of angiogenin have been observed [25]. These nuclear genome-derived tRFs bind to cytochrome C (a protein complex partially encoded by the mitochondrial genome) to prevent cells from undergoing apoptosis [25] and it has also been showed that these tRFs improve cell survival by acting in response to stress [26,27]. Although it is unknown whether tRFs of mitochondrial origin act in a similar way, differences in the expression of mitochondrial non-coding RNAs have been associated with cancer [8,28,29]. Moreover, it has been shown that the processing of the mitochondrial tRNAs at both the 5′ and 3′ ends has a substantial effect on mitochondrial gene expression [30,31]. Since mitochondrial tRFs are generated from both the 5′ and 3′ end of the mitochondrial tRNAs, and aberrant expression of mitochondrial genes leads to many disease conditions including cancer, DE mitochondrial tRFs in dataset-1 could potentially be implicated to disease condition. In dataset-2, the authors have indicated that differences in expression of piRNAs between spermatozoa from lean and obese men may increase the chances of offspring to develop obesity. No studies investigating the expression of mt-sRNAs in obesity are available; however, it has been shown that mitochondrial peptides are involved in regulating metabolism [32]. The expression of mitochondrial peptides is hypothesized to be controlled by mt-sRNAs [4]. Hence, altered expression of mt-sRNAs may result in an impaired metabolic pathway, which, in turn, might result in obesity.
Interestingly, no single mt-sRNA mapped to the termination association sequence (TAS) in the mitochondrial DNA control region, neither in the dataset-1 nor in the dataset-2. Small RNAs originating from the TAS region (co-ordinates 16,161 to 16,188 in the mouse mtDNA sequence) within the mitochondrial control region were expressed in mice [33].
Studies on tRFs have shown that a disproportionately high number of unique tRFs was derived from mitochondrial tRNA genes ($$n = 22$$) when compared to nuclear tRNA genes ($$n = 625$$) in humans [34,35]. For example, a study on samples from prostate cancer patients demonstrated that $62.0\%$ tRFs originated from nuclear tRNA genes, while the remaining $38\%$ originated from the mitochondrial tRNA genes [35]. This indicates the diversity of mitochondrial tRFs. Many of these mt-sRNAs map uniquely to the mitochondrial genome and not to the mitochondrial DNA-like sequences (NUMTs) in the nuclear genome [36]. Moreover, it has been shown that expression of mt-sRNAs is not associated with levels of NUMT but varies across different tissues depending on the mitochondrial DNA content [36]. This indicates mt-sRNAs have biological roles and, hence, mt-sRNAs were found to be differentially expressed in dataset-1 and 2 could be implicated in disease condition.
## 4.1. Implementation
The code for mtR_find is written in PYTHON 3.6.8 (also compatible with PYTHON 2.7.5) and requires dependencies that include PYTHON modules: pandas (version 0.21.0 and above) [37], multiprocessing, matplotlib [38] (optional) and other tools such as bowtie (version 1.1.2 and above) [39] and samtools (version 1.9 and above) [40].
## 4.2. Data Resources, Extraction of Mitochondrial Genome, and Annotation File
Depending on the species of interest (input parameter), mitochondrial genomes of Homo sapiens, Danio rerio, Gallus gallus, Mus musculus, and *Rattus norvegicus* have been downloaded from Ensembl [41]. In the case of *Xenopus laevis* and Xenopus tropicalis, the mitochondrial genomes have been downloaded from Xenbase [42]. A bowtie index corresponding to the particular genome was created using default parameters. *The* gene annotations were obtained by downloading the gene transfer format (GTF) annotation file for the species of interest from Ensembl/Xenbase and extracting the information pertinent to the mitochondrial genes. For any other species not listed above, the FASTA and GTF files have to be downloaded and provided manually by the user. The script mt_annotaion.py is useful to pre-process the GTF file (https://github.com/asan-nasa/mtR_find/blob/master/add-on/mt_annotation.py, accessed on 26 August 2022).
## 4.3. ncRNA Count Generation
In the ncRNA-count generation step, a dictionary of unique sequences was created from the list of all input FASTQ files. Using this as a reference, the count number for each unique sequence was determined for individual FASTQ files. The default cut-off threshold value for sequences is <200, because the counting accuracy of low ncRNA-count sequences can be erratic [5,43]. However, users can specify their own cut-off value tailored for the specific needs of their analyses. The output read count file is in comma separated value (CSV) format, in which the row names are unique sequences and column names are file names. Individual rows display the count number of a particular sequence in the corresponding library. In the case of SOLiD sequencing data, reads have to be mapped to the corresponding genome and converted from color-space to FASTQ files using adapt_find script [44], available at https://github.com/asan-nasa/adapt_find/blob/master/adapt_find.py (accessed on 26 August 2022) prior to the read-count generation step.
## 4.4. Mapping
Unique sequences from the read count file were extracted, converted to FASTA format, and mapped against the mitochondrial bowtie index using the following parameters: bowtie --best –v 1 –p 20. The mapped and unmapped sequences from the resulting SAM file were filtered out using samtools. Unmapped sequences carrying a non-templated CCA motif at their 3′ ends were retrieved, the CCA motif was trimmed, and the sequences were again mapped to the mitochondrial genome, this time under zero-mismatch stringent criterion to avoid false positive findings. The sequences mapping to the 3′ end of mitochondrial tRNA genes in the sense direction or to the 5′ end in the anti-sense direction were annotated as having a non-templated CCA additions at their 3′ ends (Figure 2).
## 4.5. Annotation
Genomic locations of mapped sequences were determined (Figure 3). Then, the gene annotation was performed using individual mitochondrial genes (Supplementary File S8, Table S2). The final sequence annotation was based on the position of a mapped sequence and its length within a gene using the MINTbase criteria [19] with some modifications (Supplementary File S8, Table S3). For both mt-sRNA and mt-lncRNA, if the sequence start site is in one gene and the end site is in another gene (Figure 3D), the gene that has the sequence start site is taken for annotation. The only exception to this rule is tRF-1. MINTbase classification of mt-sRNAs includes tRH-5′ and tRH-3′, and tRNA derived fragments (tRFs) include tRF-5′, tRF-3′, tRF-1, and i-tRF.
## 4.6. Nomenclature
Two levels of ID were produced. The specificID provides a unique annotation for every possible isoform of a sequence. *The* general ID provides the annotation of the family the given sequence belongs, in the terms of typical starting nucleotide, and skipping information on the sequence length and modifications from the main form. The nomenclature format for mt-sRNA is: “species_name”|”mt-sRNA”|”gene”|”sequence subtype”|”Strand”|”Orientation”|”Sequence start position”|”Sequence length”|Substitutions. For mt-lncRNA, the format is “species name”|”mt-lncRNA”|“gene”|”strand”|”sequence start position”|”sequence length”.
The species abbreviation is a three- or four-letter organism code as proposed in Kyoto Encyclopedia of Genes and Genomes (www.genome.jp/kegg/catalog/org_list.html (accessed on 19 February 2023)). The species abbreviations used in the present study are given in Supplementary File S8, Table S4. Gene name refers to one of the mitochondrial genes (Supplementary File S8, Table S2). If the sequence falls in a non-coding region, then it is denoted as “non-coding (“nc”) (Figure 3). The sequence subtype refers to the specific location in a gene transcript (applicable only for mt-sRNAs), as defined in Supplementary File S8, Table S3. Sequence start position refers to the genomic position of the 5′ nucleotide of the sequence. Strand refers to either heavy or light strand. Antisense orientation indicates anti-sense mapping of the sequence to a particular gene. Substitutions refer to any mismatches in the sequence as compared to the reference genome; if they occur, nucleotide position (from the start of the sequence) is given, along with the base letter to which the main form has been altered. The example nomenclature is given in Table 2.
## 4.7. Training-Experimental Dataset
We tested the tool on two small RNA (sRNA) datasets [18,21] downloaded from NCBI, and one long non-coding RNA dataset (unpublished study [45]) downloaded from European Nucleotide Archive (ENA). MINTmap was also tested on the two sRNA datasets to compare the performance of mtR_find with that of MINTmap. The two sRNA datasets were generated in studies where mt-ncRNAs were not analyzed. The dataset-1 contained information from sRNA-seq of hepatocellular carcinoma (HCC) versus non-malignant liver samples from subjects with chronic hepatitis B or C ($$n = 4$$ for each group), as well as uninfected subjects undergoing resection of metastatic tumors control group ($$n = 4$$, Supplementary File S13). In the dataset-2, the information was obtained from sRNA-seq of semen samples from 23 human subjects, classified as either lean ($$n = 13$$) or obese ($$n = 10$$; Supplementary File S13). The dataset-3 has been generated from RNA-seq of mouse oocytes ($$n = 2$$) and 1-cell embryos (Supplementary File S13).
In the case of sRNA datasets, the SRA files were downloaded using prefetch SRA utility tool. The SRA file format was converted to FASTQ files using fastq-dump tool [46]. Adapter sequences were removed from the raw FASTQ files, bases with quality score less than 20 were trimmed from the 3′ end. Sequences shorter than 15 nt were removed. The read count of mt-sRNA sequences was extracted by running mtR_find and differential expression analysis was performed using DESeq2 R package [47]. mt-sRNA sequences with a Benjamini–Hochberg adjusted p-value of <0.1 were considered differentially expressed (subject versus control). For mt-lncRNAs, paired-end FASTQ files obtained from ENA were converted to single-read FASTQ files using FLASH [48] and then run on the mtR_find tool. Due to the lack of biological replicates in the dataset-3, only the relative abundance of read counts was reported in our analysis.
## 4.8. Training-Simulated Dataset
mtR_find was tested on simulated datasets for both mt-sRNA and mt-lncRNA using separate scripts with the following command line parameters: [1] FASTA file (in this case, zebrafish mitochondrial genome); [2] GTF file (zebrafish mitochondrial gene annotation information); [3] desired number of unique sequences in each stimulated file; and [4] total number of stimulated files to be created. The GTF file was read and separated into two lists. The first list was based on the strand specificity: heavy strand or light strand, while the second one was based on genes.
The simulation script picked a random sequence start position from a random gene or from the non-coding region, in either the heavy or the light strands. Then, a random length was selected and added to the sequence start position to compute the sequence end position. Using the sequence start- and end-positions as co-ordinates, the sequence was extracted from the input mitochondrial genome. For the light strand sequences, the reverse compliment of the forward strand sequence was extracted, and a random count number for this particular sequence was assigned for each simulated file. This information was then used to create a simulated FASTQ file using the sequence and count information for each sequence. Random simulation of sequences and the corresponding read counts was performed using PYTHON module “random”. The simulation script outputs a simulated read count CSV file with sequence and annotation information, which should match the output of the mtR_find when the simulated FASTQ files are being analyzed.
Simulation scripts used different strategies to distribute reads among different sequences as described in Supplementary File S8, and Tables S2, S4 and S5. However, in both methods the total number of reads was split in such a way that 80–$95\%$ were simulated from the heavy strand and the remaining 5–$20\%$ were from the light strand. The simulated dataset has been tested using mtR_find tool, and the results were compared with the results from the simulation. The four different parameters were calculated to check the concordance: [1] number of unique sequences; [2] sequences mapping to the mitochondrial genome and the distribution of sequences between the two strands; [3] total read count and count of individual sequences in each file; and [4] annotation information and read count distribution among four bio-types. The bio-types included rRNA, tRNA, non–coding region, and protein-coding genes.
Simulation and testing of the tool were performed on a Linux server (Red Hat 4.8.5–28) with Python 3.6.8 (64 CPU cores, 504 GB RAM).
## 4.9. Identification of Novel tRFs
tRFs were downloaded from MINTbase [19] as a tab delimited file, while the mitochondrial tRFs (test sequences), obtained from mtR_find, were in CSV format. Both files were loaded as separate pandas data frames and the sequence column was extracted into two separate lists. Then, the sequences from the two lists were compared (Supplementary File S14). Only exact sequence matches were allowed.
## 5. Conclusions
Existing tools can identify only a sub-group of mtsRNAs. mtR_find is the first publicly available tool to comprehensively analyze and annotate all mitochondrial non-coding RNAs. The novel read count algorithm significantly reduces the execution time, making a high-throughput analysis of multiple datasets possible. mtR_find does not create any intermediate files and, hence, saves disk space. Moreover, mtR_find generates a single script for pre-processing data, mapping reads, and then generating count data with annotation information for files. mtR_find identifies novel mt-sRNAs, such as tRFs or mt-lncRNAs, in the existing datasets. It opens a new analytical possibility to re-examine thematic RNA-seq clusters of datasets in search for novel diagnostics markers.
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|
---
title: 'Increased Prolonged Sitting in Patients with Rheumatoid Arthritis during the
COVID-19 Pandemic: A Within-Subjects, Accelerometer-Based Study'
authors:
- Ana Jessica Pinto
- Diego Rezende
- Sofia Mendes Sieczkowska
- Kamila Meireles
- Karina Bonfiglioli
- Ana Cristina de Medeiros Ribeiro
- Eloisa Bonfá
- Neville Owen
- David W. Dunstan
- Hamilton Roschel
- Bruno Gualano
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001724
doi: 10.3390/ijerph20053944
license: CC BY 4.0
---
# Increased Prolonged Sitting in Patients with Rheumatoid Arthritis during the COVID-19 Pandemic: A Within-Subjects, Accelerometer-Based Study
## Abstract
Background: Social distancing measures designed to contain the COVID-19 pandemic can restrict physical activity, a particular concern for high-risk patient groups. We assessed rheumatoid arthritis patients’ physical activity and sedentary behavior level, pain, fatigue, and health-related quality of life prior to and during the social distancing measures implemented in Sao Paulo, Brazil. Methods: Post-menopausal females diagnosed with rheumatoid arthritis were assessed before (from March 2018 to March 2020) and during (from 24 May to 7 July 2020) social distancing measures to contain COVID-19 pandemic, using a within-subjects, repeated-measure design. Physical activity and sedentary behavior were assessed using accelerometry (ActivPAL micro). Pain, fatigue, and health-related quality of life were assessed by questionnaires. Results: Mean age was 60.9 years and BMI was 29.5 Kg/m2. Disease activity ranged from remission to moderate activity. During social distancing, there were reductions in light-intensity activity ($13.0\%$ [−0.2 h/day, $95\%$ CI: −0.4 to −0.04; $$p \leq 0.016$$]) and moderate-to-vigorous physical activity ($38.8\%$ [−4.5 min/day, $95\%$ CI: −8.1 to −0.9; $$p \leq 0.015$$]), but not in standing time and sedentary time. However, time spent in prolonged bouts of sitting ≥30 min increased by $34\%$ (1.0 h/day, $95\%$ CI: 0.3 to 1.7; $$p \leq 0.006$$) and ≥60 min increased by $85\%$ (1.0 h/day, $95\%$ CI: 0.5 to 1.6). There were no changes in pain, fatigue, and health-related quality of life (all $p \leq 0.050$). Conclusions: Imposed social distancing measures to contain the COVID-19 outbreak were associated with decreased physical activity and increased prolonged sedentary behavior, but did not change clinical symptoms sitting among patients with rheumatoid arthritis.
## 1. Introduction
A preliminary, multinational survey reporting step counts provided by smartphones showed that social distancing measures to contain the spread of SARS-CoV-2 have induced physical inactivity (i.e., not meeting the physical activity guidelines) [1]. The onset of the coronavirus disease 2019 (COVID-19) pandemic has placed further spotlight on participation in sedentary behavior (i.e., time spent in a sitting or reclining posture with a low energy expenditure [≤1.5 METs]), with reported increases in daily sitting time from pre-pandemic levels ranging from 30 min up to 3 h in different populations [2,3].
Extensive epidemiological evidence has indicated that physical inactivity is a major risk factor for early mortality and chronic diseases, including obesity, type 2 diabetes, cardiovascular diseases, metabolic syndrome, certain type of cancers, and others [4]. Even though time spent in moderate-to-vigorous intensity physical activity has the strongest detrimental associations with health outcomes [5,6,7], similar (albeit, detrimental) relationships have been broadly observed for excessive time in sedentary behaviors [7,8,9,10,11,12,13,14,15,16]. Importantly, both total sitting time and prolonged, uninterrupted sitting time are associated with increased risk of all-cause mortality even after consideration of the influence of participation in moderate-to-vigorous intensity physical activity [7,8,17]. Moreover, the deleterious associations of sedentary behavior with cardiometabolic risk and all-cause mortality are most pronounced in those who are physically inactive [6,11,18,19,20].
Rheumatoid arthritis is a rheumatic autoimmune disease characterized by chronic inflammation, pain, and physical disability [21]. Clinical disease symptoms can include joint pain, swelling, stiffness, and deformity, fatigue, muscle weakness, and reduced physical functioning [22,23]. Patients with rheumatoid arthritis have a higher risk of morbidity and mortality from cardiovascular diseases [24]. This increased risk can be at least partially explained by the complex interplay between chronic inflammation, adverse effects of drugs, associated comorbidities (e.g., dyslipidemias, insulin resistance, hypertension), and lifestyle [25,26]. Despite physical activity being advocated as an integral part of disease standard care [27], physical inactivity and sedentary behavior are highly prevalent among patients with rheumatoid arthritis [28].
Physical inactivity and sedentary behavior are modifiable risk factors considered to be potential targets to prevent morbimortality in autoimmune rheumatic diseases [28,29]. Among patients with rheumatoid arthritis, sedentary behavior is associated with higher disease scores, increased pain, fatigue [30] and number of comorbidities, reduced aerobic capacity [31] and physical function [30], and poor self-efficacy [32]. Furthermore, physically inactive patients with rheumatoid arthritis exhibit higher cardiovascular risk factors (e.g., higher systolic blood pressure and homeostasis model assessment (HOMA) index, abnormal lipid profile) when compared to their physically active counterparts.
Patients with rheumatoid arthritis have been shown to be more susceptible to COVID-19 infection [33] and, therefore, may be subjected to more restrictive measures of social distancing, potentially with significant impacts on their activity options, and, hence, on their burden of cardiovascular disease risk, the main cause of mortality in this population [26].
In this prospective study using a within-subjects design, we assessed physical activity and sedentary behavior levels using accelerometers in patients with rheumatoid arthritis prior to and during the imposed measures of social distancing to combat COVID-19 in Sao Paulo, Brazil. Additionally, we have assessed whether potential changes in physical activity and sedentary behavior levels would be associated with changes in pain, fatigue, and health-related quality of life.
## 2.1. Participants
Sixty-four patients diagnosed with rheumatoid arthritis were recruited from the Outpatient Rheumatoid Arthritis Clinic of the Clinical Hospital (School of Medicine, University of Sao Paulo) between March 2018 and March 2020 to participate in a randomized controlled trial (clinicaltrials.gov: NCT03186924). Thirty-five out of 64 patients with rheumatoid arthritis accepted to participate in this ancillary study.
Post-menopausal female patients diagnosed with rheumatoid arthritis, according to American College of Rheumatology European League against rheumatism collaborative initiative revised criteria [34], were recruited directly from the Rheumatoid Arthritis Outpatient Clinic of the Rheumatology Division. The exclusion criteria included: [1] participation in structured exercise training programs within the last 12 months; [2] unstable drug therapy in the last 3 months prior to and during the study; [3] Health Assessment Questionnaire score >2.0 (i.e., severe to very severe physical impairment).
This trial was approved by the local ethical committee (Commission for Analysis of Research Projects, CAPPesq; protocol code: 58340316.0.0000.0068; approval number: 1.735.096). Patients signed an informed consent form before participation in the study.
## 2.2. Experimental Design
All patients with rheumatoid arthritis had been through a clinical and physical activity assessment before the official set of social distancing measures to contain the COVID-19 outbreak, adopted on the 24 of March 2020. This facilitated the unique opportunity to track physical activity levels during the pandemic in a within-subjects, repeated measure design. We then obtained a new approval from the ethics committee for collecting data during the social distancing. Three members of our staff (DR, SMS, KM) delivered the accelerometers (ActivPAL micro™, PAL Technology, Glasgow, UK) and questionnaires to the patients at home from the 24 May to 7 July. The time elapsed for data collection between baseline and during social distancing was 12.5 months (9.9, 15.2). Patients were asked if they had adhered to the social distancing measures. All but two responded affirmatively. Data were assessed with and without the two non-compliers, and results remained the same. Thus, we reported the full data in this manuscript.
## 2.3. Physical Activity Level
Physical activity level was measured using activPAL micro™ (PAL Technology, Glasgow, UK) activity-based accelerometers before and during social distancing. Patients wore the accelerometer for 7 consecutive days (24 h/day), which was fitted using tape (3M, Tegaderm®, adhesive tape) on the right medial front thigh, orientated with the x-axis pointing downward, y-axis horizontally to the left and z-axis horizontally forward. Data were exported and analyzed using ActivPAL3™ software, version 8.10.9.46 (PAL Technology, UK). Data was checked by an experienced researcher and also crosschecked with a sleep diary. All data were standardized to a 16-h day in order to avoid bias from differences in patients’ wear time, by the formula: (data × 16)/wear time. Data were reported as follows: time spent sitting and lying (h/day), in prolonged sitting (h/day), standing (h/day), stepping (h/day), time spent in light-intensity physical activity (step cadency <100 steps/min [35]), time spent in moderate-to-vigorous intensity physical activity (step cadence of ≥100 steps/min [35]), and number of sit to stand (i.e., breaks) in time spent in sedentary behavior.
## 2.4. Clinical Assessment
Clinical characteristics were assessed at baseline, before the set of social distancing, Disease activity was assessed by the Disease Activity Score in 28 joints (DAS28 PCR) [36] and Clinical Disease Activity Index (CDAI) [37], in which higher scores represent more severe disease activity. The Health Assessment Questionnaire (HAQ) [38], which evaluates physical functioning in eight domains of daily life, was also used; higher scores represent greater physical disability. Disease duration, presence of comorbidities (e.g., hypertension, dyslipidemia, type 2 diabetes, depression, and other rheumatic diseases), current dose of prednisone, current use of biological agents (e.g., anti-TNF, anti-IL6, anti-IL1, B-cell depleting agents, and T-cell activation inhibiters), non-biological disease-modifying anti-rheumatic drugs (e.g., methotrexate and leflunomide), and other medications (i.e., anti-inflammatory drugs, pain killers, antihypertensive drugs, antihyperlipidemic drugs, antidiabetic drugs, and anti-depressants) were obtained by reviewing medical records and interviewing patients with rheumatoid arthritis. Blood samples (~10 mL) were collected after a 12-h overnight fast for measuring the following parameters: C-reactive protein and erythrocyte sedimentation rate. Samples were collected in vacutainer tubes and subsequently analyzed at the Clinical Hospital Central Laboratory (School of Medicine, University of Sao Paulo). C-reactive protein was determined by immunoturbidimetry. Erythrocyte sedimentation rate was assessed using an automated analyzer.
Pain, fatigue, and health-related quality of life were assessed before and during social distancing. Pain was assessed by the Visual Analogic Scale [39], in which patients graded their pain using a 10-point scale; zero means no pain and 10 means severe or unbearable pain. Fatigue was assessed by the Fatigue Severity Scale [40], in which scores range from 9 to 63; lower scores indicate lower fatigue. Physical and mental health-related quality of life were assessed by the 36-Item Short Form Survey (SF-36) questionnaire [41], in which scales (physical health: physical function, role-physical, bodily pain, and general health; mental health: vitality, social function, and role-emotional) range from 0 to 100; higher scores indicate better quality of life.
## 2.5. Statistical Analysis
Dependent variables were tested using repeated measures mixed models, with time (Before social distancing versus During social distancing) as fixed factor and participants as random factor, with a compound symmetry covariance matrix. Delta changes in all dependent variables were calculated with the following formula: delta change = data during social distancing—data before social distancing. Associations between changes in physical activity and sedentary behavior level and changes in pain, fatigue, and health-related quality of life were tested using Pearson correlation tests.
Statistical analysis was performed in SAS 9.3 (SAS Institute Inc., Cary, NC, USA). Data are presented as mean, estimated mean difference from the repeated measures mixed models, and $95\%$ confidence intervals ($95\%$ CI). The significance level was set at p ≤ 0.05.
## 3. Results
Patients’ clinical characteristics are presented in Table 1. In summary, mean age was 60.9 years ($95\%$ CI: 58.0 to 63.7) and BMI was 29.5 Kg/m2 ($95\%$ CI: 27.2 to 31.9). Disease activity ranged from remission to moderate activity, as assessed by DAS28 PCR and CDAI. Disability assessed by HAQ ranged from mild to severe. Mean disease duration was 18.5 years ($95\%$ CI: 14.7, 22.3). Mean C-reactive protein was 10.8 mg/dL ($95\%$ CI: 5.5 to 16.2) and erythrocyte sedimentation rate was 28.4 mm/H ($95\%$ CI: 15.7 to 41.1). Most of the patients were using disease-modifying anti-rheumatic drugs and prednisone ($85.7\%$ and $74.3\%$, respectively). Hypertension, dyslipidemia and type 2 diabetes were the most frequent comorbidities ($51.4\%$, $48.6\%$ and $34.3\%$, respectively). Before social distancing, mean pain was 5.0 ($95\%$ CI: 4.1 to 6.0), fatigue was 39.3 ($95\%$ CI: 33.8 to 44.8), and physical and mental health-related quality of life were 39.8 ($95\%$ CI: 33.1 to 46.5) and 62.0 ($95\%$CI: 52.3 to 71.7), respectively.
During social distancing, there were reductions in total stepping time ($15.7\%$ [−0.3 h/day, $95\%$ CI: −0.4 to −0.1; $$p \leq 0.004$$]), in light-intensity physical activity ($13.0\%$ [−0.2 h/day, $95\%$ CI: −0.4 to −0.04; $$p \leq 0.016$$]) and in moderate-to-vigorous physical activity ($38.8\%$ [−4.5 min/day, $95\%$ CI: −8.1 to −0.9; $$p \leq 0.015$$]), but no changes in total standing time (−0.1 h/day, $95\%$ CI: −0.7 to 0.5; $$p \leq 0.767$$) or total sedentary time (0.3 h/day, $95\%$ CI: −0.4 to 1.0; $$p \leq 0.335$$) in patients with rheumatoid arthritis. However, time spent in prolonged bouts of sitting ≥ 30 min increased by $34\%$ (1.0 h/day, $95\%$ CI: 0.3 to 1.7; $$p \leq 0.006$$; Figure 1A) and sitting bouts ≥60 min increased by $85\%$ (1.0 h/day, $95\%$ CI: 0.5 to 1.6; $p \leq 0.001$; Figure 1B). Sit-stand transitions were reduced by $10\%$ (−5.1/day, $95\%$ CI: −10.3 to 0.0; $$p \leq 0.051$$). Figure 1C and Figure 1D illustrate the accelerometer data from a patient who experienced decreased activity and increased prolonged sitting after social distancing.
During social distancing, there were no changes in pain (0.31 [$95\%$ CI: −1.04 to 1.67; $$p \leq 0.652$$), fatigue (−2.3 [$95\%$ CI: −10.0 to 5.4]; $$p \leq 0.550$$), and physical and mental health-related quality of life (1.2 [$95\%$ CI: −8.2 to 10.7]; $$p \leq 0.796$$ and −9.3 [$95\%$ CI: −23.0 to 4.5], $$p \leq 0.183$$, respectively) in patients with rheumatoid arthritis.
Changes in physical activity and sedentary behavior levels were not associated with changes in pain, fatigue, and physical and mental health-related quality of life during social distancing (all $p \leq 0.050$).
## 4. Discussion
To our knowledge, this is the first study to track physical activity and sedentary behavior patterns before and during the COVID-19 pandemic using validated accelerometry and a within-subjects design. Our main findings suggest that social distancing (including stay-at-home order) can lead to reduced ambulatory activities and increased physical inactivity as well as increased prolonged sitting among patients with rheumatoid arthritis. In contrast, social distancing was not associated with worsened pain, fatigue, and physical and mental health-related quality of life. Physical inactivity along with too much sitting emerge as a risk factor that could be detrimental to cardiometabolic health in such a high-risk group of patients during and possibly after the COVID-19 pandemic.
As those confined at home are less prone to perform physical activity, it has been speculated that inactivity and sedentary behavior could peak during the COVID-19 pandemic [29]. In fact, a rapid review has shown a substantial decrease in physical activity with a concomitant increase in sedentary behavior across all age groups during COVID-19 lockdown [42]. As for the Brazilian population, a national retrospective survey comprising 39,693 adults and older adults has shown a significant increase on self-reported physical inactivity and screen-based sedentary behaviors during the COVID-19 pandemic [43,44], which corroborates the objectively measured data presented herein. Such an increase in inactivity and sedentary behavior is of particular concern for those who are usually hypoactive and show higher risk of cardiovascular diseases, this being the case of patients with rheumatoid arthritis (see the patients’ comorbidities in Table 1) [26,28].
Observational and experimental evidence demonstrates that inactivity can predispose to pathological states and poor outcomes [45]. Sedentary behavior can add to the adverse impacts of physical inactivity in impairing cardiovascular health [46]. Consequently, individuals who are both physically inactive and highly sedentary are at the highest risk for poor outcomes [6,20], which might be the case for patients with autoimmune rheumatic diseases, as they commonly spent most of their daily hours engaged in sedentary behavior and did not achieve minimum levels of moderate-to-vigorous physical activity [28]. Namely in rheumatoid arthritis, the estimates of physical inactivity and sedentary behavior are comparable to those of other chronic diseases (e.g., type 2 diabetes and cardiovascular diseases), groups in which both physical inactivity and sedentary behaviors are associated with poor disease prognosis and mortality [9,10,11,13,47], as well as poor health-related outcomes (i.e., higher disease activity score, disease symptoms and number of comorbidities, and lower physical capacity and functioning) [28]. In rheumatoid arthritis, regular participation in exercise improves disease symptoms, inflammatory markers, cardiometabolic risk factors, and physical capacity [48,49]. However, regular participation in moderate-to-vigorous physical activity may not be feasible for some patients, especially those with poor mobility or during disease flares.
Interestingly, we observed that even in the absence of changes in total sedentary time, prolonged sitting time rose considerably. Prolonged, uninterrupted bouts of sedentary behavior are associated with all-cause mortality [8], whereas well-controlled studies show that very-light to light-intensity active interruptions in prolonged sedentary time (e.g., 2 min of walking for every 30 min of sitting) can elicit immediate improvements in cardiometabolic risk factors [50]. Recent evidence has shown that light-intensity physical activity is associated with lower disability, disease activity and cardiovascular risk in rheumatoid arthritis, in contrast to excessive sitting [28,51]. Additionally, a crossover randomized trial demonstrated that performing 3-min bouts of light-intensity walking every 30 min of sitting (total: 42 min) resulted in improved glycemic (i.e., glucose, insulin, and c-peptide) and inflammatory (i.e., IL-1β, IL-1ra, IL-10, and TNF-α) markers when compared to 8 h of prolonged, uninterrupted sitting in postmenopausal females with rheumatoid arthritis [52]. This raises the need for widespread recommendation of breaking-up prolonged sitting whenever possible (e.g., 3 min breaks of light-intensity walking every 30 min of sitting) to avoid poor health outcomes during the pandemic, which tend to be more restrictive for high-risk groups for COVID-19, such as those with autoimmune rheumatic diseases [33], a condition associated with lower vaccine responses, which may enforce more vulnerable patients to maintain some degree of physical distance and home isolation for as long as the pandemic endures.
Our findings suggested social distancing did not affect pain, fatigue, and physical and mental health-related quality of life. Qualitative evidence in patients with rheumatoid arthritis demonstrate that patients reported no changes in physical health outcomes. Conversely, they noted social distancing resulted in worsened mental health-related symptoms [53]. Additionally, changes in these variables did not associate with changes in physical activity and sedentary behavior. Because this study was performed 2 to 4 months after the set of social distancing measures, we cannot rule out that such a short period of exposure did not allow detecting impairments in these outcomes. Alternatively, it is possible that patients with rheumatoid arthritis may be more resilient than general population to the detrimental impacts of the pandemic on overall health.
The main strengths of this study are its within-subjects design and the use of posture-based accelerometers, which enables an objective and a comprehensive assessment of sedentary behavior patterns. The limitations include the relatively low sample size; lack of measurement of mood and use of medication and supplements during social distancing, which may also alter habitual physical activity; and the inability to stablish a cause-and-effect relationship between changes in behavior with social distancing measures, although elements of temporality and plausibility do support our assumptions.
## 5. Conclusions
Imposed social distancing measures to contain the COVID-19 outbreak were associated with decreased physical activity and increased prolonged sitting time, but no changes in clinical symptoms (pain, fatigue, and health-related quality of life) among patients with rheumatoid arthritis. Since this has the potential to increase the burden of cardiovascular diseases in such high-risk group of patients, attention to maintaining their activity levels is an urgent consideration during the pandemic, and possibly thereafter since inactivity and sedentariness may carry over as consequences of the outbreak.
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|
---
title: Measuring Sleep Quality in the Hospital Environment with Wearable and Non-Wearable
Devices in Adults with Stroke Undergoing Inpatient Rehabilitation
authors:
- Michael Pellegrini
- Natasha A. Lannin
- Richelle Mychasiuk
- Marnie Graco
- Sharon Flora Kramer
- Melita J. Giummarra
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001748
doi: 10.3390/ijerph20053984
license: CC BY 4.0
---
# Measuring Sleep Quality in the Hospital Environment with Wearable and Non-Wearable Devices in Adults with Stroke Undergoing Inpatient Rehabilitation
## Abstract
Sleep disturbances are common after stroke and may affect recovery and rehabilitation outcomes. Sleep monitoring in the hospital environment is not routine practice yet may offer insight into how the hospital environment influences post-stroke sleep quality while also enabling us to investigate the relationships between sleep quality and neuroplasticity, physical activity, fatigue levels, and recovery of functional independence while undergoing rehabilitation. Commonly used sleep monitoring devices can be expensive, which limits their use in clinical settings. Therefore, there is a need for low-cost methods to monitor sleep quality in hospital settings. This study compared a commonly used actigraphy sleep monitoring device with a low-cost commercial device. Eighteen adults with stroke wore the Philips Actiwatch to monitor sleep latency, sleep time, number of awakenings, time spent awake, and sleep efficiency. A sub-sample ($$n = 6$$) slept with the Withings Sleep Analyzer in situ, recording the same sleep parameters. Intraclass correlation coefficients and Bland–Altman plots indicated poor agreement between the devices. Usability issues and inconsistencies were reported between the objectively measured sleep parameters recorded by the Withings device compared with the Philips Actiwatch. While these findings suggest that low-cost devices are not suitable for use in a hospital environment, further investigations in larger cohorts of adults with stroke are needed to examine the utility and accuracy of off-the-shelf low-cost devices to monitor sleep quality in the hospital environment.
## 1. Introduction
Reports of sleep disturbances are common in adults with stroke and are particularly evident during the acute recovery phase while in hospital [1,2]. Sleep problems are experienced by up to two-thirds of people after having a stroke [3], with 25 to $85\%$ of people experiencing persistent fatigue [4]. Importantly, poor sleep quality has been found to affect neuroplasticity and memory consolidation post-stroke [5,6], which may have an adverse causal impact on a range of recovery outcomes. Moreover, poor sleep is associated with poorer motor function [7] and lower levels of physical activity as well as higher levels of fatigue during inpatient rehabilitation [8]. Finally, higher levels of sleep disruption are associated with slower recovery of functional independence and motor recovery throughout inpatient rehabilitation [1,9]. Sleep disruption is common during a hospital stay, regardless of the reason for admission, most often due to clinical care interventions and the noisy environment [10]. However, these impacts may have a particularly damaging effect for people who have had a stroke by impeding neurological recovery while also reducing the level of energy needed to enable optimal participation in rehabilitation [11].
Despite the known impact of sleep quality on stroke outcomes, sleep monitoring and interventions in the hospital environment during inpatient rehabilitation are not routine. In order to improve clinical outcomes, it is important that we have access to simple and valid methods for sleep monitoring. Wearable actigraphy devices are non-invasive technologies that can monitor and accurately measure objective sleep parameters. These devices have been widely incorporated for the measurement of human biometrics, as they can be used to monitor sleep and activity levels in a range of settings without causing discomfort to the wearer, including the assessment of sleep following stroke [12]. However, these monitors are relatively expensive (~USD 2000 each), preventing their routine use in research and clinical settings [13]. Commercial sleep monitoring devices are becoming more readily available on the personalized-device market and may offer low-cost alternatives to monitor sleep quality [14,15]. Consumer devices for measuring physiological and behavioral signals (e.g., heart rate, respiration, and bodily movements) in order to estimate sleep are either worn (e.g., on the wrist), or they are placed under the mattress or in the same room near the bed. In the context of neurological rehabilitation, devices placed under or near the person whose sleep is being monitored may enable superior assessment of sleep, particularly if they have impairments that affect the movement of specific limbs. The Withings Sleep Analyzer (WSA) is one such “nearable” device (~USD 200 each) that is placed under the mattress to detect body movements, cardiac activity, breathing patterns, and snoring. However, while the WSA records several aspects of sleep (e.g., sleep onset and number and duration of wakenings after sleep onset), it has been used primarily to measure decreases in or cessation of breathing during sleep for people with suspected obstructive sleep apnea syndrome [16], and it has not yet been validated in other clinical populations or settings for the measurement of sleep quality or quantity. Before the device is to be used more widely, it is important to investigate its accuracy in monitoring sleep relative to more common validated actigraphy devices. Therefore, this study aimed to describe device usability and to explore the level of agreement between a validated actigraphy device and the WSA within a sample of adults undergoing hospital-based rehabilitation after having a stroke.
## 2.1. Study Design
The design was a cross-sectional within-subject cohort study. Ethics was approved by the Alfred Health Human Research Ethics Committee (Project ID: $\frac{660}{21}$).
## 2.2. Study Participants
Adults with stroke undergoing inpatient rehabilitation were recruited from two wards: a general medicine rehabilitation ward and a specialized neurological rehabilitation ward. Participants were recruited from two separate wards to capture the differing environments in which adults typically undergo rehabilitation after having a stroke. Adults undergoing stroke rehabilitation on the specialized neurological ward slept in private rooms with more controlled lighting, whereby lighting could be independently dimmed to facilitate onset of sleep. Conversely, adults undergoing rehabilitation on general rehabilitation wards shared a room with one other patient and did not have access to controlled lighting. The potential for noise disturbance was also higher in this shared ward environment. Participants were eligible for the study if they had a diagnosis of stroke, were able to move and roll in bed independently, and did not have any cognitive deficits impacting their capacity to understand and use the sleep monitoring devices.
## 2.3. Procedure
Sleep was monitored for one night. Participants wore the Philips Actiwatch Spectrum 2 (Philips Respironics, Pittsburgh, PA, USA) on their wrist, while the WSA (Withings, Paris, France) was placed under their mattress. The Actiwatch and WSA devices were retrieved by the researchers the following morning to extract sleep data. Participants were also asked to document the time they got into bed with the intention to sleep, the approximate time it took for them to fall asleep, the time they awoke the following morning, and the number of times they awoke overnight. These data were then used to calculate sleep onset latency (SOL), total sleep time (TST), and number of awakenings as described below.
## 2.3.1. Philips Actiwatch Spectrum 2
The Actiwatch was placed on the participant’s wrist on the hemiparetic side. Placing the device on the hemiplegic side ensured that participants could safely and independently remove the device with their unaffected limb if necessary. The Actiwatch software (Actiware version 6.0, Philips Respironics, OR, USA) automatically determined sleep onset and offset times via pre-determined activity thresholds [17]. Within the lights off/lights on times, sleep onset time was classified as the first minute of a 10-minute immobile period with <2 activity counts in any 30-s period [18,19]. Ten consecutive minutes of activity was defined as sleep offset [18,19]. During sleep, activity threshold counts >40 per 30-s epoch were defined as awake. This allowed calculation of the number of awakenings and amount of awake time [18,19].
## 2.3.2. Withings Sleep Analyzer
The WSA is an air-inflated sensor mat that detects body and chest movements and respiration vibrations [16]. Once placed under the mattress, the WSA was paired to application-based software (Version 2151, Health mate, Withings, Paris, France) for data collection and storage. The WSA epoch times and activity thresholds used to detect and calculate sleep and wake are not readily available; however, the mat reportedly detects body movements to determine time spent in bed and time registered as awake and asleep [16].
## 2.4.1. Participant Characteristics
Participant characteristics included age, sex, stroke type, stroke location, rehabilitation ward type, days since stroke, and days in rehabilitation at the time of sleep monitoring. No participants were receiving pharmacological support for sleep.
## 2.4.2. Sleep Parameters and Device Recording
Sleep quality outcome measures were as follows: SOL, defined as time (minutes) between a detected commencement of a rest interval when lights were registered as ‘off’ and the sleep onset time; TST, defined as total time (hours) between sleep onset and offset; number of awakenings, defined as the total number of epoch blocks within the TST interval that were registered as awake; wake after sleep onset (WASO), calculated as the total duration of each awakening episode (minutes); and sleep efficiency (SE), defined as percentage of time spent in bed asleep relative to the total time spent in bed between getting into bed and getting up the following morning.
## 2.4.3. Self-Reported Sleep
A participant questionnaire was developed to record self-reported details of the previous night’s sleep, akin to a single night of a traditional sleep diary, including SOL, TST, number of awakenings, and reasons for awakenings. The questionnaire was administered the next morning by a clinical physiotherapist.
## 2.5. Statistical Analyses
SPSS (version 28.0, IBM, IL, USA) was used to test the level of agreement, within participants, between the Actiwatch and WSA for SOL, TST, number of awakenings, WASO, and SE via intraclass correlation coefficients (ICC) [18]. Absolute agreement with a two-way mixed effect model was used to determine average ICC. This approach was selected, as we assumed that the error from the devices would be predictable and fixed, and the error from the participants would be random. Agreement between the Actiwatch and participant-reported SOL, TST, and number of awakenings were also examined. ICC categories were established a priori to be poor (<0.50), moderate (0.50–0.75), good (0.75–0.90), and excellent (>0.90) [20]. Bland–Altman plots were generated to display the differences between the Actiwatch and WSA devices against the overall mean scores for the two devices, including the upper and lower $95\%$ limits around the combined mean, consistent with previous studies examining the validity of sleep recording devices [14,18,21].
## 3. Results
Eighteen participants wore the Actiwatch for an entire night, with no participants removing the device from their wrist. Ten participants also used the WSA, but data for two participants could not be used due to device pairing issues, and no valid data were collected on the WSA from an additional two participants who slept in a chair instead of the hospital bed. Therefore, actigraph data were available for all 18 participants, and both actigraph and WSA data were available for a subsample of 6 participants.
Participant characteristics are provided in Table 1. There was relative consistency in the characteristics within the entire cohort ($$n = 18$$) who used the actigraphy device as well as in the sub-sample who used both the actigraphy device and the WSA ($$n = 6$$). The characteristic with the greatest discrepancy was the ward type. Of the eight participants who had sleep monitored via both Actiwatch and WSA devices, just one of these participants was recruited from the specialized neurological ward, with the remaining seven recruited from the general ward (Table 1).
Figure 1 depicts the sleep and wakening events for both the Actiwatch and WSA data for one participant, and similar comparisons are available in Supplementary Figures S1–S5 for all other participants. Raw data for all participants who used both the Actiwatch and WSA are provided in Table S1.
ICC levels of agreement for each sleep parameter were poor between Actiwatch and participant-reported sleep quality and between the Actiwatch and WSA (Table 2). The Actiwatch underestimated sleep onset latency and over-estimated total sleep time and awakenings relative to both participant report and the WSA. Overall, there appeared to be poorer agreement between the Actiwatch and WSA than between the Actiwatch and self-report for sleep onset latency. The WSA also showed markedly lower sleep efficiency and wakenings after sleep onset relative to the Actiwatch.
The Bland–Altman plots demonstrated a broad range in the difference between Actiwatch and WSA device data (Figure 2). Participants whose data fell outside of the $95\%$ confidence interval around the combined mean between the Actiwatch and WSA typically had longer sleep onset latency, lower total sleep time, more awakenings, and lower sleep efficiency, highlighting that there was a potential bias towards poor agreement when people had worse sleep.
## 4. Discussion
This study found poor agreement for all sleep parameters between the low-cost WSA and widely used Actiwatch. This study also found poor agreement between participant-reported subjective sleep quality and the Actiwatch device parameters of sleep. Given that devices, such as actigraphy watches, measure different aspects of sleep compared with subjective reports of sleep quality, their poor level of agreement has been well-documented previously [22]. The results of the present study are clinically relevant, as they highlight a need for multiple modalities of effective sleep monitoring in the clinical setting. A low-cost alternative, such as the WSA, which could routinely monitor sleep quality in adults with stroke while undergoing rehabilitation, may inform and assist in guiding rehabilitation programs to optimize recovery; however, we found that this device also did not reliably agree with the well validated Actiwatch.
It is worth noting that previous studies have also found that actigraphic recordings had poorer agreement with polysomnography than other commercially available devices, including an under-the-mattress device in healthy young adults [14]. Importantly, the study by Chinoy, Cuellar, Huwa, Jameson, Watson, Bessman, Hirsch, Cooper, Drummond and Markwald [14] tested the reliability of consumer wearable and “nearable” sleep tracking devices in conditions that could be considered to mimic the type and frequency of overnight disruptions that occur in an inpatient rehabilitation hospital setting. Chinoy, Cuellar, Huwa, Jameson, Watson, Bessman, Hirsch, Cooper, Drummond and Markwald [14] found that the “under-the-mattress” device that they tested overestimated total sleep time by approximately 14 min and sleep efficiency by $2.9\%$, and underestimated time spent awake after sleep onset by 15 min relative to PSG. Similarly, we found that the WSA device underestimated total sleep time by 1.2 h and overestimated time spent awake after sleep onset by 66 min. In order to enable appropriate selection and use of sleep monitoring devices, further large-scale studies comparing the accuracy and reliability of a range of consumer accessible low-cost devices, such as the WSA, against both actigraphy and polysomnography in the context of neurological rehabilitation are necessary.
The discrepancies in sleep parameters between devices, and with self-report, may be due to a number of factors. Firstly, the small sample size may have been a contributing factor, as there was large variability in the data for each of the sleep parameters, and estimates could have been unduly influenced by outliers in the data. Future studies using larger samples of adults with stroke may reduce the observed data variability and develop further insight into whether the WSA and participant reports are comparable to the more expensive devices, which are held to be more accurate. Further, the discrepancies observed may also reflect the different activity thresholds of each device to calculate sleep parameters. Actiwatch activity thresholds are well-defined, readily available, and generally have reported high levels of agreement with polysomnography, particularly for detecting sleep time rather than wakenings, and is considered a ‘gold standard’ for sleep monitoring in settings where it is impractical to use polysomnography [18,21]. Conversely, activity thresholds are not readily available for many consumable devices, including the WSA. Obtaining transparency on the WSA activity thresholds will be key to determining whether it is comparable to established actigraphy devices in future research.
The actigraphy methodology used in this study differed from previous studies monitoring sleep quality in adults with stroke. Firstly, as this was a study investigating the feasibility and usability of the Actiwatch and WSA devices prior to further larger-scale studies, sleep was monitored over one night only. This is not consistent with recent recommendations that sleep monitoring via actigraphy be conducted over a 7–14-day period to account for night-to-night variability in sleep parameters within an individual [23,24]. These recommendations, however, are more relevant to studies investigating clinical understandings of sleep quality. Given that the current validation study focused on the agreement between these two devices, recommendations to monitor sleep for more than one night may not apply in this context.
While the activity thresholds and sleep parameters were comparable, the limb wearing the Actiwatch differed from previous studies. The Actiwatch was placed on the hemiparetic limb for safety reasons, which differed from previous recent studies investigating similar cohorts of adults with stroke [2,8,9,25]. This may have influenced the accuracy of sleep measurements. However, we found that SOL was longer than in previous studies [2,25], TST was comparable to one study [8] yet longer than others [2,25], while WASO was comparable to that found in similar studies [9], shorter than one [25], and longer than another [2]. Potential lower levels of movement in the hemiparetic limb overnight, awakenings in the hospital environment, and WASO may have been underestimated, as wakeful periods overnight may not have resulted in movements of the affected limb. Further, SOL and TST may be overestimated as participants may waken prior to sufficient movements in the hemiparetic limb can trigger the Actiwatch to register that they are awake. However, all participants were able to move independently in and out of bed, minimizing the risk of this occurring. Rather, it appeared that the participants who had poorer agreement between devices had worse sleep (i.e., longer sleep onset latency, lower total sleep time, more awakenings, and lower sleep efficiency), similar to previous studies examining the agreement between consumer sleep monitoring devices with polysomnography [14]. Moreover, it is likely that the discrepancies between devices were due to the small sample size, the inherent variability of one night of sleep monitoring, and the potential differing activity thresholds and sensitivity between devices, which may be more pronounced in people with hemiparesis.
## 4.1. Device Usabiltiy
The non-wearable WSA may offer a low-cost alternative to the Actiwatch device, measuring sleep from cardiac activity, breathing patterns, and snoring in addition to body movements. However, a number of usability issues arose that impacted its application. The WSA required continuous power supply via a plug-in power source at the hospital bedside. Additionally, the device relied upon application-based data storage, so Bluetooth pairing capabilities and reliable internet network connectivity was required on the hospital wards. Given the often-unreliable network connectivity in the hospital environment and variability in proximity of bedside power sources, these requirements presented challenges to data collection, and contributed to the low sample size. Moreover, some patients clearly prefer to sleep in a recliner chair rather than the hospital bed overnight where the WSA device cannot monitor their sleep.
## 4.2. Limitations
As discussed above, there are a number of limitations to the study that may influence the interpretation our findings. The small sample size, single night of sleep monitoring, and actigraphy sleep monitoring on the hemiparetic limb may have contributed to the reported poor level of agreement between the WSA and Actiwatch. However, despite our small sample size, the $95\%$ confidence intervals for the Bland–Altman tests were not dissimilar to those published by Chinoy, Cuellar, Huwa, Jameson, Watson, Bessman, Hirsch, Cooper, Drummond and Markwald [14] for a similar “under-the-mattress” device compared with polysomnography in 19 healthy young adults. While the WSA measures sleep from physiological and behavioral signals in addition to bodily movements, there was very large variability in the WSA estimates relative to the Actiwatch, which determines sleep metrics from limb movements only. These differences may be even more pertinent for people who have had a stroke with hemiparesis. Following on from the methodology developed in this current feasibility study, future studies addressing these limitations by increasing the sample size and wearing the Actiwatch on the non-hemiparetic limb may assist in obtaining new insights into the validity and utility of the WSA as a low-cost alternative to actigraphy for effective sleep monitoring in the hospital environment.
## 5. Conclusions
The findings from the present study suggest potential issues with the usability and accuracy of the WSA for monitoring sleep quality in adults who have been admitted to hospital for inpatient rehabilitation following a stroke. Future studies in larger samples and across multiple nights are needed to further investigate whether under-the-mattress technology can consistently and accurately monitor sleep parameters in adults with stroke. Moreover, developers of consumer devices for monitoring sleep should enable researchers to access their raw data so that the tools can be independently validated for reliable measurement of sleep in research settings and in unique clinical populations. Combining the use of wearable or under-the-mattress devices with subjective reports of sleep quality by adults using standardized outcome measures, together with review of clinical notes on patient sleep quality, may enable low-cost assessment of sleep in an inpatient rehabilitation setting. This may ultimately assist in understanding the nature and impact of sleep disturbances following stroke. Such studies may also enable the development of strategies to reduce the impact of the hospital environment on sleep quality, helping patients to have optimal engagement in rehabilitation programs to facilitate their recovery from stroke.
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|
---
title: 'The Association of Perceived Neighbourhood Environment and Subjective Wellbeing
in Migrant Older Adults: A Cross-Sectional Study Using Canonical Correlation Analysis'
authors:
- Yuxi Liu
- Huanting Liu
- Qin Chen
- Junhui Xiao
- Chonghua Wan
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001750
doi: 10.3390/ijerph20054021
license: CC BY 4.0
---
# The Association of Perceived Neighbourhood Environment and Subjective Wellbeing in Migrant Older Adults: A Cross-Sectional Study Using Canonical Correlation Analysis
## Abstract
Existing studies often focus on the impact of the neighbourhood environment on the subjective wellbeing (SWB) of the residents. Very few studies explore the impacts of the neighbourhood environment on migrant older adults. This study was conducted to investigate the correlations between perceived neighbourhood environment (PNE) and SWB among migrant older adults. A cross-sectional design was adopted. Data were collected from 470 migrant older adults in Dongguan, China. General characteristics, levels of SWB, and PNE were collected via a self-reported questionnaire. Canonical correlation analysis was performed to evaluate the relationship between PNE and SWB. These variables accounted for $44.1\%$ and $53.0\%$ of the variance, respectively. Neighbourhood relations, neighbourhood trust, and similar values in social cohesion made the most important contributions correlated with positive emotion and positive experience. A link between SWB and walkable neighbourhoods characterized by opportunities and facilities for physical activities with other people walking or exercising in their community, is positively associated with positive emotions. Our findings suggest that migrant older adults have a good walkable environment and social cohesion in neighbourhoods positively correlated with their subjective wellbeing. Therefore, the government should provide a more robust activity space for neighbourhoods and build an inclusive community for older adults.
## 1. Introduction
In recent years, people have paid an increasing amount of attention to individual happiness aside from economic utility and tend to use subjective wellbeing (SWB) to evaluate social progress [1,2]. SWB refers to an individual’s subjective feelings about his or her life, or the overall feeling of happiness in life [3]. Research on SWB is attentive to people’s values, emotions and evaluation, but does not fully recognise the external judgement of behavioural experts [4]. With regards to the study of SWB, early scholars predominantly focused on the impact of individual ‘endogenous characteristics’ on SWB, primarily referring to socioeconomic attributes such as age, gender, education, family income, marital status, and health status [5,6]. Empirical studies have demonstrated that demographic factors can only explain part of the difference in SWB, whilst some scholars began to pay attention to the influence of ‘exogenous factors’, such as social and residential environment on SWB [7,8,9]. There are relatively numerous studies on the influence of social factors on SWB, and they focus on the influence of social support on SWB [10,11,12]. Existing studies have demonstrated that subjective support, objective support, and support utilisation have moderate positive correlations with overall SWB, life satisfaction, and positive emotions, and moderate negative correlations with negative emotions [10,11,12]. There are some studies exploring SWB from the perspective of neighbourhoods. Hooghe and Vanhoutte’s [2011] research discovered that neighbourhoods with a strong homogeneity have a weaker impact on SWB than neighbourhoods with strong heterogeneity [13]. Research conducted by Chen and Ning [2015] revealed that good neighbourhood relationships, frequent participation in activities, convenient shopping, and beautiful landscapes are the primary neighbourhood environmental factors that affect residents’ SWB [14].
As a basic daily living space for urban residents, it is necessary to weigh the factors of the neighbourhood environment that directly affect people’s lives and feelings about it. Existing studies have demonstrated that the neighbourhood influences people’s SWB, however, the factors may be different at various stages of life [15,16]. The effects of the neighbourhood environment are less important in early to middle adulthood since they work and play outside the neighbourhood more often than older adults [16,17]. Compared to young people, older adults have different behaviours, mobility, and perceptions, and the demand for services and facilities can be extremely different [18,19]. These factors may influence and lead to differences in neighbourhood environmental needs and preferences at different stages of life. For numerous older adults, the neighbourhood in which they live is their primary environmental context [20]. The physical and social conditions of the neighbourhood environment may be more important to older adults, especially those who are retired or becoming frail. Thus, they may spend an increasing amount of time with neighbours in their neighbourhood [9,20].
In addition, existing studies often focus on the impact of the neighbourhood environment on SWB for the residents. However, for most people, the neighbour environment is not fixed, especially in recent years when residential migration has become increasingly common [21,22]. As an overall assessment of a person’s long-term quality of life [23], SWB is necessarily linked to life choices such as migration. Studies have demonstrated that the relocation of residence may cause changes in the living environment, which in turn have an impact on SWB [24,25]. From the perspective of spatial dimension, the longer the distance, the more the migrant may experience greater life changes. These changes are not only related to the support degree of the original social network and capital but also related to the challenge of adapting to the new environment [26,27,28]. Some studies believe that residential migration can lead to significant changes in specific areas of life, bringing about changes in life satisfaction [29,30,31]. Residential migration is often accompanied by specific life course events. However, the environmental changes conveyed by migration at different life course stages and the potentially important role of adaptation to the new environment in the relationship between the neighbourhood environment and SWB have received little attention.
Due to China’s urbanisation, a large number of workers and their families from across the country have migrated to work and live in new places, especially those with high economic status, such as Dongguan and Guangzhou, where jobs are more plentiful and lucrative [11,22]. The ‘one-child’ policy has reduced the size of Chinese families to a two parents and one-child ratio. Corresponding with filial piety as a major Chinese traditional value, Chinese older adults’ family members are brought along with their children who migrate for work to new places with the responsibility to take care of their parents. Other reasons for older adults’ migration include taking care of their grandchildren and reuniting with their families [22,32]. Internal migrant older adults in China, who are accompanied by their adult child migrate to the new place, viewed as ‘floating older adults’ or ‘senior drifters’ [5,32]. The number of migrant older adults has grown rapidly due to the persistence of internal migration and ageing trends in China [5,22]. The existing research on migrant older adults has gradually shifted from focusing on the migration patterns to the effects and causes of these people to the quality of life of migrant older adults [33]. For example, taking care of grandchildren within the family will significantly increase the life satisfaction of migrant older adults which has been reported in some studies [5,22]. The social support of the government and the community has a significant positive impact on the social integration of migrant older adults [34]. However, most of the existing research focuses on the fields of sociology and psychology, focusing on the impact of the social environment on the SWB or quality of life of this group, and few studies consider the impact of the neighbourhood environment [8,14]. Social cohesion is a social neighbourhood factor that affects SWB and is particularly relevant to older adults since it is associated with neighbourhood social order and violent crime rates [20]. In fact, after these elderlies migrated to the city, most of their outdoor and social activities were limited to the neighbourhood, which became the most important social support space for these migrant older adults [8]. The range of functional mobility and communication in cities for migrant older adults is much lower than that of local residents, and they tend to spend most of their time close to home [8,9,35]. Therefore, the study of the SWB of migrant older adults should take into account neighbourhood environment factors. Compared to objective indicators of the neighbourhood environment, the present study believes that the relationship between the subjective perception of the neighbourhood environment and SWB is more direct. In explaining SWB, subjectively perceived features of the neighbourhood environment are often more statistically significant than objective descriptions of environmental elements [36].
The attributes of the neighbourhood environment, and their relationship to SWB, are relatively well researched in Western countries [37,38], however, remain largely underexplored in China. Regarding the impact of the neighbourhood environment on SWB, most of the existing studies believe that positive features of the neighbourhood environment (e.g., walkability, availability of public services, and amenities) are associated with positive SWB [11,14], and most of these studies focus on the role of accessibility. For example, good accessibility to parks and green spaces can provide residents with open and natural public spaces, which has a positive impact on SWB [39]. Yet, few studies have explored the relationship between neighbourhood environment and SWB in Chinese migrant older adults. Even fewer studies examine the association of perceived neighbourhood environment (combined physical and social environment attributes) and SWB in Chinese migrant older adults. Is migrant older adults’ SWB associated with the physical and social environment of neighbourhoods where they live? To the best of our knowledge, no studies for the China setting are available to date examining perceived neighbourhood environment and SWB in migrant older adults.
To fill the knowledge gap, the present study aims to investigate the correlation between perceived neighbourhood environment and SWB amongst migrant older adults using canonical correlation analysis, which could inform the design of future interventions. Exploring the unique effects of neighbourhood attributes on migrant older adults’ SWB could be helpful to urban planners and public health officials in their efforts to build age-friendly neighbourhoods. The research will provide a reference and basis for individual behaviour decision making and community planning and governance.
## 2.1. Design
The present study employs a cross-sectional questionnaire survey conducted in Dongguan in South China, to determine the correlation between perceived neighbourhood environment and SWB amongst migrant older adults.
## 2.2. Subjects
This survey was performed amongst migrant older adults in Dongguan city between December 2018 and February 2019. The migrant older adults in this research were defined as any person aged not less than 60 years, those who had moved to Dongguan at least six months prior to the survey and were not listed in the household registration system of Dongguan. An eligible list of migrant older adults for the study was provided by the community committee. A multistage cluster sampling survey technique was used and 470 migrant older adults were invited to take part in the study ($98.2\%$ response rate). In the first stage, four districts were purposively selected out of 33 districts. In the second stage, 22 clusters were randomly selected from 26 communities with a probability proportional to the older adult’s density. In the third stage, within each cluster, migrant older adults were selected randomly.
## 2.3.1. Subjective Wellbeing (SWB)
SWB was assessed by the Memorial University of Newfoundland Scale of Happiness (MUNSH), which has been designed specifically for older adults and has high validity (Kaiser-Meyer-Olkin (KMO) of 0.703) and consistency (Cronbach’s alpha of 0.735) [40]. The MUNSH is a multiitem scale which has 24 items, assessing four dimensions: positive emotion (PA) [e.g., ‘Generally satisfied with the way your life has turned out?’], general positive experience (PE) [e.g., ‘Are you satisfied with your life today?’], negative emotion (NA) [e.g., ‘Bitter about the way your life has turned out?‘], and general negative experience (NE) [e.g., ‘How much do you feel lonely?’]. Numerous items on this scale cover specific content in the geriatric area with reference to age and time of life. Possible responses to each item are ‘yes’ (score 2 points), ‘I don’t know’ (1 point), and ‘no’ (0 points). The total SWB score was then calculated using the equation PA + PE − NA − NE. Total scores range between −24 to + 24 points, where higher scores indicate better SWB [40].
## 2.3.2. Perceived Neighbourhood Environment (PNE)
The perceived neighbourhood environment in the present study consists of the physical and social environment attributes, namely ‘walkability of the neighbourhood’ and ‘social cohesion’. These two environment attributes were assessed by the related module of the Neighbourhood Scales developed by Mujahid [41]. The walkability of the neighbourhood was measured with seven items (The specific items are presented in Table 1), asking the participants if they believed that their neighbourhood offered opportunities and facilities for physical activities, has adequate green space and walkable places, and if they observed other people walking in their neighbourhood. The questionnaire uses a 5-point Likert Scale, ranging from 1 = strongly disagree to 5 = strongly agree with the statements. The Cronbach’s alpha of the original scale was 0.73 [20,41]. The total score ranges from 7–35.
Social cohesion is comprised of four questions asking the respondent about their values such as interpersonal trust, and their relationship with their neighbours. This questionnaire also uses a 5-point Likert Scale, with responses ranging from 1 = strongly disagree to 5 = strongly agree with each statement. The Cronbach’s alpha of the original scale was 0.74 [20,41]. The total score ranges from 4–20.
## 2.3.3. Individual Characteristics
The socio-demographic factors recorded were gender, age, living arrangements, health insurance, and pension status. Living arrangement was categorized as “living with child only”, “living with child and spouse”, “living with child and grandchild”, “living with child, grandchild and spouse”, and “living alone”. Health insurance and pension status were divided into a “have” group and a “haven’t” group. Self-rated health was divided into three ordinal categories: “good”, “fair”, and “poor”.
## 2.4. Data Collection
Nine research assistants (second-year postgraduates) and community staff were trained at a workshop. All interviewers were trained before the formal collection of data by an experienced researcher. The workshop included an introduction to the study and the methods and skills of conducting quantitative interviews. The questionnaires were tested in a pilot study. Face-to-face interviews using the structured questionnaire were conducted. All of the participants were interviewed at their homes using their local language by trained interviewers. Each interview took about 20–25 min. The supervisors checked the completion of the questionnaire during the fieldwork. If information was missing, the interviewer went back to obtain the missing information.
## 2.5. Data Analysis
SPSS V.26.0 software was used to process the data. A Pearson correlation analysis was used to analyse the correlations between the perceived neighbourhood environment variables (X1-X11) and the SWB dimensions (PA, NA, PE and NE). Canonical correlations between the perceived neighbourhood environment variables and the SWB dimensions were analysed after standardising the scores of each variable.
Canonical correlation analysis is an approach that involves the application of structure coefficients as indices for the identification of important indicators. It is a multivariate statistical analysis method used to determine the correlation between two sets of variables using the correlation between the combined pairs of variables to reflect the overall correlation between the two sets of indicators [42]. This paper focuses on the correlation between the two sets of variables of neighbourhood environment and subjective wellbeing, so canonical correlation analysis was chosen.
Canonical redundancy reflects the percentage of variance explained by each canonical variable for each group of variables, If the canonical variables are well representative of the original variables, prediction can be made by canonical correlation. The magnitude of the redundancy analysis indicates the extent to which the pair of canonical variables can explain each other for another set of variances, and it will provide some useful information for further discussion of the relationship between many-to-many [42].
## 2.6. Ethical Considerations
Ethical approval was received from the Institutional Ethics Committee of the Ethics Review Committee of Guangdong Medical University, China (REC: PJ2018037) before the research was conducted. Privacy and data confidentiality were ensured. Voluntary participation and unconditional withdrawal were offered to all participants. A small gift was given as a thank you for their participation.
## 3. Results
The total sample consisted of 470 migrant older adults. Of those, 275 were female (58.5 percent) and 195 were male (41.5 percent). The mean age of the participants was 67.1 years (SD 5.5), with a minimum age of 60, and a maximum age of 87 years. Most participants had fair to good health ($$n = 424$$, 90.2 percent). Most of the migrant older adults lived with their families with an average of more than three members ($$n = 456$$, 97.0 percent). Approximately one-third of migrant participants lacked health insurance ($$n = 135$$, 28.7 percent) and had no pension ($$n = 166$$, 35.3 percent).
Table 2 illustrates the results of SWB variables and PNE variables. For SWB, the mean score (x ± s) of the total scores for SWB was 14.76 ± 8.31. For PNE, the mean score (x ± s) of walkability and social cohesion were 27.34 ± 5.15 and 15.69 ± 2.62, respectively.
The simple correlation analyses of PNE and SWB demonstrated that the correlations ranged between $r = 0.276$ and $r = 0.423$ for PA and PE, indicating there was a moderate level of correlation, whilst NA and NE were negatively correlated with X1-X3 and X8-X11, and it revealed a low level of correlation (Table 3).
The 11 variables X1–X11 of the above simple correlation analysis species were used as the X set, the scores of the SWB dimensions were used as the Y set for typical correlation analysis, and four common variables were obtained (Table 4).
The results revealed that within the four pairs of canonical variables, two pairs of canonical variables were statistically significant (r1 = 0.402, $p \leq 0.0001$ and r2 = 0.257, $p \leq 0.05$), demonstrating that there was a correlation between SWB and the PEN variables. The first pair of canonical variables contained 60.88 percent of the information. The first two pairs of typical variables cumulatively contributed to 82.87 percent of the information.
Table 5 reveals that in the first pair of canonical variables, residents with neighbourhood relations (X9), neighbourhood trust (X10), and similar values (X11) in social cohesion are positively correlated with PA (Y1) and PE (Y3). In the second pair of canonical variables, the walkability of X1, X2, X6, and X7 and the social cohesion of neighbours helping each other (X8) with PA (Y1), NA (Y2) in SWB, are closely correlated to each other.
Redundancy analysis (Table 6) demonstrated that amongst the first pair of canonical variables, U1 could explain 44.1 percent of the total variation in the X variable set and 7.1 percent in the Y variable set, whilst V1 could explain 53.0 percent of the total variation in the Y variable set and 8.6 percent in the X variable set. In the second pair of canonical variables, U2 could explain 3.8 percent of the total variation in the X variable set and 0.3 percent in the Y variable set, whilst V2 could explain 16.2 percent of the total variation in the Y variable set and 1.1 percent in the X variable set.
## 4. Discussion
This study explored the correlation between PNE and SWB among migrant older adults to understand the relative importance and level of the components of PNE and SWB. The results showed that migrant older adults with a high PNE have better PA and PE, which leads to a generally high SWB. This result is in line with previous studies, which suggested that higher PNE leads to higher SWB. Previous studies have confirmed that neighbourhood-built environments (e.g., walkability) and social environments affect older adults’ SWB (e.g., social cohesion) [8,11]. It is possible that this is due to the fact that older adults are more dependent on their neighbourhoods and that changes and adaptations in the neighbourhood environment have a greater impact on their lives [9,20].
Neighbourhood environments matter since they are socially structured and represent differential amenities, including access to physical resources, social support, and relationships [43]. Furthermore, the residential neighbourhood is the older adults’ predominant environmental context, particularly those who are retired or migrated with family [9,20]. Therefore, they likely spend more time increasingly with neighbours in the neighbourhood. Thus, this study provides evidence for the need to reinforce the neighbourhood environment for migrant older adults to improve their SWB by demonstrating a more comprehensive canonical correlation between the eleven elements of PNE and SWB.
The social environment attributes of PNE, their relationship with their neighbours, interpersonal trust, and sharing the same values are associated with positive emotions and wellbeing experiences, consistent with previous studies [8,20,43]. Older adults may be more affected by neighbourhood characteristics (e.g., social cohesion) than younger adults who have been reported in some studies as being more concerned about environmental pollution and neighbourhood beautification [44,45]. Social cohesion is an aspect of the neighbourhood’s social environment influencing individual health-related behaviours such as physical and recreational activities [46]. Social cohesion refers to the absence of potential social conflict and the presence of strong social bonds—usually measured by levels of trust and reciprocity norms [47].
Cohesive neighbourhoods may be better for reinforcing positive social norms for health behaviours, leading to quicker adoption of new residents since neighbours know and trust each other [11,12,22]. In addition, neighbours who trust one another are more likely to provide help and support in times of need, particularly for migrant older adults who face the dilemmas of losing geopolitical ties and have difficulty integrating into new cities [20,22]. Research has demonstrated that people may only trust those in the same in-group and may not participate in social activities outside their circle [48]. Therefore, migrant older adults who share the same values as neighbours are more likely to establish good relationships and trust each other, which leads to promoting their SWB.
The current study used SWB to examine the association of physical neighbourhood attributes and walkability. We found a link between SWB and walkable neighbourhoods characterized by opportunities and facilities for physical activities with other people walking or exercising in their community, positively associated with positive emotions and negatively associated with negative emotions. Previous studies showed neighbourhood walkability is related to leisure time physical activity among Chinese and U.S. older adults [49,50]. Walking is correlated with both improved physical and emotional health [51]. In addition, researchers found a link between walkable neighbourhood attributes that include land use diversity and well-connected transportation networks with more walking, less obesity, and lower coronary heart disease risk [52,53].
Migration, retirement, and other major life events tend to create anxiety, pessimism, depression, and other native emotions in migrant older adults [5]. However, a good walking environment provides migrant older adults with conditions for exercise and creates a platform for the older adults to communicate with their neighbours. Through walkable neighbourhoods, migrant older adults can avoid the “social isolation” phenomenon and “social insularity” caused by long-term absence from home [54]. It also helps them to improve their self-worth and maintain positive mental health while participating in social activities [54]. For example, Wiles [2012] found that a high-quality physical neighbourhood environment enhances wellbeing [55]. This effect is due to people having an innate emotional connection to their neighbourhood environment, and open spaces can increase social interaction.
This study is meaningful since our comprehensive analyses factored in the various elements of PNE to demonstrate the canonical correlation between PNE and SWB of migrant older adults. The study extends this prior research by focusing specifically on perceived neighbourhood environments—defined in this study as being the combined physical and social environments—of Chinese migrant older adults [9,11,12]. The statistical approach of using canonical correlation analysis is appropriate for identifying the associations between the two sets of variables of the physical and social environments, measured as walkability and social cohesion, with subjective wellbeing. Our study emphasized that positive physical and social environments are likely to contribute to the positive subjective wellbeing of the elderly. The findings could provide evidence to help governments design healthy ageing policies to improve the SWB at the community level. The findings also could potentially be expanded to other population groups. Positive physical and social environments are likely to contribute to positive subjective wellbeing beyond migrant older adults. However, this study has some limitations. First, we collected cross-sectional data based on self-reports. Thus, we cannot address the causality direction. Second, we conducted this study in only one city, which may not represent all migrant older adults in China. Therefore, future research should consider well-designed multicentre prospective studies of neighbour correlates of SWB. Third, the data collection instruments (i.e., focus on walkability) lacks accounts of other physical dimensions and amenities (i.e., health centres, banks, elderly activity centres, and parks) that support older people’s wellbeing and these could be highlighted as potential venues for further research. Finally, we did not assess the effect of changes in the socioeconomic status of the whole family on SWB and the participants’ ranges for time elapsed since migration which could affect migrant older adults’ SWB. Future research studies could explore this further.
There are some policy implications in this study’s findings. Our study suggests that physical and social attributes of neighbourhoods are strongly associated with migrant older adults’ SWB. Previous studies found that migrant older adults’ restricted access to social benefits and social relations was detrimental to their mental health [11,12]. Our findings confirm this point and further suggest that migrant older adults have a good walkable environment and social cohesion in neighbourhoods positively correlated with their subjective wellbeing. Therefore, the government should provide a more robust activity space for the neighbourhood and optimize the quality of life for older adults.
In addition, migrant older adults should be encouraged to participate in community activities to enrich their lives and improve their SWB. Finally, they could improve their wellbeing through inclusive community building. This approach requires breaking the closure and exclusion in the configuration of community power. Eliminating the identity segregation and social exclusion of residents in sharing community resources and promoting good neighbourliness among older adults with different identities and backgrounds will enhance migrant older adults’ sense of community cohesion and community belonging.
## 5. Conclusions
As residential migration becomes more common, the neighbourhood environment inevitably changes, and one needs to adapt to the new neighbourhood environment. This paper focused on older adults after residential migration. Initially, it explored the relationship between the new neighbourhood environment and SWB after migration, enriching the study of the relationship between neighbourhoods and SWB. However, we need further indepth analysis of how changes in the neighbourhood environment before and after residential migration affect older adults’ wellbeing and the critical role of other aspects of residential migration.
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|
---
title: 'Association between Anemia Severity and Ischemic Stroke Incidence: A Retrospective
Cohort Study'
authors:
- Hui-Fen Chen
- Tsing-Fen Ho
- Yu-Hung Kuo
- Ju-Huei Chien
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001762
doi: 10.3390/ijerph20053849
license: CC BY 4.0
---
# Association between Anemia Severity and Ischemic Stroke Incidence: A Retrospective Cohort Study
## Abstract
Stroke patients presenting with anemia at the time of stroke onset had a higher risk of mortality and development of other cardiovascular diseases and comorbidities. The association between the severity of anemia and the risk of developing a stroke is still uncertain. This retrospective study aimed to evaluate the association between stroke incidence and anemia severity (by WHO criteria). A total of 71,787 patients were included, of whom 16,708 ($23.27\%$) were identified as anemic and 55,079 patients were anemia-free. Female patients ($62.98\%$) were more likely to have anemia than males ($37.02\%$). The likelihood of having a stroke within eight years after anemia diagnosis was calculated using Cox proportional hazard regression. Patients with moderate anemia had a significant increase in stroke risk compared to the non-anemia group in univariate analyses (hazard ratios [HR] = 2.31, $95\%$ confidence interval [CI], 1.97–2.71, $p \leq 0.001$) and in adjusted HRs (adj-HR = 1.20, $95\%$ CI, 1.02–1.43, $$p \leq 0.032$$). The data reveal that patients with severe anemia received more anemia treatment, such as blood transfusion and nutritional supplementation, and maintaining blood homeostasis may be important to preventing stroke. Anemia is an important risk factor, but other risk factors, including diabetes and hyperlipidemia, also affect stroke development. There is a heightened awareness of anemia’s severity and the increasing risk of stroke development.
## 1. Introduction
Stroke is a leading cause of death and disability worldwide [1]. Stroke survivors suffer from various impairments and complications affecting motor, sensory, visual, language, and cognitive functions [2,3]. Therefore, a stroke imposes a great burden on patients as well as their caregivers and family members. Stroke patients may be hospitalized or may frequently visit the emergency department owing to their long-term sequelae and disability, which not only dramatically increases the burden on caregivers and their family’s finances, but also severely affects their quality of life. There are numerous recognized risk factors for stroke, such as hypertension, hyperlipidemia, diabetes mellitus, cigarette use, obesity, age, and physical activity [1,4,5]. Increases in the elderly population and life expectancy are also key reasons for the increase in number of stroke patients.
Anemia affects 15–$32\%$ of the world’s population, is usually present in stroke patients, and can worsen with aging [6,7]. In 2019, the age groups of 15 to 19 and 95 and older, for both males and females, had the highest global point prevalence of anemia. The mean (range) global prevalence rates of mild, moderate, and severe anemia were approximately $54.1\%$, (53.8–$54.4\%$), $42.5\%$ (42.2–$42.7\%$), and $3.4\%$ (3.3–$3.5\%$), respectively [8]. Elderly individuals may experience malnutrition and dyspepsia as their physical condition deteriorates with age, and this may affect their hematopoiesis functions, thereby causing anemia or pancytopenia. Anemia is also a risk factor for ischemic stroke and is related to high post-stroke mortality [9,10].
Nevertheless, previous research has suggested that anemia may raise the risk of stroke. However, the new stroke guidelines from the American Stroke Association (ASA) do not list anemia as a major stroke risk factor [11]. Here, we conducted a retrospective cohort study to investigate the association between the severity of anemia and stroke incidence. Owing to Taiwan’s National Health Insurance (NHI) policy, anemia is rarely listed as a primary condition and may not be documented on patient medical records on the basis of International Classification of Diseases, Tenth Revision (ICD-10) codes. The laboratory data of anemia status were not available in Taiwan’s NHI system, and the prevalence of anemia could be underestimated. Moreover, the data of association between anemia and comorbidities in the Taiwanese population are scarce. An evaluation of the stroke risk factors, especially anemia severity, could provide important information that may enhance medical care or even national healthcare planning. This study retrospectively evaluated the prevalence and characteristics of anemia in hospitalized patients and analyzed whether anemia severity based on the hemoglobin (Hb) level was associated with stroke development.
## 2.1. Study Cohort
This retrospective cohort study included 454,424 patients aged ≥20 years who had visited or were hospitalized at Taichung Tzu-Chi Hospital, Taiwan, from 2013 to 2019. A total of 71,787 patients underwent at least 1 blood Hb measurement performed using a Sysmex XE-5000 hematology analyzer (Sysmex Co., Kobe, Japan) within 1 year to confirm their anemia status. This study was approved by the Research Ethics Committee of Taichung Tzu-Chi Hospital (REC 111-02). The need for informed consent was waived owing to the retrospective nature of the study and the use of anonymous medical records.
## 2.2. Definition of Anemia and ICD Codes
Adult patients older than the age of 20 were included in this study. All participants in this study completed at least one Hb measurement, and persons who did not fulfill the predetermined criteria were not included. The date of laboratory Hb measurement was defined as the index date, and the anemia severity was classified according to the World Health Organization (WHO) criteria [12]. We categorized the patients into different groups according to their anemia severity. Anemia is defined as an Hb level of <13.0 g/dL for men and <12.0 g/dL for women. The cutoff for Hb in mild anemia was 11.0–11.9 g/dL for women and 11.0–12.9 g/dL for men, whereas the cutoffs for moderate and severe anemia were 8.0–10.9 and <8.0 g/dL, respectively, for both men and women. As shown in Figure 1, the exclusion criteria were as follows: [1] patients without Hb measurements; [2] receiving a diagnosis that might affect the Hb status, including gastric intestinal bleeding (ICD-10 code K92.2), bleeding (ICD-10 code R58), trauma (ICD-10 code T79.2), excessive bleeding associated with menopause onset (ICD-10 code N92.4), intraoperative and postprocedural complications of spleen, endocrine, and nervous system (ICD-10 code D78, E36, G97), excessive bleeding with onset of menstrual bleeding (ICD-10 code N92.2), traumatic hemorrhage of the cerebrum (ICD-10 code S06.360A), hemorrhage from respiratory passages (ICD-10 code R04.9), nontraumatic intracerebral hemorrhage (ICD-10 code I61.9), spleen diseases (ICD-10 code D73), pulmonary vessels diseases (ICD-10 code I28), stomach and duodenum diseases (ICD-10 code K31), acute myocardial infarction (ICD-10 code I21), injury to an unspecified body region (ICD-10 code T14), or absent, scanty, or rare menstruation (ICD-10 code N91), before their index date until anemia diagnosis; [3] receiving a stroke diagnosis before the index date on the basis of the ICD-10 codes I63; [4] not visiting our out-patient clinic or being hospitalized within the last 2 years; and [5] death or leaving against medical advice (DAMA) less than 1 month after the index date.
A flowchart of the patient enrollment process is illustrated in Figure 1. All patients were grouped by sex and age (20–30, 31–40, 41–50, 51–60, 61–70, 71–80, and >80 years). The *Hb status* confirmation date was identified as the index date for the case and control groups, and stroke events were followed subsequently.
## 2.3. Outcome and Associated Factors
The eligibility of all patients was retrospectively determined in this cohort study. The severity of anemia was then subgrouped based on Hb level, and the stroke patients were those who had at least two ICD-10 admission claims for clinic OPD visits or stroke-related hospitalization in our hospital during the study period. During the monitoring period, the occurrence of subsequent disease was examined. The occurrence of subsequent disease was analyzed during the observation period. Patients were individually tracked for 2–8 years, beginning on the index date, and followed thereafter. In this study, the outcome of stroke was defined as admission claims of ICD-10 codeI63, cerebral infarction. The accuracy of diagnoses from claims data was verified in a previous study showing that the PPV and sensitivity of ICD-10-CM code I63 as a primary diagnosis of acute ischemic stroke were $92.7\%$ and $99.4\%$, respectively [13]. We also analyzed the hazard ratio for comorbidities that were potentially linked to stroke: hypertension (I10–I13, I15), diabetes (E08–E11, E13), chronic kidney disease (CKD; N17–N19, I12, I13), chronic heart failure (I50), chronic obstructive pulmonary disease (J44, J60–70), hyperlipidemia (E78.0-E78.5), and atrial fibrillation (I48). The comorbidities were defined as the presence or absence of accompanying disease within one year before the index date of anemia. The national health insurance program (NHI) in *Taiwan is* mandatory for all citizens, and various medications and medical procedures were coded with unique code. In this study, six frequently prescribed drugs were included to investigate the efficacy of various anemia therapies for patients within six months after the hemoglobin measurement index date. These medications included iron (hydroxide-polymaltose complex, Yuanchou Chemical and Pharmaceutical Co., Ltd., Taiwan, NHI code AC46166100), ferric hydroxide sucrose complex (TCM Biotech international Corp. Taiwan, NHI code AC57884221), sodium ferrous citrate (Guang Heng Enterprise Co., Ltd. Taiwan, NHI code BC22097100), hydroxocobalamin (Shinlin Sinseng Pharmaceutical Co., Ltd. Taiwan, ACETATE, NHI code AC09754209), mecobalmin (Eisai Taiwan Inc., NHI code AC296301G0), folic acid (Johnson Chemical Pharmaceutical works Co., Ltd. Taiwan, NHI code AC346701G0), and blood transfusion (NHI code 94001C).
## 2.4. Statistical Analysis
Statistical analyses were conducted using the SAS statistical package (Version 9.4) and SPSS (version 28.0, SPSS Inc., Chicago, IL, USA) to examine the prevalence and clinical trends of anemia among the different age groups, sexes, and comorbidities. The categorical variables were assessed by applying a Chi-square test. The continuous variables were assessed by applying a t test. Furthermore, different predictors were used to estimate relative risks [14]. To examine the stroke risk associations with anemia, the deaths as competing risks of stroke were analyzed by using a Cox proportional cause-specific hazard model to calculate hazard ratios (HR), $95\%$ confidence intervals (CIs), and two-sided p values. A two-sided p value of <0.05 was considered statistically significant. A multivariate Cox proportional cause-specific hazard regression model was adjusted for age, sex, and comorbidities. A proportional hazard assumption was evaluated by the Kolmogorov-type Supremum test; that was not violated.
## 3. Results
As shown in Figure 1, only 71,787 of the 454,424 patients who visited our facility qualified for the retrospective cohort research. The baseline characteristics of the case and control groups are summarized in Table 1. The mean Hb level was 14.2 ± 1.3 g/dL in the normal group and 10.7 ± 1.6 g/dL in the anemia group.
Of the 16,708 anemia patients, 6185 ($37.02\%$) were men and 10,523 ($62.98\%$) were women. The mean age of the case group was 59.1 ± 18.5 years, and that of the control group was 50.6 ± 16.3 years. The case group had a higher incidence of comorbidities, including hypertension ($11.80\%$ versus $20.40\%$, $p \leq 0.001$), diabetes ($6.74\%$ versus $14.77\%$, $p \leq 0.001$), CKD ($0.90\%$ versus $6.07\%$, $p \leq 0.001$), chronic heart failure ($0.91\%$ versus $2.67\%$, $p \leq 0.001$), chronic obstructive pulmonary disease ($2.10\%$ versus $2.96\%$, $p \leq 0.001$), and atrial fibrillation ($0.49\%$ versus $1.01\%$, $p \leq 0.001$), than did the control group.
Table 2 presents the anemia severity and subsequent cases of stroke. The patients with anemia were further divided into three subgroups according to anemia severity, determined on the basis of Hb levels by WHO criteria [12]. Thus, of the 16,708 patients with anemia, 9065 ($54.25\%$) had mild anemia, 6532 ($39.09\%$) had moderate anemia, and 1111 ($6.65\%$) had severe anemia. During follow-up, a total of 447 anemia patients ($2.68\%$, $\frac{447}{16}$,708) and 744 controls ($1.35\%$, $\frac{744}{55}$,079) were diagnosed as having stroke. Moreover, there were 740 non-anemia patient deaths and 1229 anemia patient deaths throughout the 8-year follow-up period ($1.34\%$ and $7.63\%$, respectively).
We observed a positive association between the severity of anemia, determined based on Hb measurements, and the risk of stroke. Figure 2 illustrates the cumulative incidence of stroke in the three subgroups of anemia severity during the 8-year follow-up. A higher incidence of stroke events was noted in the patients with moderate anemia after their diagnosis during the 8-year follow-up (log-rank test, $p \leq 0.001$).
Table 3 illustrates the univariate and adjusted associations between the risk of stroke and the severity of anemia, sex, age, and comorbidities. The risk of stroke was higher in the case group than in the control group. In univariate regression analysis, we found moderate anemia (HR = 2.31; $95\%$ CI, 1.97–2.71) had a significant increase in stroke risk compared to the non-anemia group. After adjusting, we found the risk of stroke was higher in the patients with moderate anemia (adj-HR, 1.20; $95\%$ CI, 1.02–1.43, $$p \leq 0.032$$) than in the controls. The same results were obtained for gender and age by both univariate analysis (HR = 1.66, $95\%$ CI = 1.48–1.87, $p \leq 0.001$; HR = 1.07, $95\%$ CI = 1.07–1.08, $p \leq 0.001$, respectively) and adjusted HRs (adj-HR = 1.64, $95\%$ CI = 1.46–1.85, $p \leq 0.001$; adj-HR = 1.07, $95\%$ CI = 1.065–1.074, $p \leq 0.001$, respectively). Furthermore, the case group had a higher prevalence of comorbidities than did the control group. However, only the comorbidities diabetes mellitus and hyperlipidemia, by both univariate analysis (HR = 2.86, $95\%$ CI = 2.50–3.28, $p \leq 0.001$; HR = 1.89, $95\%$ CI = 1.54–2.31, $p \leq 0.001$, respectively) and adjusted HRs (adj-HR, 1.48; $95\%$ CI, 1.27–1.71; $p \leq 0.001$), (adj-HR, 1.13; $95\%$ CI, 0.91–1.39; $$p \leq 0.280$$), were associated with a higher risk of stroke in the case group compared to the control group.
## 4. Discussion
This retrospective study evaluated the prevalence and characteristics of anemia and the risk of stroke. The strength of this study is that it identified the association between anemia and the risk of stroke by using a hospital-based database, from which the laboratory data were retrieved to classify the severity of anemia. In contrast to previous studies, which have estimated the risk of stroke associated with anemia by using data from Taiwan’s NHI databases based on ICD codes and lacked conclusive laboratory Hb measurements [15,16], our study analyzed laboratory data and classified the patients into subgroups according to the severity of anemia to assess the associations between anemia severity and the risk of stroke. We also excluded patients with diseases that might interfere with our results, including those with a tendency of bleeding, other hemorrhagic disease, and persons who did not fulfill the predetermined criteria were also excluded. All participants in this study completed at least one Hb measurement, and persons who did not fulfill the predetermined criteria were then excluded. Our findings indicate that patients with moderate anemia showed an increased likelihood of stroke development.
In this retrospective analysis, there were more female anemic patients than male anemic patients. In the initial stage, the primary signs of mild anemia include fatigue, light skin, dizziness, debility, and headaches. Patients in the early stage of anemia or mild anemia may not seek medical care or consultations with physicians, particularly middle-aged men. Many male patients did not meet the criteria for hospital visits in 2 years. On the other hand, most women experience menopause at the age of 40–50 years; thus, some anemia symptoms, such as dizziness, fatigue, or paleness, may be overlooked or misdiagnosed as menopausal symptoms. Even when individuals visit a hospital or clinic, medical personnel tend to focus more on other maladies rather than anemia. However, if anemic condition is left untreated for a longer period, the consequences and complications can become more severe, causing shortness of breath, low blood pressure, arrhythmia, and even chronic heart failure. Results from this research demonstrated an increased risk of stroke occurrence in moderate anemia patients compared with the non-anemia control group. Additionally, the mortality rate in the severe anemia group was $12\%$, much higher than that of other patients with anemia in this study. Patients suffering from severe anemia might die from other illnesses caused by their feeble condition prior to having a stroke. As a result, the risk of stroke in the severe anemia group was observed to be lower than in the moderate anemia group.
According to statistical data from Taiwan’s Ministry of the Interior, the population aged >65 years increased from $11.15\%$ in 2012 to $16.68\%$ in 2021. In the past two decades, the average life expectancy also increased from 76.75 to 81.30 years. The Council for Economic Planning and Development estimated that Taiwan will become a super-aged society by as early as 2025; moreover, the population aged ≥65 years is expected to account for >$20\%$ of all individuals [17]. This accelerated speed of aging has become a burden to the healthcare system and society. In this study, there is an upward trend in the prevalence of anemia with age (from $6.72\%$ in the 20–30 age range to over $15\%$ in the elderly age groups; Table 1). Our results are consistent with the global prevalence of anemia, indicating that the trend of anemia burden increases with age [18,19,20]. We observed that the anemia prevalence peaked at $17.3\%$ in the 71–80 age group and at $14.4\%$ in the >80 age group. Anemia rates in the 71–80 age range in this study cohort were $4.2\%$ ($\frac{2990}{71}$,787) and $3.4\%$ ($\frac{2411}{71}$,787), respectively. In this study, the prevalence of anemia in people over 60 is approximately $11\%$, which is lower than it is in other Asian countries, such as Korea, where it is $13.8\%$ for people over 65 [20]. Anemia is a common condition in older adults and can be caused by various factors such as poor nutrition, chronic diseases, medication, and healthcare. Taiwan has a relatively high standard of living, and the population has access to a variety of nutritious foods, which helps to prevent nutrient deficiencies, including iron deficiency. Moreover, Taiwan has a well-developed healthcare and medical insurance system. The Ministry of Health and Welfare also promotes and encourages all citizens above the age of 45 to participate in adult health checkup programs. These programs enable the early detection of diseases such as cancer and other chronic disease, as well as delivery of comprehensive healthcare prior to the disease worsening [17]. Therefore, all those factors may contribute to reduce the overall prevalence of anemia in the population. In elderly people, anemia has been reported to be associated with cardiovascular disease [21], stroke [6], dementia [22], frailty [23], and high morbidity as well as mortality [24]. Because of Taiwan’s NHI policy, however, anemia has rarely been listed as a primary condition in elderly people. According to the WHO recommendation, an anemia prevalence of >$5\%$ is considered to be of public health significance [12] and may require public health attention and intervention. The increased prevalence of anemia in the elderly should be considered an important public issue in Taiwan.
In this study, we also observed a higher prevalence of pre-existing comorbidities among the anemia group compared to the non-anemia population. The moderate to severe anemia patients had higher all-cause mortality compared to the non-anemia group; this trend was mentioned in previous studies [9,25]. Other unreported comorbidities may interfere with the association between anemia and stroke. Severe anemia might be corrected well, but mild to moderate anemia might become a chronic condition which eventually becomes associated with stroke. In this study, we observed that patients with severe anemia required blood transfusions more frequently than the group with moderate anemia and the control group. However, a study by Dr. Ren that was published in Nature Communications raises the possibility that blood transfusions might be advantageous to health even up to seven hours after a stroke in a mouse model. Their team discovered that replenishing $20\%$ of the mouse’s blood was sufficient to significantly lessen brain damage [26]. However, there are few studies focusing on maintaining hemodynamic condition in severe anemia patients to prevent stroke. Therefore, more studies might help to clarify the benefit from blood transfusions on this issue in the future. Furthermore, the different therapeutic strategies may explain why severe anemia portends lower stroke risk than other anemia severities.
Studies assessing the association between anemia and comorbidities in the Taiwanese population are rare. Anemia, a direct consequence of decreases in Hb and red blood cell (RBC) levels in circulation, is a multifactorial condition; lack of iron, folate, and vitamin B12 are well-known causes of anemia. The most common type of anemia is iron deficiency anemia, which may account for as much as $50\%$ of all explained anemia cases [27]. Other diseases such as diabetes, chronic infections, inflammation, and CKD also affect RBC proliferation, erythropoietin production, androgen secretion, and myelodysplasia [28]. Anemia is also positively associated with impaired renal function. Taiwan has one of the highest number of cases of CKD and end-stage renal disease in the world; CKD is the most frequent cause of anemia [8,20,21,29]. The severity of anemia is directly related to the degree of renal dysfunction. CKD causes reduction in erythropoietin synthesis, subsequently resulting in decreased cell proliferation. At least one-third of anemia patients aged >65 years have CKD or autoimmune diseases/chronic infection [30]. Patients with CKD are also at a significant risk for stroke, including the ischemic and hemorrhagic subtypes. The mechanisms linked to higher risk of stroke in CKD patients include alterations in cardiac output, platelet function, regional cerebral perfusion, accelerated systemic atherosclerosis, altered blood brain barrier, and disordered neurovascular coupling [31]. Additionally, Dr. Poznyak also identified the atherosclerosis-specific features in chronic kidney disease (CKD) in a recent study [32]. The major symptoms of anemia may range from mild fatigue to severe systemic illnesses. In addition, accumulating evidence indicates that anemia engenders outcomes such as increased stroke [9], heart failure [33], hospitalization [25], and mortality [34], all of which impose a severe burden on healthcare systems. Furthermore, anemia is associated with increased iron overload, increased chances of viral infection [35], and increased risks of myocardial infarction [36]. We also analyzed other known conventional risk factors, such as hyperlipidemia and atrial fibrillation, that affect the development of stroke; the hazard ratio was slightly different to other investigations [9]. Hyperlipidemia is an important risk factor for stroke [4,37]. Atrial fibrillation (AF) is a frequent cardiac rhythm disease associated with various significant negative health outcomes, such as heart failure and stroke. Particularly in women, atrial fibrillation is linked to an increased long-term risk of stroke, heart failure, and all-cause death [38,39]. Many investigations also revealed that anemia is a frequently observed comorbidity in patients with AF and is associated with cardiovascular, stroke, and gastrointestinal bleeding [40].
In medical practice, those experiencing moderate to severe anemia are more likely to receive medical attention than those with mild anemia. This means that patients with moderate to severe anemia with signs of illness symptoms would be given blood transfusions, iron supplements, and vitamin B12, while mild anemia would more likely be overlooked [41,42,43]. Regarding the management of anemic patients, blood transfusions are often seen as an effective way to increase hemoglobin levels and improve their overall health. In this study, we examined patients who received transfusions and pharmacological therapy within six months of the diagnosis index date. According to our results, patients with moderate and severe anemia received a greater proportion of blood transfusion than those with mild anemia ($24.14\%$, $61.12\%$ vs. $11.22\%$, Table 1). Blood transfusions can maintain in the body’s hemodynamics and alter the viscosity of the blood. Keeping the blood in balance in the body’s circulation and offering better care may be a strategy to prevent stroke. However, blood transfusion is influenced by a number of circumstances and the decision of the healthcare professionals. Patients who receive frequent transfusions may also be exposed to an increased risk of stroke. To ascertain the beneficial effects of anemia therapies such as transfusion and other medication on reducing the chance of stroke, further research must be conducted.
Despite its strengths, our study has some limitations that should be noted. First, the different types of anemia, such as iron deficiency anemia or folic acid anemia, were not correctly defined in this study. Second, we could not analyze data regarding lifestyles or socioeconomic status, such as smoking, alcohol habits, obesity, education, or financial condition. Third, in order to confirm the validity of the diagnosis for anemia, we only included the patients with one Hb measurement, which could cause a potential selection bias in a retrospective study. The medical service of our hospital serves a population of approximately 2.8 million in the center area of Taiwan, and more than 700,000 clinical visits are made each year. Finally, we did not retrieve clinical data on atherosclerosis, nutrition, pregnancy, or endogenous hormones, which might be predisposing factors for stroke and the retrospective data from the hospital might still miss a few stroke patients who were diagnosed in other hospitals or died at home.
## 5. Conclusions
This study assessed the association between anemia and the risk of stroke. The prevalence of anemia was found to increase with age. A high prevalence of anemia is expected to impose a major medical burden in countries becoming super-aged societies. In this study, the risk of stroke was found to be associated with age, regardless of sex. Our study reveals that moderate anemia should be considered an increased risk factor associated with stroke incidence, and monitoring anemia severity as well as other risk factors and biomarkers is crucial in clinical practice.
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|
---
title: Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving
Lipid Mediator Biosynthesis in the Immune System
authors:
- Matti Hoch
- Jannik Rauthe
- Konstantin Cesnulevicius
- Myron Schultz
- David Lescheid
- Olaf Wolkenhauer
- Valerio Chiurchiù
- Shailendra Gupta
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001763
doi: 10.3390/ijms24054342
license: CC BY 4.0
---
# Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System
## Abstract
Lipid mediators are important regulators in inflammatory responses, and their biosynthetic pathways are targeted by commonly used anti-inflammatory drugs. Switching from pro-inflammatory lipid mediators (PIMs) to specialized pro-resolving (SPMs) is a critical step toward acute inflammation resolution and preventing chronic inflammation. Although the biosynthetic pathways and enzymes for PIMs and SPMs have now been largely identified, the actual transcriptional profiles underlying the immune cell type-specific transcriptional profiles of these mediators are still unknown. Using the Atlas of Inflammation Resolution, we created a large network of gene regulatory interactions linked to the biosynthesis of SPMs and PIMs. By mapping single-cell sequencing data, we identified cell type-specific gene regulatory networks of the lipid mediator biosynthesis. Using machine learning approaches combined with network features, we identified cell clusters of similar transcriptional regulation and demonstrated how specific immune cell activation affects PIM and SPM profiles. We found substantial differences in regulatory networks in related cells, accounting for network-based preprocessing in functional single-cell analyses. Our results not only provide further insight into the gene regulation of lipid mediators in the immune response but also shed light on the contribution of selected cell types in their biosynthesis.
## 1. Introduction
Inflammation is a complex and tightly regulated process that protects the body from any form of damage, insult, or infection [1,2,3]. In addition to secreted proteins (cytokines), lipid mediators (LMs) generated from polyunsaturated fatty acids (PUFAs) in the cell membrane play a key role in regulating all the phases of inflammation, from the initial acute response to its fine-tuning of inflammation transition and even termination [4]. During acute inflammation, arachidonic acid (AA) is the main PUFA that is used for the biosynthesis of over 150 different pro-inflammatory lipid mediators (PIMs) (i.e., various classes of prostaglandins, leukotrienes, and thromboxanes) that altogether act as the “fire-starters” of the inflammatory response by controlling vascular and cellular responses and by determining the cardinal signs of inflammation (redness, heat, swelling, pain, and loss of function) [5,6,7,8,9]. In the last two decades, various LMs involved in the termination of inflammation, so-called “specialized pro-resolving mediators (SPMs), have been identified and are composed of over 30 lipids derived from ω-3 PUFA such as docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) [10,11,12,13]. Unlike PIMs, SPMs promote the resolution of inflammation and tissue repair by activating the cardinal signs of resolution (removal, restoration, regeneration, remission, and relief). Their tightly regulated synthesis during the inflammatory response is a crucial step in extinguishing the fire of inflammation, thus favoring the return to homeostasis, as well as in the prevention of excessive inflammatory responses and the development of chronic inflammation [9,13,14,15].
Although acute inflammation involves a large number of cells and molecules, its initiation triggers relatively straightforward and ubiquitous cascades of various strengths depending on the type and amount of stimulus (e.g., production of PIMSs, vasodilation, chemotaxis of various immune cells) that ensure a rapid response in any tissue [1,3]. In contrast, the resolution of inflammation mechanisms (e.g., type and levels of SPM production and their downstream signaling cascades) strongly depends on the tissue microenvironment [9,12]. Although the SPM biosynthetic pathways, including regulatory enzymes, are now largely identified and are the very same as those involved in PIM production, the actual regulatory processes underlying cell-type-specific mediator profiles remain elusive. In 2018, Norris and Serhan performed a metabolipidomics analysis of human whole blood and identified functional and cell type-specific LM profiles [16]. Their results showed that haematopoietically and functionally distant cell types have similar LM profiles and, vice versa, closely related cells can synthesize substantially different LMs, indicating individual cell type-specific regulations. LMs are secreted to neighboring cells in an auto- and paracrine fashion [17,18]. Such a highly localized response would require cell-type-specific transcriptional programs and thus a cell-type-specific expression of transcriptional regulatory networks.
Usually, cell types are defined by cell-type-specific markers, morphological features, and functional properties or by their distinct (multi-)omics profiles [19]. With the advancement of single-cell RNA sequencing (scRNA-Seq), new subsets of existing cell types are constantly being defined, and the established boundaries between cell types seem to disappear [20]. Thus, modern experiments focus on single-cell data rather than bulk samples of apparently related cells. However, the idea of subsets of a defined cell type also adds new complexity to understanding cell-type-specific signal transduction that distinguishes them from others. To address the challenge of analyzing physiological or functional relationships in single cells, unsupervised machine-learning approaches proved to be extremely useful for identifying patterns in single-cell expression profiles [21,22]. In addition to clustering cells based on their omics profiles, generating topological features from cell-type-specific molecular interaction networks enable the study of functional relationships between molecules and genes [23]. However, the analysis capabilities rely on the causal interactions in the network, making network construction and curation an essential step.
Recently, we published the Atlas of Inflammation Resolution (AIR) as a publicly available, web-based knowledge platform of molecular interactions and biological processes involved in acute inflammation and its resolution [24]. We have identified key processes at each stage of inflammation and developed a standardized representation of the associated molecular interactions in so-called standardized molecular interaction maps (MIMs). The manually curated causal interactions enable the use of systems biology approaches to infer regulatory circuits, predict signal transduction pathways, or perform perturbation experiments [25]. Among others, the AIR provides a detailed description of the biosynthetic pathways of PIMs and SPMs from their precursors AA, DHA, and EPA. In this study, we investigated cell-type-specific transcriptional networks associated with LM synthesis pathways. We mapped scRNA-*Seq data* to gene regulatory networks extracted from the AIR and examined how the networks are affected by differences in the expression of transcription factors. We investigated how cellular LM profiles are modulated by changes in the cell-type-specific network topology of gene regulatory networks (GRNs). By applying unsupervised machine learning approaches to network topological features extracted from the GRNs, we clustered single cells according to their regulatory mechanisms and identified their key gene regulators. We have shown how the application of network-based approaches can improve the analysis of functional molecular pathways and their regulatory networks using scRNA-Seq data. Our results shed light on the gene regulation of LM synthesizing enzymes across various immune cell types.
## 2.1. Cell-Type-Specific LM Pathways
We clustered the cells based on the expression profile of genes included in the AIR database, i.e., being directly related to immunological processes (Figure 1A and Figure 2A). The dimensionality reduction largely restored the cell type clusters as they are defined in the metadata of both datasets. We investigated the expression of LM enzymes in the cells, and whether clusters of enzyme expression correspond to Uniform Manifold Approximation and Projection (UMAP) clustering. Additionally, for each cell, we analyzed whether the substrates of LM biosynthesis, AA, DHA, or EPA, are linked to the final products through the expression of catalyzing enzymes.
The GSE122108 dataset consists of mononuclear phagocytes, mainly macrophages, of different tissues, with various pro- and anti-inflammatory stimuli. The cell types with fewer samples, such as monocytes, dendritic cells, and microglia cells, were partially restored (Figure 1A). Macrophage samples are widely scattered and partially mixed with the clusters of the other cell types because they originate from a wide variety of tissues. One macrophage cluster separates from all other cells and consists mainly of peritoneal cells. These peritoneal macrophages also show a distinct LM enzyme profile, with an expression of many genes and the only cells with consistently high expression of Alox15 and Ptgis and, thus, are the only cell types expressing the required enzymes for all LM classes (Figure 1C). From the analysis, it emerged that while almost all cell types are fully capable to synthesize prostaglandins, leukotrienes, and thromboxanes, very few cell types can only synthesize SPMs. Indeed, lipoxins (that are generated by AA but still belong to the super-family of SPMs), protectins, and D-resolvins are produced only by macrophages, maresins only by macrophages and microglia, while E-resolvins are produced by all immune cells, including dendritic cells and monocytes (Figure 1B). Interestingly, lipoxins, protectins, and D-resolvins show a similar pattern due to the expression of the enzyme Alox15. In contrast, a group of dendritic cells expresses only those enzymes required for synthesizing E-resolvins and leukotrienes. Microglia also show a consistent expression profile, particularly of Alox5, Cbr1, Gpx4, and Ptgs1.
The GSE109125 dataset consists of many different cell types spanning the hematopoietic lineage and includes stem cells, epithelial cells, and both compartments of innate and adaptive immune cell populations, with monocytes being the only missing cell subsets. The UMAP of immune-filtered gene expression was able to restore the cell type groups to a high degree (Figure 2A). The two-dimensional projection of the UMAP graph shows the cell branching in two directions starting from the hematopoietic cell group. Except for B cells, which are placed closer to the myeloid cells, these two groups coincide with the lymphoid and myeloid lineages, respectively. The analysis revealed that the overall ability to synthesize LMs, based on the expression of required enzymes, is much lower in lymphoid than in myeloid cells (Figure 2B). In particular, cells belonging to the myeloid lineage and hematopoietic stem cells are the ones most capable to biosynthesize both PIMs and SPMs, with macrophages and granulocytes (neutrophils, basophils, and eosinophils) being the most efficient due to the high expression of LM enzymes (Figure 2C). Mast cells and ILCs show a similar biosynthetic pathway in producing PIMs and only one class of SPMs, i.e., E-resolvins. As expected, NK cells and NKT cells also share a similar ability to synthesize the same class of LMs, which are limited only to prostaglandins (except for I-prostaglandins) and thromboxanes; however, only NKT cells can produce maresins. Interestingly, epithelial cells display a biosynthetic pathway identical to NKT cells. Of note, it seems that neither T cells nor B cells are capable produce any LMs.
## 2.2. Cell-Type-Specific Gene Regulation
Despite apparently similar expression profiles of LM enzymes, cells may differ in transcriptional circuits that tightly regulate LM synthesis. Moreover, a similar expression profile may be regulated by substantially different transcription factor networks, which would be required for cell-type-specific responses to stimuli in different tissues. Thus, we analyzed the connectivity between transcription factors and enzymes of each LM class in the cell-type-specific GRNs. After dimensionality reduction for all classes, the embeddings were combined and projected into single UMAPs for each dataset (Figure 3A,B). For each cluster, we identified the genes with the most significant differences compared with all other cells (adj. p-value < 0.05, see methods). Detailed information on all clusters, their predicted genes, and included samples are available in the Supplementary Material.
In the GSE122108 dataset, we observed many separate clusters and good restoration of the main cell types, i.e., dendritic cells, macrophages, microglia, and monocytes (Figure 3A, Supplementary File S1). Of note, macrophages appeared as smaller clusters that were partially composed of tissue-specific cells, e.g., from the aorta, heart, or liver. We identified the significant (adj. p-value < 0.05 for any LM class) genes of the microglia cells, which build the most defined cluster in the UMAP plot (Figure 3C). For the two highest-ranked genes, Mef2a and Xrcc5, we additionally showed their regulatory score in relation to their expression in all samples. The plots show how the score is significantly increased in the microglia cells and, especially for Mef2a, is independent of its expression. In the literature, information on tissue-specific transcriptional regulation of LM biosynthesis is very sparse. Hence, to compare our results with experimental data, we searched the literature for any evidence supporting the immune modulatory function of the genes related to microglia. Of the thirteen genes, we found clear evidence in the literature for eight genes on their relevance in microglial function and neuronal inflammation (Mef2a [26], Hdac11 [27,28], Smad3 [29], Mef2c [30], Arid1a [31,32], Zfhx3 [33,34], Ets1 [35], and Jun [36]). *Four* genes were mentioned in experiments on microglial inflammation (Xrcc5 [37,38], Zfp191 [39], Prdm1 [40], and Usf2 [41]), whereas no information was found in the literature for only two genes (Znf383 and Nfrkb). The mode of action of the predicted genes in modulating microglia function has been attributed to their influence on cytokine expression. Our results suggest that they modulate the immune response by also regulating the expression of enzymes involved in the biosynthesis of LMs. Smad3, Jun, Usf2, and Xrcc5 have already been described in their regulation of prostaglandins, while little to no research is available on the other LM classes [42,43,44,45]. Mef2a and Mef2c have been identified as downstream effectors of PGE2, which could indicate a feedback loop on prostaglandin e synthesis [46,47].
In contrast, in the GSE109125 dataset, the original cell types are more heterogeneously distributed between clusters (Figure 3B, Supplementary File S2). The differences in the expression of immune-related genes between the major immune cell types are not reflected in the TFs associated with the LMs. However, two clusters consisting of hematopoietic stem cells and mast cells, respectively, are strongly separated. While no significant TFs were identified for the latter, the former shows a division into three subclusters, from each of which several significant TFs were identified. Interestingly, based on cell metadata, the three subclusters appear to represent stages of lymphoid hematopoiesis, namely (i) bone marrow-derived stem cells (BMSCs) followed by (ii) early (DN1 and DN2a lymphocytes) and (iii) late lymphoid progenitor cells. While BMSCs express many LM enzymes, they are downregulated in lymphoid progenitors. When comparing the regulatory scores of stem cells and early lymphoid progenitor cells, Hlf had the greatest difference in its score for all LM classes (not shown). Hlf is an important regulator of lymphoid development in the hematopoietic lineage [48]. Our results suggest that modulation of LM synthesis by gene regulation of LM enzymes may play a role in shaping the fate of lymphoid cells by Hlf.
## 2.3. Immune Cell Activation Modulates Gene Regulatory Networks of Lipid Mediators
Several samples in the GSE122108 data were treated with pro- or anti-inflammatory stimuli at several time points, including lipopolysaccharide stimulation (LPS), C. albicans infection, induction of injury, paracetamol, and thioglycolate. We compared the cells at successive time points for each stimulus and identified the TFs with the strongest changes in their gene regulatory activity for each LM class (Figure 4A). For the selected genes, we additionally show violin plots comparing their expression values (read counts) and topology scores, showing that the estimated change in connectivity is independent of their expression (Figure 4B). *In* general, the predicted that TFs show a strong variability between cells and the different stimuli, suggesting that gene regulation of LMs in the immune response is highly cell-type and environment specific. Additionally, especially at early time points, the identified TFs also differ substantially between PIMs (e.g., the prostaglandin classes) and SPMs (e.g., the resolvin classes) due to the distinct enzyme profile, arguing for fine-tuned gene regulation. At later time points, the difference between PIM and SPM classes becomes smaller, and the number of overlapping TFs increases.
Many predicted genes are well-known regulators of the immune response to respective stimuli. For example, in liver macrophages stimulated with APAP, Hes1 appears to be a key regulatory TF of most SPM classes. In vivo experiments showed that blocking the Notch signaling pathway in mice reduced Hes1 levels and increased susceptibility to APAP-induced liver injury [49]. In thioglycolate-stimulated monocytes/ macrophages, our model predicted several genes related to both PIMs and SPMs synthesis, which have also been described in the literature, such as Epas1 (prostaglandins), Egr2 (prostaglandins), Cebpb (all LM classes), and Srebp1 (SPMs). Epas1, coding for HIF-2α, is an important mediator of cellular processes and macrophage recruitment in response to hypoxia [50]. In an experimental thioglycolate periodontitis model, Egr2 and Cebpb were required for macrophage activation [51]. In Srebp1 knockdown mice, thioglycolate-elicited macrophages showed increased levels of pro-inflammatory cytokines and reduced levels of DHA and EPA during the resolution phase after Tlr4 activation [52]. Although being related cell types, the five subtypes of LPS-stimulated lung macrophages also differ in the predicted TFs. Two subtypes of lung macrophages originate from broncho-alveolar lavage (BAL) and show a similar gene regulation of prostaglandins through Klf10 and Vhl. *Both* genes have already been associated with inflammatory responses in BAL macrophages [53,54]. For the other LMs, both BAL subtypes do not overlap in the predicted TFs. The remaining lung macrophage subtypes are defined by cell sorting markers. Their samples for which data are available on days zero and three after LPS stimulation overlap at Stat1, Stat2, and Pias1. The results become more diverse at later time points (day six vs. day three). We observed that the three MHC-II- macrophage and monocyte subtypes partially overlap in Foxk2, Rora, and *Ing4* genes that are associated with cytokine production in response to LPS [55,56], while for the MHC-II+ subtype, we predicted autophagy-related genes Rb1cc1, Rb1, and Hdac2 [57,58,59]. Whether or not this difference is caused by MHC-II is yet to be determined, as only limited evidence connects MHC-II with the predicted genes.
## 2.4. LM Gene Regulation Shows Substantial Differences in Related Cell Types
Since the transcriptional regulation of LMs appears to be tightly regulated and cell-type-specific, we investigated the extent to which closely related cell types may differ in the transcriptional interaction networks of PIM and SPM synthesis. We identified the cell pairs with the smallest distance in expression-based UMAP but the largest distance in transcriptional network-based UMAP. The top-ranked sample pair consists of a macrophage from the aorta and a macrophage from the lung stimulated with LPS (Figure 5A). Both tissue-specific subtypes of macrophages appear to have a nearly identical transcriptomic profile but substantially differ in LM gene regulation. Thus, we extracted the core regulatory networks (CRNs) to gain further insight into the genes contributing to the observed differences (Figure 5B) and we additionally generated a CRN of an unstimulated sample of the same lung macrophage subtype but without LPS stimulation to ensure that the difference is not caused by the response to LPS. Interestingly, the CRN shows that the expression of most LM enzymes is similar except for Ptgs2, which is not expressed in aorta macrophages. In contrast, Ptgs2 is highly expressed in aorta macrophages with high expression levels of the TFs Jun, Egr1, and Fos. All these three genes are highly associated with atherosclerotic inflammation [60,61,62]. Egr1 is involved in the response to mechanical or oxidative stress and, thus, the development of atherosclerosis from plaques and hypertonia [60,63,64].
## 3. Discussion
The immune response is a tightly regulated system involving a large number of different cell types with specific spatiotemporal functions. Over the years, experimental research has attempted to identify and describe the molecular and functional processes involved. However, although more and more knowledge is being gained and regulatory processes are being elucidated, increasing complexity is blurring the boundaries between cell types. At the same time, it is challenging to study the role of specific cells in immunological processes and cell-type-specific immune responses. One reason for this is the enormous cost and effort required to study the effects of a single transcription factor, e.g., using gene knockout or targeted inhibition of transcripts with miRNAs. Consequently, experimental identification of novel transcription factors regulating a particular process is not feasible and, therefore, tends to be targeted based on hypotheses from other experiments. Moreover, experimental data are mostly generated by measurable changes, such as changes in their expression using RNA-Seq, but TFs do not necessarily have altered expression themselves, and cell-type-specific changes could be mediated by changes in the topology of gene regulatory networks. As a result, very little information on cell-type-specific gene regulation can be found in the literature, especially for the relatively young field of LM biosynthesis.
While the effects of LMs in cells and tissues have been extensively studied, particularly for PIMs but recently also for SPMs, the regulatory mechanisms underlying their biosynthesis in a cell-type-specific manner is still not very well investigated, which may be important to understand how various cells communicate to resolve inflammation. This complexity of the LM response is also shown by the ability of myeloid cells (i.e., macrophages and granulocytes) to synthesize both PIMs and SPMs, while lymphoid cells seem incapable to produce any LM. These results are also supported by the vast literature where both classes of pro-inflammatory and pro-resolving LMs have been detected in a low or high picomolar range in most cell populations belonging to the myeloid and innate compartment of immunity. In contrast, evidence that cells of the lymphoid and adaptive immune system can produce such LMs is very scarce (extensively reviewed in [8,9,65,66]).
Here, we investigated LM synthesis at the transcriptional level using in silico analyses of cell-type-specific gene regulatory networks from scRNA-Seq data. Our results highlight that, although cell types have similar expression profiles, they might exhibit distinct transcriptional regulations of LM synthesis and, thus, respond with different LM productions to experimental conditions. For instance, the higher expression of the stress- and inflammation-related genes Egr1, Jun, and Fos in aorta macrophages than in lung macrophages and their association with LM gene regulation, despite their similar RNA-Seq profiles, might account for a physiological advantage in the aorta by enabling a sufficient LM response to stress stimuli, such as hypertonia. Thus, our study showed that systems biology approaches could identify cell- and tissue-specific patterns of gene expression–phenotype relationships. Correlating the measured gene expression with underlying gene regulation can improve the analysis and interpretation of scRNA-Seq data.
While large numbers of gene regulatory interactions are available in public databases, identified using in silico predictions of binding motifs, information on the type and strength of these interactions is rather scarce. Even if available, including such information also introduces new challenges, such as integrating competitive TF interactions. As our study aims to compare cell-type-specific GRNs, we built the networks using qualitative data (considering whether there is an interaction between a TF and a gene) to avoid false negative information and, consequently, disruptions in the network. By integrating expression data and topology algorithms, the qualitative information is converted into quantitative regulation scores for machine learning algorithms, providing a valuable estimation of a TF’s relevance in the GRN. Similar in silico studies on gene interaction networks showed the use of network topology information to predict key regulators and motifs [67,68,69]. The resulting bias towards highly connected nodes was encountered by normalizing the regulation score by the node degree. In our approach, we include information on multiple genes per LM class in the calculation of regulatory scores as well as combining the predictions from machine learning for multiple LM classes. The approach can be translated equally to other immune mediators, such as cytokines. The interpretability of the molecular results of this study is further limited to mice, although the methodology can be easily translated into human data. We specifically chose murine RNA-*Seq data* as much more murine than human in vivo studies are available that provide experimental evidence on gene-to-phenotype associations.
With our study, we provided examples of how network-based scRNA-*Seq data* analyses could provide insights into cellular mechanisms of LM regulation and generate new hypotheses for follow-up investigations using human data. Thus, our results account for integrating systems biology approaches to stratify cellular responses more accurately in experimental settings and to discriminate or predict pathological states based on the ability of specific disease-associated cells to engage in pro-inflammatory or pro-resolving pathways.
## 4.1. Network Curation
We extracted molecular interactions from the “lipid mediator biosynthesis from arachidonic acid” (Figure S1), “lipid mediator biosynthesis from DHA” (Figure S2), and “lipid mediator biosynthesis from EPA” (Figure S3) submaps of the AIR using its Xplore tool. The maps were then extended with transcription factor (TF) and gene target interactions from the AIR MIM to create a gene regulatory network (GRN). Catalytic reactions were transformed into the activity flow format by integrating enzymes in between the source and target element with positive interactions each (Figure 6A). The resulting network can be considered as the graph G of a set of elements (vertices V(G)) and connecting interactions (edges E(G)). The edges encode whether two elements are linked by (de)activation, up-, or downregulation and are defined as a collection of triples E⊂(s×r×t) consisting of a source element s∈V, a relation r∈{−1,1}, and a target element t∈V.
## 4.2. Data Processing and Integration
Two murine single-cell RNA-seq profiles (GSE122108 and GSE109125) with preprocessed and library-size normalized read counts (q) by the Immunological Genome (ImmGen) Project were downloaded from their website (http://rstats.immgen.org/DataPage/, accessed on 10 November 2022). They include many different immune cell types from various tissues with extensive descriptions of the samples’ origins and sorting markers. Both datasets have been described in detail in their respective published studies [70,71]. While the GSE122108 dataset consists only of phagocytotic mononuclear cells, mainly macrophages and monocytes, the GSE109125 data includes cells from all major cell types of the lymphoid and myeloid lineage. We mapped the murine genes from the data with genes in the AIR using human–mouse gene identifier associations from the Ensemble database (https://www.ensembl.org/, accessed on 23 August 2020). We defined a read count of 10 as a threshold to mark a gene as expressed or unexpressed which is slightly higher than the threshold of 5 used by the ImmGen project to exclude more genes with non-functional expression levels [71,72]. Genes with read count values below the threshold in a cell type c were removed from G resulting in cell-type-specific subgraphs Gc with Vc⊆V and Ec⊆E (Figure 6B). Proteins from the manually curated submaps, i.e., enzymes directly involved in the LM biosynthesis, as well as elements with no expression, such as metabolites or phenotypes, were not removed from Gc. For cellular normalization, we divided the read count value of each gene by its highest absolute value across all cell types, resulting in the cell type normalized read count q^.
## 4.3. Topological Analysis
A path P in the MIM of the length l∈ℕ can be written as the sequence (u1→r1u2→r2…→rLul+1) with (ui,ri,ui+1)∈E. The relation r∈{−1,1} between the first and final element of any P is defined as (r1⋅r2⋅… ⋅rl) for all interactions along P. The shortest path SP between (u, v) is defined as an existing path Pu,v between u and v where l(Pu,v) is minimized. In each subgraph Gc, the shortest paths between precursors and the final products in the LM biosynthesis were identified using the Breadth-First-Search. In addition, pathing algorithms were applied to identify core regulatory networks (CRNs), which are combined pathways from genes to LM enzymes with the maximum score of genes passed. The identification of CRNs becomes a widest path problem and was solved with an adaptation of Dijkstra’s algorithm. The edge weights are based on the edge’s target node u and were set to either s¯u for CRNs of a single cell or |Δs¯u| when comparing two sets of cells.
## 4.4. Topological Weighting
For each LM class p, we calculated a weighting factor for all elements in the submaps representing their topological inclusion in the paths connected to p. We recently described this weighting approach [25]. In summary, the weighting of an element e is calculated based on the percentage of elements and paths connected to p. Npaths is the number of all paths to p and Npathse⊂Npaths are paths that go through e. Nnodes is the number of elements connected to p and Nnodese⊂Nnodes the number of elements on the path from e to p:we,p=r(SPe,p)⋅(NpathseNpaths+NnodeseNnodes)
## 4.5. Feature Extraction
*We* generated a regulatory score s¯ for each gene in Gc, representing its association to LM synthesis. We performed a stepwise signal propagation based on the approach presented by Lee and Cho [73], starting from the LM enzymes and continuing in the reverse direction through the transcription network (Figure 7A). The transcription factors’ scores were updated at each step based on degree centralities (=number of interactions) in the original GRN, their targets’ scores in the previous step, and their normalized read count q^ (Figure 7B). The simulation was performed for each cell type and initiated separately for each LM class by setting the starting scores set=0=we,p for each enzyme e in the LM class p. The final regulatory score for each node u in the network is then defined as the area under the curve (AUC) of scores over 100 signaling steps: s¯u=∫$t = 0100$sut (Figure 7C).
## 4.6. Cell Type Clustering
We performed a Uniform Manifold Approximation and Projection (UMAP) analysis for both datasets using both the filtered expression data and, for each LM class, using the regulatory scores s¯ generated from Gc (Figure 6C). UMAP reduces the high dimensionality of the input data into a two-dimensional graphical representation where each point corresponds to a cell in the data. In this way, cells with similar values are positioned close to each other, while separated cells indicate larger differences. Cell clusters were identified using manually adjusted k-means clustering on the generated embeddings. To visualize distributions across all LM classes, their embeddings were combined into a single dataset and a new UMAP was performed. Clustering in the enzyme expression heatmaps was performed using the Euclid-based hierarchical clustering method of the Python package seaborn version 0.12.1 [74].
## 4.7. Statistical Evaluation of Features
The goal of the statistical analysis is to identify features that differ in a group of samples, i.e., clusters. Since the calculation of regulatory scores is based on the expression of the feature in the cell, the final scores are biased towards q^. Therefore, instead of calculating the highest scores, the features should be analyzed in relation to q^. In an LM class, the q^ and s¯ values of a gene in all cells, which are not in the cluster, were fitted to linear regression, and a half-normal distribution was created from the absolute distances of each cell from the line (Figure 7D). The p-value of the feature in the cluster is then calculated from the z-score of the average distance of the cluster’s cells in the distribution.
## 5. Conclusions
In conclusion, this study demonstrates how the application of network-based approaches enables the identification of cell-type-specific regulatory networks from scRNA-Seq data. We showed that gene regulation of the lipid mediator biosynthesis is highly dependent on the cell type and stimuli. Our results further argue for a fine-tuned transcriptional modulation of immune cell types and emphasize the necessity of systems biology approaches in understanding the underlying mechanisms.
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|
---
title: Marshallese Mothers’ and Marshallese Maternal Healthcare Providers’ Perspectives
on Contraceptive Use and Reproductive Life Planning Practices and Influences
authors:
- Britni L. Ayers
- Rachel S. Purvis
- Jennifer Callaghan-Koru
- Sharon Reece
- Sheena CarlLee
- Nirvana Manning
- Krista Langston
- Sheldon Riklon
- Pearl A. McElfish
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001766
doi: 10.3390/ijerph20053949
license: CC BY 4.0
---
# Marshallese Mothers’ and Marshallese Maternal Healthcare Providers’ Perspectives on Contraceptive Use and Reproductive Life Planning Practices and Influences
## Abstract
Pacific Islander communities experience significant maternal and infant health disparities including high maternal and infant mortality. Contraception and reproductive life planning prevent approximately one-third of pregnancy-related deaths and neonatal deaths. We report the results of formative research devoted to understanding Marshallese mothers’ as well as their maternal healthcare providers’ practices and influences related to contraceptive use and reproductive life planning. This study used an exploratory, descriptive qualitative design to explore Marshallese mothers’ and maternal healthcare providers’ practices and influences of contraception use and reproductive life planning. Twenty participants were enrolled in the study, 15 Marshallese mothers and five Marshallese maternal healthcare providers. For the Marshallese mothers, two themes emerged: [1] Reproductive Life Planning Practices and Information; and [2] Reproductive Life Planning Influences. For the Marshallese maternal healthcare providers, two themes emerged: [1] Reproductive Life Planning Practices; and [2] Reproductive Life Planning Influences. This is the first study to document Marshallese mothers’ and maternal healthcare providers’ practices and influences with contraceptive use and reproductive life planning. Study results will inform the development of a culturally-adapted contraception and reproductive life planning tool with an educational program for Marshallese family units and maternal healthcare providers serving Marshallese women.
## 1. Introduction
Pacific Islander communities experience significant maternal and infant health disparities [1,2,3,4,5,6,7,8]. Pacific Islanders have almost twice the infant mortality per 1000 live births of non-Hispanic whites and have a higher maternal mortality compared to non-Hispanic whites (13.5 verse 12.7) [9]. One of the fastest growing communities of Pacific Islanders in the United States (US) is the Marshallese community in Northwest Arkansas [10,11,12,13,14]. Marshallese in Arkansas also experience a high rate of adverse perinatal outcomes: $19\%$ of Marshallese infants were born preterm (compared to $9.6\%$ nationally), and $15\%$ of Marshallese were low-birth-weight (compared to $8.3\%$ nationally) [8,15,16]. These disparities may be confounded by limited contraception and reproductive life planning. Reproductive life planning is a set of personal goals about having or not having children.
Contraception and reproductive life planning can prevent approximately one-third of pregnancy-related deaths and $44\%$ of neonatal deaths [17]. This is largely due to education surrounding the timing and spacing of pregnancies. It is one of the most effective ways to reduce the risk of adverse maternal and infant health outcomes [18]. Reproductive life planning, according to the World Health Organization, allows individuals and couples to anticipate and attain their desired number of children and the spacing and timing of their births, through the use of modern contraceptive methods [18]. Ensuring access and providing education about sexual and reproductive health services, including contraception and reproductive life planning, can be a cost-effective approach to prevent maternal and infant disparities [19,20].
There is limited data on the use of contraceptives and reproductive life planning within Pacific Islander communities. A systematic review documented factors influencing low contraception use patterns among Torres Strait communities and identified a lack of access to culturally appropriate health services, discomfort with condoms, and reproductive coercion by partners (e.g., partner wants a baby) [21]. Separate studies with Marshallese and Native Hawaiians identified an overall lack of knowledge of contraceptive options, cultural customs of not discussing contraceptive use and reproductive life planning methods, and cultural views that pregnancies are considered a blessing [22,23].
To help address maternal and infant health disparities in the Marshallese community, the University of Arkansas for Medical Sciences has implemented the Healthy Start program [24]. The program, which is described elsewhere [24], focuses on ensuring early prenatal care, well woman care (including contraceptives), and interconception health. Recognizing short intervals between pregnancies as a potential contributor to poor birth outcomes among Marshallese community members [24], the study team sought to understand contraceptive use and reproductive life planning among Marshallese in Arkansas.
## 2.1. Research Design
This study used an exploratory, descriptive qualitative design to explore Marshallese mothers’ and maternal health care providers’ (MHCPs) practices and influences of contraception use and reproductive life planning. While the focus of the study was qualitative, a quantitative survey was used to capture demographic data including relationship and pregnancy status. A community-based participatory research (CBPR) approach was followed to design and implement the study. CBPR methods help to ensure cultural appropriateness of research methods through the collaboration between academic partners and the community [25]. For this study, the Healthy Start Community Action Network (CAN), comprised of local Marshallese community members and healthcare professionals, was engaged in all aspects of the study. Several members of the interprofessional research team are Marshallese. All study plans and documents—including recruitment plans and materials, consent documents and forms, retention plans, quantitative surveys that document participant demographic information, and qualitative interview guides—were developed in partnership with the CAN and Marshallese research team members. This study and all procedures involving research study participants were approved on 25 June 2021 by the University of Arkansas for Medical Sciences Institutional Review Board (Protocol #26297).
## 2.2. Participant Eligibility, Consent, and Recruitment
Participants were eligible to participate in the study if they were 18 years of age or older and met the criteria for one of the following groups: [1] employed as a healthcare professional and caring for Marshallese women or those who self-identified as Marshallese, or [2] females of reproductive age (under 49 years of age) who self-identified as Marshallese and were pregnant in the past 12 months. Marshallese bilingual female study staff recruited participants during women’s health appointments, maternal health home visits, and community events. Potential participants who met the inclusion criteria were offered the opportunity to join the study and complete the consent process. Trained study staff read the consent aloud to the potential participant in their language of choice (English or Marshallese) and provided participants with a study information sheet. Participants had the opportunity to ask questions prior to verbally providing consent. Participants were provided with a copy of the information sheet used to obtain verbal consent. Interviews took place in a community setting or the participant’s home.
## 2.3. Instrumental Development and Data Collection
Data were collected from August to October of 2021. The CAN and bilingual study staff co-developed, reviewed, edited, and approved the demographic survey and qualitative interview guide prior to data collection. The survey was implemented using the web-based Research Electronic Data Capture (REDCap) system (Vanderbilt University, Nashville, TN, USA) [26] and took approximately 15–30 min to complete. The qualitative interview guide went through two iterations and took approximately 30 to 60 min to complete (see Supplementary Material S1). All interviews were conducted by trained bilingual Marshallese study staff. Interviews were audio recorded and then transcribed in the language spoken by participants. Transcripts in Marshallese were then translated into English by professional translators and checked for accuracy by Marshallese-speaking study staff. A total of 20 participants—15 Marshallese mothers and five Marshallese MHCPs—were enrolled in the study. Marshallese mothers that completed the survey and interview received a $40 gift card.
## 2.4. Data Analysis
Demographic information was summarized, and descriptive statistics are presented with frequencies and percentages. Interview transcripts were thematically analyzed following an inductive approach. Three qualitative researchers (two main coders, one confirmation coder) began with initial coding, which consisted of naming each data segment with short summations. The open codes were elaborated and refined into a final codebook through consensus discussions among the coders. All transcripts were coded according to the final codebook, and the frequency and patterns of codes were reviewed to identify and develop the most salient categories within the data [27]. The research team discussed the emergent themes to ensure scientific rigor and inter-coder agreement. Marshallese are a collectivist culture (value group over individual) and tend to use words like “us” rather than “I.” *This is* reflective in some of their quotes [28,29].
## 3. Results
A total of 15 Marshallese mothers (Table 1) and five Marshallese MHCPs (Table 2) were enrolled in the study. Results of the mothers and MHCPs are presented separately.
## 3.1. Marshallese Mothers Demographic Characteristics
Table 1 shows the Marshallese mothers’ demographic characteristics. All of the mothers were Marshallese and not pregnant. A majority of the mothers were in an unmarried partnership ($66.7\%$). The mothers’ ages ranged from 20 years old to 42 years old, and the mean age was 30 years old with a standard deviation of 7. Most of the mothers ($80\%$) had completed high school and some college or technical school.
## 3.2. Marshallese Mothers Qualitative Results
For the mothers, two themes emerged: [1] Reproductive Life Planning Practices and Information; and [2] Reproductive Life Planning Influences. Illustrative quotes and participant identification (PID) numbers are presented below.
## 3.2.1. Reproductive Life Planning Practices and Information
The mothers discussed their contraception and reproductive life planning practices and where they received their information.
Reproductive Life Planning Practices. When the mothers were asked about contraception and reproductive life planning practices, some mothers reported using no contraception and/or reproductive life planning method. Some mothers stated, “I’ve never done something like this;” (PID 2) and “Oh, I’ve never done this” (PID 22). Others reported knowledge of contraception and reproductive life planning but had not utilized it for themselves: “They know there is birth control but they don’t use it,” (PID 5) and “For most of us Marshallese, some of us don’t use controls, so our kids end up being probably a year or not even a year apart” (PID 17). One mother stated, “I think in our Marshallese community, we don’t talk about it. I feel like in our culture, they were not open to talking to each other about reproductive life planning” (PID 01).
The mothers reported they felt others in the Marshallese community might choose contraception if they did not want children at that time. One mother said: “Yes, probably the ones that probably don’t want kids yet. I would think they would probably be open with their options on what to take and what could last longer for them” (PID 17). Another mother echoed this sentiment, and this was predicated on the workload of having additional children. She said: “If they were wanting to space out the years between their kids, so that it can also help them and make it easier, because kids are a handful” (PID 17).
While participants describe a lack of contraception and reproductive life planning information and lack of contraceptive use in the community in general, when asked about their own understanding and their family and friends’ understanding of contraceptives, several participants described knowledge of a wide range of contraceptive methods. Participants said: “I would say the tube [Nexplanon]. I have some families, they use the tube” (PID 17). Another mother said: “Something like the tube [Nexplanon], the shot/vaccine, pill, and tube tied” (PID 20). One mother said: “The shot and the tube. The tube in your arm” (PID 25). Another mother said: “Pills, shot, and the withdrawal-type” (PID 3). One mother said: “The shot, pills, using condoms” (PID 26). Only one mother discussed the use of intrauterine devices (IUDs) when she said: “I’ve heard about IUDs a lot. I have a lot of friends that use IUDs and just a couple that use the implants. I don’t know a lot of people that do just the pill” (PID 1).
Mothers expressed that decisions about contraception and reproductive life planning should be a woman’s choice: “I think it should be up to our self, because it depends on how we want our life to play out, if we want kids, or we want to hold off on that. How you control your body is up to you” (PID 17). Additionally, other mothers stated: “I think you just make that choice of your own. Then you can talk to your healthcare provider with any type of what your options are and what’s best fit for you” (PID 1). Another mother said: “The woman herself. She’s the only one that can prevent it. It’s her choice” (PID 3).
Reproductive Life Planning Information. A majority of the mothers described obtaining information about contraception and reproductive life planning from their MHCPs. Mothers said they got their information from: “Healthcare providers” (PID 2); “The doctors” (PID 24); “I heard from my doctors” (PID 25); “The doctors” (PID 26); and “From the doctor” (PID 3). Mothers described getting this information during their prenatal visits, stating, “During second pregnancy, my doctor explained it to me,” (PID 23) or at their postpartum visit: In addition, mothers described learning from online videos or through social media outlets. For example, one mother said: “They learned online. They would watch videos online” (PID 25). Another mother said: “The ones that I learned about, I just come across on Facebook” (PID 17).
Some mothers discussed learning about contraception and reproductive life planning methods from family and friends. One mother said: “Also, friends and family; you talk about what kind of birth control they have and what kind of experience they have with it. It’s how I choose my birth control method” (PID 1). Other mothers said: “My family. My sisters, yes” (PID 20); “I’ve heard IUDs from a lot of friends” (PID 1); and “From my friends, families” (PID 26).
## 3.2.2. Reproductive Life Planning Influences
The mothers explained influences on contraceptives and reproductive life planning, and five subthemes emerged: [1] Partner/Family; [2] Religion; [3] Side Effects; [4] Cost; and [5] Fear.
Partner/Family. Mothers consistently described their partners as a strong influence in their contraception and reproductive life planning decisions. For example, when asked who influenced contraception and reproductive life planning choices, mothers said: “My husband” (PID 26); “Husbands/partners” (PID 23); and “Partners/husband or family members, like our moms and dads” (PID 02). Another participant stated, “I feel that in our community, it’s more of just between one’s immediate family, between the husband and a wife where there’s vows” (PID 01). Another mother echoed this sentiment: “Their husbands/partners, yes. We have to let them know though or they will wonder why we can’t get pregnant, and we have to let them know before we take birth controls” (PID 20). The women discussed that the support from their partners with regard to contraception and reproductive planning varied. One mother said: “Our husbands. Some would be supportive and some wouldn’t. Their husbands don’t want them on birth control” (PID 24). Another mother said: “He [referring to partner] doesn’t like birth control” (PID 04). Another mother said that “men” (PID 05) were the strongest influence for reproductive life planning beliefs and practices.
Religion. Mothers also discussed how religion was a dominant influence in contraception and reproductive life planning within their community. Mothers said: “Religions” (PID 02); “Because some [those that are religious], they don’t believe in birth control” (PID 03); and “Their religion could prevent them from taking those birth controls” (PID 25). Another mother said: “I think religious people may be. That’s the only one I can think. I know as Catholic, we’re not supposed to use any type of birth control except for the calendar” (PID 01). One mother specifically said: “Those that go to church and they are faithful” (PID 25). Some of the mothers described the use of contraception as connected to sin and unmarried sex. One mother said: “Christians. I think that some would be against it or some like families or people at church would be against it because they think a girl is planning to sin” (PID 05).
Side Effects. Another dominant influence that emerged in discussing contraception and reproductive life planning was the potential for side effects of contraceptives. One mother said: “Probably, because of the side effects, or because of what could happen to their body out there, getting it implanted in them” (PID 17). Other mothers agreed and said: “They are concerned because they say they have headaches, some would sleep a lot, hair loss, and plenty others reasons” (PID 24). Another mother said: “Because some say they gained weight, for some, they skin becomes darkened, and they just feel sick with it” (PID 25). And one mother said: “I hear a lot from those that use birth control that they get bigger from it, it changes their appetite and stuff” (PID 05).
The discussion around potential side effects was also interwoven with concerns of long-term effects. One mother said: Another mother said: “I feel like whenever I would tell a family member about it, they’re—the first thing they think is, ‘Oh, they’re going to put that in there? Wouldn’t that cause cancer?” ( PID 01). Additionally, there was concern of infertility in the future. One mother said, “The only thing I know they’re concerned of is, usually they say when they take any birth control, it’s harder for them to have kids. Yeah, after they take it out” (PID 3). Another mother said: “I have aunts who tell me not to take birth control because they say it messes my system up, and in the future, it’ll be hard for me to conceive” (PID 03).
Cost. Another influence on contraceptive use and reproductive life planning was the cost, as Marshallese non-pregnant women have not qualified for Medicaid/Medicare until December 2020. Marshallese residing in the US are Compact of Free Association (COFA) migrants, meaning Marshallese may migrate from the Marshall Islands to the US but are not considered US citizens. Therefore, Marshallese COFA migrants did not qualify for health insurance prior to this policy change in 2020 [30]. Mothers discussed how the lack of insurance, coupled with lack of money, was a strong influence in accessing contraceptives. One mother said: “The cost of it, depending on what kind of birth control they want. That’ll probably be the only thing preventing them from getting it” (PID 17). Another mother said: “In our community, not having insurance makes it hard to go to an appointment” (PID 2).
Lack of money and/or insurance was also interwoven with the primary care visits. One mother said: “It could be because they don’t have enough money to pay for the procedure” (PID 26). Another mother said: “They don’t go to the doctor’s as often or some just don’t have insurance” (PID 3).
Fear. Lastly, Marshallese mothers described fear as an influence to accessing contraception, and much of this was rooted in the concept of inserting a foreign object into their bodies. For example, one mother said: “Some are concerned because of the thin rod, and they are afraid when they inserted under their skin” (PID 24). Another mother said: “I would say, something that they put into your body. I think that probably would scare some women” (PID 01). Another mother said: “Some don’t get it because they are scared” (PID 05). The mothers explained their family members discouraged them from using birth control using fear. For example, one mother said: “If they talked to their family members and they tried to make them scared” (PID 25).
Fear of contraception was also embedded in fear of their doctor. One mother said, “They don’t know if the doctor doing the implants is experience enough to do the job, could it be implanted wrong, these kind of things” (PID 20). This fear also extended to fear in discussing the process with their doctor. One mother said, “A lot of Marshallese women will go into their follow up and not even ask questions because they’re too afraid or don’t know what to say” (PID 01).
## 3.3. Maternal Healthcare Providers’ Demographic Characteristics
Table 2 shows the MHCPs’ demographic characteristics and information about their practice and facilities. A majority of the MHCPs were female, and all of the MHCPs were Marshallese. The MHCPs specialties varied from clinical nurse [2], outreach specialist [1], disease intervention specialist [1], and family medical doctor [1].
For the MHCPs, two themes emerged: [1] Reproductive Life Planning Practices; and [2] Reproductive Life Planning Influences.
## 3.3.1. Reproductive Life Planning Practices
MHCPs discussed their perceptions of Marshallese women’s contraception and reproductive life planning practices and influences. The majority of the MHCPs stated that Marshallese women did not use contraception and reproductive life planning practices. For example, one MHCP said: Contraception and reproductive life planning practices appear to vary generationally. One MHCP stated: The MHCPs discussed that if their patients did use a contraceptive method, it was typically Nexplanon, birth control pills, condoms, or the pregnancy calendar method. One MHCP said: Another MHCP stated:
## 3.3.2. Reproductive Life Planning Influences
MHCPs described similar influences to contraception and reproductive life planning among Marshallese women. Within this theme, three subthemes emerged: [1] Partner; [2] Culture/Religion; and [3] Side Effects.
Partner. The MHCPs described Marshallese partners as being a strong influence on contraception and reproductive life planning. One MHCP said: Another MHCP said that they hear their patients say, “Or they will honestly tell me like ‘oh, my guy doesn’t like to use that.’” ( PID 1) The discussion about partners as a strong influence on contraceptive use and reproductive life planning was tethered to the inappropriateness of discussing sensitive topics with mixed genders. For example, one MHCP said: “Also, I feel sometimes that men have more say. They answer for them, so I feel like they think that we are taking away some power” (PID 3). Another MHCP said: Culture/Religion. Similar to the Marshallese mothers, the MHCPs described Marshallese culture and religion as a strong influence on contraception and reproductive life planning. Culture as an influence included both a deep religious belief system that encourages large families alongside a cultural belief system of not discussing contraception and reproductive life planning. For example, one MHCP said: “I think it’s that. We believe in this was our purpose. God gave us the ability to produce, and that’s our purpose” (PID 1). Another MHCP stated:
From the perspective of MHCPs, large family size is an aspect of the Marshallese community that negates conversation about contraception and reproductive life planning. One MHCP said: Some MHCPs described that discussing contraception and reproductive life planning is a highly sensitive subject and can create embarrassment when discussed. One MHCP said: “When it comes to Marshallese and their culture this subject is really sensitive to talk about and discuss” (PID 4).
Another MHCP said: Discussing contraceptives and reproductive life planning was described as difficult, even when Marshallese men are not present. One MHCP said, “From my experience, from what I’ve seen around or encountered with maybe a few or more Marshallese women, it doesn’t seem like it’s an easy open conversation between uh us Marshallese women” (PID 05). However, some MHCPs described seeing a change generationally with comfortability in having open dialogue about contraception and reproductive life planning. One MHCP said: Another MHCP echoed this sentiment when they stated: Side Effects. Similar to the Marshallese mothers, the MHCPs described the potential for side effects as a strong influence in contraception use specifically. One MHCP said:
Another MHCP described concerns of side effects from specific contraceptives from their patients: Implants were also discussed as a form of contraception that their Marshallese patients had concerns regarding the side effects. For example:
## 4.1. Principal Findings
The purpose of this study is to understand Marshallese mothers’ and MHCP practices and influences with contraception use and reproductive life planning. In this study, many of the Marshallese mothers described not using contraception or reproductive life planning practices despite having a broad understanding of what methods can be used. Much of their knowledge about contraception and reproductive life planning practices was obtained from their MHCPs.
## 4.2. Results
Numerous influences were discussed in contraception and reproductive life planning decision making. The most dominant influences were partners/family, religion, the potential for side effects from contraceptives, cost, and fear. Similar to previous studies with Native Hawaiian and Pacific Islanders, the dominant influences of partners and religion were embedded in cultural belief systems of not discussing sex or reproductive life planning concepts [21,23]. Spousal communication has been identified in other collectivist cultures as influential in contraception use and reproductive live planning practices [31,32,33].
Contraceptive use as part of reproductive life planning practices was also influenced by the potential for negative side effects, especially the fear of infertility. These discussions are likely predicated on lack of health literacy and/or Marshallese customs of not discussing reproductive life planning practices, with the addition of a desire for large families. Fear of contraceptive side effects and infertility is not uncommon and has been previously identified among ethnic and/or minority women [34,35], more specifically with collectivist women who may place more value on large families [31,32,33].
An expected influence in contraception and reproductive life planning practices among Marshallese mothers was the lack of insurance and inconsistent means to afford healthcare visits or contraception. This finding is consistent with prior literature which has shown that cost and lack of insurance constrains maternal care and chronic disease management among Marshallese in the US [22,29,36]. This finding is also consistent with prior literature in other populations which has shown reduced contraceptive uses among low income and uninsured women [37,38]. This article adds new insights on the constraints of lack of insurance in contraception and reproductive life planning practices for the Marshallese, as this community has not had access to health insurance outside of pregnancy until December 2020.
Marshallese mothers also described fear as a guiding influence in their contraception use and reproductive life planning practices. Similar to the discussion around the potential for negative side effects from contraceptives, fear was embedded in a lack of contraception health literacy, fear of talking to their healthcare provider, and Marshallese customs of not discussing contraception and reproductive life planning practices. Importantly, fear was ubiquitous in all the influences highlighting a complex challenge for contraception and reproductive life planning education within this community. Although these fears have not been documented in other Pacific Islander cultures, studies with other collectivist cultures such as those in Ethiopia, Kenya, and Pakistan have similarly identified fear of infertility and side effects from contraceptives as highly influential in contraception use and reproductive life planning practices [31,32,33].
Similar to the Marshallese mothers, MHCPs described many of their Marshallese patients as not using contraception or reproductive life planning practices. The MHCPs described this as generational, with younger generations of Marshallese women more open and more informed about contraception and reproductive life planning practices. Previous studies with Native Hawaiian and Pacific Islanders did not identify a generational difference in openness to discussing contraception and reproductive life planning practices. This suggests an opportunity to tailor interventions based on this generational divide among Marshallese communities [22,23].
MHCPs identified similar influences on contraception use and reproductive life planning practices among their Marshallese patients, and these included partners, culture/religion, and side effects. MHCPs described partners as a strong influence on contraception use and reproductive life planning practices and decision making. Similar to the Marshallese mothers, much of the discussion around partner influence included cultural customs of not discussing contraception use or reproductive life planning practices, but they also described partners as the most influential decision maker. The MHCPs expanded on the cultural beliefs around not discussing contraception and reproductive life planning practices and highlighted that in Marshallese culture, it is highly encouraged to have large families. The encouragement of large families was described as a way to pass down the traditions and customs of Marshallese culture.
Lastly, the MHCPs also identified the potential for contraception side effects as a strong influence on their Marshallese patients. Similar to the mothers, much of the discussion around side effects appears to emerge from a lack of health literacy and misconceptions and/or misinformation about contraception. A global systematic review of MHCPs identified that fear of contraception side effects and low health literacy about contraception was highly influential in contraception and reproductive life planning practices [35].
## 4.3. Clinical Implications
Health interventions not aligned with cultural values and perspectives of the target population are less effective than culturally responsive interventions that account for these factors [39,40]. Culturally-adapted approaches using community-based assets and Marshallese cultural values/practices have been demonstrated to be effective in improving healthcare [41,42] but have not been focused on contraception and reproductive life planning among Marshallese in the US. The results from this study will inform the development of a culturally-adapted contraception and reproductive life planning tool and education program for Marshallese women, couples, and health care providers.
## 4.4. Research Implications
Additional research is needed in several key areas. Future research should explore contraception and reproductive life planning practices and influences with Marshallese, and MHCPs that work with Marshallese communities, outside of Arkansas. Additionally, future research should consider conducting focus groups rather than individual interviews to foster a robust reciprocal conversation and comfortability. Lastly, future research should examine the variances among Marshallese mothers’ responses based on different age brackets and length of time in the US to explore the effects of generational beliefs and acculturation on practices and influences with contraception use and reproductive life planning.
## 4.5. Strengths and Limitations
This study’s findings should be evaluated with some limitations. All participants were recruited from Arkansas, and the result may or may not be generalizable to other Pacific Islander communities residing outside of Arkansas. Although the qualitative methods used allow participants to explore the research topic in their own words, participants may have tailored their responses for acceptability. Despite these limitations, this is the first study to document Marshallese mothers’ and MHCPs’ practices and influences with contraception use and reproductive life planning, thus adding substantially to the literature gap.
## 5. Conclusions
Although the literature on Pacific Islanders’ contraception and reproductive life planning practices and influences is scarce, studies have identified similar practices and influences to those found in this study with the Marshallese community residing in Arkansas [21,22,23]. Similarities included partner influence, lack of health literacy, and cultural customs of not discussing contraception use and reproductive life planning methods [21,22,23]. However, this study adds to the gap in literature by identifying unique challenges among Marshallese, including challenges with cost/health insurance and an overall fear of the process. The results from this study will inform the development of a culturally-adapted contraception and reproductive life planning tool and education program for Marshallese women, couples, and healthcare providers.
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|
---
title: Fluid Intake and the Occurrence of Erosive Tooth Wear in a Group of Healthy
and Disabled Children from the Małopolska Region (Poland)
authors:
- Beata Piórecka
- Małgorzata Jamka-Kasprzyk
- Anna Niedźwiadek
- Paweł Jagielski
- Anna Jurczak
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001795
doi: 10.3390/ijerph20054585
license: CC BY 4.0
---
# Fluid Intake and the Occurrence of Erosive Tooth Wear in a Group of Healthy and Disabled Children from the Małopolska Region (Poland)
## Abstract
Background: The aim of this study was to analyse the relationship between the type and amount of fluid intake and the incidence of erosive tooth wear in a group of healthy children and children with disabilities. Methods: This study was conducted among children aged 6–17 years, patients of the Dental Clinic in Kraków. The research included 86 children: 44 healthy children and 42 children with disabilities. The prevalence of erosive tooth wear using the Basic Erosive Wear Examination (BEWE) index was assessed by the dentist, who also determined the prevalence of dry mouth using a mirror test. A qualitative-quantitative questionnaire on the frequency of consumption of specific liquids and foods related to the occurrence of erosive tooth wear, completed by the children’s parents, was used to assess dietary habits. Results: The occurrence of erosive tooth wear was determined for $26\%$ of the total number of children studied, and these were mostly lesions of minor severity. The mean value of the sum of the BEWE index was significantly higher ($$p \leq 0.0003$$) in the group of children with disabilities. In contrast, the risk of erosive tooth wear was non-significantly higher in children with disabilities ($31.0\%$) than in healthy children ($20.5\%$). Dry mouth was significantly more frequently identified among children with disabilities ($57.1\%$). Erosive tooth wear was also significantly more common ($$p \leq 0.02$$) in children whose parents declared the presence of eating disorders. Children with disabilities consumed flavoured water or water with added syrup/juice and fruit teas with significantly higher frequency, while there were no differences in quantitative fluid intake between groups. The frequency and quantity of drinking flavoured waters or water with added syrup/juice, sweetened carbonated, and non-carbonated drinks were associated with the occurrence of erosive tooth wear for all children studied. Conclusions: The group of studied children presents inappropriate drinking behaviours regarding the frequency and amount of beverages consumed, which, especially in a group of children with disabilities, may contribute to the formation of erosive cavities.
## 1. Introduction
The disabled population is a group at an increased risk of oral diseases. The reasons for this phenomenon can be found not only in the existence of numerous barriers to access to dental care but also in difficulties in implementing proper dietary and hygienic habits in this group of people. People with disabilities have limited access to health services, including routine treatment, which leads to non-disability-related health inequalities [1].
Difficulties with swallowing, eating, salivating, chewing, and unsatisfactory overall oral aesthetics may be present among people with Down syndrome, the most common genetic cause of intellectual disability. A higher prevalence of periodontal lesions has been identified in this group of individuals, which may be caused by the patient’s self-injury to oral tissues. A higher incidence of dental caries was also observed in the group of people with disabilities due to different craniofacial anatomy, functional disorders, or parafunctions. Children with physical and intellectual disabilities constitute a group that needs early and regular dental care in order to prevent and limit the severity of the pathologies observed [2,3,4].
According to the 2014 Polish Population Health Survey, disabled persons, by Polish criterion in the age group 0–14 years old, constituted $3.7\%$ of the total. The data showed that the largest group of children with disabilities was recorded among 10–14-year-olds ($5\%$), among 5–9-year-olds ($4\%$), and less than $3\%$ among the youngest children. More children with disabilities lived in urban areas than in rural ones, 140,000 vs. 72,000, respectively [5].
Dental erosion is the dissolution of dental hard tissues caused by acids of a non-bacterial origin. Erosive tooth wear is tooth wear with dental erosion as the primary etiological factor. As erosive tooth wear has serious long-term implications, it is important to establish its prevalence and its associated and aetiological factors [6]. The development of erosive tooth wear lesions may depend on internal factors, such as the state of health, the structure of the tooth, the structure and amount of saliva produced, as well as on external factors, mainly eating and drinking behaviour [7].
A systematic review presented that citrus fruits had a significant positive relationship with dental erosion. In addition, carbonated drinks and the consumption of acidic drinks at bedtime increased the risk of erosive tooth wear in adolescents. For sport/energy drinks and fruit juice, results were inconclusive [8].
Dental erosion has been considered an oral manifestation of eating disorders (i.e., anorexia, bulimia) associated with vomiting practices. The meta-analysis presented that patients with eating disorders and with risk behaviour of eating disorders had more risk of erosive tooth wear [9].
The literature suggests [10,11] that pathological conditions characterised by reduced salivary flow, i.e., salivary gland inflammation, Sjögren’s syndrome, or other symptoms, are factors that may influence the formation and development of dental erosion. The composition of saliva is particularly important in protecting against erosive processes, and normal salivary flow enables the dilution of acid concentrations of non-bacterial origin.
Dental erosion affected $42.3\%$ of the participants in the young adult Polish population and $24.3\%$ of the 15-year-old adolescent population [12,13]. To the best of the authors’ knowledge, the evaluation of factors influencing the development of erosive tooth wear among children with disabilities in Poland has not yet been conducted.
The aim of this study was to analyse the relationship between the type and amount of fluid intake and the incidence of erosive tooth wear in a group of healthy children and children with disabilities.
## 2.1. Study Design
This observational cross-sectional study was conducted between June and October 2019 among children of patients of a private dental practice in Kraków contracted by the National Health Fund for orthodontic treatment. A total of 101 questionnaires were collected, of which, after applying an exclusion criterion and verifying the completeness of the collected data, responses concerning 86 children were included in the evaluation, 44 healthy children and 42 children with disabilities, mainly Down syndrome ($73.8\%$) and single cases of the following chronic conditions: childhood cerebral palsy, retinoblastoma, deletion syndrome, vertebrae damage, psychomotor retardation, body asymmetry, and motor aphasia. Inclusion criteria for this study: age 6–17 years and not taking medication affecting saliva secretion (inhaled medication used for bronchial asthma). Exclusion criteria for this study included: lack of parental consent, as well as lack of patient/child cooperation during the dental assessment.
All participants were informed about the conditions and procedure of this study and gave written consent to participate in the study. This study was conducted in accordance with the Declaration of Helsinki for medical research and received approval from the Bioethics Committee of Jagiellonian University (no. 1072.6120.138.2019 of 27 June 2019).
## 2.2. Data Collection
Parents/legal guardians of children were asked to answer a survey questionnaire related to dental treatment before their child entered the dental practice. In this study, no power calculation to estimate sample size was conducted. Dental observation of the occurrence and severity of erosive tooth wear and dry mouth was carried out in the case of children with disabilities by the orthodontics specialist Elżbieta Radwańska and in the group of healthy children by the dentist Barbara Noga. The calibration was not performed.
During the oral review, the children’s prevalence and severity of erosive tooth wear were assessed by noting the highest BEWE value for each sextant. On this basis, the child was categorised into a risk group based on the severity of erosive tooth wear and defined as 0–2—no risk (grade 1), 3–8—low risk (grade 2), 9–13—moderate risk (grade 3), and ≥14—high risk (grade 4) [14].
A mirror test was also performed to assess the presence of dry mouth. This index is based on a 3-point scale in the following categories: I no resistance (the mirror slides freely over the mucosa), II slight resistance (slight resistance is felt when moving the mirror), III significant resistance (the mirror sticks to the mucosa) [15].
To assess dietary behaviour, the authors used selected questions from the questionnaire on the frequency of consumption of specific products and liquids. These questions were modelled after the KomPAN Questionnaire developed by the Team of Behavioural Determinants of Nutrition, Committee on Human Nutrition Science, Polish Academy of Sciences (PAN) [16].
The questionnaire also included questions about selected socio-economic characteristics of the respondents, specific hygiene behaviours related to oral health maintenance, e.g., frequency of tooth brushing, and information about the general health of children, including subjective feelings of dry mouth. Parents/legal guardians were asked about the presence of medical conditions such as diabetes, asthma, Sjögren’s syndrome, xerostomia, inflammation of the salivary glands, and other conditions that increase the risk of erosive tooth wear, and whether the children were on continuous or regular medication (at least three times a week) and taking selected dietary supplements.
Parents were also asked to provide their child’s current height and weight, from which a body mass index (BMI, kg/m2) was calculated to assess the children’s nutritional status. The BMI values of each subject were related to national centile grids for age and sex, taking into account WHO criteria [17].
## 2.3. Statistical Analysis
Statistical analyses were performed using Statistica 13.0 PL. Due to the nature of the collected data, the evaluation of differences in responses tested with the χ2 test and the Mann-Whitney-U test as a non-parametric equivalent of the Student’s t-test was used. In the description of the results, group A denotes healthy children, while group B denotes children with disabilities.
Differences in respondents’ answers were checked for the presence of disability, age groups, dryness of the mouth (no dryness—level 1, presence of dryness—levels 2 and 3 in the classification of the mirror test), and for the risk of dental erosion according to the adopted interpretation of the BEWE index (group 1—no risk, group 2—low, moderate and high risk). The level of statistical significance was set at $p \leq 0.05.$
## 3.1. Characteristics of Participants
The mean age of all children studied was 10.78 ± 2.96 years. There were no differences in the age of the respondents in the distinguished groups of healthy children and children with disabilities (Table 1). In the case of mothers of healthy children, only $18.2\%$ reported not working, while $64.3\%$ of mothers of children with disabilities did not work. In the study group of children with special needs, $35.7\%$ lived in rural areas, while in the group of healthy children, significantly fewer rural residents ($11.4\%$) were treated at a dental clinic ($$p \leq 0.0075$$).
None of the examined children had the following diseases associated with an increased risk of erosive tooth wear, i.e., diabetes, peptic ulcer disease, bronchial asthma, Sjögren’s syndrome, xerostomia, or inflammation of the salivary glands. On the other hand, in the group of children with disabilities, parents reported the occurrence of gastroesophageal reflux disease and eating disorders in children in single cases.
Statistically ($$p \leq 0.0002$$), significantly more ($45\%$) parents of children with disabilities confirmed that their child was taking medications on a regular basis compared to healthy children ($9\%$).
There was no statistically significant difference in the parents’ answers regarding dietary supplements taken by the child. $40\%$ of parents gave their children supplements containing vitamin C, while $7\%$ provided preparations containing iron. As many as $71\%$ of all surveyed parents reported that their child received other supplements.
## 3.2. Prevalence and Severity of Erosive Tooth Wear and Dry Mouth in Dental Assessment
According to the BEWE classification of non-carious erosive cavities, $26\%$ of the total number of children in this study had erosive tooth wear. A statistically significant difference was observed for the cumulative value of the BEWE index between the evaluated groups ($$p \leq 0.0003$$). In the group of healthy children, the mean value of the BEWE index was 1.39 (min = 0, max = 8, SD = 2.16), while the mean value of BEWE was higher for children with disabilities, amounting to 2.60 (min = 0, max = 7, SD = 1.98). There were no differences in the occurrence of erosive tooth wear depending on the sex, age, and BMI of the child.
The risk of erosive tooth wear was non-significantly more common in children with disabilities ($31.0\%$) than in healthy children ($20.5\%$). Erosive tooth wear was significantly more common ($$p \leq 0.02$$) in children whose parents declared the presence of eating disorders.
In both groups, as interpreted by BEWE, the lesions were of low severity, and therefore the risk of erosive tooth wear in the study group was low. The severity of the changes in the occurrence of erosive tooth wear in the study groups is shown in Figure 1.
For almost all healthy children ($97.7\%$), no resistance was found when the dental mirror was moved along the cheek surface, i.e., they were properly hydrated. In contrast, for more than half of the children with disabilities ($57.1\%$), slight resistance was found during the examination (Figure 2). This result was statistically significant ($p \leq 0.0001$). The survey questionnaire asked about the subjective feeling of dryness in the mouth. There was no statistically significant difference in the group of healthy and disabled children. Only $12\%$ of parents of all the examined children reported dry mouth.
## 3.3. Oral Hygiene Behaviour in the Study Group of Children
In maintaining oral hygiene in the group of children with disabilities, parents used an electric toothbrush significantly more often, while healthy children used dental floss ($$p \leq 0.0032$$) and chewed sugarless gum after meals ($$p \leq 0.0373$$) significantly more often compared to children with disabilities (Table 2).
There are also significant differences regarding the frequency of children’s visits to the dental practice. For healthy children, $61.4\%$ visit the dental practice every six months, while $42.9\%$ of children with disabilities visit more often than every six months for dental check-ups ($$p \leq 0.0057$$).
## 3.4. Qualitative and Quantitative Fluid Intake in a Group of Healthy and Disabled Children
The frequency of consumption of specific beverages in the groups of healthy and disabled children is shown in Table 3, and the results indicate a significantly higher frequency of consumption of flavoured waters or waters with juice syrup and fruit tea in the group of children with disabilities compared to the control group. No differences were observed in the quantitative consumption of specific liquids and total fluid intake (TFI) in the study groups of children (Table 4).
## 3.5. Fluid Intake in a Study Group of Children and the Incidence of Erosive Tooth Wear
It was confirmed that erosive tooth wear changes were significantly more frequent in children consuming more sweetened carbonated and non-carbonated drinks and black tea, as well as drinking more liquids per day (Table 5). Also close to the accepted limit of statistical significance were flavoured waters or waters with added syrup/juice.
## 4. Discussion
In this study, the incidence of erosive tooth wear was $26\%$ of the total number of children, and these were mostly lesions of minor severity. The exclusion criteria for the study group of children comprised the use of inhaled bronchial asthma medications. The cumulative assessment of the BEWE index showed a significant difference between the groups of children according to the presence of a disability, while in the interpretation of the BEWE, the risk of erosive tooth wear in a study group was non-significantly more frequent for children with disabilities (mainly with Down Syndrom) than for the healthy ones.
Similar results were obtained in a study conducted in Dubai in 2019, but dental erosion was significantly higher in children with Down Syndrome compared to healthy children ($34\%$ vs. $15.3\%$) [18].
Among the group of children with disabilities, as many as $57.1\%$ showed slight resistance when moving a mirror in the mouth. This may be related to the amount of fluid consumed and the effect of medication, which, however, was not investigated in this study. A dry mouth can be one of the symptoms of dehydration. In the study group, besides the effect of frequency, the amount of fluid intake was also evaluated. Different studies show that children and adolescents in Europe do not drink enough water [19,20]. Decreased salivary flow causes a decrease in clearance rate, leading to an increase in the risk of erosive tooth wear, especially in the case of physical activity [21].
In the survey, we confirmed that erosive tooth wear was significantly more frequent in children consuming more sweetened carbonated and non-carbonated drinks and black tea, as well as drinking more fluid per day. Also close to the accepted limit of statistical significance were flavoured or syrup/juice-infused waters, the amount of consumption of which may influence the development of erosive tooth wear, which is related to the low pH of these drinks (pH < 4.5). A higher frequency of consumption of flavoured waters or waters with juice syrup and fruit tea was observed in the group of children with disabilities.
The findings of the present study are in accordance with the results of a systematic review, where carbonated drinks were significantly positively associated with dental erosion in adolescents [8].
Also, a positive correlation was observed between the erosive lesions of the anterior teeth and the frequency of consumption of carbonated and energy drinks in the population of adolescents aged 15 in Poland [12].
However, in the population of 18-year-old young adults in Poland, drinking behaviour, like frequent consumption of fruit teas and energizing beverages, was connected with dental erosion. Also, hygienic habits, medical conditions such as asthma, eating disorders, and oesophageal reflux showed statistical significance associated with erosive tooth wear [13].
Children and adolescents from Poland make mistakes regarding the frequency of beverage consumption. The vast majority of schoolchildren from Kraków and the surrounding area indicate that they consume water daily about three times a day, but more than a third of them choose flavoured water or water with added juices/syrups [22].
A national study by Jessa J. and Hozyasz K. [23] indicated that children aged 6 months to 18 years hospitalised in Warsaw in 2016 at the Department of Paediatrics of the Mother and Child Institute were significantly more likely to drink flavoured waters.
In contrast, in a group of adolescents from the region of Podkarpacie (Poland), sugar beverages (soft drinks) were consumed most frequently, and respondents chose energy drinks more often than isotonic beverages. All the beverages indicated have an adverse effect on the development of erosive tooth wear [24].
Similarly, as in the presented study of a group of children from the Małopolska region, a study by Alves et al. showed an association of dental erosion with the consumption of soft drinks (including sweet carbonated and non-carbonated drinks), but also fruit juices [25].
In a cross-sectional study on a sample of 400 children from Valencia (Spain), a positive correlation was observed between the presence of tooth erosion and frequent consumption of fruit juices, fizzy drinks, and isotonic drinks ($p \leq 0.05$), showing a higher correlation if the liquid was held in the mouth before swallowing [26].
The study among adolescents in Stockholm County [27] diagnosed that erosive lesions were significantly correlated with soft drink consumption, the use of juice or sport drinks as a thirst quencher after exercise, and tooth hypersensitivity when eating and drinking.
The presented studies lack uniform nomenclature of individual types of beverages, and they do not specify the type of fruit juice, which makes it difficult to compare the results.
It is recognized that beverages with high calcium content, like milk or calcium-enriched juices, may reduce the risk of dental erosion. Therefore, adequate consumption of milk and dairy products is important in the prevention of dental erosion [8].
In the study by Guelinckx et al. [ 28], data from 3611 children and 8109 adolescents were retrieved from 13 countries, including Poland. In the total sample, the highest mean intakes were observed for water (738 ± 567 mL/day), followed by milk (212 ± 209 mL/day), regular soft beverages (RSB) (168 ± 290 mL/day), and juices (128 ± 228 mL/day). Large contributions of hot beverages, like black or fruit tea, to total fluid intake (TFI) were reported in the total children sample of Poland, which is culturally conditioned.
In the study group of children from the Małopolska region, the amount of milk consumption was similar (median 200 mL/day).
In a study by Hasselkvist et al., the development of erosive tooth wear was influenced by lesser sour milk intake and more frequent intake of drinks between meals [29].
Besides the consumption of acidic drinks, a lifestyle that may be conducive to such consumption, such as sedentary living, excessive screen viewing activities, as well as being overweight, may contribute to the development of erosive wear [8]. Numerous studies showed a positive correlation between the frequency and quantity consumption of sugar-sweetened beverages and body mass index in children and adolescents [30].
In this study of children from the Małopolska region, there was no statistically significant difference in the occurrence of erosive tooth wear in relation to the sex, age, and BMI of the child. However, the declared amount of acidic liquid consumption was associated with the occurrence of erosive tooth wear.
In study by Tschammler et al., a total of 223 children aged 4–17 years children with obesity and extreme obesity compared to children with normal weight had significantly higher erosive wear and caries of deciduous and permanent teeth [31].
People with intellectual disability (ID) are characterised by a high prevalence of incorrect eating patterns, as well as a high risk of becoming overweight or obese. The results of this study from Poland showed that excess body weight was observed in $66.7\%$ and obesity in $38.9\%$ of the respondents (seven subjects) with ID [32].
Due to the lack of *Polish data* regarding the quality of fluid consumption of children and adolescents with ID, it is impossible to confront the results of this study with the findings of other Polish authors.
Oral hygiene is also an important protective factor in the prevention of erosive cavities. Children with disabilities have difficulty maintaining proper oral hygiene. In maintaining oral hygiene in the surveyed group of children with disabilities, parents significantly more often use an electric toothbrush, while healthy children significantly more often use dental floss and chew sugar-free gum after meals compared to children with disabilities. The surveyed group of parents of children with disabilities is aware of the importance of oral health in relation to the health of their child due to the declared high frequency of visiting the dental clinic with their child. Almost half of the parents/guardians of the studied children had a university degree.
Dental treatment of disabled people in *Poland is* provided free of charge. In addition to the services guaranteed in the Polish system of health care for disabled people, they have access to treatment with the best materials and treatment methods. People with disabilities are reimbursed by the state for treatment under general anesthesia [33].
The data obtained from parents/guardians of disabled and/or chronically ill children living in Poznań and Białystok (Poland) showed that up to $18.5\%$ of children with disabilities had never been to a dentist. The most common reasons for a dental visit were changes within a tooth noticed by a parent ($25.5\%$) or a dental check-up ($25\%$). Only $67.5\%$ of respondents reported no access barriers to dental treatment [34].
## Strengths and Limitations of this Study
In the presented study, the limitation of the interpretation of the results may be due to the specificity of the collection of material and the small size of the study group. Other limitations are the lack of a power calculation to estimate the sample size and also the lack of results regarding the assessment of the prevalence of caries and oral hygiene. Among the strengths of the study, it should be emphasised that few studies provide a quantitative assessment of the fluids drunk by a group of children with special needs.
## 5. Conclusions
The group of children studied presents inappropriate drinking behaviours regarding the quality of the beverages consumed, which, especially in a group of children with disabilities, may contribute to the formation of erosive cavities. Disabled children cannot perform hygiene procedures and make decisions in the field of eating habits on their own. Therefore, parental education on the relationship of food and fluid intake to oral hygiene and general health in the future should be increased.
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|
---
title: Identification of Novel Core Genes Involved in Malignant Transformation of
Inflamed Colon Tissue Using a Computational Biology Approach and Verification in
Murine Models
authors:
- Andrey V. Markov
- Innokenty A. Savin
- Marina A. Zenkova
- Aleksandra V. Sen’kova
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001800
doi: 10.3390/ijms24054311
license: CC BY 4.0
---
# Identification of Novel Core Genes Involved in Malignant Transformation of Inflamed Colon Tissue Using a Computational Biology Approach and Verification in Murine Models
## Abstract
Inflammatory bowel disease (IBD) is a complex and multifactorial systemic disorder of the gastrointestinal tract and is strongly associated with the development of colorectal cancer. Despite extensive studies of IBD pathogenesis, the molecular mechanism of colitis-driven tumorigenesis is not yet fully understood. In the current animal-based study, we report a comprehensive bioinformatics analysis of multiple transcriptomics datasets from the colon tissue of mice with acute colitis and colitis-associated cancer (CAC). We performed intersection of differentially expressed genes (DEGs), their functional annotation, reconstruction, and topology analysis of gene association networks, which, when combined with the text mining approach, revealed that a set of key overexpressed genes involved in the regulation of colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Adam8, Mmp7, Mmp13) occupied hub positions within explored colitis- and CAC-related regulomes. Further validation of obtained data in murine models of dextran sulfate sodium (DSS)-induced colitis and azoxymethane/DSS-stimulated CAC fully confirmed the association of revealed hub genes with inflammatory and malignant lesions of colon tissue and demonstrated that genes encoding matrix metalloproteinases (acute colitis: Mmp3, Mmp9; CAC: Mmp7, Mmp13) can be used as a novel prognostic signature for colorectal neoplasia in IBD. Finally, using publicly available transcriptomics data, translational bridge interconnecting of listed colitis/CAC-associated core genes with the pathogenesis of ulcerative colitis, Crohn’s disease, and colorectal cancer in humans was identified. Taken together, a set of key genes playing a core function in colon inflammation and CAC was revealed, which can serve both as promising molecular markers and therapeutic targets to control IBD and IBD-associated colorectal neoplasia.
## 1. Introduction
Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related deaths worldwide [1,2]. Colon inflammation, along with the particular host and environmental factors, plays a crucial role in the initiation and progression of CRC [3]. Colitis-associated cancer (CAC) is a type of CRC, which is preceded by clinically detectable inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), two highly heterogeneous, incurable, persistent, relapsing/worsening, and immune-arbitrated inflammatory pathologies of the digestive system [4,5]. Epidemiologic studies have showed that patients with IBD have a predisposition to CRC, and cancer risk is highly correlated with the duration and severity of colon inflammation [6,7].
In IBD, chronic long-term colon inflammation accompanied by oxidative stress can alter the expression patterns of key carcinogenesis-associated genes [8]. Moreover, persistent stimulation of epithelial proliferation in the colon by the pro-inflammatory stimuli and excessive cell damage with increased epithelial cell turnover result in detrimental genetic and immunological alterations, making patients with IBD prone to developing CRC [9]. Despite the proven involvement of “inflammation-dysplasia-carcinoma” axis in the malignant transformation of cells in IBD-related CRC [10], the molecular mechanism underlying this process is not yet fully understood. In particular, it remains rather unclear which core genes are involved in the regulation of acute colitis and how markedly their profiles change during colitis-associated malignant transformation of the colon tissue. In addition, the proven complexity of the colitis/CAC-related regulome underlies the low efficacy of conventional IBD/CRC therapy, making it inevitable that surgery is recommended for treating these pathologies [5,11]. Given the known adverse impact of the surgical management of colonic diseases on the quality of life, mental health, and work productivity of patients [5,11], the search for novel key genes involved in the inflammation-related tumor transformation, which can be used as potential molecular targets for IBD therapy, is urgently needed. Moreover, such regulatory genes can be considered as biomarkers of inflammation-driven tumorigenesis and serve as predictors for surveillance strategies and chemoprevention of colitis-related dysplasia and CRC in IBD patients.
To date, extensive exploration of colitis- and CAC-associated regulomes has been performed using transcriptomics-based approaches [12,13,14,15,16,17,18,19,20,21]. Reported bioinformatics studies have revealed some candidate biomarker genes and key signaling pathways susceptible to the development of the mentioned disorders [14,15,16,17,18,19,20,21,22,23], colitis-induced changes in the landscape of immune infiltration of colon tissue [14,16], and a range of hub genes probably involved in the development of CAC [15,20,23]. Despite a plethora of published studies, obtained results are still uncertain and are not well correlated with each other, probably due to insufficient usage of a multiple microarray analysis algorithm (the exploration of three or more independent microarray datasets in the same study, which gives more valid results [18,20,21]), ineffective manual searching of the published literature on the topic of study [14,15,16,17,18,19,20,21], and, in some cases, the absence of proper experimental validation [18]. Since the obtained data still remain insufficient for a thorough understanding of colitis/CAC-associated gene signature, further comprehensive bioinformatics analysis of colitis/CAC-related core genes is required.
In this study, deep re-analysis of multiple microarray datasets related to murine acute colitis (GSE42768, GSE35609, GSE64658, GSE71920, GSE35609) and CAC (GSE31106, GSE5605, GSE64658, GSE42768) was performed. Firstly, the differentially expressed genes (DEGs) were computed between injured and healthy colon tissues, followed by their functional annotation and Venn diagram analysis to identify acute colitis- and CAC-associated core genes. Next, the changes in the sets of core genes associated with the transition from colon inflammation to CRC were identified. Further reconstruction and analysis of gene association networks revealed a range of hub regulators among core genes, subsequent exploration of which by the text mining approach identified a list of candidate genes, which can be used as novel promising biomarkers and therapeutic targets for colitis and CAC. The obtained results were finally validated using an in vivo model of dextran sulfate sodium (DSS)-induced acute colitis and azoxymethane (AOM)/DSS-induced CAC. Furthermore, the role of identified core genes in the colonic carcinogenesis in the backstage of chronic long-term inflammation was analyzed with respect to IBD and CRC in humans.
## 2.1. Identification of Core Genes Related to Colitis and Colitis-Associated Cancer
To reveal key genes involved in the regulation of acute colitis and its transformation to CAC in mice, a range of independent expression profiles of murine colon tissue were retrieved from the GEO database, including samples of mice of both sexes and different strains with acute colitis stimulated by DSS (GSE42768, GSE35609, GSE64658, GSE71920) or dinitrobenzene sulfonic acid (DNBS) (GSE35609), or chronic colitis driven by azoxymethane (AOM)/DSS accompanied by the development of colorectal cancer (GSE31106, GSE5605, GSE64658, GSE42768). The analysis of selected transcriptomic datasets using the GEO2R tool revealed the sets of differentially expressed genes (DEGs) (colitis vs. control and CAC vs. control) susceptible to the mentioned pathologies, further overlapping of which identified 54 and 109 common DEGs specific to colitis and CAC, respectively (hereafter referred to as core genes) (Figure 1A).
## 2.1.1. Hierarchical Clustering and Functional Analysis of DEGs
Hierarchical clustering of the expression profiles of identified colitis-associated core genes revealed two main clades separating up- and down-regulated DEGs from each other (Figure 1B). The sub-clade of the most overexpressed DEGs included genes related to immune response (Ccl3, S100a9, S100a8, Cxcl2) and heme metabolism (Hp), whereas the most suppressed core genes in the colitis group were Hao2 and Slc26a3, associated with fatty acid metabolism and chloride ion transport, respectively (Figure 1B). Further functional analysis of colitis-specific core genes revealed high enrichment of inflammatory-related terms, including the production of pro-inflammatory cytokines IL-1 and TNF-α, IL-17, IGF1-Akt and Tyrobp signaling pathways, antiviral response, matrix metalloproteinases (MMPs), lung fibrosis, and rheumatoid arthritis (Figure 1C, upper panel).
Hierarchical clustering of CAC-specific core genes (Figure 1D) revealed two main clades, grouping activated and suppressed DEGs separately, and one outgroup consisted of the most overexpressed CAC-associated DEGs, notably, regulators of host-microbiota interplay (Reg3b, Reg3g), immune response (S100a9), and extracellular matrix (ECM) remodeling (Mmp7). In turn, the most suppressed core genes in the CAC group were involved in the regulation of cell adhesion (Zan), pH homeostasis (Car4), and ion transport (Slc26a3, Slc37a2, Aqp8) (Figure 1D). *Performed* gene set enrichment analysis revealed that CAC-specific core genes are tightly associated with cell invasiveness (wound healing involved in inflammatory response and MMPs), immune response (acute inflammatory response, antimicrobial peptides, etc.), redox imbalance, ion transport, bile secretion, and numerous metabolic processes (Figure 1C, lower panel). Interestingly, the retrieved functional annotation map specific for CAC was significantly less interconnected compared with the acute colitis-associated GO term/pathways network (Figure 1C, upper panel), which can be explained by the more discrete disposition of identified core genes in the CAC-related regulome.
## 2.1.2. Analysis of Interconnection between Acute Colitis- and CAC-Specific Core Genes
To explore how strongly identified core genes are interconnected in acute and chronic (CAC) phases of colitis, their Venn diagram analysis and the reconstruction of the gene association network were performed. Overlapping of acute colitis- and CAC-related genes demonstrated that 22 of the core genes, playing a regulatory role in acute inflammation, were involved in CAC pathogenesis (Figure 1E), including immune genes (Ifitm1, Ifitm3, Il1a, Lcn2, S100a9, Saa3, Tnf), genes encoding protease inhibitors (Serpina3n, Slpi, Wfdc18), ion transporters (Slc26a2, Slc26a3, Trpm6), ECM remodeling proteins (Mmp10, Timp1, Mep1a), signal transduction components (Igfbp4, Lrg1) and regulators of cell motility (Capg), fatty acid homeostasis (Hao2), host-microbiota interplay (Sult1a1), and heme metabolism (Hp).
Analysis of the gene association network generated from acute colitis- and CAC-associated core genes using the STRING database [24] demonstrated their relatively high interconnection: 72 of 141 uploaded core genes ($51\%$) formed interactions with each other within the network (Figure 1F). Interestingly, only 34 of 87 CAC-specific genes ($39\%$) were involved in the network, whereas the shares of acute colitis-specific and common genes in the reconstructed interactome were $66\%$ (21 of 32 genes) and $73\%$ (16 of 22 genes), respectively. Considering that highly interconnected genes can be involved in the same or similar biological processes [25], revealed low enrichment of the analyzed network by CAC-specific genes (Figure 1F) was in line with the discrete structure of the CAC-related functional annotation map shown above (Figure 1C).
Further computing of degree centrality scores of explored core genes revealed a range of genes occupying hub positions in the analyzed network (Figure 1F). It was found that the most interconnected nodes were acute colitis-specific or common genes involved in immune response (C3, Cxcl2, Il1b, Tnf) and ECM remodeling (Mmp9, Timp1). Among CAC-specific genes, the highest degree was identified for stabilizer of endoplasmic reticulum structure Ckap4, regulator of cell–cell interaction Cd44, and gene Lyz1 encoding lysozyme (Figure 1F). Given the hub position of Mmp9 and Timp1 and the formation of a highly connected cluster of MMPs in the core gene-retrieved network (Figure 1F), the changes in the MMPs profile can be involved in the regulation of malignant transformation of colon tissue during chronic colitis. This pattern needs further clarification.
## 2.2.1. Acute Colitis-Associated Hub Genes
To identify novel candidate genes for acute colitis and CAC, which can be used as both diagnostic markers and promising therapeutic targets, next we questioned how strongly evaluated core genes can be involved in the regulation of the mentioned pathologies and how well these genes have been studied in the field of inflammatory and neoplastic disorders of the colon.
To address the first issue, the degree centrality scores of the core genes in gene association networks created for each analyzed transcriptomic dataset were computed. Given that hub genes can exert key regulatory functions in reconstructed gene networks [26], the top 20 acute colitis-specific hub genes were identified and are shown in Figure 2A. The obtained results demonstrated that the most interconnected genes associated with acute colitis included genes encoding cytokines (Tnf, Il1a, Il1b), chemokines and its receptors (Ccl2, Cxcl2, Ccl3, Ccr5), growth factors and signal transduction components (Igf1, Tyrobp, Arrb2), ECM remodeling regulators (Mmp3, Mmp9, Timp1), and immune (C3, Clec7a, H2-Aa, Sell, Selp) and protective (Hp, Ugt2b35) proteins.
Next, to select genes poorly characterized for their role in colitis and colitis-associated disorders, a text mining approach was performed. Analysis of the mention of acute colitis-related core genes (Figure 1B) alongside the keywords “Colitis”, “Crohn’s”, “Dysplasia”, and “Colon cancer” in scientific texts deposited in the MEDLINE database revealed the most studied genes in the field of colitis (Tnf, Il1b, Mmp9, Igf1, Ccl2, Slc26a2, Timp1, Lcn2, Il1a, and Sell); the majority of them occupied hub positions in retrieved colitis-associated gene networks (key nodes) (Figure 2A). The rest of the genes were found to be less explored as colitis-related ones (Figure 2B), and, therefore, could be used as a source of novel promising markers/regulators of colitis. To experimentally verify the obtained data, Mmp3, C3, and Tyrobp, displaying, on the one hand, little connection with colitis in the published reports (Figure 2B), and, on the other hand, high degree centrality scores in colitis-associated gene networks (Figure 2A), were selected for further qRT-PCR analysis. Since the profile of MMPs was identified as hypothetically susceptible to transforming acute colitis into CAC (Figure 1F), expressions of Mmp9 and Timp1 (known inhibitor of MMPs) were also further validated.
## 2.2.2. CAC-Associated Hub Genes
The ranking of CAC-specific core genes according to their degree centrality scores in CAC-related gene networks identified the top 20 genes occupying hub positions, including genes encoding cyto- and chemokines (Tnf, Il1a, Cxcl16), regulators of ECM remodeling (Timp1, Mmp7, Mmp13, Gusb), immune (Ctla4, Cyba) and protective (Gstt1, Hp, Clu, Cyp2s1) response, lipid homeostasis (Acss2, Chpt1), ROS production (Maoa), cell–cell interaction (Cd44), membrane fusion (Snap25), and signal transduction (Plce1, Lgr5) (Figure 2C). Further text mining study, combined with the computing of the association of CAC-related core genes with the overall survival of patients with colon (COAD) and rectal (READ) adenocarcinomas, clearly confirmed the credibility of our bioinformatics analysis: the most reported CAC-related genes (Tnf, Cd44, Timp1, Mmp7, Ctla4, Clu, Il1a, Hp) were not only associated with poor prognosis in COAD and READ patients but also occupied the hub positions in the networks retrieved from CAC-associated DEGs (Figure 2D). These results indicate a probable important regulatory function of the listed core genes in colitis-associated neoplastic transformation of colon tissue.
To identify novel candidate genes for CAC, our attention was centered on the core genes that are, on the one hand, poorly characterized in the field of CAC, and, on the other hand, associated with ECM remodeling susceptible to “inflammation-dysplasia-carcinoma” axis (Figure 1C,F), notably, Mmp13 (key node) and Adam8 (extracellular metalloprotease-disintegrin involved in ECM digestion and markedly associated with pathogenesis of gastrointestinal malignancies [27]) (Figure 2D). In addition, the key nodes Timp1 and Mmp7 previously reported as probable regulators of CRC were also selected for qRT-PCR analysis.
## 2.3.1. Murine Model of DSS-Induced Colitis and CAC
Acute colitis was induced in mice by administration of $2.5\%$ DSS solution in drinking water for 7 days, followed by a 3 day recovery (Figure 3A). CAC was induced in mice by single intraperitoneal (i.p.) injection of AOM 1 week before DSS administration. Furthermore, mice were exposed to 3 consecutive cycles of $1.5\%$ DSS instillations for 7 days, followed by 2 weeks of recovery (Figure 3A). After the experiment termination, the colons were separated from the proximal rectum, mechanically cleaned with saline buffer, and collected for subsequent histological analysis and qRT-PCR.
Gross morphological analysis of healthy colons revealed the normal thickness of the colonic wall and mucosa structure (Figure 3B). Administration of $2.5\%$ DSS for one week led to acute inflammatory changes in the colonic tissues, clearly demonstrating the development of acute colitis and represented by thickening of the colonic wall, hyperemia, hemorrhages, and scattered ulcers (Figure 3B). Long-term cyclic administration of $1.5\%$ DSS with prior injections of carcinogen AOM caused the development of multiple adenomas in the distal part of mice colons with a significant decrease in the intensity of acute inflammatory changes in the colonic tissues (Figure 3B).
Histologically, the colon tissue of healthy mice demonstrated intact colon architecture, non-disrupted crypts, and goblet cells with active mucus vacuoles (Figure 3C). Acute administration of DSS caused severe colon tissue damage, represented by massive epithelium disruption with erosions and ulcerations, diffuse destruction of crypts, and loss of mucosal architecture (Figure 3C). Pronounced inflammatory infiltration through the whole colonic wall, due to neutrophils and lymphocytes as well as mucosa edema, was revealed (Figure 3C). In the case of CAC, chronic administration of DSS after AOM injection caused adenomatous transformation of the colon mucosa, represented by multiple adenomas in the colonic tissue with epithelial hyperproliferation and hyperplastic crypts (Figure 3C). Residual inflammatory infiltration located in the mucosa and submucosa of colon tissue with adenomas and represented by lymphocytes and macrophages was detected (Figure 3C). In the colon tissue adjacent to adenomas (colitis in CAC), signs of chronic colonic inflammation with moderate destruction of the mucosal architecture and crypt damage were found (Figure 3C).
Thus, we reproduced the process of colon carcinogenesis, starting with acute inflammation in the colon tissue, transitioning to chronic inflammation, and eventually ending up with the colonic tumor formation.
## 2.3.2. Core Genes Expression in the Colonic Tissue of Mice with Acute Colitis and CAC
Finally, the expression of the revealed hub genes related to acute colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Adam8, Mmp7, Mmp13) was validated by qRT-PCR in the colon tissue of mice with acute colitis and colitis-driven adenomas (Figure 3D). As expected, the expression of colitis-related genes C3, Tyrobp, Mmp3, Mmp9, and Timp1 was significantly up-regulated in inflamed colon tissue compared with healthy controls; among them, Mmp3 and Timp1 were found to be the most susceptible to acute colitis induction, demonstrating 306.3- and 110.6-fold increases in the expression, respectively, in DSS-treated mice compared with healthy controls (Figure 3D). The chronification of colonic inflammation led to significant reduction in the expression of C3, Tyrobp, Mmp3, and Timp1 in the adjacent to adenomas colonic tissue by 17.5, 6.6, 2.8 and 46.1 times compared with the samples from acute colitis group, and, moreover, the expression of C3 and Tyrobp in this compartment decreased to the healthy level (Figure 3D). Interestingly, chronification of colitis had no obvious effect on the expression of Mmp9: comparable induction of this gene in both DSS- and AOM/DSS-inflamed colon tissues was observed (Figure 3D), which could indicate the important role of Mmp9 in both acute and chronic colon inflammation, agreeing with [28]. The analysis of colonic adenomatous nodes revealed low expression of all the explored acute colitis-associated key genes: the expression levels of C3, Tyrobp, Mmp3, Mmp9, and Timp1 in adenoma tissue were 26.3, 3.4, 20.8, 11.9, and 27.7 times lower than those in the samples with acute colitis (Figure 3D). Note that adenomatous and adjacent tissues in mice with CAC mainly differed in the expression of the following genes: Tyrobp and Timp1 were found to be 1.9 and 1.7 times overexpressed in adenomas compared with the adjacent counterparts, respectively, whereas Mmp3 and Mmp9 were 7.6 and 13.9 times suppressed in tumor tissue, respectively (Figure 3D). Taken together, the obtained results clearly demonstrated that selected key genes associated with acute colitis indeed reached the maximum expression in the acute phase of colon inflammation, whereas chronification of the latter led to a marked decline in this parameter.
As expected, all CAC-associated hub genes (Adam8, Mmp7, Mmp13, and Timp1 (mentioned above)) were characterized by significant overexpression in tumor nodes compared with healthy tissue, which confirms the expediency of their further exploration as CAC-related marker genes (Figure 3D). Interestingly, only Mmp7 and Mmp13 displayed a significantly higher level of activation in colon adenomas compared with both the adjacent tissue (13.8- and 13.4-fold increase, respectively) and colon tissue with acute colitis (186.4- and 19.6-fold increase, respectively). Adam8 and Timp1 mentioned above were also up-regulated in adenomas by 8.3 and 1.7 times compared with the adjacent tissue; however, the maximum of their expression was revealed in the acute colitis samples (86.6- and 110.6-fold increase compared with the healthy group, respectively) (Figure 3D).
Thus, the performed qRT-PCR analysis successfully confirmed the expression of the acute colitis- and CAC-related hub genes identified by the in silico analysis in corresponding murine tissues and clearly demonstrated that colitis-driven colonic adenomatous transformation is accompanied by significant changes in the expression profiles of matrix metalloproteinases, which can be used as a novel prognostic signature for colorectal neoplasia in IBD.
## 3. Discussion
Despite the large collection of transcriptomics data from IBD and CAC studies, molecular regulators of the transition of colonic inflammatory lesions to cancer have not yet been clearly defined. The current study aimed to reveal core genes involved in the regulation of acute colitis and CAC development in mice and to explore how far their expression profiles changed during the chronification of colon inflammation.
Performed bioinformatics analysis of multiple cDNA microarray datasets of acute colitis and CAC identified a range of core genes associated with the explored pathologies, further functional annotation of which clearly confirmed the reliability of the obtained data. Indeed, high enrichment of acute colitis-related functional terms with pro-inflammatory cytokines and IGF1-Akt signaling pathway (Figure 1C, upper network) agrees well with the proven regulatory role of the latter in colon inflammation and inflammation-induced mucosal injury [29,30]. Along with this, CAC-related core genes were associated with the processes which markedly changed during colitis-driven tumorigenesis (Figure 1C, lower network): it is known that dysplastic and malignant lesions of colon tissue markedly dysregulate sodium transport [31], bile acid secretion [32], and metabolic [33] and oxidative [34] homeostasis.
Interestingly, the analysis of core genes common for both acute colitis and CAC (Figure 1E) also demonstrated the credibility of the performed in silico study. According to the published reports, the acute-phase genes Hp, Lcn2, Lrg1, and Serpina3n included in this list are not only activated in response to inflammatory stimuli, but their aberrant expression is also strongly implicated in tumorigenesis: high levels of Hp and Lcn2 resulted in glucose metabolic dysfunction, angiogenesis, and metastasis in different tumor types [35,36], and Lrg1 and Serpina3n were associated with epithelial–mesenchymal transition in colorectal cancer [37,38]. In addition, the interferon-responsive gene Ifitm3 is critical to early colon cancer development [12,39], along with S100a9 and Slpi, which, when highly expressed in inflamed colon tissues in mice and patients with colitis and IBD, respectively, can be considered as potent amplifiers of tumor invasion [40,41].
Analysis of gene association networks with subsequent processing of obtained results using the text mining approach revealed a range of core genes occupied hub positions in the acute colitis- and CAC-associated regulomes, which had not yet been extensively studied in relation to the explored diseases (acute colitis: C3, Tyrobp, Mmp3; CAC: Adam8, Mmp13) (Figure 2). Further qRT-PCR analysis clearly confirmed the overexpression of the mentioned hub genes in the colon tissue of mice with acute colitis and CAC (Figure 3D) that indicated the expediency of further exploration of these genes as promising novel biomarkers of colon inflammation and colon tumorigenesis.
To independently examine how tightly revealed hub genes were associated with inflammation and colorectal cancer, their sub-networks with first gene neighbors from rodent inflammatome [42] and the gene network related to malignant tumors of the colon (DisGeNET ID: C00071202) were reconstructed and analyzed. As depicted in Figure 4, all explored hub genes, except for Adam8, indeed form tight modules with gene partners within the evaluated regulomes, and are related to diverse processes and signaling pathways important for the pathogenesis of colitis and CAC. For instance, the detection of the functional group “Interleukin-4 and 13 signaling” is in accordance with [43]: a marked IL-13 response from CD4+ natural killer T cells was previously detected in mice with oxazolone-induced colitis and its blockage was found to ameliorate intestinal inflammation and injury. The members of the integrin family (Figure 4A, Timp1-, C3- and Mmp3-centered sub-networks) play a crucial role in the intestinal homing of immune cells and in supporting the inflammatory mechanisms in the gut [44]. uPA-mediated signaling (Figure 4, Timp1-, Mmp3-, Mmp9-centered sub-networks) controls macrophage phagocytosis in intestinal inflammation, and uPA receptor deficiency leads to marked aggravation of experimental colitis in mice [45]. Moreover, uPA-/- mice demonstrated more severe colorectal neoplasia compared with their wild-type littermates [46]. In addition, remodeling of the extracellular matrix is a hallmark of both colitis/IBD [47] and CAC [48], and prostaglandin signaling is involved in the malignant transformation of inflamed intestinal tissue [49].
The detailed comparison of obtained results revealed a group of MMPs as key participants of acute colon inflammation and its transition to malignancy: functional term “Matrix Metalloproteinases” was identified as statistically significant in both acute colitis- and CAC-associated functional annotation maps (Figure 1C), the highly interconnected cluster of MMPs related to different phases of colitis was revealed in the gene network retrieved from computed core genes (Figure 1F), and MMPs occupied hub positions in all analyzed regulomes related to both acute colitis (Figure 2A: Mmp3, Mmp9) and CAC (Figure 2B: Mmp7, Mmp13). Interestingly, the tissue inhibitor of matrix metalloproteinase-1 (Timp1) was also detected as a hub gene specific to both acute colitis and CAC (Figure 1E and Figure 2A,B) and tightly interconnected with MMPs module (Figure 1F), which clearly indicated the importance of Timp1/MMPs balance in colitis-induced tumorigenesis. Indeed, Timp1 is a known regulator of colitis, knockout of which markedly attenuated fibrosis in DSS-inflamed colon tissue [50], and, according to the recent report of Niu et al. [ 51], a hub gene in colorectal cancer regulome. High expression of MMP3 and MMP9 in mucosa-resident macrophages/neutrophils and IgG plasma cells was detected in patients with IBD [52,53]. According to Pedersen et al. [ 54], MMP3 and MMP9 are two key enzymes involved in the degradation of intestinal tissue during IBD. Interestingly, the silencing of Mmp3 by siRNA markedly ameliorated DSS-induced colitis in mice [55], whereas knockout of Mmp9 or its pharmacological inhibition surprisingly had no obvious effect on the progression of DSS- and TNBS-stimulated colitis in the murine model [56]. Thus, the master regulatory functions of MMPs in colitis pathogenesis require further clarification: in some cases, their overexpression can be considered as a consequence rather than a cause of intestinal inflammation [56]. In the case of CAC-associated MMPs (Mmp7, Mmp13) revealed in this study, focal high expression of Mmp7 was previously observed in CAC-related dysplastic lesions [48] and its overexpression was associated with tumor growth, metastasis, and worse overall survival in patients with colon cancer [57]. According to Wernicke et al. [ 58], the up-regulation of MMP-13 was considered as an early predictive cancer biomarker in patients with colon adenoma, which agrees well with the results of our qRT-PCR analysis (Figure 3D). Despite the extensive studies of MMPs as candidate marker genes of colitis and CAC, to the best of our knowledge, the complex evaluation of the expression of Mmp3, Mmp7, Mmp9, and Mmp13 in acutely inflamed, adenomatous, and adjacent colon tissues has not yet been reported. Revealed marked changes in their expression profiles during chronification of colitis (Figure 3D) can be considered as a novel gene signature for predicting CAC.
Besides MMPs, another ECM remodeling player, Adam8, a member of a disintegrin and metalloproteinase family (ADAMs), was identified as a core gene associated with CAC development (Figure 1F, Figure 2D and Figure 3D). Surprisingly, high expression of Adam8 was detected not only in CAC but also in DSS-inflamed colon tissue (Figure 3D). Along with the reorganization of ECM, ADAMs are engaged in the processing of various substrates, including cytokines, growth factors, cell adhesion molecules, and receptors, that determines their important role in a range of pathological processes [59]. The most studied ADAMs in IBD was Adam17, associated with EGFR and STAT3 signaling pathways crucial for the pathogenesis of colitis [60], high epithelial expression of which positively correlated with cell proliferation and goblet cell number in UC patients [61]. To the best of our knowledge, the involvement of Adam8 in the regulation of acute colitis and colitis-induced adenomatous transformation of colon tissue had not yet been reported. Only Christophi et al. and Guo et al. have discussed the overexpression of Adam8 in IBD patients [62] and AOM/DSS-induced colitis in mice [63]. Given the recently demonstrated ability of Adam8 to control neutrophil transmigration [64] and NLRP3 inflammasome activation [65], the processes tightly associated with colon inflammation [66,67], Adam8 can be considered as a novel promising master regulator of colitis and CAC; this requires further clarification. Interestingly, despite the revealed low interconnection of Adam8 with the colon cancer-associated gene network retrieved from DisGeNET (Figure 4), this gene seems to play an important role in the pathogenesis of CAC: Adam8 is involved in the activation of integrin, FAK, ERK$\frac{1}{2}$, and Akt/PKB signaling pathways related to cancer progression [68], its overexpression was identified in colorectal cancer compared with adjacent normal tissues [69], and the suppression of the expression of Adam8 by knockout or siRNA approaches resulted in reduced proliferation and invasiveness of colon cancer cells [69,70].
Finally, C3 and Tyrobp were also revealed as colitis-specific hub genes (Figure 2A and Figure 3D), which is in line with published reports. Previously, a high level of C3 in the serum and jejunal secretion of IBD patients was identified [71,72]. Moreover, C3 was found to be up-regulated in intestinal epithelial cells in the DSS-induced colitis model [73], and its ablation promoted inflammatory responses in the mid colon [74] and significantly reinforced DSS-induced colitis in C3 knockout mice compared with wild-type littermates [72]. Tyrobp is a known regulator of the production of pro-inflammatory mediators in macrophages and neutrophils [75], and, thus, is implicated in pathogenesis of various inflammation-associated diseases [75,76,77]. According to recent studies, Tyrobp was identified as a probable upstream regulator of UC [78], and its knockout robustly attenuated the severity of DSS-induced colitis in mice, whereas its overexpression resulted in a striking exacerbation of colon damage caused by DSS [79].
The published works discussed above demonstrated the involvement of the revealed core genes in the regulation of inflammation and malignant lesion of the colon, not only in murine models but also in patients. To independently confirm the translational bridge between our findings and the pathogenesis of colitis/CAC in humans, expression of core genes (acute colitis: C3, Tyrobp, Mmp3, Mmp9, Timp1; CAC: Timp1, Mmp7, Mmp13, Adam8) was further evaluated in the transcriptomics profiles of colon tissue from patients with UC and CD collected from GEO (Figure 5A) and colorectal cancer retrieved from The Cancer Genome Atlas (TCGA) (Figure 5B). As depicted in Figure 5A, the majority of the explored key genes were overexpressed in IBD and demonstrated more pronounced susceptibility to the induction of UC compared with CD, except for TYROBP, expression of which was more up-regulated in CD patients. Interestingly, despite the proven association with CAC (Figure 2B,D), TIMP1, MMP7, and ADAM8 were activated in IBD-affected colon tissues (Figure 5A), which is fully in line with our data: the high expression of these genes was demonstrated in DSS-inflamed and adjacent to adenomas colon tissues in mice (Figure 3D). In addition, similar to our results (Figure 3D), CAC-specific MMP13 was found to be slightly associated with IBD: its low activation in two of the four analyzed UC transcriptomics datasets and unchanged levels in CD samples were observed (Figure 5A). Presumably, Mmp13 plays a minor role in ECM remodeling in colitis, whereas CAC was associated with significant up-regulation of its expression, which makes Mmp13 a promising gene candidate for the predicting of colitis-associated tumorigenesis; this requires further detailed study. TCGA analysis of the identified CAC-related core genes revealed a significant association between high expression of TIMP1 and ADAM8 with low overall survival of patients with both colon (COAD) and rectal (READ) adenocarcinomas (Figure 5B). Despite the finding that Timp1 and Adam8 can play important regulatory functions in CAC, this supposition requires further detailed confirmation, since TCGA analysis was performed without consideration of the ratio of UC- and CD-associated CAC patients in COAD and READ cohorts. In addition, given recently reported sex disparities in the association of Timp1 expression with cancer progression [80], further exploration of its regulatory role in CAC in mice of both sexes is needed.
The obtained results were finally summarized in the scheme depicted in Figure 5C. According to our findings, (a) revealed core genes not only occupy hub positions within explored acute colitis- and CAC-specific regulomes, but also are interconnected with each other, (b) Timp1 is identified as a hub node in gene association networks retrieved for both acute colitis and CAC, which can indicate its crucial role in colitis-associated tumorigenesis, (c) chronification of colonic inflammation is accompanied by a switch in MMPs profile (acute colitis: Mmp3, Mmp9; CAC: Mmp7, Mmp13), which can serve as a gene signature panel for prognosis of malignant transformation of inflamed colon tissue; and (d) identified core genes are overexpressed in the colon tissue of patients with IBD (all explored genes) and highly aggressive colorectal cancer (TIMP1, ADAM8), confirming the interest in studying these genes within the framework of intestinal pathologies in humans (Figure 5C).
## Limitations of the Study
The limitations of the study are as follows: First, given the relatively low number of mice used for experimental validation of the obtained data ($$n = 6$$), and their belonging to only one sex (female) and one strain (C57Bl6), further study is required to validate the results using a larger sample size obtained from mice of both sexes and different strains. Second, considering that our findings are predominantly animal-based, to more clearly elucidate how closely (if at all) the identified core genes are involved in the regulation of intestinal pathologies in humans, revealed translational bridge needs further large-scale verification study, using clinical samples of patients with UC, CD, and UC/CD-associated colorectal cancer. Third, despite the identification of high degree centrality scores of the explored key genes and their tight association with crucial colitis/CAC-related signaling pathways, the master regulatory functions of these genes in colitis and CAC should be further verified experimentally (for instance, using knockout models).
## 4.1. Microarray Data Collection and Differential Expression Analysis
*The* gene expression profiles associated with murine acute colitis and CAC, as well as ulcerative colitis and Crohn’s disease, in patients were acquired from the Gene Expression Omnibus database [81] (Table 1). The fold changes between the mean expression values of the genes in the experimental (pathology) versus control groups were computed using the GEO2R tool [82]. The Benjamini–Hochberg false discovery rate method was selected for adjusting p-values. *The* genes with a p-value < 0.05 and |fold change| > 1.5 were identified as differentially expressed genes (DEGs) and were collected for further analysis. Overlapping of the DEGs from different datasets was performed using the InteractiVenn tool [83]. Hierarchical clustering of DEGs according to their expression profiles was carried out using the Euclidean distance metric, using the Morpheus tool (https://software.broadinstitute.org/morpheus, accessed on 12 December 2022).
## 4.2. Functional Analysis of DEGs
Functional annotation of acute colitis- and CAC-associated DEGs was performed using the ClueGO 2.5.7 plugin in Cytoscape 3.7.2, using the latest updates of Gene Ontology (Biological Processes), Kyoto Encyclopedia of Genes and Genomes (KEGG), WikiPathways, and REACTOME databases. The GO Tree interval was ranged from 3 to 8 and the minimum number of genes per cluster was set to 3. Enrichment of functional terms was tested using the two-sided hypergeometric test corrected using the Bonferroni method, followed by selecting significantly enriched terms with a p-value < 0.05. To cluster similar functional groups retrieved from different databases in the common pathway-specific modules, the GO Term Fusion was used. Functional grouping of finally selected functional terms was performed using kappa statistics (kappa score ≥ 0.4). Functional annotation of gene modules, consisting of core genes and their first gene partners extracted from murine inflammatome and colon cancer-related regulome, was performed using the ToppFun tool (databases: KEGG, REACTOME, MSigDB C2 BIOCARTA, BioSystems: Pathway Interaction Database, Pathway Ontology; Bonferroni adjustment) [84].
## 4.3. Reconstruction of Gene Association Networks
Gene association networks were reconstructed from the genes of interest using the Search Tool for the Retrieval of Interaction Genes (STRING) database, using the stringApp 1.5.1 tool [85], and were visualized using Cytoscape 3.7.2. The cutoff criterion of the confidence score was set as >0.7 to eliminate inconsistent “gene–gene” pairs from the dataset. The number of neighbors of a gene of interest within reconstructed networks was calculated using the NetworkAnalyzer plugin [86] and visualized using the Morpheus platform [87].
## 4.4. Data Mining Analysis
The search for the co-occurrence of the names of core genes with various colitis- and CAC-related terms in the same sentences in abstracts of published reports deposited in the MEDLINE database was performed using the GenCLiP3 tool [88], with the following settings: impact factor of 0–50 and year of publication of 1992–2022. The results were visualized using Circos [89].
## 4.5. Murine Models of Acute Colitis and Colitis-Associated Cancer (CAC)
Eight-week-old female C57Bl6 mice with an average weight of 22–24 g were obtained from the Vivarium of the Institute of Chemical Biology and Fundamental Medicine SB RAS (Novosibirsk, Russia). Mice were housed in plastic cages (7 animals per cage) under normal daylight conditions. Water and food were provided ad libitum. Experiments were carried out in accordance with the European Communities Council Directive $\frac{86}{609}$/CEE. The experimental protocols were approved by the Committee on the Ethics of Animal Experiments at the Institute of Cytology and Genetics SB RAS (Novosibirsk, Russia) (protocol No. 56 from 10 August 2019).
Acute colitis was induced in mice ($$n = 10$$) by administration of $2.5\%$ DSS solution in drinking water for 7 days, followed by 3 days of recovery. Mice were sacrificed on day 10 after colitis initiation. CAC was induced in mice ($$n = 10$$) by a single intraperitoneal (i.p.) injection of carcinogen AOM (10 mg/kg) 1 week before DSS administration, as described in [90]. Furthermore, mice were exposed to 3 consecutive cycles of $1.5\%$ DSS instillations with drinking water for 7 days, followed by 2 weeks of recovery. The mice were sacrificed 10 weeks after the start of the experiment. At the end of the study, the colons were separated from the proximal rectum, mechanically cleaned with saline buffer, and were then collected. Only 8 of 10 samples had well-formed adenomas in the colon, which were selected for the subsequent gross examination, histological analysis, and qRT–PCR.
## 4.6. Histology
For the histological study, colon specimens were fixed in $10\%$ neutral-buffered formalin (BioVitrum, Moscow, Russia), dehydrated in ascending ethanol and xylols, and embedded in HISTOMIX paraffin (BioVitrum, Moscow, Russia). The paraffin sections (5 μm) were sliced on a Microm HM 355S microtome (Thermo Fisher Scientific, Waltham, MA, USA) and stained with haematoxylin and eosin. The images were examined and scanned using an Axiostar Plus microscope equipped with an Axiocam MRc5 digital camera (Zeiss, Oberkochen, Germany) at magnifications of ×100.
## 4.7. Quantitative Real-Time PCR (qRT-PCR)
Total RNA was isolated from the colons of experimental animals using TRIzol reagent (Ambion, Austin, TX, USA) according to the manufacturer’s instructions. Briefly, colon tissue was collected in 1.5 mL capped tubes, filled with 1 g of lysing matrix D (MP Biomedicals, Irvine, CA, USA) and 1 mL of TRIzol reagent, then homogenized using a FastPrep-24 TM 5G homogenizer (MP Biomedicals, Irvine, CA, USA) with QuickPrep 24 adapter. The homogenization was performed at 6.0 m/s for 40 s. After homogenization, the content of the tubes was transferred to the new 1.5 mL tubes without lysing matrix. Total RNA extraction was performed according to the TRIzol reagent protocol.
Due to the known ability of DSS to linger in the RNA extracted from the colon tissue, and, thus, interfere with both reverse transcription and PCR reactions, the extracted total RNA was diluted to a volume of 250 μL and purified using Microcon Centrifugal Filter Devices (MilliPore, Burlington, MA, USA) by centrifuging for 1 h at 14,000× g.
The first strand of cDNA was synthesized from total RNA ($$n = 6$$ per group, the samples with the highest RNA purity and integrity) in 100 μL of reaction mixture containing 2.5 μg of total RNA, 20 μL of 5× RT buffer (Biolabmix, Novosibirsk, Russia), 250 U of M-MuLV-RH revertase (Biolabmix, Novosibirsk, Russia), and 100 μM of dT[15] diluted to a volume of 100 μL. Reverse transcription was performed at 25 °C for 10 min followed by the incubation at 42 °C for 60 min with subsequent termination at 70 °C for 10 min.
Amplification of cDNA was performed in a 25 μL PCR reaction mixture containing 5 μL of cDNA, 12.5 μL of HS-qPCR (2×) master mix (Biolabmix, Novosibirsk, Russia), 0.25 μM each of the forward and reverse primers to Hprt and Hprt specific ROX-labeled probe, 0.25 μM each of the forward and reverse gene-specific primers, and FAM-labeled probe (Table 2). Amplification was performed as follows: [1] 94 °C, 2 min; [2] 94 °C, 10 s; [3] 60 °C, 30 s (steps 2–3: 50 cycles). The relative level of gene expression was normalized to the level of Hprt expression according to the ΔΔCt method.
Amplification was performed using a C1000 Touch with CFX96 module Real-Time system (BioRad, Hercules, CA, USA), and the relative level of gene expression was calculated using BioRad CFX manager software (BioRad, Hercules, CA, USA). Three to five samples from each experimental group were analyzed in triplicate. The sequences of the primers used in the study are listed in Table 2.
## 4.8. The Association of DEGs Expression with Survival Rates of Patients with Colorectal Cancer
To explore the association of revealed core genes with the progression of colon (COAD) and rectal (READ) adenocarcinomas, analysis of the survival rates and their correlation with the expression of studied genes was performed using The Cancer Genome Atlas (TCGA) clinical data for patients with COAD and READ. Kaplan–Meier survival curves for COAD and READ patients depending on the mRNA expression level of core genes were constructed using the OncoLnc tool [91].
## 4.9. Statistical Analysis
The statistical analysis was performed using Benjamini–Hochberg false discovery rate method (identification of DEGs; GEO2R tool), two-sided hypergeometric test with Bonferroni correction (functional analysis of DEGs; ClueGO plugin and ToppFunn tool), and two-tailed unpaired Student’s t-test (qRT-PCR analysis; Microsoft Excel). p-values of less than 0.05 were considered statistically significant.
## 5. Conclusions
In summary, this animal-based research revealed a range of core genes associated with acute colitis (C3, Tyrobp, Mmp3, Mmp9, Timp1) and CAC (Timp1, Mmp7, Mmp13) in mice. The observed high rate of interconnection of these genes with gene networks retrieved for intestinal inflammation and malignancy, their significant association with key colitis/CAC-related signaling pathways, and probable involvement in the pathogenesis of IBD and colorectal cancer in patients demonstrated the expediency of further detailed studies of identified core genes as novel master regulators and promising therapeutic targets for colitis and CAC.
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---
title: 'Social Support: The Effect on Nocturnal Blood Pressure Dipping'
authors:
- Wendy C. Birmingham
- Anna Jorgensen
- Sinclaire Hancock
- Lori L. Wadsworth
- Man Hung
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001803
doi: 10.3390/ijerph20054579
license: CC BY 4.0
---
# Social Support: The Effect on Nocturnal Blood Pressure Dipping
## Abstract
Social support has long been associated with cardiovascular disease risk assessed with blood pressure (BP). BP exhibits a circadian rhythm in which BP should dip between 10 and $15\%$ overnight. Blunted nocturnal dipping (non-dipping) is a predictor of cardiovascular morbidity and mortality independent of clinical BP and is a better predictor of cardiovascular disease risk than either daytime or nighttime BP. However, it is often examined in hypertensive individuals and less often in normotensive individuals. Those under age 50 are at increased risk for having lower social support. This study examined social support and nocturnal dipping in normotensive individuals under age 50 using ambulatory blood pressure monitoring (ABP). ABP was collected in 179 participants throughout a 24-h period. Participants completed the Interpersonal Support Evaluation List, which assesses perceived levels of social support in one’s network. Participants with low levels of social support demonstrated blunted dipping. This effect was moderated by sex, with women showing greater benefit from their social support. These findings demonstrate the impact social support can have on cardiovascular health, exhibited through blunted dipping, and are particularly important as the study was conducted in normotensive individuals who are less likely to have high levels of social support.
## 1. Introduction
Social support can be defined as the perceived availability of resources [1] (functional support). Research has shown a strong association between social support and mortality and morbidity [2,3,4,5], including cardiometabolic diseases such as cardiovascular disease (CVD), type 1 and type 2 diabetes, and chronic obstructive disease, which are the global leading causes of death [6,7,8,9,10]. Lower levels of social support have been associated with higher incidence and progression of colorectal cancer in men, higher recurrence of breast cancer in women, worse outcomes in older adults with lung cancer, worse outcomes for single individuals with gastric cancer [11,12,13,14], and worse diabetes outcomes [15]. Social support has also been linked to psychological factors such as depression, distress, and satisfaction with life, which have influences on cardiovascular health [16,17]. A review by Holt-Lunstad, Smith & Layton [18] found that the link between supportive relationships and health was as predictive of disease as known risk factors such as smoking and lack of physical exercise. Additionally, individuals with poor social connectedness are $29\%$ more likely to develop CVD and $32\%$ more at risk for stroke [19].
Hypertension is the most common disease in industrialized nations [20] and is the predominant risk factor for CVD [21]. In 2020, more than 670,000 deaths in the U.S. had hypertension as a primary or contributing cause [20], and nearly half of adults in the U.S. ($47\%$) have hypertension, as defined by the American Heart Association [22]. In adults who have not been diagnosed with CVD, there is a strong association of slightly elevated levels of both systolic blood pressure (SBP) and diastolic blood pressure (DBP) with increased risk for developing hypertension in a relatively short time [23] and is associated with early target organ damage [24,25,26]. This is important, as blood pressure (BP) shows an increased trajectory over time and is associated with poor cardiovascular outcomes even 25 years later [27]. Thus, several meta-analyses have shown the effectiveness of lowering BP to reduce CVD risk [28,29].
BP shows a circadian rhythm, such that a healthy cardiovascular profile includes a decrease of 10–$20\%$ from day to night (i.e., nocturnal dipping) [30]. Blunted nocturnal dipping (non-dipping) is defined as blood pressure which does not dip at least $10\%$. It is associated with increased risk for cardiovascular events in both normotensive and hypertensive adults, higher risk of cardiovascular morbidity and mortality [31,32,33], composite kidney endpoint, and increased risk of all-cause mortality [34]. Recent research has shown blunted nocturnal dipping to be a better predictor of cardiovascular disease and mortality than 24-h averages alone [35,36]. Indeed, abnormalities of the circadian dipping patterns are associated with both total and cardiovascular mortality [36].
Ambulatory blood pressure (ABP) has been demonstrated to be a better predictor of mortality than blood pressure taken in an office setting, typically a physician’s office (clinical BP) [37,38,39]. ABP allows for a more accurate assessment of BP, as it takes multiple readings spread throughout intervals across the day and night and can thus provide a more accurate evaluation of BP fluctuations, rather than a single reading in a physician’s office, which may be influenced by the white-coat effect [38]. However, while hypertension is the primary predictor of CVD and has been consistently linked to social support which has been assessed with ABP [40,41,42,43], a recent meta-analysis by Uchino and colleagues [44] found no association between daytime ABP and social support. This is an interesting finding given the amount of literature detailing the association between the two. For nocturnal dipping research, these are important findings to take into consideration, as nocturnal dipping is calculated using daytime and nighttime ABP. It would be worth considering that social support may differentially impact ABP when using the ratio of daytime and nighttime dipping ABP rather than just daytime ABP. Uchino and colleagues suggested that an examination of other indices of ABP such as nocturnal dipping would be beneficial, as such studies were not included in the meta-analysis. This could be informative given that nocturnal dipping as assessed with ABP has been linked to social support, such that those with lower levels of social support/social integration showed blunted dipping [45,46,47].
However, much of the research on nocturnal dipping has focused more heavily on hypertensive individuals and older adults, or it has not differentiated between younger and older adults. Yet adults under the age of 50 tend to report a lack of social connections or more loneliness than those over the age of 50 [48,49]. In fact, younger generations (Gen Z and Millennials) report a lack of social support and fewer social interactions than baby boomers [49]. Further, the literature on dipping has varied, with some studies showing social support impacting dipping on both SBP and DBP, while other studies show dipping on only one or the other (either SBP or DBP), or with no significant effects on either. A 2013 meta-analysis by Fortmann and Gallo [50] showed that of the studies that used the Interpersonal Support Evaluation List (ISEL) measure to assess social support, only one study showed social support associated with both SBP and DBP dipping [51], one study associated social support with only SBP dipping [52], one study found no results for either SBP or DBP dipping [53], and one study [54] found a marginally significant association between social support and SBP dipping. Thus, one aim of our study was to examine the discrepancies between findings.
Social support can be broadly assessed by the ISEL, measuring perceptions of functional support including tangible, emotional, and informational support, and feelings of belonging. However, none of the studies noted above from Fortmann and Gallo’s meta-analysis examined the specific domains of the ISEL. Because different domains of social support can be more beneficial, depending on one’s needs, these specific types should be examined individually. Additionally, a significant portion of the literature has focused on hypertensive individuals, yet research shows that an increased blood pressure trajectory over time is associated with poor health outcomes even 25 years later [27]. Thus, it would be beneficial to understand the impact of social support on nocturnal dipping in a normotensive sample under 50 years of age, a point in life where individuals could make social changes that could decrease the risk of developing hypertension later in life.
In an effort to better understand this impact, we collected ABP on 179 normotensive individuals under 50 years of age over a 24-h period, and data on their social support. Because social support has been associated with stress, and stress can influence BP, we also looked at the association of stress on dipping. Additionally, based on recent work showing the prognostic value of nocturnal dipping at predicting cardiovascular disease over the prognostic value of 24-h blood pressure readings [35,36], and on the recent work on the association of social support on daytime BP [44], we looked at the impact of social support on daytime, nighttime, and 24-h ABP. Finally, we examined the effect of social support on nocturnal dipping using the full ISEL measure. Because social support can be seen in different facets as measured in the ISEL, we also examined the specific dimensions of social support. Additionally, we expected that this association would be moderated by sex based on the literature that has identified sex as an independent predictor of daytime and nocturnal BP and nocturnal BP dipping [55].
## 2.1. Criteria
Participants having the following conditions were excluded: medical conditions/medications with a cardiovascular component (e.g., hypertension or psychological problems for which they were being medically treated; see Cacioppo, Malarkey [56]) and a self-reported body mass index (BMI) no higher than 29.9, as 30 or higher is classified as obese, and hypertension and obesity are highly correlated. Participants were required to have a smartphone in order to complete a diary reading (see Measures below), at each BP reading. Each participant was given a personalized access code to the diary website.
## 2.2. Participants
In total, 179 participants (male, $$n = 91$$, $55\%$; female, $$n = 88$$, $45\%$) were recruited through a university, social media, and the community. All participants were over 21 and under 50 years of age, married, and currently living with their spouse. The mean age of participants was 24.85 years (SD = 4.10, range 21–46), and average length of their marriage was 2.99 years (SD = 2.04; range 1–18). Most were White ($91.53\%$) and college educated ($46.89\%$ with college degree or higher; $51.98\%$ currently pursuing a college degree), with $46.88\%$ reporting an income over USD 30,000 (See Table 1).
## 2.3. Procedure
Following informed consent, eligible participants completed questionnaires related to perceptions of social support. Participants were then fitted with an ABP monitor and given detailed instructions on its use, shown how to stop a reading if needed (e.g., while driving, in a work meeting, etc.) and how to stop all readings if they chose to end the study early. Monitors were set to take a reading randomly twice an hour throughout the day and once per hour overnight. Participants were also given instructions on completing the diary entry (see Measures section below) and instructed to complete the entry within three to five minutes after the ABP monitor took a reading; diary entries were not required overnight. Participants returned the equipment the following day and received compensation. Participants were paid USD 75 each in cash.
## 2.4.1. Physiological Measures
Ambulatory blood pressure was obtained using the Oscar 2 (Suntech Medical Instruments, Raleigh, NC, USA). The Oscar 2 was designed specifically for ABP assessment and has been validated for both SBP and DBP by international guidelines [57]. It utilizes codes that may signify problems with the estimation of ABP readings. Based on prior research [58], readings associated with weak Korotkoff sounds, measurement timeout, and air leaks were deleted. Outliers associated with artifactual readings identified using criteria by Marler, Jacob [59] were also discarded; these included: (a) SBP <70 mmHg or >250 mmHg, (b) DBP <45 mmHg or >150 mmHg, and (c) SBP/DBP < [1.065+ (0.00125 × DBP)] or >3.0.
## 2.4.2. Psychological and Relationship Measures
The Perceived Stress Scale (PSS). The PSS is a ten-item assessment to measure stress perceptions and predict health-related outcomes associated with stress appraisal. This widely used assessment has been shown to have adequate psychometric properties and is related to other stress, health, and satisfaction measures [60]. Good reliability was demonstrated for the current study at 0.86.
Diary. Each participant completed a diary entry on their smartphone for each BP reading during the day. Piloting showed an entry took less than 2 min to complete. The diary collected information on standard control BP variables, and participants were instructed to complete the diary within 5 min following the BP reading. A time/day stamp allowed us to verify the diary entry was completed on time. Readings which were not completed within the 5 min window were discarded.
Sleep Quality. Sleep was assessed using a single item measure the following morning in which participants rated their sleep the previous night compared to an ordinary night on a 1–7 scale (1 = extremely bad; 7 = extremely good).
Interpersonal Support Evaluation List (ISEL). The ISEL [61] assesses network-level functional social support, measuring specific domains of appraisal, self-esteem, belonging, and tangible support. The ISEL has shown an overall internal consistency of 0.83. Our study demonstrated good reliability at 0.78.
## 2.5. Statistical Methodology
Data were analyzed using SAS version 9.4. Descriptive statistics were computed to examine demographics and baseline SBP and DBP in addition to average daily SBP and DBP, sleeping SBP, ISEL, and PSS averages. Mixed model (MIXED PROC) regressions were used to analyze associations between social support and nocturnal blood pressure dipping. Three steps were followed in running the regression models. The first step was to determine which covariates were significant predictors of the dependent variable (nocturnal dipping) by using forward selection methods. The second step was to run regression models. One regression model had PSS as the dependent variable, controlling for significant covariates from step one (age, BMI, posture, consumption of foods or drinks, and activity since the prior reading). The second regression model used ISEL as the dependent variable controlling for the same significant covariates. The last step was to conduct multiple group analysis to investigate gender differences in the models. Statistical significance was set at $p \leq 0.05.$
Nocturnal dipping was calculated as the change from daytime to nighttime BP. It has been measured by some researchers using the night–day ratio, with dipping (>0.8 and <0.9), extreme dipping (≤0.8), non-dipping (>0.9 and ≤1.0), and reverse dipping (>1.0). Using these criteria, among our sample, $16.77\%$ would be classified as extreme dippers, $48.04\%$ as dippers, $24.58\%$ as non-dippers, and $10.61\%$ as inverted dippers. Thus, it was more heavily distributed among dippers and non-dippers, and extremes in either direction were reasonably equivalent. We therefore treated nocturnal dipping dichotomously (dippers classified according to a dipping ratio of BP night/day; dippers were ≤0.90 and non-dippers were >0.90) taking the average of the daytime BP and the average of the night-time BP readings (time from self-reported bedtime to self-reported rising).
## 3.1. Preliminary Analysis
The mean number of readings per participant was 36.98 (range 22–46) for the 24 h period. All outliers due to artifactual readings were discarded as noted above. Percentage of discarded readings per participant was $1.48\%$ ($M = 0.55$, range 1–6). The SBP baseline average was 122 (SD = 12.19), and DBP baseline was 71.9 (SD = 7.84). Daily SBP average was 135.4 (SD = 18.83), and daily DBP average was 77 (SD = 9.92). Sleeping SBP average was 119.67 (SD = 20.82), and sleeping DBP average was 60.47 (SD = 10.26). ISEL scores ranged from 15–45, with a mean score of 36.39 (SD = 5.59). The PSS average was 16.5 (range 1–33; SD = 6.74), and sleep quality average was 4.06 (range 1–7; SD = 1.29) (Table 2).
We found social support associated with stress, such that those with greater perceived social support demonstrated less stress (B = −0.69, SE = 0.01, t[8141] = −62.55, $p \leq 0.001$). We next examined whether stress was associated with SBP or DBP dipping. Stress was associated with both SBP and DBP dipping and was thus included in the model.
We then examined daytime blood pressure readings and social support. Consistent with the Uchino findings, neither daytime SBP (B = −0.08, SE = 0.08, t[651] = −0.89, $$p \leq 0.37$$) nor DBP ($B = 0.01$, SE = 0.05, t[849] = 0.14, $$p \leq 0.37$$) was associated with social support. We then looked at nighttime readings and social support. SBP ($B = 0.36$, SE = 0.18, t[183] = 1.98, $$p \leq 0.04$$) was associated with social support, but DBP was not ($B = 0.13$, SE = 0.10, t[223] = 1.34, $$p \leq 0.18$$). Neither 24 h SBP (B = −0.07, SE = 0.08, t[677] = −0.89, $$p \leq 0.37$$) nor 24 h DBP ($B = 0.00$, SE = 0.04, [865] = 0.09, $$p \leq 0.92$$) was associated with social support.
## 3.2. Primary Analysis
Age, BMI, sex, position at time of reading, caffeine consumption, activity level, and sleep quality were significant predictors and were added to the model. We ran our first analysis on the full ISEL measure, capturing all domains in one score. As expected, nocturnal dipping was associated with perceptions of social support, such that those reporting low levels of social support showed blunted DBP dipping (B = −0.41, SD = 0.06, t[3801]= −7.24, $p \leq 0.001$). SBP dipping was not associated with social support ($$p \leq 0.118$$). We then looked at each domain separately to parse out the effect. Self-esteem was associated with both SBP dipping and DBP dipping (B = −0.64, SE = 0.09, t[3824] = −6.59, $p \leq 0.001$; B = −0.79, SE = 0.14, t[3801] = −5.74, $p \leq 0.001$), such that lower self-esteem support was associated with blunted dipping. Tangible support was associated with both SBP (B = −0.39, SE = 0.06, t[3824] = −6.20, $p \leq 0.001$) and DBP dipping (B = −1.75, SE = 0.19, t[3801] = −9.31, $p \leq 001$), such that lower tangible support was associated with blunted dipping. Belonging was associated with SBP ($B = 0.36$, SE = 0.05, t[3824] = 6.68, $p \leq 0.001$) and DBP ($B = 0.38$, SE = 0.11, t[3809] = 3.27, $$p \leq 0.001$$), such that less belonging support was associated with blunted dipping. Appraisal support was associated with blunted DBP (B = −1.68, SE = 0.19, t[3809] = −8.97, $p \leq 0.001$, but not with SBP (B = −0.07, SE = 0.05, t[3824] = −1.4, $$p \leq 0.16$$) (Table 3).
## 3.3. Effect of Gender
We examined whether the effects of social support on nocturnal dipping varied by sex. We found sex significantly interacted with dipping, such that women benefited more from total social support for SBP dipping (B = −0.298, SE = 0.034, t[3823] = −8.68, $p \leq 0.001$), but not DBP ($B = 0.004$, SE = 0.05, t[3800], $$p \leq 0.94$$). Looking at specific domains, women benefited more than men from tangible support for SBP (B = −0.55, SE = 0.097, t[3823] = −5.64, $p \leq 0.001$), but neither benefited for DBP (B−34, SE= 0.18, t[3800] = −1.86), $$p \leq 0.06$$). Women benefited more from belonging support for SBP (B = −0.64, SE = 0.12, t[3823] = −5.16, $p \leq 0.001$), but men benefited more for DBP ($B = 0.88$, SE = 0.13, t[3813] = 6.59, $p \leq 0.001$). Neither sex benefited from self-esteem support. Women benefited more than men from appraisal support for SBP (B = −1.154, SE = 0.09, t[3823] = −12.50, $p \leq 0.001$), but neither benefited for DBP (B = −0.07, SE = 0.05, t[3808] = −1.4, $$p \leq 0.161$$) (Table 4).
## 4. Discussion
Our main findings show social support associated with dipping, such that those individuals who perceive they have less social support demonstrate blunted nocturnal dipping for DBP but not for SBP. When we examined support by specific domains, we found both SBP dipping and DBP dipping associated with social support within the specific domains of tangible and self-esteem support. Further, we extended the prior literature showing the association between social support and health by examining normotensive individuals under 50 years of age. It is important to note that while overall social support was not associated with blunted SBP dipping, overall social support was associated with DBP dipping, and DBP carries its own risks separate from SBP. Whereas high SBP readings indicate an increase in the risk for heart disease such as heart attacks, heart failure, kidney disease, and overall mortality, high DBP is linked to higher risk of abdominal aortic aneurysm. The American Heart Association notes that there is an emphasis on SBP, yet research has shown that each increase of 10 mmHg in DBP is associated with a $28\%$ risk of developing an abdominal aortic aneurysm [62]. It is therefore important to take both SBP and DBP into account when assessing risk.
While we found DBP dipping to be associated with social support, we also found the same results as Uchino and colleagues on daily blood pressure and social support, such that daytime SBP and DBP were not associated with social support. We also found that nighttime DBP was not associated with social support, nor did we find an association for either 24 h SBP or DBP, although nighttime SBP was associated with social support. This is interesting, as both daytime and nighttime ABP are used to determine nocturnal dipping. This seems to suggest that it is the combination of daytime and nighttime blood pressure, specifically using the ratio of daytime and nighttime blood pressure, that is more useful as a measure of cardiovascular disease risk than using daytime or nighttime measures alone. The lack of association between 24 h SBP or DBP and social support is also indicative of the benefits of using nocturnal dipping as a health outcome, rather than BP alone, whether 24 h, daytime, or nighttime.
Social support is multidimensional and can influence health through various pathways. We gained a better picture of the contributions of social support to nocturnal dipping when we divided social support into its component parts. Tangible support predicted both SBP and DBP dipping. It can be expected that tangible support is an aspect of social support that was associated with both SBP and DBP dipping. Tangible support is one specific support that individuals find most beneficial. It can include provisions of shelter, food, or financial help, and the perception of the availability of such assistance when needed can significantly reduce stress. Financial stress can be a particular source of distress and is related to poor psychological and physiological health for those with low levels of perceived tangible support, with a six- to seven-fold increased odds ratio for poor psychological well-being and psychosomatic symptoms [63]. While there is a large body of literature on the benefits of tangible support on health outcomes, there is little addressing tangible support and nocturnal dipping. Our findings address this gap and demonstrate that in addition to contributing to other health-related outcomes, tangible support impacts nocturnal dipping.
SBP dipping and DBP dipping were also associated with self-esteem social support. Self-esteem social support assessments include items such as “Most people I know think highly of me.” Such support can be beneficial in terms of feeling valued by others. Self-esteem support can increase one’s ability to ask for help when help is needed, as it may help decrease feelings of burdensomeness. Thus, being able to ask for needed support could be manifest in healthy nocturnal dipping, as shown in this study.
These findings indicate the importance of examining social support within the differing domains, as one kind of support may be more effective at reducing stress than another. This is not to say that of benefits generally, only these two aspects of support (tangible and self-esteem) are beneficial, and the other aspects are not. Rather, our findings indicate that normotensive individuals under the age of 50 may benefit more from these specific types of support than older normotensive or hypertensive individuals. This is important to our understanding of the link between social support and health, as the benefits of social support have been shown to be more effective if they are applicable to the needs of the individual. In other words, one who needs emotional support in a time of stress will not find informational support helpful. Support is most effective if tailored to the specific needs of the individual.
It is expected that women would benefit more from social support. Traditionally, women offer more social support to others and when facing stress, tend to give and receive more social support than males. Part of this phenomenon, as noted in the literature, may be because men are more likely to react to stress with a “fight or flight” orientation, while women tend to use the “tend-and-befriend” response, with ‘befriend’ referring to creating and maintaining social networks. Men have more diverse social ties, but also more negative interactions. Women’s networks tend to be more homophilic and homogeneous and family-based than men’s, which may make it easier for a woman to ask for help from someone similar to her. Further, women are more likely to offer support to their same-sex friends than men are to offer support to their same-sex friends, and men are less likely than women to seek or provide support. These differences in social network structure and function may lead to women having a larger network from which to draw social support, be more likely to ask for help, and thus more likely to benefit from their social support.
Women’s SBP dipping and DBP dipping were also associated with belonging and appraisal social support. Belonging assessments include items such as “If I decide one afternoon to go to a movie, I could easily find someone to go with me.” Such belonging support can be beneficial in terms of enhancing mood and feeling a sense of acceptance and belonging by others. This sense of acceptance and belonging can help individuals to cope with stressors more effectively and could help one to avoid certain stressors to begin with. Appraisal support is measured with items such as “*There is* someone I can turn to for advice about handling problems with my family.” This type of advice or guidance can be a particularly effective type of support for women as demonstrated in the healthier dipping profile seen in our participants.
The current study is a novel contribution to the literature, as we have examined social support and health in a relatively under-investigated health outcome, which is a known contributor to cardiovascular disease. Further, we examined those at greater risk of loneliness and lack of social support and have done so by looking at the overall ISEL measure and the individual domains of social support. Lastly, we have specifically focused on a normotensive sample, which is likely more representative of those in the population of individuals under 50 years of age.
## Limitations and Future Work
While these findings are important, certain limitations apply. Our sample was predominately White and educated, and all were heterosexual and married; thus, it is not clear how these findings would relate to unmarried individuals, people of color, or non-heterosexual individuals. High BP is also more common is non-Hispanic Black adults than in non-Hispanic White adults and is more common in African Americans than in other ethnic groups. It would then be important to look at nocturnal BP in a more diverse sample. We used BMI as a criterion for exclusion based on the large amount of prior research that has used this measure. BMI is also a quick way to determine qualification before participants are scheduled to come to the lab. However, the body adiposity index (or hip-to-waist ratio) is now generally the preferred method and may have more accurately assessed obesity. We did not use the body adiposity index, as it could not be assessed until the participant arrived at the lab. Decisions on eligibility needed to be assessed earlier in the screening process. Additionally, we did not assess whether participants had taken a nap during the day, which could impact sleep time/quality. Our study was also cross-sectional; thus, the social support needs of the individual at this specific point may have influenced their response to the individual types of support (e.g., tangible vs. emotional). We also measured nocturnal blood pressure over a single 24 h period. It would be beneficial to measure over several 24 h periods. Finally, it is important to note that while ABP is a valuable and well-validated tool for measuring daytime and nighttime BP and has consistently found associations between cardiovascular measures and social support, the recent meta-analysis on daytime ABP found no such connection [44]. Our findings are consistent with these findings, and yet we showed social support associated with total DBP dipping, and with both SBP and DBP dipping within the various component parts of the ISEL measure. It is therefore important that future research examine how the findings of this study and from current research fit into the overall literature on social support and cardiovascular health.
## 5. Conclusions
Despite these limitations, our study demonstrated the importance of social support for normotensive individuals who may be at greater risk for insufficient support, and the importance of examining social support more broadly. Further, our study demonstrated the importance of examining nocturnal blood pressure in addition to using daytime or overnight blood pressure to assess the benefits of social support on cardiovascular health. Future studies should also consider an examination of nocturnal dipping in a more diverse sample.
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|
---
title: A Novel Combination of Sotorasib and Metformin Enhances Cytotoxicity and Apoptosis
in KRAS-Mutated Non-Small Cell Lung Cancer Cell Lines through MAPK and P70S6K Inhibition
authors:
- Pedro Barrios-Bernal
- José Lucio-Lozada
- Maritza Ramos-Ramírez
- Norma Hernández-Pedro
- Oscar Arrieta
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001819
doi: 10.3390/ijms24054331
license: CC BY 4.0
---
# A Novel Combination of Sotorasib and Metformin Enhances Cytotoxicity and Apoptosis in KRAS-Mutated Non-Small Cell Lung Cancer Cell Lines through MAPK and P70S6K Inhibition
## Abstract
Novel inhibitors of KRAS with G12C mutation (sotorasib) have demonstrated short-lasting responses due to resistance mediated by the AKT-mTOR-P70S6K pathway. In this context, metformin is a promising candidate to break this resistance by inhibiting mTOR and P70S6K. Therefore, this project aimed to explore the effects of the combination of sotorasib and metformin on cytotoxicity, apoptosis, and the activity of the MAPK and mTOR pathways. We created dose–effect curves to determine the IC50 concentration of sotorasib, and IC10 of metformin in three lung cancer cell lines; A549 (KRAS G12S), H522 (wild-type KRAS), and H23 (KRAS G12C). Cellular cytotoxicity was evaluated by an MTT assay, apoptosis induction through flow cytometry, and MAPK and mTOR pathways were assessed by Western blot. Our results showed a sensitizing effect of metformin on sotorasib effect in cells with KRAS mutations and a slight sensitizing effect in cells without K-RAS mutations. Furthermore, we observed a synergic effect on cytotoxicity and apoptosis induction, as well as a notable inhibition of the MAPK and AKT-mTOR pathways after treatment with the combination, predominantly in KRAS-mutated cells (H23 and A549). The combination of metformin with sotorasib synergistically enhanced cytotoxicity and apoptosis induction in lung cancer cells, regardless of KRAS mutational status.
## 1. Introduction
KRAS mutations occur in up to $35\%$ of patients with non-small cell lung cancer (NSCLC) [1] and represent $50\%$ of oncogenic mutations in adenocarcinoma histology. Clinically, these genetic alterations are usually related to age over 65 years, smoking history, mutual exclusivity from alterations in the Epidermal Growth Factor Receptor (EGFR), and EML4-ALK translocations [2,3]. Furthermore, alterations in the KRAS oncogene have been considered predictors of poor response in chemotherapy-treated NSCLC patients harboring advanced, or metastatic, disease stages [4]. The biological importance of KRAS mutations is focused on impairing GTP hydrolization, keeping it aberrantly activated [5], which then results in constitutive activation of cell signaling pathways, such as mitogen-activated protein kinase (MAPK) and AKT-mTOR-P70S6K [6]. In NSCLC, these alterations occur mainly at codon 12 ($80\%$), mostly as a substitution of glycine by cysteine (G12C, $42\%$), but there are also reported interchanges of glycine for valine (G12V, $21\%$), glycine for aspartate (G12D, $17\%$), and glycine for alanine (G12A, $7\%$) [7]. Particularly, G12C mutation is relevant, as it binds to specific KRAS inhibitors, such as sotorasib and adragasib [8], then inhibiting the phosphorylation of p-ERK in cells with this mutation [6], correlating with important reductions in tumoral size [5,6], and even showing promising results in clinical trials [9,10] of lung, colorectal, pancreatic, and endometrial cancers [11]. However, although the antineoplastic effects of sotorasib have been clearly described, their short-lasting clinical responses have become its most important drawbacks [10,11,12]. Consequently, preclinical evidence has suggested that sotorasib efficacy in KRAS-mutated tumors may be affected by diverse off-target resistance mechanisms [13], among which the most relevant is MAPK pathway reactivation by AKT-mTOR-P70S6K signaling [14]. Thus, metformin represents a pharmacological alternative that may overcome this resistance mechanism, since this biguanide inhibits complex 1 of the mitochondrial respiratory chain, subsequently activating AMP-activated protein kinase (AMPK). This triggers the activity of intracellular intermediaries to inhibit mTORC1, finally decreasing proteinic synthesis in cancer cells through p70S6K inhibition [15]. Accordingly, diverse studies have evidenced the cytotoxic role of metformin as monotherapy, its capacity to promote apoptosis and inhibit the mTOR pathway, as well as the correlation of these findings with reduced tumoral sizes in murine models [16,17,18].
Additionally, the combination of metformin with tyrosine kinase inhibitors, like afatinib, synergistically increased cell cytotoxicity, induction of apoptosis, and inhibition of PI3K-AKT-mTOR pathway in A549 (KRAS G12S) cell lines, even if these cells lack of EGFR mutations. This further suggests a sensitizing effect on afatinib mediated by metformin [19], which was further supported by in vivo studies reporting that combining metformin with diverse other targeted therapies decreased tumoral size and inhibited mTOR signaling in mouse neoplasms derived from A549 cell line (KRAS G12S) [20].
As well, in vitro evidence has demonstrated that metformin also regulates MAPK pathway; for instance, Ko et al. [ 17] showed that increasing metformin concentrations exhibited a dose-dependent inhibition of p-MEK$\frac{1}{2}$ and p-ERK$\frac{1}{2}$ in A549 and H1975 cells. Comparably, Do et al. [ 16] identified that metformin inhibited p-Raf and p-ERK$\frac{1}{2}$ in a dose-dependent manner. Thus, the effects of this biguanide extend beyond mTOR pathway.
Furthermore, emerging clinical evidence supports the concomitant use of metformin with antineoplastic drugs; for example, Arrieta et al. [ 21] reported that the combination of metformin with tyrosine kinase inhibitors (TKIs) increased the overall and progression-free survival periods in patients with EGFR-mutated NSCLC. Similarly, a phase II clinical trial showed a significant increase in the progression-free survival (PFS) of NSCLC patients after combined treatment with metformin and paclitaxel, carboplatin, or bevacizumab [22]. These findings suggest that metformin may enhance the clinical effectivity of other antineoplastic agents [20,23,24,25].
Finally, the molecular consequences derived from combining metformin and sotorasib remain unexplored; therefore, this study aimed to analyze their effects on cell viability, apoptosis and the activity of MAPK and AKT-mTOR pathways in lung cancer cell lines harboring different KRAS mutational statuses.
## 2.1. Metformin Increases Sotorasib-Driven Cytotoxicity in KRAS-Mutated Lung Cancer Cell Lines
First, we found a greater decrease in cellular viability using the combination of metformin and sotorasib, compared to their corresponding monotherapies. Specifically, we found significant differences between the combination and sotorasib alone in KRAS-mutated cell lines H23 ($56.2\%$ vs. $44.6\%$; $$p \leq 0.0457$$; Figure 1A) and A549 ($31.6\%$ vs. $53.9\%$; $$p \leq 0.0223$$; Figure 1B). Differently, the wild-type KRAS cell line (H522) did not display statistical significance in this comparison ($57.4\%$ vs. $47.6\%$; Figure 1C). Moreover, the pharmacodynamic analysis reported synergy between sotorasib and metformin in all tested cell lines, including H23 (CI = 0.62450; Figure 1A), A549 (CI = 0.73647; Figure 1B), and H522 (CI = 0.91655; Figure 1C).
## 2.2. Increased Apoptosis Induction by the Addition of Metformin to Sotorasib, Regardless of KRAS Status
After, we measured membrane markers of apoptosis (annexin-V) or necrosis (7-AAD). Consequently, as shown in Figure 2, all cell lines exhibited increases in apoptosis induction driven by the combination, compared to controls, including H23 ($22.3\%$ vs. $70.27\%$ p ≤ 0.0001), A549 ($8.02\%$ vs. $80.99\%$ p ≤ 0.0001), and H522 cells ($1.6\%$ vs. $49.47\%$ p ≤ 0.0001). Particularly, sotorasib showed significant differences compared to controls in H23 ($24\%$ vs. $66.2\%$ p ≤ 0.0001) and A549 cells ($5.6\%$ vs. $71.7\%$ $$p \leq 0.0127$$). Differently, H522 was the only cell line showing notable differences between sotorasib and the combination ($64.5\%$ vs. $80.9\%$ $$p \leq 0.0217$$).
## 2.3. Combined Therapy Significantly Decreases MAPK Pathway Activity
After confirming that metformin and sotorasib concomitantly induced cell death, we assessed their biological impact on diverse intermediaries of MAPK pathway, such as KRAS, CRAF, BRAF, and ERK$\frac{1}{2.}$ As expected, KRAS expression was importantly reduced in H23 cells after treatment with sotorasib alone ($$p \leq 0.0103$$) or in concomitance with metformin ($$p \leq 0.0013$$). In A549 cells, p-CRAF was importantly inhibited by all treatments, while BRAF expression was only reduced in the combined group (p ≤ 0.01). Additionally, p-CRAF was inhibited by the combined treatment in H522 cells (p ≤ 0.05). Furthermore, p-ERK$\frac{1}{2}$ (p-MAPK) expression was decreased in H23 by all treatments, in A549 cells by metformin (p ≤ 0.01) and the combination (p ≤ 0.01), and in H522 by the combination (p ≤ 0.01) and metformin alone (p ≤ 0.01) (Figure 3).
## 2.4. Combined Treatment of Metformin and Sotorasib Inhibits AKT and P70S6K Activation
Next, we explored the inhibitory efficacy of the combination over the AKT-mTOR-P70S6K pathway, since this is the main resistance mechanism to KRAS inhibitors (Figure 4). Specifically, AKT expression was reduced after sotorasib alone (p ≤ 0.05) or the combination (p ≤ 0.01) in H522 cells. Moreover, p-AKT was significantly inhibited by the combination in H23 ($$p \leq 0.0163$$) and H522 cells (p ≤ 0.05), but only as a non-significant trend in A549 cells. Furthermore, p-P70S6K was significantly inhibited by the combination in H23 ($$p \leq 0.0071$$) and H522 cells (p ≤ 0.01), but only as a non-significant trend in A549 cells.
## 3. Discussion
Treatment with sotorasib has modified response and survival of patients with KRAS G12C mutations. However, despite showing promising responses, intrinsic or acquired resistance mechanisms have prevented the development of better clinical results. In this sense, the most important mechanism of resistance to sotorasib is the activation of the AKT-mTOR-P70S6K pathway. As metformin has previously been demonstrated to inhibit this signaling pathway, we explored whether combining this biguanide with sotorasib resulted in an improvement of sotorasib effectiveness in lung cancer cells. Consequently, our results exhibited that the combination exerted synergistic effects over cytotoxicity and apoptosis in cells with G12C and G12S KRAS mutations. The most similar example to this phenomenon in the literature is a study of our research group, showing that combining metformin and afatinib (EGFR tyrosine kinase inhibitor) induces a synergistic effect on A549 cells, even if this cell line lacks EGFR mutations. This effect was mainly attributable to metformin-driven AMPK activation, which then inhibited mTOR-P70S6K signaling [19]. Analogously, metformin also potentiates apoptosis in combination with selumetinib (MEK inhibitor) [26], implying that the inhibition of the MAPK pathway is important for metformin-driven apoptosis as part of a wide mosaic of other reported mechanisms, such as lowering of Bcl-2 protein levels, increasing Bax expression [27], and promoting G0/G1 cell cycle arrest [18]. Otherwise, sotorasib has also been combined with other drugs to overcome its resistance, like buparlisib (PI3K inhibitor) [28] or DT2216 (BCL-XL) [29], thereby supporting the assertion that the PI3K-AKT-mTOR pathway plays an important role in the apoptosis of KRAS-mutated cells [30].
After assessing the cytotoxic effects of our concomitant therapy, we explored its impact on MAPK and AKT-mTOR-P70S6K signaling pathways, showing an important inhibition of them in all cells tested, regardless of KRAS mutational status. This is relevant, since MAPK pathway inhibition is a well-known consequence of sotorasib monotherapy in models with G12C mutation [31], and it is equally expected that its high specificity to this alteration prevents sotorasib from inhibiting p-ERK in cells without G12C mutation [6]. Therefore, our results show, even at the proteomic level, an important sensitization of metformin to sotorasib effects in non-common KRAS mutations. Furthermore, mTOR inhibition has special importance in reaching effective cytotoxicity in cells with an over-activated MAPK pathway, as important cytotoxic effects in cell lines with KRAS or MEK mutations are reported from the use of mTOR inhibitors, whether alone [32,33] or in combination with MAPK inhibitors [34]. Finally, we found that metformin synergizes with sotorasib due to an important inhibition of AKT and P70S6K in all cells. These findings match with those results previously reported by our research group for combining metformin and afatinib in lung cancer cells, in which we described that this biguanide potentiates apoptosis induction by inhibiting the EGFR-AKT-P70S6K pathway [19]. Furthermore, previous studies have also reported that inhibiting PI3K-AKT-mTOR pathway positively correlated with apoptosis induction [32,33]. These findings are further consistent with preclinical evidence testing the concomitant use of metformin and figitumumab, showing inhibition of PI3K-AKT and MAPK signaling pathways [20], thus placing these drugs as potential enhancers of KRAS inhibitors, such as sotorasib. Differently, metformin has demonstrated variable outcomes over MAPK, as some studies stand that this biguanide increases B-RAF and C-RAF activity [35,36], while others report the exact contrary effect [16,17]. This phenomenon can be explained by differences in the concentrations used during in vitro tests; for instance, metformin IC50 concentrations > 20 mmol in A549 cells are reported to cause an active inhibition of the AKT-mTOR pathway, which decreases the inhibitory activity of Rheb over the dimerization of C-RAF and B-RAF [35], indirectly promoting MAPK activity. Meanwhile, lower concentrations of this biguanide (1–10 mmol) are not reported to inactivate Rheb, then allowing MAPK pathway inhibition, as evidenced in this study for A549 (CRAF and P-MAPK), and H522 cells (P-CRAF, P-MAPK, CRAF, and MAPK) after treatment with metformin, either as a monotherapy or in combination with sotorasib. Altogether, our results suggest that combining metformin and sotorasib finds its main mechanism of action in the concomitant inhibition of the AKT-mTOR-P70S6K pathway by metformin, and MAPK by sotorasib, thus simultaneously decreasing protein synthesis and cell growth. This mechanism of action is further illustrated in Figure 5.
Moreover, as part of the wide mosaic of intracellular effects of metformin, plenty of evidence demonstrates that this biguanide modifies diverse metabolic pathways to avoid the development of Warburg effect in cells with KRAS mutations. Although we did not evaluate metabolism in this study, we previously reported that combining metformin and afatinib showed strong inhibition of GLUTs, and a marked increase in AMPK activity, regardless LKB1 involvement [19]. This may be explained by AMPK-driven inhibition of energy generation [37]. Importantly, our study shows that cells lacking LKB1, such as A549, decreased MAPK and p-MAPK expressions, which may also promote metabolic consequences, such as decreased lactate levels and AMPK-mediated glycolysis. Therefore, the metabolic importance of metformin may be of special interest in cells with KRAS mutations, as this driver alteration is metabolically involved in cancer progression [38].
## Strengths and Limitations
The main strength of this study is exploring the combined effect of metformin and sotorasib in cells having or not KRAS G12C mutation of susceptibility to sotorasib, demonstrating a synergistic relationship between metformin and sotorasib for the first time. Nevertheless, we are aware of the limitations of this investigation; first, it only was used one cell line belonging to each of the most representative groups of mutational profiles (KRAS G12C, G12S, and non-KRAS mutated). Second, although our results in A549 cells are in line with previous reports, evidence is lacking for H23 and H522 cells, not allowing complete generalization of our results to studies involving these cell lines.
## 4.1. Cell Lines and Reagents
Human lung adenocarcinoma cell lines H23 (KRAS G12C), A549 (KRAS G12S), and H522 (without KRAS mutations) were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). H522 and H23 cells were cultured in RPMI-1640 medium (Gibco, Waltham, MA, USA. 31800-022), meanwhile A549 cells were cultured in F12 medium (Gibco, Waltham, MA, USA. 21700-075), and both media were supplemented in a $10\%$ concentration with Fetal Bovine Serum (FBS) (Gibco, New York, NY, USA. 26140-079) and penicillin–streptomycin–amphotericin B in a $1\%$ concentration (MP Biomedicals. Fountain Pkwy, OH, USA, 091674049). They were incubated in an atmosphere of $5\%$ CO2 at 37 °C. As cells constituted an 80–$90\%$ confluent monolayer, they were subcultured using 400 µL of Trypsin-EDTA 1X solution (Sigma Aldrich. St. Louis, MO, USA. 549430C).
Metformin (Sigma Aldrich. St. Louis, MO, USA. PHR1084) was diluted in the appropriate culture medium of each cell line at a concentration of 100 mmol. Similarly, sotorasib (Medkoo Biosciences. Morrisville, NC, USA. 207085) was diluted in dimethyl sulfoxide (DMSO) at 5 µmol, 10 µmol, 15 µmol, 20 µmol, and 25 µmol concentrations.
## 4.2. Cell Viability Assay
A quantity of 1 × 104 cells per well were seeded in triplicate in 96-well plates. After 24 h of incubation, cells were treated for 72 h with metformin at different concentrations per well triplicate (5 mM, 10 mM, 15 mM, 20 mM, and 25 mM). In the same way, cells in three independent experiments were treated for 72 h with 5 µmol, 10 µmol, 15 µmol, 20 µmol and 25 µmol of sotorasib as monotherapy.
Subsequently, the MTT solution (3,4,5-dimethylthiazol-2-yl-2,5-didiphenyltetrazolium bromide) at a concentration of 5 mg/mL (Sigma Aldrich. St. Louis, MO, USA. Catalog number: M2128) was added to wells, which were incubated for 4 h. After this period, the culture medium was removed and replaced by 200 µL of isopropanol-DMSO (1:1) solution to dissolve the formazan crystals. Cell viability resulting from this experiment was quantified by measuring absorbance at 570 nm (BioTek, Saint Clare, CA, USA, ELX 808) to calculate optical density values.
The results of such measurements were averaged and normalized at $100\%$ in relation to controls. According to cytotoxicity results, we determined IC50 and IC10 doses of sotorasib and metformin, respectively, for each cell line, which are shown in Table 1.
Then, each cell line was seeded in 96-well plates in an amount of 1 × 104 cells per well, ordering them in five triplets of wells, representing the following treatment groups: control, DMSO, metformin, sotorasib and the combination of sotorasib and metformin. After 24 h of incubation, each cell line was treated with its respective IC10 and IC50 doses of metformin and sotorasib, respectively, either as monotherapy or as a combination. After that, the viability test was carried out using MTT solution and a spectrophotometer, as previously described.
## 4.3. Analysis of Drug Combination Index
To determine the type of pharmacodynamic interaction between metformin and sotorasib, we calculated their combination index (CI) for each cell line using Compusyn 1.0 software (Biosoft, Cambridge, UK). Combination index values <1 were interpreted as synergistic, values from 1 to 1.10 as additive, and values > 1.10 as antagonistic.
## 4.4. Apoptosis Assay
To assess the level of apoptosis induction, cell lines were seeded in 24-well plates in a confluence of 4 × 104 and incubated overnight. After 24 h, cells were incubated with IC10 and IC50 doses of metformin and sotorasib, respectively, either as monotherapies or as a combination for 72 h at 37 °C and $5\%$ CO2. Then, the cells were detached using trypsin, washed three times with 1X PBS, and later they were marked with FITC Annexin V Apoptosis Detection Kit with 7-AAD (Biolegend, San Diego, CA, USA 640922). Finally, the cells were evaluated through flow cytometry in accordance with manufacturer’s instructions.
## 4.5. Western Blot Analysis
After 72 h of treatment, cell lines were washed three times with PBS solution and lysed with RIPA lysis buffer system (Santa Cruz Biotechnology. Dallas, TX, USA. SC-24948) according to the manufacturer’s instructions. Subsequently, extracted proteins were quantified using Bradford’s assay (Bio-Rad, Hercules, CA, USA, #5000205). Then, 40 µg of total protein was separated by electrophoresis for 110 min at 100 V on $10\%$ SDS-PAGE gel, and then transferred onto 0.2 μmol nitrocellulose membranes by the Trans-Blot Turbo Transfer System set at 20 V and 2.5 amps. The efficacy of this process was checked by Ponceau Red stain. Subsequently, membranes were blocked with a $10\%$ BSA-PBS tween solution for 30 min, underwent three washes of 10 min with PBS-Tween solution, and were incubated with their corresponding primary antibodies (dilution 1:1000) overnight at 4 °C. Secondary antibodies were directed against the following molecules: KRAS (Santa Cruz Biotechnology. Dallas, TX, USA. SC-30), B-RAF (Cell signaling. Danvers, MA, USA. 9433), C-RAF (Cell signaling. Danvers, MA, USA. 53745), p-CRAF (Cell signaling. Danvers, MA, USA. 9421), MAPK (Cell signaling. Danvers, MA, USA. 9102), p-MAPK (Cell signaling. Danvers, MA, USA. 4370), AKT (Santa Cruz Biotechnology. Dallas, TX, USA. SC-5298), p-AKT (Santa Cruz Biotechnology. Dallas, TX, USA. SC-514032), P70S6K (Cell signaling. Danvers, MA, USA. 9202), p-P70S6K (Cell signaling. Danvers, MA, USA. 9205), and GAPDH (Santa Cruz Biotechnology. Dallas, TX, USA. SC-47724).
After incubation, each primary antibody was removed from its corresponding membrane. Later, each membrane underwent three washes of 15 min with PBS-Tween solution. Once completed, membranes underwent incubation for 1 h with a 1:10,000 dilution of their corresponding secondary antibodies. After that, membranes took 5 of 10 min to reduce background derived from secondary antibodies. Finally, proteins of interest were visualized using an enhanced chemiluminescence kit (LI-Cor, Lincoln, NE, USA), and band intensities were quantified by densitometry using ImageJ software (1.49 version, National Institutes of Health, Bethesda, MA, USA).
## 5. Conclusions
Metformin exhibits a sensitizing role to sotorasib in non-KRAS-mutated cells. Furthermore, the combination of sotorasib and metformin exerts a synergistic enhancement of cytotoxicity and apoptosis induction, likely driven by the concomitant inhibition of MAPK and mTOR-P70S6K pathways in KRAS-mutated cells.
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---
title: Short-Term Ambient Air Ozone Exposure and Components of Metabolic Syndrome
in a Cohort of Mexican Obese Adolescents
authors:
- Jorge Octavio Acosta Montes
- Albino Barraza Villarreal
- Blanca Gladiana Beltrán Piña
- Karla Cervantes Martínez
- Marlene Cortez Lugo
- Isabelle Romieu
- Leticia Hernández Cadena
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001840
doi: 10.3390/ijerph20054495
license: CC BY 4.0
---
# Short-Term Ambient Air Ozone Exposure and Components of Metabolic Syndrome in a Cohort of Mexican Obese Adolescents
## Abstract
Ambient air pollution is a major global public health concern; little evidence exists about the effects of short-term exposure to ozone on components of metabolic syndrome in young obese adolescents. The inhalation of air pollutants, such as ozone, can participate in the development of oxidative stress, systemic inflammation, insulin resistance, endothelium dysfunction, and epigenetic modification. Metabolic alterations in blood in components of metabolic syndrome (MS) and short-term ambient air ozone exposure were determined and evaluated longitudinally in a cohort of 372 adolescents aged between 9 to 19 years old. We used longitudinal mixed-effects models to evaluate the association between ozone exposure and the risk of components of metabolic syndrome and its parameters separately, adjusted using important variables. We observed statistically significant associations between exposure to ozone in tertiles in different lag days and the parameters associated with MS, especially for triglycerides (20.20 mg/dL, $95\%$ CI: 9.5, 30.9), HDL cholesterol (−2.56 mg/dL ($95\%$ CI: −5.06, −0.05), and systolic blood pressure (1.10 mmHg, $95\%$ CI: 0.08, 2.2). This study supports the hypothesis that short-term ambient air exposure to ozone may increase the risk of some components of MS such as triglycerides, cholesterol, and blood pressure in the obese adolescent population.
## 1. Introduction
Ambient air pollution is a major public health concern globally. Over $90\%$ of the world’s population is estimated to live in zones where air pollutant concentrations exceed the World Health Organization guideline limits (WHO) [1]. In Mexico, ozone is found in concentrations higher than what is established as acceptable by the Official Mexican Standard, as estimated by the System of Atmospheric Monitoring of the Metropolitan Area of the Valley of Mexico (SIMAT) [2].
Several epidemiological studies have linked ambient air pollution with respiratory (chronic obstructive pulmonary disease, asthma, lung function decrease, and inflammatory airways) [3,4,5,6] and cardiovascular diseases [7,8,9] and lungs cancers [10,11]. These effects indicated that exposure to ambient air pollutants might cause events during the later stages of life and initiate chronic disease processes. However, the effects of air pollutants on the earlier stages of developing chronic diseases are less studied. Metabolic syndrome (MS) comprises a cluster of major modifiable risk factors for non-communicable diseases, including abdominal obesity, dyslipidemia, elevated blood pressure, and high glucose concentrations [12,13]. MS affects approximately 10–$25\%$ of the global population and its prevalence is rapidly increasing worldwide, and it has been suggested that the increase in the prevalence of MS is related with genetic factors, low physical activity, and an unhealthy lifestyle; however, the ambient air pollution could also be a risk factor for components of MS [14]. Under this context, the inhalation of air pollutants can participate in the development of oxidative stress, systemic inflammation [15,16], insulin resistance [17], endothelium dysfunction [18,19], and epigenetic modification. These negative responses can independently and/or interactively be involved with the development of cardiovascular symptoms, all of which are components in the diagnosis of MS. Previous epidemiological and experimental studies have explored the relationship between air pollution exposure and individual MS components [14,20,21]. However, the existing evidence focuses mainly on the adult population, one of the main reasons being the complexity of the diagnosis of metabolic syndrome in adolescents, so it becomes very important to study how exposures to environmental pollutants behave in metabolic disorders in this population group. Two previous epidemiological studies in humans investigated the relationship between air pollutants and MS, and both reported significant associations [14,22].
Additionally, a recent animal study showed that exposure to air pollutants (exposure to particulate matter) resulted in weight gain and cardiorespiratory and metabolic dysfunction [20]. More recently, studies in rats and humans reported that acute or short-term exposure to ozone, under controlled conditions, can lead to metabolic disturbances within hours or days since changes in the metabolome in blood samples were observed [21,23]. Even though these studies involving the metabolome have provided important information regarding new metabolites and the possible mechanisms of action of acute exposure to ozone, it is still necessary to evaluate the short-term effects on macromolecules derived from these metabolic processes more closely in population groups exposed to environmental fluctuations in ozone. Additionally, to our knowledge, no prior study has been conducted in Mexico to evaluate the association between short-term air pollution and MS in the adolescent population. Therefore, considering the current MS epidemic, the higher air pollution, and the scarcity of such an evaluation, our study would be the first to assess the relationships between short-term exposure to ozone and MS in Mexican adolescents.
## 2.1. Design and Study Population
A dynamic cohort study of 415 adolescents living in the *Metropolitan area* of Mexico City was conducted from January 2006–August 2013. Participants were enrolled during the first three years and followed during one year on average (from 6 months until three years maximum) from when they attended the obesity clinic at Mexican Children’s Hospital Federico Gómez (HIM-FG), which provides health care to people from 0 to 18 coming from the entire metropolitan area of the Mexico City.
Mexico *City is* part of the metropolitan zone in the Valley of Mexico (MZVM), with nine million inhabitant; approximately $52\%$ of the population are women, and $13.5\%$ belong to the 10–19 age group [24]. This is considered the largest and most complex city in the country with high levels of traffic-related pollutants emissions [25].
The main objective of the cohort was to evaluate if weight loss improved lung function and reduced local inflammation in obese adolescents aged between 10 to 18 years old with and without asthma. All adolescents who met the criteria for being overweight and obese according to Cole et al. [ 26] and agreed to participate in the study were included in a program of nutritional, physical, and psychological orientation to improve their “healthy” life. Adolescents were given recommendations to increase their physical activity for half an hour per day, and a nutritionist give orientation for a healthier diet based on the WHO recommendations according to age and sex ($60\%$ of carbohydrate, $20\%$ proteins, and $20\%$ fat).
All participating adolescents signed an informed consent letter in addition to the consent letter from both parents. The protocol was approved by the ethics committees of the Children’s Hospital of Mexico Federico Gómez and the National Institute of Public Health. The adolescents were cited for the first time for an evaluation, where they had a clinical history and blood samples were taken to evaluate the metabolic profile (cholesterol, triglycerides, high- and low-density lipoproteins, uric acid, glucose). Participants were cited every three months for taking blood samples and for receiving dietary guidelines and the questionnaires on the frequency of food consumption and physical activity were applied. For every 15 days during the first three months and every month during the following months up until one year, the child received psychological attention through trained personnel.
As part of this cohort, and preserving the longitudinal character of the base study, we selected a subsample of 372 adolescents aged 9 to 19 years, diagnosed with overweight and obesity, in which metabolic alterations in blood were evaluated every three months.
## 2.2.1. Components of Metabolic Syndrome Evaluation and Other Measures
To determine the biochemical parameters of metabolic syndrome, blood samples were taken by trained personnel, and a sample of approximately 7 mL was extracted from a vein of the participant’s arm and duly safeguarded to maintain its integrity. The sample was obtained during the first hours of the morning, asking the adolescent to come fasting and in optimal hydration conditions. The sample was centrifuged and separated into 2.5 mL vials, and then it was sent to freeze for storage and subsequent analysis. All the extractions were conducted according to the manufacturer’s instructions.
Blood pressure was taken through a baumanometer by trained personnel. The adolescent was left with 15 min of rest, and the shot was taken twice to obtain an average of the measurements and have a more accurate value. Information on anthropometric measures was obtained from participants at the baseline and during the follow-up period. Each participant was weighted while wearing light clothing and standing without shoes on a calibrated platform scale (brand healthometer, model 402 KL, with a minimum capacity of 100 g). The height (cm) was obtained using a Holtain Limited Crymych, Dyfec stadiometer barefoot on a flat surface, making a right angle with the vertical bar of the stadiometer and asking each patient to inhale before sliding the headboard over the top point of their head. The BMI (BMI = weight/height2) was calculated to indirectly quantify body fat, considering the following cut-off points: 20, 25, and 30, corresponding to the categories of normal weight, overweight, and obesity, according to Cole.
The cut-off points for the parameters related (triglycerides, HDL cholesterol, and fasting glucose) to the diagnosis of MS were those established by the FID [27]. A participant was considered as positive for MS if he had a waist circumference greater than the 90th percentile or the threshold or, failing that, the condition of overweight or obesity according to the body mass index, plus two criteria of the following: [1] triglycerides levels > 150 mg/dL, [2] HDL cholesterol levels < 40 mg/dL, [3] systolic blood pressure > 130 mmHg, [4] diastolic blood pressure > 85 mmHg, and [5] fasting glucose levels >to 100 mg/dL.
The parameters of the MS were managed in two forms; in the first, the values for each component were considered continuously and each one was handled as an individual variable. In the second, a joint variable (yes and no) was explored and constructed from the different parameters that make up the MS based on the definition of the FID [27].
## 2.2.2. Exposure Assessment
All the information related to the air pollutants, as well as the information of meteorological variables (direction and wind speed, humidity, and temperature), were obtained through the Atmospheric Monitoring System of the Metropolitan Zone of the Valley of Mexico (SIMAT), which makes measures continuously for 365 days of the year. Currently, in the MZVM, atmospheric monitoring networking (AMN) has 40 air quality monitors.
The daily exposures to ozone and the other pollutants were constructed considering two important times of the day, the shift attended at school (morning: 7:00 to 14:00 h and evening: 15:00 to 19 h), and the remaining hours were assigned to the corresponding exposure to the home address. According to the above, hourly averages were used to obtain a maximum of 24 h hours or a daily maximum (1-h maximum): a maximum of 8 h according to school or home address was estimated per adolescent once their exposure diary was constructed, and delays of 1 up to 15 days prior to the visit to the blood sample collection were recorded.
The exposure to O3 was assigned using a geographic information system (GIS), which considered the distance between the monitor and the area where both the home and school of the participants were located, based on their address and zip code, estimating the closest monitor’s exposure either to the school or home according to the school shift. Additionally, during the study period (specifically in 2010), the AMN made some changes both in the location of some monitors and in the placement or elimination of others; therefore, these were considered in the assigning of the closet monitor.
## 2.3.1. Diet
The dietary intake was assessed using a validated food frequency consumption questionnaire, which indicates how many times a week and how many servings of food the participant consumed per day. This report was intended to represent dietary consumption during the three months that elapse between one measurement and another. The questionnaire was applied by trained nutritionists and was answered by the adolescent, supported by the person who accompanied them to the consultation. To obtain the consumption of kilocalories consumed in one day, the portions of each of the foods that the participant consumed during a week were calculated to then obtain a daily average of carbohydrates, lipids, and proteins; the calculation of micronutrients consumed during a day (antioxidants: vitamin C and vitamin E) was conducted in the same way. The nutritional contribution of each of the foods was calculated based on the reference values of food composition established by the Tables for Practical Use of Foods of Greater Consumption in its third edition [28], as well as by what is established in the Mexican System of Equivalent Foods in its third edition.
## 2.3.2. Physical Activity
Physical activity was categorized as mild, moderate, and intense through a short physical activity questionnaire assessed according to each adolescent visit. The questionnaire consisted of 6 questions evaluating practice about physical activity in the last 7 days and the time it took: vigorous (running, swimming, riding a bike, or playing in some team), moderate (quick walk or jog of 20 min or more), or light (walking 20 min). Additionally, it explores the time spent remaining sitting in front of a television, a computer, or playing video games. The questionnaire was applied by trained personnel and was answered by the adolescent, supported by the person who accompanied them to the consultation.
## 2.4. Statistical Analysis
An exploratory analysis of the information was carried out, where each variable’s quality and distribution were evaluated. Added to this, the minimum and maximum ranges of the variables were analyzed to know if there was any extreme value that would affect the distribution, eliminating two low main criteria and entry to those that were not biologically plausible, and ensuring that these values do not represent more than $5\%$ of the total of the values within the variable. The short-term association of ozone exposure with the metabolites outcomes was evaluated using linear mixed-effects models, considering the ID of the participant as a random intercept, and using models for continuous and binary responses (only for the metabolic syndrome condition). We also evaluated as potential confounders the physical activity, BMI, antioxidant intake (vitamin C and vitamin E), asthma presence, kilocalories consumed, and meteorological variables, considering only those that were significant in the final model. Likewise, statistical significance was evaluated from the inclusion or exclusion of each variable to elucidate how the coefficient was affected by time, with the interest of finding the best estimate of the model with the least number of variables.
A mixed-effects model with random intercept was used since some variables had different measurements over time; however, there were some more that were maintained through the study. Within the mixed models, the short-term exposure was evaluated based on tertiles, leaving the lowest tertile as a reference category for the remaining two. The cut-off points for each tertile were different depending on the number of days before the sample was taken, within which the exposure (lags) was considered. Lag days from 0 to 15 were considered, according to that reported in the previous literature, in which multiple lags due to short-term ozone exposure are evaluated for cardio metabolic risk [22]. To prevent the results from being biased via exposure to other pollutants, especially particulate matter, which has been widely evaluated as associated with ischemic disease and cardiovascular risk, we adjusted the model by adjusting for PM2.5 concentrations of the same lag as for ozone using the maximum of 24 h. A stratified analysis was performed with the conditions of asthmatic or non-asthmatic; however, no statistically significant association was obtained. All the statistical analysis was carried out using the statistical package STATA 14.0.
## 3. Results
The mean age of participants was 12.8 years (SD = 2.1 years). More than half of the subjects were males ($56.5\%$). The characteristics of the study participants are summarized in Table 1. Based on the main definition of MS for the study, the prevalence of MS was $10.0\%$. In Table 2, we can see the results related to the parameters are indicative of MS. We found that $45\%$ of the participants were obese; in terms of fasting glucose levels, only $8\%$ of the participants presented values above what was considered normal, $40\%$ had HDL cholesterol levels below established limits, $35\%$ had triglyceride levels above the cut-off point of normality, only $3\%$ of the participants presented high blood pressure of both the systolic and the diastolic kind, and $9\%$ of participants met criteria for the classification of metabolic syndrome in the baseline data.
Table 3 summarizes the descriptive statistics of air pollution concentrations and meteorological variables based on the geographical area of the Metropolitan Area of the Valley of Mexico. We found that the ozone concentration is lower in the northern part and must increase concentration in the areas further south. This coincides with a slight increase in the average temperature for the central and southern areas, which, added to the wind and relative humidity conditions, means that the population living in these areas is exposed to slightly higher concentrations than the rest.
The association between ozone ambient air concentrations and components of MS are summarized in Table 4. *In* general, we observed a statistically significant tertile trend increase between tertile exposure to ozone and some of the parameters related to MS. The lags that showed a statistically significant association were those corresponding to 2, 7, 8, 11, and 13 days prior to the visit. We found an increase in triglyceride levels of 20.24 mg/dL ($95\%$ CI: 9.54, 30.95) for ozone exposure on lag day 2, as well as an increase of 12.55 mg/dL ($95\%$ CI: 1.44, 23.66) on lag day 11 for those in the third tertile relative to the lowest tertile for ozone exposure. Comparing in a similar way for those in the third tertile with the lowest tertile, regarding HDL cholesterol, a decrease in blood concentration of −2.56 mg/dL ($95\%$ CI: −5.06, −0.05) was observed in the ozone exposure on lag day 7; however, on lag day 8 of ozone exposure, the decrease was more significant: −3.46 mg/dL ($95\%$ CI: −5.96, −0.95). Blood pressure in general showed a statistically significant change on lag day 13, increasing by 1.14 mmHg ($95\%$ CI: 0.08, 2.2) in systolic blood pressure, while for diastolic blood pressure, the increase was 0.91 mmHg ($95\%$ CI: −0.03, 1.86), this being marginally significant. According to the adolescents who had the characteristics of metabolic syndrome, we observed an OR of risk of 2.23 ($95\%$CI: 1.10, 4.56) and 1.99 ($95\%$CI: 0.93, 4.25) for third tertile ozone exposure on lag days 2 and 3, respectively. We also observed increased risk with lag 15.
All models were carried out using the mixed-effects model with random intercept adjusted by physical activity, BMI, antioxidant intake (Vitamin C and Vitamin E), and asthma presence. Meteorological variables were tested as adjustment variables, but these were not statistically significant, nor did either change the sense of association between ozone and the metabolite evaluated (Table 4). Additionally, we evaluated these models by adjusting PM2.5 concentrations (as a continuous variable, not in tertiles) using the same lag time than ozone. The results are shown in Table 4. In most cases, the significance of ozone remains. PM2.5 was significant principally at lag 4 (Supplementary Materials).
## 4. Discussion
In this cohort study, we found that short-term ambient air exposure to ozone was significantly associated with an increase in some parameters related to MS in young populations. Although exposure to outdoor ambient levels of PM2.5, NO2,, and O3 has been associated with asthma, respiratory diseases, and respiratory symptoms in children mainly, there are few previous longitudinal studies that have studied this type of association in populations between this age range and particularly in obese adolescents; to our knowledge, this is the first study to evaluate these effects in a low–middle income country.
We found associations in different exposure lags between 2 and 14 days in most of the components of MS, which could lead adolescents, given their condition of obesity, to a greater risk of being classified as positive for metabolic syndrome based on the classification of the FID. The fact that some components were related to 2-day lags and more lag days may also be due to the possible correlation that exists in the pollutants due to weekly cyclical trends. In one study in rats, it was reported that short-term exposure to ozone can lead to higher levels of leptin and blood glucose, as well as other changes in metabolites involved with the metabolism of glucose, lipids, and amino acids after subjecting a group of rats for a few hours to high ozone concentrations [21]. Subsequently, the same researchers showed, in a controlled human study, that after short-term exposure to ozone circulating lipid metabolites were altered as a result of changes in metabolism, giving rise to the saturation of certain metabolic pathways [23], suggesting alterations in membrane phospholipids linked to proinflammatory mechanisms due to ozone. We believe that if these effects are sustained for longer periods of time, they can trigger permanent damage to the metabolic system or even the immune system. One study that indicated that long-term exposure to ambient air pollutants may increase the risk of metabolic syndrome, especially among males, results that are consistent with our findings; however, their results come from a cross-sectional study conducted in the adult population [29]. Another study reported a positive association between ozone exposures and type 1 diabetes as well as alterations in plasma lipid profiles and lower levels of glucagon-like peptide one after exposure to highly polluted air, respectively, indicating that sub-chronic exposure to ozone-induced beta-cell dysfunction may secondarily contribute to other tissue-specific metabolic alterations, due to an impaired regulation of glucose, lipid, and protein metabolism in young adult rats [30]. Similarly, an increase in oxidative stress has been observed in rats exposed to high concentrations of ozone, leading to mitochondrial DNA damage as well as an endothelial vascular decrease in nitric oxide synthetase, producing a significant increase in atherogenesis in comparison with rats that are exposed to filtered air, results that provide further experimental evidence for the possible link between air pollution and MS [31].
There are different hypotheses that describe the possible biological mechanisms that support our findings. Although these mechanisms are still not completely clear, it has been described that air pollutants may perturb autonomic nervous system balance by activating afferent pulmonary autonomic reflexes; additionally, when ozone enters the body through the respiratory tract, it reacts with the existing biomolecules in the fluid that covers the lungs, generating highly reactive products that enter the bloodstream, promoting cascade inflammation mechanisms that can lead to damage in the cardiac vasculature, which in turn can induce arrhythmias, myocyte reduction, contractility, and decreased coronary blood flow due to acute vasoconstriction, which can increase blood pressure [15,22,32]. Similarly, it has been described that exposure to air pollutants may induce the generation and release of endogenous pro-inflammatory mediators and vasculo-active molecules, which can disrupt insulin signaling and impair vasorelaxation [30,33]. The oxidative stress promotes the activation of Nrf2, the heat shock protein 70, NF-kB, increases the expression of a variety of proinflammatory cytokines (TNF-alpha and interleukin 1β), chemokines (Interleukin 8), and adhesion genes, and, finally, some studies report that air pollution exposure is associated with abnormal methylation levels of global DNA and specific genes involved in glucose homeostasis and lipid metabolism pathways [34,35]. One study in 2015 proposes that short-term exposure to ozone can increase circulating cortisol and is reflective of an activation of a neurohormonal-mediated stress response, likely through the activation of the HPA axis and altered lipid metabolic processes stimulating the adipose lipolysis of triglyceride stores and being liberated into the circulation. The increased lysolipids, likely released from the hydrolysis of cellular and membrane phospholipids, and serum polyunsaturated fatty acids in ozone-exposed humans may be linked to proinflammatory mechanisms due to ozone exposure and showed elevated circulating metabolites of β-oxidation and ω-oxidation; overall, this study demonstrates that ozone exposure in humans is associated with increased release of stress hormones causing lipolysis, as in rodents [21].
Some limitations must be considered when interpreting our results. First, daily variations in ozone air pollutant exposure were evaluated through the daily records of the fixed central monitoring locations (RAMA). The temporal variations in each adolescent’s exposure were assumed to follow those at the central monitoring site, and we did not obtain detailed information about the time spent by each participant; instead, we presumed that exposure was primarily associated with the amount of time spent in the outdoors. To strengthen the validity of this assumption, each adolescent was assigned to the monitoring site closest to his or her home or school by means of a spatial GIS, providing greater variability in the data. Second, the possibility that the results were a consequence of the poor control of confounders, such as socioeconomic status, however, is unlikely because all our participants came from the same study area and attended the same public school system; also, the design of the study excluded women with a high risk of pregnancy and/or pre-existing illness and the models were adjusted for potential confounders.
On the other hand, within the main strengths of this study, we can mention that it is a longitudinal study with a good participation rate ($89\%$ of participants from the cohort entered this evaluation). Additionally, the fact of having valuable information on other variables of importance at different times of the follow-up gives greater support to the findings. In this sense, being a cohort study, we can highlight that we had the possibility of observing that the exposure precedes the event, and our results were based on an observational analysis of the cohort; also, we used mixed linear multivariate models to account for the strong patterns of association among the outcome and exposure variables and for the control of confounders.
In this study, we adjusted for variation in physical activity, an important prediction factor in metabolic syndrome. In previous studies [23], it has been evaluated that certain metabolic changes due to ozone exposure can vary after exercising. Even so, we did not find significant differences when it was explored in a stratified way between adolescents who did vigorous activity and those who did not. Perhaps in a future study, it would be advisable to expand the sample size and evaluate these activities more precisely, as well as to support the results presented in this study.
## 5. Conclusions
Our data show that short-term ambient air ozone exposure is associated with the components of MS. These adverse effects were observed in a longitudinal setting in a free-living population, more specifically in a cohort of obese adolescents. These results could have significant public health policy implications, and derived that metabolic syndrome was defined by a combination of various cardiovascular disorders (hypertension, dyslipidemia), elevated triglycerides and lowered high-density lipoprotein cholesterol, raised fasting glucose, obesity, and is associated with systemic inflammation and increases the risk of cardiovascular disease in the early stages of life.
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|
---
title: Prediction of Relevant Training Control Parameters at Individual Anaerobic
Threshold without Blood Lactate Measurement
authors:
- Claudia Römer
- Bernd Wolfarth
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001845
doi: 10.3390/ijerph20054641
license: CC BY 4.0
---
# Prediction of Relevant Training Control Parameters at Individual Anaerobic Threshold without Blood Lactate Measurement
## Abstract
Background: Active exercise therapy plays an essential role in tackling the global burden of obesity. Optimizing recommendations in individual training therapy requires that the essential parameters heart rate HR(IAT) and work load (W/kg(IAT) at individual anaerobic threshold (IAT) are known. Performance diagnostics with blood lactate is one of the most established methods for these kinds of diagnostics, yet it is also time consuming and expensive. Methods: To establish a regression model which allows HR(IAT) and (W/kg(IAT) to be predicted without measuring blood lactate, a total of 1234 performance protocols with blood lactate in cycle ergometry were analyzed. Multiple linear regression analyses were performed to predict the essential parameters (HR(IAT)) (W/kg(IAT)) by using routine parameters for ergometry without blood lactate. Results: HR(IAT) can be predicted with an RMSE of 8.77 bpm ($p \leq 0.001$), R2 = 0.799 (|R| = 0.798) without performing blood lactate diagnostics during cycle ergometry. In addition, it is possible to predict W/kg(IAT) with an RMSE (root mean square error) of 0.241 W/kg ($p \leq 0.001$), R2 = 0.897 (|R| = 0.897). Conclusions: *It is* possible to predict essential parameters for training management without measuring blood lactate. This model can easily be used in preventive medicine and results in an inexpensive yet better training management of the general population, which is essential for public health.
## 1. Introduction
Reluctance to undertake physical activity and obesity are associated with an increase in cardiovascular diseases, in particular in coronary heart disease, diabetes mellitus and a higher level of inflammation [1,2,3]. Research has demonstrated that engaging in regular physical activity leads to a reduction in both morbidity and mortality rates [4,5]. As our society continues to face an increasing burden of disease from conditions such as diabetes mellitus, arterial hypertension, and obesity, the cost of treating these cardiovascular diseases will also become a growing financial strain on the healthcare system in the future [6,7,8,9,10,11]. Especially, during the COVID-19 pandemic, regular exercising decreased significantly [12]. Consequences may be not only increasing obesity and cardiovascular diseases but also mental health conditions [13,14]. The WHO Guidelines recommend regular activity (150–300 min per week of moderate intensity, or 150 min per week of intensive physical activity) [15]. In order to achieve comprehensive prevention, simple and inexpensive training recommendations and the prescription of physical activity are required [16]. Optimizing training intensity recommendations in cardiopulmonary training requires that the essential parameters heart rate (HR) and training load (W/kg) at individual anaerobic threshold (IAT) are known [17]. It is necessary to define individual training parameters, as several studies have confirmed that training adherence depends on training intensity [18,19]. Adherence to physical activity is one of the most relevant factors to better health [20]. Realization of exercise recommendations by health workers is reported to be insufficient [21,22], which might be caused by the lack of personalized trainings programs. Overexertion, defined as the transition from the aerobic to the anaerobic metabolism [23], may therefore reduce training adherence. To perform optimal training, knowing the heart rate at the individual anaerobic threshold is essential for better training control. Measuring these important parameters (HR(IAT) and W/kg(IAT)) is largely limited to competitive athletes in dedicated sports medicine centers. Performance diagnostics with blood lactate is one of the most established methods for these kinds of diagnostics [24,25], but it is time- and cost-intensive [26].
The prediction of IAT was early performed by Conconi et al. by determining heart rate threshold which shows a high correlation in runners [27]. There are only a few studies using linear regression models to predict anaerobic threshold on cycling ergometry in the general population. Mostly athletes were examined, and the number of examined subjects is low [27,28,29,30]. Simple methods for measuring performance are crucial for allowing the general population access to the appropriate training parameters, particularly for individuals who are new to sports. There is a lack of studies examining prediction models for essential training parameters in the general population using non-invasive methods [31,32,33,34]. The aim of this study was to assess HR(IAT) and W/kg(IAT) by linear regression models to establish an easy access training recommendation for the general population, as physical inactivity is an important predictor of mortality [35].
## 2. Methods
In this study, a retrospective analysis was performed. Secondary data of the Sports Medicine Institute of the University Medical Center Charité Berlin were analyzed for the prediction of HR(IAT) and W/kg(IAT) without lactate measurement. All of the ergometry protocols conducted between 2015 and 2017 were obtained from the institutional sports medicine information system. Patients who were not included in the study were excluded for specific reasons: For the present analysis, the following inclusion criteria were applied: patients (I) with missing lactate data, (II) with missing heart rate data, and (III) with insufficient protocols and implausible data. Exclusion criteria were cardio-pulmonary and musculoskeletal diseases. The study was conducted in accordance with the Declaration of Helsinki and with the approval of the local ethics committee of Humboldt University Berlin.
## 2.1. Peak Performance Test
The performance test on the cycle ergometer started at 50 Watt (W) and was raised in 25 W steps after 3 min. Resting heart rate, blood pressure and blood lactate were measured before the lactate step test was initiated. During the test, heart rate was continuously measured by electrocardiogram. Blood pressure, blood lactate and RPE (rate of perceived exertion) were measured in the last thirty seconds of each step. Determination of lactate threshold (LT = first significant increase in blood lactate during exercise test starting from the resting lactate values) and individual lactate threshold (IAT = second significant increase in blood lactate and transition from aerobic to anaerobic metabolism) were assessed using the method of Dickhuth et al. [ 36].
## 2.2. Statistical Analysis
The Kolmogorov–Smirnov test was used to determine whether the continuous variables were normally distributed, and a descriptive analysis was carried out. The power per kilogram body weight at the individual anaerobic threshold (W/kg(IAT)) was used as a measure of individual physical performance. The Pearson correlation coefficient and root mean square error (RMSE) were used to assess correlation. A two-sided significance level of α = 0.001 was set as the threshold for determining statistical significance. Before performing multiple regression analysis of HR (IAT), all parameters were checked individually for their respective correlation and linear regression with a very high level of significance $p \leq 0.001.$ *Descriptive analysis* was performed and is shown in Table 1. Minimum, mean and maximum HR and HR after one-, three- and five-minutes post-workout were examined. All parameters with a significance level $p \leq 0.001$ were removed. All statistical analyses were performed using the SPSS software (IBM Corp. Released 2016. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY, USA: IBM Corp.) and Matlab (MATLAB and Statistics Toolbox Release 2022b, The MathWorks, Inc., Natick, MA, USA).
## Study Population
The population consisted of 188 competitive athletes (football, handball, athletics, volleyball, etc.), 226 prevention and rehabilitation athletes (with various chronic diseases, e.g., orthopedic, rheumatological or other autoimmune diseases) and 820 recreational athletes. None of the athletes had known coronary artery disease or heart failure. A total of 579 had a BMI greater than 25. Overall, 141 individuals of the 226 prevention and rehabilitation athletes had a BMI greater than 25, 52 individuals in this subgroup had a BMI greater than 30, and 13 individuals had a BMI greater than 35. Descriptive analysis is shown in Table 1.
## 3. Results
We performed multiple linear regression analyses for both HR(IAT) and W/kg(IAT) using personal parameters such as gender, age, height and weight as well as performance measurements such as heart rate and power as input parameters.
After each multiple regression analysis, we removed one parameter with the highest p-value until the desired significance level of $p \leq 0.001$ was met by all remaining input parameters.
After completing this process, the following input parameters are included in the multiple linear regression analysis for determining the HR(IAT); see equation in Figure 1: gender; weight; mean power (Pmean); maximum power (Pmax); mean HR (HRmean); and minimal HR (HRmin). Using these parameters in multiple linear regression, the determination of HR(IAT) is possible with an RMSE = 8.77 bpm. The adjusted R-squared is 0.798.
The proposed linear regression model for determining HR at IAT was compared to the *Karvonen formula* (Figure 2). The proposed method shows a lower RMSE (8.77 bpm) than the *Karvonen formula* (RMSE of 11.2 bpm), and HR determination at IAT is more exact using linear regression.
The essential parameter W/kg(IAT), which is especially important for determining changes in performance, was also examined. As explained above, the respective input parameters were iteratively removed unless they met a level of significance $p \leq 0.001.$ This includes the removal of the heart rate recovery (HRR = HRmax-HR after 5 min of recovery) parameter, as its significance level was $$p \leq 0.057.$$ *As a* result, only the following four parameters were included for multiple linear regression analysis to determine W/kg(IAT): gender; body weight (kg); mean power (Pmean); maximum power (Pmax); maximum HR (HRmax). Using these parameters in multiple linear regression (Figure 3), the determination of W/kg(IAT) is possible with a root mean square error, RMSE = 0.241 W/kg. The adjusted R-squared was 0.897. Figure 3 shows the comparison between W/kg(IAT) values on the horizontal axis determined by means of blood lactate values and the W/kg(IAT) values on the vertical axis determined by means of multiple linear regression.
To better understand the impact of individual input parameters on the W/kg(IAT), we have visualized the regression parameters using an effect plot in Figure 4. For this, we multiplied the weights of the formula in Figure 3 with the actual values in our database. The latter are normalized by subtracting their respective mean values, as this offset is already modeled in linear regressions, in our case 2.2306 W/kg.
## 4. Discussion
This retrospective analysis of this dataset was examined to predict heart rate at IAT as well as training load (W/kg) at IAT without measuring blood lactate values for cycle ergometry. Both heart rate and the number of watts at the individual anaerobic threshold are essential parameters for training control. These parameters are currently best determined via blood lactate diagnostics during ergometry performance testing. A total of 1234 performance protocols with blood lactate in cycle ergometry were analyzed. Multiple linear regression analyses were performed to predict the essential parameters heart rate at individual anaerobic threshold (HR(IAT)) and workload at individual anaerobic threshold (W/kg(IAT)) by using routine parameters for ergometry without blood lactate. HR(IAT) can be predicted with a root mean square error, RMSE of 8.77 bpm ($p \leq 0.001$). The intention of this regression model is the acceleration of preventive medicine by using every ergometry to compile an individual training recommendation in primary and secondary prevention. At once, the greatest challenge and the utmost benefit is a continuous training adherence. To avoid overexertion, knowing the individual anaerobic threshold is necessary. This applies for preventive medicine as well as for pre-habilitation to meet the proposed exercise recommendation of 150–300 min per week by the WHO. Future work implies to supervise pre-habilitation patients with the recommended regression model, as standard cycle ergometry can be performed by every general practitioner, and patients can be examined close to the place of residence.
There is a need for more research in preventive medicine that focuses on developing better preventive training control methods for the general population. The main leverage point is the prevention of cardiovascular diseases, which is one of the most causes of morbidity and mortality. Furthermore, as societies are becoming older, frailty amongst the elderly population is a growing financial burden [37]. Increasing frailty goes in line with a decrease in quality of life. Regular physical activity can reduce frailty [38], and research has also shown an improvement for quality of life [39]. This challenge for health systems needed to be addressed by establishing easy access methods for training control parameters and training programs for the main population.
Although lactate performance diagnostics is a well-established method for recording performance, methodological errors must be considered. Due to constantly increasing blood lactate values and only intermittently measured lactate values by using the capillary blood of the earlobe, measurement inaccuracy must be assumed. Furthermore, certain nutritional methods (e.g., low-carb) are associated with an altered lactate curve [40]. Due to glycogen depletion, incorrect low blood lactate is measured, which can lead to a misinterpretation of the lactate curve. A large number of studies examined different threshold models, whereby an exact determination of the aerobic and anaerobic threshold is better to be regarded as an aerobic and anaerobic transition [41,42]. The earlier assumption of fixed aerobic and anaerobic thresholds soon showed individual differences in further investigations and the need to consider individual threshold methods. Despite these challenges of metabolic threshold models, the lactate determination for training recommendation was established as daily routine in contrast to other methods, in the last decades. The cardio-pulmonary exercise test (CPET), which is applied to determine VO2max and ventilatory thresholds, is also a method of assessing an individual’s physical fitness. For the collection of respiratory and metabolic parameters, this is a complex measurement, and expensive equipment with regular calibration is needed. This method is significantly more time-consuming, requires special trained nurses or sport scientists, and is therefore primarily reserved for patients with cardiac and pulmonary diseases. The RPE and the walking test are simple methods to avoid overexertion in preventive and recreational sports. Nevertheless, the application of RPE is difficult for people who are inexperienced in sports and can easily lead to overexertion or unchallenged activity. In preventive medical examination, an individual training recommendation is increasingly demanded by patients.
It could be shown that the lactate accumulation shows inter-individual differences [43,44,45], and fixed submaximal threshold concepts (of 2 mmol/and 4 mmol/l) should not be applied for individual training recommendations. The lactate concentration in the aerobic–anaerobic transition range is also dependent on muscle recruitment in different movement patterns [46,47]. Individual training recommendations should therefore be specific to the sport. Several studies have been able to prove the training effect of exercise based on the individual anaerobic threshold. The determination of the HR(IAT) during the ergometry without lactate diagnostics can be used for recommendations of basic endurance and interval training and for prevention programs.
Lactate measurement examination is an expensive, as lactate measurement equipment and special trained nurses are required, and time-consuming examination and has until now rarely been covered by health insurance companies. Depending on the individual, the test takes forty to fifty minutes, including warm-up, measuring resting heart rate and recovery time in the end. In various studies, the lactate transition range and the maximum lactate steady state showed a connection with hormonal and immunological changes, which at least supports the assumption of an upper anaerobic threshold [48,49,50]. Therefore, the individual anaerobic threshold should be considered when making training recommendations for the general population, since long-term training with a disproportionate increase in lactate can lead to training non-adherence and vulnerability for infections or injuries, thus bringing the known advantages of regular physical activity [49,50]. Our regression model allows a good prediction of HR(IAT) with an RSME 8.77 bpm and a prediction of W/kg(IAT) with a deviation of 0.241 W/kg. Shen et al. examined the velocity at lactate threshold on a treadmill by using several prediction models with different heart rates [31]. As with the data in this study, age was not a significant parameter and was excluded in the regression models. However, body mass index was excluded [31], and this study only included body weight for predicting W/kg(IAT). Interestingly, women seem to show a slightly higher W/kg unless body height is considered to be negative, in which case these effects neutralize each other. Differences between the results of Shen et al. also might be attributed to different physical activity on a treadmill and a cycle ergometry [31]. Sport-specific differences for HR(IAT) and W/kg(IAT) needed to be considered [51,52,53], and further research on regression models for running and rowing should be addressed in future studies.
The exclusion of heart rate recovery for the prediction of W/kg(IAT) was justified by not meeting the significance level of $p \leq 0.001.$ This suggests that HRR may not be a singular predictor for evaluating physical fitness [54]. As research results are inconclusive and the evidence is weak [55,56,57], further research should be performed in larger studies. However, HRR should be recorded as a longitudinal parameter [54], since changes in HRR showed good results in recognizing cardiopulmonary diseases [58,59]. In this context, HRR is an essential parameter, which should be monitored regularly to detect changes in autonomic function [60].
The *Karvonen formula* is mainly used for training control in popular sports. The *Karvonen formula* uses the heart rate reserve, and it requires that the maximum heart rate and resting heart rate are determined to apply the formula [61]. By multiplication with a fitness level factor (0.8 for athletes; 0.6 for recreational athletes; and 0.3 for untrained people), the heart rate at the anaerobic threshold can be calculated [61]. The *Karvonen formula* was applied to the examined measurement protocol results in this study. The results of using the *Karvonen formula* with a factor of 0.7, due to the predominantly athletic clientele of the sports medicine university outpatient clinic, are shown in Figure 2. In comparison to the measured heart rate at IAT, the scatter diagram reveals a good correlation with a higher RMSE of 11.2 bpm in comparison to the regression model of this study (RMSE 8.77 bpm). The shape of the curve indicates an overestimation of the low values and an underestimation of the high HR values at the IAT. The *Karvonen formula* also uses resting HR and maximum heart rate to determine HR(IAT). Both heart rates are individual values, and maximum heart rate is especially difficult to determine for the general population, especially as maximum heart rate changes with age [62,63,64]. Thus, an initial determination of maximum heart rate is also required for the *Karvonen formula* and should be acquired under medical supervision, especially for individuals > 35 years to cardiovascular adverse events. Due to the improved prediction based on a regression model determined in this study, we recommend cycle ergometry in medical supervision with the regression model identified in Figure 1.
In preventive medicine, ergometry is also recommended for every sports beginner and returner over the age of 40 (for men) and over 55 (for women), according to the German guideline for preventive medical check-ups in sports. In contrast to lactate performance diagnostics, ergometry can be carried out by almost any general practitioner or as part of an occupational medical examination. However, an individual training recommendation is usually only given by sports physicians, since a respective specialization for individual training advice is missing. Due to the increasing number of cardiovascular events, obesity and an increasingly aging population, there is a health gap to reach the general population with individual training recommendations and to examine the full scope of preventive medicine. The proposed regression model differs from other studies with a significantly higher number of study protocols examined in a heterogeneous population [27,28]. In addition, further research should examine whether shorter exercise tests, such as the 6-MWT (6-min walking test), can be used for a regression model prediction of essential parameters for training control [65]. Studies in obese individuals demonstrated promising potential to assess individual respiratory threshold [65,66,67]. Especially obese and older subjects or individuals with other disabilities which rule out cycle ergometry might benefit [65]. Thus, a regression model for cycle ergometry with a shortened protocol should be addressed in future studies, as these shorter tests can be performed more regularly to examine training improvement and address the changed HR(IAT) after consistent training [66]. Considering the results of this retrospective study, we recommend the output of a training program with an individual training heart rate at IAT and watt range at IAT, provided after every check-up examination using cycle ergometry in medical supervision, including the recommendation of the WHO [15].
## Future Work
As it is known that also children and adolescent obesity has been continuously rising during the last few years [68], there is a need to find approaches for physical activity in these age groups, since chronic diseases will start in early ages and will have a huge impact on GNP. Further studies are necessary to establish regression models for HF(IAT) for adolescents to teach them a healthy and adequate regular exercise program with a potentially better exercise adherence, since exercise adherence is one of the most encouraging parameters for health [20]. These exercise programs should be established in schools under supervision and with regular physical examinations. Furthermore, research has shown that pre-habilitation can be a relevant benefit prior to chemotherapy or extensive operations [69,70]. As neoadjuvant chemotherapy is associated with decreasing aerobic endurance [70,71], there is a need for easy access training therapy not only in primary but also in secondary and tertiary prevention. Measuring HF(IAT) at routine secondary and tertiary preventive examinations may improve exercise adherence; further research in these subgroups is necessary.
Further goals of these examinations are an establishment of pre-habilitation offers close to home, besides the expansion of the preventive individual training recommendations for the general population. An individual training recommendation for pre-habilitation could therefore be made directly by the attending general practitioner or cardiologist. A gap in care, of mostly only a few sports medicine offers, could thus be closed. Further examinations with other ergometer types (rowing ergometer, elliptical) are planned in order to enable a conversion of HR(IAT) and different ergometer types in prevention and pre-habilitation.
## 5. Limitations
Incorrect entries during the manual transmission of the lactate values must be considered. These were minimized in advance by means of a plausibility check of the entire data set. The sample size in this study is appropriate for generating a valuable prediction in comparison to other studies [31,72]. The age and gender distribution may vary in comparison to the general population, as the examined population includes more physically active individuals, especially in the younger age groups. Furthermore, a heart rate deviation of 8.77 bpm is not appropriate for athletes in professional sports, although a blood lactate test or cardiopulmonary exercise test (CPET) is still recommended for this clientele. This regression model is suitable for cardiorespiratory endurance sports. It should be noticed that it is not applicable for resistance or interval training; individual training recommendations for these kinds of training should be considered.
At the same time, ergometry offers a simple and inexpensive measuring method that can be performed in the outpatient and inpatient sector and represents a suitable procedure for popular sports and preventive medicine to monitor cardiorespiratory training.
## 6. Conclusions
In conclusion, it is possible to derive relevant parameters for training control after a standard cycle ergometry without performing a blood lactate test by using regression models to predict HR(IAT) and W/kg(IAT) for the general population. This enables training control without blood lactate diagnostics or CPET and does achieve enormous time and financial savings for active exercise therapy as well as for preventive and rehabilitative medicine. Regular individual test repetition allows the consideration of short-term training adaption and supports continuous training progress.
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|
---
title: 'The Effects of Acute Sleep Curtailment on Salt Taste Measures and Relationships
with Energy-Corrected Sodium Intake: A Randomized Cross-Over Trial with Methodology
Validation'
authors:
- Chen Du
- Russell Keast
- Sze-Yen Tan
- Robin M. Tucker
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001849
doi: 10.3390/ijerph20054140
license: CC BY 4.0
---
# The Effects of Acute Sleep Curtailment on Salt Taste Measures and Relationships with Energy-Corrected Sodium Intake: A Randomized Cross-Over Trial with Methodology Validation
## Abstract
[1] Background: Sleep may be a factor that influences the taste–dietary intake relationship. The effect of sleep on salt taste measures has not been adequately studied, and no standardized methodology has been developed for measuring salt taste preference. [ 2] Methods: A sweet taste forced-choice paired-comparison test was adapted and validated to determine salt taste preference. In a randomized cross-over trial, participants slept a curtailed night ($33\%$ reduction in sleep duration) and a habitual night, confirmed by a single-channel electroencephalograph. Salt taste tests were conducted the day after each sleep condition using five aqueous NaCl solutions. One 24-h dietary recall was obtained after each taste test. [ 3] Results: The adapted forced-choice paired-comparison tracking test reliably determined salt taste preference. No changes in salt taste function (intensity slopes: $$p \leq 0.844$$) or hedonic measures (liking slopes: $$p \leq 0.074$$; preferred NaCl concentrations: $$p \leq 0.092$$) were observed after the curtailed sleep condition compared to habitual sleep. However, sleep curtailment disrupted the association between liking slope and energy-corrected Na intake ($p \leq 0.001$). [ 4] Conclusions: The present study serves as the first step toward more standardized taste assessments to facilitate comparison between studies and suggests accounting for sleep when exploring taste–diet relationships.
## 1. Introduction
Although taste plays a critical role in determining dietary intake [1,2,3], laboratory-based taste measurements are often unreliable predictors of intake [4,5]. Using salt taste as an example, some studies reported that the hedonic measures, including liking and preference, of salty stimuli were positively correlated with dietary salt and salty food intake [6,7,8], while other studies reported that salt liking did not predict dietary salt intake [9,10]. Relationships between diet and measures of salt taste function, such as taste thresholds and intensity ratings, are also varied; some studies report that these measures do not predict intake [11,12,13,14,15,16], while others report that they do [17,18,19,20]. Given the conflicting findings, exploring factors that may influence the taste–diet relationship is warranted.
Sleep is one physiological process that has been shown to alter measures of sweet taste [21,22] as well as dietary intake [23,24,25]. Increases in preferred concentrations for sucrose [21,26] and sucralose were observed after one night of curtailed sleep [21]. When testing sweet taste using solid foods, sweeter oat crisps were liked more after curtailed sleep compared to habitual sleep [26]. In summary, one night of short sleep altered hedonic measures of sweet taste, which can lead to undesirable dietary behaviors such as increased consumption of sweets and high calorie foods [24,27,28,29,30].
While alterations in sweet taste after sleep curtailment have been demonstrated, there is limited investigation into the effects of sleep on salt taste. One cross-sectional study reported an association between short sleep duration (defined as <6 h) and increased odds of self-reported altered taste perception in general [31]. A separate study demonstrated self-reported sleepiness was not associated with salt taste sensitivity, but positively associated with cravings for savory foods [32]. Two experimental studies investigated the effects of complete absence of sleep (sleep deprivation) and shortened sleep duration (sleep curtailment) on salt taste function. One study reported no change in salt taste detection threshold after 24, 48, and 72 h of sleep deprivation [33], while the other study demonstrated no differences in salt taste sensitivity between individuals with long (>7 h) versus short (<7 h) sleep [34]. To our knowledge, these are the only investigations that investigated the effects of sleep on salt taste measures.
Apart from limited studies in this area, there are also several limitations in these previous studies that investigated the effects of sleep on salt taste measures. First, none of the studies included hedonic measures of salt taste, which are more reliable predictors of dietary intake than thresholds [4,5]. Second, the complete absence of sleep is not commonly experienced in the general adult population [35,36]. Third, the study that compared short versus long sleep duration did not prescribe the curtailed sleep duration based on the participants’ habitual sleep duration, which could have created an unequal sleep curtailment duration for individuals, i.e., some individuals were curtailed more or less than others. This methodological limitation could reduce the ability to detect differences, should they exist. Given the small number of studies and these limitations, research investigating the effects of sleep curtailment on both hedonic and functional measures of salt taste is needed.
To address the lack of understanding regarding salt taste hedonics, it is important to develop a method that enables the classification of adults based on their salt-liking phenotypes, e.g., likers and dislikers. This is important, as previous work suggests that sweet-liking phenotype and sleep curtailment interact [21,22,26,37]. Sweet-liking phenotypes are used to broadly categorize individuals as sweet likers or dislikers, and have been repeatedly identified in the literature by our group [22,26,37,38] and others, e.g., [39,40,41]. Whether salt phenotypes are identifiable and whether they influence taste responses after sleep curtailment have not been previously explored.
The present work includes two components: part one validated a salt taste preference evaluation method, and part two applied this method in an experiment that investigates the effect of salt taste curtailment on salt taste preference. Four aims were addressed: First, since a standardized salt preference test does not exist [4], the methodology validation component of the study aimed to examine whether an adapted version of a forced-choice paired-comparison test developed for sweet taste [42] could be used in determining salt taste preference (aim 1). Part two investigated the effects of sleep curtailment on salt taste hedonic measures (liking and preferred concentration) and function (intensity) (aim 2); examined whether any of the taste tests were associated with energy-corrected sodium intake (aim 3); and explored whether identifiable salt-liking phenotypes were present (aim 4). The researchers hypothesized that:The adapted forced-choice paired-comparison tracking test could serve as a valid and reliable tool to determine salt taste preference. Salt taste liking, measured by the slope of liking ratings of five salty solutions across different concentrations would be steeper after a night of curtailed sleep compared to a night of habitual sleep; however, salt taste function, measured by the slope of intensity ratings would not be affected. Additionally, preferred salt concentration, measured by the newly developed test, would increase. Hedonic measures of salt taste (liking slope and preferred salt concentration) and salt taste function (intensity slope) would be positively associated with energy-corrected Na intake after both the habitual and the curtailed night of sleep. Distinguishable salt-liking phenotypes would be identified.
## 2.1. Participants
Participants, including both males and females ($$n = 59$$), between the ages of 18 and 45 years with no diagnosed sleep conditions who reported habitually sleeping 7 to 9 h per night and had regular weekday bedtimes were recruited for the study. Potential participants who had conditions that may affect taste function and dietary intake, such as type 2 diabetes and cardiovascular diseases, were excluded from the study. A screening questionnaire was used to check eligibility. Sleep quality, measured with the Pittsburg Sleep Quality Index (PSQI), was examined at screening for recruiting approximately equal numbers of good and poor sleepers [43]. PSQI scores range from 0 to 21 with higher scores indicating worse sleep quality; scores ≥ 5 indicate poor sleep quality, while scores < 5 indicate good sleep quality.
## 2.2. Study Protocol and Timeline
This was a randomized, crossover study that included a consent visit, plus two lab visits after a night of habitual and curtailed sleep in random order. The two lab visits were at least seven days apart to provide a washout period to recover from the one night of shortened sleep. After each lab visit, participants were asked to fill out one 24-h dietary recall the following morning using the Automated Self-Administered 24-h (ASA24®) Dietary Assessment [44] to record everything consumed during the day of the taste test, including meals, snacks, and beverages. ASA24® has been validated and widely used in clinical studies [44,45,46,47].
## 2.3. Compliance to Sleep Protocol
For the sleep protocol, participants were instructed to wear the Zmachine (General Sleep, Columbus, OH, USA), a single channel electroencephalogram (EEG) that monitors sleep duration and stages, during sleep on both nights. The Zmachine has been validated against polysomnography (PSG) [48] and is widely used in sleep research studies [22,26,30,49]. Participants were instructed to put the Zmachine on 30 min before going to bed. For the habitual night, bedtime and waketime were determined based on typical self-reported bed and wake times while curtailed bed and wake times were calculated by reducing self-reported total habitual sleep time by $33\%$ and delaying bedtime [22,26]. For example, one participant reported typically going to bed at 10:00 p.m. and waking up at 7:00 a.m. The total bedtime for the participant was 9 h. For the curtailed night of sleep, a 3-h reduction of sleep ($33\%$ of reduction in sleep duration) was applied, and the bed and wake times were 1:00 a.m. and 7:00 a.m., respectively. If a participant did not follow the sleep protocol they were assigned, they were asked to repeat the protocol.
## 2.4. Consent Visit
During the consent visit, eligible participants completed the demographic questionnaire and were measured for weight, height, and percent body fat (%BF). Demographic questions included gender, race, ethnicity, and age. The height of participants was measured using a standing stadiometer (HM200P, Charder, Taichung, Taiwan). Weight and %BF were evaluated using a bioelectrical impedance scale (TBF-400, Tanita, Arlington Heights, IL, USA).
## 2.5. Lab Visits
Participants returned to the lab after one night of curtailed sleep and one night of habitual sleep. These two lab visits were identical. At each visit, the sleep recording data were reviewed in the *Zmachine data* viewer for each participant prior to taste testing to confirm that the participant followed the intended (habitual vs. curtailed) sleep protocol. Participants who did not follow the sleep protocol, which was defined as more than a 30-min discrepancy between actual sleep time and protocol determined sleep time, were asked to repeat a night of sleep following the relevant protocol at least a week later. After confirmation that the participant had adhered to the assigned protocol for that night, they were asked how they would rate the previous night’s sleep quality and duration on a visual analog scale (VAS) of 0 to 100. Zero indicated far below average while 100 indicated far above average.
## 2.6. Taste Testing
The five NaCl solutions presented to participants included concentrations of 0.05, 0.09, 0.15, 0.19, and 0.25 M NaCl, which were selected to reflect the spectrum of salt taste in a real-world food environment [50]. For example, the lowest concentration salt solution (0.05 M) represents the salt concentration of milk, while the most concentrated salt solution (0.25 M) reflects the salt concentration of pickle juice. Concentrations were pilot tested in the laboratory prior to the study to reflect commonly experienced dietary salt exposures [50] and to ensure they were distinguishable from each other.
First, five 10 mL salty solutions made with NaCl and distilled water in different concentrations were presented to participants in 30 mL plastic portion cups, in order of increasing concentration (0.05, 0.09, 0.15, 0.19, and 0.25 M). Participants were asked to put each solution in their mouth and swish for as long as needed to thoroughly evaluate the solution and then expectorate. Next, participants were asked to rate the liking of each solution on a VAS of 0 to 100, where 0 reflected ‘not at all’ and 100 represented ‘extremely.’ Immediately after the liking task, participants were asked how intense they thought the salty solutions were on a VAS of 0 to 100, where 0 indicated ‘not at all’ and 100 signified ‘extremely.’ After rating their liking and intensity, participants were asked to rinse their mouth with distilled water after each sample until no saltiness was perceived. After 30 s, the next sample was tasted.
The preferred salt concentration test followed examination of liking and intensity. This test adapted the two-series forced-choice paired-comparison tracking procedure developed by Mennella et al. [ 2011] [42]. The forced-choice paired-comparison tracking procedure has been validated in determining preferred sweet concentrations [42]. The same 5 concentrations of aqueous NaCl solutions as those used for the liking and intensity tests were used. In the first series of tests, participants were presented a pair of salt solutions, lower concentration first, and asked to taste both solutions while rinsing in between with distilled water. After tasting solutions in pairs, participants identified their preferred salt solution. As with the sweet taste forced-choice paired-comparison tracking protocol, the 0.09 M and 0.19 M NaCl solutions, were presented to participants first for each trial. Then, each subsequent pair presented contained the participants’ previously preferred concentration paired with an adjacent solution, either higher or lower in concentration. At each presentation, the lower concentration was presented first. The tests were repeated until one preferred concentration was sequentially selected twice while the adjacent concentrations to the preferred concentration had been tasted. This preferred concentration was recorded as the preferred concentration for trial 1. Participants were given two minutes to rest in between trial 1 and the second trial to avoid fatigue. For trial 2, the series of tests were repeated; however, the higher concentration of each pair of solutions were presented to participants first to reduce the possibility of an order effect [42]. The preferred concentration for the second series of tests was recorded as the preferred concentration for trial 2. The geometric mean of the preferred concentration for trial 1 and 2 was calculated for each participant to avoid position bias and improve the accuracy in estimating the preferred concentration [42].
## 2.7. Statistical Analysis
Descriptive statistics were performed. Variables are presented as mean ± standard deviations unless specified otherwise. Sample size calculations were performed based on a power of 0.8, an alpha of 0.05, and a median effect size of 0.5. A total sample size of 40 participants was required to achieve $80\%$ power. The geometric mean of preferred salt concentrations from trial 1 and 2 under each sleep condition was calculated and used in analyses. Paired t-tests were used to determine the differences of salt taste function (slope of intensity ratings), preference (slope of liking ratings and preferred concentration), sodium intake, energy-corrected sodium intake, macronutrient intake between the curtailed and the habitual night, after confirming the linearity of these variables were met. Additionally, time in bed, total sleep time, deep sleep, and REM sleep were compared between the curtailed and the habitual night of sleep to verify that the sleep protocol was implemented correctly. To examine whether the adapted forced-choice paired-comparison tracking test is a valid tool for determining salt taste preference, only data from the habitual night of sleep was used. First, the geometric means of preferred salt concentration were compared between visit 1 and visit 2 using one-way ANOVA to ensure no order effect was present. Then, the intensity rating of each concentration of NaCl solution was compared to all others using paired t-tests [42], and a false discovery rate (FDR) of $q = 0.05$ was employed to reduce the risk of Type 1 error. The geometric mean of preferred salt concentrations for trial 1, trial 2, and the overall sample were compared between each other. In addition, bivariate correlations were used to examine the associations between the liking slope and the preferred salt concentration. Additionally, hierarchical cluster analysis with between groups linkage was performed to identify salt-liking phenotypes using the habitual sleep data only, as this night reflects typical behavior. Further, a zero-order Pearson correlation matrix was created to examine the relationships between age, BMI, %BF, PSQI, total sleep time, liking slope, intensity slope, preferred salt concentration, and energy-corrected Na intake. FDR was again used to correct for multiple comparisons. To explore whether variabilities in salt-liking ratings influenced the relationship between liking and intake under different sleep conditions, the variance of liking ratings for each solution was compared between the habitual and the curtailed condition using the Levene test, which is commonly used to determine differences in variances between samples [51,52]. Data analysis was completed using SPSS version 27 (IBM Corporation, Armonk, NY, USA). $p \leq 0.05$ was used to determine statistical significance in all analyses.
## 3.1. Anthropometric and Demographic Information
A total of 59 participants complied with the sleep protocols and completed the study. More than two-thirds of the participants were female, nearly half were white, more than one-third were Asian, and the average BMI was considered to be in the healthy range (Table 1).
## 3.2. Validation of the Adapted Forced-Choice Paired-Comparison Tracking Test in Determining Salt Taste Preference
Analyses indicated that the preferred salt concentration was not different between those who experienced the habitual night first and those who experienced the habitual night second. This result confirmed that no order effect was detected (Table 2).
To evaluate the utility and reliability of the adapted procedure, it was necessary to first confirm that all salt stimuli concentrations were perceptibly different in terms of intensity. This was confirmed ($p \leq 0.001$ for all concentration comparisons, Figure 1).
Next, preferred concentrations across the two trials were compared and no significant difference was found between the two sleep conditions; that is, the preferred salt concentration determined in Trial 1 was not different from Trial 2 ($$p \leq 0.078$$) or from the geometric mean of the two trials ($$p \leq 0.948$$). The concentration of Trial 2 also did not differ from the geometric mean ($$p \leq 0.089$$). Out of the 59 participants, 48 ($82\%$) participants either selected the same concentration for Trial 1 and 2 ($$n = 27$$, $46\%$) or selected neighboring concentrations in Trial 1 and 2 ($$n = 21$$, $36\%$). To further demonstrate the validity of this method, the preferred salt concentration was positively and moderately to strongly correlated with the liking slope of the five test solutions ($r = 0.593$, $p \leq 0.001$).
## 3.3. Compliance of the Sleep Protocol
As intended, the time in bed as well as total sleep, slow wave sleep (SWS), and REM sleep times were different between the habitual and the curtailed nights (Table 3). Additionally, the reduction of total sleep time from the habitual to the curtailed night was $36.1\%$, which confirmed that the $33\%$ sleep duration reduction was achieved, indicating that the sleep protocol for each night was implemented correctly by participants.
## 3.4. No Difference in Salt Taste Hedonic Measures and Function, Sodium and Macronutrient Intake, and Food Cravings between the Curtailed and Habitual Nights
Slopes of liking ($$p \leq 0.074$$, Figure 2) and intensity ratings ($$p \leq 0.844$$, Figure 3) of salt solutions were not different between the habitual and the curtailed nights of sleep. Further, neither preferred concentration of saltiness (habitual 0.12 ± 0.06 M vs. curtailed 0.13 ± 0.06 M, $$p \leq 0.092$$), sodium intake, nor energy-corrected sodium intake differed after the night of habitual sleep compared to the curtailed night (Table 4). In terms of dietary intake, energy consumption (kcal/d), carbohydrate (g/d), protein (g/d), fat (g/d), and Na (mg/d) intake were not different between sleep conditions.
## 3.5. Correlations between Hedonic Measures, Salt Taste Function, and Energy-Corrected Sodium Intake under the Habitual and the Curtailed Sleep Condition
In the habitual sleep condition, the liking slope was positively correlated with preferred concentration and energy-corrected Na intake. Whereas, after sleep curtailment, the association between the liking slope and energy-corrected Na intake no longer existed.
The liking slope was positively associated with higher preferred salt concentration and higher sodium intake after one night of habitual sleep but not after one night of curtailed sleep (Table 5).
Age, BMI, %BF, sleep quality and duration were not associated with hedonic measures of salt, intensity, or energy-correlated Na intake. Additionally, salt taste intensity and preferred salt concentration were not associated with energy-corrected Na intake.
Under the curtailed sleep condition, liking slope positively correlated with preferred salt concentration; however, sleep curtailment disrupted the relationship between liking and energy-corrected Na intake (Table 6). All other relationships did not change when compared to the habitual sleep condition, including the positive correlation between the liking slope and the preferred salt concentration.
In order to explore why sleep curtailment disrupted the relationship between liking and energy-corrected Na intake, standard deviations of liking ratings between the habitual and the curtailed night were compared to test whether the variance in liking ratings under the curtailed condition was increased. The results revealed that variances in liking ratings were larger only for the highest NaCl concentration solution ($$p \leq 0.032$$) after the curtailed night of sleep compared to the habitual night of sleep.
## 3.6. Exploration of Salt-Liking Phenotypes
As there was no significant difference in salt-taste liking between the two sleep conditions, data from habitual sleep was used to explore salt-liking phenotypes. Two phenotypes were identified using hierarchical cluster analysis (Figure 4). Participants in cluster 1 expressed a higher level of liking compared to participants in cluster 2 for each concentration ($p \leq 0.001$ for all). The mean liking ratings of all concentrations in cluster 1 are above the midpoint of the VAS scale while mean liking ratings in cluster 2 are below the midpoint for all concentrations. Based on these results and following the conventions established in the sweet-liking phenotype literature, we propose that cluster 1 represents salt likers and cluster 2 represents salt dislikers. Due to the small sample size ($$n = 19$$) in the salt likers group, statistical comparisons between the two groups were not conducted.
## 4. Discussion
The purpose of the present study was to examine whether the adapted version of the forced-choice paired-comparison tracking procedure could be used as a reliable and valid tool for determining salt taste preference, and to evaluate the effects of sleep on salt taste hedonic measures and function. The results from the methodology validation component of the study demonstrated that the adapted forced-choice paired-comparison tracking procedure could serve as a reliable and valid test to determine salt-taste preference when compared to conventional methods. The randomized cross-over trial demonstrated there were no significant changes in salt-taste function or hedonic measures after one night of curtailed sleep compared to one night of habitual sleep. However, the slope of liking was associated with energy-corrected Na intake, but only under the habitual sleep condition. These results suggest that the lack of associations between salt-taste measures and dietary intake reported by the current literature [4,5] could be attributed, in part, to not accounting for sleep duration.
## 4.1. Validity of the Adapted Forced-Choice, Paired-Comparison Tracking Procedure in Determining Salt Taste Preference
The forced-choice, paired-comparison tracking procedure was originally developed to determine sweet taste preferences for adults [42], but the present study is the first to adapt and validate the same forced-choice paired-comparison tracking test procedures in assessing salt-taste preference. Three validation steps were undertaken. First, participants were able to distinguish the intensity of the salt solutions, hence demonstrating the appropriateness of the salt concentrations used in the test. Second, two trials to determine preferred concentrations were performed, and the concentrations selected for each trial were not significantly different from each other or from the geometric mean of the two trials. Additionally, more than $80\%$ of participants picked either the same or neighboring concentrations for the first and the second trial, indicating the results were reproducible between the two trials. Third, the preferred salt concentration was positively associated with the liking slope, which suggests that the adapted forced-choice paired-comparison tracking test could be used in place of assessing liking slope, should the researcher wish to conduct only one hedonic measure. Given these outcomes, the present study supports the use of the adapted forced-choice paired-comparison tracking test in determining a preferred salt concentration.
One major limitation in sensory research is that there is no consensus on salt-taste test procedures, which makes compairing results from one study to another difficult [4]. For example, some studies measured salt liking on a 9-point hedonic scale, using 1 representing “dislike extremely” while 9 representing “like extremely” (e.g., [53]) while others measured liking using a general Labeled Magnitude Scale (e.g., [1]).
Liking is commonly evaluated with Likert scales or visual analog scales (VAS) [54]. For Likert scales, the options are usually limited to five to seven choices, which may not truly reflect the attitude or feeling of participants [55]. Additionally, it is arbitrary to translate the ordinal results of Likert scales into a continuous variable as is commonly done when analyzing Likert scale data [55]. In terms of VAS, even though such scales can provide continuous data, the common disadvantage of VAS is that participants may use the scale differently, for example, two participants may wish to indicate that the stimulus is liked “slightly,” but that same perception could be marked on very different locations on the scale [54]. Therefore, repeated measures testing is strongly recommended when using VAS so that each participant serves as their own control.
Methods for assessing preference are highly variable, making comparisons across studies challenging, and limitations to these methods exist. For example, some studies ask participants to add salt into broth until the preferred concentration is achieved (e.g., [14]) while others assess self-reported salt preference in foods (e.g., [15]) or frequency of consuming common salty foods (e.g., [56]). One advantage of using the concentrations delineated in the current study is that they have been shown to be distinguishable from each other and span a range of concentrations that encompass commonly encountered sensations when eating or drinking [50]. However, preferred concentration testing is limited in that it identifies only one concentration, which can be heavily influenced by the concentrations selected for testing as well as the differences between each concentration [57]. Therefore, if only relying on one test to assess preference, researchers should carefully consider which one best meets their needs in the context of the proposed research question.
When comparing liking slopes and preferred concentration approaches in determining salt-taste liking, the liking slopes use multiple data points to describe a function, which provides a more comprehensive understanding of taste responses over a range of concentrations. This more holistic evaluation could explain why energy-corrected Na intake was associated with liking slope but not preferred concentration. Results from the present study suggest that if a researcher is attempting to predict sodium intake from taste hedonics, the slope of liking responses is the better choice.
## 4.2. The Effects of Sleep on Salt Taste Function
Sleep curtailment did not affect salt-taste intensity assessments, which aligns with our hypothesis and agrees with what has been reported regarding the effects of sleep curtailment on sweet taste function [16,20,22,26,35,58,59,60,61,62]. Two previous studies examined the effects of sleep duration alteration on salt taste function; one implemented sleep deprivation [33] while the other investigated the effects of short sleep (<7 h) versus long sleep (>7 h) on salt taste function [34]. Both studies reported sleep duration had no effects on salt taste sensitivity; however, it is difficult to compare these results with ours because of the differences in taste measures assessed. The present study examined salt-taste sensitivity at a suprathreshold level using an intensity rating method while the other two studies focused on examining detection thresholds [33,34]. Detection thresholds, on the other hand, are assessed using salt concentrations that span a range from undetectable to detectable [35], with the goal to determine the lowest salt concentration that can be detected. Thus, detection threshold testing and intensity testing measure two different attributes of taste function [63]. In terms of the effects of sleep on sweet-taste function, previous studies demonstrated that sweet taste function, measured by both intensity rating [22,26] and detection threshold [34], did not differ after one night of curtailed sleep. Thus, current and previous findings suggest both salt- and sweet-taste function is relatively robust after one night of short sleep. Future work should focus on investigating the effects of chronic sleep curtailment on sweet- and salt-taste functions.
## 4.3. The Effects of Sleep on Hedonic Measures of Salt Taste
Contrary to our hypothesis, sleep curtailment for one night did not affect hedonic measures of salt taste, which contradicts our findings on the negative effects of curtailed sleep on sweet-taste hedonics [22,26]. This difference could be attributed to differences in the activation of the reward system of the brain by these two tastes [64]. Neural and behavioral reactivity to pleasurable experiences increase after sleep deprivation and curtailment [65]. For example, under conditions of shortened sleep, consuming sweets becomes more pleasurable after one night of short sleep; therefore, liking and consumption of sweets increase [66]. However, previous studies noted the anterior insula of the brain, which is the putative primary taste cortex [67], was activated more with NaCl than with sucrose solutions [64]. The anterior insula plays a role in negative valence-specific responses in taste [68], responding more to taste that is unpleasant. Salt solutions are often rated as less pleasant compared to sucrose solutions [64]. Additionally, under sleep deprivation or curtailment, the ability for the anterior insula to discriminate reward versus punishment decreased [69,70,71]. Given the anterior insula is activated more for salty taste and is less sensitive under short sleep conditions, acute sleep curtailment may not increase the liking of salt. In summary, differences in the effects of sleep on salty vs. sweet hedonics could be because of differences in the valence and neural activity produced by these tastes.
## 4.4. Associations between Salt Taste Measures and Dietary Intake
Curtailed sleep eliminated the expected relationship between salt taste liking and energy-corrected Na intake, likely due to curtailed sleep contributing to greater variance in salt liking of the highest concentration. We noted that participants reported a significantly higher variability in liking ratings only for the highest NaCl concentration solution (1.46 g NaCl/100 mL), which is within the range of high salt foods. For example, NaCl concentration of pickle juice is 3.18 g NaCl per 100 mL, which exceeds the highest concentration tested [72]. Sleep curtailment has been shown to negatively affect the prefrontal cortex of the brain, which decreases decision-making and self-monitoring abilities [73,74]. This may explain why liking ratings varied more under the curtailed sleep condition; however, why only ratings of the highest NaCl concentration solution were affected is unknown. Further exploration regarding the relationship between taste measures and diet intake under curtailed sleep conditions is needed.
The association between salt-taste liking slope and energy-corrected Na intake was only present under the habitual sleep condition, which provides one possible explanation for the lack of associations between taste measures and dietary intake reported in the literature [4,5]. Almost all studies investigating the relationships between taste measures and dietary intake fail to account for sleep curtailment or deprivation [4,5]. In the present study, we observed that the association between salt-taste liking slope and energy-corrected Na intake disappeared after one night of curtailed sleep, which suggests that sleep curtailment disrupted the previously observed association between liking and intake. Given that one in three adults worldwide do not routinely achieve adequate sleep [75,76,77,78] failing to account for short sleep duration could obscure taste test-dietary intake relationships. Therefore, the findings from the present study suggest that sleep duration should be considered when investigating relationships between taste and diet.
## 4.5. Identification of Salt-Liking Phenotypes
Salt-liking phenotypes, including salt likers and salt dislikers, were detected. Individuals who rated liking of all sodium concentrations above the mid-point of the liking scale were identified as “salt likers” while “salt dislikers” rated likings of all solutions below the mid-point. Both salt-liking phenotypes demonstrated a decline in liking as concentrations of sodium increased.
The salt-liking phenotypes identified in this study differ from the established sweet-liking phenotypes reported in the literature, e.g., [38,39,40,79]. Sweet-liking phenotypes reliably identify sweet “likers” who show increased liking as concentrations of sweeteners increase; sweet “dislikers” who report decreased liking as concentrations of sweeteners increase; and “inverted U-shape” responders, whose liking increases and then falls as the concentrations of sweeteners go above the most liked concentrations. Salt “liker” and “disliker” liking patterns followed the same curve but were at different places on the scale. The reasons why salt-liking and sweet-liking phenotypes are different warrant further exploration, particularly using real foods, as aqueous salt solutions are not routinely consumed; whereas, some sweet taste exposures are fairly limited in terms of sensory input, e.g., sugar sweetened beverages.
## 4.6. Strengths and Limitations
This work has several strengths. First, the study included objective sleep data to validate sleep duration and verify compliance to sleep protocols. Second, both salt-taste function and hedonic measures were examined, which provide a more comprehensive evaluation of salt taste. Third, sleep curtailment was individualized based on habitual sleep duration of participants, which ensured the consistency of sleep curtailment between participants. Fourth, the study incorporated a one-week wash-out period between the two taste test sessions for study 2 to avoid carry-over effects. Finally, this project employed a randomized cross-over design, the gold standard for experimental rigor.
Several limitations are also noted. Results from this work may have limited generalizability, as mostly young adults were tested. Future studies should consider testing the forced-choice paired-comparison tracking procedure in other populations, such as teenagers and older adults. Additionally, the study included only testing short-term sleep curtailment, and only one 24-h dietary recall was obtained after the habitual and curtailed nights of sleep; hence, this may not represent habitual intake. While menstrual cycle status was not accounted for, studies of young, healthy women report limited effects on total sleep time [80] or sleep quality throughout the cycle [81]. Taste and intake changes of women measured over the course of three months reported no difference in sodium intake or salt-taste sensitivity between the luteal and follicular phases [82]. If there were variations in sleep or taste based on menstrual cycle status, the randomization used should have helped to control for these effects. Some taste measures, such as sensitivity, appear to be robust to acute sleep curtailment; however, future studies should consider investigating the effects of chronic sleep curtailment on salt-taste measures. Future research should consider using multiple dietary assessment methods and/or consider using indirect methods of measuring intake, such as urinary sodium. In addition, studies should focus on the effects of sleep curtailment on salt-taste measures among salt likers and dislikers in the future.
## 5. Conclusions
The present study demonstrated that the forced-choice paired-comparison tracking procedure is a reliable and valid tool for determining salt taste preference. Results also indicated that acute sleep curtailment did not affect salt taste function or hedonic measures; however, the expected relationship of salt-taste liking and energy-corrected Na intake was disrupted after sleep curtailment. These findings suggest that sleep duration should be considered in taste studies examining relationships between taste and dietary intake. Salt-taste measures appear to be robust after acute sleep curtailment; therefore, future studies should consider examining the effects of chronic sleep curtailment on salt taste. Further, researchers should consider adopting the NaCl concentrations used in the present study for salt-taste testing and use the forced-choice paired-comparison tracking tool as a standard procedure to measure salt taste preference to facilitate direct comparisons between future taste studies. Finally, two salt-liking phenotypes were detected in the present study. Replication of salt-liking phenotypes and, if present, exploration of their importance to human health is warranted.
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|
---
title: Eating Behavior and Obesity in a Sample of Spanish Schoolchildren
authors:
- Andrea Calderón García
- Ana Alaminos-Torres
- Roberto Pedrero Tomé
- Consuelo Prado Martínez
- Jesús Román Martínez Álvarez
- Antonio Villarino Marín
- María Dolores Marrodán Serrano
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001860
doi: 10.3390/ijerph20054186
license: CC BY 4.0
---
# Eating Behavior and Obesity in a Sample of Spanish Schoolchildren
## Abstract
From the point of view of prevention, it is convenient to explore the association between eating behavior and the obese phenotype during school and adolescent age. The aim of the present study was to identify eating behavior patterns associated with nutritional status in Spanish schoolchildren. A cross-sectional study of 283 boys and girls (aged 6 to 16 years) was carried out. The sample was evaluated anthropometrically by Body Mass Index (BMI), waist-to-height ratio (WHtR) and body fat percentage (%BF). Eating behavior was analyzed using the CEBQ “Children’s Eating Behavior Questionnaire”. The subscales of the CEBQ were significantly associated with BMI, WHtR and %BF. Pro-intake subscales (enjoyment of food, food responsiveness, emotional overeating, desire for drinks) were positively related to excess weight by BMI (β = 0.812 to 0.869; $$p \leq 0.002$$ to <0.001), abdominal obesity (β = 0.543–0.640; $$p \leq 0.02$$ to <0.009) and high adiposity (β = 0.508 to 0.595; $$p \leq 0.037$$ to 0.01). Anti-intake subscales (satiety responsiveness, slowness in eating, food fussiness) were negatively related to BMI (β = −0.661 to −0.719; $$p \leq 0.009$$ to 0.006) and % BF (β = −0.17 to −0.46; $$p \leq 0.042$$ to $$p \leq 0.016$$).
## 1. Introduction
Eating habits acquired during childhood and adolescence tend to become established during adulthood. For this reason, achieving a healthy diet at an early age is a definite factor in avoiding obesity and chronic diseases. Several experimental studies and reviews on the subject have shown that parents have a strong influence on their children’s eating behavior [1,2]. This is particularly important in childhood, during which children learn what, when and how to eat according to the cultural transmission of family patterns and attitudes [3,4]. Parental prohibition or restriction of food, or the use of food as a reward, are factors that impact the emotional domain and predict children’s enjoyment of food or their response to satiety [5]. Similarly, healthy nutrition education by families is associated with positive attitudes towards food and appropriate regulation of food intake which is reflected in children’s improved nutritional status [6]. Obviously, parents also pass on their genes, which also play a proven role in the regulation of appetite and food preferences [7,8,9]. In any case, eating behavior, which undoubtedly has a genetic and environmental component, is reflected in the nutritional condition of the subject and modulates the risk of obesity.
Different studies conclude that the capacity to respond to satiety is lower in overweight children and adolescents, especially in those who are obese, as well as a more noteworthy response to food cues. They have understood this as a higher desire to eat and greater likelihood of ingestion in the presence of food. For this reason, overweight children and adolescents seem to be more likely to eat food in the absence of hunger, out of mere desire or pleasure [10]. In addition, food enjoyment and speed of intake appear to be higher in obese children, who have a delayed sense of satiety [11]. Therefore, this bidirectional association leads to children with a greater enjoyment or taste for food being at greater risk of obesity [12]. It is worth noting that a greater increase in intake under emotional stress has also been observed in overweight children and adolescents compared to medium and underweight subjects [13,14]. However, the results in this aspect are controversial as recent meta-analysis studies show that the relationship between emotional intake and body composition is not as direct in children and adolescents as in adults [15]. Consequently, it is necessary to explore the association between eating behavior and the obese phenotype during the school and adolescent age range.
Previous findings show the usefulness of analyzing the eating behavior of children in detail using questionnaires such as the Children’s Eating Behavior Questionnaire (CEBQ) [16]. This test identifies different phenotypes related to habits such as food avoidance, early or late satiety, gluttony, or tendency for emotional overeating, habits that may eventually alter nutritional status [17,18]. Research using the CEBQ relates overweight and obesity in children and adolescents with higher scores on the pro-intake scales and lower scores on the anti-intake scales, pointing to higher consumption and enjoyment of food, lower satiety and more emotional overeating behaviors. Conversely, low weight is associated with lower scores on the pro-intake scales and higher scores on the anti-intake scales, relating to avoidance eating behaviors, early satiety and lower enjoyment of food [19].
Initially used in British children [16], the CEBQ has been applied to schoolchildren from different populations, such as the United States [20], Sweden [21], Saudi Arabia [22], Bosnia [23], Portugal [24] and Chile [25]. In Spain, the only precedent is the study of Jimeno Martinez et al. [ 26] as part of the MELI-POP (Mediterranean Lifestyle in Pediatric Obesity Prevention) pilot study. On the other hand, in most of the mentioned studies, the association between eating behavior assessed by CEBQ and obesity has been established through weight and BMI, with very few studies that include other indicators of adiposity [27]. For this reason, the main objective of the present study is to identify, in a sample of Spanish schoolchildren, the eating behavior associated with nutritional status assessed by anthropometric parameters that identify, in more outstanding detail, body composition and fat distribution.
## 2.1. Participants
This is a cross-sectional study in a convenience sample of 283 Spanish schoolchildren aged 6 to 16 years ($33.21\%$ [94] girls); ($66.69\%$ [189] boys). A total of $54.6\%$ were aged between 6 and 10 years (107 boys and 48 girls). The remaining $45.40\%$ (84 boys and 44 girls) were between 11 and 16 years of age. The sample was recruited between 2019 and 2021 in public schools and municipal sports centers in middle-class neighborhoods in the Community of Madrid, Spain. In these sports centers, schoolchildren perform soccer, basketball, gymnastics, or swimming activities as part of after-school classes.
In $42.20\%$ of families, both parents had primary education. In $25.30\%$, at least one parent had secondary or university education and in $32.50\%$ of the cases, both parents had advanced specific vocational training or university education. All the schoolchildren performed between 100 and 120 min of physical activity per week during school hours in two sessions. A total of $93.20\%$ also participated in out-of-school physical activity (mean = 3.61 SD = 1.84 h/week) with no differences between sexes (Table A1).
Data collection was carried out as part of a school health program developed by the Spanish Society of Dietetics and Food Sciences in coordination with local councils. It should be noted that data collection was partially affected by the COVID 19 pandemic, which forced special precautions and decreased the potential number of children finally included in the present study. The data were anonymized and were disaggregated from information that could identify the subject. Participants’ assent and informed consent from parents or guardians were required following the bioethical principles of the Declaration of Helsinki in its most updated version [28]. The Ethics Committee approved the project of the Autonomous University of Madrid (CEI-91-1699).
## 2.2. Instruments
Each participant was assessed anthropometrically through direct measurements, body composition indicators and adiposity distribution. Their parents or guardians completed the CEBQ [16] questionnaire.
## 2.2.1. Anthropometric Study
The anthropometric assessment was carried out according to the protocol of the International Biological Program (IBP) [29]. Height (cm) was measured with a Tanita Leicester measuring rod with an accuracy of 1 mm; weight (kg), umbilical waist circumference (cm) with a Cescorf tape and bicipital, tricipital, subscapular and suprailiac skinfolds (mm) with a Holtain adipometer with an accuracy of 0.2 mm and constant pressure (10 g/mm2).
For prevalence analysis, the sample was stratified by sex. Nutritional categories were established based on the Body Mass Index [BMI = weight (kg)/height (m2)] using the cut-off points of Cole et al. [ 30,31] and the waist-to-height ratio (WHtR = waist circumference/height), using the criteria established by Marrodán et al. [ 32] which define abdominal obesity as >0.51 in boys and 0.50 in girls, and abdominal overweight as >0.48 in boys and >0.47 in girls. Body fat percentage (%BF) was estimated by plicometry using the Siri equation [33], with a previous calculation of density [34,35]. Adiposity levels were classified according to the references for the Spanish youth population [36].
## 2.2.2. CEBQ Questionnaire
As indicated above, the CEBQ [16], provides information on the response to satiety, taste for food, speed of intake, and emotional food consumption. It is a validated questionnaire with 35 items that assess eight sections of eating behavior and whose questions are answered on a Likert-type scale with an option to score from 0 to 4 according to the intensity of the behavior (where 0 = never, 1 = rarely, 2 = sometimes, 3 = often and 4 = always).
The items are classified into eight subscales: food responsiveness (FR; 5 items), enjoyment of food (EF; 4 items), emotional overeating (EOE; 4 items), desire for drinks (DD; 3 items), slowness in eating (SE; 4 items), satiety responsiveness (SR; 5 items), food fussiness (FF; 6 items) and emotional under-eating (EUE; 4 items). The first four items (FR, EF, EOE and DD), have a positive focus or pro-intake dimension, while the last four (SE, SR, FF and EUE) relate to anti-intake habits. Pro-intake behaviors integrate those habits that favor food consumption, while anti-intake behaviors encompass those habits that lead to avoidance of food consumption. The questions corresponding to each subscale are defined according to the CEBQ’s classification (Table A2). The Spanish version of the CEBQ has been validated [26] and used previously [37].
## 2.3. Statistical Procedures
The internal consistency of the eight subscales of the CEBQ questionnaire and reliability estimates were determined using Cronbach’s alpha. Depending on the normality of the variables, ANOVA, Mann Whitney U tests were performed to compare the mean scores of each subscale of the CEBQ according to nutritional categories. Logistic regression models were applied to establish, as independent variables, the CEBQ subscale score and, as dependent variables, nutritional categories categorized dichotomously according to excess weight, abdominal obesity or high %BF. In these models, sex, age and level of physical activity previously coded according to WHO recommendations were included as covariates [38]. Statistical analysis was performed using R 4.1.2 software. Statistical significance was considered when $p \leq 0.05.$
## 3.1. Internal Consistency of the Subscales and Factor Structure of the CEBQ Questionnaire
First, the internal consistency of the CEBQ questionnaire in the present sample was assessed using Cronbach’s Alpha. Internal consistency was adequate (Cronbach’s alpha above 0.7) for all factors except subscales 1 and 8. The unweighted mean factor scores (±SD) and internal reliability estimates (Cronbach’s Alpha) for the CEBQ factors are presented in Table 1.
## 3.2. Sample Characterization
According to BMI, $6.70\%$ of the participants were underweight and $35\%$ had excess weight ($24\%$ overweight and $11\%$ obese). Regarding the WHtR, $14.80\%$ were overweight, and $31.80\%$ abdominal obese. According to %BF, $51.20\%$ were classified as having high adiposity ($19.40\%$ between 90th–97th percentiles and $31.80\%$ > 97th percentile). Significant differences were found between sexes in the categorization of the sample based on BMI, WHtR and %BF ($p \leq 0.001$ *), with the male sex having the highest percentage of overweight in all three classifications (Table A3).
## 3.3. Comparison between Mean Scores of CEBQ Scales and Nutritional Status
Figure 1, Figure 2 and Figure 3 show a clear trend towards higher scores on the pro-intake subscales and lower scores on the anti-intake subscales as BMI, abdominal obesity, and relative adiposity categories increase. Figure 1 represents separately the trend of the mean scores on the pro-ingestion and anti-ingestion scales, classified according to the nutritional category of each participant according to the body mass index (BMI) categories [30,31]. The trend observed is that the higher the level of overweight, the higher the mean score on the pro-intake scales and the lower the score on the anti-intake scales. Figure 2 represents the trend of the mean scores on the pro-intake and anti-intake scales according to the nutritional category of the sample diagnosed from the waist-to-height ratio (WHtR) [32]. Participants with overweight or abdominal obesity achieved higher mean scores on the pro-intake scales and lower scores on the anti-intake scales. Figure 3 represents the trend of the mean scores on the pro-ingestion and anti-ingestion scales as a function of the nutritional category established on the basis of body fat percentage (%BF) [36]. *The* general trend observed is that the higher the percentage of body fat, the higher the mean score achieved in the pro-intake scales and the lower in the anti-intake scales.
Table 2 compares the mean scores of the different subscales of the CEBQ as a function of nutritional status as assessed by BMI, WHtR and %BF. In the pro-intake dimension, scores for the subscales EF, FR and EOE were higher ($p \leq 0.05$) in overweight schoolchildren according to BMI or above the cut-off point for WHR and %BF. The score for the DD subscale was higher only for the abdominal obese. On the other hand, they obtained lower scores ($p \leq 0.05$) for the SR and SE subscales for the anti-intake dimension than their no obese peers.
As the regression model (Table 3) shows, in general terms, higher mean scores on the pro-intake scales translate into a higher risk of excess weight, abdominal fat, or high %BF. For example, each point scored on the FR and EOE subscales increases the risk of overweight by 2.385 and 2.253 times, respectively. Likewise, each point obtained in the EF subscale increases the likelihood of having high adiposity by 1.8 times. In contrast, the higher the score on the anti-intake subscales (SR and SE), the lower ($p \leq 0.05$) the risk of being overweight or obese, and the lower the risk of having a high %BF.
## 4. Discussion
Previous research yields results similar to those obtained in our study, showing a significantly lower satiety response capacity in children and adolescents with obesity, as well as a greater enjoyment of food, high responsiveness to external stimuli associated with increased food intake, and a tendency to eat at a faster rate [24,39,40]. Two recently published major studies provide a comprehensive review of eating behaviors linked to childhood obesity, with an emphasis on appetite control and satiety regulation. They have shown that aspects such as satiety responsiveness, responsiveness to food and the tendency to overeat, which are collected in CEBQ, are positively associated with BMI in children [41,42]. Several theories have been put forward to explain delayed satiety in overweight schoolchildren. These include the ability to ingest food without hunger, larger gastric size, metabolic-hormonal dysregulation associated with appetite–satiety control, and greater sensitivity to external factors that predispose to caloric, fatty or sweet products [43]. Similarly, emotional overeating, primarily associated with situations such as anxiety or boredom, or emotional eating due to food restrictions, is associated with an increased risk of developing obesity. On the other hand, several studies suggest that non-hunger eating may be an exciting predictor of weight and obesity at an early age, although the evidence is limited. This is because children who eat more in the absence of hunger are more likely to be able to eat again in a shorter time after a meal, especially more palatable, high-fat and high-calorie foods [44].
A sample of 240 Portuguese schoolchildren aged 3–13 years also found a significant association between scores on all pro-intake subscales of the CEBQ and increased risk of elevated BMI. In particular, the risk of obesity was associated with a weaker satiety response and greater food enjoyment [24]. Another study in Portugal involving 2951 schoolchildren concluded that high scores on the pro-intake and low scores on the anti-intake subscales at seven years of age were associated with increased cardiometabolic risk at ten years of age and vice versa [40]. Similar research involving 406 London schoolchildren aged 7–12 years found significant associations between subscales of emotional overeating, increased enjoyment of food, and increased desire to drink with higher adiposity and weight [39]. However, as in the present study, no relationship was observed between EUE score and nutritional status. It is worth noting that some review papers report a close relationship between EOE and emotional disturbances, especially if they are of a negative nature [42]. At the same time, other authors underline an evolutionary tendency to overeat, which generally promotes a higher intake of snacks and low-quality foods [45].
Our results are also consistent with previous findings on the association between lower scores on the anti-intake subscales of the CEBQ in overweight schoolchildren and higher scores in underweight schoolchildren. In particular, a study with a sample of 7295 schoolchildren from the Generation R Study cohort found that children rated by the CEBQ as “more irritable towards food,” less enjoyable, more avoidant, or more likely to be satiated sooner, had significantly lower BMI and %BF [46]. Similarly, a study involving 2500 schoolchildren aged 3–10 years in Bosnia and Herzegovina also found a linear increase in BMI as a function of scores on the pro-intake subscales, except for the desire to drink, and a decrease in BMI as a function of scores on the anti-intake subscales [23]. *In* general, underweight and normal-weight schoolchildren appear to exhibit certain behavioral traits that protect against the obesogenic environment, while overweight schoolchildren exhibit the opposite traits considered risk factors, supporting the theory of “behavioral susceptibility to obesity” [47].
Several lines of research reflect the possibility that overweight children may have been more vulnerable to the obesogenic environment. This means they have been more receptive to advertising and other external stimuli that encourage a higher intake of caloric and unhealthy products. In addition, behavioral patterns predisposing to obesity that begin in childhood may become more pronounced in adolescence and even more so in adulthood [48]. Since interventions to modify eating behavior are more effective at earlier ages, it is of interest to prevent overweight and obesity and to understand the eating behavior of children and adolescents by using validated questionnaires for an individualized approach [49].
The present study has some limitations. As indicated in the material and methods section, fieldwork was conducted during the COVID-19 pandemic. Although children attended school and the sports center relatively usually, security measures slowed anthropometric measurements and limited the number of subjects finally included in the study. It was impossible to obtain a sufficient sample size to separate by age group. On the other hand, it is possible that the COVID-19 pandemic had some effect on the eating behavior of schoolchildren. Another issue is that an exclusively anthropometric nutritional diagnosis was performed, assessing both the weight status and the amount and distribution of fat. Moreover, we have tried to associate eating behavior with this physical condition. For this nutritional diagnosis, we did not use blood biochemistry indicators, as this was not the aim of the study.
It should be noted that the subjects in the sample are school children, and a certain number of them eat part of their meals at school. For this reason, the answers to the test refer exclusively to the eating behavior of the children at home. Finally, as a limitation to be taken into account, we should mention that Cronbach’s alpha, which measures the reliability of internal consistency, is questionable for subscale 8 (FF) of the CEBQ. However, other authors have obtained similar values for this same subscale. Such is the case of Gao et al. [ 50], who, analyzing a sample of Chinee schoolchildren, estimated a score of 0.49 for this item.
In the near future, we intend to analyze whether the pro-intake and anti-intake subscales of the CEBQ also show an association with a genetic risk score constructed from a battery of SNPs that we found to be associated with the anthropometric obesity profile in children [51]. We will thus verify whether eating behavior mediates the phenotypic expression of the genetic component of childhood obesity.
## 5. Conclusions
The present study shows the apparent association between anthropometric nutritional status and scores on the subscales of the psychometric test CEBQ. In all pro-intake subscales, schoolchildren with overweight, abdominal obesity or high %BF scored higher. In contrast, in the anti-intake subscales, the average scores were lower than those of their normal-weight peers. This confirms that overweight or obese schoolchildren have a lower satiety response, faster food intake and a pattern of emotional overeating. Given the association between eating behaviors and obesity, it would be essential to know the food-related behavior pattern of the child and adolescent population for a more complete and comprehensive nutritional approach. In this sense, tools such as the CEBQ can be very useful.
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|
---
title: FHL2 Genetic Polymorphisms and Pro-Diabetogenic Lipid Profile in the Multiethnic
HELIUS Cohort
authors:
- Jayron J. Habibe
- Ulrika Boulund
- Maria P. Clemente-Olivo
- Carlie J. M. de Vries
- Etto C. Eringa
- Max Nieuwdorp
- Bart Ferwerda
- Koos Zwinderman
- Bert-Jan H. van den Born
- Henrike Galenkamp
- Daniel H. van Raalte
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001862
doi: 10.3390/ijms24054332
license: CC BY 4.0
---
# FHL2 Genetic Polymorphisms and Pro-Diabetogenic Lipid Profile in the Multiethnic HELIUS Cohort
## Abstract
Type 2 diabetes mellitus (T2D) is a prevalent disease often accompanied by the occurrence of dyslipidemia. Four and a half LIM domains 2 (FHL2) is a scaffolding protein, whose involvement in metabolic disease has recently been demonstrated. The association of human FHL2 with T2D and dyslipidemia in a multiethnic setting is unknown. Therefore, we used the large multiethnic Amsterdam-based Healthy Life in an Urban Setting (HELIUS) cohort to investigate FHL2 genetic loci and their potential role in T2D and dyslipidemia. Baseline data of 10,056 participants from the HELIUS study were available for analysis. The HELIUS study contained individuals of European Dutch, South Asian Surinamese, African Surinamese, Ghanaian, Turkish, and Moroccan descent living in Amsterdam and were randomly sampled from the municipality register. Nineteen FHL2 polymorphisms were genotyped, and associations with lipid panels and T2D status were investigated. We observed that seven FHL2 polymorphisms associated nominally with a pro-diabetogenic lipid profile including triglyceride (TG), high-density and low-density lipoprotein-cholesterol (HDL-C and LDL-C), and total cholesterol (TC) concentrations, but not with blood glucose concentrations or T2D status in the complete HELIUS cohort upon correcting for age, gender, BMI, and ancestry. Upon stratifying for ethnicity, we observed that only two of the nominally significant associations passed multiple testing adjustments, namely, the association of rs4640402 with increased TG and rs880427 with decreased HDL-C concentrations in the Ghanaian population. Our results highlight the effect of ethnicity on pro-diabetogenic selected lipid biomarkers within the HELIUS cohort, as well as the need for more large multiethnic cohort studies.
## 1. Introduction
Type 2 diabetes mellitus (T2D) is a highly prevalent and complex metabolic disorder affecting millions of people worldwide [1]. T2D is characterized by insulin resistance accompanied by progressive pancreatic β-cell failure, leading to hyperglycemia [2]. Additionally, it is known that approximately $50\%$ of T2D patients develop dyslipidemia, resulting in increased fasting triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) concentrations, as well as decreased high-density lipoprotein cholesterol (HDL-C) concentrations [3,4,5,6]. The combination of hyperglycemia and dyslipidemia is a strong driver of cardiovascular disease. Furthermore, while the global prevalence of T2D is on the rise, it also appears that there are differences in the risk of developing T2D, dyslipidemia, and associated cardiovascular complications across ethnic groups [7,8], which may be caused by differences in the genetic background of individuals. Indeed, several genome-wide association studies (GWASs) have linked multiple single-nucleotide polymorphisms (SNPs) to T2D [9] and dyslipidemia [10]. However, many of these GWASs have been conducted in mostly European descent populations and provide less insight into the contribution of genetics to differences in T2D and dyslipidemia across ethnic groups.
Four and a half LIM domains 2 (FHL2) is a member of the FHL domain family of proteins. FHL2 is expressed most abundantly in the heart and muscles, and to a lesser extent in other organs [11,12,13,14]. FHL2 serves as an interaction platform that acts through various protein–protein interactions [15,16]. Upon binding to a target protein, FHL2 may either enhance or repress the binding of the target protein to another protein or may alter the conformation of the target protein [17]. Through binding with a target protein, FHL2 can regulate various protein signaling pathways [15]. Thus far, FHL2 has been researched extensively in the field of oncology and cardiovascular diseases, as well as inflammation and cell differentiation, although far less is known regarding its involvement in metabolism [11,18,19,20,21]. It is only recently, however, that publications have surfaced which demonstrate a link between FHL2 and metabolism. As such, GWASs have shown an association between FHL2 loci and body mass index (BMI) [22,23]. Interestingly, studies have also implicated FHL2 in glucose metabolism and diabetes-related complications [24,25,26]. Most recently, we demonstrated that FHL2-deficient mice are protected from weight gain on a high-fat diet. These mice show increased energy expenditure involving browning of the white adipose tissue and increased glucose uptake in the heart [27]. In line with these observations, we confirmed that, in human adipose tissue, the expression of FHL2 negatively associates with the expression of browning genes. Additionally, we also showed that FHL2 expression was higher in individuals with T2D than non-diseased individuals using publicly available human pancreatic islet datasets and that FHL2-deficient mice possessed improved glucose clearance compared to wild type (WT) mice [26]. In the same pancreatic islet datasets, we also observed a correlation between higher FHL2 expression and higher HbA1c levels. The purpose of the current study was to determine whether FHL2 genetic loci are associated with the incidence of T2D and various aspects of lipid metabolism in a large multiethnic cohort (HELIUS cohort) and, thus, to further elucidate the role of FHL2 in human T2D and dyslipidemia. Here, we hypothesized that FHL2 SNPs associate with specific markers of glucose and lipid metabolism such as fasting plasma glucose values and plasma TG concentrations in humans.
## 2.1. Baseline Characteristics
The cohort we analyzed in this study consisted of 10,056 subjects of both male and female gender from different ethnic backgrounds including European Dutch, African Surinamese, South Asian Surinamese, Ghanaian, Turkish, and Moroccan. The relative contribution of participants from each ethnic group was unequal in this study. In order of relative size, the largest groups were Moroccan ($30.1\%$), Turkish ($26.2\%$), South Asian Surinamese ($14.9\%$), European Dutch ($12.8\%$), African Surinamese ($11.5\%$), and Ghanaian origin ($4.4\%$) (Table 1). The percentage of males per ethnicity differed across groups, with the highest percentage of males being present within the European Dutch group ($50\%$). Interestingly, we also observed differences in the percentage of T2D individuals within groups, with European Dutch participants having the lowest prevalence ($5.8\%$) and the South Asian Surinamese group having the highest ($21.4\%$). The baseline characteristics of the cohort varied across ethnicities. The mean age was lowest in the Turkish and Moroccan groups (41 ± 12 years and 41 ± 13 years, respectively) and highest in the European Dutch (51.8 ± 13 years) and African Surinamese (52 ± 11 years) groups. Mean BMI also differed across groups, with European Dutch participants having on average lower BMI (25.5 ± 4.4 kg/m2) than Turkish participants (28.5 ± 5.6 kg/m2). South Asian Surinamese participants showed the largest waist-to-hip ratio (WHR), while the Moroccan participants showed the smallest. Fat percentage was lowest in the European Dutch group ($29.4\%$ ± $7.5\%$) and highest in the Moroccan group ($32.8\%$ ± $8.3\%$). Fasting plasma glucose and HbA1c concentrations were lowest in the European Dutch group (5.4 ± 0.8 mmol/L and 36.9 ± 4.9 mmol/mol, respectively) and highest in the South Asian Surinamese group (5.9 ± 1.5 mmol/L and 42.6 ± 10.1 mmol/mol, respectively). Blood TG concentrations were lowest in the Ghanaian group (0.7 ± 0.4 mmol/L) and highest in the Turkish group (1.2 ± 0.9 mmol/L), while the inverse was true for blood HDL-C concentrations. TC and blood LDL-C concentrations were both highest in the European Dutch participants (5.2 ± 1.0 mmol/L and 3.2 ± 0.9 mmol/L) and lowest in the Moroccan participants (4.6 ± 0.9 mmol/L and 2.9 ± 0.8 mmol/L), respectively (Table 1).
## 2.2. FHL2 Genetic Polymorphism Distribution
A schematic representation of the FHL2 gene with the exons (1–6) and introns along with the location of each FHL2 SNP is illustrated (Figure 1). Additionally, the FHL2 polymorphisms with their respective reference and alternative alleles, position within the genome, SNP type classification, and ethnicity-specific allele frequency in this study are indicated (Table 2). The distribution of FHL2 SNP reference allele and alternative allele among the different ethnicities differed substantially in some cases. The Ghanaian subjects demonstrated the highest prevalence of the reference alleles for SNPs rs11124029, rs3087523, rs2278502, rs257678, rs880427, rs2376740, rs4851770, and rs7583367. In contrast, the Ghanaian group also showed the lowest proportion of the reference allele for SNP rs2278501, rs4640402, and rs6750100 compared to the other ethnicities. The alternative allele for the missense SNP rs137869171 leading to an Asn226Lys amino-acid change was only present within the European Dutch and Moroccan groups. Of the 19 FHL2 genetic polymorphisms that we evaluated, rs11124029 and rs3087523 lead to synonymous polymorphisms which do not alter the amino-acid sequence of the resulting protein.
## 2.3. Associations between FHL2 SNPs and Lipid Metabolism and Glucose Tolerance
Univariate analysis of FHL2 SNPs with age, gender, BMI, and ancestry as covariates showed nominally significant associations between FHL2 SNPs alternative alleles and plasma TG, HDL-C, LDL-C, and TC concentrations (Figure 2). The rs11124029 SNP was associated with a decreased HDL-C concentration ($$p \leq 0.045$$, beta = −0.009). The SNP rs4640402 was associated with a decreased TG concentration ($$p \leq 0.018$$, beta = −0.017), whereas it was associated with an increased HDL-C concentration ($$p \leq 0.025$$, beta = 0.008). On the other hand, the rs880427 SNP was associated with a decreased HDL-C concentration ($$p \leq 0.003$$, beta = −0.011), as well as increased HbA1c ($$p \leq 0.037$$, beta = 0.004). Furthermore, the SNP rs4851770 was associated with both an increased LDL-C ($$p \leq 0.018$$, beta = 0.01) and TC concentration ($$p \leq 0.024$$, beta = 0.006). In addition to our analysis of the complete HELIUS cohort, the multiethnic composition allowed us to evaluate whether the SNP association with the outcomes were similar across ethnic groups. To this end, we stratified our association analysis by ethnicity and conducted the same test with the same covariates in each subset. In the ethnicity-stratified analyses, we saw 31 nominally significant associations. We saw the most associations in the Moroccan group, where two different SNPs were associated with a decreased LDL-C concentration (rs11891016 and rs4851765), and the SNP rs4851770 was associated with an increased LDL-C concentration ($$p \leq 0.015$$, beta = 0.018). This SNP was also associated with an increased TC concentration ($$p \leq 0.006$$, beta = 0.014). In this group, the SNPs rs137869171 ($$p \leq 0.005$$, OR = 3.757) and rs2278501 ($$p \leq 0.031$$, OR = 1.224) were associated with an increased risk of T2D. In this group, we additionally saw that the SNP rs3087523 was associated with a decreased TG concentration ($$p \leq 0.047$$, beta = −0.047). Lastly, we also observed that rs2376740 was associated with a decrease in HDL-C concentration ($$p \leq 0.04$$, beta = −0.013).
Interestingly, in the African Surinamese group, the SNP rs3087523 was associated with decreased HDL-C ($$p \leq 0.035$$, beta = −0.067), and the SNPs rs1914748 and rs11884297 were associated with decreased TG concentrations ($$p \leq 0.047$$, beta = −0.047 and $$p \leq 0.041$$, beta = −0.044). Furthermore, rs2576778 was associated with an increase in LDL-C ($$p \leq 0.042$$, beta = 0.045). Additionally, the SNPs rs11124029 ($$p \leq 0.021$$, beta = 0.025) were associated with increased HbA1c. Lastly, rs118884297 was associated with a decrease in TC ($$p \leq 0.015$$, beta = −0.023), as well as plasma glucose ($$p \leq 0.043$$, beta = −0.021). In the Ghanaian group, we found that the SNP rs2278501 was associated with an increased risk of T2D ($$p \leq 0.027$$, OR = 1.61), and the SNP rs880427 was associated with a decreased HDL-C concentration ($$p \leq 0.002$$, beta = −0.071). On the other hand, the rs11884297 SNP was associated with an increased HDL-C concentration ($$p \leq 0.009$$, beta = 0.053) and an increased TC concentration ($$p \leq 0.028$$, beta = 0.036). Moreover, the rs4640402 SNP was associated with a decreased HDL-C concentration ($$p \leq 0.035$$, beta = −0.037), an increased TG concentration ($$p \leq 0.001$$, beta = 0.103), and an increased HbA1c concentration ($$p \leq 0.033$$, beta = 0.028). Three SNPs were associated with a decreased TG concentration (rs11891016: $$p \leq 0.02$$, beta = −0.08; rs1914748: $$p \leq 0.007$$, beta = −0.08; rs4851765: $$p \leq 0.018$$, beta = −0.09). Lastly, the SNP rs48511772 was associated with a decrease in HDL-C ($$p \leq 0.046$$, beta = −0.04), and rs880427 was associated with an increase in HbA1c ($$p \leq 0.015$$, beta = 0.04).
In the Turkish group, the SNP rs4851770 was associated with an increased TC concentration ($$p \leq 0.038$$, beta = 0.011). Furthermore, the rs880427 SNP was associated with a decreased HDL-C concentration ($$p \leq 0.02$$, beta = −0.016) and increased HbA1c ($$p \leq 0.032$$, beta = −0.008). In the European Dutch group, SNP rs2576778 was associated with an increase in HDL-C concentration ($$p \leq 0.044$$, beta = 0.024). Only two of the nominally significant associations passed multiple testing adjustments, namely, the association of the SNP rs4640402 ($$p \leq 0.002$$, beta = 0.103) with increased TG and the association of the rs880427 ($$p \leq 0.003$$, beta = 0.07) with decreased HDL-C concentrations in the Ghanaian population. All FHL2 SNP associations listed here are indicated in Table 3 and Supplementary File S1.
## 3. Discussion
In this study, we elucidated the associations between several FHL2 SNPs and multiple parameters of lipid metabolism including TG, HDL-C, LDL-C, and TC, as well as T2D status, HbA1c, and glucose concentrations, in the HELIUS cohort. In addition, this is one of the first studies to make use of the genotype data available from the HELIUS cohort and investigate the novel metabolism-related gene FHL2 and its polymorphisms in a multiethnic setting. In doing so, we illustrate for the first time the association of several FHL2 polymorphisms with plasma lipid concentrations and hyperglycemia. We also demonstrate that there appears to exist not only concordant but also opposing associations of these SNPs with outcomes between ethnic groups.
We identified the SNP rs4851770 to be associated with increased LDL-C and TC concentrations in the complete cohort, as well as in the Turkish group, and with increased TC concentration in the Moroccan group. The SNP rs2278501 was associated with an increased risk of T2D in the Ghanaian and Moroccan groups. Lastly, rs880427 was associated with a decreased HDL-C concentration in the total cohort and in the Ghanaian and Turkish groups.
On the other hand, we also identified seemingly opposing effects. The SNP rs3087523 SNP was associated with a decreased HDL-C concentration in the African Surinamese, but with a decreased TG concentration in the Moroccan group. The power of our associations varied greatly, from $5\%$ to $89\%$, with a mean power of $53\%$. If considering all association tests, the power varied from $5\%$ to $89\%$, with a mean power of only $12\%$. This is likely a reflection of the still rather small sample size in this cohort and suggests that, in addition to environmental factors, multiple SNPs may be involved in driving these phenotypes. Thus, a validation study in a larger sample size could elucidate whether these associations are robust. Furthermore, while various lipid measurements were performed in this cohort, these were by no means exhaustive and did not include, for example, ceramides or plasmalogens. Subsequent studies in large multiethnic cohorts will benefit greatly from including a more exhaustive lipid panel.
Both T2D and dyslipidemia are complex metabolic disorders that affect large portions of the global population and are associated not only with one another, but also with other metabolic diseases. In this study, we aimed to uncover the link between FHL2 genetic polymorphisms and dyslipidemia, as well as T2D, using the large multiethnic HELIUS cohort. FHL2 is still a relatively unknown gene in the field of metabolism with currently only a handful of publications. Given that FHL2 has been mechanistically associated with insulin secretion [24,26], diabetic kidney disease [25], and obesity [27], and that SNPs and epigenetic changes in FHL2 are associated with T2D [24] and body fat mass [28], we hypothesized that FHL2 genetic variants may also be associated with specific markers of glucose and lipid metabolism such as fasting plasma glucose values and plasma TG concentrations in humans.
We focused on T2D-related parameters such as plasma glucose and HbA1c concentrations as previous work by our group showed a correlation between FHL2 expression and HbA1c levels [26]. While we did not observe any significant associations between the FHL2 SNP variants and plasma glucose concentrations, we did uncover nominally significant associations with blood TG, HDL-C, LDL-C, and TC concentrations, as well as with T2D status and HbA1c concentration. FHL2 SNPs were associated with a pro-diabetogenic lipid profile with elevated LDL-C and TC, as well as decreased HDL-C. However, some FHL2 SNPs were also associated with increased HDL-C and decreased TG. This is interesting as we recently elucidated the protective role of FHL2 deficiency against developing obesity in mice and highlighted the association between FHL2 expression and browning of white adipose tissue in humans [27]. Adipocytes also regulate serum TG and HDL-C. Considering that FHL2 expression plays a role in adipocyte phenotype in mice and potentially in humans, and that adipocytes regulate serum TG and HDL-C, genetic variants in the FHL2 gene may also affect blood TG and HDL-C concentrations in humans.
Of the 19 FHL2 genetic polymorphisms that we evaluated, rs11124029 and rs3087523 lead to synonymous polymorphisms which do not alter the amino-acid sequence of the resulting FHL2 protein, while rs137869171 does lead to a missense polymorphism that alters the amino-acid sequence of FHL2 (Asn226Lys). FHL2 is composed of nine zinc fingers, and this variation is located in the eighth zinc finger, changing a polar uncharged amino acid into a positively charged amino acid, which may have functional consequences for the protein that are at present unknown. In our analyses, however, we only observed a nominally significant association between rs137869171 and an increased risk of T2D in the Moroccan group. The remaining FHL2 genetic loci were located in noncoding regions such as introns and intergenic regions upstream of FHL2.
Our results showed that only two of the nominally significant associations passed multiple testing adjustments, namely, the association of SNP rs4640402 with increased TG and the association of SNP rs880427 with decreased HDL-C concentrations in the Ghanaian population. These SNPs highlight the potential contribution of FHL2 to the risk of developing pro-diabetogenic lipid profile in this group. Our results within the Ghanaian group present similarities with previous work, which demonstrated the impact of a pro-diabetogenic polymorphisms in Japanese men associated with increased susceptibility to T2D [29]. However, whether the FHL2 SNP associations we highlight here in the Ghanaian group are truly causal requires further inquiry. *The* genetic variation within intronic and intergenic regions may still have functional implications for FHL2 expression through the regulation of alternative splicing, in addition to affecting promoter and enhancer regions upstream of FHL2. In addition, it has also been demonstrated that synonymous polymorphisms may elicit non-neutral effects in mRNA gene expression and, thus, negatively impact the organism in which they occur [30]. However, this would still need to be studied in further detail and is currently beyond the scope of this study.
In conclusion, our data indicate a link between FHL2 polymorphisms and dyslipidemia that is dependent on ethnic differences between individuals but does not occur through an effect on glucose metabolism. This was most clearly visible in the Ghanaian group after correcting for multiple testing. Given the vast array of targets that FHL2 can bind to, as well as recent publications demonstrating its role in metabolism, it stands to reason that we do not yet fully understand the role of FHL2 in metabolism or the underlying mechanisms such as genetic variation that determine its expression and function.
## 4.1. Population
The Healthy Life in an Urban Setting (HELIUS) study is a large multiethnic cohort study conducted in Amsterdam, the Netherlands, from which data was collected from January 2011 to November 2015; this study was described in detail elsewhere [31,32]. Briefly, the cohort contains individuals of European Dutch, South Asian Surinamese, African Surinamese, Ghanaian, Turkish, and Moroccan descent ranging from 18 to 70 years old, living in/near Amsterdam. Potential participants were sampled with a simple random sampling method from the municipality registry, after stratification by ethnicity as defined by registered country of birth.
The complete study population consisted of 24,789 participants of European Dutch ($$n = 4671$$), South Asian Surinamese ($$n = 3369$$), African Surinamese ($$n = 4458$$), Ghanaian ($$n = 2735$$) Turkish ($$n = 4200$$), Moroccan descent ($$n = 4502$$), and unknown Surinamese or unknown descent ($$n = 854$$), of which a subset had whole-genome genotyping data used for further analysis in this study [28,31]. Specifically, we analyzed the data of 10,056 individuals from the subset of the HELIUS cohort with genotyping data from the six largest ethnic groups, which equated to 1286 European Dutch, 1502 South Asian Surinamese, 1156 African Surinamese, 445 Ghanaian, 2636 Turkish, and 3031 Moroccan. In the total results, all subjects were included.
The study protocols were previously approved by the Amsterdam Medical Center ethical review board, and all participants provided written and informed consent. Ethnicity was defined by the country of birth of the participants, as well as that of their parents. The exact distinction of ethnicity in the HELIUS cohort was also described more extensively elsewhere [31]. Briefly, subjects were classified as European Dutch if they were born in the Netherlands and if both parents were also of European Dutch origins. All non-European Dutch participants in this study were classified on the basis of whether they were born outside of the Netherlands and had at least one parent who was also born outside of the Netherlands, or they were born in the Netherlands but both parents were born elsewhere. A limitation of the country of birth indicator for ethnicity is that people who were born in the same country might have a different ethnic background, which, in the Dutch context, applies to the Surinamese population (Table 1). Therefore, after data collection, participants of Surinamese ethnic origin were further classified according to self-reported ethnic origin (obtained by questionnaire) into ‘African’, ‘South-Asian’, ‘Javanese’, or ‘other’. The homogeneity of each ethnic group was demonstrated previously for the genome [33], microbiome [34], and diet [35].
## 4.2. Phenotypical Assessments
Participants completed a structured questionnaire with records on demographic, socioeconomic, and health-related behavior. Height measurement was performed without shoes with SECA 217 stadiometer to the nearest 0.1 cm. Weight was measured without shoes and in light clothing with SECA 877 scales to the nearest 0.1 kg. Body mass index (BMI) was determined by dividing measured body weight (kg) by height squared (m2). Fasting blood samples were drawn, and plasma samples were used to determine the concentration of glucose by spectrophotometry, using hexokinase as the primary enzyme (Roche Diagnostics, Tokyo, Japan). In this study, we defined individuals suffering from T2D according to whether they self-reported as such, had increased fasting glucose (≥7 mmol/L), or used glucose-lowering medication. Blood samples were drawn from all participants in a fasted state (>8 h of fasting). Serum TG, total cholesterol (TC), HDL-cholesterol (HDL-C), glucose, and LDL-cholesterol (LDL-C) concentrations were measured/calculated from plasma samples, while whole blood was used to determine hemoglobin A1C (HbA1c) concentrations as described previously, using an in-house assay [36]. The continuous measurements were log10-transformed prior to association testing.
## 4.3. Genotyping and Polymorphism Quality Control
Genotyping of HELIUS participants was performed as described elsewhere [37,38]. After the original quality control, the autosomal chromosomes were imputed using the Sanger Imputation Service (https://imputation.sanger.ac.uk, accessed on 1 May 2021). Phasing was performed with EAGLE2 and the PBWT method using the HAPLOTYPE Reference Consortium (release 1.1). Thereafter, poorly imputed SNPs were filtered using a 0.5 imputation quality score cutoff. All chromosome locations are based on the GRCh37 coordinates. Additional quality control was performed using PLINK version 1.9 (the following parameters for quality tests were used: --geno 0.05 --mind 0.05 --indep-pairwise 50 5 0.5 --genome --min 0.1875 --hwe 0.00001). The following SNPs were directly genotyped on the array, whereas the rest were imputed: rs880427, rs1914748, and rs6750100. In the full cohort, SNP variant rs137869171 had a minor allele frequency (MAF) <$1\%$, while variants rs2278502, rs2376740, and rs11891016 were in linkage disequilibrium. Despite this, all FHL2 genetic polymorphisms were used for further analysis. No quality issues were observed for any of the participants. All FHL2 SNP variants underwent quality control per ethnic group for the ethnicity-specific analyses. For the European Dutch group, all variants passed the MAF threshold of $1\%$ with rs2278502, rs2376740, and rs11891016 being in LD. In the South Asian Surinamese group, all variants passed MAF $1\%$ with only rs2278502 and rs11891016 being in LD. All variants in the African Surinamese group passed MAF $1\%$. In the Ghanaian group, rs137869171 did not meet the criteria of MAF $1\%$ and was in LD alongside rs11891016. For the Turkish group, all FHL2 SNP variants met the MAF $1\%$ criteria while rs2278502, rs11891016, and rs2376740 were in LD. Lastly, in the Moroccan group, all SNP variants met the criteria of MAF $1\%$; however, the variants rs2278502, rs2376740, rs11891016, and rs11124029 were in LD.
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|
---
title: Trends of Antidiabetic and Cardiovascular Diseases Medication Prescriptions
in Type 2 Diabetes between 2005 and 2017—A German Longitudinal Study Based on Claims
Data
authors:
- Batoul Safieddine
- Florian Trachte
- Stefanie Sperlich
- Jelena Epping
- Karin Lange
- Siegfried Geyer
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001865
doi: 10.3390/ijerph20054491
license: CC BY 4.0
---
# Trends of Antidiabetic and Cardiovascular Diseases Medication Prescriptions in Type 2 Diabetes between 2005 and 2017—A German Longitudinal Study Based on Claims Data
## Abstract
Background: With an attempt to understand possible mechanisms behind the severity-dependent development of type 2 diabetes (T2D) comorbidities, this study examines the trends of antidiabetic and cardiovascular diseases (CVD) medication prescriptions in individuals with T2D. Methods: The study is based on claims data from a statutory health insurance provider in Lower Saxony, Germany. The period prevalence of antidiabetic and CVD medication prescriptions was examined for the periods 2005–2007, 2010–2012, and 2015–2017 in 240,241, 295,868, and 308,134 individuals with T2D, respectively. ( Ordered) logistic regression analyses were applied to examine the effect of time period on the number and prevalence of prescribed medications. Analyses were stratified by gender and three age groups. Results: The number of prescribed medications per person has increased significantly for all examined subgroups. For the two younger age groups, insulin prescriptions decreased but those of non-insulin medications increased, while both increased significantly over time for the age group of 65+ years. Except for glycosides and antiarrhythmic medications, the predicted probabilities for CVD medications increased over the examined periods, with lipid-lowering agents demonstrating the highest increase. Conclusions: Results point towards an increase in medication prescriptions in T2D, which is in line with the evidence of the increase in most comorbidities indicating morbidity expansion. The increase in CVD medication prescriptions, especially lipid-lowering agents, could explain the specific development of severe and less severe T2D comorbidities observed in this population.
## 1. Introduction
Temporal change in morbidity has been of a high concern due to its solid effects on health policy planning and public health programming [1]. While non-communicable diseases have become the leading cause of premature morbidity globally [2], type 2 diabetes (T2D) has reached alarming levels due to its increasing prevalence and associated quality of life impairment [3].
In Germany, research suggests that morbidity in the context of T2D is expanding. As well as the fact that prevalence rates of T2D have been increasing in Germany [4], the age at onset has been shown to be declining among younger individuals [5]. On the other hand, life expectancy for individuals with T2D has been progressively increasing between the years 2005–2014 [4], indicating more years lived with the disease. Adding to that, the extra years lived with T2D are associated with more comorbidities [6]. Based on a large population of a health insurance provider in the state of Lower Saxony, Germany, our previous research has examined the development of comorbidities in individuals with T2D between the years 2005 and 2017. It indicated that individuals with T2D have significantly elevated risks of having more comorbidities diagnosed in the time period 2015–2017 compared to 2005–2007 [7]. Moreover, our study showed that the prevalence of severe adverse cardiovascular (CVD) events, such as myocardial infarction (MI) and stroke, has either remained constant for some of the age and gender groups examined, or slightly decreased over time for the other age and gender groups. However, at the same time, a clear and substantial increase was observed in other CVD comorbidities that are counted as risk factors, such as hypertension, cardiac insufficiency, and hyperlipidemia among the men and women of all age groups examined [7]. The study also reported a significant increase in the risk of having other vascular diseases, such as retinopathy, nephropathy, and polyneuropathy. Accordingly, it was concluded that the extra years lived with T2D are spent with more comorbidities, which signifies a deterioration in the quality of life and, thus, indicates an expansion of morbidity in the population of individuals with T2D.
However, the mechanisms behind the different development patterns of CVD comorbidities in individuals with T2D depending on severity remains unclear. It can be hypothesized that as a result of new treatment guidelines [8], different medication prescription practices have been developing, leading to the postponement of severe adverse health events. At the same time, deterioration of lifestyle risk factors [9] might lead to an increase in the prevalence of other chronic comorbidities, despite better treatment and diagnoses. This study will focus on the first premise of the abovementioned hypothesis through exploring temporal trends of medication prescriptions in T2D.
Studies from Europe reported evidence on an increase in the number of medications prescribed per patient [10] and polypharmacy [11] during the last two decades. Nevertheless, while studies examining time trends of antidiabetic medication use in Germany exist [12,13,14], evidence on the time trends of specific medication groups aimed at managing diabetes as well as the comorbidities accompanying it is scarce. Moreover, considering gender and age differences in the management of T2D is essential for understanding mechanisms that lie behind the morbidity development patterns in specific subgroups.
Based on the same data used in our previous research to examine the development of comorbidities in T2D [7], this study aims to explore possible mechanisms behind the severity-dependent developmental trends of T2D comorbidities through examining the gender- and age-stratified development of medication prescriptions in T2D. We hypothesize that individuals with T2D have been more routinely medicinally treated over time between the years 2005 and 2017, leading to the delay of severe CVD events. In order to examine this hypothesis, the following research questions will be addressed:How has the number of prescribed medications per person been developing between the years 2005 and 2017 in individuals with T2D?How has the prevalence of antidiabetic and CVD medication prescriptions been developing between the years 2005 and 2017 in individuals with T2D?
## 2.1. Data
The database for this study is anonymized claims data of individuals insured by “AOKN: Allgemeine Ortskrankenkasse Niedersachsen” that cover the years 2005–2017. AOKN is a large statutory health insurance provider in the state of Lower Saxony, Germany which insures around one-third of the population in this state [15]. Given that health insurance is mandatory in Germany, about $90\%$ of the population are statutory insured, with insurance premiums defined individually based on income [16]. All individuals in the statutory health insurance system receive the same health care coverage. The datasets include demographic information, in- and outpatient diagnoses, medical prescriptions and medical treatments. The data are currently available for the years from 2005 to 2017, allowing for a longitudinal analysis of diagnoses and prescribed treatments in this time period. The scientific use of the pre-existing anonymized claims datasets is regulated by German law in the German Civil Code “Bürgerliches Gesetzbuch”. The data protection officer of the Local Statutory Health Insurance of Lower Saxony-AOK Niedersachsen where the headquarter is located in Hannover, Germany) has given permission to use them for scientific purposes. Therefore, no ethical approval was required for this study.
## 2.2. Definition of T2D Cases and Medications
The population of this study includes all insured individuals with T2D aged 18 years and older. T2D is defined based on individual diagnosis data, based on the German version of the International Classification of Diseases (ICD-10 GM) and on medication data. The exact definition and plausibility mechanism of this definition have been described in an earlier publication [7].
Medication groups were identified according to the anatomical therapeutic chemical classification (ATC) with daily doses defined for the German pharmaceutical market [17].
After consulting clinicians, 12 discrete diabetes and CVD medication groups were first identified, namely insulin, non-insulin, blood thinning medications, vasodilators, diuretics, beta blockers, calcium channel blockers, renin–angiotensin agents, glycosides, antiarrhythmic and antiadrenergic agents. The corresponding ATC codes of the 12 medication groups are presented in the Supplementary Materials in Table S1. Then, the discrete CVD medication groups were simplified into four major medication groups to be used in the analyses: [1] antihypertensive agents (beta blockers, calcium channel blockers, diuretics, renin–angiotensin agents, vasodilators, and antiadrenergic agents), [2] lipid-lowering agents, [3] blood thinning medications, and [4] glycosides and antiarrhythmic medications.
In order to avoid overestimation of prescription prevalence, prescriptions were only considered plausible if they were present for individuals at least twice in each time period examined, with the exception for individuals who were insured for only one quarter in the corresponding period.
## 2.3. Time Period
In this study, the trend of medication prescriptions was examined over three time periods between 2005 and 2017, which are the years for which the data are currently available, with equal intervals and gaps in-between. The three time periods were 2005–2007 (p1), 2010–2012 (p2), and 2015–2017 (p3). In order to limit bias, T2D, as well as all medication prescriptions, were newly defined in each period using the same criteria, allowing for the same potential errors and, thus, improving comparability among the time periods. The time periods approach was used in order to better illustrate clear directions of temporal development. Two-year gaps were left between the three time periods to provide sufficient time for possible changes in morbidity and prescription frequency to happen.
## 2.4. Statistical Analysis
In order to detect age and gender differences in the trends of medication prescriptions in individuals with T2D, all analyses in this study were applied separately for men and women, and for three age groups: 18–45 years, 46–64 years, and 65+ years.
Period prevalence rates of the single medication groups in the three examined time periods were calculated for all subgroups and are displayed in the Supplementary Materials in Table S2. Denominators are based on the aggregated insurance duration in each period in terms of person-years to correct for censoring that might result due to different observation periods.
## 2.4.1. Trend of the Number of Prescribed Medications
The number of prescribed discrete medication groups (that could range between 0 and 12) was grouped into the following categories: “0 Medications”, “1–2 Medications”, “3–4 Medications” and “5+ Medications”. Ordered logistic regression was applied to examine the effect of time period on the number of medications. Separate models were created for each of the age and gender groups, resulting in six models (model 1: men, 18–45 years; model 2: men, 46–64 years; model 3: men, 65+ years; model 4: women, 18–45 years; model 5: women, 46–64 years; model 6: women, 65+ years). In each model, the outcome or dependent variable was the number of prescribed medications with its four above described categories. The main independent variable was the time period with its three categories: p1 (2005–2007), p2 (2010–2012), and p3 (2015–2017), with p1 being the reference group. Age (as a metric variable, displaying the age of individuals within each age subgroup) and duration of observation (days of observation or insurance within each time period) were added as covariates to all models to adjust for their influence.
Cluster-robust standard errors were used in all models in order to correct for the possible effects of within cluster variation due to having individuals in more than one period, which can lead to autocorrelation.
Ordered logistic regression provides an odds ratio (OR) indicating the odds of being one category higher in the outcome (category of number of medications) for the examined group (p2 or p3) compared to the control group (p1). Even though time period is treated as a predictor variable in the regression models, the aim is to examine the trends of medication prescriptions by interpreting the odds of having a higher number of prescribed medications over time, i.e. in p2 and p3 compared to p1. No additional potential influencing factors were added to the models to concretize the effect of time period within the context of temporal development.
## 2.4.2. Trend of the Prescription Prevalence
Logistic regression analyses were applied to examine whether there was a significant change in the prevalence of the prescribed medication groups over the three time periods. Cluster-robust standard errors were used to correct for within cluster variation.
In this line of analysis, the outcome or dependent variables were the six medication groups, namely [1] insulin, [2] non-insulin antidiabetic medications, [3] antihypertensive agents, [4] lipid-lowering agents, [5] blood thinning medications, and [6] glycosides and antiarrhythmic medications. Each of these dichotomous outcomes had two categories, yes/no, where “yes” implies having medications prescribed from the corresponding medication group within the examined time period. For each of these outcomes, separate logistic regression models were applied for each gender and age groups, resulting in six models per outcome. Similar to the abovementioned analyses of the trend of the number of prescribed medications, the main independent variable was time period, and age within each age category and duration of observation were adjusted for in all models.
Since odds ratios tend to either overestimate (if OR > 1) or underestimate (if OR < 1) effects when dealing with outcomes of more than a $10\%$ prevalence rate [18], prevalence ratios (PR) were calculated instead in this analysis.
## 2.4.3. Predicted Probabilities
Based on the examined regression models described above, predicted probabilities using time period as the main independent variable with margins at means for age and duration of observation were estimated and graphically displayed. Predicted probabilities provide the possibility of interpreting the results more accurately than prevalence rates because they display adjusted effects [19].
The software STATA v15.1 was used for all statistical analyses in this study.
## 3. Results
This study involved 240,241, 295,868, and 308,134 individuals with T2D over the three time periods 2005–2007, 2010–2012, and 2015–2017, respectively. The distributions of age, gender, and insurance durations are presented in Table 1.
## 3.1. Number of Medications
In men, the number of prescribed medications increased over the three periods among all examined age groups. The predicted probability of having no medications prescribed decreased by up to $3\%$ for the youngest age group, while that of taking five or more medications increased by up to $9\%$ for the oldest age group. Nevertheless, the differences were mostly apparent between the first two time periods, while the change between the periods 2010–2012 and 2015–2017 was minimal (Figure 1).
In women, the change in the number of prescribed medications was only present for the age group of 65+ years. While women in this age group had a slightly lower probability of having only one–two medications prescribed, the probability of having five or more medications prescribed was up to $6\%$ higher during the latest period (Figure 1).
The ordered logistic regression analysis showed that in men, the probability of having at least one more medication prescribed significantly increased over time for all age groups, while it only significantly increased for the age group of 65+ years in women. Men aged 18–45 years were $16\%$ and $21\%$ more likely to have one additional agent prescribed if they were in p2 and p3, respectively (compared to p1). While these probabilities were slightly higher for the middle age group ($18\%$ and $22\%$ for p2 and p3, respectively), they were more pronounced for the age group of 65+ years, where men were $30\%$ and $40\%$ more likely to have at least one additional prescribed medication in p2 and p3 respectively. Though significant, the increase was less pronounced for this age group in women, where the odds increased by $21\%$ and $24\%$ for p2 and p3, respectively (Table 2).
## 3.2. Antidiabetic Medications
In both men and women, the predicted probabilities of having insulin prescribed decreased by $4\%$ for the youngest age group, while the predicted probability of prescriptions entailing non-insulin antidiabetic medications increased considerably, where they were $14\%$ and $6\%$ higher in p3 compared to p1 for men and women, respectively. Though less pronounced, the change in predicted probabilities for insulin and non-insulin medications exhibited similar attitudes for the middle age group. For the oldest age group, however, the predicted probabilities increased for both insulin and non-insulin antidiabetic medications in men but remained almost unchanged in women (Figure 2).
The logistic regression analysis showed that being in the second or the third time periods was significantly associated with a higher chance for non-insulin prescriptions and a lower chance for insulin prescriptions among the youngest and the middle age groups. In the oldest age group, the chances of having both insulin and non-insulin medications prescribed were significantly higher in men, while, in women, only non-insulin prescriptions increased significantly over time (Table 3).
## 3.3. CVD Medications
While the prescriptions of antiarrhythmic medications and glycosides were minimal for the two younger age groups in p1, their predicted probabilities slightly decreased over time. For the oldest age group, the predicted probabilities of having prescriptions from this medication group was $15\%$ in men and $18\%$ in women in p1, but these probabilities decreased by more than a half in p3 (Figure 3). These results were also shown in the logistic regression analyses, with significant reductions in the PRs for most of the age and gender subgroups examined (Table 3) In lipid-lowering and blood thinning medications, there was barely any change in the predicted probabilities for the youngest age group. For the two older age groups, however, there was a clear increase in the predicted probabilities for lipid-lowering and blood thinning medications being prescribed, with the oldest age group being the most affected. While this applied for both genders, men had higher probabilities than women in all three time periods (Figure 3). These conclusions were mostly reproduced through the logistic regression analyses, where it was shown that men and women aged 65 years or older had an up to $38\%$ and $60\%$ higher chance of having lipid-lowering agents prescribed in p2 and p3, respectively. They also had 9–$11\%$ and 23–$29\%$ higher chances for blood thinning medications in p2 and p3, respectively (Table 3).
The predicted probabilities for antihypertensive agents were the highest among all medication groups examined. Approximately a third of the individuals in the youngest age group had antihypertensive medications prescribed in p1, with an increase by a few percentage points in p2. In the middle age group, $69\%$ of men and three-quarters of women were predicted to have had prescriptions from this medication group in p1. While this probability remained almost constant in women, it increased by a few percentage points in p2 in men. In the oldest age group, there was also a slight increase in the predicted probabilities in p2, but these started off in p1 with $87\%$ and $91\%$ in men and women, respectively. Among all the subgroups, almost no change appeared between p2 and p3 (Figure 3). The logistic regression analyses showed a slight but significant increase in the chance of having this medication group prescribed for all age groups in men and the oldest age group in women (Table 3).
## 4. Discussion
In an attempt to understand possible mechanisms behind different patterns of morbidity expansion in the context of T2D, this study examined the temporal development of medication prescriptions in men and women with T2D.
Overall, the study reported an increase in the number of discrete prescribed medications per individual between the time periods of 2005–2007 and 2015–2017. This is in accordance with the finding from our previous study on the development of comorbidities [7], which reported that the number of comorbidities per individual increased over the same time periods in individuals with T2D. The increase in the number of prescribed medications per person with T2D is also in line with evidence from other European studies. Higgins et al reported a significant increase in the number of prescribed medical agents per person between the years 2000–2015 [10]. Similarly, Oktora et al. reported an increase in “polypharmacy” in individuals with T2D between the years 2012 and 2016 [11]. Nevertheless, while the use of multiple medications can be essential for the treatment of diabetes and its comorbidities, the increase in the number of medications prescribed per person can be associated with related adverse effects and a higher risk for potentially inappropriate medication [11,20].
The trend of the prevalence of antidiabetic medications overall remained constant in women but increased slightly in men. When splitting this group, different attitudes could be observed, pointing towards a decrease in insulin but an increase in non-insulin prescriptions for the two younger age groups. This could be partly attributed to the change in medical therapeutic practices, such as the delay of insulin prescription in individuals with T2D [21,22,23]. A longitudinal study from Germany and UK suggests that the time to insulin therapy as well as the average glycated hemoglobin levels before insulin therapy have increased between 2005 and 2010 [23]. Nevertheless, the increase in the prevalence of non-insulin prescriptions is of a notably higher extent than the decrease in insulin prescriptions in the two younger age groups. In addition, both the prescription prevalence of insulin and non-insulin medications increased for the older age group (65+ years). While this might partly be the result of earlier detection of T2D, it also signposts a deterioration in the management of T2D and an expansion of morbidity in this population, despite changes in prescription practices. Evidence from our previous research which was carried out on the same study population suggests that in the age group of 65+ years, there has been a marked increase in the prevalence of diabetes-related nephropathy [7], for which non-insulin therapy is counter-indicated [24]. Individuals with T2D who suffer from this complication are, thus, left with the only choice of insulin therapy. This in turn also reflects an expansion of the morbidity level, especially among the age group of 65+ years.
Except for glycosides and antiarrhythmic medications that have been prescribed less frequently, possibly due to potential side effects [25], and the existence of medical alternatives, the predicted probabilities as well as the odds for having CVD medications prescribed increased for the two older age groups. Although studies that examined trends for the use of CVD medications in T2D are limited, results from the available evidence from Germany [14,26], as well as other countries, such as Taiwan [27] and the USA [28], designate a similar conclusion based on the trend of CVD medication prescriptions in T2D. Evidence from two German studies indicates that the proportion of individuals with T2D who receive antihypertensive and lipid-lowering medications have increased between 2000 and 2007 [14] and between 1990 and 2011, [26] respectively. The increase in the prevalence of the prescription of CVD medications is consistent with the manifest increase in the prevalence of CVD comorbidities (hypertension, hyperlipidemia, and cardiac insufficiency) that was observed in our previous research carried out on the same population of the current study [7], which reflects a higher morbidity level in this population. Moreover, research also indicates a temporal increase in the prevalence of CVD risk factors in individuals with T2D, such as obesity [29], which can also explain the higher prescription rates of related CVD medications in this population. Nonetheless, changes in medical practices could still be partly responsible for the trend of some CVD medication prescriptions, such as lipid-lowering agents, which had the most pronounced increase among all CVD medication groups between p1 and p3 for both men and women. In 2016, the European Society of Cardiology (ESC) reduced target levels for low-density lipoprotein cholesterol (LDL-C) levels circulating in the blood and, thus, the level from which lipid-lowering medications would be prescribed was decreased [30]. The more recent ESC guidelines from 2019 recommend even lower target blood LDL-C levels [31], which forecasts that apart from the increasing morbidity, a higher prevalence of prescriptions would be potentially observed in future studies that consider later periods.
The increase in the prescription of CVD and antidiabetic medications in T2D could, thus, provide a possible explanation for the different patterns of development of CVD comorbidities in T2D, depending on severity. Our previous findings suggest that the development of severe CVD comorbidities, such as MI and stroke, either remained constant or decreased for some examined subgroups between 2005 and 2017. On the other hand, the predicted probabilities of other comorbidities, such as hypertension and hyperlipidemia that also act as risk factors for MI and stroke, increased markedly and significantly among almost all examined subgroups [7]. It was, thus, hypothesized that differences in medication prescription practices could be associated with the delay of serious health events, such as MI and stroke, in individuals with T2D. The results of the present study support this hypothesis and could be interpreted as an improvement in the medicinal management of risk factors in terms of medication prescription practices, thus, delaying serious health events. The results also reapprove that morbidity is expanding in the population of individuals with T2D. Nevertheless, it still remains an open question whether the expansion of morbidity in T2D in terms of a higher risk of milder CVD comorbidities over time is due to the temporal increase in lifestyle risk factors. Research suggests that lifestyle modification could be more effective than medications in the management of CVD in T2D [32]. The results of a German longitudinal study that examined the development of cardio-metabolic risk factors between the years of 1990 and 2011 suggest that while the prevalence of using antidiabetic, lipid-lowering, and antihypertensive medications increased significantly, there was a simultaneous significant increase in the prevalence of smoking and obesity [26]. Thus, the increase in milder CVD comorbidities that are associated with a deterioration of quality of life in individuals with T2D [29] could be a result of an increase in lifestyle risk factors, such as unhealthy eating habits [33] and lack of adequate physical activity [34], despite the existence of the Disease Management Program (DMP) [35] and a better adherence to the guidelines of the DMP over time [36].
## 5. Strengths and Limitations
The database for this study is routine data of a large population of statutory insured individuals in the state of Lower Saxony, thus, providing adequate power. All medication prescription information is available, ruling out any recall or selection bias. One limitation of the study is that information about the actual intake of medications, and not just prescription information, is not available. However, there is no clear evidence on the existence of temporal differences in the medication adherence in T2D, which makes the three periods comparable in terms of the proportion of individuals who actually took the medications after buying them. Moreover, since the study aims to discuss results in the context of morbidity development, trends of medication prescriptions would presumably reflect how the “need” for these medications have been developing. In addition, certain medication combinations, and not only the number of medications, can be relevant in terms of morbidity development in T2D. However, this was not considered due to the scope of the paper, and will be addressed in future studies. Additionally, the results are not fully generalizable to all individuals with T2D in Germany since the socioeconomic distribution of AOKN differs to some extent from the general population [37].
## 6. Conclusions
This study provides evidence for the temporal increase in the prevalence of medication prescriptions in T2D. The results of this study support the hypothesis of morbidity expansion in the population of T2D. The increase in CVD medication prescriptions, especially lipid-lowering agents, could explain the severity-dependent developmental pattern of T2D comorbidities. Further investigations are planned to examine the temporal development of lifestyle risk factors in T2D to provide a better understanding for the mechanisms behind morbidity expansion in this population.
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|
---
title: The Relationship of Left Ventricular Diastolic Dysfunction and Asymmetrical
Dimethylarginine as a Biomarker of Endothelial Dysfunction with Cardiovascular Risk
Assessed by Systematic Coronary Risk Evaluation2 Algorithm and Heart Failure—A Cross-Sectional
Study
authors:
- Livija Sušić
- Lana Maričić
- Ines Šahinović
- Kristina Kralik
- Lucija Klobučar
- Mateja Ćosić
- Tihomir Sušić
- Josip Vincelj
- Antonio Burić
- Marko Burić
- Matea Lukić
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001866
doi: 10.3390/ijerph20054433
license: CC BY 4.0
---
# The Relationship of Left Ventricular Diastolic Dysfunction and Asymmetrical Dimethylarginine as a Biomarker of Endothelial Dysfunction with Cardiovascular Risk Assessed by Systematic Coronary Risk Evaluation2 Algorithm and Heart Failure—A Cross-Sectional Study
## Abstract
Background: Cardiovascular (CV) risk factors, causing endothelial dysfunction (ED) and left ventricular diastolic dysfunction (LVDD), contribute to an increased risk of heart failure (HF). The aim of this study was to determine the relationship between the occurrence of LVDD and ED with CV risk assessed by the Systematic Coronary Risk Evaluation2 (SCORE2) algorithm and HF. Methods: In the period from November 2019 to May 2022, a cross-sectional study that included 178 middle-aged adults was conducted. Transthoracic echocardiography (TTE) was used to assess left ventricular (LV) diastolic and systolic function. ED was assessed using the plasma values of asymmetric dimethylarginine (ADMA) and was determined using the ELISA method. Results: The majority of subjects with LVDD grades 2 and 3 had high/very high SCORE2, developed HF and all were taking medication ($p \leq 0.001$). They also had significantly lowest plasma ADMA values ($p \leq 0.001$). We found that the reduction of ADMA concentration is influenced by certain groups of drugs, or more significantly, by their combinations ($p \leq 0.001$). Conclusions: In our study, we confirmed a positive correlation between LVDD, HF and SCORE2 severity. The results showed a negative correlation between the biomarkers of ED, LVDD severity, HF, and SCORE2, which we believe is due to medication effects.
## 1. Introduction
According to the World Health Organization’s (WHO) 2021 report, cardiovascular diseases (CVD) are still the leading cause of death in the world [1]. Despite significant progress in diagnosis and therapy, we continue to see a high prevalence of coronary heart disease (CHD) [2], which could be caused by the formation and accumulation of atherosclerosis or by vasoconstriction, both due to impaired endothelial function. Heart failure (HF) is another prevalent form of CVD which is continuously rising [3]. It occurs as a result of increased intracardiac pressure and/or inadequate cardiac output due to structural and/or functional abnormalities of the heart, the etiology of which varies according to age and geographical location. In adult populations that have Western lifestyles and in developed countries, CHD and hypertension are dominant factors. Characteristic symptoms of HF are dyspnea, ankle swelling, and fatigue, which may be accompanied by signs of fluid accumulation. In addition to the presence of characteristic symptoms and signs, in order to confirm the diagnosis of HF it is necessary to determine cardiovascular (CV) risk factors and the plasma concentration of natriuretic peptides (NP), as well performing an ECG and TTE. When confirming the diagnosis of HF, it is also necessary to determine the phenotype and its severity. The severity of HF is determined by the New York Heart Association (NYHA) and includes four classes: with NYHA class I representing the mildest, almost asymptomatic form, while NYHA class IV represents the most severe form, in which symptoms occur even at rest. The HF phenotype is determined based on the measurement of the systolic function of the left ventricle (LV) using TTE, the so-called left ventricular ejection fraction (LVEF), whereby HF is divided into HF with reduced (HFrEF), mildly reduced (HFmrEF), and preserved ejection fraction (HFpEF) [4]. In all these phenotypes, a certain degree of left ventricular diastolic dysfunction (LVDD) is always present. LVDD, the earliest change common to many CVDs and CV risk factors [5,6,7], is defined as the inability of the LV to receive blood from the left atrium (LA) and pulmonary veins (PV) during diastole without an increase in filling pressure. According to the severity, LVDD can be divided into three grades: grade 1 is characterized by the slowing down of ventricular relaxation without significant hemodynamic consequences, grade 2 is characterized by an increase in LV filling pressures and LA dilatation, and grade 3 is characterized through the development of PV congestion. Although LV catheterization is the gold standard for the diagnosis of LVDD, the most common method for its evaluation in clinical practice is TTE [8]. In order to reduce the burden of atherosclerotic CVDs, the European Society of Cardiology (ESC) proposed new algorithm for estimating the 10-year risk of fatal and non-fatal CV events called the Systematic Coronary Risk Evaluation2 (SCORE2) [9]. Based on the estimated SCORE2, individuals are divided into three risk groups, namely low to moderate, high, and very high CV risk, which further determines the time of specific therapy introduction and the target values of systolic blood pressure (SBP) and low density lipoprotein cholesterol (LDL-C) plasma concentration. Given that SCORE2 does not cover all known CV risk factors, some patients remain underestimated; so in recent decades, scientists have been focused on researching the molecular connection between CV risk factors, CVDs, CHD, and HF. The universal pathophysiological change that connects all entities is endothelial dysfunction (ED) [10]. ED is a disorder responsible for reducing the bioavailability of nitric oxide (NO), which leads to the development of atherosclerosis directly and, via the NO-soluble guanylate cyclase-protein kinase G (sGC-PKG) signaling pathway, to disturbed cardiomyocyte remodeling indirectly [11,12,13]. Although the flow-mediated dilatation (FMD) of the brachial artery is still considered the gold standard for the assessment of systematic ED, recently, biomarkers have been increasingly used for this purpose [12,14]. One of the popular biomarker is asymmetrical dimethylarginine (ADMA), whose increased plasma concentration is associated with major CV events and mortality, even in subjects without previously proven CVD [15]. ADMA is an endogenous inhibitor of NO synthase (NOS), an intracellular enzyme responsible for the production of NO. It is produced in cells via the process of the methylation of arginine residues of nuclear proteins. Under normal circumstances, most ADMA is degraded within the cytosol of the cell in which it was formed. In situations of excessive intracellular concentration, some of the ADMA exits the cell into the bloodstream so that it can be degraded in a cell other than the one in which it was formed and only $20\%$ of circulating ADMA is excreted in the urine [16]. All CV risk factors known so far, as well as CVDs, increase the concentration of ADMA in such a way that they lead to the excessive formation or inhibition of its degradation [17]. We found in the literature that ADMA showed a positive correlation with the incidence of LVDD [18] and N-terminal pro-brain NP (NT-proBNP) values [19], and, similarly to NT-proBNP, can further improve the stratification of the risk of cardiac decompensation, major adverse CV events, and mortality in patients with HF [20,21]. Through our research, we aimed to determine whether we could contribute to a personalized understanding of the (residual) CV risk assessed via the SCORE2 algorithm by measuring the plasma concentration of the biomarkers ADMA and NT-proBNP with dependence on LVDD. The objectives of the study were to examine the associations between the prevalence and severity of LVDD and SCORE2, to examine the association between the occurrence and severity of LVDD and the plasma concentration of ADMA and NT-proBNP, to assess the association of SCORE2 with the plasma concentration of ADMA and NT-proBNP and to determine the effectiveness of ADMA and NT-proBNP in predicting LVDD, HFrEF, and NYHA class III–IV in subjects from the general population.
## 2. Materials and Methods
A cross-sectional population study that included 178 adults (99 men, 79 women), aged 40 to 65 years, who came for a cardiology examination at the Osijek-Baranja County Health Center and Clinical Hospital Center Osijek, was conducted in the period from November 2019 to May 2022. The study complied with the provisions of the Declaration of Helsinki and was approved by the Ethics Committee of Osijek-Baranja County Health Center (ID: 03-939-$\frac{2}{19}$), Clinical Hospital Center Osijek (ID: R2-$\frac{7882}{2019}$), and the University “Josip Juraj Strossmayer”, Faculty of Medicine in Osijek (ID: 2158-61-07-19-134). The inclusion criteria were age 40–65 years and willingness to participate in the study was demonstrated by signing a written consent. The exclusion criteria were as follows: malignant disease; acute coronary syndrome; acute infectious disease; congenital heart disease in adults; poor echocardiographic presentation, in which the heart structures were not properly seen; and additionally, in women, the use of oral contraceptives, pregnancy, and being in the 6-month period after childbirth.
Anamnesis data on past illnesses and current symptoms, family history, functions, habits, addictions, and medication intake were taken from all subjects. A basic 12-channel ECG, ambulatory blood pressure (BP), body mass, height, waist, and hip circumference were recorded for each subject. All subjects were given the results of laboratory analysis, which contained the following parameters: red blood count, urea, creatinine, total cholesterol (TC), LDL-C, high density lipoprotein cholesterol (HDL-C), triglycerides, and fasting blood glucose (FBG). From the above data, body mass index (BMI), body surface area (BSA), waist/hip ratio, non-HDL-C, and glomerular filtration rate (eGFR) were calculated using the relevant well-known formulas [9,22,23,24]. To estimate the 10-year risk of fatal and non-fatal CV events, we used the SCORE2 algorithm for high-risk countries (to which Croatia belongs) [9].
To establish the diagnosis of targeted CV risk factors and CVDs, such as dyslipidemia, arterial hypertension (AH), overweight, obesity, metabolic syndrome (MetS), CHD, family history of premature atherosclerotic (ATS) disease, diabetes mellitus (DM), and chronic kidney disease (CKD) of stage 3 or higher, we used criteria in accordance with current guidelines and recommendations of the ESC, the American Heart Association (AHA), and the International Society of Nephrology, as well as reports by WHO [9,25,26,27,28,29,30,31]. When discussing dyslipidemia, we mean values of TC greater than 5 mmol/L, LDL-C greater than 2.6 mmol/L, HDL-C of less than 1.2 mmol/L in women and less than 1 mmol/L in men, non-HDL-C of greater than 3.3 mmol/L, or triglyceride values greater than 1.7 mmol/L, as well as the use of hypolipemic drugs [9,25]. By AH, we mean values of SBP greater than 139 mmHg and diastolic BP (DBP) values greater than 89 mmHg, as well as taking antihypertensive drugs [26]. By overweight, we mean a BMI greater than 25 kg/m2, while values of 30 kg/m2 and more were defined as obesity [27,28]. The diagnosis of MetS was made in situations where, in addition to abdominal obesity (waist circumference greater than 88 cm in women or greater than 102 cm in men), at least two of the following four criteria were present: 1. impaired fasting glycaemia (IFG), defined by FBG values greater than 5.5 mmol/L, 2. SBP greater than 129 mmHg, 3. DBP greater than 84 mmHg, and 4. dyslipidemia [28,29]. We considered physical inactivity as physical activity of moderate intensity with a total weekly duration of less than 150 min [30]. The diagnosis of CHD was established by reviewing the existing medical documentation, based on functional non-invasive tests for the detection of myocardial ischemia during exercise or by showing the anatomy of the coronary arteries through CT angiography or invasive coronary angiography [31]. By a positive family history of premature ATS disease, we mean illness or death related to ATS in first-degree relatives under 55 years of age in men and 65 years of age in women [32]. DM was diagnosed in subjects who met one of the following criteria: 1. FBG greater than 6.9 mmol/L, 2. blood glucose 2 h postprandial or at any time greater than 11 mmol/L, 3. value of hemoglobin A1c (HbA1c) greater than $6.5\%$, 4. taking oral antidiabetic drugs, or 5. insulin [33]. Chronic kidney disease (CKD) stage 3 or higher was established in subjects with an eGFR of less than 60 mL/min/1.73 m2 [34].
For the two-dimensional TTE examination we used Phillips Affiniti 30 ultrasound (Philips Ultrasound Inc, Bothell, WA, USA) with a 2–4 MHz multifrequency probe. Two-dimensional TTE was carried out in all subjects, using the M-mode, two-dimensional (2D)-mode, pulsed-wave (PW), continuous-wave (CW), and tissue Doppler imaging (TDI), respecting the current guidelines of the American Society of Echocardiography (ASE) and the European Association of Cardiovascular Imaging (EACVI) from 2016 [8]. In assessing the diastolic function of the LV, we used the following four variables: 1. the ratio of the maximum LV filling velocities during the early (E) and late phases of diastole (during atrial contraction [A]), obtained using a PW Doppler positioned over the tips of the mitral valves, the so-called E/A ratio; 2. E/e′ (where e′ represents the maximum speed of early relaxation of the myocardium obtained by TDI positioned over the septal part of the mitral annulus); 3. tricuspid regurgitation systolic peak velocity (TR v max); and 4. the left atrium volume index (LAVI) obtained by the biplane area-length method. If only two parameters were available for evaluation, then we utilized the ratio of the maximum flow velocity over the upper right PV during systole (S) and diastole (D), the so-called S/D ratio, and the difference in the duration of atrial contraction measured over the upper right PV (a dur) and over the tips of the mitral valves (A dur), the so-called a dur–A dur, as an additional parameter.
Based on the estimated LV diastolic function (LVDF), we further divided the subjects into four numerically equal groups according to the following criteria: 1. Normal LVDF was established if half or more variables were within normal values: E/A 0.8–2, E/e′ septal ˂ 10, TR v max < 2.8 m/s, LAVI ˂ 34 mL/m2, S/D ratio ≥ 1, a dur–A dur ˂ 30 msec 2. LVDD grade 1 was established if half or more variables were within these values: E/A ≤ 0.8 (with E ≤ 50 cm/s), E/e′ septal < 10, TR v max < 2.8 m/s, LAVI ≤ 34 mL/m2, S/D ratio ≥ 1, a dur–A dur ˂ 30 msec 3. LVDD grade 2 was established if half or more variables were within these values: E/A 0.8–2, E/e′ septal 10–14, TR v max > 2.8 m/s and LAVI > 34 mL/m2, S/D ratio ˂ 1, a dur–A dur ≥ 30 msec and
4. LVDD grade 3 was established if half or more variables were within these values: E/A > 2, E/e′ septal > 14, TR v max > 2.8 m/s, LAVI > 34 mL/m2, S/D ratio ˂ 1, a dur–A dur ≥ 30 msec.
The laboratory analysis of ADMA and NT-proBNP was carried out at the Clinical Institute for Laboratory Diagnostics of the Clinical Hospital Center Osijek. It was performed exactly according to the manufacturer’s protocol.
ADMA was determined using the ADMA ELISA reagent (BioVendor Group, Brno, Czech Republic) on the Etimax 3000 ELISA processor (DiaSorin S.p. A, Saluggia (Vercelli), Italy). For its determination, 3 mL of venous blood was sampled in a test tube with ethylendiaminetetraacetic acid (EDTA)-anticoagulant. The sample was then centrifuged for 10 min at 3500 rpm and aliquoted into two 300 µL aliquots. Aliquots were then stored at −70 degrees Celsius until analysis.
NT-proBNP was determined with the Roche NT-proBNP reagent (Roche Diagnostics GmbH, Mannheim, Germany) on the immunochemical analyzer Cobas 6000 (Roche Diagnostics GmbH, Mannheim, Germany) using the ECLIA method. For the determination of NT-proBNP, an additional 3 mL of venous blood was sampled in a test tube without additives. The sample was left for 30 min until the clotting process was completed and then centrifuged for 10 min at 3500 rpm. Immediately afterwards, NT-proBNP was determined in the serum supernatant.
The diagnosis, phenotype, and severity of HF were confirmed based on symptoms, signs, resting ECG, TTE, and the plasma concentration of NT-proBNP, according to ESC Guidelines for HF, published in 2021 [4].
## Statistical Analyses
To observe a mean effect in the difference of numerical variables between four independent groups of subjects, with a significance level of 0.05 and a power of 0.85, the calculated minimum required sample size was 164 subjects (G*Power, 3.1.2). Categorical data were represented by absolute and relative frequencies. Numerical data were described by the arithmetic mean and standard deviation in the case of distributions that followed the normal one, and in other cases by the median and the limits of the interquartile range (IQR). Differences in categorical variables were tested using the chi-square (χ2) test and, if necessary, with Fisher’s exact test. The normality of the distribution of numerical variables was tested with the Shapiro–Wilk test. Differences of normally distributed numerical variables between two independent groups were tested with Student’s t-test, and in the case of deviation from normal distribution with the Mann–Whitney U test. Differences of normally distributed numerical variables in the case of three or more independent groups were tested using analysis of variance (ANOVA) and, in cases of deviation from the normal distribution, via the Kruskal–Wallis test. Using logistic regression, we evaluated the influence of plasma concentrations of ADMA and NT-proBNP on the probability of occurrence of LVDD, HFrEF, and NYHA class III–IV and high/very high SCORE2. Receiver operating characteristic (ROC) analysis [35] was applied to determine the optimal threshold value, area under the ROC curve (area under the curve, AUC), and specificity and sensitivity of the tested parameters. The association of normally distributed numerical variables was assessed by Pearson’s correlation coefficient r and, in cases of deviation from the normal distribution, by Spearman’s correlation coefficient ρ (rho). All p values were two-sided, and the significance level was set at Alpha = 0.05. The statistical program MedCalc® Statistical Software version 20.215 (MedCalc Software Ltd., Ostend, Belgium; https://www.medcalc.org; accessed on 5 February 2023) was used for data analysis.
## 3.1. General, Clinical, and Laboratory Characteristics and the Prevalence of CV Risk Factors of the Subjects in the Study Groups
The study was conducted on 178 patients (99 male, 79 female), with a median age of 61 years and an IQR of 49 to 65 years. The most common reasons for referral to cardiology examination were dyspnea ($65\%$), unregulated hypertension ($48\%$), palpitations ($35\%$), angina ($32\%$), positive family history of premature ATS disease ($29\%$), and FH or markedly elevated TC or LDL-C ($15\%$). After we estimated LVDF, subjects were divided into four numerically equal groups (Table 1).
Table 1 shows that there were slightly more men in the sample ($56\%$), who had LVDD grade 2 and 3 ($$p \leq 0.001$$) at significantly higher rates compared to women. The median age of the subjects increased with the severity of LVDD ($p \leq 0.001$). The subjects in the LVDD grade 3 group had a significantly higher prevalence of atrial fibrillation (AF), AH, obesity, CHD, DM ($p \leq 0.001$), CKD stage ≥ 3 ($p \leq 0.001$), and IFG ($$p \leq 0.001$$). They also had a significantly higher prevalence of diagnosed dyslipidemia ($$p \leq 0.04$$), but with lower blood lipid values compared to the subjects in the other groups. The reason for this was a significantly higher percentage of the use of statins. There were no significant differences between the groups in the prevalence of overweight, MetS, physical inactivity and active smoking.
## 3.2. Echocardiographic Parameters in the Study Groups
Regarding the TTE examination, Table 2 shows that the group with LVDD grade 3 had the statistically significantly lowest values of LVEF and the highest LV end-diastolic diameters (LVEDd) and LV end-systolic diameters (LVESd), as well as the highest LV mass index compared to all other groups ($p \leq 0.001$). Other parameters were within the expected values with respect to the estimated LVDF.
## 3.3. Differences between Groups Regarding the Prevalence of HF, NYHA Class, and SCORE2
Table 3 shows a statistically significant difference in the prevalence of HF among all groups, i.e., in the group with normal LVDF, not a single case of HF was recorded, therefore it was not applicable to determine the phenotype and severity of HF in that group, while in all other groups, the prevalence was high and the grade of LVDD tended to be more severe ($p \leq 0.001$). As for SCORE2, most patients, as many as 104 ($58\%$) had very high CV risk, more significantly in the LVDD grade 2 and 3 groups ($p \leq 0.001$).
## 3.4. Differences between Groups Regarding Therapy
A total of 152 ($85\%$) respondents were taking drugs from the antihypertensives, angiotensin receptors and neprilysin inhibitors (ARNIs), diuretics, mineralocorticoid receptor antagonists (MRAs), hypolipemics, antidiabetics, antiarrhythmics, antianginal, antiplatelet and anticoagulant drug groups. The percentage of medication intake within each group depended on the prevalence of CV risk factors, CVDs, HF, and estimated SCORE2 and was therefore highest in the LVDD grade 2 and 3 groups, where all subjects ($100\%$) were taking some form of medication from the previously mentioned groups, often in combination. Using the χ2 test, we assessed differences between groups regarding therapy intake. Subjects in the LVDD grade 3 group took the following groups of drugs significantly more often ($p \leq 0.001$): angiotensin converting enzyme inhibitors (ACEI), ARNI, beta blockers (BB), diuretics, MRAs, statins, amiodarone, trimetazidine, sodium-glucose co-transporter 2 (SGLT2) inhibitors, warfarin, and non-vitamin K antagonist oral anticoagulants (NOACs).
## 3.5. Biomarkers of HF and ED
The median concentration of NT-proBNP of all subjects was 139 ng/L (IQR from 41 ng/L to 680 ng/L), and the median concentration of ADMA was 0.436 µmol/L (IQR from 0.231 µmol/L to 0.528 µmol/L).
In Table 4, we demonstrate the distribution of subjects depending on the observed parameters (LVDD, SCORE2, HF, and NYHA class) and the plasma concentrations of ADMA and NT-proBNP. The lowest values of ADMA (up to 0.329 µmol/$L = 1$st tertile) had subjects with LVDD grades 2 and 3, very high SCORE2 ($p \leq 0.001$), and HF (p ˂ 0.001), while the highest values (≥0.510 µmol/$L = 3$rd tertile) were recorded significantly more often in subjects with normal LVDF and LVDD grade 1 ($p \leq 0.001$). In the case of NT-proBNP, the lowest values (up to 63 ng/$L = 1$st tertile) had subjects with normal LVDF and LVDD grade 1, while the highest values (≥344 ng/$L = 3$rd tertile) were more often found in subjects with LVDD grades 2 and 3, very high SCORE2 ($p \leq 0.001$), HF (predominantly HFrEF ($p \leq 0.001$)), and NYHA class III-IV ($$p \leq 0.001$$).
Using Fisher’s exact test and the χ2 test, we compared the concentration of NT-proBNP and ADMA with the general and laboratory characteristic of subjects, as well as the prevalence of CV risk factors listed in Table 1. The highest values of NT-proBNP in plasma were observed in men; subjects aged over 60 years; those with AH, DM, or CKD stage ≥ 3; CHD ($$p \leq 0.001$$); and IFG ($$p \leq 0.006$$), while the lowest values had the subjects with higher values of TC and non-HDL-C ($p \leq 0.001$), lower values of FBG ($$p \leq 0.01$$), and a positive family history of premature ATS disease ($$p \leq 0.02$$). Regarding ADMA, the highest values in plasma belonged to the subjects with higher values of LDL-C ($$p \leq 0.002$$), non-HDL-C ($$p \leq 0.007$$), and TC ($$p \leq 0.01$$), while the lowest plasma values belonged to subjects aged over 60 years and those with AH, IFG, ($p \leq 0.001$) and CHD ($$p \leq 0.002$$). Other CV risk factors listed in Table 1, as well as ambulatory measured SBP and DBP, did not have a statistically significant effect on NT-proBNP or ADMA values in the observed population. There was no statistically significant difference in ADMA concentrations between genders.
Finally, using Fisher’s exact test, we compared the values of the aforementioned biomarkers with the type of drugs used. The subjects who used ACEIs, BBs, statins, insulin, acetylsalicylic acid (ASA), ARNI, MRAs, SGLT2 inhibitors, and diuretics significantly more often had the lowest values of ADMA and highest values of NT-proBNP in plasma. The subjects who used antiarrhythmics, antianginal drugs, NOACs, warfarin, angiotensin receptor blockers (ARBs), and P2Y12 inhibitors were statistically significantly more likely to also have NT-proBNP values in third tertile.
Using logistic regression, we assessed whether plasma concentrations of NT-proBNP and ADMA predict the prevalence of any grade of LVDD (compared to normal LVDF), HFrEF, NYHA class III–IV, and a high/very high SCORE2. Table 5 shows that subjects with higher NT-proBNP values were more likely to have any grade of LVDD, HFrEF, NYHA class III–IV, and high/very high SCORE2. In contrast, the subjects who had lower ADMA values were more likely to have any grade of LVDD and high/very high SCORE2, while ADMA did not show statistical significance according to prevalence of HFrEF and NYHA class III–IV.
As can be concluded from Table 5, we found low but significant odds ratio (OR) values combined with a very narrow confidence interval (CI) of $95\%$, and therefore, we also performed ROC analysis to determine the optimal threshold value, the area under the ROC curve (AUC), and the specificity and sensitivity of the tested parameters. All the ROC curves demonstrate the superiority of NT-proBNP over ADMA (Figure 1, Figure 2, Figure 3 and Figure 4).
Figure 1, Figure 2 and Figure 3 show the ROC curve comparing NT-proBNP and ADMA plasma concentrations in different groups of subjects depending on the estimated LVDF.
Figure 4 shows the ROC curve comparing NT-proBNP and ADMA plasma concentrations in the subjects with a high/very high SCORE2. This shows that an estimated high/very high SCORE2 is a good diagnostic indicator for low ADMA and high NT-proBNP values ($p \leq 0.001$).
## 3.6. Medical Therapy and ADMA Values
We determined the connection between therapy and ADMA values. Out of a total of 152 subjects, 33 ($22\%$) were undergoing monotherapy and 119 ($78\%$) were undergoing polytherapy. We found that the subjects who took drugs from the groups listed in Table 6 had lower plasma ADMA values. Although the correlations were weak (r < −0.5), they were significant. The strongest correlation was among those taking ARNI (r = −0.454), ACEIs (r = −0.418), and statins (r = −0.395). They had significantly lower plasma ADMA values compared to those who were not taking drugs from these groups. The situation was the same in those who were undergoing polytherapy—they had significantly lower ADMA values compared to those undergoing monotherapy (r = −0.431).
Using multivariate linear regression, we determined that two predictors were significant for explaining the value of ADMA: polytherapy (β = −0.142) and statins (β = −0.092), which explain $22.3\%$ of the total variance of ADMA (F[2, 175] = 26.5, $p \leq 0.001$).
## 4. Discussion
The key findings of this study are as follows. First, certain groups of drugs, and more strongly, in combination, significantly affect the lowering of circulating ADMA concentration in plasma. Second, the same groups of drugs did not have the same effect regarding lowering plasma NT-proBNP concentration. Third, in contrast to previously published studies [15,18,19], we did not confirm a positive correlation of plasma ADMA concentration with NT-proBNP or the prevalence and severity of LVDD, HF, NYHA class, or CV risk, probably due to the previously mentioned drug influence. Fourth, similarly to other studies, we also confirmed that there is a positive correlation between the prevalence and severity of LVDD, CV risk factors, CVDs [5,6,7], estimated CV risk [36], NT-proBNP concentration [37], and HF [38,39]. However, contrary to other authors, we used SCORE2 to assess CV risk. Fifth, we confirmed that a higher concentration of NT-proBNP in the plasma is able to predict the severity of HF and NYHA class III–IV, which was also previously reported by other authors [40,41,42].
In the following section, we will refer only to the results that we obtained in a different manner to those previously published and attempt to justify our opinions, primarily related to the biomarkers that we used in our study.
It is already known that coronary ED is a condition involved not only in vasoconstriction, inflammation, fatty streak formation, and progression to plaque rupture and coronary artery thrombosis but also in the development and progression of LV hypertrophy and LVDD, ultimately resulting in HF development [11,12,43,44,45,46]. Moreover, Reriani et al. [ 47] found that measuring coronary ED provided better prognostic results compared to the Framingham risk score. It is also known that if risk factors are treated appropriately, coronary and systemic ED are reversible conditions, suggesting that ED becomes a surrogate endpoint of a therapeutic approach to reducing CV risk and CVD incidence [43,44]. However, the gold standard for the evaluation of coronary ED is the angiographic evaluation of vasodilation responses to the administration of vasoactive substances in the coronary arteries, but due to its invasiveness and cost, it is used only in patients who require cardiac catheterization for indications other than ED evaluation. The FMD of the brachial arteries provides a non-invasive alternative but is technically challenging and requires extensive training and standardization [48]. Therefore, it is not surprising that in recent decades there has been a great demand for biomarkers of ED that can be measured from a plasma sample with minimal invasiveness, which would be simple for daily use and inexpensive and accessible for a large number of patients. So far, a large number of such biomarkers have been found, and each of them may have important implications, but also limitations, in the clinical setting, which is why their use is still scientific in terms of researching additional information regarding the risk of developing CVDs and new treatment targets [49].
In our study, we used ADMA as a biomarker of ED and NT-proBNP as a biomarker of HF. ADMA is one of the most potent endogenous inhibitors of the three isoforms of NOS that have been related to many CV risk factors and a wide range of CV and other diseases [50]. In 2017, Nemeth, B., et al. [ 51] published a systematic review and meta-analysis based on which they estimated the reference values of ADMA in a healthy population. The meta-analysis included a total of 5528 adults without hypertension, diabetes, or obesity, who were not taking any medications and were included in one of 66 studies that examined plasma ADMA levels. In 24 of these studies, encompassing a total number of 1435 respondents, the ELISA method was used and reference values of 0.25–0.92 µmol/L were obtained, with an average value of 0.57 µmol/L. In a study published by Deneva-Koycheva et al. [ 52], the reference range for plasma ADMA concentration using an ELISA method in a sample of 150 healthy Bulgarian residents (74 men and 76 women, aged between 18 and 65 years) were from 0.22 to 0.69 µmol/L, with a mean concentration of 0.48 µmol/L. In the same study, another important fact was established—that there were no gender and age differences in ADMA concentration. Similarly, in our study, the range of ADMA concentration in plasma, also using the ELISA method, was between 0.23 µmol/L and 0.53 µmol/L, with a median of 0.44 µmol/L. Contrary to the previously mentioned study, the concentration of ADMA in our subjects was inversely proportional to age. However, we must emphasize that our study included a general population in which only a few subjects were healthy, while a large percentage had been diagnosed with one of the traditional CV risk factors and CVDs, the prevalence of which increased with age, which is why even $85\%$ of respondents took drugs from the antihypertensive, antidiabetic, hypolipemic, antianginal, antiarrhythmic, anticoagulant, or antiplatelet therapy groups. Those who were taking drugs, especially combinations of drugs from several groups, had statistically lower values of ADMA concentration in plasma (often lower than the specified reference range) compared to the other subjects. In previous reviews and meta-analyses, it has been confirmed that elevated plasma ADMA concentration correlates with the presence and functional significance of plaque in the coronary, cerebral, and peripheral circulation and, consequently, is able to predict CV mortality and morbidity [53,54]. From this, we can conclude that an elevated level of ADMA is associated with an increased CV risk. However, cross-sectional studies that compared ADMA values in the plasma of patients with confirmed CHD in dependence to number and percentage of stenosis of coronary arteries affected by atherosclerosis gave conflicting results. While Mangiacapra et al. [ 55] confirmed that the level of ADMA in the plasma of 281 patients with CHD is an independent predictor of the expansion and functional significance of atherosclerotic CHD, Ghayour-Mobarhan M et al. [ 56] did not confirm a significant relationship between the concentration of ADMA in plasma and the presence or severity of coronary artery stenosis in a sample of 165 subjects without traditional CV risk factors. What is not known, regarding both studies, is whether the effect of drug therapy on ADMA concentration was examined. Although a specific ADMA-lowering agent has not yet been found, many drugs have been reported to lower ADMA levels in clinical studies by either increasing the activity/expression of the enzyme responsible for ADMA degradation or by decreasing the expression of the enzyme responsible for the formation of ADMA in cells or via some other unknown method [17,43,44,57,58]. Some of these are ACEIs, ARBs, BBs, hipolipemics, oral hypoglycemics, and ASA. In our study, subjects with AH, IFG, and CHD, who mostly belonged to the groups with LVDD grades 2 and 3, in which the percentage of subjects who were taking one or more drugs from the groups mentioned above was $100\%$, had the lowest plasma ADMA values, while subjects with dyslipidemia had the highest plasma ADMA values. Although dyslipidemia was the most common CV risk factor in the population we observed, with a total prevalence of $92\%$, statins were taken by only $45\%$ of respondents and were statistically most significantly used in the LVDD stage 3 group (even $81\%$). Therefore, we believe that the obtained results are the result of the drugs, and we confirmed this through multivariate regression, according to which, the variations of ADMA values in plasma are most significantly affected by polytherapy and statins.
NT-proBNP is the second biomarker that we examined in our study. It is a biologically inactive N-terminal fragment that is formed from the cleavage of prohormone (proBNP) into the biologically active hormone BNP and is produced by LV myocardial cells in response to myocardial stretching under the influence of increased pressure and/or volume. NT-proBNP, as well as BNP, has an important place not only in the diagnosis of HF [59] but also in the prevention of both HF and major adverse CV events [60,61]. It has been confirmed in several studies so far [40,62] that the value of NT-proBNP is significantly higher in women compared to men at any age, while the levels increase with age in both sexes. Although, in our study, we also confirmed that NT-proBNP concentration increased with age, men had higher values of plasma NT-proBNP concentrations compared to women. We explain this by the fact that there were slightly more men in the sample, who had significantly higher rates of LVDD grades 2 and 3 compared to women, and the higher LVDD was, the higher NT-proBNP concentrations in plasma was. In terms of current knowledge concerning the effects of drugs on the concentration of NPs in plasma, theories are quite contradictory. Recent studies confirm the reduction of NT-proBNP with the use of new drugs in the treatment of HFrEF—while ARNI lowers the initial values of NT-proBP by approximately $30\%$, SGLT2 inhibitors and vericiguat lower these values more modestly (by about 10–$15\%$) [63]. The effects of other drugs that we are used in daily practice for HF, CHD, and AH are more complex and depend not only on the underlying disease for which the given drug is introduced but also on the length of therapy as well as on the active substance within a particular group of drugs [64,65].
We believe that our study has important implications for clinical practice, indicating that the therapy we use today can have a great impact on the improvement of ED, and if introduced early enough, it can influence the sequence of the development of CVDs and HF. In this context, ADMA can serve as a good biomarker of treatment effectiveness.
The main limitation of our study lies in the fact that we conducted a cross-sectional study in which we compared the values of NT-proBNP and ADMA between different groups of people depending on estimated LVDF, and not, as in previous prospective studies, comparing the same groups of people after a certain period of undergoing certain therapies. Therefore, we cannot say with certainty how significant each therapy is in lowering NT-proBNP and ADMA, because we did not monitor these values over time. Therefore, a longitudinal study is highly needed.
## 5. Conclusions
In the pathophysiological continuum of CVDs, the first link is ED, caused by traditional and non-traditional CV risk factors, which eventually lead to ischemia, myocardial remodeling, and finally, to HF. The biomarkers of ED can be measured from a plasma sample with minimal invasiveness, they are suitable for daily use, inexpensive and accessible to a large number of patients. ADMA, similar to other biomarkers of ED, gives us information about the systemic state of endothelial function, and with the help of LVDF assessment and measurement of NT-proBNP concentration in plasma, we are able to non-invasively assess the coronary consequences of ED. Further research should determine how the biomarkers of ED, LVDD, and NT-proBNP could be included in the CV risk assessment tables. It is also necessary to clarify the knowledge regarding which period of life it becomes advisable to start measuring and how often to examine the biomarkers of ED and what the target values would be, with the aim of slowing the progression of the development of CVDs, which could ultimately result in a decrease in the incidence of CVDs and HF. Furthermore, a longitudinal study is needed to determine the effect of therapy on the plasma values of NT-proBNP and ADMA.
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|
---
title: 'mHealth Technology as a Help Tool during Breast Cancer Treatment: A Content
Focus Group'
authors:
- Angeles Fuentes
- Clara Amat
- Raimundo Lozano-Rubí
- Santiago Frid
- Montserrat Muñoz
- Joan Escarrabill
- Imma Grau-Corral
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001870
doi: 10.3390/ijerph20054584
license: CC BY 4.0
---
# mHealth Technology as a Help Tool during Breast Cancer Treatment: A Content Focus Group
## Abstract
Purpose: To assess the usability and preferences of the contents of mHealth software developed for breast cancer patients as a tool to obtain patient-reported outcomes (PROMs), improve the patient’s knowledge about the disease and its side effects, increase adherence to treatment, and facilitate communication with the doctor. Intervention: an mHealth tool called the Xemio app provides side effect tracking, social calendars, and a personalized and trusted disease information platform to deliver evidence-based advice and education for breast cancer patients. Method: A qualitative research study using semi-structured focus groups was conducted and evaluated. This involved a group interview and a cognitive walking test using Android devices, with the participation of breast cancer survivors. Results: The ability to track side effects and the availability of reliable content were the main benefits of using the application. The ease of use and the method of interaction were the primary concerns; however, all participants agreed that the application would be beneficial to users. Finally, participants expressed their expectations of being informed by their healthcare providers about the launch of the Xemio app. Conclusion: Participants perceived the need for reliable health information and its benefits through an mHealth app. Therefore, applications for breast cancer patients must be designed with accessibility as a key consideration.
## 1. Introduction
Breast cancer is the most common form of cancer among women [1,2]. In 2018, breast cancer mortality trends decreased by $41\%$ due to various factors, including early diagnosis, advancements in treatment, lifestyle changes, improved nutrition, and research [3,4]. This increased survivorship highlights the importance of focusing on the long-term goals and consequences of treatment to enhance quality of life and overall well-being, promoting a proactive approach to health [5].
Technological developments in recent years have been essential in supporting methodologies for diagnosing health and cancer. These developments include the standardization of portable and wearable devices for data collection and health biomarkers, as well as advances in data analysis through artificial intelligence [6,7,8,9,10].
The use of apps to promote health and well-being has grown exponentially [11]. Smartphones facilitate the creation and development of millions of apps, including communication apps, geolocation with maps, video games, video streaming, and health apps. These can be easily downloaded from app stores and offer low-cost solutions that can be accessed by a large global population.
Using mHealth (mobile Health), also known as health apps, to support breast cancer patients during treatment and post-treatment can be a helpful complement to the usual treatment for patients [12]. mHealth can be an effective tool for obtaining patient-reported outcome measures (PROMs) that reflect patients’ perceptions of their own health. Within the framework of Value-Based Healthcare (VBHC), there is a value change from volume-driven to value-driven care, empowering patients by allowing them to report on their disease-related side effects and quality of life, and reinforcing treatment adherence [13,14,15,16,17,18,19].
PROMs are thought to be central to the understanding of the effectiveness of treatments in cancer [20], improving communication between patients and providers, patient satisfaction [21], daily life [22], and survival [23]. According to Osborn et al., a small number of mHealth applications have been used in clinical studies examining a variety of cancer types and age groups. The studies found that the positive impact was largely limited to improved symptom control, although some studies reported increased symptoms. Data on other outcomes, including health economic measures, were limited [17].
Xemio (www.xemio.org (accessed on 3 March 2023)) is a digital platform that comprises a website, social network, and app, providing access to a virtual environment for meetings, debates, support, and accompaniment. It was developed by Fundación ISYS, with patients as the primary focus, specifically for those with breast cancer. The project has created the Xemio app (Figure 1), an app designed by patients and doctors, and all its content is reviewed and updated by oncology professionals to help patients with their disease self-management and social issues. The platform, built for smartphones, helps patients and their families track side effects and treatments, as well as participate in activities and social events organized by various associations. The Xemio platform is endorsed by the SOLTI scientific societies dedicated to breast cancer research and the Catalan Society of Family and Community Medicine (CAMFiC). It has received support from the “la Caixa” Foundation, a Europe Horizon 2020 grant, and crowdfunding.
In order to assess the patients’ preferences for the prototype of the Xemio app, the research group decided to conduct a focus group as the first step in a series of participatory user-centered activities to develop a mobile app that is well received by patients.
The results presented in this article from the focus group are part of a larger research project. The Xemio app is integrated with the Electronic Medical Record of Hospital Clínic de Barcelona [24]. This will allow oncologists to access and interact with data recorded by patients participating in the pilot study. This integration is part of the European project “Artificial Intelligence Supporting Cancer Patients across Europe” (ASCAPE) (ClinicalTrials.gov Identifier: NCT04879563). ASCAPE aims to identify quality-of-life problems based on PROMs and support treatment recommendations.
In order to establish the design process of the Xemio app, a qualitative observational study design was previously conducted [25,26]. The study design incorporated semi-structured interviews with five patients from a local patient association [27], with the aim of identifying the desired content and features of a mobile app to assist individuals living with breast cancer [28]. The smartphone app prototype was developed with the help of an oncologist and two general practitioners belonging to the research group, and it is based on this prototype that this study was carried out.
## 2. Objectives
The aim of this focus group was to gain an in-depth understanding of the needs of breast cancer patients during treatment and assess the feasibility of a smartphone application prototype developed by a research team of patients and oncology professionals.
## 3. Methodology
Qualitative research methods provide a deeper understanding of social issues. These techniques offer more opportunities for gaining in-depth knowledge about a specific topic compared to quantitative research methods [29,30]. A focus group is a commonly used qualitative research technique [25,26] that does not require extensive resources and enables interactive feedback and suggestions from participants during the sessions. It helps to identify key areas for improvement in a product or service [31]. This study followed the flowchart of steps for conducting a focus group discussion [29].
A focus group session began with the presentation of the content to be discussed after a brief presentation by the moderator. The moderator asked the participants about their experiences.
## 3.1. Patient Identification and Patient Recruitment
Before patient selection, it was decided that the group should be composed of breast cancer survivor patients treated at the Hospital Clinic. None of the patients were on active cancer treatment when the focus group took place. The patients invited to participate were women who represented the prototype patient cases designed for the focus group. These patients represent different age groups, between 50 and 65 years old and over 65 years old, considering that the average age of breast cancer patients is 63 in white women, combining situations of employment or unemployment and living alone or with family. Although there is a generational gap in the use of new technologies, researchers decided to model the focus group, including older adult patients, as a very suitable methodology for marginalized groups [32,33].
Due to the COVID-19 emergency in June 2020, it was challenging to recruit and invite patients to increase participation in the focus group. In accordance with the COVID-19 regulations regarding the gathering of people in enclosed spaces and hospitals, the focus group consisted of 5 participants aged between 52 and 71 years old. The archetypes of the type of patients that were of interest were the following:Patient 50–65 years old, employed;Patient 50–65 years old, unemployed;Patient > 65 years old that lives alone;Patient > 65 years old that lives with family members.
Participant identification was followed by participant recruitment. For the recruitment of participants, the collaborating oncologist drew up a list of possible candidates following the mentioned archetypes as best as possible. The oncologist contacted the candidates over the phone.
## 3.2.1. Data Collection on Patient Information Needs, Services, and Activities (Session I)
The focus group was divided into two sessions. Both sessions occurred on the same day. This first session was done without giving the patients any prior proposals of what they would evaluate in the second session, and it aimed at exploring the immediate impressions that patients have regarding information, services, and activities that they considered helpful during cancer treatment.
During the first part of the focus group, the moderator, an experienced doctor in charge of the Patient Experience department of Hospital Clínic de Barcelona, proposed the topics to be discussed with the participants. The topics of discussion were agreed upon beforehand with two other experts that also acted as observers: an oncologist and an Information Society expert. The topics to be discussed were as follows:Treatment of symptoms;Advice on how to cope with side effects;Services or activities needed throughout the cancer process;News that would be of interest during this period.
The proposed contents of the conversation were elaborated through a thematic study group and collective alignments, considering the participants’ previous experiences. It was planned as a 50 min session.
Data collection in the first session was done by recording an audio tape (and a subsequent transcription), taking notes, and participant observation.
## 3.2.2. Cognitive Walkthrough Test with Users (Session II)
Cognitive walkthrough (CW) is a method of inspecting the usability of an interactive system that focuses on evaluating the ease of learning a new tool [30]. Its purpose is to analyze how a user thinks and behaves when they first use an interface. It is known that if users are given a choice, they prefer to learn based on exploration and observation, rather than reading manuals or following instructions [34].
The patients were allowed to interact with the Xemio app in the second part of the focus group. During this activity, the patients were given a smartphone with the app and commands about what activities to do with the app. Their impressions were collected regarding the content and usefulness of the tool. Data collection in this session was achieved using questionnaires, registering the navigation of the app, and the participant’s observations recorded by the observers.
The research team developed two questionnaires and a user test to evaluate the ease of use, effectiveness, and efficiency of the Xemio application. The session was well defined and guided by “Usability Inspection Methods, Jakob Nielsen, 1994”, taking special care with some golden rules such as one task = one action.
The order of the CW session was as follows:First questionnaire: this was aimed at understanding the degree of literacy the participants had in handling smartphone applications;User test: a selected member of the research group with expertise about the app acted as the facilitator by explaining the tasks to be completed;Second questionnaire: This was aimed at assessing the usefulness and contents of the application.
It also contrasted the answers that emerged from the first part of the focus group. A 50 min session was planned to complete this part.
## 3.3. Venue for the Discussion
The focus group was held in the living lab of the Hospital Clinic, a space dedicated to sharing experiences with patients, called Espai de Intercanvi d’Experiències (EIE) within the Hospital Clínic de Barcelona. The space for the Exchange of Experiences (EIE) is a physical space within the hospital that facilitates reflection, rethinking, and co-creating solutions to improve care services and increase their value from the patient’s perspective.
## 3.4. Data Analysis
The content analysis was carried out by coding the four thematic categories proposed, grouping and classifying the comments as positive and negative, locating the areas of interest, collecting the scores from the questionnaires, and analyzing the fluidity of navigation in the app.
## 4. Results and Reporting
The results are organized into three distinct sections. The first section includes an analysis of the results from the first session of the focus group. The second section focuses on the analysis of the questionnaires and the tasks performed with the Xemio app. Finally, the third section compares the results from both the first and second sessions.
## 4.1. The Capture of Information and Follow-Up Needs
The topic of the first part of the session was symptoms from the treatment (side effects) and their intensity. The conversation focused on treatment effects on body image, such as hair loss, spots on the skin, weight gain, and increased sweating. Afterward, the moderator directly asked about other side effects such as the effect on sleep, sexual life, or nutrition. Three participants pointed out that they experienced a metallic taste when eating food. In addition, some participants pointed out a weight loss at the beginning of the treatment that was recovered later. Topics related to surgery side effects, especially lymphedema (cork-like tenderness in the arm), were also mentioned in the discussion without going into much detail. Finally, two patients were referred for mental focus and memory problems. The focus participants maintained an objective and positive attitude throughout the discussion.
During the session, the moderator collected most of the relevant information in a Metaplan board meeting, which constituted four main topics: symptoms, side effects, services, and news about cancer treatment advancements (Table 1).
## 4.1.1. Textual Phases Catch from Patients
In the second part of the session, the moderator focused on how patients manage the treatments’ side effects. During that section, the participants recalled digestive side effects, nausea, mouth sores, skin burns from radiotherapy, fever, fatigue, and general malaise. However, most patients claimed to have received complete information on managing their symptoms from the hospital oncology staff. The positive perception was that they had been fully informed and had help when needed.
In the third part of the session with the moderator, the patients of the group were asked about which services outside the hospital they used during their treatment and about their participation in activities carried out by patient associations. The first thing the participants mentioned was information of a practical nature to adapt to their new reality, such as the location of stores where they could buy wigs and scarves. The youngest patient admitted searching for terminology on the Internet. One of the older patients explained how she signed up for adult classes at the university. One of the patients expressed that she had attended a patient association session of the “Kálida” space at the Sant Pau hospital in Barcelona. When asked about the reason for not participating in patient association activities, they replied that the hours were unsuitable for them and that they maintained other personal activities.
The fourth section of the discussion with the moderator was about news consumption preferences. Participants were asked about the need for the consumption of specific news. The participants expressed that they thought there was an excess of information on the Internet. Another conversation topic about their cancer was information from conventional media that created false expectations. When asked about topics of interest to generate news, the general agreement was the preference for practical news with content such as nutrition and aesthetics tips and an agenda for group activities.
## 4.1.2. Cognitive Walkthrough Test with Users
After a short break, the second session of the focus group was presented to the patients. This session started with a pre-test to find out the participants’ everyday use of Information Communication Technologies (ICT) resources. The results of this questionnaire are shown in Table 2.
A digital generation gap is visible in the use of tools by age, with the patients of the age group of 50 years being the most likely to use Internet tools and the older patients being less likely to use Internet tools.
## 4.2. Results of the Xemio App User Test
Observational comments on required tasks:Task [1] *Find a* side effect in Xemio: Patients were asked to find and record side effects in the app. P1, P3, and P4 had no difficulty, P2 had many navigation issues, and P5 also had some difficulties. *They* generally believed that navigating the side effects area and move intensity and recommendations could be more intuitive. Patients were looking for specific effects that were not included in the app (i.e. heart side effect) and expected to find a free text field where they could record these side effects; this is a use case we hadn’t developed yet. Task [2] *Register a* treatment in Xemio: Patients were asked to register the Intensity of effects on nails. Participant P5 could not find the option to get to the functionality to select and register an intensity.. General difficulty registering the intensity (it is not intuitive). Participants have problems returning to the previous screen when recording side effects and intensities. The image of the body to record dry skin is very well understood and the body part can be chosen; however, participants have problems understanding how to record the intensity. For example, the head only lets them select moderate intensity. Task [3] Consult information on types of cancer: *There is* confusion between the side menu and the bottom menu. P3 asks if she is able to zoom in. She expresses that the letters and symbols cannot be seen well. Task [4] Register for an event in the social agenda: the moderator decided not to complete this task when she realized that the patients were starting to have difficulty processing more new information and were experiencing difficulties following the pace of the session. Tasks [5] Configure my personal data and [6] Generate PDF document with my histories: it gave errors to some participants when they entered their data; however, they could access my diary.
After the experience of interacting with the Xemio app, participants were asked about their opinion of the application.
To ensure a better representation of opinions by having more options than Yes/No or True/False answers, a seven-point Likert scale questionnaire was designed, providing participants with options to express themselves more accurately and a better representation of their assessment. The seven-point Likert Scale was also chosen to reduce the possibility of random or inconsistent responses and to avoid neutral judgments as occur with five-point Likert Scales. *In* general, the seven-point Likert scale can be a good choice for collecting detailed information about a participant’s evaluation. The results of this questionnaire are shown in Table 3, and the results of the open-ended questionnaire are shown in Table 4.
## 4.3. Combined Results
The results of the two parts of the study were somewhat different. In the first part, patients expressed complete confidence in the information provided by the oncology unit, describing it as accessible, complete, and understandable. In contrast, they expressed concern about information found on the Internet and the possibility of encountering false information.
In the second part of the study, after using the application, the participants viewed it as a positive addition to their existing sources of information. They expressed interest in the ease of access to information about practical events organized by other entities.
Comparing the results of each session, the participants who struggled with the tasks in the second part were the same individuals who do not use smartphones to access the Internet. Additionally, participants P2 and P5 had more difficulty navigating the application than the other participants.
## 5. Discussion
This focus group helps to choose functionalities and define the process of evolution and continuous improvement of the Xemio application. Selecting suitable candidates to participate in the focus group was essential to generate critical feedback and the necessary knowledge to identify unmet needs. A wide range of focus group participants provided valuable additional input from each participant.
The age of the patient is a key factor in determining the probability that the patient will incorporate technology, specifically this app, into their daily routine. The younger participants in the group had no difficulty navigating the app, while the older patients required assistance to complete tasks. Applications designed to support patients with cancer or chronic diseases may not be appropriate for those who have not acquired basic technology skills. As a result, these technological tools should not yet be considered a standard of care as they may exclude a significant portion of patients.
However, mHealth applications have the potential to become a normal part of the standard of care in the near future, as more cancer patients acquire the necessary technological skills to use these tools. It is important to involve potential users from the beginning of the design process and throughout its evolution. There is currently a lack of evidence regarding patient knowledge and participation in the development and evaluation of medical applications [11,35]. Typically, technologies are presented to patients without their involvement in the design process and only later are they asked about their usefulness in clinical practice. To address this issue, it is crucial to adopt a patient-centered approach and prioritize identifying unmet needs before beginning the design process.
## 6. Principal Findings
Despite the limitations of this focus group, the results suggest that while breast cancer patients believe they receive adequate care and that the hospital services meet their needs, there is still room for improvement in patient care and support. This highlights the need for more research and efforts to enhance patient care in this field, even though patients are currently satisfied with the care they receive.
The adoption of mHealth tools, such as the Xemio app, has the potential to revolutionize the way chronic patients receive care in hospitals. By using smartphones, tablets, and wearable devices, patients can remotely monitor their health and communicate with their healthcare providers, without having to visit the hospital as frequently. This not only saves time and resources for patients but also reduces the burden on hospitals and healthcare providers, enabling them to focus on providing more complex care to those who need it most.
Additionally, mHealth tools can provide real-time health data, allowing providers to make more informed decisions about a patient’s care. This can lead to improved outcomes and a higher quality of care for patients. With the increasing availability of sophisticated mHealth technologies, the potential for improving care for chronic patients is enormous, and it is an area that is receiving increasing attention from researchers, healthcare providers, and policymakers alike. This could eventually lead to overall quality improvements in patient care. This was demonstrated during the second session of the focus group when patients expressed how much they appreciated the app and found it informative. This conversation led to the patients wishing they had the option to use the app on their phones during their initial diagnosis, treatment, and ongoing cancer process.
## 6.1. Comparison with Prior Work
A few years ago, researchers conducted a review to evaluate the effectiveness of mHealth tools to support patients with chronic disease management [36]. The study, which referred to mHealth tools used for disease management as “mAdherence”, also explored the usability, feasibility, and acceptability of these tools. The researchers found that mAdherence tools and platforms were generally highly usable, feasible, and acceptable. However, they also pointed out that there is limited information available on how mHealth tools are designed to meet the needs of specific patient populations. For example, they noted that older patients may have difficulty traveling to a healthcare provider’s office and that mAdherence tools could ease this burden. The researchers recommended an iterative design process that includes systems and content development and multiple stages of user experience testing.
The following review article by Hamine et al. [ 36] found that 62 out of 107 studies explored the usability, feasibility, acceptability, or patient preferences for mAdherence interventions. The authors found that 27 studies in their search used randomized controlled trials (RCTs) to explore the impact on adherence behaviors, and significant improvements were observed in 15 of those studies. There were 16 out of 41 RCTs that showed significant differences between groups regarding effects on disease-specific clinical outcomes. The conclusion of the review article is that mHealth tools have the potential to facilitate adherence to disease management; however, the evidence to support its effectiveness is, so far, mixed.
## 6.2. Limitations
The oncologist treating the patient was present at the first session of the focus group. The presence of the oncologist may have changed what the participants revealed. They may have chosen not to share specific experiences because they thought it might affect their treatment or relationship with their doctor or the hospital.
Another limitation is that the sample size was very small and limited to a single focus group. Recent publications [37,38] suggest having at least three clusters to capture significance and saturation is necessary. Because of the COVID-19 emergency in June 2020, it was difficult to increase the number of focus group participants through recruitment and invitations to patients. The focus group was held at the Hospital Clínic de Barcelona, which was facing a shortage of resources due to the pandemic, making it challenging to schedule additional dates for the process. The focus group was carried out following all hygiene and safety regulations established by the government, and additional precautions were taken to avoid contact between participants considered to be at high risk.
## 7. Conclusions
While patients currently receive adequate care, there is always room for improvement, and mHealth tools have the potential to play a major role in enhancing patient care and support in the field of health and wellness.
Upcoming work will involve a long-term randomized pilot to investigate how using the Xemio app impacts the quality of life of breast cancer survivors, expected to be published during 2023. Further work will also involve the continuous evolution of the app to provide better and updated services to the users to support them throughout the cancer process, including patient evaluation tools, such as interviews and PREMS questionnaires.
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|
---
title: 'Risk Factors for Mortality of Hospitalized Adult Patients with COVID-19 Pneumonia:
A Two-Year Cohort Study in a Private Tertiary Care Center in Mexico'
authors:
- Carlos Axel López-Pérez
- Francisco J. Santa Cruz-Pavlovich
- Juan Eduardo Montiel-Cortés
- Adriana Núñez-Muratalla
- Ruth Bibani Morán-González
- Ricardo Villanueva-Gaona
- Xochitl Franco-Mojica
- Denisse Gabriela Moreno-Sandoval
- Joselyn Anacaren González-Bañuelos
- Alan Ulises López-Pérez
- Marily Flores-González
- Cristina Grijalva-Ruiz
- Edna Daniela Valdez-Mendoza
- Luis Renee González-Lucano
- Martín López-Zendejas
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001871
doi: 10.3390/ijerph20054450
license: CC BY 4.0
---
# Risk Factors for Mortality of Hospitalized Adult Patients with COVID-19 Pneumonia: A Two-Year Cohort Study in a Private Tertiary Care Center in Mexico
## Abstract
During the COVID-19 pandemic, the high prevalence of comorbidities and the disparities between the public and private health subsystems in Mexico substantially contributed to the severe impact of the disease. The objective of this study was to evaluate and compare the risk factors at admission for in-hospital mortality of patients with COVID-19. A 2-year retrospective cohort study of hospitalized adult patients with COVID-19 pneumonia was conducted at a private tertiary care center. The study population consisted of 1258 patients with a median age of 56 ± 16.5 years, of whom 1093 recovered ($86.8\%$) and 165 died ($13.1\%$). In the univariate analysis, older age ($p \leq 0.001$), comorbidities such as hypertension ($p \leq 0.001$) and diabetes ($p \leq 0.001$), signs and symptoms of respiratory distress, and markers of acute inflammatory response were significantly more frequent in non-survivors. The multivariate analysis showed that older age ($p \leq 0.001$), the presence of cyanosis ($$p \leq 0.005$$), and previous myocardial infarction ($$p \leq 0.032$$) were independent predictors of mortality. In the studied cohort, the risk factors present at admission associated with increased mortality were older age, cyanosis, and a previous myocardial infarction, which can be used as valuable predictors for patients’ outcomes. To our knowledge, this is the first study analyzing predictors of mortality in COVID-19 patients attended in a private tertiary hospital in Mexico.
## 1. Introduction
Two years after being declared a global pandemic by the World Health Organization (WHO) on 11 March 2020, the coronavirus disease-2019 (COVID-19), has caused more than 449,000,000 cases and 6.6 million deaths [1,2]. COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is transmitted primarily through large respiratory droplets. This disease presents with a wide array of clinical presentations, ranging from asymptomatic, mild respiratory, or extrapulmonary disease, to life-threatening respiratory failure, multi-organic failure, and death [1,3,4].
Due to the magnitude of the pandemic and the current absence of an effective curative treatment, several studies have reported the clinical and epidemiological characteristics of their respective populations [1,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. These can be used as a proxy for the prediction of patients’ outcomes. Currently, risk factors related to worse clinical outcomes and mortality include older age; male sex; obesity; comorbidities such as diabetes, hypertension, and heart failure; and laboratory features compatible with an inflammatory state [1,2,3,4,7,8,9,12,14,21,26,28,29].
Latin America and the Caribbean (LAC) has arguably been one of the areas most impacted by the pandemic, five of the region’s countries being among the 20 with the highest number of reported cases and deaths [30,31]. The pandemic has had a very elevated socioeconomic impact on the region, particularly affecting vulnerable populations: groups with a high poverty index or a lack of formal employment [21,31] as well as those with preexisting comorbidities, exacerbated by deficiencies of the health institutions in vulnerable countries [32], with most of these regions being unable to guarantee public healthcare to a considerable percentage of the population. As a response to the lack of complete public coverage, health systems in countries such as Mexico are forced to rely heavily on private spending [32,33,34].
The country has experienced six waves of the disease, resulting in more than 7.2 million cases and 331,407 deaths to date [35]. Despite not having the highest mortality rate of LAC, Mexico currently stands as the fifth country with the most deaths worldwide [35]. The alarming mortality, correlated with the aforementioned risk factors [21,23,36,37], can also be associated with the differences among healthcare institutions. Evidence suggests that the lack of homogeneity among available resources, infrastructure, quality of care, and standardized protocols may have resulted in a higher probability of dying from COVID-19 in public healthcare facilities than in private institutions [21,38,39,40]. Considering this, it is necessary to analyze the statistical behavior of the pandemic in public and private institutions independently. This would in turn present us with an image depicting the interaction between the pandemic and the two different healthcare environments, correlating with socioeconomic implications such as inequalities in healthcare access and cultural disparities of marginalized groups, which continue to impact the evolution of the pandemic in Mexico.
In this study, the findings from a 2-year retrospective large cohort study from a private tertiary care center in Guadalajara, Mexico, are reported. This study aims to describe and compare clinical characteristics, laboratory and radiological findings, and mortality among adult patients hospitalized with COVID-19 pneumonia in a Mexican private tertiary care center from April, 2020 to March, 2022.
## 2.1. Study Design
A retrospective cohort study was conducted at San Javier Hospital (SJH), a private tertiary care center located in Guadalajara, Jalisco, Mexico, that included all adult patients admitted to the hospital with a confirmed diagnosis of COVID-19 from 4 April, 2020 to 3 March, 2022. Patient admission was based on the National Institutes of Health (NIH) severity of illness categories [41], admitting all those with COVID-19 with severe or critical illness and those with moderate illness at high risk of progressing to severe disease, as determined by each attending doctor.
The primary outcome was in-hospital mortality without a set timeframe for it to occur. Inclusion criteria were: [1] adult age (≥18 years old), [2] patient admitted to SJH with a new diagnosis of COVID-19 pneumonia, [3] SARS-CoV-2 infection confirmed with RT-PCR of nasopharyngeal swab with the Berlin protocol, and [4] definite discharge or COVID-19-related death outcome. Exclusion criteria were: [1] interhospital transfer from our institution to another hospital and [2] patient discharged against medical advice. The present research was conducted in accordance with the Declaration of Helsinki, as we adhered to the General Principles of the World Medical Association; the importance of the objective outweighed the risks to the participants of the study, our research used accepted scientific principles based on the scientific literature, and all data was maintained with confidentiality [42]. The study was approved by the research ethics committee of the SJH with the register number 002-08-2022-MLZ. Due to the observational and retrospective nature of the study, no informed consent was required. Decisions regarding diagnostic approach, treatment, and follow-up were the responsibility of the attending physician, with consideration that during the pandemic different medical treatments were used based on the best scientific information available at each moment.
## 2.2. Data Collection
Epidemiological data were retrieved from the electronic medical record (TASY) of the primary and secondary evaluations performed by first-contact physicians at the respiratory care unit. Additional clinical and laboratory information, clinical outcome (survival or mortality), and pathway to death was obtained from the electronic medical record (EMR). Initial laboratory tests were defined as the first results available, typically within 24 h of hospital admission [24], including complete blood count, liver panel, basic metabolic panel, C-reactive protein (CRP), D-dimer, and Troponin I, among others.
## 2.3. Definitions
Co-morbidities were defined as follows: chronic obstructive pulmonary disease (COPD) as a diagnosis of postbronchodilator FEV1/FVC ratio of <0.70 [43]; asthma as established by the Global Initiative for Asthma 2020 [44]; chronic kidney disease (CKD) as a glomerular filtration rate below 60 mL/min for more than three months [45]; diabetes according to the guidelines of the American Diabetes Association [46]; hypertension as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg [47]; and immunosuppression as neutropenia (less than 500 neutrophils), with active malignant disease, asplenia, or under immunosuppressive treatment (prednisone >20 mg/day or other immunosuppressive drugs for at least 30 days) [21,22].
Definitions for the causes of death include: acute respiratory distress syndrome (ARDS) according to the Berlin definition [21,25], septic shock according to the 2016 Third International Consensus Definition for Sepsis and Septic Shock [48], and myocardial infarction following the guidelines of the Fourth Universal Definition of Myocardial Infarction [49].
## 2.4. Statistical Analysis and Tools
According to their distribution and type, the variables are summarized as mean and standard deviation or median with ranges and percentages (%), as appropriate. Demographic and clinical characteristics were compared between survivors versus non-survivors using a chi-square test and a t-Student test, as appropriate. Variables that proved to be statistically significant in the univariate analysis underwent multivariate ANOVA to discriminate confounding variables. The variables that remained significant with this analysis were assessed by Cox’s regression analysis method: forward likelihood ratio. We considered a two-tailed $p \leq 0.05$ as statistically significant.
The statistical software used for the analysis was SPSS 24.0 (SPSS Inc. Chicago, IL, USA). Figure 1 was created with Microsoft Excel version 2301 (Microsoft, Redmond, WA, USA). Supplementary Figure S1 was created with GraphPad Prism v.6 (GraphPad, Boston, MA, USA).
## 3. Results
In the study period, spanning from 4 April, 2020 to 3 March, 2022, 1377 patients were admitted under the diagnosis of confirmed SARS-CoV-2 pneumonia, 119 of which were excluded due to interhospital transfer or voluntary discharge against medical advice. The study population consisted of 1258 patients, of whom 1093 recovered ($86.8\%$) and 165 died ($13.1\%$). The median age was 56.2 ± 16.5 years, being 68.3 ± 14.2 years for non-survivors and 54.4 ± 16.0 for survivors. The mean length of stay was 12.2 ± 13.7 days, being significantly higher in those patients who died compared with survivors. In total, 243 ($19.3\%$) of patients were admitted to the intensive care unit (ICU), and 200 ($15.9\%$) were mechanically ventilated (MV). A significant association was observed between the need for MV or ICU admission and in-hospital death. Among survivors, 86 ($7.8\%$) received mechanical ventilation, and 107 ($9.7\%$) were managed in the ICU. Figure 1 shows patient distribution regarding the number of hospital admissions, hospital discharges, ICU admissions, and in-hospital deaths during the study period. Similar to those observed in the general population, three waves of disease are denoted in the figure during the study period, reaching the peak of hospital admissions in December 2020, August 2021, and January 2022.
Demographic, clinical, and laboratory characteristics at admission of survivors and non-survivors are shown in Table 1, Table 2 and Table 3. Several of these variables showed statistical significance ($p \leq 0.05$) in univariate analysis.
The mechanisms for death are summarized in Supplementary Table S1. The most common cause was multi-organic failure ($42.4\%$), followed by ARDS ($33.9\%$) and septic shock ($10.9\%$). Other causes included unstable bradycardia, pulmonary embolism, myocardial infarction, and hypovolemic shock, which were much less common. Vaccination status in both survivors and non-survivors can be seen in Supplementary Table S2. Observed and expected values of the variables that were analyzed using the chi-square test can be found in Supplementary Table S3. Nonparametric plots comparing MULBSTA, Charlson, and NEWS scales scores between survivors and non-survivors can be found in Supplementary Figure S1.
In the multivariate analysis (Table 4), the variables that independently predicted mortality, identified by Cox regression analysis, were older age (>60 yo), cyanosis, and previous myocardial infarction.
## 4. Discussion
To our knowledge, this is the first large cohort study of COVID-19 in-hospital mortality and the associated risk factors of patients attended exclusively in a private hospital in Mexico, and one of the few in LAC. In 2021, LAC was the region with the highest number of COVID-19 deaths and deaths per 1000 population, representing $28.8\%$ of global reported deaths while having only $8.4\%$ of its population [50]. In our cohort, in-hospital overall mortality was $13.1\%$, which contrasts with the mortality reported by other hospitals in this country (22–$53\%$) [21,22,23,25,26] as well as with some cohorts in other LAC countries [5,18].
The significantly lower mortality rate found in our cohort can be explained by several factors, namely, the fact that our hospital belongs to the private health subsystem compared with other Mexican cohorts based in public health services [21,22,23,25,26]. Márquez-González et al. [ 27], Carrillo-Vega et al. [ 51], and Salinas-Escudero et al. [ 52] analyzed the national database to identify the risk factors for hospitalization and death in the Mexican population, showing a lower patient survival rate among those hospitalized in public institutions. This problem is prevalent among health systems in most LAC countries, which, to varying degrees, all lack universal public health regimes, instead relying heavily on private subsystems and, in most cases, considerable out-of-pocket expenses [32,53,54,55]. In their cohort study of a private healthcare network in Brazil, De Oliveira et al. also reported a considerably lower mortality rate compared with other cohorts from the public subsystem in Brazil and other parts of the world [5]. Aside from age, which was also lower than the reported mean in other studies, they attributed the disparities between private and public hospitals to be a possible factor involved in this difference. The Mexican health system’s highly heterogeneous organization and quality of care have allowed discrepancies in healthcare to persist to date. The system of care is divided into four main subsystems (private healthcare providers and the public institutions Instituto Mexicano del Seguro Social (IMSS), Instituto de Seguridad y Servicios *Sociales para* los Trabajadores del Estado (ISSSTE), and Secretaría de Salud (SS)), all of which remain fragmented and incapable of delivering universal care [32,33,34,38,56]. Public institutions represent the health services with the highest demand, which puts them at a higher risk of exceeding their operating capacity—resulting in hospital saturation and heightened mortality [51].
Another factor to consider for the difference in mortality rates is that, while many Mexican cohorts analyzed the first months of the pandemic, our study spanned a 2-year period. Thus, the evolution of our clinical knowledge of COVID-19, a lesser degree of bed-saturation and overcrowding of critical areas, and the effect of vaccines over the last months of our studied period, most likely contributed to a decrease in in-hospital mortality. On the other hand, the inclusion of patients with an initial moderate NIH severity of illness probably contributed to this result, although only 90 patients with this characteristic were present in the study population. Finally, a factor that was not considered in our study was the effect of the newly developing COVID-19 variants. One of particular relevance is the Omicron variant reported in November 2021, the fifth variant of concern (VOC) posing a threat to global public health. Omicron emerged as the variant most mutated, transmissible, and resistant to immunotherapeutics and vaccines. Nonetheless, Omicron proved to be milder than the previous variants, mostly causing upper respiratory tract symptoms and resulting in low mortality rates [57,58].
In our study, $19.3\%$ of patients received care in the ICU and $15.9\%$ were MV. ICU and MV mortality were $55.9\%$ and $57\%$, respectively, similar to other Mexican [21,23,24,25] and global [5,20] cohorts. Both ICU admission and the need for MV were significantly more frequent in non-survivors, which has been commonly reported amongst many cohorts, highlighting the importance of ICU management and MV as predictors of death in patients hospitalized due to COVID-19 [5,20,25,26,27].
Hypertension and diabetes are comorbidities identified by several studies as risk factors for mortality. Although they were identified as predictive in the univariate analysis, they were not included in the final multivariate model. They both present in a high prevalence in LAC and the Mexican population [21,25,59]. Hypertension is one of the comorbidities that has most commonly been associated with increased mortality in COVID-19 patients, though the exact mechanism remains unclear [10,11,22,25,59,60,61]. Its prevalence in our cohort was similar to the national average ($31\%$) and to that of other LAC countries [4,21,25]. The use of ACEI/ARBS represented a significant difference between both groups. Although mediated by a possible mechanism by which RAAS blockers increase ACE2 expression, potentially increasing the risk of SARS-CoV-2 infection, the effect of ARB or ACEI use on disease severity is still controversial [22,25]. In our cohort, diabetes presented with a higher prevalence than the national average ($13.7\%$) [21]. As with hypertension, it has been associated with COVID-19 severity and mortality [3,19,59,61,62], with many proposed mechanisms, including reduced resistance to viral infections as a consequence of a sustained low level of immunity as well as vascular and heart damage due to longstanding disease [62].
Overweight status and obesity showed no difference between the two groups. Though it has been associated with increased disease severity in COVID-19 patients in some studies, the association remains unclear, with mixed results among the bibliography [5,18,25]. A meta-analysis conducted by Mesas et al. [ 61] showed that increased mortality was present only in studies with fewer chronic or critical patients, by which BMI did serve as a prominent prognostic factor only in studies with these conditions, which was not the case in our study.
Immunosuppression [3,5,12,15,62,63], cancer [10,59,61,62], and chronic kidney disease [12,17,20,52,61] are other important comorbidities that have been reported as predictive risk factors for mortality in different cohorts. Despite the fact that they were significantly more frequent in the mortality group in our cohort, they did not remain significant in the final multivariate model.
After the univariate analysis, significant variables were analyzed by multivariate ANOVA and then by Cox regression analysis to determine the explicative and predictive variables. In the resulting model, older age, the presence of cyanosis, and previous myocardial infarction were the main predictors of mortality, consistent with the findings amongst other cohorts. In several studies, age was found to be a main determinant of COVID-19-related in-hospital mortality, independent of other pre-existing comorbidities [64,65,66,67]. The median age in our study was 56.2 ± 16.5, similar to other large cohorts in our country [21,22,23,25,26]. As previously established, age has been reported as one of the most important risk factors, being associated with higher mortality plus extended hospital and ICU times [6,27,59]. In our study, age was identified as a risk factor for mortality (non-survivors, were, on average, 14 years older than survivors) and remained as an independent mortality risk factor after multivariate analysis. This may be explained by contributing factors such as age-related physiological changes, impaired immune function, and preexisting illnesses [18,20,59,62]. At this point in the pandemic, older age is well established as a strong predictor of severity and mortality in patients with COVID-19, which prompts early referral of older individuals for inpatient care [11,19,28,60]. In one study conducted in the same city as our present research, age, along with other factors, was also found to be a mortality predictor in multivariate analysis [25]. Another predictive variable was the presence of cyanosis. Although identified as a mortality-related risk factor in the univariate analysis of some studies, our cohort, to the best of our knowledge, is the first to include it in the final multivariate model [18,68]. Finally, the history of previous myocardial infarction was also an important predictor of mortality. The presence of cardiovascular disease has been extensively reported with worse outcomes in patients with COVID-19 [3,8,12,61,69]. Specifically, a history of ischemic heart disease was found to be a significant variable by some cohorts [3,20]. Similar to our results, one study also reported myocardial infarction as a predictor of mortality in the multivariate analysis [67].
An important aspect to consider while analyzing COVID-19 mortality is the evaluation of the role of SARS-CoV-2 infection in such deaths [70]. At the start of the pandemic, some COVID-19 deaths may have been misclassified as being due to other causes, while conversely, during peak pandemic periods, a bias in the opposite direction probably occurred [19]. Due to the lack of knowledge of the pathophysiology of COVID-19 death, as well as the high prevalence of comorbidities observed in deceased people who tested positive for SARS-CoV-2, the question of whether a patient died with or due to COVID-19 is still very much debated [71,72]. Assigning a primary cause of death to a deceased patient with multiple principal diagnoses that could lead to death has been challenging since before COVID-19 [73]. The problem of objectively identifying the “real” cause of death is not only relevant from a conceptual standpoint, but also has many practical consequences regarding epidemiology, public health interventions and policies, health communication to the public, and political decisions [74]. Although numerous observational studies have reported outcomes and risk factors for mortality in COVID-19, the accuracy of the causes of death has seldom been reported [75]. This can be due to many factors, including the methods of assigning primary cause of death, the impossibility of performing necropsies, and countries’ laws allowing only one cause to be reported on a death certificate [70,71,73,74,76] Regarding the limitations of this study, its retrospective nature makes it prone to under-documentation of many clinical variables, limiting the researchers’ capacity to obtain comprehensive data due to incomplete medical records. This was particularly relevant for determining the actual role of SARS-CoV-2 infection in each death, as the EMR often lacked the information necessary to evaluate whether COVID-19 was only an epiphenomenon for that particular death. Social determinants of the study population, such as median household income, were not assessed. As genomic sequencing data was not available, analysis of the predominant variants of concern in each wave could not be performed. Due to the changing nature of the pandemic, along with the growing understanding of the disease, clinical practice improvements were implemented, with the evaluation of such changes exceeding the scope of this study [5]. Finally, we excluded patients that did not have the entire course of disease in our institution, such as those discharged against medical advice or because of interhospital transfer, as we were therefore unable to assess their evolution. Despite these limitations, the size and duration of this study allowed us to provide a reasonably complete overview of the pandemic as it presented in our hospital [77].
Our study gains relevance as the socioeconomic impact of COVID-19 continues to impact the population of our country, worsening socioeconomic inequality: while nonvulnerable groups are given the option of more reliable services, the more marginalized populations are left with no choice but to attempt to receive care in saturated, underfunded, and often uncoordinated public health subsystems [78]. These disparities further heighten inequalities affecting vulnerable groups, including indigenous communities, migrants, people in overcrowded living conditions, informal workers, people with disabilities, and older adults, even more so in cases involving chronic diseases, which are also correlated with these same vulnerabilities [21,30,37,50,54,55,78]. While this is not limited to Mexico or LAC—the syndemic relationship among social inequalities, chronic diseases, and COVID-19 has been reported at an international level [79]—the public and private subsystems’ conditions, low healthcare spending, infrastructure, and other health-related policies have all had a considerably higher socioeconomic impact in LAC [32].
## 5. Conclusions
Mortality in hospitalized patients with COVID-19 in this Mexican private tertiary care center was $13.1\%$. Older age, the presence of cyanosis, and a previous myocardial infarction were the most significant independent risk factors for mortality in adult patients hospitalized with COVID-19 pneumonia in our 2-year cohort. Considering the significant disparities in the quality of care that exist between the private and public health subsystems in Mexico, our results gain special significance, as they contribute to a more complete overview of the healthcare system and its interaction with the pandemic.
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|
---
title: Becoming a Paralympic Champion—Analysis of the Morpho-Functional Abilities
of a Disabled Female Athlete in Cross-Country Skiing over a 10-Year Period
authors:
- Wojciech Gawroński
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001877
doi: 10.3390/ijerph20053909
license: CC BY 4.0
---
# Becoming a Paralympic Champion—Analysis of the Morpho-Functional Abilities of a Disabled Female Athlete in Cross-Country Skiing over a 10-Year Period
## Abstract
Changing medical classification into the functional one in disabled cross-country skiing means that the athlete’s predispositions and performance abilities most of all determine the final result in cross-country skiing. Thus, exercise tests have become an indispensable element of the training process. The subject of this study is to present a rare analysis of morpho-functional abilities in relation to the implementation of training workloads during the training preparation for a Paralympic champion in cross-country skiing when she was close to her maximal achievements. The study was performed to investigate abilities evaluated during laboratory tests and how they relate to performance outcomes during major tournaments. An exercise test to exhaustion on a cycle ergometer was performed three times a year on a cross-country disabled female skier over a 10-year period. The morpho-functional level which enabled the athlete to compete for gold medals in the Paralympic Games (PG) is best reflected in the results obtained by her in the tests in the period of direct preparation for the PG and confirms optimal training workloads in this time. The study showed, that the VO2max level is presently the most important determinant of physical performance achieved by the examined athlete with physical disabilities. The aim of this paper is to present the level of exercise capacity of the Paralympic champion based on the analysis of the results of the tests in relation to the implementation of training workloads.
## 1. Introduction
From the beginning, as a rehabilitation tool, sport for the disabled athlete has evolved to competitive sport at the Olympic level [1]. A real breakthrough in competition regarding cross-country skiing for the disabled took place at the beginning of the new millennium. Changing medical classification into the sport-specific functional one in disabled sports means that the athlete’s predispositions and performance abilities determine the final result [2]. The number of sporting events was limited by reducing the number of competition classes [3,4,5]. In the 2006 Paralympic Games (PG) in Turin, competition in cross-country skiing was narrowed down to three groups of participants: visually impaired (VI) athletes, standing skiers with physical disability, and athletes using a sit-ski [3]. For example, standing skiers with physical disability are grouped into the following sport classes: with lower limb impairments (LW 2, LW 3, LW 4) and upper limb impairments LW $\frac{5}{7}$, LW 6, LW 8, and LW 9, which combined upper and lower limb impairments [6]. In the case of the athletes with those enumerated sport classes, VO2max is different because of different disabilities and less muscles mass are involved during skiing. That is why Realistic Handicap Competition and Kreative Renn Ergebnis Kontrolle (RHC-KREK) was introduced in 2006 PG with regard to individual athletes in order to level out the chances of athletes with various physical disabilities competing in the same competition group [7].
The aforementioned changes led to the fact that training became more professional. During the 2002 PG preparations, training was divided into annual cycles as there were 4 years ahead to prepare the cross-country skiing Paralympic team [8]. Due to the fact that training-related overloads and, as a consequence, general overtraining [9] and injuries occurred [10,11], risks similar to those of able-bodied cross-country skiers increased. Subsequently, assessment of general health with preliminary screening, and particularly, assessment of endurance, has gained significance [12]. Hence, endurance tests have become an indispensable element of the training process, particularly in cross-country skiing for the disabled. A rational training program must be based on objective premises, while the selection of load should be compliant with the athlete’s individual endurance predispositions [13]. The literature of the subject lacks data concerning the physiological profile of the disabled skiers and, to date, little information has appearedabout performance abilities of disabled cross-country skiers. In the literature, there are no data published concerning the physiological profile of the top class athletes with physical disability, especially in the standing position (LW 2-LW 9). It seems that the scarcity of publications results mainly from the fact that there are only small groups of athletes with disabilities and various types and levels of disability who were examined, which is an obstacle in analyzing data and publishing the results. Additionally, there are rare data concerning athletes, e.g., with visual impartment (VI) and intellectual disability (ID). For example, Bernardi described research only on athletes with lower-limb dysfunctions, including sit-skiers or athletes performing winter sports other than cross-country skiing [14,15]. In turn, Bhambhani discussed physical capacity of a narrow group of skiers with various disabilities but included only one female with visual impairment who was skiing in a standing position [16]. Therefore, a review of the literature confirmed the scarcity of publications concerning the assessment of physical capacity of cross-country skiers with motor disabilities. Among the literature, there is one study on aerobic capacity of cross-country skiers, including three females, but the study is of athletes with intellectual disabilities (ID) [17].
The aim of this paper is to present the level of exercise capacity of a Paralympic champion based on the retrospective analysis of the laboratory test results in relation to the implementation of training loads.
## 2.1. Study Participant
The study subject was a female with physically disabled upper limbs, which she lost in a post-traumatic amputation after an agricultural accident at the age of 3. She began training and participating in athletic runs at the age of 22, and at the age 23 (in the year 2000) she took up professional cross-country skiing and qualified as an athlete to sports class LW $\frac{5}{7}$, i.e., skiing without poles. It was confirmed before the first international competition according to the Paralympic sport classification [18].
She was 29 years old when she won two gold medals in the 2006 PG and 33 years old when she won a bronze medal in the 2010 PG in Vancouver [19].
## 2.2. Health Evaluation
Every time before the physical exercise testsuntil exhaustion, a pre-participation examination (PPE) was carried out which consisted of a general medical examination and an assessment of ECG and blood and urine tests.
## 2.3. Exercise Testing
In the absence of contraindications, the exhaustion tests were performed three times a year from 2001 to 2010, i.e., before the General Preparation Phase (I), before the Specific Preparation Phase (II), and during the Competitive Phase (III) [8]. The procedure started with a general warm-up and was followed by the exercise test performed on a Monark cycle ergometer. Exercise loads starting from 60 W were increased by 30 W every two minutes until volitional exhaustion, normally occurring after 10–15 min. Before, during, and until the 3rd minute after the test, such variables as minute ventilation of the lungs (VE), oxygen consumption (VO2), and carbon dioxide production (VCO2) were registered with the use of a ergospirometer (MES system, Dymek, A., Kraków, Poland). At the same time, heart rate (HR) was monitored by a Polar-Electro device. Then, the maximal oxygen uptake (VO2max) and ventilatory threshold (VT) were calculated. The VT was determined based on criteria described in the literature [20,21]. Arterialized blood was taken from an earlobe twice, i.e., before the test and 3 min after finishing the work on the ergometer. Blood lactate (La) concentration was determined using DR.LANGE LP 20 device. Body weight (BW) and the percentage of fat (%F) were measured with the use of a Tanita BF-662W scale before and after the test. In order to determine if a mechanical effect of the performed test worked, general quantity of the performed work (kJ) and achieved power (W) was calculated. The tests were always performed in the same room and with the use of the same equipment. Each time, the ergospirometer was calibrated with a standard mixture of calibration gases and the flow sensor was calibrated using a 3-litre hand calibration pump, taking into account atmospheric pressure as well as air temperature and humidity of the room where the tests were conducted. The calibration was performed under the supervision of a representative from MES, the manufacturer of the ergospirometer. The load was chosen on an ergometer which was calibrated each day before starting the exercise protocol. The cycling frequency was provided by a metronome. The described procedures guaranteed the repeatability of the test.
## 2.4. Training Data
Data from the realization of training workloads before the PG in Salt Lake City in 2002 and in Turin in 2006 were based on annual reports of subsequent coaches of the Paralympic team published by Chojnacki [8]. In turn, the realization of training loads in the $\frac{2009}{2010}$ season is based on the reports that the athlete herself prepared according to the same scheme.
## 2.5. Analysis
In this study, selected results of exercise tests carried out in the periods directly preceding participation in the PGs are presented. During these periods, the athlete reached the highest exertional capability due to training optimization.
The data obtained from endurance tests underwent descriptive analysis. The level of VO2max and blood La as well as work performed during the tests were assessed retrospectively in the context of the applied training workloads with the use of a visual inspection method and qualitative analysis. Additionally calculated were the mean, standard deviation, and coefficient of variation for observation values.
This research was approved by the Bioethical Commission of District Medical Chamber (No. 70/KBL/OIL/2007). The athlete was fully informed of the purpose, terms, and conditions of the tests. The participant gave written informed consent in accordance with the Declaration of Helsinki before the start of the study and provided her consent to publish the reports in the future.
## 3.1. Health Status
The PPE carried out periodically revealed no significant health-related contraindications to competitive sport. The athlete did not take part in only one test due to an upper respiratory tract infection in the season of $\frac{2001}{2002.}$ The athlete’s training did not cause any overload changes in particular spine sections due to asymmetry in the length of the left and right forearm stumps.
## 3.2. Performance Abilities in Selected Seasons
During the follow-up in the years 2001–2010, a number of physical exercise tests were conducted by the athlete. The scores that were chosen for this study are characterized by morpho-functional features of the athlete that she demonstrated in her representative preparation season, i.e., directly preceding her participation in PG 2002, 2006, and 2010.
## 3.2.1. Season 2001/2002 before the Salt Lake City PG
The athlete took part in the laboratory physical exercise tests twice, i.e., during and after the preparation period. Anthropometric measurements performed during further tests revealed that her body weight increased and the amount of fat was between 17 and $24\%$, which meant a normal value for women.
The athlete’s HR on finishing Test 1 was above 190 bpm (beat per minute). The HR scores at the level of anaerobic threshold represented $84\%$ of HR. Test 2 produced similar results. However, on this occasion the HRmax of the athlete was much lower and amounted to only 175 bpm. This resulted in a significant decrease in maximal oxygen intake, which in Test 1 amounted to 42.8 mL/kg/min and in Test 2 only 40.2 mL/kg/min. It was also influenced by an increase in body weight from 48.8 to 51.0 kg. Additionally, minute ventilation decreased from 84.4 to 74.9 L/min.
The work performed in this test characterized by the duration of the effort. The athlete did not enhance her performance compared to the first test. In this case, the duration of the effort was 10 min in both tests with the power output at the level of 180 W. The blood La at the time of exhaustion amounted to 12.6 mm/L in the first and 10.7 mmol/L in the second test.
## 3.2.2. Season 2005/2006 before the Turin PG
During the training season of $\frac{2005}{2006}$ (Table 1), the examined athlete took part in three tests. The morphological indices in consecutive examinations showed body weight and body fat decreased by $16.3\%$, $13.3\%$, and $14.9\%$. Heart rate during the tests was (187, 192, 188 bpm in successive tests), which proves the athlete’s full engagement in the activity. The percentage value of HRVT ranged between 83 and $89\%$ HRmax VO2max in two tests reached the level of 51.8/mL/kg/min, but it was lower (45.5/mL/kg/min) in Test 2.
The athlete’s minute ventilation in Test 1 (95.0 L/min) and Test 2 (99.6 L/min) was at a higher level than average and increased to the level of 107.4 L in Test 3. The duration of effort in the three tests was 14′30″, 10′, and 13′, respectively. The maximal power output achieved was 240, 210, and 240 W and at VT power was 150, 165, and 150, respectively. The blood La at the time of exhaustion amounted to 10.6 mmol/L in the first test, 12.45 mmol/L in the second test, and 9.65 mmol/L in the third test. Respiratory exchange ratio (RER) in all tests was always above 1.1.
## 3.2.3. Season 2009/2010 before the Vancouver PG
As in the previous season, the subject took part in three tests (Table 1). Compared to the results from the tests carried out 4 years before, morphological indices in subsequent tests revealed a slight increase in body mass and fat tissue which, in turn, meant a decrease in non-fat body mass.
HR in Tests 2 and 3 conducted in this period was lower than the expected level calculated with the formula “220 minus age”. The values of VO2max in Test 1 were low, while in Tests 2 and 3 they reached the level of 51.3 and 53.8 mL/kg/min, which proved the subject’s high aerobic performance. The duration of effort and achieved power were the same in all the tests, i.e., 11 min and 210 W. The blood La at the time of exhaustion, which amounted to 11.7 mmol/L in the first test, decreased to 7.36 mmol/L in the third test.
## 3.3. How to Become a Paralympic Champion—Training Data in the 2005/2006 Season
In the season of peak sport achievements ($\frac{2005}{2006}$), 265 training hours were realized, where the majority of the training work ($75.9\%$) was below at the VT, i.e., HR not exceeding 164 bpm, which was $85\%$ HRmax. The remaining training loads included effort at the VT and sporadically above the VT. There was only small fraction of the training workload performed above the VO2max in a form of short accelerations lasting a few seconds each. Therefore, in general more than $75\%$ of the training workload was performed below the VT and about $25\%$ in the range between the VT and VO2max [8].
A competition period is a period of training camps divided by two cycles of competitions. Afterwards, a direct preparation period for the Turin PG in 2006 started. At that time, a 10-day training camp in the high mountains was held followed by a 1-week microcycle with low training capacity. The intensity of training effort in that period at the ventilatory threshold was lowered to $50\%$ at the cost of more intensive effort at the ventilatory threshold and above. General training capacity in the $\frac{2005}{2006}$ season increased slightly by $3.1\%$ compared with the $\frac{2001}{2002}$ season [8]. In turn, in the $\frac{2009}{2010}$ season, training capacity was by far bigger and included 414 h in total, i.e., as much as $22\%$ more than in the $\frac{2005}{2006}$ season. Training capacities in particular seasons in the preparation and competition periods are compared with selected results as presented in Figure 1.
## 3.4. The Morpho-Functional Abilities of the Paralympic Champion in 2006 in Cross-Country Skiing
The morpho-functional level which enabled the athlete to compete for gold medals in the Paralympic Games 2006 is best reflected in the results obtained by her in Test 3 in the period of direct preparation for the PG. In this period of top performance, the results of morphological tests were as follows: height—160 cm, body mass—51.7 kg, fat tissue—$14.9\%$, body water—$58.9\%$, and non-fat body mass—44 kg. In this period, the most stable body mass level, the lowest percentage of fat, and the highest non-fat body mass level were observed compared to the previous training periods. Maximal values of the selected exercise physiological variables reached by the subject in the period of $\frac{2005}{2006}$ were as follows: heart rate—188 bpm, maximal oxygen intake—51.30 mL/kg/min, maximal minute ventilation—107.4 L/min and maximal blood lactate concentration—9.65 mmol/L with the duration of exercise—13 min. Enumerated oxygen pulse at VO2max amount 14.09 mL/bt. Values at the VT were as follows: heart rate—164 bpm, maximal oxygen intake—39.3 mL/kg/min, power 150 W, pedaling economy 13.54 mL/W, and in the case of VO2 net,12.08 mL/W.
## 4. Discussion
The longitudinal analysis of the laboratory test results in relation to the training workloads is very important for successfully facilitating potential modifications of the training process and thereby obtaining optimal performance. It is common knowledge that physical endurance, i.e., an ability to sustain long-term or hard work without signs of fatigue leading to profound systemic changes, as well as post-exertion recovery abilities, determine the performance of cross-country skiers. Physical exercise tests are used to provide information regarding a current level of endurance capabilities of athletes [22]. Exercise tolerance for disabled sports depends on a number of factors, such as metabolic profile/capacity [23], body type, and build [24,25]. The impact of these factors on endurance varies depending on the type, intensity, and duration of physical activity during cross-country competition [26]. Sports results in cross-country skiing are alsoaffected by other external factors, such as equipment, ski waxing, snow conditions, area configuration or skiing tactics, and internal factors, e.g., economy of skiing, biomechanical techniques, or anthropometric attributes [27]. Additionally, in disabled sports, a type of disability, e.g., visual disability (VI), intellectual disability (ID), or motor, muscle, and joint coordination, affects all the aforementioned factors [28]. However, the physical attributes of an athlete, particularly physical endurance, constitute the main factor. It is affected by the performance of many systems, including the cardiovascular system and the respiratory system which, to a large extent, are responsible for the aerobic potential of an athlete and especially for maximal oxygen intake [22,29]. However, there is a scarcity of studies regarding endurance capabilities of disabled athletes performing winter sports [30].
## 4.1. Maximal Oxygen Uptake
First of all, the plateauing of VO2- in all tests should be mentioned, which was usually visible at 30–60 s before termination of the exercise. Accordingly, the adopted observation statement classified the criterion for reachingVO2max (the stabilization of oxygen uptake plateaued despite the increase of the load in the exercise and there was notable stabilization of HRmax. Additionally, RER was minimum above 1.0 or more, >1.10–1.15, and La > 8–10 mmol/L). Admittedly, plateau in oxygen consumption is the primary means of confirming that maximal oxygen uptake is attained during an exercise test to exhaustion, but it may be of crucial importance for athletes with intellectual disabilities due to a misunderstanding of test methodology [31] and the need to maintain peak effort as long as 30–60 s.
However, what causes expression of a plateau in VO2 at the end of incremental exercise is still unresolved. It is arguable that plateauing depends on the adopted definition and may be a primarily methodological issue and not a physiological issue. Demonstrating data may encourage the use of more objective and accurate plateau criteria and modify the current practice of using the obsolete criterion to confirm VO2max [32].
The VO2max, as in this case, which can be compared with data from the literature [29,33] is the most significant factor in cross-country skiing. In the direct preparation period before the 2006 PG, the VO2max that was achieved by the subject was 51.30 mL/kg/min (2.65 L/min). In turn, the highest level of the maximal oxygen uptake, 2.74 L/min (53.80 mL/kg/min), in the whole long-term observation period was noted during the test carried out before the 2010 PG. As a comparison, an average VO2max in the test performed by three female athletes with intellectual disability was 51.8mL/kg/min [15] and theVO2max reached by a female with visual impairment was 56.9 mL/kg/min [14]. Taking this into account, the maximal oxygen uptake at the level of 51.30 or 53.8 mL/kg/min may be seen as similar to other results and, according to the literature, it is a high level for a female athlete.
However, the maximal oxygen uptake achieved by the examined athlete and the above cited results of athletes with various types of disabilities differ from the VO2max levels of able-bodied female skiers who obtain results at the level of 65–70 mL/kg/min and more [33]. It is worth noting that with regard to the initial tests on a cycling ergometer in 2001, the maximal oxygen uptake of the subject increased from 40.2 to 53.80 mL/kg/min. However, it should be emphasized that the studied athlete, although physically active since her trauma, started professional cross-country skiing training very late, i.e., at the age of 23, i.e., when the possibilities to improve oxygen uptake are limited for those over 30 years old [29]. However, I think the regular training compensates for the age and it is possible even after turning 100 years old [34].
Furthermore, in general, VO2max during cycling exercise—as in this case—can be 10–15 % lower than during running or skiing due to the involvement of less muscle mass during cycling than during running or skiing. In the case of the athlete studied, however, VO2max during cycling was probably not very different from VO2max during skiing as she did not fully use her upper body during skiing due to her disability (a partial upper limbs amputation). In fact, the best measurement of oxygen uptake is the measurement taken during the field test with the use of a mobile ergospirometer. Bernardi carried out such research focusing on the biomechanics of running and training implications [15]. However, that research did not include any athlete with the dysfunction of the upper limbs, so it is hard to compare the results. In turn, other research confirmed that significantly lower levels of VO2max and maximal heart rate are achieved by female and male athletes with spinal cord injuries (paraplegia, tetraplegia) competing in cross-country skiing in a sitting position [14].
For a complete analysis of endurance capabilities of the athletes, it is significant to know the values of the described indices at the ventilatory threshold. The presented results of the study participant show that in the training process, the VO2max not only increased, but what is even more significant is that the anaerobic processes contributed at a higher percentage of VO2max (Table 1). *In* general, the ventilatory threshold moved to the right, thus increasing the possibility to continue exercising without suddenly increasing fatigue. Moreover, during the peak effort, in the $\frac{2009}{2010}$ season, decreasing concentration of La to 7.36 mmol/L in tests was noted, although time of effort (11 min), i.e., the quantity of work, did not change. It suggests a successive improvement in exercise tolerance.
## 4.2. Selected Physiological Parameters
Certainly, apart from maximal oxygen intake, other physiological indices affecting its level (e.g., HRmax and VEmax) are also significant in terms of assessing endurance capabilities of an athlete. Thus, HRmax of the study participant reached high values in the tests during the periods of her top performance in subsequent Paralympic seasons (191,192,189 bpm, respectively) and were higher than the age-predicted maximum HR. Similar high values of HRmax (192 bpm) were noted by Bhambhani in a 30-year-old athlete with visual impairment in the test [16]. Both examples confirm that the achieved VO2max was at the highest possible level. However, it seems interesting that HRmax values noted in three young athletes (aged 17–19) with mental disabilities were at the level of 179, 178, 179 bpm which, in turn, was much lower than their age-predicted maximum HR. In this case, the achieved oxygen intake value may be seen only as VO2peak.
In turn, the levels of VEmax in athletes with mental disabilities differed (84.9, 99.8, 120.1 L/min), while in an athlete with visual disabilities, the maximum minute ventilation reached the value of as much as 140.3 L/min [16].
## 4.3. Training Data and Analysis
Across all measures, BM, FFA, HRmax, HR at ventilatory threshold, HR (max%), and VO2 (max%) were the most stable, with coefficient of variation (CV) in the 3–$6\%$ range. La at rest and effort, with CV in the 19–$25\%$ range, and fat (CV = $17\%$) had the highest coefficient of variation. The analysis of the presented training workloads showed a considerable domination of aerobic training, which is compliant with the recommendations resulting from subsequent tests in the years 2001–2006. Simultaneously, a considerable intensity of training and control competitions gave proper results during the first Test 1 in season $\frac{2005}{2006}$ in the form of an increase not only in VO2max by approximately 17 % but also in the ventilatory threshold level. Despite high oxygen consumption and other physiological test results, poorer results achieved during the 2010 PG (may be explained by a large increase in training capacity compared to the $\frac{2005}{2006}$ season (Figure 1). Theoretically, these assumptions are confirmed by sports results in the following season ($\frac{2010}{2011}$), in which the athlete won a 5 km freestyle run during World Championships as well as one year later, when she received the Crystal Globe as a winner of the World Cup Series $\frac{2011}{20129}$ [35]. Soon after this, the athlete became pregnant. As her maternity leave began 1 year prior to the next PG, she did not manage to achieve the required level of performance, which was confirmed by the control competitions. For this reason, she did not take part in the 2014 PG.
## 5. Practical Applications
The studied example shows morpho-functional capabilities (requirements to become a Paralympic champion) which should characterize an athlete competing for medals in disabled cross-country skiing during the Paralympic Games. It may be concluded that implementing a regular health assessment aimed at improving endurance capabilities of the studied athlete in 2001 was justified and innovative since, with the present level of competition in Paralympic skiing, achieving a high level of endurance and optimal health disposition during the PG is a factor determining the sports result. Thus, the laboratory exercise tests preceded by a health status evaluation are becoming an indispensable element of the training process, while the selection of loads must be based on objective factors and individual endurance predispositions of an athlete. British experiences from the Olympic and Paralympic Games in London in 2012 recommend implementing the model of combining medical care with a training process in order to achieve ethical and functional balance between medical care and optimization of sports performance [36]. This underlines the importance and the validity of the comprehensive sports and medical care system applied in Paralympic cross-country skiers. In summary, the above data provide a unique insight into the characteristics required to succeed in cross-country skiing at the PG.
## 6. Limitations and Future Research Directions
A low number of subjects, especially as described in this paper, is one of the major limitations of this study, but there is only one champion always. Additionally, other factors that may influence an athlete’s performance, such as diet, rest, supplements, and medications, were not taken into account in the study.
In the future, similar laboratory physical exercise tests should be carried out among other disabled athletes at top levels from class LW 2, LW 3, LW 4, LW 6, LW 8, and LW 9, and the obtained data should be compared. It could be useful to more objectively estimate “handicap” (RHC-KREK) for different disabilities.
## 7. Conclusions
The VO2max level is presently the most important determinant of physical fitness, achieved by the examined athlete (51.3–53.8 mL/kg/min) with physical disabilities (a partial upper-limbs amputation) and is comparable with the level achieved by athletes with intellectual disabilities or visually impaired competitors which start in a standing position too.
Maximal values of the selected exercise physiological variables reached by the subject in the period of direct preparation for the PG 2006 were as follows: heart rate—188 bpm, maximal minute ventilation—107.4 L/min, and maximal blood lactate concentration—9.65 mmol/L.
The oxygen pulse at VO2max amount 14.09 mL/bt. Values at the VT were as follows: heart rate—164 bpm, maximal oxygen intake—39.3 mL/kg/min, power—150 W, pedaling economy—13.54 mL/W, and in the case of VO2 net—12.08 mL/W.
The presented physiological, biochemical, and mechanical values show the level of endurance capabilities which characterized the female athlete who won 2 gold medals during PG 2006.
Overdose of training workloads in the season $\frac{2009}{2010}$ was one of the reasons for a decrease in athlete performance despite the high level of VO2max.
Systematic medical supervision and pre-participation evaluation of a disabled athlete is very important in order to detect underlying medical problems and overtraining markers that may limit the performance or even place the athlete at “increased risk”.
The analysis of the author’s own study confirms that, in light of introduced changes in medical classification into a sport-specific functional one, the final results in cross-country skiing depend more on physical performance level than on disability type.
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|
---
title: 'Life Habits of Healthcare Professionals during the Third Wave of COVID-19:
A Cross-Sectional Study in a Spanish Hospital'
authors:
- Enedina Quiroga-Sánchez
- Natalia Calvo-Ayuso
- Cristina Liébana-Presa
- Bibiana Trevissón-Redondo
- Pilar Marqués-Sánchez
- Natalia Arias-Ramos
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001878
doi: 10.3390/ijerph20054126
license: CC BY 4.0
---
# Life Habits of Healthcare Professionals during the Third Wave of COVID-19: A Cross-Sectional Study in a Spanish Hospital
## Abstract
[1] Background: To describe sleep quality, eating behaviour and alcohol, tobacco and illicit drug use among healthcare staff in a Spanish public hospital. [ 2] Methods: Cross-sectional descriptive study examining sleep quality (Pittsburg Sleep Quality Index), eating behaviour (Three Factor Eating Questionnaire (R18)), tobacco and drug use (ESTUDES questionnaire) and alcohol use (Cut down, Annoyed, Guilty, Eye-opener). [ 3] Results: 178 people, of whom $87.1\%$ [155] were women, with an average age of 41.59 ± 10.9 years. A total of $59.6\%$ of the healthcare workers had sleep problems, to a greater or lesser degree. The average daily consumption was 10.56 ± 6.74 cigarettes. The most commonly used drugs included cannabis, occasionally used by $88.37\%$, cocaine ($4.75\%$), ecstasy ($4.65\%$) and amphetamines ($2.33\%$). A total of $22.73\%$ of participants had increased their drug use, and $22.73\%$ had increased their consumption during the pandemic, with beer and wine accounting for $87.2\%$ of drinks consumed during this period. [ 4] Conclusions: In addition to the psychological and emotional impact already demonstrated, the COVID-19 crisis has repercussions on sleep quality, eating behaviour and alcohol, tobacco and drug consumption. Psychological disturbances have repercussions on physical and functional aspects of healthcare workers. It is feasible that these alterations are due to stress, and it is necessary to act through treatment and prevention as well as promote healthy habits.
## 1. Introduction
In the healthcare field, the different stress-generating situations have multiple repercussions, not only on business operations but also on the health of the patient and the workers involved. With this in mind, reviews of the literature describe how exhaustion derived from work stress affects the worker’s commitment to the organization, their productivity, patient safety and satisfaction and the quality of care [1,2]. Similarly, it can also modify the health behaviours of the worker and push them away, in certain situations, from the recommended guidelines [3].
Along these same lines, sleep is one of the factors that most contribute to physical and psychological well-being. Nursing professionals are usually, within the healthcare community, the most affected by sleep disorders [4]. In fact, those who suffer from this type of alteration have a high risk of developing performance decreases and making errors when administering medication [5]. Likewise, sleep disorders are related to the presence of multiple health problems [6]. An example of this is the consequences derived from the alteration of the circadian rhythm that occurs in workers who have night shifts or irregular shifts. Going even further, it has been possible to verify the relationship between this type of alteration and the presence of diabetes mellitus [7], cardiovascular diseases, metabolic syndrome or cancer [8].
Another consequence of suffering from sleep disorders is the limitation that those affected have in managing stress [9]. In this sense, although it has been seen how nurses (specifically) have a series of coping resources that can be classified as healthy (for example, socialization), they can also activate other resources that are not beneficial for their health, such as alcohol and tobacco use, social avoidance or anger displacement [10]. Regarding the consumption of alcohol, tobacco and illicit drugs, Baldisseri et al. [ 2007] [11] estimated that approximately $10\%$ to $15\%$ of healthcare professionals will abuse drugs or alcohol at some point in their professional life. In fact, they demonstrated a higher rate of abuse of benzodiazepines and opiates. Blake et al. [ 2011] [12] found that, of a sample of 325 nurses, two-thirds exceeded the recommended maximum daily intake of alcohol, and nearly one-fifth were smokers. A review of the literature carried out by Nilan et al. [ 2019] [13] reported a prevalence of tobacco use in nursing between $21\%$ and $25\%$, varying according to the socioeconomic level of the country.
Similarly, shift work, as an example of a stress-causing agent, negatively influences dietary habits and the weight of workers, increasing the prevalence of obesity. A higher frequency of food intake and/or consumption of poor-quality food has also been noted among shift workers [14,15,16]. The emotions that are generated in complex situations are capable of modifying eating behaviour, marking, for example, certain preferences for some foods or even modifying caloric intake [4].
In Spain, the SARS CoV 2 COVID-19 pandemic hit hard. By 30 April 2021, 78,216 people had died, and more than 80,000 healthcare workers had been infected [17]. This situation has meant that healthcare workers in our country have faced numerous work stressors, such as long working hours and/or work overload, among others. As a consequence, wave after wave, levels of anxiety and depression have progressively increased in nursing staff [18], and so have multiple sleep disorders in the healthcare community [19], with a significant impact on both physical and mental health [20,21,22,23,24]. Given healthy lifestyles among healthcare professionals may be compromised by the multiple consequences of the current pandemic, it is necessary to explore what impact the third wave of COVID-19 has had on the health of our healthcare professionals. Thus, the aim of this present study was to describe the sleep quality, eating behaviour and alcohol, tobacco and drug consumption of healthcare workers in a Spanish public hospital. In doing so, we aim to highlight the importance of health-related habits, as well as the need to promote strategies to improve these habits and, consequently, the well-being of these workers.
## 2.1. Design
An intervention-free cross-sectional descriptive study was proposed, carried out in the months of February to March 2021 through an online, anonymous, and completely voluntary questionnaire and developed through the Google Forms® application. With the aim of reaching the largest possible number of healthcare workers in the shortest possible time, the dissemination was carried out through an instant messaging platform (WhatsApp).
The STROBE checklist guidelines for observational research have been followed.
## 2.2. Participants and Selection Criteria
All healthcare personnel of legal age, who had a working relationship with the health centre, were considered to participate. As an inclusion criterion, the healthcare personnel had to present the informed consent document covered and signed. The final sample was made up of 178 subjects.
## 2.3. Measurements and Instruments
Several sociodemographic variables were considered (age, sex, marital status, work service, seniority, type of contract, professional category and type of cohabitation), and through the Pittsburgh Sleep Quality Index (PSQI), sleep quality was evaluated. This questionnaire validated in Spanish [25] contains a total of 24 items that are grouped into 7 dimensions, which provide information on the different factors that affect sleep quality. Thus, subjective sleep quality refers to the subject’s assessment from 0 (very bad) to 3 (very good). Similarly, sleep latency measures how long the subject thinks it takes them to fall asleep, while sleep duration reports the actual number of hours a person sleeps at night. The efficiency of habitual sleep results from the percentage relationship between the time the subject believes they are asleep and the time they have been lying down. On the other hand, sleep disturbances inquire about the frequency with which alterations are noticed. Finally, it also contemplates the use of sleep medication and daytime dysfunction, understood as the impact of sleep on the development of daytime activities. The graphic representation of the scores obtained in each of the components allows us to clearly see where the problems related to sleep lie. The sum of the scores of the 7 dimensions gives a score varying from 0 to 21 points (the higher the score, the worse quality of sleep). Setting a cut-off point of 5 points for its interpretation, a difference is made between good sleep quality (scores below 5) and poor sleep quality (higher scores) [25].
Eating behaviour was measured using the Three Factor Eating Questionnaire (R18) (TFEQ-R18). This validated tool [26] consists of 18 items with a Likert-type response model with 4 possible options from 1 (rarely) to 4 (always). Thus, it evaluates three dimensions of eating behaviour: uncontrolled intake (tendency to eat more than usual due to loss of control when eating with a subjective sensation of hunger); emotional eating (the inability to resist emotional cues or eat in response to negative emotions); and cognitive restriction (the conscious restriction of eating aimed at controlling body weight and/or promoting weight loss). The three domains are converted to a scale from 0 to 100 (de Lauzon et al., 2004) according to the following equation [(raw score−lowest possible raw score)/possible raw score range) × 100]. Thus, higher scores indicate a higher probability of the domain to which they refer. This test presents appropriate reliability coefficients for the three subscales (ranging from 75 to 85) that are also indicated in a nursing population (85 to 90) [27].
Related to tobacco use (daily use, in the last 30 days, in the last year and starting age) and use of drugs that are illegal in Spain (use, type of drug and use in the last 12 months), the ESTUDES-validated questionnaire (Survey on Drug Use in Secondary Education in Spain) belonging to the National Drug Plan was used. This validated tool aims to collect information on drug use and other addictions in order to design and evaluate policies aimed at the prevention of this type of substance and the problems derived from it, mainly focused on the family and/or school environment. A total of 10 items referring to the consumption of said substances (6 items on tobacco and 4 on illicit drugs) were selected for this study.
Finally, data on alcohol consumption were collected using the CAGE (Cut down, Annoyed, Guilty, Eye-opener) validated questionnaire [28,29] to detect cases of alcohol dependence or abuse. Developed by Ewing [1984] [28] and validated by Mayfield et al. [ 1974] [30], it is characterized by its brevity, simplicity and ease of application. It comprises a total of 4 questions, which can be administered in the context of a clinical interview or in isolation. Each affirmative answer adds 1 point so that the existence of problems is evidenced when 2 or more questions are answered affirmatively. It has a sensitivity between 65–$100\%$ and a specificity of around 88–$100\%$ [31,32].
## 2.4. Data Analysis
A descriptive analysis of the variables described was carried out using the SPSS statistical package in version 26.0. The description of the variables analysed was carried out using measures of central tendency and dispersion, in the case of quantitative variables, mean and standard deviation (SD). Qualitative variables were expressed as absolute frequencies and percentages. The normality of the variables was tested using the Kolmogorov–Smirnov test with Lilliefors modification.
## 2.5. Ethical Considerations
This study was approved by the Research Ethics Committee of the Health Areas of León and Bierzo (registration number: 20205) and the Ethics Committee of the University of León (ETICA-ULE-044-2020). Likewise, it was designed in such a way as to respect the ethical principles for global medical research that are reflected in the Declaration of Helsinki and its subsequent modifications. Prior to participation in the study, it was specified through informed consent documents that participation was completely voluntary, making it clear that the exploitation of the registered data would be carried out completely anonymously and confidentially for research purposes.
## 3. Results
A total of 178 people participated, out of which $87.1\%$ [155] were women, with a mean age of 41.59 ± 10.9 years, with 22 being the minimum and 68 the maximum. Table 1 shows the descriptive data of the sample.
Figure 1 shows the graphic representation of each of the PSQI components. Thus, $59.6\%$ [106] of the healthcare workers presented sleep problems of greater or lesser importance. A total of $19.7\%$ [35] of participants showed optimal levels of sleep quality, measured by the PSQI. In Table 2, we can see the descriptive results of each of the PSQI components.
Descriptive data on eating behaviour according to the Three Factor Eating Questionnaire (R18) (TFEQ-R18) are described in Table 2 and Figure 2. The results of the dimensions of “uncontrolled intake” and “emotional eating” are highlighted as they were exceptionally high (74.45 ± 20.50 and 70.6 ± 25.78 out of 100, respectively).
Regarding the consumption of toxic substances, tobacco, alcohol and illicit drugs were considered for this study. About tobacco use, according to ESTUDES, the average age of onset is 16.23 ± 2.67 years, with 9 being the minimum age of onset and 29 being the maximum. Meanwhile, the average daily consumption was 10.56 ± 6.74 cigarettes (with a minimum of 1 and a maximum of 30 cigarettes per day with a frequency of consumption mainly daily in $25.28\%$ [45] of the participants but also sporadic, weekly in $5.06\%$ [9] and even less in $3.37\%$ [6].The most used illicit drugs were cannabis, used at some point by $88.37\%$ [38], followed by cocaine in $4.75\%$ [2] of the participants, ecstasy ($4.65\%$ [2] and, finally, amphetamines used at some point by $2.33\%$ of the respondents). On the other hand, regarding alcohol, $22.73\%$ [25] had increased its consumption during the pandemic, with beer and wine representing $87.2\%$ of the drinks consumed in this period. Cocktails ($3.67\%$), liquors ($0.92\%$) and other types of alcoholic substances ($8.26\%$) were also consumed. In Table 3, we can see more extensively the data referring to the consumption of tobacco, drugs and alcohol.
All respondents consumed alcohol. In terms of consumption pattern analysed using the CAGE questionnaire, we noted that $94.94\%$ [169] were social drinkers. Risk consumption was also observed by $2.25\%$ [4]. Likewise, the existence of harmful consumption was noted in $1.69\%$ [3] of the respondents and alcohol dependence in $1.12\%$ [2] of the participants.
## 4. Discussion
The literature has shown that the work dynamics of healthcare personnel generate high levels of stress and exhaustion, with important implications for health, both physical and mental [20,21,22,23,24], a situation that has been aggravated due to the COVID-19 pandemic [17]. This research has addressed aspects that interfere not only with the biopsychosocial development of the individual but also with the work quality of healthcare personnel during COVID-19. Thus, sleep quality, general and emotional eating behaviour and the consumption of alcohol, tobacco and illicit drugs have been studied.
Our study confirmed that the characteristics of healthcare professionals are similar to those of other studies carried out in care units [33,34]. The sociodemographic data show that $87.1\%$ of the sample were women with a mean age of 41.6 years, with the female gender being represented in more than $80\%$ of health-related jobs. Healthcare workers are mainly women with an average working age superior to 40 years [35]. In addition, $68.5\%$ turned out to be nursing professionals, making it the largest group among the employees surveyed. Indeed, nurses rank as the largest healthcare workforce, representing more than $50\%$ of the total healthcare workforce globally [35,36]. In spite of this, these figures are far from reaching the ideal levels to offer quality care since a greater number of nurses are needed to guarantee optimal health levels [37]. Along the same lines, the employment profile of the hired personnel has been described. The results of this study show that only $36\%$ of healthcare personnel have a tenure, which they obtained by passing an examination for Public Service, while $64\%$ have a temporary employment contract. However, it is true that the comparison with other countries can be somewhat complex as labour contracts vary depending on the health scenarios. Thus, a Brazilian study prior to the pandemic informs us that, indeed, most healthcare professionals were hired as service providers (temporary staff) [38]. In this line, the results of this work highlight the Spanish problem related to the shortage of healthcare personnel. Observed by public administrations and in an attempt to deal with this pandemic, staff from the different existing job placement offices and newly graduated professionals (mostly novices) were hired to help with the health emergency situation. The enormous spreading speed of COVID-19, added to the high number of infections among healthcare personnel, has forced these administrations to increase the supply of temporary jobs [39,40,41].
Regarding the quality of sleep, the results of this study showed that around $60\%$ of healthcare personnel have some sleep disorder, while around $20\%$ reflect optimal levels. This data is similar to those found in other comparable works, in which healthcare personnel are presented as a group with poor sleep quality, not only before the pandemic but also after [42,43]. Although the proportions obtained in this study are high, other investigations that evaluated the sleep quality among healthcare personnel during COVID-19 show even higher percentages. For example, in one of the most important studies carried out during the pandemic period, it was found that $75\%$ of healthcare workers had poor sleep quality [44]. Similarly, in Saudi Arabia, using the same questionnaire, a prevalence of sleep deprivation of $83\%$ was found among healthcare personnel [45]. If it were not for the pandemic period, this fact could be justified by the alteration of circadian rhythms derived from rotating shifts (usual shifts in care units), which in turn causes less stable sleep rhythms [46]. In the current context, the authors think it is important to point out that the high prevalence of sleep disorders found in this study, conducted one year after the start of the pandemic, may be related to concerns related to the contagious nature of COVID-19 and the work dynamics established in this period. On the other hand, when comparing the overall PSQI score of our work (7.92 ± 4.18) with other studies, we can see how our figures are lower than those obtained in other investigations. For example, in China, in a study carried out on 180 healthcare workers who worked during COVID-19, in addition to presenting a higher PSQI score (8.6 ± 4.6), they also showed high levels of stress and anxiety had a negative impact on sleep quality [47].
Furthermore, regarding emotional eating, this study observed that the sample presented a high level of emotional eating (the inability to resist emotional signals or eat in response to negative emotions) (70.6 ± 25.8). Likewise, the data obtained regarding uncontrolled intake (74.4 ± 20.5) (tendency to eat more than usual due to loss of control when eating with a subjective feeling of hunger) were high. In other studies, these data were obtained in response to negative or uncomfortable emotional states [48]. Regarding cognitive restriction (conscious restriction of eating aimed at controlling body weight and/or promoting weight loss), although the results are better compared to the other dimensions of the questionnaire (66.3 ± 16.3), they are also high. Job changes, job demands and uncertainty reflect how healthcare personnel “compensate with food” the negative emotions experienced in stressful situations [21].
In relation to tobacco consumption, our results show how one out of three healthcare professionals increased their consumption during this third wave. The published literature has already indicated that cigarette consumption increased in Spanish healthcare professionals during COVID-19 [49]. This may be motivated by the increase in cigarette consumption in the face of environmental stressors of various kinds, such as conflicts, disasters in the presence of depressive symptoms or post-traumatic stress disorders [50,51].
As for alcohol consumption, in this work, it is noted that one out of every four healthcare workers has increased consumption. Although such consumption has been associated with social life for decades, the pandemic has shown that its absence has not led to a reduction in alcohol consumption. In the healthcare environment, alcohol abuse or dependence may be associated with having been working as a healthcare personnel during the pandemic period and may again be related to situations that potentially generate post-traumatic stress disorders [50,51,52].
Here, we present the limitations and future lines of research. This study shows a series of limitations to highlight. The first is the sample size and having carried out this study in a single hospital. Furthermore, the difficulty in answering the Pittsburgh questionnaire can be considered another limitation. Indeed, our results show 37 people did not complete this instrument.
In future lines of study, we propose to expand this research with the inclusion of not only other variables of interest that have not been contemplated yet but also professionals from other centres from different parts of Spain and even Europe. It would also be interesting to include healthcare professionals from hospital centres as well as those dedicated to community health in the study sample.
## 5. Conclusions
The literature reports that the COVID-19 crisis has affected all types of healthcare workers, generating a significant emotional impact. The feeling of fear, anxiety or uncertainty, as well as the care overload or the pressure to which healthcare workers have been subjected during the pandemic, has considerably increased the consequences on their physical and psychological health. This study provides data on the quality of sleep and diet and the consumption of alcohol and tobacco of healthcare professionals in times of the pandemic. It is reflected in our results that the alterations already demonstrated in psychological health transcend other fields of the physical sphere of the individual and that they are basic for their functioning not only as workers but also as people. Since it is thought that stress levels could be the cause of such affectation, it would be interesting to implement strategies not only dedicated to the prevention and treatment of psychological consequences but also address fundamental aspects that favour healthy habits.
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|
---
title: Clinical-Scale Mesenchymal Stem Cell-Derived Extracellular Vesicle Therapy
for Wound Healing
authors:
- Jieun Kim
- Eun Hee Kim
- Hanbee Lee
- Ji Hee Sung
- Oh Young Bang
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001880
doi: 10.3390/ijms24054273
license: CC BY 4.0
---
# Clinical-Scale Mesenchymal Stem Cell-Derived Extracellular Vesicle Therapy for Wound Healing
## Abstract
We developed an extracellular vesicle (EV) bioprocessing platform for the scalable production of human Wharton’s jelly mesenchymal stem cell (MSC)-derived EVs. The effects of clinical-scale MSC-EV products on wound healing were tested in two different wound models: subcutaneous injection of EVs in a conventional full-thickness rat model and topical application of EVs using a sterile re-absorbable gelatin sponge in the chamber mouse model that was developed to prevent the contraction of wound areas. In vivo efficacy tests showed that treatment with MSC-EVs improved the recovery following wound injury, regardless of the type of wound model or mode of treatment. In vitro mechanistic studies using multiple cell lines involved in wound healing showed that EV therapy contributed to all stages of wound healing, such as anti-inflammation and proliferation/migration of keratinocytes, fibroblasts, and endothelial cells, to enhance wound re-epithelialization, extracellular matrix remodeling, and angiogenesis.
## 1. Introduction
Cutaneous wounds are common injuries caused by trauma, burns, ulcers, or surgery. Non-healing cutaneous wounds can impose severe clinical burdens on patients without effective treatment strategies. The beneficial effects of exogenous mesenchymal stem cells (MSCs) on wound healing have been observed in various animal models and clinical cases [1,2] Clinical test results using MSCs to enhance wound healing have been promising [3,4]. Notwithstanding the promising results obtained in clinical trials, MSC-based therapies are not considered a standard of care in clinical settings due to various limitations to their applicability [5,6].
A cell-free treatment paradigm using MSC-derived extracellular vesicles (EVs) can avoid the cell-related problems associated with stem cell therapy and exert the paracrine actions of MSCs. In addition, the “off-the-shelf” use of allogeneic MSC-derived EVs from healthy and young stem cells, such as MSCs derived from the umbilical cord, has the advantage of scalable production and storage with standardized procedures with high restorative capacity. However, critical hurdles remain in the translation of MSC-EVs into clinical therapeutics. Previous studies have used EV preparations obtained from the conventional 2D culture of MSCs; however, to date, no preclinical or clinical studies have examined the effects of MSC-EVs via scale-up production with customized therapeutic properties. We have previously reported that MSCs 3D-cultured as size-controlled cellular aggregates on a large scale better preserved the innate phenotype and properties of MSCs compared to 2D monolayer cultures, which resulted in the significantly augmented secretion of therapeutic MSC-derived EVs and their therapeutic contents (miRNAs and cytokines) from MSCs compared to conventional 2D cultures [7].
In the present study, we hypothesized that a clinical-scale EV product using a 3D micropatterned well system would enhance the wound healing process. To verify this, we developed an EV-bioprocessing platform designed using a cell non-adhesive microwell-patterned array for the scalable production of human Wharton’s jelly (WJ)-MSC-derived EVs in serum-free media. The effects of clinical-scale EV products on wound healing were tested in two different wound models: subcutaneous injection of EVs in a conventional full-thickness rat model and topical application of EVs using a sterile re-absorbable gelatin sponge in a chamber mouse model that was developed to prevent the contraction of wound areas. In addition, we performed in vitro and in vivo mechanistic studies using multiple cell lines involved in the wound healing process.
## 2.1. EV Characterisation
The amount of EVs obtained from 3D culture system was estimated to be approximately 8155.28 EVs per cell. The EVs had a typical round shape as seen on electron microscopy (TEM and Cryo-EM) (Figure 1A), and the mean particle diameter was 146.0 nm (Figure 1B). We investigated the expression of CD9, CD63 and CD81 using the Exoview Tetraspanin kit. EVs were primarily captured by antibodies against each tetraspanin, and then fluorescently labeled by detection antibodies for the three tetraspanins. It was demonstrated that the subpopulation of CD63+ was higher than CD9+ or CD81+ (Figure 1C). The presence of EV-specific positive markers (CD63, CD81 and Syntenin-1) further confirmed the identity as EVs (Figure 1D). The particle/protein ratio was 6.5 × 108 particles/μg. Specific contaminating proteins, including histone H2A.Z, GM130, and antibiotics, were identified by Western blot or ELISA. Antibiotics GM130 and histone H2A.Z were not detected (Figure 1E). The characteristics of EVs and their cargo contents did not change at room temperature after 1 week (Figure 1F).
## 2.2. MSC-EVs Induce Re-Epithelialization in Both Types of Wound Models
To investigate the efficacy and mechanism of MSC-EVs in a full-thickness rat wound model, rats were induced into a full-thickness wound model, and 2 × 108 EVs/rat were injected subcutaneously for 3 d (Figure 2A). Wound closure in the MSC-EVs group was higher than that in the PBS-treated group (Figure 2B,C). In addition, 14 d after wound induction, the contractility and repair ability of the wound center were measured, and the percentage of re-epithelialization was analyzed for wound repair capacity (Figure 2D,E). MSC-EV treatment significantly increased re-epithelization (Figure 2D) and reduced the size of the wound area compared with the controls (Figure 2E).
In addition, we tested the effects of EVs in a mouse chamber wound model because, unlike in humans, rodent skin wounds contract soon after wound formation (Figure 2F). In the chamber model, topical application of EVs using a sterile re-absorbable gelatin sponge (Cutanplast) in the chamber mouse model induced wound closure (Figure 2G,H) and improved re-epithelialization and granulation tissue in the chamber (Figure 2I,J). MSC-EVs induced a significant reduction in the size of the wound areas (%) in the chamber, strengthened the newly formed epidermal layer, and promoted the production of granulation tissue in the chamber.
## 2.3. MSC-EVs Accelerate Wound Healing by Promoting the Migration of Keratinocytes
MSC-EVs stimulated epithelial regeneration in both wound models. MSC-EVs promoted hypertrophy of the epithelial cell layer after 3 d of treatment with EVs (Figure 3A,B). Immunohistochemical examination 7 d after wounding showed that the number of keratinocytes was increased in the epithelial cell layer, suggesting that MSC-EVs promote the proliferation of keratinocytes for re-epithelization (Figure 3E,F). Interestingly, the epithelial cell layers returned to normal thickness after 2 weeks of MSC-EV treatment (Figure 3C,D), suggesting that EV-mediated regeneration of the epidermis occurs mainly during the initial phase of wound healing and the remodeling of the scar tissue maturation phase. In a study of wound tissue treated with MSC-EV, it was observed that the skin tissue underwent stabilization and thinning during the maturation stage. Immunohistochemistry for keratin 14 (a marker of keratinocyte cells) and Ki-67 (a marker of proliferating cells) showed that MSC-EVs stimulated the proliferation and migration of keratinocytes (Figure 3E,F).
## 2.4. MSC-EVs Promote the Migration of Mature Fibroblasts into the Granulation Tissue
MSC-EV therapy stimulated the proliferation of fibroblasts to promote the maturation of granulation tissue in both the full-thickness and chamber wound models (Figure 4). Treatment with MSC-EVs increased the number of proliferating fibroblasts that were positive for both Ki67 and vimentin (a marker of fibroblast cells) in immunological staining (Figure 4B,E). In addition, the migration of proliferating fibroblasts to the granulation tissue was increased after treatment with MSC-EVs, from subcutaneous areas in the chamber model and from the non-injured regions in the full-thickness model (Figure 4A,D).
## 2.5. MSC-EVs Promote the Formation of New Blood Vessels in the Wound Area
Immunohistochemical staining for CD31 (a marker of vascular structure) showed that MSC-EVs enhanced the vascular structure in both the epithelial cell layer and the wound center region during the wound healing process (Figure 5A). Similarly, immunohistochemical staining for a vascular endothelial growth factor (VEGF, a blood vessel marker) showed that MSC-EVs promoted angiogenesis (Figure 5C). We also measured the tissue levels of pro-angiogenic growth factors and found that VEGF, angiopoietin (Anpt)-1, and Anpt-2 levels were significantly increased in tissue lysates obtained from the dorsal wound area in the EV group compared to those in the control group (Figure 5E–G).
## 2.6. In Vitro Assay for MSC-EV Effects on Four Major Cell Types, Fibroblasts, Keratocytes, Endothelial Cells, and Inflammatory Cells
We performed in vitro studies to investigate the mechanisms of MSC-EVs using multiple cell lines involved in the wound healing process: keratinocytes (HaCaT), fibroblasts (NIH-3T3), endothelial cells (HUVECs), and inflammatory cells (RAW264.7). For both NIH-3T3 and HaCaT cells, cell motility was assessed using a scratch wound model. Various MSC-EVs (2, 5, and 10 × 108 EVs) were administered for 24 h (Figure 6A,B). MSC-EVs promoted the proliferation of both keratinocytes and fibroblasts, although the maximal effective dose was lower in fibroblasts than in keratinocytes. The tube formation assay using HUVECs showed a dose-dependent increase in angiogenesis (Figure 6C). Lastly, inflammation-induced macrophage RAW264.7 cells were tested using the Griess reagent for NO production (Figure 6D). Treatment with MSC-EVs promoted the polarization of M2-type macrophages (Figure 6E). In addition, compared to the control group, the levels of inflammatory cytokines were significantly decreased, but the levels of anti-inflammatory cytokines (IL-10) were increased in the EV group (Figure 6F).
## 3. Discussion
This study is the first to show that clinical-scale EV therapeutics are feasible using a micro-patterned well system and can improve the wound healing process. In this study, the effects of EV treatment were tested in different wound injury models under different treatment modes, which showed consistent findings. The mechanisms of action of MSC-EVs were assessed using both in vivo and in vitro models. The therapeutic potential of EVs can contribute to multiple stages of wound healing, such as cell proliferation and differentiation, inflammation, angiogenesis, and extracellular matrix remodeling. Specifically, our clinical-scale EV therapeutics could effectively induce the proliferation and migration of endothelial cells, keratinocytes, and fibroblasts to improve angiogenesis and re-epithelialization and regulate inflammatory cells in rodent wound models.
To date, multiple studies have investigated the effects of stem cell-derived EVs in wound models [8,9,10,11,12,13,14,15,16,17,18]. MSC-EV therapies obtained from various MSC sources, such as bone marrow, adipose tissue, and umbilical cord, have been used to improve recovery in various wound models. However, the development of MSC-EV therapeutics faces several hurdles, including establishing a consistent, scalable cell source and developing robust GMP-compliant upstream and downstream manufacturing processes [19]. MSCs undergo senescence, and their intrinsic ability to secrete EVs significantly declines in conventional 2D cultures; therefore, MSC-EV preparations may differ in their therapeutic potential. In addition, according to the US FDA conversion guideline documents for industry estimating the maximum safe starting dose in adult healthy volunteers (July 2005), one patient in clinical testing requires more than 100 times higher doses than those of one mouse or rat. Low output limits of EV preparations obtained from the conventional 2D culture of MSCs limit the clinical application of EVs. EVs obtained under 3D cultures, such as micro-patterned well systems, as shown in the present study, hollow fiber bioreactor-based 3D culture systems, and 3D scaffolds cultures, exhibited enhanced EV yield and a heightened damage-repair ability [20,21]. Therefore, for effective clinical-scale production of therapeutic EVs, large batches of MSCs are needed, which significantly affects the labor, time, and cost of production. In this study, we established a cell bank, used the 3D culture method, and the combination of filter and TFF system, as it allowed the large-scale production of EVs (the yield of EVs is more than 10–20 fold that of conventional 2D culture) without the use of serum. Compared to conventional stem cell-based therapeutics, our EV therapy has potential benefits in terms of cost-effectiveness when WJ-MSCs are cultured in a 3D micropatterned well system and isolated using a TFF system (Supplementary Figure S2). More importantly, our scalable 3D-bioprocessing EV production method reduced the donor/batch variation. Lastly, our small RNA sequencing data revealed that MSC-EVs containing miRNAs played important roles in angiogenesis, cytoprotection, immune modulation, and rejuvenation, and miRNAs, such as miR-21-3p, miR-125a, and miR-126-3p, were involved in the wound healing process after treatment with MSC-EVs (Supplementary Figure S3) [8,9,10,14,22,23]. MSC-EVs treatment has been found to promote wound healing by increasing the expression of VEGF-A, Wnt, and PI3K/AKT in fibroblast and keratinocyte cells. These findings suggest that EV-contained miRNA and cargo play a key role in wound healing by regulating specific signaling pathways, but more research is needed to fully understand the mechanism and potential therapeutic applications of MSC-EVs in wound healing (Supplementary Figures S3 and S4) [8].
In this study, the effects of MSC-EV treatment were tested in different species (mouse and rat) and wound models (mild [traditional full-thickness model] and severe [chamber model]), which showed consistent therapeutic benefits. The chamber model prevents the migration of keratinocytes into the wound and the closure of the wound via contraction [24]. It facilitates the de novo generation of epithelial tissues from the surface of the skin ulcers. Our results suggest that the application of EVs stimulates wound-resident stem cells to promote the wound-healing process; however, further studies are required to evaluate the de novo generation of epithelial tissues from wounded tissues [24].
Wound healing is classically divided into four stages: hemostasis, inflammation, proliferation, and remodeling. Each stage is characterized by key molecular and cellular events and is coordinated by a host of secreted factors that are recognized and released by the cells of the wounding response [25]. As various cellular components are involved at different stages of the wound healing process, we performed an in vitro assay to determine EV effects on four major cell types: fibroblasts, keratocytes, endothelial cells, and inflammatory cells. Depending on the severity and chronology (time interval from the onset of wound injury) of the wound and the presence of any comorbidities, such as infection and diabetes mellitus, in patients, one stage may be more prominent than others, and the target of treatment could be different among patients. For example, therapies with anti-inflammatory effects are needed in the inflammatory phase, the first phase after the cutaneous wound, while enhancing angiogenesis can be an important strategy in patients with diabetes mellitus. Proliferation and remodeling are important targets for the treatment of chronic deep wounds. The in vitro assay can aid in assessing the targets for different wound healing treatments. The results of this study showed that MSC-EV therapeutics exert their effects in most phases of wound healing.
This study has several limitations. First, the molecular action mechanisms of MSC-EVs could not be investigated. Of the cargo in exosomes, miRNAs are of prime importance in mediating the therapeutic effects on wound healing [8,9,10,11]. Molecular pathways of EV-miRNAs involved in wound healing are under investigation. In addition, we studied the effects of MSC-EVs in healthy young mice and rats. Cutaneous wounds are difficult to heal in older patients and those with comorbidities, especially diabetes mellitus. We are currently investigating the effects of MSC-EVs in diabetic wound animal models. Lastly, further in vivo studies are needed to determine the dose-responsiveness and optimal dose of EVs based on the specific phase of wound healing, as the optimal doses for angiogenesis and proliferation of keratinocytes and fibroblasts were different in our in vitro studies.
In conclusion, the present study demonstrated that our scalable 3D-bioprocessing production method is feasible for clinical-scale MSC-EV therapy. Moreover, our results showed that MSC-EVs promote wound healing in both mild and severe injuries via the regulation of various wound-healing phases.
## 4. Materials and Methods
All studies involving human subjects were approved by the Institutional Review Board of Samsung Medical Center. WJ was provided to the healthy volunteers. All volunteers or their guardians provided written informed consent to participate in the study. All experimental animal procedures were approved by the Institutional Animal Care and Use Committee (Laboratory Animal Research Center, AAALAC International approved facility) of Samsung Medical Center.
## 4.1. Preparation of EV-Three-Dimensional (3D) Spheroid Cultures of WJ-MSCs
MSCs derived from human WJ of the umbilical cord (WJ-MSCs) were culture expanded at passage five with growth medium in a $5\%$ CO2 incubator at 37 °C. WJ-MSCs were used at passage six to generate 3D spheroid cultures. WJ-MSCs were seeded into a micro-patterned well system (EZSPHERE; ReproCELL Inc., Tokyo, Japan), washed with phosphate-buffered saline (PBS), and trypsinized using TrypLE Express (GIBCO, NY, USA). After the WJ-MSCs were centrifuged, a fresh serum-free medium without heterologous proteins was added, and the cells were counted using a hemocytometer. After cell counting, 60 mL of the cell suspension was placed in a microarray containing approximately 69,000 microwells, each with a diameter and depth of 500 μm × 200 μm coated with 2-methacryloyloxyethyl phosphorylcholine polymer at a density of 400 cells/well. For the 3D spheroid culture of WJ-MSC, serum-free medium (α-minimal essential medium) was used, without any antibiotic. A 3D spheroidal cell aggregate was prepared by inducing spontaneous spheroidal cell aggregate formation while maintaining a static state by dispensing uniformly and culturing in a CO2 incubator at 37 °C for 4 d.
## 4.2. Isolation of EVs
EV isolation was performed in a biological safety cabinet. The culture medium was collected via gentle pipetting at the top of each well. To remove the cell debris and apoptotic bodies, 1800 mL of culture medium was centrifuged at 2500× g for 10 min, followed by filtration through a 0.22-μm membrane. The filtered medium was separated using a 300-kDa MWCO mPES hollow fiber MiniKros filter module (Spectrum Laboratories, Rancho Dominguez, CA, USA) on a commercially available KrosFlo KR2I tangential flow filtration (TFF) system (Spectrum Laboratories, Rancho Dominguez, CA, USA), which facilitates the large-scale processing of samples. EV-containing samples were recirculated into a filtration bottle. Small molecules, including free proteins, were passed through the membrane pores, eluted as a permeate, and collected. The collected solution was used as the secretome. EVs were maintained in circulation as retentate and concentrated in the bag. We conducted five volume exchanges of EVs with PBS, and EVs were subsequently concentrated to a final volume of 300 mL of recovery solution (PBS). The recovered solution was filtered through a 0.22-μm membrane. After harvesting the conditioned media, the EV isolation process was started immediately using the TFF procedure.
All processes were performed according to the guidelines on quality, non-clinical, and clinical assessment of EV therapy products of the Korean Food and Drug Administration (FDA, released December 2018) using good manufacturing practice (GMP)-compliant methods. Schematics of the processes of EV production, isolation, and quality control are shown in Supplementary Figure S1.
## 4.3. Characterization of EVs
Following the guidelines recommended by the International Society for Extracellular Vesicles (Minimal Information for Studies of Extracellular Vesicles 2018) and the Korean FDA, EVs isolated from the WJ-MSC culture medium were characterized in terms of their morphology, size distribution, surface markers, purity, potency markers, efficacy, stability, and safety [26].
See the Supplementary detailed methods for nanoparticle tracking analysis, Western blotting, transmission electron microscopy (TEM), enzyme-linked immunosorbent assay (ELISA), Exoview analysis, quantitative reverse transcription-polymerase chain reaction, and small RNA sequencing.
## 4.4. Two Animal Models of Cutaneous Wound
All animal experiments were approved by the Institutional Animal Care and Use Committee of Samsung Biomedical Research Institute and performed in accordance with the Institute of Laboratory Animal Resources guidelines. All animals were maintained in compliance with the relevant laws and institutional guidelines of the Laboratory Animal Research Center (AAALAC International-approved facility) at Samsung Medical Center.
## 4.4.1. Conventional Full-Thickness Skin Wound Rat Model
A conventional full-thickness cutaneous wound model was used in this study. Briefly, excisional wounds were created using an 8 mm diameter punch (Acuderm, Inc., Ft. Lauderdale, FL, USA) on the shaved dorsal skin under ketamine (100 mg/kg) and xylazine hydrochloride (5 mg/kg) anesthesia. Silicone splints were fixed around the excised wound. EVs were injected subcutaneously at four different points around the wounds, while an equal volume of PBS was injected subcutaneously in the same position in the control group rats. Based on the results of our preliminary experiments, a dose of 2 × 108 EVs/rat was selected for further experiments using the rat model.
## 4.4.2. Mouse Chamber Wound Model
Unlike human skin, rodent skin has panniculus carnosus, a thin layer of muscle attached to the subcutaneous tissue that acts as a contractile force for wound closure. Therefore, in the full-thickness rat model, it was difficult to measure the regeneration and recovery mechanisms of skin epithelial cells because of rapid wound healing by contraction. Therefore, we tested the effects of EVs in a mouse chamber model [24,27].
We surgically removed the skin from the back of the mice to generate an ulcer and isolated the resulting wound from the surrounding skin using a skin chamber sutured to the deep fascia. A chamber-made EP tube was placed inside the skin layer and fixed to the skin layer by a simple suture. Since mice are half as small as rats based on their body surface area, a dose of 1 × 108 EVs/mouse was selected for the mouse model and applied for 3 d after a full-thickness excision wound. Cutanplast was moistened with EVs and placed inside the chamber. To prevent inflammation in the chamber, antibiotics (Baytril) were injected for 2 weeks after surgery.
## 4.5. Measurement of Wound Contraction
Measurements of wound contraction and wound closure were performed using surgical calipers, and the wound areas were quantified using Aperio Image Scope V 12 software. Wounds were photographed on days 0, 1, 3, 5, 7, 10, 14, and 21 post-wounding, and wound size was determined using the ImageJ software (National Institutes of Health, Bethesda, MD, USA) to measure the wound area. The percentage of wound closure was calculated using the following equation: Wound closure=Initial wound size−Specific day wound sizeInitial wound size×100 Using histological samples, the general linear model for the determination of time versus wound closure (re-epithelialization) and granulation tissue formation for each treatment was evaluated. Wound contraction was calculated as a percentage of the original wound size, taken as $100\%$ of each animal in the group using the equation given above. The percentage of wound area was calculated using the following formula:*Wound area* (%)=Area at biopsyArea on incision day×100
## 4.6.1. Histological Analysis
Skin tissue samples were fixed in $4\%$ paraformaldehyde for 24 h and underwent dehydration with graded ethanol. The samples were then embedded in an optimal cutting temperature compound and cut into 10–30-μm thick sections. Hematoxylin and eosin (H&E) staining was performed using commercial staining kits (H&E Staining Kit (ab245880), Abcam, Cambridge, UK)), according to the manufacturer’s instructions. Images were captured using a microscope (ScanScope image, USA).
## 4.6.2. Immunohistochemistry
After 15 d of induction of wound models, the effect of MSC-EVs was compared with that of the control (basal medium) by immunostaining with Ki-67 (a cell proliferation marker) and vimentin (a fibroblast marker), according to the manufacturer’s instructions. Dorsal skin tissues were fixed in $4\%$ paraformaldehyde and blocked with $10\%$ normal goat serum. Dorsal skin was incubated overnight at 4 °C with rabbit anti-Ki-67 (1:50; Abcam, UK) and goat anti-vimentin (1:500; Abcam, UK) antibodies. The cells were then washed with PBS and incubated with secondary DyLight-labeled anti-goat IgG (1:200, 594 nm; Abcam, UK) and DyLight-labeled anti-rabbit IgG (1:200, 488 nm; Vector Laboratories, Burlingame, CA, USA) antibodies. Samples were imaged using a fluorescence microscope (EVOS; Advanced Microscopy Group, Bothell, WA, USA), and positively stained cells were quantified using ImageJ software.
## 4.6.3. Measurement of Cytokine Levels via ELISA
ELISA was performed using commercial kits according to the manufacturer’s instructions. The following ELISA kits were used: tumor necrosis factor-α (MBS140025, MyBioSource, San Diego, CA, USA), Ang-1 (MBS2601637, MyBioSource, San Diego, CA, USA), Ang-2 (MBS8420366, MyBioSource, San Diego, CA, USA), interleukin (IL)-10 (MBS140013, MyBioSource, San Diego, CA, USA), IL-6 (MBS 824703, MyBioSource, San Diego, CA, USA) and IL-beta (MBS 175967, MyBioSource, San Diego, CA, USA). All kits included standard proteins; therefore, the amount of protein and EV counts were determined based on the standard curve from each kit.
## 4.7.1. Measurement of Nitric Oxide Production in RAW264.7 Cells
The level of NO was determined by measuring the quantity of nitrite in the supernatant using the Griess reaction. Macrophage RAW264.7 cells (1.0 × 105) were seeded into a 24-well plate and treated with lipopolysaccharide (LPS; 100 ng/mL) for 24 h. To measure the amount of NO produced, 50 μL of conditioned medium was mixed with an equal volume of Griess reagent (Sigma, Saint Louis, MO, USA) and incubated for 15 min at room temperature. Absorbance was measured at 540 nm using a microplate reader, and the absorbance versus sodium nitrite concentration plot was constructed.
## 4.7.2. Fibroblast Wound Healing Assay in NIH-3T3 Cells
NIH-3T3 cells were seeded at 1.8 × 105/well into a 12-well plate. The wells were then scratched longitudinally using a yellow tip. After washing twice with high glucose media, cultures were treated with the same medium containing 5 μg/mL mitomycin C (Sigma, Saint Louis, MO) with or without MSC-EVs (2, 5, and 10 × 108 /mL). Cell migration was assayed 24 h after MSC-EV treatment using optical microscopy. Wound areas were measured using the ImageJ software, and the percentage of cell motility was calculated using the following equation: ([Area at 0 h − Area at 12 h]/Area at 0 h) × 100.
## 4.7.3. Keratinocyte Wound Healing Assay in HaCaT Cells
HaCaT cells were seeded at 2.2 × 105/well into a 12-well plate. The experimental procedure was the same way as the one used in the NIH-3T3 fibroblast wound-healing assay.
## 4.7.4. Angiogenesis Assay in Human Umbilical Vein Endothelial Cells
In vitro capillary network formation was determined using a tube formation assay on Matrigel (354248; Corning, Glendale, AZ, USA). Human umbilical vein endothelial cells (HUVECs) (1.5 × 104 cells/mL) were seeded onto Matrigel-coated wells of a 96-well plate and cultured in $1\%$ fetal bovine serum-supplemented Dulbecco’s Modified Eagle’s medium (10567014; Gibco, Waltham, MA USA) in the presence of 5 × 108/mL MSC-EVs or PBS. Tube formation was observed using an inverted microscope (Leica DMi8, Wetzlar, Germany). The number of network structures was quantified by randomly selecting five fields per well using ImageJ software.
## 4.8. Statistical Analyses
Statistical analyses were conducted using the SPSS program (SPSS Statistics Version 24.0, IBM Corp, Armonk, NY, USA) and GraphPad Prism 9 software (GraphPad Software, San Diego, CA, USA). The normality of the data was evaluated using the D’Agostino–Pearson test. One- and two-way analyses of variance with Tukey’s multiple comparison tests were used to analyze the three groups. Student’s t-test and Wilcoxon–Mann–Whitney test were used for paired and unpaired analyses of the two groups. Statistical analysis results are indicated in the figure legends. Results are expressed as the mean ± standard error. Statistical significance was defined as $p \leq 0.05.$
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---
title: Getting Connected to M-Health Technologies through a Meta-Analysis
authors:
- Luiz Philipi Calegari
- Guilherme Luz Tortorella
- Diego Castro Fettermann
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001891
doi: 10.3390/ijerph20054369
license: CC BY 4.0
---
# Getting Connected to M-Health Technologies through a Meta-Analysis
## Abstract
The demand for mobile e-health technologies (m-health) continues with constant growth, stimulating the technological advancement of such devices. However, the customer needs to perceive the utility of these devices to incorporate them into their daily lives. Hence, this study aims to identify users’ perceptions regarding the acceptance of m-health technologies based on a synthesis of meta-analysis studies on the subject in the literature. Using the relations and constructs proposed in the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) technology acceptance model, the methodological approach utilized a meta-analysis to raise the effect of the main factors on the Behavioral Intention to Use m-health technologies. Furthermore, the model proposed also estimated the moderation effect of gender, age, and timeline variables on the UTAUT2 relations. In total, the meta-analysis utilized 84 different articles, which presented 376 estimations based on a sample of 31,609 respondents. The results indicate an overall compilation of the relations, as well as the primary factors and moderating variables that determine users’ acceptance of the studied m-health systems.
## 1. Introduction
The literature indicates an increase in the concern with people’s health, resulting in a rise in the development of technological products [1]. The continuous development of technologies applied to healthcare has provided patients with a better quality of life while increasing their expectancy of better treatments in the health system [2,3,4]. As a way to improve healthcare, new technologies have started to be incorporated into healthcare systems, such as e-health technologies.
E-health technologies are considered an emerging and growing field in the medical sector [5,6]. The evolution of the development of e-health technologies presents promising alternatives for healthcare carried out effectively and at a low cost [7]. Faced with the growing concern of people with their health, the development of e-health technologies for the remote monitoring of users has presented a significant market evolution [8,9,10]. Technological advances in Internet of Things (IoT) devices, big data strategies, and portable biosensors have generated alternatives to provide personalized e-health services [11]. The greater flexibility in the use of IoT devices, such as wearables, provided by the evolution of cloud computing technologies, promotes the expansion of the use of mobile devices aimed at health services, called m(mobile)-health [12,13]. M-health technologies propose providing health services anytime and anywhere, overcoming temporal and geographic barriers [14]. Highlighting m-health technologies, the demand for wearable devices continues in constant growth [15]. For the wearables market, an annual growth rate of $20\%$ is estimated for the following years, moving about 150 billion euros up to 2028 [16]. The proliferation of wearables on the market predicted for the next decade will stimulate the technological advancement of such devices, improving intelligent systems and their resources [17].
Despite the benefits of using e-health technologies, there needs to be more understanding of the relations among suppliers, technologies, and potential consumers [18]. Furthermore, the rollout of e-health devices must consider all factors that affect the utility perceived by consumers [18]. As a way to understand the consumer’s acceptance, it is essential to interpret the factors that explain the acceptance of new technologies by potential users [19,20,21,22,23]. In the context of studies with models of technological acceptance, studies with small samples hardly consolidate general trends in terms of acceptance [24]. In the particular case of m-health technologies, the literature reports several studies with divergent estimations of m-health acceptance [25,26,27]. A case of this divergence is the relation of the effort expectation construct to the Behavioral Intention to Use construct, reported as having a significant and positive effect [26,28] and in other cases reported as having no significant effect [29,30]. The same case has been reported in the relation between the Facilitating Conditions and the Behavioral Intention to Use constructs, which is significant in some studies [27,31] and not significant in others [32,33]. The frequent divergences in the estimations of the acceptance of m-health technologies in the literature raise the need to identify a general trend among various estimations carried out in particular contexts. In order to deal with the variety of estimations, the literature suggests applying the meta-analysis methodology, which establishes a robust research model based on gathering studies from a specific area [34]. The meta-analysis also raises general trends between divergent results and makes evident the consensus among similar relations [35].
This study aims to identify users’ perceptions regarding the acceptance of m-health technologies based on a synthesis of meta-analysis studies on the subject in the literature. A meta-analysis was carried out using the relations and constructs proposed in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model of technology acceptance proposed by Venkatesh et al. [ 36] and widely utilized in the m-health literature [37,38,39]. Moreover, the moderation effect of variables proposed in the UTAUT2 was estimated using a meta-regression [34,40] procedure. The results indicate an overall compilation of the relations, as well as the primary factors and moderating variables that determine the users’ acceptance of m-health technologies.
## 2. Acceptance Models
IoT adoption promotes many benefits for industry, companies, and users [21]. However, it is possible to observe in the literature various barriers related to the lack of acceptance of these new technologies by their potential users [41,42,43,44,45]. The divergence among the technology acceptance estimations reported in the literature undermines the reliability of these results [46]. The diversity in the results could be associated with the use of small samples as well as the sampling procedures utilized [6], and meta-analysis is the technique suggested to deal with it and raise robust and reliable estimations [47].
Technology acceptance models have been widely applied to understand user behavior toward various solutions. For example, we have its application in studies on applications and information systems for agricultural activities [48,49], virtual reality systems [50], home devices [24,51,52], autonomous cars, [53], safety systems for construction workers [54], learning environments or e-learning [55,56], e-shopping [57], e-services [58], digital content marketing for tourism [59], mobile payments [60], the visual design of wearables [61], and wearable locating systems [62], among others. The number of new e-health technologies has increased the use of technology acceptance models to improve the comprehension of the factors that affect the user’s acceptance of m-health technologies [63,64,65].
The literature reports various approaches for measuring the acceptance and use of new technologies, such as the technology acceptance model (TAM) [66], the Theory of Planned Behavior (TPB) [67], the Theory of Reasoned Action (TRA) [68], and the Unified Theory of Acceptance and Use of Technology (UTAUT) [69]. Despite the various alternatives, the TAM is one of the most disseminated in the literature [70,71,72,73,74]. However, the TAM model also is criticized for providing an overly generic estimate of user perception relative to the acceptance of new technologies [75].
The UTAUT model was developed as a result of the TAM model’s limitations, presenting a broad application in the literature to measure the acceptance of new e-health technologies [76,77,78]. The UTAUT model proposed by Venkatesh et al. [ 69] is formed by the following constructs: Performance Expectancy (PE, also expressed as Perceived Usefulness, Extrinsic Motivation, Job-fit, Relative Advantage, and Outcome Expectation), defined as the extent to which a person believes that using a specific system will improve their performance in carrying out a specific action [66]; Effort Expectancy (EE, also expressed as Perceived Ease of Use, Complexity, and Ease of Use), defined as the extent to which a person believes that using a given system will be effortless [66,79]; Social Influence (SI, also expressed as Subjective Norm, Social Factors, and Image), defined as the extent to which an individual believes that people of reference may influence the use of a given system [69]; Facilitating Conditions (FC, also expressed as Perceived Behavioral Control and Compatibility), defined as the extent to which an individual believes in the existence of technical and organizational infrastructure and favorable environmental conditions that motivate them to use technological systems [69]; Behavioral Intention to Use (BI), defined as the extent to which an individual formulates a conscious plan to execute or not execute a future behavior [8,80]; and use behavior (UB), defined as the Usage Behavior measured from the actual frequency of use of a given technology [69].
In order to improve the UTAUT model prediction, the authors proposed including factors related to the consumer’s context, creating the UTAUT2 model [36]. The update brought three new constructs: Hedonic Motivation (HM), defined as the pleasure or fun derived from using the new technology [81,82]; Price Value (PV), which refers to the exchange that the consumer deems fair between the perceived benefits and the monetary costs [83,84]; and Habit, which refers to a reflexive behavior by people or automatic behaviors stemming from their experiences and learning [82,85,86]. Some authors also use UAUT2 variations, adding the Attitude construct (AT) [87,88], which refers to the degree to which the person has a behavior favorable to the use of the technology studied [67].
## 3.1. Proposed Model
Due to the context of m-health technologies, the literature recommends including other constructs in the UTAUT2 model [89]. Hence, besides the relations proposed by the UTAUT2 model, the literature also suggests five other relations of constructs considered critical to the acceptance of m-health technologies. The first relation included is between the Effort Expectancy (EE) > Performance Expectancy (PE) constructs, as suggested by the literature [90,91]. The second relation added is between Performance Expectancy (PE) > Attitude (AT) [92,93]. The third relation suggested is between Effort Expectancy (EE) > Attitude (AT) [94,95]. The fourth relation frequently estimated in the literature is between the constructs Attitude 138 (AT) > Behavioral Intention (BI) [96,97]. The fifth relation added is between Privacy Risks (RP) > Behavioral Intention (BI), also often estimated in the m-health literature [90,98].
The literature suggests that the perceived utility is more significant insofar as wearable technologies are easy to use, i.e., require less user effort [99,100]. Hence, it becomes important to consider the positive effect on the Effort Expectancy (EE) > Performance Expectancy (PE), the sixth relation in the proposed model. The Performance Expectancy (PE) and the Effort Expectancy (EE) are constructs within the cognitive scope that affect the Attitude (AT) of users and, subsequently, determine their intention to use [91]. Attitude is defined as an affective reaction by an individual when using a technology [69]. The literature suggests that more positive attitudes by an individual toward a technology tend to positively influence the Behavioral Intention to *Use this* technology [91,92]. Hence, the seventh relation, PE > AT, the eighth relation, EE > AT, and the ninth relation, AT > BI, will also be analyzed in this study. Lastly, the increase in the frequency of health data sharing in cloud computing environments stimulates the concern of digital media users with the privacy and security of personal information [98]. The behavior of users avoiding using digital media that require access to personal data becomes one of the most common reasons for not accepting a technology [101]. Hence, it is essential to observe if there is a negative PR > BI effect.
## 3.1.1. Moderators
Previous studies have pointed out that social characteristics significantly impact users’ acceptance of new technologies and must be incorporated into the acceptance models [102,103]. The current meta-analysis includes the moderator effect on the relationships of two user variables, gender and age. Moreover, the meta-analysis also estimates the moderator effect of time on the relationships. The literature suggests the inclusion of moderator variables in the models to improve prediction capacity and deal with the heterogeneity of the correlations considered in the meta-analysis [103].
## Gender
Despite various inconclusive and diverging results, the literature emphasizes the importance of including the moderation of the gender variable in the new technology acceptance models [69,104,105]. The moderating variable of gender was encoded from the proportion of male respondents relative to the total (number of male respondents/total respondents).
## Age Range
The literature reports a lower acceptance of e-health technologies by senior users than people in other age ranges [30,77]. In the studies considered in this meta-analysis, the demographic data referring to the age of the respondents is reported by grouping the ages in age ranges. Given this restriction, the age range variable corresponding to each study was estimated in this meta-analysis from the mean value between the limits of each age range considered in the studies weighted by the sample percentage corresponding to each studied range. When the age range’s maximum and minimum age limits were not defined (e.g., over sixty years old), 18 years was considered the minimum age, and 85 was the maximum age.
## Timeline
The literature on meta-analysis suggests including the study year [40] as a moderator variable. Upon analyzing users’ acceptance of new technologies, the relations among its variables may change over time. A better understanding of this change over time still needs to be addressed in the literature [106]. For this reason, the publication year is considered a moderating variable in this study.
For the meta-analysis, the moderating variable “Timeline” was encoded as follows (Equation [1]), where Year = publication year of the analyzed study; YearMax = the most recent publication year among the studies considered for the analyzed relations; YearMin = the oldest publication year among the studies considered for the analyzed relations:[1]Timeline=Year−YearMinYearMax−YearMin From the relations presented in the previous topics, the model proposed for this meta-analysis is represented in Figure 1. Hence, besides the relationships proposed by the UTAUT2 model (PE > BI, EE > BI, SI > BI, HM > BI, HB > BI, FC > BI, FC > UB) and the moderations considered in each relation (age range, gender, timeline), five relations of constructs considered important to the studied problem were included, primarily for presenting themselves frequently and with relevant results in the e-health literature (EE > PE, PE > AT, EE > AT, AT > BI, RP > BI).
## 3.2. Sample
The meta-analysis depends on the primary data. Thus, the execution of a comprehensive and quality bibliographic search becomes essential [107]. The methodological approach of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA—keywords protocol, eligibility criteria, information source, search and selection of studies) was used to elaborate the study. The search was carried out in the literature through the scientific databases “Scopus”, “Web of Science”, “Emerald Insight”, “IEEE Xplore” “Science Direct”, “PubMed”, and “MedLine”. The initial search on m-health technology acceptance articles [108,109,110] and technology acceptance models articles [36,111] raised the keywords used for the search. The keywords were organized into four search fields. The first search field corresponds to the analyzed technologies, “m-health” and “wearables,” applied with health purposes, combined with the words “health” and “fitness”. The second field refers to the user’s acceptance of new technology. The second field uses the combination of the words “accept*,” “engag*”, and “user”, with the asterisk indicating the inclusiveness of similar terms that have the same root. The third search field refers to the technology acceptance models. According to Taherdoost et al. [ 58], the most popular technology acceptance models are the following: “Technology Acceptance Model”, “Decomposed Theory of Planned Behavior” (DTPB), “Theory of Planned Behavior” (TPB), “Model of PC Utilization” (MPCU), “Theory of Reasoned Action” (TRA), “Innovation Diffusion Theory” (IDT), “Motivational Model” (MM), “Social Cognitive Theory” (SCT), “Unified Theory of Acceptance and Use of Technology” (UTAUT), and Unified Theory of Acceptance and Use of Technology 2”. The fourth search field corresponds to the statistical methods for estimating the models considered for the meta-analysis: “Partial Least Squares” and “Structural Equation Modeling.” The search used the topic procedure, restricting the search to the article’s field of title, abstract, and keywords. Table 1 displays the search string used in the databases. The search resulted in 273 studies. The details of the filtering mechanism of the materials selected for the meta-analysis are shown in Figure 2. The studies were filtered firstly based on the publication language. Next, 31 duplicated and 15 unavailable (articles with limited access to their full content) studies were excluded, leaving 238 papers to be analyzed fully.
## 3.3. Coding
The methodology approach used coding rules to guarantee consistency among the studies considered for this meta-analysis. As suggested by the literature [112], the initial pool of articles was assessed following criteria: empirical study containing at least one construct of the UTAUT2 model [113] or a similar one [36]; (ii) the presence of correlations among the constructs (ro); (iii) the internal consistency of the constructs (rxx and ryy); and (iv) the size of the sample utilized (Ni). From the initial pool of 238 articles, the coding procedure found 84 articles that met all requirements. These 84 articles presented a total of 376 correlations among the constructs incorporated into the proposed model (Figure 1) and are based on a sample of 31,609 respondents (Appendix A). The number of articles, correlations, and the sum of respondents utilized in the current meta-analysis is relevant compared to other meta-analyses in the health area [114,115,116,117].
## 3.4. Analysis
The Hunter–Schmidt method [118] has been widely applied in studies that relate items measured by Likert scales and latent variables [119,120,121]. In this research, the correlations stemming from the studies considered for the meta-analysis (ro) were unattenuated using the reliability of the constructs for each relation as suggested by Hunter and Schmidt [47] (Equation [2]), where rm is the size of the effect corrected for the measurement error, and rxx and ryy are the reliabilities of the constructs involved in the relations, stemming from Cronbach’s alpha [122] or the Composite Reliability (CR) [123]:[2]rm=rorxxryy The correlations were corrected for the sampling error using the sample size of each observation as the weight (Equation [3]), where rc is the average corrected correlation for the bivariate relations, and Ni and ri are the sample size and size of the effect corrected for the measurement error for each sample i, respectively:[3]r¯c=∑Nirmi∑Ni Moreover, it is also possible to calculate the sampling error variance (ei) for each study (Equation [4]):[4]ei=1−r¯c22(Ni−1)rxxryy2 The results of the relation estimates (r¯c) and estimation errors (ei) were used to calculate the compiled effect of the relations. The individual estimates were treated as random effects, assuming that the correlations among the studies are different [40].
The method-denominated meta-regression was used to verify the need to incorporate moderators into the relations. Meta-regression is indicated as a way to analyze the heterogeneity of the residuals of the estimates through moderating variables [40,118]. To analyze the heterogeneity of the residues, the Qresiduals statistic, which corresponds to a weighted measure of the square of errors, and the inconsistency test I2, which represents the proportion of studies in which the proposed model does not explain the coefficient, were considered [124].
From the techniques developed by Hunter and Schmidt [125] and presented by Borenstein et al. [ 118] and Card [40], we intended to identify the correlations among the constructs proposed in the model to measure the acceptance of e-health technologies by users. Statistics software Stata® v. 16 was used to estimate all effects presented in this paper.
## 4.1. Overview of the Studies Considered for the Meta-Analysis
Appendix B shows a growing trend of publications on the subject from the 84 studies selected and presented in the present meta-analysis. For example, this fact can be observed in the growth in the number of articles published over the years. It is also possible to observe that most of the studies considered for the meta-analysis come from China [17], followed by Bangladesh [9], the USA [7], and Taiwan [7]. There is also a need for studies from Latin American countries. This fact may indicate the need to develop future studies to understand Latin American consumers’ acceptance of m-health technologies.
## 4.2. Reliability of Constructs
Table 2 presents the descriptive analysis of Cronbach’s alpha of the constructs considered in the meta-analysis. The results display that Cronbach’s alpha from all constructs is above 0.6, indicating that the constructs used in this meta-analysis are reliable [126].
## 4.3. Meta-Analysis of Model Correlations
The results of the estimates proposed in the model are presented in Table 3, indicating a significant effect for all relations proposed in the model. It is possible to observe that the AT > BI relation presented the most significant effect (β = 0.647; p-value < 0.05) among the considered relations. It is also possible to observe a negative effect resulting from the PR > BI relation. These results indicate that while Attitude has a more significant impact among users on their Behavioral Intention to Use an e-health device, the risk to privacy may cause resistance to this same intention to use.
It is also possible to observe that the HM > BI relation presented the smallest range in its Confidence Interval (β = 0.003; p-value < 0.05) and, consequently, a lower I2 value. These results indicate a smaller resulting variance among the effects corresponding to the HM > BI relation. However, the other values obtained for I2 point to high heterogeneity in the other relations considered for the meta-analysis, indicating the need to incorporate other moderating variables into the proposed model.
Regarding the moderating variables (Table 4), it is possible to verify that the effect of the moderating variables was significant for most of the relations proposed. Among the moderating effects that presented significant moderation (p-value < 0.05), it is possible to observe that the moderating effect of the “Timeline” in the FC > UB relation presented the highest coefficient (β = 1.2735; p-value = 0.026). The PE > BI, PV > BI, and BI > UB relations did not present a significant moderating effect by the variables considered in this study. It is also important to emphasize the high values obtained for I2 (except for the HM > BI relations), which indicate that even with the incorporation of three moderators, as suggested by the results in Table 3, the incorporation of other moderating variables is still necessary to better understand and estimate the relations.
## 5. Discussion
Figure 3 presents the results referring to the effects of the model proposed by the current meta-analysis, presenting the results for the coefficients previously shown in Table 3 and Table 4.
## 5.1. Main Relations of the Model
The results indicate a positive and significant relation (β = 0.339; p-value < 0.01) for the PE > BI relation. Although studies are verified indicating that the effect of the PE predictor is the most significant among the other constructs toward the BI [127,128], the effect of PE was the third-largest relative to the BI in this analysis. As a strategy to improve user understanding of the potential utility of e-health technologies, marketing professionals must communicate clearly the effectiveness of using the technology for health [1]. This indication is based on the positive perception that benefits stemming from using technology reinforce the intention to use a product [1].
For the EE > BI relation, the results indicate a positive and significant effect (β = 0.2320; p-value < 0.05). Although some studies indicate no significance for the relation EE > BI [129,130], the positive effect has been reported in many estimations in the literature [32,131,132]. The positive effect of the EE > BI relation is related to offering functions that meet user needs, promoting the increase in the acceptance of the effort required for use [133]. If the consumers perceive that using the technological device is intuitive and easy, they will more easily perceive the benefits and value of this technology [134]. As an alternative for those who are not acquainted with the used technologies, it would be possible to promote the reduction in the effort required to use the technology from the incorporation of graphical resources that allow the user greater facility to become familiarized with the available functionalities [135].
Users perceive greater utility in e-health devices (m-health/wearables) when they observe more ease in using the technology [99]. The results indicate a positive and significant effect (β = 0.4680; p-value < 0.01) for the EE > PE relation. The positive value for the coefficient indicates that the easy operation of e-health devices induces an increase in user expectations related to the desired performance for the technology to be acquired [69].
The meta-analysis estimated a significant (p-value < 0.01) and positive effect for the EE > AT and PE > AT relations (β = 0.3490 and β = 0.5250, respectively). Therefore, it is also important to highlight the effect of the AT construct on BI, which presented the most magnitude estimated in this meta-analysis (β = 0.6470; p-value < 0.01). In summary, these results indicate that users have a more positive attitude relative to e-health devices if the technology is perceived as useful [136] and easy to use [100]. The results also pointed out that a positive attitude directly influences Behavioral Intention.
Although some studies reported the non-significance of the relation SI > BI [130,137,138] and others still suggest a negative effect [139], the results of the meta-analysis indicate a positive and significant relationship (β = 0.2800; p-valor < 0.01) between SI > BI. The result may be explained by people’s desire to share views and behaviors perceived in specific groups [140]. The usefulness and reliability of the content were two criteria that predicted the participants’ intention to share digital information media [141]. The spread of misinformation on social media still causes fear and distrust among technology users [78]. ( When the users indicate their acceptance of the technology before the community, the perception of risks tends to decrease, promoting more confidence in using the technological product [142]. In this sense, social networks are an important tool for forming opinions regarding products and brands due to the wide dissemination of information [32]. A better understanding of health outcomes from online information sharing becomes important for healthcare-related prevention and optimization [143]. Hence, the investment in resources directed at support and data collection from social media becomes essential.
For the relation FC > BI, the meta-analysis indicates a positive and significant trend (β = 0.4880; p-value < 0.01), although some studies indicate non-significance for this relation [30,33]. This result reflects the need for resources that improve internet services and increase compatibility among intelligent devices with health-monitoring functionalities [144]. The FC construct presents the fourth-largest effect among all 56 relations investigated in this meta-analysis. However, the literature considers that this construct is deemed one of the most important to determine BI due to the dependence of wearable and m-health devices on wireless network support and internet providers with high data transfer capacity [145].
The meta-analysis indicates a positive and significant effect (β = 0.2790; p-value < 0.01) for the relation FC > UB, although some studies reported non-significance for the same relation [77,146]. The positive effect of the FC > UB relation results from the positive influence of the presence of training and/or technical support capable of helping the user overcome concerns with technological innovations [145]. The presence of an operational structure capable of guiding the user simply or of a support system to obtain help positively influences the adoption of e-health technologies [32]. Training programs, technical support, and financial aid provided by professionals or family members would be crucial for using e-health devices [145]. The updates to enhance e-health product functionalities may even occur through continuous improvement, employing big data analyses related to medical care [139].
Several studies suggest that Hedonic Motivation plays a direct role in the Behavioral Intention to Use e-health technologies [30,147]. The meta-analysis indicates a positive and significant effect (β = 0.1150; p-value < 0.01) for the HM > BI relationship. This positive effect may indicate that the studied devices improve social communication and pleasure in using these technologies, besides the use purpose related to health monitoring [147].
Comparing the estimations obtained in studies aimed at m-health acceptance by teenagers [129] and elderlies [148], it appears that the HB > BI relationship is non-significant for the first case and significant for the second. Such divergence suggests that age may be a relevant moderating factor to be considered. Even with these divergences, the meta-analysis indicates a positive and significant coefficient (β = 0.364; p-value < 0.10) for the relation HB > BI. A positive effect of this relationship indicates that adopting a permanent habit increases the likeliness of accepting the studied technologies [36]. These results may also represent the user’s dependence on the habitual use of such devices [130].
For the relation PV > BI, although some studies in the literature on the subject suggest the significance of this relation [149,150], the meta-analysis estimated a non-significant relationship (p-value > 0.05). The acceptance of a given technology tends to increase insofar as the user perceives that the benefits of using such technologies are superior to the cost of their adoption [132,151]. The non-significant coefficient for the PV > BI relationship may be related to the great variety of e-health devices available and the benefit provided by these devices [144].
Among the studies considered for the present meta-analysis, only one showed a positive sign for the PR > BI relationship [152]. Intuitively, it is reasonable to expect that PR has a negative effect since this construct represents a consumer concern. Nonetheless, the authors justified the result because older adults are less concerned about privacy, and PR would not hinder older people’s acceptance of the e-health device [152]. Despite that, the meta-analysis estimated a negative and significant coefficient for the relationship between PR > BI (β = −0.1600; p-value < 0.01). The literature points to the concern of patients with the possibility of disseminating personal health information [153]. M-health devices such as m-health and wearable devices are more vulnerable to attacks and information interception, contributing to user insecurity regarding the privacy of such devices [101]. The perception of privacy becomes even more important when disclosing personal health information that may cause embarrassment to the user [154,155,156]. From this, developers must make sure that e-health devices comply with the data collection, processing, and storage regulations and provide transparency to consumers regarding data collection and use [101]. In summary, managers must conduct product design plans aligned with marketing strategies and privacy protection policies to attract consumers [157].
The literature reported that behavioral intention does not always indicate the actual use of the technology [158,159]. However, many estimations indicated that the Usage Behavior (UB) of an e-health technology is preceded and strongly affected by Behavioral Intention (BI), [26,160,161,162]. The meta-analysis indicates a positive and significant relationship for BI > UB (β = 0.525; p-value < 0.01). Hence, Behavioral Intention (BI) is an efficient indicator of the actual Usage Behavior of users.
## 5.2. Relations of the Moderating Variables
Firstly, it is possible to observe that only three relationships were significantly moderated by the three moderating variables proposed for the model (EE > AT, AT > BI, and FC > UB). Secondly, it was verified that some relations were not significantly influenced by the moderators proposed in this work (PE > BI, EE > PE, PV > BI, BI > UB). However, as presented before, most relationships present I2 values close to $100\%$ (except the HM > BI relations). The I2 values suggest that the inclusion of more moderating variables into the model is necessary to deal with the heterogeneity of the residuals. Hence, although the PE > BI, EE > PE, PV > BI, and BI > UB relations were not significantly influenced by the proposed moderators, the incorporation of other moderations could reveal significant effects on these relations, enabling an adjustment for the proposed model.
## 5.2.1. Gender
Gender exerts an important effect on adopting e-health technologies [146], which may be observed in the meta-analysis results. The moderation of the gender variable is significant (p-value < 0.05) for six of the relationships (PE > AT, β = 0.5012; EE > BI, β = 0.7610; EE > AT, β = 1.0522; AT > BI, β = 0.4486; HM > BI, β = 0.0078; PR > BI, β = 1.0522). These significant moderating effects indicate a greater influence of these relationships in men than in women. The effect was more relevant in men for relations involving HM and AT, which can be explained by the fact that men are more adventurous and are more likely to explore new technologies. At the same time, women desire factors that give them security (support) for the use of a technological system [163]. Although women tend to be more attracted by mobile technologies [164], men are more inclined to adopt m-health systems [165].
Some authors have suggested that women use less technology [166] and are less acquainted with new technologies [167,168]. The literature also indicates that the EE > BI relationship may influence men more to accept e-health devices [28,146], agreeing with the results obtained in the current meta-analysis.
## 5.2.2. Age Range
User age range affects adopting e-health technologies [98]. The current meta-analysis results indicate that the age range’s moderation was significant for five of the relations (p-value < 0.05). Most of the significant effects of the “age range” moderator resulted in positive coefficients (EE > AT, β = 0.5799; SI > BI, β = 0.3163; AT > BI, β = 0.5863; HB > BI, β = 0.8443), which point out that older people are more susceptible than younger people to these relationships. Previous studies on adopting new technologies have suggested that the perceived benefits of technology influence the intention of senior citizens to adopt the technology [30,146,169]. It is verified in the literature that older people tend to be more susceptible to the complexity of technology [170]. Older adults with relatively less experience with the internet find a more challenging environment searching for reliable information [171]. Despite e-health motivating the elderly with health care, this motivation can be reduced over time due to the perception of the incompatibility of these technologies with the social environment of the elderly [172]. However, senior citizens have a positive attitude toward adopting technologies that render their lives more convenient [173] and make them more independent and with better quality of life [174]. The attitude most strongly linked to the perception of older people relative to the Behavioral Intention to Use technology may be related to health problems and concerns, which tend to increase with age [175]. With the increase in age, it is also possible to perceive that ease of use is considered a relevant factor for the attitudes of users toward the adoption of the technology (EE > AT) [100]. As the older population acquires a more relevant proportion relative to the general population, understanding their specific needs is essential to increase their technological acceptance level [98,131]. The FC > UB relation for this analysis was the only one significant for younger people (FC > UB, β = −0.8599). The development of mobile devices influences more and more youths to monitor their health and have healthier lifestyles continuously [176].
## 5.2.3. Timeline
From the results obtained, it is possible to observe the significance of the moderating variable of timeline. The meta-analysis showed that the moderation presented a negative sign in five relationships (PE > AT, β = −0.4415; EE > AT, β = −0.5324; AT > BI, β = −0.3046; SI > BI, β = −0.3046; FC > BI, β = −0.1898; HB > BI, β = −0.1767). These results indicate that the magnitude of estimated relationships has decreased over time. The resulting values may be explained by the fact that people are more used to a technological environment, which would enable more considerable reliability of the performance of the technology, less concern regarding the effort required for its use, and more regularity in using technological devices.
In contrast, the FC > UB and PR > BI relationships presented positive values (FC > UB, β = 1.2735; PR > BI, β = 0.3693), which indicates that the intensity of the effects of FC on UB and PR on BI has increased over the years. The result corresponding to the FC > UB relation may be translated through the greater need for a structure that serves health requirements and allows speed in the transport of information and technological ubiquity. In turn, the result referring to the PR > BI relation may indicate the increase in the consumers’ concern with the security of confidential information, corresponding to the vulnerability of mobile digital services stemming from the high rate of information transfer among networks [101].
## 5.3. Implications for Theory and Practice
As the main contribution, this study presents a general guide to understanding how the process of accepting new e-health technologies takes place, indicating overall guidance for future research and the development of such technologies considering their acceptance by users. In the academic context, the results of this meta-analysis present significant variables (e.g., PE > AT, AT > BI, BI > UB) that must serve as guidelines for future research on the acceptance of other e-health technologies. Furthermore, the non-significance of some of the relations among the analyzed constructs (e.g., PV > BI and some moderations of the relationships) suggests that future investigations should explore such relations considering incremental alterations to the proposed model.
As for the practical implications, the results guide the marketing and product development activities of m-health and wearable devices. Managers and developers can obtain direction for their activities from the degree of importance of each construct analyzed. It is necessary to consider the individuality of each user to provide more flexible solutions with greater capacity for the customization of health information-sharing services [177]. Understanding consumers’ needs enables focusing on developing components essential to the market acceptance of the studied devices.
## 5.4. Limitations and Directions for Future Research
Calculating the I2 made it possible to verify the need to include other moderating variables to improve the model fit. The results indicate that these relationships still present significant heterogeneity, evincing that other factors still not considered by this study also affect the acceptance by users of e-health technologies. Hence, future research may explore the model proposed in this study, adding other moderating variables that may help explain the studied phenomenon.
There is a concern for the World Health Organization in reaching equity in health services for medical issues and social matters. Hence, similar studies must be able to analyze possible users residing in underdeveloped or developing countries, as is the case for countries located in Latin America, especially Brazil. This fact becomes relevant due to the variation in the cultural and economic characteristics of the population and characteristics associated with the regulation and structuring of health services that may influence technology acceptance. Moreover, future research must assess which components are considered essential for these devices from the perception of general and specific users.
## 6. Conclusions
This study sought to understand consumers’ acceptability relative to e-health technologies associated with m-health and wearable devices. A meta-analysis was carried out considering 84 previous studies, a total of 31,609 respondents, and 376 correlations.
Through the meta-analysis, fourteen relations among the constructs were estimated as significant. The effect of the AT construct on BI (AT > BI) presented the highest intensity (β = 0.6470; p-value = 0.00). The model is also composed of 23 effects of moderating variables on the relationships. Only the PE > BI, EE > PE, PV > PC, and BI > UB relations were not significantly affected by any proposed moderating variables.
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|
---
title: 'Changing perceptions of general health in the Kayseri Province, Turkey in
2004 and 2017: A population-based study'
authors:
- Vesile Senol
- Ferhan Elmali
- Fevziye Cetinkaya
- Melis Nacar
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10001896
doi: 10.3389/fpubh.2023.1095163
license: CC BY 4.0
---
# Changing perceptions of general health in the Kayseri Province, Turkey in 2004 and 2017: A population-based study
## Abstract
### Aim
Self-rated health (SRH) and health-related quality of life (HRQoL) have closely related outcomes in measuring general health status in community-based studies. The aim of this study is to determine changes in the self-perceived overall health of people and affected factors by comparing the findings of two studies conducted in the same research area.
### Methods
Both studies were conducted using the same measurement tools in households determined by random sampling techniques in the same research areas. The first and second studies were conducted with 1,304 and 1,533 people residing in 501 and 801 households in 2004 and 2017, respectively. The demographic data form, the Nottingham Health Profile (NHP), and a single-item SRH questionnaire were used for data collection.
### Results
The rate of good SRH increased from $56\%$ to $70\%$ while the average NHP score decreased from 30.87 to 20.34. The predictors of negative health perceptions were the presence of chronic diseases (OR 3.4–2.7-times higher), being female (OR.1.4–1.5 times higher), and the completion of primary education only (OR. 2.7–2.8 times higher) both 2004 and 2017. Living 500–1,000 m from the nearest healthcare facility was the main protective variable against poor SRH.
### Conclusions
Good SRH and HRQoL have increased significantly over time. Chronic diseases, education, and gender are the strongest predictors of poor SRH.
## Introduction
Self-rated health (SRH) and health-related quality of life (HRQoL) are two outcome measures that are used to evaluate people's perceptions of their health status in population-based studies. Both of these measures are self-reported, inexpensive, and easy to conduct. SRH (also known as self-assessed health or self-perceived health) is evaluated according to the answer to a single-item question: “*In* general, how would you rate your health: poor, fair, good, very good, or excellent?” [ 1, 2]. According to the World Health Organization, the SRH question is a simpler [3], less expensive [4], more precise and objective [5], and culturally sensitive [6] outcome measure than the clinical assessment tools [7]. Despite this, the single item about which SRH is concerned is sufficient to reveal people's health status, but it cannot provide more specific health status information.
Self-perceived overall health can also be measured using HRQoL, which is often used in community-based studies and defines the general health perceptions of the individual's or the group's subjective health status (or QoL) in physical, social, and emotional domains [8, 9]. It is determined by many factors and can be arranged according to several dimensions. A parameter related to HRQoL is self-rated health [1, 10]. Self-perceived health (SPH) is a powerful and independent predictor that is affected by general and disease-specific mortality and the incidence rate of chronic disease and includes many components related to public health. Studies in this area state that SPH can be related to behavioral, biological, psychological, and social dimensions, such as general and functional status, age, gender, marital status, education, household income, chronic diseases, lifestyle factors, culture, health beliefs, and healthcare service utilization (11–22).
Self Perceived Health, is a powerful predictor which reflects the rate of use of health-care, can vary depending on time, structural-financial reforms, and epidemiological transformation. As a matter of fact that, McCallum et al. [ 23], Waidmann et al. [ 24], and Leinonen et al. [ 25] suggested that SRH follows a change in health.
The aim of this study is to determine the change in the self-perceived overall health status of people and the affecting factors by comparing two different years, using the same research methods.
## Study design and settings
This cross-sectional descriptive-analytic study is a two-part study, which was carried out in two different years, and describes the level of healthcare services used by people, their level of general health perception, and the change it has shown over time. The first of these studies was carried out in Kayseri in 2004 and the second one was carried out in 2017 in the same region (Figure 1). The findings of these studies on healthcare use, influencing factors, and changes in usage patterns will be published in a separate study due to an excess of data. This article contains the results regarding perceived health.
**Figure 1:** *The map of research regions in the years 2004 and 2017.*
## Study population and sampling
This study was carried out in Kayseri, which is one of the biggest cities in Turkey and an important commercial and industrial center in central Anatolia. Its population is nearly 1.5 million.
In 2004, 21 urban Primary Health Centers (PHCs), and in 2017, 71 urban Family Health Centers (FHCs) provided healthcare services in the same region (Figure 1). With the Health Transformation Program in 2008, the healthcare service delivery model in Turkey was changed. In the provision of primary care services, the health center model was replaced by the family medicine system.
The study area was stratified according to socio-economic levels as good, middle, and low according to local health authorities. Of the 21 PHCs that were providing health care services in the research area, seven were recruited for the study using the simple random sampling technique. Seven PHCs were stratified according to socio-economic status and included in the study, with three PHCs classed as “low,” three PHCs as “middle,” and one PHC as “good.” Of the 68 health clinic units connected to the seven PHCs, 34 were chosen by selecting half of the total number of Community Health Centers (CHCs) affiliated with each PHC region. In the study, 13–15 households from each CHC were visited, and data were collected via face-to-face interviews.
In 2017, 30 of the 71 FHCs that provided healthcare services in the region of the previous study in 2004 were included in this study. Of the 30 FHCs stratified according to socioeconomic status, nine FHCs were “good,” seven FHCs were “middle,” and 12 FHCs were “low.” In the study, 26–29 households were visited in each FHC unit and data were collected via face-to-face interviews. In determining the sample size of the study, the prevalence of healthcare service use ($49\%$ for 2004 and $35\%$ for 2017) and the average number of individuals aged 15 and over [2.89 (≈3)] were calculated for each household for the measurement of general health perception.
In 2004, the size of the sample was based on the rate of healthcare service use, which was accepted as $49\%$ throughout Turkey, and the number of people to be included in the sampling was calculated as 1,288, with an interval of confidence of $95\%$, α = 0.05, β = 0.20 and effect size of $d = 0.08$, using the NCSS (Statistical and Power Analysis Software-PASS). The number of PHCs in the center of the province [168,064] was compared to the urban population [648,845] to determine the number of people aged 15 and over in each dwelling. It was calculated that there could be ~2.89 (≈3) persons aged 15 and over in each dwelling. Based on this result, it was considered sufficient to include 430 dwellings in the study to achieve a sample size of 1,288 people. In the study, 1,304 people aged 15 and over in 501 households were reached. A questionnaire was provided to each of the 4.03 ± 1.03 people in the household.
In 2017, the sample size was determined as 2,000 people; to achieve a minimum of $80\%$ power of representation using the NCSS, the rate of PHC use was accepted as $35\%$, with a confidence interval of $95\%$, α = 0.05, β = 0.20 and effect size of $d = 0.10.$ In 2017, it was considered appropriate to include 670 households in the scope of the research to reach the target sample size of 2,000 people, depending on the target of reaching ~3 people in each household. In the study, 1,533 people, aged 15 and over in 801 households were reached. A questionnaire was provided to 3.19 ± 0.98 persons per household (Figure 2).
**Figure 2:** *Distribution of the number of people reached in 2004 and 2017.*
## Data collection tools
Research data were collected via face-to-face interviews, upon visiting the members of the households, through demographic data forms for families and adults (≥15 years) and the elderly (≥65 years), the single-item Self-Rated Health (SRH) question, and the Nottingham Health Profile.
Self-rated health (SRH) is measured with the single-item question, “*In* general, how would you rate your overall health?”. The responses were based on a five-point scale, ranging from excellent to poor. For the analyses, where it was considered a continuous variable, “poor” was coded as 1, “fair” as 2, “good” as 3, “very good” as 4, and “excellent” as 5. Regression analyses dichotomized these responses into “good self-rated health” (i.e., excellent, very good, and good) and “poor self-rated health” (i.e., fair and very poor) [1, 2].
The Nottingham Health Profile (NHP) is a generic and simple scale designed to measure subjective health status (or QoL) in physical, social, and emotional domains [8]. The NHP is composed of two parts. In the first part, there are 38 dichotomous items (yes/no answers) covering six health dimensions: energy (three items), pain (eight items), emotional reactions (nine items), sleep (five items), social isolation (five items), physical mobility (eight items). “ No” answers to each statement in the profile are coded as 0 and “yes” answers are coded as 1. Total score ranges for the NHP are from 0 to 600.
In this study, firstly, the “yes” answers given to the scale questions were scored using weighted values, and the possible range of scores for each dimension is 0 to 100 points. In part 1, the scores close to 100 points indicated “poor” perceived health, and those close to 0 points indicated “good” perceived health. In 2004 and 2017, Cronbach's alpha coefficient for the total scale was 0.91 and 0.92, respectively. The six dimensions ranged from 0.71 to 0.87 in 2004 and from 0.72 to 0.89 in the current study, confirming its validity and reliability for the Turkish version.
## Statistical analysis
Data analysis was performed with the statistical package program IBM Corp., 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp. The Shapiro-Wilk Test was used to determine the convenience of quantitative variables in a normal distribution. A brief representation of the quantitative variables according to the normal distribution was indicated as the mean, standard deviation, and median (Q1–Q3) of the non-matching variables. The Mann-Whitney U Test was used in the comparison of the two independent groups. The Kruskal Wallis Test was used to compare more than two groups. The Bonferroni Test was used to identify groups that cause differences. Single and multiple binary logistic regression analyses were used to identify the determinants of perceived health status. The dichotomous SRH (good and poor perception) was evaluated as a dependent variable in the model. Variables that showed a significant relationship in univariate analyses, such as age, gender, marital status, monthly household income, educational status, family type, distance from the home to health institutions, perceived health, presence of chronic disease, and hospitalization were evaluated as independent variables. In two regression models for 2017 and 2004, the odds ratio (OR), $95\%$ confidence interval (CI), and Nagelkerke R squared were calculated for each variable. The Hosmer–Lemeshow goodness-of-fit test was used to determine how well the model fits with the data. Categorical variables were shown as percentages and frequencies. The Pearson Chi-Square Analysis was used to examine the relationship between categorical variables. The statistical significance level was accepted at $p \leq 0.05.$
## Results
A total of 1,304 and 1,533 questionnaires were analyzed in 2004 and 2017, respectively. The mean age was 37.05 ± 15.46 and 39.24 ± 14.51 in 2004 and 2017, respectively. The sample groups in 2004 and 2017 had no statistically significant differences regarding age group or gender (Table 1).
**Table 1**
| Sociodemographic variables | Sociodemographic variables.1 | 2004 | 2004.1 | 2017 | 2017.1 | Statistical assessment | Statistical assessment.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | n | %* | n | %* | χ2 | p |
| Gender | Male | 588 | 45.1 | 677 | 44.2 | 0.229 | 0.632 |
| Gender | Female | 716 | 54.9 | 856 | 88.8 | 0.229 | 0.632 |
| Age groups | 15–44 | 921 | 70.6 | 1077 | 70.3 | 0.303 | 0.860 |
| Age groups | 45–64 | 288 | 22.1 | 336 | 21.9 | 0.303 | 0.860 |
| Age groups | ≥65 | 95 | 7.3 | 120 | 7.8 | 0.303 | 0.860 |
| Total | 1304 | 100.0 | 1533 | 100.0 | | | |
The number of people who responded to the SRH question was 2826. In 2004 and 2017, respectively, the percentage of people who rated their health as excellent was $2.1\%$ vs. $1.8\%$, very good was $14.6\%$ vs. $14.7\%$, good was $39.2\%$ vs. $53.5\%$, fair was $34.9\%$ vs. $25.5\%$, and very poor was only $9.1\%$ vs. $4.5\%$. The rate of good health perception increased from $56\%$ in 2004 to $70\%$ in 2017 ($p \leq 0.001$). In addition, it was found that some sociodemographic and clinical variables were significantly associated with SRH (Table 2). The prevalence of poor SRH was significantly higher in females, those aged 65 and over, illiterates and those who completed primary education only, low-income earners, those with chronic diseases, and those who had used healthcare services or been hospitalized within the 12 months preceding the survey in both 2004 and 2017 (Table 2).
**Table 2**
| Sociodemographic and clinical variables | Self-rated health | Self-rated health.1 | Self-rated health.2 | Self-rated health.3 | Self-rated health.4 | Self-rated health.5 |
| --- | --- | --- | --- | --- | --- | --- |
| | 2004 ( n : 1,304) | 2004 ( n : 1,304) | 2004 ( n : 1,304) | 2017 ( n : 1,522) | 2017 ( n : 1,522) | 2017 ( n : 1,522) |
| | Good | Poor | χ2 / p | Good | Poor | χ2/p |
| | n (%) | n (%) | | n (%) | n (%) | |
| All group | 730 (56.0) | 574 (44.0) | 59.918/ <0.001 | 1066 (70.0) | 456 (30.0) | 59.918/ <0.001 |
| Gender | Gender | Gender | Gender | Gender | Gender | Gender |
| Male | 379 (64.5) | 205 (35.5) | 31.207/ <0.001 | 506 (75.5) | 166 (24.7) | 15.854/ <0.001 |
| Female | 351 (49.9) | 365 (51.0) | 31.207/ <0.001 | 560 (65.9) | 290 (34.1) | |
| Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) |
| 15–24 | 227 (69.4) | 100 (30.6) | 57.358/ <0.001 | 192 (81.0) | 45 (19.0) | 98.519/ <0.001 |
| 25–44 | 341 (57.4) | 253 (42.6) | 57.358/ <0.001 | 641 (76.3) | 199 (23.7) | |
| 45–64 | 130 (45.1) | 158 (54.9) | 57.358/ <0.001 | 183 (54.5) | 153 (45.5) | |
| ≥65 | 32 (33.7) | 63 (66.3) | 57.358/ <0.001 | 50 (45.9) | 59 (54.1) | |
| Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status |
| Single | 220 (72.6) | 83 (22.4) | 53.909/ <0.001 | 211 (76.2) | 66 (23.8) | 21.809/ <0.001 |
| Married | 475 (52.5) | 429 (47.5) | 53.909/ <0.001 | 811 (70.1) | 346 (29.9) | |
| Divorced/widowed | 35 (36.1) | 62 (63.9) | 53.909/ <0.001 | 44 (50.0) | 44 (50.0) | |
| Education level | Education level | Education level | Education level | Education level | Education level | Education level |
| Illiterate, primary education | 305 (44.8) | 376 (55.2) | 72.488/ <0.001 | 254 (53.1) | 224 (46.9) | 94.862/ <0.001 |
| Secondary and high school and faculty | 425 (68.2) | 198 (31.8) | 72.488/ <0.001 | 812 (77.8) | 232 (22.2) | |
| Household monthly income | Household monthly income | Household monthly income | Household monthly income | Household monthly income | Household monthly income | Household monthly income |
| Low | 360 (50.6) | 351 (49.4) | 24.413/ <0.001 | 64 (55.7) | 51 (44.3) | 14.995/0.001 |
| Middle | 328 (61.2) | 208 (38.8) | 24.413/ <0.001 | 882 (70.5) | 369 (29.5) | |
| Favorable | 42 (73.7) | 15 (26.3) | 24.413/ <0.001 | 120 (76.9) | 36 (23.1) | |
| Presence of chronic diseases | Presence of chronic diseases | Presence of chronic diseases | Presence of chronic diseases | Presence of chronic diseases | Presence of chronic diseases | Presence of chronic diseases |
| No | 633 (65.7) | 331 (34.3) | 140.655/ <0.001 | 905 (77.7) | 260 (22.3) | 46.316/ <0.001 |
| Yes | 97 (28.5) | 243 (71.5) | 140.655/ <0.001 | 163 (43.1) | 196 (56.9) | |
| Number of chronic diseases | Number of chronic diseases | Number of chronic diseases | Number of chronic diseases | Number of chronic diseases | Number of chronic diseases | Number of chronic diseases |
| 1 | 88 (31.1) | 195 (68.9) | 5.166/0.024 | 138 (50.9) | 133 (49.1) | 13.583/ <0.001 |
| Comorbidity (2–4) | 9 (16.1) | 47 (83.9) | 5.166/0.024 | 25 (28.4) | 63 (71.6) | |
| Use of healthcare services in the last year | Use of healthcare services in the last year | Use of healthcare services in the last year | Use of healthcare services in the last year | Use of healthcare services in the last year | Use of healthcare services in the last year | Use of healthcare services in the last year |
| No | 209 (69.9) | 90 (30.1) | 30.497/ <0.001 | 209 (82.0) | 46 (18.0) | 20.746/ <0.001 |
| Yes | 521 (51.8) | 484 (48.2) | 30.497/ <0.001 | 857 (67.6) | 410 (32.4) | |
| Hospitalization in the last year | Hospitalization in the last year | Hospitalization in the last year | Hospitalization in the last year | Hospitalization in the last year | Hospitalization in the last year | Hospitalization in the last year |
| No | 669 (58.0) | 485 (42.0) | 16.133/ <0.001 | 1,013 (72.4) | 376 (27.6) | 43.316/ <0.001 |
| Yes | 61 (40.7) | 89 (59.3) | 16.133/ <0.001 | 53 (43.1) | 70 (56.9) | |
## Distribution of HRQoL
*The* general health perception of people aged 15 years and over was evaluated using the NHP. In 2004, 1,304 people responded to the questionnaire, and 1,508 people responded in 2017. The total NHP score was 30.87 in 2004 and it decreased to 20.34 in 2017. In addition, the NHP total and subdimension scores also decreased to a remarkable level in 2017 compared to 2004. “ Energy” and “Physical Mobility” were the highest and lowest scores in 2004, and they decreased from 36.81 to 14.72 (Table 3). Total NHP scores varied significantly by sociodemographic and clinical variables in both 2004 and 2017 (Table 4).
## SRH and its relation to HRQoL
SRH was closely associated with HRQoL in both 2004 and 2017. The NHP total and subdimension scores were significantly different in individuals with positive self-perceived health status when compared to people with negative self-perceived health status. Self-perceived health was more prominent in all dimension scores for those who performed well. Likewise, while the NHP total score was 20.22 in those with good general health perception in 2004, this decreased to 13.91 in 2017 ($p \leq 0.001$). In 2017, the levels of QoL related to pain, social isolation, and physical mobility were highest in subjects with good SRH (Table 5).
**Table 5**
| NHP dimensions | Self-rated health | Self-rated health.1 | Self-rated health.2 | Self-rated health.3 | Self-rated health.4 | Self-rated health.5 |
| --- | --- | --- | --- | --- | --- | --- |
| | 2004 ( n : 1,304) | 2004 ( n : 1,304) | 2004 ( n : 1,304) | 2017 ( n : 1,508) | 2017 ( n : 1,508) | 2017 ( n : 1,508) |
| | Good | Poor | Z * / p | Good | Poor | Z * / p |
| | x ±SD | x ±SD | | x ±SD | x ±SD | |
| Energy | 32.88 ± 36.02 | 69.24 ± 37.70 | 15.994, <0.001 | 27.12 ± 36.91 | 60.19 ± 41.65 | 14.004, <0.001 |
| Pain | 11.16 ± 21.10 | 41.60 ± 35.32 | 17.749, <0.001 | 7.51 ± 17.39 | 28.20 ± 33.11 | 15.040, <0.001 |
| Emotional reactions | 41.71 ± 32.13 | 30.70 ± 28.68 | 13.911, <0.001 | 18.48 ± 25.52 | 40.10 ± 34.56 | 12.060, <0.001 |
| Sleep | 18.47 ± 24.84 | 37.79 ± 32.05 | 11.291, <0.001 | 13.59 ± 23.34 | 32.48 ± 32.02 | 11.792, <0.001 |
| Social isolation | 16.74 ± 25.37 | 28.70 ± 30.63 | 7.784, <0.001 | 8.54 ± 19.91 | 23.69 ± 31.98 | 10.469, <0.001 |
| Physical mobility | 11.37 ± 18.35 | 33.39 ± 26.05 | 16.427, <0.001 | 8.32 ± 18.51 | 30.31 ± 31.51 | 15.684, <0.001 |
| Total (1st section profile point) | 20.22 ± 18.11 | 44.41 ± 22.83 | 18.368, <0.001 | 13.91 ± 16.90 | 35.84 ± 25.33 | 16.627, <0.001 |
| Overall POINT | 30.87 ± 23.60 | 30.87 ± 23.60 | 47.231, <0.001 | 20.34 ± 22.13 | 20.34 ± 22.13 | 35.693, <0.001 |
## Determinants with SRH
The common determinants that increased negative health perception in 2004 and 2017 were being female (1.4–1.5 times higher), having at least one chronic disease (3.4–2.7 times higher), and having completed primary education only (2.7–2.8 times higher). Whereas being married (1.7 times higher), use of healthcare services in the last year (1.8 times higher), and middle income (2.3 times higher) were variables specific to 2004, being between the ages of 45 and 64 years (2.3 times higher) and hospitalization in the last year (2.4 times higher) were the main factors associated with poor health perception specific to 2017. However, living 500–1,000 m from the nearest health institution was the main protective factor (1.5–1.7 times higher) against poor health perception in both 2004 and 2017 (Tables 6, 7).
In the 2004 study, hospitalization (2.0 times higher) and age (1.6–4.5 times higher) significantly increased poor health perception in the single regression analysis and were dropped from the model because a significant relationship could not be maintained in the multiple regression analysis. In the 2017 study, in univariate regression analysis, use of healthcare services, which increased poor health perception by 2.2 times, and being separated from a spouse, which increased it by 2.9 times, were dropped from the model because they could not maintain a significant relationship in the multiple regression analysis. The variables of middle- and good-income levels (0.533 and 0.381) and nuclear family structure (0.650), which provided protective effects against poor health perception, did not show a significant relationship in multiple regression and therefore could not persist in the model.
## Discussion
To the best of our knowledge, this study is one of the limited number of studies in which both parameters, SRH and HRQoL, are used together to determine the general health perception in the general population, and in this context, the factors affecting both are defined. Furthermore, it presents a time-dependent change in the study with results that define the factors affecting perceived individual health using the same measurement tools in the same region.
The findings of this study show that the prevalence of good SRH increased significantly over time. In fact, the rate of respondents who had “good” health perception, which was $56.0\%$ in 2004, increased to $70.0\%$ in 2017 (Table 2). In Turkey, according to the 2019 OECD health statistics, $68.8\%$ of the population rated their health as good. In this context, it can be assumed that the rate of good health perception obtained from this study is comparable with the overall rate reported for Turkey. The rate of good SRH obtained in both of the current studies in *Turkey is* higher than Korea, Japan, Portugal, and Poland and is almost homogeneous with other OECD countries (Austria, Finland, Denmark, and Luxemburg), but it is lower than New Zealand, the USA, Switzerland, Norway, Spain, and Australia [26]. The differences might be partially due to the methodologies used for measuring SRH and reported SRH status being exposed to biological, psychological, and social dimensions, such as age, sex, place of residence, education, occupation, level of income, and lifestyle factors, as well as the possibility of being affected by perceptual differences and cultural factors [1, 2, 27]. In addition, previous studies conducted by Dong et al. [ 28] indicate that good SRH is higher in married, non-smoking, and non-alcohol users. A study carried out by Liu et al. [ 12] reports that marriage is the main determinant of good SRH. Similarly, Darviri et al. [ 29] revealed that a healthier diet and regular exercise are closely related to good health perception. In contrast to these studies, Orea et al. [ 13] reported that strong physical activity and adequate nutrition are among the determinants of poor health perception. Coinciding with the studies in the literature (12, 30–33), the present study reveals that the prevalence of poor SRH is significantly higher in females, those who are of advanced age, those with low income and a low level of education, those with chronic diseases, and those who had used healthcare services or been hospitalized within the 12 months prior to the survey in both 2004 and 2017 (Table 2).
## SRH and HRQoL
In this study, self-perceived overall health status was evaluated using the NHP. It was observed that the total and subdimension scores, obtained from the profile in 2017, decreased significantly compared to 2004. This confirms that people who rated their health had experienced a positive change in all domains (Table 3).
However, it was observed that self-perception of overall health is closely related to QoL. Better HRQoL was found to be consistent with better SRH status. Similarly, as the NHP total and subdimension scores improve, positive self-perceptions of health increase significantly. In particular, pain, social isolation, and physical mobility QoL scores are significantly better in those who rate their health positively. On the other hand, energy, emotional reactions, and sleep QoL scores were found to be better in 2017 compared to 2004 in individuals with a good perception of their health, but the improvement in scores is relatively low when compared to other areas (Table 5). This is consistent with the findings of a study conducted by Uutela et al. [ 34] and Kara [35] on patients with chronic diseases, which found that NHP dimensions for pain, energy, emotional reactions, and mobility were significantly associated with health perception. Previous studies have found that, similar to our study findings, dynamism and daily activities are important components of QoL and that mental and physical functions, physical exercise, and rich social relationship networks are positively correlated with QoL levels [28, 36, 37].
In this study, the relationship between HRQoL and self-reported health status may be mediated by several factors in both periods (Tables 2, 4), which has been confirmed in other studies (35, 38–43). In this regard, our study reveals that sociodemographic and clinical factors not only mediate the change in NHP scores but also impact the deterioration of SRH perception. The findings, in relation to impaired HRQoL and poor perception of health, are significantly associated with females, the elderly, widowed and divorced people, those with a low income and level of education, those with one or more chronic diseases, and those who had been hospitalized within the 12 months prior to the survey (Tables 2, 4). It is known that men and women typically have different health outcomes when exposed to similar risks, which may account for the gender disparity in reporting poor SRH and impaired QoL. High educational attainment often explains the beneficial relationship between education and health directly through the improvement of health due to rewarding employment, favorable social and economic circumstances, and the adoption of healthy lifestyle habits. Respondents with higher levels of education are more aware of health issues and the importance of maintaining their protective actions against poor health perception and reduced quality of life. Poor perception of health and impaired QoL in widowed or divorced individuals may be associated with a lack of emotional and practical support and a feeling of loneliness. However, marriage might be considered a protective factor against these deprivations. When sociodemographic characteristics are used as control variables in people with a poor perception of their general health, we can conclude that the severity of fatigue, inadequate social participation, physical activity limitations, sleep dissatisfaction, and emotional reaction problems are significantly higher in the above-mentioned sensitive groups. Consistent with our results in previous studies, physical activity, social participation, and sleep quality have been defined as the main determinants affecting both QoL and SRH status [29, 40]. In some studies [16, 44], it has been found that sleep dissatisfaction is closely related to poor SRH and impaired QoL. However, in other studies [40, 41], it is emphasized that physical activity levels and social participation may improve perceptions of SRH and QoL in support of the above-mentioned findings.
## Comparison of predictors of poor SRH
In this study, the determinants of poor SRH were evaluated using single and multiple regression analysis (Tables 6, 7). The regression analysis revealed that the rate of poor health perception is higher in women, those with a low level of education, and those with chronic disease, supporting the univariate relationship results. The risk of negative health perception due to being female has increased over time; while the relative risk was 1.4 times higher in 2004, it was found to be 1.8 times higher in 2017. In accordance with the findings in our study, some research has consistently shown that gender has a significant influence on poor SRH status [28, 30, 45, 46]. These studies state that poor SRH is between 1.2 and 3.4 times higher in females when compared to their male peers. Our study findings may have been affected by the fact that most of the women in the study group did not work in a job that generates an income ($65\%$ housewives), had a low level of education ($62.2\%$ illiterate and individuals who completed primary education only), and were of advanced age ($53.4\%$ aged 65 and over). These results indicate that more attention is needed on women's health and appropriate public health interventions should be implemented to improve their health and social status in Turkey.
Regression analysis revealed a significant interaction between poor SRH and literacy in this study. The relative risk of negative health perception due to primary education level increased at a similar rate over time. The odds ratio for poor SRH was 2.8 times higher in 2017 and 2.7 times higher in 2004. In consistency with our findings, Stanojevic Jerkovic et al. [ 47] reported that the completion of primary education only is the strongest factor [OR: 4.3 (2.5–7.3)] associated with poor SRH. However, some studies indirectly support our findings; Dong et al. [ 28] stated that higher education is a protective factor (OR: 0.9 vs. 0.7) against poor SRH. Orea et al. [ 13] reported that the relative risk of poor health perception was 0.70 (0.5–0.8) for university graduates in comparison with 0.75 (0.6–0.9) in those who completed secondary education (Tables 6, 7).
In this study, while the odds ratio for poor SRH was 3.4 times higher in 2004 in those who had one or more chronic diseases, it decreased to 2.8 times in 2017 (Tables 6, 7). Previous studies have indicated that the relative risk of poor perception of health is higher, between 1.3 and 2.6 times, in people who had one or more chronic diseases. In patients with chronic diseases, the odds ratio for poor SRH was found to be 1.3–1.4 times higher by Orea et al. [ 13], 2.6 times higher by Stanojevic Jerkovic et al. [ 47], 2.0 times higher by Liu et al. [ 12], 2.3 times by Wang et al. [ 45], and 1.6 times higher by Cau et al. [ 46]. These studies correspond with the current study's findings, which show that subjective health perception also depends on objective health [3, 48].
In this study, regression analysis revealed that place of residence is a significant determinant of self-perceived overall health status. Living near a health center (500–1,000 m) is conducive to better (OR: 0.63 vs. 0.56) health perception (Tables 6, 7).
Consistent with this result, previous studies [45, 49] have demonstrated that increased physical access to healthcare services also influences respondents' reported health.
Negative health perception, which was found to be 1.7–4.5 times higher in all age groups in single regression in 2004, was omitted from the model because it did not show a significant relationship in the multiple regression step. However, the perception of poor health, which was seen at 1.3–4.0 times higher in all age groups in single regression in 2017, was only found to be 2.3 times higher in the 45–64 age group in the multiple regression analysis (Tables 6, 7). In previous studies, Dong et al. [ 28] found that in those aged 75 and older, the odds of reporting poor health were 4.9 times higher than in those aged 18–24 years old. Liu et al. [ 12] reported that poor SRH was 1.9 times higher in people aged 41–56 and 3.0 times higher in those aged 57–72. Wang et al. [ 45] showed that poor perceived health was 1.8 times higher in people aged 45, while it was 3.9 times higher in those aged 65.
The increase in negative health perception due to hospitalizations in the year prior to the study showed a significant relationship only in univariate regression analysis in 2004. In contrast, an increase in negative health perception (2.07-fold) due to hospitalizations was found to be a significant relationship in both single and multiple regression analysis in the 2017 study (Tables 6, 7). Previous studies [30, 45] have consistently demonstrated that hospitalization is associated with poorer SRH status, which is consistent with our findings. In these studies, it is reported that the odds ratio for the poor perception of health is 2.2 times and 1.9 times higher in those who had been hospitalized than in those not hospitalized in the year prior to the study, respectively.
In the 2004 study, multivariate regression analysis revealed that the strongest factors associated with poor SRH were middle household income (OR: 2.2, 1.1–4.4), being married (OR: 1.7, 1.2–2.4), and using healthcare services in the 12 months prior to the survey (OR: 1.8, 1.3–2.4) (Table 6). In the single regression analysis conducted in the 2017 study, while the determinants of the increase in poor health perception included middle and favorable income levels (0.53- and 0.58-fold), being divorced or widowed (2.9-fold), and use of healthcare services in the 12 months prior to the survey (2.2-fold), the variables mentioned above were dropped from the model because the significant relationship did not persist in the multiple regression analysis (Table 7).
In the literature, some studies indicate that marital status plays a decisive role in poor SRH, which is consistent with our previous study findings. In this context, the components of marital status that affect negative health perceptions differ from study to study. For example, Liu et al. [ 12] showed marriage to be a protective (OR: 0.8, 0.7–0.9) factor against negative health perception, and Cau et al. [ 46] revealed that poor health perception was 4.7 times higher in single people and 2.1–1.8 times higher in widowed or divorced people. However, Khabir et al. [ 33] reported that the ratio of poor health perception was 1.8 (1.6, 2.0) times higher in married people and 4.0 (3.3, 4.9) times higher in widowed or divorced people.
In addition, similar to the findings of our 2004 study, previous studies (50–52) have consistently revealed that the use of healthcare services is associated with poorer SRH status. These studies state that people who perceive their own health status as poor are more likely to use healthcare services, 76.9 times [50] and 3.8 times [51] more than those who perceive their health status as good in the 12 months prior to the survey. In other words, poor SRH status has also been shown to be independently predictive of higher healthcare utilization rates [53].
As a result, it's possible that the variables covered by the health transformation program, which has been in effect in the study's region of Turkey since 2003, are strongly related to the gradual improvement in self-reported health and quality of life, indirectly. Furthermore, in our study, the increase in the use of healthcare services from $79.6\%$ in 2004 to $84.8\%$ in 2017 ($p \leq 0.001$) and the increase in the use of primary healthcare centers from $30.3\%$ to $45.8\%$ ($p \leq 0.001$) can be attributed to the relative effect of the implementation of the health transformation program in the research region. It is thought that the improvement in positive health perception and quality of life may have been relatively affected by the changes in the demographic and economic characteristics of the participants over time as well as the increased physical and financial access to primary healthcare services.
## Conclusions
Based on the results of this study, the levels of good self-rated health have significantly improved over time. In the same time period, the mean total NHP score decreased from 30.87 (±23.60) to 20.34 (±22.13). The improvement in the total NHP and subdimension scores support an increase in good health perception. Poor SRH is associated with being female, being 45–64 years old, having a low level of education, having chronic diseases, and having been hospitalized. Proximity to health facilities is the main protective factor against poor SRH. According to the findings of the study, local and national governments can be informed about the factors that influence negative health perception and take steps to improve physical, psychosocial, and economic health in disadvantaged groups. Thus, preventive measures can be taken in order to establish health-promoting policies and improve public health.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
This study was approved by the Ethics Committee in Clinical Research of Human Subjects at Erciyes University, Faculty of Medicine (Decision date and no: $\frac{2005}{240}$ vs. $\frac{2015}{399}$) and permission was obtained from the governor's office of Kayseri. All respondents provided written consent to participate in both studies in 2004 and 2017 before data collection. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
VS: idea, concept, design, supervision/consulting, data collection, processing analysis, interpretation, and paper writing. MN: literature review. FC: idea, concept, design, interpretation, and supervision. FE: processing analysis and interpretation. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: A Nucleus Accumbens Tac1 Neural Circuit Regulates Avoidance Responses to Aversive
Stimuli
authors:
- Zi-Xuan He
- Ke Xi
- Kai-Jie Liu
- Mei-Hui Yue
- Yao Wang
- Yue-Yue Yin
- Lin Liu
- Xiao-Xiao He
- Hua-Li Yu
- Zhen-Kai Xing
- Xiao-Juan Zhu
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001899
doi: 10.3390/ijms24054346
license: CC BY 4.0
---
# A Nucleus Accumbens Tac1 Neural Circuit Regulates Avoidance Responses to Aversive Stimuli
## Abstract
Neural circuits that control aversion are essential for motivational regulation and survival in animals. The nucleus accumbens (NAc) plays an important role in predicting aversive events and translating motivations into actions. However, the NAc circuits that mediate aversive behaviors remain elusive. Here, we report that tachykinin precursor 1 (Tac1) neurons in the NAc medial shell regulate avoidance responses to aversive stimuli. We show that NAcTac1 neurons project to the lateral hypothalamic area (LH) and that the NAcTac1→LH pathway contributes to avoidance responses. Moreover, the medial prefrontal cortex (mPFC) sends excitatory inputs to the NAc, and this circuit is involved in the regulation of avoidance responses to aversive stimuli. Overall, our study reveals a discrete NAc Tac1 circuit that senses aversive stimuli and drives avoidance behaviors.
## 1. Introduction
Reward and aversion are critical for motivated behaviors and are associated with many mood disorders. Unexpected stimuli and threats drive aversive behaviors, an innate response crucial to the survival of animals [1]. Aversive stimuli engage negative emotions and contribute to prominent psychiatric disorders. Enormous advances have been made in understanding the neural circuits underlying reward [2,3,4,5,6,7]. However, the neural circuits underlying aversion remain elusive.
It is widely thought that the nucleus accumbens (NAc) is a critical brain region in the reward and aversion circuits that integrate different inputs, leading to motivated behaviors [8,9,10,11,12,13,14]. Anatomically, the NAc can be divided into the core, lateral shell, and medial shell [15]. It has been found that distinct NAc neural circuits are involved in different brain functions [16,17,18,19,20,21]. Dopamine transmissions from the ventral tegmental area have been linked to reward and aversion processing [18]. Glutamatergic inputs from the thalamic paraventricular nucleus to the NAc regulate aversion [19,22]. How the NAc regulates opposite behaviors at the same time remains elusive. Thus, it is worth investigating whether distinct NAc subregions are included in discrete neural circuits involved in aversion.
The major projection neurons in the NAc are medium spiny neurons (MSNs), distinguished by their dopamine receptor expression (D1-MSNs and D2-MSNs) [23,24,25]. Markers for D1-MSNs and D2-MSNs also include the expression of different peptides [26,27]. Substance P, the major peptide encoded by tachykinin precursor 1 gene (TAC1), and dynorphin are exclusively expressed in D1-MSNs [28,29,30]. Previous work demonstrated that dynorphin-containing neurons in the NAc mediate negative affective states [16,31,32]. This raises the possibility that Tac1 neurons in the NAc medial shell may be involved in the regulation of aversion.
The NAc has received attention as a crucial convergence point of reward and aversion circuits, as it receives multiple projections from the ventral tegmental area (VTA), medial prefrontal cortex (mPFC), basolateral amygdala (BLA), and hippocampus [33,34]. The mPFC is strongly related to neural circuits encoding aversion and decision making [35,36,37]. The prelimbic and infralimbic regions of the mPFC have been implicated in aversion [38,39,40]. However, studies have yielded conflicting findings. How the mPFC regulates aversion through specific neural circuits remains underexplored.
Here, we show that tachykinin precursor 1 (Tac1) neurons in the NAc medial shell mediate avoidance responses to aversive stimuli. Neural tracing and electrophysiological data show that NAcTac1 neurons project inhibitory signals to the lateral hypothalamic area (LH) and modulate avoidance behavior in the presence of aversive stimuli. Additionally, neurons in the NAc medial shell receive inputs from mPFC glutamatergic (mPFCGlut) neurons, and optogenetic manipulation of the mPFCGlut → NAc circuit regulates aversive behaviors. These results indicate the essential role of Tac1 neurons in encoding aversive stimuli and regulating behavioral responses.
## 2.1. NAcTac1 Neurons Regulate Avoidance Behavior in Response to Aversive Stimuli
To investigate the expression of Tac1 neurons in the NAc, we crossed the Tac1-internal ribosome entry site 2 (IRES2)-Cre mouse line [41] with a Cre-dependent tdTomato reporter line, Ai9 [42] (Figure 1A). We observed that Tac1-tdTomato cellular expression closely matched endogenous substance P and Dopamine Receptor 1 (Figure 1B–G). To mimic aversion in mice, Tac1-Cre male mice were given an injection of formalin in the plantar surface of a hindpaw, as previously described [43,44]. Patch-clamp recordings were performed on Tac1 neurons in the NAc (Figure 1H). We observed decreased excitability of Tac1 neurons in the medial shell, but not in the lateral shell (Figure 1I,J and Figure S1A,B). These data indicate that Tac1 neurons in the NAc medial shell are involved in the circuit regulating aversion.
To determine whether NAcTac1 neurons in the NAc medial shell regulate aversive behaviors, we performed chemogenetics using designer receptors exclusively activated by designer drugs (DREADDS). To selectively manipulate the activity of Tac1 neurons, we bilaterally injected AAV-DIO-hM3D(Gq)-mCherry, AAV-DIO-hM4D(Gi)-mCherry, and AAV-DIO-mCherry into the NAc medial shell of Tac1-Cre male mice (Figure 1K). Formaldehyde has been shown to act as an unfamiliar aversive stimulus for rodents without altering their motor activity [45]. We thus measured the approach-avoidance behaviors of male mice to an aversive stimulus (formaldehyde) while inhibiting or activating the activity of NAcTac1 neurons. A piece of cotton dipped in $5\%$ formaldehyde was placed on one side of a three-chamber arena. Mice tend to explore a novel object, but animals display strong avoidance behaviors when exposed to formaldehyde. Mice were introduced into the chamber containing formaldehyde. Interactions with formaldehyde were recorded for 5 min. The opposite chambers of the arena were designated the ‘safe’ area and ‘center’ area. We observed that hM3D(Gq)-injected mice spent significantly more time exploring the aversive stimulus than hM4D(Gi)- and mCherry (control)-injected mice (Figure 1L–O). To investigate whether the activity of Tac1 neurons regulates interactions with a neutral stimulus in mice, we performed the approach experiment and replaced the piece of $5\%$ formaldehyde cotton with a piece of regular cotton. We found that the activity of NAc Tac1 neurons did not affect time spent interacting with neural stimuli (Figure S2A–C). Moreover, olfaction and locomotion were not affected by hM4D(Gi) or hM3D(Gq) injection (Figure S3A,B). These results suggest that NAcTac1 neurons in the medial shell are crucial to avoidance behaviors in response to aversive stimuli.
## 2.2. NAcTac1 Neurons Project to the LH
To identify possible downstream targets of NAcTac1 neurons that may encode aversive stimuli, we injected AAV-DIO-mCherry into the NAc medial shell of Tac1-Cre mice. Four weeks later, the animals were euthanized, and the distribution of neurons that NAcTac1 neurons target in the brain was examined (Figure 2A–C). The whole-brain mapping results indicated that dense mCherry-labeled terminals were found in the lateral hypothalamic area (LH) (Figure 2D–F, Figure S4).
We next assessed the synaptic function of NAcTac1 neurons projecting to the LH. We first expressed channel rhodopsin-2 (ChR2) in NAcTac1 neurons, and then, selectively activated the terminals of NAcTac1 neurons in the LH via optogenetic stimulation (5 ms pulse, 20 Hz) (Figure 2G,H). In the whole-cell patch-clamp configuration, inhibitory postsynaptic currents (IPSCs) were recorded in 17 out of 42 LH neurons (Figure 2I). However, no excitatory postsynaptic currents were recorded. IPSCs were eliminated via pretreatment with the GABA-A receptor antagonist bicuculline (Figure 2J,K, Figure S5). These results suggest that NAcTac1 neurons send inhibitory inputs to the LH.
## 2.3. NAcTac1-to-LH Projection Mediates Avoidance Behaviors in Response to Aversive Stimuli
To assess whether the NAcTac1→LH circuit regulates avoidance responses to aversive stimuli. Male Tac1-Cre mice were unilaterally injected with AAV-DIO-mCherry and AAV-DIO-ChR2-mCherry (Figure 3A,B). Six weeks later, we carried out an approach-avoidance assay for evaluation (Figure 3C). A piece of cotton dipped in $5\%$ formaldehyde was placed in one corner of a square chamber. Mice were introduced into the chamber. Their interactions with formaldehyde were recorded. Compared with the control stimulation, the selective delivery of blue light (5 ms pulse, 20 Hz for 5 min) to the LH of ChR2-expressing terminals elicited a significant increase in interaction time with formaldehyde (Figure 3D,E). We also calculated the total distance traveled by the mice in the arena and found that locomotion was not affected (Figure 3F).
We then selectively inhibited the NAcTac1 terminals in the LH by delivering continuous yellow light to the LH of male mice bilaterally infected with AAV-DIO-NpHR-eYFP in the NAc medial shell (Figure 3G,H). In the approach-avoidance assay, the photoinhibition of NAcTac1→LH projection significantly decreased interaction time with formaldehyde without affecting locomotion (Figure 3I–L). We also carried out a real-time place aversion (RTPA) assay and found that the photoinhibition of NAcTac1→LH projection elicited avoidance of the photoinhibition-paired chamber (Figure S6A–D). Taken together, these data indicate that the NAcTac1→LH circuit is crucial to avoidance behaviors in response to aversive stimuli.
## 2.4. mPFCGlut Inputs Activate NAc Neurons
Next, we sought to identify upstream brain regions of NAcTac1 neurons that might mediate aversive behaviors. We employed a monosynaptic viral tracing strategy in Tac1-Cre mice. The NAc medial shell of Tac1-Cre mice was injected with AAV-DIO-RVG and AAV-DIO-TVA-GFP. Four weeks later, RV-EnVA-dsRed was injected into the LH (Figure 4A–C). We found that the medial prefrontal cortex (mPFC) was projected to NAcTac1 neurons (Figure 4D). Based on emerging studies [37,38,46,47,48] showing that the mPFC is critical for neural circuits of aversion, we focused on neurons in the mPFC projecting to NAcTac1 neurons. To determine the kinds of mPFC neuron that are involved in the NAcTac1 circuit, we performed immunofluorescence experiments and found that mCherry-labeled neurons co-expressed the glutamatergic marker VGLUT2 (Figure 4E and Figure S7).
We next evaluated the synaptic function of mPFCGlut neurons projecting to NAc neurons. We first expressed ChR2 in mPFCGlut neurons by injecting AAV-CaMKIIα-ChR2-eYFP into the mPFC, and then, selectively activated NAc neurons that were receiving projections from mPFCGlut neurons via optogenetic stimulation (5 ms pulse, 20 Hz) (Figure 4F,G). In the whole-cell patch-clamp configuration, excitatory postsynaptic currents (EPSCs) were recorded in 32 out of 60 NAc neurons (Figure 4H). However, no inhibitory postsynaptic currents were recorded. EPSCs were eliminated via pretreatment with the AMPA receptor antagonist CNQX (Figure 4I,J and Figure S8). To identify whether Tac1 neurons in the NAc medial shell received inputs from the mPFCGlut, we expressed ChR2 in mPFCGlut neurons in Tac1-Cre; Ai9 mice. In the patch-clamp recording, EPSCs were recorded in Tac1 neurons in the NAc medial shell (Figure S9A–D). These results suggest that mPFCGlut neurons project excitatory signals to Tac1 neurons in the NAc medial shell.
## 2.5. The mPFCGlut-to-NAc Circuit Modulates Avoidance Behaviors in Response to Aversive Stimuli
To investigate whether activation of the mPFCGlut →NAc circuit decreases avoidance behaviors in response to aversive stimuli. Male mice were unilaterally injected with AAV-CaMKIIα-eYFP and AAV-CaMKIIα-ChR2-eYFP (Figure 5A,B). Six weeks later, we carried out an approach-avoidance assay for evaluation (Figure 5C). Compared with the control stimulation, the selective delivery of blue light (5 ms pulse, 20 Hz for 5 min) to ChR2-expressing terminals in the NAc medial shell elicited a significant increase in interaction time with formaldehyde (Figure 5D,E). We also calculated the total distance traveled by mice in the arena and found that locomotion was not affected (Figure 5K).
Next, we selectively inhibited mPFCGlut terminals in the NAc medial shell by delivering continuous yellow light to the LH of male mice bilaterally infected with AAV-CaMKIIα-eNpHR3-mCherry in the mPFC (Figure 5G,H). In the approach-avoidance assay, inhibition of the mPFCGlut →NAc pathway significantly reduced interaction time with formaldehyde without affecting locomotion (Figure 5I–L). We also carried out a real-time place aversion (RTPA) assay and found that inhibition of the mPFCGlut →NAc pathway elicited avoidance of the photoinhibition-paired chamber (Figure S10A–D).
Furthermore, we determined whether the activation of mPFCGLUT neurons could attenuate aversive behaviors following the inhibition of NAcTac1 neurons. AAV-CaMKIIα-hM3D(Gq)-mCherry or AAV-CaMKIIα-mCherry was injected in the mPFC, while AAV-DIO-hM4D(Gi)-mCherry was injected in the NAc (Figure S10A). We found that the activation of the mPFC neurons was able to attenuate avoidance behaviors in response to aversive stimuli (Figure S11B,C). Taken together, these data indicate that the mPFCGlut →NAc circuit is crucial to avoidance behaviors in response to aversive stimuli.
## 3. Discussion
Using neural circuit tracing, chemogenetics, electrophysiology, and optogenetics approaches, we found that NAcTac1 neurons in the medial shell mediate avoidance responses to aversive stimuli in 10–14-week-old male mice. NAcTac1 neurons send inhibitory inputs to the LH, and the NacTac1→LH circuit is required for aversive behaviors in mice. Moreover, Nac neurons receive glutamatergic inputs from mPFCGlut neurons, and mPFCGlut→Nac projection regulates behavioral responses in the presence of aversive stimuli.
Our mapping study of output circuits demonstrated that NacTac1 neurons in the medial shell project to LH neurons. The LH is a brain region that contains heterogeneous cell populations [49] and is involved in the regulation of multiple behaviors, such as feeding, aversion, and reward-seeking [50,51]. Previous studies have reported that the activation of LH neurons causes avoidance and aversive behaviors [52,53,54]. This is consistent with our data, which show that the optogenetic inhibition of NAcTac1 terminals in the LH induced aversive behaviors in mice. However, the LH receives multiple excitatory and inhibitory inputs from both cortical and subcortical structures, further research is needed to fully resolve the neuron populations receiving inhibitory inputs from NAcTac1 neurons and the mechanisms underlying aversion in the LH.
Previous studies have reported that mPFC neurons project to the NAc and that these neurons are able to elicit avoidance [55]. It has also been reported that projections from the mPFC to the NAc have no effect on aversion [37]. These conflicting results may partially be caused by the heterogeneity of the different regions of the NAc: the core, medial shell, and lateral shell. These regions contain similar classes of medial spiny projection neurons (MSNs). NAc medial shell MSNs have been described as “medium-small spiny neurons” with low density [56]. In addition, the NAc medial shell shows a perplexing phenotype that opposes the classical direct and indirect pathway model [16,57,58]. In addition, previous work indicates that D1 neurons in the NAc also represent a portion of the classical indirect pathway and are activated by aversive stimuli [59,60]. Taken together, the classical striatal direct and indirect pathway models are not applied to the NAc. In this study, we found that mPFCGlut neurons send excitatory signals to neurons in the NAc medial shell. Moreover, this mPFCGlut→NAc circuit is involved in the regulation of aversion. However, aversion is a multidimensional construct, and we cannot rule out the possibility that other neurons in the NAc receive inputs from the mPFC and contribute to aversive behaviors.
In summary, we delineated distinct Tac1 neurons as encoding aversive stimuli. Furthermore, we dissected the dedicated function of this circuit and identified it as a critical component of the aversion circuit. These results may improve our understanding of the aversion circuit. By understanding the structure and mechanisms underlying aversion and negative prediction, it will be possible to design intervention strategies for pathological depressive conditions.
## 4.1. Animals
All experimental procedures were approved by the Animal Advisory Committee of Northeast Normal University, China. The laboratory was kept under specific pathogen-free (SPF) conditions. All mice were maintained on a 12–12 h light–dark cycle (lights on from 6:00 to 18:00 every day), with food and water provided ad libitum. All behavioral tests were performed during the light period. C57BL/6J mice were obtained from Huafukang Animal Center, Beijing, China. Tac1-IRES2-Cre mice (Jax No. 021877) were obtained from Jackson Laboratory (USA). Ai9 mice (Jax No. 007905) were kindly provided by Prof. Chunjie Zhao from Southeast University.
## 4.2. Viral Vector Generation
For monosynaptic tracing, AAV-EF1α-DIO-His-EGFP-2a-TVA (AAV$\frac{2}{9}$, 5.53 × 1012 particles mL−1), AAV-EF1α-DIO-RG (AAV$\frac{2}{9}$, 5.22 × 1012 particles mL−1), and RV-ENVA-ΔG-dsRed (3.10 × 108 particles mL−1) were purchased from BrainVTA (Wuhan, China). AAV-EF1α-DIO-mCherry (AAV$\frac{2}{9}$, 1.47 × 1013 particles mL−1) was purchased from GeneChem (Shanghai, China).
For functional analysis, AAV-EF1α-DIO-hM4D(Gi)-mCherry (AAV$\frac{2}{9}$, 1.044 × 1012 particles mL−1), AAV-EF1α-DIO-hM3D(Gq)-mCherry (AAV$\frac{2}{9}$, 2.205 × 1012 particles mL−1), and AAV-EF1α-DIO-ChR2-mCherry (AAV$\frac{2}{9}$, 1.25 × 1013 particles mL−1) were purchased from GeneChem (Shanghai, China). AAV-CaMKIIα-ChR2(H134R)-eYFP-WPRE-hGH polyA (AAV$\frac{2}{9}$, 2.77 × 1012 particles mL−1), AAV-CaMKIIα-eYFP-WPRE-hGH polyA (AAV$\frac{2}{9}$, 6.6 × 1012 particles mL−1), AAV-CaMKIIα-eNpHR3.0-mCherry-WPRE-hGH polyA (AAV$\frac{2}{9}$, 4.25 × 1012 particles mL−1), and AAV-CaMKIIα-mCherry-WPRE-hGH polyA (AAV$\frac{2}{9}$, 2.29 × 1012 particles mL−1) were purchased from BrainVTA (China).
## 4.3. Viral Tracing
For output mapping, the NAc medial shell of Tac1-Cre mice was injected with with AAV-EF1α-DIO-mCherry (200 nL). For input mapping, AAV-EF1α-DIO-His-EGFP-2a-TVA and AAV-EF1α-DIO-RG (1:1, total 150 nL) were injected into the NAc medial shell of Tac1-Cre mice. Four weeks later, 300 nL of RV-ENVA-ΔG-dsRed was injected into the LH. Thus, we only infected NAcTac1 neurons in the medial shell that projected to the LH, and traced their inputs. The mice were sacrificed one week after RV injection.
## 4.4. Stereotaxic Injection
Mice were anesthetized with $1.0\%$ sodium pentobarbital (0.1 g/kg body weight, i.p.). Viruses were delivered at a rate of 100 nL/min using a stereotaxic instrument (RWD Co, Shenzhen China) and a 5 µL syringe (Hamilton, Sigma, USA). After each injection, the syringe was left in place for 15 min, and then, slowly withdrawn. Experiments were performed at least 4–6 weeks after virus injection.
Stereotaxic coordinates were derived from the Paxinos and Franklin Mouse Brain Atlas and empirically adjusted. The coordinates for injection into the NAc medial shell (total volume of 400 nL) were +1.9 mm AP, ±0.6 mm ML, and −4.4 mm DV. The coordinates for injection into the LH (total volume of 150 nL) were −1.5 mm AP, ±0.9 mm ML, and −5.1 mm DV. The coordinates for injection into the mPFC (total volume of 400 nL) were +2.2 mm AP, ±0.3 mm ML, and −1.35 mm DV. For monosynaptic circuit tracing and the ChR2 experiment, viruses were delivered unilaterally. For other functional analysis, viruses were delivered bilaterally.
## 4.5. Implantation of Optical Fibers
Optogenetic behavioral experiments were performed as previously described [16,18,19], and optic fibers (NA: 0.37; INPER, Wuhan, China) were unilaterally (ChR2) or bilaterally (NpHR3.0) implanted over the LH (AP: −1.5 mm; ML: ±0.9 mm; DV: −4.9 mm) and NAc medial shell (AP: +1.9 mm; ML: ±0.5 mm; DV: −4.2 mm). The mice were subjected to behavioral tests after 2 weeks of recovery. For optogenetic activation experiments, both control and ChR2-injected mice were stimulated using a 20 Hz 465 nm blue laser (INPER, China) with 2–5 mW light power at the fiber tips. For optogenetic inhibition experiments, both control and NpHR-injected mice were continuously stimulated using a 589 nm yellow laser (INPER, China) with 2–5 mW light power at the fiber tips.
## 4.6. Immunohistochemistry
As previously described [61], mice were deeply anesthetized with sodium pentobarbital (0.5 g/kg, i.p.) and perfused transcardially with 0.1 M PBS followed by $4\%$ paraformaldehyde (PFA) in PBS. Their brains were then post-fixed overnight at 4 °C and transferred to $30\%$ sucrose solution. Sagittal and coronal sections were cut on a freezing microtome (Leica, CM 1950, USA) at a thickness of 40 µm. The sections were rinsed in PBS, and then, incubated in blocking solution ($0.2\%$ Triton X-100, $10\%$ serum, and $2\%$ BSA in 0.1 M PBS) for 2 h. After washing with PBS, the sections were counterstained with DAPI (1:2000, Life Technologies, D3571, USA) for 8 min. The sections were then covered with ProLong gold mounting media (Thermo Fisher, P36930, USA). The following primary antibodies were used: NeuN (1:1000; EMD Millipore, MAB377, USA), substance P (1:1000; Abcam, ab10353, USA), VGLUT2 (1:500; Synaptic Systems, 135 402, Germany), and DRD1 (1:500; Novus Biologicals, NB110-60017, USA). The following secondary antibodies were used: Alexa Fluor 488-conjugated goat anti-mouse (1:1000; Invitrogen, A21121, USA), Alexa Fluor 488-conjugated goat anti-rabbit (1:1000; Invitrogen, A11008, USA), and Alexa Fluor 488-conjugated goat anti-guinea pig (1:1000; Invitrogen, A11073, USA). All images were acquired using a Zeiss LSM 880 confocal microscope (USA).
## 4.7. Ex Vivo Electrophysiology
Mice were deeply anesthetized with sodium pentobarbital and quickly decapitated to remove their brains. Acute slices (300 μm thick) were cut using a vibrating microtome (Leica, VT 1000S). The sections were quickly transferred to a recovery chamber and incubated at 35 °C for 30 min in recovery solution comprising 93 mM NMDG, 1.2 mM NaH2PO4, 30 mM NaHCO3, 20 mM HEPES, 25 mM D-Glucose, 5 mM Na-ascorbate, 2 mM Thiourea, 3 mM Na-pyruvate, 3 mM KCl, 10 mM MgSO4, 0.5 mM CaCl2, 93 mM HCl, and 12 mM NAC (pH 7.4). The slices were then incubated at room temperature for 1 h in carbogenated artificial cerebral spinal fluid (aCSF) comprising 120 mM NaCl, 2.5 mM KCl, 1.0 mM NaH2PO4, 26 mM NaHCO3, 11 mM D-glucose, 2.0 mM MgCl2, and 2.0 mM CaCl2 (pH 7.4) before recording. Recordings were made at 33 °C (TC-324B; Warner Instruments, USA). All solutions were saturated with $95\%$ O2/$5\%$ CO2.
Whole-cell patch-clamp recordings were performed using an EPC-$\frac{10}{2}$ amplifier (HEKA, Germany). The recording pipettes were pulled from borosilicate glass tubes (Sutter Instruments, USA) and had a resistance of 3–6 MΩ; only whole-cell patches with a series resistance < 15 MΩ were used for recordings. EPSC and IPSC were recorded by holding the membrane potential at −70 mV.
For optical recording in the LH, AAV-DIO-ChR2-mCherry was injected into the NAc medial shell of Tac1-Cre mice, and LH neurons in areas with a high density of mCherry terminals were patched. ChR2 with 465 nm blue light was delivered via a laser (INPER-B1–465, INPER, China). To record optically evoked IPSCs (oIPSCs) in LH neurons, CNQX (50 µM, Tocris Bioscience, 1045, USA) was added to the aCSF. Patch pipettes were filled with 135 mM CsCl, 1 mM EGTA, 4 mM Mg-ATP, 0.6 mM Na-GTP, and 10 mM HEPES (pH 7.4).
For optical recording in the NAc medial shell, AAV-CaMKIIα-ChR2-mCherry was injected into the mPFC of C57 mice, and NAc medial shell neurons in the areas with a high density of mCherry terminals were patched. ChR2 with 465 nm blue light was delivered via a laser (INPER-B1–465, INPER, China). To record optically evoked EPSCs (oEPSCs) in NAc medial shell neurons, bicuculline (20 µM, Tocris Bioscience, 0130) was added to the aCSF. Patch pipettes were filled with 130 mM K-gluconate, 1 mM EGTA, 5 mM Na-phosphocreatine, 2 mM Mg-ATP, 0.3 mM Na-GTP, and 10 mM HEPES (pH 7.4).
Data were acquired using PATCHMASTER 1.3 (HEKA, Germany) and analyzed using MiniAnalysis 1.0 (Synaptosoft), Clampfit 10.0 (Molecular Devices), and Igor 5.03 (Wavemetrics) software.
## 4.8. Behavioral Assays
All mice used for the behavioral assays were male mice and their littermates. An experimenter blinded to the genotypes performed all the tests.
## 4.9. Approach-Avoidance Test
The avoidance test was conducted to measure avoidance of an unfamiliar aversive stimulus. For the chemogenetics experiments, control-, hM3D(Gq)-, and hM4D(Gi)-injected mice were i.p. injected with clozapine N-oxide (CNO; 5 mg/kg or JHU37160; 0.5 mg/kg), and introduced into the chamber half an hour later. The chamber (70 cm × 70 cm) contained three sides (safe, center, and form). A piece of cotton dipped in $5\%$ formaldehyde was placed on the form side. The opposite sides of the chamber were designated the ‘safe’ area and ‘center’ area. As previously described [18], mice tend to explore a novel object, but animals display strong avoidance behaviors when exposed to formaldehyde. Interactions with formaldehyde were recorded and analyzed by the EthoVision XT system (Noldus, Wageningen, The Netherlands).
For the optogenetics (ChR2 and NpHR) experiments, after recovery from the surgery for virus injection and fiber implantation, mice were introduced into an arena (40 cm × 40 cm). A piece of cotton dipped in $5\%$ formaldehyde was place in a corner of the arena. Interactions with formaldehyde were recorded in 5 min segments and analyzed using the EthoVision XT system (Noldus, Wageningen, The Netherlands).
## 4.10. RTPA Assay
On the day of habituation, the mice were introduced into a Plexiglas box with two chambers (30 cm × 30 cm × 50 cm each) and allowed to explore the chamber freely for 15 min. One chamber was randomly designated the stimulation side, and the other was designated the non-stimulation side. The time spent in each of the chambers was recorded. Mice that spent more than $60\%$ of the total time in either compartment were excluded from the experiments. On the day of the experiment, the mice were randomly introduced into either chamber and received continuous 589 nm yellow light (or 20 Hz 465 nm blue light) every time they entered the stimulation chamber until they moved into the non-stimulation chamber. The time spent in each chamber was recorded and analyzed using the EthoVision XT system.
## 4.11. Olfaction Test
Before the test, all pellets were removed from the home cage, but the water bottle was kept in place. On the day of the experiments, a mouse was introduced into a clean cage containing clean bedding with a depth of 3 cm. The animal was allowed to explore the arena freely for 5 min. The animal was then transferred to an empty clean cage. In the cage containing the bedding, food was buried approximately 1 cm beneath the surface in a random corner. The surface of the bedding was smoothed out, and the animal was reintroduced into the cage. The latency to find the buried food was recorded. The food was considered uncovered when the mouse started to eat it.
## 4.12. Quantification and Statistical Analyses
All experimental procedures and data analyses were conducted in a blinded manner. The number of replicates (N or n) indicated in the figure legends refers to the number of experimental subjects independently treated in each experiment. All statistical analyses were performed in Graph Pad Prism (GraphPad Software) unless otherwise stated. For normally distributed data, a Student’s test and one-way ANOVA, followed by Scheffe’s post hoc test, were used to analyze the significance between groups. For non-normally distributed data, Mann–Whitney U-tests were used to calculate the significance between groups. All statistical data can be found in the figure legends. Statistical significance was set at * $p \leq 0.05$, ** $p \leq 0.01$, and *** $p \leq 0.001.$ *The data* are presented as the means ± s.e.m.
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|
---
title: Prevalence of Diabetic Retinopathy and Use of Common Oral Hypoglycemic Agents
Increase the Risk of Diabetic Nephropathy—A Cross-Sectional Study in Patients with
Type 2 Diabetes
authors:
- Wei-Ming Luo
- Jing-Yang Su
- Tong Xu
- Zhong-Ze Fang
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001907
doi: 10.3390/ijerph20054623
license: CC BY 4.0
---
# Prevalence of Diabetic Retinopathy and Use of Common Oral Hypoglycemic Agents Increase the Risk of Diabetic Nephropathy—A Cross-Sectional Study in Patients with Type 2 Diabetes
## Abstract
Objective: This study investigated the effect of amino acid metabolism on the risk of diabetic nephropathy under different conditions of the diabetic retinopathy, and the use of different oral hypoglycemic agents. Methods: This study retrieved 1031 patients with type 2 diabetes from the First Affiliated Hospital of Liaoning Medical University in Jinzhou, which is located in Liaoning Province, China. We conducted a spearman correlation study between diabetic retinopathy and amino acids that have an impact on the prevalence of diabetic nephropathy. Logistic regression was used to analyze the changes of amino acid metabolism in different diabetic retinopathy conditions. Finally, the additive interaction between different drugs and diabetic retinopathy was explored. Results: *It is* showed that the protective effect of some amino acids on the risk of developing diabetic nephropathy is masked in diabetic retinopathy. Additionally, the additive effect of the combination of different drugs on the risk of diabetic nephropathy was greater than that of any one drug alone. Conclusions: We found that diabetic retinopathy patients have a higher risk of developing diabetic nephropathy than the general type 2 diabetes population. Additionally, the use of oral hypoglycemic agents can also increase the risk of diabetic nephropathy.
## 1. Introduction
Diabetes is a metabolic disorder caused by absolute or relative insufficient secretion of insulin, with type 2 diabetes (T2D) being the most common. In 2021, 537 million adults aged 20–79 years had diabetes around the world, and by 2045, the number of diabetic adults is expected to rise to 783 million [1]. T2D is associated with many adverse complications, and its complications are a major cause of death for T2D. Diabetic microangiopathy is a group of common complications of diabetes, in which diabetic retinopathy (DR) and diabetic nephropathy (DN) are the most common. DN is proteinuria and a progressive decrease in glomerular filtration rate (GFR) due to prolonged diabetes. The incidence of DN is also on the rise in China, and it has become the second cause of end-stage renal disease, second only to various glomerulonephritis. As both belong to microvascular disease, the correlation between DR and the risk of DN incidence has become a research topic. According to an UK study, DR occurs earlier than other complications [2]. The presence of DR means not only vision problems, but also an increased risk of other microvascular and macrovascular complications [3]. At present, some studies have found that DR occurs earlier than DN, can promote the development of DN, and can also help diagnose DN [4]. Additionally, DN patients with concurrent DR have an increased risk of rapid renal disease progression and generally worse renal outcomes [5]. Additionally, with the further application of metabolomics in the study of the pathogenesis of T2D and its complications, researchers have found that amino acid metabolism is closely related to microangiopathy. Amino acids, such as leucine (Leu) [6], histidine (His) [7,8], phenylalanine (Phe), and tyrosine (Tyr) [9], have been confirmed to be significantly related to the occurrence and development of DN in past studies. Metabolomics has gradually become an important method system to reveal the risk factors of DN. However, it is still unclear whether the relationship between amino acids and DN exists in people with different DR Status.
Metformin, Acarbose, and Sulfonylureas are all common oral hypoglycemic agents during the treatment of diabetes. At present, the protective effect of Metformin on the kidney has been widely studied. A study found that Metformin has a potential protective effect on DN through the AMPK/SIRT1-FoxO1 pathway [10], while other scholars thought there is no relation between them. However, less research has been done on the effects on DN risk of the other two drugs at the same time. Whether there are interactions between different drugs remains unclear. Thus, it is time to identify risk factors and early prediction of diabetes and its complications, which is critical for reducing diabetes complications [11,12] and economic burden [13]. Additionally, this is beneficial from both clinical and public health perspective [14]. It is against this background that we conducted the research on the effect of DR and the use of drugs on the risk of DN.
## 2.1. Study Method and Population
All the information of T2D patients is retrieved from the First Affiliated Hospital of Liaoning Medical University (FAHLMU), which is a tertiary general hospital located in Jinzhou, Liaoning Province, China. Inclusion criteria for this study were: [1] Patients diagnosed as T2D or treated with anti-hyperglycemic therapy; [2] The information of diabetic microvascular disease including DN and DR is completed. [ 3] The information on the use of Metformin, Acarbose, and Sulfonylureas drugs is completed. Exclusion criteria were: [1] T2D patients under the age of 18; [2] Subjects lacking amino acid indicators, height, weight, or blood pressure. [ 3] Patients with extreme outliers of amino acids. A total of 1821 patients with T2D were preliminarily included in this study. According to the exclusion criteria, 1031 subjects were finally included in this study, including 188 patients in the DN group and 843 T2D patients in the control group (Figure 1). Then, we used multiple imputation to deal with the missing data.
The ethics of the study was approved by the Ethics Committee for Clinical Research of FAHLMU. Additionally, due to the retrospective nature of the study, informed consent was waivered, which is consistent with the Declaration of Helsinki.
## 2.2. Data Collection and Clinical Definitions
We retrieved the data from electronic medical records which included demographic and anthropometric information, and current clinical factors and information of diabetic complications. Demographic data included gender and age. Anthropometric measurements included weight, height, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Clinical parameters included total cholesterol (TC), triglycerides (TG), glycosylated hemoglobin (HbA1c), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), creatinine (Crea), and uric acid (UA). Additionally, the duration of DN was recorded to exclude the interference of the duration of the disease on the results.
The measurements of anthropometric indicators were measured by standardized procedures in the hospital. Participants were allowed to wear light clothes and no shoes. weight and height were measured to the nearest 0.1 kg and 0.5 cm, respectively. Blood pressure in adults was measured with a standard mercury sphygmomanometer after a cuff on the right arm and after rest of 10 min in a seated position at an appropriate size. Age was calculated in years from the date of birth to the date of medical examination or hospitalization. The body mass index (BMI) was obtained according to the formula as the ratio of body weight (kg) to squared height (m) and classified according to the overweight and obesity criteria recommended by the National Health Commission of China [15]. The diagnosis and classification criteria of T2D in the study were based on the publishment of World Health Organization (WHO) or treated with antihyperglycemic therapy [16]. DR diagnostic standard based on eye exam results for T2D [17]. The diagnostic standard for DN was based on the criteria of care for T2D [18]. According to the RCS curve, His, tryptophan (Trp), Valine (Val), and threonine (Thr) were stratified according to 51 μmol/L, 46 μmol/L, 133 μmol/L, and 24 μmol/L, respectively (Figure 2).
## 2.3. Amino Acid Quantification and Equipment
Details of the metabolomics assessment method have already been published [19]. Briefly, 8 h of fasting blood sample was collected at admission. A total of 22 amino acids were detected via LC-MS, i.e., asparagine (Asn), alanine (Ala), arginine (Arg), citrulline (Cit), Leu, lysine (Lys), Trp, Tyr, Thr, Val, glycine (Gly), proline (Pro), Phe, glutamine (Gln), His, methionine (Met), serine (Ser), ornithine (Orn), glutamate (Glu), aspartate (Asp), piperamide (Pip), and cysteine (Cys). AB Sciex 4000 QTrap system (AB Sciex, Framingham, MA, USA) was used to conduct direct injection MS metabolomic analysis. Analyst v1.6.0 software (AB Sciex) was used for data collection. ChemoView 2.0.2 (AB Sciex) was used for data preprocessing. Isotope-labeled internal standard samples were purchased from Cambridge Isotope Laboratories (Tewksbury, MA, USA). Standard samples of the amino acids were purchased from Chrom Systems (Grafelfing, Germany).
## 2.4. Statistical Analysis
Continuous data were expressed as mean ± standard deviation (SD), non-normally distributed data were expressed as median (interquartile range), and categorical variables were expressed as numbers (percentages). In two populations with different DR prevalence status, it was tested whether there were differences between the different indicators of the patients in the DN group and the non-DN group. Continuous variables were normally distributed with t-test or ANOVA, non-normal with rank-sum test, and categorical variables with chi-square test.
Characteristics of participants were described and compared according to the prevalence of DN. According to the results, amino acids with significant differences in DN prevalence were screened out for further analysis. Spearman correlation was performed on the correlation between amino acids and DR. A binary logistic regression model stratified by DR condition was then used to obtain odds ratios (OR) for different amino acids to DN and their $95\%$ confidence intervals ($95\%$CI). Traditional risk factors for DN in T2D patients were adjusted by a structural adjustment program: model adjusted age, gender, BMI, SBP, DBP, TG, TC, HbA1c, HDL-C, LDL-C, Duration of DN, UA, and Crea. Then, the additive interaction between different drugs and DR was analyzed and the correlation coefficient of additive interaction was calculated. All analyses were performed using R version 4.1.0.
## 3.1. Description of Study Subjects
Table 1 summarizes the select characteristics of the DN group and the non-DN group stratified by DR condition in the total population. The study included 1031 participants, with a mean age of 57.24 years old (SD: 13.82) and a mean BMI of 25.29 (SD: 3.85). Additionally, 548 patients were male ($53.15\%$).
Then, we divided the total population into two groups based on prevalence of DR. In the DR group, the mean age was 57.77 years old (SD: 9.96) and the mean BMI was 25.09 (SD: 3.31). Additionally, there were 73 males ($45.1\%$) in this group. When compared according to the outcome of DN, we found that differences in TC, UA, and use of three oral antidiabetic drugs were statistically significant between the two groups. Patients with DN had higher TC and UA, and a greater proportion of using all three common drugs. In the T2D population without DR, the mean age was 57.14 years old (SD: 14.43) and the mean BMI was 25.33 (SD: 3.95). Additionally, there were 475 males without DR ($54.7\%$). In this group, age, BMI, SBP, HDL-C, UA, Crea, and the use of Acarbose was statistically different between the second level groups divided by DN. The patients in the DN group were older, and have higher BMI, SBP, HDL-C, UA, and Crea. A larger percentage of people with DN use Acarbose.
## 3.2. Differences in Individual Amino Acids According to the Appearance of DN
It is observed that the 10 amino acids of Leu, Phe, Trp, Tyr, His, Val, Gly, Thr, Cit, and Ser had significant differences in T2D patients with different DN prevalence (Table 2). Except for Cit, the concentrations of the other 9 amino acids in DN patients were lower than those in T2D patients.
## 3.3. Correlations between Amino Acids and DR and the Impacts of DR on Amino Acids for DN
We did correlation analysis on the selected 10 amino acids and DR (Figure 3). The results showed that except Cit, the remaining amino acids were positively correlated with DR, and the correlations were all statistically significant. Among amino acids, the correlation between Leu and Val was the strongest, reaching a strong correlation level ($r = 0.84$). Due to the significant correlation between amino acids and DR, we next analyzed the relationship between amino acids and DN risk stratified by the prevalence of DR (Table 3). The results showed that the protective effect of His, Trp, Val, and Thr on the risk of DN was no longer significant in the DR group.
## 3.4. Addictive Interaction between Oral Hypoglycemic Drugs and DR
Table 4 shows the additive interactions between different drugs and DR. Concomitant use of Acarbose and Metformin increased the risk of DN (OR: 1.61, $95\%$CI: 1.13–2.29), and, although either Acarbose or Metformin alone increased the risk of DN, the risk of concomitant use of both drugs was higher than that of single use any one. Similar results can be observed in the additive interaction results of Acarbose and Sulfonylureas. In the interaction analysis of Sulfonylureas, Metformin, and DR, the highest risk of DN was using Sulfonylureas only in the presence of DR (OR: 2.95, $95\%$CI: 1.5–5.81). Followed by DR and the use of both Sulfonylureas and Metformin (OR: 2.56, $95\%$CI: 1.56–4.21).
## 4. Sensitive Analysis
After random forest imputation of missing values (UA = 187, TG = 288, TC = 289, Crea = 147), the effects of special amino acids for the risk of DN stratified by DR in T2D remained stable and significant in multi-variable analyses (Table 5).
## 5. Discussion
In recent years, many studies have found that the risk of DN in DR patients is higher than that in the general T2D population. A study has found a significant association between DR and subsequent increased risk of DN in T2D patients, with younger patients at greater risk than older patients [20]. A study in Sudanese shows a significant association between DR and DN in adults with diabetes [21]. Additionally, there is a study which found that DR contributes to the diagnosis of DN in patients with T2D and kidney disease, but its severity may not be parallel to the presence of DN [4]. Klein et al. and Kofoed-Enevoldsen et al. suggested that the occurrence of DN and DR may be regulated by similar molecular pathways, which means that patients with DN may have already developed DR and patients with DR are vulnerable to develop DN. However, results in T2D have been inconsistent [22,23]. Excepting for population heterogeneity, the duration of T2D in different study populations is also an important influencing factor for the inconsistent results. At the same time, because DN can cause proteinuria, which shows significant renal lesions in patients, the inclusion of some patients with glomerulopathy who have the same results but do not have DN will also affect the results [24].
Our results showed that the protective effects of His, Trp, Val, and Thr on the risk of DN were affected by the prevalence of DR. A metabolomic study of DR found Asn, dimethylamine, His, Thr, and Gln to be the most variable metabolites in DR patients [25]. Another study confirmed that the metabolism of Gly, Ser Trp, and *Thr is* significantly disturbed in DR patients, especially the Trp metabolism [26]. Additionally, it is found that valine–leucine–isoleucine biosynthesis was also significantly disturbed in DR patients [27].
Some published studies have shown that Metformin can protect the kidney. However, the result obtained in our study shows that Metformin increases the risk of DN. We believe that excepting for population heterogeneity, the reason for the different results is that the use of Metformin leads to changes in the metabolism of other nutrients. Multiple studies have shown that long-term use of Metformin reduces Vitamin B12 (VB12) levels in the body [28,29,30]. A study in a North Indian population found that VB12 supplementation prevented the development of DN and improved the overall management of people with diabetes [31]. In animal experiments, it was found that both folic acid and VB12 can reduce the 24 h urinary albumin, and the combined effect is better [32]. High concentrations of homocysteine (Hcy) have been identified as a risk factor for DN [33,34]. Vitamin B supplementation can effectively reduce Hcy levels, of which VB12 is more effective in reducing Hcy concentration [35].
The study found that, although Acarbose can significantly improve blood sugar levels in DN patients, proteinuria did not improve [36]. Additionally, in mice experiments, Acarbose was found that it does not significantly reduce the incidence and severity of glomerulosclerosis [37]. This is consistent with our findings. Using Acarbose alone had no significant effect on the development of DN. However, the results showed that the effect of Metformin on DN was amplified when Metformin and Acarbose were used together. Additionally, our study showed that the effect of Sulfonylureas on DN was not significant when used alone. In published studies, some scholars have pointed out that gliquidone can improve the antioxidant response and delaying renal interstitial fibrosis by inhibiting the Notch/Snail1 signaling pathway, thereby improving the symptoms of DN. Gliquidone, as one Sulfonylureas, can ameliorate the diabetic symptoms of DN through inhibiting Notch / Snail1 signaling pathway, improving anti -oxidative response, and delaying renal interstitial fibrosis [38]. On the other hand, studies have also found that Glibenclamide (another kind of Sulfonylureas) should be used cautiously in patients with stages 2 and 3 DN. Additionally, Sulfonylureas are contraindicated in patients with stage 4 DN [39]. We believe that the difference may be caused by the different duration and stage of the DN in the study.
Based on the current research on microvascular disease, our study further explored the effect of amino acid metabolism changes on the risk of DN in different DR conditions. At present, there are few published studies on the relationship between amino acid metabolism and DN. The amino acids selected from our results can provide certain directions and ideas for further refinement of DN metabolism research. At the same time, our research has some shortcomings. [ 1] Due to the nature of the cross-sectional study, we could not prove the causality between DR and progression of DN [40,41], and the order of occurrence between DR and DN cannot be determined. At present, there are also some studies that believe the occurrence of DN promotes the development of DR. For example, a study conducted in Pakistan indicated that DN is an independent risk factor for the development and progression of DR [42]. However, unlike DN, the DR is not only a kind of microvascular disease, but can also cause a certain degree of nerve damage. Due to its more complex pathogenic factors and more related lesions, it is believed that its onset time has been studied more extensively before DN. More prospective studies are needed to prove the causal relationship between them; [2] The lack of available vitamin indicators makes it difficult to verify the effects of drugs on vitamins. [ 3] Other than the three drugs mentioned in the article, the effects of other oral hypoglycemic agents were not included in the study. Our laboratory team is conducting monitoring of vitamin indicators and improving the information of more types of hypoglycemic drugs to subsequently validate our results.
In conclusion, we selected amino acids that have protective effect on the risk of DN, and found that DR patients had a higher risk of developing DN. Additionally, the use of oral hypoglycemic drugs can also increase the risk of DN, with combining use of drugs has worse effect than that of any one drug alone. The occurrence of the disease is a complex process with multiple effects, and more follow-up studies are needed to confirm the association between different risk factors, so as to better intervene in the disease.
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|
---
title: 'Development of a Person-Centred Integrated Care Approach for Chronic Disease
Management in Dutch Primary Care: A Mixed-Method Study'
authors:
- Lena H. A. Raaijmakers
- Tjard R. Schermer
- Mandy Wijnen
- Hester E. van Bommel
- Leslie Michielsen
- Floris Boone
- Jan H. Vercoulen
- Erik W. M. A. Bischoff
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001916
doi: 10.3390/ijerph20053824
license: CC BY 4.0
---
# Development of a Person-Centred Integrated Care Approach for Chronic Disease Management in Dutch Primary Care: A Mixed-Method Study
## Abstract
To reduce the burden of chronic diseases on society and individuals, European countries implemented chronic Disease Management Programs (DMPs) that focus on the management of a single chronic disease. However, due to the fact that the scientific evidence that DMPs reduce the burden of chronic diseases is not convincing, patients with multimorbidity may receive overlapping or conflicting treatment advice, and a single disease approach may be conflicting with the core competencies of primary care. In addition, in the Netherlands, care is shifting from DMPs to person-centred integrated care (PC-IC) approaches. This paper describes a mixed-method development of a PC-IC approach for the management of patients with one or more chronic diseases in Dutch primary care, executed from March 2019 to July 2020. In Phase 1, we conducted a scoping review and document analysis to identify key elements to construct a conceptual model for delivering PC-IC care. In Phase 2, national experts on Diabetes Mellitus type 2, cardiovascular diseases, and chronic obstructive pulmonary disease and local healthcare providers (HCP) commented on the conceptual model using online qualitative surveys. In Phase 3, patients with chronic conditions commented on the conceptual model in individual interviews, and in Phase 4 the conceptual model was presented to the local primary care cooperatives and finalized after processing their comments. Based on the scientific literature, current practice guidelines, and input from a variety of stakeholders, we developed a holistic, person-centred, integrated approach for the management of patients with (multiple) chronic diseases in primary care. Future evaluation of the PC-IC approach will show if this approach leads to more favourable outcomes and should replace the current single-disease approach in the management of chronic conditions and multimorbidity in Dutch primary care.
## 1. Introduction
Over the last decades, the increasing prevalence of chronic diseases has cast a huge burden on healthcare systems worldwide [1]. Currently, chronic diseases are the leading cause of death globally, with cardiovascular diseases, diabetes, and chronic lung diseases causing the highest mortality [1]. In the Netherlands, $59\%$ of the population had one or more chronic diseases in 2020 [2]. In addition, between 2004 and 2017, the prevalence of patients with two or more chronic diseases (multimorbidity) [3] in central Europe has increased in adults aged 50 and over [4]. Most importantly, chronic diseases have a major impact on patients’ health-related quality of life, especially when they have multiple chronic conditions [5,6,7,8].
To reduce the burden of chronic diseases on patients and healthcare providers, single disease management programs (DMPs) have been developed [9,10,11]. Based on Dutch primary care [12,13,14], we define DMPs as long-term chronic care programs in primary care that are predominantly run by general practice nurses (PNs) under the responsibility of a general practitioner (GP) and focus on assessing, monitoring, and treating a single chronic disease. DMPs for chronic obstructive pulmonary disease (COPD), cardiovascular diseases (CVD), and diabetes mellitus type 2 (DM2) are currently the most widely implemented. Although DMPs have shown some minor improvements in process indicators, such as coordination of care and communication between caregivers [15,16], they have failed to show improvement in patients’ health-related quality of life (HRQoL) [17,18]. A possible explanation could be that DMPs mainly focus on the medical aspects of a specific condition, with less attention being paid to other chronic diseases or social problems that may also impact HRQoL. In addition, an organisation in which patients with multiple chronic diseases attend multiple DMPs provided by multiple healthcare professionals (HCP) is not desirable, both from an economical and patient perspective [19]. Patients may receive overlapping or conflicting treatment advice [20]. Furthermore, the DMP approach seems to conflict with the core competencies of primary care professionals, i.e., medical generalism, community orientation, focusing on social determinants of health and societal factors, and working from a personal–professional relationship with patients [21,22].
An alternative approach for DMPs might be found in Person-Centred and Integrated Care (PC-IC), as increasingly advised by international guidelines on multimorbidity and chronic conditions [23,24,25]. Instead of focusing on a standard set of disease management processes determined by health professionals, PC-IC aims to ensure that patients’ values and concerns shape the way long-term conditions are managed [26]. This approach encourages patients to select treatment goals and to work with clinicians to determine their specific needs for treatment and support of their chronic diseases [27]. A PC-IC approach is believed to improve the quadruple aims [28,29] of better patient and HCP experience, population health, and cost-effectiveness [26,30]. Currently, several studies on such PC-IC approaches to managing chronic conditions in primary care are emerging, but descriptions of their scientific foundation are lacking [31].
In addition, in the Netherlands, a shift is taking place from DMPs to PC-IC approaches initiated by primary care HCP organizations. To scientifically support this movement, this paper describes a mixed-method multiphase development of a PC-IC approach for the management of patients with one or more chronic diseases in Dutch primary care. We co-designed the approach with all stakeholders involved, i.e., academics, HCPs, patients, and healthcare insurers.
## 2.1. Design
A multiphase process to develop a PC-IC approach for patients with one or more chronic conditions, but at least DM, COPD, or CVD was started in March 2019 and finished in July 2020. We conducted the process together with three large primary care cooperatives in the eastern part of the Netherlands, i.e., the Nijmegen region (168 GPs, approximately 290,000 inhabitants), the Arnhem region (193 GPs, ~440,000 inhabitants), and the Doetinchem region (116 GPs, ~150,000 inhabitants). We followed a four-phase process in which the information collected in each phase was commented on by stakeholders and used in the next phase (see Table 1). The four subsequent phases were all a priori defined by the project team based on criteria for reporting the development of complex interventions in healthcare and including all relevant stakeholders [32,33]. In short, in Phase 1 we conducted a scoping review and a document analysis to identify key elements to construct a conceptual model for delivering PC-IC care. In Phase 2, national experts on DM2, CVD, and COPD and local HCPs commented on the conceptual model using online qualitative surveys. In Phase 3, patients with one or more chronic conditions commented on the conceptual model in individual interviews. To conclude the development process, in Phase 4 the conceptual model was presented to the local primary care cooperatives and finalized after processing their comments. We used the Standards for Reporting Qualitative Research (SRQR) guidelines to design and report the methods and results of the respective sub-studies [34].
The medical ethics review board of the Radboud University Medical Center declared that ethics approval for the study was not required under Dutch National Law (registration number: 2019-5756). All participants received written information about the study and their written informed consent was obtained prior to their participation.
## 2.2. Scoping Review and Document Analysis (Phase 1)
In this phase, we aimed to identify which process elements and which interventions a PC-IC approach should contain. We identified the key process elements (e.g., history taking or discussing patients’ goals) for successful (multiple) chronic disease management by conducting a scoping review. We identified the key interventions by conducting a document analysis.
For the scoping review, we searched PubMed, EMBASE, Cochrane, Turning Research into Practice (TRIP) Medical Database, and the Guidelines International Network (GIN) to identify key elements for the successful management of (multiple) chronic diseases in primary care (see Appendix A for the search strategies). All eligible publications up until 27 August 2019 were included, and no lower limit with regard to publication date was applied. Forward citation tracking was used and the reference lists of relevant publications were hand searched for additional relevant publications. Two of the authors (LR and MW) independently screened the titles and abstracts of the publications and reviewed the full text of those that seemed eligible for the scoping review. Publications were included if the language was English or Dutch, if the target population consisted of patients with multiple chronic conditions, and if the target setting was primary care. Primary care was defined as a non-hospital community setting with medical care continuity by (the equivalent of) a GP. Publications were excluded if they were study protocols, commentaries, or cost-effectiveness analyses. Next, one author (LR) extracted data on publication details, methods used, and recommendations on important elements of clinical care from the included publications. The extracted details were cross-checked by a second author (MW). The results of the scoping review were used to create a conceptual model including key process elements for PC-IC.
For the document analysis, we analysed all Dutch chronic disease care standards and GP guidelines relevant to the DMPs for COPD, CVD, and DM2 [12,13,14,35,36,37] to identify all unique interventions that were used in the management of these conditions. The documents were analysed by two authors (LR and MW) using inductive thematical coding (Table 2). Using an affinity diagram, a schematic overview of unique key interventions to be included in the PC-IC approach was developed. The resulting intervention model was combined with the process model from the scoping review to form our conceptual PC-IC approach, which was further adjusted in the subsequent phases.
## 2.3. Online Surveys with Healthcare Professionals (Phase 2)
We conducted online surveys among healthcare professionals using open-ended responses, with a thematic analysis of wordings in order to further adjust the conceptual model of our PC-IC approach. This method was chosen because it enabled HCPs from different disciplines to give their individual opinions and flexibility to contribute to the study at a time that suited participants. Each regional primary care cooperative purposively selected a heterogenous group of 10 to 15 HCPs in the following professions or disciplines: GPs with a special interest in CVD, DM, or COPD, regular GPs, PNs, allied HCPs (e.g., physiotherapists, dieticians), social workers and other HCPs involved in the care for patients with chronic diseases. In addition, six GPs with a special interest in CVD, DM, or COPD who were involved in the national guidelines or health policy committees were asked to participate. All participants were monetarily compensated for their time and received written information on the conceptual model of the PC-IC approach before the online survey started.
The online survey was performed in five subsequent parts in which open-ended questions were sent to participants through an adapted secured version of LimeSurvey (LimeSurvey GmbH, Hamburg, Germany). Each survey focused on a predetermined part of the conceptual model of the PC-IC approach. Questions concerned the strength and limitations of different parts of the PC-IC approach. If there were doubts about the responses to the questionnaire items, we asked follow-up questions via e-mail or phone until the answers could be sufficiently interpreted. Analysis of the questionnaire data was performed by three researchers (LR, MW, and AO) using thematical coding, as described in Table 2. To conclude this phase, we organized a virtual meeting with all participants in which we presented the results of the surveys and checked for agreement. This resulted in an adapted version of the conceptual model of the PC-IC approach.
## 2.4. Individual Interviews with Patients (Phase 3)
We then organized individual semi-structured telephone interviews with chronic disease patients to explore their opinions on the conceptual model of the PC-IC approach. Each primary care cooperative recruited patients with DM2 and/or COPD and/or CVD who received chronic disease management from their general practitioner. Participating patients received written information on the study and the conceptual model of the PC-IC approach by e-mail or postal mail before being interviewed. Patients were recruited until data saturation was reached. Patients did not receive financial compensation for their participation.
The interviews were conducted by two researchers (LR and FB). The interviewer first explained the goal of the interview and presented the conceptual model before asking questions regarding expected strengths, weaknesses, and points for improvement of the different elements and interventions (see Appendix B). The interviews were audio recorded, transcribed verbatim, coded, and analysed according to the thematic analysis approach, see Table 2. A summary of the results was offered for member checking. This resulted in an adapted version of the conceptual model of the PC-IC approach.
## 2.5. Finalization of PC-IC Approach (Phase 4)
In this last phase of the development process, we aimed to collect final feedback from the remaining stakeholders (see Table 1) on the adapted version of the conceptual model of the PC-IC approach. Because of their vital role in the organisation and reimbursement of primary healthcare for chronic patients, representatives of the three primary care cooperatives involved and three healthcare insurance companies were invited to and participated in a joint meeting to give oral feedback on the adapted version of the PC-IC approach from their perspectives. Neither patients nor HCPs were invited to this meeting. After the presentation of the PC-IC approach by one of the authors (LR) an open discussion with the ten participants was moderated by another author (EB). Notes were taken by one of the authors (LR) during the discussion.
Finally, to improve the comprehensibility of the approach for people with limited health literacy, two experts from the Dutch Centre of Expertise on Health Disparities (Pharos) were asked to provide written feedback on the comprehensibility of the conceptual model. Their feedback was collected and summarized by one of the authors (LR).
All input from phases one through four was processed by the research team in a report of the feedback on the PC-IC approach. This report was shared with the participants and a meeting was held with stakeholders of the primary care cooperatives for the finalization of the PC-IC approach.
## 3.1.1. Scoping Review
We identified 203 unique publications, of which 18 were included in the review (Table 3). Included publications were published between 2007 and 2019, of which $67\%$ were in the last five years (2015–2019). All publications were in English and most were from the United States or the United Kingdom.
Most publications stated there is still a lack of research and thus insufficient evidence for optimal clinical management of people with multiple chronic diseases [5,23,38]. Only a few of the included studies focused on person-centred outcomes [38,39]. Nonetheless, authors generally agreed that interventions that are generic in nature (i.e., not specific for the underlying condition(s)) and with a person-centred approach are most likely to result in health benefits for patients with chronic diseases and multimorbidity, in comparison to a single disease approach [5,39,40,41].
## Assessment of Multiple Domains—Integral Health Status
Besides the medical domain, authors recommended paying attention to other domains of life as well, i.e., to functional limitations, mental health, and social functioning [5,24,39,40,41,43,47,48,50,51]. Patients with limited physical, emotional, and financial capacities are most disrupted by their chronic illness, but interventions to support these particular patient capacities have been scarcely studied [39]. With regard to mental health, it is recommended to discuss this domain with patients and to actively monitor signs of anxiety, distress, and depression [24,47]. For the social domain, social circumstances, including social support, living conditions, and financial constraints should be considered [47]. Health professionals are encouraged to involve relatives or other informal caregivers in key decisions about the management of the patient’s health, if the patient so desires [24,40,48]. In addition, the needs of these relatives should be considered as well [41]. By including all of these domains, interventions have the potential to better address health inequalities in the population [50]. We summarized the multiple domains in the concept of integral health status (Figure 1).
**Table 3**
| Category | Item | Factors |
| --- | --- | --- |
| Physiological functioning | Physiological functioning | Physiological functioning |
| Medical parameters | Treatment of risk factors CVD | Treat risk factors CVD according to CVD guideline |
| | | Optimize cardiovascular risk profile |
| | Treatment of elevated cholesterol | Reduce elevated cholesterol |
| | Treatment of elevated blood glucose | Treatment of elevated blood glucose/reach target value blood glucose |
| | | Reach target value blood glucose with education and information |
| | | Education on influence of physical activity on blood glucose |
| | | Improve self-management by education on self-check of blood glucose |
| | Treatment of elevated blood pressure | Reduce elevated blood pressure |
| Lifestyle | Promote healthy body weight | Advise balance for persons with healthy body weight |
| | | Promote reduction of waist circumference for overweight or obese persons |
| | | Promote weight loss for overweight or obese persons |
| | | Reduce body weight |
| | | Improve self-management with healthy diet and physical activity |
| | Promote healthy diet | History taking diet |
| | | Promote healthy alcohol use |
| | | Promote healthy diet |
| | | Give dietary advice |
| | Promote physical activity | Attention for inactivity |
| | | Advise to prevent excessive sitting |
| | | Advise strengthening exercises for muscles and bones |
| | | Promote physical activity |
| | | Coaching on physical activity |
| | | Improve endurance |
| | Promote quitting tobacco use | Advise or give coaching to quit tobacco use |
| | | Advise strongly to quit tobacco use |
| | | Advise to prevent inhaling smoke from others |
| | | Treatment to improve motivation to quit tobacco use |
| Medication | Promote correct use of medication | Pay attention to correct use of medication |
| | | Counselling for oxygen therapy |
| | | Evaluate use of inhalation medication |
| | | Deliver adequate pharmaceutical care through patient counselling |
| | | Detect suboptimal medication use |
| | | Education on goal and pharmacology of medication |
| | | Education on use of inhalation medication aids |
| | | Education on oxygen therapy |
| | | Improve self-management by education on correct use of medication |
| | Improve therapy compliance | Discuss therapy compliance |
| | Improve medication safety | Deliver adequate pharmaceutical care by reviewing pharmacotherapy |
| | | Deliver adequate pharmaceutical care by medication surveillance |
| | | Discuss medication safety |
| | | Medication review when prescribing new medication |
| | | Periodical medication review by pharmacy and physician |
| Physical functioning | Physical functioning | Physical functioning |
| Prevention | Attention for oral care | Attention for oral care |
| | Attention for foot care | Advise patients on foot care and shoes |
| | | Improve self-management with foot checks |
| | Attention for flu vaccinations | Yearly invitation for flu vaccination |
| | | Education on flu vaccinations |
| Actual complaints | Reduce burden of cough | Reduce burden of cough by advising physical activity |
| | | Reduce burden of cough by education on clearing sputum |
| | | Reduce burden of cough by breathing and relaxation exercises |
| | Reduce dyspnoea | Reduce fear for dyspnoea by psychological counselling |
| | | Reduce dyspnoea by breathing and relaxation exercises |
| | | Reduce dyspnoea by adapting pace |
| | | Reduce dyspnoea with medication |
| | | Reduce dyspnoea by improving respiratory muscles |
| | Attention for self-management of exacerbations | Attention for exacerbation management |
| | | Reduce disease burden in future: prevent exacerbations |
| | | Education on reducing chances of exacerbations |
| | | Education on early detection of exacerbations |
| | | Improve self-management by composing an action plan for exacerbations |
| Functional (dis)abilities | Functional (dis)abilities | Functional (dis)abilities |
| Mobility | Attention for mobility | Education on possible limitations of fitness to drive |
| Functioning at home | Functioning at home | Advise aids |
| Quality of Life | Quality of Life | Quality of Life |
| Treatment burden | Reduce treatment burden | Reduce treatment burden |
| | | Identify treatments with limited effects |
| | | Identify medication with elevated chance of side effects |
| | | Identify non-pharmaceutical interventions as alternatives to medication |
| | | Identify alternative planning of appointments |
| | | Identify ways to maximalise effect of current treatments |
| | | Identify causes of high treatment burden |
| Emotional well-being | Learn coping with condition through psychological counselling | Advise contact with peers |
| | | Treatment of adaptation problems |
| | | Use psychosocial interventions (training for coping skills, relaxation exercises, stress-management) |
| | | Psychosocial counselling to learn coping strategies |
| | | Education on the condition |
| | | Education on coping with the (consequences of) chronic condition |
| | | Pay attention to psychosocial factors (fear of dyspnoea, shame, sexual problems, social isolation, depression) A |
| | Support for psychological problems | Support for psychological problems |
| | | Reduce stress, fear, sadness, and depression |
| Social functioning | Social functioning | Social functioning |
| Social relationships | Attention for social relationships | Discuss important themes in partner relationship |
| | | Discuss communication to others |
| | | Pay attention to social context of patient |
| Labour participation | Improve labour participation | Advise occupation physician for work related problems |
| | | Discuss communication at work |
| | | Support for reintegration: reduce burden, improve capacity |
| | | Reduce disease burden in future: prevent invalidity and incapacity for work |
| | | Education on the possible limitations for work |
## Case Management
Case management is considered to be an effective way to support patients in achieving their goals and communicating with other HCPs [41]. Case managers are advised to perform regular face-to-face assessments with the patient [41]. Establishing a partnership between different disciplines (i.e., primary care physicians, medical specialists, nurses, mental health professionals, and social care workers) may provide the key to improving care for patients with multimorbidity and psychological distress [45,48]. The patient should also be part of this team [41]. Communication and coordination across health professionals are considered essential in providing multimorbidity care [24,39,40,47,48,49]. To improve partnership and communication between health professionals and the patient and family, it is recommended to work in small teams with dedicated contact persons on both sides [44].
## Clinical Assessment
Multiple publications recommend assessing disease burden by determining how day-to-day life is affected by the patient’s health problems and establishing how health problems and treatments interact [24,48]. Examples of health problems influencing disease burden are chronic pain, depression and anxiety, and incontinence [48]. Another recommendation is to assess the burden of treatment because this can greatly influence patients’ quality of life [23,24,39,42,47,48]. For example, NICE recommends discussing the number of healthcare appointments a patient has and the format in which they take place, the number of non-pharmacological treatments, the assessment of polypharmacy, and the effects of all treatments on mental health or well-being [24]. An annual medication review is recommended to evaluate the risks, benefits, possible interactions, and treatment adherence for each drug the patient uses [24,48]. Finally, Muth et al. noticed that the management of risk factors for future disease can be a major treatment burden for patients with multimorbidity and should be carefully considered when optimizing care [48].
## Patient Preferences and Priorities
Many studies described the importance to elicit patients’ preferences and priorities for care [23,24,40,42,45,47,48,50]. Addressing a patient’s priorities helps to minimize adverse effects of psychological distress [45]. Using these preferences and priorities, together with the health professional’s clinical expertise and based on the best available evidence, individual goals for care should be determined [46,47,48]. In this conversation, health professionals should also explore, without any assumptions, to what extent a patient wants to be involved in decision-making [48]. Another important factor to consider when discussing goals with patients with multimorbidity is life expectancy and prognosis of the conditions [23].
## Care Plan
After prioritizing the patient’s problems, a care plan should be drafted, which sets out realistic treatment goals, monitoring, treatment, prevention, (self-)management advice, responsibility for coordination of care, and timing of follow-up through shared decision-making [24,47,48]. The plan should be shared with other involved professionals, the patient, and the family [41,44]. When choosing interventions, it is advised to use the best available evidence, but to also recognize the limitations of the evidence base for patients with multimorbidity [23,48] and to check if an intervention is effective in terms of patient-related outcomes [24]. Possible interventions should be tailored and adapted to a patient’s individual needs [41,50] and shared decision-making should be used to maximize the impact of interventions [19,40,41]. The key process elements of the PC-IC approach that we could retrieve from the included publications are summarized in Figure 2.
## 3.1.2. Document Analysis
For this phase, we analysed three clinical guidelines of the Dutch College of General Practitioners and three national care standards for DM2, COPD, and CVD [12,13,14,35,36,37]. The document analysis resulted in a list of categories with unique key interventions for disease-specific and holistic care (Appendix C) which was converted into a draft conceptual intervention model for the PC-IC approach. After processing feedback from stakeholders as described in Section 3.2, Section 3.3 and Section 3.4 below, this resulted in a graphical representation of the final intervention model for use in daily practice.
## 3.2. Online Qualitative Surveys with Healthcare Professionals (Phase 2)
A total of 56 HCPs were invited to participate in the online qualitative survey study. Fifty-two ($93\%$) responded and 10 were asked follow-up questions to clarify the responses of their initial input. The majority of the participants consisted of GPs ($$n = 16$$) and PNs ($$n = 15$$), but several other disciplines were also involved (Table 4). The results of the survey were categorized as: general comments on the PC-IC approach and comments on the individual phases of the care process (i.e., assessment; setting personal health goals; choosing interventions; individual care plan; evaluation).
## 3.2.1. General Comments
*In* general, most participants agreed with the underlying vision of the PC-IC approach, namely that person-centred and holistic care would improve the quality of care for patients with one or more chronic diseases (Q1, see Table 5). It is likely to lead to more insight into the patient’s health status and any underlying problems. Using the PC-IC approach could increase the motivation of the patient for behavioural change and therefore may improve therapy compliance and health status. Many participants expect that this approach will initially take up more time, but that this time will be restored in the future. In the long term, therefore, the approach could save time and lead to more efficient provision of care (Q2).
Another anticipated advantage of the PC-IC approach is the cyclical aspect, which ensures that the process continues and the patient’s health status is checked repeatedly. Some participants liked the fact that the PC-IC approach has a strong theoretical basis and would give patients more control and responsibility (Q3).
According to some participants, a potential disadvantage of the approach could be that it may be too time-consuming, both for the HCP and for the patient (Q4). Therefore, some participants considered it not feasible to implement the approach in daily practice in its current form. In addition, some participants doubted the magnitude of the positive effects on the quality of care and patients’ health of the PC-IC approach.
In addition, participants questioned which patients the care program would be suitable. Some thought it would be useful for all patients, whereas others suggested using it only for the more complex patients. Others indicated that the program may be too complicated for people with limited health skills (Q5).
## 3.2.2. Assessment of Integral Health Status
Assessing patients’ integral health status was considered a positive development by almost all participants, who indicated that a broader assessment of health status may have positive effects for both the patient and the HCP. It provides insight into the connection between health problems and their underlying causes for both parties. This creates more awareness and motivation for change in patients, especially if the underlying cause of these health problems concerns a domain other than the medical domain (Q6). Involving family members or informal caregivers in discussing the overall health situation was also mentioned as a strong point. They can often provide useful additional information and may be supportive during treatment. Assessing and discussing integral health status also provides clear goals and priorities for the patient. Therefore, participants considered the integral health status a suitable way to map out complex patients. Filling out a questionnaire online (at home) helps the patient better prepare and saves time during the consultation.
A disadvantage of focusing on integral health status instead of the disease-oriented approach might be that the medical aspects may not be sufficiently addressed and the severity of individual chronic diseases becomes less clear to patients. In addition, HCPs feared a patient may not want to talk about other areas of life as he/she may consider them irrelevant to the condition. It was also mentioned that making a more elaborate assessment of the patient’s health status could be confrontational for some, especially for those with many problems (Q7).
## 3.2.3. Setting Personal Health Goals
Most participants were enthusiastic about setting personal health goals through shared decision-making. The most important advantage mentioned was that it may motivate the patient toward behavioural change. Contributing factors to motivation were awareness, commitment, and responsibility on the side of the patient. Setting personal goals also benefits the HCP, who gains more insight into the patient’s priorities, and is more in tune with the patient, which could make interventions more effective (Q8). Participants also mentioned the disadvantages and pitfalls of setting personal goals, such as that the importance of disease control might be overlooked (Q9). In addition, HCPs mentioned the risk that the patient sets unattainable goals, which can demotivate both the patient and the HCP.
## 3.2.4. Choosing Interventions
Although the graphical representation of the PC-IC conceptual model for use in daily practice and the accompanying schematic overview of existing key interventions to support the management of patients with chronic conditions (see Section 2.2) was appreciated by many participants, the graphical representation in its initial form as presented to the participants was deemed confusing by some of them due to the inclusion of too much information in one visualisation. Without further explanation, this makes the model difficult to understand (Q10). Participants also mentioned that it is difficult to create a static model for the supply of interventions, which will usually vary between regions and possibly change over time.
## 3.2.5. Individual Care Plan
Participants saw many advantages of a care plan, both for the patient and for the HCP. The most important advantage is that the patient and the various HCPs involved may share the same specific personal goals, which makes communication between patient and caregiver and between different caregivers easier. The care plan provides a clear structure and benefits evaluation of personal goals. Participants also indicated that it fits well within a holistic approach (Q11). Disadvantages could be that it is time-consuming to draw up the plan, that the conversation with the patient can become subordinate to the plan, that making a plan is not yet sufficiently integrated into the ICT systems, and that a care plan can lead to the medicalisation of non-somatic problems. Disadvantages for patients could be that it can be invasive, that it can evoke resistance, and that it can create ambiguity if not all HCPs are on the same page (Q12).
Participants wanted to include the following information in the proposed individual care plan format: the patient’s specific goals; the selected interventions; an overview of the HCPs involved and their responsibilities; and time of evaluation.
## 3.2.6. Evaluation
Many participants found it unclear whether a patient-level evaluation had been included in the process of the PC-IC approach. They indicated that they missed this essential step and would prefer to add it (Q13). An advantage of an evaluation is that it provides new information which can be used in the next cycle. No disadvantages of an evaluation were mentioned.
## 3.3. Individual Interviews with Patients (Phase 3)
Twelve patients were invited for the interview study. One patient did not want to participate, two were not eligible as they did not receive care in a DMP, and nine consented to be interviewed. Data saturation was reached after the first eight interviews. Eight patients ($88.8\%$) were male, their mean age was 65 years (range 58–79 years). One patient had COPD, three had CVD, two had DM2, and three had any combination of these chronic diseases. The median duration of being in the DMP was ten years (range 2–10 years).
The following main categories were”Ide’Iified during the analysis of the interview transcripts: personalized care, cooperation, patient role, and PN role.
## 3.3.1. Personalized Care
Patients were generally positive towards the presented manner of personalized care, especially regarding the integral health status assessment and use of individual care plans. The integral health assessment may give patients and HCPs better insight and focus on holistic well-being, and may account better for comorbidity, disease interaction, and psychological factors. It may detect issues affecting patients’ well-being and identify those who require more support. It may also improve working relationships by shifting towards a more personal approach rather than a disease focus. Two participants were content with their current care and expected no benefits from the new approach.
The PC-IC approach would be inviting patients to be more involved in their healthcare. Having the care plan at home could help remind and motivate them and allow easier involvement of informal caregivers/social support systems. The wording of the care plan should be easy to understand. Some participants called for more flexibility regarding individual care plans to be adaptive to patients’ needs and unexpected circumstances and requested options to communicate their questions and concerns to their PN after the plan is formulated.
Longer consultation time would allow for more personal attention, and opportunities for better patient education, and is expected to benefit health outcomes. One participant believed that too much consultation time was reserved for patients. Some participants suggested adjusting the consultation frequency and the duration according to each patients’ individual needs. This may allow PNs to direct their efforts more efficiently (Q14).
## 3.3.2. Co-Operation
Participants saw the benefits of being equal stakeholders in their own care. This may improve care participation and help them carry responsibility for their own health. Greater equality may also improve the working relationship with HCPs. Giving patients the opportunity to prepare for care consultations was seen as a way to improve participation and equality. Using digital questionnaires for assessing integral health status was appreciated by the participants. Avoiding time constraints when answering health-related questions may also cause more reflection on health, better quality answers, and time during consultations to explore the answers. One participant noted that completing the questionnaire allows patients to share thoughts about their health with their informal caregivers/social support system more easily (Q15).
Some participants worried that patients with low literacy, facing language barriers, insufficient health skills, or insufficient computer skills may have difficulties using the questionnaire. One participant affirmed this, saying his low literacy made him feel insecure and uncertain when filling out questionnaires. One participant thought that thirty minutes was too long to fill out a questionnaire. Participants gave several suggestions regarding accessibility. Intelligible and straightforward questions were seen as important. Further suggestions were: visual instead of numeric response scales, a paper-version alternative, and a narrator function. One participant suggested a shorter alternative to the questionnaire.
Using the questionnaire results would support the patient and the PN, as both may get better insight into the patient’s current health status and its long-term course. This may provide a sense of control and assurance for the patient and may help both sides to prepare for consultations and help discover previously unacknowledged problems that affect the patient’s health when the responses yield unexpected results. Several participants mentioned that the color-coding of the results made them more insightful, while one participant found this too confrontational and judgmental towards patients.
Several participants saw potential flaws in using the questionnaire results. Two participants warned that this could lead to a search for non-existent problems. One participant thought paying attention to psychological stressors was lacking, while these can cause or amplify illness. Participants mentioned that the results should also be kept simple, some suggesting that a summary would suffice. Some participants suggested additional questions, one suggesting a question about literacy, another suggesting to include socioeconomic background, and a third suggesting questions regarding what mattered most in the life of patients (Q16).
Participants also provided advice on the quality of communication with their HCPs, from which five requirements for communication emerged: trust, authenticity, empathy, constructiveness, and specificity. Trust improves patient openness and working relationships and requires continuity of care. Authentic personal interest makes patients feel seen and heard, and is conducive to developing trust. Being empathetic may provide a sense of safety and comfort, and let patients know that they are supported. Being constructive may create a positive and motivational focus on the patient. Finally, adjusting one’s approach to specific patient abilities and needs may benefit mutual understanding and working relationships.
## 3.3.3. Role of the Patient
Participants thought that gaining ownership and self-management was important and that patients are ultimately responsible for their actions regarding their well-being, but may often be unaware of their potential influence on it. Being aware of this may stimulate self-management. They noted the potential benefits of self-management but also expressed thoughts on factors limiting its attainability. Participants thought that experiencing ownership in care may motivate better adherence to treatment and healthcare advice, and may facilitate acceptance of advice. They noted that any level of self-management may be beneficial for this. Similar to the HCPs the participating patients also mentioned that formulating personal health goals may contribute to more personalized care. Patient-specific factors such as personality traits, acceptance, and knowledge were thought to have an important limiting influence on attaining self-management (Q17). Several participants noted that patients’ responsibility extended to communication with the PN, as patients may choose to withhold information on topics such as mental problems or illiteracy, but this may prevent them from receiving optimal care.
Three participants elaborated on involving informal caregivers/social support systems or primarily spouses, during consultations and at home. Patients bringing their spouses to consultations may be a source of information for the PN. The spouse may help retain information and provide support at home, as well as develop more understanding of the patient and their problems themselves. One participant noted that this involvement should be balanced with professional care, as a patient might value the opinion of their spouse more than that of the PN (Q18).
## 3.3.4. Role of the Practice Nurse
Participants saw benefits in the proposed role of the PN in providing a patient-centred model of care, but also mentioned several limiting factors and provided feedback on their perception of PNs’ responsibilities in the process. The PN taking on the case-manager role may provide a more central viewpoint of patient wellbeing, in line with the integral health assessment. Having a central point of responsibility may also benefit from continuity of care. However, some PNs may lack the knowledge and skills to deal with complex cases or the affinity to handle certain aspects of patient well-being. Guiding patients toward appropriate care when faced with these limitations was marked as a responsibility of the PN. One participant noted that patients may still prefer GP visits for certain problems regardless of the PN’s capability. Resource constraints, such as available time per patient, were seen as a potential limitation.
Other PN responsibilities mentioned were on supporting self-management and communications within the healthcare team. Participants noted that improving self-management is dependent on the PN creating opportunities to do so, which might require them to develop flexibility in their approach according to patients’ needs. Two participants suggested that HCPs could provide summaries of consultations as additional support when formulating personal goals. Sharing of relevant information between HCPs was seen as important in keeping care teams informed on patient health and may prevent patients from having to repeat their story several times.
Participants had different opinions on GP involvement in their care. Most participants advised that their GP did not need to be ‘visibly’ involved in their care, one saying that he expected the PN to have more relevant expertise than the GP, and another saying that the GP should be involved when deemed required. One participant saw merit in some but limited GP visibility, even if only once a year (Q19).
## 3.4.1. Health Insurers
*In* general, health insurers found the PC-IC approach a good and positive development to move towards integral and holistic tailor-made care. Their suggestions for further improvement were: to describe the inclusion criteria for the PC-IC approach in practice more clearly, for example, every patient with two or more chronic diseases; pay more attention to the consequences of a shortage of HCPs in the future by having patients prepare their consultation at home and using more e-health applications; pay more attention to the required change in organisations and practices, because the implementation of the intervention determines if the intervention is successful.
## 3.4.2. Dutch Centre of Expertise on Health Disparities (Pharos)
The experts from Pharos felt positive about the PC-IC approach to health and treatment because they found that, for a lot of people in vulnerable positions, not only disease, but also context, abilities, and possibilities influence health. A digital questionnaire for the assessment of health status that is already used in Dutch hospitals and general practices (the Nijmegen Clinical Screening Instrument, or NCSI) [52] was tested with people with limited health skills and led to suggestions for improvement of the language use and layout of this digital questionnaire. In addition, Pharos provided feedback on the conceptual intervention model, which was found to be an unsuitable way to visualise and discuss these treatments. The model was considered too complicated and interfered with the integral approach.
## 3.4.3. Finalization of the PC-IC Approach
Based on the scientific literature, current practice guidelines, and input of a variety of stakeholders, the holistic, PC-IC approach for the management of patients with (multiple) chronic diseases in primary care was finalized in a meeting with relevant stakeholders of each primary care cooperative (Figure 3 and Figure 4).
## 4.1. Summary of Results
To our knowledge, this paper is the first to describe in detail the subsequent steps in the development of a person-centred and integrated care approach for people with (multiple) chronic conditions in primary care. In the first phase, the scoping review identified that a PC-IC approach for multimorbidity should comprise multiple domains of health status, a case manager, and a thorough assessment of patient preferences and priorities. These essential elements were incorporated into a conceptual model for the PC-IC approach. The document analysis resulted in a list of unique interventions. In the second phase, HCPs commented on the (dis)advantages of the conceptual model, and provided suggestions for the improvement of the conceptual intervention model. The third phase consisted of a patient-level evaluation step to the PC-IC approach. Patients commented on the conceptual model and indicated that this approach could have many advantages, such as being more responsible for their own health and having a partnership with the HCP. In the final phase, health insurers and the Dutch Centre of Expertise on Health Disparities (Pharos) provided feedback on the model, after which the PC-IC approach was finalized in a meeting with relevant stakeholders of each of the three primary care cooperatives involved.
## 4.2. Comparison to Existing Literature & Interpretation
Our findings are supported by other interventions to deliver personalized primary care for patients with chronic conditions that have been reported [31,53,54]. Similar to our approach, these interventions all include a PC-IC consultation, case management, personal goal setting, and network support. Differences between the respective approaches consist mainly of the targeted population and the way eligible patients are selected. The most recent interventions focus on targeting multimorbidity or ‘high-need’ patients. For example, Salisbury et al. developed and evaluated the 3D approach for people with multimorbidity in the UK, in which general practices offered greater continuity of care and biannual person-centred, comprehensive health reviews [31]. They selected patients with at least three types of chronic diseases and, although patients experienced the provided care as more person-centred, no favourable effects on HRQoL, general well-being, or patients’ treatment burden were observed [31]. Another intervention, which was also developed in the Netherlands, divides patients into low-, moderate-, and high-care-need subgroups, and only the high-care-need subgroup receives the intervention [55,56]. The effects of this intervention have not been reported yet, but a likely advantage of targeting all patients with chronic conditions, as we aim with our intervention, is that it may reduce overtreatment in patients who actually need less care than they currently receive according to the strict DMP protocols. This may create more time for patients who need more attention from their primary care HCPs.
The results from our interviews with patients suggest that the developed PC-IC approach may solve several problems in current chronic care. For example, Rimmelzwaan et al. found that people with multimorbidity missed an approach that focuses on the patient “as a whole” [57]. In addition, these authors also observed that the participants in this study reported that HCPs should treat their patients as equals. Our study shows that patients believe that this new PC-IC approach could improve holistic care, time, and attention in consultations with the NP, as well as the partnership between patients and HCP. Furthermore, our findings are similar to research by Rijken et al. [ 58], who found that people with multimorbidity have the following priorities in their chronic care: having one health record shared by all HCPs involved in their care, regular comprehensive assessments, and receiving support from their HCPs to self-manage their chronic conditions.
In our study, we have predominantly focused on the micro-level service delivery aspects of PC-IC care. However, to support the PC-IC approach, other levels, and components of integrated care, i.e., the meso and macro levels of service delivery, leadership and governance, workforce, financing, technologies and medical products, and information and research, have to be considered and studied as well [10,59]. For financing, Bour et al. have studied a complementary payment model to this PC-IC approach, which is published elsewhere in this journal [60].
## 4.3. Strengths & Limitations
A particular strength of our study was the rigorous and extensive development process per region with relevant stakeholders. The development of the PC-IC approach based on the existing literature and the input from stakeholders makes the foundation of the conceptual model the best it can be before the scheduled feasibility study is executed, making the feasibility study even more effective. Because the development process was finalized per region, it could be tailored to fit the regional situation. We did, however, not further analyse regional differences, which limits the generalisability of the results to other regions in the Netherlands or other countries. Another advantage of our study was that HCPs and patients could comment on a tangible conceptual model, which made their feedback more specific and useful to modify the concept. A final strength of the study was the high participation rate of HCPs. This may be due to the method of online interviews, because of the advantages of online interviewing: significant savings in time for participants and the opportunity for participants to carefully formulate a response to a particular question [61]. Another explanation could be the compensation HCPs received from the regional primary care cooperatives to participate in the study.
We also acknowledge some limitations. First, in the beginning of the project we performed a scoping review on multimorbidity, but the scope of the project later expanded to people with one or more chronic diseases, also because of the feedback from participating HCPs. Nonetheless, we think the findings are also relevant for patients with single chronic diseases, as problems may still arise in other areas of life, and PC-IC seems also effective in single disease cases [26]. In addition, the scoping review is currently somewhat outdated. However, we decided not to update the scoping review at this stage, as the intervention is based on the consecutive phases of the development process.
Second, in the interview study (Phase 3), eight of the nine patients interviewed were males, which limited our ability to take the role of gender into account when adapting the draft conceptual PC-IC model from the patient perspective. This clearly reduced the diversity of the study sample and may also explain why data saturation was reached rather quickly.
Third, due to the influence of COVID-19 restriction measures, the method of interviewing patients had to be revised. To limit the potential exposure of patients with chronic diseases to the SARS-CoV-2 virus, we chose to conduct the interviews by phone. The pitfall of this method is that non-verbal signals cannot be seen, which might lead to different conversations and different observations from the interviews. An advantage might be that the patient feels more anonymous and is more likely to respond frankly, although the topic of our study was not particularly sensitive.
Fourth, HCPs and patients commented on a theoretical model. After actually experiencing it in their practices, their views and opinions may be different. Therefore, the experiences of patients and HCPs should also be examined after having implemented the model in the upcoming feasibility study.
## 4.4.1. Recommendations for Future Research
Our next studies will focus on the feasibility and the actual effects of the developed PC-IC approach in terms of the Quadruple Aim, in which we will focus on health-related quality of life, self-management behaviour, and patient experience, as outcome variables in research on the effects of PC-IC should be tailored to be person-centred [62]. As part of the cluster, in the randomised trial that is currently underway we assess barriers and facilitators of switching from the current to the new (PC-IC) approach in several domains (i.e., professionals, patients, organizational, and financial domains). The insights we gain from this will be part of the recommendations regarding the implementation of the PC-IC approach elsewhere. Furthermore, more research is needed on the acceptability of this approach in patients with limited health literacy.
## 4.4.2. Recommendations for Practice
Although this study offers some important insights for HCPs searching for a PC-IC approach to chronic care, the anticipated superiority of this approach relative to the current DMPs has yet to be studied.
## 5. Conclusions
Based on the scientific literature, current practice guidelines, and the input of a variety of stakeholders, we developed a holistic, person-centred and integrated approach for the management of patients with (multiple) chronic diseases in primary care. Future evaluation of the PC-IC approach will show if this approach leads to more favourable outcomes and should replace the current single-disease approach in the management of chronic conditions and multimorbidity in Dutch primary care.
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|
---
title: 'Nutritional Health Knowledge and Literacy among Pregnant Women in the Czech
Republic: Analytical Cross-Sectional Study'
authors:
- Klára Papežová
- Zlata Kapounová
- Veronika Zelenková
- Abanoub Riad
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001919
doi: 10.3390/ijerph20053931
license: CC BY 4.0
---
# Nutritional Health Knowledge and Literacy among Pregnant Women in the Czech Republic: Analytical Cross-Sectional Study
## Abstract
Adequate nutrition and the nutritional status of pregnant women are critical for the health of both the mother and the developing foetus. Research has shown a significant impact of nutrition on the child’s health and the future risk of developing chronic noncommunicable diseases (NCDs), such as obesity, diabetes, hypertension, and cardiovascular disease. There is currently no data on the level of nutritional knowledge of Czech pregnant women. This survey aimed to evaluate their level of nutritional knowledge and literacy. An analytical cross-sectional study was conducted in two healthcare facilities in Prague and Pilsen between April and June 2022. An anonymous self-administered paper-form questionnaire for assessing the level of nutritional knowledge (40 items) and the Likert scale for assessing nutrition literacy (5 items) were used. A total number of 401 women completed the questionnaire. An individual’s nutritional knowledge score was calculated and compared with demographic and anamnestic characteristics using statistical methods. The results showed that only $5\%$ of women achieved an overall nutritional score of $80\%$ or more. University education ($p \leq 0.001$), living in the capital city ($p \leq 0.001$), experiencing first pregnancy ($$p \leq 0.041$$), having normal weight and being overweight ($$p \leq 0.024$$), and having NCDs ($$p \leq 0.044$$) were statistically significantly associated with a higher nutritional knowledge score. The lowest knowledge scores were found in the areas of optimal energy intake, optimal weight gain, and the role of micronutrients in diet during pregnancy. In conclusion, the study shows limited nutrition knowledge of Czech pregnant women in some areas of nutrition. Increasing nutritional knowledge and nutrition literacy in Czech pregnant women is crucial for supporting their optimal course of pregnancy and the future health of their offspring.
## 1. Introduction
The number of people suffering from overweight, obesity and related diseases is increasing worldwide. According to the World Health Organization (WHO), chronic noncommunicable diseases (NCDs) are the leading cause of morbidity and mortality worldwide. By 2022, 41 million people will die from these causes each year, accounting for $74\%$ of all deaths worldwide. Therefore, great emphasis is placed on early prevention of these NCDs, e.g., hypertension, overweight and obesity, hyperglycemia, and hyperlipidemia [1]. Over time, however, it has become apparent that several diseases may have their origins in an individual’s intrauterine development, and experts have pointed out that maternal lifestyle during pregnancy is linked to serious health consequences and diseases in the child that may develop later in life [2,3,4,5,6,7,8,9]. This concept of ‘nutritional programming’, based on the theory of exposure to specific conditions and lifestyle factors during pregnancy that can determinate an individual’s health later in life, has become accepted dictum [2,3,4,5,6,7,8].
A healthy lifestyle, including a balanced diet and weight control, is undoubtedly crucial for the adequate development of pregnancy in terms of maternal and fetal health [2,4,10,11]. Despite this well-known fact, suboptimal maternal nutrition and weight gain has been seen more often over the past few decades [12]. Women’s pregnancies are increasingly complicated by extreme or morbid obesity, with all its consequences for maternal and fetal health, and despite concerted public health efforts, the proportion of overweight pregnant women continues to rise [13,14].
In today’s world, the online environment offers countless possibilities for obtaining information. With the internet being more accessible to an increasing number of people, it could be thus assumed that easy access to information allows women to obtain all the necessary knowledge related to adequate nutrition, elimination of food risks, and nutrition-related complications associated with pregnancy. However, studies show that this is far from the case and that women do not follow nutritional recommendations [15,16,17,18].
A woman’s eating behaviour during pregnancy is influenced by many multifaceted and complex factors. One of the most important factors is the level of nutritional knowledge, the lack of which can be a barrier to adopting healthy behaviours and other postnatal weight management practices. Nutrition knowledge is not only about facts and processes but also about how to apply them in practice [19]. Most studies have documented that the behaviour of pregnant women varies according to, for example, their level of education, age, BMI, number of pregnancies, and sociocultural factors [20,21,22,23]. The influence of socioeconomic factors is also often discussed, such as lower net household income, average educational attainment, and availability of health insurance [24].
Lee et al. noted that there is a lack of published research on the assessment of pregnant women’s comprehensive nutrition knowledge [23]. Generally, most previous studies monitored the level of nutritional knowledge around the intake of specific nutrients, such as folic acid [25,26], optimal weight gain [13], iodine intake [27], and fruit and vegetable intake [28], but only a few dealt with comprehensive knowledge [20,23]. One study even addressed areas of nutrition within the context of lifestyle factors of pregnant women [24]. Several studies showed that non-adherence to pregnancy-specific nutritional recommendations was associated with lower levels of nutritional knowledge [20,22,23,29] and indicated that nutrition education during pregnancy was associated with positive pregnancy outcomes [20]. Another study documented that recommendations are often insufficient unless accompanied by support (e.g., through nutrition counselling) to achieve optimal and healthy eating [22]. Many women expect to get all the information they need from a private gynaecologist. Obtaining this type of care has been shown to be a particularly effective method of prevention [24]. Despite this, healthcare providers are not routinely prepared to help pregnant women make informed decisions, and nutritional care is often lacking in primary care for pregnant women [20,30].
There is no definition of the minimum nutritional knowledge that pregnant women should know. The abovementioned studies focusing on the nutritional knowledge of pregnant women are very heterogeneous in this regard, making it difficult to establish a single tool and score system. Women should demonstrate a general overview of all areas of dietary recommendations (which are often country-specific according to national dietary recommendations) without favouring any one area. Each recommendation has a particular rationale related to the health of the woman and the developing foetus. The nutrition knowledge classification is an indicator of success that provides an overall picture; however, far more important is to identify areas in which the level of nutrition knowledge is lowest [12,19].
Nutritional knowledge is one of the cornerstones of health and nutrition literacy, which represents the ability to obtain, understand, and use information that ultimately leads to an increase in one’s own influence on the quality of one’s health. Nutritional knowledge alone does not completely influence an individual’s behaviour, but it can significantly shape their attitudes, which can be reflected in a person’s actions [20].
As for the health and nutritional policy in the Czech Republic on pregnant women regarding nutrition advice and nutritional supplement recommendation during medical appointments, there is not anything like that. For these reasons, it is not entirely clear who should provide this care and counselling to pregnant women, so it is fragmented among health professionals. Although gynaecologists are at the frontline of basic prenatal care, the number of patients and the lack of dedicated time for them detract from prevention, including both nutritional education and the recommendation of dietary supplements [31]. Other competent healthcare professionals in this respect are trained dietitians, of whom there is also a critical shortage and low awareness of their existence in the Czech Republic [32].
In the Czech Republic, no data are available on pregnant women’s nutritional knowledge. Insufficient attention is paid to this issue, and there is a lack of current nutritional recommendations at the national level, and it is not obvious to what extent healthcare professionals should devote time to nutrition education. Thus, women primarily depend on available sources of information (e.g., internet blogs and forums). To increase awareness of this issue, this study aimed to describe the nutritional knowledge level of Czech pregnant women with attention to the influence of selected sociodemographic and anamnestic factors that may contribute to the level of this knowledge and nutrition literacy.
## 2.1. Design and Settings
This analytical cross-sectional survey-based study was conducted between April and June 2022. The questionnaires were distributed to the target participants in a paper form, which they completed on the spot and then dropped in a box. There was no time limit for completion, and the average completion time was 20–30 min. Fully anonymised completed questionnaires were collected securely before being transferred to electronic format.
## 2.2. Participation
The target group was Czech pregnant women. Inclusion criteria were as follows: Czech citizenship, last month of the third trimester (≥36th week of pregnancy), and singleton pregnancy. Exclusion criteria included: non-Czech nationality, low gestational age (˂38 weeks of pregnancy), multiple pregnancies, and age < 18 years.
Data collection occurred in the Gynaecology and Obstetrics Clinic in Pilsen and the Institute for Maternal and Pediatric Care in Prague. These medical facilities were randomly selected; however, there is a significant difference between Prague and Pilsen. Prague is the capital of the Czech Republic (1.3 million inhabitants), and Pilsen represents a smaller town (with 169,000 inhabitants) in the western part of the country. The purpose of the visits to these medical facilities was regular last prenatal check-ups before they come under the control of the birthing facility. Women were asked to participate in the study when they visited an antenatal clinic. Trained health professionals provided women with information about the study, which was also given in the written form.
The minimum sample size required for this study was estimated using Epi InfoTM version 7.2.5 (CDC, Atlanta, GA, USA, 2021) utilising the following assumptions [33]:i. Confidence level (CI) = $95\%$;ii. Acceptable error margin = $5\%$;iii. Target population size ≈ 111,425 (the average number of live births in the Czech Republic between 2010 and 2021);iv. Number of clusters = 2;v. Expected frequency of the primary outcome, which is the satisfactory level of knowledge score > $80\%$.
At least 384 valid responses were required to establish statistically robust inferences between putative demographic and anamnestic predictors and the current levels of nutritional knowledge. A total of 457 questionnaires were completed, of which 31 were discarded because of incomplete demographic or anamnestic data, and 25 because of the woman’s low gestational age (<38 weeks of pregnancy). Only fully completed questionnaires were used to assess nutritional knowledge ($$n = 401$$).
## 2.3. Instrument
This study is the first of its kind in the Czech Republic. Thus, it was not possible to use any existing validated measurement. For this purpose, an in-house tool was developed to test women’s nutritional knowledge in multiple areas of nutrition recommendations for pregnancy. The development of the original measurement tool (questionnaire) was thus preceded by an extensive search of scientific literature related to the topic, the selected target group, and the questionnaire methodology. The selection of studies was gradually narrowed down, and the instrument construction was based on modified questionnaires according to selected studies [20,23]. The questions were designed to reflect the national dietary recommendations and, at the same time to test women’s knowledge of several aspects of nutrition, including their ability to understand the meaning of recommendations and to apply them in practice. The accuracy and terminological correctness of the questions were checked and modified in collaboration with dietitians from the Faculty of Medicine at Masaryk University in Brno.
The resulting questionnaire was pilot tested on a sample of 30 women representing the target population and modified based on the feedback as necessary. After final modifications, the reliability of the questionnaire was tested, and the kappa value for test–retest reliability was calculated. The mean value of 0.914 showed perfect test–retest reliability.
The questionnaire consisted of two parts. The first part focused on basic sociodemographic and anamnestic data such as age, level of education, place of residence, pregnancy order, body mass index (BMI), chronic diseases, medication and supplements, adherence to an alternative diet and anthropometric measurements, specifically pregestational height and weight, which were taken from the medical records of each pregnant woman. The cutoff point of age groups (≤28 and 28 years) was determined according to the average age of the first-time mother, which is 28 years in the Czech Republic.
The second part of the questionnaire consisted of 40 multi-choice questions testing nutritional knowledge divided into five categories. The first category focused on knowledge of micronutrients (iron, calcium, iodine, folic acid, vitamin A, vitamin D, and omega-3 unsaturated fatty acids), the second category focused on knowledge of macronutrients, and the third category on knowledge of nutritional recommendations (e.g., consumption of fruits and vegetables, fish, salt, and fibre) and optimal daily energy intake, weight gain, and the effect of excess weight on a woman’s health. The fourth and fifth categories were devoted to food supplements and food safety concerns (e.g., mercury intake in pregnancy and the risks related to *Listeria monocytogenes* and Salmonella).
## 2.4. Outcomes
The nutrition knowledge test (NKT) consisted of 40 items with one correct answer; therefore, the items were considered binary (correct = 1/incorrect = 0). Each item had a ‘do not know’ option to eliminate guessing. The sum of the correct answers (maximum 40) represented the final nutrition knowledge score, which was assessed in relation to the main variables (age, education, BMI, pregnancy order, and presence of disease). A satisfactory level of nutritional knowledge was defined to be >$80\%$.
## 2.5. Ethics
The study was approved by the Ethics Committee of the Faculty of Medicine, Masaryk University, Brno (ref. no. $\frac{4}{2022}$), and by the Ethics Committee of the University Hospital in Pilsen and the Faculty of Medicine, University of Pilsen (ref. no. $\frac{74}{2022}$), and the Ethics Committee of the Institute for Mother and Child Care in Prague (ref. no. $\frac{1}{1}$/2022). The respondents confirmed their consent to participate in the study before completing and submitting a questionnaire.
## 2.6. Analyses
All statistical analyses were carried out using the Statistical Package for Social Sciences (SPSS) version 28.0 (SPSS Inc., Chicago, IL, USA, 2022) and the R-based open software jamovi [34,35]. Initially, the normality of numerical variables, e.g., age, BMI, and knowledge score, was tested using the Shapiro–Wilk test with a significance level (Sig.) of <0.05. Consequently, descriptive statistics were used to summarise the sample characteristics (independent variables) and nutritional health knowledge and literacy (dependent variables). Qualitative variables such as education level, pregnancy order, and city were summarised using frequencies and percentages; while numerical variables such as knowledge score were summarised using means and standard deviations (µ ± SD). Then, inferential statistics were carried out to perform hypothesis testing using the chi-squared test (χ2), Fisher’s exact test, Mann–Whitney test (U), and Kruskal–Wallis test (H). All analytical tests were carried out with a significance level (Sig.) of <0.05.
## 3.1. Demographic and Ananmnestic Characteristics
Of the 401 questionnaires collected, 264 ($65.8\%$) were collected in Prague and 137 ($34.2\%$) in Pilsen. Most women were aged 30–34 years ($40\%$). The average age of women was 31.6 years, and the median age was 31 years. All of the women were in the third trimester of pregnancy. More than half of the participants had a university degree ($57.4\%$), more of them in Prague ($44.9\%$) than in Pilsen ($12.5\%$). Fifty-three percent of the women were expecting their first child, and their median age was 30.3 years. Most women had a normal body weight at the beginning of pregnancy ($63.7\%$).
The most commonly reported disease accompanying pregnancy was thyroid disease in $14.5\%$ of women, $6\%$ of women reported gestational diabetes mellitus, and $1\%$ of women reported high blood pressure. About a quarter ($24.9\%$) of women were taking multivitamin preparations, $23.7\%$ of women took iron, $14\%$ magnesium and $10.7\%$ folic acid supplements. However, multivitamin supplements are also likely to contain folic acid and iron; contents of these were not controlled in this work due to a huge variability in multivitamin preparations. Only $2.5\%$ of the women reported adherence to an alternative diet that was mostly vegetarian or vegan (Table 1).
## 3.2. Nutritional Knowledge Items
The test of the level of nutritional knowledge of pregnant women showed limited knowledge of nutrition. Only $5\%$ of women achieved a level of nutritional knowledge higher than $80\%$. Ten questions with the highest error rate were: a question about the mercury content in fish, a question about the promotion of iron absorption in the diet, a question focused on the energy content of food, a question about vitamin A intake during pregnancy, knowledge about vitamin D intake, recommendations for optimal weight gain, plant sources of calcium, the recommended daily intake of folic acid in pregnancy, the reason for the need for iodine in pregnancy, and the reason for folic acid intake in pregnancy. Among the items where women scored best were sources of folic acid, essential sources of iodine, recommended frequency of fish consumption, the importance of calcium in the diet of a pregnant woman, starting to take folic acid, sources of calcium, foods with high-fat content, the risk of contamination of the diet with Salmonella, sources of protein in nutrition, sources of ω − 3 unsaturated fatty acids (Supplementary Table S1).
## 3.3. Nutritional Knowledge Items by Education Level and Age Group
The analysis of NKT showed that the level of nutritional knowledge in pregnant women was highly dependent on the achieved education level. The pregnant women with a university education level (undergraduate and postgraduate degrees) demonstrated better results in questions related to iron (q. #1 and #4), folic acid (q. #6 and #7), omega-3 unsaturated fatty acids (q. #11), vitamin D (q. #14), iodine (q. #15 and #16), vitamin A (q. #17), major nutrients (q. #19 and #20), nutritional recommendations (q. #21–23, #25–26, #28–29, #31–32, and #34), nutritional supplements (q. #35), and food safety (q. #37) knowledge as compared with pregnant women with pre-university educational level (Supplementary Table S2).
Notably, pregnant women with a pre-university education level were more successful with the question about salt consumption in pregnant women (q. #24) compared with pregnant women with a university education level. For further analysis, the effect of the age group was also evaluated. As the average age of first-time mothers in the Czech *Republic is* 28 years, the pregnant women involved in this study were separated into two groups: [1] ≤28 years old and [2] >28 years old women [36].
The evaluation of the questionnaire showed only a limited effect of age on performance in the NKT. The statistical analysis revealed that pregnant women with higher average age (>28 years old) demonstrated better results in questions related to vitamin D (q. #13), iodine (q. #15 and #16), nutritional recommendations (q. #21 and #28), and nutritional supplements (q. #35) as compared to pregnant women with an average age of 28 years or less; however, the group of ≤28 years-old pregnant women showed a higher percentage of successful answers in the question about salt consumption in pregnant women (q. #24) Table 2.
## 3.4. Nutritional Knowledge Items by BMI and Pregnancy Order
Next, the effect of body mass index (BMI) and pregnancy order on the performance in the NKT was evaluated. It was found that pregnant women scored as underweight or extremely obese demonstrated worse nutritional knowledge in questions related to iron (q. #3 and #4), omega-3 unsaturated fatty acids (q. #12), iodine (q. #15), major nutrients (q. #19 and #20), and nutritional recommendations (q. #30) as compared with pregnant women scored as normal weight, overweight, or obese. For pregnancy order, the pregnant women tested in this study were separated into two groups: [1] primiparous and [2] multiparous. Primiparous women demonstrated a higher percentage of successful answers in questions related to calcium (q. #9), vitamin A (q. #17), nutritional recommendations (q. #22), and food safety (q. #37, #38, and #39) compared with multiparous women. On the other hand, multiparous women demonstrated better results in questions related to iron (q. #3) and vitamin D (q. #14) than primiparous women (Table 3).
## 3.5. Nutritional Knowledge Scores
The nutritional knowledge score (total score) analysis revealed several main factors that influenced the NKT outcome. It was found that one of the most important factors in nutritional knowledge was the education level of pregnant women. Women with a university level of education (undergraduate and graduate degrees) demonstrated better results than those with a pre-university education level. Further, a significant effect was also found in the city of origin. Pregnant women in Prague demonstrated better performance compared with women from Pilsen. Additionally, pregnancy order was also found as a significant factor in the NKT outcome. Primiparous pregnant women showed higher total nutritional knowledge scores than multiparous women. The Kruskal–Wallis test also revealed a significant effect of BMI on the total NKT score. The following analysis showed that the pregnant women with extreme values of BMI (underweight or extremely obese) showed lower scores in NKT compared with pregnant women who scored as normal weight, overweight, or obese. Notably, pregnant women with NCDs demonstrated better results in the NKT than pregnant women without NCDs (Table 4).
A linear regression model was established for the overall nutritional health knowledge score incorporating all the independent variables that were found to be significant in the univariate analyses. According to the model, education level had an adjusted odds ratio (aOR) of 3.06 (CI $95\%$: 1.97–4.15) and noncommunicable diseases had an aOR of 1.13 (0.03–2.23). On the other hand, city and pregnancy order were not found to be statistically significant in this model (Table 5).
## 3.6. Nutritional Health Literacy
Analysing the results of nutritional health literacy items revealed that education level was the most prominent factor. On comparing the pre-university vs. university groups, differences were statistically significant in information appraisal ($$p \leq 0.036$$), utilisation ($$p \leq 0.031$$), and help-seeking ($$p \leq 0.036$$). For university-educated women, it was more difficult to recognise valuable sources of information, but after receiving the proper information, it was much easier for them to use it. Interestingly, both groups (pre-university and university) reported that it was mainly easy and very easy for them to seek professional help when needed (Table 6).
## 3.7. Determinants of Nutritional Health Literacy
In Table 7, we can observe the subjective assessment of women’s ability to obtain the necessary information, understand it, assess its meaning, use it, and seek professional help in correlation with the main variables. Statistically significant results can be observed for the order of pregnancy, where women who were expecting their first child, despite having higher nutritional knowledge, reported that it was more difficult for them to find the necessary information (information acquisition), understand and appraise it. Similarly, university-educated women with more nutritional knowledge reported that it was more difficult for them to appraise information, use it, and seek professional help than lower-educated women.
## 4. Discussion
The results of the study showed a low level of nutritional knowledge among Czech pregnant women, with only $5\%$ of women achieving more than $80\%$ of correct answers. These results could be comparable to a study where Lee et al. reported very similar overall nutritional knowledge of pregnant women. Out of a group of 114 women, only $2\%$ demonstrated a level of nutrition knowledge during pregnancy higher than $80\%$ [23]. The results are therefore very similar to those of our study. The next level of assessment of nutrition knowledge is problematic because there is no cutoff score that clearly delineates the boundaries of each grade/level of nutrition knowledge. For this reason, studies usually use similar scores to allow comparison of results [12].
Tests based on a total score usually require the use of proven methods. When assessing nutritional knowledge, it is more meaningful to assess individual areas, as good knowledge may be recorded in some, while bad knowledge may be noted in others [37]. For this reason, and in line with other studies, our evaluation focused on the error rate of questions with the lowest nutritional scores. Focusing on questions with the highest error rates can help to identify areas where preventive interventions need to be strengthened.
In the studied population of pregnant women, the lowest knowledge was demonstrated in the question asking about the types of fish with mercury content: $84\%$ of women answered incorrectly. At the same time, $87.8\%$ of women correctly answered the question, “How many times a week it is recommended for pregnant women to eat fish?”. The connection between these two questions may point to the difficulty of using knowledge in practice. Lee et al. also found women had limited knowledge of risky foods containing mercury [23]. The second question with the highest error rate was related to the promotion of dietary iron absorption. During pregnancy, the need for iron is many times higher than the need for other micronutrients [13,30]; $74.3\%$ of women did not know that vitamin C supports the absorption of iron from the diet. The questions on iron absorption were asked primarily to test the level of nutritional knowledge in women at risk of low iron intake, which includes those on alternative diets (vegetarianism, veganism). Due to the low number of these women in the study population ($2.5\%$), it was not possible to evaluate this fact. However, the results surprisingly showed the absence of this knowledge in the majority of pregnant women. This may be due to a lack of awareness of the increased need for iron in pregnancy with little or no interest in this issue, even though at least $23.7\%$ of women supplemented with iron.
Knowledge of daily energy needs is very important for pregnant women, which is one of the prerequisites for weight management; $73.8\%$ of women did not answer correctly which of the presented dishes contained 1500 kJ and $56.4\%$ of women did not know by how many kJ the daily energy intake increased in the second and third trimesters. Additionally, $66.3\%$ of women did not correctly state the optimal weight gain during pregnancy for women who had a normal body weight at conception. Downs et al. also concluded in their study that women did not have the necessary knowledge about the recommended weight gain during pregnancy [13]. Shub et al. also confirmed in a study of 364 pregnant women that women had limited knowledge about weight gain during pregnancy [38]. A systematic review and meta-synthesis of qualitative research gathering evidence on the understanding, perception, and evaluation of women’s optimal weight gain during pregnancy reported that women were not aware of optimal weight gains during pregnancy [14].
The question about vitamin A and its need during pregnancy was difficult for most women to answer. When asked whether its need is increased or decreased in pregnancy, $70\%$ of women answered incorrectly. Similarly, low knowledge was evident for vitamin D. In this study, $66.3\%$ of women answered incorrectly on the importance of increased need in pregnancy; $50.9\%$ of women could not even identify the source of vitamin D in their diet. Other studies on knowledge about vitamin D showed that $78.5\%$ of women presented a good knowledge [20].
Another very interesting finding was on the use of folic acid during pregnancy. Even though $88.8\%$ of women knew when to start taking folic acid, $60.8\%$ of women did not know the recommended daily intake and $56.6\%$ of women did not know the reason for taking folic acid in pregnancy. The same difficulty was encountered in a study of 150 women in New South Wales, Australia, which found that most women in the study population did not know the reason for taking folic acid or the adequate recommended daily intake of folic acid and iodine from food and supplements during pregnancy [39]. Different results were found in an Iranian cross-sectional study of 265 women, which found good knowledge of folic acid use during pregnancy and that only $34.1\%$ of women expressed negative attitudes towards its use [26]. Other similar studies also demonstrate the lack of nutritional knowledge of pregnant women regarding folic acid intake [25].
The results of the assessment of nutritional knowledge showed the absence of knowledge of pregnant women in important areas of nutritional recommendations. It is important for pregnant women to strengthen their knowledge on recommended energy intake during pregnancy, optimal weight gain, and in terms of micronutrients, especially knowledge about the use of folic acid. The results indicate difficulties with using information in practice, and women often did not know the reason for specific nutritional recommendations.
The factor of level of education proved to be a very strong factor related to the level of nutritional knowledge, where university-educated women had a higher level of nutritional knowledge than other women with a lower level of education. In our sample, $57.4\%$ of women were university educated, which could have influenced the results due to the low proportion of women with the lowest education in the sample. The bigger proportion of university-educated women in this study could be explained by their higher willingness to fill in the questionnaire. For the investigation, we also chose large cities where universities are represented, and it could be thus assumed many educated women would live there.
The group of women was divided into two groups according to the age of first-time mothers, which in the Czech *Republic is* 28 years. The difference between the level of nutritional knowledge of women aged ≤ 28 years and older women was investigated. The reason for dividing the group by age was the assumption that first-time mothers may have a higher level of nutritional knowledge. The results, however, showed a higher level of nutritional knowledge of women older than 28 years, but it was not statistically significant. The assumption of a higher level of nutritional knowledge according to the national average age of first-time mothers was wrong. In our group of women, the average age of first-time mothers was 30.4 years, which may be related to the high proportion of university-educated women who have children at a later age than the national average. Regarding women’s age, these results are consistent with the studies mentioned above, where younger women demonstrated lower levels of nutritional knowledge.
Women’s weight at the beginning of pregnancy has become the subject of many expert discussions and is very important for the health of both the woman and the foetus. If a woman’s weight at conception is not optimal, the woman must be aware of the possible risks and, above all, the energy value of the diet and optimal weight gain [11,40].
Statistical relationships between the level of nutritional knowledge and women’s weight at the beginning of pregnancy have been demonstrated. Underweight and extremely obese women showed lower knowledge than other women. The problem is found in the lower level of education of these risk groups in the issue of recommending energy intake in the second and third trimesters ($$p \leq 0.014$$). In this regard, it is desirable that especially at risk groups of women have sufficient knowledge about the optimal increase in energy requirements for proper weight gain during pregnancy.
In a survey of Scottish women were pregnant women recruited from a cohort study of severely obese pregnant women. Severely obese pregnant women in this study also had lower scores on general nutrition knowledge than the group with normal weight. The results remained significant after controlling for education level [41]. Education should be aimed especially at women who do not have a normal body weight at the beginning of pregnancy and they should be consistently educated about appropriate energy intake and optimal weight gain during pregnancy.
Our results showed a higher level of nutritional knowledge of first-time mothers compared to that of women with more children. It was hypothesised that there might be a significant relationship between pregnancy order and level of nutritional knowledge. These women may have more time to educate themselves or perceive a higher degree of responsibility for the optimal course of pregnancy. Indeed, primiparous women demonstrated a higher level of knowledge in the area of food safety (mercury content in fish ($$p \leq 0.011$$), risks associated with *Listeria monocytogenes* ($$p \leq 0.003$$) and risks of this bacterium to the foetus ($$p \leq 0.01$$). This assumption thus may be correct.
A cross-sectional study from Ghana found satisfactory knowledge of food risks among pregnant women but suggested that food safety knowledge may not be associated with appropriate nutritional behaviour [42]. In this study, the following factors were observed in relation to the level of nutritional knowledge of pregnant women: age, level of education, city, pregnancy order, body mass index (BMI), noncommunicable diseases (NCDs), medications and supplements, and alternative diet. Statistical significance was found for the following factors: education ($p \leq 0.001$), city ($p \leq 0.001$), pregnancy order ($$p \leq 0.041$$), BMI ($$p \leq 0.024$$), and NCDs ($$p \leq 0.044$$).
Our study demonstrated a higher nutritional knowledge score among women with higher education compared to other groups of women. The factor of the place of data collection is debatable, as it was a question of large cities with a high proportion of university-educated respondents, and the connection of this result is not entirely clear. According to the national survey from 2021, the population level of university education was $18.7\%$, the highest concentration was in Prague ($35.9\%$), and lower percentage was in Pilsen ($14.5\%$) [43]. The sample presented included a higher percentage of university- educated people overall ($57.4\%$), of which $44.9\%$ were in Prague and $12.5\%$ in Pilsen. The first pregnancy was found to be significant when women demonstrated a higher level of nutritional knowledge. Very interesting was the finding on BMI, where women with underweight and extreme obesity turned out to be very risky groups. These women should be given significant care as part of prevention, as there is a risk of harming the health of both the woman and the child from incorrect nutritional behaviour during pregnancy. The presence of disease was also found to be a statistically significant factor. Here we can assume a higher level of education of women by health professionals due to the existence of health problems, as well as a possibly higher interest of women in their health and the health of their children [44].
In the field of research, we do not find many studies to compare with our results. We can compare the results from the Istanbul study on a sample of 736 pregnant women, which showed a relationship between the age of the women and the level of nutritional knowledge. Women aged < 18 years presented the lowest level of knowledge compared to the other age groups, 25–29 years, 30–34 years, and ≥35 years. In this study, high school graduates had higher scores of nutritional knowledge than primary school graduates, and finally, the order of pregnancy emerged as significant, with women in their first pregnancy having higher nutritional knowledge scores than those with more than five pregnancies. BMI was not a statistically significant factor in this study [18]. The results are consistent with our study when considering the factors of age, education, and pregnancy order. Unlike in this study, BMI was proven to be a statistically significant factor in our study.
In contrast, a cross-sectional study assessing the nutritional knowledge of pregnant women according to the Australian Dietary Guidelines confirmed significant demographic differences in nutritional knowledge scores. Multiple regression analysis confirmed significant independent effects of education level, income, age, stage of pregnancy, language, and health/nutrition qualifications on respondents’ nutritional knowledge scores [12].
Some studies focus on isolated knowledge of nutrition in conjunction with sociodemographic data of pregnant women. A Turkish study investigated the knowledge of iodine in 150 pregnant women aged 19 to 45 in relation to the sociodemographic characteristics of the study participants. Only $68\%$ of women knew that iodine deficiency could have serious consequences during pregnancy. Knowledge was significantly associated with the level of education ($p \leq 0.001$), but women’s age, trimester, and parity were statistically insignificant [45].
Assessing the influence of sociodemographics and other factors on nutritional knowledge is very important from the point of view of searching for risk groups of pregnant women on whom preventive and intervention strategies should focus.
This study assessed the ability of women with higher education and women with less education to obtain, use, and recognise valuable information, and the difficulty of seeking professional help. Responses were compared between women with a university and non-university education. Only statistically significant items were assessed. These abilities correspond to health literacy, and nutritional knowledge is one of the cornerstones of health literacy, which represents the ability to acquire, understand, and use information that ultimately leads to an increase in one’s own influence on the quality of health. Nutritional knowledge itself will not completely influence an individual’s behaviour, but it can significantly shape their attitudes, which can be reflected in a person’s actions [46].
From the results, we can conclude that women who demonstrated a higher level of knowledge in the study could think more about where to find valuable information, think more about its use in practice, etc., compared to women who demonstrated a lower level of knowledge.
## 4.1. Limitations
A limitation of the study was the inability to ensure an equal distribution of the group in terms of education. The tertiary level of education predominated, which limits the generalisation of the results. This can be explained by the greater interest of women with higher education in this issue and, thus, their willingness to complete the questionnaire. A limit was also maternity hospitals in large cities, where there may be a higher concentration of university-educated women. Another limitation could be the questionnaire’s scope and the questions’ difficulty. Another limitation may have been guessing responses, although each question was provided with a “do not know” option.
## 4.2. Strengths
This study is the first to be conducted in the Czech Republic. So far, this topic has not been addressed. The harvested sample has an optimal size and decent representativeness. The selection of a group of women in the third trimester of pregnancy reflected all the knowledge gained during pregnancy. This heterogeneous sample allowed for subgroup analysis across education level, BMI, and pregnancy order, among other factors. Finally, the current study highlighted the problem of nutritional knowledge of pregnant women and the absence and limited availability of expert and evidence-based recommendations. We appeal to all interested parties to continue to address this situation, as this issue is currently greatly underestimated in the Czech Republic.
## 4.3. Implications
The results of this study point to the need to appeal to professionals and politicians to raise awareness about the need for changes that can lead to an increase in the level of nutritional knowledge of pregnant women. This issue appears to be severely underestimated at present because of the potential impact on pregnant woman’s health and children’s health in the future. So, in the future, it is essential to focus on constantly improving the level of nutritional knowledge and nutritional literacy. One of the objectives of national health policy should be nationwide primary prevention, which should already be aimed at the young generation, as prenatal interventions often come at a time when they are not effective to a sufficient degree. Pre-conception care and preparation for parenthood should be an obvious part of not only health care but also the education system in any developed society. Attention needs to be focused on increasing the level of education of pregnant women on nutritional recommendations and making them available, for example, through the portals of the Ministry of Health or professional societies. Awareness of the importance of dietary recommendations should be strengthened by developing effective campaigns and national programs aimed at the target group of women. Healthcare providers should be the first and reliable source of nutritional information, interprofessional cooperation should be strengthened, and free care by dietitians should be increased. Last but not least, regular measurement and evaluation of the level of nutritional knowledge of pregnant women should be ensured and a validated tool should be developed at national level for this regular measurement and evaluation.
The need for regular measurement and evaluation of pregnant women’s nutritional knowledge level must be emphasised and continued. Knowing the level of knowledge is a prerequisite for effective prevention, which includes the development of effective and comprehensible nutrition-based recommendations that can target areas of concern and help in effectively developing campaigns and programs aimed at target groups.
## 5. Conclusions
The study’s results showed low nutritional knowledge among Czech pregnant women: only $5\%$ of women demonstrated knowledge above $80\%$. Lack of knowledge was demonstrated in key areas such as optimal energy intake, optimal weight gain, and micronutrients. Women found it difficult to understand the meaning of the recommendations and their use in practice. Level of education ($p \leq 0.001$), city ($p \leq 0.001$), order of pregnancy ($$p \leq 0.041$$), BMI ($$p \leq 0.024$$), and NCDs ($$p \leq 0.044$$) were statistically significant factors. For targeted prevention, it is necessary to continue to measure and evaluate the level of internal knowledge. Nutritional education should therefore increase the level of knowledge in the areas mentioned above, which may lead to changes in inappropriate eating habits and support the adoption of healthy eating habits.
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|
---
title: 'Asking the Experts: Using Cognitive Interview Techniques to Explore the Face
Validity of the Mental Wellness Measure for Adolescents Living with HIV'
authors:
- Zaida Orth
- Brian Van Wyk
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001944
doi: 10.3390/ijerph20054061
license: CC BY 4.0
---
# Asking the Experts: Using Cognitive Interview Techniques to Explore the Face Validity of the Mental Wellness Measure for Adolescents Living with HIV
## Abstract
There has been an increased focus on the mental health of adolescents living with HIV (ALHIV), because evidence shows that poor mental health outcomes are associated with lower rates of adherence and retention in HIV care. However, the research to date has predominantly focused on addressing mental health problems and reducing symptoms of mental illness rather than strengthening mental wellness [positive mental health]. Consequently, little is known about the critical mental wellness indicators that should be targeted in services for ALHIV. There is a need for valid and appropriate measures of mental wellness to drive research and provide evidence on the mental wellness needs of ALHIV that would inform service delivery as well as the monitoring and evaluation of treatment outcomes. To this end, we developed the Mental Wellness Measure for Adolescents Living with HIV (MWM-ALHIV) for ALHIV in the South African context. In this paper, we report on the findings from a cognitive interview study with nine ALHIV aged 15–19 years receiving treatment at a public healthcare facility in the Cape Metropole, South Africa. Through interviews, participants identified key issues related to the wording, relevance and understanding of the items and provided suggestions to improve the instrument’s overall face validity.
## 1. Introduction
Mental wellness (positive mental health) has been identified as a significant driver of adolescent health and well-being [1]. This is evidenced in the inclusion of mental health (wellness) in the Sustainable Development Goal (SDG) target 3, which highlights mental health promotion as a critical factor in reducing premature mortality from non-communicable diseases and ensuring good health for all by 2030 [2]. This target is especially relevant for adolescents living with HIV (ALHIV), as they learn to navigate living with a highly stigmatized chronic condition and other challenges and health risks associated with adolescence [3]. Research aimed at understanding the lived experiences of ALHIV has shed light on the various biopsychosocial challenges face, including delayed disclosure, stigma, dysfunctional families, community violence, substance use, poverty, gender inequality and bullying, to name but a few [4,5,6,7]. Their exposure to these various stressors places them at greater risk of developing mental health disorders in comparison to their peers who do not have HIV [4,5,6,7]. Various studies have reported high prevalence rates of mental health disorders such as anxiety, depression, and post-traumatic stress disorder (PTSD) [7,8,9]. Moreover, poor mental health has been significantly associated with low adherence to antiretroviral therapy (ART) and retention in care (RiC) among ALHIV [10,11]. Consequently, the low rates of adherence to ART and RiC are associated with higher AIDS-related deaths among ALHIV in comparison to children and adults living with HIV [12,13].
South *Africa is* at the epicenter of the HIV pandemic, and is home to approximately $20\%$ of the global adolescent HIV population aged 10–19 years [13]. The increased prevalence of ALHIV in South *Africa is* attributed to new infections as well as the successful roll-out of the ART program, which has increased the survival rate of perinatally inflected children [14]. In addition to navigating the physical, social and psychological changes experienced during adolescence and the challenges associated with managing HIV, ALHIV also have to navigate the complex myriad of socio-economic, systemic and environmental factors which shape their daily lives and impact their physical and mental health and wellbeing [14]. The current context of HIV treatment and care is rooted in the socio-political economic system from the apartheid era, which continues to perpetuate racial-based inequalities, thereby fostering conditions in which certain groups are more susceptible to HIV-infection [15]. For example, results from a 2012 population survey indicated that Black South African women were disproportionally affected by HIV in comparison to other groups [15].
Within the South African context, research has shown that structural factors such as poverty, housing, a lack of service provision in communities, racial inequalities, violence, and gender inequalities negatively impact the mental health of ALHIV [14]. For example, reports indicate that the majority of ALHIV belong to marginalized populations characterized by poverty. As a consequence, the impact of poverty is associated with limited access to resources, food insecurity and education, which in turn is associated with poor mental health outcomes and adherence [14]. Furthermore, a study by Woollet et al. [ 9] aimed at identifying the mental health risks among ALHIV accessing care at public healthcare facilities in Johannesburg found that there were high levels of reported mental health problems: $27\%$ were symptomatic for depression, anxiety, or PTSD; and $24\%$ reported suicidality. Additionally, the findings indicated that hunger, violence, gender, and illness were significantly correlated with mental health problems, while mental wellness factors such as hope for the future and knowledge of status were considered as protective factors [9]. These findings indicate that ALHIV require additional support to manage their condition in relation to the contextual risks that they experience. It is critical to address the structural problems and inequalities ALHIV face, yet solutions to these problems are complex and require transforming the macro contexts and political systems. However, preventative strategies that improve the mental wellness of ALHIV are useful in strengthening their capacities to be resilient and grow to become productive members of society.
In support of mental wellness, studies evaluating the effectiveness of psychosocial interventions in improving mental health and adherence among ALHIV have shown promising results [16,17,18,19]. This evidence has increased calls and advocacy to improve mental health (wellness) promotion for ALHIV and to integrate mental health care into adolescent-friendly services [20,21]. However, many of the studies have focused on reducing mental health problems rather than promoting mental wellness. Quantitative studies that measure the effectiveness of psychosocial interventions in improving mental health among ALHIV often use instruments measuring symptoms of mental illness. Therefore, improvements to mental health in these studies are based on the measured reduction of mental illness symptoms. To truly establish the effectiveness of psychosocial interventions or mental health services in improving mental health among ALHIV, we need to develop holistic evaluations by measuring both mental health problems and mental wellness [21,22].
There has been a proliferation of positive psychology research that focused on a range of dimensions from theory development, evaluation of positive psychology interventions, and instrument testing [23]. However, despite the relevance of positive psychology as a tool to promote health and prevent illness (especially in the context of low-resourced settings), the majority of such research has been done in Western contexts, with only small pockets of evidence emerging in the global South [23]. Furthermore, despite the increased focus on positive mental health, a challenge of the field is the lack of consensus on how mental wellness should be conceptualized [1]. Research has identified a range of mental wellness constructs such as self-acceptance, hope, connectedness, and life satisfaction, among others, that are associated with improved well-being and positive functioning in adults and adolescents [1,24,25]. As mentioned, these concepts have mostly emerged from research in the Western context, with a number of growing studies exploring these concepts among indigenous populations and those living in the global South [26]. For example, a study on Thai ALHIV [27] found that spirituality and dignity played an essential role in maintaining mental wellness; in turn, this was associated with living responsibly and experiencing a better quality of life. There has also been an increase in instruments that measure singular mental wellness constructs (i.e., self-esteem) or general mental well-being [26,28,29,30]. Many of these instruments were developed with general adult and adolescent populations living in high-income countries (HICs) [26,28,29,30]. Due to the cost and time associated with instrument development, instruments developed in HICs are typically validated for use in other contexts, such as the KIDSCREEN measures or WHO-5 well-being index [31,32,33]. However, we need to be critical of how these instruments were validated and of the type of validity that was established. For example, the study by Balthip et al. indicates that spirituality is an essential indicator of mental wellness among Thai ALHIV. Nevertheless, this indicator is often not included in commonly used and validated measures such as the KIDSCREEN measure. This raises pertinent questions regarding the relevance of such instruments and what indicators of mental wellness should be targeted to improve outcomes among ALHIV in different contexts. Relatedly, the World Health Organization (WHO) published guidelines on mental health promotion and prevention interventions for adolescents in 2020 [21]. A leading recommendation of the report states that psychosocial interventions should be provided for ALHIV, as these are shown to promote positive mental health and reduce mental disorders [21]. However, the report also indicates that due to a lack of evidence, it was not possible to provide specific recommendations on psychosocial interventions to promote positive mental health, and that additional research is required to improve mental health trajectories. Throughout the report, mental-wellbeing and mental functioning were listed as positive mental health outcomes, while no specific indicators of positive mental health were stated [21].
As indicated, there is a limited understanding in terms of which mental wellness constructs would be most relevant to improving the health and well-being of ALHIV, especially in low- and -middle-income countries (LMICs) [21]. While there are mental wellness instruments that have been validated with adolescents in various contexts, and these mental wellness construct measures may be potentially relevant to ALHIV, we need to consider that mental wellness as a social construct is influenced by time, culture, and age. Therefore, the lived experiences of ALHIV shape their perception and understanding of mental wellness and its associated constructs [20]. For example, HIV is a highly stigmatized condition; therefore, approaches aimed at improving self-acceptance among ALHIV will not be the same as approaches used to improve self-acceptance among adolescents who are not living with HIV. From a health equity perspective, it is crucial that we develop a comprehensive understanding of how mental wellness is perceived by ALHIV to identify relevant indicators and develop appropriate instruments to measure mental wellness in this population in the South African context. Considering the increased conversation around defining concepts such as mental wellness and positive mental health, this is an opportune time to explore the meaning and conceptualization of these from an African perspective to ensure that cultural and indigenous views of health and wellbeing are prioritized rather than transmuting concepts from the West.
## 2. The Mental Wellness Measure for Adolescents Living with HIV
There is an increasing need for mental wellness measures for ALHIV to provide much-needed data on the context and impact of mental wellness outcomes which can then be targeted in interventions and service delivery [21,34]. To address this, we developed a Mental Wellness Measure for ALHIV (MWM-ALHIV) instrument in the South African context. The MWM-ALHIV was developed by first conceptualizing mental wellness for ALHIV through a systematic review of mental health instruments used in research with adolescents [26,28], a photovoice study with ALHIV accessing treatment at three public healthcare facilities in the Cape Metropole District in South Africa [35,36], and an integrative review of mental wellness concepts emerging from research done with ALHIV in Africa [37]. The findings from the systematic review indicated that there is a lack of mental wellness measures developed specifically for adolescents living with a chronic condition (such as HIV) in the African context, thereby proving support for the development of a new instrument [26,28]. Furthermore, through the photovoice study, participants were able to lead the conversation on what mental wellness means to them and what mental wellness factors are most salient in their lives, while the integrative review provided insight on the mental wellness concepts that are relevant in the African context, and how these are shaped by the cultural context [35,36,37]. From these findings, we developed the Salutogenic Model of Mental Wellness (SMoMW) (Figure 1) adapted from Antonovsky’s [38] Salutogenic Model of Health as a theoretical guide to develop the MWM-ALHIV.
Antonovsky originally developed the Salutgogenic Model to emphasize the conditions that lead to health rather than the determinants of health [38]. The Salutogenic Model was born out of observations that people experience various stressors in their daily lives resulting in tensions which, if left unresolved, develops into the health damaging condition of stress [38]. However, exposure to stressors does not necessarily lead to stress and illness. Rather, Antonovsky noted that people who have access to resources are better able to cope with and resolve tensions than people with little or no resources [38]. Through the model, Antonovsky highlights the importance of focusing on the interplay of stressors and life experiences to move an individual towards health [38]. Two key concepts of this model are the generalized resistance resources (GRR), referring to any factor or characteristic which can be used to facilitate tension management, and a sense of coherence (SOC), referring to an individual’s capacity to manage and overcome stressors [38]. Based on the Salutogenic paradigm, the SMoMW was developed following extensive reviews of the literature and participatory research with ALHIV accessing treatment in the Cape Metropole District in South Africa, which helped us to identify key mental wellness constructs that are relevant to ALHIV. Therefore, the SMoMW can be used to guide health promotion interventions, inform youth-friendly services, and facilitate research activities. According to the SMoMW, mental wellness is expressed as overall SOC, which comprises cognitive (comprehensibility), behavioral (manageability) and motivational (meaning) mental wellness factors. SOC, in turn, is influenced by a range of ecological factors including the life experiences of ALHIV, their access to resources, potential exposure to stressors, and their life situation. In this sense, ALHIV who have a high SOC are more likely to access and mobilize resistance resources, which will strengthen their SOC, leading to better mental wellness. The SMoMW can be used to understand how these factors influence the mental wellness of ALHIV in context, and what points of intervention would be most useful, as shown in Table 1. The MWM-ALHIV was developed to measure the SOC aspect of the model, highlighting the key mental wellness concepts identified by ALHIV as being salient to their experiences of living with HIV [39]. Due to the heterogeneity of the group, it would be challenging to develop a measure that takes into account all contextual aspects such as the GRRs or the potential life stressors. However, the strength of the model is that it allows for such factors to be considered when designing health promotion interventions or guiding research. Therefore, the MWM-ALHIV can be used in conjunction with the model to guide the collection of demographic data (culture, age, mode of infection etc.), select supplementary tools to measure other aspects (i.e., exposure to violence, adherence, treatment fatigue), or interpret findings.
Ensuring validity is a crucial step in instrument development to determine the extent to which a measure accurately captures what it is intended to measure [31,40,41]. Validity testing is usually done during the pilot phase of a study, using methods such as factor analysis or Item Response Theory (IRT) [31,40,41]. However, it is equally important to test for content and face validity. In a previous study, we established content validity by engaging with experts in a modified Delphi Study to determine how adequately the domains and items represent the measurement of mental wellness among ALHIV [39]. The Delphi Study participants endorsed the measure by providing consensus on the relevance and representation of the domains and items. In its present state, the MWM-ALHIV includes 113 items and measures mental wellness as an overall SOC represented through three domains and 11 sub-domains (Table 1).
As mental wellness is increasingly prioritized, we need to ensure that ALHIV are part of the conversation (especially those living in LMICs) and are included in efforts to improve their health and well-being. According to the WHO and UNAIDS, including ALHIV in the research process is a key priority [34]. Therefore, the next step is to establish the face validity and improve on the content validity of the MWM-ALHIV by engaging with the target population as the next group of experts [34]. To this end, we conducted cognitive interviews with a group of ALHIV to determine to what extent the items in the measure are appropriate, acceptable, sensible, and relevant for the intended users. According to UNAIDS, the participation and leadership of ALHIV are crucial at all stages of informing HIV programming, including the design, implementation, and monitoring and evaluation. The MWM-ALHIV reflects this commitment, as it was developed using a participatory photovoice method, allowing ALHIV to lead the conversation and express what aspects of mental wellness are important to them and what role it plays in their lives. The cognitive interviews followed as a logical next step to meaningfully engage with participants and collaborate with them to improve the MWM-ALHIV. Once finalized, the MWM-ALHIV can be used to implement, monitor, and evaluate youth-friendly services and HIV programming for ALHIV [21].
## 3. Methods
Cognitive interviewing is a technique used to evaluate the content and face validity and applicability of a survey or instrument [41,42,43]. The cognitive interview methodology was first established in the 1980s as a means to improve the validity of an instrument by understanding the cognitive processes involved in answering response items [41,42,43]. This technique is used during the preliminary stages of instrument development to gain insight into the participant’s cognitive processes when responding to ensure they understand the questions as intended [41,42,43]. There are two main approaches to cognitive interviewing, namely the ‘think-aloud’ and ‘verbal probing’ techniques [41,42,43]. The think-aloud method involves participants verbalizing their thought processes while responding to each item in the instrument, with the interviewer documenting the participant’s thought processes [41,42,43]. On the other hand, the verbal probing approach involves the interviewer asking a series of probing questions aimed at eliciting detailed information from the participants after they have responded to the items. While the think-aloud method is advantageous in reducing biased responses, asking participants to verbalize their thoughts while answering a question can be an unnatural and difficult practice for participants, resulting in a significant cognitive burden [43]. As such, the interviews were conducted according to the verbal probing approach outlined by Willis and Artino, as this was considered appropriate for the given sample of adolescents [43].
## 3.1. Participants and Procedures
The MWM-ALHIV is intended to measure mental wellness among ALHIV aged 14–19 years. As such, we recruited participants matching those criteria from a public healthcare facility in the Cape Metropole District of South Africa. The Cape Metropole *District is* located in the Western Cape Province, with reports indicating that $6.76\%$ of people living with HIV reside in the province [15]. The healthcare facility is located in a ‘low-income’ community and provides free healthcare to members of the community and the surrounding areas. Additionally, to be included in the study, participants had to speak English as a first or second language. Due to the sensitive nature of the study, we first made contact with a doctor at the facility who was previously in charge of running the Youth Adherence Clubs. The doctor was given the relevant information with the study and asked to aid in the recruitment due to issues around disclosure. Those who were interested in participating were put in contact with the researcher and received information sheets as well as assent and consent forms for those younger than 18 years. Following this, the researcher set up a time and date to interview each participant at their convenience. All interviews were conducted at the public healthcare facility.
## 3.2. The Lexical Context in South Africa
Cognitive interviews are based on the assumption that we can use language (think-aloud and verbal probes) to tap into the cognitive processes of the participant and explain the way participants mentally process and respond to items in surveys or questionnaires [43].
As language plays a significant role in this process, it is salient to consider the multilingual context in South Africa. There are 11 official languages in South Africa, with IsiZulu identified as being spoken by the majority of the population ($23\%$), followed by isiXhosa ($16\%$), Afrikaans ($13.5\%$), English ($10\%$), *Sesotho sa* Leboa ($9\%$), Setswana ($8\%$), Sesotho ($8\%$), Xitsonga ($4.5\%$), siSwati ($2.5\%$), Tshivenda ($2.5\%$) and isiNdebele ($2\%$) [44]. In the Western Cape province, where this study is based, the main languages are Afrikaans ($49\%$), isiXhosa ($24.7\%$) and English ($20.3\%$) [44]. During the apartheid era, English and Afrikaans were identified as the official national languages of South Africa, while Indigenous languages were marginalized [44,45,46]. In addition to declaring the 11 official languages, other efforts aimed at redressing the language inequality include policies which state that South African children be taught in their mother tongue in the first three years of schooling, after which they are taught through an English or *Afrikaans medium* until their final year of high school [46].
In post-apartheid South Africa, Afrikaans continues to be used widely in the media and basic education system, with English dominating as the language of urban life and that predominantly used in the media, business, government, and basic and higher education systems [44,46]. Thus, despite being spoken as a home language by a minority of the population, *English is* used as a second language and a common language of communication in urban areas [44,46]. Furthermore, language in South *Africa is* fluid, with the majority of the population speaking more than two languages [44]. Census data from 2011 indicated that the average South African speaks between two and three languages. As such, South Africans are considered to be a ‘code-switching’ people, meaning that they may use more than one language during a conversation [44].
## 3.3. Data Collection
The interviews were carried out by a researcher who is experienced in qualitative research, has had previous training and experience conducting cognitive interviews with adolescents, and has experience doing research with ALHIV. The interviews were conducted in a private, quiet space to allow the participant to answer honestly. Before the start of each interview, the researcher reiterated the purpose of the study, and that the participant had the right to stop the interview process at any point if they no longer wished to continue. To reduce social desirability bias and to help participants feel comfortable, the researcher explained that the questions in the instruments were derived from photovoice interviews with other ALHIV who attended the healthcare facility in 2019. The interviews were conducted in December 2022. During the interview, the researcher sat next to the participant and read each question in the MWM-ALHIV aloud, along with the answer options, and then gave the participant the opportunity to select a response option. Following this, the researcher would ask probing questions based on the participant’s answer and their experience to assess the cognitive match between the intent of the question and the participant’s understanding and interpretation of the question. The probing questions included ‘why did you choose that answer?’, ‘ what does [key term from questionnaire] mean to you?’, ‘ can you explain what [key term] means to you in your own words?’, ‘ what popped in your head when I said [key term]’, ‘I noticed you hesitated before answering that question, can you tell me more?’, ‘ were any of the questions easy to answer?’, ‘ were any of the questions hard to answer?’ These probes allowed participants the opportunity to reflect on their answers and to provide explanations to demonstrate their understanding. Additionally, the researcher asked questions to elicit macro-level engagement from the participants, which included ‘how would you ask [question from instrument] to your friends?’ or ‘what word [key term] would you and your friends use?’ *As a* psychologist and a woman of color who grew up in a post-apartheid South Africa, the researcher was aware of the power differences that could affect how participants interacted with her. She tried to minimize these by encouraging casual interaction with the participants, using local colloquialisms, and building a rapport with the help of the doctor. After each session the researcher had a debriefing session with the participant, giving them an opportunity to reflect on the experience and ask questions. Following this, the participants were given an incentive of ZAR 150 (USD 9) as a thank you.
## 3.4. Data Analysis
All interviews were audio recorded and transcribed. Additionally, the researcher made detailed field notes during and after the interviews to aid with the analysis. The transcripts were analyzed thematically to identify common themes and patterns that may indicate problems with the items, ambiguous wording, or potential sources of bias.
## 3.5. Ethics
Ethical clearance for this study was obtained by the University of the Western Cape. Each participant received and returned signed consent/assent forms before the start of the interviews. Participants who were younger than 18 years provided signed parental consent forms. Participants were reminded that they could withdraw from the study at any time without any negative consequences and that all of their information would be kept private and confidential.
## 4. Findings
We conducted interviews with nine participants accessing treatment at the public healthcare facility. The aim of the current study is to establish the face validity of the instrument rather than assess the mental wellness of the participants. Therefore, according to Willis [43] this sample size is deemed sufficient to confirm patient understandability of an item. Additionally, the analysis revealed no new themes emerging. The findings yielded enough information to identify key issues in the items to be revised. As cognitive interviewing is an iterative approach, it would be more useful to integrate the revisions and then conduct additional rounds.
The participant demographics are presented in Table 2. As shown in the table, the majority of participants ($$n = 7$$) indicated isiXhosa as their home language and English as their second language. As indicated earlier, this would mean that the seven Xhosa-speaking participants would receive their secondary education in English. The two Afrikaans participants were educated in their home language and study English as a second additional language, in line with the Department of Basic Education’s curriculum. All of the participants completed or are currently enrolled in school and are therefore considered to be literate. Furthermore, all participants were able to converse fluently in English, which, as mentioned, is a reflection of the urban setting. Two of the participants stated that they completed level 4 in the School of Skills—an alternative education institution for pupils who are unable to cope with or develop in mainstream institutions [47]. Pupils are enrolled at age 14 or 15 years and complete 4 years of schooling [47]. As Afrikaans in the predominantly spoken language in the Western Cape, it may be surprising that most of the participants in this sample spoke isiXhosa as their home language. However, this may also reflect the racial disparities and economic inequalities which drive the HIV epidemic in South Africa. Furthermore, all participants indicated that they were perinatally infected; thus, suggesting that the overrepresentation of Xhosa speaking participants in this study may be associated with the high HIV prevalence among Black South African women who lacked access to ART and the prevention of mother to child transmission (PMTCT) services (which was only initiated in 2002) [48].
The findings from this research highlight the value and importance of working with ALHIV; through engagement and feedback from the participants, we were able to work together to identify question failures and problems related to the face validity of the instrument which were not identified during the Delphi study.
These issues are classified into three themes, namely: comprehension mismatch, ‘big’ or difficult words, and sentence structure and question relevance (Table 3). These question failures should be understood in the context of the South African language and education landscape, and raise questions about survey development in South Africa. Generally, the older participants ($\frac{18}{19}$) were more confident in verbalizing their answers and thought processes than the younger adolescents. Both participants A01 and A02 attended a School of Skills; however, A01 spoke about her learning difficulties and demonstrated an awareness of her strengths and limitations. As such, she was able to clearly explain how she answered specific questions and engaged with the researcher on a macro level by making suggestions.
The younger adolescents ($\frac{15}{17}$) struggled to verbalize their thought processes. As one participant said, ‘I know what it means, I am just struggling to find the words to explain’. In these cases, the researcher would try to elicit a response by asking participants if they could provide examples or a similar word or suggested that they say it in their home language. For example, upon prompting, A02 said she would describe a valuable person as an ‘important’ person. In a similar study aimed at adapting a measure of grief among South African adolescents, it was noted that since cognitive interviewees are tasked with explaining how they experience and interpret specific words and phrases, polyglot contexts represent an exceedingly complex environment for research implementation [49]. This raises the question of whether participants’ struggles to verbalize their thoughts are related to a lack of understanding. Indeed, the relationship between language and cognition is a persistent question in scientific inquiry [43,49].
The participants’ responses to items were analyzed in light of this context. Given that all participants were bilingual and had different literacy levels, items were revised to reflect the lowest literacy level. In other words, an item was flagged for revision even if only one participant struggled to comprehend it.
## 5. Discussion
There is a growing recognition on the importance of improving mental wellness among ALHIV to support lifelong adherence to ART and ensure that they reach their full potential across the course of their lives. Strengthening mental wellness among ALHIV in South *Africa is* especially critical considering the high prevalence rates of HIV, the impact of the epidemic, and the health risks they are exposed to as a result of pervasive inequalities within the country. While it may take years to address the structural problems, focusing on mental wellness through mental health promotion interventions may offer protection from health risks to ALHIV and strengthen their capacity to thrive. However, in resource restrained contexts such as in South Africa, we need evidence-based responses to maximize the impact and outcome of interventions and services. *To* generate quality evidence, we require robust tools. Therefore, we set out to validate the MWM–ALHIV as a first step towards developing an age and culturally appropriate measure of mental wellness made for ALHIV, with ALHIV.
This study provides an example of how cognitive interviews with ALHIV in South Africa can be used to improve the face validity of the measuring instrument, MWM-ALHIV. The MWM-ALHIV was developed to address the gap in research on mental wellness among ALHIV. While there are numerous instruments aimed at measuring positive mental health, our goal was to develop an instrument that is culturally sensitive and captures the world view of ALHIV in the South African context. A strength of this measure is that it has been developed using participatory approaches with ALHIV that enabled the researchers to understand which aspects of mental wellness are important to them, how they talk about and understand it, and the role it plays in facilitating adherence to ART. The current study is concerned with improving the face validity of the instrument and represents a snapshot in the process towards establishing the psychometric properties of the instrument.
Similar to Taylor et al. [ 49], we found that the language issues which emerged from the interviews reflect the challenges of the South African landscape. Translating the questionnaire to Xhosa or *Afrikaans is* necessary to accommodate the larger population. However, this may be a challenging process. For example, when asked if the questions would be easier if they were translated to Xhosa, participant A01 suggested that it may not make a difference—even though she mainly speaks Xhosa at home, she also speaks other languages with her family and she never learned how to read or write in Xhosa. She further stated that due to her learning difficulties, it is easier for her to hear the questions verbally and then answer rather than reading them on her own. However, this may reflect a more complex problem in South Africa related to verbal and written language use. According to Chimbga and Meier, South African learners that were tested during an international comparative evaluation of reading literacy through the Progress in Reading International Literacy Study performed poorly, despite most writing in their home language [46].
Therefore, given the complex language issues, we agree with previous recommendations made by Taylor et al. [ 49] in that it may be advisable to adopt a multilingual approach in conducting cognitive interviews and even the survey design itself within the South African context. In other words, during the interviews, participants should be allowed to answer in their language of choice or to code-switch. Following this, decisions can be made regarding the appropriate language choices for the instrument [49]. To be representative of the fluid multilingual context and code-switching nature of South Africans, considerations should be made to have the instrument translated into a version which may include multiple languages or colloquialisms – such as Afrikaaps - in addition to a standard English version of the instrument or other translations [49]. Afrikaaps (also known as Kaaps) is a language created in settler colonial South Africa which developed as a result of encounters between indigenous African groups (Khoi and San) and slaves brought in from Southeast Asia and Portuguese, Dutch and English settlers [50]. In contemporary South Africa, the language is commonly spoken by working class speakers in the Cape Flats (an area in Cape Town where people were forcibly moved during the apartheid era) [50]. The language has been established since the 1500s and was first taught in madrassahs (Islamic schools). In later years it was appropriated by Afrikaner nationalists [50].
Additionally, we found that the interviews supported the appropriateness of the instrument and supported the rationale of the SMoMW. Therefore, even though certain domains in the model and instrument are believed to reflect Western concepts and values, such as self-esteem and self-acceptance, we found that participants resonated with and responded well to these categories and reflected the findings from our previous photovoice study, thereby demonstrating the confirmability of the findings. Furthermore, even though the mental wellness concepts originated from Western perspectives, we included and adapted these based on our engagement with ALHIV participants. It may be that these concepts perform well cross-culturally, as South *Africa is* considered a multi-cultural country; yet many of the systems and institutions are based on Western principles and values. While participants in this study were raised within collectivist cultures, they were also exposed to and expected to adapt to individualistic cultures and values that are perpetuated in their social circles. For example, when explaining an answer to ‘someone in my family accepts me’, participant A02 stated that it helped her feel like she was ‘just like’ her other family members and that she was ‘a normal person’. Therefore, the connectedness and acceptance she felt from her family members enabled (representing the collective values) her own self-acceptance and boosted her self-esteem (representing individualistic values) [35]. Similarly, A06 mentioned that living with HIV did not define who he is ‘because if it did, I would have killed myself’, indicating that self-acceptance is a motivator to continue living and receiving treatment.
On the other hand, participant A05 indicated that she ‘somewhat agrees’ with self-acceptance questions such as ‘I am kind to myself’, ‘I am happy with the way I am’, and ‘I am living with HIV, and I am okay with that’. When asked to explain her answers, the participant revealed that a few months prior she was would have answered ‘disagree’ to those questions, as she felt bad about herself as someone living with HIV. Consequently, this impacted her adherence to treatment. However, she described that she was on a journey, and she has started feeling more accepting of herself, but she is not ‘quite there yet’. The participant was one of the younger adolescents (15 years) and was informed of her HIV status when 11 years of age. Based on the SMoMW, and her answers to the MWM-ALHIV, we can hypothesise that even though she struggled with adherence, the connectedness she feels from her family and friends and the support she received from the doctor provided a buffer which helped her along her journey of self-acceptance, which in turn is giving her the tools she needs to re-commit to her treatment. Additionally, age is considered part of the life context in the SMoMW, which can play a role in mental wellness and health outcomes. For example, in comparison to the older participants, A05 (like her peers) is at the stage where she may be more impulsive, rebellious and/or forgetful when it comes to her treatment. During the interview, she mentioned that sometimes when she is out with her friends, she has so much fun that she forgets to take her medication, but when she does remember she would rather skip the dose if it is too late. On the other hand, older participants may have been through more life experiences which brought them further along the journey of self-acceptance. Additionally, they have been in mainstream adult care for a longer period of time, which would require them to manage their treatment independently.
The aforementioned indicates that the MWM-ALHIV has the potential to screen for mental wellness among ALHIV in a way that can indicate high levels of mental wellness, but can also pick up on areas where they may be struggling, without necessarily being in ‘crisis’. For example, the struggles A05 speaks about in her journey to adherence and maintaining self-acceptance may not have been flagged using instruments that are frequently used in assessing mental health among ALHIV such as the Child Depression Inventory (CDI) or the strengths and difficulties questionnaire [51]. However, her answers on the MWM-ALHIV suggest that she may benefit from additional support that specifically targets her self-acceptance and addresses issues around treatment reminders that are appropriate for her age.
In addition, findings from our systematic review of mental wellness instruments for adolescents indicated that there are relatively few instruments measuring mental wellness [often referred to as general mental well-being] (e.g., the Warwick-Edinburgh Mental Wellbeing Scale [52] and the Mental Health Continuum-Short Form [53]). Instead, the majority of the instruments measured singular indicators of mental wellness, such as connectedness (the Milwaukee Youth Belongingness Scale [54]), or self-esteem (e.g., the Rosenberg Self-esteem Scale [55]). Instruments measuring multiple indicators are preferred over instruments that focus on one mental wellness measure, because the former can provide a more comprehensive overview of both eudemonic and hedonic dimensions of mental wellness [56]. Various scholars have argued that both hedonic (feeling well) and eudemonic dimensions (functioning well) should be emphasized and included in measures to provide a more holistic view of adolescent mental wellness [57,58]. Particular strengths of the MWM-ALHIV are that the measure includes both general questions reflecting eudemonic and hedonic dimensions of mental wellness, as well as those specific to living with HIV. Furthermore, unlike the other instruments identified in the review [26,28] that originated in developed countries, the MWM–ALHIV was developed after extensive research to first conceptualize mental wellness for ALHIV in the African context. The MWM–ALHIV is the first mental wellness measure for ALHIV in the South African context that was developed in a South African setting.
## 6. Conclusions
This study was principally undertaken to determine the face validity and improve upon the content validity of the MWM–ALHIV. The MWM–ALHIV was developed as an age and culturally appropriate measure for ALHIV in the South African context. Unlike other instruments measuring general mental wellness or aspects of mental wellness that have been developed in the Western context and subsequently adapted to other cultures and contexts, the MWM–ALHIV was developed with ALHIV to ensure the appropriateness and relevance of the domains and to reflect their lived experiences. The cognitive interviews represent the next logical step in the study to include the voices of ALHIV in the instrument development process. Based on the responses from participants, revisions were made to improve the overall readability and comprehension of the measure. Additionally, the interviews also provide further insight into the appropriateness and confirmability of the domains of the measure and the SMoMW. Furthermore, the findings provide insight into considerations of language and implementation related to cognitive interviews and survey development with adolescents in the South African context. This study represents a snapshot of a larger project aimed at conceptualizing and developing a measure of mental wellness for ALHIV. Following the instrument development process, we aim to pilot the instrument and engage in further rounds of cognitive interviews to establish its psychometric properties.
## 7. Study Limitations and Recommendations
As a qualitative study, certain limitations are noted. However, due to the amount of rich data gathered from the interviews, we view this as a pilot stage which provided lessons learned for future rounds of cognitive interviews, which is in line with the cognitive interview iterative approach; we may conduct these rounds before the pilot testing of the instrument and after. The sample of participants in this study lived in an urban area and accessed treatment at a public healthcare facility from a specific community. We recommend further rounds of the cognitive tests be done with participants from other urban communities in addition to rural communities. Furthermore, based on the lessons learned, we would aim to recruit interviewers that speak isiXhosa so that participants may answer in the language of their choice. While the researcher offered the participants the opportunity to explain some concepts in their own language, she was limited in following up with further probing questions. As mentioned, to reflect the multilingual, fluid language of the South African landscape, we would recommend that this instrument be translated into appropriate languages and different dialects, or include some code-switching between languages, as participants may find this more relatable and easier to navigate.
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---
title: Physical, Psychiatric, and Social Comorbidities of Individuals with Schizophrenia
Living in the Community in Japan
authors:
- Masaaki Matsunaga
- Yuanying Li
- Yupeng He
- Taro Kishi
- Shinichi Tanihara
- Nakao Iwata
- Takahiro Tabuchi
- Atsuhiko Ota
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001945
doi: 10.3390/ijerph20054336
license: CC BY 4.0
---
# Physical, Psychiatric, and Social Comorbidities of Individuals with Schizophrenia Living in the Community in Japan
## Abstract
The physical, psychiatric, and social comorbidities interfere with the everyday activities of community-dwelling individuals with schizophrenia and increase the risk of their readmission. However, these comorbidities have not been investigated comprehensively in Japan. We conducted a self-reported internet survey in February 2022 to identify individuals aged 20–75 years with and without schizophrenia using a prevalence case-control study. The survey compared physical comorbidities such as being overweight, hypertension, and diabetes; psychiatric comorbidities such as depressive symptoms and sleep disturbances; social comorbidities such as employment status, household income, and social support between participants with and without schizophrenia. A total of 223 participants with schizophrenia and 1776 participants without schizophrenia were identified. Participants with schizophrenia were more likely to be overweight and had a higher prevalence of hypertension, diabetes, and dyslipidemia than participants without schizophrenia. Additionally, depressive symptoms, unemployment, and non-regular employment were more prevalent in participants with schizophrenia than those without schizophrenia. These results highlight the necessity of comprehensive support and interventions addressing physical, psychiatric, and social comorbidities in individuals with schizophrenia in the community. In conclusion, effective interventions for managing comorbidities in individuals with schizophrenia are necessary to enable them to continue to live in the community.
## 1. Introduction
Schizophrenia is a common illness with a reported lifetime prevalence of approximately $1\%$ [1]. The World Health Organization’s Comprehensive Mental Health Action Plan 2013–2030 advocates for deinstitutionalization of individuals with schizophrenia, shifting the place of care from long-term inpatient psychiatric hospitals to non-specialized community-based health settings and providing comprehensive, integrated, and responsive mental health and social care. In Japan, approximately 150,000 individuals with schizophrenia are hospitalized, representing $60\%$ of admissions for mental and behavioral disorders and $12\%$ of all disease admissions [2]. Since the proposal of “The Vision for Reforming Mental Health Care and Welfare” in 2004, there has been a shift toward community-based care for individuals with schizophrenia [3]. Meanwhile, approximately 15–$30\%$ of individuals with schizophrenia are readmitted within 90 days of discharge worldwide [4]. Physical, psychiatric, and social comorbidities have been associated with readmission [5,6,7]. Investigating the physical, psychiatric, and social comorbidities of individuals with schizophrenia in the community can aid in identifying their needs and improving their care.
Evidence on physical, psychiatric, and social comorbidities associated with schizophrenia is accumulating [1]. For example, physical comorbidities include obesity [8], diabetes [9], hypertension [8], and hyperlipidemia [8]; psychiatric comorbidities include depression [10] and sleep disorder [11]; social comorbidities include low employment rate [12] and functional impairment such as community living and work [13]. In Japan, some comorbidities, such as overweight, hypertension, diabetes, depressive symptoms, quality of life, employment rate, and household income, have been reported [14,15]. However, there are no comprehensive reports on the physical, psychiatric, and social comorbidities of Japan’s community-dwelling individuals with schizophrenia [1].
Community-dwelling individuals with schizophrenia face unique challenges related to their physical, psychiatric, and social comorbidities. These comorbidities, such as obesity, depression, and low employment rate [16,17,18], can make it difficult for individuals with schizophrenia to function in everyday life. In addition, obesity is a risk factor for diabetes, and diabetes is one of the most significant mortality risk factors for individuals with schizophrenia [19]. Depression in individuals with schizophrenia can exacerbate the symptoms of schizophrenia, worsen the quality of life, and increase the risk of suicide [20]. Unemployment in individuals with schizophrenia can reduce the quality of life and place an extended burden on social support and disability services [21,22,23]. Addressing physical, psychiatric, and social comorbidities in primary care in the community is crucial in improving the health outcomes of individuals with schizophrenia.
To aid in the development of better treatment and support services for individuals with schizophrenia in the community, we conducted an internet survey to compare the prevalence of physical, psychiatric, and social comorbidities between individuals with and without schizophrenia living in the community in Japan.
## 2.1. Study Design and Participants
We conducted a prevalence case–control study using an internet research agency’s pooled panels (Rakuten Insight, which had approximately 2.3 million panelists in 2022). We collected data from those currently without schizophrenia and those currently with schizophrenia in February 2022.
For those currently without schizophrenia, we sampled 28,000 participants in the Japan Society and New Tobacco Internet Survey (JASTIS) [24] and the Japan COVID-19 and Society Internet Survey (JACSIS) [25,26,27] conducted by the Rakuten Insight Panel. Responses were obtained from 6656 respondents, who were asked the following questions before the survey. Those who answered no to all four questions were considered not to have schizophrenia: [1] Are you currently suffering from mental illness?; [ 2] Have you had mental illness in the past?; [ 3] Have you experienced auditory hallucinations?; [ 4] Have you ever used stimulants or other illegal drugs, been an alcoholic, or received psychiatric treatment? Finally, we obtained 1776 participants between the ages of 20 and 75 according to the sex and age structure of the Rakuten Insight Panel.
For those currently with schizophrenia, we sampled 5584 individuals aged 20 to 75 years who self-reported schizophrenia in the Rakuten Insight disease panel, a subset of the Rakuten Insight Panel. Responses were obtained from 3256 respondents, who were asked the following questions before the survey. Those who answered yes to all four questions were considered to currently have schizophrenia: [1] Are you currently suffering from schizophrenia only, schizophrenia and migraine, schizophrenia and a sleep disorder, or schizophrenia, migraine, and a sleep disorder?; [ 2] Have you experienced auditory hallucinations lasting more than one month?; [ 3] Have you never used stimulants or other illegal drugs and never been an alcoholic?; [ 4] Have you experienced the first auditory hallucination lasting more than one month at less than 60 years of age? A final response was received from 223 respondents.
## 2.2. Study Variables
A self-administered questionnaire assessed demographic and health-related backgrounds, physical comorbidities, psychiatric comorbidities, and social comorbidities.
Demographic and health-related backgrounds include age, body mass index (BMI) (underweight: <18.5 kg/m2, normal: 18.5–24.9 kg/m2, or overweight: ≥25.0 kg/m2), smoking status (current, past, or never), reason for quitting smoking (bad for health, illness, or other (e.g., financial reasons)), alcohol drinking (current drinker (≥23.0 g/day of ethanol), current drinker (<23.0 g/day ethanol), ex-drinker, or never drinker), reason for quit drinking (bad for health, illness, or other (e.g., financial reasons)), sports (<1 times per week or ≥1 times per week), tendency to overeat, eating speed (fast, normal, or slow), eating instant foods (<1 times per week, 1–4 times per week, or ≥5 times per week), bowel movement (<3 times per week, 3–7 times per week, or ≥2 times per day), stool (soft, normal, hard, or recurrent diarrhea and constipation), restriction in functional capacity, and bad self-rated health status (SRHS).
Physical comorbidities include overweight, cancer, cardiovascular disease, heart failure, hypertension, diabetes, dyslipidemia, gout, sleep apnea syndrome, and fracture.
Psychiatric comorbidities include depressive symptoms (absent or present), sleep time (<5 h, 6–7 h, 8–9 h, or ≥10 h), hypnagogic disorder (<3 times per week or ≥3 times per week), deep sleep disorder (<3 times per week or ≥3 times per week), middle wakening or early wakening (<3 times per week or ≥3 times per week), perceived stress (absent or present), ikigai (absent or present), happiness (absent or present), and internet use time per week (h).
Social comorbidities include taking regular medical checkups, educational background (junior/senior high school, university, junior college, or vocational school), occupation (unemployed, homemaker, white-collar workers, or blue-collar workers), type of employment (regular, non-regular, or self-employed/business people), household income (million Japanese yen) (<3, 3–6, 6–9, or ≥9), marital status (unmarried, married, divorced, widowed, or others), family structure (living alone, living with parents, living with spouse, living with children, and living with other people), social support, and social capital (cognitive and structural dimensions).
## 2.3. Statistical Analysis
T-tests were used to compare the averages of continuous variables, and Fisher’s exact tests were used to compare the proportions of categorical variables between participants with and without schizophrenia.
A logistic regression analysis was used to calculate sex- and age-adjusted odds ratios (AORs) and $95\%$ confidence intervals (CIs) of participants with schizophrenia compared to participants without schizophrenia for physical comorbidities, psychiatric comorbidities, and social comorbidities. All the analyses were conducted using R4.2.1 software (R Foundation: Vienna, Austria). The level of significance was set at $p \leq 0.05$ (two-sided).
## 3. Results
The study presented in Table 1 provides a comparison of demographic and health-related characteristics of participants with and without schizophrenia. Males with schizophrenia were more prevalent ($52\%$) than females with schizophrenia. Overall, males were older than females, but there was no significant difference in age between participants with and without schizophrenia for males. On the other hand, women with schizophrenia were older than women without schizophrenia.
A higher percentage of participants with schizophrenia were overweight (BMI ≥ 25) in both sexes ($53\%$ for participants with schizophrenia vs. $28\%$ for participants without schizophrenia in men and $39\%$ vs. $9.3\%$ in women, respectively). Smoking was more prevalent among women with schizophrenia. Fewer participants with schizophrenia were drinkers, and more participants with schizophrenia were abstinent drinkers compared to participants without schizophrenia. Eating habits were also compared between the two groups. Overeating was more prevalent among female participants with schizophrenia. Speed-eating was more prevalent among participants with schizophrenia. Additionally, participants with schizophrenia tended to consume instant foods more frequently. Bowel movements were also compared, and it was found that participants with schizophrenia had more frequent bowel movements and soft stools than participants without schizophrenia.
Participants with schizophrenia reported a higher level of functional restriction and bad self-rated health status than participants without schizophrenia. The percentage of those who felt a restriction in functional capacity was small in participants without schizophrenia ($7.4\%$ in men and $4.6\%$ in women). In comparison, that percentage was about $40\%$ in participants with schizophrenia ($39\%$ in men and $41\%$ in women). A total of $94\%$ of participants with schizophrenia went out daily. Similarly, the proportion of bad self-rated health status was higher among participants with schizophrenia than participants without schizophrenia.
As for physical diseases in participants with schizophrenia, in men, hypertension ($18\%$), diabetes ($14\%$), and dyslipidemia ($13\%$) were common. In women, dyslipidemia ($11\%$), hypertension ($7.4\%$), and diabetes/cancer ($6.5\%$) were common (Table 2).
Figure 1 shows the results of a sex- and age-adjusted logistic regression model investigating the association between schizophrenia and physical comorbidities. Compared to community dwellers without schizophrenia, community dwellers with schizophrenia more frequently reported a history of fracture (AOR: 7.17, $95\%$ CI = 2.81 to 18.1), sleep apnea syndrome (AOR: 4.04, $95\%$ CI = 1.23 to 11.9), overweight (AOR: 3.85, $95\%$ CI = 2.83 to 5.24), diabetes (AOR: 3.25, $95\%$ CI = 1.90 to 5.44), and dyslipidemia (AOR: 2.60, $95\%$ CI = 1.60 to 4.13).
Table 3 shows the psychiatric comorbidities of participants with and without schizophrenia. Participants with schizophrenia had more severe depressive symptoms (CES-D ≥ 8) and were more predominant among participants with schizophrenia compared to participants without schizophrenia ($63\%$ for participants with schizophrenia vs. $23\%$ for participants without schizophrenia in men; $77\%$ vs. $29\%$ in women).
In terms of sleep patterns, participants with schizophrenia reported sleeping longer. They were more likely to report sleep disturbances, including hypnagogic disorder, deep sleep disorder, and middle or early awakenings, compared to participants without schizophrenia.
Perceived stress (PSS-4 scores) was higher in participants with schizophrenia than in participants without schizophrenia. More participants with schizophrenia had an absence of ikigai (a positive reason for living) and absence of happiness than participants without schizophrenia. Internet use was significantly longer in participants with schizophrenia compared to participants without schizophrenia in men, while no significant differences were found in women.
Figure 2 shows the results of a sex- and age-adjusted logistic regression model investigating the association between psychiatric comorbidities and schizophrenia. Depressive symptoms (CES-D ≥ 8) were associated with more strongly than other psychiatric comorbidities (AOR: 7.54, $95\%$ CI = 5.52 to 10.4). Compared to community dwellers without schizophrenia, community dwellers with schizophrenia more frequently reported long-hour sleep (≥10 h) (AOR: 3.95, $95\%$ CI = 2.89 to 5.39), stressful (PSS-4 > 7) (AOR: 3.60, $95\%$ CI = 2.61 to 5.07), middle wakening or early wakening (AOR: 3.57, $95\%$ CI = 2.62 to 4.84), hypnagogic disorder (AOR: 2.98, $95\%$ CI = 2.20 to 4.02), absence of happiness (AOR: 2.58, $95\%$ CI = 1.94 to 3.43), absence of ikigai (AOR: 2.26, $95\%$ CI = 1.70 to 3.00), deep sleep disorder (AOR: 2.07, $95\%$ CI = 1.55 to 2.75), and longtime internet use (>14 h per week) (AOR: 1.50, $95\%$ CI = 1.13 to 1.98).
Table 4 shows the social comorbidities of participants with and without schizophrenia. Regarding health literacy and behaviors, participants with schizophrenia did not have regular health examinations compared to participants without schizophrenia. In terms of socioeconomic status, compared to participants without schizophrenia, participants with schizophrenia were less educated (junior/senior high school), unemployed ($50\%$ vs. $15\%$ in men; $33\%$ vs. $6.8\%$ in women), non-regular employment ($56\%$ vs. $15\%$ in men; $79\%$ vs. $42\%$ in women), low household income (<3 million Japanese yen) ($53\%$ vs. $18\%$ in men; $44\%$ vs. $22\%$ in women), unmarried (especially among men), and living with parents. Regarding social support and social capital, the differences in ESSI scores with and without schizophrenia were small. Participants with schizophrenia had a lower cognitive social capital compared to participants without schizophrenia.
Figure 3 shows the results of a sex- and age-adjusted logistic regression model investigating the association between schizophrenia and social comorbidities. Unemployment (AOR: 6.25, $95\%$ CI = 4.56 to 8.55) and non-regular employment (AOR: 6.24, $95\%$ CI = 3.94 to 10.0) were significantly more strongly associated with schizophrenia than other social comorbidities. For other social comorbidities, compared to community dwellers without schizophrenia, community dwellers with schizophrenia more frequently reported living with parents (AOR: 4.55, $95\%$ CI = 3.37 to 6.16), low household income (<3 million Japanese yen) (AOR: 3.76, $95\%$ CI = 2.82 to 5.02), unmarried (AOR: 3.53, $95\%$ CI = 2.58 to 4.84), not taking regular medical checkups (AOR: 2.20, $95\%$ CI = 1.65 to 2.94), less cognitive social capital (AOR: 2.08, $95\%$ CI = 1.57 to 2.78), low education background (Junior/senior high school) (AOR: 1.99, $95\%$ CI = 1.49 to 2.65), and less social support (ESSI < 17) (AOR: 1.48, $95\%$ CI = 1.09 to 2.00).
## 4. Discussion
This study revealed the physical, psychiatric, and social comorbidities of individuals with schizophrenia living in the community in Japan, which were seldom reported comprehensively.
The mean age of participants with schizophrenia in this study was 48 years for males and 44 years for females, which is consistent with the mean age of 42.7 years reported in The Japan National Health and Wellness Survey [15]. However, it should be noted that this estimated age of participants with schizophrenia may be younger than the mean age of individuals with schizophrenia in Japan. This is due to the fact that participants in this study were recruited via the internet, which could lead to a higher representation of younger adults with high internet usage [39]. Additionally, this recruitment method may not include hospitalized patients, many of whom are elderly.
Participants with schizophrenia had a higher percentage ($53\%$ of men and $39\%$ of women) with a BMI of 25 or higher than participants without schizophrenia. The prevalence of BMI ≥ 25.0 kg/m2 among Japanese outpatients with schizophrenia has been reported to be $48.9\%$ [14]. Individuals with schizophrenia have a shorter life expectancy due to death from cardiovascular causes [40,41]. Previous studies have also shown that outpatients have a higher rate of obesity than inpatients [14], highlighting the need for measures to control obesity in individuals with schizophrenia living in the community.
Approximately $10\%$ of participants with schizophrenia in this study defecated less than three times per week, a prevalence lower than that reported in outpatients in Finland ($31.3\%$) [42] and inpatients in Japan ($36.6\%$) [43]. One potential explanation for this discrepancy may be that participants with schizophrenia had more than two bowel movements per day and a higher percentage of soft stools than those without schizophrenia, some of whom may be constipated. Antipsychotic medications are known to cause constipation, and it has been reported that over $50\%$ of individuals with schizophrenia taking antipsychotic drugs experience constipation [44]. Many participants in this study likely took laxatives, such as osmotic laxatives such as magnesium hydroxide, which can soften stools. However, the anticholinergic effect of antipsychotics may reduce peristalsis in the bowel, leading to constipation with limited stool volume and a feeling of incomplete defecation.
Participants with schizophrenia have been found to engage in overeating and faster eating patterns compared to those without schizophrenia. Overeating may be due to increased appetite caused by the side-effects of antipsychotic medications [45]. In systematic reviews, the prevalence of binge eating among individuals with schizophrenia taking antipsychotic medications ranges from $4.4\%$ to $45\%$, with the majority of participants being of Western origin [46]. As far as we know, no studies have compared the prevalence of eating fast in individuals with schizophrenia with that of the general population. Eating fast has been reported to be associated with obesity [47]. In clinical practice, it is crucial to instruct individuals with schizophrenia on appropriate food intake in terms of both quantity and speed to prevent obesity.
A comparison of smoking rates between male participants with schizophrenia and those without schizophrenia revealed no significant differences. However, female participants with schizophrenia were found to have smoking rates that were approximately 2.5 times higher than those without schizophrenia. Similarly, a meta-analysis of smoking rates among Japanese individuals with schizophrenia showed that compared to the general population, male individuals with schizophrenia had an odds ratio 1.53 times higher for smoking rates ($52.9\%$ for individuals with schizophrenia and $40.1\%$ for the general population) and female individuals with schizophrenia had an odds ratio 2.40 times higher for smoking rates among females ($24.4\%$ for individuals with schizophrenia and $11.8\%$ for the general population) [48]. This disparity in smoking rates may be attributed to the fact that the smoking rate in the Japanese population has been decreasing over time among men, but the decline is less pronounced among women [49].
The primary physical diseases among participants with schizophrenia were hypertension, diabetes mellitus, and dyslipidemia, all of which are associated with obesity. The prevalence of these diseases was relatively low compared to a previous study conducted in Japan [14], which may be attributed to the younger age of the participants in this study. However, the prevalence of diabetes mellitus and dyslipidemia was significantly higher than that of participants without schizophrenia. These diseases, as well as obesity, require caution when examining individuals with schizophrenia.
Although the absolute prevalence of fracture and sleep apnea was low, both conditions had high sex- and age-adjusted odds ratios for the prevalence of schizophrenia. Fracture is associated with antipsychotic medications, analgesics, and physical diseases such as hypertension in individuals with schizophrenia [50,51]. It has been reported that bone mineral density is decreased in Japanese outpatients with schizophrenia [52], highlighting the need for osteoporosis prevention. A meta-analysis reported a comorbidity of obstructive sleep apnea as high as $15.4\%$ in schizophrenia [53]. In a Japanese survey, $19\%$ of hospitalized individuals with schizophrenia had sleep apnea [54]. Because the medical history was self-reported, there may be undiagnosed obstructive sleep apnea. Potentially, the prevalence of obstructive sleep apnea could be even higher. Early detection and intervention for obstructive sleep apnea are needed to protect against sleep disturbance and cardiovascular disease [55].
Cardiovascular disease and heart failure, associated with schizophrenia in previous studies of Westerners [56,57], were not associated with schizophrenia in the present study. This may be partly because these diseases typically have an elderly onset and the sample size of participants with schizophrenia in the study was small. The present study also found no association between gout and schizophrenia, which is consistent with a systematic review and meta-analysis that found no difference in uric acid levels between individuals with and without schizophrenia [58]. Cancer was not associated with schizophrenia in this study, although the prevalence of cancer was higher in female participants with schizophrenia than in female participants without schizophrenia. This result is consistent with previous findings that schizophrenia is associated with a higher risk of breast cancer [59], although the incidence of cancer in individuals with schizophrenia has been reported to vary in comparison to the general population [60].
Depressive symptoms were present in more than two-thirds of participants with schizophrenia, making it the greatest risk factor for psychiatric comorbidities in schizophrenia (AOR: 7.54). This prevalence is higher than that reported in Japan’s National Health and Wellness Survey, where $47.8\%$ of individuals with schizophrenia had depressive symptoms ($\frac{85}{178}$) [15]. The lifetime prevalence of depression in individuals with schizophrenia ranges from 16 to $69\%$, depending on factors such as the definition of depression, patient setting, and period of observation [10]. This is higher than that in the general population, which is consistent with our findings [17]. Depression in schizophrenia has been reported to be associated with factors that interfere with living in the community, such as schizophrenia relapse, early rehospitalization, impairment of social and occupational functioning, and family and community burden [10,61]. Additionally, depression in individuals with schizophrenia is strongly associated with an increased risk of suicide [62]. Therefore, addressing depressive symptoms is a crucial intervention for individuals with schizophrenia.
Participants with schizophrenia reported longer sleep duration and more sleep disturbances than those without schizophrenia. A systematic review reported that those with remitted schizophrenia showed a longer sleep duration, time in bed, and sleep latency than the healthy control did [63], which is consistent with our results. Individuals with schizophrenia complain of sleep disturbances not only in the acute phase but also in the remission phase [64]. In a Japanese study, the prevalence of individuals with schizophrenia with any sleep disturbances was $49.4\%$ ($\frac{88}{178}$) [15]. In Chinese outpatients, the prevalence of at least one type of insomnia was $28.9\%$ ($\frac{180}{623}$), while those with difficulty initiating sleep, difficulty maintaining sleep, and early morning wakening were $20.5\%$, $19.6\%$, and $17.7\%$, respectively [65]. The participants with schizophrenia in this study, most presumed outpatients, showed sleep disturbances at a higher rate compared to the previous research in China. Further studies are needed in different populations. Sleep disturbances tend to precede the onset of schizophrenia, and management of sleep disturbances can prevent acute exacerbation of psychiatric symptoms [63].
Perceived stress was stronger in participants with schizophrenia than in those without schizophrenia, which is consistent with previous evidence in Western populations [66,67]. A common finding is an association between stress and pathophysiology in all stages of schizophrenia [66]. Appropriate coping with stress is associated with improved quality of life in individuals with schizophrenia [68].
Participants with schizophrenia had less ikigai and happiness than participants without schizophrenia, which is consistent with less well-being, happiness, and life satisfaction in individuals with schizophrenia among Westerners [67,69]. However, the difference between young adult individuals with schizophrenia and the general population in subjective well-being scores is small [69]. In an interview survey conducted with mentally disabled persons living in a community in Japan, some of them realized ikigai through dialogue with interviewees [70]. Ikigai or happiness may vary depending on the patient background or may be difficult to realize in individuals with schizophrenia.
Individuals with schizophrenia were reported to spend more time using the internet compared to those without schizophrenia, especially in men. In South Korea, $22\%$ of individuals with schizophrenia were reported to suffer from problematic internet use, which was associated with higher levels of perceived stress and lower coping skills [71].
Participants with schizophrenia did not receive regular medical checkups as compared to participants without schizophrenia. This result is consistent with reports that Korean people with psychosis demonstrated lower knowledge of physical illnesses and did not receive regular medical checkups [72]. Therefore, individuals with schizophrenia must be educated and encouraged to undergo medical checkups.
A higher percentage of participants with schizophrenia were unemployed or had non-regular employment, had lower household incomes, were less likely to be married, and lived with their parents than participants without schizophrenia. Despite evidence indicating that individuals diagnosed with schizophrenia are more likely to be unemployed [22] or have lower income [73] than the general population, there is a paucity of research investigating whether they are more likely to be unmarried or residing with their parents. However, our finding is consistent with a previous Japanese study, which also found a high prevalence of unmarried, unemployed, and low household income among individuals with schizophrenia [15]. Additionally, the number of claims for mental and behavioral disorders per population was lower in the Japanese Medical Data Center (JMDC) database, consisting of corporate health insurance claims, compared to the National Database (NDB), consisting of all claims data constructed by the Japanese government [74]. This aligns with the high percentage of non-regular employment among participants with schizophrenia in the present study.
Sociodemographic features specific to individuals with schizophrenia are interrelated. A survey on family support for individuals with mental disorders in Japan found that $85\%$ of respondents were parents [75], indicating that many individuals with schizophrenia are unmarried and live under parental support. Furthermore, the patients reported that they were unemployed or had non-regular employment, and their household income was low. In addition to the patient’s work arrangement, family members are expected to work fewer hours to support the patient’s daily needs, resulting in lower household income. With the parents’ aging, further measures are needed to ensure that individuals with schizophrenia can continue to live in the community because the parents are concerned about livelihood support ($74.8\%$) and financial aspects ($60.1\%$) after the parents’ death [75]. From a medical and social perspective, there is a need for educational programs that can help individuals with schizophrenia support themself while also managing their mental health or programs that can help parents better understand the condition and how to support their children with schizophrenia.
Social support tended to be lower among participants with schizophrenia than those without schizophrenia. Cognitive social capital was significantly lower in participants with schizophrenia than in participants without schizophrenia, while structural social capital did not differ between participants with and without schizophrenia. It has been reported in Westerners that schizophrenia was associated with low social support [76] and low cognitive social capital at the ecological level [77], which is consistent with the results of this study. Community development from the perspective of social support and social capital is required to improve community residents’ mental health.
The present study has several strengths and limitations. First, online surveys may be susceptible to sampling bias and response bias, compared to population-based surveys. However, we did not use a stratified sampling technique to obtain as many responses as possible from respondents with schizophrenia. Second, the diagnosis of schizophrenia in this study was based on self-reports, which may limit the accuracy of the diagnosis. To address this limitation, we asked preliminary questions based on the DSM-5 diagnostic criteria [78] to exclude psychiatric disorders other than schizophrenia, such as depression, delusional disorder, and alcoholism, and to increase the specificity of the self-reported schizophrenia status. However, the sensitivity of the self-report survey may be low due to the lack of insight that often accompanies schizophrenia [79], potentially leading to an underestimation of the prevalence of the condition. In future studies, we plan to consider alternative approaches for assessing schizophrenia, such as clinical interviews or medical records, to improve the validity of our results. Third, the potential for underestimation of the prevalence of physical conditions among individuals with schizophrenia is due to the self-reported nature of the data, which can be influenced by cognitive deficits and low health literacy. Fourth, some of the participants with schizophrenia might not be living in the community, because we did not collect data about whether they lived there. This misclassification may cause the prevalence of comorbidities in schizophrenia to be biased, while the estimate of $94\%$ with schizophrenia being out daily would support that most of the participants with schizophrenia live in the community. In addition, due to low health literacy, their comorbidities may be underreported. A more focused sample of community-dwelling individuals with confirmed psychiatric and medical diagnoses would be needed in future research. Fifth, the study design did not adequately include self-reflective components critical to understanding the daily experiences and perceptions of individuals with schizophrenia. Future research should consider self-reflective components because self-reflection could influence the perception of comorbidities. Sixth, the study has not collected sufficient data on other confounding variables, such as medication and menopause, that may have influenced the outcomes. Hence, in future research, it is imperative to collect data on potential confounding variables associated with the identified risk factors for schizophrenia. This would allow for a more comprehensive understanding of the underlying factors and their association with schizophrenia. Finally, this study was cross-sectional, and causal relationships must be carefully evaluated.
## 5. Conclusions
This study provides an overall description of comorbidities in individuals with schizophrenia living in the community in Japan using an internet survey. Physical comorbidities included overweight, hypertension, dyslipidemia, and diabetes. As for psychiatric comorbidities, depressive symptoms and sleep disorders were common. Social comorbidities included low education, unemployment/non-regular employment, low income, and living with parents. These findings suggest that a comprehensive approach is necessary to manage the physical, psychiatric, and social comorbidities in individuals with schizophrenia to continue to live in the community. The interventions should include lifestyle modifications, psychological therapies, vocational rehabilitation programs, job coaching, and supported employment programs. Such interventions can help individuals with schizophrenia to manage their comorbidities, improve their overall health outcomes and quality of life, and reduce the risk of readmission to hospitals. Healthcare providers, policymakers, and other stakeholders should work together to develop and implement interventions that address these comorbidities and improve the health outcomes and overall quality of life of individuals with schizophrenia living in the community.
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|
---
title: 'The Influence of the COVID-19 Pandemic Emergency on Alcohol Use: A Focus on
a Cohort of Sicilian Workers'
authors:
- Emanuele Cannizzaro
- Luigi Cirrincione
- Ginevra Malta
- Santo Fruscione
- Nicola Mucci
- Francesco Martines
- Fulvio Plescia
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001951
doi: 10.3390/ijerph20054613
license: CC BY 4.0
---
# The Influence of the COVID-19 Pandemic Emergency on Alcohol Use: A Focus on a Cohort of Sicilian Workers
## Abstract
The period between the beginning and the end of the COVID-19 pandemic emergency generated a general state of stress, affecting both the mental state and physical well-being of the general population. Stress is the body’s reaction to events or stimuli perceived as potentially harmful or distressing. Particularly when prolonged over time, it can promote the consumption of different psychotropic substances such as alcohol, and thus the genesis of various pathologies. Therefore, our research aimed to evaluate the differences in alcohol consumption in a cohort of 640 video workers who carried out activities in smart working, subjects particularly exposed to stressful situations due to the stringent rules of protection and prevention implemented during the pandemic. Furthermore, based on the results obtained from the administration of the AUDIT-C, we wanted to analyse the different modes of alcohol consumption (low, moderate, high, severe) to understand whether there is a difference in the amount of alcohol consumed that could predispose individuals to health problems. To this end, we administered the AUDIT-C questionnaire in two periods (T0 and T1), coinciding with annual occupational health specialist visits. The results of the present research showed an increase in the number of subjects consuming alcohol ($$p \leq 0.0005$$) and in their AUDIT-C scores ($p \leq 0.0001$) over the period considered. A significant decrease in subgroups who drink in a low-risk ($$p \leq 0.0049$$) mode and an increase in those with high ($$p \leq 0.00012$$) and severe risk ($$p \leq 0.0002$$) were also detected. In addition, comparing the male and female populations, it emerged that males have drinking patterns that lead to a higher ($$p \leq 0.0067$$) health risk of experiencing alcohol-related diseases than female drinking patterns. Although this study provides further evidence of the negative impact of the stress generated by the pandemic emergency on alcohol consumption, the influence of many other factors cannot be ruled out. Further research is needed to better understand the relationship between the pandemic and alcohol consumption, including the underlying factors and mechanisms driving changes in drinking behaviour, as well as potential interventions and support strategies to address alcohol-related harm during and after the pandemic.
## 1. Introduction
11 March 2020 has now become a historic date. On that day, the World Health Organisation (WHO), following a careful analysis of the risks associated with the spread of severe acute respiratory syndrome by a coronavirus (SARS-CoV-2), declared that the COVID-19 epidemic could be considered a real pandemic [1,2,3,4]. Since then, the world’s population has had to change its lifestyle, aligning with the rules laid down by the various governments [e.g., Italian, British, French, American] [5,6,7,8] concerning the prevention and protection methods to be implemented in private life, in public places, in school and university environments and in the workplace [9,10,11,12,13,14,15]. Moreover, this health emergency has forced public and private administrations to resort to smart-working, or agile working, as a suitable method to manage and contain the pandemic.
These changes have had a profound impact on working and social life. All this, together with the continuous evolution of rules to be followed, has led to the genesis of a condition of general consistent malaise that has facilitated the beginning of various forms of stress and related disorders [16,17,18,19,20,21,22], including the one now identified as ‘COVID-19 stress’ [23].
Stress is a generic term often used to indicate adverse life conditions [24]. Exposure to a stressful stimulus over a long period can promote the onset of different moods such as anxiety, fear, anger, excitement, and sadness that can, in the case that they exceed the individual’s coping abilities, promote the occurrence of different pathologies [25,26,27,28,29] and increase vulnerability to use of substances of abuse [30,31,32].
Furthermore, continued exposure to aversive stimuli is influenced by different contexts, such as education (school, university) and work [33,34,35,36]. It has been pointed out that the working environment, its organisation, and work-related behaviour are themselves stressors, and as such can influence workers’ psychological well-being [37]. Recently, different research has focused on the relationship between stress at work, aggravated by the new prevention and protection guidelines due to the pandemic emergency, and the development of mental disorders and risk behaviours such as the use of substances of abuse [37,38]. In this context, the risk of developing such conditions is related to the type of work performed, the potential for social interaction (prolonged or not), and exposure to different environmental contaminants that would promote the genesis of other pathologies.
Notably, among the addictive behaviours related to stressful conditions, alcohol abuse leads the way due to alcohol’s easy obtainability and organoleptic properties [39,40]. In this context, additional scientific evidence shows that people who experience periods of severe economic or psychological stress are more inclined to consume alcoholic beverages with the consequent onset of abuse and addiction behaviour [41,42]. The pandemic has led to changes in alcohol consumption patterns, with some individuals drinking more due to increased stress and isolation. In contrast, others have reduced or abstained due to health or financial concerns. Interesting research by Sohi and colleagues has shown that during the pandemic, the amount and mode of alcohol intake are substantially heterogeneous and depend on the country in which the research was conducted. These authors suggest that further research is needed to understand better the relationship between the pandemic and alcohol consumption, including the underlying factors and mechanisms driving changes in drinking behaviour, and to create potential interventions and support strategies to address alcohol-related harm during and after the pandemic [43].
Based on the aforementioned, this research aimed to assess how both the approach and the mode of consumption of alcoholic beverages changed during the pandemic period in a population of video workers who were forced by the pandemic to carry out activities in smart working. Before administering the AUDIT-C questionnaires, we excluded part of the population based on different criteria. In particular, we decided to exclude subjects with a body mass index above or equal to or greater than 32, with dysmetabolic and oncological pathologies. This decision stems from knowledge of these variables’ influence on alcohol consumption. On the other hand, it has been reported that individuals with a high BMI, particularly those with obesity, are at increased risk of developing dysmetabolic pathologies such as diabetes, metabolic syndrome, and non-alcoholic fatty liver disease, which in turn can increase the risk of developing alcohol addiction by altering the body’s response to alcohol and affecting brain’s reward pathways [44,45]. Moreover, some cancer treatments such as chemotherapy can be less effective in individuals who consume alcohol. Therefore, it is probable that individuals with an oncological pathology will limit or avoid alcohol consumption to reduce the risk of cancer progression and other health complications [46].
Finally, based on the data collected through the administration of the AUDIT-C test, it was possible to classify the population into different categories that accounted for the risk of encountering pathologies related to improper consumption of alcohol.
Given the scientific evidence on the increasing consumption of alcoholic beverages, the hypothesis of our study focuses on the idea that the pandemic period, marked by stringent norms of prevention and protection, was a risk factor that could exert such pressure as to influence the mode of alcohol consumption as much as the amount of alcohol consumed.
## 2.1. Experimental Design
This observational study was conducted on a cohort of video workers, considering the recommendations indicated by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) [47]. The sample of this study is “opportunistic” because data were collected based on the availability of participants at a private practice of occupational medicine in Palermo, Italy.
The study was conducted in two different periods: T0: June 2020 and T1: April 2022, the date on which, given the end of the emergency state (31 March 2022), apart from vulnerable subjects, the majority of the working population was considered to have returned to work in person.
For this study, subjects of both sexes aged between 25 and 65 years with a video work history of at least four years were enrolled. The population was initially 800 (T0) workers (400 males and 400 females). Among these, $11\%$ (63 F and 25 M) refused to participate in the study, and $6\%$ (11 F and 37 M) were excluded from the study because they did not show up for the specialist visit as they were no longer employed by their companies or were absent due to illness or other causes. A further $3\%$ (9 F and 15 M) of subjects who, at the time of the first medical examination, were on drug therapy for anxiety disorders, depression, or other psychiatric disorders, were also excluded; workers with a body mass index greater than or equal to 32, employees on drug therapy for dysmetabolic pathologies, all workers with previous or current oncological pathologies, and workers with a previous history of pathological addictions were also excluded. All subjects admitted to the study met the inclusion criteria considered in our study.
Eventually, the study enrolled 640 adults: 321 males and 319 females (M/F ratio 1.006). The number of subjects analysed at time T0 was identical to that of T1, although their number varied within the subgroups considered in this study.
At the end of the patients’ general anamnesis, all participants were asked to fill in a questionnaire to establish their alcohol consumption patterns and degree of alcohol dependence.
All participants were informed about the purpose of the study and signed the informed consent before participating. Respondents were asked not to mention their or the organisation’s names in the questionnaire to ensure privacy and anonymity.
All data have been handled according to Italian law to protect privacy (Decree No. 196, January 2003). A multidisciplinary team of health experts collected and analysed the data through the questionnaires administered on alcohol habits.
## 2.2. Assessment of Alcohol Consumption and Degree of Dependence
The assessment of alcohol consumption and the relative risk associated with its use was conducted by administering the Alcohol Use Disorders Identification Test-Concise (AUDIT-C), a modified version of the 10-question Alcohol Use Disorders Identification Test (AUDIT) developed by the World Health Organisation. This test is valuable for investigating alcohol consumption and how it occurs. It also allows us to identify patients who are hazardous drinkers and those who are particularly at risk of developing alcohol-related disorders.
This instrument is a 3-item survey with a total score ranging from 0 to 12 points. Each item has five response options ranging from 0 to 4 points. A score of 3 or more points on the AUDIT-C may indicate that people are risk drinkers or have alcohol use disorders. A score of 4 or more for men and 3 for women is predictive of potential alcohol abuse. A person’s likelihood of developing an alcohol use disorder is directly proportional to a higher test score [48].
Furthermore, based on the score obtained from the AUDIT-C test, we divided our population into five different categories: abstainer (score = 0), low risk (score = 1–3 men; 1–2 female), moderate risk (score = 4 men; 3–4 female); high risk (score = 5–7 men and female) and severe risk (8–12). The groups were structured based on previous research on the association between alcohol intake and health risks [49,50,51,52].
## 2.3. Statistical Analysis
The statistical analysis of the data was conducted using the GraphPadPrism 8.01 statistical software package (GraphPad Company, San Diego, CA, USA). Initially, the collected data were analysed to understand whether they were normally distributed and, consequently, to choose the most suitable statistical analysis to apply. To do this, we applied the D’Agostino–*Pearson omnibus* normality. Given that our data did not follow a normal distribution, we used the non-parametric Chi-square test to determine whether the frequency values obtained with the survey were significantly different from those obtained with the theoretical distribution. Specifically, the Chi-square test was applied to understand whether there were differences in the number of total consumers and between the male and female samples in the two periods, and to assess possible variations in the risk categories obtained from the analysis of the AUDIT-C test data over the time interval considered. Moreover, logistic regression was also performed to calculate the probability of the association between alcohol consumption and gender. Data are expressed as odds ratio (OR).
The Wilcoxon test was applied for paired data, and the Mann–Whitney U test was used for unpaired data to assess the differences in AUDIT-C scores among the population under our study. A descriptive analysis of the data obtained was also conducted to understand the consumption pattern and the amount of alcohol consumption. Data were reported as mean with $95\%$ CI. Statistical significance was set at $p \leq 0.05.$
## 3.1. Alcohol Consumption in the General Population
The collection and analysis of data useful for the identification of the number of subjects consuming alcohol and their risk of developing problems related to the misuse of the substance were conducted by the administration of the AUDIT-C.
In detail, within the sample analysed, a more significant number of subjects who consumed alcoholic beverages, both at T0, 467 ($72.97\%$; audit score (AS) 3.229, confidence interval (CI) 3.072–3.386) and at T1, 519 ($81.09\%$; AS 3.925, CI 3.746–4.104) compared to those who claim not to drink at both T0, 173 ($27.97\%$) and T1, 121 ($18.91\%$) was highlighted. Moreover, among the subjects consuming alcoholic beverages, there were differences regarding the percentage of subjects who consume alcohol with different risk modes. Indeed, the subjects who consume alcohol in a manner considered to be a low risk both at T0, 298 ($63.81\%$; AS 2.168, CI 2.069–2.258) and at T1, 245 ($47.21\%$; AS 2.139, CI 2.032–2.245) prevail over those who consume it in a riskier manner (Table 1).
When we analysed the consumption of alcoholic beverages in a subgroup of drinkers, the descriptive analysis of the data showed that there was a reduction in the percentage of the number of low-risk subjects and an increase in those at moderate, high and severe risk between the time intervals analysed (Figure 1).
Considering the data obtained from the descriptive analysis, we assessed whether there were differences in the number of consumers and those belonging to the different risk categories in the two periods considered. In detail, statistical analysis by the Chi-square test showed a significant increase in the percentage of total consumers (χ2 = 11.94, $z = 3.455$, $$p \leq 0.0005$$). The analysis of the data on the number of subjects consuming alcohol in different risk modes revealed a reduction in the percentage of subjects consuming alcohol in a low-risk manner (χ2 = 7.915, $z = 2.813$, $$p \leq 0.0049$$) and an increase in the high (χ2 = 10.54, $z = 3.247$, $$p \leq 0.0012$$) and severe (χ2 = 13.92, $z = 3.731$, $$p \leq 0.0002$$) risk groups at T1 compared to T0. There were no significant differences in the percentage of moderate-risk drinkers between T1 and T0 (χ2 = 0.8292, $z = 0.9106$, $$p \leq 0.3625$$) (Figure 2).
## 3.2. Differences in Alcohol Consumption between Males and Females
Given the data obtained on drinking behaviour in the sample analysed, we wondered whether there were differences between the percentages of male and female subjects regarding alcohol consumption and differences in the risk related to alcohol consumption (Table 2).
The analysis conducted by applying the Chi-square test did not reveal any significant differences in the percentages of alcohol drinkers between males and females (χ2 = 1.230, $z = 1.109$, $$p \leq 0.2675$$; χ2 = 1.150, $z = 1.072$, $$p \leq 0.2836$$) in the two timeframes considered.
When we evaluated the differences in the consumption of alcoholic beverages obtained from the analysis of the AUDIT-C test, the analysis of the data by the Chi-square did not reveal statistically significant differences both at T0 and at T1 between males and females regarding low risk (χ2 = 0.05162, $z = 0.2272$, $$p \leq 0.8203$$; χ2 = 0.7793, $z = 0.8828$, $$p \leq 0.3774$$), moderate (χ2 = 0.3778, $z = 0.6146$, $$p \leq 0.5388$$; χ2 = 0.06674, $z = 0.2583$, $$p \leq 0.7961$$) and high (χ2 = 0.06298, $z = 0.2509$, $$p \leq 0.8019$$; χ2 = 0.1887, $z = 0.4344$, $$p \leq 0.6640$$). When we went to analyse the data concerning the drinking mode, the data analysis showed that at T0, there were no differences (χ2 = 2.823, $z = 1.680$, $$p \leq 0.0929$$) between males and females. On the contrary, at time T1, we found statistically significant differences in the percentage of males drinking in a manner that exposes them to a severe health risk (χ2 = 7.350, $z = 2.711$, $$p \leq 0.0067$$) compared to females (Figure 3).
Furthermore, based on the data obtained, we calculated the probability of the association between alcohol consumption and gender in the two time periods considered. Specifically, there was no more of a significant probability of drinking in male subjects than in female subjects in the two times considered (OR: 0.9863—$95\%$ CI: 0.9148–1.063; OR: 1.042—$95\%$ CI: 0.9763–1.112).
We also analyzed the differences in the AUDIT-C score between T0 and T1. Statistical analysis was conducted using the Wilcoxon test to understand any differences in the AUDIT-C score in the two times covered by our study. The analysis showed a significant increase in the score at time T1 ($p \leq 0.0001$) compared to that obtained at time T0.
## 4. Discussion
The pandemic emergency experienced in recent years has drastically changed many aspects of daily life. The two waves of the contagion, which occurred over a relatively short period of time, have led to isolation, forced living in confined spaces and profound changes in everyone’s working life [15,53]. All of these things have exerted intense pressure on the adaptive capacities of the population; while in the first phase, these capacities served to cope with adversity by drawing on our instinctive spirit of survival, with the prolongation of the pandemic, they have fostered the development of a condition of persistent stress which may alter an organism’s internal homeostasis and lead to the onset of different pathologies and/or the establishment of addictive behaviour. This may include an increase in alcohol consumption [21,54,55].
The trend recorded for the consumption of alcoholic beverages is well in line with the data obtained from the present observational study, in which there was an increase in the number of alcoholic drinkers (+$10.02\%$) over the time interval examined. It is also interesting to note that the percentage of subjects who consume alcohol is always higher ($72.97\%$; 81.09) than those who claim not to drink ($27.03\%$; $18.91\%$).
The result concerning alcohol consumption behaviour is a sobering thought. In particular, the data showed a different pattern of alcohol consumption. Specifically, following analysis of the subgroup categories, a reduction in the number of subjects who consume alcohol in a manner that exposes them to a low health risk emerged both in males (−$32.14\%$) and females (−$12.78\%$) over the time interval considered. In addition, a significant increase in the number of subjects who consume alcohol in a manner that exposes them to higher ($52.51\%$) and severe ($80.65\%$) health risks were revealed.
In addition to the increase in the consumption of alcoholic beverages, our data also showed an increase in the AUDIT-C score, both when we evaluated all the population subjects of our study ($p \leq 0.0001$) and when we analysed the subgroup of drinkers ($p \leq 00001$). An increase in the AUDIT-C score can predict the development of physical or social problems related to alcohol consumption. In particular, there are risks associated with alcohol consumption that may vary depending on gender, age, general health and the amount and frequency of alcohol consumption. Excessive alcohol consumption can have serious adverse health consequences and increase the risk of liver disease, pancreatitis and certain types of cancer. It can also lead to different mental health problems such as depression and anxiety [56].
Increased consumption of alcoholic beverages and changes in the mode of consumption can be traced back to the emotional distress experienced during the COVID-19 pandemic. During this period, a large part of the population had to drastically change their daily routines, starting with their mode of work.
Remote working has, for example, encouraged social isolation and the onset of general malaise due to the impossibility of setting up a good workplace and/or reconciling work and private commitments effectively [57,58,59].
The office is a space, but it is, above all, a community. Working in the office means being surrounded by workers, collaborating, asking for help, chatting over a coffee, and having pure and simple human contact that makes us feel part of a group. Working from home means giving up completely the social and human component of office work.
Working from home and limiting opportunities for sociability and collaboration can only lead to a growing sense of isolation and increased health risks. This analysis may seem overly alarmist and pessimistic, but it is confirmed by various studies [59,60,61,62,63]. It has been shown that homeworking aligns with workers’ satisfaction only if it is not protracted for a long time. In fact, after an initial period of enthusiasm, there is a widespread desire to return to office life, even in the face of losing time and money for travel [64]. The reason for this choice is mainly the feeling of loneliness that affects home workers [65].
In-person working also underwent profound changes due to the implementation of multiple measures to contain the contagion [66,67]. All of the above were able to generate a solid stimulus to interrupt the normal internal balance of the body and make the condition experienced highly stressful.
This condition may partly explain the increase in the consumption of alcoholic beverages in a manner that exposes health risks, as was recorded in our study.
Stress is a factor closely correlated with often uncontrolled consumption of alcohol and with relapses back into its use after a period of abstinence [68]. Different studies have shown that particularly dangerous and demanding work environments and family stress are factors associated with increased alcohol consumption [69,70,71,72]. This is partly attributable to increased cortisol release which is triggered by activation of the hypothalamic-pituitary–adrenal axis, one of the main modulators of the adaptive stress response [73]. In particular, impaired regulation of the HPA axis is associated with problematic alcohol consumption, and the nature of this dysregulation varies with the stages of progression towards alcohol dependence [74,75,76].
The motivation that drives people to consume more and more alcohol can be traced back to the molecule’s action. In fact, alcohol exerts anxiolytic effects, and its intake promotes a reduction in the perception of stress [77,78]. Alcohol can modulate the activation of the hypothalamic–pituitary–adrenal (HPA) axis both directly and indirectly, resulting in a different regulation of glucocorticoid release and the consequent alteration of the adaptive stress response [79,80,81].
This reduction in the state of tension facilitated by alcohol intake is attributable to its ability to stimulate the action of different inhibitory neurotransmitters, such as γ-aminobutyric acid [GABA] and opioids. These, through inhibition of the hypothalamus’s paraventricular nucleus (PVN), modulate the release of neuropeptides that are helpful in stimulating the synthesis and subsequent release of cortisol [72,82,83,84], thereby attenuating the stress response.
Alcohol can thus assume a positive reputation among the general population, who may use it as ‘self-medication’ to combat incredibly unpleasant living conditions and sources of stress. This encourages a growing amount of alcohol to be consumed, thereby promoting an increased risk of alcohol-related diseases.
## 5. Limitations of the Study
Although this research provides further evidence of the influence of stress on alcoholic beverage consumption, it does not lack some limitations that could be considered for future studies. In particular, the study, although carried out on a reasonably homogeneous population, could not consider the correlation between the stress biomarkers assessed at the times considered and alcohol consumption patterns. This would have provided intriguing evidence of the risk of alcohol-related disease in the population examined.
## 6. Conclusions
Our study highlighted the way in which the imposition of smart working during the pandemic was one of the factors that negatively impacted the psycho-physical wellbeing of workers by causing stress that encourages the onset of risky behaviour.
In the population examined, it emerged that during the COVID-19 pandemic, the number of alcohol users and the modes of consumption of alcoholic beverages changed. From our study, the increase in alcohol consumption in ways that increase health risk is a result to be treated with particular concern, and to which we should pay particular attention.
This result was related to difficult working conditions, which are a source of intense stress. In-depth knowledge of the risky ways in which an individual worker consumes alcohol can enable the implementation of preventative actions to safeguard their health and to improve the safety of the worker and those who work with them.
Further studies are necessary to determine the close correlations between work-related stress and risky alcohol consumption in individual video workers, especially after the COVID-19 pandemic.
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|
---
title: 'A Dual Coverage Monitoring of the Bile Acids Profile in the Liver–Gut Axis
throughout the Whole Inflammation-Cancer Transformation Progressive: Reveal Hepatocellular
Carcinoma Pathogenesis'
authors:
- Luwen Xing
- Yiwen Zhang
- Saiyu Li
- Minghui Tong
- Kaishun Bi
- Qian Zhang
- Qing Li
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001964
doi: 10.3390/ijms24054258
license: CC BY 4.0
---
# A Dual Coverage Monitoring of the Bile Acids Profile in the Liver–Gut Axis throughout the Whole Inflammation-Cancer Transformation Progressive: Reveal Hepatocellular Carcinoma Pathogenesis
## Abstract
Hepatocellular carcinoma (HCC) is the terminal phase of multiple chronic liver diseases, and evidence supports chronic uncontrollable inflammation being one of the potential mechanisms leading to HCC formation. The dysregulation of bile acid homeostasis in the enterohepatic circulation has become a hot research issue concerning revealing the pathogenesis of the inflammatory-cancerous transformation process. We reproduced the development of HCC through an N-nitrosodiethylamine (DEN)-induced rat model in 20 weeks. We achieved the monitoring of the bile acid profile in the plasma, liver, and intestine during the evolution of “hepatitis-cirrhosis-HCC” by using an ultra-performance liquid chromatography-tandem mass spectrometer for absolute quantification of bile acids. We observed differences in the level of primary and secondary bile acids both in plasma, liver, and intestine when compared to controls, particularly a sustained reduction of intestine taurine-conjugated bile acid level. Moreover, we identified chenodeoxycholic acid, lithocholic acid, ursodeoxycholic acid, and glycolithocholic acid in plasma as biomarkers for early diagnosis of HCC. We also identified bile acid-CoA:amino acid N-acyltransferase (BAAT) by gene set enrichment analysis, which dominates the final step in the synthesis of conjugated bile acids associated with the inflammatory-cancer transformation process. In conclusion, our study provided comprehensive bile acid metabolic fingerprinting in the liver–gut axis during the inflammation-cancer transformation process, laying the foundation for providing a new perspective for the diagnosis, prevention, and treatment of HCC.
## 1. Introduction
HCC is one of the most serious malignancy tumors threatening human health, the third leading cause of cancer-related death in the world [1,2,3]. Persistent inflammation leading to the formation of the tumor microenvironment is an important factor in the formation of HCC, whose mechanism is very complicated. The morbidity trend of HCC appears to be closely related to hepatitis B (HBV) infection, and it has been reported that HCC patients caused by HBV still account for more than half of the global cases [4,5,6]. Although the inflammation-cancer transformation process of “hepatitis-cirrhosis-HCC” has become a research highlight to reveal the pathogenesis of HCC, there is still no effective clinical treatment strategy. Hence, it has become important to clarify the pathogenesis of the process to achieve early diagnosis of HCC and identify new therapeutic targets.
Among the various endogenous metabolites originating from the co-metabolism of the liver–gut axis, bile acids (BAs) have received increasing attention because of their neoplasm-promoting properties [7,8,9,10]. BAs are synthesized in the liver, and the size and composition of the liver bile acid pool are closely regulated by translocation proteins [11]. When liver organic solute transporter-alpha/beta (OSTα/OSTβ) expression is downregulated, abnormal retention of BAs in hepatocytes within the organism occurs, leading to chronic liver injury [12], while patients diagnosed with HCC between 13 and 52 months concerning bile acid transporter deficiency resulted in a suppression of liver bile acid efflux [13]. In addition to the effect of liver bile acid accumulation on hepatocarcinogenesis, disruption of intestinal bile acid pool homeostasis can contribute to cancer development and a variety of chronic disease phenotypes. Elevated levels of secondary BAs in feces are capable of causing structural and functional abnormalities in the colonic epithelium through various mechanisms, including oxidative damage to DNA, activation of nuclear factor kappa-B, and enhanced cell proliferation [14]. However, depicting the spectrum of BAs and their interactions in plasma, liver, and intestine, covering the entire enterohepatic circulation, during the overall disease course of “health-hepatitis-cirrhosis-HCC” still requires research.
In this paper, based on an N,N-diethyl-1,4-butanediamine (DEABA) derivatization method for absolute quantification of BAs, the systematic bile acid profiles in plasma, liver, and intestine in the whole progression of HCC have been obtained. Combined with analysis of independent sample t-tests, principal component analysis (PCA), orthogonal partial least squares discrimination analysis (OPLS-DA), and bayesian linear discriminant analysis (BLDA), key BAs biomarkers were screened out to distinguish different disease stages, which was valuable for the early diagnosis of HCC. Next, gene set enrichment analysis (GSEA) and the cancer genome atlas (TCGA) database were employed to explore the effect of core genes on the distribution of bile acid pools, which was crucial for promoting the development of HCC. Our study revealed the change of BAs in the liver–gut axis during the inflammation-cancer transformation process and provided a novel perspective for treating HCC.
## 2.1. Histology Assessment and Total Bile Acid Features in the Inflammation-Cancer Transformation Process
Changes in total bile acid (TBA) levels can reflect the physiological status and injury degree of the organism. Studies have confirmed that the TBA profiles of patients with HCC have unique metabolic characteristics, and the homeostasis of TBA is dependent on liver synthesis and intestinal absorption [15,16]. To elucidate the etiopathogenesis of HCC underlying TBA metabolism disorders, the present study evaluated the various canceration stages of DEN-induced rats based on the results of hematoxylin-eosin (H & E) stained liver tissue sections and quantified the TBA level in rats plasma, liver, and intestine at different stages of HCC progression.
H & E staining showed that hepatocytes began to exhibit severe impairment in the 8th week compared to healthy controls (Figure 1A), termed the hepatitis stage (Figure 1B). The liver tissue was infiltrated with lymphocyte-dominated inflammatory cells, with a small amount of bile duct hyperplasia and localized vascular stasis. The cirrhosis stage occurred in the 12th week (Figure 1C), with an obvious structural disorder of liver lobules, the proliferation of perivenous connective tissue, formation of pseudo lobules with hepatocyte regeneration nodules, and bile duct hyperplasia. The 16th week was the initial stage of HCC (Figure 1D). Microscopically, hepatocyte empty valve degeneration and a small number of adenoid structures were observed, and a large amount of bile duct hyperplasia was visible. At the same time, massive vascular stasis and brownish-yellow pigmentation were observed. The 20th week was described as an advanced HCC stage (Figure 1E). The hepatic tissue showed obvious adenoid structures, all cells had enlarged deep-stained nuclei, and different degrees of vacuolar degeneration were observed.
Based on the histological results, we found that TBA levels significantly increased in all disease groups (Figure 2A). The TBA level of intestinal contents samples gradually decreased with disease progression, which showed an opposite trend to plasma and liver samples (Hepatitis & Cirrhosis vs. Control ** $p \leq 0.01$; HCC & Advanced HCC vs. Control * $p \leq 0.05$), while the TBA levels in plasma and liver gradually increased in all stages (* $p \leq 0.05$, ** $p \leq 0.01$). Therefore, we speculate that there is a close relationship between the inflammation-cancer transformation process and enterohepatic circulation.
To further analyze the specific reasons for the gradual decrease of TBA levels in the intestine, we subsequently analyzed total primary and secondary BAs in plasma, liver, and intestinal contents. We found that total primary and secondary BAs were markedly elevated in plasma and intestinal contents. However, we observed a specific phenomenon of elevated total primary BAs but decreased secondary BAs in liver samples only (* $p \leq 0.05$, ** $p \leq 0.01$). With the development of HCC, total primary BAs in the intestine decline in the advanced HCC stages, in contrast to the continuous increment of total primary BAs in the plasma and liver (Figure 2B). In addition, it is noteworthy that the total secondary BA level in plasma and liver showed an abnormal rebound at the advanced HCC stage, which was not seen in intestinal contents (Figure 2C).
## 2.2. Observing Liver–Gut Axis BAs Environment and Screening HCC Biomarkers for Early Diagnosis
To figure out the key driving BAs for the evolving of HCC, we quantified the changes in the levels of 5 free BAs (cholalic acid, CA; chenodeoxycholic acid, CDCA; ursodeoxycholic acid, UDCA; lithocholic acid, LCA; deoxycholic acid, DCA; Figure 3), and their associated 10 conjugated BAs in plasma, liver, and intestine (Figure 4). The quantitative results of 15 BAs in plasma, liver, and intestinal contents samples from different disease stages of HCC and healthy controls are included in Table S3, and the results are expressed as mean ± SD.
For free BAs, we found the same trend in three samples, with a significant increase in CA, CDCA, and DCA and a marked decline in LCA (* $p \leq 0.05$, ** $p \leq 0.01$). In addition, the different phenomena in UDCA are noteworthy, which were reduced in the liver and intestinal contents but elevated in plasma.
In rodents, free BAs are more likely to be coupled to taurine, the glycine-conjugated BAs accounting for a small proportion of conjugated BAs [17]. The report supports that glycine-conjugated BAs are present at low levels in rats [18]. Due to the low levels and some errors in the quantitative analysis, individual disease groups did not show significant differences compared to the control group. However, from an overall perspective, glycocholic acid (GCA), glycochenodeoxycholic acid (GCDCA), glycodeoxycholic acid (GDCA), glycolithocholic acid (GLCA), and glycoursodeoxycholic acid (GUDCA) all showed similar trends to their prototypes in three samples (Figure 4A–E). Taurocholic acid (TCA), taurochenodeoxycholic acid (TCDCA), tauroursodeoxycholic acid (TDCA), taurolithocholic acid (TLCA), and tauroursodeoxycholic acid (TUDCA) showed consistent trends concerning prototypic BAs only in plasma and liver. Surprisingly, all five taurine-conjugated BAs were reduced in the intestine and found a progressive decrease in TCA, TUDCA, and TDCA with disease progression (* $p \leq 0.05$, ** $p \leq 0.01$, Figure 4F–J).
Next, the association between discrepancies in bile acid levels and inflammatory-cancer transformation was established by two multivariate modeling approaches, PCA and OPLS-DA. The results showed that they were distinguished by respective disease stages. As the HCC progresses, the PCA score plot demonstrated a definite trend, confirming the potential of BAs to predict disease staging (Figure 5A–C). Next, combining the contribution degree of OPLS-DA (VIP > 1) and the significance of independent t-test ($p \leq 0.05$), CDCA, LCA, UDCA, and GLCA in plasma, and CDCA in liver and intestine were seen as biomarkers that have a positive role in the early diagnosis of HCC (Figure 5D–F). To date, liver biopsy is currently the gold standard for early diagnosis of HCC, but patient acceptance of this standard invasive technique is poor. A BLDA diagnostic model was constructed by CDCA, LCA, UDCA, and GLCA in plasma to achieve non-invasive detection. The coefficients of the four biomarkers and constants in the BLDA diagnostic model are listed in Table 1. By substituting the bile acid concentrations into the respective equations, the probability of being classified in the corresponding disease group was calculated. The result indicated a reliable model; $86.7\%$ of the samples could be correctly distinguished (Table S4).
## 2.3. BAAT was Associated with Altered Composition of the Intestinal BA Pool and Disruption of Enterohepatic Circulation
To explore the potential mechanisms of bile acid metabolism changes in HCC patients and to screen out valuable key target genes, 373 HCC samples and 50 healthy samples from the TCGA database were involved in the present analysis. GSEA enrichment analysis was used to screen out 15 gene sets related to the biological functions of bile acid (Table S5, Figure S1). We obtained 125 genes from 15 gene sets to import into the STRING database to complete the visualization of Protein-Protein Interaction (PPI) Networks with a confidence level > 0.4 (Figure 6). Finally, Cytoscape software was applied to calculate the key node genes based on the cyto Hubba plug-in and maximal clique centrality (MCC) algorithm, the most core gene BAAT was obtained, ranking first.
BAAT is the final modification before catalyzing the generation of conjugated BAs from free BAs into the enterohepatic circulation [19]. Evidence indicates that BAAT promotes glycine-conjugating BAs with extremely low efficiency but efficient conjugating with taurine in rats [20,21]. The TCGA database supports that BAAT is significantly under-expressed in HCC cases (** $p \leq 0.01$, Figure S2), suggesting that BAAT deficiency is partly responsible for the decrease in taurine-conjugated BAs in the intestine, which would alter the composition of the intestinal bile acid pool and increase its toxicity, thereby promoting the progression of inflammation to HCC.
## 3. Discussion
In recent years, HBV infection has progressively developed into a major cause of HCC. At the same time, 80–$90\%$ of new cases occur in the context of cirrhosis, suggesting that hepatitis and cirrhosis play important roles in the precancerous liver environment [22,23]. It is confirmed through research that early diagnosis of HCC by monitoring BAs may improve prognosis and the feasibility of curative treatment [24]. Meanwhile, the bidirectional communication of the liver–gut axis is an essential part of coordinating the dynamic balance of the bile acid pool in the body [25]. However, there are few existing articles describing whole bile acid profiling in enterohepatic circulation during the process of “hepatitis-cirrhosis-HCC”. The pathogenesis of HCC has not been clear till now. We clarified the four disease stages of HCC development based on the previous literature [26,27] and histopathological analysis, first achieving the dual coverage monitoring of the dynamic changes of bile acid levels and distribution during the enterohepatic circulation and the evolution of “hepatitis-cirrhosis-HCC”, and found that the imbalance of the enterohepatic circulation system was the key driver of the inflammation-cancer transformation process, which contributes to cognitive the pathogenesis of HCC.
This study indicated a significant sludge of BAs in the liver–gut axis, while TBA levels in plasma and liver are positively correlated with HCC progression. High levels of bile acid environment have been known to induce reactive oxygen species production and apoptosis in hepatocytes, further leading to impaired liver function [28]. It was accepted that the gradual accumulation of TBA is a major risk factor for the development of HCC, while it is well established that TBA levels and enterohepatic circulation profoundly influence each other [29]. Enterohepatic circulation is the process by which BAs pass from the liver to the intestine and then return to the liver through reabsorption from the portal vein [25,30]. The above process is intricately linked to processes that mainly undergo extensive feedback and feed-forward regulation by specialized absorption and excretion transport systems in the liver and intestine [31]. Furthermore, defective expression and function of bile acid export, as well as reabsorption, have been recognized as important causes of progressive cholestasis in the liver and plasma [32,33]. BAs in the above process are circulated through specialized absorption and excretion transport systems in the liver and intestine. Bile salt export pump (BSEP) and multidrug resistance-associated protein (MRP2) are key transport proteins for the hepatic efflux of BAs, while sodium bile acid/taurocholic synergistic polypeptide (NTCP) and organic anion transport peptide (OATP) are the main transport proteins in the liver responsible for uptake of circulating BAs in the portal vein [34,35]. Reports on patients with HCC also indicate that BSEP, MRP2, NTCP, and OATP expression is downregulated [29,36], corroborating the disruption of enterohepatic circulation in the development of HCC.
The intestine is the site of secondary BA synthesis. Primary BAs synthesized in the liver are further metabolized in the intestine [37]. We provide dysregulation of the primary and secondary BAs in the liver–gut axis, revealing a unique metabolic regulation of BAs in the intestine. The organic solute transporter-alpha/beta (OSTα/OSTβ) are exporters of BAs from the intestine and are an important link in enterohepatic circulation [38]. It has been confirmed in the literature [39] that the absence of OSTα/OSTβ expression causes an increased level of BAs in the intestinal contents as well as in the small intestine. Our quantitative results showed that total secondary BAs were most significantly elevated in the intestine, in addition to being equally elevated in plasma but reduced in the liver, a characteristic phenomenon that likewise suggests a deficiency of the liver bile acid transport system.
Mechanisms underlying the failure of the intestinal barrier and the development of a leaky gut are not fully understood. Still, abnormal retention of toxic BAs is recognized as an important contributing factor [40,41,42]. Secondary BAs are generated from primary BAs through reactions such as 7α-dehydroxylation, so they have the highest hydrophobicity compared to all BAs, a property thought to be linked to hepatotoxicity [43]. On the other side, secondary BAs and their derivatives are a major component of the intestinal bile acid pool, and their elevation represents a change in the toxicity of the intestinal bile acid pool [44]. With the progressive development of HCC, we concluded that due to the large accumulation of secondary BAs in the intestinal epithelium, the intestinal permeability is altered, which eventually causes intestinal fistula. Therefore, we believe that the phenomenon of an abnormal rebound of total secondary bile acids in plasma and the liver is caused by the development of intestinal fistula and the massive efflux of toxic substances accumulated in the intestine at the advanced HCC stage. The above processes also coincided with a progressive decrease of total and secondary BAs in the intestine of the disease group.
CA and CDCA are two primary BAs, and DCA and LCA are secondary BAs from their conversion, respectively. According to the report that the hydrophobic-hydrophilic balance of BAs is closely related to metabolic homeostasis in vivo [45], more hydrophobic BAs can act as cancer promoters and further amplify the development of HCC [46,47]. The high hydrophobicity of CDCA and DCA makes them cytotoxic and pro-inflammatory [48,49]. CA is not highly hydrophobic, but studies have shown that feeding mice with CA increases the size and hydrophobicity of the bile acid pool while causing cholestasis and hepatic steatosis [50]. LCA also has hydrophobic properties, but that’s a small fraction of BAs. UDCA is a primary bile acid in rats, a non-toxic hydrophilic bile acid [51]. Evidence supports the ability of UDCA to accelerate enterohepatic circulation and its cytoprotective properties [52,53]. Therefore, the elevation of CA, CDCA, and DCA in the liver and intestine and the downregulation of LCA and UDCA imply a hydrophobic change in the composition of BAs and a progressive accumulation of toxic BAs that inhibit the enterohepatic circulation. Bile flow is primarily dependent on the drive of conjugated BAs. Congenital defects in BA conjugating can lead to malabsorption of fat-soluble vitamins and, thus, severe liver disease [54,55]. BAAT is the key enzyme capable of mediating bile acid coupling [19]. As mentioned earlier, it has been demonstrated that BAAT -/- mice are almost completely devoid of taurine-conjugated BAs in the liver, suggesting that BAAT is the primary taurine-coupled enzyme in mice [56,57]. Our figures showed that the TBA level in the intestines remained significantly elevated. At the same time, all the taurine-conjugated BAs were continuously reductive in the intestine of model rats. We speculate that the down-regulation of BAAT expression is the key reason for the above phenomenon. Consistent with this, the gene enrichment results confirm our previous speculation about the variation of taurine-conjugated BAs level in the intestine.
## 4.1. Reagents
Acetonitrile, isopropanol, and methanol were purchased from Fisher Scientific (Fair Lawn, NJ, USA), while formic acid, dimethyl sulfoxide, and acetone were purchased from Yuwang Co. Ltd. (Yucheng, China). The distilled water used in the experiments was purchased from Wahaha Group Co., Ltd. (Hangzhou, China). DEN used in animal experiments was purchased from Sigma-Aldrich (St. Louis, MO, USA).
The commercial standards selected for this study, the bile acid used for quantitative analysis, their abbreviations, CAS numbers, and manufacturers are included in Table S1.
## 4.2. Animals
For this study, Wistar male rats, weighing 100 ± 20 g, purchased from by the Animal Ethical Committee of Changsheng Biotechnology (IACUC No. CSE202106002), were used and provided a constant relative humidity of 65 ± $15\%$ and a temperature of 23 ± 2 °C environment with 12 h-light dark cycles. At the same time, the rats have full access to food and water. The rats were fed and acclimatized to their environment for one week prior to the experiment. Then, 64 rats were randomly divided into two groups, the HCC model group and the healthy control group. Rats in the model group ($$n = 32$$) were injected intraperitoneally with DEN solution at a dose of 70 mg/kg once a week for 10 weeks, while rats in the control group ($$n = 32$$) were injected intraperitoneally with an equal volume of saline as a control.
## 4.3. Histopathological Analysis
Liver tissue sections were deparaffinized with xylene and dehydrated in ethanol. Making tissue into 3 µm slice samples and then stained with H&E. Images were acquired using a NIKON digital sight DS-FI2 imaging system after observation with a NIKON Eclipse ci optical microscope.
## 4.4. UFLC-MS/MS Conditions for Quantitation of BAs
A previously published method by our group was used to quantify the BAs [58]. The method was based on a polar response homogeneous dispersion strategy with DEABA labeling, which reduces the polarity and response gap of the analytes and improves selectivity compared to non-derivatization. The ultra-performance liquid chromatography—tandem mass spectrometer (UPLC-MS/MS) systems and chromatographic column were used for the analysis, and liquid phase conditions can be found in previous methods. The positive ion gradient elution program was: 0.01–10.00 min, $20\%$B→$50\%$B; 10.00–17.00 min, $50\%$B→$85\%$B; 17.00–22.00 min, $85\%$B→$90\%$B. The negative ion gradient elution program was 0.01–4.00 min, $20\%$B→$35\%$B; 4.00–6.00 min, $35\%$B→$70\%$B; 6.00–10.00 min, $70\%$B→$85\%$B. 10.00–10.10 min, $85\%$B→$90\%$B, and continued with $90\%$ B running at 10.10–12.00 min.
We used the electrospray ionization (ESI) source in both positive and negative ion form to accomplish the analysis and determination of BAs by multiple reaction monitoring (MRM) modes. The ion spray voltage was 5500 V(+)/4500 V(−), and the other parameters of the mass spectrum were as follows: curtain gas (N2), 20 psi; nebulizer gas (gas 1, N2), 50 psi; heater gas (gas 2, N2), 50 psi; and source temperature, 500 °C(+)/500 °C(−). The corresponding mass spectrometer (MS) parameters for the 15 BAs can be found in Table S2.
## 4.5. Sample Collection and Pretreatment
For plasma samples, the whole blood samples were collected from each group following forbidden food for 12 h, placed in heparinized sterile eppendorf tubes, and centrifuged at 10,142× g for 10 min at 4 °C to transfer plasma. Then, BAs were extracted from plasma samples as described in the previous method [58].
For liver samples, rats in each group were killed by cervical dislocation after plasma collection. Liver tissue was immediately peeled out, bathed in physiological saline, blotted through filter paper, and transferred to a dry ice box soon afterward. Liver tissue samples (50.00 ± 0.50 mg each) were homogenized in 100 μL physiological saline for 5 cycles (5 s at 300 w, with 3 s between each cycle) by using an ultrasonic cell disruptor (JY92-IIDN, SCIENTZ, Zhengjiang, China) in an ice bath. One liver homogenate was added to 10 µL of internal standard and 10 µL of methanol, the same internal standard used for plasma samples. After vortex shaking for 30 s, 500 µL of precipitated protein reagent, methanol:isopropanol (v/v, 1:2), was added. The homogenate was centrifuged (4 °C, 10,142× g) with vortex shaking for 5 min for 10 min, and the upper layer was dried under a stream of nitrogen. The dried liver samples were derivatized in the same manner as the plasma samples and then subjected to subsequent analysis.
For intestinal contents samples, on the day before the rats were killed, the rats were placed in metabolic cages to collect 24 h intestinal contents. The collected intestinal contents samples were lyophilized for 48 h and ground into powder. 50 mg ± 0.50 mg was taken from intestinal contents lyophilized powder and spiked with 500 µL of physiological saline, then vortexed for 10 min to obtain Intestinal contents homogenate. The pretreatment procedure for intestinal contents samples was approximately the same as for liver samples. The difference is that for protein precipitation, 600 µL of methanol:acetonitrile:acetone (v/v/v, 1:1:1) was added to the intestinal contents sample, and the supernatant before drying was filtered through 0.22 μm organic filter membrane. The dried intestinal contents sample were derivatized in the same manner as the plasma samples and then subjected to subsequent analysis.
## 4.6. Gene Enrichment Analysis
We collected samples from The TCGA genomic data commons data portal (https://portal.gdc.cancer.gov/ (accessed on 15 September 2022)) and obtained their RNA sequencing fragments per kilobase million data.
In this study, we selected the gene sets associated with biological functions of bile acid (shown in Table S3) from the GSEA data set (https://www.gsea-msigdb.org/ (accessed on 5 September 2022)) and performed enrichment analysis between the two groups by GSEA software (version 4.2.3). Among them, gene sets whose p-value < 0.05, false discovery rate (FDR) < 0.05, and normalized enrichment score (NES) > 1.5 were collected for subsequence procession. We visualized the PPI network using STRING 11.5 (https://cn.string-db.org/ (accessed on 18 November 2022)) and the cytoHubba plug-in of Cytoscape (version 3.9.1) software for screening key genes.
## 4.7. Statistical Analysis
*The* generated raw data files were processed using the Analyst® application (version 1.5.1, AB SCIEX™, Foster City, CA, USA), based on which standard curves were created, and all BAs were quantified. The significant differences between the experimental groups were determined using the SPSS Statistics (version 26.0, CHI, Chicago, IL, USA) and GraphPad Prism (version 9.2.0, GraphPad Software Inc., San Diego, CA, USA). The BLDA discriminant analysis was carried out with SPSS software, while PCA and OPLS-DA analysis used the SIMCA-P program (version 14.1, Umetrics, Malmö, Sweden). When the p-value < 0.05 or less, we considered the data evidently different and statistically significant.
## 5. Conclusions
In this study, we achieved a dual coverage monitoring of the bile acid profile in the liver–gut axis throughout the whole inflammation-cancer transformation progression. We found that the enterohepatic circulation is disrupted during HCC development after intensively researching the differences in levels of TBA, primary/secondary BAs, and single BAs. Next, we used GSEA gene enrichment analysis to obtain the key node gene BAAT, which dominates the synthesis of taurine-conjugated BAs in rats. We also validated our specific phenomenon of taurine-conjugated BAs in the intestine.
In summary, our results suggest that the disruption of the enterohepatic circulation in the internal environment is an important factor dominating the inflammation-cancer transformation process. The lack of BAAT may be one of the potential mechanisms interrupting the enterohepatic circulation. Additionally, we developed the BLAD diagnostic model, and found that GLCA, CDCA, UDCA, and LCA in plasma samples can be used as biomarkers to distinguish the different disease stages of HCC, enabling early diagnosis of HCC from the perspective of non-invasive detection. However, immunotherapy has been a hot research topic for treating HCC. It has been recently suggested that regulatory T cells, the most abundant immunosuppressive cell population of the HCC-related tumor microenvironment, might suggest a potential target for HCC immunotherapy [59]. Evidence supports that intestinal flora influences the differentiation, accumulation, and function of regulatory T cells [60], and the influence of intestinal flora on BAs metabolism is well established [61,62]. In future studies, it is of great interest and necessity to focus on the link between BAs metabolism, intestinal flora, and the immune cell population of the tumor microenvironment, which will contribute to the further development of HCC therapy.
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|
---
title: 'Socioeconomic Background and Self-Reported Sleep Quality in Older Adults during
the COVID-19 Pandemic: An Analysis of the English Longitudinal Study of Ageing (ELSA)'
authors:
- Adam N. Collinge
- Peter A. Bath
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001974
doi: 10.3390/ijerph20054534
license: CC BY 4.0
---
# Socioeconomic Background and Self-Reported Sleep Quality in Older Adults during the COVID-19 Pandemic: An Analysis of the English Longitudinal Study of Ageing (ELSA)
## Abstract
The COVID-19 pandemic negatively impacted sleep quality. However, research regarding older adults’ sleep quality during the pandemic has been limited. This study examined the association between socioeconomic background (SEB) and older adults’ sleep quality during the COVID-19 pandemic. Data on 7040 adults aged ≥50 were acquired from a COVID-19 sub-study of the English Longitudinal Study of Ageing (ELSA). SEB was operationalized using educational attainment, previous financial situation, and concern about the future financial situation. Sociodemographic, mental health, physical health, and health behavior variables were included as covariates. Chi-squared tests and binary logistic regression were used to examine associations between SEB and sleep quality. Lower educational attainment and greater financial hardship and concerns were associated with poor sleep quality. The relationship between educational attainment and sleep quality was explained by the financial variables, while the relationship between previous financial difficulties and sleep quality was explained by physical health and health behavior variables. Greater financial concerns about the future, poor mental health, and poor physical health were independent risk factors for poor sleep quality in older adults during the pandemic. Healthcare professionals and service providers should consider these issues when supporting older patients with sleep problems and in promoting health and wellness.
## 1. Introduction
In March 2020, the World Health Organization (WHO) declared the novel coronavirus disease 2019 (COVID-19) outbreak to be a pandemic. Since then, the virus has mutated into several variants that have spread around the world [1]. Common symptoms include fever, sustained coughing, and shortness of breath [2]. The virus can cause long-term fatigue (i.e., ‘long COVID’), injury to key organs, and death [3,4,5].
To date, more than six and a half million people have died as a result of COVID-19 [6]. However, while undoubtedly severe, the consequences of the virus have spread far beyond its physical symptoms. Notably, research has identified a general decline in sleep quality during the pandemic [7,8,9]. This is likely to be due to an increase in psychological challenges attributable to the pandemic [10,11,12,13], which may have disrupted sleep quality [9,14,15].
Broadly, disruptions to sleep quality are a primary concern for both scholars and health clinicians due to the overall importance of sleep. On the one hand, good-quality sleep has been shown to improve cognitive functioning, mood, and mental well-being, alongside allowing for somatic regeneration [16,17,18,19]. Conversely, long-term poor-quality sleep may lead to a variety of negative health outcomes, including dementia, cardiovascular and cerebrovascular complications, depression, and cancer [20,21,22,23,24,25,26].
Given these detrimental outcomes, it has been important to identify those who are most at risk of experiencing poor sleep quality and to understand the factors associated with poor sleep so that interventions can be targeted to those in most need. For example, it is now widely accepted that older adults are more likely to experience poor sleep quality than younger and middle-aged adults [27]. This is predominantly due to the natural compression of circadian rhythm waves in old age, which results in a decrease in peak melatonin production [27]. As melatonin is the key moderator of the sleep–wake cycle [28], this natural biological change means that sleep fragmentation and arousal become more frequent, and sleep duration becomes reduced [29], thereby disrupting sleep quality.
In addition to natural biological changes, it is quite possible that the negative relationship between age and sleep quality has been exacerbated by the COVID-19 pandemic. According to Ring et al. [ 30], older adults typically have a heightened awareness of death, i.e., adults tend to feel closer to death in the later stages of life. However, this heightened awareness is likely to have been compounded by the fact that adults aged 65 and over have been disproportionately affected by the virus in terms of mortality [31,32]; this has been widely publicized throughout the media [33,34]. Consequently, older adults may be more likely to develop an overwhelming fear of COVID-19 [33,35,36,37,38], otherwise known as ‘coronaphobia’, which can present as anxiety, depression, and loneliness [39,40,41]. Such psychological adversity has been shown to negatively impact sleep quality [14,42,43].
Despite the well-documented links between aging and sleep quality, alongside the likelihood that this negative relationship has been compounded by the pandemic, COVID-related research has, so far, failed to thoroughly examine the relationship between aging and sleep quality within the context of the pandemic. Instead, scholarship has generally focused on students, parents, and healthcare professionals (e.g., [44,45,46]). Although such a focus is undeniably important, it has meant that older adults have been somewhat neglected in the literature. Further, the extant literature focusing on age, sleep, and COVID-19 has tended to treat older adults homogeneously (e.g., [29,47]). This is likely to lead to results that are too general; however, this could be corrected by accounting for further factors.
Alongside age, researchers have identified socioeconomic background (SEB) to be a key contributor to sleep problems and health disparities. In other words, lower SEB has been shown to be associated with sleep disorders, including insomnia, alongside overall poor sleep quality [48,49,50,51]. This is likely to be due to higher stress levels experienced by this demographic group compared with the rest of the population [52]. These stressors predominantly revolve around increased economic burdens [53,54], heightened job insecurities [54], and childhood adversity [55,56], which is likely internalized and persists into adulthood [57,58]. Moreover, studies suggest that adults from lower SEBs are more likely to take part in behaviors that disrupt sleep quality, such as alcohol and tobacco consumption [59,60,61,62,63,64,65].
Importantly, SEB, and any health complications associated with it, persists into old age [66]. This suggests that, when analyzing sleep quality in older adults, heterogeneity can be observed by accounting for SEB. The aim of this study was, therefore, to examine the relationship between SEB and sleep quality in older adults in England, in the context of the COVID-19 pandemic. In doing so, this study is important in that it provides new insights into a previously underexplored area and offers new evidence to the growing discourse of sleep problems during the COVID-19 pandemic.
## 2.1. Data
The data used in this study were acquired from the English Longitudinal Study of Ageing (ELSA) [67]. Since 2002, the ELSA has interviewed a large representative cohort of adults aged 50 or over living in England at biennial intervals regarding their health, social, and financial circumstances. In addition, ELSA also undertook a COVID-19 sub-study, which collected data on pandemic-related variables alongside the usual health and socioeconomic data. The COVID sub-study had two waves. Data collection for wave 1 took place between June and July 2020, and data collection for wave 2 took place between November and December 2020. This study used the COVID-19 sub-study to assess sleep quality in older adults during the COVID-19 pandemic. Specifically, wave 1 data were used as the dataset and had a higher response rate ($$n = 7040$$; $75\%$) than wave 2 ($$n = 6794$$; $72\%$). The data used in this study are freely available at https://www.elsa-project.ac.uk/ (accessed on 27 March 2022).
## 2.2. Sleep Quality
The dependent variable used in this study was sleep quality, which was assessed using the ELSA variable ‘CvHesleep’. For this interview question, participants were asked how well they had slept over the past month using a five-point Likert scale (1 = ‘excellent’, 2 = ‘very good’, 3 = ‘good’, 4 = ‘fair’, and 5 = ‘poor’). This variable was recoded into a binary format for the analyses. Original responses ranging from ‘excellent’ to ‘good’ were recoded as ‘excellent–good’ (=1), whilst ‘fair’ and ‘poor’ responses were recoded as ‘fair–poor’ (=2). This split was based on the premise that good subjective sleep quality is determined by high satisfaction levels [68,69]. Conversely, ‘fair’ responses suggested that participants slept less than adequately, rather than satisfactorily. Hence, ‘fair’ responses were considered to be more closely related to ‘poor’ sleep quality.
## 2.3. Socioeconomic Background (SEB)
The primary independent variable to be used in this study was SEB. However, there was no single variable measuring SEB in the ELSA dataset. Instead, SEB was operationalized using three proxy indicators: educational attainment (‘w9edqual’), participants’ self-reported financial situation in the three months before the coronavirus outbreak (‘CvPreFn’), and the extent to which participants were worried about their future financial situation (‘CvFinS_CvFinS1_1’). Financial proxies were used on the premise that adults from lower SEBs tend to accumulate fewer savings over their lifetime, and therefore may be more prone to financial difficulties in old age [70]. To this end, greater exposure to financial difficulties and greater concern over future finances were considered to be indicators of low SEB. Similarly, lower levels of education (i.e., <NVQ3/A-Levels) were considered indicative of lower SEB. Neither of the financial proxies were recoded for the purposes of this study. However, the category ‘foreign/other’ ($$n = 573$$; $8.1\%$) was excluded from the education variable as it gave no insight into the level of qualification, and, therefore, could not be used to determine SEB reliably. The educational attainment variable was coded as follows: 1 = ‘Degree or equivalent’ (reference category), 2 = ‘Higher education below degree’, 3 = ‘NVQ3/A-Level’, 4 = ‘NVQ2/O-Level’, 5 = ‘NVQ1/CSE’, and 6 = ‘No qualifications’. The financial variable representing the participants’ self-reported financial situation in the three months before the coronavirus outbreak was coded as: 1 = ‘Living comfortably’ (reference category), 2 = ‘Doing all right’, 3 = ‘Just about getting by’, 4 = ‘Finding it quite difficult’, and 5 = ‘Finding it very difficult’. The financial variable representing the extent to which participants were worried about their future financial situation was coded as: 1 = ‘Not at all worried’ (reference category), 2 = ‘Not very worried’, 3 = ‘Somewhat worried’, 4 = ‘Very worried’, 5 = ‘Extremely worried’.
## 2.4. Covariates
Alongside the primary dependent and independent variables, a selection of covariates were also included in the statistical analyses. These were categorized as either additional sociodemographic factors, mental health factors, or physical health and health behavior factors, and are described below.
## 2.4.1. Sociodemographic
Age, gender, ethnicity, and urbanicity were used as sociodemographic covariates. The gender, ethnicity, and urbanicity variables were originally recorded as binary data, so further recoding was not required. Age was recoded from continuous to ordinal data using the following groups: 50–59 years, 60–69 years, 70–79 years, 80–89 years, and 90+ years. Notably, several participants were recorded as having an age of <50 years. As the required age to participate in ELSA is ≥50 years, these were deemed to be errors and these participants were excluded from the analyses.
## 2.4.2. Mental Health
Feeling depressed or lonely over the past week, and feeling nervous, anxious, or on edge over the past two weeks were used as mental health covariates. The depression and the loneliness variables were used in their original binary form. The anxiety variable was originally based on a four-point Likert scale (1 = ‘not at all’, 2 = ‘several days’, 3 = ‘more than half the days’, and 4 = ‘nearly every day’). This variable was recoded into a binary format, where ‘not at all’ was recoded as ‘no’ (=2) and all other responses were recoded as ‘yes’ (=1).
## 2.4.3. Physical Health and Health Behaviors
Self-reported health over the past month, isolating due to increased risk from coronavirus, alcohol and tobacco consumption, and the presence of a longstanding illness, disability, or infirmity (LSIDI) that limits activities were used as physical health and health behavior covariates. The latter variable was a composite, recoded from whether the participant had an LSIDI and whether the LSIDI limits activities. Participants who reported no LSIDI were coded 0. If participants had a LSIDI that did not limit their activities, the response was coded as 1. If participants had a LSIDI that did limit their activities, the response was coded as 2. The alcohol use variable was also recoded as 1 (‘yes’) if they drank alcohol or 2 (‘no’) if they did not drink or had never drunk alcohol.
## 2.5. Statistical Analysis
Descriptive statistics were first produced to characterize the study sample in terms of sociodemographic characteristics, mental health characteristics, and physical health and health behavior characteristics. Chi-squared tests for independence were conducted to examine bivariate associations between the independent variables and sleep quality. Finally, binary logistic regression was used to determine whether the covariates had a moderating effect on the relationship between the SEB proxy variables and sleep quality in older adults. In total, four regression models were produced. Model 1 included and compared all three SEB proxy variables simultaneously with sleep quality, after which the covariates were added iteratively. Model 2 also included the sociodemographic data, model 3 also included mental health data, and model 4 also included physical health and health behavior data. p values, odds ratios, and $95\%$ confidence intervals were calculated to estimate the increased or decreased risks of variables and categories in relation to sleep quality. Variables that were non-significant in the bivariate analysis were excluded from the multivariate analysis. Alpha was set to ≤0.05 throughout this study. Data management and analysis were conducted using IBM SPSS v28 (IBM, Armonk, NY, USA).
## 2.6. Patient and Public Involvement and Engagement (PPIE)
The research for this study was presented to four members of a Patient and Public Involvement and Engagement (PPIE) panel as part of a post-graduate training scheme organized by Health Data Research (HDR) UK North. Panel members were offered the opportunity to express potential research avenues for this study and which shape suggestions for future research.
## 2.7. Ethics
Ethics approvals for the original and subsequent waves of the English Longitudinal Study of Ageing were obtained from appropriate NHS Research Ethics Committees. The University of Sheffield Research Ethics Committee confirmed that this study involved only existing anonymized data and did not require further ethics approval (ref. 046081).
## 3.1. Characteristics of the Sample
Table 1 shows the general characteristics of the study sample. Among the 7040 individuals who took part in this wave of the study, just over half ($$n = 3726$$; $52.9\%$) reported having experienced good quality sleep over the past month. The indicators of socioeconomic background (SEB) were relatively high in the study sample, with $42.6\%$ ($$n = 2999$$) of all participants achieving A-Levels or higher, $61.2\%$ ($$n = 4309$$) of all participants claiming to have been ‘living comfortably’ in the three months before the COVID-19 pandemic, and $77.7\%$ ($$n = 5470$$) of all participants claiming to be ‘not very worried’ or ‘not at all worried’ about their future financial status.
The most frequently occurring age group was people aged 60–69 ($$n = 2348$$; $33.4\%$); $56.5\%$ of the sample were women ($$n = 3980$$); the majority came from a non-ethnic minority background ($$n = 6726$$; $95.5\%$) and lived in an urban location ($$n = 5110$$; $72.6\%$). Generally, the frequency of poor mental health was low, with $17.5\%$ of participants experiencing depression ($$n = 1232$$), $17.2\%$ reporting loneliness ($$n = 1209$$), and $36.5\%$ experiencing anxiety ($$n = 2572$$). Overall, $77.6\%$ ($$n = 5466$$) of participants rated their health over the past month as ‘good’ or better, while $23.5\%$ ($$n = 1656$$) of the sample were self-isolating due to an increased risk from COVID-19. The majority of participants drank alcohol ($$n = 4596$$; $65.3\%$) and did not smoke ($$n = 6532$$; $92.8\%$), while $69.1\%$ ($$n = 4871$$) of participants either had no longstanding illness, disability, or infirmity (LSIDI), or their LSIDI did not limit activities. Three hundred and ninety-nine people ($5.7\%$) had had a COVID-19 test; of these, 34 ($0.5\%$) were waiting for results, while and 23 had tested positive ($0.3\%$).
## 3.2. Bivariate Associations with Sleep Quality
Table 2 presents the results from the Chi-squared tests, which were used to examine associations between the independent variables and sleep quality. Regarding SEB, all three proxy variables were significantly associated with sleep quality in older adults during the COVID-19 pandemic (educational attainment: $$p \leq 0.005$$; previous financial situation: $p \leq 0.001$; and concern over future finances: $p \leq 0.001$). In addition to these SEB proxies, the following variables were found to be significantly associated with sleep quality in older adults during the COVID-19 pandemic: age ($p \leq 0.001$); gender ($p \leq 0.001$); urbanicity ($$p \leq 0.004$$); all mental health covariates (all: $p \leq 0.001$); and all physical health and health behavior covariates (all: $p \leq 0.001$).
## 3.3. Multivariable Relationships between SEB and Sleep Quality
Table 3 presents the results from the binary logistic regression analysis, which was used to test associations between the socioeconomic background (SEB) proxy variables and sleep quality, in the unadjusted model (model 1, containing only the three SEB variables) and when adjusting for the sociodemographic (model 2), mental health (model 3), and physical health and health behavior (model 4) covariates.
Educational attainment ($$p \leq 0.321$$) was not significantly associated with sleep quality in model 1 and remained non-significant when adjusting for the other covariates (model 2: $$p \leq 0.551$$; model 3: $$p \leq 0.635$$; and model 4: $$p \leq 0.694$$).
Conversely, both financial SEB proxies showed a highly significant association in the unadjusted model (model 1) (both: $p \leq 0.001$). People who felt that they were doing all right (OR = 1.32; $95\%$ CI = 1.16, 1.51), just about getting by (OR = 1.76; $95\%$ CI = 1.36, 2.29), or finding it difficult financially (OR = 2.50; $95\%$ CI=1.28, 4.88) three months before the coronavirus outbreak were significantly more likely to have poor sleep quality during the pandemic compared with people who were living comfortably. Similarly, people who were not very worried (OR = 1.22; $95\%$ CI = 1.07, 1.39), somewhat worried (OR = 2.14; $95\%$ CI = 1.79, 2.54), very worried (OR = 4.11; $95\%$ CI = 2.70, 6.24), or extremely worried (OR = 7.10; $95\%$ CI = 3.19, 15.83) during the pandemic were significantly more likely to experience poor sleep quality compared with people who were not at all worried in the unadjusted model (model 1).
In model 2, when adjusting for age, gender, and urbanicity, both the financial SEB proxies retained a high level of significance (both: $p \leq 0.001$). Similarly in model 3, when adjusting for anxiety, depression, and loneliness in addition to the sociodemographic variables, the person’s financial situation three months before the pandemic ($$p \leq 0.003$$) and being worried about the future financial situation ($p \leq 0.001$) were significantly associated with poor sleep quality. When adjusting additionally for physical health and health behavior variables (model 4), being worried about the future financial situation remained significantly associated with poor sleep quality ($p \leq 0.001$), although the financial situation three months before the pandemic was no longer significant ($$p \leq 0.879$$). In this final adjusted model, people who were somewhat worried (OR = 1.46; $95\%$ CI = 1.16, 1.83), very worried (OR = 2.02; $95\%$ CI = 1.15, 3.55), or extremely worried (OR = 8.09; $95\%$ CI = 1.62, 40.30) during the pandemic were significantly more likely to experience poor sleep quality compared with people who were not at all worried.
In the final adjusted model, age group ($$p \leq 0.008$$), gender ($$p \leq 0.004$$), feeling depressed ($p \leq 0.001$), lonely ($p \leq 0.001$), and anxious ($p \leq 0.001$), and self-rated health ($p \leq 0.001$) were all independently associated with poor sleep quality.
## 4. Discussion
Using data from a large cohort study of adults aged ≥50, this study aimed to explore whether socioeconomic background (SEB) was associated with sleep quality in older adults during the early stages of the COVID-19 pandemic. Further, it looked to examine the effects of additional covariates on the relationships.
The bivariate results indicated that educational attainment, the person’s financial situation in the three months before the coronavirus outbreak, and the person’s degree of concern over their future financial situation were all associated with sleep quality. More specifically, lower educational attainment and perceived greater financial hardship and concerns were associated with poor sleep quality. Given that these were considered to be indicators of low SEB, it may be concluded that low SEB is related to poor sleep quality in older adults, which is supported by research conducted prior to the COVID-19 pandemic [71,72,73]. Our results show that these relationships between low SEB and poor sleep quality in older adults were present during the pandemic, although, given the cross-sectional nature of our analyses (as further discussed below), it is not possible to say whether they were caused, or exacerbated, by the COVID-19 pandemic. Although COVID-19 infection has been associated with reduced sleep quality, given the low proportion of people who had received a positive COVID test in this wave ($0.3\%$), any differences in sleep quality or the relationship with SEB, were more likely to have been due to concerns about the virus and the pandemic generally, rather than due to actual infection.
The three education/finance variables were used as proxies for SEB. It may, therefore, be that lower educational attainment and greater financial difficulties and concerns were in themselves important in determining sleep quality in a general sense, rather than as indicators of SEB. This is supported by the finding that the three SEB proxies had varying levels of significance in the multivariate analyses, which might suggest that they are not necessarily indicative of SEB as a whole, and/or that they had a differential role in relation to sleep quality. The three variables are, therefore, each discussed separately below.
Although educational attainment was significantly associated with sleep quality in the bivariate analyses, the association was not significant in the unadjusted regression model. This suggests that the association between educational attainment and sleep quality was explained by the financial variables, i.e., people with lower educational attainment had associated financial concerns, and that these were more important in predicting sleep problems than the level of education itself. Educational attainment remained non-significant when adjusting for sociodemographic, mental health, and physical health and health behavior variables; in other words, while education is related to sleep quality in older adults, the relationship is moderated by a variety of wider factors that may be more directly associated with sleep quality. This finding contrasts with those from previous studies, which found education to have direct causality with poor health [66,71,74]. However, this is likely to be due to the ages of the participants in this study, i.e., an individual’s highest level of education is typically achieved during early adulthood, meaning that a considerable amount of time is likely to have passed between the completion of education and old age. Consequently, the effects of education on (older) adults’ health become less pronounced over time [75], particularly when compared with more recent factors, such as occupation, access to financial resources, or health problems.
Both the person’s financial situation in the three months before the coronavirus pandemic and the person’s degree of concern about their future financial situation were highly significant in the unadjusted model and when adjusted for sociodemographic characteristics. This suggests that older adults with greater financial difficulties and concerns are likely to experience poor sleep quality regardless of their sociodemographic characteristics, e.g., age and gender.
The relationship between the person’s previous financial situation and sleep quality was partially moderated when adjusting for mental health variables, although the category ‘finding it very difficult’ was non-significant across all models. This may have been because of the relatively small numbers in this category (a possible Type II error). This is supported by the finding that, although the category ‘finding it quite difficult’ was significantly associated with poor sleep quality in models 1 and 2, it became non-significant in model 3 as further variables were included. The other categories (‘just about getting by’ and ‘doing all right’) remained significant until model 4, when the health-related variables were included. At this point, the financial situation three months before the coronavirus outbreak was no longer significant. This may indicate that older adults experiencing financial hardship had more physical and mental health problems that were more important in negatively affecting sleep quality. Notably, studies have shown that older adults facing greater financial strain are typically less able to purchase food, pay bills, and own their accommodation outright [76,77,78], which can lead to heightened stress and anxiety [76,77,79]. Feelings of financial inadequacy compared with peers can also promote general life dissatisfaction, resulting in depression [80]. There is, therefore, evidence in the extant literature to support the idea that greater financial difficulty is associated with poor mental health, which can, in turn, negatively affect sleep quality. However, the present study did not directly test for causality, and it is likely that this observed relationship between poor sleep quality and mental health among older adults experiencing greater financial strain is bidirectional.
The inclusion of physical health and health behavior variables into the logistic regression model fully moderated the relationship between participants’ previous financial circumstances and sleep quality. This may suggest that participants experiencing greater financial strain are likely to also have poor physical health, which, in turn, may disrupt sleep quality. In a recent study, König et al. [ 81] found that older adults experiencing greater financial difficulties are more likely to delay retirement. However, delayed retirement has been found to be associated with musculoskeletal pain [82,83,84], which negatively impacts sleep quality [85,86]. Thus, causal pathways to explain the results of this study may be inferred from the extant literature. However, as with the mental health variables, this relationship may well be bidirectional, given that direct causality was not tested.
With regard to the person’s degree of concern about their future financial situation, this remained significantly associated with sleep quality across all models, and there was limited evidence of a moderating effect when adjusted for mental health variables (model 3). Previous research has shown that financially related concerns about the future have been linked to depression [87,88] and anxiety [89], both of which likely disrupt sleep quality [90]. However, the results of this study suggest that concern over future finances is an independent risk factor for poor sleep quality, i.e., above and beyond the effects of mental health problems. Similarly, the relationship between financial concerns about the future and poor sleep quality was not moderated by the inclusion of physical health variables, similar to findings reported by Morris et al. [ 91]. It may therefore be concluded that participants who were greatly concerned about the future experienced poor sleep quality, irrespective of any physical health problems. Again, this situates financial concern about the future as an independent risk factor for poor sleep quality.
## Limitations
Several limitations became apparent whilst undertaking this research. First, much of the data were susceptible to recall bias [92] and under-evaluation [93,94], given that they were acquired using self-reported measures, e.g., the sleep quality and the perceived health variables. Future research could seek to acquire data on sleep quality in a more objective manner in order to allow for improved accuracy. For example, polysomnography could be used to measure sleep quality [95,96], although this requires greater resources and may reduce response rates. The self-reported health measure we used is also subjective, and although self-reported health has long been established as an independent predictor of various health outcomes (e.g., mortality, health service utilization, and prescribed drug use) in older people [97,98], variables derived from a more detailed past medical history may provide a more nuanced understanding of the moderating effect of previously diagnosed physical and mental health on the relationships with sleep problems observed here. Future research could seek to include data on such diagnoses from patient records.
Second, SEB was constructed using three proxy variables for the purposes of this study. While this provided insight into different components of SEB, it did not allow for holistic inferences regarding SEB to be made. Future research should therefore look to use a single SEB composite variable, for example, through measures such as the indices of multiple deprivation (IMD) scale [99]. This may allow for more conclusive inferences to be drawn regarding the relationship between SEB and sleep quality in older adults during the COVID-19 pandemic.
Third, the categories ‘BAME’ and ‘non-BAME’ were used in the ELSA ethnicity variable. However, these terms may be perceived as too generalized and may misrepresent the disparities faced by certain ethnic groups [100], such as Roma groups [101]. Accounting for the complexity of ethnicity by either using the terms ‘ethnic minority’ and ‘non-ethnic minority’ or by using a more comprehensive list of ethnicities may allow for more representative research to take place.
Fourth, the dataset used in this study did not account for the range of components that form sleep quality, including sleep duration, sleep fragmentation, and daytime sleepiness [102]. Adopting a narrower focus might yield further interesting results and may highlight greater sleep disparities as a result of SEB.
Finally, the two waves of data collection during the COVID-19 pandemic were relatively early on, and the data from COVID Wave 1 analyzed here were collected between June and July 2020, when relatively little was known about the virus and how it was transmitted. It is possible that the associations observed here may have been moderated or exacerbated as the pandemic progressed, or the findings may have been different at other times during the pandemic. For example, the UK came out of the initial lockdown period in June 2020; on the one hand, this might have reduced older people’s anxieties about COVID-19 (because the prevalence and risk of COVID-19 were reducing); on the other hand, the increased social activity and lack of rapid testing and vaccination may have increased people’s fears about being infected. These changes may have had a further impact on people’s sleep quality and on the relationship between SEB and sleep. In the medium term, as the pandemic progressed, the understanding of the virus and its transmission increased and rapid tests and vaccines were developed, which probably helped reduce fears, although this may have been offset by the emergence of variants of the virus, e.g., the Beta and Omicron variants, which may have increased uncertainty and fears. Further research should explore whether the relationship between SEB and sleep quality was moderated as the pandemic progressed, e.g., using the (smaller) ELSA COVID Wave 2 study or using other available datasets. Similarly, a longitudinal analysis of individuals before, during, and following the COVID-19 pandemic would provide insights into both the effect of the pandemic on sleep quality and the extent to which the SEB–sleep quality relationship was moderated or exacerbated during this period.
Despite the limitations discussed above, the study involved a large representative sample of people aged 50 and over, and a number of conclusions can be drawn about this study that may be applicable to older people in other countries.
## 5. Conclusions
This study is important in being the first to examine the association between socioeconomic background (SEB) and sleep quality among older people during the early stages of the COVID-19 pandemic in England (June–July 2020). It has demonstrated that, in relation to sleep quality, SEB may need to be conceptualized in relation to component parts in order to understand the factors associated with poor sleep quality.
In summary, this study found that lower educational attainment, greater exposure to financial difficulties, and greater concern over future finances were all associated with poor sleep quality in older adults during this phase of the COVID-19 pandemic. However, the relationship between educational attainment and sleep quality was moderated and explained by the person’s degree of concern over their future financial situation. Meanwhile, the relationship between the person’s previous financial situation and sleep quality was moderated by mental health, physical health, and health behavior variables. To this end, it cannot be concluded that low SEB as a whole was a significant risk factor for poor sleep quality in older adults. Instead, greater concern over future finances as a specific component of low SEB, poor mental health, and poor physical health were found to be the biggest risk factors for poor sleep quality in older adults during the COVID-19 pandemic. Future research involving longitudinal analysis should investigate how financial concerns, sleep quality, and the relationship between financial concerns and sleep varied before, during, and after the pandemic.
From a practical perspective, the findings of this study highlight ways in which sleep quality can be better managed in older adults. Principally, any financial concerns should be eased as much as possible. While this would probably be best achieved through state pension reforms, such political change does not offer the most practical approach for clinicians. Instead, the establishment of free financial advice centers for older adults in areas of greater deprivation may help ease anxieties relating to future finances. However, such centers must adopt a person-centered approach, given that older adults tend to be reluctant to discuss personal problems with strangers [103]. Other possible options to ease financial strain during future waves of COVID-19, or during future pandemics, may be to provide temporary financial support to the people in greatest need, e.g., older people on low incomes.
In a similar vein, the provision of free and accessible mental health services for older adults should be made a priority in order to reduce cases of anxiety, depression, and loneliness. Efforts must simultaneously be made to promote a mental health discourse in older adult communities, given that mental health is often a stigmatized or unrecognized concept for many older adults [104]. Finally, an active health literacy program could be developed in order to reduce preventable illnesses and behaviors that impede good-quality sleep, such as alcohol consumption, smoking, and obesity.
This study has developed important insights that could help healthcare professionals, policymakers, and health service providers to support older people generally, as well as during future waves of the COVID-19 pandemic, and in future pandemics more generally. Healthcare professionals should consider the underlying financial or physical/mental health problems faced by individual patients who seek support for sleep problems: although poor sleep may be the problem being present, other associated factors may require consideration. Policymakers and health service providers may wish to consider interventions to promote health and wellness among older people experiencing sleep problems.
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---
title: 'A Cluster-Randomised Stepped-Wedge Impact Evaluation of a Pragmatic Implementation
Process for Improving the Cultural Responsiveness of Non-Aboriginal Alcohol and
Other Drug Treatment Services: A Pilot Study'
authors:
- Sara Farnbach
- Alexandra Henderson
- Julaine Allan
- Raechel Wallace
- Anthony Shakeshaft
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001979
doi: 10.3390/ijerph20054223
license: CC BY 4.0
---
# A Cluster-Randomised Stepped-Wedge Impact Evaluation of a Pragmatic Implementation Process for Improving the Cultural Responsiveness of Non-Aboriginal Alcohol and Other Drug Treatment Services: A Pilot Study
## Abstract
There is limited evidence regarding implementing organisational improvements in the cultural responsiveness of non-Aboriginal services. Using a pragmatic implementation process to promote organisational change around cultural responsiveness, we aimed to (i) identify its impact on the cultural responsiveness of participating services; (ii) identify areas with the most improvement; and (iii) present a program logic to guide cultural responsiveness. A best-evidence guideline for culturally responsive service delivery in non-Aboriginal Alcohol and other Drug (AoD) treatment services was co-designed. Services were grouped geographically and randomised to start dates using a stepped wedge design, then baseline audits were completed (operationalization of the guideline). After receiving feedback, the services attended guideline implementation workshops and selected three key action areas; they then completed follow-up audits. A two-sample Wilcoxon rank-sum (Mann–Whitney) test was used to analyse differences between baseline and follow-up audits on three key action areas and all other action areas. Improvements occurred across guideline themes, with significant increases between median baseline and follow-up audit scores on three key action areas (median increase = 2.0; Interquartile Range (IQR) = 1.0–3.0) and all other action areas (median increase = 7.5; IQR = 5.0–11.0). All services completing the implementation process had increased audit scores, reflecting improved cultural responsiveness. The implementation process appeared to be feasible for improving culturally responsive practice in AoD services and may be applicable elsewhere.
## 1. Introduction
There are substantial inequalities in health status and health care access between Aboriginal and Torres Strait Islander people (hereafter referred to as Aboriginal) and non-Aboriginal people in Australia [1], including disproportionate drug and alcohol-related morbidity and mortality [2]. Although all health services should be culturally safe, effective and welcoming to Australians from any cultural backgrounds, there is evidence that Aboriginal people receive less benefit from non-Aboriginal health services than non-Aboriginal people [3]. Ensuring that mainstream health services (that is, services that are not specifically developed for Aboriginal people) are responsive to Aboriginal peoples’ needs is a key strategy to reduce inequalities in healthcare access and enhance the quality of care provided to Aboriginal people [4,5,6]. Cultural responsiveness is an ongoing process of adapting systems, services and practice to fit with culturally diverse user preferences [7], and providing high-quality care that is culturally appropriate and safe [8]. While the importance of culturally responsive health services is well acknowledged [8], there is a lack of consensus on effective methods to develop health services that are culturally responsive [6].
Cultural responsiveness initiatives have been shown to improve healthcare worker cultural knowledge, awareness and sensitivity [9,10,11,12], improve patient satisfaction with providers [9,12,13] and increase access and frequency of visits by Aboriginal people [14]. However, the quality of many existing studies is low, frequently using observational study designs and interventions that provide one-off staff training, but which tend to be ineffective if not implemented as part of a systematic approach [6,15].
While many non-Aboriginal clinicians are individually committed to practising in a culturally responsive way, improving cultural responsiveness needs to be a whole-of-service activity that involves multiple strategies across all levels of the workforce and organisational policy, management and practices to be effective [5,6,16,17,18]. There is limited evidence, particularly in the Australian context, regarding effective systematic methods for implementing organisational-level change to improve cultural responsiveness [6]. Aiming to provide a structured method to implement best-evidence cultural responsiveness practices, the current project developed a pragmatic implementation process for facilitating organisational change in services. The first step of this process involved combining a number of recommended cultural responsiveness strategies [19] into a best-evidence guideline for improving the cultural responsiveness of non-Aboriginal AoD services [20]. The co-designed best-evidence guideline details a wide range of evidence-based strategies including: engaging management [21,22]; enhancing communication and relationships between mainstream and Aboriginal services and communities [3,23,24]; improving staff knowledge of the social and historical determinants of health [25]; and tailoring programs to suit the local community [26].
The core components, or themes, of the guideline were then operationalised into flexible activities that could be tailored to suit each service [27,28,29], and implemented in non-government organisation (NGO) non-Aboriginal AoD services. The implementation fidelity, barriers and facilitators to implementation, and their acceptability and feasibility, are described elsewhere [27,30]. The current study aims to identify the impact of the implementation process on the cultural responsiveness of participating services, as measured by the mean change in audit scores from baseline to three-month follow-up. Secondary aims were to identify the areas of the guideline that were most frequently selected as priority areas for change and most successfully actioned by services during the project. We also aimed to build on the services’ insights to develop a program logic to identify how the standardised core components were flexibly applied by services to support future implementation.
## 2.1. Study Design
The project was co-designed and implemented using a community-based participatory research approach [29,31] that facilitated iterative development of the best-evidence guideline and the pragmatic implementation process through collaboration between the project team (RW and JA, who have experience working in NSW AoD services), the researchers (SF, AH, AS), the Network of Alcohol and other Drugs Agencies (NADA; the peak organisation for the NGO AoD sector in NSW), the Primary Health Networks (PHNs) as the project funders and an Aboriginal Advisory Group (which included Aboriginal community members with professional and community connections to NGO AoD treatment services or government treatment services). The project was overseen by the Aboriginal Advisory Group to ensure the priorities and world views of Aboriginal experts were centralised into the guideline and the project implementation. Members of the Group were offered reimbursement for expenses arising from their involvement. Project implementation expenses were covered by the project. The impact of the project on the cultural responsiveness of participating services was evaluated using a cluster-randomised stepped-wedge design with 12 services and six clusters.
## 2.2. Participating Services
Seventeen non-Aboriginal NGO AoD treatment services from six PHN districts in New South Wales (NSW) were identified by the PHNs as being potentially willing to participate, with fifteen providing formal consent to participate ($88\%$) (hereafter referred to as participating services). Participating services included a variety of AoD service types, including residential rehabilitation ($$n = 3$$), day programmes ($$n = 2$$), centre-based counselling and support ($$n = 3$$), outreach counselling and support ($$n = 4$$), groupwork and phone support ($$n = 1$$) and group or individual youth services ($$n = 2$$). Twelve services completed all project activities ($80\%$; Table 1). No data related to Aboriginal clients or organisations were accessed or used in this phase of the project.
## 2.3. Cultural Responsiveness Project
The project was delivered in these sequential phases: (i) engage stakeholders, develop co-design structures and secure approvals from ethics and participating sites; (ii) co-design the implementation process and best-evidence guideline; (iii) implement the guideline and monitor uptake. Phase 1 and the process evaluation outcomes are described in detail elsewhere [27] and the co-design, implementation and monitoring steps are described below (phases 2 and 3). Aboriginal author RW was involved in all aspects of the project and was provided training in research methods, manuscript development and presenting research findings. Non-Aboriginal members of the research team (JA, AH, SF, AS) have extensive experience of working with Aboriginal communities over multiple projects and have completed training in cultural responsiveness. RW provided cultural mentoring to non-Aboriginal researchers. Findings from the project were presented to participating services and local Aboriginal peak bodies via ongoing discussions about the project and at formal events, such as the Aboriginal Corporation Drug and Alcohol Network of NSW (ACDAN) Symposium.
## 2.4. Co-Designed Best-Evidence Guideline for Cultural Responsiveness in Non-Aboriginal AoD Services (Phase 2)
A best-evidence guideline that describes key elements of culturally responsive service delivery in non-Aboriginal AoD treatment services was co-designed at the beginning of the project and this process is described fully in the guideline document [20] (See Supplementary File S1 or also published online at https://www.nada.org.au/resources/alcohol-and-other-drugs-treatment-guidelines-for-working-with-aboriginal-and-torres-strait-islander-people-in-a-non-aboriginal-setting/ (accessed on 1 June 2022)). Briefly, the guideline co-design process was facilitated by an Aboriginal project team member (RW) and overseen by the Aboriginal Project Advisory Group [27]. The guideline identifies six themes: [1] *Creating a* welcoming environment, [2] Service delivery, [3] Engagement with Aboriginal organisations and workers, [4] Voice of the community, [5] Capable staff, and [6] Organisation’s responsibilities.
## 2.5.1. Clustering of Participating Services and Randomisation to a Starting Date
Services were clustered based on PHN district/geographical region ($$n = 6$$). Each cluster of services was randomised to an implementation starting date between June and October 2019, with approximately one month between clusters, as shown in Table 2. Cluster randomisation was conducted by a statistician independent of the project using random number generation. Owing to varying numbers of services within regions, and attrition, clusters included different numbers of services; cluster 1 included one service, cluster 5 included three services and the remaining clusters included two services each.
The following implementation and monitoring steps were completed with each participating service.
## 2.5.2. Baseline Audits of Participating Services
Services were advised of their allocated start date and structured baseline audits of current culturally responsive practice, using a standardised audit tool, were completed individually with each participating service. The audit process identified the extent to which services addressed the guideline, rating cultural responsiveness according to 21 actions areas which corresponded with the six guideline themes. Audit tools were developed which framed the 21 action areas as questions in order to collect information from staff at participating services. Audits were conducted by two trained auditors (RW, JA or another trained auditor) in the setting where the service is delivered and took between 90 min to two hours to complete. Auditors were independent of the service being audited and at least one auditor at each audit was Aboriginal.
## 2.5.3. Audit Feedback to Participating Services
Individualised written feedback from the audit findings was provided to each participating service, listing all guideline action areas with a descriptive assessment for each area reflecting the level of evidence observed during the audit (limited, some, good or excellent) and recommendations for areas where potential improvements could be made.
## 2.5.4. Guideline Implementation Workshops with Participating Services
Implementation workshops were held with key staff from services (CEOs/managers and direct service delivery staff) to explain the guideline, review the audit feedback, set goals for improvement and develop a detailed action plan tailored to their service (to operationalise action areas from the guideline themes). Workshops were facilitated by JA and RW. Staff identified and prioritised specific activities that they would implement from the 21 action areas and were encouraged to select three key action areas for their service to progress over the next three months. For example, activities that operationalise guideline Theme 1: *Creating a* welcoming environment, might include processes to ensure that all clients are welcomed respectfully at first contact with the service, providing tea/coffee/water in the waiting room, accommodating children or other family members in the service, or displaying local Aboriginal artwork. These self-designed activities provide flexibility in how individual services operationalised and implemented the core components, enabling the practice change activities to be tailored to the needs and resources of individual services and the communities they serve [1,2,3,4,5].
## 2.5.5. Follow-Up Audits of Participating Services
Follow-up audits of services were conducted after three months to assess change in culturally responsive practices in the 21 action areas, following the same procedure as for the baseline audits. Where possible, the same service staff attended the follow-up audit. Services were provided with a second individualised feedback report, including discussion of any changes that had occurred.
## 2.6. Measures
To privilege Aboriginal values and views throughout analysis and reporting, we used the guideline themes that were developed by the Aboriginal Advisory Group to assess culturally responsive practices. The study aimed to identify the impact of the project on the cultural responsiveness of services using the following outcomes:Change in audit score from baseline to follow-up audit on the three key action areas identified by staff at the implementation workshops (possible score 0–9).Change in audit score from baseline to follow-up audit in all other action areas from the guideline (other than the three key action areas selected by each service (possible score 0–54).
## 2.7. Statistical Analysis
The audit responses provided by staff were recorded into the audit tool. After each audit was completed, ratings of 0–3 were allocated to each of the 21 audit criteria, according to pre-specified rating rules, by one of the researchers conducting the audit (RW). A second researcher (SF) then independently reviewed the audit tool and rated the 21 criteria. The two sets of ratings were compared and any disagreement around ratings were resolved by discussion, until a consensus was reached. A two-sample Wilcoxon rank-sum (Mann–Whitney) test was used to analyse the difference in audit scores between baseline and follow-up audits, on the three key action areas (outcome 1) and all other action areas (outcome 2). All analyses were conducted using Stata 16 [32].
The extent of change across the six guideline themes was identified by summing item ratings within each theme and calculating the rates of change for each theme. The frequency with which each individual action area was selected by service staff (during workshops to operationalise their improvement goals), and whether improvements were subsequently observed in those action areas, were descriptively explored.
## 2.8. Development of a Program Logic
We used a program logic structure developed in previous work [5,7] to build a standardised logic model specifically for improving cultural responsiveness in non-Aboriginal NGO AoD services. The program logic model was developed by reviewing the audit findings and activities chosen by staff during the workshop and linking these to the core components (themes) of the guideline.
## 3.1. Implementation Process
Twelve of the fifteen participating services completed all service-specific project components. Some delays in completing the three-month follow-up audits occurred, with an average time between audits of 18 weeks (range 14–28 weeks) (See Table 2). The longest delays in completing the follow-up audits were for services “J”, “D” and “F”, with the audits completed at 19, 24 and 28 weeks, respectively. Service “B” was part of cluster 5; however, due to delays in completing the baseline audit, service “B” ultimately completed the project components in line with cluster 6. Further detail on implementation and process outcomes are reported elsewhere [27].
## 3.2. Change in Cultural Responsiveness of Services in Three Key Action Areas
Outcomes are reported for services that completed baseline and follow-up audits ($$n = 12$$).
Ten of 12 services increased their audit score on their three key action areas at follow-up. The median follow-up scores were statistically significantly higher than the median baseline scores (median change = 2.0, IQR = 1.0–3.0, z = −2.79, $p \leq 0.005$) (Table 3).
## 3.3. Change in Cultural Responsiveness of Services in All Other Action Areas
All 12 services showed an increase in score on all other action areas (excluding the three key action areas). The median follow-up scores were statistically significantly higher than the median baseline scores (median change = 7.5, IQR = 5.0–11.0, z = −1.97, $p \leq 0.05$) (Table 3).
## 3.4. Guideline Themes with the Most Improvement
Overall, there were improvements in scores across all six themes of the guideline and all showed similar rates of improvement; Theme 5: Capable staff (+$22\%$), Theme 3: Voice of the community (+$18\%$), Theme 6: Organisation’s responsibilities (+$18\%$), Theme 1: *Creating a* welcoming environment (+$17\%$), Theme 2: Service delivery (+$16\%$) and Theme 4: Engagement with Aboriginal organisations and workers (+$16\%$).
## 3.5. Action Areas Most Frequently Selected (by Staff) and Most Frequently Improved
Service staff chose a wide variety of action areas from the guideline to prioritise; 16 of the 21 areas were selected at least once. Those most frequently selected as key action areas were: 1B: The physical environment is welcoming to Aboriginal people ($$n = 6$$ services); 3Ai: Aboriginal community engagement to develop relationships ($$n = 4$$); 3Aiii: Local history and protocols are reflected in practice and/or policy ($$n = 5$$); and 4A: Developing connections with Aboriginal organisations and workers ($$n = 5$$) (see the guidelines in Supplementary File S1 for further description). The action areas that services most frequently improved on were in Theme 2 (2B: Immediate triage options are available for Aboriginal people ($$n = 8$$ services) and 2C: Staff are culturally responsive in therapeutic practice ($$n = 7$$)), Theme 3 (3B: Local Aboriginal protocols are reflected in practice and/or policy ($$n = 7$$)) and Theme 6 (6Aii: There are Aboriginal-identified positions and Aboriginal publications and networks are used to advertise jobs ($$n = 7$$), 6Aiii: Service induction includes materials about working with Aboriginal people and materials are developed/reviewed by a local Aboriginal person ($$n = 8$$)).
## 3.6. Development of a Program Logic
To facilitate future implementation of improvements to cultural competence, the researchers (AH, AS, SF) developed a program logic that is directly tied to guidelines, shown in Table 4. This program logic was developed post-implementation to clearly delineate the standardised core components (guideline themes), flexible components (service level activities) approach and likely mechanisms of change, for future iterations of this project, based on previous work by the authors [28,29]. The second column lists the six best-evidence themes/principles that comprise the core components of the guideline. These are standardised across all services, as are the aims/goals/target areas for improvement (first column) and the articulation of why these core components would impact on cultural responsiveness (third column). The fourth column provides examples of specific activities that services can implement, with flexibility to choose practice change activities tailored to the needs and resources of individual services [28,31,33,34,35]. The remaining columns identify the measures of processes (the extent to which services engaged in the intervention process), outcomes (the extent to which indicators of culturally responsive practice improved) and data sources.
## 4. Discussion
The current study used a community-based, participatory research approach to develop a best-evidence, service-level practice change process, supported by a program-logic framework previously developed by the authors [28,29]. The pragmatic implementation approach is supported by existing evidence [33,34,35] and means that individual services could implement areas of the guideline that were most relevant to their local context and current level of cultural responsiveness. All participating services increased their overall audit scores and most increased scores on their chosen priority action areas, reflecting an increase in compliance with the guidelines and improved cultural responsiveness. The results are consistent with previous research demonstrating that audits and practice improvement interventions can be effective methods of identifying where improvements are needed, engaging with workers, and improving culturally responsive practices in a variety of health settings [14,36,37,38]. Our results support the effectiveness of the guideline and implementation process as a meaningful way of identifying and operationalising best-evidence principles of cultural responsiveness and enabling staff to understand and enact components of the guidelines that were relevant to their service. The program-logic model links the best-evidence core components of the guideline to the flexible service level activities, likely mechanisms of change, processes and outcomes, and can be used to guide future work on improving cultural responsiveness in AoD and potentially other health and human services. Our approach demonstrates a process to improve the cultural responsiveness of service delivery, and it is hoped that this approach impacts on inequalities in health status and healthcare access between Aboriginal and non-Aboriginal people.
An important strength of the project is that the co-design and implementation was led by an Aboriginal researcher and AoD worker, with extensive consultation with senior Aboriginal AoD clinicians (via the Aboriginal Advisory Group), funders, researchers, as well as links with workers (via the peak organisation for the NGO AoD sector) [6,21,24]. Furthermore, the guidelines recommend multiple evidence-based strategies across all organisational levels [6,16], such as: tailoring of service delivery to local communities [26]; enhancing relationships with Aboriginal services and communities [3,23,24]; improving staff knowledge and competency [25]; and implementing organisation-wide policies and practices [39]. The improvements in audit scores observed across all themes of the guideline, indicating that these concepts and strategies were clearly operationalised. Improvements were frequently observed in areas related to enhancing relationships with Aboriginal communities (e.g., having local Aboriginal protocols reflected in practice and/or policy), improving staff knowledge and skills (e.g., improved crisis triage options and staff demonstrating cultural responsiveness in direct service delivery) and organisation-wide policies or practices (e.g., including materials about working with Aboriginal people in service induction training). A larger evaluation with a longer timeframe would allow a more detailed exploration of specific components of the audits and guidelines and whether there are critical activities that services can enact to improve cultural responsiveness.
In addition to measuring change in audit scores (reflecting change in cultural responsiveness), future studies should also aim to examine the impact of these changes on service delivery or utilisation outcomes, potentially through using routinely collected administrative data. Previous reviews of cultural responsiveness programs have highlighted the need for valid indicators of change and objective outcome measures [6,12], and routinely collected administrative data represent objective, pragmatic, low-cost and easily tracked outcomes. As services improve their levels of cultural responsiveness, we would hope to also see improvements in service utilisation by Aboriginal people (for example, the number of episodes of care provided to and completed by Aboriginal people). The short time frame of the current evaluation limited our ability to examine these types of outcomes. Not only was the time for services to enact changes limited [27], but three months was likely not sufficient for any changes implemented to impact on service utilisation or client outcomes.
The project used a methodologically strong assessment process involving a standardised audit tool that reflected the best-evidence guidelines and a double-scoring system to enhance inter-rater reliability [40]. The possibility of practice effects should be noted; service staff may have had a more thorough knowledge of the audit criteria after completing the baseline audit, leading to more positive reporting of activities in follow-up audits. The practical implication of this is that some of the improvement in follow-up audit scores may be due to improvements in staff understanding of the audit, rather than the specific cultural competence activities they enacted. This is an issue about the true mechanisms of change: it is likely that the observed changes in cultural responsiveness are a combination of both the activities themselves and greater familiarisation with the audit process and content. Some services had limited capacity for improvement in audit scores for their three priority action areas; three services chose to prioritise an area that already had a full score at baseline, and one service only selected two priority areas. For future implementations, services should be encouraged to choose priority areas that have room for improved practice, providing maximum opportunity for improvements.
Participating services were self-selected, and it is possible that they may have had a pre-existing active interest in and/or resources to dedicate to improving their cultural responsiveness. The significant improvements in audit scores achieved by these services may not occur so quickly in other services. However, the participating services do represent a broad geographic and demographic area of NSW (including both urban and regional locations), as well as a variety of service delivery types. Service frontline and managerial staff rated the project as highly acceptable [27]. A key next step is a longer-term follow-up of participating services to establish whether the improvements in culturally responsive practice can be maintained or extended over time. Importantly, in line with the logic model presented, this will include an examination of administrative data to assess any changes in service utilisation. Then, if indicated, a randomised controlled trial evaluation of the implementation process in a larger sample of services may be warranted to demonstrate the generalisability, and costs and benefits of the process.
## 5. Conclusions
The co-designed best-evidence guideline and pragmatic implementation process represents a feasible and acceptable method [27] for implementing service-wide improvements in cultural responsiveness and may be applicable to improving the cultural responsiveness of a wide variety of health and human services. The randomised stepped-wedge evaluation design, double-rated audit scoring, and standardised core intervention increased methodological rigour, while the flexibility with which individual services can operationalise and implement the guidelines allowed tailoring to available resources and needs.
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|
---
title: The Novel RXR Agonist MSU-42011 Differentially Regulates Gene Expression in
Mammary Tumors of MMTV-Neu Mice
authors:
- Lyndsey A. Reich
- Ana S. Leal
- Edmund Ellsworth
- Karen T. Liby
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10001983
doi: 10.3390/ijms24054298
license: CC BY 4.0
---
# The Novel RXR Agonist MSU-42011 Differentially Regulates Gene Expression in Mammary Tumors of MMTV-Neu Mice
## Abstract
Retinoid X receptor (RXR) agonists, which activate the RXR nuclear receptor, are effective in multiple preclinical cancer models for both treatment and prevention. While RXR is the direct target of these compounds, the downstream changes in gene expression differ between compounds. RNA sequencing was used to elucidate the effects of the novel RXRα agonist MSU-42011 on the transcriptome in mammary tumors of HER2+ mouse mammary tumor virus (MMTV)-Neu mice. For comparison, mammary tumors treated with the FDA approved RXR agonist bexarotene were also analyzed. Each treatment differentially regulated cancer-relevant gene categories, including focal adhesion, extracellular matrix, and immune pathways. The most prominent genes altered by RXR agonists positively correlate with survival in breast cancer patients. While MSU-42011 and bexarotene act on many common pathways, these experiments highlight the differences in gene expression between these two RXR agonists. MSU-42011 targets immune regulatory and biosynthetic pathways, while bexarotene acts on several proteoglycan and matrix metalloproteinase pathways. Exploration of these differential effects on gene transcription may lead to an increased understanding of the complex biology behind RXR agonists and how the activities of this diverse class of compounds can be utilized to treat cancer.
## 1. Introduction
Retinoid X receptor (RXR) agonists bind to and activate the nuclear receptor RXR. RXR is a type II nuclear receptor, which is found in the nucleus bound to DNA and corepressor proteins [1,2]. Upon activation by a ligand, conformational changes in the structure of RXR promote dissociation of corepressor proteins and recruitment of diverse coactivator proteins. Because of its flexible dimerization domain, RXR homodimerizes or heterodimerizes with other nuclear receptors, including peroxisome proliferator-activated receptor (PPAR), liver X receptor (LXR), pregnane X receptor (PXR), or vitamin D receptor (VDR), to initiate transcription [3]. Upon activation, RXR regulates the transcription of target genes, involved in proliferation, differentiation, survival, and immune cell function [4].
Bexarotene is an RXR agonist, currently FDA approved to treat cutaneous T cell lymphoma (CTCL) [5]. Bexarotene has been tested in clinical trials for breast and non-small cell lung cancer but failed to attain approval for these indications, despite promising responses in some patients and manageable side effects [6,7]. Many have sought to improve the efficacy of bexarotene via novel drug delivery systems and formulations [8] or have made structural modifications to identify new RXR agonists [9,10]. Our new analog, MSU-42011, is effective for treatment in the MMTV-Neu model of HER2+ breast cancer [11], an established mouse model which recapitulates the human disease, as has been validated by gene expression profiling [12,13]. This model expresses wild-type, unactivated Neu in mammary tissue under the mouse mammary tumor virus (MMTV) promoter [14]. MSU-42011 also effectively reduces established tumor burden in the A/J mouse model of carcinogen-induced lung cancer [9]. In both of these preclinical models, changes in immune cell populations differed in the tumors of mice treated with MSU-42011 vs. bexarotene [9], suggesting that these compounds have distinct patterns of immunomodulatory activity.
Nuclear receptor biology is complex, and gene transcription varies based on the nuclear receptor binding partner of RXR [15]. For example, target pathways under the control of RXR:RAR heterodimers include genes which induce the enzymes phosphoenolpyruvate carboxykinase (PEPCK) and tissue transglutaminase 2 (TG2), immune-related genes such as B cell translocation gene 2 (Btg2), and retinoic acid response genes such as aberrant cellular retinol binding protein 1 (Crbp1) and cellular retinoic acid-binding protein 1 (Crabp1) [16]. *Several* genes involved in lipogenesis (Agpat2, Acsl1, Gpat3) and glucose metabolism (Hk2, Taldo1) are regulated by RXR:PPAR dimerization in adipocytes [17]. VDR, another nuclear receptor for which RXR is an obligate heterodimer, regulates expression of an extensive list of genes which act as VDR response elements. In quiescent hepatic stellate cells, binding of calcipotriol to the VDR nuclear receptor initiates binding to a cistrome of 6281 target sites, which expands to 24,984 sites when these cells are activated by lipopolysaccharide (LPS) or transforming growth factor beta (TGFβ) [18]. Through dimerization with the PXR nuclear receptor, RXR regulates transcription of genes involved in xenobiotic and endobiotic metabolism, cytoprotective mechanisms, and detoxification, including enzymes such as CYP3A4 and efflux pumps such as MDR1 [19,20]. Because the network of nuclear receptor target genes is vast, the biological effects of RXR activation are numerous and diverse.
Others have previously investigated the effects of bexarotene on the transcriptional regulatory network in mammary glands of mouse models of breast cancer [21], but to date no one has analyzed gene expression data from tumors treated with different RXR agonists. To this end, we used RNA sequencing to compare pathways activated by treatment with MSU-42011 versus pathways activated by bexarotene and validated selected genes by qPCR and immunohistochemistry. These data provide additional information about the cancer-relevant transcriptional regulation of RXR agonists and the diversity of activities of these compounds.
## 2.1. RXR Agonists Regulate Pathways Relevant in Breast Cancer
To characterize differential expression across the whole transcriptome, high-throughput techniques such as RNA sequencing (RNA-seq) allow us to parse differentially expressed genes into biological pathways for comprehensive analysis of RXR agonist response in tumors. For these studies, MMTV-neu mice (four per group) were fed control diet, MSU-42011 (100 mg/kg diet), or bexarotene (100 mg/kg diet) for 10 days. Tumors were harvested and RNA was analyzed by RNA-seq (Figure 1A). Relative to control tumors, tumors treated with both RXR agonists had higher expression of canonical immune pathways such as binding of antigen presenting cells and proliferation of immune cells, mononuclear leukocytes, and lymphocytes (Figure 1B). Causal network analysis [22], a means of identifying upstream regulators of differentially expressed genes from RNA-seq, identified SMAD4, IRF3, IRF7, and ZBTB10 as possible regulatory nodes.
## 2.2. Top Genes Differentially Expressed in Tumors Treated with MSU-42011 and Bexarotene Correlate with Patient Survival
Differential expression analysis revealed a list of genes (GSE211290) differentially expressed in control tumors vs. tumors from mice treated with MSU-42011 vs. tumors from mice treated with bexarotene. This list of 289 significantly (padj < 0.05) upregulated or significantly downregulated genes was sorted by adjusted p value. Of the top 10 most significant differentially expressed genes, high levels of expression of five genes correlate with improved overall survival in breast cancer patients—GRIA3 (logrank $$p \leq 3.1$$ × 10−7) (Figure 2A), CLEC10 (logrank $$p \leq 0.0035$$) (Figure 2B), FNDC1 (logrank $$p \leq 9.7$$ × 10−5) (Figure 2C), ISLR2 (logrank $$p \leq 4.8$$ × 10−5) (Figure 2D), and ITGA11 (logrank $$p \leq 2.4$$ × 10−6) [23] (Figure 2E). Survival curves were generated using the Kaplan–Meier Plotter (KmPlot) [24], without further stratification of breast cancer patients. *These* genes code for a glutamate receptor linked to migration and invasion (GRIA3) [25]; a c-type lectin with a role in cellular adhesion, signaling, and inflammation which serves as a dendritic cell marker (CLEC10) [26]; a fibronectin protein associated with invasion and chemoresistance (FNDC1) [27]; a member of the immunoglobulin superfamily which participates in nervous system development (ISLR2) [28]; and an alpha integrin which regulates adhesion to the extracellular matrix and the organization of collagen (ITGA11) [29].
## 2.3. RXR Agonists Regulate Cancer-Relevant Biological Pathways in MMTV-Neu Tumors
Enrichment analysis on control vs. MSU-42011 vs. bexarotene differential expression data using EnrichR reveals a set of pathways regulated by treatment with the various RXR agonists (Figure 3). The KEGG 2019 mouse database was used for these analyses; analysis using the Wikipathways 2019 mouse database is also shown (Supplemental Figure S1). Identified pathways include genes associated with ECM-receptor interaction, chemokine signaling, focal adhesion, PI3K-Akt signaling, complement and coagulation cascades, and the phagosome. Genes within these pathways encode for macromolecules involved in cellular structure and function, cellular behavior such as adhesion and migration, and downstream signaling pathways.
## 2.4. MSU-42011 and Bexarotene Induce Unique Gene Expression Profiles with Some Unifying Characteristics in Treated Tumors of a HER2+ Murine Model
Enrichment analysis was used to compare differentially expressed genes in control vs. MSU-42011 and control vs. bexarotene groups. Bar charts of these analyses reveal enrichment of shared pathways (focal adhesion, ECM-receptor interaction), as well as pathways unique to MSU-42011 (rheumatoid arthritis, ribosome) and pathways unique to bexarotene (PI3K-Akt signaling pathway, Rap1 signaling pathway) (Figure 4A,B). These unique pathways include genes which code for critical components related to cellular proliferation, immunity, and cellular migration and invasion. Scatterplot depictions of pathways regulated by MSU-42011 (Figure 4C) and by bexarotene (Figure 4D) highlight the similarities and differences in pathway enrichment within a particular cluster across different drug treatments. Volcano plot depictions of pathways regulated by MSU-42011 (Figure 4E) and bexarotene (Figure 4F) highlight the pathways unique to MSU-42011, especially the ribosome pathway. This pathway contains genes which encode for components necessary for rapid cellular turnover, which is particularly relevant to tumor biology [30,31]. KEGG 2019 was used as a database for these analyses.
## 2.5. MSU-42011 Increases Col6a3 and Map9 Expression in Mouse Mammary Tumors
*Several* genes were selected from the differential expression analysis for validation of mRNA expression by qPCR and protein levels by IHC. Collagen type VI a3 chain (COL6A3) is an extracellular matrix protein which is altered in several types of cancer [32]. Col6a3 mRNA expression (Figure 5A) is increased in tumors treated with MSU-42011 ($$p \leq 0.0425$$) but not in tumors treated with bexarotene. IHC (Figure 5B) demonstrates a $41\%$ increase in Col6a3 protein levels in tumors treated with MSU-42011 ($$p \leq 0.0096$$), and no apparent increase in Col6a3 in bexarotene-treated tumors (Supplemental Figure S2D). Kmplot was used to investigate the relevance of Col6a3 expression in human breast tumors (Figure 5C). High expression of COL6A3 is correlated with increased relapse-free survival ($$p \leq 0.031$$) in HER2+ breast cancer patients. qPCR (Figure 5D) also confirms a significant ($$p \leq 0.0026$$) increase in Map9 mRNA in MSU-42011-treated tumors, while there was no significant increase observed in bexarotene-treated tumors. MAP9 is a microtubule-associated protein which regulates cell cycle and the DNA damage response [33]. High expression of MAP9 is positively correlated with relapse-free survival (Figure 5E) in breast cancer patients ($$p \leq 0.0023$$).
## 2.6. MSU-42011 Increases IL-18 and H2-AA Expression in Mouse Mammary Tumors
As shown in Figure 4, the rheumatoid arthritis pathway is differentially regulated by MSU-42011 but not by bexarotene. *The* genes within this pathway include immune response genes which may contribute to the anti-tumor immunomodulatory activity of MSU-42011 [34]. The cytokine IL-18 was selected from the rheumatoid arthritis pathway for validation (Figure 6A). In tumors of mice treated with MSU-42011, but not bexarotene (Supplemental Figure S2A), mRNA expression of IL-18 increased ($$p \leq 0.0116$$). IHC (Figure 6B) revealed an increase in IL-18 in tumors treated with MSU-42011 ($$p \leq 0.04825$$).
Interestingly, tumors from the bexarotene group display an apparent paucity of IL-18, even in comparison to control tumors (Supplemental Figure S2B). Importantly, *Kmplot analysis* reveals that high IL-18 expression is correlated with increased relapse-free survival in breast cancer patients ($$p \leq 0.00022$$) (Figure 6C). MSU-42011-treated tumors also demonstrate a significant ($$p \leq 0.040822$$) upregulation of the gene coding for major histocompatibility complex (MHC) component H2-AA by qPCR (Figure 6D).
## 2.7. MSU-42011 Polarizes Bone Marrow-Derived Macrophages (BMDMs) towards an Anti-Tumor Phenotype
RXR agonists regulate pathways relevant to the function of the immune system, such as rheumatoid arthritis, complement and coagulation cascade, and cytokine–cytokine receptor interaction. To validate and further characterize the immunomodulatory activity of these compounds, BMDMs treated with RXR agonists were evaluated for expression of cancer-relevant genes within these pathways. Monocytes were harvested and differentiated with MCSF (20 ng/mL). On Day 5, BMDMs were treated with conditioned media from E18-14C-27 cells, derived from MMTV-Neu mammary tumors, to induce a tumor-educated macrophage phenotype. BMDMs were treated with conditioned media alone, or with 300 nM of either MSU-42011 or bexarotene. After 24 h, the relative proportion of F$\frac{4}{80}$+CD206+ macrophages was significantly ($$p \leq 0.02726$$) lower in BMDMs treated with conditioned media and 300 nM MSU-42011 compared to conditioned media alone (Figure 7A) In comparison, treatment with 300 nM bexarotene and conditioned media did not significantly alter the relative proportion of F$\frac{4}{80}$ + CD206+ BMDMs ($$p \leq 0.9423$$). Treatment with 300 nM of either RXR agonist significantly ($$p \leq 0.0016$$) decreased mRNA expression of IL-13, an immunosuppressive cytokine (Figure 7B). A trend of increasing TLR9 and IRF1 mRNA expression, associated with a pro-inflammatory, anti-tumor phenotype was observed in BMDMs treated with both RXR agonists (Figure 7C,D). RXR agonists also induce a significant ($$p \leq 0.00015$$) increase in expression of CCL6, a pro-inflammatory cytokine (Figure 7E).
## 3. Discussion
RXR agonists are a class of drugs with anti-tumor activity in preclinical models of breast and lung cancer [9,10,35]. While the known target of these drugs is the nuclear receptor RXR, different RXR agonists have markedly different effects on downstream gene expression. The nature of nuclear receptors—their ability to homodimerize or to heterodimerize with other nuclear receptors, the diversity of the structures of their ligands, and the vast number of target genes—makes RXR an interesting drug target. These characteristics likely differ among RXR agonists, potentially initiating heterodimerization with different nuclear receptor partners or recruiting different coactivators, leading to variations in resulting gene expression which may be clinically beneficial.
For the first time, using RNA-seq, we compared pathway activation and biological activity of the novel RXR agonist MSU-42011 and the FDA-approved bexarotene. The regulation of many similar pathways, including focal adhesion and extracellular matrix components, are shared by these two molecules (Figure 4). Immune-related pathways such as cytokine signaling pathways, complement activation, and genes related to phagosome activity are also shared by both MSU-42011 and bexarotene. Interestingly, validation of individual genes within these pathways shows that while one RXR agonist upregulates an immune- or ECM-related gene, the other RXR agonist does not. For example, MSU-42011 increases expression of Il-18 and Col6a3 at both the mRNA and protein level (Figure 5 and Figure 6), but neither of these two gene products are increased in tumors treated with bexarotene.
Several pathways were identified through enrichment analysis that were unique to a single RXR agonist. For example, the ribosome pathway and the fatty acid biosynthesis pathway, through which macromolecules critical to cellular function are synthesized, were prominent in enrichment analysis for MSU-42011 but not bexarotene. Conversely, the proteoglycans in cancer pathway, containing genes which code for matrix metalloproteinases (MMP), WNT signaling molecules, and growth factors such as IGF1 and FGF2, is prominent in bexarotene differential expression analysis but not MSU-42011.
The increase in Il-18 expression seen at both the level of mRNA and protein in tumors treated with MSU-42011, but not bexarotene, suggests that this RXR agonist promotes a pro-inflammatory tumor microenvironment, which can be harnessed for breast cancer treatment. IL-18 expression has been investigated as a possible prognostic indicator in breast cancer patients [36] and augments the cytotoxicity of NK cells [37]. Further investigation into the mechanism of MSU-42011 is necessary to determine if Il-18 is a critical mediator of anti-tumor immune response, and if it can be used as an indicator of response to therapy.
Furthermore, the increase in H2-Aa mRNA observed in tumors treated with MSU-42011 provides further evidence of its immune modulatory properties. H2-AA is an MHC class II component, higher expression of which is correlated with increased survival in ovarian cancer [38]. MHC II is responsible for antigen presentation to CD4+ T cells, which have recently gained recognition supporting the activation of cytotoxic T cells and mediating checkpoint inhibition response in cancer [39]. The MHC II pathway is necessary for antitumor immunity in several cancer types and is upregulated by treatment with histone deacetylase (HDAC) inhibitors [40,41]. In triple negative breast cancer, high expression of genes associated with the MHC II pathway correlates with progression-free survival [42]. Pharmacologic means of augmenting MHC II signaling may be a valuable therapeutic strategy for enhancing anti-tumor immunity. The increase in expression in Il-18 mRNA and protein and H2-aa mRNA observed in tumors treated with MSU-42011, but not bexarotene, may provide insight into the unique immunomodulatory properties of these two RXR agonists.
While COL6A3 expression has been explored as a prognostic biomarker in colorectal cancer [43], less is known about the role of COL6A3 in breast cancer. There is a trend of decreased COL6A3 expression with increasing tumor stage in breast cancer patients [32], which suggests a propensity for invasion and metastasis in these tumors [44]. Further, increased expression of COL6A3 in breast cancer after chemotherapy may predict for responsiveness to chemotherapy [45]. Finally, a cleavage fragment of COL6A3 known as endotrophin recruits macrophages through induction of monocyte chemoattractant protein-1 (MCP1) and increases IL-6 and TNFα in the tumor microenvironment [46]. Similarly, in obesity, collagen VI expression in omental white adipose tissue is correlated with expression of MCP-1, CD68, and CD86, providing further evidence that this collagen influences macrophage infiltration and phenotype [47]. As the role of COL6A3 is complex and can vary between cancer types and across tumor staging, the increase in expression of Col6a3 mRNA and protein in tumors treated with MSU-42011 and resultant effect on invasion and immunity merits further investigation.
The expression of the microtubule-associated protein MAP9 is altered in both colorectal cancer and breast cancer, leading to cell cycle dysregulation [33]. MAP9 hypermethylation in breast cancer leads to decreased expression and may have utility as an epigenetic biomarker [48]. Further, MAP9 transcription is induced upon DNA damage, and MAP9 protein interacts with and stabilizes p53 in Sa-OS-2 cells, leading to increased tumor suppressor activity [49]. As mRNA expression of Map9 is increased in tumors treated with MSU-42011, an exploration of the effects of MSU-42011 on cell cycle control and the ways this may be exploited for therapeutic purposes is warranted.
Based on our RNA sequencing data, particularly differentially expressed genes and pathways relating to immunity, we investigated the effects of MSU-42011 treatment on cell surface marker and gene expression in BMDMs (Figure 7). MSU-42011 decreased the relative proportion of F$\frac{4}{80}$ + CD206+ BMDMs by flow cytometry, indicating that treatment with MSU-42011 decreases immunosuppressive macrophages, while bexarotene did not have any effect. Further markers of immunosuppressive and pro-inflammatory macrophages were evaluated in BMDMs treated with RXR agonists by qPCR. MSU-42011 decreased expression of Il-13, an immunosuppressive cytokine, and increased expression of Ccl6, a pro-inflammatory cytokine. Furthermore, treatment with MSU-42011 increased expression of Tlr9 and Irf1, an interferon-regulatory factor known to be induced by ligation of TLR9. The TLR9-IRF1-IFN signaling axis has been implicated in macrophage polarization [50]. Taken together, these data provide additional evidence that MSU-42011 skews macrophages away from a tumor-promoting, immunosuppressive phenotype and toward an anti-tumor, proinflammatory phenotype. This effect on macrophages may be important for the anti-tumor activity of MSU-42011.
In conclusion, treatment with RXR agonists results in modulation of gene expression that are consistent with effective cancer treatments. As a drug class, RXR agonists display a broad range of activities, regulating different genes and biological pathways. The diversity of these compounds may allow them to be utilized for targeted or personalized cancer therapy.
## 4.1. Drugs
MSU-42011 was prepared as previously described [9,10,11]. Bexarotene was purchased from LC Laboratories (Woburn, MA, USA). For in vivo studies, RXR agonists were dissolved in a vehicle of 1 part ethanol: 3 parts highly purified coconut oil (Neobee oil, Thermo Fisher Scientific, Waltham, MA, USA). A total of 50 mL vehicle or drug dissolved in vehicle was mixed into 1 kg of powdered 5002 rodent chow (PMI Nutrition, St. Louis, MO, USA) using a stand mixer (KitchenAid, Benton Harbor, MI, USA).
## 4.2. In Vitro Experiments
Bone marrow-derived macrophages (BMDM) were isolated from femurs of adult C57BL/6 mice and differentiated using 20 ng/mL MCSF (Biolegend #576406, San Diego, CA, USA), as previously described [51]. Conditioned media was harvested from E18-14C-27 cells, derived from MMTV-Neu tumors, after 48 h of culture. BMDMs were treated using $75\%$ conditioned media supplemented with $25\%$ fresh media, with or without 300 nM RXR agonists for 24 h. IL-4 (10 ng/mL)(Biolegend #574304) was used as a positive control to induce a CD206+ immunosuppressive macrophage phenotype.
## 4.3. Flow Cytometry
BMDMs were harvested after 24 h treatment with conditioned media, with or without RXR agonists, filtered, and stained with fluorescent antibodies against F$\frac{4}{80}$ (APC, BM8, Biolegend) and CD206 (PE, MR6F3, Thermo Fisher Scientific). Live/dead green (Thermo Fisher Scientific) was used as a viability dye. Samples were run on BD Accuri C6 (BD Biosciences, San Jose, CA, USA.
## 4.4. In Vivo Experiments
MMTV-Neu mice [14] from our breeding colony (founders were purchased from Jackson Laboratory, Bar Harbor, ME, USA) were fed pelleted chow and palpated for tumors. Once tumors were detected, mice were switched to powder 5002 chow. Tumors were measured twice weekly with a caliper until 4 mm in diameter, at which time mice were randomized and fed control diet or 100 mg per kg per day diet of RXR agonist diet (~25 mg per kg per day body weight) for 10 days. Tumors were harvested and sections were either flash frozen for RNA-seq/qPCR or saved in neutral buffered formalin for immunohistochemistry.
## 4.5. RNA Sequencing
Frozen tumor sections (4 samples per treatment group) were weighed and homogenized. RNA was extracted using a RNeasy Mini Kit (Qiagen, Hilden, Germany), and the quality of the RNA confirmed with an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). RNA sequencing was completed by Novogene (Sacramento, CA, USA) as described previously [52]. Raw read counts were analyzed using the DESeq2 package in R (R for Windows v. 4.1.2; R Studio v. 1.4.1717) to generate differential expression profiles, and EnrichR and Ingenuity Pathway Analysis (Qiagen) were used for enrichment analysis. Raw and processed date were deposited in the Gene Expression Omnibus and are available through GSE211290.
## 4.6. qPCR
RNA harvested from frozen tumor sections was normalized across samples using Nanodrop (Thermo Fisher Scientific), and 500 ng of RNA was used to synthesize cDNA using a High Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). PCR was run on QuantStudio 7 Flex (Thermo Fisher Scientific) using SYBR green fluorescence. PCR data was analyzed using the delta-delta CT method using GAPDH as a housekeeping control. Error bars represent standard error of biological replicates, as indicated in figure legends. The following forward/reverse primers (Integrated DNA Technologies, Coralville, IA, USA) were used: IL-18, 5′-TCCTTGAAGTTGACGCAAGA-3′/5′-TCCAGCATCAGGACAAAGAA-3′, Col6a3, 5′ AAGGACCGTTTCCTGCTTGTT-3′/5′-GGTATGTGGGTTTCCGTTGAG-3′. Map9, 5′-GAAGAGTGCTACAGCCAACAC-3′/5′-ACAACAAGGTTTTTCCCCTTCC-3′, H2-AA, 5′-TCAGTCGCAGACGGTGTTTAT-3′/5′-GGGGGCTGGAATCTCAGGT-3′.
## 4.7. Immunohistochemistry
Formalin-fixed tissues were embedded in paraffin and sectioned by the Histology Core. Boiling citrate buffer was used for antigen retrieval, and endogenous peroxidase activity was quenched using hydrogen peroxide. Tissue sections were stained with antibodies against IL-18 (1 μg/mL, PA5-79481, Thermo Fisher Scientific), and Col6a3 (20 µg/mL, PA5-49914, Thermo Fisher Scientific), as described [34]. Sections were then labeled with biotinylated secondary antibodies (anti-rabbit, Cell Signaling Technology, Danvers, MA, USA; anti-rat, Vector Labs, Burlingame, CA, USA), as previously described. [ 34] A DAB substrate (Cell Signaling) was used for signal detection, as per manufacturer-provided protocols, and sections were counterstained with hematoxylin (Vector Labs). The Fiji ImageJ image processing package (version ImageJ2) was used for quantification of intensity of DAB staining by the color deconvolution method [53] and mean gray value was used to calculate optical density by the formula OD = log (max intensity/mean intensity, with a maximum intensity of 255 for 8–bit images [54].
## 4.8. KmPlot Generation
Survival curves were generated using Kaplan–Meier Plotter (https://kmplot.com/analysis/, accessed on 26 July 2022). This tool allows for correlation of gene expression to publicly available patient survival data [23]. KmPlot sources this patient data from GEO, EGA, and TCGA databases. The patient samples are split into two groups, high and low expression of the gene in question, using a robust autoselect algorithm to determine the most appropriate cutoff [24]. Breast cancer data was used, and overall or relapse free survival was compared.
## 4.9. Statistical Analysis
Results were expressed as the mean ± standard error. Data from tumor qPCR experiments were analyzed by one-tailed t test. $p \leq 0.05$ was considered statistically significant throughout all experiments. For RNAseq, differential expression analysis was performed using DESeq2. Outliers are detected by Cook’s distance and removed [55]. p values were adjusted to correct for multiple comparisons using the Benjamini and Hochberg method, and padj < 0.05 was considered statistically significant [56]. Data from in vitro experiments were analyzed using one-way ANOVA, and significant differences between groups were determined by the Tukey HSD multiple comparisons test.
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|
---
title: Associations of COVID-19 Hospitalizations, ICU Admissions, and Mortality with
Black and White Race and Their Mediation by Air Pollution and Other Risk Factors
in the Louisiana Industrial Corridor, March 2020–August 2021
authors:
- Qingzhao Yu
- Wentao Cao
- Diana Hamer
- Norman Urbanek
- Susanne Straif-Bourgeois
- Stephania A. Cormier
- Tekeda Ferguson
- Jennifer Richmond-Bryant
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001987
doi: 10.3390/ijerph20054611
license: CC BY 4.0
---
# Associations of COVID-19 Hospitalizations, ICU Admissions, and Mortality with Black and White Race and Their Mediation by Air Pollution and Other Risk Factors in the Louisiana Industrial Corridor, March 2020–August 2021
## Abstract
Louisiana ranks among the bottom five states for air pollution and mortality. Our objective was to investigate associations between race and Coronavirus Disease 2019 (COVID-19) hospitalizations, intensive care unit (ICU) admissions, and mortality over time and determine which air pollutants and other characteristics may mediate COVID-19-associated outcomes. In our cross-sectional study, we analyzed hospitalizations, ICU admissions, and mortality among positive SARS-CoV-2 cases within a healthcare system around the Louisiana Industrial Corridor over four waves of the pandemic from 1 March 2020 to 31 August 2021. Associations between race and each outcome were tested, and multiple mediation analysis was performed to test if other demographic, socioeconomic, or air pollution variables mediate the race–outcome relationships after adjusting for all available confounders. Race was associated with each outcome over the study duration and during most waves. Early in the pandemic, hospitalization, ICU admission, and mortality rates were greater among Black patients, but as the pandemic progressed, these rates became greater in White patients. However, Black patients were disproportionately represented in these measures. Our findings imply that air pollution might contribute to the disproportionate share of COVID-19 hospitalizations and mortality among Black residents in Louisiana.
## 1. Introduction
Coronavirus Disease 2019 (COVID-19) severity and mortality have been associated with several vulnerability factors, including comorbidities, environmental exposures, natural disasters, sociodemographic factors, and residence in congregate settings [1,2]. During the first wave of COVID-19 cases in the U.S., transmission in congregate settings was responsible for most disease spread [3], while comorbidities among older residents likely elevated risk of death [4]. The second wave of COVID-19 cases in the U.S. saw disproportionate numbers of severe disease and deaths among Black, Hispanic, Native American, and immigrant population groups [2,5,6]. The third wave may have occurred in part due to asymptomatic transmission in congregate settings including prisons and long-term care facilities, disproportionately impacting Black and Hispanic populations [2].
Soon after the start of the pandemic, some evidence emerged of an association between long-term average air pollution concentrations and the prevalence or severity of COVID-19. Notably, significant associations were observed for long-term average concentration of particulate matter (PM) having a diameter smaller than 2.5 μm (PM2.5) with SARS-CoV-2 infection prevalence [7,8,9], COVID-19 disease severity [10], intensive care unit (ICU) admission [11,12], ventilator use [12], and mortality [7,11,12,13]. Associations were also observed for long-term average diesel PM concentration estimates for COVID-19 prevalence and mortality [7]; average nitrogen dioxide (NO2) concentrations for prevalence [9,10,14], hospitalization [12], ICU admission [12], ventilator use [12], and mortality [12,14]; ozone (O3) concentration for mortality [12]; and hazardous air pollutant indices for respiratory and immunological hazard and mortality [15]. Chen et al. [ 12] also calculated associations with hospitalization, ICU admission, ventilator use, and mortality for 1-month average concentrations of PM2.5 and NO2. However, evidence was mixed, with some studies showing no association for NO2 [11], O3 [7,9,10,14], or PM2.5 [14,15]. Although many studies suggested a relationship between air pollutant concentration and COVID-19 outcomes, these studies primarily occurred early in the pandemic. Less is known about the association between air pollutant exposure and COVID-19 over time.
Strategies to respond effectively to public health emergencies such as the COVID-19 pandemic require understanding potential causal pathways for disease outcomes [16,17]. Mediation models can be useful to test how conditions present in populations may influence disease status either directly or indirectly. Disparities in COVID-19 outcomes by race combined with evidence about the relationship between COVID-19 and comorbidities, insurance status, and pollution exposure led to the hypothesis that there is a causal pathway between race and COVID-19 mediated by comorbidities, insurance status, and pollution exposure (Supplemental Figure S1).
Louisiana parishes routinely score well below the national average for quality of life, morbidity, and mortality indices such as low birthweight, child poverty, and median household income [18]. Based on the most recently available data, Louisiana ranks 46th among the states in air quality given by average daily PM2.5, 47th in percent smokers among adults, and 45th in the COVID-19 death rate. For the period of 1 March 2020–31 August 2021, $37.7\%$ of Louisiana’s COVID-19 deaths occurred in people identifying as non-Hispanic Black (hereafter referred to as “Black patients”) [19]. In 2020, $41.7\%$ of Louisiana’s COVID-19 deaths occurred among Black patients, compared with $31.2\%$ of Louisiana residents identifying as Black [20]. This is consistent with a recent analysis that connected disparities, systemic racism, economic stress, and COVID-19 mortality [21].
Given the disproportionate impact of COVID-19 on communities of color in Louisiana and the U.S., the goals of this research were to investigate the association of race and COVID-19 outcomes over time and to identify if exposures to air pollution and other characteristics, if any, may mediate associations of race with COVID-19 hospitalizations, ICU admissions, and mortality. We combined datasets from a Louisiana hospital system distributed across the Industrial Corridor and an air pollution database to include both individual and environmental level risk factors. We investigated factors including race, insurance status, comorbidity, and pollutant exposure for four waves of COVID-19 between 1 March 2020 and 31 August 2021.
## 2.1. Study Population and Health Data
In our cross-sectional study, we evaluated associations between race and COVID-19 hospitalizations, ICU admissions, and mortality and tested for factors that may mediate relationships. We used the Franciscan Missionaries of Our Lady (FMOL) Health System COVID-19 registry to identify patients at ten Louisiana locations distributed across the Industrial Corridor (Supplemental Table S1). The study was approved by the Louisiana State University Health Sciences Center-New Orleans Institutional Review Board (protocol #1986).
A total of 13,454 patients aged eighteen years or older who tested positive by a polymerase chain reaction (PCR) test for SARS-CoV-2 were identified using the Epic healthcare software between 1 March 2020 and 31 August 2021. This period is broken down by waves: 1 March–10 June 2020 (First Wave), 11 June–6 October 2020 (Second Wave), 7 October 2020–30 June 2021 (Third Wave), and 1 July–31 August 2021 (Fourth Wave). These were chosen to minimize both cases and mortality at the beginning and end of each period using the Johns Hopkins database for Louisiana [22].
Patient-level variables included hospital department, SARS-CoV-2 test date, SARS-CoV-2 test result, age, insurance status (private insurance, Medicaid, Medicare, and self-pay), self-reported race, self-reported ethnicity, sex, admission date, discharge date, length of hospital stay, admission status, ICU stay, ICU admission date, ICU discharge date, length of ICU stay, discharge dispatch, body mass index (BMI), presence of comorbidities, census tract, and census block group. Specific comorbidities were not listed consistently in the database, so they were simply recoded as presence [1] or absence [0] of any comorbidities for each patient in the database. To minimize bias in the patient database, negative PCR tests were not included in the database because tests were often obtained for non-medical reasons (e.g., work, travel, recreation, routine medical procedures).
Records were complete for hospitalization and ICU admission; records were missing for mortality for 171 Black patients and 128 White patients. Data with missing hospitalization, ICU, or mortality information were removed from the dataset. The final sample size was 11,331. Ethnicity data were missing for 9977 patients. A total of 113 patients (<$1\%$) responded that their ethnicity was “Hispanic or Latino/a”, “Mexican, Mexican American, or Chicano”, or “Other Hispanic, Latino/a, or Spanish origin”, while 1271 patients responded that they were “Not of Hispanic or Latino/a or Spanish Origin”. Therefore, ethnicity was not included in the statistical analyses.
## 2.2. Air Pollution Data
Air pollution burden calculations were based on Mikati et al. [ 23]. Absolute burden for each respiratory hazardous air pollutant was calculated by census tract as the weighted average of the emissions over the block groups within each tract. Facility-level air pollutant emissions data across the state of Louisiana were obtained from the 2017 National Emissions Inventory [24], and data for the census block groups and census tracts, including shape files and demographic characteristics, were obtained from the 2015–2019 American Community Survey [25]. Air pollutant emissions for each facility were assigned to a census block group when the block group’s centroid fell within a 2.5-mile radius of the facility. Air pollution burden was calculated as the sum of assigned facility-level emissions for each block group. Air pollution burden was then summed for each census tract. Air pollutants included PM2.5 and hazardous air pollutants (HAPs) known to have respiratory health effects: 1,3-dichloropropene, 2,4-toluene-diisocyanate, acetaldehyde, acrolein, acrylic acid, arsenic, beryllium, cadmium, chlorine, chloroprene, chromium, diesel PM, formaldehyde, hexamethylene-1,6-diisocyanate, hydrazine, hydrochloric acid, naphthalene, nickel, polycyclic organic matter (POM), propylene, and triethylamine. Oil and gas wells and refineries, which are prevalent naphthalene sources, and a neoprene plant, a chloroprene source, fall within the hospital service area (Supplemental Figure S2). Emissions burdens were assigned to 12,031 individual COVID-19 patients in the FMOL Health System database based on their census tract of residence. Bias minimization related to spatial assignment of emissions burdens is described in Mikati et al. [ 23].
## 2.3. Statistical Analysis
Differences in population characteristics, including air pollutant burden, were first illustrated using summary statistics. Direct relationships of race with other demographic variables (age, sex, BMI, presence of comorbidities, insurance status) or with disease-related variables (hospitalization, ICU admission, mortality) were screened via χ2 or ANOVA for categorical or continuous variables, respectively. Patient status was determined using hospital data for admission status, length of hospital stay, ICU status, and length of ICU stay. p-value < 0.05 for the χ2 or ANOVA test signified a potential significant difference between Black and White COVID-19 patients.
We used mediation analysis to test for environmental risk factors, called third variables, that might explain widely reported racial disparities in the COVID-19 outcomes. Mediation analysis is used here because it tests for causal associations from the explanatory variable (race) to third variables (environmental risk factors) and then to the outcome (COVID-19 hospitalization, ICU admission, or mortality) to determine if the pollutants are responsible for the association [26,27,28]. Potential mediators that intervene in the associations of race with COVID-19 outcomes (hospitalization, ICU admissions, mortality) were first evaluated. The variables included age, insurance status (private insurance, Medicaid, Medicare, and self-pay), ethnicity, sex, presence of comorbidities, and pollutant emissions. ANOVA or χ2 testing was performed to check the relationship between race and each variable, and between each variable and health outcomes. Potential mediators and potential covariates in the association between race and health effect were identified. Associations of each variable with both race and health effect indicated that the variable is a potential mediator. Variables associated with just health effects but not with race were identified as covariates to be controlled in the mediation analysis. Mediation analysis was then used to test if a portion of the race–outcome relationship could be accounted for by each intermediate variable after adjusting for all potential mediators, covariates, and confounders [26,27,28]. Significant mediators with the same sign as the total effect were considered as part of the racial differences explained by the mediator, while those with opposite sign suggested that the potential mediator caused greater uncertainty.
We used the R software v4.0.5 for data organization (packages dplyr, tidyr, bit65, and data.table) and for the merger of geographic data with air pollution emissions data and output of shape files containing emissions burdens (packages tigris, Hmisc, sp, and rgdal). The R package mma was used to perform the mediation analysis [29]. Confidence balls [30] were created to control the overall confidence level at $95\%$. We confirmed each of the criteria listed under the STrengthening the Reporting of OBservational Studies in Epidemiology checklist for cross-sectional studies during completion of this manuscript [31].
## 3. Results
Of the 11,331 patients in the final sample, 5708 ($50.4\%$) identified as non-Hispanic Black, and 5623 ($49.6\%$) identified as non-Hispanic White (Table 1). In comparison, $33.8\%$ of the population of *Louisiana census* tracts associated with patients’ residential addresses (referred to hereafter as the “patient population”) identified as non-Hispanic Black, and $58.8\%$ identified as non-Hispanic White. Census tract population data were available for $89\%$ of patients. A total of 6210 ($54.8\%$) cases identified as female, and 5119 ($45.2\%$) identified as male. On average, Black patients were 7.9 years younger than White patients. Black patients had a higher average BMI (p-value < 2 × 10−16), but average BMI for both groups was in the obese range (BMI > 30). Length of hospital and ICU stays were both significantly higher among White patients, although that difference diminished for Medicare recipients and those without insurance. More Black patients had Medicaid ($61.9\%$) or were uninsured ($61.6\%$), while more White patients had private insurance ($62.5\%$) or Medicare ($59.4\%$). Among the twenty-two pollutants tested, emissions burden was statistically significantly higher for Black patients in seventeen compounds and for White patients in three compounds, with no significant difference for two pollutants, hydrazine and propylene.
For the study duration, hospitalizations were significantly higher among White patients ($53.4\%$), while ICU admissions were significantly higher among Black patients ($52.4\%$). Table 2 provides the frequency of hospital and ICU admissions and deaths for the full study period and for each wave of the study. Equitable Black and equitable White indicate the ratio of the share of the population of patients in each group compared with the number of patients that would be expected for each group based on the proportion of each group in the *Louisiana census* tracts sending patients to the FMOL Health System. Compared with their share of the patient population, Black patients were over-represented among hospitalizations by $28\%$, among ICU admissions by $43\%$, and among total COVID-19 patients by $38\%$ (Table 2). Hospital and ICU admissions significantly exceeded the share of the population for Black patients by $86\%$ and $89\%$, respectively, during the first wave and by $40\%$ and $56\%$, respectively, during the second wave. By the third wave, the proportions of hospital and ICU admissions were higher among White patients with a significant χ2, but the proportion of hospital and ICU admissions among Black patients were $16\%$ and $36\%$ greater, respectively, than the share of the population identifying as Black.
Information regarding mortality (patients who expired while at the hospital or within 7 days of discharge) was available for 11,032 ($97.3\%$) cases (Table 2). For the study duration, the proportion of those who died was significantly higher for White patients, but the proportion of Black patients who died was still $25\%$ greater than the proportion of Black people in the *Louisiana census* tracts sending patients to the FMOL Health System. The proportion of patients who died was nearly $65\%$ for Black patients during the first wave, with the share of the patient population that is Black over-represented by $78\%$, but was significantly higher for White patients during the second and third waves and not significantly different in the fourth wave. During the second wave, mortality among Black patients was still $28\%$ higher than the share of patient population identifying as Black.
The mediation analysis figures (Figure 1, Figure 2 and Figure 3 and Figures S3–S14) illustrate the relative relationships between effect estimates for Black and White patients and how much the health effect (hospital admissions, ICU admissions, or mortality) can be explained by other factors. Based on the coding (1 = White, 2 = Black), a positive total effect suggests a larger effect in Black patients compared with White patients, and a negative total effect suggests a larger effect in White patients compared with Black patients. The direct effect illustrates how much of the health effect with respect to race can be explained only by race. The other effects show how much the health effect with respect to race can be explained by other factors, such as age, sex, comorbidity, or air pollution. For each factor, an effect that is the same sign as the total effect with a confidence interval that does not include zero suggests that the specific factor can explain some of the race–health effect relationship. An effect with a sign that is different from the total effect and/or large confidence intervals can suggest large uncertainty in the total effect or may indicate that a direct effect or mediated effect may partially explain effect on a different race than is represented in the total effect.
Age and, with a smaller contribution, presence of comorbidities were significant mediators of the race–hospitalization relationship (Figure 1) for the entire study period. The negative sign of the total effect and direct effect indicated greater hospital admissions among White patients, with age and comorbidities as significant mediators for each wave. Naphthalene and arsenic were significant mediators of the total effect for the duration of the study. Naphthalene was not a significant mediator for any of the individual waves, and arsenic was only for the fourth wave. PM2.5 and chromium exposures may have increased the effect among Black patients. However, these exposures may have added uncertainty to the race–hospitalizations total effect because the different sign of these mediation coefficients widened the confidence intervals around the total effect.
The model for race–ICU admission for the entire study period (Figure 2) included a direct effect that was larger than and opposite in sign to total effect, widening the confidence interval around total effect to suggest uncertainty. The direct effect of different sign may suggest that mediating factors, such as age, comorbidity, sex, and exposure to chloroprene, naphthalene, and propylene dichloride, may contribute to a greater total effect in White patients but that Black patients may be more likely to experience COVID-19 ICU admissions in the absence of the mediating factors. PM2.5 and chromium emissions burden potentially contribute to a greater effect in Black patients but widened the confidence intervals around total effect. Age was a mediator of the race–ICU admission effect during each wave. During the third wave, the total effect between race and ICU admission was near zero, but there was a greater direct effect on Black patients and greater indirect effect of PM2.5 emissions on Black patients balanced by greater indirect effects of age, cadmium emissions, and nickel emissions on White patients. The fourth wave produced a large total effect for the race–ICU admission model that included a direct effect comprising more than half of the total effect and indirect effects from age, insurance status, sex, and emissions of POM.
The mediation analysis results indicate that for the total duration and for each wave, there was a greater total effect in White patients, with age consistently a significant mediator of the total effect of race on mortality (Figure 3). The direct effect of different sign may suggest that being of Black race predicts a greater race-based mortality effect in COVID-19 patients, and the greater total mortality effect in White patients may have been driven by mediating factors. Sex and comorbidities had smaller indirect effects for the entire study period but were still significant. Naphthalene was identified as a mediator of the total effect, contributing to a greater effect in White patients for the total duration, while hydrochloric acid added uncertainty to the assessment of mediation. Hydrochloric acid burden may have contributed to the effect in Black patients. Naphthalene was identified as a potential mediator during the first wave but was not significant and added uncertainty to that model. POM was a significant mediator of the race–mortality relationship during the fourth wave. POM emerged as a potential mediator in the total duration model but was of small magnitude.
## 4. Discussion
A complicated picture of racial disparities in COVID-19 hospitalization, ICU admission, and death emerges from these results. For the entire study period, hospitalization and mortality rates among those who were diagnosed with COVID-19 in Louisiana’s Industrial Corridor were greater for White patients than for Black patients, while ICU admission rates were higher for Black patients. These proportions shifted towards White patients by late 2020. However, the proportion of those diagnosed with COVID-19 as well as those hospitalized, admitted to the ICU, and who died remained disproportionately higher for Black patients compared with the patients’ residential areas, despite the 7.9-year age difference between Black and White patients. For example, across the entire study period, COVID-19 mortality among Black patients was $25\%$ greater than what would be anticipated based on the proportion of the patient population identifying as Black, while COVID-19 mortality among White patients was $14\%$ below what would be anticipated based on the patient population identifying as White.
Among the population of those who had to be hospitalized due to COVID-19, most of the association of race could be explained by mediators, i.e., third variables. Age was the strongest mediator, accounting for the largest share of the association between race and COVID-19 hospitalization. In each wave, the average age of Black patients was 8–9 years younger than the average age of White patients. In fact, life expectancy for Black Louisiana residents is 3.4 years shorter than for White Louisiana residents [32]. These factors make it difficult to disentangle the effect of race from the effect of age. Cronin and Evans [33] calculated the U.S. COVID-19 mortality rate throughout 2020 by race-ethnicity and age and found higher mortality for Black males and females for every age group (0–44 y, 45–64 y, 65–74 y, and 75+ y) with a greater effect of age than race or sex.
Findings that naphthalene and chloroprene explained part of the associations between White race and ICU admissions and that naphthalene also explained part of the associations of White race with hospital admissions and mortality were surprising given that their burdens among Black patients in Louisiana were 8.9 and 4.5 times higher, respectively, than for White patients. Chlorine was found to explain ICU admissions among Black patients, and hydrochloric acid was found to explain mortality among Black patients. These findings are consistent with chlorine’s burden being 17 times greater and hydrochloric acid’s burden being 8.0 times greater among Black patients than White patients. Terrell and James [15] noted higher COVID-19 incidence in locations with a higher respiratory hazard index, where the index was computed by the U.S. EPA based on HAPs emissions. PM2.5 explained ICU admissions and mortality among Black patients and was 5.2 times greater among Black patients compared with White patients. Several studies [7,11,12,13] found associations of PM2.5 with COVID-19 using data from the first few months of the pandemic, but they either used a nationwide domain or studied different parts of the country. Sidell et al. [ 9] studied how the relationship between air pollution and COVID-19 infection changed in a southern California cohort over four waves spanning 1 March 2020 through 28 February 2021. They observed associations to persist for each wave and the entire duration of their study for both 1-month average and 1-year average PM2.5 and NO2 concentrations and between COVID-19 infection and 1-year average O3 concentrations for the second, third, and fourth waves and entire study duration. However, the magnitude of the associations declined over the third and fourth waves, especially for PM2.5. Uncertainties persist about the influence of air pollution on COVID-19 outcomes over the course of the pandemic. Terrell and James [15] calculated a correlation of 0.21 for PM2.5 concentration with COVID-19 mortality for Louisiana, and Xu et al. [ 34] noted for a study of COVID-19 in Texas that PM2.5 concentrations were not associated with COVID-19 mortality.
There were some limitations specific to this dataset. These analyses reflect the data and results of the full population that interfaced with the FMOL Health System based primarily in the Industrial Corridor. This selective population was not representative of all Louisiana COVID-19 hospitalizations and thus limits some generalizability of our results for the full state. The most recent HAP emission data were from 2017. Additionally, vaccination status was not included in the dataset but could have affected severe outcomes during the last two waves.
Mediation analysis showed a clear relationship between race and outcome at the beginning of the pandemic, but race appeared less influential over time. Mediation analyses highlighted the uncertainty in the race–outcome relationships across waves. Although several air pollutants were associated with race, with higher emissions burdens among predominantly *Black census* tracts, air pollution did not appear to consistently mediate the total race–outcome relationship for most waves. Uncertainties in the mediation analyses raise questions about unmeasured confounding. VanderWeele [35] asserted four necessary assumptions for mediation analysis: [1] control for confounding of the exposure–outcome relationship, [2] control for confounding of the mediator–outcome relationship, [3] control for confounding of the exposure–mediator relationship, and [4] no confounder of the mediator–outcome relationship is affected by the exposure. The first three were accomplished through the process of checking for significant associations among the exposure, potential mediator, and outcome. However, the final assumption is more difficult to enforce for this study given that long-standing racialization may introduce other, uncontrolled factors [36]. Similarly, it is difficult to ascertain whether any mediators were omitted from the analysis. Additionally, exposure measurement error or exposure misclassification has the potential to weaken the associations between the exposure and mediators. In the case of the HAP burdens, Mikati et al. [ 23] sought to control this by testing different assignment radii and found little difference. Use of census tract-level assignments also helps to localize the exposure estimates.
## 5. Conclusions
The wave-by-wave results of this study indicate that the role of race in the associations of COVID-19 outcomes has evolved over the course of the pandemic in Louisiana. Early in the pandemic, the association of race with hospitalization, ICU admission, and mortality appeared to be mediated by age. However, the younger age profile of Black COVID-19 patients contradicts findings of enhanced risk to older patients [33], suggesting that race rather than age played a role, especially early in the pandemic. As time went on, the analysis revealed greater impact on White patients in terms of overall numbers, but still with a disproportionate impact on Black patients compared with the local population. These findings reveal a need for strategies that focus on disadvantaged communities and individuals to protect each population group from exposure to the SARS-CoV-2 virus and from the severe impacts of COVID-19. Our findings also highlight a need to disentangle the associations of COVID-19 outcomes with race as a marker for measures of disadvantage and social determinants of health.
Burden from air pollutants may have explained some of the race–outcome associations. Findings that greater effect of chlorine and PM2.5 in Black patients on ICU admissions and greater effect of hydrochloric acid in Black patients on mortality were not surprising because their burdens among Black patients were 17, 5.2, and 8.0 times higher, respectively, than among White patients. Our results suggest that disparities in environmental conditions may have exacerbated inequities in COVID-19 impacts among Black patients.
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|
---
title: Can Adipose Tissue Influence the Evaluation of Thermographic Images in Adolescents?
authors:
- Hamilton H. T. Reis
- Ciro J. Brito
- Manuel Sillero-Quintana
- Alisson G. Silva
- Ismael Fernández-Cuevas
- Matheus S. Cerqueira
- Francisco Z. Werneck
- João C. B. Marins
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10001993
doi: 10.3390/ijerph20054405
license: CC BY 4.0
---
# Can Adipose Tissue Influence the Evaluation of Thermographic Images in Adolescents?
## Abstract
Infrared thermography (IRT) is a technology easy to use for clinical purposes as a pre-diagnostic tool for many health conditions. However, the analysis process of a thermographic image needs to be meticulous to make an appropriate decision. The adipose tissue is considered a potential influence factor in the skin temperature (Tsk) values obtained by IRT. This study aimed to verify the influence of body fat percentage (%BF) on Tsk measured by IRT in male adolescents. A total of 100 adolescents (16.79 ± 0.97 years old and body mass index of 18.41 ± 2.32 kg/m²) was divided into two groups through the results of a dual-energy X-ray absorptiometry analysis: obese ($$n = 50$$, %BF 30.21 ± 3.79) and non-obese ($$n = 50$$, %BF 11.33 ± 3.08). Thermograms were obtained by a FLIR T420 infrared camera and analyzed by ThermoHuman® software version 2.12, subdividing the body into seven regions of interest (ROI). The results showed that obese adolescents presented lower mean Tsk values than the non-obese for all ROIs ($p \leq 0.05$), with emphasis on the global Tsk (0.91 °C) and anterior (1.28 °C) and posterior trunk (1.18 °C), with “very large” effect size values. A negative correlation was observed in all the ROI ($p \leq 0.01$), mainly in the anterior (r = −0.71, $p \leq 0.001$) and posterior trunk (r = −0.65, $p \leq 0.001$). Tables of thermal normality were proposed for different ROIs according to the classification of obesity. In conclusion, the %BF affects the registered Tsk values in male Brazilian adolescents assessed by IRT.
## 1. Introduction
Skin blood flow has been studied for many years, especially for its important role in human thermoregulation. The physiology and vascular anatomy of the skin create a typical pattern of temperature distribution, which must remain within a certain distribution range to be considered healthy. When temperature values deviate from this standard considered ideal, this can be a sign of some kind of illness.
Infrared thermography (IRT) is a non-invasive, radiation-free, and easy-to-apply technology particularly suitable to precisely map the skin temperature (Tsk) through the analysis of thermographic images, which is frequently used for clinical purposes as an auxiliary tool in the process of diagnosis of diseases [1,2,3] and the prevention and rehabilitation of injuries [4,5,6,7]. The procedure is performed using a thermographic camera with a sensor responsible for capturing the heat radiated from the skin’s surface and transforming it into a temperature scale. The camera sensor is positioned close to the evaluated and provides a real-time representation of the Tsk distribution pattern in high resolution.
To obtain a quality thermographic image, the acquisition process must follow specific guidelines, such as the suggested by Moreira et al. [ 8], and observe several factors that may influence image evaluation. This is suggested in the review written by Fernández-Cuevas et al. [ 9], which presents studies that indicate that technical, environmental, and individual internal and external factors can influence the analysis by IRT, which is relevant for medical diagnosis purposes or for understanding human thermoregulation processes.
One of the two main factors to be observed, including a group of internal factors, is related to body composition [9]. Body fat has a lower level of thermal conductivity than other tissues involved in the thermoregulation process [10], acting as a “body thermal insulator” [11] since it acts as thermal resistance, making the process of heat conduction of the body more difficult to the internal region of the body compared to peripheral regions (e.g., skin) by $40\%$ to $50\%$ [10] and being able to influence the Tsk of the area where it is more concentrated [12]. Adipose tissue has lower thermal conductivity values than muscle tissue [13], dermis [13], and epidermis [14]. Furthermore, obesity is associated with increased inflammatory cytokines TNF-a or IL-6 to perivascular adipose tissue around healthy blood vessels, which free radical scavengers or cytokine antagonists can block, directly affecting the mechanisms of skin vasodilation and vasoconstriction [15,16].
Some studies have investigated whether the amount of body fat can interfere with the Tsk assessed by IRT in the population of men [17,18,19,20] and women [17,18,21,22,23] and, in general, observed that individuals with a more significant amount of fat presented lower Tsk values in body regions of interest (ROI) such as the trunk, arms, and legs. This factor should be considered during the evaluation of thermal images for a more precise assessment of the results.
Given the need for more precise knowledge on this subject, since the few existing studies are restricted to the adult population [17,18,19,20,21,22,23], and given that the use of IRT is more frequently used in clinical settings, investigating the influence of this characteristic on other age groups seems crucial for increasing the thermal image evaluation capacity of professionals working with IRT.
Thus, the objective of this study was to verify the influence of body fat on the Tsk values of male adolescents and to provide tables of thermal normality that help in the process of evaluating thermographic images and subsequent diagnosis of possible diseases or sports injuries or in helping the physical rehabilitation process. It is hypothesized that %BF will present a negative correlation with Tsk values and that participants with higher amounts of body fat will have lower Tsk values pattern in the regions of the trunk, arms, and lower limbs.
## 2.1. Participants
After evaluating 216 male high school students from public and private schools in a city in the interior of Brazil, we included 100 participants in the study. This amount was based on the total number of participants considered obese after the initial assessment. Thus, we intentionally selected the 50 individuals considered obese (16.83 ± 0.93 years, 78.94 ± 10.08 kg, 1.76 ± 0.07 m height, and a body mass index of 25.63 ± 2.96 kg /m2), and to perform a statistical evaluation with the same number of non-obese participants, we randomly selected, among the remaining 166 evaluated, 50 non-obese individuals (16.75 ± 1.01 years, 56.49 ± 8.51 kg, 1.75 ± 0.07 m height, and a body mass index of 18.46 ± 2.50 kg/m2). The final characteristics of the sample were 16.79 ± 0.97 years, 67.71 ± 14.61 kg of body weight, 1.75 ± 0.07 m height, and a body mass index of 18.41 ± 2.32 kg/m2. As a characterization criterion for individuals with or without obesity, we used the classification proposed by Williams et al. [ 24] specifically for teenagers. The randomization process of the 166 evaluations was carried out using the website https://www.randomizer.org/ (accessed on 19 December 2022).
As inclusion criteria, we selected male individuals who were apparently healthy, without apparent motor or intellectual deficiency, and aged between 14 and 19. Those excluded from the research were those without a signed informed consent or presenting some of the following exclusion criteria: smoking; history of kidney problems, musculoskeletal injury in the last two months, skin burns, or symptoms of pain in some body region; or sleep disturbances or fever over the previous seven days, physiotherapy or dermatological treatments with creams in the last two days, ointments or lotions for local use in the last two days, consumption of medication affecting Tsk (i.e., anti-inflammatory, antipyretic, or diuretics), or any dietary supplement with potential interference with water homeostasis or body temperature in the last two weeks. In addition, participants could not perform resistance training.
The study was approved according to ethical criteria for research involving human beings by the Ethics Committee of the local Institution under the registration number CAAE 40934275729. After explaining the characteristics and study objective, all the participants (or their person in charge in case of been under 18 years old) voluntarily signed the written consent before participating in the study.
## 2.2.1. Anthropometric Assessment of the Body Fat Percentage (%BF)
All the anthropometric variables were collected by trained professionals with level II certification from the International Society for the Advancement of Kinanthropometry (ISAK) [25]. Initially, height was measured using a portable stadiometer (Cescorf®, Porto Alegre, Brazil) with a precision of 1 mm and body mass with a digital balance (Welmy w $\frac{200}{5}$, Brazil) with a precision of 0.1 kg. The %BF was determined by dual-energy X-ray absorptiometry (DXA) by a single technician duly qualified for this function, using a GE Healthcare® densitometer, Lunar Prodigy Advance DXA System (software version: 13.31), which provides the values of total and segmented fatness (i.e., trunk, arms, and lower limbs). The equipment was calibrated daily according to the manufacturer’s specifications to guarantee the quality of the measurements.
## 2.2.2. Thermography Assessment
The thermographic image collection protocol was carried out following what was established by Moreira et al. [ 8], carefully observing all the factors that need to be considered to obtain a quality image.
Four thermographic images from the upper and lower body (see Figure 1), in the anterior and posterior positions, were registered from each subject using a T420 infrared camera (FLIR®, Stockholm, Sweden) located perpendicularly to the center of the recorded body areas. The imager had an accuracy of $2\%$, a spectral band of 7.5–13 µm, 60 Hz rate, automatic focus, and a resolution of 320 × 240 pixels and could detect temperature variations ≤ 0.05 °C. It was connected at least 30 min before all the evaluations to allow the stabilization of its thermal sensor, setting the emissivity at 0.98. During data collection, ambient temperature (21.3 ± 0.7 °C) and humidity (55.3 ± $2.2\%$) were controlled according to specific recommendations for this type of evaluation [8,9] and monitored through a portable meteorological station (Instrutherm®, THAL-300, São Paulo, Brazil). After stabilizing the temperature and humidity values in the room, the subjects remained standing, wore only slippers and shorts, and avoided any contact with surfaces or scratches for 10 min [26] before the thermographic images were captured. All the thermograms were obtained in the morning to reduce the influence of circadian rhythm on the results [27,28]. The thermal imager was positioned perpendicular to the ground [8] and at a distance allowing the subject to fit into the avatar generated by the software used for analysis so that all ROIs could be satisfactorily evaluated, as shown in Figure 1. After 10 min, following the methodology of Yasuoka et al. [ 29], they were asked to report the thermal sensation (TS) on a 9-point scale (+4, very hot; +3, hot; +2, warm; +1, slightly warm; 0, neutral; −1, slightly cool; −2, cool; −3, cold; −4 very cold) and the comfort sensation (CS) on a 7-point scale (+3, very comfortable; +2, comfortable; +1, slightly comfortable; 0, neutral; −1, slightly uncomfortable; −2, uncomfortable; −3, very uncomfortable).
The thermograms were automatically analyzed with ThermoHuman® software version 2.12 (PEMA THERMO GROUP S.L., Madrid, Spain), a validated system [30,31] that has been used in other studies with human population [32,33,34]. The software provides mean Tsk and standard deviation values and the number of pixels, which are automatically quantified in 48 ROI for the upper body and 36 ROI for the lower body. Those initial values were integrated, considering the average Tsk values and the corresponding number of pixels of each ROI, into seven groups (see Figure 1): Whole body (TskGlobal): considering the 84 ROIs; trunk: considering 10 ROIs from the anterior view (TskTrunkANT) and 10 ROIs from the posterior view (TskTrunkPOST); arms: considering 12 ROIs of both arms from the anterior view (TskArmsANT) and 12 ROIs from the posterior view (TskArmsPOST); and legs: considering 16 ROIs of both lower limbs from the anterior view (TskLegsANT) and 16 ROIs from the posterior view (TskLegsPOST). The ROIs were integrated with the use of the equation: Tskintegrated = (TskROI1 × npixROI1 + TskROI2 × npixROI2 + …+ TskROIn × npixROIn)/(npixROI1 + npixROI2 + … + npixROIn), where “n” is the number of ROI to be integrated, and “npix” is number of pixels included in the ROI. The data of the head, hands, gluteus, hips, and feet were excluded from the analysis.
## 2.2.3. Statistical Analysis
The Kolmogorov–Smirnov test was applied to confirm the normality of the dependent variables. As the normality was confirmed, the results are presented as average, minimum, and maximum values and their standard deviations. A Student’s t-test for independent samples was run to verify whether TS, CS, and Tsk differed between groups (obese and non-obese). Moreover, Cohen’s test was used to assess the effect size, which was interpreted following the scale proposed by Sawilowsky [35], which classifies the values of d as very small (0.01), small (0.2), medium (0.5), large (0.8), very large (1.2), and huge (2.0). The correlation between these variables was analyzed using the Pearson correlation test.
Furthermore, we elaborated a normative table to establish the thermal profile of the adolescents based on the %BF for each ROI analyzed. For this, we used the percentiles (P) as a reference to classify if an ROI was “strongly hypo-radiant” ($P \leq 5$), “hypo-radiant” ($P \leq 25$), in “thermal normality state” ($$P \leq 50$$), “hyper-radiant” ($P \leq 75$), or “strongly hyper-radiant” ($P \leq 95$). The choice of terms for characterizing the ROI was based on other studies [36,37].
The statistical analyzes were carried out by statistical software (SPSS, version 22.0), with a significance level of $5\%$.
## 3. Results
Table 1 presents the data on the quantity of fatness of the two participant groups ($$n = 100$$) based on their classification of obesity.
No differences were observed ($p \leq 0.05$ and $95\%$ CI = −$\frac{0.122}{0.482}$) in the values reported for TS and CS by obese (TS = 1.01 ± 0.40 and CS = 1.53 ± 0.58) and non-obese (TS = 0.83 ± 1.00 and CS = 1.27 ± 0.95) individuals in the thermographic collection environment.
Table 2 presents the results obtained by the thermographic evaluation of the two participant groups ($$n = 100$$) and their respective means, standard deviation, and minimum and maximum values as well as a comparison between the values observed in the participants with and without obesity. The main Tsk differences were observed for the TskGlobal (0.91 °C), TskTrunkANT (1.28 °C), and TskTrunkPOST (1.18 °C), being lower in obese individuals with “very large” effect size values.
This pattern of negative variation observed between the Tsk values of obese and non-obese adolescents was also verified in the correlation between the variables. We found a negative relationship between %BFglobal and Tskglobal (r = −0.57, $p \leq 0.001$), between the %BFTrunk and TskTrunkANT (r = −0.71, $p \leq 0.001$) and TskTrunkPOST (r = −0.65, $p \leq 0.001$), %BFArms and TskArmsANT (r = −0.29, $p \leq 0.01$) and TskArmsPOST (r = −0.36, $p \leq 0.001$), and %BFLegs and TskLegsANT (r = −0.45, $p \leq 0.001$) and TskLegsPOST (r = −0.44, $p \leq 0.001$), with emphasis on the values observed in the trunk region, as illustrated in Figure 2.
Based on the results obtained, Table 3 suggests breakpoint values to classify the person (both obese or non-obese) according to their level of infrared radiation as “strongly hypo-radiant” ($P \leq 5$), “hypo-radiant” ($P \leq 25$), “in thermal normality state” ($$P \leq 50$$), “hyper-radiant” ($P \leq 75$), or “strongly hyper-radiant” ($P \leq 95$) on all the considered integrated ROIs.
## 4. Discussion
The main results observed in this study suggest that the Tsk of individuals considered obese is lower than those without obesity (Table 2). Among the results, we highlight the effect size values observed in the evaluations of the TskGLOBAL, TskTrunkANT, and TskTrunkPOST, which presented “d” values of 1.23, 1.64, and 1.57, respectively, representing a probability of $80.8\%$, $87.6\%$, and $86.7\%$ for an obese adolescent presenting lower Tsk values than a non-obese adolescent for these ROIs. Additionally, Tsk values are inversely related to %BF for all ROIs analyzed in the study, highlighting the results observed between %BFglobal and Tskglobal (r = −0.57, $p \leq 0.001$), %BFTrunk and TskTrunkANT (r = −0.71, $p \leq 0.001$), and %BFTrunk and TskTrunkPOST (r = −0.65, $p \leq 0.001$). These data make it possible to affirm that this parameter should be considered in studies evaluating Tsk by IRT once the range of thermal normality varies according to the obesity classification of the evaluated patient. For this reason, we propose tables for the characterization of thermal normality to minimize any error in evaluation of the thermal images according to the classification of obesity for male adolescents.
The influence of %BF on Tsk values assessed by IRT has already been verified in other studies with the adult population based on different analysis models and presenting similar results to the present study. Chudecka et al. [ 22] and Chudecka and Lubkowska [23] used the bioimpedance technique and manual marking of ROIs to assess the impact of %BF on Tsk in adult women. Chudecka et al. [ 22] compared 20 obese women (23.2 ± 1.57 years, 90.7 ± 5.12 kg, 167.2 ± 3.75 cm height, and 37.8 ± 2.25 %BF) with 20 non-obese women (22.4 ± 1.22 years, 60.4 ± 2.56 kg, 169.0 ± 2.68 cm height, and 25.7 ± 2.44 %BF), verifying that women with obesity presented lower values ($p \leq 0.05$) of Tsk in the anterior and posterior regions of the arms, thighs and calves, the abdomen, and lower portion of ribs. In addition, they presented a negative correlation with %BF on the anterior (r = −0.77, $$p \leq 0.001$$) and posterior (r = −0.63, $$p \leq 0.008$$) regions of the thigh and abdomen (r = −0.88, $$p \leq 0.000$$). The body fat of the abdomen region was also negatively correlated (r = −0.59, $$p \leq 0.052$$) with Tsk in the study by Chudecka and Lubkowska [23], who compared 15 women with anorexia nervosa (18–24 years, 44.9 ± 4.49 kg, 169.90 ± 6.16 cm of height, and 13.30 ± 1.43 %BF) with 100 apparently healthy women (21–23 years old, 62.0 ± 4.84 kg, 168.8 ± 6.12 cm of height, and 22.8 ± 3.77 %BF). In both situations, the women stayed 20 min at a room temperature of 25.0°C and $60\%$ relative humidity before imaging.
Neves et al. [ 17] and Salamunes et al. [ 21] used DXA for analyzing body composition of an adult population including both men and women, and the impact of %BF on the observed Tsk values also presented results equivalent to those of the present study. In the study by Neves et al. [ 17] that evaluated the Tsk in 47 men and 47 women aged between 18 and 28 years, after 15 min at a room temperature of 23.0 ± 1 °C (no mention of humidity), they observed that the highest value of %BF was negatively correlated with the average Tsk of the anterior (r =−0.76, $p \leq 0.05$) and posterior trunk (r = −0.69, $p \leq 0.05$), anterior (r = −0.57, $p \leq 0.05$) and posterior lower limbs (r = −0.63, $p \leq 0.05$), and anterior (r = −0.42, $p \leq 0.05$) and posterior arms (r = −0.47, $p \leq 0.05$) in males and also negatively correlated with the anterior (r = −0.27, $p \leq 0.05$) and posterior trunk (r = −0.47, $p \leq 0.05$), anterior (r = −0.36, $p \leq 0.05$) and posterior lower limbs (r = −0.40, $p \leq 0.05$), and anterior (r = −0.30, $p \leq 0.05$) and posterior arms (r = −0.21, $p \leq 0.05$) in women [18]. This negative correlation in women was also reported by Salamunes et al. [ 21], who evaluated 123 women aged between 18–35 years after 15 min at a room temperature of 21.0 °C (no mention of humidity), observing this behavior in the anterior and posterior regions of the trunk (r = −0.33 and r = −0.36, $$p \leq 0.000$$, respectively), anterior and posterior arms (r = −0.40 and r = −0.43, $$p \leq 0.000$$, respectively), and anterior and posterior lower limbs (r = −0.38 and r = −0.49, $$p \leq 0.000$$, respectively).
The results in the present study, corroborated by those who observed the same Tsk pattern and its relation with the %BF, clearly demonstrate that the adipose tissue influences the Tsk values, probably due to its low thermal conductivity [10,11]. Thus, taking body fat into account is important when analyzing thermographic images. For this reason, we present values for the characterization of thermal normality according to the subject’s obesity classification (Table 3). We propose the points of thermal normality ($$P \leq 50$$) and cutoff points of $P \leq 25$ for low radiating and $P \leq 75$ for high radiating ROIs and cutoff points of ($P \leq 5$) for “very low” and (>95) for “very high” radiating ROIs. This proposal is very innovative, and it has not been conducted by other studies that evaluated the thermal profile in adults [18,38,39,40,41,42] or that observed differences between Tsk values as a function of body composition [17,21,22,23] or anthropometric indexes [22,23].
To the best of our knowledge, this is the first study that evaluated the impact of %BF on Tsk values in adolescents using DXA to estimate body composition and presents a different analysis methodology from previous studies, proposing a table of thermal normality. While previous studies used manual marking methods for ROIs selection, this study used software with automatic selection that has already been used in other studies for thermographic evaluation [5,43]. This characteristic can reduce individual error and promote greater reliability of the data obtained.
Our results can contribute to the process of thermographic evaluations, providing a new understanding of previous studies that sought to understand the population Tsk profile [38,39,40,41,42] without taking the %BF into account, which can lead to a misevaluation of the characteristics of the evaluated individuals and may cause an erroneous diagnostic action. Therefore, it is important that future studies that aim to draw a population’s thermal profile carry out their characterization in terms of %BF or anthropometric indexes related to this variable. In order to allow a better understanding of thermal images, a possible suggestion is to stratify different %BF classification ranges to establish more specific normality values for differences in body fat. Despite being considered the reference method for assessing %BF, DXA is an expensive technology with limited accessibility. In this way, researching the influence of body composition on Tsk, the body mass index (BMI) appears as a viable option; however, it requires a specific evaluation since different BMI classification ranges can also influence Tsk values in adolescents, as indicated by Reis et al. [ 34,44]. However, it is important to observe whether the subject performs resistance training activities since the total amount of muscle mass can influence the BMI. We emphasize that it was considered an inclusion criterion in this study to refrain from performing resistance training.
The observed results demonstrate that the Tsk values considered normal for individuals considered obese are different from those considered non-obese. Thus, male adolescents evaluated by IRT in search of diagnostic help on some muscle group pain should be framed in their respective body composition range to avoid general errors on the part of the clinical staff, for example. In addition, knowing this relationship can also influence an evaluation in sports where it is common for players to start the pre-season with higher body fat values or in sports that can be categorized by body weight, where it is normal to find different BF% and BMI patterns. Given the subject’s characteristics, understanding how and to what extent this factor can influence the IRT helps in decision making and in the evaluation process, mainly when the professional assembles a thermographic mapping of the subject throughout the season. Another possibility is to check for pathological skin changes ranging from malignancies (e.g., melanomas) and autoimmune disorders (e.g., atopic dermatitis or AD) to infectious conditions such as herpes simplex, which also lead to unique types of changes.
Since one of the limitations of the study was that it was only carried out with Brazilian male adolescents aged 16.79 ± 0.97 years, we suggest performing similar studies with different genders and age groups; for example, women, who tend to have greater %BF and may suffer more considerable alterations in Tsk values for that reason, or the elderly, who suffer orthopedic, metabolic, and thermoregulatory disturbances. Thus, these two population groups can benefit considerably from this strategy, allowing better evaluation of the resulting images and allowing the professional to understand whether the evaluated region is “hypo-radiant”, in a “thermal normality state”, or “hyper-radiant” depending on the clinical context that the patient is undergoing. In addition, we suggest conducting similar studies at different temperature and humidity ranges in the thermographic collection room to verify whether this can influence the results. We emphasize that the study was carried out within established standards for thermographic collection, and it was demonstrated that the participants felt thermally comfortable subjected to the temperature and humidity of the room. We also suggest that in future studies, the activity of the sympathetic neuro vegetative system be controlled since it may influence the measurement in comparative cases, as it was in the present study, by promoting changes in blood flow. It is essential to improve the application of the technique continuously.
It is important to highlight that the procedures for obtaining thermographic images in this study followed specific guidelines related to the collecting device. However, as the evaluation of IRT in humans is in constant technological evolution, we also suggest that other evaluations investigate whether different thermographic cameras (mainly with better resolution and precision) observe the same pattern of results presented in the present study.
Understanding the factors that can influence the Tsk values obtained by IRT is crucial for evaluating thermographic images to be used as an auxiliary tool in the diagnosis of the alterations in the individual’s normality pattern. Regarding the present study, the results make it clear that the %BF is a variable that must be considered in the thermographic image analysis, which can improve the use of IRT in clinical and sports environments and/or in the physical rehabilitation process.
## 5. Conclusions
Adolescents with a higher amount of body fat had lower Tsk values, with a negative correlation shown between them and influencing the evaluation of the thermographic image, which should be carefully observed. Normality classification values for Tsk were proposed according to the evaluated classification—with or without obesity—which can be used as a reference in the evaluation of thermographic images obtained in a collection environment similar to the one in the present study.
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|
---
title: Quantitative Analysis of Online Labor Platforms’ Algorithmic Management Influence
on Psychological Health of Workers
authors:
- Gengxin Sun
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002000
doi: 10.3390/ijerph20054519
license: CC BY 4.0
---
# Quantitative Analysis of Online Labor Platforms’ Algorithmic Management Influence on Psychological Health of Workers
## Abstract
Online labor platforms (OLPs) can use algorithms to strengthen the control of the labor process. In fact, they construct work circumstances with higher work requirements and pressure. Workers’ autonomy in behavior is limited, which will have a great influence on their labor psychology. In this paper, taking the online take-out platform as an example and by using a qualitative study of take-out riders’ delivery processes, which were supplemented by semi-structured, in-depth interviews with online platform executives and engineers, we used grounded theory to explore the influencing factors of OLPs’ algorithmic management on take-out riders’ working psychology. The quantitative analysis results showed that, in the context of conflict between work autonomy and algorithmic management, platform workers experienced psychological tensions relating to work satisfaction, compensation, and belonging. Our research contributes to protect public health and labor rights of OLP workers.
## 1. Introduction
With the deep application of the Internet and algorithm technology in the field of labor, the gig economy expands rapidly. In the gig economy, the construction of internet platforms and the application of algorithmic management enable employers to use online labor platforms (OLPs) to achieve direct and comprehensive contact with freelancers, so as to complete work tasks assignment of different complexities. The OLPs are attracting a large number of independent contractors and freelancers in a flexible way. In Europe and the U.S., 20–$30\%$ of the working-age population perform temporary, flexible jobs in OLPs [1]. In China, the number of participants in the gig economy had been about 830 million in 2020, including about 84 million OLP workers [2]. OLPs are considered as a new employment mode. It is believed to give workers the power to allocate time and energy independently through information technology [3,4]. Yet, OLPs also offer a mode for organization of work, which relies on the use of algorithms to monitor and control platform workers. Therefore, under the working circumstances of real-time monitoring algorithm technology, the OLP workers’ autonomy seems to be more limited. At the same time, workers work hard under the control of real-time quantitative and hidden intelligent platform algorithms, which not only leads to increasing workload, but also affects the physical and mental health of workers. Sun et al. [ 5] point out that take-out platforms increase the control and predictability of meal delivery through giving consumers a kind of “God’s Vision” that overlooks the overall situation. This vision adds considerable invisible mental pressure to take-out riders, and the rider’s flexibility in delivering meals is greatly compromised. Based on the power dependence theory, Feng et al. [ 6] concluded that, in online labor platforms, due to the influence of the power dependence relationship, the reduction of autonomy further leads to over-investment in working time and physical exhaustion, thus affecting the health of workers, and workers seem unable to control when to stop working, thus falling into a negative cycle of work engagement upgrading.
According to the definition of the World Health Organization, people with positive mental health are full of positive emotions and can work effectively. Relevant research [7] shows that the overall performance of employees with positive mental health is $16\%$ higher than that of colleagues with negative mental health. Due to the increasing workload and work pressure of workers on the online labor platforms, and the limited space for career development, workers not only easily to lose their enthusiasm for work, but also think that their work is worthless. Therefore, the research on the potential influence of OLPs on workers’ psychology is of great significance to comprehensively grasp the influence of OLPs’ algorithmic management on work autonomy and labor control.
The take-out industry is the fastest growing field in the gig economy; the number of take-out riders in *China is* close to 7 million. Take-out platforms have become the most typical and representative OLPs. With the emergence of a large number of take-out riders, urban problems, such as increasing traffic violations and traffic accidents, have also taken place [8]. These problems are caused by the shorter and shorter delivery time, strict requirements from customers, and severe punishment system, which also reflect the algorithmic management labor control of OLPs over take-out riders and the resulting psychological pressure. Relevant research [9,10] found that there was uncertainty in delivery time and customer evaluation in delivery labor. Take-out riders developed emotional labor strategies for different subjects in the labor process to flexibly cope with and overcome these double uncertainties. The emotional labor of riders makes up for the loopholes and blind spots behind the OLPs’ algorithmic management in a human way, but these strategies can only alleviate the tension between the platform and riders in the sense of representation, and also cause many psychological problems for riders. Therefore, we selected the take-out platform and take-out riders as the research object to study the influence of OLPs’ algorithmic management on the psychological health of workers.
In this paper, we conducted a qualitative study of more than three hundred take-out riders in Qingdao, China, which was supplemented by interviews with take-out platforms executives and engineers, to address the question: “how does OLPs algorithmic management which is implemented by the take-out platforms affect psychological health of platform workers”? We used grounded theory [11,12] to analyze the collected empirical fact data, and abstracted the theory on the basis of empirical facts, so as to comprehensively and objectively reveal the correlation between OLPs’ algorithmic management and workers’ psychological health factors.
## 2. Related Works
At the early development stage of OLPs, researchers generally believed that it was an effective mode for enterprises to achieve flexible employment and workers to achieve independent employment. Sloboda [13] believes that platform workers can flexibly and autonomously choose when, where, and how much work to do, and they can also choose to work on multiple platforms at the same time to reduce their dependence on any one platform. Thomas [14] also found that workers could freely arrange working hours, based on the business operation architecture designed by programs and customer information resources, without being controlled and constrained by employers. However, with the exposure of many problems of OLPs and the continuous progress of related studies, researchers gradually found that this so-called flexibility and autonomy are closely related to the uncertainty of various factors. Stewart et al. [ 15] believe that the work autonomy promised by flexible employment arrangements is unrealistic, and the work autonomy of OLPs is also affected by algorithm control. In the process of studying crowdsourcing workers, Schoerpf et al. [ 16] found that the platform had an important impact on the working hours, income, and creativity of crowdsourcing workers, as well as their working and living conditions, by using online evaluation mechanisms and other algorithms. Through the research on take-out riders, Ingrao [17] found food delivery workers’ job characteristics and how they can change these job characteristics to improve levels of well-being via job crafting in Italy. Duggan et al. [ 18] pointed out that the OLPs would manage and supervise the labor process in which workers participate in production and create value, and their autonomy in working time and task arrangement would be reduced due to the influence of intelligent algorithm control system.
Algorithmic management had emerged in OLPs as a method of organizing and coordinating extremely large groups of workers and clients in an automated way. The existing research mainly explored the role of algorithmic management in OLPs from two aspects: how to realize the rapid matching between supply and demand of the online labor market and how to implement and innovate the traditional human resource management process (including task allocation, behavior control, and performance evaluation). Wood et al. [ 19] believed that algorithmic management realized the rapid and accurate matching of labor supply and demand, and OLPs could quickly complete the accurate matching of labor and task demands by virtue of its technical information advantages. This efficient value creation method under the on-demand economy provided more opportunities for workers to work flexibly and independently. Idowu et al. [ 20] concluded, when studying the digital labor market, that algorithmic management has weakened the demand for human resource managers, and online platforms that use algorithmic technology as a virtual automation management role can save a lot of marginal costs and labor costs. Rani et al. [ 21] believe that the increasingly detailed division of labor led to the gradual quantification of work task content, which provided an opportunity for workers on OLPs to automatically assign and evaluate work tasks through algorithms.
Based on the above researches and analysis, it can be found that OLPs built a supply and demand matching transaction platform through digital technology, which allows customers to release task demands independently and workers to obtain flexible and independent employment. They adopt big data-driven algorithmic management methods to build a control system, so as to avoid uncertainty risks and ensure the quality of business operations. But the algorithmic management of OLPs also has a huge influence on the work autonomy of workers. Grabher et al. [ 22] found that Uber designed a set of automatic matching algorithms to ensure the high scheduling rates of online resources by limiting the rejection rate of drivers, and the intelligent algorithm would give priority to those drivers with high comprehensive ratings, thus reducing the autonomy of workers in obtaining jobs from a technical perspective. Matherne et al. [ 23] found that the Uber platform often pushed information to drivers through an algorithm system to remind drivers to improve their service attitude and work quality in order to control workers’ deviant and uncivilized behaviors. Jadhav et al. [ 24] found, in their research on take-out platform riders, that the platform not only grasped information sources and riders’ data, but also conducted real-time dynamic monitoring based on riders’ personal characteristics. The intelligent voice assistant developed by platforms, instead of a manual one, could constrain and control riders.
The existing OLPs’ algorithmic management research focused more on the influence mechanism on work autonomy and the influence on labor process. However, the implementation of algorithmic management on the online labor platform has actually constructed a work situation with higher work requirements and pressure [25,26], and there are few studies on how workers can independently choose to continuously extend working hours and improve labor intensity, thereby affecting the physical and mental health of workers. Shevchuk et al. [ 27] pointed out that in response to the “task crisis” brought on by OLPs, workers usually work hard to stay online 24 h a day, which greatly increased the labor reinforcement from mental level and behavioral levels. Those workers with poor economic conditions who need to bear various family responsibilities will independently extend their working hours and also bear various risks. This paper will quantitatively analyze how OLPs’ algorithmic management has a specific impact on the psychological health of workers through labor reinforcement from mental and behavioral levels.
## 3. Materials and Methods
Proceeding from the labor process theory and combining the research status of the gig economy, we conducted an intensive study of a single case with the purpose of generalizing from description to theory. In this paper, an online take-out platform (Meituan) and its dynamic relationship with take-out riders was taken as observed unit. Meituan is the largest takeout platform in China. According to the data released on the Meituan official website, in 2021, the Meituan takeout platform will have 250 million customers, 5.27 million active takeout riders, covering more than 2800 cities, with a transaction amount of 702.1 billion yuan and a total of 14.4 billion orders.
Our case selection was guided by the extreme case selection method [28], which is particularly useful for constructing new theory. The online take-out platform scores highly on both algorithmic matching and algorithmic control, which make it a particularly useful case from which to construct new theory on OLPs’ algorithmic management influence on psychological health of workers on OLPs.
## 3.1. Data Collection and Analysis
In process of data collection, multiple sources of qualitative data, including platform public data, written dialogue, and psychological interviews were used. All data, except those drawn from platform public data, were anonymized to ensure the privacy.
The data collection of our research is the riders of Meituan takeout platform in Qingdao, China. Qingdao Federation of Trade Unions established labor unions for take-out riders and accelerated to attract them to join trade unions to the greatest extent. The author, as the vice chairman of Qingdao Federation of Trade Unions, is responsible for this work all the year round. Therefore, 220 Meituan riders, who all work at a take-out distribution station affiliated with the Meituan platform in Qingdao, were selected as the research objects in this study. Through improving the working mechanism of labor rights protection and providing mental health services through the trade union, we gradually contacted the takeout riders to understand their work and service process.
In the first stage of data collection, unstructured interview was adopted to encourage the take-out riders to express their personal views around the take-out theme. The content of the communication was converted into text storage, and, then, topics and questions with high repeatability were comprehensively extracted. At last, we interviewed the riders with specific questions until the interview outline was completed.
In the second stage of data collection, semi-structured interview was adopted, and the interview content was flexibly adjusted according to the outline and the take-out riders’ answers, until the 220th rider, which basically covered the needs of this study. By October 2022, 275 in-depth interviews had been conducted in this study, with an average of 30 min to 10 h per respondent. Meanwhile, EAP (Employee Assistance Program) mental health services were provided for 220 take-out riders at least twice each. The mental and emotional conditions of take-out riders were recorded through professional mental scales and mental counseling.
The period, method, role, focus, and purpose of data collection in each stage are given in Table 1.
The interviewees in this study include 220 take-out riders. The demographic characteristics of the interviewees are shown in Table 2.
From Table 2, it can be seen that the majority of riders were male, and the age was mainly between 21 and 30. This means that the OLPs attract a large number of young male workers. In addition, the qualifications of riders were generally low: some riders had worked in the manufacturing industry and traditional service industry for some time, especially during COVID-19, while some riders chose to join the take-out riders because of the closure of the original factory or the layoff of the company.
The job characteristics of the interviewees are shown in Table 3.
From Table 3, it can be seen that the income of take-out riders is attractive (the average annual income of interviewees last year was 72,327 yuan). However, their work intensity is also very high, and they raise their income based on the amount of delivery orders, which is obtained at the expense of physical labor and sleep time. According to the data analysis of interviews, most of the take-out riders worked longer than the legal 40 h per week, and nearly $94\%$ of the take-out riders worked longer than 8 h per day. From 11 to 12 noon was their busiest time, and nearly $65\%$ of the take-out riders worked seven days per week. On average, a rider needs to deliver 46 orders every day and travels nearly 170 km.
## 3.2. Coding of Grounded Theory
The grounded theory emphasizes the promotion of theory from data, and believes that only through in-depth analysis of data the theoretical framework could be gradually formed. The core of grounded theory is to transform complicated empirical data into theoretical expression through coding.
Open coding is the process of splitting materials, comparing the similarities and differences between keywords and sentences, and conceptualizing and categorizing topics and events [29]. Because the data in the materials are usually inaccurate and scattered, it is necessary to redefine the interview data, analyze the materials sentence by sentence, and generate three-level nodes (A1,…, An) around the labor process of the take-out platform. This paper used qualitative analysis software to obtain 37 open coding nodes, including normative constraints, punishment mechanism, appeal mechanism, safeguard mechanism, etc. Some open coding is shown in Table 4.
Open coding will decompose and interpret the data, and get the possible internal connection of different concepts. Axial coding is intended to divide appropriate categories according to the similarity conditions, context, and interaction strategies of the analysis facts, and explore the correlation between the initial concepts through research and discussion. First of all, the interview outline focused on the labor control of the take-out platform, from which many factors about the platform control system could be obtained. The most talked about topics were rules regulations and science technology, so, in this paper, delivery specification, compensation system, and algorithm technology were classified. Secondly, according to demographic statistical characteristics, this paper classified the interviewees’ educational experience, household registration, marital status, and career planning as group characteristic factors. According to relevant research theories [30,31], this paper classified the concepts of high psychological pressure and higher economic income as social adaptation after comprehensive proofreading of the initial concepts. Finally, the factors related to platform participants were classified into agent factors, merchant factors, and consumer factors. To sum up, this paper classified 37 free nodes obtained by open coding and identified 8 categories. Axial coding results are shown in Table 5.
Selective coding is intended to abstract and categorize more than two categories formed by axial coding again. Explaining the relationship between main categories and corresponding categories is the core of the whole model. All corresponding categories were closely related to the main category, which provided a reasonable explanation for the factors that affected the mental health of workers caused by the online labor platforms’ algorithmic management. Through comparison with relevant research, the axial coding was further refined into four main categories: management system, technical means, labor factor, and participant factor, which are shown in Table 6.
According to the grounded theory, we can determine the influence factors of riders’ labor process. Due to the application of algorithmic management in OLPs, the labor control of take-out riders revolves around the core factor of time. Therefore, the influence factors of riders’ labor process can be divided into objective factors (system control factor, algorithmic control factor, and participant factor) and subjective factors (labor factor), which are shown in Figure 1.
In order to accurately identify the influence of online labor platforms’ algorithmic management on psychological health of workers, regression analysis modeling was used to analyze the relationship between influence factors and psychological health. Firstly, the psychological health of OLP workers was selected as the dependent variable, and, then, eight variables were selected from management system, technical means, labor factor, and participant factor. Variable selection and symbol description are shown in Table 7.
The regression analysis formula is defined as follows:y=β0+β1x1+β2x2+β3x3+β4x4+β5x5+β6x6+β7x7+β8x8+ε where β0,β1,β2… are coefficients, and ε represents error.
Because the unit of selected variables are different, and the dimension difference of units is large, in order to eliminate the influence of dimension on regression, it is necessary to convert the data into standardized data with mean values of 0 and standard deviations of 1, and then the standardized multiple linear regression coefficient is obtained through regression. The regression results are shown in Table 8.
From Table 8, we can see that the adjusted R-square of the model was 0.9637, but the results of most parameter significance tests were not ideal. It shows that the model has some problems. First, is the question of multicollinearity should be considered. So, we needed to further calculate the variance inflation factor. Calculation result of variance inflation factor is shown in Table 9.
Generally, when the variance inflation factor of variables is greater than 100, it is considered that there is serious multicollinearity between them. From Table 9, it can be clearly observed that x1, x6, x7 and x8 had multicollinearity.
The regression results of the optimized model after removing the variable x6, x7, x8 are shown in Table 10.
It can be seen from Table 10 that, when the model contained x1, x2, x3, x4, x5, and intercept terms, the adjusted R-square was 0.9827, and the fit goodness of the model was the best. The parameters in the optimized model are significant. From the last column in the Table 10, we can see that the multicollinearity had been improved.
The results prove that delivery specification, compensation system, algorithm technology, group characteristic factors, and social adaptation, which are brought on by OLPs’ algorithmic management, directly affected the psychological health of workers.
## 3.3. Analysis of Psychological Scale
Perceived social support [32] refers to the social support that individuals can perceive subjectively, including the emotional feeling and satisfaction of being understood, respected, and supported that individuals perceive. Social adaptation can be reflected by perceived social support. Job burnout [33] refers to a state of exhaustion caused by long time, high intensity and high load of work. Its cause is that workers have high expectations of their work, resulting in bad moods, cognition effects, and other aspects. Delivery specification, compensation system, and algorithm technology can be reflected by job burnout. Psychological capital [34] is an individual’s understanding of self and a positive psychological state that an individual shows in the process of growth, including self-efficacy, hope, optimism, and resilience. Group characteristic factors can be reflected by psychological capital.
The positive effect of perceived social support on mental health has been confirmed by many studies, and the level of mental health can be further improved by improving perceived social support. Therefore, the hypothesis is proposed that perceived social support can significantly predict the mental health of take-out riders. The influence of perceived social support on mental health is expected to be positive, while the influence of job burnout on mental health is expected to be negative. Some studies show that, the more serious job burnout is, the worse the mental health is. Therefore, the higher the level of perceived social support of individuals, the higher the level of positive psychology, and the lower the possibility of job burnout. The hypothesis is proposed that job burnout plays an intermediary role in perceived social support and mental health. Previous studies have proved that there is a significant positive correlation between perceived social support and psychological capital. The higher the level of perceived social support, the more inclined people are to treat others with an optimistic and positive attitude, be good at building good interpersonal relationships, and show a more positive and healthy psychological state. Therefore, the hypothesis is proposed that psychological capital plays an intermediary role in perceived social support and mental health. Therefore, starting from perceived social support, job burnout, and psychological capital, this paper studies the influence factors of algorithmic management on the mental health of take-out riders.
In this study, 220 take-out riders were investigated with the Perceived Social Support Scale (PSSS), Maslach Burnout Inventory General Survey (MBI-GS), Psychological Capital Scale (PCS), and General Health Questionnaire (GHQ-12), and 52 riders were provided with mental counseling. The research carried out descriptive statistical analysis and correlation analysis on the scores of each scale.
According to previous research [35,36,37] on the relationship between perceived social support, job burnout, psychological capital, and mental health, the following research hypotheses were proposed: By using the data of theses scales to conduct descriptive statistical analysis on riders’ perceived social support, job burnout, psychological capital, and mental health, the analysis results showed that the average score of perceived social support was (56.28 ± 16.26), the average score of job burnout was (32.79 ± 12.07), the average score of psychological capital was (92.36 ± 19.53), and the average score of mental health was (39.71 ± 8.06). The descriptive statistical results of research variables are shown in Table 11.
The independent sample t-test results of different gender riders on each variable are shown in Table 12.
As shown in Table 12, there were significant differences in perceived social support, psychological capital, and mental health variables between different genders, but there was no significant difference in job burnout.
Variance analysis on various variables of take-out riders with different daily working hours is shown in Table 13.
As shown in Table 13, there were significant differences in the scores of riders with different daily working hours for perceived social support, job burnout, psychological capital, and mental health variables.
Variance analysis on various variables of take-out riders with different daily delivery order quantity is shown in Table 14.
As shown in Table 14, there were significant differences in the scores of riders with different daily delivery order quantities for perceived social support, psychological capital, and mental health variables. However, there was no significant difference in job burnout.
Variance analysis of various variables of take-out riders with different monthly income is shown in Table 15.
As shown in Table 15, there are significant differences in the scores of riders with different monthly income for perceived social support, job burnout, psychological capital, and mental health variables.
The correlation analysis of perceived social support, job burnout, psychological capital, and mental health showed that there were correlations between each pairwise variable ($p \leq 0.01$). There was a significant negative correlation between perceived social support and job burnout, and the correlation coefficient r was between −0.139 and −0.336 ($p \leq 0.01$). There was a significant positive correlation between perceived social support and psychological capital, and the correlation coefficient r was between 0.436 and 0.582 ($p \leq 0.01$). There was a significant positive correlation between perceived social support and mental health, and the correlation coefficient r was between 0.337 and 0.497 ($p \leq 0.01$). There was a significant negative correlation between job burnout and psychological capital, and the correlation coefficient r was between −0.117 and −0.539 ($p \leq 0.01$). There was a significant negative correlation between job burnout and mental health, and the correlation coefficient r was between −0.471 and −0.512 ($p \leq 0.01$). There was a significant positive correlation between psychological capital and mental health, and the correlation coefficient r was between 0.468 and 0.668 ($p \leq 0.01$). Based on the proposed hypothesis and the analysis of the relationship between various variables, the intermediary effect between perceived social support, job burnout, psychological capital, and mental health was investigated. The Bootstrap method [38] was used to repeat sampling for 1000 times, and the confidence level was set at $95\%$ to test the significance of these paths, as shown in Table 16.
The estimated value interval of all paths did not include 0. Perceived social support could directly predict the mental health of take-out riders, with a direct effect of 0.195. It could also mediate mental health through job burnout and psychological capital, and job burnout and psychological capital played a chain intermediary role between perceived social support and mental health of take-out riders, with a total indirect effect of 0.373.
## 4. Discussion
The management of large groups of highly independent, highly skilled workers on OLPs has been achieved through a variety of algorithms, which haveacted as a means of coordinating and controlling workers. Algorithmic control [39] refers to the use of algorithms to monitor platform workers’ behavior and ensure their alignment with the platform organization’s goals. Uber quantifies the driver’s work habits by recording all details of the driver’s whereabouts, thereby supervising the driver’s work process and improving the driver’s service quality. Although Uber has repeatedly promoted so-called hands-off management to give drivers full freedom and autonomy, it is implementing a higher level of monitoring, because it records a series of personal data of the driver. Shestakofsky [40] pointed out that algorithms in Uber give the company vast leverage over work processes and the mental health of drivers.
From the labor process of take-out riders’ perspective, we found that algorithmic management seriously affected the mental health of riders. In the rider’s whole labor process, the take-out platform is responsible for directing the rider’s work, the consumer is responsible for evaluating the rider’s work, and the take-out platform completes the final reward and punishment for the riders. The take-out platform can allocate orders to riders in a short time, calculate the estimated delivery time, plan the delivery route, while, at the same time, riders also face the arbitrary supervision of algorithm management. Any rider who fails to deliver the goods within the specified delivery time may be punished, a certain amount of money will be deducted from the rider once the delivery is overdue, and the rider cannot click on the delivery in advance. The take-out platform will determine whether the rider has violated the operation according to the location of the rider and the customer feedback mechanism. Once the rider clicks on the delivery in advance without the customer’s consent, a certain amount of money will be deducted. In addition, bad comments from customers are also one of the important reasons for the rider’s money deduction. When the harsh punishment system is used as the management basis and workers sell their emotions to the enterprise as part of the labor force, the workers need to face the dual emotional control of the platform and customers. OLPs transfer part of the labor supervision right to consumers. On the surface, the evaluation score is given by customers according to their service experience, but in fact, it is given by the platform through the algorithm scoring system. Riders rely on the dispatch mode, which is set by the platform algorithmic management to obtain remuneration. They usually choose to extend working hours and improve distribution intensity to increase income. From the above analysis, we can see that algorithmic management had a direct influence on the rider’s labor process, labor experience, and mental health.
According to the research and descriptive statistical analysis results, we can obviously conclude that there is a significant correlation between perceived social support, job burnout, psychological capital, and mental health. Among them there was a significant positive correlation between perceived social support and mental health, and perceived social support could positively predict the mental health of the take-out riders. Research hypothesis H1 is confirmed. From the perspective of different average daily working hours and daily delivery order quantity, the longer the working hours or the more the orders were, the lower the riders’ perceived social support, psychological capital, and mental health were, which is consistent with previous research results [41]. For the take-out riders, constructing a good interpersonal relationship, getting the understanding and support of family and friends, as well as social respect and recognition, is a process of strengthening social support, which will involve emotional experience and other issues, which will ultimately have a relationship with their mental health.
The social support that individuals feel can help them cope with stressful events and relieve emotions, which is conducive to mental health. The level of job burnout of the take-out riders who work more than 12 h a day was the highest, which may be due to the long working hours and high work intensity, because workload and time distribution are not only the influencing factors of job burnout, but also affect the enthusiasm of work and the sense of success in work. At the same time, the platform algorithmic management results in take-out riders working long hours and receiving more orders per day, which makes riders spend less time communicating with their family and friends. When there are fewer channels to obtain emotional communication from the outside, the level of perceived social support of individuals will also be reduced. The analysis results from different monthly income levels showed that the higher the monthly income was, the higher the perceived social support. Some studies have shown that the income gap may cause individuals with poor economic conditions to deviate in self-cognition, thus resulting in a reduction in subjective support in social support, namely, a reduction in perceived social support. In terms of job burnout, the level of job burnout of riders with incomes of 5000–7000 was lower than that of riders with monthly incomes of 5000 and below. It may be because the riders with higher monthly income need more working hours or order quantity, which easily leads to physical fatigue, and physical fatigue also easily leads to psychological fatigue. The highest level of job burnout of riders with monthly incomes of more than 10,000 indicated that high income makes job satisfaction higher and work enthusiasm higher, so it is not as easy to be tired, and further makes their psychological capital and mental health higher. Some studies [42,43] have found that, if individuals feel more supportive resources, such as organizational support or family support, it will help prevent job burnout. Therefore, perceived social support can negatively predict job burnout. If workers are stuck in job burnout for a long time, they will inevitably have an impact on their mental health. It can be seen that perceived social support not only had a direct impact on mental health, but also indirectly affected mental health through the partial intermediary role of job burnout. Research hypothesis H2 is confirmed.
Through interviews and research, it was found that most of the take-out riders were able to look optimistically at many problems encountered in their work (complaints, weather, etc.), and they believed that they could solve the problems themselves. On the whole, the psychological capital level of take-out riders was high. The level of psychological capital and mental health of riders with incomes above 10,000 were lower than those of riders with other monthly incomes, respectively. This result is different from the existing research results [44], wherein the higher the family income, the healthier the mental health. The specific reason for this result may be related to the relative satisfaction of work input and work income. Descriptive statistical analysis results show that perceived social support can positively predict psychological capital level. Therefore, perceived social support can indirectly affect mental health through psychological capital. That is, psychological capital played a partial intermediary role in perceived social support and mental health. Research hypothesis H3 is confirmed.
According to the above relationships between perceived social support, job burnout, psychological capital, and mental health, it was shown that perceived social support can affect the job burnout of the take-out riders, and job burnout has an impact on psychological capital, thus further affecting mental health. They proved that job burnout and psychological capital played a chain intermediary role in perceived social support and mental health. Research hypothesis H4 is confirmed.
Prolonging working hours and increasing labor intensity for a long time will not only cause physical damage, but also lead to mental health problems. Many studies [45,46] have shown that labor reinforcement can significantly reduce employees’ job satisfaction and happiness, and then have a negative influence on their psychology and behavior. According to the results of psychological evaluation and analysis in this paper, the mental health problems of riders were caused by the reduction of workers’ autonomy, the continuous occupation of life time by work tasks and the increasing workload caused by algorithmic management. Ogbonna et al. [ 47] pointed out that, with the increase in labor intensity, the energy and physical strength consumed by many consumptive work tasks needs to be recovered through rest, and the continuous consumption and inadequate recovery would lead to the gradual exhaustion of individual energy, which will have a negative influence on workers’ mood. Those findings are consistent with the conclusion of this paper. In addition, through psychological evaluation and analysis, it was found that the work identity of the rider was inconsistent with the current environment, which would lead to a sense of tension. This sense of tension caused by role conflict and cognitive ambiguity often leads to work stress and job burnout. The rider and other workers engaged in OLPs would put themselves in a high-risk and uncertain environment, and they were tired of running and being strictly controlled by the platform algorithm, which would make them feel confused about their own identity, which would then unconsciously adapt to their own identity. It shows that workers’ independent extension of working hours and improvement of labor intensity under the control of their autonomy would reduce their satisfaction and happiness, increase emotional exhaustion, and reduce the level of work performance.
## 5. Conclusions
The rise of the gig economy platform largely depends on internet technology and new organizational management models. This paper studied the labor process of take-out platforms from the perspectives of algorithmic management. It tried to find the answer to how online labor platforms’ algorithmic management affected the psychological health of workers. In this paper, the quantitative analysis method was used to analyze the various data, and the grounded theory method was used to find the main factors affecting the labor process and psychology of take-out riders. This study found that the mental health of the take-out riders was generally good, and there was less job burnout. However, there were significant differences in working hours, daily delivery order quantity, and monthly income; these factors directly affected the psychological health of take-out riders. This study deeply analyzed the influence of an online labor platform’s algorithmic management on work autonomy and the threat to workers’ physical and mental health. It provides a reference for standardizing the employment of platform enterprises to promote the sustainable development of the gig economy. In the future, I will define the concept of algorithmic management in a structured and systematic way, and I will take various methods to further promote empirical research.
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|
---
title: Availability of Medical Services and Teleconsultation during COVID-19 Pandemic
in the Opinion of Patients of Hematology Clinics—A Cross-Sectional Pilot Study (Silesia,
Poland)
authors:
- Kamila Jaroń
- Angelika Jastrzębska
- Kamil Mąkosza
- Mateusz Grajek
- Karolina Krupa-Kotara
- Joanna Kobza
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002034
doi: 10.3390/ijerph20054264
license: CC BY 4.0
---
# Availability of Medical Services and Teleconsultation during COVID-19 Pandemic in the Opinion of Patients of Hematology Clinics—A Cross-Sectional Pilot Study (Silesia, Poland)
## Abstract
Summary: A new virus, SARS-CoV-2, emerged in December 2019, triggering the COVID-19 pandemic in 2020 due to the rapid spread and severity of cases worldwide. In Poland, the first case of COVID-19 was reported on 4 March 2020. The aim of the prevention efforts was primarily to stop the spread of the infection to prevent overburdening the health care system. Many illnesses were treated by telemedicine, primarily using teleconsultation. Telemedicine has reduced personal contact between doctors and patients and reduced the risk of exposure to disease for patients and medical personnel. The survey aimed to gather patients’ opinions on the quality and availability of specialized medical services during the pandemic. Based on the data collected regarding patients’ opinions on services provided via telephone systems, a picture was created of patients’ opinions on teleconsultation, and attention was drawn to emerging problems. The study included a 200-person group of patients, realizing their appointments at a multispecialty outpatient clinic in Bytom, aged over 18 years, with various levels of education. The study was conducted among patients of Specialized Hospital No. 1 in Bytom. A proprietary survey questionnaire was developed for the study, which was conducted on paper and used face-to-face interaction with patients. Results: $17.5\%$ of women and $17.5\%$ of men rated the availability of services during the pandemic as good. In contrast, among those aged 60 and over, $14.5\%$ of respondents rated the availability of services during the pandemic as poor. In contrast, among those in the labor force, as many as $20\%$ of respondents rated the accessibility of services provided during the pandemic as being well. The same answer was marked by those on a pension ($15\%$). Overwhelmingly, women in the age group of 60 and over showed a reluctance toward teleconsultation. Conclusions: Patients’ attitudes toward the use of teleconsultation services during the COVID-19 pandemic varied, primarily due to attitudes toward the new situation, the age of the patient, or the need to adapt to specific solutions not always understood by the public. Telemedicine cannot completely replace inpatient services, especially among the elderly. It is necessary to refine remote visits to convince the public of this type of service. Remote visits should be refined and adapted to the needs of patients in such a way as to remove any barriers and problems arising from this type of service. This system should also be introduced as a target, providing an alternative method of inpatient services even after the pandemic ends.
## 1. Introduction
For a long time, coronaviruses were considered benign pathogens that cause respiratory symptoms of minor severity that resolve within a few days. The arrival of new infectious virus species has given rise to an increase in interest in these viruses. Before the emergence of the new SARS-CoV-2 coronavirus, a highly infectious species of SARS coronavirus had already appeared in the public, in 2002, causing a worldwide outbreak. Ten years after the SARS outbreak, new cases of the respiratory disease caused by the MERS coronavirus emerged, but this virus did not entail an outbreak. In contrast, a new SARS-CoV-2 virus emerged in December 2019, which triggered the COVID-19 pandemic in 2020 due to the rapid spread and severity of cases worldwide [1]. The Wuhan live animal and seafood market is considered the epicenter of COVID-19. In Poland, the first case was reported on 4 March 2020. The aim of the prevention effort was primarily to stem the spread of infection to prevent overburdening the healthcare system [2]. The most common symptoms present at the onset of SARS-CoV-2 coronavirus infection were dry cough, fever, general weakness, and muscle aches. The course of the infection largely depends on the age of the patient, and more severe symptoms are observed more often in the elderly than in children [1]. Most symptomatic patients have a mild form of the disease ($80\%$ of patients). In contrast, $14\%$ of symptomatic patients have a severe course of the disease, i.e., accelerated breathing, significant resting dyspnea, involvement of more than $50\%$ of the lung parenchyma, and saturation below $94\%$. A minority, of $6\%$ of patients have a critical course of the disease with acute respiratory distress syndrome, with multiple organ failure and septic shock [2]. In about $20\%$ of people, the disease is asymptomatic. To a large extent, the course of the disease and its severity depend on the patient’s immune response to infection. Coronavirus, SARS-CoV-2 is primarily transmitted between people by the droplet route, where close person-to-person contact is not necessary. For infection to occur, the virus must be transmitted to the mucous membranes of the throat, nose, or eyes. The minimum infectious dose of the virus has not been determined [3].
The pandemic continues to be a global threat to health care and the availability of health services. It has affected all countries and therefore health systems have had to adapt to the new situation to ensure rapid access to medical care. However, due to reduced access to medical services during this time, the functioning of the healthcare system has been disrupted. To curb the spread of the virus, many diseases were treated through telemedicine, primarily using teleconsultation. Telemedicine has reduced personal contact between doctors and patients and reduced the risk of exposure to disease for patients and medical personnel. However, telemedicine does not fully replace the interaction that occurs in face-to-face interactions [4]. In Poland, the majority of teleconsultations within the framework of so-called telemedicine and medical advice are carried out in contact through a telecommunications device (such as a telephone). According to estimates, this is $95\%$ of all teleconsultations. Other forms, such as video chat, are marginal [2,3,4]. Nonetheless, alternative modes of communication, such as online consultations and teleconsultation, have significant benefits in emergencies. Among other things, they provide patients with real-time information and professional advice from physicians during times of inaccessibility to medical facilities [5].
The purpose of the survey was to gather patients’ opinions on the quality and availability of specialized medical services during the pandemic. Based on the data collected regarding patients’ opinions on services provided via telephone systems, a picture was created of the opinions of clinic patients regarding teleconsultation, and attention was paid to emerging problems. It was assumed that the coronavirus pandemic negatively affected the quality and availability of medical services provided by public health care providers.
## 2.1. Study Organization
The study included a 200-person group of patients, completing their visits to specialized hematology outpatient clinics in Bytom (Silesia, Poland) (Scheme 1), aged over 18 years, with various levels of education. To anonymize the study, only data on gender, age, and the fact of treatment were collected. All data were coded with appropriate symbols, preventing the identification of patients by the Act of 29 August 1997, on the Protection of Personal Data (Journal of Laws of 1997, No. 133, item 883).
The primary criteria for inclusion were the patient’s written consent, expressed through participation in the survey, and that the patients be aged 18 or over. Participation in the study was anonymous and completely voluntary. The study adhered to the provisions of the Declaration of Helsinki and received a positive opinion from the Bioethics Committee of the Silesian Medical University in Katowice (ID: PCN/0022/KB/$\frac{211}{20}$).
## 2.2. Research Tool
A proprietary survey questionnaire was developed for the study, which was conducted on paper and used face-to-face interaction with patients. The survey questionnaire contained 17 closed questions. The first five questions (metric) were about gender, age, place of residence, education, and current occupational status. The remaining 12 questions were aimed at finding out the patients’ opinions on the teleconsultations conducted and assessing their availability and quality. The questionnaire was validated by administering it twice, two weeks apart, to a group of 30 people; in the first version, respondents were given a chance to express their opinion and indicate comments on the content of the questionnaire. The second time, the repetition of responses was tested. The reliability of the questionnaire was assessed using Cronbach’s alpha coefficient and was shown to be 0.83, which in psychological research indicates good reliability.
## 2.3. Study Sample
The study included 200 patients, most of whom were women ($58\%$). The largest number of respondents belonged to the age group of 60 years and older ($44\%$), and the smallest number belonged to the age group of 18–28 years ($9\%$). Of the respondents, $94.5\%$ were city residents and most had a secondary/vocational education ($68\%$). The surveyed patients were mostly employed ($50\%$) or retired ($49\%$) (Table 1).
## 2.4. Statistical Compilation
Statistical analysis was carried out using Statistica software (Statsoft, Poland). Multivariate tables were used in the calculations, individual groups of respondents were compared, and relationships between variables were analyzed. Mann–Whitney U and Kruskal–Wallis tests were used in statistical inference. The p-values <0.05 were considered statistically significant. For the results of the statistical inference, the abbreviation T is adopted in the text.
## 3. Results
In response to the question “How do you rate the availability of services provided during the COVID-19 pandemic?”, the majority of respondents rated the availability of services during the COVID-19 pandemic as good ($35\%$), and $25.5\%$ as definitely good. In contrast, $21.5\%$ of respondents marked the answer “difficult to say”, 34 people ($17\%$) rated the availability as bad, and only two people ($1\%$) as definitely bad. To the next question, i.e., “How do you rate the quality of services provided during the COVID-19 pandemic?”, $32\%$ of respondents rated the quality of services provided as good, and $27\%$ of people answered: “hard to say”. Another $20\%$ of respondents rated the quality as good, $15.5\%$ of respondents marked the answer “bad”, while only $5.5\%$ of people answered, “definitely bad”. When asked to evaluate the quality of the services provided through ICT systems, $30.5\%$ of respondents thought that the introduction of teleconsultation and its quality were good. $27.5\%$ had no opinion on the subject, while $21.5\%$ of respondents rated the quality of services provided through ICT systems badly. Of the respondents, $17\%$ gave a decidedly good rating, and $3.5\%$ gave a decidedly bad rating. Furthermore, $56\%$ of respondents indicated that the creation of teleconsultation during the COVID-19 pandemic was a good idea, while $44\%$ indicated that it was not a good idea. In response to the question “What do you like best about the advice provided through telephone or online systems?” ( respondents could indicate more than one answer), most respondents indicated the convenience of visiting without leaving home ($49.5\%$), $45.5\%$ marked safety related to the possibility of contracting a virus; however, $45\%$ indicated the answer “I don’t like this type of visit”. Additionally, $30.5\%$ of respondents indicated the lack of waiting in line, while only $17\%$ of people marked the answer that they had better contact with the doctor. In a question about possible problems arising when providing advice via ICT systems (again, it was possible to mark more than one answer), the largest number of people ($56\%$) indicated that they had not noticed any problems in this regard, $40.5\%$ of respondents had problems with connectivity, while $38.5\%$ of people had problems understanding the information provided, $32.5\%$ of respondents indicated poor contact with the doctor, and $26.5\%$ of people indicated a lack of examination. To the question “Do you think it would have been a good idea to conduct visits via ICT systems without the pandemic?”, $54\%$ said yes, while $46\%$ of people indicated a “no” answer. The same number of respondents, as with the previous question, answered the question “Are you willing to use the advice provided by the telephone method?” and $54\%$ indicated “yes”, while $46\%$ indicated “no”. Regarding the question about the attitude of medical personnel to the advice given by the telephone method, $34.5\%$ of respondents answered “difficult to say”, $28.5\%$ of people rated the attitude of medical personnel to the advice given as being well, as did $20.5\%$ of respondents. In contrast, the answer “bad” was marked by $14\%$ of people, and “definitely bad” by $2.5\%$ of respondents. In response to the question “Have you used other medical facilities that also provided telehealth appointments?”, $77.5\%$ of people answered that they had used telehealth elsewhere, while $22.5\%$ of people had not used this type of service elsewhere. The last question included only those who answered yes to the previous question, i.e., “Have you used other medical facilities where teleconsultation visits were also conducted?” and referred to 155 people. This question was about the evaluation of conducted visits to another facility via telehealth systems and $31.6\%$ of people rated the conducted visits to another facility via telehealth systems badly, $29\%$ of people did not comment, $26.5\%$ of respondents rated the visits well, $11\%$ of people marked the answer “definitely badly”, and $1.9\%$ of people marked the answer “definitely well”.
Referring to the question: “How do you rate the availability of services provided during the COVID-19 pandemic?”, a breakdown was made in the responses in terms of the number of women and men (Figure 1). Of men and women, $17.5\%$ rated the availability of provided services during the pandemic well, $15\%$ of women rated this availability strongly well, while only $10.5\%$ of men gave this rating (“strongly well”). A bad rating was given by $12.5\%$ of women and $4.5\%$ of men. The answer “definitely bad” was indicated by $1\%$ of men, and $0\%$ of women. In contrast, $13\%$ of women and $8.5\%$ of men had no opinion. There was no relationship between the variable’s gender and the evaluation of the availability of medical services during the COVID-19 pandemic ($p \leq 0.05$).
For the same question—How do you rate the availability of services provided during the COVID-19 pandemic?” for respondents by age (Figure 2), in the age group of 60 and over, $14.5\%$ of respondents rated the availability of services provided during the pandemic poorly. The answer good was marked by $4.5\%$ of people, and bad by $1\%$ of respondents. The same number, i.e., $12\%$ of respondents, marked the answer “good” and “hard to say”. In the 50–59 age group, the largest number of respondents answered “good” ($7.5\%$ of people). Six percent of respondents marked the answer “definitely good”, and “hard to say” was indicated by $4.5\%$. No one marked the answers “bad” and “definitely bad”. On the other hand, in the 40–49 age group, the highest number of responses was “good” ($7\%$). “ Good” was marked by $4\%$ of respondents, $2.5\%$ of people had no opinion on the subject, and $2\%$ of respondents indicated the answer “bad”. No one marked the answer “definitely bad”. Respondents in the 29–39 age group mostly indicated the answer “definitely good” ($5.5\%$), $5\%$ of people indicated the answer “good”, $0.5\%$ indicated the answer “bad”, and $2.5\%$ had no opinion. Additionally, no one marked the answer “definitely bad”. In contrast, in the 18–28 age group, there are only two ratings, i.e., “definitely good” ($5.5\%$) and “good” ($2.5\%$). A statistically significant relationship was found between the variable age and the evaluation of the availability of services during the pandemic. Those over 60 were more likely to negatively evaluate the availability of medical services provided during the COVID-19 pandemic ($T = 11.868$; $r = 0.632$; $$p \leq 0.001$$).
About the professional status of the respondents, the answers to the above question—“How would you rate the availability of services provided during the COVID-19 pandemic?” ( Figure 3)—were as follows: among working people, as many as $20\%$ of respondents rated the availability of provided services during the pandemic well, $19\%$ of working respondents indicated the answer “definitely well”, $3\%$ “poorly”, while $8\%$ had no opinion. No one marked the answer “definitely bad”. Those on a pension, on the other hand, mostly ($15\%$) marked the answer “good”. Of respondents, $14\%$ marked the answer “bad”, while $13.5\%$ had no opinion on the subject. In contrast, “definitely good” was marked by $5.5\%$ of people, and “definitely bad” by $1\%$. Those who were pupils or students ($1\%$) marked one answer—”definitely good”. There was a statistically significant relationship between the variable of occupational status and the assessment of the availability of services during the pandemic. Those who were employed/retired were more likely to negatively evaluate the availability of medical services provided during the COVID-19 pandemic ($T = 12.003$; $r = 0.614$; $$p \leq 0.002$$).
Another question asked “Are you willing to use telephonic advice?”, and respondents were grouped by age and gender (Figure 4). Overwhelmingly, reluctance to teleprompting was shown by women in the age group of 60 years and older ($T = 10.099$; $r = 0.703$; $$p \leq 0.001$$). The rest of the respondents’ answers were similar, so no differences were noted ($p \leq 0.05$). The more frequent response was “yes” among both women and men, regardless of age.
## 4. Discussion
The pandemic has changed the way healthcare services are delivered to patients around the world. To provide precautions and physical distancing during the COVID-19 pandemic, telephone consultation was provided as an alternative method to face-to-face visits, primarily in primary care (PCP) [6]. However, telemedicine also has some drawbacks, as it primarily focuses on the symptoms presented by the patient, patients are often not comprehensively examined and visual cues are often lacking. In addition, there are issues regarding the relationship between doctor and patient, or problems regarding the quality of the information provided [6]. Despite the drawbacks, telephone consultations were used during the pandemic because of their ability to deliver remote, essential health care to patients and to halt the spread of the virus [6].
A study by Zammit, et al. found that there was a significant improvement in patient satisfaction and an increased preference for telephone consultations [7]. Telemedicine during the pandemic made a huge impact mainly among older patients or patients with chronic diseases. The advantages of telephone telemedicine, in addition to preventing the transmission of infections, are convenience and saving time. However, the difficulty of checking and explaining the condition to patients, the possibly incomplete assessment of their health status, and the misunderstandings that can arise from a telephone consultation between a doctor and a patient negatively affect this type of medical service [8].
The COVID-19 pandemic has proven that telemedicine is a very helpful and desirable tool in healthcare. It allows for a personalized approach on the part of healthcare professionals toward patients and the establishment of positive interactions between them. This represents a very valuable aspect from the perspective of both parties. The use of telemedicine has made it possible to access medications (so-called e-prescriptions, electronic prescriptions), make diagnoses, implement comprehensive treatment, and, in addition, carry out health education processes, including issues related to the prevention of chronic diseases. Studies related to teleconsultation, which were conducted before the outbreak of the SARS-CoV-2 virus pandemic, did not show a significant decrease in effectiveness compared with traditional visits made in a stationary manner [9,10].
A study that was conducted in the context of the role and importance of telemedicine in the initial wave of the COVID-19 pandemic was the original work carried out by Fatyga et al. [ 11]. This study was related to elderly patients of a Silesian diabetes clinic. It involved 86 patients, aged ≥60 years, whose leading disease was type 2 diabetes. The study did not include patients with microvascular complications of diabetes, those who had suffered a stroke, were struggling with depression or other mental disorders, or were consuming excessive amounts of alcoholic beverages. The results obtained by the authors show that, for the most part, a significant number of patients—despite complying with all restrictions related to the sanitary-epidemiological regime, i.e., taking preventive behaviors—declared frequent or constant feelings of fear of contracting coronavirus disease. Consequently, alternatives such as the use of telemedicine were far more favorable to them due to the lack of real contact with other people, thereby offsetting the risk of potential illness due to COVID-19. The conclusions of the survey demonstrate the validity of the use of telemedicine, although it is worth considering measures to improve it. In addition, it seems non-negligible to conduct further scientific research, including clinical research, focusing on the issue of telephone and electronic medicine from the point of view of patients, which will allow more accurate interpretations regarding the adequate management of medical personnel in this area, as well as strengthening behavioral health strategies among the elderly population.
Patient satisfaction with the use of telemedicine can also vary depending on the availability of both face-to-face visits and teleconsultation [8]. In a study conducted on the satisfaction and importance of teleconsultation during the coronavirus pandemic among patients with rheumatoid arthritis, $62.3\%$ said the quality of teleconsultation was not as satisfactory when compared with in-person consultations [12]. In contrast, in another study on determining patients’ satisfaction with the quality of teleconsultation. Patients in the surveyed PCPs rated communication with the doctor and comprehensiveness of medical care the highest. The treatment used helped $47.5\%$ of patients improve their health [13]. Additionally, studies have been conducted on the use of telemedicine among asthma patients. However, the disadvantages brought to the fore regarding teleconsultation were the limited ability to perform tests, or the lack of personal contact between doctor and patient [14,15]. From a subsequent study conducted among 14,000 respondents on the satisfaction of patients using teleconsultation with their PCP during the pandemic, more than $40\%$ of respondents were satisfied with the teleconsultation provided and said that the quality of services provided in this way was comparable to the advice given in an inpatient manner. In contrast, $36.3\%$ of people rated the quality of an in-person visit to a PCP higher than a teleconsultation [16]. Thanks to telemedicine, people in high-risk groups, for example those with cardiovascular disease, diabetes, or Parkinson’s disease, were able to effectively monitor their health status during the pandemic, while maintaining constant contact with medical personnel [17].
The study also found that doctors and nurses showed lower satisfaction with teleconsultation than patients. Above all, medical personnel were concerned about emergencies that could occur due to the patient’s limited visualization during a telephone consultation. Telephone consultations tended to convey less information than video consultations; however, despite this, teleconsultation was preferred over video visits by both providers and patients, especially those who were less technologically advanced [8].
The nature of telemedicine may limit a provider’s ability to obtain a comprehensive physical examination, which is fundamental to a physician’s diagnostic arsenal. Of course, telemedicine does not apply to every scope, such as invasive procedures, dental procedures, or critically ill patients requiring in-person visits [8]. Lack of easy access to PCPs and specialized treatment has also been associated with widespread and higher levels of perceived anxiety among patients [18]. Inadequate access to reliable information has also fostered anti-vaccine movements [19].
In an era of efforts to curb the epidemic, it is essential to safeguard the health needs of both COVID-19-infected patients and other patients. It is also important that people who identify worrisome symptoms in themselves that may indicate the development of a condition should not give up on early diagnosis [20,21]. It should also be noted that the earlier a patient is diagnosed, the greater the chances of a faster recovery, which serves to minimize treatment costs burdening the healthcare system. Therefore, it is recommended that health promotion and disease prevention activities be increased, as well as broader health education for the public for both citizens as a whole and for patients suffering from various diseases [22]. Undoubtedly, the e-health solutions implemented so far, such as e-prescription, e-referral, teleconsultation, or video consultation with a doctor, have made it possible to secure the basic needs of patients to a large extent; nevertheless, it is necessary to improve them further as doing so will make the healthcare system more resilient to emergencies (including further epidemics) in the future [23,24]. Nevertheless, when implementing such solutions, intensified information and education campaigns should also be carried out, especially those that emphasize the development of digital competencies among senior citizens burdened with multiple diseases. The elderly, for example, have repeatedly reported difficulties in using the Internet Patient Account. In the future, it should also be pointed out that, among other things, hospitals should have procedures in place to take appropriate and proportionate action, particularly about restricting the exercise of patient rights [25,26]. This restriction should not be tantamount to a ban, leading to the deprivation of patients’ rights, and should not prevent the realization of the rights of persons authorized by the patient, or relatives [27]. There is an urgent need to further standardize the provision of health services using solutions that allow remote communication [28]. Telemedicine or video consultations should not completely replace in-person highly specialized medical consultations, they should be a form of support for the patient’s treatment process in emergencies, such as in the case of the next wave of COVID-19 or the emergence of a new pandemic. However, the development of telemedicine during the pandemic was undoubtedly necessary and essential but still needs to be refined [20,22]. During the pandemic, telemedicine was an alternative method of diagnosing, treating, monitoring, and distantly supporting patients who did not require face-to-face contact with medical personnel [27,29,30]. The study conducted by the authors of this paper indicates that patients’ attitudes toward the use of telemedicine services during the COVID-19 pandemic varied. Younger people rate the quality and accessibility of teleconsultation services well, in contrast to those over 60.
## Strengths and Limitations
The study is not free of limitations. The first limitation of the conducted survey is the scope of the research sample, which includes only one specialist outpatient clinic provider from one country. However, this sample was sufficient to test and validate the research tool—a questionnaire to assess patient satisfaction with the quality of remote medical care. In addition, despite the pandemic, the survey was conducted using a face-to-face survey method, which helped reduce researcher error and the risk of “bot/fake responders”, as is the case with similar surveys conducted using the computer-assisted web interview (CAWI) method. A survey of a larger number of respondents from across the country is planned for the follow-up survey stage, which will be conducted to finalize and update the results. The second limitation is that the very evaluation of the quality of remote advice came only from the point of view of patients, who are not qualified to substantively assess the effectiveness and selection of appropriate treatment methods. The indicated research limitation provides an interesting direction for further research that could address the evaluation of the quality of the treatment by qualified medical personnel or healthcare coordinators.
## 5. Conclusions
Patients’ approach to the use of teleconsultation services during the COVID-19 pandemic varies, primarily due to attitudes toward the new situation, the age of the patient, or the need to adapt to specific solutions not always understood by the public. The availability of medical services during the COVID-19 pandemic is rated significantly lower by the elderly (over 60) and the group of pensioners/retirees. There is no gender variation in respondents’ opinions.
Telemedicine cannot completely replace inpatient services, especially among the elderly. It is necessary to refine remote visits to convince the public of this type of service. Remote visits should be refined and adapted to the needs of patients in such a way as to remove any barriers and problems arising from this type of service. This system should also be introduced as a target, providing an alternative method of inpatient services even after the pandemic ends.
## Figures, Scheme and Table
**Scheme 1:** *Location of the research conducted.* **Figure 1:** *Evaluation of medical services during the COVID-19 pandemic compared with gender.* **Figure 2:** *Evaluation of medical services during the COVID-19 pandemic compared with age.* **Figure 3:** *Evaluation of medical services during the COVID-19 pandemic compared with professional status.* **Figure 4:** *Assessment of teleconsultation during the COVID-19 pandemic compared with age and gender.* TABLE_PLACEHOLDER:Table 1
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|
---
title: 'Nutritional Content of Popular Menu Items from Online Food Delivery Applications
in Bangkok, Thailand: Are They Healthy?'
authors:
- Nongnuch Jindarattanaporn
- Inthira Suya
- Lara Lorenzetti
- Surasak Kantachuvesiri
- Thaksaphon Thamarangsi
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002036
doi: 10.3390/ijerph20053992
license: CC BY 4.0
---
# Nutritional Content of Popular Menu Items from Online Food Delivery Applications in Bangkok, Thailand: Are They Healthy?
## Abstract
The rise in online food delivery (OFD) applications has increased access to a myriad of ready-to-eat options, which may lead to unhealthier food choices. Our objective was to assess the nutritional profile of popular menu items available through OFD applications in Bangkok, Thailand. We selected the top 40 popular menu items from three of the most commonly used OFD applications in 2021. Each menu item was collected from the top 15 restaurants in Bangkok for a total of 600 items. Nutritional contents were analysed by a professional food laboratory in Bangkok. Descriptive statistics were employed to describe the nutritional content of each menu item, including energy, fat, sodium, and sugar content. We also compared nutritional content to the World Health Organization’s recommended daily intake values. The majority of menu items were considered unhealthy, with 23 of the 25 ready-to-eat menu items containing more than the recommended sodium intake for adults. Eighty percent of all sweets contained approximately 1.5 times more sugar than the daily recommendation. Displaying nutrition facts in the OFD applications for menu items and providing consumers with filters for healthier options are required to reduce overconsumption and improve consumer food choice.
## 1. Introduction
Each year, noncommunicable diseases (NCDs) are responsible for 41 million deaths globally [1]. In Thailand, NCDs cause $75\%$ of all deaths, with cardiovascular diseases (CVDs) accounting for the highest proportion [2]. Dietary factors, including increased intake of salt, fats, and sugars, are the biggest contributor to CVD risk [3]. Transnational food and beverage corporation practices have reshaped the dietary landscape through a combination of food availability, pricing, and social and cultural desirability [4], all of which have made unhealthy foods more readily accessible. In particular, food and beverage businesses have expanded their service channels to online food delivery (OFD) applications in order to provide convenience for consumers, a strategy which has also led to increased product sales [5]. The proliferation of OFD applications has provided a broader portion of the Thai population with direct access to non-traditional and ready-to-eat foods, which have the potential to disrupt good health and well-being [6].
OFD applications are currently considered a significant predictor of food choice [7] and eating [8] among the general population. The Thai food delivery market has grown rapidly, expanding from 61,000 million baht in 2019 to 68,000 million baht during the COVID-19 pandemic in 2020 [9], and further still to 105,000 million baht in 2021 [10]. The percentage of foods ordered from OFD applications increased from $3.9\%$ to $10.7\%$ between 2019 and 2020 [11]. In 2020, $85\%$ of Thai people utilised OFD applications, with $61\%$ of that group ordering fast food, such as fried chicken, burgers, and pizzas [12]. Use of OFD applications has grown more popular than restaurant dining or takeout among Thai people due to the convenience of searching for food items and finding new restaurants through these applications [13]. Food delivery trends in Thailand are similar to those found in other countries. Evidence from Australia, New Zealand, Canada, the United States, and the Netherlands have shown that most menu items, including the most popular items on OFD applications, were unhealthy [14,15,16,17] because of their high levels of salt, sugar, and/or saturated fats [14,15,16].
Raising public awareness about dietary guidelines and package labelling are some of the most common strategies utilised to educate the public about healthy diets [18]. Thailand established government policies to tackle unhealthy diets, specifically for packaged foods. Interventions such as the Guideline Daily Amounts (GDAs) label and “Healthier Choice” nutritional logos on selected packaged food products have been used to raise awareness about healthy eating [19,20]. However, these interventions only apply to packaged foods and do not encompass OFD applications. Although a previous study has assessed the nutrition information displayed on ready-to-eat packaged foods and the nutritional quality of those food products in Thailand [21], no data currently exists on the nutritional content of foods offered through OFD applications in Thailand. This study aims to address this gap by exploring the nutritional profile of popular menu items (food and non-alcoholic beverages) available through OFD applications. The goal is to raise public awareness about the nutritional content of foods delivered through these services and inform ongoing policy development and implementation for tackling unhealthy diets and NCDs in Thailand.
## 2. Materials and Methods
We conducted a cross-sectional, exploratory study to describe the nutritional contents of the most popular food and drink items available on OFD applications in Bangkok, Thailand. We summarised the nutritional contents from the 40 most popular menu items based on energy, total fat, sodium, and total sugar, and compared them against recommended daily intake values. This study received approval from FHI 360’s Office of International Research Ethics (report number 1892564-2).
## 2.1.1. Selection of Online Food Delivery Applications
Three OFD applications (Grab, Lineman, and Robinhood) were purposively selected due to their high cumulative utilisation rate among all OFD platforms; approximately $89\%$ of Thai people used these applications when ordering their food and drinks through an OFD application [12]. Furthermore, they have consistently maintained their positions as leaders in Thailand’s OFD market [22]; Grab had the highest market share ($50\%$), followed by Lineman ($20\%$), and Robinhood ($7\%$) [23].
## 2.1.2. Identification of Popular Menu Items
Data on the most popular menu items from Grab, Lineman, and Robinhood were compiled and saved in Microsoft Excel between May and June 2021 [24,25]. Each application had its own list of most popular items, with each list differing slightly due to varying consumer preferences. All popular menu items across the three applications were selected for a total of 80 menu items. Next, 20 menu items were removed due to duplication. The remaining 60 menu items consisted of 20 items from Grab ($33\%$ of total menu items), 21 from Lineman ($35\%$), and 19 from Robinhood ($32\%$). However, given budget constraints for food nutrition analysis, the target population was reduced to 40 menu items. These items were selected based on their popularity ranking in each OFD application while maintaining the same proportion of items from the original sample size. Therefore, the target population comprised the top 13 items from Grab, the top 14 from Lineman, and the top 13 from Robinhood (Figure 1).
## 2.2. Data Collection
After identifying the most popular items across the three applications, Grab was ultimately used as the sole OFD application to order these items for data collection as it is the most popular OFD application in Bangkok [25,26,27,28]. The final 40 menu items were categorised into three different types: 25 ready-to-eat items, 5 sweets, and 10 non-alcoholic beverages. Research team ordered each menu item from each restaurant from the top 15 restaurants in Grab as nominated by consumers. For consistency, the order time was set between 8.00 a.m. and 12.00 p.m. during standard restaurant operating hours. The research team selected one standard portion of each menu as the default. Since restaurants have varying portion sizes, the research team recorded the weight of each sample to calculate a portion size average for each item and ensure more accurate results. All items were ordered within a one-month period (4 January to 1 February 2022). Delivery drivers for OFD applications delivered menu items to a laboratory, and each menu item was tested for nutritional content the day it was received (minimum of 500 g of sample needed).
## 2.3. Data Analysis
Each menu item’s nutritional contents were evaluated in terms of energy, total fat, sodium, and total sugar, as overconsumption of these nutritional contents is one of the risk factors associated with NCDs [29,30,31]. We opted to evaluate total fat instead of saturated fats due to budget and time constraints. Nutritional analysis for the items was conducted by Central Laboratory Co., Ltd., Bangkok, Thailand [32], with nutritional contents classified using chemical analysis [33]. The research team summarised the average, minimum, and maximum values of each item’s nutritional profile. SPSS version 26 was used for analysing the variation of nutritional content among the menu items.
The outcomes of this study were compared with national and international standards, as listed in Table 1. Since recommendations for total fat and total sugar intake are calculated on a daily basis, the research team calculated the recommended intake per portion for total fat and total sugar. This entailed dividing the daily recommended intake by three based on the assumption that one portion is equivalent to one meal and there are three meals in a day. Menu items with contents higher than the recommended criteria were categorised as “unhealthy menu items”.
## 3.1. Nutritional Composition per 100 Grams
Overall, 40 menu items from 15 restaurants in Bangkok were classified into three food types: 25 ready-to-eat items, 5 sweets, and 10 non-alcoholic beverages.
Table 2 shows the nutritional content per 100 g for each of the 40 items. Overall, fried streaky pork and grilled pork neck were extremely high in energy and total fat per 100 g compared to other ready-to-eat items. Fried streaky pork had the highest energy and total fat (mean energy = 440.5 per 100 g; mean total fat = 36.3 per 100 g), followed by grilled pork neck (374.5 g and 30.8 g, respectively). In terms of sodium content, spicy papaya salad with northeastern style fermented crab and fish was especially high in sodium (1.6 g per 100 g)—nearly equivalent to the daily recommended maximum sodium threshold of 2 g. Grilled pork balls (0.8 g per 100 g) and grilled pork (0.8 g per 100 g) were also high in sodium. In terms of sugar, pandan and coconut chiffon cake ranked highest in total sugar (23 g per 100 g), followed by iced honey lemon tea (19.7 g per 100 g), and iced cocoa (16.2 g per 100 g).
## 3.2.1. Energy
Figure 2 illustrates the energy content of all 40 menu items. Fried streaky pork contained the highest average energy content per portion (814.9 kcal), followed by grilled pork (811.5 kcal), and rice with stir-fried minced pork, chili, and basil (734.4 kcal). Among sweets, pandan and coconut chiffon cake was the highest in average energy (1098.8 kcal), followed by egg tart (678.5 kcal). For non-alcoholic beverages, bubble milk tea was the highest in average energy (417.9 kcal), followed by iced green tea Frappuccino (382.2 kcal) and iced coffee (336.5 kcal).
The WHO’s recommended average daily energy requirement for adults is 2100 kcal per person per day, i.e., not more than $30\%$ of recommended daily total energy [34]. Fried streaky pork contained an average of 814.9 kcal per portion (around $39\%$ of total daily intake), and the pandan and coconut chiffon cake (307 g) provided an average of 1099 kcal per portion (around $50\%$ of total daily intake). Bubble milk tea had an average 418 kcal per portion (around $20\%$ of total daily intake).
Additionally, among all menu items, 7 were categorised as “unhealthy” in terms of energy content (five ready-to-eat foods and two sweets). The most “unhealthy” item was fried streaky pork followed: grilled pork; grilled pork neck; grilled pork balls; papaya salad, spicy, with dried shrimp and roasted peanuts; papaya salad, spicy, with fermented crab and fermented fish northeastern style; pandan and coconut chiffon cake; and egg tart. Notably, none of the non-alcoholic beverages fell into the “unhealthy” category.
## 3.2.2. Total Fat
Six ready-to-eat items and three sweets contained higher fat than the WHO’s recommendation, but none of the non-alcoholic beverages were above the recommended threshold. The average total fat content per portion was highest for fried streaky pork (67.1 g), followed by grilled pork (55.6 g) and grilled pork neck (46.8 g). For sweets, the average total fat content per portion was greatest for pandan and coconut chiffon cake (65.1 g), followed by egg tart (45 g) and coconut milk ice cream (30.9 g) (Figure 3). Although these menu items consist of just one meal, they already contain nearly all of the WHO’s recommended total daily fat intake [34,35].
## 3.2.3. Total Sodium
Overall, 8 out of 25 ready-to-eat items were very high in sodium (as measured by the daily sodium intake threshold of 2 g) and 23 of 25 ready-to-eat “unhealthy” menu items contained more than the recommended sodium intake for adults of 0.6 g per meal (Figure 4). For reference, the WHO suggests that a person should consume less than 5 g of salt (approximately 2 g sodium) per day [36].
Mean sodium levels were much higher when reported per portion rather than per 100 g. The average total sodium content per portion was greatest for spicy papaya salad with fermented northeastern style crab and fish, Chinese pork bun, and iced coffee. One portion of spicy papaya salad with fermented northeastern style crab and fish (313 g) contained 5 g of sodium, and the average portion for Chinese pork bun contained 0.8 g of sodium. High sodium was not only found in ready-to-eat items but also in non-alcoholic beverages. Iced coffee was found to have the highest amount of sodium per portion among non-alcoholic beverages at 0.3 g.
## 3.2.4. Total Sugar
Eight non-alcoholic beverages were considered unhealthy (more than 25 g of sugar per portion) (Figure 5). The WHO recommends that adults and children reduce their daily intake of free sugars from less than $10\%$ of their total energy intake to less than $5\%$, or roughly 25 g (6 teaspoons) per day [35,37,38,40]. All non-alcoholic beverages, except for soy milk and iced Americano, contained an average of 33.9 g of sugar per portion, and all sweets except for egg tart and deep-fried Chinese dough contained an average of 31.5 g of sugar per portion; this is almost 1.5 times higher than the daily recommendation.
Notably, the average sugar content per menu item may not be indicative of whether a certain item is “healthy” or “unhealthy” in the Thai context when compared to the WHO’s recommendation. Although the average sugar content of an item may show that it is “healthy”, this is also based on the average portion size and the standardisation of ingredients. Thus, if a certain item’s portion size happens to be much larger than the average, or if a certain restaurant’s recipe uses more sugar than normal, it is possible that the item may be categorised as “unhealthy”. For example, the average sugar content of rice with salmon was 7.7 g, which is considered “healthy”. However, the sugar content range for this menu item was 0–21.6 g, with the maximum value close to the WHO recommended daily sugar intake (25 g).
## 4. Discussion
Most of the menu items were considered unhealthy, with higher levels of energy, total fat, sodium, and total sugar compared to the recommended daily intake. The findings of this study correspond to similar studies in China and Canada where the nutritional quality of OFD foods was generally low [41] and did not meet healthy eating recommendations [16]. The nutritional information generated from analysing the 40 menu items can serve as a launching point for both practical actions in the form of regulating information provided through OFD applications and raising consumer awareness about nutritional contents. The large variations in total fat, sodium, and sugar content observed when comparing menu items per portion and per 100 g indicate that opportunities exist for improvement. This can be achieved by standardizing portion size or showing nutritional facts for menu items through the OFD applications, particularly for sodium, sugar, and fat. These approaches may reduce the overconsumption of unfavourable nutrients, and are strategies advocated for addressing NCDs [42].
## 4.1. Energy Content
When analysing these menu items, a portion of fried streaky pork delivered via OFD applications contained $39\%$ of the WHO’s recommended daily energy intake for adults, and $37\%$ and $46\%$ of the recommended daily energy intake for Thai men and women aged 19 to 50 years, respectively, based on the Department of Health (DOH), Ministry of Public Health (MoPH). This does not account for any additional accompaniments, such as rice (one ladle) that can add approximately 80 extra calories [38]. Furthermore, the DOH recommends aiming for approximately 400–600 calories for a main meal [38]. Many menu items, including drinks, contain nutrients that are higher than the DOH recommendations for daily caloric intake. Restaurants should consider improving the overall nutritional profile of these items by reformulating the recipe or cooking method, or by reducing portion size.
## 4.2. Total Fat Content
Six ready-to-eat items and three sweets had higher fat content than the WHO’s and DOH’s recommended total fat intake, which is 20–$35\%$ (44–78 g) of total energy intake for Thai adults [35]. This is particularly problematic since desserts are likely to be consumed alongside a main meal, meaning consumers are consuming more fat than recommended in a single meal. Restaurants should consider substituting ingredients with lower fat alternatives. For example, since one of the main ingredients in pandan and coconut chiffon cake is high fat oil, bakery shops should consider substituting this with reduced fat oils.
## 4.3. Sodium Content
Sodium content was particularly high among the menu items assessed, which did not include any condiments that are often added to meals. WHO evidence revealed that Thais consume an average of 10.8 g of salt per day or 4.2 g of sodium in their current lifestyle, which was more than double the recommended daily amount of salt in 2015 [43]. A cross-sectional population-based survey conducted in Thailand in 2021 revealed that average sodium consumption among Thai adults was 3.6 g per day [44]. Our study supports this finding since many popular menu items in our analysis were also found to be high in sodium, and recipes with alternatives to sodium, such as low sodium condiments, were not popular due to higher prices. Thailand has set an ambitious goal of reducing the population intake of salt/sodium by $30\%$ [45]; this is in line with the WHO’s global voluntary targets for a $30\%$ relative reduction in mean population intake of salt/sodium by 2025 (relative to 2010 levels) [46]. Based on the WHO’s and DOH’s recommendations for daily and per meal sodium intake, restaurants should reduce sodium content by reformulating their recipes and providing nutritional information through OFD applications to enhance consumer awareness and transparency.
## 4.4. Sugar Content
All non-alcoholic beverages (except for soy milk and iced Americano) and all sweets (except for egg tart and deep-fried Chinese dough) contained average sugar content higher than the daily recommendation. A new WHO guideline recommends that ‘free’ sugars make up no more than $10\%$ of daily kilojoule intake [37]. Notably, total sugar refers to the total amount of sugar from all sources (free sugars plus those from milk and those present in the structure of foods such as fruit and vegetables). Our nutritional analysis does not distinguish between naturally occurring sugars and free sugars. However, it is likely that the sugar content of the various papaya salads, pandan and coconut chiffon cakes, and iced honey lemon teas exceeded the WHO’s and DOH’s daily sugar recommendation for adults [35,37,38,40].
## 4.5. Policy Implications
Revising the Thai national policy could be another method for tackling sugar consumption. An updated excise tax has been applied to sugary drinks since 16 September 2017 [47]. The levy on sugary drinks is capped at $20\%$, with beverages containing more sugar carrying a larger tax burden than less sweet beverages [48]. However, this policy focuses on sweetened beverages in the form of packaged foods sold at retailers or supermarkets. The results of our study found that almost all sweets and non-alcoholic beverages are not categorised as packaged food since foods sold at restaurants are not required to be labelled. Despite their lack of inclusion in the policy, there is scope for restaurants to revise their recipes to reduce sugar content while concurrently displaying nutritional facts on OFD applications to help consumers make informed food choices that contribute to a healthy diet.
In addition to the policy strategies suggested above to reduce consumer intake of foods high in fats, sodium, and sugars, relevant public entities could collaborate or partner with OFD application developers to provide healthier food options. This can be accomplished in several ways. First, a voluntary upper limit could be set for sugar, fat, and sodium in the OFD applications or in restaurant menu details to indicate that the item is a “healthier” option. If a menu item is under the threshold, it can be indicated as “healthier”. Second, OFD application developers could design settings to allow consumers to filter options when they order. For example, they can choose to filter foods or restaurants by “less salt”, “less sweet”, and “less fat”. Third, public entities and developers could work together with restaurants to set and implement standardised portions for menu items available through the application. Finally, a logo can be designed for use on OFD applications to inform consumers that the food is healthy.
Restaurants should know whether the foods they are selling are unhealthy or not. The Bureau of Nutrition, MoPH has produced the Thai Nutri Survey Program (TNS) and the relevant manuals to address this issue. Therefore, social marketing should be used to promote this program among restaurants or public to raise awareness and provide the tools to analyse and monitor the nutritional content of their menu items. Consequently, restaurants will know how healthy their menu items are. This nutrition content should also be shown on the application to provide information for consumers. This will enable them to make informed food choices when ordering.
## 4.6. Strength and Limitations
To the best of our knowledge, this study is the first to investigate the nutritional content of popular menu items from OFD applications in Thailand. It analysed nutritional content with assistance from a professional laboratory, thus providing objective data results and helping to reduce the knowledge gap related to nutritional information for some of Thailand’s most popular foods and drinks. However, the study also has several limitations. First, this study only considered 15 restaurants in Bangkok that were ranked based on popularity by Grab and did not consider popular menu items from other applications or locations. In addition, popular menu items obtained through the OFD applications are only valid within the study period and may only be relevant to Bangkok. Therefore, these findings may not be relevant to popular menu items outside the study duration if recipes are changed, or to other parts of the country. However, because this study had wide-ranging results, adding more restaurants is unlikely to significantly affect the results. Second, budget constraints prohibited the addition of condiments into the analysis. Future studies should include condiments to provide more accurate results that represent a complete meal and improve understanding of typical consumption patterns. In addition, no data exists that compares home-cooked foods with OFD foods; it would be helpful to investigate whether the same menu items made at home are healthier.
Finally, the WHO’s most recent guidelines for daily energy, fat, salt, and sugar intake were used to evaluate salt and sugar levels in popular menu items. Although these guidelines are based on scientific evidence [46], limitations exist. The guidelines do not classify gender and age so average values may not be fully applicable in the Thai context due to differences in physiology between Thais and people of other races/ethnicities. Moreover, most menu items in this study did not meet international standards for energy and fat per meal. Further exploration is required to obtain a more accurate standard to assess the healthiness of foods.
## 5. Conclusions
OFD platforms are becoming popular, with an increasing number of orders for ready-to-eat foods, sweets, and non-alcoholic beverages. However, we found that most single items purchased through OFD applications in Bangkok contained levels of energy, total fat, sodium, and total sugars that were close to or exceeded recommended daily intakes. This creates additional challenges for public health nutrition policymakers, though OFD platforms may also provide an opportunity to improve public health nutrition and diet-related health outcomes using certain policy levers. It will be important for relevant entities under the MoPH—NCD Division and DOH—to collaborate with OFD application developers to use their influence and promote healthy food consumption. Such a public-private partnership may help increase the availability of healthy choices while also nudging consumers towards these options. Going forward, the nutritional contents of popular menu items should be randomly assessed. Condiments and other menu items from OFD applications not included in this assessment, as well as items from restaurants in other Thai provinces, should be included in future studies to increase the comprehensiveness of nutritional content measurement and analyses in Thailand.
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|
---
title: 'Caring for a Child with Congenital Adrenal Hyperplasia Diagnosed by Newborn
Screening: Parental Health-Related Quality of Life, Coping Patterns, and Needs'
authors:
- Laura Rautmann
- Stefanie Witt
- Christoph Theiding
- Birgit Odenwald
- Uta Nennstiel-Ratzel
- Helmuth-Günther Dörr
- Julia Hannah Quitmann
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002056
doi: 10.3390/ijerph20054493
license: CC BY 4.0
---
# Caring for a Child with Congenital Adrenal Hyperplasia Diagnosed by Newborn Screening: Parental Health-Related Quality of Life, Coping Patterns, and Needs
## Abstract
Diagnosing a child by newborn screening with classic congenital adrenal hyperplasia due to 21-hydroxylase deficiency (CAH) causes multiple challenges for the affected parents and the whole family. We aimed to examine the health-related Quality of Life (HrQoL), coping, and needs of parents caring for a child with CAH to develop demand-responsive interventions for improving the psychosocial situation of affected families. In a retrospective cross-sectional design, we assessed HrQoL, coping patterns, and the needs of parents caring for a CAH-diagnosed child using specific questionnaires. Data of 59 families with at least one child diagnosed with CAH were analyzed. The results show that mothers and fathers in this study reached significantly higher HrQoL scores compared to reference cohorts. Decisive for the above-average parental HrQoL were effective coping behaviors and the parental needs being met. These findings verify the importance of helpful coping patterns and rapid fulfillment of parental needs for maintaining a good and stable HrQoL of parents with a child diagnosed with CAH. It is crucial to strengthen the parental HrQoL to build a reasonable basis for a healthy upbringing and improve the medical care of CAH-diagnosed children.
## 1. Introduction
The introduction of expanded newborn screening in Germany in 2005, including rare congenital inborn errors of metabolism such as congenital adrenal hyperplasia (CAH), allows for diagnoses in newborns before the onset of symptoms. Therefore, early initiation of therapy can prevent newborns from severe consequences [1,2,3].
CAH is a rare disease with an incidence of 1:12,000 [3]. It is characterized by prenatal masculinization of the external genitalia in females. CAH may occur with or without a salt-wasting crisis. A salt-wasting crisis can lead to failure in thriving but also to shock or even coma. Children with CAH show an increased production of male hormones. When untreated, children will be short-statured. In adulthood, CAH patients often show obesity, metabolic changes, and infertility [4]. To prevent grave consequences such as potentially fatal salt-wasting crises or the above-mentioned long-term effects, it is crucial to detect CAH early in newborns. With an early diagnosis, it becomes possible to start the substitution of hormones rapidly and ensure a healthy growing up of the child. The newborn screening constitutes an efficient procedure to systematically screen for rare diseases with easy implementation by applying blood on filter paper.
CAH is characterized by an increased amount of 17-hydroxyprogesterone, which is crucial for the detection within the screening process. Approximately $94\%$ of all CAH cases are allocated to classic CAH [5,6,7,8]. Classic CAH due to 21-hydroxylase deficiency (CAH) is an autosomal recessive disorder of cortisol biosynthesis caused by mutations in the active 21-hydroxylase gene (CYP21A2). In approximately two-thirds of patients with a 21-hydroxylase defect, there is a complete loss of function of the enzyme. This defect can lead to a life-threatening salt-wasting crisis within the first two to three weeks of life, which can recur in stressful situations without appropriate treatment. The clinical manifestations mainly include genital ambiguity in females due to androgen excess and life-threatening adrenal crises in both sexes due to impaired cortisol and aldosterone production [8]. In female children, external virilization of the genitals may occur, usually surgically corrected within the first two years of life [9]. In addition, despite treatment, patients usually achieve only below-average height and premature onset of puberty. The signs of hyperandrogenism may develop in girls, i.e., hirsutism, oligomenorrhea, and prolonging the menstrual cycle to more than 35 days [10].
Additionally, CAH is associated with many long-term implications relating to hormonal replacement therapy, clinical and biochemical monitoring, surgical interventions, fertility issues, and quality of life [5,6,7,8]. With a prevalence of approximately 1:14,000, CAH is a rare health condition [2].
Diagnosing a child with a chronic disease such as CAH causes multiple challenges for the parents and constitutes a profound change in parental thinking. The parents must understand and accept the disease and realize that the disease is lifelong. Many studies verify an increasing psychosocial burden on the entire family after a newborn is diagnosed with a chronic disease [11,12,13,14].
We know less about the psychosocial situation of parents of children with CAH, particularly those diagnosed by newborn screening. A few studies indicate an increasing strain on these parents, especially shortly after the initial diagnosis [6,15,16,17,18,19]. The initial shock about the diagnosis can lead to anxiety, grief, helplessness, feeling overwhelmed, and depressive symptoms [20].
Immediate and appropriate education by medical professionals about the diagnosis is essential in alleviating parents’ psychosocial distress. It is crucial that parents comprehensively understand the information [20,21,22]. Therefore, professionals must be trained in communication skills. In this conversation between parents and medical professionals, individual needs and gender-specific aspects must be considered. Fathers often need more medical information about their children’s health condition than mothers to gain sufficient understanding [23]. Parents associate uncertainty about the child’s health status with negative emotions. These negative emotions can negatively affect the parental relationship, especially in the case of genetically inherited conditions, affecting family dynamics [21]. As a result, there is a risk of disrupting the early attachment process to the child, which is of great importance for further child development. Accepting the diagnosis and adjusting expectations for one’s child is essential for developmentally supportive parenting behaviors [24]. Coping with the diagnosis depends on the family and social structures. However, it can be positively reinforced by specialized counseling, support services, and contact with other parents of CAH children [25,26].
Although there is evidence of increased psychosocial distress among affected parents, there is often a lack of professional support and psychosocial support services [27]. Due to the rarity and complexity of the diseases detected in newborn screening, there are usually problems finding specialized medical professionals to treat and care for the child [28].
In addition to the psychosocial burden, health-related quality of life (HrQoL) is an essential outcome of successful treatment. HrQoL describes a person’s subjective psychological, physical, mental, and social health-related well-being and functioning ability [29].
Studies examining the HrQoL of parents with chronically ill children reported a reduced HrQoL in parents caring for a chronically ill child due to an increased burden and additional responsibility [30]. Parents of children with chronic health conditions reported an increased time requirement, resulting in putting aside parental interests and social needs [27]. Due to the time-intensive necessary care of a chronically ill child, parents often reduce paid work, which may result in financial problems. In particular, mothers of chronically ill children often only work part-time or give up their employment as they are the primary caregivers [31]. Other factors include emotional distress, lack of support in housekeeping, or marital problems [30].
Only a few studies focus on HrQoL and the psychosocial consequences for the parents of children with CAH. Waldthausen [19] reported an average to above-average HrQoL of parents of CAH children compared to parents of healthy children. However, the parental burden seemed to depend on the children’s age and was highest in the postnatal period. Parents experienced feelings of overwhelming shock when receiving the child’s diagnosis. Due to insufficient information that is understandable for non-medical people, parents reported additional frustration and uncertainty [16]. Inadequate parent education includes missing information about the condition and the treatment (e.g., medication administration), coping strategies, and support in talking about the health condition with the affected child, siblings, family, and friends [32]. Parents of CAH children reported an increased need for psychosocial support; however, this support was often unavailable [33].
Nevertheless, parents seem to cope with their child’s chronic health condition over time and worry less about the child. $97\%$ of parents of children with CAH aged 4 to 12 years reported being satisfied with their child’s health, and $76\%$ had no fear of a salt-wasting crisis [34].
However, it is crucial to understand the needs of affected families to improve the psychosocial well-being of parents caring for a child with CAH. Gathering the dimensions of the HrQoL, coping patterns, and the specific needs of affected parents allows clinicians and researchers to gain insight into these families and thus provide adequate support. Therefore, our study aims to examine the HrQoL, coping patterns, and special needs of parents caring for a child with CAH.
## 2. Materials and Methods
This retrospective cross-sectional study examined the HrQoL, coping patterns, and needs of parents caring for a child with CAH diagnosed by newborn screening in Bavaria, Germany. It was a cooperation project of the Department of Medical Psychology of the University Medical Center Hamburg-Eppendorf (UKE), the Bavarian State Office for Health and Food Safety, and the University Children’s Hospital in Erlangen. In a mixed-method approach, quantitative and qualitative data were analyzed using validated questionnaires and semi-structured telephone interviews. The presented analysis focuses on the quantitative data, aiming to gain knowledge about HrQoL, coping patterns, and the needs of parents of children born with CAH. The Bavarian Medical Association Ethics Committee approved the study in September 2018 (No. 18003).
## 2.1. Participants
The Bavarian State Office for Health and Food Safety registry identified the families and asked them to participate in this study. Inclusion criteria were: [1] parents of children with classic CAH (21-OH deficiency), [2] children aged up to 18 years, and [3] CAH diagnosis by newborn screening in Bavaria [35,36]. The newborn screening is regularly performed 36 to 72 h after the child’s birth. If screening results are positive, parents were informed immediately for further examinations of their newborn.
Parents were excluded because of [1] missing informed consent, [2] insufficient knowledge of the German language, or [3] other diseases than CAH being the center of attention of the family.
## 2.2. Data Collection
The Bavarian State Office for Health and Food Safety sent questionnaires to eligible families via the postal service, including an informative letter about this study, a declaration of consent, and a pre-paid return envelope between September 2018 and September 2019. Parents reported sociodemographics, the child’s health condition, and circumstances regarding the diagnosis process. Based on self-reports, parents provided information about their HrQoL within the last week, coping strategies for dealing with the situation due to their child’s disease, and their current particular needs.
Parental HrQoL was measured using the chronic–generic Ulm Quality of Life Inventory for Parents (ULQIE) [37]. The ULQIE consists of 29 items divided into 5 subscales. The subscales include the dimensions [1] physical and daily functioning, [2] satisfaction with the situation in the family, [3] emotional distress, [4] self-development, and [5] well-being, as well as four single items without any scale assignment. Each item is rated on a 5-point Likert scale from 0 to 4. The scales’ internal consistency (Cronbach’s Alpha) varies between 0.74 and 0.92.
Parental coping strategies were assessed using the chronic–generic Coping Health Inventory for Parents (CHIP) questionnaire [38,39]. The CHIP consists of 45 items that examine the usefulness of different coping strategies of parents of chronically ill children. The items are assigned to three subscales: [1] maintaining family integration, cooperation, and an optimistic definition of the situation; [2] maintaining social support, self-esteem, and psychological stability; and [3] understanding the medical situation through communication with other parents and consultation with medical staff. All items are rated on a 4-point Likert scale from zero to three. Cronbach’s Alpha for the scales is between 0.79 and 0.90 [39].
Chronic–generic needs of parents were measured using the Scale of needs for parents of chronically ill children. This questionnaire contains 19 items about the parental need for information, psychosocial care, exchange with others, and support in everyday life. The items are rated on a 5-point Likert scale ranging from 1 to 5: the higher the item’s score, the more intense the parent’s need. The reliability of the Scale of needs for parents of chronically ill children shows internal consistency of 0.95 [40].
## 2.3. Data Analysis
We used the statistic software SPSS, version 25 (IBM, Armonk, NY, USA, 2017) for data analysis. Descriptive statistics were computed for each subscale of the questionnaires examining HrQoL and coping. Missing values were replaced by the individual mean score for each variable if missing data were random or less than $20.00\%$ of all scale items [41]. We compared the scores from our sample to reference values using Student’s t-tests. Multiple linear regression analyses were performed to identify variables that predict parental HrQoL. Sociodemographic and clinical variables, the CHIP total score, and the total score of the Scale of needs for parents of chronically ill children were entered as predictors into the model. The clinical data comprised the child’s age, sex, and current hormonal replacement therapy. Since no data on the pubertal status of the children were reported, we set the age of 10 years, according to Sawyer, Azzopardi [42], as the beginning of adolescence. If families live with more than one CAH-diagnosed child, we used the data for the firstborn CAH child for further analysis.
The examined sociodemographic aspects consisted of the parents’ age and sex, number of children, number of children diagnosed with CAH, place of residence, educational level, and employment status in the last 12 months. Further examined aspects were the use of psychosocial consultation and contact with other parents who have children with CAH.
For the confirmatory approach, a Bonferroni adjustment for multiple testing was conducted. Based on an initial significance level of α = 0.05, the significance level was reduced to α = 0.0033. The reduction was justified by simultaneously calculating multiple linear regression analyses and one one-sample t-test. The linear regression model investigates 14 relevant influencing factors to analyze the influence on the HrQoL measured by the ULQIE total value. With the one-sample t-test, the computed ULQIE total value was compared to a reference cohort’s total value.
Exploratory regression analyses were conducted to investigate the impact of clinical or sociodemographic aspects on the CHIP’s total score, the total score of the Scale of needs for parents of chronically ill children, and each subscale of the ULQIE. In addition, we explored whether the coping behavior or parents’ intensity of needs influenced the ULQIE-subscales.
## 3. Results
The 120 eligible families received the study information as well as the questionnaires. Of these families, 62 agreed to participate and filled out the questionnaires, resulting in a response rate of $51.67\%$. Among the 62 families with at least one child diagnosed with CAH, three were excluded due to one or more exclusion criteria, resulting in a final sample size of 59 families (Figure 1).
## 3.1. Sample Characteristics
We included 59 families in the final analysis, consisting of 100 parents (59 mothers and 41 fathers). The mean age of the parents was 43.18 ± 7.48 (SD) years, with 42.12 ± 7.50 for mothers and 44.71 ± 7.27 (SD) for fathers. In 41 families, both parents answered the questionnaires, whereas in 18 families, only the mother participated. The mean time of conducting the presumptive diagnosis of the child was 7.79 days after birth. Ten of the fifty-nine families had two affected children diagnosed with CAH, including two families giving birth to twins with CAH (Figure 1). The number of children living in the household ranged between 1 and 5, with a mean of 2.13 ± 0.91 (SD). Fifty-nine children with CAH (26 girls, 33 boys) plus ten siblings diagnosed with CAH (six girls and four boys) were identified. The mean age of the children was 11.01 ± 4.86 (SD) years (range: 0.68–18.12 years). Parents of $\frac{23}{32}$ girls reported virilization of external genitalia at birth, and genital surgery was already performed in 22 girls. All children were treated with hydrocortisone, and 56 children ($81.16\%$) received fludrocortisone. Further detailed information concerning parents’ and children’s characteristics and psychological support at the time of diagnosis are listed in Table 1, Table 2 and Table 3.
## 3.2. HrQoL of Parents
Parental HrQoL scored in the upper segment of the 0–4 scale, with the highest means for the subscale satisfaction with the situation in the family (3.38 ± 0.59) and the lowest means for the subscale self-development (2.28 ± 0.77) (Table 4). In comparison to the used reference values [37], parents of CAH children reported significantly higher HrQoL scores for physical and daily functioning (t[97] = 9.15, $p \leq 0.01$), emotional distress (t[97] = 12.31, $p \leq 0.01$), self-development (t[97] = 4.53, $p \leq 0.01$), well-being (t[98] = 6.53, $p \leq 0.01$), as well as for the ULQUIE total score (t[97] = 8.36, $p \leq 0.01$).
No statistically significant differences between the HrQoL scores of mothers and fathers were found. The same results were found for parents of one affected child with CAH compared to parents of two CAH children. However, the analyses showed differences in the ULQUIE total score between parents of younger (<10 years) and older CAH children (≥10 years), as well as between parents of CAH girls and CAH boys (Table 5). Parents of children with CAH in adolescence reached higher ULQIE total scores than parents of children younger than ten years. Further, parents of a girl with CAH seemed to have a higher HrQoL than parents of a CAH boy.
The regression analysis explained a substantial variance in parental HrQoL assessed using the ULQIE total score (F[14,44] = 1.83, $$p \leq 0.06$$, $$n = 58$$). The results showed that sociodemographic or clinical factors did not significantly affect the total score of the ULQIE. Nonetheless, the examined HrQoL of the parents depended considerably on the effectiveness of applied coping strategies and the intensity of the needs of these parents. Confirmatory regression analyses showed a significant positive correlation between the total score of the CHIP and the ULQIE total value. Additionally, the data conveyed that parents with intense needs achieved significantly lower HrQoL scores (Table 6). The coefficient of determination R2 for the linear regression model was 0.40.
Exploratory regression analyses showed a correlation between particular subscales of the ULQIE and several factors involved. The subscales daily functioning, family satisfaction, and emotional distress seemed to depend on coping strategies and needs in the same way as the total score of the ULQIE (Table 6). The HrQoL measured by the subscale family satisfaction was further influenced by the child’s age (r = −0.05, $$p \leq 0.01$$) and the use of psychosocial consultation (r = −0.49, $$p \leq 0.04$$). Parents who used psychological or social-pedagogic help reported a lower HrQoL, measured by the family satisfaction subscale. The same is true for parents with an older CAH child. Concerning the subscale self-development, regression analyses showed impacts from the parents’ educational qualification, the gender of their child, and their applied coping strategies. The higher the parental educational degree, the lower the HrQoL measured by the subscale self-development (r = −0.48, $$p \leq 0.04$$).
Additionally, the HrQoL on the self-development scale of parents of a boy decreased by about 0.45 units compared to parents of a girl ($$p \leq 0.06$$). The coping strategies influenced the parents’ self-development in the same way as the total score (Table 7). The subscale well-being resulted in high p-values. Therefore, no well-founded tendencies could be suggested. Nevertheless, we did not prove these findings in a significant manner due to the exploratory approach.
## 3.3. Coping of Parents with CAH Children
The parents’ individual coping behavior was measured using the CHIP. Mothers and fathers in this study stated that maintaining family integration, cooperation, and an optimistic definition of the situation was their most helpful coping pattern (2.31 ± 0.45). Maintaining social support, self-esteem, and psychological stability was deemed less helpful than the other subscales. With a mean total score of 2.01 (±0.47), the effectiveness of coping mechanisms of parents of children with CAH was comparable to German reference cohorts [39,44]. Mothers rated the helpfulness of particular coping patterns higher than fathers (Table 8).
The exploratory regression analyses showed high p-values ($p \leq 0.11$) on every independent variable, including sociodemographic or clinical particulars for the CHIP total score as the dependent variable within the model.
## 3.4. Needs of Parents with CAH Children
We measured the intensity of the needs of parents with CAH-diagnosed children using the Scale of needs for parents of chronically ill children. Comparing mothers and fathers, the mean total scores differed by about 0.12 points, resulting from mothers’ mean score of 2.87 (±0.83) and a mean score of 2.75 (±0.76) for fathers. The most frequently indicated needs were primarily information, such as needing more information about therapy options, dealing with departments and councils, diagnostic methods, and dealing with the ill child. Another often-mentioned requirement was having more time for the partner. The three needs least indicated were the need to obtain relief in everyday life from relatives or friends and to conduct problem-centered conversations with friends (Table 9).
Furthermore, parent-reported needs showed differences depending on the child’s gender. Exploratory regression analyses indicated that parents of a boy declared their needs more severely than parents giving birth to a girl with a 0.69 unit difference (p ≤ 0.01). Other factors included in the model did not show any substantial dependences.
## 4. Discussion
Diagnosing a child with CAH raises multiple challenges for the parents concerning lifelong hormonal substitution therapy, potential risk of adrenal crisis, feminizing surgery, gender identity, and long-term morbidity. From the start of the diagnosis, the parents relied on healthcare providers, particularly the pediatric endocrinologist, to provide them with all information about the disease and their child’s care [16]. Conflicting recommendations by physicians, nurses, and persons in the parental environment can cause parental uncertainties [32]. A few studies have pointed out an increasing psychological burden on these parents [6,15,16,18,19,45]. However, overall data on the psychological adjustment of parents are scarce. This study investigated parental HrQoL, coping strategies, and needs in families with CAH children.
The parents of CAH children in our study reported an above-average HrQoL compared to data from other pediatric samples of oncology patients, patients with a cardiological disease, or patients with cystic fibrosis [43,46]. We assume the time since diagnosis might be decisive for the parental HrQoL. At the time of the study, the mean age of the CAH children diagnosed by newborn screening was 11.01 years, whereas the cited study was performed during the first three months after diagnosis [47]. It has been shown that HrQoL increased over time [43].
Our results show that the most important influencing factors on the HrQoL of parents of children with CAH are the effectiveness of applied coping patterns and the intensity of needs among these parents. Regression analyses revealed that effective coping and low intensity of particular needs positively affect parental HrQoL, and no other influencing factors significantly impact the parental HrQoL. Although the R2 marks 0.40, this regression model sufficiently explains the influence on the parental HrQoL since we included many possible influencing factors. The parents in the present study rated maintaining family integration, cooperation, and an optimistic definition of the situation as their most helpful coping patterns. Moreover, the surveyed mothers and fathers reached the highest means of the HrQoL measurement at the subdimension satisfaction with the situation in the family, which emphasizes the importance of well-functioning family structures. This observation is congruent with Van Schoors et al. [ 48], who found that the parents reported a better HrQoL if they experienced higher emotional closeness in their families. In particular, the perceived level of expressiveness within the family was shown to be decisive [48].
McCubbin et al. [ 39], Senger et al. [ 49], and Clever et al. [ 44] showed that the subdimension maintaining family integration, cooperation, and an optimistic definition of the situation was evaluated as most helpful by parents. These results underline the potential of enhancing family cohesion as a critical coping pattern to improve parental HrQoL; the child’s specific disease might not be as important.
Additional important coping patterns influencing the parental HrQoL are understanding the disease itself and a feeling of competence in disease management. Disturbed communication with healthcare providers, especially during diagnosis and subsequent treatment guidelines, negatively impacts on parental management of the disease [16]. A limited understanding of CAH, especially its genetic implications, including recurrence risk and carrier status, was detected in a small study of five CAH families from Manila [50]. On the other hand, Fleming, Van Riper [51] showed a significant, positive relationship between detailed provider instruction on managing adrenal crises and perceived parental management ability in parents of children with CAH. They reported that the impact of CAH on the family decreases with a gain in parental management ability. A direct link between perceived management ability and parental HrQoL was found in children with cancer [48]. These findings highlight the importance of understanding and competence in managing upcoming disease-related challenges for good parental HrQoL.
With the first three needs most frequently reported being explicitly informational, parents pointed to receiving sufficient information about their child’s disease and upcoming management challenges. The findings are comparable to the results provided by parents of children with other chronic diseases [40]. As elaborated earlier, well-informed parents have a higher perceived management ability and reach higher HrQoL scores [17,48]. Therefore, it is crucial to convey thorough and understandable information and, in this way, meet parental needs as early as possible.
In the context of information collection, parents in the present study accentuated contact with other affected families. Almost one-third stated initiating active contact with other parents in the same situation through disease-specific patient self-help groups. Furthermore, a detailed information educational brochure about CAH presented by a patient organization was appreciated as very supportive. The parents described the patient organization as helpful for gaining information, practical advice, and social support. However, around half of our sample’s parents ($59.57\%$) did not contact other affected families. Parent-to-parent communication is a meaningful intervention to improve the knowledge, self-efficacy, and HrQoL of mothers of ill children [52]. Other studies have highlighted self-help groups’ positive effect on patients’ HrQoL [53,54]. These findings suggest that parent-to-parent contact for mothers and fathers of CAH children is desirable and highlight the need for early involvement in patient-organizations, in the best case, parallel with the initial diagnosis.
Parents in this study did not declare a high need for psychosocial support at the survey time. Altogether, only $16.00\%$ of the mothers and fathers used psychosocial consultation in the past. However, about two-thirds of this group evaluated psychosocial support as helpful or very helpful. Studies have shown that psychological support, e.g., through cognitive behavioral therapy, is highly recommended for parents of children with chronic health issues. Multiple emotional stressors and coping challenges for the families of children with CAH can increase the risk of developing psychopathological symptoms [55]. Immense psychological stress of the parents was reported in a study of parents in Sri Lanka [56].
The clinical practice guideline published by the Endocrine Society in 2010 also recommends regular behavioral/mental health consultation and evaluation for parents to address any concerns related to CAH [5].
We assume that the parents’ need for psychosocial help was no longer present at the time of this survey since, in most cases, many years have passed after disclosing the child’s diagnosis. Another reason could be that there was impeded access to psychosocial consultation. Approximately only every tenth family in the present study received a professional offer of psychosocial support during their first clinical stay after learning of the presumptive diagnosis of their child. Consequently, early psychosocial support could be a valuable topic with scope for improvement.
## 5. Practical Recommendations
Different dimensions of care should be considered to improve the situation of CAH-affected families. Sensitizing physicians at maternity and pediatric clinics to this rare disease is essential. Medical professionals should be encouraged to consult experts early, such as pediatric endocrinologists, to accompany respective families from the diagnosis process onwards. In addition, our results show that even after a mean time of 11 years after the disclosure of the CAH diagnosis of their child, mothers, and fathers still indicated more information regarding the disease as being their primary need. This knowledge gap implies that with a complex disease such as CAH, new arrival questions, and informational needs are shared continuously.
Consequently, improving the mediation of first and ongoing information is essential for improving care for these families. Another critical aspect constitutes the mediation of the (presumptive) diagnosis. Studies show that parents value thorough, timely, and understandable information without too many specialist terms [16,18]. Furthermore, physicians should be aware of the process of announcing the diagnosis of a child as a potentially traumatizing event for mothers and fathers [16]. Therefore, it is recommendable to additionally offer: [1] written material with accessible and understandable information about the disease, upcoming obstacles, and practical advice, as well as [2] early psychosocial support for the entire family directly at the disclosure of the diagnosis.
Furthermore, families should be encouraged to participate in self-help groups early. With the results of this study, the development of demand-responsive, purposeful interventions can be initiated. Family-oriented coping should focus on early interventions to strengthen family solidarity and improve communication. Another leading aspect depicts information-oriented coping. Early and repeated training for managing adrenal crises is desirable to strengthen the mother’s and father’s management ability and improve the situation of CAH-affected families.
## 6. Conclusions
A child with a chronic disease diagnosis marks an unexpected, extraordinary, and stressful change in the prevailing family-life system. Since CAH constitutes a rare chronic disease with a potentially life-threatening character, parents must overcome additional obstacles in caring for their children. Thus, they are even more at risk of undergoing psychological strain and decreasing their HrQoL [56,57,58]. Parental well-being is fundamental for the child’s psychosocial development [12,59]. Therefore, it is essential to consider the psychosocial situation of parents of children diagnosed with CAH. The present study is the first to examine parental HrQoL in families with pediatric CAH patients extensively. The results show that despite the complex and seemingly threatening nature of CAH, parents are doing pretty well and do not experience extensive restrictions in their daily lives through their child’s disease. The present data imply an above-average HrQoL in mothers and fathers of children and adolescents with CAH.
On the one hand, the established parental HrQoL is highly affected by the effectiveness of the mother’s and father’s applied coping patterns. Parents in the present study described their coping strategies as helpful so that effective coping can be assumed. On the other hand, the intensity of needs in parents of a child with CAH significantly influences their perceived HrQoL. Hence, this study verifies the importance of helpful coping patterns and the rapid fulfillment of parental needs for maintaining a good and stable HrQoL of parents with a child diagnosed with CAH.
## 7. Strengths and Limitations
To our knowledge, this is the first study investigating the HrQoL of parents of CAH-diagnosed children. The findings provide important impulses for improving the situation of CAH-affected families since decisive influencing factors on the parental HrQoL were found.
Even though the examinations concern a rare disease, many participants could be gathered for this study. Questionnaires were addressed to $83.90\%$ of all registered parents with a child diagnosed with CAH through newborn screening in Bavaria. With a response rate of $51.67\%$, the sample covers a large number of affected families in this region. Further, contrary to many other studies, mothers and fathers were included in the survey to include the family perspective. A strength of this study is based on the applied methods. Using a Bonferroni adjustment for multiple testing ensures the prevention of possible interference factors to a large extent.
Nevertheless, various limitations of the study have to be acknowledged. Since the study format was retrospective and not based on clinical data, recall bias cannot be excluded. Further, even though there was a satisfying number of returns, the parents who did not participate may have struggled with their situation the most and could not answer the survey. An important fact to add is that the children’s ages in this study were spread over a wide range (0.68–18.12 years). Therefore, parents newly confronted with their child’s initial diagnosis and parents who had years to adapt to the new situation filled out the questionnaires, making the study population heterogeneous. The present study also describes a regional sample, including only parents in Bavaria, Germany. However, this study still delivers a profound gain in knowledge about the psychosocial situation of CAH-affected families and elaborates important impulses to improve the provision of care. Further studies should concentrate on accompanying families with CAH-diagnosed children early on and in other federal states and countries. A longitudinal format is suggested.
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|
---
title: Bidirectional Comorbid Associations between Back Pain and Major Depression
in US Adults
authors:
- Haiou Yang
- Eric L. Hurwitz
- Jian Li
- Katie de Luca
- Patricia Tavares
- Bart Green
- Scott Haldeman
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002070
doi: 10.3390/ijerph20054217
license: CC BY 4.0
---
# Bidirectional Comorbid Associations between Back Pain and Major Depression in US Adults
## Abstract
Low back pain and depression have been globally recognized as key public health problems and they are considered co-morbid conditions. This study explores both cross-sectional and longitudinal comorbid associations between back pain and major depression in the adult population in the United States. We used data from the Midlife in the United States survey (MIDUS), linking MIDUS II and III with a sample size of 2358. Logistic regression and Poisson regression models were used. The cross-sectional analysis showed significant associations between back pain and major depression. The longitudinal analysis indicated that back pain at baseline was prospectively associated with major depression at follow-up (PR 1.96, CI: 1.41, 2.74), controlling for health behavioral and demographic variables. Major depression at baseline was also prospectively associated with back pain at follow-up (PR 1.48, CI: 1.04, 2.13), controlling for a set of related confounders. These findings of a bidirectional comorbid association fill a gap in the current understanding of these comorbid conditions and could have clinical implications for the management and prevention of both depression and low back pain.
## 1. Introduction
Low back pain and depression have been recognized as major public health problems in the world. Low back pain has been globally ranked the highest cause of disability and years lived with disability among various diseases [1]. Depression has similarly been documented as a leading cause of global health-related burden and disability [2].
Low back pain and depression frequently occur together and are seen as co-morbid conditions [3]. Substantial research has been conducted on the comorbid association between low back pain and depression in the past few decades, but the published research has been marked with inconsistency and controversy [4,5,6,7]. The primary question arising from this literature is whether depression is the cause of low back pain or the result of the chronicity of back pain. There have been three hypotheses related to this question: (a) depression increases the risk of low back pain, (b) low back pain increases the risk of depression, and (c) the association of chronic low back pain and depression is bidirectional [8,9]. Compared with the first two hypotheses, there has been less research investigating the possible bidirectional association hypothesis [6,7,9].
Much of the controversy in the literature can be attributed to the type of study population that has been commonly used, which is that of patients [9]. Patients with low back pain usually have a higher prevalence of depression, and patients with depression have an increased likelihood of symptoms of low back pain than the general population [10].
There have been limited population-based studies and even fewer using U.S. population databases. An initial examination of the cross-sectional association between chronic musculoskeletal pain and depression in the U.S. population indicated significantly increased risk for depression in participants with chronic musculoskeletal pain than those without [11]. However, the data for that study was the first National Health and Nutrition Examination Survey (NHANES I, 1971–1974) and is over a half century old [11]. Other cross-sectional studies conducted in different parts of the world have also indicated a linkage between depression and low back pain [12,13,14].
Longitudinal associations between depression and low back pain have been under studied and the limited evidence has not been consistent [4]. In a randomized controlled clinical trial with 18 months follow-up, Hurwitz and colleagues found bi-directional associations between low back pain and psychological distress using both cross-sectional and longitudinal assessments [6]. Another longitudinal study conducted in Canada focused on a population-based, random sample of adults followed up at 6 and 12 months. This study indicated an independent and robust relationship between depressive symptoms and onset of an episode of spinal pain [4]. However, a third study using adult twins conducted in Spain indicated no significant association between chronic low back pain and the future development of depression [14].
The goal of this study is to explore the cross-sectional and longitudinal comorbid associations between major depression and back pain in a national sample of adults in the U.S. using data from the Midlife in the United States Survey (MIDUS) with a population-based prospective design. The analysis focuses on the comorbid association between depression and back pain, controlling for demographic and socioeconomic factors, and health behavioral factors.
## 2. Materials and Methods
The data used for this study came from the MIDUS, which is aimed at investigating behavioral, psychological, and social factors for health and wellbeing in a national sample of Americans. The MIDUS was developed with a prospective population design. The MIDUS I was conducted in 1995–1996, MIDUS II was conducted in 2004–2006, and MIDUS III was conducted in 2013–2014 [15]. This study used the longitudinal data of MIDUS II and III, with a 9-year follow-up period.
## 2.1. Study Population
The MIDUS collects data through telephone interviews and a self-administered questionnaire (SAQ). In total, 4963 participants who were 30 years of age and above in the MIDUS II were included in the baseline (T-1) for the study, as indicated in Figure 1. However, there were 922 participants who were not part of the SAQ and did not provide data for back pain. An additional 41 participants had no answer to the question on back pain and there were 557 participants with missing data for covariates. Thus, those without data on back pain or covariates were excluded, leaving 3443 participants for T-1, which we used as the sample for the cross-sectional analysis. After 9 years, 882 participants who were lost to follow up, 203 participants who did not have data on back pain (161 participants were not part of the SAQ, and 42 participants had missing data for back pain) at MIDUS III (T-2) were excluded. The final sample size used for the current analysis was 2358 (Figure 1). For the longitudinal analysis on the association between back pain at T-1 and major depression at T-2, we included 2109 participants who were free of major depression at T-1 (249 participant with major depression at T-1 were excluded). For the longitudinal analysis on the association between major depression at T-1 and back pain at T-2, we included 1790 participants who were free of back pain at T-1 (568 participants with back pain at T-1 were excluded).
## 2.2. Measurements
For detailed information on the key variables for the analysis, please see Appendix A.
## 2.2.1. Back Pain
Back pain was assessed by an independent question that focused on frequency of backaches. A respondent’s answer of experiencing backaches “almost every day” or “several times a week” in the past 30 days was defined as back pain.
## 2.2.2. Major Depression
Major depression was assessed through a pre-coded dichotomous variable based on the Composite International Diagnostic Interview Short Form (CIDI-SF) [16]. Two domains were included in the assessment: Depressed Affect and Anhedonia. For more information, please see Appendix A.
## 2.2.3. Health Behaviors
Assessments of health behavioral factors included four variables: leisure-time physical activity, tobacco use, alcohol consumption, and obesity. Leisure-time physical activity was coded as a variable with three categories: active (vigorous or moderate physical activity several times a week), insufficiently active (vigorous or moderate physical activity once a week to less than a month), and inactive (no moderate or vigorous physical activity at all). For more information, please see Appendix A. Current tobacco use was coded as a dichotomized variable with four questions. “ Yes” was based on the question, “Do you now smoke cigarettes regularly?”, “ No” was based the questions, “Age had first cigarette?”, “ Ever smoked cigarettes regularly?”, and “Do you now smoke cigarettes regularly?” Alcohol consumption was coded as a nominal variable with three categories: non-drinkers, light to moderate drinkers, and heavy drinkers. Obesity was assessed based on self-reported weight and height and was classified as a body mass index (BMI) > 30.
## 2.2.4. Demographic and Socioeconomic Characteristics
Demographic and socioeconomic factors included in the analysis were: sex, age, race/ethnicity, education, and personal earning. Race/ethnicity was coded as two groups: Non-Hispanic White and others. Age was coded into three age groups by years: 30–49; 50–59; and 60–76 and over. Education was assessed through the question: “*What is* the highest grade of school or year of college you completed?” The response was coded into three categories: high school or less than high school; some college; and college and above. Personal earning was based on the original income variable with a sum of responses to the questions on personal earning income of the respondent, pension income of the respondent, and social security income. It was coded into three categories: <$19,999; $20,000–$59,999; $60,000–$200,000 and above.
## 2.3. Statistical Analysis
The main goal of the analysis is to investigate the question whether major depression is prospectively associated with back pain and whether back pain is linked to subsequent major depression. All data were analyzed using Stata12.1 [17]. Analyses were performed on individuals with complete data.
To describe the characteristics of the study sample at T-1, we first conducted the descriptive analysis on the prevalence of major depression and back pain, characteristics of the study participants in terms of (age, sex, and race/ethnicity), socioeconomic status (education and personal earning), and behavioral factors (leisure-time physical activity, tobacco use, alcohol consumption, and obesity). In addition, we conducted the bi-variate analysis between the two key health outcome variables at T-1 and the characteristics of the participants, with the Pearson’s test.
We then conducted multivariable cross-sectional and longitudinal analyses to explore comorbid associations between major depression and back pain. We constructed models based on several studies that examined the association between back pain and depression, using the demographic characteristics (age, sex, education, and earning) and health behavioral factors (leisure-time physical activity, tobacco use, alcohol consumption, and obesity) as confounders [4,5,6]. Race/ethnicity was not controlled in the four models of cross-sectional and longitudinal associations due to the disproportionally high percentage of Non-Hispanic White participants in the data.
Model 1 focused on cross-sectional comorbid association between major depression at T-1 and back pain at T-1 with multivariable logistic models, controlling for demographic characteristics (age and sex), socioeconomic status (education and personal earning), and behavioral factors (leisure-time physical activity, tobacco use, alcohol consumption, and obesity). Model 2 focused on the cross-sectional association between back pain at T-1 and major depression at T-1, controlling for demographic characteristics, socioeconomic status, and behavioral factors.
Models 3 and 4 were constructed to focus on longitudinal comorbid associations of major depression and back pain with Poisson Regression models. Model 3 focused on longitudinal associations of back pain at T-1 and major depression at T-2, following a group of participants with no major depression at T-1 and controlling for demographic characteristics, socioeconomic status, and behavioral factors. Model 4 focused on major depression at T-1 and back pain at T-2, following a group of participants without back pain from T-1, controlling for demographic characteristics, socioeconomic status, and behavioral factors.
## 3.1. Baseline Characteristics
Table 1 shows the prevalence of major depression and back pain, characteristics of the study participants, and bi-variate associations at the baseline (T-1). The prevalence of major depression for those with back pain was $17.4\%$, which was higher than that of the general study population ($10.5\%$) at T-1. The prevalence of back pain within those with major depression ($37.4\%$) was also higher than that of the general study population ($22.5\%$) at T-1.
For demographic characteristics, $55\%$ were female, $36\%$ were aged 60 to 70 and over, and over $90\%$ of the participants were Non-Hispanic White. The bi-variate association between major depression and the main demographic factors were significant, with the exception of race and ethnicity. Age was inversely related to major depression. There was a higher proportion of female participants with major depression ($24.1\%$) and a higher proportion of female participants with back pain ($14.1\%$). For socioeconomic status, education and earning distributions were both inversely related to both major depression and back pain, although the prevalence levels varied. For health behavioral factors, the distribution of the level of leisure-time physical activity was inversely related to complaints of back pain. The lower the level of leisure-time physical activity, the greater the likelihood of back pain. However, current smoking was significantly related to both back pain and major depression.
## 3.2. Cross-Sectional Multivariable Associations
The cross-sectional analysis of multivariable associations is shown in Table 2. Model 1 in Table 2 indicates that major depression at T-1 was significantly associated with back pain at T-1 (aOR 2.13, CI: 1.68, 2.71), controlling for demographic and health behavioral factors. Model 2 in Table 2 shows that back pain at T-1 was significantly associated with major depression at T-1 (aOR 2.11, CI: 1.66, 2.69), controlling for demographic and health behavioral factors. Bidirectional cross-sectional associations between major depression and low back pain were seen.
## 3.3. Longitudinal Associations
In exploring the bidirectional associations between back pain and major depression, Model 3 in Table 3 shows that back pain at T-1 was significantly associated with major depression at T-2 (PR 1.96, t: 1.41, 2.74), controlling for demographic variables and health behavioral factors. Female adults had a prospectively increased risk of major depression (PR 1.87, CI: 1.32, 2.65), and adults who currently smoked at T-1 were more likely to have major depression at T-2 (PR 1.75, CI: 1.17, 2.62). Furthermore, light to moderate drinkers of alcohol at T-1 may have had a lower risk of major depression at T2.
Model 4 in Table 4 shows that major depression at T-1 was associated with back pain at T-2 (PR 1.48, CI: 1.04, 2.12), controlling for a set of confounders. Older adults aged 60 to 75 and older at T-1 were more likely to have back pain at T-2 (PR 1.39, CI: 1.05, 1.84). Furthermore, compared with heavy drinkers, light to moderate drinkers of alcohol at T-1 may have a lower risk of major depression (PR 0.72, CI: 0.53, 0.99).
## 4. Discussion
This study is the first population-based longitudinal study on the bi-directional comorbid association between major depression and back pain in adults in the United States. The findings of this study show that major depression is likely to be prospectively associated with back pain, and that back pain is linked to subsequent major depression. This study provides evidence to support the bidirectional association between these two disabling disorders and is consistent with the findings in the prior study by Hurwitz et al. [ 7]. This study also readdresses several controversial research issues in terms of hypotheses, study population, measurement, and data analysis in the understanding of the bi-directional associations [3,4,5,6,7,8].
Using data from a national sample of the U.S. population, this study shows the prevalence of major depression and back pain in the U.S. general population. This study shows an increased prevalence of major depression in people reporting back pain ($17.4\%$) when compared to study subjects without back pain ($10.5\%$). At the same time, the prevalence of back pain in study subjects with major depression ($37.4\%$) was higher than those without major depression ($8.5\%$). This finding is consistent with studies conducted in South Korea and Qatar. In the study conducted in South Korea on patients with depressive symptoms, $20.3\%$ reported chronic low back pain, which was much higher than the prevalence of $4.5\%$ of the general population reporting low back pain [13]. The study in Qatar [10] reported a similar pattern, with $13.7\%$ of the general population with depression complaining of low back pain compared to $8.5\%$ in the general population. In this study, male subjects with chronic low back pain reported a higher prevalence of depression compared to the general population. ( $32\%$ vs. $16\%$) [18].
One strength of the current study is the instrument used for assessing major depression, the Composite International Diagnostic Interview Short Form (CIDI-SF) [19]. This instrument is considered to have satisfactory reliability and internal consistency [17]. Another strength of the current study was the longitudinal design, which made it possible to explore the impact of major depression as a precursor of back pain compared with a cohort of participants free of major depression, and vice versa.
A main limitation of this study may be attributed to the general goal of the MIDUS, which was not designed for assessing the association between major depression and back pain. The second limitation may be related to the definition of back pain based on the MIDUS question on “backache”. Although it is not clearly defined as conventional “low back pain”, it may imply any spinal pain inferior to the neck, and therefore, could be thoracic pain and could be operationalized as low back pain. The third related limitation is the long follow–up period of 9 years, which was longer than several other published studies using 6- to 12-month follow-up periods [5,7]. With a 9-year follow-up period, different changes in low back pain and depression may be missed. This study sample also had a disproportionately high proportion of Non-Hispanic White racial population, which may limit its generalizability.
Understanding the mechanism of bidirectional association between chronic pain and depression may come from insights provided by recent brain imaging research. Chronic pain and depression appear to have a common neuroplasticity mechanism, which could explain their bidirectional relationship [4,20,21,22]. On the other hand, the bidirectional associations could be explained by shared environmental, clinical, psychosocial, or other factors for back pain and depression [7]. Job strain, a workplace psychosocial factor, has also been linked to both low back pain and major depression [23,24]. However, we did not control for the possible environmental, clinical, or psychosocial factors as confounders. These confounders may be common to both depression and back pain, and they might explain the findings. However, exploring these confounders is beyond the scope of our current study.
This study indicates back pain and depression are not isolated conditions. Understanding the comorbid and bidirectional associations between chronic pain and depression is important, as it may have implications for the management of patients with both depression and low back pain [25,26].
Since both these disorders cause high levels of disability and may be causally related in a bidirectional manner, it would perhaps be of value to assess and manage patients presenting with depression by enquiring about back pain (and vice versa), and addressing those complaints at the same time, rather than considering the management as isolated health concerns. Future population-based longitudinal studies in large scale are needed to explore factors related to the onset, progression, and reoccurrence of low back pain and major depression, as well as psychosocial, behavioral, and other factors that may impact bidirectional comorbid associations.
## 5. Conclusions
This study indicated low back pain and depression are not isolated conditions and that they have a prospective bidirectional association. This study fills a gap in the field and may have implications for the management and prevention of disability associated with both depression and low back pain. Future population-based longitudinal studies in large scale are needed to explore factors related to temporal precedence, onset, progression, and reoccurrence of low back pain and major depression, as well as psychosocial, behavioral, and other factors that may impact bi-directional associations.
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|
---
title: 'Effects of PM2.5 Exposure on the ACE/ACE2 Pathway: Possible Implication in
COVID-19 Pandemic'
authors:
- Laura Botto
- Elena Lonati
- Stefania Russo
- Emanuela Cazzaniga
- Alessandra Bulbarelli
- Paola Palestini
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002082
doi: 10.3390/ijerph20054393
license: CC BY 4.0
---
# Effects of PM2.5 Exposure on the ACE/ACE2 Pathway: Possible Implication in COVID-19 Pandemic
## Abstract
Particulate matter (PM) is a harmful component of urban air pollution and PM2.5, in particular, can settle in the deep airways. The RAS system plays a crucial role in the pathogenesis of pollution-induced inflammatory diseases: the ACE/AngII/AT1 axis activates a pro-inflammatory pathway counteracted by the ACE2/Ang[1-7]/MAS axis, which in turn triggers an anti-inflammatory and protective pathway. However, ACE2 acts also as a receptor through which SARS-CoV-2 penetrates host cells to replicate. COX-2, HO-1, and iNOS are other crucial proteins involved in ultrafine particles (UFP)-induced inflammation and oxidative stress, but closely related to the course of the COVID-19 disease. BALB/c male mice were subjected to PM2.5 sub-acute exposure to study its effects on ACE2 and ACE, COX-2, HO-1 and iNOS proteins levels, in the main organs concerned with the pathogenesis of COVID-19. The results obtained show that sub-acute exposure to PM2.5 induces organ-specific modifications which might predispose to greater susceptibility to severe symptomatology in the case of SARS-CoV-2 infection. The novelty of this work consists in using a molecular study, carried out in the lung but also in the main organs involved in the disease, to analyze the close relationship between exposure to pollution and the pathogenesis of COVID-19.
## 1. Introduction
Particulate matter (PM), as a major component of air pollutants, contains a complex mixture of smoke, dust, and other solid particles, as well as liquid droplets, present in the air [1].
PM differs in size, shape, and chemical composition. Among the various methods of PM classification, the aerodynamic diameter is certainly the one that best defines its property of being transported in the atmosphere and its ability to be inhaled. Based on this parameter, PM is categorized into three classes: coarse particles or PM10 (ranging from 2.5 to 10 µm); fine particles or PM2.5 (smaller than 2.5 µm); ultrafine particles or PM0.1 (UFP, smaller than 0.1 µm) [2,3].
While larger particles show greater fractional deposition in the extra-thoracic and upper tracheobronchial regions, smaller particles (e.g., PM2.5) are mostly deposited in the deep lung [4]. Direct effects may occur via agents that are able to cross the pulmonary epithelium into the circulation, such as possibly soluble constituents of PM2.5 (e.g., transition metals and polycyclic aromatic hydrocarbons, PAHs) [4,5,6,7,8,9].
This subsequently may contribute to a systemic inflammatory state via increased oxidative stress, potentially leading to increased health risk [9].
It is well known that pollution impairs the first line of upper airway defense [10]; thus, people living in an area with high levels of pollutants are more prone to develop chronic respiratory conditions and susceptible to any infective agent [11].
SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) [12] is the pathogen of COVID-19. This disease was first reported in December 2019 in Wuhan, Hubei Province, China, and then spread worldwide. The course of the disease is usually mild, but in many cases, it may require hospitalization, and degenerate into acute respiratory distress syndrome (ARDS) leading even to death.
A significant overlap was observed between increased mortality and morbidity and pollution levels.
Based on this correlation, many epidemiological studies, summarized in an exhaustive review by Marquès and Domingo [13], have investigated a possible relationship between the high level of SARS-CoV-2 lethality and atmospheric pollution.
Lombardy is one of the Italian regions with the highest level of virus lethality in the world and one of Europe’s most polluted areas [11].
Samples of PM2.5 gravimetrically collected during the winter of 2008 in the urban area of Milan (North Italy) were chemically characterized based on the potential toxicological relevance of its components. Milan winter PM2.5 contains high concentrations of pro-oxidant transition metals and PAHs and is mainly composed of particles ranging in size from 40 nm to 300 nm. Although the chemical composition is similar to that of other European cities, the annual levels of PM2.5 in Milan are higher [6].
PM2.5 induces an inflammation state with consequent production of cytokines that activate the pathways mediated by MAPK or JAK-STAT3, which in turn modulate the expression of matrix metalloproteinases (MMPs).
Phosphorylated STAT3 and phosphorylated ERK act as transcription factors at the nuclear level by increasing inflammation and MMPs expression.
MMPs are zinc-dependent endopeptidases that are capable of degrading the matrix, but also perform other functions, such as activation or inactivation of chemokines/cytokines, and are involved in inflammation [14,15].
The relationships between PM2.5 and inflammation have been mentioned in many pulmonary diseases, such as acute lung injury (ALI), asthma, and chronic obstructive pulmonary disease (COPD) [16,17,18]. Lin and collaborators [19] demonstrated, using a mouse model, that PM2.5-induced ALI is regulated by the Renin-Angiotensin System (RAS) and the Angiotensin-Converting Enzyme II/angiotensin 1-7/Mas receptor (ACE2/Ang[1-7]/Mas) axis has a crucial role in the pathogenesis of lung injury.
RAS is an essential endocrine system, strongly related to the cardiopulmonary system and inflammation which, by activating inflammatory factors in the lung, participates in pulmonary injury [20,21].
ACE and ACE2 are enzymes expressed in various organs, and are two key enzymes of RAS, generating two pathways with opposite effects [22].
In the Angiotensin-Converting Enzyme/AngiotensinII/AngII Type I Receptor (ACE/AngII/AT1R) axis, Ang II produced by ACE1 starting from Ang I interacts with the AT1 receptor, inducing the expression of IL-6, TNF-α, and TGF-β1 [23]. These cytokines activate transduction pathways involving STAT3 and ERK, and lead to increased production of MMPs and pro-inflammatory molecules. Consequently, this pathway is pro-inflammatory.
Instead, in the ACE2/Ang[1-7]/Mas axis, Ang 1-7 produced by ACE2 starting from Ang II interacts with MAS, which represses the STAT3 and ERK transduction pathway reducing the expression of MMPs and pro-inflammatory molecules [24]. Therefore, Ang 1-7 acts by inhibiting inflammatory pathways, JAK-STAT, MAPK, and NF-KB, but also activates anti-inflammatory molecules such as IL-10, and protective pathways such as NRF2, against ROS. Consequently, this pathway has an anti-inflammatory role [25].
Interestingly, the initial cell entry phase of the SARS-CoV-2 requires binding of its homo-trimeric spike glycoprotein to the membrane-bound form of angiotensin-converting enzyme 2 (ACE2) on the target cell [26,27].
Then, the relationship between exposure to PM2.5 and SARS-CoV-2 infection seems to actually converge on the RAS, and in particular on ACE2.
ACE2 acts as a cellular receptor of the virus, and the binding leads to the internalization of the complex in the target cell with consequent down-regulation of ACE2 [28].
Therefore, the imbalance of ACE2/ACE levels in COVID-19 and the dysregulated AngII/AT1R axis may partially be responsible for the cytokine storm and the resulting pulmonary damage [29,30].
The loss of the modulatory effect of Ang(1–7), obtained by its binding to the Mas receptor that attenuates inflammatory response, may be a further contributing factor in the hyper-inflammation status of severe cases of COVID-19 [31].
Our previous studies showed that UFP-induced inflammation and oxidative stress are associated with the alteration of COX-2, HO-1, and iNOS levels [32,33].
Lung and systemic inflammation are responsible for many of the severe cases of COVID-19 [34], which may ultimately cause severe respiratory failure, multi-organ dysfunction, and death [35].
The search for possible therapeutic strategies against SARS-CoV-2 is rapidly proceeding. Several potential target therapies have been proposed, including acetylsalicylic acid for its anti-inflammatory, analgesic, antipyretic, and antithrombotic effects [36].
These effects are obtained because ASA inhibits prostaglandin and thromboxane synthesis by irreversible inactivation of cyclo-oxygenase-1 (COX-1) and cyclo-oxygenase-2 (COX-2). Additional actions have been described to explain the ability of ASA to suppress inflammation, including heme oxygenase (HO) expression induction [37] and iNOS acetylation, resulting in the release of nitric oxide [38].
Based on these assumptions, here we examine, in a mouse model, the effects of PM2.5 exposures on ACE, ACE2, COX-2, HO-1, and iNOS in the main organs involved in COVID-19 pathology (lung, heart, liver, and brain), to test the potential close relationship between PM2.5 exposure and disease severity.
## 2.1. Animals
Male BALB/c mice (7–8 weeks old) were purchased from Harlan and housed in plastic cages under controlled environmental conditions (temperature 19–21 °C, humidity 40–$70\%$, lights on from 7:00 a.m. to 7:00 p.m.) where food and water were administered ad libitum. Animal use and care procedures were approved by the Institutional Animal Care and Use Committee of the University of Milano-Bicocca (protocol number: PP$\frac{10}{2008}$) and were in compliance with the guidelines set by the Italian Ministry of Health (DL $\frac{116}{92}$). Invasive procedures were performed under anesthesia, and an attempt was made to minimize animal suffering.
## 2.2. PM Sources and Characterization
Atmospheric winter PM2.5 was collected in Torre Sarca (Milano) and has already been described [6]. The details of the sampling and chemical analysis performed on PM2.5 were described by Perrone et al. [ 8,39], while the chemical composition of Milano PM2.5 was summarized in Sancini et al. [ 40].
The filter extractions were conducted by using an ultrasonic bath (Sonica®, SOLTEC, Milan, Italy), specifically developed to maximize the detachment efficiency of the fine PM. Particles were extracted from the filters in ultra-pure water with four cycles of 20 min each, then dried into a desiccator and weighed. PM2.5 aliquots were properly diluted in sterile saline, sonicated, vortexed, and immediately instilled in mice.
## 2.3. Dose
The purpose of this study is to analyze the adverse effects of exposure to PM2.5 on the different organs analyzed. For this reason, we reduced the cumulative PM2.5 dose proposed by Happo et al. [ 7] to 0.3 mg/mouse to apply the same treatment scheme used by Farina et al. [ 41] and Sancini et al. [ 40]. Indeed, this protocol allows for an increase in extrapulmonary adverse effects, the lungs being still affected.
## 2.4. Intratracheal PM2.5 Instillation
Animals were randomly divided into two experimental groups: sham (isotonic solution), and PM2.5-treated mice. Five mice for each experimental group were intratracheally instilled.
Male BALB/c mice were exposed to a mixture of $2.5\%$ isoflurane (Flurane, Merial, Toulouse) anesthetic gas and kept under anesthesia for the whole instillation procedure. Intratracheal instillations with 100 µg of PM2.5 in 100 µL of isotonic saline solution or 100 µL of isotonic saline solution (sham) were administered through a MicroSprayer® Aerosolizer system (MicroSprayer® Aerosolizer- Model IA-1C and FMJ-250 High-Pressure Syringe, Penn Century, Wyndmoor, PA, USA), as previously described [42,43,44].
The intratracheal instillation was performed for a total of three instillations on days 0, 3, 6, and 24 h after the last instillation, mice were euthanized with an anesthetic mixture overdose (Tiletamine/Zolazepam-Xylazine and isoflurane), (Figure 1).
## 2.5. Organ Homogenization
Organs (lung, heart, liver, and brain) of sham and PM2.5-treated mice, after being excised quickly, were washed in ice-cold isotonic saline solution, minced, and suspended in $0.9\%$ NaCl plus protease inhibitors cocktail (Complete, Roche Diagnostics S.p. A Milano, Monza, Italy). The samples were then homogenized for 30 s at 11,000 rpm with Ultra-Turrax T25 basic (IKA WERKE) and sonicated for 30 s. All the above procedures were performed on ice. The samples were stored at −20 °C for subsequent biochemical analyses.
## 2.6. Electrophoresis and Immunoblotting
Lung, heart, liver, and brain homogenates of sham and PM2.5-treated mice were analyzed for protein content by quantification with a micro-bicinchoninic acid (BCA) assay (Sigma-Aldrich Cat# B9643, Cat# C2284 St. Louis, MO, USA); then, 30 µg of total proteins for each sample were subjected to SDS-PAGE ($10\%$) followed by Western blot.
Protein analysis was assessed with specific antibodies: ACE2 (2.5 µg/mL) and ACE (0.05 µg/mL) (R&D Systems, Minneapolis, MN, USA), COX-2 (1:250) (BD Transduction Laboratories, Franklin Lakes, NJ, USA), HO-1 (1:1000) (Cell Signaling Technology, Danvers, MA, USA), iNOS (1:300) (Byorbyt, Cambridge, UK). The secondary antibodies were appropriate horseradish peroxidase (HRP)-conjugated rabbit anti-goat (1:4000) and goat anti-rabbit or anti-mouse (1:5000) (Thermofisher Scientific, Milano, Italy).
Immunoreactive proteins were revealed by enhanced chemiluminescence (ECL SuperSignal detection kit, Thermofisher Scientific, Milano, Italy) and semi-quantitative analysis was estimated by ImageQuant™ 800 (GE Healthcare Life Sciences, Milan, Italy), program 1D gel analysis. No blinding was performed.
Staining of total proteins versus a housekeeping protein represents the actual amount of loading more accurately due to minor procedural and biological variations, as demonstrated by recent studies [45,46]. Accordingly, samples were normalized with respect to the total amount of proteins detected by Ponceau staining, allowing a straightforward correction for lane-to-lane variation [45,47]. Each protein was then expressed as a percentage of the sham, which represents the control.
## 2.7. Statistical Analysis
For each parameter measured in sham and PM2.5-treated mice, the means (±standard error of the mean, S.E.) were calculated.
Statistical differences were tested by one-way ANOVA and t-test and were considered significant at the $95\%$ level (p-value < 0.05).
## 3. Results
In this project, we analyzed the effects of PM2.5 sub-acute administrations on mouse lungs, heart, liver, and brain, evaluating their possible implications in COVID pathology.
In 2018, Lin and collaborators hypothesized that acute lung injury (ALI) induced by PM2.5 was regulated by RAS, with a crucial role for the ACE2/Ang[1-7]/MAS axis in the pathogenesis of the damage. In fact, the atmospheric particulate, through the activation of pro-inflammatory pathways, is implicated in different respiratory and cardiovascular diseases, and the RAS system is strongly related to the cardiopulmonary system and inflammation.
To test this hypothesis, they studied the ACE2 expression in the lung tissue of mouse models of ALI induced by PM2.5 and found a significant up-regulation of this protein. In addition, following the ACE2 knockdown, they observed an increase in lung levels of p-STAT3 and p-ERK$\frac{1}{2}$ as well as a reduction in injury recovery and tissue remodeling. These results confirm that ACE2 is closely involved in the pathogenesis of PM2.5-induced ALI, playing a protective role [19]. The increase of ACE2 in the lung, after PM2.5 exposure, was confirmed by [48].
However, the ACE2 protein, besides counter-regulating the inflammatory effects triggered by PM exposure acting as an organ-protective factor, is also the main receptor of SARS-CoV-2, the virus responsible for the COVID-19 pandemic [49].
The dual function of ACE2, together with the overlapping between the geographic distribution of COVID-19 outbreaks and high local pollution levels, led to the hypothesis of a correlation between the PM concentration, viral infection susceptibility, and severity of symptoms [50,51].
Induction of inflammation and oxidative stress was observed in mice exposed to UFP, resulting in increased COX-2, HO-1, and iNOS, not only in the lung and heart [32] but also in the cerebellum and hippocampus [33].
Interestingly, as recently demonstrated, these proteins have been related to the pathogenesis of COVID-19, once again suggesting a close relationship between air pollution and SARS-CoV-2 infection [52,53,54].
Based on this evidence, we analyzed the ACE/ACE2 protein pathway and COX-2, HO-1, and iNOS protein levels in a mouse model after sub-acute PM2.5 exposure, in order to evaluate a possible molecular correlation between air pollution and susceptibility to SARS-CoV-2 infection.
This study was performed in the lungs and other organs involved in the COVID-19 syndrome, such as the heart, liver, and brain. Although it is known that SARS-CoV-2 infection causes respiratory disease, it also induces adverse effects at the extrapulmonary level.
The effects of PM2.5 exposure vary according to the organs analyzed.
In the lung of PM2.5-treated mice, the levels of ACE2 (+ $40\%$) and COX-2 (+ $40\%$) increased compared to the sham (Figure 2).
In the heart tissue, PM2.5 treatment induced a significant decrease in ACE and ACE2 (−$34\%$ and −$27\%$ respectively) while showing a significant increase in COX-2 level (+$21\%$) and HO-1 (+$60\%$), compared to the sham (Figure 3).
PM2.5-treated mice showed increased levels of ACE (+$80\%$) in the liver, compared to sham (Figure 4), resulting in a significant change in the ACE/ACE2 ratio (+$83\%$) (Table 1).
In the brain, as well as in the liver, PM2.5-treated mice showed increased levels of ACE (+$39\%$), compared to sham (Figure 5), resulting in a significant change in the ACE/ACE2 ratio (+$40\%$) (Table 1).
All the other investigated biomarkers were not affected by PM2.5 repeated instillations, in all the organs considered (Figure 2, Figure 3, Figure 4 and Figure 5).
## 4. Discussion
Several studies have shown that the ACE2/Ang[1-7]/MAS axis is critically involved in lung pathophysiological processes. It can antagonize the negative effects mediated by RAS or the ACE/AngII /AT1 axis, such as lung inflammation, fibrosis, pulmonary arterial hypertension, and apoptosis of alveolar epithelial cells, suggesting an anti-inflammatory and organ-protective role of the ACE2 protein which, however, is also the receptor of SARS-CoV-2 [55].
Therefore, the significant ACE2 increase observed in the lungs after PM2.5 sub-acute exposure might favor SARS-CoV-2 pulmonary entry in case of infection.
Furthermore, many inflammatory and oxidative stress mediators are known to be impaired in COVID-19 and are associated with multi-organ damage and poor disease prognosis [56,57].
Following sub-acute exposure to PM2.5, COX-2 increased, indicating an inflammatory state that a possible infection could exacerbate.
The COX-2 up-regulation is typical of viral infections and COVID-19. In particular, SARS-CoV-2 acute respiratory syndrome induces severe tissue damage by releasing “cellular debris”. Both primary infection and accumulation of cellular debris initiate the stress response of the endoplasmic reticulum and increase the regulation of inflammatory enzymes, including microsomal prostaglandin E synthase-1 (mPGES-1) and prostaglandin-endoperoxide synthase 2 (also known such as COX-2), which subsequently produce eicosanoids: prostaglandins (PG), leukotrienes (LT) and thromboxanes (TX). These pro-inflammatory lipids, named autacoids, trigger the cytokine storm, which mediates the widespread inflammation and organ damage found in patients with severe COVID-19 [58].
Instead, subacute treatment with PM2.5 does not induce changes in HO-1 and iNOS levels in the lungs. These data are in agreement with previous in vivo work [40], in which the huge amount of polycyclic aromatic hydrocarbons (PAHs) characterizing the PM2.5 samples increased lung cytochrome expression, in particular the Cyp1A1 and Cyp1B1, responsible for the metabolism of PAHs. However, PAH metabolism within the lungs did not promote an increase in HO-1 levels.
It is possible to speculate that the treatment with PM2.5 in the lung mainly involves the alveolar-capillary barrier. The increase in vascular permeability following endothelial activation would facilitate the translocation of fine particles from the lungs into the bloodstream.
Concerning the heart, several studies have highlighted the ACE2/Ang[1-7]/MAS axis cardioprotective effect against the damage generated by the ACE/AngII/AT1 axis [59]. Ferreira et al. [ 2001] observed, for the first time, that the activity of Ang[1-7], produced by ACE2, induced a significant reduction in cardiac arrhythmias related to ischemia/reperfusion (anti-arrhythmogenic effect) beyond a post-ischemic heart function improvement. Subsequent studies have highlighted the ability of Ang[1-7] to attenuate cardiac hypertrophy, suggesting an anti-hypertrophic role [55]. Consequently, the significant decrease in ACE2 observed following PM2.5 sub-acute exposure might be related to ACE reduction. Therefore, the ACE level reduction, inducing a decrease in AngII, would make ACE2 “less necessary” but might lead to increased inflammation and impaired heart function, predisposing to greater severity in cases of SARS-CoV-2 infection [27].
As in the lung, COX-2 level significantly increases in treated mice, compared to sham, following PM2.5 sub-acute exposure. This result suggests a highly compromised inflammatory contest in the heart that could degenerate in case of SARS-CoV-2 infection.
Furthermore, in the heart following sub-acute exposure to PM2.5, HO-1 increased, as noted in our previous work [40], in an attempt probably at a protective response.
Numerous studies have reported the beneficial effects of the ACE2/Ang1-7/MAS axis in counteracting steatosis and non-alcoholic inflammation, liver fibrosis, and insulin sensitivity in the liver. These observations are in agreement with the increase in ACE2 observed in chronic liver lesions in animal and human models. Furthermore, Ang[1-7] is known to suppress the growth of hepatocellular carcinoma and angiogenesis [55].
Lubel et al. [ 60] demonstrated that patients with liver cirrhosis showed remarkably high concentration levels of both plasma Ang[1-7] and AngII. In cirrhotic rat liver, Ang[1-7] significantly inhibited the vasoconstriction induced by intrahepatic AngII, through the NO signaling pathways dependent on eNOS and guanylate cyclase.
Sub-acute exposure to PM2.5, instead, induces an increase in ACE but not ACE2, showing that exposure to PM2.5 in the liver does not activate the anti-inflammatory ACE2 /Ang[1-7]/MAS axis to counteract the increased ACE. This event causes a significant increase in the ACE/ACE2 ratio and, consequently, in the pro-inflammatory pathways, indicating an inflammatory state that could be exacerbated by possible infection. No significant changes in HO-1 and iNOS were observed in the liver.
Finally, ACE2 is present in the brain, predominantly in neurons [61]. In an interesting review, the physiological aspects of the ACE2/Ang[1-7]/MAS axis in different organs were analysed, and in particular the role of Ang[1-7] in the brain. ACE2 appears to be essential in the central nervous system for cardiovascular regulation. Indeed, transgenic mice overexpressing the synapsin promoter-driven human ACE2 exhibit protective phenotypes for cardiovascular disease. This suggests that the balance between Ang[1-7] and AngII in brain regions, which regulates the autonomic nervous system, is critical [52].
The increase in ACE levels, observed following sub-acute exposure to PM2.5, causes a significant increase in the ACE/ACE2 ratio. Alteration of the balance between Ang[1-7] and AngII indicates a compromised situation in the brain following exposure to PM2.5, which can degenerate in the case of SARS-CoV-2 infection, with serious outcomes also at the heart level.
Furthermore, the slight decrease in HO-1 observed in the brain suggests a lower countering power against the inflammatory cascade in the case of SARS-CoV-2 infection [62], since HO-1 exerts a powerful antioxidant effect degrading heme, a pro-inflammatory mediator. Indeed, a lower expression of HO-1, due to different polymorphisms, has been associated with greater difficulty in counteracting SARS-CoV-2-induced inflammation [63].
## 5. Conclusions
Sub-acute exposure to PM2.5 causes alterations in the ACE/ACE2 system, with possible consequences on COVID-19 pathogenesis.
In an in vivo model of male BALB/c mice, PM2.5 exposure causes variations in the ACE2 and/or ACE levels in all the organs considered.
It is known that ACE2 can counteract the pro-inflammatory pathways activated by ACE, acting as an organ-protective factor, but also acts as a receptor for the entry of SARS-CoV-2 into host cells in case of infection. An alteration of the ACE/ACE2 ratio, when in favor of ACE, suggests a greater probability of manifesting severe symptoms under infection due to the pro-inflammatory pathways’ enhancement. In contrast, a condition favoring ACE2 increase can involve greater susceptibility to SARS-CoV-2 entry in case of contact with the virus.
Therefore, exposure to PM2.5 causes organ-specific changes in the ACE/ACE2 pathway. In all the organs analyzed, HO-1 and iNOS never undergo significant changes, except in the heart, where an increase in HO-1 was observed in agreement with our previous work [40], although we observed a significant COX-2 increase in the lungs and heart, and a considerable increment in the brain.
However, COX-2 plays a central role in viral infections. It is known that SARS-CoV-2 induces the over-expression of COX-2 in human cell cultures and mouse systems [64] and that it could be involved in regulating lung inflammation and disease severity.
In the concept of “risk stratification,” living in a polluted environment can significantly increase the possibility of developing a severe form of COVID-19, especially in individuals with predisposing risk factors (diseases, lifestyle, genetics). This concept could at least partially explain the greater lethality of the virus observed in highly polluted areas, including Lombardy.
The novelty of this work is the use of a molecular approach on an in vivo and non-epidemiological model carried out not only at the pulmonary level but also in the primary organs involved in the disease, in order to analyze the close relationship between pollution exposure and the pathogenesis of COVID-19.
It could be interesting to repeat the analyses following exposure to UFP which, given their aerodynamic diameter of less than 100 nm, have greater penetration and higher translocation rates with possibly worse toxicity profiles.
In our opinion,. experimental studies evaluating the role of air pollution in specific populations are urgently needed for a deeper understanding of the mechanisms leading to a worse prognosis.
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|
---
title: 'Public Support for Nutrition-Related Actions by Food Companies in Australia:
A Cross-Sectional Analysis of Findings from the 2020 International Food Policy Study'
authors:
- Ebony Yin
- Adrian J. Cameron
- Sally Schultz
- Christine M. White
- Lana Vanderlee
- David Hammond
- Gary Sacks
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002083
doi: 10.3390/ijerph20054054
license: CC BY 4.0
---
# Public Support for Nutrition-Related Actions by Food Companies in Australia: A Cross-Sectional Analysis of Findings from the 2020 International Food Policy Study
## Abstract
Unhealthy food environments contribute to unhealthy population diets. In Australia, the government currently relies on voluntary food company actions (e.g., related to front-of-pack labelling, restricting promotion of unhealthy foods, and product formulation) as part of their efforts to improve population diets, despite evidence that such voluntary approaches are less effective than mandatory policies. This study aimed to understand public perceptions of potential food industry nutrition-related actions in Australia. An online survey was completed by 4289 Australians in 2020 as part of the International Food Policy Study. The level of public support was assessed for six different nutrition-related actions related to food labelling, food promotion, and product formulation. High levels of support were observed for all six company actions, with the highest support observed for displaying the Health Star Rating on all products ($80.4\%$) and restricting children’s exposure to online promotion of unhealthy food ($76.8\%$). Findings suggest the Australian public is strongly supportive of food companies taking action to improve nutrition and the healthiness of food environments. However, given the limitations of the voluntary action from food companies, mandatory policy action by the Australian government is likely to be needed to ensure company practices align with public expectations.
## 1. Introduction
Unhealthy diets are a key risk factor for non-communicable diseases (NCDs) and a global health priority [1]. It is widely accepted that food environments have a major influence on dietary intake [2,3]. In Australia, food environments generally do not promote healthy eating [4,5,6,7], with “discretionary” foods that are high in energy, sugar, salt and/or saturated fat widely available and heavily promoted [7]. The supply and marketing of discretionary food in *Australia is* led by a relatively small number of large food companies with substantial market power [7,8]. These food companies use a wide range of strategies to influence consumers as part of integrated marketing campaigns, including: traditional and digital marketing tactics (e.g., television and outdoor advertisements, social media and gamification) [9]; retail-based promotion (e.g., price promotions, positioning and shelf space) [8]; and on-package marketing (e.g., cartoon characters and health claims) [10,11].
There have been consistent calls for government-led policy action to improve the healthiness of food environments as part of efforts to address unhealthy diets [2,3,12]. Some countries have implemented a suite of mandatory food-related policies including: restricting exposure of children to marketing of unhealthy food [13]; providing front-of-pack nutrition labelling [14]; and increasing the prices of unhealthy foods (e.g., taxes on sugary drinks) [15]. In contrast, the Australian government’s policy response to unhealthy diets falls far short of global benchmarks [16]. Currently, Australia’s nutrition-related policies rely heavily on voluntary action by food companies, including the voluntary Health Star Rating (HSR) front-of-pack nutrition labelling system [17], industry codes for adult and children’s marketing guidelines [18], and the Healthy Food Partnership Reformulation Program [19]. The lack of mandatory action has been attributed to multiple factors, including food industry lobbying to limit regulations that may harm their profits, and the prioritisation of economic wealth over public health [20,21,22,23]. Reliance on voluntary action has for the most part been shown to be ineffective, with limited uptake of such policies by food companies coupled with weak or incomplete implementation where there is uptake [24,25,26]. A 2018 assessment of Australian food company nutrition-related policies and commitments found that most companies fell short of global recommendations [27].
In the absence of government regulation, pressure on food companies from external stakeholders such as the general public and investors can lead to increased implementation of nutrition-related actions (e.g., via corporate sustainability strategies) [28,29,30]. An understanding of the extent of public support for food company action is an important advocacy tool to inform strategies to influence food industry efforts to improve the healthiness of Australian food environments. Public expectations of food companies can also guide government policy development [31].
Previous research has found that public support for various nutrition-related policies differs between countries, due to factors such as differing cultural norms, political ideology, and stage of implementation [32,33]. Research examining public support for nutrition-related policies in Australia has largely focused on support for government-led policy solutions [34,35], with limited research focused on public perceptions related to food company action [36,37,38]. Two previous studies investigated public perceptions of unhealthy food sponsorship at community events and in community sport [37,38]; and one study investigated the perceived responsibility of food companies to address population health outcomes, generally [36]. While these studies found strong support for increased food company action to improve population diets, they were very limited in the scope of the nutrition-related actions they explored. To contribute to addressing this knowledge gap, this study aimed to understand public support for food company actions targeting front-of-pack nutrition labelling, exposure of children to marketing of unhealthy foods and product reformulation in Australia, and how the level of support varied by socio-demographic factors.
## 2.1. Study Design and Sampling
Data are from the 2020 International Food Policy Study (IFPS), an online annual repeat cross-sectional survey conducted across five countries: Australia, Mexico, Canada, the USA, and the UK [39]. The current study used data collected between November and December 2020 from respondents in Australia.
Participants aged 18 to 100 residing in Australia were recruited through Nielsen Consumer Insights Global Panel and their partners’ panels, using non-probability sampling methods. Email invitations were sent to a random sample of eligible panellists. Participants provided informed consent prior to survey completion. Participants received remuneration in line with the panels’ existing incentive structure (e.g., points-based or monetary) [40]. The study received ethics clearance through a University of Waterloo Research Ethics Committee (ORE# 30829). Deakin University Human Research Ethics Committee provided an ethics exemption in 2018. A full description of the study methodology has been published elsewhere [40].
## 2.2.1. Support for Food Company Action
Public support was assessed for six actions food companies can take to improve the overall healthiness of the food supply, as outlined in Table 1. The set of actions was derived from global, nutrition-related recommendations for food companies [27]. Respondents were randomly selected to answer only one of the six questions to reduce overall survey length and response fatigue. Support was measured by asking respondents, “Please tell us whether you agree or disagree with the following statement”. A 5-point Likert scale was used to assess support including “strongly agree”, “agree”, “neutral”, “disagree” and “strongly disagree”. Each question also had a “refuse to answer” and “don’t know” option.
## 2.2.2. Sociodemographic Variables
Self-reported demographic variables included age group (18–29, 30–44, 45–59, 60+ years), sex, education, body mass index (BMI), household income, whether respondents had children, and the respondents’ food shopping responsibility. Education was categorised into three levels; “low” (year 12 or lower), “medium” (trade certificate or diploma) and “high” (bachelor’s degree or above). BMI was calculated using self-reported height and weight and was categorised according to World Health Organization classification [41]. Household income was reported in ranges of AUD 10,000 from “Less than AUD 10,000” to “AUD 150,000 and over”. Equivalised household income was calculated using the OECD-modified equivalence scale [42]. This scale is used by the Australian Bureau of Statistics to adjust for economies that occur from sharing resources within households, allowing for more meaningful comparisons of household income [43]. The equivalisation scale assigns a value of 1 to the household head, 0.5 to each additional adult and 0.3 to each child [42]. The categorical data collected for income were assigned a value in the middle of each income range (e.g., AUD 20,000–30,000 became AUD 25,000). The OECD-modified equivalence scale was applied to this value to determine an estimated equivalised household income. Income was then recategorized into low, medium, and high tertiles. Variables representing socio-demographic characteristics were selected for inclusion in regression models a priori based on being both assessed in the IFPS study and known to influence diet-related behaviours [32,44,45].
The extent of food shopping responsibility was categorised as “most”, “shared equally”, “some, but less than others” and “none”. Dietary health was categorised as “poor”, “fair”, “good”, “very good” and “excellent”. Each variable also had “refuse to answer” and “do not know” options.
## 2.3. Data Management and Analysis
A total of 5500 respondents completed the survey. Respondents were excluded for the following reasons: invalid response to a data quality question; survey completion time under 15 min; and/or invalid responses to at least 3 of 21 open-ended measures ($$n = 1211$$), leaving an analytic sample of 4289 respondents. Participants with missing results for the sociodemographic variables were included in the descriptive analysis, but were excluded in the logistic regression models that included these variables. Missing data, “refuse to answer”, and “do not know” responses were excluded from analysis. Data were weighted using post-stratification sample weights constructed using a raking algorithm with population estimates based on age, sex at birth, region, ethnicity, and education [40]. Estimates reported are weighted. Analyses were conducted using Stata/BE-17 [46].
Explanatory variables used in the models included age, sex, BMI, education, equivalised household income, shopping role, guardian/parental status, and health of diet. These were chosen as covariates based on the existing literature [34,44].
Additional sensitivity analysis was undertaken to determine best fit of the model through exploratory univariate logistic regression modelling for each covariate [47]. To determine the impact of “neutral” responses, a separate multivariable logistic regression analysis was conducted on all outcome measures, excluding “neutral” responses. The results from this analysis were similar to the final model that included the “neutral” response option. The final model was tested for goodness of fit using the Hosmer–Lemeshow test [47]. Due to the number of response options being tested, the significance level was set at the 0.01 level.
## 3.1. Sample Characteristics
The weighted sociodemographic characteristics of respondents are detailed in Table 2. The mean age of respondents was 46.6 years (min 18–max 92) and there was an approximately equal proportion of male and female respondents. The majority of respondents reported low to medium education levels, having no children, doing most of the food shopping in their household and rated their overall diet quality as “good” to “excellent”.
## 3.2. Support for Food Company Action
The proportion of respondents who supported the various food company nutrition-related actions is detailed in Figure 1. There was more than $60\%$ support for all actions, with the highest level of support for food companies displaying the Health Star Rating on packaging of all food and drinks ($80.4\%$). The lowest support was for food companies not placing “cartoon characters or other images that appeal to children on product packaging for unhealthy food and drinks” ($61.6\%$) and only making “nutrition claims on products that are healthy overall” ($61.9\%$). Across all food company actions, the proportion of participants who opposed the actions was low ($2.0\%$ to $10.1\%$), while the proportion of participants reporting a neutral response ranged from $15.4\%$ to $29.6\%$.
## 3.3. Support for Food Company Actions by Sociodemographic Characteristics
Results from the multivariable logistic regression model fitted to examine associations between sociodemographic characteristics and level of support for voluntary food company action are detailed in Table 3. Overall, age was a significant covariate for three of the six initiatives. Respondents aged over 60 years old were more than twice as likely than 18–29 year-olds to support food companies “not placing cartoon characters or other images that appeal to children on product packaging for unhealthy food and drinks”, and “not advertising unhealthy food and drinks on TV at times when children and teenagers are likely to be watching”. Those aged above 60 years were more than three times as likely than 18–29 year olds to support food companies “not targeting children and teenagers with online ads for unhealthy food and drinks”. No significant differences in support were found for any other age groups.
Females were almost twice as likely as males to report support for not targeting “children and teenagers with online ads for unhealthy food and drinks”. Sex was not significantly associated with support for any other initiative. Respondents with bachelor’s degrees or above were more than twice as likely to support food companies not targeting “children and teenagers with online ads for unhealthy food and drinks” compared to respondents with low education levels.
No significant associations were found between categories of household income, BMI, parental status, shopping responsibility, and the overall health of diet and level of support for any initiative. For three food company initiatives (that food companies “have a responsibility to make food and drinks healthier for consumers”, “should clearly display the Health Star Rating on the packaging of ALL food and drinks” and “should only make nutrition claims on products that are healthy overall”), no significant associations were found between any sociodemographic variables or BMI and level of support.
## 4. Discussion
This study found strong public support for food companies to take action to improve the healthiness of Australian food environments. The highest level of support was observed for displaying the Health Star Rating on all products, restricting exposure of children to promotion of unhealthy food online, and manufacturing healthier food and drinks. Support for restricting other types of marketing of unhealthy products to children and the responsible use of nutrition claims was also high.
Public support for voluntary nutrition-related action by food companies in this study was generally consistent with findings related to the support of government regulation of food companies from previous studies in Australia and internationally [33,34,35,37,38,48]. A scoping review of 18 studies that explored Australians’ views on regulatory nutrition policies found high levels of support for implementation of interpretive front-of pack nutrition labelling, and moderate to high levels of support for restricting unhealthy food marketing to children and reformulation to improve product healthiness [35]. Likewise, an international study examining public support for nutrition interventions in seven countries, including Australia, found high support across all countries for reformulation interventions and interpretive front-of-pack nutrition labelling (e.g., Health Star Rating, Nutriscore) [48].
The strong level of support for Health Star Rating labelling corresponds with previous studies that have found support for health-related policies and actions increased after their widespread implementation [32,49]. In Australia, the Health Star Rating system was first introduced in 2014, with uptake increasing to $43\%$ of eligible products by 2021 [7]. Some studies have posited that increased acceptance of an initiative after implementation may be associated with the public observing positive impacts or not observing negative consequences [49].
The association between demographic characteristics and the extent of support for various food company nutrition-related actions was generally uniform, with some variation across the different actions. Of note, support for food companies not targeting children with online advertisements for unhealthy food and drinks was significantly higher for those over 60 years compared with 18–29 year olds. Other studies have also found that those above 60 years old were more likely to support nutrition-related policies that were similar to the ones examined in this study [33,50]. The lack of association between parental status and support for food company actions is consistent with previous research which found that parental status was not significantly associated with support for government policies focused on restricting the marketing and promotion of unhealthy food and beverages to children [37,50,51]. While previous literature has identified being female and having a higher level of education as common demographic characteristics associated with increased support for food-related interventions (i.e., sugar sweetened beverage tax, food placement, price-promotion, and restriction of unhealthy food marketing to children), the current study found no significant association between education and most nutrition-related actions [34,44,50]. The exception was a significant association between education and support for online advertising restrictions. The lack of significant differences in the results across different socioeconomic groups likely reflects the broad support for such measures across the population.
Despite this study’s findings that there is both strong public support for companies to take action to improve nutrition, and minimal public opposition to such action, voluntary uptake of globally recommended nutrition-related actions by food companies in Australia has generally been limited. The most recent report [2020] measuring uptake of the Health Star Rating system showed that, six years post-implementation, only $41\%$ of eligible products displayed the Health Star Rating [7]. Reformulation efforts have also been limited, with little change in the overall nutritional quality across all packaged food categories between 2019 and 2021, and few companies formally committing to the Healthy Food Partnership’s reformulation program [7]. There is also consistent evidence to demonstrate the inadequacy of current industry self-regulation in protecting Australian children from unhealthy food marketing online, on television, outdoors, and through sport sponsorships [52,53,54,55]. An assessment of Australia’s largest food and beverage manufacturers found there were significant opportunities to improve nutrition-related policies and practices across the sector, including those related to reformulation, nutrition labelling, and food marketing [27].
## Implications
Overall, the relatively low level of implementation of globally recommended nutrition policies by food companies likely indicates that public support for nutrition-related action is not sufficient to drive policy and practice change for the food industry as a whole. Nevertheless, there appears to be potential to capitalise on the high levels of public support for action to better advocate for change by food companies. Such advocacy is likely to prove most influential if it involves coalitions working together [3]. Due to their potential to influence the actions of public companies, including the large multi-national food companies that dominate food systems in Australia, the institutional investment community may represent a potential lever for increased action [56].
The Australian government currently relies heavily on voluntary actions to improve population diets. Not only do such policies fall short of global recommendations, over the past five years (2017–2022) little policy progress has been observed at the federal government level [16]. The recently released National Obesity Strategy (2022–2032) [57] and National Preventive Health Strategy (2021–2030) [58] have a strong focus on policies for creating healthier food environments, including in the areas of food labelling, food promotion, and food composition. Public support for food company actions in this area is an important consideration as part of policy development processes [21], with the current study indicating strong public support for greater action. A number of other countries, including the United Kingdom [59] and Chile [60], have recently implemented mandatory regulations in these areas, providing a clear pathway for action for the Australian government.
The findings from the current study provide important insight into the current perceptions of the Australian public towards nutrition-related actions by the food industry. The study’s main strength is that it drew data from a relatively large sample of Australians (with selection of participants weighted to ensure the sample closely resembled the population sociodemographics in Australia). Respondents were recruited using nonprobability-based sampling from a commercial panel, meaning that despite the national sample, the findings should not be presumed to provide nationally representative estimates [61,62]. Importantly, the survey measures did not specify whether the relevant food company action would be implemented voluntarily or in response to government legislation. As such, this study is not able to provide any indication of whether the Australian public prefers a voluntary or mandatory approach to food company nutrition-related actions [63].
## 5. Conclusions
This study found strong public support in Australia for food companies to take action to improve nutrition and the healthiness of food environments. The findings from this study support greater implementation of nutrition-related policies and initiatives focused on improving the healthiness of food products, transparent labelling practices and socially responsible marketing strategies. With the current reliance on voluntary action from food companies in Australia, mandatory policy action may be needed to ensure company practices align with public expectations.
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|
---
title: Heart Rate from Progressive Volitional Cycling Test Is Associated with Endothelial
Dysfunction Outcomes in Hypertensive Chilean Adults
authors:
- Cristian Alvarez
- Marcelo Tuesta
- Álvaro Reyes
- Francisco Guede-Rojas
- Luis Peñailillo
- Igor Cigarroa
- Jaime Vásquez-Gómez
- Johnattan Cano-Montoya
- Cristóbal Durán-Marín
- Oscar Rojas-Paz
- Héctor Márquez
- Mikel Izquierdo
- Pedro Delgado-Floody
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002090
doi: 10.3390/ijerph20054236
license: CC BY 4.0
---
# Heart Rate from Progressive Volitional Cycling Test Is Associated with Endothelial Dysfunction Outcomes in Hypertensive Chilean Adults
## Abstract
Background: A progressive volitional cycling test is useful in determining exercise prescription in populations with cardiovascular and metabolic diseases. However, little is known about the association between heart rate during this test and endothelial dysfunction (EDys) parameters in hypertensive (HTN) patients. Objective: To investigate the association between EDys markers (flow-mediated dilation [FMD], pulse wave velocity of the brachial artery [PWVba], and carotid-intima media thickness [cIMT]) and heart rate during a cycling test in HTN adults. A secondary aim was to characterize cardiovascular, anthropometric, and body composition outcomes in this population. Methods: This was a descriptive clinical study in which adults (men and women) were assigned to one of three groups: HTN, elevated blood pressure (Ele), or a normotensive control group (CG), and completed a progressive cycling test. The primary outcomes were FMD, PWVba, cIMT, and heart rate (HR) at 25–50 watts (HR25–50), 50–100 watts (HR50–100), and 75–150 watts (HR75–150) of the Astrand test. Secondary outcomes included body mass index (BMI), waist circumference, body fat percentage (BF%), skeletal muscle mass (SMM), resting metabolic rate (RMR), and estimated body age, as measured by a bio-impedance digital scale. Results: Analyses of the associations between FMD, PWV, and HR25–50, HR50–100, and HR75–150 watts revealed no significant association in the HTN, Ele, and CG groups. However, a significant association was found between cIMT and HR75–150 watts in the HTN group (R2 47.1, β −0.650, $$p \leq 0.038$$). There was also a significant trend ($$p \leq 0.047$$) towards increasing PWVba in the CG, Ele, and HTN groups. Conclusion: Heart rate during a progressive cycling test is associated with the EDys parameters cIMT in HTN patients, with particularly strong predictive capacity for vascular parameters in the second and third stages of the Astrand exercise test compared to normotensive control.
## 1. Introduction
Atherosclerosis is a chronic disease characterized by the accumulation of lipoproteins in the inner layer of artery walls. This accumulation is often due to oxidative damage to low-density lipoprotein (LDL-c) [1]. The accumulation of LDL-c can lead to inflammation in the major arteries (e.g., carotid and brachial arteries), which typically progress to fibroatheromas [2]. However, before the development of atherosclerosis, an endothelial dysfunction (EDys) state is usually found. EDys is a phenotypic condition that is an intermediate pathology, characterized by a pro-thrombotic and pro-inflammatory state. This is the result of an imbalance between the actions of vasodilators and vasoconstrictors, which modifies the “function” and “structure” of the vasculature [3]. Traditional methods for detecting EDys are highly invasive, expensive, and time-consuming, such as coronary epicardial vasoreactivity and venous occlusion plethysmography. Therefore, non-invasive methods based on ultrasound imaging have been rapidly implemented in clinical management [4,5].
EDys is often associated with several health conditions, including arterial hypertension (HTN), obesity, coronary artery disease, chronic heart failure, peripheral artery disease, diabetes, metabolic syndrome, non-alcoholic fatty liver disease, and chronic renal failure [6]. In Chile, $26.9\%$ of adults have HTN, and this prevalence is highly superior in older adults [7]. Therefore, it is estimated that an important number of adults and older adults will develop EDys, which will progress to atherosclerosis, or to an atheromatous plaque that, in turn, will increase the risk of stroke. Regarding “functional” parameters, the percentage of flow-mediated dilation (FMD) has been a more suitable and strong marker of vascular health in adults. Low values of FMD (i.e., <$6.5\%$) denote an impaired vascular function associated with cardiometabolic risk [4,8]. Furthermore, pulse wave velocity of the brachial artery (PWVba) is a recognized marker of arterial stiffness in adults. Although different values have been proposed for cardiovascular risk identification (e.g., PWVba > 18 m·s−1 [9]), the >10 (m·s−1) PWVba value is well accepted as an indicator of high cardiovascular risk [10]. On the other hand, carotid-intima media thickness (cIMT) is a well-established marker of “structural” vascular health [11]. Despite this, there is a scarcity of proposals and clear agreement about cut-off points for considering high cardiovascular risk in adults. Values of cIMT > 0.9 mm have been suggested by expert panels as part of the proposals for considering high cardiovascular risk [10].
Physical inactivity, which refers to not following the international physical activity guidelines of 300 min per week of low-moderate physical activity or at least 150 min per week of vigorous-intensity physical activity [11,12], is more prevalent in sedentary, obese, and hypertensive populations, as well as those with dyslipidemia or metabolic syndrome, and is associated with negative effects on both functional and structural vascular parameters, such as flow-mediated dilation (FMD), pulse wave velocity of the brachial artery (PWVba), and carotid intima-media thickness (cIMT) [10,12,13,14]. Several expert panels have recommended moderate-intensity continuous training (MICT) for 30–60 min per session most days of the week for individuals with elevated blood pressure or hypertension [15]. MICT has been shown to be crucial for preventing and treating hypertension [16,17], and recent evidence has highlighted the time-efficiency of high-intensity interval training [18,19] and resistance training for improving EDys in a similar manner [20].
However, before starting any exercise training program in clinical populations such as those with elevated blood pressure or HTN, it will necessary to know the baseline cardiovascular response to physical effort through a progressive exercise volitional test, such as a cycling test [16,17]. The Astrand test is a useful progressive volitional cycling test that provides information about cycling power output in watts, which increases at different levels. For example, in women, power output increases by 25 watts per level, while in men, it increases by 50 watts. Heart rate should also increase progressively at each level. [ 17,18]. Interestingly, the theoretically predicted heart rate maximum (HRpredicted) using the well-known formula (i.e., 220-age) is often overestimated or underestimated in physically inactive individuals [19]. Additionally, the use of heart rate maximum (HRmax) is poorly reported in physically inactive hypertensive populations who are generally unable to maintain a steady state at maximal intensity. Therefore, the heart rate peak (HRpeak) use, is a more easy and useful cardiovascular marker to obtain under exercise test conditions in physically inactive populations and has been widely reported for exercise prescription. This aim of this study was to assess the association between the EDys markers FMD, PWVba, and cIMT with the heart rate during a cycling test in HTN adults. A secondary aim was to characterize cardiovascular, anthropometric, and body composition outcomes in this population.
## 2.1. Participants
This preliminary descriptive study is part of an experimental randomized controlled clinical trial in which 75 adult men and women were invited to participate in an exercise training intervention and were assigned to one of three groups based on their blood pressure levels: arterial hypertension (HTN), elevated blood pressure (Ele), or a normotensive control group (CG). The study was conducted in Concepción, Chile between September 2022 and January 2023.
To determine the sample size, we used a G*Power 3.1.9.7 statistical sample size software calculator with an alpha error probability of $p \leq 0.05$ and a $95\%$ confidence interval (CI) for three groups, expecting a medium-to-large effect size. Thus, a minimum of ten subjects per group would give a statistical power of ≥$90\%$)].
The eligibility criteria for this study were as follows: (i) HTN, elevated blood pressure (controlled and on updated pharmacotherapy), or healthy normotensive; (ii) normal weight, overweight, or obese (as determined by body mass index [BMI]); (iii) normal or hyperglycaemic (T2DM, controlled and on updated pharmacotherapy); (iv) living in urban areas of the city of Concepción; and (v) the demonstrated ability to adhere to all measurements and stages of the study. Exclusion criteria included: (i) abnormal ECG; (ii) uncontrolled HTN (SBP ≥ 169 mmHg or DBP > 95 mmHg); (iii) morbid obesity (BMI ≥ 40 kg/m2); (iv) type 1 diabetes mellitus; (v) cardiovascular disease (e.g., coronary artery disease); (vi) diabetes complications such as varicose ulcers on the feet or legs, or a history of wounds, nephropathies, or muscle-skeletal disorders (e.g., osteoarthrosis); (vii) recent participation in weight loss treatment or exercise training programs (within the past 3 months); and (viii) the use of pharmacotherapy that can influence body composition.
All participants were informed about the study procedures and potential risks and benefits, and provided written consent. The study was conducted following the Declaration of Helsinki and was approved by the Ethics Committee of Universidad Andres Bello, Chile (Approval N° $\frac{026}{2022}$). The clinical trial is registered under the clinical trials.gov international scientific platform under the code NCT05710653.
In the first stage of the enrolment ($$n = 75$$), subjects were screened, and after exclusion criteria ($$n = 10$$) participants were excluded for several reasons; ([$$n = 3$$] due to bone diseases, [$$n = 3$$] due to a history of heart disease, [$$n = 3$$] because they were already enrolled in other exercise activities, and ($$n = 1$$) due to be under weight loss treatment). Thus, a total of ($$n = 65$$) subjects participated in this first stage of our clinical trial study. The final sample size was as follows per group: (HTN $$n = 18$$, Ele $$n = 22$$, and CG $$n = 21$$). The study design can be seen in (Figure 1).
## 2.2. Endothelial Dysfunction Outcomes
To the three main EDys outcomes (FMD, PWVba, and cIMT), an ultrasound imaging 7–12 MHz linear-array transducer (GE Medical Systems, Model LOGIQ-E PRO, Milwaukee, WI, USA) for non-invasive vascular measurements of the brachial and carotid arteries was used. All participants were informed about refraining from eating, exercising, consuming caffeine, or taking vasoactive drugs before the test.
## 2.2.1. Flow-Mediated Dilation
To measure FMD, each participant was positioned in a supine position and allowed to rest for 20 min. An ultrasound probe with a 60° inclination angle was then used in a longitudinal plane to explore the anterior and posterior lumen-intima interfaces at a site 1–3 cm proximal in the antecubital fossa to measure the brachial diameter and central flow velocity (pulsed Doppler) before the occlusion. The arm was abducted approximately 80° from the body and the forearm was supinated, and an adjustable mechanical metal arm precision holder with a magnetic base for a three-axis (X-Y-Z) positioning stage (EDITM, Progetti e Sviluppo, Italy) was used to standardize the position and avoid evaluator bias. Next, a blood pressure cuff was positioned on the left arm and inflated at 50 mmHg (over the SBP baseline) for 5 min. Information was recorded during this time, including (i) a baseline image that was obtained before the occlusion, (ii) a 3-min video obtained (60 s before the stopping of the occlusion that was maintained until 2 min after cuff deflation), and (iii) a final image that was taken after the occlusion. The peak artery diameter after cuff deflation was recorded, and FMD was calculated as the percentage (%) rise in peak diameter from the preceding baseline diameter and the image after deflation [21], using the following formula: FMD (%)=[(peak diameter −baseline diameter)]∗100baseline diameter The intra-session coefficient of variation has been ≤$1\%$ for the baseline diameter in our previous studies [18]. Reliability was estimated by intra-class correlation coefficients (ICC) based on four baseline measurements, with ICC values of 0.91 for the baseline diameter and ICC of 0.83 for FMD% (previously used data).
## 2.2.2. Carotid Intima-Media Thickness
To assess cIMT, we used the same ultrasound Doppler with the 7−12 MHz linear-array transducer. The participants were instructed to lie in a supine position and turn their heads slightly to the left and right. Once the carotid bulb was identified, a B-mode image was obtained for longitudinal right orientation of the common carotid artery. The scan was focused on 1 cm far from the bifurcation on the far wall of the common carotid artery. All images were recorded and analyzed offline using ultrasound software. Measurements were recorded at the end-diastolic stage, and the value for each side was obtained from the mean of three wall measurements of the cIMT [22]. A cIMT value of ≥0.9 mm was considered pathological [10], and a maximum thickness of ≥1.2 cm was indicative of pathological atherosclerosis [23].
## 2.2.3. Pulse Wave Velocity
The PWVba was measured by analyzing oscillometric pressure curves that were registered from the upper arm in the brachial artery, and the measurement was represented in (m·s−1). An electronic device with a cuff for inflation/deflation positioned on the left arm (Arteriograph, TENSIOMEDTM, HU) was used for the measurement. This equipment automatically inflates/deflates the cuff and maintains occlusion in the left arm for 5 min to complete a pre-test/post 5-min test occlusion. After the measurement, the information was analyzed by a computer program (Arteriograph Software v.1.9.9.2; TensioMed, Budapest, Hungary) and a PDF information sheet was downloaded. The algorithm used to measure blood pressure in the ArteriographTM device had been previously validated [24]. A cut-off point of PWVba > 10 (m·s−1) denotes a high arterial stiffness risk, and thus a high cardiovascular risk [10]. An example representation of FMD, PWVba, and cIMT measurements can be seen in (Figure 2).
## 2.2.4. Blood Pressure and Heart Rate at Rest
In a seated position and with at least 10 min of rest, systolic (SBP) and diastolic blood pressure (DBP) were classified by arterial hypertension (HTN), elevated blood pressure (Ele), or normotensive control condition (CG) following the last American Heart Association categorization (i.e., ‘normal’ blood pressure SBP/DBP <120/<80 mmHg, ‘Elevated’ blood pressure SBP/DBP 120–129/<80 mmHg, ‘stage 1′ HTN 130–$\frac{139}{80}$–89 mmHg, ‘stage 2′ HTN SBP/DBP ≥140/≥90 mmHg) [25]. Measurements were performed with an OMRONTM digital electronic BP monitor (model HEM 7114, Chicago, IL, USA). Two recordings were made using the electronic device with a cuff for inflation/deflation positioned on the left arm. Immediately after the BP measurement, each subject was provided with a heart rate watch monitor in the left hand (Model A370, PolarTM, Kempele, Finland), where the heart rate at rest was registered.
## 2.2.5. Progressive Volitional Cycling Test and Heart Rate during Exercise
The modified Astrand progressive volitional cycling test was used to determine both heart rate and power output in watts in each HTN, Ele, and CG participant [26,27]. During the test, the heart rate was measured in the first (HR25–50), second (HR50–100), third (HR75–150), fourth (HR100–200), and fifth (HR125–250) stages of the test progression, with different load graduations for men and women. Considering the evident differences in cycling performance from our HTN, Ele, and CG, some individuals will perform more than others in the test, thus, to ensure more robust statistical analyses with our associative regression models, we only included the first three stages of the Astrand test, particularly due to all subjects adhering to the completion of these stages. For the test, an electromagnetic cycle ergometer (model Ergoselect 200, ERGOLINETM, Lindenstrasse, Germany) was used. The heart rate was continuously monitored using a telemetric heart rate sensor (Model A370, PolarTM, Finland), where we registered the maximum heart rate of each Astrand test stage.
We used the modified Astrand progressive volitional cycling test to measure both heart rate and power output in watts for each participant in the HTN, Ele, and CG groups [26,27]. The test involved measuring the heart rate in five different stages of test progression, with different load graduations for men and women: the first (HR25–50), second (HR50–100), third (HR75–150), fourth (HR100–200), and fifth (HR125–250) stages. However, due to differences in cycling performance among participants, we only included the first three stages of the test in our statistical analyses in order to ensure more robust results in our associative regression models. We used an electromagnetic cycle ergometer (model Ergoselect 200, ERGOLINETM, Germany) to conduct the test and continuously monitored the heart rate using a telemetric heart rate sensor (Model A370, PolarTM, Finland), recording the maximum heart rate for each stage.
## 2.2.6. Anthropometric and Body Composition (Secondary Outcomes)
We measured body mass (kg), waist circumference (cm), body fat (%, kg), and skeletal muscle mass (%), as well as height (m). The first four variables were measured using a digital bio-impedance scale (OMRONTM model HEM 7114TM, Chicago, IL, USA), while height was measured using a stadiometer (SECATM, Model 214, Hamburg, Germany). Participants wore light clothing and no shoes during the measurements. We calculated body mass index (BMI) using body mass and height measurements to determine the degree of obesity according to standard criteria for normoweight, overweight, or obesity. We also recorded the basal metabolic rate and estimated body age. Table 1 presents the baseline characteristics of the study groups.
## 2.3. Statistical Analyses
Data are presented as mean with standard deviation (±SD). The Shapiro-Wilk test was used to test the normality assumption of all variables. The Wilcoxon rank sum test was used for variables that were not normally distributed. A one-way ANOVA was performed to test differences between groups, adjusted for weight, height, gender, SBP, and the use of beta-blockers. Additionally, a post-hoc Tukey’s test was applied after the ANOVA for group comparisons (HTN × Ele × CG). We also reported a trend analysis (ptrend) to test for potential (linear) tendencies to increase or decrease a particular outcome through the categories of different blood pressures. These analyses were applied using the Graph Pad Prism 8.0 software (Graph Pad Software, San Diego, CA, USA).
Finally, linear regression was applied to associate EDys outcomes (FMD, PWVba, cIMT) with heart rate (beats/min) in the first three steps of the progressive volitional cycling Astrand test (i.e., $\frac{25}{50}$, $\frac{50}{100}$, and $\frac{75}{150}$ watt). The βeta value (for association), and R2 (for predictive capacity) were tested with heart rate for these EDys outcomes. In the regression model, each HR25–50, HR50–100, and HR75–125 watt was used as an independent model predictor of FMD, PWVba, and cIMT (in backward manner) adjusted for weight, height, gender, and SBP. These statistical analyses were performed with SPSS statistical software version 18 (SPSS™ Inc., Chicago, IL, USA), and statistical significance was set at p ≤ 0.05.
## 3.1. Baseline Characteristics
As was the nature of the study, there were higher significant values of blood pressure in SBP comparing HTN vs. CG (143.2 ± 9.1 vs. 110.4 ± 7.0, $p \leq 0.0001$), and Ele vs. CG (124.9 ± 2.6 vs. 110.4 ± 7.0, $p \leq 0.001$) (Table 1). Similar results were shown in DBP comparing HTN to. CG (87.3 ± 10.7 vs. 73.8 ± 7.2, $p \leq 0.0001$), and Ele to. CG (83.3 ± 7.9 vs. 73.8 ± 7.2, $p \leq 0.001$) (Table 1), in MAP comparing HTN to CG (105.9 ± 10.2 vs. 86.0 ± 7.1, $p \leq 0.0001$), and Ele to CG (97.1 ± 6.1 vs 86.0 ± 7.1, $p \leq 0.001$) (Table 1), and PP comparing HTN to CG (55.9 ± 1.6 vs. 36.6 ± 2.2, $p \leq 0.0001$), and Ele to CG (41.6 ± 5.3 vs. 36.6 ± 2.2, $p \leq 0.001$) (Table 1).
## 3.2. Anthropometric and Body Composition (Secondary Outcomes)
In BMI, there were significant differences between HTN vs. the Ele group (29.5 ± 4.7 vs. 29.7 ± 3.8 kg/m2, $p \leq 0.001$), and between Ele vs. the CG group (29.7 ± 3.8 vs. 26.2 ± 3.1 kg/m2, $p \leq 0.001$). There was a significant trend ($$p \leq 0.004$$) to increase BMI from the CG to the Ele and HTN groups (Figure 3A). In waist circumference, there were significant differences between HTN vs. the Ele group (99.8 ± 8.7 vs. 100.1 ± 10.1 cm, $p \leq 0.001$), and between the Ele vs. CG group (100.1 ± 10.1 vs. 90.2 ± 10.5 cm, $p \leq 0.001$) (Figure 3B). There was a significant trend ($$p \leq 0.001$$) to increase waist circumference from the CG to the Ele and HTN groups (Figure 3B). There were no differences among groups in terms of body fat (%), skeletal muscle mass, or body age.
## 3.3. Endothelial Dysfunction Parameters (Main Outcomes)
In FMD, PWVba, and cIMT there were no significant differences among groups (Figure 4A–C). There was a significant trend ($$p \leq 0.047$$) to increase PWVba from the CG to the Ele and HTN group (Figure 4B). In cIMT, there were no significant differences among groups (Figure 4C).
## 3.4. Heart Rate during Progressive Volitional Cycling Test in the HTN, Ele, and Control Groups
The description of heart rate during the progressive volitional cycling test is shown in (Figure 5). In the HTN group, the HRpredicted was of 177.7 beats/min, while the HRpeak in the cycling test was of 166.0 beats/min (Figure 5A). In the Ele group, the HRpredicted was 181.6 beats/min, while the HRpeak in the cycling test was 163.5 beats/min (Figure 5B). In the CG group, the HRpredicted was 180.0 beats/min, while the HRpeak in the cycling test was 152.8 beats/min (Figure 5C). Overall, each HTN, Ele, and CG normotensive group had a progressively increased heart rate from each cycling stage of 25–50 w, 50–100 w, 75–150 w, 100–200 w, and 125–250 w, from 96.8 to 166.0 in HTN (+69.2 beats/min), from 93.8 to 163.5 in Ele (+69.7 beats/min), and from 907 to 152.8 in CG (+62.1 beats/min) (Figure 5A–C). There were no significant differences in the HRR, HRpredicted, and HRpeak among groups (Figure 5D–F). The HRpeak showed a significant increasing trend from CG (146.4) to the Ele (156.2), and the HTN group (159.5 beats/min) (Figure 5F).
## 3.5. Association between EDys Outcomes FMD, PWVba, and cIMT with Different Heart Rate during a Progressive Volitional Cycling Test in HTN, Ele, and Control Normotensive Subjects
When FMD and PWVba were correlated with the HR25–50, HR50–100, and HR75–150 watts of power output in the progressive volitional cycling test, no significant correlations were found in each HTN, Ele, and CG group (Figure 6A–F). Similarly, no significant correlations were found between cIMT with the HR25–50, HR50–100 steps of the Astrand test (Figure 6G,H). Although cIMT do not show an association with HR75–150 in the CG and the Ele group, there was a significant correlation between cIMT with HR75–150 in the HTN group (R2 47.1, β −0.650, $$p \leq 0.038$$) (Figure 6I).
## 4. Discussion
The study found that there was a significant association between the vascular outcome cIMT and heart rate during the third stage of the cycling test in individuals with HTN (i.e., HR75–150). The heart rate during different stages of the test had a high predictive range for EDys outcomes, including FMD, PWVba, and cIMT in the HTN group. Additionally, there was a trend towards increased cIMT and PWVba in individuals with HTN compared to those with normal blood pressure, but no differences were observed in FMD. These findings were observed in HTN patients who had a higher prevalence of overweight/obesity as indicated by weight, BMI, and WC measurements.
Participants with HTN showed a significant association between the vascular outcome cIMT and heart rate during the third stage of the cycling test. The heart rate during different stages of the test had a high predictive range for EDys outcomes, including FMD, PWVba, and cIMT in the HTN group.
Previous studies have reported a direct relationship between cIMT and an attenuated chronotropic response in a stress test, as was observed in this study [28]. It seems that the link between cIMT and the attenuated chronotropic response to exercise is the imbalance of the sympathovagal activation, which reflects the baroreflex sensitivity dysfunction. This dysfunction has been described as a probable cause of early atherosclerosis risk factors such as inflammation [29,30]. Therefore, an impaired chronotropic response to exercise could be an indicator of the EDys presence in subjects without cardiovascular risk factors, including HTN, as we found [28]. The study also observed that individuals with HTN had a higher prevalence of overweight/obesity as indicated by weight, BMI, and WC measurements. These findings emphasize the importance of monitoring body weight and body composition as part of the HTN management and EDys.
The findings are consistent with previous research indicating that individuals with HTN are more likely to be overweight or obese, as evidenced by measures of weight, body mass index (BMI), and waist circumference (WC) (Table 1). Furthermore, studies have shown that individuals with HTN and other cardiovascular risk factors, such as physical inactivity and a significant consumption of unhealthy foods tend to have impaired vasodilation after standardized FMD trials, high PWVba, and high cIMT [3,6].
The present study’s findings regarding PWVba are consistent with those of Park et al., who found that PWVba and FMD increased as frailty status increased in older adults. Specifically, PWVba increased from the non-frail group (1615.7 ± 209.9 cm/s [i.e., 16.1 m·s−1]) to the pre-frail (1815.2 ± 265.0 cm/s [i.e., 18.1 m·s−1) and frail (1829.9 ± 256.0 cm/s [i.e., 18.2 m·s−1) groups, which is similar to the increasing trend observed in our normotensive (7.7) to elevated (8.4) and hypertensive blood pressure group (8.7 m·s−1) (Figure 4). Furthermore, Park et al. found that FMD was lower in the pre-frail and frail groups ($3.4\%$ and $3.1\%$, respectively) compared to the non-frail group ($5.2\%$), and with the frail group showing approximately two times lower FMD than the non-frail group [31]. In contrast, our study found that FMD was higher in the normotensive group ($17.3\%$) compared to the HTN group ($15.2\%$). However, this difference may be partially explained by the age difference between the two studies, as the average age of the groups in our study (HTN: 42.2 years, Ele: 38.3 years, CG: 39.9 years) was younger than the groups in the study by Park et al. ( non-frail: 74.1 years, pre-frail: 75.3 years, frail: 75.3 years).
Our study found a significant association between the vascular outcome cIMT and heart rate during the third step of a cycling exercise test in individuals in the elevated blood pressure group (Figure 5). Additionally, heart rate during the different stages of the exercise test had a high predictive range for EDys outcomes, including FMD, PWVba, and cIMT. There was also a trend towards increased cIMT in individuals with elevated blood pressure compared to those with normal blood pressure, but no differences were observed in FMD or PWVba. Previous research has not found an association between resting heart rate and vascular parameters such as FMD. Our findings, which show an association between heart rate during exercise and EDys parameters, could be useful for predicting the behavior of vascular parameters and avoiding more invasive, expensive, and lengthy clinical tests.
In conclusion, this study provides evidence for the association between markers of endothelial dysfunction and heart rate during a progressive volitional cycling test in individuals with HTN. These findings suggest that monitoring heart rate during exercise testing could be a useful tool for assessing the risk of EDys in individuals with HTN. In addition, the study highlights the importance of monitoring body weight and composition as part of the management of HTN and EDys.
## Strengths and Limitations
As for limitations, our study had several constraints. Firstly, all variables were only measured in the afternoon, and PWVba was measured using an oscillometric cuff digital device instead of the more commonly used tonometry method. However, the equipment used for PWVba measurement has been validated previously. Secondly, we did not measure metabolic or plasma parameters, as they were not the primary objectives of this study. Nevertheless, none of the participants reported receiving hypercholesterolemia/dyslipidemia treatment within the past 6 months.
## 5. Conclusions
Heart rate during a progressive cycling exercise test is associated with vascular parameters in hypertensive patients, particularly during the second and third stages of the Astrand exercise test, indicating a high predictive capacity for vascular parameters in HTN when compared to control normotensive peers. Further research is needed to investigate the mechanisms behind these results.
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---
title: The Association between the Differential Expression of lncRNA and Type 2 Diabetes
Mellitus in People with Hypertriglyceridemia
authors:
- Shoumeng Yan
- Nan Yao
- Xiaotong Li
- Mengzi Sun
- Yixue Yang
- Weiwei Cui
- Bo Li
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10002095
doi: 10.3390/ijms24054279
license: CC BY 4.0
---
# The Association between the Differential Expression of lncRNA and Type 2 Diabetes Mellitus in People with Hypertriglyceridemia
## Abstract
Compared with diabetic patients with normal blood lipid, diabetic patients with dyslipidemia such as high triglycerides have a higher risk of clinical complications, and the disease is also more serious. For the subjects with hypertriglyceridemia, the lncRNAs affecting type 2 diabetes mellitus (T2DM) and the specific mechanisms remain unclear. Transcriptome sequencing was performed on peripheral blood samples of new-onset T2DM (six subjects) and normal blood control (six subjects) in hypertriglyceridemia patients using gene chip technology, and differentially expressed lncRNA profiles were constructed. Validated by the GEO database and RT-qPCR, lncRNA ENST00000462455.1 was selected. Subsequently, fluorescence in situ hybridization (FISH), real-time quantitative polymerase chain reaction (RT-qPCR), CCK-8 assay, flow cytometry, and enzyme-linked immunosorbent assay (ELISA) were used to observe the effect of ENST00000462455.1 on MIN6. When silencing the ENST00000462455.1 for MIN6 in high glucose and high fat, the relative cell survival rate and insulin secretion decreased, the apoptosis rate increased, and the expression of the transcription factors Ins1, Pdx-1, Glut2, FoxO1, and ETS1 that maintained the function and activity of pancreatic β cells decreased ($p \leq 0.05$). In addition, we found that ENST00000462455.1/miR-204-3p/CACNA1C could be the core regulatory axis by using bioinformatics methods. Therefore, ENST00000462455.1 was a potential biomarker for hypertriglyceridemia patients with T2DM.
## 1. Introduction
Diabetes has become the third leading chronic disease that seriously endangers human health. In 2021, there were about 537 million people with diabetes worldwide, and this number is projected to reach 643 million by 2030 and 783 million by 2045. The prevalence of diabetes is on the rise, and over 6.7 million people will die from diabetes-related causes [1]. Type 2 diabetes mellitus (T2DM) is an endocrine and metabolic disease caused by a combination of genetic and environmental factors and characterized by fasting and postprandial hyperglycemia, which account for more than $90\%$ of diabetes [2]. Existing evidence indicates that people with T2DM have a $15\%$ increase in all-cause mortality compared with people without diabetes [3].
Pancreatic β cells play an essential role in maintaining glucose homeostasis [4]. Glucose is a major physiological regulator for pancreatic β cells and can be metabolized via pancreatic β cells, thereby stimulating insulin secretion [5,6]. However, in chronic hyperglycemic environments and sustained glucose metabolism, pancreatic β cells are prone to damage and dysfunction, resulting in defective insulin secretion [7]. In addition, dyslipidemia also plays an important role in the development of T2DM. On the one hand, the lipotoxicity caused by dyslipidemia could affect the development of insulin resistance, which in turn aggravates the occurrence of lipid metabolism disorders, and a vicious circle is established [8]. On the other hand, the accumulation of abnormally elevated triglycerides in pancreatic β cells leads to their dysfunction and the further apoptosis of pancreatic β cells, which eventually causes the disorder of insulin secretion and the increase of blood glucose, thus inducing T2DM [9]. Meanwhile, T2DM complicated with hyperlipidemia is more likely to induce complications such as cardiovascular and cerebrovascular diseases [10]. Therefore, whether from a public health or a clinical perspective, hypertriglyceridemia patients with T2DM should be paid more attention.
Long noncoding RNAs (lncRNAs) represent a class of transcripts longer than 200 nucleotides with limited protein-coding potential [11]. They affect downstream gene expression and promote/inhibit disease development mainly by binding to targeted mRNAs or serving as endogenous competing RNAs for miRNAs [12]. Studies have found that lncRNAs are related to the development of T2DM and its related diseases. For example, lncRNA PVT1 can regulate insulin secretion and lipid metabolism by affecting miR-20a-5p expression, and it is also associated with end-stage renal disease in T2DM patients [13,14]. The lncRNA MALAT 1 plays an important role in the pathophysiology, inflammation, and progression of T2DM through regulating gene transcription [15]. MEG3 is overexpressed in patients with T2DM and is closely related to the occurrence of diabetic retinopathy [16]. Meanwhile, more than 1000 lncRNAs have been found in human islet cells, many of which are highly islet-specific, suggesting that they could have important and unique roles in regulating pancreatic function [13]. Our study aims to screen the differentially expressed lncRNA between new-onset T2DM and normal blood glucose control in hypertriglyceridemia subjects, and then explore the effects and possible mechanism of lncRNA on pancreatic β cell function and activity, thus providing some references for the prevention and treatment of T2DM in people with hypertriglyceridemia.
## 2.1. Screening and Validation of Differentially Expressed lncRNAs
Blood samples of six newly diagnosed T2DM patients and six patients with normal blood glucose were used to perform RNA sequencing. Basic information of subjects and the situation of data filtering are shown in Tables S1 and S2, respectively. The cleaned data is used for subsequent analysis to ensure the quality of the analysis. We obtained a total of 3163 differentially expressed lncRNAs (1439 up and 1724 down) between the T2DM group and the control group based on a p value less than or equal to 0.05. The corresponding volcano plot and heat map are shown in Figure S1. Meanwhile, a total of 25 differentially expressed lncRNAs (10 up and 15 down) were found between the above two groups based on an adjusted p value less than or equal to 0.05 (Table 1).
Firstly, we analyzed the genes corresponding to the above 25 lncRNAs through the GSE 130,991 dataset, and a total of 13 genes were found in the dataset. Specially, the gene PLEKHM2, corresponding to the lncRNA ENST00000462455.1, was statistically significant (Table 2). Meanwhile, RT-qPCR was used to verify the expression levels of lncRNA ENST00000462455.1 in 120 hypertriglyceridemia T2DM patients and 120 hypertriglyceridemia patients with normal FPG. The results indicated that the expression level of ENST00000462455.1 in the T2DM subjects was decreased ($t = 5.673$, $p \leq 0.001$), and the same results were observed in gender and age subgroups (Figure 1). In addition, the ROC curve was used to assess the diagnostic power of ENST00000462455.1 (Figure S2 and Table S3).
## 2.2. Effects of lncRNA ENST00000462455.1 on the Activity and Function of MIN6 Cells
Firstly, we detected the localization and distribution of ENST00000462455.1 in MIN6 cells by FISH. As internal reference genes, 18S was almost located in the cytoplasm and U6 was almost located in the nucleus. The results of the FISH indicated that the ENST00000462455.1 was distributed in both the cytoplasm and the nucleus (Figure 2). Next, we analyzed the expression of ENST 00000462455.1 in MIN6 cells cultured for 24 h, 36 h, 48 h, 72 h, and 96 h for the control, HG, HF, and HG + HF groups. The results indicated that, compared with the HF group, the expression level of ENST00000462455.1 in MIN6 cells in the HG + HF group decreased after 48 h (HF vs. HG + HF: 1.92 ± 0.05 vs. 0.95 ± 0.17, $p \leq 0.001$), 72 h (HF vs. HG + HF: 2.06 ± 0.29 vs. 1.21 ± 0.17, $p \leq 0.01$), and 96 h (HF vs. HG + HF: 1.37 ± 0.05 vs. 1.07 ± 0.03, $p \leq 0.01$) of culture in the corresponding environment (Figure 3).
To further explore the effect of lncRNA ENST00000462455.1 on the activity and function of MIN6, the siRNA against ENST00000462455.1 was transfected into MIN6 to silence the expression of the lncRNA. The results of RT-qPCR confirmed that the silencing effect was stable (Figure S3). Subsequently, we explored the effect of ENST00000462455.1 on MIN6 activity by the CCK-8 assay. Taking the HF group as a reference, we found that the relative survival rate of MIN6 in the HG + HF group with si-lncRNA was lower than that in the si-NC group (si-NC vs. si-lncRNA: 1.24 ± 0.21 vs. 1.06 ± 0.16, $p \leq 0.05$) (Figure 4A). Similarly, by flow cytometry, we observed that the relative apoptosis rate of MIN6 in the HG + HF group with si-lncRNA was higher than that in the si-NC group (Figure 4B). Meanwhile, the insulin level in the supernatant of the MIN6 cultured under the corresponding glycolipid environment for 48 h was detected by ELISA, thus assessing the effect of ENST00000462455.1 on the insulin secretion of MIN6. The results showed that the insulin secretion of MIN6 in the HG + HF group with si-lncRNA was lower than that in the si-NC group (si-NC vs. si-lncRNA: 12.06 ± 0.70 mIU/L vs. 9.07 ± 1.20 mIU/L, $p \leq 0.001$; si-NC vs. si-lncRNA(relative): 1.90 ± 0.11 vs. 1.33 ± 0.18, $p \leq 0.001$) (Figure 4C). In addition, RT-qPCR was also used to detect the expression levels of relevant key transcription factors. Taking the HF group as a reference, we found the expression levels of Ins1, Pdx-1, Glut2, FoxO1, and ETS1 in the HG + HF group with si-lncRNA were lower than those in the si-NC group ($p \leq 0.05$) (Figure 4D). Therefore, under a high-glucose and high-fat environment, the decreased expression of lncRNA ENST00000462455.1 could lead to the lowering of MIN6 cell activity and the occurrence of dysfunction.
## 2.3. Exploration of ceRNA Mechanism for lncRNA ENST00000462455.1
We further explored the possible mechanism of ENST00000462455.1 by constructing a ceRNA network which included lncRNA ENST00000462455.1 and its corresponding 14 miRNAs and 118 mRNAs (Figure 5). Given that miRNAs play an important role in the ceRNA network, we identified key miRNAs by searching the literature. Based on the available evidence, we found that miR-204-3p and miR-125a-3p were associated with type 2 diabetes or pancreatic β cells dysfunction, and 29 mRNAs corresponding to these two miRNAs were found in the ceRNA network (Table S4). Subsequently, GO and KEGG analysis were performed on these mRNAs (Figure 6A,B). The results indicated that CACNA1C, CSRP1, ANXA6, KCNIP2, and DPYSL2 are enriched in multiple pathways of BP, CC, and MF (Tables S5–S7). In particular, the results of the KEGG analysis showed that CACNA1C was enriched in multiple pathways including type 2 diabetes and insulin secretion (Table S8). Meanwhile, compared with the control group, the GSEA results found that CACNA1C was a core gene and the expression of it was decreased in hypertriglyceridemia subjects with T2DM (Table S9, Figure S4). In addition, we explored the interaction of key mRNAs in the ceRNA by establishing a PPI network, and a key network module was identified by cluster analysis: CSRP1-ANXA6-DPYSL2-CACNA1C-RCAN1-KCNIP2 (MCODE score: 2.8) (Figure 6C,D). Based on the above results, the possible ceRNA regulatory axis of lncRNA ENST00000462455.1 is shown in Figure 6E. Among them, ENST00000462455.1/miR-204-3p/CACNA1C may be the core regulatory axis.
## 3. Discussion
Protein-coding RNAs account for only about $2\%$ of the genome [17,18]. Although noncoding RNAs do not have traditional RNA functions in protein translation, they have become novel basic regulators of gene expression. Existing evidence indicated that some lncRNAs in islet often map to the proximal end of related genes that related to function or development of pancreatic β cells and thus may have specific regulatory functions for the gene expression of pancreatic β cells [19,20,21]. In our study, transcriptome sequencing was first performed on whole blood samples of hypertriglyceridemia subjects with T2DM or normal FPG to get the differentially expressed lncRNAs. Subsequently, the differentially expressed lncRNA ENST00000462455.1 was verified by GEO and RT-qPCR, and its potential value in clinical settings was also assessed via ROC. In addition, compared with the HF environment, we found that the expression of ENST00000462455.1 in MIN6 cells decreased under the HG + HF environment. Therefore, lncRNA ENST00000462455.1 was viewed as a differentially expressed lncRNA in hypertriglyceridemia patients with T2DM and normal FPG.
We further explored the effect of ENST00000462455.1 on the function and activity of MIN6 cells. After silencing ENST00000462455.1, we found that the activity of MIN6 cells decreased and the apoptosis rate increased. Meanwhile, the insulin secretion was also reduced. In addition, the expression levels of transcription factors, including Ins1, Pdx-1, Glut2, FoxO1, and ETS1, were decreased after silencing ENST00000462455.1. As an inherent regulatory gene of insulin, Ins1 is regulated by circulating levels of glucose and plays an important role in maintaining mature pancreatic β cells mass and function, insulin secretion and reserve, and glucose homeostasis [22,23]. Similarly, the function of Pdx-1 is to maintain mature islet function, mass, and the regeneration of pancreatic β cells [24]. Meanwhile, Pdx-1 may also be a key factor related to the adverse effects of lipid metabolism disorders on pancreatic islets [25]. FoxO1 could regulate the proliferation, apoptosis, and differentiation of pancreatic β cells and play a role in insulin secretion and resistance to oxidative stress [26]. Simultaneously, FoxO1 is closely related to Ins1 and Pdx-1. Previous study found that FoxO1 transgenic mice significantly elevated the expression levels of Ins1 and Pdx-1 [27]. In fact, the relationship between FoxO1 and Pdx-1 has been confirmed during the development of the body. FoxO1 can activate itself in the early stage of pancreatic development by mediating the expression of Pdx-1 [28]. Specially, although the function of Glut2 is merely to catalyze the passive transport of glucose across plasma membranes, this transport activity is important for the control of cellular mechanisms impinging on gene expression, the regulation of intracellular metabolic pathways, and the induction of hormonal and neuronal signals, which together form the basis of an integrated interorgan communication system to control glucose homeostasis [29]. In addition, previous study also found that the overexpression of Ets-1 in MIN6 cells could protect them from severe hypoxic injury in a mitochondria-dependent method [30].
One of the main mechanisms of lncRNAs is that they can become endogenous competing RNAs for miRNAs affecting the expression of downstream genes, thereby promoting or inhibiting the development of diseases. In our study, ENST00000462455.1 was observed in both the cytoplasm and nucleus by FISH. Existing evidence indicated that lncRNAs stably expressed in the cytoplasm are ideal ceRNAs (although recent studies also found that some nuclear-localized lncRNAs could also act as ceRNAs). Therefore, we further constructed the ceRNA network of ENST00000462455.1 by the bioinformatics method and found that ENST00000462455.1/miR-125a-3p/RCAN1/DPYSL2 may be one of the regulatory axes. Previous studies have shown that miR-125a-3p could inhibit the expression of insulin receptors via the insulin signaling pathway, resulting in insulin resistance, thus leading to lipid and carbohydrate metabolism disorder [31]. Meanwhile, miR-125a-3p is also related to diabetic cardiomyopathy and diabetic nephropathy [32]. RCAN1 has a role in the pancreatic β cell dysfunction for T2DM [33]. Some studies found that the acute induction of RCAN1 by increased reactive oxygen species and hyperglycemia could inhibit endocrine cell apoptosis and protect them from damage. However, some evidence indicated that chronic overexpression of RCAN1 could also adversely affect cells, leading to pathological changes in neurons and endocrine cells associated with T2DM [33]. Therefore, more studies for the molecular mechanisms of RCAN1 need to be performed.
Another possible ceRNA regulatory axis is ENST00000462455.1/miR-204-3p/KCNIP2/CACNA1C/ANXA6/CSRP1. Among them, ENST00000462455.1/miR-204-3p/CACNA1C may be the core regulatory axis. Previous studies found that the expression of miR-204 is increased in pancreatic islets of T2DM and elevated serum miR-204 is a marker of ongoing pancreatic β cell death [34]. Meanwhile, miR-204 can directly target and inhibit the endoplasmic reticulum transmembrane factor protein kinase R-like endoplasmic reticulum kinase (PERK) and its downstream signaling pathways, thereby aggravating ER-stress-induced pancreatic β cell apoptosis [35]. As a chain of miR-204, miR-204-3p is involved in various diabetic complications. In diabetic cataract, miR-204-3p can regulate the migration and epithelial-to-mesenchymal transition in lens epithelial cells [36]. Meanwhile, miR-204-3p also plays a role in high-glucose-induced podocyte apoptosis and dysfunction [37]. In addition, for diabetic cardiomyopathy, miR-204-3p can regulate cardiomyocyte autophagy, thus affecting myocardial ischemia/reperfusion injury [38].
Voltage-gated calcium channels (VGCCs) and potassium channels are important to insulin secretion [39,40,41]. Among them, the L-type voltage-gated calcium channels (LVGCCs) are present in pancreatic β cells and are involved in glucose transport, lipolysis, and lipogenesis [42,43]. Although LVGCCs account for only ∼$50\%$ of the total Ca2+ current, their inhibition reduces glucose-induced insulin secretion by $80\%$ and nearly abolishes insulin release in vivo [44]. In humans, the two main LVGCCs are Cav1.2 and Cav1.3, and CACNA1C is the encoding gene of Cav1.2. It was found that Cav1.2 was required for first-phase insulin secretion and rapid exocytosis in pancreatic β cells, and the expression level of CACNA1C was also high in the cells [45,46]. In mice, Cav1.2 was the only LVGCC and the knockout of CACNA1C was lethal (glucose intolerance and loss of first-phase insulin secretion were observed) [47]. In addition, CACNA1C is also involved in diabetic peripheral neuropathy, diabetic heart disease, and diabetic cataract [48,49,50]. KCNIP2 (encodes the KChIP2 protein) interacts with the subfamily of the voltage-gated potassium channel to increase the current density, accelerate the recovery from inactivation, and slow inactivation kinetics [51]. Existing evidence indicated that the lack of insulin signaling in the heart of T2DM patients may be one of the mechanisms for the decreased expression of KCNIP2, which in turn leads to abnormal changes in cardiac electrophysiology [52]. In addition, ANXA6 is involved in cholesterol transport, accumulation, and storage of TG, and plays an important role in the glucose and lipid balance by regulating the release of adiponectin [53,54,55].
Some limitations exist in this study. We used MIN6 cells, a mouse pancreatic beta cell line, for the experimental verification of lncRNA ENST00000462455.1 functions. Considering the species difference, the effect of this lncRNA on T2DM of human needs further evaluation. Moreover, the study lacked corresponding animal model verification. Meanwhile, our study only used bioinformatics methods to explore the possible ceRNA regulatory mechanism of ENST00000462455.1, and further experimental verification is required.
## 4.1. Participants
Six newly diagnosed T2DM patients and six patients with normal blood glucose were recruited to perform RNA sequencing. All subjects were Han Chinese, aged 40–65 years, and were recruited at the First Hospital of Jilin University from July to September 2020. Patients were diagnosed based on the guidelines for the prevention and control of type 2 diabetes in China (2017 Edition): Patients with type 2 diabetes were defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L or oral glucose tolerance test (OGTT) two-hour blood glucose ≥ 11.1 mmol/L. FPG < 6.1 mmol/L and OGTT < 7.8 mmol/L were defined as the normal controls. Meanwhile, the level of triglycerides (TG) in all participants was ≥1.7 mmol/L according to the guidelines for prevention and treatment of dyslipidemia in China (2016 Edition). All patients had not previously controlled their blood glucose through drugs or other treatments. Moreover, the corresponding genes of the lncRNAs were verified via the GSE 130,991 dataset (910 samples). A total of 92 T2DM and 96 controls with hypertriglyceridemia were selected from the dataset based on the above guidelines. Meanwhile, we also collected 120 T2DM and 120 controls with hypertriglyceridemia to perform RT-qPCR validation at the First Hospital of Jilin University from July to August 2021.All patients with a history of coronary artery disease (CAD), hypertension, atrial fibrillation, myocardial infarction, tumor, acute infectious disease, immune disease, and hematological disease were excluded from the study. All participants provided written informed consent and the study was approved by Ethics Committee of the Public Health of the Jilin University, and the privacy of the participants are strictly confidential.
## 4.2. RNA Sequencing
Total RNA in blood was isolated and purified using a total RNA extraction kit. The NanoPhotometer® spectrophotometer (IMPLEN, Westlake Village, CA, USA) and RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA) were used to assess the RNA purity and integrity, respectively. The chain-specific library was constructed by removing the ribosomal RNA. After the library was qualified, Illumina PE150 sequencing was performed according to the pooling of the effective concentration of the library and the data output requirements. Followed by the sequencing, data filtering was conducted: we removed reads with adapter and N (N means that the nucleobase information cannot be determined) ≥ 0.002, and the paired reads that contain low-quality nucleobases (>$50\%$) in single-end reads were also removed. Meanwhile, the Q20, Q30, and GC content were calculated, and the clean reads were obtained. Subsequently, the mapping analysis was performed by the software Hisat2 for the corresponding clean reads. The reference database was GRCh38.p12 (human) and GRCm38.p6 (mouse). Based on the mapping results, we further assembled, filtered, and quantified the transcripts by using the Stringtie and Cuffmerge software. Finally, the expression level matrix was obtained. All analyses in the study were based on the data and the data could be found in GEO database (GSE193436).
## 4.3. Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR)
The total RNA was extracted using the MolPure® Blood RNA Kit (19241ES50, YEASEN) or MolPure® Cell RNA Kit (19231ES50, YEASEN) based on the sample type. Subsequently, we used the lnRcute lncRNA First-Strand cDNA Kit (KR202, TIANGEN) or FastKing gDNA Dispelling RT SuperMix (KR118, TIANGEN) to conduct reverse transcription. The cDNA was then analyzed by RT-qPCR using lnRcute lncRNA qPCR Kit (FP402, TIANGEN) or SuperReal PreMix Plus (SYBR Green) (FP205, TIANGEN) on the QuantStudio 3 system (Applied Biosystems, Waltham, MA, USA). The PCR primers are shown in Table S10. Expression data were normalized to the expression of β-actin with the 2−ΔΔCt method.
## 4.4. Cell Culture
MIN6 cells (mouse pancreatic beta cell line) were cultured in RPMI Medium 1640 (31800, Solarbio, Beijing, China) supplemented with $10\%$ fetal bovine serum (FBS) (04-001-1A, Biological Industries, Cromwell, CT, USA) at 37 °C with $5\%$ CO2.
## 4.5. Fluorescence In Situ Hybridization (FISH)
RiboTM lncRNA FISH Probe Mix (lnc11001001, RIBOBIO) and RiboTM Fluorescent in Situ Hybridization Kit (C10910, RIBOBIO) were used for the FISH of lncRNA, thus detecting the distribution of the target lncRNA. The cell slides were placed at the bottom of a 24-well plate and each well was plated with 1 × 105 cells. After the cells had grown to about $80\%$, the cells were washed with phosphate-buffered saline (PBS) and fixed with $4\%$ paraformaldehyde. Subsequently, the cells were washed again and treated with permeabilization solution, then 200 μL of prehybridization solution was added and the cells were blocked at 37 °C for 30 min. The prehybridization solution was discarded and 100 μL of the hybridization solution containing the lncRNA FISH probe was added for overnight hybridization at 37 °C. Next day, the cells were washed by PBS and stained with DAPI and photographed by fluorescence microscopy, with 18S and U6 as the reference genes.
## 4.6. Construction of Cellular Environment and Determination of lncRNA Expression
Based on different glycolipid environments, our experiment was divided into four experimental groups: control (5 mmol/L D-glucose + PBS), high glucose (HG) (30 mmol/L D-glucose + PBS), high fat (HF) (5 mmol/L D-glucose + 400µmol/L sodium palmitate), and high glucose and high fat (HG + HF) (30 mmol/L D-glucose + 400µmol/L sodium palmitate) [56,57]. The expression of the target lncRNA in each group was determined by qRT-PCR after 24 h, 36 h, 48 h, 72 h, and 96 h.
## 4.7. Cell Transfection
The siRNA was transfected by liposome reagent transfection to silence the target lncRNA. Corresponding sequence of siRNA was shown in Table S11. Firstly, six-well plates were seeded with 2 × 105 cells per well. After 24 h, siRNA against target lncRNA (GenePharma) was transfected into cells by using Lipofectamine 2000 (11668019, Invitrogen). After incubation at 37 °C with $5\%$ CO2 for 6 h, the medium was changed to complete medium (supplemented with $10\%$ FBS) for another 24 h. Subsequently, the RNA in the cells was directly extracted or further cultivated in different glycolipid environments for 48 h and the expression level of the target lncRNA in the negative control group (si-NC) and experimental group (si-lncRNA) was detected to evaluate the effect of transfection.
## 4.8. CCK-8 Assay
The cells were seeded in 96-well plates (4 × 103 cells per well). After the lncRNA was silenced, corresponding glycolipid environment were constructed for 48 h, and then 10μL of CCK-8 reagent (CK04, Dojindo) was added to each well. Subsequently, the plate was incubated for another 1–4 h and the absorbance values were measured at 450 nm with an enzyme-linked immunometric meter.
## 4.9. Apoptosis Assay
Cell apoptosis was detected by the FITC Annexin V Apoptosis Detection Kit I (556547, BD BIOSCIENCES PHARMINGEN). Firstly, cells were seeded in 6-well plates (2 × 105 cells per well). After the lncRNA was silenced, the corresponding glycolipid environments were constructed for 48 h. Then, the original medium in the plate was discarded and cold PBS was added to wash the cells. Subsequently, 1 × binding buffer was added to each well and the cells were stained with FITC and PI. After 15 min incubation protecting from light, flow cytometry analysis was performed by using a FACSCalibur (BD BIOSCIENCES PHARMINGEN).
## 4.10. Enzyme-Linked Immunosorbent Assay (ELISA)
Insulin secretion was assessed by ELISA. Similarly, cells were seeded in 6-well plates (2 × 105 cells per well). After the lncRNA was silenced, the corresponding glycolipid environments were constructed for 48 h. Then, the supernatant was collected and detected by Mouse INS ELISA kit (ml001983, mlbio). All experiments were performed strictly in accordance with the manufacturer’s instructions.
## 4.11. Detection of Transcription Factor Levels of Pancreatic β Cell Function and Activity
Cells were seeded in 6-well plates (2 × 105 cells per well). After the lncRNA was silenced, corresponding glycolipid environments were constructed for 48 h. Subsequently, RT-qPCR was used to detect the transcription factors of pancreatic β cell function and activity (Ins1, Pdx-1, MafA, Glut2, TCF7L2, FoxO1, ETS1, Pax6, Ngn3).
## 4.12. Statistical Analysis
Normal continues variables were described by mean and standard deviation. Meanwhile, median and interquartile ranges were used to describe the skewed continues variables. Correspondingly, the t-test and Wilcoxon rank-sum test were conducted based on the data distribution. Chi-square test was conducted for categorical variables. One-way ANOVA was used for comparison among multiple groups, and LSD was performed for pairwise comparison. The diagnostic value of the lncRNA for T2DM in hypertriglyceridemia subjects was evaluated by the ROC curve. All above analyses were mainly performed by SPSS 24.0 and GraphPad Prism 7.0 software. A 2-sided p value less than 0.05 was considered significant. Independent replicated experiments were conducted in our study.
R 4.0.4, Cytoscape 3.8.2 and GSEA 4.2.1 software were used to conduct bioinformatics analysis. Differentially expressed genes were screened using the limma package [58] and the correlation between genes was analyzed by Pearson correlation. Meanwhile, the ggplot2 [59] and pheatmap [60] packages were used to draw the volcano plot and heat map, respectively. The ceRNA network construction strategy of the target lncRNA is shown in Figure S5, and Cytoscape was used to draw the networks. The clusterProfiler package [61] was used for GO (including Biological Process (BP), Cellular Component (CC), and Molecular Function (MF)) and KEGG enrichment analysis, and corresponding enrichment circle maps were drawn via the online analysis tool (https://www.omicsshare.com/tools/, accessed on 13 November 2021). Gene Set Enrichment Analysis (GSEA) was performed using GSEA software. In addition, PPI network analysis was performed by STRING 11.5 (http://string-db.org, accessed on 12 November 2021) and Cytoscape, and the MCODE was used to conduct cluster analysis in PPI network.
## 5. Conclusions
The lncRNA ENST00000462455.1 is a potential biomarker for hypertriglyceridemia patients with T2DM. More experimental studies are needed to verify the function of the lncRNA and analyze its possible mechanism.
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|
---
title: 'Evaluating Community Capability to Prevent and Control COVID-19 Pandemic in
Shenyang, China: An Empirical Study Based on a Modified Framework of Community Readiness
Model'
authors:
- Xiaojie Zhang
- Xiaoyu Liu
- Lili Wang
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002099
doi: 10.3390/ijerph20053996
license: CC BY 4.0
---
# Evaluating Community Capability to Prevent and Control COVID-19 Pandemic in Shenyang, China: An Empirical Study Based on a Modified Framework of Community Readiness Model
## Abstract
Community plays a crucial role in the successful prevention and control of the COVID-19 pandemic in China. However, evaluation of community capability to fight against COVID-19 is rarely reported. The present study provides a first attempt to assess community capability to combat COVID-19 in Shenyang, the capital city of Liaoning province in Northeast China, based on a modified framework of a community readiness model. We conducted semi-structured interviews with ninety key informants from fifteen randomly selected urban communities to collect the data. The empirical results indicate that the overall level of community capability for epidemic prevention and control in Shenyang was at the stage of preparation. The specific levels of the fifteen communities ranged from the stages of preplanning to preparation to initiation. Concerning the level of each dimension, community knowledge about the issue, leadership, and community attachment exhibited significant disparities between communities, while there were slight differences among communities on community efforts, community knowledge of efforts, and community resources. In addition, leadership demonstrated the highest overall level among all the six dimensions, followed by community attachment and community knowledge of efforts. Community resources displayed the lowest level, followed by community efforts. This study not only extends the application of the modified community readiness model to evaluate community capability of epidemic prevention in the Chinese community context, but also offers practical implications for enhancing Chinese communities’ capabilities to deal with various future public health emergencies.
## 1. Introduction
The coronavirus disease 2019 (COVID-19) pandemic, which first broke out at the end of 2019 in China [1], was called a “public health emergency of international concern” by the World Health Organization because of its high infection and mortality rate [2]. It has caused global social disruption and economic recession and continues to pose a major threat to public health. As of 2 December 2022, over 639 million people have been infected; of these, 6.6 million deaths, an unprecedented rate of spread around the world [3]. While the losses of the pandemic are numerous, many countries are still struggling to prevent and control the COVID-19 disease. China was the first country to identify and report a confirmed case, and it has effectively controlled the wide spreading of the pandemic through strict and effective measures since the first outbreak of COVID-19 in Wuhan of China up until the Chinese government deregulated the epidemic control in December 2022. The number of deaths due to the infection of COVID-19 is fewer than 32,000 in China since the first outbreak of the pandemic [4].
During the process of the COVID-19 prevention and control in China, communities played a crucial role in implementing government policy, mobilizing public participation, and providing public service. For example, communities set up checkpoints at the entrances of the blockaded communities and provided the necessities of life for residents [5,6], helped organize and conducted large-scale community-based biotechnology testing of COVID-19, offered medical assistance, provided isolation rooms for suspected cases or those who came from the epidemic areas, disinfected public spaces regularly, recruited and trained volunteers, disseminated anti-epidemic knowledge, and provided psychological counseling [7]. A few studies have shown that these measures taken by communities effectively reduced the risk of the COVID-19 outbreaks [8,9,10,11]. However, the performance of communities in the pandemic prevention and control varies widely in China. Some communities can provide efficient and effective preventive services while others cannot. Some communities have offered sufficient and satisfactory residents’ demand-oriented services while other counterparts have not. The variation of the performance is mainly due to the different levels of communities’ preventive and controlling capabilities [12]. As a result, the main questions that this research addresses are (i) How can community capability be evaluated in China? and (ii) What are the levels of community capability for epidemic prevention and control in China?
Community capability, also called community capacity [13], refers to the residents’ ability to collectively affect community opportunities [14]. According to George et al., community capacity refers to the combined influence of a community’s social systems and collective resources, which is generally applied to address community problems and broaden community opportunities [15]. Since the community is not a passive recipient of outside influences but an active initiator in the effort to achieve specific goals [16], Chavis emphasized more the initiative in the defining process. Chavis identified community capacity as ‘‘the ability to develop, mobilize, and use resources to manage change’’ [17]. With the emphasis on community capacity increasing, assessing community capability has emerged rapidly from the fields of community participation and development in recent years. Brock et al. maintained that the development of capability assessment allows the community to understand its strengths and room for further improvement [18].
Previous research has addressed a few assessment frameworks that are used directly or adapted slightly in evaluating community capability. For example, the NSW Health Capacity Building *Framework is* frequently used to evaluate community capacity in public health management [19,20]. The framework provides a guide for enhancing the capability of the community system to improve health [21]. Goodman et al. constructed a ten-dimension evaluative framework of the community capacity, and subsequent studies further verified the validity of some dimensions and also highlighted the importance of others, such as cultivation of leadership [22,23]. Liu constructed an evaluation index based on Chinese communities’ characteristics, which involves six dimensions, i.e., community participation, community consciousness, horizontal and vertical interaction, leadership, problem assessment ability, and resource mobilization ability [24]. Lee also developed a six-dimension scale for assessing community capacity. It is comprised of leadership and organization, administrative management, resource mobilization, residents’ participation, collaborative work and network, and public relations and initiatives [25].
It is clear that there are some common indices within those evaluative frameworks. They are community leaders who can initiate mobilization, partnerships of community connectedness and concern, the availability of and access to internal and external resources, and linkages and networks that can facilitate collective action [26]. Most of these common indices are incorporated in the community readiness model (hereafter CRM), which is based on structured interview guidelines and scoring systems. The model is widely used to evaluate communities’ preventive ability in drug and alcohol, intimate partner violence, childhood obesity, cardiovascular disease, HIV/AIDS, and cancer prevention [27,28,29,30], demonstrating high validity and wide applicability. Furthermore, the CRM can help theorists and practitioners have a deep understanding of community capability from the perspective of group dynamics [31]. The application of the key informant method inherent in the model contributes to better knowledge of the practical progress of the targeted intervention programs in the communities. In addition, the model provides an easily operationalized measurement tool for scholars and accurate intervention strategies for policymakers to push the community to change.
With regard to studies assessing community readiness for COVID-19 prevention, Adane et al. evaluated community readiness level for COVID-19 pandemic prevention in the Awi Zone of northwest Ethiopia by employing four evaluative criteria including residents’ knowledge, vulnerability perception, attitude, and practice towards prevention measures [32]. Bumyut et al. used the CRM to estimate the community readiness for implementation of the Safety and Health Administration for COVID-19 prevention in the tourism community of Southern Thailand [33].
Previous studies focused mainly on the establishment of evaluation frameworks and the adoption of those frameworks to assess community capability of mobilizing and taking action to prevent chronic disease, serious disease, addictive behavior, domestic violence, and health promotion. Very few studies evaluated community capability to deal with the worldwide COVID-19 epidemic [33]. As far as we know, no studies have been published to evaluate community ability to deal with public health emergencies including COVID-19 in China. Therefore, the main purpose of this study is to estimate community capability to prevent and control the COVID-19 pandemic in China by employing a modified framework of the widely used CRM.
## 2. A Modified Framework of the CRM
The CRM was originally developed by the Tri-Ethnic Center for Prevention Research at Colorado State University to measure a community’s level of readiness to implement a prevention program [34,35]. The aim of constructing this theoretical model is to identify whether a local prevention program can be effectively and successfully carried out and supported by a community, and to offer strategies to help communities’ mobilization for better changes. The original CRM includes five dimensions of readiness: (a) community efforts, (b) community knowledge of the efforts, (c) leadership, (d) community knowledge about the problem, and (e) funding for community efforts [35,36]. In the subsequent development of the theory, the fifth dimension “funding for community efforts” was changed to “resources for community efforts”, in order to incorporate other important resources besides money, including people, time, space, and other factors that also influence community efforts. A sixth dimension called community climate, which refers to characterization of a community, was also added according to the suggestions of community members, who participated in a workshop where the model was presented. These dimensions cover a variety of aspects that can help guide a community in moving their readiness levels forward [29].
The CRM divides community readiness level into nine stages that range from “community tolerance or no awareness of the issue” to “a high level of community ownership”. The nine stages of readiness are (a) community tolerance or no awareness, (b) denial/resistance, (c) vague awareness, (d) preplanning, (e) preparation, (f) initiation, (g) institutionalization or stabilization, (h) confirmation/expansion, and (i) professionalization [27,34]. The specific definition of each stage, which can also be used as an “anchored statement” to evaluate community readiness level, is shown in Table 1. Specifically, stage 1 to stage 9 represents a continuum of low to high level of community readiness to implement a specific program. Stage 1, which corresponds to the anchored scales score of 1, means the lowest level, while stage 9 corresponding to the scales number of 9 means the highest level. Once the stage of community readiness is identified, intervention strategies can be formulated and implemented to raise levels of community readiness [37].
Since the creation of the CRM, it has been extensively used to identify the level of community readiness to develop and implement prevention and treatment programs for addressing a variety of problems ranging from environmental problems such as air and water pollution, litter, and recycling; health and nutritional problems such as obesity, cancer, drug and alcohol abuse, cardiovascular disease, and sexually transmitted diseases; to social problems such as violence, transportation safety, poverty, and homelessness [27,29,30,35,38,39,40,41]. The wide application of this model demonstrates its appropriateness, effectiveness, and high diagnostic power in determining the stages of community readiness and enhancing its level to deal with a wide range of problems. As community readiness is often used interchangeably with community capability [23], and CRM has been shown to be a very effective tool for building community capability [42], we chose to adopt the CRM to evaluate community capability to prevent and control the COVID-19 pandemic in China. We also used community readiness and community capability in the same sense.
With the continuously extended application of the CRM, it has evolved over time and became a flexible organic system that can be adapted to new and different problems [36]. According to Kostadinov et al. [ 43], in order to better tailor the model to the subject area and particular community, scholars make both minor modifications, including modifying the methodology and interview scripts, and substantial changes, including removing or adding the core questions, changing dimensions, adding new dimensions, and altering existing dimensions. For example, Apriningsih et al. examined the school readiness for the implementation of a school-based Weekly Iron Folic Acid Supplementation Program by integrating the social ecological model into the CRM [44]. York et al. substituted a new political climate dimension for the knowledge of existing efforts dimension [45]. Jason et al. divided the community climate dimension into town climate and police department climate, in order to reflect the differences between two sections of the community [46]. Gansefort et al. combined the community efforts and the community knowledge of the efforts into one single dimension [47]. Bumyut et al. removed the community efforts dimension when evaluating the community readiness for effective implementation of COVID-19 prevention measures [33]. Liu et al. also removed community efforts in assessing community readiness for disseminating evidence-based physical activity programs to older adults [48].
The studies mentioned above indicate that modifications to the CRM are widely accepted and employed to make the model better fit the community, the subject area, and the particular issue. Just as Jumper-Thurman et al. addressed, “the model is a research-based tool” [49]. In this study, we substitute community attachment for the community climate dimension to better tailor the model to the Chinese community and the COVID-19 pandemic issue. The community climate is originally defined as the level of community support for specific programs, such as the opportunities, policies, services or staffs, and so on [50]. In its subsequent application to measure community readiness, it mainly refers to community attitudes [51] and is defined as prevailing attitudes within the community concerning the issue [28]. The measuring questions of community climate focus mainly on community members’ attitudes toward the issue, the primary obstacles to community efforts, community members’ support on efforts to address the issue, and the circumstance in which community members tolerate the issue. Most of the measures are similar to the measures of community knowledge of efforts, resources, and community knowledge, especially in the Chinese context. For example, measurements of community members’ support and the primary obstacles can be covered by the measures of resources, and the measurement of community attitudes can be substituted by the measures of community knowledge. Therefore, we remove the dimension of community climate.
Simultaneously, we add the community attachment dimension. Community attachment refers to the feeling of being part of a group that is a source of security and a kind of emotional connection with the community [52]. Attachment also implies that this sense of belonging is positively evaluated, and that one is happy and proud to belong to the community and will take on more responsibility in the community [53]. Community attachment significantly impacts residents’ participation in community activities of improving its members’ well-being and addressing social needs and other urban issues [54]. A number of prior studies have demonstrated the importance of community attachment in explaining community participation and satisfaction [55,56,57,58,59]. For example, community attachment or the sense of community has been shown to significantly predict residents’ involvement in substance abuse prevention activities [60]. Since community participation is an essential element of community capability, the community attachment is thus added, in order to enhance the applicability, appropriateness, and explanatory power of the CRM to evaluate community capability to prevent and control the COVID-19 pandemic in China.
## 3.1. Design and Sample
In this study, community refers to the jurisdiction of the community neighborhood committees. To assess the level of community capability for COVID-19 prevention and control in China, we selected two communities randomly in each of the nine administrative districts in Shenyang, the capital city of Liaoning Province in Northeast China. The reasons why we chose the city of Shenyang as the targeted research area were as follows.
First of all, *Shenyang is* the most important central city in Northeast China. It has a population of 9.118 million and is classified as a mega-city region by the Central People’s Government of China. Conducting a case study of Shenyang could provide some community governance experiences and intervention strategies to enhance community capability to cope with various public health emergencies to other similar mega-cities. Secondly, Shenyang has experienced several serious outbreaks of the COVID-19 pandemic since 2020 and the communities in the city have made tremendous efforts in effectively fighting against the epidemic. Therefore, the communities in Shenyang are representative in the implementation of epidemic prevention and control programs. Evaluating the community capability in Shenyang may contribute to a better understanding of a variety of strengths and weaknesses of the communities and their readiness to prevent and control the epidemics.
After choosing the targeted research communities, we adopted the qualitative approach to assess the community capability level of the prevention and control of COVID-19. Key informants’ interview and anchor scoring method were both used to obtain the research data and identify the accurate capability level, the details of which are displayed in Section 3.2 and Section 3.3.
## 3.2. Data Collection
The CRM is used to evaluate a community’s level of readiness through a semi-structured interview of key informants. Key informants are formal and informal community leaders or decisionmakers who can provide comprehensive and informed opinions about various problems in the community. They represent different sectors of communities and have extensive experience of working with their communities. The criteria of key informant selection in this study are as follows: [1] at least one year of work experience in the community neighborhood committees or living in the community for more than three years; [2] deeply involved in the COVID-19 prevention and control; and [3] willing to participate in the study. Six key informants in each community were selected. Finally, ninety key informants from fifteen communities, including Communist Party of China community neighborhood committee secretaries, community workers, community volunteers, and community elites, were interviewed to acquire the qualitative research data. These interviewees had the best understanding of what happened in the communities and what the communities had done to prevent and control the COVID-19 pandemic.
To ensure the quality of the interview, we developed the final interview outline based on the instructions of the Tri-Ethnic Center, and we also conducted some pre-interviews to modify the outline to make it more suitable to the Chinese community context and more comprehensible for the key informants. The final modified interview outline is shown in Table 2.
The semi-structured interviews with the community key informants were conducted from April to June in 2022. Due to the COVID-19 pandemic, all the interviews were conducted by telephone separately. At the beginning of the formal interview, we ensured that all interviewees had a full understanding of the purpose and process of the research, and we also informed all interviewees that the interview was confidential. Moreover, we obtained the electronic signatures of the interview participants on the informed consent form for the use of the research data. Finally, each interview lasted for about thirty to forty-five minutes, and the interviews were fully audio-taped and transcribed verbatim for subsequent analysis.
## 3.3. Data Analysis
Firstly, according to the instructions given by the Tri-Ethnic Center, anchor scoring method was used to evaluate the specific capability level of different communities. Since the statements of the anchor rating scale were rooted in the Western community context, the contextual differences between China and the West may lead to biased evaluation to a certain extent. Thus, we adjusted the anchor sentences according to experts’ advice, the pre-surveys, and Chinese language expressions. For the newly added dimension of community attachment, we developed the anchor sentences based on the studies of Castañeda et al. and Foster-Fishman [61,62], and the instructions of scoring dimensions by the Tri-Ethnic Center. The final anchor rating scale is shown in Table 3.
Secondly, two raters analyzed and scored the qualitative data obtained from the interviews of the 90 key informants independently based on the modified anchoring rating scale (see Table 3). As the anchored scales define each dimension by using a 9-point rating scale, with 1 representing the lowest levels of capability for that dimension and 9 representing the highest levels of capability, the raters finally scored each interview from 1 to 9 for the 6 dimensions, respectively. In order to ensure the reliability of the rating process, discussions of the items or phrases were made when disagreement occurred, and a third rater was invited to repeat the scoring until reaching a consensus.
Finally, we summed the consensual scores of the six key informants in every community and averaged them to calculate a final score for each dimension. The overall capability level of a community was determined by calculating the average scores of the six dimensions, and the overall level of each dimension for all communities under study was obtained by averaging the dimensional score of every individual community. All the averages were rounded down to generate whole numbers, which were used to identify the overall as well as specific dimensional levels of community capability.
## 4.1. Sample Characteristics
The majority of the key informants were female ($$n = 52$$), accounting for about $58\%$ of the total 90 interviewees. As for occupation, about $40\%$ of the respondents ($$n = 37$$) were secretaries of the community neighborhood committees, who were regarded as the formal or official heads of the urban communities in China. The second largest number of the respondents were the volunteers in COVID-19 prevention ($$n = 24$$). There were also 18 community elites and 11 medical workers participating in the interviews. Moreover, the age of most of the respondents was 30 to 50 years, with the oldest being 54 years old. More than $50\%$ of the key informants have lived in their communities for more than 5 years.
## 4.2. Overall Level of Community Capability
Figure 1 and Table 4 show the final community capability scores and levels of each dimension, respectively. On the whole, we found that the average capability score (ACS) of the total 15 communities was 4.97, which means that the overall level of community capabilities to prevent and control the COVID-19 pandemic in the city of Shenyang was located at the stage of preparation. Furthermore, the capability scores of the 15 communities ranged from 4.28 to 5.61, which indicates that their levels were mainly distributed in the stages of preparation, preplanning, and initiation.
Specifically, $26.67\%$ of the evaluated communities were in the initiation stage, which demonstrates that a quarter of the communities in the city had taken some measures or considerable efforts to control and prevent the pandemic, and some active community members began to participate in the relevant programs. About $20\%$ of the communities stayed in the preplanning stage, which means that there was recognition of the seriousness of the COVID-19 pandemic and the need to take some actions, but there were no concrete plans about how to control and prevent it. Moreover, no efforts or specific plans had been made to deal with the epidemic. More than $50\%$ of the communities were found in the preparation stage, indicating that more than half of the communities were planning to solve the problem, which means that active leaders began to take some actions or formulate some schemes to fight against the disease and various resources were ready to put into use.
## 4.3. Overall Average Level of Each Dimension
Figure 2 displays visually the average scores for each dimension of the community readiness. Leadership, with an average score of 5.86, received the highest score among all the six dimensions. This puts leadership in the stage of initiation, demonstrating that the community leaders were actively running and supporting plans for combating the COVID-19 epidemic. This result was easily understood in the Chinese context because the whole country from the head of the state to the head of the communities all attached great importance to the prevention and control of the COVID-19 pandemic. Community attachment had the second highest mean score (5.20) of all the dimensions, placing it at the stage of preparation. This means that some community members felt that they had the responsibility to participate in the fight against the COVID-19 pandemic and tried best to make their contributions. Community knowledge of efforts and community knowledge about the issue received average scores of 5.09 and 4.87, respectively, both of which are in the stage of preparation. The evaluation results indicate that general information on COVID-19 was available in the communities and some community members had basic knowledge of the pandemic. In addition, the community members also had general knowledge of the plans, policies, emergency management projects of COVID-19 prevention and control, and the leaders and the people involved in combatting the pandemic. The overall average scores of community resources (4.36) and community efforts (4.43) were the lowest among all dimensions, indicating the preplanning stage. The results demonstrate that most of the evaluated communities were facing the dilemma of insufficient resources and inadequate efforts in the prevention and control of COVID-19.
## 4.4. Specific Levels of Each Dimension for the Communities
First, regarding the dimension of community knowledge about the issue, seven communities were at the stage of preparation, three communities were at the initiation stage, while one community was at the institutionalization stage. The remaining four communities were at the preplanning stage. The assessment results reveal that there was a significant difference between the communities in the level of knowledge about COVID-19. According to the interviews, members in some communities were well informed about COVID-19 and had some information about its causes and consequences through communities’ bulletin boards, WeChat groups, and government notices. However, residents in other communities were ill-informed. As a leader from community F said, “the publicity of COVID-19 was inadequate, especially in communities with large aging population, they don’t use the smartphones or internet”.
Second, in terms of community efforts, about half of the communities were at the stage of preparation while the other half were at the preplanning stage, which showed that there were no significant differences in the communities’ efforts, including preparing health emergency schemes, making emergency exercises, popularizing knowledge of tackling public health emergencies, and improving infrastructure construction, to prevent and control the COVID-19 pandemic. Nonetheless, some community efforts were irregular and temporary. As an informant of community G addressed in the interview, “In fact, emergency exercise is not often made and equipment is inadequate, especially for pandemic prevention and control”. In addition, most of the key informants interviewed noted that the lack of grant funding and experts were barriers for communities to make efforts.
Third, concerning the community knowledge of efforts, the levels of this dimension for all communities assessed were higher than that of the community efforts. Eleven communities were at the level of preparation and four communities were at the initiation stage, indicating that there were slight disparities in the communities’ level of knowledge of efforts. The evaluative result also means that community members in most of the communities had general knowledge of the efforts but lacked specific knowledge as well as in-depth understanding of the local efforts. Some key informants described the difficulties to acquire official and up-to-date information regarding the specific plans, policies, and emergency management programs of the COVID-19 prevention and control. As an informant from community L suggested, “further information of the efforts should be widely disseminated in the communities”.
Fourth, regarding the dimension of leadership, four communities were at the level of institutionalization, seven communities were at the stage of initiation, and the remaining four were at the preparation level. The evaluative results revealed that almost all the communities had a higher level of leadership than their level on the five other dimensions. The reason for this result might be that the leadership was very identifiable in the Chinese community context. They were composed of leaders in the community neighborhood committees, community self-government organizations, professional service organizations, and intermediary agencies. They made great efforts on preventing and controlling COVID-19. Most of the key informants interviewed stated that community leaders provided direct and enormous support in the prevention and control of COVID-19, such as distributing resources quickly, organizing staff orderly, and serving the residents wholeheartedly.
Fifth, with respect to the community resources, six communities stayed at the stage of preparation, while nine communities remained in the preplanning stage. The empirical results show that most of the communities had the lowest level of resources among all the dimensions. A possible explanation might be that China had the largest population and the Chinese government attached greatest importance to the prevention and control of the pandemic, which required a great many of financial, human, material, and many other resources to control the spread of COVID-19. In contrast, the various resources needed for preventing the COVID-19 emergency were limited. According to the interviews, the government appropriation and donations from the Red Cross Society were not sufficient to meet financial requirements. The number of community volunteers was also insufficient, with less than 30 volunteers in some communities.
Finally, regarding the dimension of community attachment, four communities were at a higher level of initiation, eight communities were at a medium level of preparation, and three communities were at a lower level of preplanning. The analytical results disclose a big difference in the level of various communities’ attachment. Furthermore, the level of community attachment for most of the communities was higher than the overall level of the community capability, which illustrates the higher level of community attachment than other dimensions. This result corresponded to the fact of Chinese community members’ high sense of participation and high level of involvement in the pandemic prevention and control.
## 5. Discussion
This study applied a modified framework of the CRM for the first time to evaluate community capability to prevent and control the COVID-19 epidemic in the Chinese context. The assessment results demonstrate that the overall capability levels of all the communities evaluated in this research were at the stages of preplanning, preparation, or initiation, which can be together called the “intermediate stages” group in the spectrum of the readiness model, according to Kelly et al. [ 63]. Apart from identifying the overall level of each community’s capability, we also assessed and presented the levels of the six different dimensions, on which community capability was based. The research findings of the present study not only have important theoretical contributions but also show some practical implications to inform policymakers in promoting community capabilities in the prevention and control of COVID-19 and other epidemics.
Firstly, the CRM has been widely used to assess community readiness levels in different countries, such as Australia, the USA, the UK, and other European countries [37,40,47,64]. This study contributed to prior studies by employing the model in the Chinese community context. Moreover, previous research adopting the CRM mainly focused on evaluating community readiness of general prevention programs, such as drug and alcohol [65], obesity [66], violence [67], and healthy lifestyles [68]. To the best of our knowledge, only Adane et al. used the model to assess the level of community readiness for COVID-19 pandemic prevention, which they assessed in the Awi Zone of northwest Ethiopia [32]. Thus, our study also helps to extend the application of the CRM to the evaluation of community capability in the prevention of a public health emergency.
Secondly, the CRM was modified in this study by substituting community attachment for the community climate dimension, in order to improve the appropriateness and applicability of the model in the context of Chinese community. Community attachment mainly refers to community members’ sense of belonging to their community. In other words, it emphasizes the emotional ties that community residents have to their communities. The final results illustrate that community attachment demonstrated a higher level than other dimensions of community capability except leadership. According to the interviews with key informants, the emotional and psychological ties between the residents and their community could activate residents’ willingness to devote various resources and take positive actions to support relevant programs in the prevention and control of the COVID-19 epidemic. This result was consistent with the findings of Peterson and Reid [69]. In addition, we refined a community readiness evaluation index to make it suitable for the assessment of Chinese community capability based on the anchor rating scale developed by the Tri-Ethnic Center, which provides guidance for the operationalization of the CRM in the Chinese context and promotes the application of the model in a wider range of subject areas and issues in China [70].
Thirdly, the number of evaluated communities in most previous studies was less than ten, and one third of prior studies focused on only one community [36]. This research made further contributions by assessing both the overall capability level of the fifteen communities as a whole and the individual level of capabilities for each community. Moreover, the average level of each dimension in the modified model was also reported for the fifteen communities, demonstrating the overall stages of the six dimensions. Thus, this study provides a comprehensive evaluation of community capabilities, by not only regarding the fifteen communities as a geographically larger community, namely the urban city, but also focusing on the different levels of capabilities to prevent and control COVID-19 between each individual community.
Fourthly, the analytical results of this study indicate that leadership is located at the highest level among all the six dimensions to measure community capability of preventing and controlling the COVID-19 pandemic in China. This result corresponds with the findings of Coroiu et al. [ 71] and Kostadinov et al. [ 72]. However, some previous studies found that the dimension of community efforts received the highest scores in both Western settings, such as the UK and the USA, and in the Middle East context, such as Iran [73]. A possible explanation for the different results may be that community leaders in China played an irreplaceable and vitally important role in the process of combating COVID-19, through following the leadership of the Communist Party of China, implementing epidemic prevention and control policy promulgated by the government, and mobilizing community residents to participate. They had authority, resources, and personal influence, which were all critical to improve the community capability level.
Fifthly, community resources and community efforts received the lowest scores in this evaluation, which demonstrates that these two dimensions could be regarded as the biggest weaknesses of the communities in the process of combating COVID-19 in China. Just as some scholars said, “resources are vital to any health intervention program’s success and they serve as potential indicator of future sustainability of the effort” [74]. According to the interviews with the key informants, relevant resources, including people, money, equipment, time, and space, were all insufficient to effectively deal with the pandemic. Specifically, limited financial support was a significant barrier when implementing some prevention programs. Infrastructures such as isolation space and hospital beds were in critical shortage, especially in the old and remote communities. There was also a huge shortage of medical workers and professional nursing staff. The finding that community resources were at the lowest level of capability conformed with existing research that inadequate budgets and expertise, and the underfunding of public health, were major and well-known problems [75,76,77]. Community effort was found to be the second lowest dimension, which contradicted prior studies on ordinary community prevention programs. The different results might be explained by the characteristics of health emergencies, including suddenness, complexity, and harmfulness, which usually made it difficult for communities to devote enough effort.
Finally, the evaluative results of this study also provided some important guiding suggestions and precise intervention strategies to promote the community capability of coping with COVID-19 and various other health emergencies to a higher level. As the results reveal, the levels of the community capability to prevent and control COVID-19 in Shenyang of China were in the stages of preplanning, preparation, and initiation. Therefore, we put forward a few policy suggestions based on the Tri-Ethnic Center Community Readiness Handbook and the evaluative results of the six dimensions for each community. Specifically, for the communities at the stage of preplanning, community members’ awareness of the various impacts of COVID-19 should be continuously enhanced through different forms of media; the engagement of various formal and informal community leaders in the preventive efforts should be increased via mobilization; and the emotional connection between residents and their communities should be reinforced through the provision of diversified forms of daily care and services and volunteer platforms.
For the communities at the stage of preparation, more local data about COVID-19 should be collected and made available for community residents; community key leaders and influential people should be motivated to mobilize community residents to provide more supports for the prevention and control of the pandemic; the effectiveness of preventive policies and programs should be evaluated; and community surveys and public forums should be conducted to solicit new preventive strategies from community leaders and community members. For the communities at the stage of initiation, professional in-service training of the prevention and control of the pandemic should be conducted; publicity efforts regarding the COVID-19 prevention should be further promoted; the progress of the epidemic prevention and control should be continuously updated; evaluation of the various preventive efforts should be increased; interviews with community members should be conducted to obtain their comments about improving the prevention strategies; and more resources including money and people should be invested.
## 6. Conclusions
This study evaluated the community capability to prevent and control the COVID-19 pandemic in Shenyang, the capital city of Liaoning province in Northeast China. The evaluation adopted a modified framework of a CRM by removing the dimension of community climate and adding a new dimension of community attachment. The assessment results demonstrate that the overall level of community capability in the pandemic prevention and control in the city of Shenyang was at the stage of preparation. The specific levels of the selected fifteen communities ranged from the stages of preplanning to preparation to initiation, showing a moderate level of difference. Regarding the level of each dimension, the levels of community knowledge about the issue, leadership, and community attachment exhibit significant disparities among different communities, while the levels of community efforts, community knowledge of efforts, and community resources show slight differences. In addition, leadership shows the highest overall level among all the six dimensions, followed by community attachment and community knowledge of efforts. Community resources present the lowest level, closely followed by community efforts.
Although this study has important theoretical contributions as well as practical implications, there are still some limitations that need to be emphasized. The most important limitation is that the evaluation of the community capability levels relied on the interviews with key informants, mainly including community neighborhood committee leaders, community workers, and community elites, who might overstate their roles and performance and underestimate the performance of others. Future studies could include a broader range of community informants. We also suggest that a combination of qualitative and quantitative approaches be used in future community capability assessment, in order to reduce the impact of subjectivity inherent in the key informant interviews. Another limitation is that the evaluative result of the overall level of community capability in Shenyang might be biased due to the limited number of selected communities and the lower administrative level of key informants in this research. It is suggested that more different types of communities be included in future analysis. Moreover, community key informants at a higher administrative level, including the sub-district, urban district, and the city, could be incorporated to obtain a better understanding of the overall level of community capability in the city. A third limitation of this study lies in its failure to analyze the impact of China’s COVID-19 policy on community capabilities to deal with public health crises. While policy formulation and implementation are crucial in strengthening community capacity, the influencing mechanism of the policies is worth further exploration. The last limitation is that the present study estimates the overall average level of community capability in Shenyang by randomly selecting 15 communities at the jurisdictional level of community neighborhood committee, rather than focusing on a single community. Although this research design has some theoretical contributions, it fails to analyze the specific characteristics of individual communities. An in-depth case study to evaluate a single community might provide more targeted intervention strategies for policymakers to enhance the level of community capability to manage public health emergencies.
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|
---
title: The Management of Obstructive Sleep Apnea Patients during the COVID-19 Pandemic
as a Public Health Problem—Interactions with Sleep Efficacy and Mental Health
authors:
- Anca Diana Maierean
- Damiana Maria Vulturar
- Ioana Maria Chetan
- Carmen-Bianca Crivii
- Cornelia Bala
- Stefan Cristian Vesa
- Doina Adina Todea
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002103
doi: 10.3390/ijerph20054313
license: CC BY 4.0
---
# The Management of Obstructive Sleep Apnea Patients during the COVID-19 Pandemic as a Public Health Problem—Interactions with Sleep Efficacy and Mental Health
## Abstract
With the onset of the COVID-19 outbreak, it was stipulated that patients with obstructive sleep apnea (OSA) may have a greater risk of morbidity and mortality and may even experience changes in their mental health. The aim of the current study is to evaluate how patients managed their disease (sleep apnea) during the COVID-19 pandemic, to determine if continuous positive airway pressure (CPAP) usage changed after the beginning of the pandemic, to compare the stress level with the baseline, and to observe if any modifications are related to their individual characteristics. The present studies highlight the level of anxiety, which was high among patients with OSA during the COVID-19 pandemic ($p \leq 0.05$), with its influence on weight control ($62.5\%$ of patients with high levels of stress gained weight) and sleep schedule ($82.6\%$ reported a change in sleep schedule). Patients with severe OSA and high levels of stress increased their CPAP usage (354.5 min/night vs. 399.5 min/night during the pandemic, $p \leq 0.05$). To conclude, in OSA patients, the presence of the pandemic led to a greater level of anxiety, changes in sleep schedule and weight gain because of job loss, isolation, and emotional changes, influencing mental health. A possible solution, telemedicine, could become a cornerstone in the management of these patients.
## 1. Introduction
COVID-19, a coronavirus-transmitted infectious disease, was first identified in Wuhan, China, and declared a pandemic by the World Health Organization on 11 March 2020 [1,2,3]. The pandemic spread of COVID-19 is an undefined medical challenge and unprecedented measures have been taken worldwide. Moreover, the COVID-19 pandemic placed an enormous burden on the global healthcare system and had a substantial impact on patients with chronic diseases, making their follow-up appointments and surveillance more difficult [4,5,6,7,8,9]. The COVID-19 pandemic had a profound effect on vulnerable populations and, in Romania, it has continued to spread more and more until the present day [1].
The COVID-19 pandemic has had a deeply negative impact on every aspect of patients’ daily lives. Moreover, many of COVID-19’s risk factors are also frequently diagnosed comorbidities of obstructive sleep apnea (OSA), which has become a highly prevalent sleep-related breathing disorder. These comorbidities are associated with high mortality in patients with COVID-19 and include arterial hypertension, coronary heart disease, hyperlipidemia, type II diabetes mellitus, and obesity, being responsible for more severe disease and worse prognosis. In addition, for those with low incomes, in terms of COVID-19 disease, lower socioeconomic status has been associated with severe illness and increased mortality [1,10,11,12].
OSA is defined as a sleep disorder that involves the cessation or a significant decrease in expired airflow in the presence of breathing effort. It is the most common type of sleep-disordered breathing and is characterized by recurrent episodes of upper airway collapse during sleep. These episodes are associated with recurrent oxyhemoglobin desaturations and arousing from sleep [13,14]. OSA is diagnosed if the apnea-hypopnea index (AHI) is greater than or equal to 15 times per hour, or between 5 and 14.9 events per hour, with documented symptoms of unintentional sleep episodes during wakefulness, daytime sleepiness, insomnia, mood disorders, loud snoring, breathing interruptions during the patient’s sleep, or documented hypertension, ischemic heart disease, or a history of strokes [15,16,17].
These sequences of obstructive events, such as apnea or hypopnea, are responsible for a high oxidative stress level and for sympathetic activation, which is involved in the determination of comorbidities associated with OSA. Moreover, it is a well-known fact that the prevalence of these comorbidities is directly correlated with elevated levels of mental distress. It could be argued that the additional mechanisms observed in OSA patients, such as inflammation, oxidative stress, or immune system function issues, could be involved in this link [18,19].
In addition, OSA symptomatology affects day-to-day life, as OSA increases the risk of traffic accidents, being associated with excessive daytime sleepiness (EDS) in approximately $50\%$ of patients [20,21]. Even if the reports differ, in most cases, the driving risk in OSA is more closely related to the degree of daytime sleepiness than to the objectively measured severity of sleep-disordered breathing [22,23,24]. Effective OSA treatment, usually with continuous positive airway pressure (CPAP), rapidly reduces both the apnea-hypopnea index and excessive daytime sleepiness in most of the affected patients, leading to a reduced number of road crashes [25,26,27,28]. These facts have led to a revision of Annex III of the European Union (EU) Directive on driving licenses, which is subject to mandatory implementation by all member states from 31 December 2015 and has become important in Romania as well [29,30,31].
In addition, in the current context, which is the rapid spread of infection, the number of deaths caused by COVID-19, the imposition of home confinement for indefinite periods of time, and the growing financial losses incurred can convey an increased risk of psychiatric conditions among all layers of society, which will also adversely affect the risk of car crashes. Moreover, several studies on many related subjects have been published, showing a high prevalence of anxiety, stress, depression, and post-traumatic stress disorder (PTSD) in healthcare workers, a higher risk of distress, anxiety, depression, and sleep disturbance in nurses, and a sevenfold increase in depression rates in the general population [32,33].
Depression and anxiety have a clinically relevant association with OSA, and, as Lee et al. observed in a female cohort, excessive daytime sleepiness and lower education levels were related to anxiety [34]. OSA patients with depression could experience higher levels of fatigue and lower quality of life than OSA patients without mood disorders. A recent meta-analysis showed relatively high rates of symptoms of anxiety (ranging from $6.3\%$ to $50.9\%$), depression (14.6–$48.3\%$), post-traumatic stress disorder (7–$53\%$), psychological distress (34–$38\%$), and stress (8.1–$81.9\%$) in the general population across the globe during the COVID-19 pandemic. Anxiety and depression are known to have reciprocal relationships with insomnia [10,35] and it has been shown that the prevalence of all forms of psychological distress in the general population has been higher during the pandemic [36,37]. In these conditions, depression and anxiety could also make CPAP therapy difficult in OSA patients. This suggests that undiagnosed or untreated OSA patients, those with lower continuous positive airway pressure compliance, and those with mood disturbances may be at higher risk of developing severe forms of COVID-19 than the general population [38,39].
Anxiety is a normal reaction to a stressful situation and the response to supportive interventions and coping strategies is generally positive. For example, increased anxiety levels during the pandemic are associated with fuller compliance with governmental measures and hygienic practices. Moreover, during pandemic times, anxiety regarding health matters can rapidly become excessive. For these subjects, this stressful situation can determine anxious behavior (repeated medical consultations, avoiding health care even if needed, etc.); in the general population, it can lead to mistrust of public authorities’ safety measures or non-adherence to infection control strategies and the stigmatization of certain groups [32,40].
In the context of the risk of car crashes and depression or anxiety, during the COVID-19 pandemic it was observed that, of all road accidents, there was an increased number of speed-related crashes and fatalities. Vingilis et al. identified several potential factors that affected road safety during the pandemic, with personal factors such as a propensity for risky behavior, along with situational factors such as fuel price increases and the improper application of laws [41]. We must also consider that, during the pandemic, high levels of stress, depression, and anxiety were reported in some population groups, while increased alcohol sales and use were observed [42,43,44]. With these well-known factors affecting road crashes, the multifactorial impact of the pandemic on road safety requires an interdisciplinary approach [45].
To determine the anxiety or depression status of OSA patients, the stress perception questionnaire (PSQ), developed by Levenstein [46], is a useful multiple-choice questionnaire (Table S1 in the Supplementary Materials).
There were 3 main objectives of the current study:[1]To evaluate how patients managed their sleep apnea during the COVID-19 pandemic, considering their symptomatology and comorbidities and the higher risk of severe disease with a fatal outcome;[2]To establish if CPAP adherence has increased/decreased after the onset of the pandemic;[3]To compare the stress level in OSA patients with their pre-pandemic levels and to observe if its modification is related to their individual characteristics (age, gender, and BMI) or to comorbidities and apnea severity.
The aim of the study was to assess the level of stress and anxiety in OSA patients during the COVID-19 pandemic and to compare them to the pre-pandemic period, to quantify the subjects’ characteristics (the severity of OSA and their comorbidities), their impact on individual perceptions regarding COVID-19 infection, and to quantify if the stress or other factors (age, gender, environment, individual perception about COVID-19, and the level of knowledge) influence their CPAP compliance. This paper studies the impact of the COVID-19 pandemic on patients from Transylvania, who were diagnosed with OSA in the Sleep Laboratory of the “Iuliu Hatieganu” University of Medicine and Pharmacy, regarding their stress level and, as a consequence, their CPAP usage. Information obtained from this survey indicated if OSA subjects who were treated at home with a CPAP device were careful enough to ensure that their disease was being effectively controlled during the pandemic. The study also took into consideration the degree of awareness of OSA patients that they are more susceptible to COVID-19 and are more likely to develop more severe complications of the disease.
The present article is organized with an introduction, materials and methods, results, a discussion, and our conclusions. Each chapter has subchapters pointing out the parameters for analysis and the main findings of the study.
## 2.1. Study Design and Setting
Between 16 March and 14 May 2020, we conducted a retrospective observational study in the Sleep Laboratory of the “Iuliu Hatieganu” University of Medicine and Pharmacy. The study included patients diagnosed with OSA from September 2019 to November 2019 who underwent CPAP therapy at home.
At their diagnosis, all participants underwent a cardiorespiratory sleep study using a VitalNight Pro polygraph device, which incorporates continuous recordings from a nasal cannula, heart rate, oxygen saturation, tracheal sounds (microphone), thoracic and abdominal movement, and body position. The sleep study results were analyzed and approved by trained personnel. CPAP titration was made using a CPAP device (Philips Respironics Dream Station Auto CPAP) after a validated protocol [47]. The initiation of therapy was indicated according to Medicare guidelines, as follows: all patients with an AHI greater than 15 were considered eligible for CPAP, regardless of symptomatology; for patients with an AHI of 5–14.9/h, CPAP was indicated only if the patient had one of the following symptoms: excessive daytime sleepiness, impaired neurocognitive function, mood disorders, insomnia, cardiovascular disease (e.g., hypertension or ischemic heart dis-ease), or a history of stroke [6]. After the initiation of CPAP therapy, the patients underwent a medical visit one month after the titration, which included reading the CPAP therapy device cards to evaluate the residual AHI and therapy compliance.
During the national state of emergency (SOE), a telephone questionnaire survey of 46 OSA patients was conducted, and telemedicine was used for the first time in the management of OSA subjects in our center. Patients from the Transylvania region who had been diagnosed in our department were contacted by us, either for a follow-up visit or to examine the progress of their OSA management. Patients offered their written consent and the documents were sent to them via e-mail. After they agreed to participate in the form of a video or telephone call, the patients returned the signed documents and they were enrolled in the study.
Moreover, for those OSA subjects included in the survey, information was offered to ensure that the CPAP device was being used effectively, and that adequate supplies were available, with appropriate masks, tubing, and CPAP machine sanitization, to encourage regular adherence to nightly CPAP use, and that patients were following the Centers for Disease Control and Prevention guidelines.
The data recorded by the CPAP devices were downloaded by our sleep technicians, using VitalNight EasyScore software version 5.22afrom the database wherein the information from patients’ CPAP devices is stored via telehealth services and was interpreted according to the guidelines [13] issued by the sleep-medicine doctors involved.
## 2.2. Participants
During the study period, a total of 108 patients who were diagnosed in our clinic were contacted.
The inclusion criteria for patients were an age ≥ 18 years, a diagnosis of OSA established between September 2019 and November 2019, with CPAP therapy at home for at least three months, and, at the moment of the survey, to be undergoing CPAP therapy. We considered that patients who do not meet the inclusion criteria were excluded from the study. The exclusion criteria were that the patients should be aged ≤18 years, with a diagnosis of OSA established before September 2019 or after November 2019, and those patients diagnosed with OSA who were not using CPAP therapy at home.
After applying the inclusion criteria, a cohort of 46 patients was obtained. From the total number of patients contacted, $24.07\%$ (26 subjects) had abandoned CPAP therapy of their own free will (from their diagnosis of OSA until the time of the survey), $25\%$ (27 subjects) could not be reached (9 of them had moved from the city, 10 changed their phone numbers or did not answer, and 8 had died) and $8.3\%$ (9 patients) did not offer their informed consent (Figure 1).
All subjects gave their informed consent for inclusion before participating in the study. The study was carried out in accordance with the Declaration of Helsinki [48] and the protocol was approved by the Ethics Committee of the “Iuliu Hatieganu” University of Medicine and Pharmacy Cluj-Napoca, with the reference number $\frac{270}{02}$ February 2020.
## 2.3. Variables
The collected data (Table 1) comprised personal information (sex, age, environment, and smoker status), anthropometric measures (weight, height, BMI, neck circumference, and abdominal circumference), associated diseases (hypertension, chronic ischemic cardiopathy, myocardial infarction, dyslipidemia, cardiac failure, diabetes mellitus, asthma, and COPD), sleep parameters collected at diagnosis according to the database (AHI, ODI, SaO2 minimum, average SaO2, and nocturnal and diurnal symptomatology), Epworth sleepiness scale rating (at diagnosis, one month after the diagnosis, and during the pandemic) (Table S2 in the Supplementary Materials), CPAP parameters one month after the diagnosis, according to the database, and during the pandemic (average time of use, compliance above 4 h, residual AHI), and PSQ score (at baseline and during the pandemic).
## 2.4. Data Sources
As a classical instrument to evaluate stress levels, we used the perceived stress questionnaire (PSQ), an assessment used as a routine measurement tool in our laboratory and during the pandemic period, along with an original questionnaire designed for OSA patients that were on CPAP treatment. The perceived stress questionnaire is used as an instrument to assess the stressful life events and circumstances that tend to trigger disease symptoms [46]. With stress significantly affecting the quality and consistency of the sleep cycle, the PSQ is a potentially valuable tool to evaluate the underlying cause of sleep disturbances. To complete the PSQ, respondents receive one of two sets of scoring instructions: the general questionnaire queries stressful feelings and experiences over the course of the previous year or two, while the more recent questionnaire concerns stress during the previous month. Respondents indicate on a scale of 1 (“almost never”) to 4 (“usually”) how frequently they experience certain stress-related feelings. Higher scores indicate a greater level of stress. The interviewer must remind the subjects that they must circle the number that describes how often the sentences applied to their situation in the last month. A total score is found by tallying each item (questions 1, 7, 10, 13, 17, 21, 25, and 29 are positive and are scored according to the directions accompanying the scale). A score between 30 and 59 is classified as reduced stress, between 60 and 89 is classified as moderate stress, and between 90 and 120 is classified as high stress (Table S1 in the Supplementary Materials).
The original questionnaire was conceived considering our experience with COVID-19 patients and included 7 questions regarding the individual’s COVID-19 status, the modifications of the daily schedule (working from home), the subjective considerations about feeling depressive or anxious, the changes in sleep schedule and sleep quality and the factors that can influence it, and the individual’s perception of the risk of contracting COVID-19 (Table S3 in the Supplementary Materials).
## 2.5. Statistical Analysis
Statistical analysis was performed using MedCalc® Statistical Software, version 20.014 (MedCalc Software Ltd., Ostend, Belgium; https://www.medcalc.org; 2021, accessed on 16 October 2022). Quantitative data were examined for normality of distribution, using the Shapiro–Wilk test, and were expressed as mean ± standard deviation or median and 25th–75th percentiles. Qualitative data were expressed as frequency and percentage. Regarding the changes in CPAP compliance (nights with CPAP usage of over 4 h) during the COVID-19 pandemic, compared to one month after diagnosis, for our sample size, we calculated the power of the study to be at $99\%$ (α = 0.01) for a level of significance of $1\%$ (β = 0.01). Comparisons between groups regarding qualitative variables were performed using the chi-square test. Comparisons between groups regarding quantitative variables were performed using the Mann–Whitney test. Correlations between variables were verified using Spearman’s rho, a non-parametric test used to measure the strength of association. The Wilcoxon test, a non-parametric test, was used to evaluate the change of a variable between two repeated measurements. A p-value of lower than 0.05 was considered statistically significant.
## 3.1. General Characteristics at Baseline of the Included Patients
The information was collected from patients diagnosed with OSA from September 2019 to November 2019. We chose this period because, after three months of home CPAP therapy, the doctor can determine the individual pattern of CPAP for the patient, so the adherence parameters are more stable [49].
The mean age of the studied population was 56.7 years, ranging between 37 years and 76 years; $80.4\%$ of the subjects were males and $19.6\%$ were females. In addition, considering the smoker status, $28.3\%$ of the subjects were non-smokers and $71.7\%$ were active smokers.
One of the most important comorbidities was obesity, so the mean body mass index (BMI) in our study group was 36.33 (33.42–41.21) kg/m2, with most of the patients being obese. In addition, the anthropometric measurements recorded at diagnosis indicate a median neck circumference of 47.4 ± 4.3 cm and a median abdominal circumference of 124.5 (115.75–141.00) cm, suggesting central disposition of the adipose tissue, which predisposes patients to the development of OSA.
Most of the included patients also presented associated pathologies correlated with OSA; $78.3\%$ had hypertension, $47.8\%$ had dyslipidemia, $10.9\%$ of the patients had suffered cardiac failure, and $4.3\%$ of the patients had a myocardial infarction prior to the evaluation, leading to high morbidity and mortality levels in the context of SARS-CoV-2 infection. Of the patients, $23.9\%$ were diabetic; $17.4\%$ were treated with oral antidiabetic medication, $4.3\%$ were insulin-dependent, and $2.2\%$ needed insulin, a disease with a high impact on SARS-CoV-2 patients’ evolution. Moreover, 10 patients presented chronic respiratory diseases: $4.3\%$ had asthma, and $17.4\%$ suffered from COPD (Table 2).
We included the PSQ questionnaire as a routine evaluation; the mean score at diagnosis was 54 (41.75–66.25), with 17 ($36.9\%$) of the subjects having a minimum stress level and 29 ($63.1\%$) having a moderate stress level (Table 2).
## 3.2. The Sleep Parameters at the Baseline
As seen in Table 3, we quantified the nocturnal respiratory parameters at the time of diagnosis. The mean AHI was 63.75 (39.9–81.2) events/hour of sleep and the desaturation index was 59.6 (38.8–79.1) events/hour of sleep, indicating that most of the subjects have severe OSA. In addition, the minimum oxygen saturation was $65\%$ (60–$74.3\%$) with a mean oxygen saturation of $87.5\%$ (81.8–$90.3\%$), showing that the patients have important nocturnal desaturations. We also quantified the presence of nocturnal and diurnal symptoms at the time of diagnosis; $95.7\%$ reported snoring, $65.2\%$ had apnea episodes during the night that had been observed by their family, $15.2\%$ had nightmares, $78.3\%$ had nocturia, $80.4\%$ reported daytime sleepiness, $45.7\%$ reported a morning headache, $67.4\%$ reported morning fatigue, $43.5\%$ reported that it influenced their work capacity, those being the most frequent symptoms in people with sleep apnea. The Epworth sleepiness scale showed a median value of 16 (12–19.25) and, in $82.6\%$ of subjects, the total score showed values above 10 points; therefore, in 38 cases, we identified excessive daytime sleepiness (Table 3).
## 3.3. CPAP Compliance Parameters at One Month after Diagnosis
One month after diagnosis, the patients diagnosed with OSA performed a routine evaluation. By reading their CPAP cards data, we analyzed their CPAP compliance and concluded that the average duration of use was 354 (288.7–389.2) min/night, their compliance above 4 h was 69.5 (54.7–76.0)% and their residual AHI was 5.6 (3.35–9.3) events/hour of sleep (Table 4). As seen in Figure 2, $28.26\%$ had a residual AHI above 5 events/hour of sleep, $52.18\%$ had between 5–14.9 events/hour of sleep, and $19.56\%$ had a residual AHI above 15 events/hour of sleep, one month after their OSA diagnosis. As seen in Table 4, we followed the patients’ CPAP compliance and compared it one month after their diagnosis to one month during the pandemic. The average use per night of CPAP increased from a median of 354.5 min one month after their diagnosis to 399.5 min during the state of emergency, with statistical significance ($$p \leq 0.00$$). As a result, compliance above 4 h showed an improvement from a median value of $69.5\%$ one month after diagnosis to $79\%$ during the state of emergency ($$p \leq 0.000$$). This finding leads to a reduced number of events per night, from a median of 5.6 one month after diagnosis to 2.4 during the state of emergency ($$p \leq 0.000$$). CPAP compliance during the state of emergency increased in the case of patients diagnosed with a COVID-19 infection. Those subjects had a median compliance of $73\%$ (58.75–$79.00\%$) one month after their OSA diagnosis and of $82\%$ (72–$84\%$) during the state of emergency. In addition, CPAP compliance during the state of emergency increased in the case of patients with COVID-19. In addition, the patients increased their CPAP usage from 363 (311–402.75) min/night to 429 (398.75–464.5) min/night ($$p \leq 0.012$$).
As mentioned before, we evaluated the patients using a questionnaire comprising 7 questions. Regarding question no. 1 of our evaluation, $43.47\%$ of the subjects were diagnosed with COVID-19 at the beginning of the pandemic ($70\%$ men, $30\%$ women) and of those, $70\%$ presented important symptomatology, more frequently showing fever, cough, shortness of breath, myalgia, and expectoration. Of all subjects infected with SARS-CoV-2, $80\%$ had arterial hypertension, $75\%$ had diabetes mellitus, and $25\%$ had dyslipidemia, these being the most prevalent comorbidities. This fact is very important since those comorbidities are the most prevalent in OSA subjects.
In addition, considering question no. 2 of the questionnaire, $56.52\%$ of the participants had at least one member of the family diagnosed with COVID-19; $80.4\%$ of the subjects of the study population lived isolated from their family during the state of emergency, which had a profound impact on their mental state.
Regarding question no. 3 about each subject’s financial situation, during the state of emergency, $47.8\%$ of patients worked from home and $17.4\%$ became unemployed, with the rest of the study group already being unemployed at that time. Even if, of the entire population, $45.65\%$ of the subjects reported that they had experienced symptoms of depression, especially a lack of motivation and concentration, and $36.95\%$ experienced anxiety, in the case of those patients that lost their jobs, subjective depression symptoms were reported in $100\%$ of cases ($$p \leq 0.000$$, compared with those who worked from home) and $75\%$ had worries about financial loss and experienced nightmares, but not anxiety. In addition, we identified a strong correlation between unemployment and the modification of sleep patterns. Therefore, all the patients who lost their jobs presented a changed sleep schedule: $75\%$ of the patients slept less and $25\%$ slept more.
Regarding question no. 4 of our evaluation, $82.6\%$ reported a sleep schedule that had been modified in the last 3 months; from those, $50\%$ had a COVID-19 infection. Other complaints concerning sleep quality were as follows: $13.1\%$ of cases experienced difficulties in maintaining sleep, $13.1\%$ had subjective excessive daytime sleepiness, waking up early was reported in $8.7\%$ of the cases, and $4.3\%$ stated that they had unrefreshing sleep. The usage of different substances (question 4b) to improve the subjective quality of sleep was analyzed. While melatonin, cannabis, or other substances were used in reduced proportions, we observed that $52.2\%$ of the patients included in the study had used alcohol to obtain a better quality of sleep.
In terms of question no. 5 (both a and b) regarding the high prevalence of obesity, we analyzed weight fluctuations during the state of emergency, compared to basic values, and the correlation with different parameters. Therefore, $84.8\%$ of the patients reported that their weight was modified in the last three months; of these, $64.1\%$ gained weight, with an average weight gain of 6.24 kg, while $35.9\%$ lost weight, with an average weight loss of 6.78 kg. Conversely, $11\%$ reported that their weight was constant, while $4.2\%$ did not know how to answer the question. The correlation between weight and compliance with CPAP was approached. As a result, $52.3\%$ of the patients that gained weight used their CPAP more, mostly because of the accentuation of OSA symptoms, compared with only $28.57\%$ of those who had lost weight. Regarding weight gain and sleep schedule modifications, $84.61\%$ of the patients who gained weight had a modified sleep pattern and claimed unrefreshing sleep, subjective daytime sleepiness, and morning fatigue.
On collating the answers to question no. 6, we concluded that $73.9\%$ of the subjects felt fear regarding COVID-19 infection, while $84.8\%$ considered that they had an increased risk of COVID-19 infection. From the total number of subjects, only $65.2\%$ of the included people had information about the pandemic, obtained in equal proportions from the Internet, television, and from their attending doctor.
Regarding question no. 7 on the state of emergency, the emotional aspect of OSA patients was influenced in $34.7\%$ of cases by worries about themselves, $39.13\%$ were affected by loneliness, $19.56\%$ experienced nightmares, $17.39\%$ were influenced by worries about financial loss, and $10.86\%$ were influenced by worries about family/friends, with most of the patients experiencing more than one emotional problem.
We also analyzed the Epworth sleepiness score during the state of emergency and we observed that only $39.1\%$ of the patients currently had a score above 10, compared to $71.3\%$ at the time of diagnosis. Moreover, the median value of the Epworth score at diagnosis was 16 versus 5 during the state of emergency ($$p \leq 0.00$$) and the Epworth score at diagnosis correlated with compliance above 4 h during the state of emergency ($$p \leq 0.02$$).
In addition, as stated before, the perceived stress questionnaire (Table S1 in the Supplementary Materials) was applied two times, at the time of diagnosis and at another evaluation during the emerging cases of COVID-19, between 16 March and 14 May 2020; this was included in the patient’s medical records. The median value for the stress perception scale before the state of emergency was 54 and increased during the state of emergency to 93, a difference with clinical significance ($$p \leq 0.000$$). During the state of emergency, $17.39\%$ of the subjects were in the lower stress category, $30.43\%$ in the moderate stress category, and more than half of the subjects, $52.17\%$, were in the intensive stress group. Regarding the weight modification and stress perception scale categories, both of these being factors with implications for CPAP compliance, a correlation was made; $75\%$ of the patients included in the lower stress category lost weight. For the medium level of stress, 2 ($14.3\%$) lost weight, 3 ($21.4\%$) maintained a constant weight, and 9 ($64.3\%$) gained weight. In the category of a high level of stress, 6 ($25\%$) of the patients were represented by those who lost weight, 3 ($12.5\%$) maintained the same weight, and 15 ($62.5\%$) gained weight. Moreover, the anxiety and depression modification percentages may be influenced by the fact that we included only 9 females in our study; this is significant because they are underdiagnosed with OSA due to the lack of classic symptoms. Studies that include a greater number of patients need to be developed, in order to clarify if the emotional changes are more important in the female population of OSA patients.
As stated in Table 5, we observed that compliance above 4 h before the state of emergency was negatively correlated with PSQ at the time of diagnosis ($$p \leq 0.001$$), AHI ($$p \leq 0.006$$), and desaturation index ($$p \leq 0.000$$), and was positively correlated with the minimum saturation of oxygen ($$p \leq 0.40$$). Events per hour of sleep one month after diagnosis were correlated with AHI (0.031) and desaturation index ($$p \leq 0.012$$), and were indirectly correlated with the minimum saturation of oxygen ($$p \leq 0.030$$).
Average use of AutoCPAP therapy per night during the state of emergency correlated with AHI ($$p \leq 0.001$$), the desaturation index ($$p \leq 0.020$$), and PSQ evaluated during the state of emergency ($$p \leq 0.002$$).
Taking into consideration that there might be other factors that influence the usage of CPAP, we evaluated the correlation between compliance before and during the state of emergency, along with the grade of apnea or level of stress. Therefore, a statistically significant correlation was identified between compliance during the state of emergency and AHI at the time of diagnosis ($$p \leq 0.049$$). Moreover, the correlation between compliance during the state of emergency and the PSQ score during the state of emergency is also statistically significant ($$p \leq 0.014$$).
## 4. Discussion
The COVID-19 pandemic brought a new challenge to healthcare systems worldwide. The concern for patients with comorbidities was founded on high morbidity and mortality levels in this category. Although there are currently few studies regarding the prevalence of COVID infection in patients who had previously been diagnosed with OSA, the data show that the presence of this pathology increases the risk of a negative outcome in COVID-19 patients [4,50,51,52].
Furthermore, infection with the new virus leads to the development of an aggressive type of pneumonia with a high risk of mortality in elderly patients, particularly in those with comorbidities, such as diabetes, obesity, and hypertension [4,5,12,53]. The association between OSA and COVID-19 is currently undergoing research. To evaluate the impact of the COVID-19 pandemic on an OSA patient’s status, we conducted a study that included 46 subjects diagnosed with OSA who received CPAP treatment at home for at least three months before the beginning of the pandemic, because the usage pattern and the adherence parameters were more stable at this period of time.
The treatment of OSA patients and the use of CPAP devices remain major challenges for healthcare professionals. Good adherence and the proper use of CPAP devices are the main elements in the successful management of disease in these patients [10,54].
OSA is a highly prevalent disease that occurs in $24\%$ of young to middle-aged men and $70\%$ of older men, in $9\%$ of young women, and in $56\%$ of older women [55]. The results are comparable with those of our study, in which the disease was found in $86\%$ of cases in men. It has been postulated that the higher clinical ratio may be a result of the fact that women do not show the “classic” symptomatology and, thus, may be underdiagnosed.
Obesity has long been known to be associated with OSA; for both genders, the body mass index (BMI) correlates positively with the severity of the disease [56,57]. This hypothesis is supported by our study, the mean BMI of our cohort being 36.33 kg/m2, with a mean neck circumference of 47.4 ± 4.3 cm and a median abdominal circumference of 124.5 cm, indicating a central disposition of the adipose tissue.
In addition, as Bonsignore et al. showed, OSA patients show a high prevalence of cardiovascular diseases (systemic hypertension, coronary artery disease, arrhythmias, and ischemic stroke), respiratory diseases (COPD and asthma), and metabolic disorders (diabetes mellitus, dyslipidemia, and gout) [58]. In our study, $78.3\%$ of patients had systemic arterial hypertension, $47.8\%$ had dyslipidemia, $10.9\%$ suffered cardiac failure, $4.3\%$ suffered a myocardial infarction, $23.9\%$ were diabetic, and $21.7\%$ had respiratory diseases ($4.3\%$ had asthma, and $17.4\%$ had COPD). OSA and COVID-19 disease share common comorbidities; studies showed that many patients who had developed a severe form of COVID-19 infection also had diabetes, systemic arterial hypertension, and other respiratory diseases [59]. The profile of patients at greater risk of developing complications or even of death associated with COVID-19 infection [4,53,54,60] is similar to our population of OSA patients, many of them being diagnosed with obesity ($75\%$), hypertension ($80\%$), dyslipidemia ($25\%$) and other associated respiratory pathologies.
Moreover, we included the PSQ questionnaire as a routine evaluation; the mean score at the time of diagnosis was 54 (41.75–66.25), with 17 ($36.9\%$) subjects having a minimum stress level and 29 ($63.1\%$) having a moderate stress level. As shown in the study by Celik et al., OSA patients are predisposed to a significant level of stress, which is reduced while using CPAP [61].
At the beginning of the COVID-19 pandemic, we elaborated an original questionnaire (Table S3 in the Supplementary Materials) which showed that $43.47\%$ of the OSA subject sample was infected with COVID-19 and had important symptomatology. In addition, $56.52\%$ of the participants had had at least one family member diagnosed with COVID-19, and most of the patients lived in isolation. In the current situation of the global pandemic, studies show that OSA patients are generally aware of their supplementary risk level when compared to the general population [35,62]. The results of our research show similar results, taking into consideration that most of the patients lived in isolation from their families, in order to minimize the spread of the virus through CPAP-generated aerosols. Furthermore, most of these subjects had more than one comorbidity, increasing their risk of developing a severe form of COVID-19 infection, as stipulated in the literature [59].
In our study, most of the patients had severe OSA, with a mean AHI of 63.75 (39.9–81.2) events/hour of sleep, a highly important aspect considering that severe and moderate OSA becomes a risk factor when a patient is diagnosed with COVID-19, as the literature reveals [63]. The patients’ associated symptoms, such as snoring, apnea episodes reported by the family, daytime sleepiness, morning fatigue, and morning headache influenced work capacity, nightmares, and nocturia. In addition, as the literature shows, the main symptoms seen in OSA patients include excessive daytime sleepiness, non-refreshing sleep, fatigue, morning headache, memory loss, nocturia, and irritability. Untreated OSA is also associated with a lack of concentration leading to a loss of productivity in the workplace and road crashes, as previously shown in many studies, that resulted in fatality [21,24,64]. Even if we did not take into consideration the risk of road crashes, it is important to mention that the coexistence of untreated OSA and depression or anxiety during the COVID-19 pandemic has become an important risk factor for these events.
Data from question 3 showed that $17.4\%$ of the subjects lost their jobs and $47.8\%$ worked from home, the rest of the study population already being unemployed at that time. The patients that lost their jobs or worked from home reported symptoms of depression, especially a lack of motivation and concentration, along with anxiety, factors that have a substantial influence on the subjects’ mental health. These data are similar to those from the literature, which stipulated that financial and social concerns resulting from the pandemic may increase rates of insomnia and anxiety during a lockdown [65]. In the case of the subjects who lost their jobs, a change in sleep pattern was observed; $75\%$ slept less and $25\%$ slept more, a finding that is supported by the specialized literature, which showed that during the lockdown, many subjects had irregular sleep schedules, less exposure to daylight, less physical activity, depression, anxiety, and more screen time, with many reporting lower sleep quality despite, perhaps, longer sleep duration [65].
We analyzed the weight fluctuation patterns during the state of emergency and observed that $64.1\%$ of the patients gained weight (6.29 kg median) and $35.9\%$ of the patients lost weight (a median of 6.78 kg). Additionally, $84.61\%$ of the patients had a modified sleep schedule and sleep disturbances and $52.3\%$ of them used CPAP more. It is a well-known fact that patients with mild OSA who gain $10\%$ of their baseline weight are at a sixfold-increased risk of progression of OSA, while an equivalent weight loss can result in a more than $20\%$ improvement in OSA severity. In addition, obesity may negatively influence the control of OSA because of fat deposition at specific sites in the body, which is the reason why OSA symptoms are more frequent and important, meaning that the patients feel more confident in using their CPAP devices [66,67].
Furthermore, our patients were adherent to CPAP treatment during the time of the COVID-19 pandemic because they were concerned about complications due to COVID-19. In addition, a significant percentage, $63\%$, of the patients increased their usage of CPAP during the pandemic. The presence of a high level of anxiety and depression, as well as increased worry concerning contracting the virus, may be a possible explanation for their good compliance with the treatment of OSA.
Data from the literature show that OSA patients have an increased level of stress regarding COVID-19 infection and the possible outcomes of the disease, an aspect that influences their mental health and their behavior [68,69]. The studies showed that high levels of stress during the state of emergency correlated with increased CPAP adherence and an improved outcome and management of OSA, with fewer sleep events [68]. Moreover, a study from France, which included 7485 OSA patients and CPAP users and lasted for a year and four months, has also demonstrated that CPAP adherence increased remarkably during the lockdown period ($3.9\%$, $p \leq 0.001$). Moreover, when comparing the data with the same period in 2019, from 15 March to 15 April, they stated that very low adherence to CPAP therapy decreased from $18.5\%$ in 2019 to $4.48\%$ in 2020 ($p \leq 0.001$) [68]. Our data showed that the level of stress increased significantly during the state of emergency with a median PSQ value of 54, versus 9.3 before the pandemic ($$p \leq 0.000$$). Moreover, the average use per night (354.5 min vs. 399.5 min, $$p \leq 0.000$$) and compliance above 4 h/night ($69.5\%$ versus $79\%$, $$p \leq 0.000$$) increased significantly. The number of events per night (5.6 versus 2.4, $$p \leq 0.000$$) decreased significantly. Therefore, better adherence to the treatment was obtained during the state of emergency; these data offer useful information suggesting that OSA patients were triggered to obtain greater control over their OSA during the COVID-19 pandemic [68,70].
Of our studied population, $82.6\%$ reported sleep disturbances, $45.7\%$ reported signs of depression, and $37\%$ reported anxiety. In total, $71.7\%$ were worried about contracting the new virus. Multiple data from the literature reported sleep disturbances during the COVID-19 outbreak in patients with OSA [69,71,72]. Still, the improvement of the median Epworth score before and during the state of emergency (16 versus 5), despite the sleep disturbances reported by the patients, is an additional factor in favor of increased adherence to the CPAP regimen (the score correlated with compliance above 4 h, $$p \leq 0.02$$). The correlation between a lack of sleep and depression and anxiety was proven [69]. Mood disturbances and modified sleep schedules have become issues that worsen during a public health crisis, such as in the COVID-19 pandemic [73]. As Grubac et al. showed in two studies on rats, the duration of sleep fragmentation is a significant determinant of anxiety-linked behavior and, thus, may also have influenced the anxiety level in our cohort [74,75].
Multiple sleep societies have formulated the advice that OSA patients who use CPAP devices should self-isolate if they have COVID-19-compatible symptoms. Our results show that $72\%$ of those patients with members of the family who tested positive slept in different rooms. Still, the recommendation was not feasible during the stressful pandemic period [76].
One study from New York, which included 122 patients using CPAP therapy, reported that most of the subjects included did not believe that they had an increased risk of contracting COVID-19 or could suffer a more complicated outcome. Still, $88\%$ of the patients continued to use their CPAP device [77]. From our studied population, $84.8\%$ of the subjects thought that they had an increased risk of COVID-19 infection. Moreover, $100\%$ of the patients included in our study continued to use their CPAP devices. These differences may be explained by the different spreads of the disease and a later maximum incidence in the USA, compared to Europe. As a result, in the abovementioned study, $9\%$ of the patients tested positive for coronavirus infection [78], while $43.5\%$ of the patients were COVID-19 positive.
Treatment and the efficient management of OSA patients during the COVID-19 outbreak was a major challenge for healthcare specialists, mainly because of the closing down of most of the sleep centers. The opportunity to communicate with patients via telemedicine offered better control over the situation by encouraging many OSA patients to control their symptoms, adjusting the settings of the ventilator, and offering therapeutic indications [79,80].
The management of OSA subjects during the COVID-19 pandemic has been a challenge, and discordant statements have been made. While some authors recommend that patients with OSA should give up the use of CPAP at home during the COVID-19 pandemic [81], others have recommended that CPAP therapy be continued, especially for those with other comorbidities [82,83]. Still, most of the patients included in the present study described feeling greater health security when using CPAP therapy, reducing their level of anxiety and depression.
The lack of use of CPAP devices at home would affect their quality of life and the return of their symptoms, with an increased risk of cerebrovascular and cardiovascular diseases for OSA patients [83,84]. In addition, untreated OSA can increase the risk of car accidents, along with other factors, such as unrefreshing sleep, sleep schedule alterations, sedative medications, alcohol intake, shift work, inadequate sleep time, and poor sleep hygiene [24,25]. To obtain satisfactory results with OSA subjects during the COVID-19 pandemic, the healthcare specialist from the sleep laboratories was advised to make changes to their usual routine and keep in close contact with their patients through adapted methods. In this context, the role of the dentist becomes very important, especially because patients with certain risk factors (anomalies of the jaws (micrognathism and retrognathism) or of the soft tissues (macroglossia)) that can lead to a reduction in oropharyngeal space are followed up more intensively and their OSA can be detected earlier in order to implement therapy measures. This screening is of great importance considering the risk of OSA complications; thus, a multidisciplinary approach is needed [85].
The main limitation of our analysis was the epidemiological situation, which led to decreased access and then the closure of the Sleep Laboratory. Moreover, the small number of patients included, especially females, may bias the results by influencing the emotional change scores. The study group size was restricted by the method of communication, refusals to participate in the study, or the abandonment of the use of CPAP by patients of their own free will.
In the case of patients who need a close follow-up and chronic monitoring for their sleep disease, telemedicine can play a vital role in providing individualized treatment, which can assure good adherence to CPAP treatment. Interestingly, the Sleep Societies have implemented a unique platform by which they can offer sleep telemedicine resources, to introduce this into current clinical practice, and to guide sleep physicians in the proper facilitation of this useful and new tool across sleep laboratories [78,86].
Therefore, we conclude that the presence of the pandemic has led to a greater level of anxiety in patients with OSAS, largely due to the increased risk of developing serious complications if SARS-CoV-2 infection is contracted. Given that during the pandemic, patients experienced an unhealthy lifestyle that led to weight gain, sleep disorders, and an impaired social life, thus exacerbating the symptoms of obstructive sleep apnea syndrome, they became more aware of the harmful consequences of the disease and used CPAP therapy more.
## 5. Conclusions
Taking into consideration that during the pandemic, OSA patients’ disease management has become a challenge, it is highly recommended that sleep centers continue their activity and develop new approaches to diagnosing and treating sleep apnea patients. Close communication between medical professionals and people suffering from sleep apnea must be maintained via new methods such as telehealth, preventing the interruption of patient care and avoiding unnecessary travel. Moreover, those centers that were unable to operate during the pandemic should try to establish procedures to recommence sleep apnea management after the COVID-19 peak as quickly as feasible, depending on the local COVID-19 conditions.
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|
---
title: 'Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis
of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern
Province of Saudi Arabia'
authors:
- Sunday O. Olatunji
- Nawal Alsheikh
- Lujain Alnajrani
- Alhatoon Alanazy
- Meshael Almusairii
- Salam Alshammasi
- Aisha Alansari
- Rim Zaghdoud
- Alaa Alahmadi
- Mohammed Imran Basheer Ahmed
- Mohammed Salih Ahmed
- Jamal Alhiyafi
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002108
doi: 10.3390/ijerph20054261
license: CC BY 4.0
---
# Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia
## Abstract
Multiple Sclerosis (MS) is characterized by chronic deterioration of the nervous system, mainly the brain and the spinal cord. An individual with MS develops the condition when the immune system begins attacking nerve fibers and the myelin sheathing that covers them, affecting the communication between the brain and the rest of the body and eventually causing permanent damage to the nerve. Patients with MS (pwMS) might experience different symptoms depending on which nerve was damaged and how much damage it has sustained. Currently, there is no cure for MS; however, there are clinical guidelines that help control the disease and its accompanying symptoms. Additionally, no specific laboratory biomarker can precisely identify the presence of MS, leaving specialists with a differential diagnosis that relies on ruling out other possible diseases with similar symptoms. Since the emergence of Machine Learning (ML) in the healthcare industry, it has become an effective tool for uncovering hidden patterns that aid in diagnosing several ailments. Several studies have been conducted to diagnose MS using ML and Deep Learning (DL) models trained using MRI images, achieving promising results. However, complex and expensive diagnostic tools are needed to collect and examine imaging data. Thus, the intention of this study is to implement a cost-effective, clinical data-driven model that is capable of diagnosing pwMS. The dataset was obtained from King Fahad Specialty Hospital (KFSH) in Dammam, Saudi Arabia. Several ML algorithms were compared, namely Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Trees (ET). The results indicated that the ET model outpaced the rest with an accuracy of $94.74\%$, recall of $97.26\%$, and precision of $94.67\%$.
## 1. Introduction
Chronic diseases are generally identified as illnesses that tend to last over a long period, requiring continuing medical attention and causing limitations and disabilities [1]. According to the World Health Organization (WHO), 41 million people die of chronic diseases yearly [1]. Chronic diseases are not just inherited but are also caused by exposures throughout life [2]. Many chronic diseases have been related to lifestyle habits, such as smoking, consuming unhealthy foods, and not being physically active [3]. Even though an early diagnosis is crucial in managing chronic diseases, they often exhibit no symptoms in their early stages, necessitating the emergence of the latest technologies contributing to the pre-emptive diagnosis of these diseases.
Several chronic diseases, including Multiple Sclerosis (MS), are prevalent in Saudi Arabia. MS affects approximately 2.8 million individuals worldwide. Since 2013, the prevalence of MS has increased [4]. According to Al Jumah et al. [ 5], the prevalence of MS in Saudi Arabia was greater in the central region and lower in the southern region. Moreover, 40.40 per 100,000 of all populations and 61.95 per 100,000 Saudi citizens were diagnosed with MS. Females were also shown to be more likely than males to develop MS at a ratio of 2:1, and young, educated individuals are more likely to be affected in various Saudi Arabian regions [5].
MS is a lifelong chronic inflammatory demyelination illness that can damage the spinal cord (central nervous system) and the brain, causing the immune system to attack the myelin that protects nerve fibers. MS causes miscommunication between the brain and the rest of the body, leading to major disability [6]. Many potential symptoms that vary from one patient with MS (pwMS) to another are experienced, such as cognitive deficiencies, weakness, sensory impairment, visual loss, dizziness, and spasticity [7]. Since MS affects everyone differently, there is presently no reliable approach to anticipate how the condition will progress in a particular pwMS. In addition, some pwMS may appear healthy for years after the diagnosis, while others may advance more swiftly. Furthermore, it is currently not known whether MS can be cured. However, it has been found that disease-modifying medications help to manage symptoms and stop the course of the disease [8]. Therefore, screening for MS before symptoms develop and following early treatment plans are crucial to improving the patient’s quality of life [9].
As disease-modifying medications help in the symptomatic treatment and disease progression, an accurate and reliable MS diagnosis is essential for enabling pre-emptive therapies for the disease [10]. In addition to ruling out any other conditions that might resemble MS clinically or radiologically, MS is diagnosed by having central nervous system lesions that are distinct from one another in both time and space [11]. For the disease’s diagnosis, there is no specific laboratory test that can precisely identify the disease. Considering this, the most recent McDonald diagnostic criteria for MS, released in 2017, encompass clinical assessment, imaging, and laboratory data [12]. Nowadays, Magnetic Resonance Imaging (MRI) is the most effective technique for diagnosing MS, as well as tracking the disease’s progression and testing treatment effectiveness. However, utilizing MRI to diagnose MS is expensive, time-consuming, and prone to human errors [13].
Machine Learning (ML) is a branch of computer science that focuses on the theory of pattern recognition and computational learning. The implementation of algorithms takes place through the process of learning from data and making predictions using unseen data. The growing capabilities of ML facilitated the process of identifying patterns not visible to humans using the massive medical data available. Therefore, several studies were conducted to diagnose MS using ML and DL algorithms. However, most studies focused on diagnosing MS using imaging datasets, and only some used clinical data, which added extra workload associated with data collection and the challenge of using complexly constructed models. Therefore, by using the latest technologies, this study aims to overcome the limitations of previous work by utilizing simple clinical data to detect pwMS pre-emptively.
This study’s dataset was obtained from King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia. It contains clinical data records of 569 patients (365 pwMS and 205 without MS). Various ML algorithms were utilized, including Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Extra Tree (ET). The results showed that ET attained the highest accuracy of $94.74\%$ with 11 features only. To better understand how an AI model reaches decisions, researchers have developed Explainable Artificial Intelligence (XAI). In this approach, ML models are modified to generate explainable models, enabling the end users to confidently manage, comprehend, and trust emerging AI systems [14]. Shapley Additive Explanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used in this study to explain the outperforming model’s findings.
This paper is divided into the following sections. Section 2 comprises a thorough literature review. Section 3 details the materials and methods used, including the dataset description, statistical analysis, a description of the employed ML algorithms, the performance measures used to assess the developed models, and finally, the optimization strategy chosen. The findings and the feature selection method utilized are also explained in Section 4, and the results of the models employing XAI approaches are described in Section 5. Finally, the conclusions and future work are discussed in Section 6.
## Study Objectives
Aiding medical personnel in pre-emptively screening and treating MS can help slow the disease’s progression. This study aimed to develop a valuable tool for predicting MS that can be deployed in local hospitals. The following is a synopsis of the study’s contributions:Developed the first clinically applicable and cost-effective ML model to screen MS pre-emptively in Saudi Arabia. Utilized the SelectKBest technique based on the chi-squared test to reduce the number of features needed to produce accurate results. Compared and evaluated the diagnostic performance of simple and ensemble classifiers. Applied Explainable Artificial Intelligence (XAI) techniques to assist medical professionals in comprehending how features affect the top-performing ML model in this study.
## 2. Literature Review
Based on clinical information and Retinal Nerve Fiber Layer (RNFL) thickness determined by Optical Coherence Tomography (OCT), a study [15] was conducted to diagnose MS better and forecast the long-term course of impairment in pwMS. The dataset was obtained from Miguel Servet University Hospital, which includes 212 records (104 healthy individuals and 108 pwMS). The ML algorithms used were Multiple Linear Regression (MLR), SVM, DT, Naive Bayes (NB), Long Short-Term Memory (LSTM), K-Nearest Neighbors (KNN), and an Ensemble Classifier (EC). The results demonstrated that the EC attained an accuracy of $87.7\%$, a sensitivity of $87\%$, a precision of $88.7\%$, a specificity of $88.5\%$, and an Area Under the Curve (AUC) of 0.8775. As for forecasting the long-term impairment course in pwMS, LSTM achieved the highest accuracy of $81.7\%$, sensitivity of $81.1\%$, precision of $78.9\%$, specificity of $82.2\%$, and AUC of 0.8165.
Using the same techniques, a recent study [16] used RNFL thickness measured by OCT to diagnose MS. Only 102 records were obtained in this study from the hospital mentioned above (30 healthy individuals and 72 pwMS) using three different Spectralis OCT protocols to perform structural assessments of RNFL thickness. The macular RNFL was measured using the fast macular thickness protocol, whereas the peripapillary RNFL was measured using both fast RNF and fast RNFL-N thickness protocols. The fast macular thickness protocol with KNN was the best acquisition procedure for MS diagnosis, achieving an accuracy of $95.8\%$, sensitivity of $94.4\%$, precision of $97.1\%$, specificity of $97.2\%$, and an AUC of 0.958. Furthermore, DT performed best for MS prognosis with an accuracy of $91.3\%$, a sensitivity of $90\%$, a precision of $92.3\%$, a specificity of $92.5\%$, and an AUC of 0.913 for the fast macular thickness protocol, and SVM for fast RNFL-N thickness protocol with an accuracy of $91.3\%$, a sensitivity of $87.5\%$, a precision of $94.6\%$, a specificity of $95\%$, and an AUC of 0.913.
Similarly, the study [17] used the dataset mentioned above from Miguel Servet University Hospital, consisting of 260 records (180 healthy individuals and 80 pwMS). The authors in this study aimed to use different ML techniques to compare axonal loss in ganglion cells observed by means of the Swept-Source OCT (SS-OCT). Three ML classifiers were used and evaluated, including DT, Multilayer Perceptron, and SVM. The DT classifier obtained the best results, with an accuracy of $97.24\%$ and an AUC of 0.995, using RNFL data in the macular area. Consequently, the authors concluded that SS-OCT provides excellent differentiation between healthy controls and MS patients based on measurements of RNFL thickness.
Likewise, the authors in [18] obtained 96 records (48 pwMS and 48 healthy individuals) from the same hospital to use SS-OCT to diagnose MS earlier. The proposed Feed-Forward Neural Network (FFNN) classifier achieved promising results with an accuracy of $97.9\%$, a sensitivity of $98\%$, and a specificity of $98\%$.
By analyzing exhaled breath, the authors of [19] aimed to diagnose MS using an electronic nose (eNose). A diagnostic test tool called eNose (Aeonose) can identify volatile organic component patterns in exhaled breath. The authors tested Aeonose’s ability to distinguish between the breath patterns of pwMS and healthy people. The dataset included 253 case controls (124 pwMS and 129 healthy individuals) who each breathed into the Aeonose for five minutes. The data from exhaled air were used to construct a predictive model using an Artificial Neural Network (ANN). With a subgroup of pwMS who had not been prescribed any medication for their MS, the authors developed a second predictive model to examine the impact of drug use. With a sensitivity of $75\%$ and a specificity of $60\%$, the ANN model built using the entire dataset was able to discriminate pwMS from healthy individuals. The sensitivity and specificity of the model developed using the subgroup of pwMS not taking medication and the healthy control participants were $93\%$ and $74\%$, respectively.
The authors in [20] trained a Convolutional Neural Network (CNN) using brain MRI to distinguish between MS and its imitators. The CNN model achieved an accuracy of $98.8\%$ using a total of 268 T1 and T2 weighted brain MRI scans.
More recently, the authors in [21] used CNN to predict the progression of the disease using brain MRI. The data of 373 pwMS were collected from the Italian Neuroimaging Network Initiative (INNI) repository. CNN was used to predict clinical worsening, cognitive deterioration, or both. The results showed that the clinical and cognitive worsening achieved an accuracy of $83.3\%$ and $67.7\%$, respectively. On the other hand, when the system was trained using both clinical and cognitive data, it achieved $85.7\%$ accuracy.
Furthermore, the study [22] aimed to detect MS using MRI. The dataset contains 130 brain MRI scans (30 pwMS and 100 healthy individuals). The authors used transfer learning to train the model by using SoftMax as an activation function to classify disease development. By using CNN, the model achieved an accuracy of $98.24\%$, specificity of $95.45\%$, and sensitivity of $100\%$.
Furthermore, the authors in [23] presented an approach that combines CNN and the two-dimensional discrete Haar wavelet transform to identify pwMS using MRI scans. The University of Cyprus’ Laboratory of eHealth provided the dataset for this study, consisting of 58 records (38 pwMS and 20 healthy individuals). The experiments on the image data attained an accuracy of $99.05\%$, precision of $98.43\%$, and sensitivity of $99.14\%$.
A review of the literature on the early prediction of MS revealed that most previous studies focused on diagnosing MS using imaging datasets, whereas few used clinical data. Additionally, it has been found that relatively small datasets were explored in previous studies. Therefore, this study aims to build an ML model using simple clinical features that could predict MS accurately with the least amount of workload and computation. In addition, the work provides medical specialists with a rationale for trusting the prediction using XAI techniques. Consequently, local hospitals with low incomes gain from deploying the pre-emptive diagnosis model.
## 3. Materials and Methods
This study developed a pre-emptive model for diagnosing MS using Python programming language. A fixed seed value of 0 was set throughout all operations. Before modeling, the dataset was subjected to various pre-processing techniques, as shown in Figure 1. The SelectKBest technique with the chi-squared test and $k = 11$ was used to extract the best features. Additionally, the dataset was split into stratified proportions, where $80\%$ of the data were reserved for training and were further validated using stratified 10-fold cross-validation, whereas the rest were used for testing the proposed models. The min–max scaler was then fitted to the training set and transformed into the testing set. Furthermore, seven ML algorithms were trained with the selected 11 features: SVM, DT, LR, RF, XGBoost, AdaBoost, and ET. GridSearchCV was then used with stratified 10-fold cross-validation to optimize the hyperparameters of the models using the training set. The models were assessed using a variety of performance metrics, including accuracy, precision, recall, F1-score, and AUC). Subsequently, the best model was interpreted using SHAP and LIME techniques. The process used to build the prediction models is summarized in Figure 1.
## 3.1. Data Description
The dataset used in this study was obtained from KFSH in Dammam, Saudi Arabia. The dataset includes records of 570 patients (365 pwMS and 205 healthy), with 44 demographical features and laboratory biomarkers. Table 1 demonstrates the features’ names and types. After applying the SelectKbest approach, 11 features remained, namely age, ALT (dimension), LDH, creatinine, blood urea nitrogen, total bilirubin, gamma glutamyl transferase, alkaline phosphatase, AST, platelet, and BP—systolic.
## 3.2. Statistical Analysis
In this section, statistical analysis was carried out to understand the data and the underlying patterns. Statistical analysis aids in determining the pre-processing methods that should be used to prepare the data for modeling. The data used in this study consisted of numerical features and only one categorical feature. The numerical attributes of the data were analyzed using well-known statistical metrics. The numerical properties of the data and their accompanying statistical breakdown are shown in Table 2. As the table demonstrated, the big difference between the 75th quartile and the maximum values indicates the presence of outliers and skewness in some features. Moreover, Figure 2 displays the value count of the gender attribute after removing the duplicates in the pre-processing stage.
## 3.3. Data Pre-Processing
One of the crucial processes in converting raw data into valuable data for training is data pre-processing. The Python Sklearn and Pandas packages were used in the current study to perform several pre-processing techniques. Initially, the dataset included 177 features and 570 records, most of which contained null values; thus, features with ≥300 null values were dropped, and any duplicated row was eliminated using the Pandas duplicate() method. Consequently, only 44 features and 569 instances remained. Furthermore, categorical data were transformed into a numerical format before training and evaluating models using the Sklearn LabelEncoder() method [24].
Missing values significantly influence the inferences drawn from the dataset. Therefore, it can result in several complications, including a decrease in statistical power, inaccurate parameter estimates, and difficulties with data processing. Different imputation strategies were used for missing values based on the types of attributes. In this study, the numerical null values were imputed by checking the STD to observe how the data are distributed. Whenever the STD is high, the data are more skewed; therefore, the null values are filled using the median, as shown in Equation [1], where n refers to the total number of observations. [ 1]Median=(n+1)2 In contrast, if the STD was low, the forward-filling and the mean imputation techniques were utilized. The forward-filling method states that the nearest value before the targeted point will be utilized if the value is null. Besides the forward-filling requirement, the resampling procedure allows a maximum of one usage of each value. The subsequent missing values will be marked as missing if the closest preceding value has already been utilized once for resampling. Hence, the mean was used to impute the remaining null values, as shown in Equation [2], where n represents the total number of values in a column and X represents a single data point [25]. [ 2]Mean =∑ Xn Next, a univariate feature selection method called Select K-Best (SelectKBest) was used. Utilizing a variety of univariate statistical tests, it chooses the K-best features from the feature set. This study selected the top 11 features using the chi-square test. Only positive features can be used in the test; hence, each non-negative and target feature receives a score from the algorithm. Pairs of expected and observed frequencies can be used to determine the score using Equation [3]. [ 3]x2=∑$i = 1$n(OFi−EFi)2EFi where OFi is the frequency that was observed for the feature F’s i-th value, and EFi is the frequency anticipated for feature F’s i-th value [26].
Following pre-processing, the dataset was split into two stratified sets: $80\%$ for training and validation and $20\%$ for testing. The values were then scaled between 0 and 1 using the min–max scaler, which has been fitted to the training set and transformed to the testing set using Equation [4]. [ 4]MinMaxScaler (v′i)=xi−minAmaxA−minA(new_maxA−new_minA)+new_minA where xi represents the ith value, maxA and minA denote a feature’s maximum and minimum values, and new_maxA and new_minA are the values 0 and 1, respectively.
## 3.4.1. Support Vector Machine (SVM)
In 1990, Cortes and Vapnik proposed Support Vector Machine (SVM). Since then, its popularity has increased among the ML community [27]. SVM is a supervised learning algorithm that provides solutions to classification and regression problems, mainly used in binary classification problems [28]. In classification, a hyperplane is located in feature space by SVM to separate different classes [29]. The training points are mapped onto the feature space and separated by a maximum margin between classes. In the same space, the testing data points are then mapped and categorized according to which side of the margin they fall.
## 3.4.2. Decision Tree (DT)
DTs are supervised ML classifiers that may be thought of as rule-based classifiers. Using a training set, DT develops a set of binary rules that can properly identify the majority of training set samples [30]. Thus, given a sample, DT evaluates whether multiple rules are met and produces a result. DT is straightforward and beneficial for interpretation. However, in terms of generalization, they are often not competitive with more sophisticated supervised learning algorithms and can quickly overfit if no limitations on the highest number of rules are applied [30].
## 3.4.3. Logistic Regression (LR)
In 1958, David Cox developed LR, an ML approach based on supervised learning and statistical analysis. It is known as a log transformation of linear regression. However, unlike linear regression, it is only used for classification [31]. LR is robust and fast in predicting discrete categorical target classes [32]. Furthermore, its simplicity allows it to rapidly reach a high level of performance. Depending on the dataset, the fundamental purpose of LR is to establish linear and noncomplex decision boundaries across classes.
## 3.4.4. Random Forest (RF)
In 2001, Leo Breiman proposed RF while introducing the concept of bagging, also known as “bootstrap aggregation” [33]. The RF classifier comprises several DTs representing various subjects from the dataset. Instead of depending exclusively on one DT, RF uses the majority vote predictions from each tree to anticipate the outcome, increasing the predictive accuracy [34].
## 3.4.5. Extra Trees (ET)
ET is an ensemble ML classifier that Mingers first familiarized in 1989 [35]. The idea behind ET is to use several small decision trees, each of which is a weak learner on its own. ETs are comparable to other tree-based ensemble techniques, such as RF; however, unlike RFs, all the trees in an ET are trained using the same training set. Additionally, while RF simply splits a node based on variable value, ETs split a node based on both variable indexing and variable splitting values. Because of this, ETs are both generalizable and more computationally efficient than RFs [36].
## 3.4.6. Extreme Gradient Boosting (XGBoost)
XGBoost is a model that was initially introduced by Carlos Guestrin and Tianqi Chen in 2011 and has since been continuously optimized to be used with modern data science tools and challenges [37]. XGBoost is a boosting tree-based learning framework with a high degree of expansion and versatility. It combines several models to create a robust model [37]. The most well-known benefits of XGBoost include its high scalability and parallelizability, speed of execution, and ability to frequently outperform competing algorithms. Additionally, it controls over-fitting using a more regularized model formalization, which enhances performance [38].
## 3.4.7. Adaptive Boosting (AdaBoost)
The first genuinely effective boosting algorithm, known as Adaptive Boosting (AdaBoost), was introduced by Freund and Schapire for binary classification. It is a meta-algorithm that can enhance the performance of numerous other learning algorithms by pairing up with them [39]. AdaBoost is adaptive in the sense that cases that were incorrectly identified by earlier classifiers are considered while creating new classifiers. In other terms, the fundamental principle behind AdaBoost is to call a weak classifier repeatedly while modifying the weights given to the samples for each call [40].
## 3.5. Performance Measure
In this study, the models’ performance was assessed using a variety of performance metrics, including accuracy, precision, recall, F1-score, and AUC. In order to further assess the models, confusion matrices were used, which include True Negative (TN), True Positive (TP), False Negative (FN), and False Positive (FP), where:TN indicates patients who were correctly identified as non-MS patients. TP indicates patients who were correctly identified as pwMS.FN indicates patients who were incorrectly identified as non-MS patients. FP indicates patients who were incorrectly identified as pwMS.
Accuracy is the ratio of correctly identified MS and non-MS patients over the total number of patients in the dataset. It is mathematically represented in Equation [5]. [ 5]Accuracy=Correctly identified MS and non−MS patientsTotal number of patients in the dataset *Precision is* the ratio of correctly identified positive instances across all predicted positive instances. It is mathematically represented in Equation [6]. [ 6]Precision=Correctly identified MS patients Total number of predicted postive instances *Recall is* the ratio of correctly identified positive instances to all the positive instances in the actual class. It is mathematically represented in Equation [7]. [ 7]Recall=Correctly identified MS patients All the positive instances in the actual classt Correspondingly, the F1-score is the weighted average of recall and precision. It is mathematically represented in Equation [8]. [ 8]F1−Score=2×(Precision×Recall)Precision+Recall
The AUC measures a classifier’s ability to distinguish between classes, as stated in Equation [9], where n1 and n0 are the numbers of negative and positive observations, respectively, and ri in S0 = Σri denotes the degree of the ith positive observation. [ 9]AUC=S0−n0(n0+1)/2n0n1
## 3.6. Optimization Strategy
The hyperparameters of ML models are essential factors impacting the model performance. Setting an appropriate value for these hyperparameters can considerably enhance the model performance. The GridSearchCV technique was utilized to build models that are capable of providing accurate solutions to these problems. GridSearchCV was used in this study to find the best hyperparameters in a search space that included a range of values. *It* generates all possible combinations of hyperparameter values to determine the best combination using the training set. Stratified 10-fold cross-validation was used to validate the model performance. The optimal hyperparameters generated by the GridSearchCV for each algorithm are outlined in Table 3. Moreover, the hyperparameters used in the grid are outlined in the Supplementary File.
## 4. Empirical Results
In accordance with the described performance indicators, Table 4 assesses the developed models using the ideal hyperparameters and features subset produced by the GridSearchCV and SelectKBest techniques, respectively. Overall, the results reveal that neither overfitting nor underfitting affected the model due to the low difference between training and testing accuracy. Furthermore, the ET classifier outperformed other algorithms with an accuracy of $94.74\%$, a precision of $94.67\%$, a recall of $97.26\%$, and an F1-score of $95.95\%$. Following that, XGBoost achieved an accuracy of $93.86\%$, a precision of $94.59\%$, a recall of $95.89\%$, and an F1-score of $95.24\%$. Algorithms including SVM, LR, and RF achieved identical accuracies after implementing GridsearchCV. RF, however, performed differently in terms of precision and recall. On the other hand, DT and AdaBoost achieved the lowest performance measures after optimization with an accuracy of $92.11\%$, a precision of $93.24\%$, a recall of $94.52\%$, and an F1-score of $93.88\%$. Figure 3 displays the confusion matrices for optimized selected models.
Figure 3 reveals that the ET model, which achieved the fewest FNs (two), is the best algorithm for predicting unwanted occurrences of the targeted disease, followed by the RF and XGBoost models, which failed to classify three cases of MS. Meanwhile, ET, XGBoost, LR, and SVM achieved the lowest rates of FP. Misdiagnosis of MS may occur due to pressure to deliver a timely diagnosis, as several alternative diagnoses may mimic MS, such as functional neurologic disorders, migraines, and arterial disease [41]. The misdiagnosis of MS could lead to serious repercussions, including losing the chance for early treatment and possibly accelerating the course of the condition. Moreover, the risk of prolonged, unnecessary healthcare hazards and death is often attributed to misdiagnosed MS patients [42]. To determine the best-performing model, the lowest FN and FP values must be achieved. Therefore, it is concluded that the ET outperforms all other models for the pre-emptive diagnosis of MS.
Figure 4 illustrates the Receiver Operating Characteristics (ROC) curve that evaluates the discrimination ability of the classifiers with different thresholds. Accordingly, Figure 4 reveals that the AUC values for the executed classifiers ranged from 0.91 to 0.94. However, XGBoost and ET achieved the highest values at 0.93 and 0.94, respectively.
## 4.1. Interpretation of the Final Recommended Model
Since ML applications have become more popular over the past eight years or so, they are now having a major influence on humanity in various ways, such as lending decisions and making judgments. However, because most models are opaque by nature, mindlessly implementing their recommendations in applications that affect people could result in problems with justice, safety, and reliability, among many other concerns [43]. Accordingly, this has caused a branch of AI known as XAI to emerge, which is an essential component of enhancing the trust and dependability of AI and ML. Currently, many techniques, most notably ML and DL techniques, are not visible in how they operate and are hence referred to as black-box models. In order to gain adequate trust, and in some situations, achieve even greater performance through human–machine collaboration, XAI is particularly focused on comprehending or interpreting the judgments made by the proposed opaque or black-box models. However, it has been recognized that this poses serious issues for several industries, including health sciences and criminal justice. Consequently, arguments have been made in favor of AI that is explicable [44]. Therefore, this work implements two XAI techniques, including SHAP and LIME.
## 4.1.1. Shapley Additive Explanation (SHAP)
Black-box ML models are frequently used, making comprehending their results challenging. Accordingly, explainable ML algorithms that dissect the results are used to identify characteristics that influence the model’s output [45]. SHAP is one of the proposed methods, explaining each feature’s impact on the model and permitting both local and global analysis for the intended dataset. Each case prediction is proved using this method by computing all impact-considered attributes and employing SHAP values generated from the coalition game theory. The effect of each attribute on the SHAP value is roughly averaged across all possible permutations. Furthermore, the absolute SHAP value represents the degree of the feature’s impact on model prediction, making it possible to utilize it as a measurement of feature relevance [46].
Figure 5 reveals that the high values of all features, except platelets, strongly influence the prediction, whereas their low values negatively influence the positive prediction. Overall, features including “Age”, “BP- Systolic”, and “Alkaline Phosphatase” have significant importance in model prediction, whereas those including “ALT (Dimension)” and “LDH” have a slight effect in comparison to other features.
## 4.1.2. Local Interpretable Model-Agnostic Explanations (LIME)
LIME is a technique for explaining black-box models, or models whose inner logic is obscure and difficult to comprehend [47]. LIME adjusts the feature values for a single data sample and monitors its impact on the output. In this method, data around an instance are simulated via random perturbation, and specific selection techniques are employed to assess the significance of attributes. Accordingly, a feature selection process is developed, which selects the features from the new data that best characterize the model result. Finally, a straightforward model is developed, fitted to the newly chosen data, and used to create an explanation for the intended model [48].
The positive probability prediction generated by the ET model, shown in Figure 6a, was $88\%$. The figure reveals that features including “Age”, “Alkaline Phosphatase”, and “LDH” contributed to the correct classification of the model for pwMS. Contradictorily, Figure 6b explains the negative prediction generated by the ET model, where the negative probability prediction was $84\%$. The figure reveals that all features except for “Alkaline Phosphatase”, “AST”, and “LDH” have contributed to the correct prediction of non-MS patients.
## 5. Discussion
With the significant advancement of technology in the past couple of decades, emerging technologies, such as ML, have shown a promising revolution in healthcare. The value of ML lies in its ability to interpret the vast amounts of healthcare data generated daily by electronic health records, allowing healthcare providers to improve and speed up care delivery by evaluating a broader range of data [49]. Moreover, it has been proven that deploying ML models in healthcare systems could contribute significantly in terms of automating primary/tertiary healthcare systems and introducing intelligent decision-making techniques, resulting in lower medical testing costs and time and higher average life expectancy [50].
Over the past two decades, MS has become more prevalent, especially in Saudi Arabia, with a prevalence of approximately 40.4 people per 100,000 of the general population and 61.95 per 100,000 of the Saudi national population [5]. Few pwMS are diagnosed in the early stages of the illness, and pwMS in Saudi Arabia may not be provided with optimum delivery care. Therefore, it is suggested that utilizing ML to pre-emptively diagnose MS may contribute to reducing the associated risks.
Several studies have been conducted to diagnose pwMS using ML techniques, attaining optimistic outcomes. However, very small datasets with few positively confirmed pwMS were used, which may cause the proposed models to be biased to certain patterns [17,20,22,23]. Additionally, most of the studies that achieved significant outcomes relied on MRI images, which are known to have a large sample requirement. Additionally, it is observed that the MRI market in Saudi Arabia reached USD 100.14 million in 2021 and is expected to reach USD 140.77 million by 2027 at a compound annual growth rate of $5.61\%$ [51]. Therefore, acquiring MRI data is an inherently costly and lengthy procedure. The long data-capture times result in limited patient throughput, discomfort, and motion artifacts [52]. Accordingly, it is essential to overcome the limitations of the previous work by developing a timely model for detecting MS. As an alternative to MRI, blood testing can be a less invasive and cost-effective test. Globally, blood sample-handling infrastructures and clinical routines for blood testing have already been well established. Accordingly, it can play an essential role in cutting the costs of diagnosing diseases [53].
This study’s main objective was to build the first ML model to diagnose MS using demographical data and clinical biomarkers collected from the Eastern Province of Saudi Arabia. Several ML algorithms have been developed and compared to find the best-performing model. The results indicated that, with only minor variations in their performance metrics, practically all models performed similarly. However, the ET model outperformed the remaining proposed models, attaining an accuracy of $94.74\%$, a recall of $97.26\%$, a precision of $94.67\%$, an F1-score of $95.95\%$, and an AUC of 0.94 using 11 attributes. The SHAP XAI technique showed that features including “Age”, “BP- Systolic”, “Alkaline Phosphatase”, “Platelet”, and “Creatinine” have the greatest impact on the model’s prediction.
The patient’s age is ranked as the most important feature in screening MS patients from others, which aligns with the research [10]. The research showed that age is a significant predictor for the diagnosis of MS because aging induces changes in the brain. The second most important feature, BP—systolic, was also shown to have a gradient association with MS [54]. Moreover, platelet was proven to have an association with MS by the study [55]. Their investigation aimed to evaluate the MS patients’ platelet adhesiveness. Both pwMS and the control group had blood samples collected, and the final platelet count percentage showed the degree of platelet stickiness. The results showed that platelet stickiness raised in pwMS compared to the control subjects. Additionally, creatine was also proven to have a significant effect on the prediction of MS, where the study [56] showed that MS patients experienced higher levels of creatinine than the control group, which consisted of healthy subjects having the same age and gender.
The fact that several studies are in line with the SHAP findings shows the reliability of the proposed model. In addition, as opposed to previous studies in the literature, this study focused on the prediction of MS using clinical data instead of MRI imaging, ensuring that the model is computational and cost-efficient.
## 6. Conclusions
MS is a chronic inflammatory disease that causes long-term functional impairment and disability. It is usually misdiagnosed as other diseases, such as functional neurologic disorders, migraines, and arterial disease, since the symptoms vary depending on the impacted areas and the damage. Furthermore, there are no specific tests that can identify MS with certainty. Therefore, specialists must use a differential diagnosis that depends on ruling out other conditions that might have a similar set of symptoms. Few people have been diagnosed accurately in the early phases of their disease and receive timely care that contributes to reducing the course of the disease, which proves the importance of early screening and diagnosis in preventing complications that negatively impact patients’ quality of life. Accordingly, this study aimed to propose a clinically applicable and cost-effective ML model to screen MS pre-emptively using clinical laboratory biomarkers. According to the comparison between the results achieved from the implemented models, the ET classifier outperformed other models with an accuracy of $94.74\%$, a precision of $94.67\%$, a recall of $97.26\%$, and an F1-score of $95.95\%$ using 11 features after using the SelectKBest approach. Furthermore, XAI was employed to ensure that medical specialists could easily understand how the algorithms could comprehend or interpret the judgments and the most relevant features of the predictive model. The findings of SHAP indicated that features including “Age”, “BP-Systolic”, and “Alkaline Phosphatase” have significant importance on the model prediction.
Since the occurrence of MS cases is greater in the central part than in the eastern part of KSA, it is recommended to collect data from different regions that could improve and verify the obtained results for future work. Consequently, the trained algorithm will be evaluated on new patients to confirm its dependability level and improve it to acquire better accuracy. Additionally, the model could be upgraded to diagnose the stages of MS reliably.
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|
---
title: Clustering of the Adult Population According to Behavioural Health Risk Factors
as the Focus of Community-Based Public Health Interventions in Poland
authors:
- Anna Poznańska
- Katarzyna Lewtak
- Bogdan Wojtyniak
- Jakub Stokwiszewski
- Bożena Moskalewicz
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002111
doi: 10.3390/ijerph20054402
license: CC BY 4.0
---
# Clustering of the Adult Population According to Behavioural Health Risk Factors as the Focus of Community-Based Public Health Interventions in Poland
## Abstract
Effective lifestyle health promotion interventions require the identification of groups sharing similar behavioural risk factors (BRF) and socio-demographic characteristics. This study aimed to identify these subgroups in the Polish population and check whether local authorities’ health programmes meet their needs. Population data came from a 2018 question survey on a random representative sample of 3000 inhabitants. Four groups were identified with the TwoStep cluster analysis method. One of them (“Multi-risk”) differed from the others and the general population by a high prevalence of numerous BRF: $59\%$ [$95\%$ confidence interval: 56–$63\%$] of its members smoke, $35\%$ [32–$38\%$] have alcohol problems, $79\%$ [76–$82\%$] indulge in unhealthy food, $64\%$ [60–$67\%$] do not practice recreational physical activity, and $73\%$ [70–$76\%$] are overweight. This group, with an average age of 50, was characterised by an excess of males ($81\%$ [79–$84\%$]) and people with basic vocational education ($53\%$ [50–$57\%$]). In 2018, only 40 out of all 228 health programmes in Poland addressed BRF in adults; only 20 referred to more than one habit. Moreover, access to these programmes was limited by formal criteria. There were no programmes dedicated to the reduction of BRF exclusively. The local governments focused on improving access to health services rather than on a pro-health change in individual behaviours.
## 1. Introduction
Over the past ten years, local authorities have increasingly prioritised the health and well-being of local communities. Their activities concern various aspects of social life, including organisation of medical care, ensuring a clean environment, stable and affordable housing, safety, preventing addictions, and many others. In many countries, public health services have been entrusted to local governments by acts of national parliaments (in force, e.g., in the Netherlands since 2008, in Norway since 2012, and in England since 2013) [1]. Their actions are to equalise the distribution of factors that directly or indirectly affect the health of individuals and communities. Also, in Poland, local authorities, by law, carry out public health tasks. They develop, implement, and finance health programmes (called health policy programmes)—sets of actions targeting specific problems in their communities. This paper discusses interventions in the area of health promotion and disease prevention proposed by Polish local authorities to reduce behavioural risk factors.
Such actions are crucial in Poland, where the percentage of deaths due to cardiovascular diseases is distinctly higher than the average in the EU (in 2017, $43\%$ vs. $37\%$) [2]. According to the Global Burden of Disease Study 2019, in Poland the high percentage of total deaths is attributable to behavioural (thus modifiable) risk factors ($44\%$ vs. $37\%$ in UE) [3]. These numbers do not acknowledge the burden connected with excessive body weight, considered a metabolic factor, that contributes to a further $14.2\%$ of deaths (against $10.9\%$ in UE). Moreover, health problems in Poland are concentrated on specific demographic and social groups. According to EUROSTAT [4], the difference between men’s and women’s life expectancy equals eight years and is one of the highest among EU countries. Differences in health status and life expectancy have been reported for inhabitants of large cities and small towns [5]. The prevalence of smoking or overweight/obesity varies with the level of education [6].
An effective health policy in this area must recognise how harmful habits are distributed in the population [7]. Moreover, behavioural risk factors tend to aggregate, which has important implications for preventive medicine and health promotion [8]. This co-occurrence and the concentration of risk factors in specific population subgroups have already been thoroughly documented in many countries [9,10]. These are the cases of associating excessive drinking with smoking [8,10,11] and the concentration of bad habits among people who are less educated [10,12] and with lower socio-economic status [10,11,13]. Some of these correlations are ambiguous, e.g., the relationship between the level of physical activity and smoking differs in various countries and for various social groups [14,15].
In Poland, such studies are undertaken rarely and usually on a local scale—regarding professionally active subpopulations or inhabitants of one region [16]. The exception is the Polish Multicentre National Population Health Examination Survey (WOBASZ) that has been conducted twice (2003–2004 and 2013–2014). The comparison of both editions of this survey suggests that despite changes over ten years (both favourable—regarding smoking, as well as unfavourable—more frequent obesity, reduction in physical activity among men), the percentage of Poles with a healthy or unhealthy lifestyle remained unchanged: $2\%$ and $25\%$, respectively [17]. However, no attempt has been made to describe population groups characterised by multiple risk factors. Considering the mentioned needs and the gap in knowledge concerning the co-occurrence of behavioural risk factors in the population of adult Poles, we undertook the study presented in this paper.
The main objectives of the study were:Identification of the groups of adult individuals in Poland who share various features in terms of risk factors for health (behavioural, overweight, lack of vaccination and preventive medical examinations) and socio-demographic characteristics based on the results of a nationwide survey;Checking whether the most exposed people find support in the interventions taken by local authorities by reviewing all health programmes proposed for realisation in the year of the survey.
## 2.1. Survey
The questionnaire survey on the prevalence of health risk factors conducted in Autumn 2018 was based on a random sample of 3000 inhabitants of Poland aged 20 and above. The sample was drawn from the Universal Electronic System for Registration of the Population (PESEL). In order to ensure the intended number of subjects at the expected response rate of $50\%$, 6000 people were drawn; the interviews were conducted until the assumed sample of 3000 respondents was reached. The sampling scheme used included the population stratification according to the province of living and residence location class (in 6 categories: rural areas, towns with a population of up to 20,000, 20–100,000, 100–500,000, 500,000–1 million, and the largest city in the country with 1.8 million inhabitants—Warsaw) and two stages of drawing lots (first communes within the strata, then inhabitants of the selected communes in the gender and age proportions appropriate for the stratum). The obtained sample was representative for the national population in terms of sex, age, province of living, and the share of urban and rural residents.
Experienced interviewers conducted the survey using the computer-assisted personal interviewing (CAPI) method. The collected data regarded socio-demographic characteristics, height and body weight (in self-assessment), selected lifestyle-related health behaviours, use of medical care, and financial difficulties.
The results of the survey allow for the estimation of risk factor prevalence in the national population (after corrections for differences in sex and age structures between the final sample and the population). They also enable the identification of groups of individuals who share health behaviours and socio-demographic characteristics, i.e., involving people potentially in need of assistance in similar scope and form.
## 2.2. Statistical Analysis
In order to identify the groups mentioned above, cluster analysis was used, grouping individuals not risk factors. The TwoStep cluster analysis method was applied. It is often utilised in similar studies because it enables the simultaneous use of continuous and categorical variables and aids in determining the optimal number of clusters—their number does not necessarily need to be known a priori [11,13]. The following variables were used for cluster identification:➢Binary Sex;*Marital status* (in the following layout: married or cohabitant vs. single);Living in rural (vs urban) areas;Smoking (currently);Problems with alcohol (affirmative answer for 3 questions: (a) Have you ever thought you were drinking too much alcohol? ( b) Have people ever irritated or annoyed you with their comments regarding your alcohol-drinking habits? ( c) Have you ever felt bad or felt guild because of drinking alcohol?);Overweight (BMI ≥ 25);Lack of recreational physical exercises (sport, gymnastics, jogging, cycling, etc.)—spending less than 10 min per week on physical activity resulting in at least raised respiratory or heart rate during the spring–summer and autumn seasons;Unhealthy products in one’s diet (fast food meals; sweet, carbonated beverages; or sweets several times a week);Too little vegetable/fruit intake in one’s diet (fewer than 5 portions a day);Eating fish less frequently than once a week;Lack of preventive medical examinations (diagnostic laboratory tests, cytology, mammography, colonoscopy) or vaccination in the last 3 years;➢Categorical Education (in the following layout: primary, basic vocational, secondary, tertiary);➢Quantitative Age (in years).
After identifying clusters, their characteristics were found within the scope of the above variables (percentage or median with a $95\%$ confidence interval—$95\%$ CI presented in square brackets). Analogous values were also calculated for additional features (including financial difficulties—insufficient money to buy food, basic clothes, or paying monthly bills in the last year—and the need for medical consultation in the last year) not included directly in the clustering procedure due to their correlations with the used variables. They were used in the discussion of obtained results.
The chi-square and Kruskal–Wallis tests were applied for qualitative and quantitative variables, respectively, when comparing the characteristics determined for particular clusters. The statistical significance of observed differences was adjusted for multiple comparisons (Bonferroni correction). In order to eliminate the influence of differences in the age structure and education level between the compared groups on the prevalence of overweightness and obesity, direct standardisation of rates was applied; the national population served as the reference population.
In all statistical tests, the assumed significance level was 0.05. The analysis was conducted with the use of SPSS12.PL package.
## 2.3. Health Programmes
In the next stage of the study, the health programmes planned for implementation in the year of the survey [2018] were analysed to determine the extent to which they reflected the population’s needs in limiting behavioural risk factors.
The complete data come from the ProfiBaza information system [18], which stores information about public health interventions in Poland, including all health programmes submitted for assessment by the state Agency for Health Technology Assessment and Tariff System (AOTMiT). Under the provisions of law, the realisation and financing of each programme needs the sanction of the President of this institution.
## 3.1. Cluster Analysis
The prevalence of main health risk factors in the studied group can be considered an estimate for the Polish population; differences in results after adjustment for the age structure do not exceed 0.5 percentage points. In Poland, $30\%$ [29–$32\%$] of the population smoke, $13\%$ [12–$15\%$] have drinking problems, $67\%$ [65–$68\%$] indulge in unhealthy products in their diet, the same percentage eat too few vegetables and fruit, $47\%$ [45–$48\%$] do not practice physical activity in their free time, $50\%$ [48–$51\%$] are overweight, and $44\%$ [42–$45\%$] do not undergo preventive medical examinations or vaccination (Table 1).
The analysed characteristics are not distributed evenly in the population; thus, four population clusters can be distinguished. These received the subjective names: 1—“The youngest” (covering $29\%$ [27–$31\%$] of the adults), 2—“Multi-risks” ($26\%$ [25–$28\%$]), 3—“The oldest” ($27\%$ [25–$28\%$], 4—“Healthy lifestyle” $18\%$ [17–$20\%$]. Relative to the general population, the main risk factors in cluster 1 are a high number of unhealthy products in the daily diet and no vaccination/preventive medical examinations. In cluster 2, all risk factors occur much more frequently than in the general population. Cluster 3 is characterised by a lack of physical activity and overweight/obesity. In cluster 4, all risk factors are significantly less common than in the general population. The description of the clusters is shown in Table 2.
The distributions of particular features and statistical significance of differences between the clusters are presented in Table 1 (variables used in the clustering procedure) and Table 3 (additional characteristics of identified clusters not directly used in the clustering process).
The “Multi-risks” cluster unfavourably deviates from the other clusters in terms of the prevalence of behavioural risk factors; only the lack of recreational physical activity is as frequent ($64\%$) as among “The oldest”. The latter group, however, consists of people on average 14 years older and, as indicated by the age-specific rates, more active up to the age of 69 (Figure 1). Equal percentages of inactive people result from a high excess of people over 70 years of age in this cluster. The age structure of members of all clusters is presented in Figure 2.
Extra body weight constitutes a severe problem in two clusters—it concerns $90\%$ of “The oldest” and $73\%$ of “Multi-risks” clusters; the percentage of obese people is $24\%$ and $14\%$, respectively. In this case, age-specific coefficients among “The oldest” are much higher—after standardisation by age, the percentage of overweight people was $94\%$ vs. $71\%$, whereas the obesity rate was $22\%$ vs. $14\%$. The effects also do not originate from differences in the education structure—after standardisation by education level, the overweight rate is $93\%$ vs. $75\%$. Among “The oldest”, the problem prevails more often, despite the clearly healthier diet (Table 1 and Table 3).
## 3.2. Review of Health Programmes
In 2018, AOTMiT received 228 health programmes for assessment. Local governments had developed $97\%$ of them for realisation in their administrative units. The Ministry of Health submitted the remaining seven programmes ($3\%$ of the total) for nationwide application. Their nature is summarised in Figure 3.
Almost $40\%$ of the total were intended solely for children and adolescents. Adults were most often (in 51 out of 139 programmes) offered vaccination (optional in the country, the most often against influenza—Figure 3). In $80\%$ of cases available to people over 60 years of age. As many as 46 programmes were devoted to the improvement of accessibility of healthcare for people with diagnosed health problems. Forty-two programmes offered participation in the screening examination.
Among health programmes aimed at adults, 40 ($29\%$) dealt with the issue of behavioural risk factors, either in the context of healthcare or diagnostics. Half of the programmes in question acknowledged intervention in the scope of one behavioural factor (physical activity in 12 cases, nutrition in 5, smoking in 3), 11 programmes combined physical activity and nutrition, whereas 9 addressed three or more factors (Table 4).
All health programmes precisely specify the age range of recipients; $34\%$ of programmes directed at adults involved people solely over the age of 60 or 65. However, there are no consistent, medically, and socially justified criteria for determining the age limits for the availability of these programmes, e.g., the difference in the eligibility age between particular osteoporosis prevention programmes is 10 years.
The aim of this study has not been to assess the substantive aspects of the presented health programmes (AOTMiT negatively reviewed $19\%$ of programmes directed at adults, but formal shortcomings of the projects could also cause it). However, the presented data indicate that their authors neither consider the co-occurrence of risk factors nor the characteristics of groups with such unfavourable habits.
## 4. Discussion
This study identified four clusters in the Polish population, each of which shared the same health risk factors. Similarly to other countries, a group with a healthy lifestyle was found [9]. Age-related factors characterised the following two clusters: “The youngest”—the most physically active, overusing unhealthy food, and not interested in vaccination or preventive examinations; and “The oldest”—mostly women, avoiding smoking and alcohol, low physical activity, and generally overweight ($90\%$ overweight, $24\%$ obese). Such phenomena as excessive consumption of fast-food meals by young people or low physical activity of older women are well known [11].
The most significant outcome is the identification of the “Multi-risks” cluster that combines most behavioural health risk factors. It seems that this group, constituting approximately one-fourth of the adult population, determines the high excess mortality rate of men in Poland. It mainly consists of males ($81\%$), $59\%$ smoke, $35\%$ have alcohol problems, $83\%$ eat too few vegetables and fruit, $79\%$ indulge in unhealthy food, $64\%$ are physically inactive, and $73\%$ are overweight. The average age of its members is 50; more than half have basic vocational education. The existence of such a group has been reported in other countries [9,13]. It was also observed that subgroups with lower education engage in poor behaviours more often [7,10,11]. This “Multi-risks” group needs urgent intervention in the field of health promotion, also undertaken at local levels, aimed at lifestyle changes to help eliminate or limit several risk factors in one person.
Meanwhile, local authorities in Poland mainly focus on providing access to medical services—one-third of the health programmes directed at adults were devoted to treating or rehabilitating people with a diagnosed disease. In the scope of prevention, adult inhabitants were most often offered free vaccination ($37\%$ of programmes).
Other limitations in the availability of programmes result from the recipients’ age; $40\%$ of programmes are intended for children and adolescents and $34\%$ target adults over 60 or 65. Consequently, there is a shortage of programmes involving people at about 50 years of age who have multiple health risk factors but have not been diagnosed with one of the supported diseases—only 12 such programmes were available in 2018 ($9\%$ of these devoted to adults).
Local authorities do not implement health programmes aimed solely at reducing the prevalence of health risk factors. Although included in $29\%$ of adult-oriented programmes, they were always combined with rehabilitation (thus intended for patients) or screening. Moreover, half of them regarded only one risk factor.
The effectiveness of multiple-risk interventions remains open. *In* general, considering the synergy of individual risk factors and the economic aspect of intervention or the lack of it, such actions have a more significant impact on public health than those targeted at single risk factors [9]. However, comparing the effectiveness of both strategies (simultaneous vs. sequentially delivered multiple health behaviour change interventions) can be inconclusive [19]. Moreover, a meta-analysis of 69 trials involving over 73,000 people revealed that interventions covering education and skill training aimed at many risk behaviours simultaneously, only result in changes concerning daily diet and physical activity, whereas the strategy of simultaneous reduction of smoking and other risk factors might be sub-optimal [20]. Regardless of the effectiveness of particular strategies of multiple risk interventions, even if a person manages to eliminate one risk factor, they may have no chance of receiving support for the successive elimination of further factors.
The efficiency of Polish health programmes is additionally affected by the fact that they do not differentiate the scope or methods regarding recipients’ sex or education level and neglect their culture of health (conditioned by age, education, and social status). People from the identified clusters differ in terms of lifestyle and attitude towards their own health—they represent diverse cultures of health. Over one-third of people in the “Multi-risks” cluster did not feel the need to receive medical help or even consultancy in the last year, and almost three-quarters did not undertake any preventive measures. Their physical activity is substantially lower than among “The youngest” (inactive $64\%$ vs. $24\%$). This difference does not result only from their age. The percentage of inactive members of the “Multi-risk” cluster at 20–40 already exceeds $60\%$ (Figure 1). On the other hand, both “The oldest” and “Healthy lifestyle” clusters comprise mainly females who do care for their health (diet and preventive actions). They clearly differ, however, in terms of age, education level, financial resources (frequency of financial difficulties), and also, possibly, the social support level (frequency of being in a long-term relationship) and opinion on socially accepted women’s behaviours (smoking and alcohol). Different problems in these groups require individualised solutions regarding the provided information and training of particular personal skills. Meanwhile, to increase the persuasive effect of communications in health promotion, effectively broadening recipients’ knowledge and their preparation for making medical decisions, it is recommended to apply culture-sensitive health communication adjusted to beneficiaries’ cultural backgrounds [21].
Thus, tailored interventions, even those conducted using computer-aided methods, are strongly recommended [22,23]. For instance, the acknowledgment of the occupational setting discussed concerning blue-collar workers [24] can be of great importance in Poland, where three-quartes of people in the “Multi-risks” cluster are of professionally active age (below 60) and over half have a basic vocational education.
It should be concluded that local governments’ activities in health promotion and disease prevention are insufficient to ensure control over risk factors for a national population of almost 38 million. There is a need for interventions at the central level, realised with the use of primary health care [25] and maybe also occupational medicine (the effectiveness of workplace-based policies is still under debate) [26]. Nevertheless, any party undertaking such actions, including local governments, should consider the existence of a group particularly affected by behavioural risk factors that need urgent and comprehensive help [11]. These people should be the target of appropriate interventions for this reason, not as residents of a certain age or patients needing treatment or rehabilitation for a specific disease. This study is aimed at identifying and describing this group.
The limitation of the study that should be discussed is the age of the subjects. The analysis of the prevalence of behavioural risk factors, the identification of clusters, and the review of available health programmes concern people aged 20 years or older. However, it has already been proven that many harmful health behaviours start at a younger age. Adult smoking begins in adolescence [27] and nutrition in childhood influences the risk of later obesity [28]. The Health Behaviour in School-aged Children (HBSC) study results show that among 15 year olds in Poland in 2018, $12\%$ regularly smoked (including $5\%$ daily), $26\%$ ate sweets every day, and only $27\%$ met the WHO recommendations for moderate-to-vigorous physical activity [29]. The analysis of health programmes addressed to children and adolescents is purposeful and planned to be carried out in the future.
The timing of the question survey [2018], i.e., before the outbreak of the COVID-19 pandemic, may also be questionable. However, it turned out that in the following years, the number of health programmes decreased significantly [18]. This tendency was evident during the COVID-19 pandemic. In 2019, 195 programmes were submitted for assessment, in 2020 it was 97, whereas in 2021there were only 80. At the same time, the need for aid increased. In many countries, the lockdown unfavourably affected the population’s health behaviours. The prevalence of overweightness and obesity increased due to limited physical activity and changes in dietary habits (eating more frequently and snacking) [30,31,32,33]. In Poland, there is a visible aggravation of previously practised unfavourable habits—over $45\%$ of smokers did it more frequently during the lockdown and a stronger tendency to drink more was found among alcohol addicts. Similarly, older, so in general, heavier people were more likely to gain weight, whereas those underweight tended to lose it further [34]. These results confirm that the survey has not lost relevance and suggest that the population grouping according to risk factors could become even stronger. Thus, tailored interventions aimed at reducing multiple risk factors will be increasingly needed to prevent further consolidation of risk factors in certain social groups that would exacerbate the previously observed health inequalities [33].
## 5. Conclusions
Among inhabitants of Poland, one can distinguish four population groups that differ in terms of the prevalence of behavioural health-related risk factors and socio-economic situation. Similarly to other countries, a “Multi-risks” cluster was identified. It constitutes approximately one-quarter of the adult population and differs from other groups and the general population, with a high prevalence of numerous lifestyle-related health risk factors.
The existence of the said group, comprising mostly men, can be related to the phenomenon of excess male mortality and the big difference (8 years) in the life expectancy between men and women in Poland.
The content and conditions of participation in health programmes indicate a need for better recognition of this problem by local authorities.
Most health policy programmes focus on providing inhabitants with free vaccination and complementing limited access to healthcare (mainly in terms of rehabilitation). *In* general, lifestyle-related health risks are rarely considered and always in the context of a specific disease combined with screening or therapeutic activity.
The recruitment criteria for programmes are formal (age and diagnosed medical problem). They do not consider the recipients’ education level or health culture—their attitude towards their health, which is expressed by, among other things, practicing various harmful behaviours.
People affected with multiple risk factors, mostly men aged about 50 with vocational education, cannot expect effective support under health policy programmes proposed by local governments.
One can expect that both the lifestyle-related differences discussed in this article and their health outcomes will be exacerbated in the future. These are side effects of the COVID-19 pandemic, when harmful behaviours intensified, especially among already affected people. At the same time, the number of proposed health programmes has significantly decreased.
The results of this study should contribute to improving health programmes to reduce the prevalence of behavioural risk factors and their co-occurrence.
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|
---
title: '“We’re Not Going to Be as Prepared”: A Qualitative Study of Healthcare Trainees’
Experiences after One Year of the COVID-19 Pandemic'
authors:
- Holly Blake
- Alex Brewer
- Niki Chouliara
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002117
doi: 10.3390/ijerph20054255
license: CC BY 4.0
---
# “We’re Not Going to Be as Prepared”: A Qualitative Study of Healthcare Trainees’ Experiences after One Year of the COVID-19 Pandemic
## Abstract
The COVID-19 pandemic had significant impacts on the mental health and academic experiences of healthcare trainees. Building on findings from earlier in the pandemic, we explore the impacts on healthcare trainees after a sustained pandemic period of 12–14 months, involving multiple lockdowns, changes in government COVID-19 regulations and the delivery of health education. A qualitative study was conducted between March–May 2021. Participants were 12 healthcare trainees (10 women, 2 men) of medicine, nursing, and midwifery, registered at one of three higher education institutions in the United Kingdom. Interviews were fully transcribed, and data were thematically analysed using a combination of deductive and inductive approaches. We identified three key themes with eight subthemes: (i) academic experiences (adjustment to online learning, loss of clinical experience, confidence in the university), (ii) impacts on wellbeing (psychosocial impacts, physical impacts, pandemic duration and multiple lockdowns), and (iii) support frameworks (university preparedness for increased student support needs, importance of relationship with academic tutors). Findings shed light on the long-lasting and emerging impacts of the pandemic over time. We identify support needs for trainees both during their academic studies, and as they move forwards into professional roles within the healthcare workforce. Recommendations are made for higher education institutions and healthcare employers.
## 1. Introduction
The coronavirus (COVID-19) pandemic has negatively impacted the mental wellbeing of the general population worldwide [1,2,3,4]. Globally, there has been high rates of anxiety, depression, post-traumatic stress disorder, psychological distress and stress [1,2]. In 2020, anxiety and depression were found to be highest in people living with pre-existing conditions and those infected with COVID-19 [3]. Common risk factors associated with mental distress during the COVID-19 pandemic in the general population include female gender, younger age group (≤40 years), presence of chronic/psychiatric illnesses, unemployment, student status, and frequent exposure to social media/news concerning COVID-19 [2]. Healthcare workers (HCWs) are at the forefront of the pandemic response and have experienced significant psychological impacts of COVID-19 [5,6,7,8]. The ICON study explored the impacts of COVID-19 on the UK nursing and midwifery workforce across three time points in 2020 and found that nurses and midwives experienced a high prevalence of negative psychological effects, including severe stress, severe anxiety, and signs of post-traumatic stress disorder [5]. Similarly, a systematic review and meta-analysis focused on doctors, nurses and allied health professionals found that post-traumatic stress disorder was the most common mental health disorder associated with the COVID-19 pandemic among health care workers, followed by anxiety, depression, and distress [7]. There has been a high prevalence of insomnia and burnout in HCWs during the pandemic [6]. Increased psychological distress in HCWs has been associated with personal factors, such as younger age and caring responsibilities, and workplace factors such as a lack of personal protective equipment (PPE) and lack of access to, or confidence in, essential training [5]. It has been suggested that the mental health impacts of the pandemic on HCWs may be higher in certain occupational groups (e.g., health technicians, medical students, and frontline health workers) [8] and, overall, may be underestimated [6].
As the future healthcare workforce, trainees are capable and willing to be involved in global health emergencies [9], although the mental health of healthcare trainees has also been impacted during the COVID-19 pandemic [10,11,12,13,14,15,16]. Systematic reviews and meta-analyses found that student nurses reported suffering from fear, stress, anxiety, depression, and sleep disturbance [10,12]. Similarly, rates of stress, anxiety and depression are high in medical trainees [13,14,15,16], and some studies identified evidence of suicidal ideation and burnout [17]. The mental health impacts of the pandemic have influenced healthcare trainees’ intentions to leave their training [11]. This is not unexpected since systematic reviews show that students and younger age groups are particularly at risk for pandemic-related distress [2,17], and college/university students more broadly have experienced high rates of anxiety and depression during the pandemic [18,19,20,21,22], often associated with social restrictions and periods of self-isolation [23,24,25]. However, the prevalence of depression and anxiety during COVID-19 is relatively higher among healthcare trainees than both the general population and healthcare workers (e.g., [8,13]).
A study conducted in the first few months of the outbreak of COVID-19 in the UK, highlighted the impacts of the pandemic experienced by healthcare trainees studying medicine, nursing, midwifery and other healthcare disciplines [26]. Among myriad challenges were the disruption to academic studies, rapid transition to online learning, social isolation, mental health impacts and challenges to accessing mental health support. Importantly, trainees in this study raised concerns about the future in terms of the negative impact of the pandemic on their education, and whether resulting gaps in their knowledge would leave them unprepared for their future clinical practice. These were early experiences, 4–5 months after the pandemic outbreak in 2020, in the context of high fear associated with a new and rapidly escalating virus, and higher education institutions operating in ‘crisis mode’ [27], rapidly implementing virus containment and mitigation strategies, and overhauling systems and processes for the delivery of teaching and learning. Many of the reported reviews also draw on evidence from earlier in the pandemic. Therefore, in the current study, we build on early findings to explore the impact of the COVID-19 pandemic on healthcare trainees after a sustained pandemic period of 12–14 months. Findings will shed light on any long-lasting or emerging impacts of the pandemic and identify support needs for trainees going forwards.
## 2.1. Study Design
This was a qualitative interview study, conducted as part of the larger PoWerS research programme [26] which had explored the impacts of COVID-19 on healthcare students six months after a pandemic was declared in the UK. Here, we explore the impact of the COVID-19 pandemic on healthcare students in the UK after a sustained pandemic period, specifically 12–14 months on. This follows multiple lockdowns and changes in government regulations relating to COVID-19 and social restrictions, alongside an extended period for higher education institutions to adjust approaches to the delivery of healthcare education and student support for learning. The consolidated criteria for reporting qualitative research (COREQ-32) checklist [28] was used to ensure the quality of reporting this study (Supplementary File S1). Ethical approval for the study was obtained from the University of Nottingham Faculty of Medicine and Health Sciences Research Ethics Committee in March 2021 (FMHS REC ref 39-0620).
## 2.2. Participants and Setting
Participants were medicine, nursing and midwifery trainees registered at higher education institutions in the United Kingdom (UK). Trainees from other disciplines and those who were not registered for study during this period were excluded. Participants were recruited via social media and email promotion of a study advertisement via student society circulation lists.
## 2.3. Procedure
Qualitative data were collected over a 7-week period between March and May 2021. In response to the study promotion, potential participants were asked to contact the researcher by email to express interest in taking part. They were emailed a participant information sheet that explained the purpose of the study, the research processes and an invitation to take part in a single interview to share their views. All participants provided written informed consent before the interview took place. To optimise recruitment during a challenging pandemic period, trainees were given the option of entering a prize draw to win a 25GBP shopping voucher which has been shown to increase response rates in research [29]. No trainees refused to participate after expressing interest or dropped out after consenting to take part. Recruitment continued until the dataset was deemed to hold sufficient information power [30]. Semi-structured interviews were conducted online using Microsoft Teams and were audio-recorded with consent. Interviews were fully transcribed using an online transcription software and then checked by the researcher to ensure their accuracy. Due to time constraints, the transcripts were not returned to the participants for comment and/or correction. An interview topic guide (Supplementary File S2) was generated to provide a foundation for the interviews. The topic guide was informed by the PoWerS study [26] and was finalised through discussion between the research team and a healthcare trainee who was not a participant in the study to agree on the relevance of the questioning. Our approach and interpretation were informed by published recommendations for virtual qualitative health research conducted during a pandemic [31].
## 2.4. Data Analysis
The data analysis process used in the current study employed thematic analysis [32] using a combination of deductive and inductive approaches. Initially, a coding framework was developed based on key areas identified in a previous study [26]. The key areas of discussion were as follows: impacts on wellbeing, impacts on academic studies and learning, and support for healthcare students. Under each overarching category, codes were generated that were grounded in the interview data. One researcher (AB) coded the data, and a second researcher (NC) checked and verified the codes. We did not code data that did not fall under these categories and was not relevant to our research questions. The initial overarching framework was then refined based on the data and generated codes. In accordance with the process of thematic analysis, a process of abstraction then took place whereby codes were grouped together into subthemes and then into main, overarching themes.
## 3. Results
In total, 12 participants provided informed consent and were interviewed (10 women, 2 men) registered at one of three universities in the UK (site 1: $$n = 9$$; site 2: $$n = 2$$, site 3, $$n = 1$$). Interviews lasted between 20 and 37 min, with an average time of 33 min. Participant age ranged from 19–42 years. Nine had worked in the UK health or social care environments in areas considered high-risk for COVID-19 during the pandemic (e.g., as defined in [26]; this included dedicated COVID + ve ward, intensive care unit, emergency department or ambulance services, ward with COVID + ve patients, entrance meet and greet, staff or regular visitor to care or residential home, or other self–defined high–risk area). Participants were trainees of medicine ($$n = 7$$), nursing ($$n = 2$$) and midwifery ($$n = 3$$). Table 1 shows participant characteristics.
Drawing on the results of the thematic analysis of the qualitative data, three main themes and eight sub-themes were generated (Figure 1).
## 3.1.1. Adjustment to Online Learning
Trainees had diverse experiences and opinions relating to the sudden transition to online learning due to social restrictions during the early stages of the pandemic. The lack of social interaction with online learning was challenging for some who had: “gone from everyday being in-person and being really interactive to just sitting in your room” (P5). Some trainees raised concerns about the increased use of pre-recorded lecture materials, particularly in the early pandemic days, referring to: “very poor-quality recordings of lectures from previous years, some of which were completely incomprehensible” (P2). For others, pre-recorded sessions provided an opportunity for study without interruption and offered greater flexibility for trainees, which was valued. Live online lectures were viewed more positively than pre-recorded materials as they allowed trainees the opportunity for interaction and conversation, either verbally or via chat functions within the video-conferencing platform. Despite the challenges experienced by trainees over the course of a year, there was a clear recognition of the workload and challenges involved for educators in diverting traditional approaches to teaching and learning to online platforms at pace and scale. Trainees spoke positively of the immense efforts and achievements of academic staff in continuing to deliver higher education through such unprecedented circumstances: “…apart from the odd technical blip at the beginning, it’s been pretty seamless. I mean, from, from people that have not done this before to pull off what they pulled off has been amazing. I can’t fault them at all.” ( P7).
## 3.1.2. Loss of Clinical Experience
Many trainees felt they had lost essential clinical experience during the pandemic: “It felt like taking a step backwards. Having all the course moved online…that’s what we were doing in pre-clinical years… suddenly, we’re back in front of the computer and it was quite demoralising” (P2). The impact on clinical experience varied according to the level of study and degree programme. Those in their earlier stages of study were less concerned about the impact of the pandemic on their clinical learning. Due to the strong emphasis on theory and less time in clinical practice in year one, students lost little or no practice experience and were confident they would have the necessary experience by the time they graduated. For those in the later stages of study, the pandemic had a greater impact on clinical learning as they were more likely to have missed clinical placement time due to lockdowns or social restrictions. A second-year medical student reported they “haven’t really had clinical placements this year” (P4); a fourth-year medical trainee reported having “missed a good six months of placement” at the start of the pandemic (P2). This led to worry about the impact of missed or altered clinical exposure on academic learning and performance: “…our clinical opportunities are greatly reduced because of the pandemic...we’ve definitely had a huge impact on our education” (P2). Since the pandemic was long-lasting, this worry continued even after social restrictions were relaxed and placements had been reintroduced. Some trainees reported that clinical experiences during the pandemic were not of the same quality, whereas others felt well supported on placements (by clinical mentors and placement coordinators) and valued the learning experiences these opportunities provided. Views towards clinical learning on placements varied according to where trainees were placed (e.g., depending on the hospital or department, or level of mentor support). Some trainees had signed up to aid healthcare organisations in various roles (e.g., healthcare assistant, logistics) in which they gained further exposure to clinical environments and felt a sense of contribution to the pandemic response.
Trainees expressed concerns about preparedness for future clinical job roles, with some speaking of “imposter syndrome” (P2) as they felt they may not graduate at the same professional standard as others and would be unworthy of their professional title: “something we’ve had to come to terms with that we’re not going to be as prepared” (P6). Trainees highlighted a need for clinical mentors to recognise the impact of the pandemic on clinical learning when students transition to professional posts (e.g., lost opportunities to practice clinical skills in clinical environments; associated loss of confidence), and a desire for future supervisors to recognise that they may need more support than their predecessors: “hope that our seniors are understanding and… are able to guide us when we get to that stage” (P6). Although there were concerns about preparedness for future practice, and an impact on confidence levels, trainees did not equate this with an impact on their future employability. Irrespective of their perceived amount of (and confidence in) clinical experience, it was perceived that: “doctors are going to be needed anyway” (P4).
## 3.1.3. Confidence in the University
Participants had mixed views relating to their confidence in the university’s approach during the pandemic to the conduct of assessments and preparing trainees for clinical practice. Due to COVID-19 restrictions, several participants had some, or all, of their practical assessments changed to online format. Some alluded to inequity in the experience of online assessments; one trainee referred to the challenges of finding an appropriate environment in which to undertake online examinations: “[I] didn’t have a quiet place in my house to do the exam…family was going in and out of the house and being somewhat noisy” (P3). Some participants raised concerns about the lack of standardisation in the university’s approach to conducting and monitoring take-home examinations and the potential for cheating, particularly among medical students: “I heard a lot of rumours that people were doing the exams together… and some of my friends had suspiciously high marks compared to what they usually get relative to me. So, I think that was very upsetting.” ( P3); “there wasn’t any sort of system put in place to stop people cheating” (P6). Other trainees offered a more positive perspective on their university’s approach, both to the assessment of learning and preparation for future clinical practice. They believed that the challenges of the pandemic would be taken into consideration in grading. In relation to clinical examinations, one medical trainee reported: “I haven’t had much clinical experience, but then again, I think the uni did tell the examiners to take it into account. And I do think everyone’s in the same boat so, you know, the marks will naturally shift.” ( P8). Some trainees suggested that the university was likely to generate opportunities to recoup any lost clinical learning time.
## 3.2.1. Psychosocial Impacts of the Pandemic
Most of the trainees commented on the negative impacts of the pandemic on their mental wellbeing. After more than a year of lockdowns, social restrictions (e.g., social distancing, self-isolation), and studying from home, several students felt this had impacted on their ability to live and study as they had done previously. Some trainees spoke of having to take time away from studies or placements due to the impacts of COVID-19 on their mental wellbeing: “…some time off placement to make up at the end of the course” (P10).
Several of the trainees experienced anxiety that was directly associated with the pandemic; for some, this was related to concerns about catching and transmitting the virus (e.g., to peers, colleagues, or vulnerable patients). This was discussed either in relation to their potential for contact with COVID-19 patients on placements or in the context of social mixing. In terms of socialising, for some, the virus transmission risk outweighed the desire to socialise. For others, their anxiety centred around a perceived loss of social skills and the return to interacting with others after long periods of isolation. One midwifery trainee referred to feeling “slightly agoraphobic…it’s just got worse the longer it’s gone on” (P7). For this trainee, the negative impact of the pandemic on their mental wellbeing was profound and led to them considering terminating their studies: “(the pandemic) has made me feel incredibly low… at times not knowing if I can carry on my course” (P7). Not all students were impacted to the same extent. Those who considered themselves to be less outgoing and sociable by nature, reported being less impacted by remote working and social restrictions during the pandemic than those who would usually socially interact to a greater extent: “I’m quite introverted anyway, so it (isolation) has not had a massive psychological impact on me” (P1). Other trainees proposed that their mental health during the pandemic could be improved through increased opportunities to socialise, even if this needed to be online (e.g., “pen pals” or evening social events).
Some trainees experienced a drop in their motivation for study during the pandemic, which worsened as the pandemic went on, with trainees: “starting to struggle to maintain any kind of momentum, finding the will to do it” (P7). Low motivation was associated with poor mental wellbeing, a loss of social interaction with peers and removal of in-person contact with academics during lockdowns: “no-one’s really keeping you accountable” (P3).
## 3.2.2. Physical Impacts of the Pandemic
Although participants spoke more frequently about the psychosocial impacts of the pandemic, several of the trainees discussed the impacts of COVID-19 on their physical health. Impacts could be positive or negative depending on the individual, their circumstances and prior lifestyle behaviours. With the increased time spent at home to study, some trainees were consuming more unhealthy foods, whereas others reported they had less access to ‘junk food’ and their diet had improved: “…when you’re going about doing everything, you just have a pack of crisps here, a cake there, whatever. But when you’re just sat about the house, you can feel it clogging up your arteries” (P9). Similarly, some had exercised more, and others less, compared with pre-pandemic behaviours. The closure of gyms and running tracks had impacted those who had previously been very physically active. Two participants reported experiencing sleep issues during periods of pandemic-related lockdown or social restrictions, and this was associated with working either in environments that were not fit for study or balancing work and home life: “very hard … sleeping and doing everything in the same environment” (P4).
## 3.2.3. Pandemic Duration and Multiple Lockdowns
At the point of interviews, the COVID-19 pandemic had continued for over a year, with key lockdowns and social restrictions introduced between March 2020 and May 2021. Several participants commented on how the impact of these restrictions varied across time. This was attributed to the progression of the pandemic, the weather, the timing (whether lockdowns occurred within or outside of academic term-time) and the presence (or lack) of social support through the year from peers, friends, and family. The wellbeing impact of the first lockdown had been less significant for some, as it occurred during a period of warm weather (spring/summer), and trainees had returned to family homes and were therefore accessing social support: “… because I’ve got a lot of siblings the house it was still quite busy and it kind of felt like normal life to be honest” (P3); “… just being home for the lockdown was just nice because they’re my support system and they’re always there when I need them.” ( P4). Some trainees raised that returning to family homes during lockdowns was a protective factor, providing a sense of safety and security in a rapidly changing global context. However, for those with young families, balancing childcare alongside studies was a particular challenge, especially during lockdowns and periods of self-isolation. With exceptions, as time went on, the pandemic took a heavy toll and wellbeing declined over time for most: “the duration of the lockdowns has definitely affected my mental wellbeing.” ( P12); “…the thought of working from home and actually the pace being slower was actually quite nice because the course moves at a hundred miles an hour… but it quickly became apparent actually that it wasn’t a gift” (P7).
## 3.3.1. University Preparedness for Increased Student Support Needs
There was a consensus that structured mental health support services offered by universities during the pandemic were poorly communicated (i.e., in terms of what was available and how it could be accessed) and inadequate. Perceived problems with structured services seemed to be associated with access rather than the quality of provision. A minority of those raising psychological impacts of the pandemic did not know where or how to access help and support. A few trainees experiencing difficulties had reached out to counselling services. While those who had accessed counselling generally reported a good experience, several indicated that support was often not timely due to long delays in accessing appointments. Some trainees referred to specific efforts being made to improve wellbeing within their academic faculties, for example, course leaders providing protected time-out from study, and advocating attendance at structured wellbeing sessions. However, it was viewed that there was a focus on quantity of wellbeing sessions, rather than quality. Some trainees felt that the impact of wellbeing provisions on trainees was not fully considered, as trainee workloads were not reduced to allow attendance at wellbeing sessions. Therefore, academic activity was compressed into a shorter timescale to compensate for this, resulting in an increased work intensity, and paradoxically, negatively impacting wellbeing. While experiences were broadly comparable across trainees from different institutions, disciplines and years of study, the lack of clarity around mental wellbeing support provisions seemed to be particularly notable for medical trainees: “If I was going through something, I wouldn’t know who to contact” (P5).
## 3.3.2. Importance of Relationship with Academic Tutors
While structured support services and online mental health support were viewed more negatively, most trainees spoke about the value of regular contact with academics, particularly personal tutors, in ensuring they had a positive learning experience and supporting their wellbeing throughout the pandemic. Student representatives augmented support from personal tutors, and trainees applauded their efforts to support the communication of information about course-related changes and welfare support at critical times during the pandemic. In contrast, some trainees had reached out to academics for support, but were dissatisfied with the response and felt that support needs were not being met: “…when we’ve raised concerns as a cohort, about, you know, our deteriorating mental health as a group… A couple of the lecturers didn’t seem to be quite so sensitive to that. It was very much “well we’re all in the same boat, everybody’s struggling”, that was difficult to swallow.” ( P7). Trainees’ experiences varied depending on their relationship with their personal tutor and the level of support that individual was willing or able to provide. Trainees who reported a good relationship with their personal tutor highlighted the support they provided and the positive impact of this on their mental wellbeing, particularly during lockdowns. *In* general, trainees placed a high value on tutors who actively reached out to provide support, rather than waiting passively for trainees to contact them with issues: “he just checks in, makes sure I’m okay… he’ll ask if I have any concerns” (P5); “I’ve got an amazing personal tutor who’s really supportive and really responsive” (P3).
## 4. Discussion
This study explores the impact of the COVID-19 pandemic after a sustained pandemic period of 12–14 months. Three key themes were generated from the data: (i) academic experiences, (ii) impacts on wellbeing and (iii) support frameworks.
In terms of academic experience, as identified in prior research [26], the rapid transition to online learning had been a significant stressor for healthcare trainees, although this was primarily related to the loss of social interaction with peers and tutors and was more problematic earlier in the pandemic and during periods of lockdown. Healthcare students have needed to adapt to a rapid transition to the use of technology to deliver education remotely and enable the continuation of teaching through periods of lockdown and social restrictions [33]. According to trainees in our sample, there was a difference in the value attributed to online teaching according to the nature of its delivery; live online lectures were generally perceived more positively than pre-recorded materials (particularly low-quality recordings). Our trainees felt that live online delivery allows interactivity and active collaborative learning, which is known to enhance critical thinking [34] and active learning [35] in a higher education context. While virtual teaching is purported to be effective, work is needed to further enhance student engagement and interactivity [36] and as noted by Rudolph and colleagues, “technology should not be revered as a panacea” [37]. With online learning likely to constitute at least a proportion of higher education teaching in the future, it seems advisable to ensure adequate attention is paid to strategies that foster online social interactions between faculty staff, clinical mentors, and healthcare trainees.
There were some aspects of taught course delivery that were viewed to be better delivered in-person. Specifically, the experience of technology-delivered remote examinations was not positive, in our sample. Remote examinations exposed inequalities in student experience during this time, largely due to the differences in home environments as some trainees did not have access to suitable environments to sit examinations, which they felt affected their performance. Social and digital inequalities have been noted in other student samples. For example, Bashir and colleagues [38] found that $61\%$ of biosciences students at a UK institution were able to study uninterruptedly during an online examination period, with students (particularly those from more deprived households) reporting inadequate home working space/environment and lacking necessary items such as a desk [38]. Moving forwards, higher education institutions should consider exploring whether all trainees have access to reliable and affordable physical devices (e.g., computers, laptops), Internet connectivity and quiet study spaces in which to take online assessments—and ensure available institutional facilities are well-promoted to all trainee cohorts. While some trainees adopted a “we’re all in the same boat” attitude and believed the university would take the uniqueness of the pandemic situation into consideration with regards grading, other trainees were concerned about quality standards during this time and the lack of fairness in remote assessment processes. These concerns impacted their confidence in the university to deliver and monitor online assessments fairly. This has been found in other higher education settings, and participants have raised concerns relating to security, validity and fairness in the implementation of online assessments [39]. To mitigate these challenges, Shraim [39] recommended that online assessments may be best utilised for formative rather than summative assessments. However, there may be times when online summative assessments are necessary for trainees’ progression (e.g., for distance learning professional development courses or during future pandemics). Further work is therefore needed to ensure equity in implementation and quality standards, instill confidence in student cohorts relating to the fairness of the system.
Having had time to reflect on the year, trainees in our study highlighted the efforts made by academic staff to continue the delivery of teaching and learning during the COVID-19 pandemic. This is an important observation. COVID-19 has impacted higher education institutions around the world [40]. Adjusting to online learning has been as challenging for academic staff as for students [25,41]; academics have needed to overcome gaps in digital skills and reconfigure their pedagogical approaches to the online learning environment [41]. The challenges for academic staff have been immense, including unfamiliarity with the learning management systems, privacy concerns, issues with student engagement, increased preparation time and technological issues [42]. Over time, educators have made progress in transitioning from emergency measures to more pedagogically consistent approaches, albeit there remains a need for better integration of theoretical and practical learning [43]. Our study highlights that trainees are aware of the efforts made by academic staff to keep higher education functioning during this crisis. With a greater focus on students compared to academic staff in the published literature, we advocate that wellbeing and technological/pedagogical support would benefit staff engaged in the delivery of remote, online or hybrid education at all times (not least during a pandemic), coupled with monitoring of workload and stress levels as proposed elsewhere [25]. Supporting staff is an essential part of ensuring the provision of adequate and equitable support for healthcare trainees, and indeed, all students in higher education settings.
A key concern for healthcare trainees was the loss of clinical experience during the long-lasting pandemic, resulting in missed placements or lower-quality placement experiences. This was primarily an issue for those in later years of study, as found in previous research [26]. A lack of preparedness for future practice has been identified in other samples of nursing [44,45] and medical trainees [46]. Interestingly, trainees in our sample did not view this loss of clinical experience as a risk to their employability. However, trainees reported experiencing “imposter syndrome” due to a belief that their knowledge and skills would be lacking compared to predecessors. Imposter syndrome is not a new concept in healthcare and has previously been identified in both nursing [47] and medical [48] trainees. Since imposter syndrome is evident at every stage of the career [49] and is linked to burnout, anxiety, and depression [50], higher education organisations may consider addressing imposter syndrome as part of the preparation for the transition to professional practice, through workshops and training (e.g., [49]). Further, healthcare organisations and line managers should be mindful of the COVID-19 impacts on new recruits’ confidence to practice and ensure additional mentoring and training is in place to build confidence and address any perceived gaps in knowledge or skills in the initial employment period.
The impact of the pandemic and the shift to remote learning impacted on engagement in physical health behaviours for some of the trainees, including dietary and/or exercise habits and sleep patterns. With some exceptions, our trainees reported primarily negative impacts, which aligns with other studies showing that the COVID-19 pandemic impacted negatively on university students’ dietary intake, physical activity, sedentary behaviour, and sleep [51,52]. Advocating health behaviours is important for health status across the life course, since diet, physical activity and sleep are independently associated with health-risk indicators and all-cause mortality [53,54] and negative lifestyle behaviours are associated with lower psychological wellbeing [55,56]. With direct relevance to healthcare trainees, the prior finding that poor lifestyle choices in healthcare professionals or trainees can influence the likelihood of them role modelling health behaviours to others (e.g., colleagues, students), their views and actions towards promoting health to patients, and the willingness of patients to heed their advice [57,58,59,60]. Promoting the value of healthy lifestyles to health trainees (and health professionals) themselves during and beyond the pandemic, remains an important priority for health educators and healthcare employers alike. This will support future engagement with health promotion in patient care, and importantly, establish a healthy future workforce for health and care organisations.
Most notable were the psychological impacts of the pandemic on healthcare trainees, with participants in our study experiencing anxiety, low motivation for study, and low mood. Additionally, our trainees felt a sense of social responsibility as the future healthcare workforce; they feared contracting and transmitting the virus to vulnerable others. Fear of contracting the virus is high among college/university students more generally, irrespective of their subject discipline [61,62]. Our findings align with other studies that identified mental health impacts of the COVID-19 pandemic in the general population [1,2,3,4], healthcare professional [5,6,7,8] and healthcare trainee samples [10,11,12,13,14,15,16]. For some, the negative impacts of the pandemic on mental wellbeing were mitigated by environmental factors (e.g., good weather lifting mood), social support (e.g., from family members at home), or personality traits (e.g., those who reported introversion traits perceived reduced social contact to be less problematic). Conversely, other participants experienced significant wellbeing impacts that involved time out from studies and were sustained or increased over the duration of the year.
Our study shows that mental health impacts are still evident 12–14 months after COVID-19 was declared a pandemic. Using a conceptual model (Supplementary File S3), a prior study presented relevant actions for mitigating the impacts of the pandemic on health and care workers; this showed to have relevance for healthcare trainees from diverse health and medical disciplines [26]. The main areas for action described within this model include proactive organisation approaches; psychologically supportive teams; communication strategies; managing emotions; social support and self-care. However, our study findings suggest that increased investment in mental health support may be required in the long term and not just as a short-term pandemic response. Mental health impacts of the pandemic were exacerbated by ongoing challenges in accessing supportive services. One year into the pandemic, some healthcare trainees still did not know where to access information about help and support for mental wellbeing, and others were struggling to access counselling services due to long waiting lists. Poor communication from their institutions relating to mental health support and inadequate capacity of student support services were issues identified by healthcare trainees in the first few months of the pandemic (e.g., [26]), but our study suggests this situation has not greatly improved. Given the continuation of the pandemic well beyond the current study data collection period, and the ongoing mental health impacts of COVID-19 for healthcare trainees, higher education organisations need to urgently invest additional resources into structured mental health support services. More attention is needed to raising awareness of mental health, and signposting to support, particularly in disciplines where mental ill-health can be considered taboo [63], and in student communities in which access to structured services for mental health is known to be low [23].
Outside of structured services, academics play a key role in student support. We observed that proactive approaches to trainee support were valued, particularly check-ins from academic tutors. We advocate that academic tutors are mindful of the sustained impacts of the pandemic on trainee cohorts and trainees’ concerns for their future as clinical practitioners. Tutors should be aware of the importance of their role in providing support and signposting to trainees, who may have increased support needs due to the long-lasting pandemic and its aftermath. This may require additional training and support for academics related to the role of the personal tutor. This would increase the parity of support that is provided across cohorts and subject disciplines and ensure that signposting to supportive services is both appropriate and timely. It has been argued that a combination of academic and mental health support is needed for healthcare trainees during a pandemic [64] and, of course, beyond. Recommendations for the study are summarised in Box 1.
## Study Strengths and Limitations
Our findings are based on the views of a small sample of healthcare trainees from three healthcare disciplines. Although we gained insights across subject disciplines, our data did not allow us to explore similarities or differences between participants registered at different academic institutions. There were more female than male participants in our study (no trainees identified as non-binary), and so the views of male and non-binary trainees warrant further exploration. However, the gender imbalance in our sample broadly reflects trends in student cohorts and professional registrants from the disciplines included. For example, there is a higher proportion of female than male entrants, both to higher education in the UK [65] and to medical schools [66]. Further, the UK Nursing and Midwifery *Council data* shows that $89.3\%$ of all nursing and midwifery registrants are female [67]. Our findings align with gender-based research, as participants in our sample reported beliefs aligned with “imposter syndrome”, which is more common in female than male healthcare trainees [68].
There may be a risk of selection bias in the study since trainees who were impacted more, or less, may have been more, or less, likely to take part in the research. For example, trainees who had experienced greater impacts from the pandemic may have been more likely to agree to take part in the study. Conversely, however, trainees struggling with mental health concerns may have felt less able to engage in research. Recruitment was challenging during the COVID-19 pandemic. This was not surprising given that our study highlights the impact of the pandemic on healthcare trainee mental wellbeing, which may have impacted on research engagement. In addition, the challenge of virtual recruitment into research studies during the COVID-19 pandemic has been recognised [69]. In our study, recruitment may have been facilitated by traditional, in-person recruitment efforts. However, the pandemic shifted research recruitment approaches to fully remote online platforms. Here, we focused primarily on the use of social media, although this has been identified as a valuable strategy for online recruitment to qualitative research studies [70]. We added a prize-draw incentive to encourage participation, which has been identified as a useful mechanism to increase uptake in research studies [70] and in this instance, helped us to achieve sufficient information power [30].
Future research may benefit from exploring the impacts of the pandemic on other healthcare disciplines. Further, there is a paucity of longitudinal research in this area and future studies might seek to explore whether there are changes in support needs over time, and the experiences of healthcare trainees who studied during the pandemic, as they transition into employment within healthcare organisations.
## 5. Conclusions
Building on findings from earlier in the pandemic, we conducted qualitative interviews to explore the impacts on healthcare trainees after a sustained pandemic period, involving multiple lockdowns, changes in government COVID-19 regulations and the delivery of health education. The COVID-19 pandemic had significant impacts on the wellbeing and academic experiences of healthcare trainees. The long-lasting duration of the pandemic has taken its toll; trainees’ mental wellbeing declined over time, and trainees fear the impacts of a loss of clinical learning on their future job roles in the healthcare workforce. Healthcare employers should be mindful of trainees’ perceived gaps in knowledge and skills and risk for “imposter syndrome”. Organisations should consider providing additional mentoring and support for new recruits to the healthcare workforce. This may help to increase their opportunities to discuss or practice clinical skills and build their confidence with relation to their clinical competencies. Despite challenges with new approaches to teaching delivery, trainees value the efforts made by academic and support staff in ensuring the continuation of theory, practice learning and assessments through a challenging time. Support from academic tutors is highly valued, but the quality of support varies. Over a year into the pandemic, there were evident deficiencies in structured support systems for student mental wellbeing, particularly around awareness of and access to services. Higher education institutions should consider further resource investment in structured support services to expand service capacity.
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|
---
title: 'Cognitive Function and Depressive Symptoms among Chinese Adults Aged 40 Years
and Above: The Mediating Roles of IADL Disability and Life Satisfaction'
authors:
- Yixuan Liu
- Xinyan Yang
- Yanling Xu
- Yinghui Wu
- Yiwei Zhong
- Shujuan Yang
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002125
doi: 10.3390/ijerph20054445
license: CC BY 4.0
---
# Cognitive Function and Depressive Symptoms among Chinese Adults Aged 40 Years and Above: The Mediating Roles of IADL Disability and Life Satisfaction
## Abstract
The purpose of this study was to investigate the relationship between cognitive function and depressive symptoms among Chinese adults aged 40 years and above, as well as the series of multiple mediating effects of Instrument Activities of Daily Living disability and life satisfaction on this relationship. The data was obtained from the China Health and Retirement Longitudinal Study (CHARLS, 2013–2018), including 6466 adults aged 40 years and above. The mean age of the adults was 57.7 ± 8.5. The SPSS PROCESS macro program was conducted to examine the mediating effects. The results indicated that there was a significant association between cognitive function and depressive symptoms five years later (B = −0.1500, $95\%$CI: −0.1839, −0.1161), which could also be demonstrated through three mediation pathways: [1] the mediating pathway through IADL disability (B = −0.0247, $95\%$CI: −0.0332, −0.0171); [2] the mediating pathway through life satisfaction ($B = 0.0046$, $95\%$CI: 0.0000, 0.0094); and [3] the chain mediation pathway through IADL disability and life satisfaction (B = −0.0012, $95\%$CI: −0.0020, −0.0003). Both IADL disability and life satisfaction have been proven to be crucial mediators for the relationship between cognitive function and depressive symptoms five years later. It is necessary to improve individuals’ cognitive function and reduce the negative impact of disability on them, which is important to enhance their life satisfaction and prevent depressive symptoms.
## 1. Introduction
Depression is a common clinical mental disorder and is characterized by a persistent depressive mood [1,2], and it has become one of the most common medical illnesses [3,4]. It not only places a heavy burden on society because of long-term medicines and health services but also severely affects the health and quality of life of individuals [5,6]. A study reported that direct and indirect spending on treating major depression has been steadily increasing each year in the United States [7]. The prevalence of depressive symptoms was also quite common in China [8]. There were $2.2\%$ of males and $3.3\%$ of females in China suffering from major depressive disorders [9]. Wen et al. found that the incidence of depressive symptoms was as high as $22.3\%$ through a 4-year follow-up among Chinese adults [10]. As such, it is essential to identify the factors related to depressive symptoms and probe into the mechanism among these factors.
As part of the aging process, increasing age is often accompanied by a decline in cognitive function [11], which is characterized by decreased memory, attention, and reasoning ability [12]. The link between cognitive function and depression has attracted a lot of attention, and there are many studies that have proven the relationship between them. The relationship between cognitive function and depression is bidirectional. That is, depression affects cognitive function, and, conversely, cognitive decline can also lead to depression. For example, depression can accelerate brain aging and increase the risk of cognitive impairment [13] through peripheral and cerebral microvascular dysfunction [14]. At the same time, studies have demonstrated that cognitive decline reduces people’s learning and thinking ability and then affects all aspects of life, work, and social interaction, which could increase their psychological stress and even lead to depression or other mental illnesses [15]. Tatiana et al. found that cognitive decline might predict depressive symptoms among older Hispanic adults living in the community [16]. Archana et al. used dynamic change models and potential difference scores to find that memory performance related to cognitive function predicted the changes in depression two years later [17]. By establishing the relationship between cognitive impairment and mood, Jennifer et al. found that participants with mild cognitive impairment had increased odds of depressive symptoms, but participants without cognitive impairment had no change in the rates of depressive symptoms [18]. In China, Yang et al. found that people with cognitive decline have a higher incidence of depression [19]. A cohort study has shown that participants with cognitive impairment had poorer mental status and an increased risk of depression one year later [20]. Clinical studies involving younger and elderly individuals have also established the inverse relationship between cognitive function and depression [21,22,23]. Moreover, in terms of gender differences, females in their mid-to-late 40s will go through menopause, which is the time of life when women in their mid-to-late 40s experience 12 consecutive months of amenorrhea because of a loss of follicular activity [24]. Due to the relative deficiency of androgens, estrogen, and progestin, postmenopausal women may experience depression and cognitive decline, which severely impairs postmenopausal females’ quality of life [25,26]. In consequence, it is also worth considering that the effect of cognitive function on depressive symptoms seems to differ by gender.
Although previous studies have explored the relationship between cognitive decline and depression, the impact of individual physical and psychological changes following cognitive decline on depression is also worthy of attention. As one of the adverse physical consequences of cognitive decline [27,28], disability can be considered as a series of physical limitations that influences individuals’ daily social, recreational, and work activities, which is generally measured by the activities of daily living (ADL) scale or the instrumental activities of daily living (IADL) scale. A cross-sectional study of elderly people in China indicated that nearly one in five individuals had difficulties with ADL disability but two in five had difficulties with IADL disability. Most elderly people need help with IADL, such as bathing and shopping [29]. IADL generally involve the more complex and varied activities of daily living compared with ADL, which require multiple cognitive domains and cognitive flexibility to complete together [30]. A study suggests that the association between cognitive function and ADL depends substantially on IADL [31]. Moreover, hippocampal and cortical gray matter volumes are correlated with IADL [32], suggesting that cognitive decline contributes to the incidence of IADL disability. According to a study involving 10,898 Chinese people, one of the most common risk factors for males regarding IADL disability was cognitive impairment [33]. Therefore, we chose IADL disability, which was more closely associated with cognitive function, as one of the indicators in this study. Regarding whether disability affects depressive symptoms in adults, prior studies have shown that compared with individuals without disabilities, individuals with disabilities were at increased risk of onset depression [34,35]. By constructing a Back Propagation neural network model, Chinese scholars found that disability ranked fourth among the risk factors of depression among Chinese individuals aged 45 or older [36]. These findings strongly suggest that disability is not just a consequence of cognitive decline but is also a key predictive factor for depression. In terms of IADL disability, previous research has confirmed that people with worse IADL performance were more likely to develop depressive symptoms over time [37]. A nationally representative study has shown that depressive symptoms were associated with an increase in IADL disability among Latinos [38]. In China, Li et al. found that IADL disability was significantly associated with an increased incidence of depression among older adults in both males and females [39]. Decreased ability of IADL may be a precursor of depression [40]. Therefore, it is of interest to explore the effect of IADL disability on the relationship between cognitive function and depressive symptoms.
Life satisfaction is a subjective judgment process, which is often considered a fundamental dimension for measuring individuals’ quality of life [41]. Among the studies on the relationship between cognitive function and life satisfaction, previous research has shown that elderly people with cognitive decline had lower life satisfaction [42]. A national study on 10,081 elderly South Koreans showed that cognitive function was an important factor in life satisfaction [43]. In a longitudinal study, life dissatisfaction was found to be related to the development of mild cognitive impairment among older adults [44]. However, there are few reports about the association between cognitive function and life satisfaction among Chinese people, which is worth exploring. In terms of the relationship between disability and life satisfaction, research has demonstrated that people with ADL and IADL disabilities were negatively associated with life satisfaction. The loss of independence for daily living abilities, especially for IADL ability, would trigger a significant decline in perceptions of quality of life and a lower level of life satisfaction [45]. In addition, life satisfaction has been proven to be linked to mental disorders, such as depression [42,46]. Zhang et al. studied nationally representative data in China and found that compared with those who were satisfied with their lives, the elderly with lower life satisfaction were more than twice as likely to be depressed [47]. Scholars have also found that cognitive decline was related to disability incidence, which was more common among elderly people who were dissatisfied with their lives [48]. It is concluded that cognitive function, IADL disability, and life satisfaction are related to each other. Given the relationship between cognitive function, life satisfaction, and depressive symptoms, life satisfaction may mediate the relationship between cognitive function and depressive symptoms.
Exploring the effect of IADL disability and life satisfaction on the relationship between cognitive function and depressive symptoms is conducive to a better understanding of the relationship between cognitive function and depressive symptoms and its internal mechanism, which also provides a reference for prevention and intervention for depression after cognitive decline. This study aimed to assess the relationships between cognitive function, IADL disability, life satisfaction, and depressive symptoms five years later among Chinese adults aged 40 years and above. We proposed three hypotheses for this study: H1, Cognitive function can have an impact on depressive symptoms five years later; H2, IADL disability and life satisfaction may have an independent mediating effect on the association between cognitive function and depressive symptoms five years later; and H3, IADL disability and life satisfaction would have a serial mediation effect between cognitive function and depressive symptoms five years later. We used data from three waves of the China Health and Retirement Longitudinal Study (CHARLS) that was conducted in 2013, 2015, and 2018, respectively, to empirically test the serial multiple mediating effects of IADL disability and life satisfaction between cognitive function and depressive symptoms five years later. At the same time, the influence of gender differences on this study was also considered.
## 2.1. Data and Study Design
The data were freely obtained from three waves of the China Health and Longitudinal Retirement Survey (CHARLS) conducted in 2013, 2015, and 2018. The CHARLS is a national longitudinal survey implemented by the National School for Development (China Center for Economic Research), which was first performed in 2011, and the participants have been followed up every two years [49]. The survey covers 28 provinces, 150 county-level units, and 450 communities in China, including information about Chinese adults, such as demographic background, family structure, socioeconomic status, and health behaviors [50].
We ascertained each participant’s cognitive function at baseline in 2013, his/her condition of IADL disability and life satisfaction in 2015, and his/her depressive symptoms in 2018. For the time frame, we excluded the participants who had already developed IADL disability, dissatisfaction with life, and depressive symptoms at baseline, as well as the participants with memory-related disorders, such as Alzheimer’s disease, brain atrophy, and Parkinson’s disease. Using data from each time frame, we evaluated the associations among cognitive function, IADL disability, life satisfaction, and depressive symptoms.
At baseline in 2013, the total sample consisted of 18,612 participants. We excluded 4349 individuals who were lost to follow-up from 2013 to 2018. Meanwhile, 28 participants were excluded due to memory-related disorders, such as Alzheimer’s disease, brain atrophy, and Parkinson’s disease, while 36 participants under 40 years old were also excluded. We further excluded those who had already developed IADL disability ($$n = 3758$$), life dissatisfaction ($$n = 507$$), or depressive symptoms ($$n = 2046$$) at baseline in 2013. Then, 1422 participants without complete information on the core variables, such as IADL disability and life satisfaction or other covariates, were also excluded. The final number of participants aged 40 years and above who were available for the follow-up survey was 6466. The details are shown in Figure 1.
## 2.2.1. Cognitive Function
The cognitive function in the CHARLS [2013] was assessed by the TICS-10 (orientation and attention), word recall (episodic memory), and figure drawing (visual-spatial abilities) [51]. The TICS (Telephone Interview of Cognitive Status) included the serial subtraction of 7 from 100 (up to five times), date (day, month, and year), day of the week, and season of the year. The scores of the TICS-10 ranged from 0 to 10. Word recall was used to assess episodic memory. After being shown 10 Chinese nouns, the participants were asked to recall as many words as they could immediately (immediate memory), in any order, and to recall them again four to ten minutes later (delayed recall). The episodic memory score includes the average number of immediate and delayed word recalls and ranged from 0 to 10. In terms of visuospatial ability, the respondents were shown a picture of two overlapped pentagons and asked to draw a similar figure. The participants received a score of 1 if they drew it correctly and no score otherwise [52,53]. The overall score ranged from 0 to 21, with higher scores indicating better cognitive function.
## 2.2.2. IADL Disability
Disability in the instrumental activities of daily living (IADL) was described as dependence on at least one IADL task: doing housework, preparing meals, shopping, taking medication, managing money, and making a phone call [54]. The answers included 0 (no, I do not have any difficulty), 1 (I have difficulty but still can do it), 2 (yes, I have difficulty and need help), or 3 (I cannot do it). Those respondents were seen as dependent when they could not carry out the IADL scale activities independently (last three options) [55]. The total score ranged from 0 to 18, with higher scores indicating the more severe the dependence on the IADL item.
## 2.2.3. Life Satisfaction
Life satisfaction was assessed by one broad question: “How satisfied were you about your life?” The respondents rated based on a 5-point Likert scale in which the higher scores indicated lower levels of life satisfaction. Assessing life satisfaction with an intuitive single question is easier to understand and accept, especially for older adults, which has been used in previous research [56,57].
## 2.2.4. Depressive Symptoms
Depressive symptoms in the CHARLS were assessed by the 10-item short form of the Center for Epidemiologic Studies Depression Scale (CESD-10) [58]. Compared with the original CESD, the Chinese version of the CESD-10 also showed considerable accuracy in classifying the participants’ depressive symptoms (kappa = 0.84, $p \leq 0.01$) [58]. The CESD-10 comprised 10 questions about depression, and the answers included four options: 0 (rarely), 1 (some days; 1–2 days per week), 2 (occasionally; 3–4 days per week) and 3 (most of the time; 5–7 days per week) [59]. The total score ranged from 0 to 30, with a higher value indicating more depressive symptoms [60]. Individuals who scored more than 10 were identified as having depressive symptoms [61].
## 2.2.5. Demographic Characteristics
We also considered the demographic characteristics of the individuals from the baseline in 2013, including age (years), gender (male, female), marital status (not married, married), smoking (yes, no), drinking (yes, no), social activities (yes, no), physical activities (yes, no), chronic disease (inapplicable, no, one, two and above), and self-rated health (very healthy, healthy, general, unhealthy, very unhealthy).
## 2.3. Data Analysis
In this study, IBM SPSS Statistics version 24 was employed for analysis and processing. We used descriptive analysis to describe the general characteristics of the study population. t-tests or chi-squared tests were applied to compare the group differences in gender. The PROCESS macro (Model 6) designed by Hayes [62] was used to examine whether IADL disability and life satisfaction mediated the association between cognitive function and depressive symptoms five years later. We also stratified the entire sample by sex to explore whether this relationship still existed. Based on bias-corrected bootstrapping with 5000 samples, we set the bootstrap confidence interval (CI) at $95\%$. Bootstrap intervals are considered to be significant when the $95\%$CI does not contain zero [63].
## 3.1. Characteristics of Participants
As shown in Table 1, a total of 6466 participants aged 40 years or above were included in our study, and their mean age was 57.7 ± 8.5. The majority of the participants were married ($92.8\%$), didn’t smoke ($84.0\%$), and performed some physical activities ($89.4\%$) or social activities ($64.5\%$). A total of $7.6\%$ of the participants clearly knew they had more than one chronic disease, and $40.4\%$ of the participants drank. Among all the participants, only $6.9\%$ and $15.9\%$ had rated themselves as “very healthy” or “healthy”, respectively. In terms of gender, there were 3506 males and 2960 females, accounting for $54.2\%$ and $45.8\%$, respectively. The mean age for the males was 58.8 ± 8.4 and for the females was 56.4 ± 8.3. The results of the t-tests or chi-squared tests showed that compared with the males, the females were more likely to be married, smoke, have lower than moderate self-rated health status, and were less likely to drink. More detailed demographic characteristics are shown in Table 1.
## 3.2. Correlation between the Core Variables
Correlation analysis revealed that cognitive function was negatively correlated with IADL disability (r = −0.214, $p \leq 0.01$) and depressive symptoms five years later (r = −0.152, $p \leq 0.01$). Cognitive function was positively correlated with life satisfaction ($r = 0.026$, $p \leq 0.05$). IADL disability ($r = 0.152$, $p \leq 0.01$) and life satisfaction ($r = 0.147$, $p \leq 0.01$) were positively correlated with depressive symptoms five years later. IADL disability ($r = 0.039$, $p \leq 0.01$) was positively correlated with life satisfaction (Table 2).
## 3.3. Mediating Effect Analyses
To further elucidate the underlying mechanisms by which cognitive function is associated with depressive symptoms five years later, we explored the mediating roles of IADL disability and life satisfaction in this relationship. All the analyses in this study were conducted on the basis of adjusting for all the demographic characteristics. The analysis results are shown in Table 3 and Figure 2. Cognitive function had a significant and negative association with depressive symptoms five years later (B = −0.1712, $95\%$CI: −0.2050, −0.1374). Cognitive function had a significant and negative association with IADL disability (B = −0.0655, $95\%$CI: −0.0747, −0.0564). IADL disability had a significant and positive association with depressive symptoms five years later ($B = 0.3765$, $95\%$CI: 0.2874, 0.4655). Cognitive function had a significant and positive association with life satisfaction ($B = 0.0048$, $95\%$CI: 0.0003, 0.0094). Life satisfaction had a significant and positive association with depressive symptoms five years later ($B = 0.9587$, $95\%$CI: 0.7771, 1.1402). When controlling for IADL disability and life satisfaction, cognitive function was still negatively correlated with depressive symptoms five years later, although the coefficient decreased (B = −0.1500, $95\%$CI: −0.1839, −0.1161).
In addition, Table 3 presents the total and direct effects of cognitive function on depressive symptoms five years later and the mediating effect of IADL disability and life satisfaction. The results demonstrated that the total and direct effects of cognitive function on depressive symptoms five years later were −0.1712 and −0.1500, respectively. When IADL disability and life satisfaction were modelled as mediators, respectively, the path coefficients of cognitive function on depressive symptoms five years later indicated that IADL disability and life satisfaction had a significant mediating effect (Indirect effect1 = −0.0247, $95\%$CI: −0.0332, −0.0171; Indirect effect2 = 0.0046, $95\%$CI: 0.0000, 0.0094). In addition, IADL disability and life satisfaction played a serial mediating role in the association between cognitive function and depressive symptoms five years later (Indirect effect 3 = −0.0012, $95\%$CI: −0.0020, −0.0003). Therefore, three types of mediating effects were found in the relationship between cognitive function and depressive symptoms five years later: first, the mediating effect of IADL disability (effect = −0.0247); second, the mediating effect of life satisfaction (effect = 0.0046); and third, the serial mediating effect of IADL disability and life satisfaction (effect = −0.0012). All the results confirmed the hypothesis we made at the beginning of the study.
## 3.4. Gender Differences
With respect to gender differences, the full sample was divided into male ($$n = 3506$$) and female ($$n = 2960$$) groups for the mediating effect analyses. As shown in Table 3, IADL disability and life satisfaction partially mediated the relationship between cognitive function and depressive symptoms five years later for females. Additionally, the indirect roles of IADL disability and life satisfaction were also significant, respectively. However, for males, there is only one significant mediation path: cognitive function→ IADL disability→ depressive symptoms, which means that IADL disability was a mediator in the relationship between cognitive function and depressive symptoms five years later.
## 4. Discussion
Based on the national longitudinal dataset from CHARLS (2013, 2015, and 2018), we explored the relationship between cognitive function and depressive symptoms five years later among Chinese individuals aged 40 years and older and formulated a mediation model to examine the underlying mechanisms behind this specific association. The results showed that cognitive function is significantly associated with depressive symptoms five years later. In other words, cognitive decline is a risk factor for future depressive symptoms. Disability and life satisfaction play partial mediating roles and a serial mediation role in the relationship between cognitive function and depressive symptoms five years later.
The results suggested that cognitive function is significantly associated with subsequent depressive symptoms five years later, which is in accordance with previous studies [64,65]. This means that the worse the cognitive function, the higher the risk of depressive symptoms in the future. Several longitudinal studies have provided evidence that cognitive decline precedes the onset of depressive symptoms [66]. Clinically speaking, cognitive impairment has several pathophysiological mechanisms, such as disturbances in the hypothalamic–pituitary–adrenal axis and abnormalities in brain-derived neurotrophic signaling [67], which as risk factors might lead to increased chances of future depressive symptoms. *In* general, the risk of depression is most commonly diagnosed in relation to cognitive decline, such as memory lapses, slower thoughts, and confusion [68]. At the same time, people with cognitive decline experience depressive symptoms, which can be interpreted as a psychological response. In other words, depression can be conceptualized as a kind of psychological reaction to the perception of cognitive decline [69]. In addition, cognitive impairment may also make individuals more susceptible to cognitive distortions (e.g., unrealistic expectations, hyper-responsive to external stimulation), which can impair peoples’ regulatory emotions and further lead to depression [70,71].
After exploring the internal mechanism of the relationship between cognitive function and depressive symptoms five years later, we demonstrated that the indirect effect of this association can be mediated by IADL disability and life satisfaction, respectively. On the one hand, the results revealed that better baseline cognitive performance reduced the incidence of future IADL disability, which is consistent with previous findings that participants with impaired cognition were less likely to be independent [72,73]. A systematic review and meta-analysis established that IADL disability existed over a continuous course of cognitive decline [74]. Cognitive decline can affect people’s operational skills and fine control ability through neuropathological damage, resulting in IADL disability [75] and leading to losses of independence and productivity. When people become aware of the various adverse effects of cognitive decline on their daily life, such as the inconvenience of life and behaviors, it will break the psychological balance to cause many individuals obvious psychological burdens and will bring a series of depressive symptoms in the future [76]. On the other hand, life satisfaction played a mediating role between cognitive function and depressive symptoms. Interestingly, contrary to previous studies [77,78], we found that cognitive decline actually increased people’s life satisfaction, which in turn reduced the risk of developing depressive symptoms. With aging, there is a gradual decline in cognitive function among some people. Correspondingly, they may receive more material and emotional help from friends and relatives, which may prevent them from experiencing more negative emotions, improve their life satisfaction, and, thus, reduce the development of depressive symptoms [79]. Furthermore, the relevant policy guarantees and medical services for people with cognitive disorders provided by the government and departments also make them feel the care and support from society to a large extent [80,81], which will also improve their quality of life and life satisfaction to effectively prevent the occurrence of depression.
We also found that IADL disability and life satisfaction played partial mediating roles in the relationship between cognitive function and depressive symptoms five years later. In detail, baseline cognitive decline was significantly associated with future IADL disability and then reduced life satisfaction, which was in turn related to depressive symptoms in the future. Poor baseline cognitive ability increases the incidence of future IADL disability [27,82]. Adverse outcomes of IADL disability, such as social withdrawal, lack of energy/interest, and decreased self-efficacy, have been identified as strong predictors of reduced life satisfaction. Meanwhile, a large number of studies have shown that lower life satisfaction is an effective indicator of an individual’s exposure to significant depressive symptoms [83]. Compared with the general population, individuals with life dissatisfaction are more likely to have depressive symptoms and other mental health problems [84]. Therefore, IADL impairment caused by cognitive decline renders most adults unable to perform their social roles and daily life normally, thus affecting their life satisfaction [85]. To a certain extent, this will cause personal psychological distress that is difficult to adjust to and may even develop into depression in severe cases [86].
From the perspective of gender difference, our findings showed that IADL disability and life satisfaction played a chain mediating role between cognitive function and depressive symptoms five years later in females, while for males, only IADL disability had a significant mediating effect, which may be due to personality and biological differences between males and females. Moreover, menopause seems to expose women to the odds of cognitive impairment due to changes in sex hormone levels. The decline in cognitive function may interfere with an individual’s activities of daily living [87]. Increased sensitivity to hormonal changes in some menopausal women makes them more susceptible to the negative emotions associated with cognitive decline and IADL disability, leading to lower life satisfaction and an increased risk of depressive symptoms.
Finally, there are some limitations in this study. Firstly, the assessments of variables through self-report questions or a single item may have led to results bias and a lack of sensitivity [88], which made it difficult to detect subtle changes between the samples and also resulted in significant but very small correlations between some variables. In order to make the research results more convincing, future studies should try to introduce more objective and rich measurement methods to provide multidimensional information about the participants’ related indicators. Secondly, the dependent variable of depressive symptoms in this study was continuous. In order to explore the association between cognitive function and future depressive symptoms and its internal mechanism more clearly, clinical diagnosis results and more complex psychological tests should be combined, with the incidence of major depression as the endpoint for in-depth analysis. Thirdly, all the variables were collected through three waves of data from different years. This was neither a cross-sectional nor longitudinal study in nature, so in order to make the findings more convincing, longitudinal research should be conducted to analyze the changes regarding the relationship between cognitive function and future depressive symptoms over time and their causality.
## 5. Conclusions
This study provides evidence of the association between cognitive function and depressive symptoms five years later among Chinese individuals aged 40 years and older, and the support for the sequential mediating effects of IADL disability and life satisfaction between this relationship was confirmed. Future studies on this topic should reconsider and scrutinize in more depth the relationship between cognitive function and depressive symptoms while considering the differences in other factors. It is necessary to improve cognitive function and reduce the negative impact of disability on individuals, especially females, which is very important to enhance their life satisfaction and prevent depressive symptoms.
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|
---
title: 'The Individual- and Organization-Related Stressors in Pandemic Scale for Healthcare
Workers (IOSPS-HW): Development and Psychometric Properties of a New Instrument
to Assess Individual and Organizational Stress Factors in Periods of Pandemics'
authors:
- Caterina Primi
- Monica Giuli
- Emanuele Baroni
- Vanessa Zurkirch
- Matteo Galanti
- Laura Belloni
- Costanza Gori
- Maria Anna Donati
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002142
doi: 10.3390/ijerph20054082
license: CC BY 4.0
---
# The Individual- and Organization-Related Stressors in Pandemic Scale for Healthcare Workers (IOSPS-HW): Development and Psychometric Properties of a New Instrument to Assess Individual and Organizational Stress Factors in Periods of Pandemics
## Abstract
The validation and psychometric properties of the Individual and Organization related Stressors in Pandemic Scale for Healthcare Workers (IOSPS-HW) were presented. This is a new measure to assess individual factors related to the health and well-being of individuals, such as family and personal relationships, as well as organizational factors related to the management of the pandemic, including workplace relationships, job management and communication. Across two studies conducted at different time points of the pandemic, psychometric evidence of the IOSPS-HW is presented. In Study 1, through a cross-sectional design, we conducted exploratory and confirmatory factor analysis through which the originally developed 43 items scale was reduced to a 20-item bidimensional scale with two correlated dimensions: Organization-related Stressors (O-S; 12 items) and Individual- and Health-related Stressors (IH-S; 8 items). Internal consistency and criterion validity were also provided by investigating the relationship with post-traumatic stress. In Study 2, we provided evidence for the temporal invariance of the measure and for temporal stability through a Multigroup-CFA through a longitudinal design. We also supported the criterion and predictive validity. The results suggest that IOSPS-HW is a good instrument to simultaneously investigating individual and organizational factors related to sanitary emergencies in healthcare workers.
## 1. Introduction
Stress can be defined as physiological or psychological change that forces the person to deviate from his normal functioning [1]. This change is caused by the perception of the subject regarding environmental demands [2]. When biological, social or psychological conditions of the environment are perceived to be greater than the available resources, a stressful situation arises [3]. Similarly, work-related stress corresponds to the set of physical and emotional responses due to the inability to face the demands of one’s work [4]. Stress at work is an increasingly common occurrence in modern life and can emerge from a variety of sources, affecting people in different ways [5,6]. Psychosocial stressors in the workplace may occur due to the characteristics of the role, as well as the lack of control and social support. The interaction of these elements can result in harmful effects on the health of individuals and organizations [7,8]. Burnout is one of the multiple common outcomes of chronic work stress, and it is defined as a persistent negative mental state characterized by physical and emotional exhaustion [9]. Employees who experience a high level of work stress have an even greater likelihood of experiencing health problems, poor motivation, less productivity and a sense of occupational safety [10]. Organizations with such employees are less likely to be successful in a competitive market [11].
Each work occupation is characterized by various types and loads of stress. However, Selye [12] indicated that healthcare is one of the most stressful professions. The need to consider and investigate occupational stress in health contexts has been emphasized, as performance strongly decreases in stressful situations [11]. It has been found that stress contributes to organizational inefficiency, high staff turnover, absenteeism due to illness, a decrease in quality and the amount of care, an increase in healthcare costs and a decrease in job satisfaction [11]. The risk of developing health problems for health professionals is $10\%$ higher than the average for other professions, indicating the presence of additional sources of stress [13]. The COVID-19 pandemic has proved to be stressful for people in the community and healthcare professionals. According to the studies conducted on past outbreaks of SARS (Severe Acute Respiratory Syndrome) and MERS (Syndrome Respiratory System of the Middle East), frontline health workers report high levels of stress that often results in depression, anxiety and post-traumatic stress disorder (PTSD) [14,15]. Italy was the first Western country to report cases of COVID-19, and in February 2020, the government declared a state of emergency [16]. During the peak of cases produced by the first wave, many of the major Italian cities suffered overloads of intensive care [17]. Thus, Italian authorities announced strict quarantines along with a country-wide lockdown. Despite all the efforts made by government authorities and the health system, Italy, as well as many other countries, was not ready to face such a pandemic [18]. Healthcare professionals worked continuously at the forefront of the treatment of infected patients, exposing them to critical situations every day [19]. The risk of infection and transmission of the virus to other patients and families was very high, sometimes making it necessary to isolate themselves [20]. The lack of adequate personal devices of protection was another factor that increased concern and the sense of insecurity at work [21]. In an unprecedented situation such as this one, health professionals were certainly the most exposed category. About $86\%$ of the interviewed operators reported increased perceived work stress. Symptoms of anxiety, depression and acute stress were reported in $24.5\%$, $35.9\%$ and $33.3\%$ of the cases, respectively. In addition, $13.9\%$ of the subjects seemed to be at risk of developing PTSD [18].
To better understand the mental health burden of the COVID-19 pandemic among healthcare professionals, a validated measure of the pandemic-specific stressors is required. Such a measure may examine the stressors that are most burdensome and the way in which these stressors impact well-being and mental health. During the COVID-19 pandemic (Table 1), several measures were developed to capture distress or anxiety, sleep problems, or post-traumatic stress symptoms in the population, such as the COVID Stress Scales (CSSs) [22]; the COVID-19 Fear Scale (FCV-19S) [23]; the Coronavirus Anxiety Scale (CAS) [24], and the COVID-19 Burnout Scale (COVID-19 BS) [25]. These measures assess the perceived global level of stress during the COVID-19 pandemic but do not distinguish between different stressors, especially individual stressors, such as anxiety and fear for the health of oneself, family or colleagues, and organizational stressors, such as lack of proper communications and work overload. Recently, new measures have been developed to assess different stressors related to the pandemic, such as the Pandemic Stress Questionnaire (PSQ) [26]; the COVID-19 Stressors Scale [27]; the COVID-19 Stress Scale (CSS) [28]; the COVID-19 Stressors Score [29]; and the Pandemic Stressor Scale (PSS) [30] (Table 1).
Nevertheless, none of these measures have been built on the specific target of health workers despite being a population that was significantly affected by the COVID-19 pandemic, experiencing high levels of stress both on an individual (anxiety about health) and organizational level (lack of support and adequate communication in excessive workload) [18]. Starting from these premises, the goal of the present work was to develop a new scale to measure a broad range of different kind of stressors in healthcare operators during a highly critical and emergency period, such as that of the COVID-19 pandemic. To assess the multiple stressors of a pandemic or epidemic, it is necessary to have a valid and reliable measuring instrument that can capture as many as possible aspects that can induce high levels of stress. In order to construct the scale, we organized an integrated research project aimed specifically at developing such a measurement tool as one of the actions conducted by the Regional Reference Center on Relational Criticalities (RCRC), with Careggi University Hospital of Florence (Italy) as leading center, and it is based on the scientific collaboration with the Psychometric Lab of the NEUROFARBA (Neuroscience, Psychology, Drug Research and Child Health) Department of the University of Florence. Since its foundation in 2007 (DGR n$\frac{.356}{2007}$), the RCRC deals with the organizational well-being of hospitals and structures of the Regional Health System of Tuscany, promoting the psychological well-being of health care professionals through research, planning, consultancy and training. To reach the goal, a two-waves study, i.e., at T0, from June to September 2020, and at T1, from February to May 2021 (see Table 2 for the descriptive across the studies), was implemented.
We began the study in June 2020 during the first phase of the pandemic when healthcare professionals were involved at the forefront of the emergency management. In light of the limitations of the scales internationally generated during this period to assess psychological distress symptoms, the instrument that we present in the current paper was properly developed to measure distress derived from different individual and organizational stressors relevant during a pandemic or epidemic. This was done such that it could represent a useful tool to be used in possible future pandemic situations. For this reason, we named the scale Individual and Organization-related Stressors in Pandemic Scale for Healthcare Workers (IOSPS-HW). The goal was to build a new tool that allows to consider both the individual factors related to the health and well-being of individuals, such as family and personal relationships, fear of infecting others, difficulty in family management, uncertainty about the future and general disturbance of life, and organizational factors related to the management of the pandemic, such as workplace relationships, fear of infection, job management and communication, limited access to resources, and difficult working conditions. The construction of this new scale in the COVID-19 era highlighted the strong need to assess the severity of distress for different stressors relevant during a pandemic or epidemic in this category of workers. Combining aspects of individual and organizational stressors in a single tool that is valid and reliable might facilitate the implementation of actions with the purpose of reducing individual stress on the one hand and increasing organizational efficiency in emergency conditions on the other. In sum, the measurement of stress and its factors is essential to recognize more of the at risk groups of workers to prevent extremely stressful situations and perform adequate interventions. Given that there are no existing instruments created to comprehensively measure stressors during a pandemic among healthcare operators, the purpose of the present work was to develop a two-dimension and brief instrument to measure both individual and organizational factors. Indeed, we aimed to provide a scale appropriate for large, multivariate studies wherein several tests and scales must be administered together. In Study 1, we described the construction of the scale and its psychometric properties, in particular the analysis of its dimensionality. Study 2 offered a contribution to the measurement of its validity and reliability.
## 2. Study 1
The aim of Study 1 was to construct the scale and examine its psychometric properties through a cross-sectional design. Development of the IOSPS-HW began with the preparation of detailed construct specifications describing various aspects of stressors. In order to identify the macro areas from which to derive the potentially stressful factors for healthcare professionals in pandemic management, we analyzed the existing studies concerning previous pandemics (due to pathogens other than SARS-CoV2) [31] as well as the recommendations and guidelines published by international organizations, such as the World Health Organization (WHO) and Occupational Safety and Health Administration (OSHA) on the management of COVID-19 [32]. From such analysis, several factors, both related to individual health and organizational themes, emerged as potentially stressful. Following an initial examination of all these contributions, a first set of items was created to be submitted to a panel of healthcare workers based in the COVID-19 departments (doctors, nurses, psychologists, physiotherapists and social health workers) who analyzed the content and wording of the items. These specifications guided the creation of 43 candidate items with a 5-point Likert scale from 1 (Not stressful) to 5 (Very stressful). After this first phase, the scale was administered to a sample of healthcare professionals with the aim of studying dimensionality. We hypothesized that, in order to obtain the scale, we would need to assess two correlated dimensions measuring individual and organizational stress factors. In other terms, we expected to have a scale with a two-factor structure. In this way, we could obtain a single score for each dimension. Moreover, the benefit of a bi-correlated-factor construct is that scoring is simplified as items are added for the whole domain to achieve the total score.
We also assessed the reliability with the omega coefficient for each dimension and for the total scale, and the validity by examining the correlation with the post-traumatic level of stress [33]. In line with previous studies, we expected to find a positive correlation between work stress and distress experienced [18,34]. Finally, we investigated differences in the IOSPS-HW subscales and total scores by considering the work experience in a COVID-19 Unit, in line with other studies that have found substantially higher levels of stress among those healthcare operators who worked in COVID-19 units compared to those who did not work in COVID-19 units [17,21,35].
## 2.1. Participants
The sample was composed of 950 health workers ($77\%$ females) aged from 23 to 68 years (mean age = 47.76 years, SD = 10.20) recruited in the hospital of Careggi, Tuscany, in the period immediately following the first lockdown (from June to September 2020, T0). Table 2 depicts the socio-demographic variables investigated, i.e., marital status, level of education, type of job, and length of service. In addition, each participant was asked: “Did you work/do you work in a COVID-19 Unit?” Thirty-six percent of participants ($$n = 346$$) worked in a COVID-19 Unit.
## 2.2. Measures and Procedure
The research protocol included a form for gathering socio-demographic data (age, sex, marital status, educational qualification, professional qualification, and years of service). Additionally, it was requested to indicate whether the participants worked or had worked in a COVID-19 Unit.
The Individual and Organization related Stressors in Pandemic Scale for Healthcare Workers (IOSPS-HW) administered in Study 1 consisted of 43 items describing two areas of stressors: One related to individual health (19 items) and the other to organizational themes (24 items). Item examples were “Fear of getting severely ill” and “Work in solitude”, respectively. Items were rated on a 5-point scale ranging from 1 (Not stressful) to 5 (Very stressful) (see Appendix A).
To measure post-traumatic stress symptoms, the Impact of Event Scale-Revised (IES-R) [36,37] was used. The IES-R is a 22-item self-report evaluation scale to assess current subjective distress for a specific traumatic life event. Participants responded by considering a specific stressful life event while managing the COVID-19 health emergency and then indicating the level of distress they experienced in the past two weeks. The IES-R is composed of three subscales: Intrusions (e.g., repeated thoughts about the trauma), Avoidance (e.g., effortful avoidance of situations that serve as reminders of the trauma), and physiological hyperarousal. The IES-R score is defined as the sum of the average of the three subscales by providing an indication of the level of distress experienced, with a higher score indicating a greater psychological impact.
Participants filled out the research protocol online. The inclusion criterion included being a health worker actively working in the hospital during the epidemic’s first lockdown. Interested health workers were able to access the survey only after signing the informed consent form. Anonymity was preserved and the median survey completion time was approximately 30 min.
This study was not preregistered. Data will be available under request and after the permission of the Hospital.
## 2.3. Results
First, a missing analysis was conducted in order to identify cases with more than $10\%$ of missing values. No such case was found. For cases with less than $10\%$ of missing values, missing was replaced with the mean value of the total sample. This replacement was conducted only for $2\%$ of the participants ($$n = 19$$).
## 2.3.1. Item Selection and Dimensionality
The following process was used to reduce the original 43-item questionnaire to a shortened version. Item distributions and descriptions were examined for assessment of normality. Skewness and Kurtosis indices revealed that the departures from normality were acceptable [38]. An Exploratory Factor Analysis (EFA) with the Principal Component Analysis (PCA) with Oblimin rotation was performed, and two components were extracted. Results showed that eight items had factor loadings on both components (e.g., “Discomfort due to work wearing individual safety devices (e.g., masks, visors, overalls”) *For this* reason, these items were eliminated.
An EFA with the Principal Axis Factoring (PAF) and an Oblimin rotation was also performed on the resulting 35 items since the potential dimensions were expected to be correlated. The factorability was supported by Bartlett’s test of sphericity (χ2 = 22,219.32, df = 595, $p \leq 0.001$) and the Kaiser-Meyer-Olkin measure of sampling adequacy (0.95) [39]. The resulting two factors were defined as Individual- and Health-related Stressors (IH-S; 16 items) and Organization-related Stressors (O–S; 19 items). Results showed two correlated factors ($r = 0.63$). All items possessed factor loadings greater than 0.40, ranging from 0.40 to 0.78 on the expected factor.
Subsequently, the two-factor structure was tested by a confirmatory factor analysis (CFA) that employed the maximum likelihood estimator (AMOS software) [40]. To verify the model’s fit, the following indices were taken into account: The comparative fit index (CFI) [41], the Tucker–Lewis’s index (TLI) [42], and the root mean square error of approximation (RMSEA) [43]. For the TLI and CFI, values above 0.90 are indicative of acceptable fit, while values above 0.95 were indicative of excellent fit [44]. The RMSEA value is considered acceptable when it is below 0.08 and good when it is below 0.05 [45].
The results showed a poor overall fit (TLI = 0.713, CFI = 0.746, RMSEA = 0.097, $90\%$ CI [0.095, 0.100]). Modification indices (MIs) suggested adding error covariance between several items. Scrutiny of the content of items revealed a similarity in item content that may have led to error covariances [46]. Following the analysis of the factor loading of each item with the expected factor, we selected the items with the highest factor loading [47]. In this way, we eliminated 15 items. For example, between the item “Excessive circulation of information/communications” and the item “Difficulty in identifying suitable interlocutors according to needs” that had error covariance, we kept the second because it had a higher factor loading. The final version of the scale resulted in 20 items: Twelve items for Organization-related Stressors (O–S) and eight items for Individual- and Health-related Stressors (IH–S) (see Appendix A). This form demonstrated a good fit (TLI = 0.902, CFI = 0.913, RMSEA = 0.069, $90\%$ CI [0.064, 0.073]). Each item loaded strongly and significantly on its hypothesized factor with factor loadings ranging from 0.42 to 0.77. The correlation between the factors was found to be 0.76 (Figure 1).
## 2.3.2. Reliability and Validity
The omega value for the overall scale was 0.93 ($95\%$ CI [0.92, 0.93]) and all the corrected item-total correlations were above 0.30, ranging from 0.33 to 0.60. The value of omega did not increase if an item was deleted. The omega was 0.91 ($95\%$ CI [0.90, 0.92]) for the Organization-related Stressors (O–S) subscale, and 0.84 ($95\%$ CI [0.82, 0.85]) for the Individual and Health related Stressors (IH–S) subscale. Following the cut-offs proposed by the European Federation of Psychological Assessment (EFPA) [48], the values of internal consistency were good for the scale and the subscales.
With respect to validity, in order to investigate the relationships between organizational and individual stressors and post-traumatic stress, the correlations between the IOSPS-HW score and its subscale scores, as well as the IES-R, were calculated. The results illustrated that a high perception of organizational and individual factors as stressors was associated with a high perception of post-traumatic stress. In detail, the total score for the IOSPS-HW was significantly and positively correlated with the total score at the IES-R. Both the subscale scores were correlated with the total score at IES-R (Table 3).
All the correlation values were adequate or good measures of validity according to the cut-offs proposed by the EFPA [48]. Specifically, for criterion-related validity, values between 0.20 and 0.35 are deemed adequate, values between 0.35 and 0.50 are good, and values higher than 0.50 are excellent. To further address the validity, we assessed work environment differences in the IOSPS-HW score. Participants were divided into two groups according to their work unit (No-COVID-19 Unit vs. COVID-19 Unit). Independent sample t-tests were performed to explore the differences between the groups on the IOSPS-HW total score and each subscale. As hypothesized and reported in Table 4, participants who worked or had worked in a COVID-19 Unit ($72\%$ females; mean age = 44.78, SD = 10.27) demonstrated significantly higher scores on the total score and on each subscale compared to the participants who did not work in a COVID-19 Unit ($79\%$ females; mean age = 49.47, SD = 9.76).
## 2.4. Discussion
The aim of this study was to develop a new scale to measure organizational and individual stressors during a pandemic in healthcare operators. Our findings indicate that the IOSPS-HW is a two-factor structure assessing individual and organizational factors related to perceived stress. This scale might allow capturing a broader range of stressor domains. A strength of this study was the use of large-sized samples along with the combined use of EFA and CFA. The developed scale was confirmed to have convergent validity with an existing post-traumatic stress measure. Indeed, the IOSPS-HW score and the subscales significantly and positively correlated with the IES-R. Additionally, the IOSPS-HW was able to discriminate against health workers considering their unit. As expected, health workers who worked in COVID-19 units had a higher level of stress in each subscale and in the total score. Additionally, the IOSPS-HW presents all the advantages of the short scale and resulted to be appropriate for large multivariate studies where several measures must be administered together. In summary, it could be used to identify individuals who require additional psychological support. Nevertheless, this study had the limitation to be cross-sectional by involving a sample of healthcare operators during the first lockdown of the emergency in Italy. Thus, generalizability to other times of the pandemic is limited. For this reason, we conducted Study 2 to investigate the psychometric properties of the IOSPS-HW in a different wave of the pandemic.
## 3. Study 2
Following a second wave of infections occurring between September and December 2020, leading to the health situation being critical, a high number of infections, mortality rate and hospitalizations forced the hospitals to open numerous COVID-19 units. In the months of February to May 2021, there was a stabilization of the positivity and mortality rate and the number of hospitalizations. A characterizing aspect of this period was concerned with the introduction of anti-COVID-19 vaccines. Beginning on 27 December 2020 (“Vaccine Day”), the vaccination campaign began in Italy, which established the priority in the administration of vaccines to certain categories, including over 80, fragile categories, and health professions. In the following months from February to May 2021, the administration of the first dose of the vaccine began, and in the same months, the vaccination obligation for all those who practiced a health profession took over.
Starting from these considerations, the first aim of Study 2 was to confirm the factor structure of the final 20-item IOSPS-HW across various waves of the pandemic through a multigroup confirmatory procedure. Thus, we followed a longitudinal design. Additionally, we further tested the validity of the scale by taking into account depression, anxiety, well-being and post-traumatic stress as criterion variables by considering them indicators of the healthcare workers’ mental health [49]. Moreover, we investigated the predictive validity of the IOSPS-HW by testing the predictive power of each dimension at T0 with PTSD symptoms at T1. In this way, the weight of each dimension related to individual and organization stressors offering important support in the assessment for future crisis intervention for healthcare workers was tested. Finally, we provided further evidence for the reliability of the scale in terms of temporal stability.
## 3.1. Participants
Eight hundred and forty-three health workers ($76\%$ females) aged 22 to 69 years (mean age = 47.91 years, SD = 10.66) recruited in the hospital of Careggi, Tuscany, participated in a second survey (T1) that took place between February and May 2021. Table 2 depicts the socio-demographic variables for the time of administration, i.e., marital status, level of education, type of job, and length of service. In addition, each participant was asked: “Did you work/do you work in a COVID-19 unit?”. Thirty-eight percent of participants ($$n = 322$$) worked in a COVID-19 Unit. Among the participants, 522 cases (Mage(SD) = 47.00 (11.09), $74\%$ female) participated only in the second survey, and 321 (Mage(SD) = 49.38 (9.78), $80\%$ female) participated in both the waves (T0 and T1).
## 3.2. Measures and Procedure
As in Study 1, the research protocol included a form for gathering socio-demographic data (age, sex, marital status, educational qualification, professional qualification, and years of service) and participants were requested to indicate if they worked or had worked in a COVID-19 Unit. Participants then answered the IOSPS-HW and the IES-R, as in Study 1. However, in this study, the IOSPS-HW was the 20-item version with twelve items for the Organization-related Stressors (O-S) and eight items for the Individual and Health related Stressors (IH-S). Additionally, the Generalized Anxiety Disorder-7 (GAD-7) [50,51], the Patient Health Questionnaire (PHQ-9) [51,52], and the World Health Organization Well-being Index (WHO-5) [53,54] were used to assess the criterion validity variables.
The GAD-7 [50,51] is a 7-item questionnaire developed to identify probable cases of generalized anxiety disorder and to measure the severity of symptoms based on the DSM-IV [55] such as nervousness, inability to stop worrying, excessive worry, and restlessness. The GAD-7 asks participants to rate how often they have been concerned by seven core symptoms over the past two weeks. Response categories are not at all, several days, more than half the days, and nearly every day, scored as 0, 1, 2, and 3, respectively. The total score of the GAD-7 ranges from 0 to 21. Among primary care patients and the general population, the GAD-7 has demonstrated good internal consistency, test-retest reliability, and convergent, construct, criterion, and factorial validity [50,52,56].
The PHQ-9 [51,52] is a self-report measure consisting of nine questions based on the nine DSM-IV criteria for a major depressive episode [55]. It refers to symptoms experienced during the two weeks prior to answering the questionnaires. Scores for each item range from 0 (not at all), to 1 (several days), 2 (more than half of the days) and 3 (nearly every day), while summed scores range from 0 to 27. The PHQ-9 can be used as a screening tool with recommended cut-off scores of ten or greater for the diagnosis of major depression.
The WHO-5 [51,52,53] allows for a brief assessment of well-being over a two-week period. Subjective well-being refers to how people experience and evaluate their lives. Individuals were asked to indicate for each of the five statements how they felt over the past two weeks, using a six-point Likert scale ranging from 0 (at no time) to 5 (all of the time). The scale has shown good psychometric properties in both general and clinical populations [57].
As in Study 1, participants filled out the questionnaire online and inclusion criterion was being a health worker actively working in the hospital during the epidemic. Anonymity was preserved and the median survey completion time was approximately 30 min.
## 3.3. Results
Similar to Study 1, we checked for missing data. However, no cases with more than 10 of the missing value were found. For cases with less than $10\%$ of missing values, the missing value was replaced with the mean value of the total sample. This replacement was conducted only for $2\%$ of the participants ($$n = 17$$).
As reported in Table 2, $42\%$ of participants who took part in T1 worked in a COVID-19 Unit. Due to the health emergency, $35\%$ of health workers were assigned to a different unit from the one in which they usually worked.
## 3.3.1. Validity across Time
Invariance of the IOSPS-HW was tested by comparing the T0 sample, including all the subjects who participated only in the first survey ($$n = 617$$, Mage(SD) = 47.32(10.35), $76\%$ female) to the T1 sample, including all the subjects who participated only in the second survey ($$n = 522$$, Mage(SD) = 47.00(11.09), $74\%$ female). First, the two-factor model was tested separately in the two groups, and the model showed acceptable fit indices among T0 sample (TLI = 0.900; CFI = 0.901; RMSEA = 0.073, $90\%$ CI [0.068, 0.079]), with standardized factor loadings ranging from 0.43 to 0.76 and two correlated factors ($r = 0.74$, $p \leq 0.001$). A good fit was also obtained for T1 sample (TLI = 0.914; CFI = 0.924; RMSEA = 0.066, $90\%$ CI [0.060, 0.072]). The standardized factor loadings ranged from 0.51 to 0.81 and the correlation between the two factors was 0.71 ($p \leq 0.001$).
To test for cross-validation with a multigroup confirmatory analysis on the two-waves samples, analyses were conducted by performing hierarchically nested confirmatory factor analyses and invariance was evaluated using the criteria of ∆CFI less than 0.01 and the equivalent cut-off of 0.015 for RMSEA [46,58]. Following the guidelines for testing measurement invariance, the preliminary independence model was fitted (χ2 = 11,047.70, df = 380, $p \leq 0.001$) [59]. Thereafter, the configural invariance was established (CFI = 0.912, RMSEA = 0.050), and metric invariance was assessed (Table 5). Although the ΔCFI criterion was met (.001), further levels of structural variances and covariances, as well as measurement error variances and covariances were supported by referring to the same criterion (respectively 0.000 and 0.004).
## 3.3.2. Criterion Validity
In order to analyze the criterion validity of the IOSPS-HW, we investigated its associations with depression, anxiety, well-being and post-traumatic stress. In line with other studies, a positive correlation was found between the total and subscale scores at the IOSPS-HW with depression, general anxiety and post-traumatic stress studies [18,60,61]. Moreover, a negative correlation was found between the IOSPS-HW total and subscale scores with well-being (Table 6).
## 3.3.3. Predictive Validity
We also assessed the predictive validity of the IOSPS-HW scale by investigating its predictive power on PTSD symptoms by considering the participants in both the waves. In particular, a stepwise linear regression analysis was run with PTSD symptoms as the dependent variable and the IOSPS-HW subscale scores as the independent variables. Preliminarily, we verified that the change of the rate of infections between T0 ($2.18\%$) and T1 ($8.41\%$), i.e., +$6.23\%$, was not significantly related to PTSD symptoms (β = 0.01, $$p \leq 0.872$$).
Thus, we included only the IOSPS-HW subscale scores in the regression analysis. In the first step (Model 1), only the Individual and Health related Stressors (IH–S) score was entered, and it was a significant and positive predictor. In Model 2, the Organization-related Stressors (O-S) was also introduced, which depicted a significant increase in explained variance (ΔR = 0.03) (Table 7).
## 3.3.4. Temporal Stability
In order to analyze the reliability of the scale we measured the test-retest correlation ($$n = 297$$) for each dimension and the total score by considering the scores at T0 and T1. We obtained a coefficient of 0.67 ($p \leq 0.001$) for Individual and Health related Stressors (IH-S), 0.65 ($p \leq 0.001$) for Organization-related Stressors(O–S), and 0.70 ($p \leq 0.001$) for the IOSPS-HW total score scale. In line with the criteria for the EFPA, the scale showed good stability with correlations that were higher than 0.60 [48].
## 3.4. Discussion
In Study 2, the psychometric properties of the IOSPS-HW in a different wave of the pandemic were analyzed. This analysis was necessary as the scale was developed at a time when the response capacity of professionals was under threat, as they were insufficient resources to care for COVID-19 patients, contradictory instructions, or interruptions in the continuity of care of non-COVID-19 patients. Results confirmed the structural validity of the scale as it was found to be invariant across time. As expected, we found positive correlations between individual and organizational factors with anxiety, depression and post-traumatic symptoms and negative correlations with well-being. The predictive validity of the scale was also confirmed. To summarize, the IOSPS-HW scale resulted to be a valid tool to monitor stressor levels in order to check on the effective recovery of health care professionals.
## 4. Conclusions
During previous pandemics (e.g., Severe Acute Respiratory Syndrome (SARS), influenza A/H1N1, and the Middle East Respiratory Syndrome (MERS)), healthcare workers experienced severe problems such as emotional stress, including anxiety, depressive symptoms, and insomnia [14,62,63,64]. However, with respect to recent previous cases, the COVID-19 pandemic has been found to be more global and temporarily more widespread. The construction of this new scale in the COVID-19 era responds to the strong need to assess the severity of distress for different stressors relevant during a pandemic or epidemic in this category of workers. Through the development of this scale, our goal was to establish an evidence-based assessment that could be used as a basis for future crisis intervention for healthcare workers during any infectious disease outbreak. Combining aspects of the individual and organizational stressors in a single tool that is both valid and reliable may facilitate the implementation of actions that have the purpose of reducing individual stress on the one hand and increasing organizational efficiency in emergency conditions on the other. In summary, the measurement of stress and its factors is essential to recognize groups that are more at risk to prevent extremely stressful situations and carry out adequate interventions. This information is crucial to deploy targeted interventions to support professionals in terms of individual, group and organizational support. Specifically, thanks to the information obtained from the tool, information and psychoeducational materials can be prepared to help professionals recognize the signs of stress, and if necessary, ask for specialist help. In addition, initiatives can be arranged to support team working, and psychological support services can be strengthened. In line with the findings, more extensive training can be carried out for newly hired workers.
Through future surveys, the employment of the scale will allow to monitor any changes over time in the factors that may determine a source of stress for healthcare professionals who are managing various phases of the pandemic trend, thus guiding support actions. Overall, the emerging results and the experience gained could have significant consequences in terms of planning further actions aimed at mitigating the impact both on health professionals, and by extension, on the general population.
The IOSPS-HW has the valuable advantage of providing two separate scores for individual and organizational stress factors, compared to all the scales that have been built during the pandemic that do not separate these two dimensions. This is important because the literature suggests that healthcare workers experience high levels of stress which negatively affect their well-being. Moreover, research indicates that high levels of stress and low levels of well-being negatively affect productivity, that is a fundamental aspect for hospitals as the main providers of public health. The drop in productivity could lead to an increase in demand, with repercussions on levels of organizational stress and therefore healthcare workers’ well-being. Finally, the advantage of having the possibility of separately monitoring individual and organizational stressors allows for the structuring of diversified interventions which aim to work on different aspects. Other values of the IOSPS-HW regard the fact that it can be useful to monitor healthcare workers’ stress levels in other future possible pandemics. Indeed, the items do not refer specifically to the COVID-19 pandemic-contrary to some scales built during the pandemic itself.
This study had a few limitations. The first limitation is that it does not discriminate between professional categories, nor does it consider critical services separately during this crisis, such as critical care and resuscitation, internal medicine, pneumology, and infectious diseases. Moreover, these data are limited to Italy. A measurement of the invariance among languages could be conducted in future studies. Despite the limitations, the IOSPS-HW scale could be used in future outbreaks to check the level of acute stress of the health care workforce and to give them proportional support according to their needs in these conditions.
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|
---
title: 'Cross-Sectional Analysis of Family Factors Associated with Lifestyle Habits
in a Sample of Italian Primary School Children: The I-MOVE Project'
authors:
- Francesco Sanmarchi
- Alice Masini
- Carolina Poli
- Anna Kawalec
- Francesco Esposito
- Susan Scrimaglia
- Lawrence M. Scheier
- Laura Dallolio
- Rossella Sacchetti
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002146
doi: 10.3390/ijerph20054240
license: CC BY 4.0
---
# Cross-Sectional Analysis of Family Factors Associated with Lifestyle Habits in a Sample of Italian Primary School Children: The I-MOVE Project
## Abstract
The acquisition of healthy dietary and exercise habits during childhood is essential for maintaining these behaviors during adulthood. In early childhood, parents have a profound influence on a child’s lifestyle pursuits, serving as both role models and decision-makers. The present study examines family factors as potential contributors to healthy lifestyle habits and their child’s overall diet quality among a sample of primary school children. A secondary aim is to evaluate several aspects of diet quality using the Mediterranean adaptation of the Diet Quality Index-International (DQI-I). This cross-sectional study involved 106 children enrolled in a primary school located in Imola, Italy. Data were collected from October to December 2019 using an interactive tool used to assess parent characteristics, children’s lifestyle, food frequency (ZOOM-8 questionnaire), and actigraph accelerometers to capture children’s physical activity and sedentary behavior. Adherence to the Mediterranean Diet (expressed by KIDMED Index) was positively associated with fathers’ educational level, parental sport participation, and the parent’s overall nutritional knowledge. Higher mothers’ educational level was inversely associated with children’s leisure screen time. Parents’ nutritional knowledge was positively related to children’s average daily minutes of organized sport activities. The better score for DQI-I was for consumption adequacy, followed by variety and moderation. The lowest score was for overall balance. The present study reinforces the importance of family factors in young children’s lifestyle choices, particularly their dietary, leisure time, and exercise habits.
## 1. Introduction
An imbalance between calorie intake and expenditure, often due to unbalanced diets high in saturated fats, trans fatty acids, sugar and salt, as well as excessive sedentary behavior contribute to numerous adverse health effects (e.g., increased adiposity; poorer cardiometabolic health, fitness, behavioral conduct/prosocial behavior, and reduced sleep duration) [1]. Healthy nutrition is defined as the intake of an adequate and well-balanced diet to support the body’s energy needs, and a healthy lifestyle is characterized by physical activity (PA) and other behaviors that promote overall health and well-being [2]. In recent decades, there has been a transition from traditional, healthier dietary patterns such as the intake of fruits, vegetables, whole grains, and lean protein sources, as well as limited intake of added sugars, saturated and trans fats, and sodium) [2] to less healthy ones, particularly among young children, which has greatly affected diet quality [3,4]. Despite this trend, the Mediterranean Diet (MD), in particular, has received significant attention due to its demonstrated protective effects against metabolic risk, cardiovascular disease, and various types of cancer [5,6,7].
The MD is characterized by a high intake of fruits and plant-based foods (i.e., legumes, nuts, and seeds), unprocessed cereals, extra-virgin olive oil, dairy products (milk, yogurt, and cheese), low to moderate intake of fish and poultry meat, and reduced consumption of red meat [8]. These dietary ingredients have been shown to be beneficial for countering the risk of developing various diseases (e.g., cardiovascular, endocrine, and psychiatric conditions), improving physical and mental health [9,10], and increasing lifespan [11,12]. Recent studies have specifically confirmed numerous benefits of the MD for children and adolescents [13].
In light of this evidence, a growing number of literature reviews [14,15] and published guidelines [14,16] have suggested the importance of implementing health promotion interventions targeting young children in primary school settings [17]. Physical activity and nutrition are crucial components of healthy lifestyle interventions, as they provide essential protective measures that can positively guide children’s health and development [18]. In contrast, the absence of PA and poor dietary quality are significant determinants of chronic diseases and further compound associated risk factors, including childhood and adolescent obesity [19,20]. The World Health Organization (WHO) has recommended that children and adolescents should perform at least 60 min of moderate-to-vigorous PA (MVPA) per day in order to avoid the risk of metabolic and cardiovascular diseases. Additionally, according to recent WHO guidelines [21], children and adolescents should limit the time they spend engaging in sedentary activities in order to reap physical and mental health benefits. Sedentary behavior (SB) is defined as any waking behavior that is performed while sitting, reclining, or lying down with low energy expenditure [22]. Included in this definition is the recreational use of computers and television viewing, activities that require little sustained physical effort. This type of behavior is prevalent and pervasive in developed countries [23] and has been linked to negative health outcomes such as an increased risk of type 2 diabetes, cardiovascular disease, all-cause mortality in adults, and a reduction of sleep duration, reduced prosocial behaviors and increased depressive symptoms in children and adolescents as well as increased adiposity and poorer cardiometabolic health [24,25]. According to the last available Childhood Obesity Surveillance Initiative (COSI) round, after Cyprus and Greece, Italy was the country with the highest both obesity and overweight prevalence in children. In particular, in the Emilia-Romagna region, the prevalence of overweight children was $20.4\%$, while the prevalence of obesity and severe obesity was, respectively, $6.9\%$ and $2.4\%$. Although there has been a slight reduction over the years of all morbidity indicators, overweight and obesity remains an important public health issue. Furthermore, only 6 in 10 children spend less than two hours a day watching TV, video games, tablets and mobile phones and $16\%$ did not have any physical activity the day before the survey [26].
A child’s rearing environment has an important influence on their choice of a healthy lifestyle [27,28]. Two prominent sources of influence that make up this rearing environment include their family and school [29]. The family (i.e., parents and caregivers) plays a prominent role because they can transmit beliefs and attitudes that support a positive outlook on healthy nutrition and dietary practices as well as convey important cultural and emotional ties to food. This includes extolling the importance and favorable outcomes of health living. Consistent reinforcement of healthy living can have a tremendous influence on a child’s healthy lifestyle pursuits including selection of foods to eat, consumption patterns, and whether a child will engage in PA [30,31,32]. When parents are active as part of their own lifestyle pursuits and routinely eat healthy foods, they serve as role models to their children, who engage in similar health-engendering behaviors. Children can learn vicariously from their parents by watching them shop at the supermarket and also viewing food preparation in the home. They can also assist their parents in food preparation and thus learn directly by monitoring what their parents provide for breakfast, lunch, and dinner as well as the different kinds of snacks they are offered. Parents can also encourage children to actively play sports and motivate them through family activities that center on athletic endeavors. Added to the family’s influence, schools are also a place where children can learn about healthy lifestyles including what foods are available in the cafeteria for lunch and the school’s reinforcement of PA as part of a child’s physical education [33]. Children and adolescents spend a significant portion of their daily time in school and are influenced by their physical and social environments, such as school health policies, nutrition education and support, and physical education [34]. In this respect, schools are an ideal setting for reaching as many children as possible, regardless of gender, socio-economic status, or background, and represent a favorable environment for reducing health inequalities.
While most school-based interventions aimed at promoting healthy lifestyles have focused solely on the school setting, some reviews have found that multicomponent interventions involving the family, in addition to the school, are likely to be the most effective [35]. Previous studies have demonstrated the significant influence of parents on the PA patterns and food intake of young people, as well as their role in promoting behavior change [36]. Of note, few studies have investigated these associations in an Italian population of primary school children, presenting mixed results [37]. With this in mind, the current study examines family factors as potential correlates of healthy lifestyle habits (adherence to the MD, leisure screen time, moderate to vigorous PA, and engagement in organized sports) among primary school children living in Bologna, Italy. A secondary aim is to evaluate several aspects of diet quality using the Mediterranean adaptation of the Diet Quality Index-International (DQI-I) [38] in a sample of primary school children. The study offers a novel use of assessment strategies to map the influence of family factors on children’s healthy lifestyles and dietary habits.
## 2.1. Study Design and Participants
This cross-sectional study was conducted with a sample of children enrolled in the “I-MOVE” project [39], with all children from a primary school in Imola (70,075 inhabitants, Bologna, Italy), located in the Emilia Romagna region of northeastern Italy. The Bioethics Committee of the University of Bologna approved the “I-MOVE” project on 18 March 2019 (approval number: 0054382), and the study was conducted in accordance with the Declaration of Helsinki. The research team obtained written informed consent from the parents of participating children. Invitation letters were sent to the principals of primary schools located in Imola. One school expressed interest in participating in the I-MOVE project and 10 teachers representing 5 classes agreed to participate. Children from these classes were then recruited if their teachers provided access, parents consented, and the child assented to participate.
Children were recruited to participate from the first to fifth grades (corresponding to elementary school) and inclusion criteria included not having any health issues or physical disabilities that might interfere with or impact their PA performance. Children in primary school in Italy attended 2 h per week of physical education lessons. Children normally had lunch in the school’s canteen. Therefore, examining parental influence on dietary practices refers only to dinners and weekends’ meals. The study was designed based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines [40].
## 2.2. Instruments
Data from parents and their children were collected from October to December 2019 using the ZOOM-8 questionnaire [41] and actigraph accelerometers worn by each child. The ZOOM-8 questionnaire is an interactive tool used to assess parental characteristics and parents’ reports of their child’s lifestyle (over the past year), including nutrient and food intake, PA levels, and sedentary behaviors [41]. The questionnaire was completed by a parent/caregiver, and was evaluated for accuracy and completeness by one of the investigators prior to data analysis. The ZOOM-8 questionnaire contains two parts: Part 1 includes items that assess the child’s lifestyle (e.g., leisure screen time, sleeping hours, participation in organized and structured sport activities) and characteristics of the parents (e.g., age, educational level, weight and height, PA, and nutritional knowledge).Part 2 consists of a semiquantitative food frequency questionnaire (FFQ), following the methodology described and validated by Willett [42]. The instrument consists of 53 commonly used food items categorized into 11 food groups [43]. Frequency response categories for all food servings ranged from daily, weekly, monthly, annually, to never. A reference food was considered for different items, and photographs of different foods were provided to help respondents gauge portion-sizes.
Energy density, macro- and micro-nutrient intakes were calculated using the MetaDieta software Professional 4.0.1 (Me. Te. Da., San Benedetto del Tronto, Italy, 2019), which includes an Italian database of food composition.
Each child’ body max index (BMI) was calculated based on age and sex using International Obesity Task Force (IOTF) cut-off values [44,45], with children’s height and weight measured and recorded directly by the researchers. A dichotomous variable was created to indicate whether at least one of the parents engaged in PA at least once a week (yes/no).
The child’s PA levels and SB were monitored over a seven-day period using actigraph accelerometer models GT3X (ActiGraph LCC: Pensacola, FL, USA). The data were analyzed using ActiLife 6.13.3 software (ActiGraph LCC: Pensacola, FL, USA) with an epoch length of 10 s to allow for a detailed estimate of PA intensity [46]. Children wore the accelerometers around their waist with an elastic belt over a seven-day period (five weekdays and two weekend days), only removing them for bathing, swimming, and showering [47]. We analyzed the accelerometer’s data only when children complied with specific inclusion criteria: having worn the accelerometer on at least 3 weekdays and 1 weekend day, and for at least 10 h every day. Minutes spent in PA (light, moderate, and vigorous) per day were calculated using the Evenson cut-points [48].
## 2.3. Adherence to the Mediterranean Diet
We calculated adherence to the MD using the modified Mediterranean Diet Quality Index for Children and Adolescents (KIDMED) score [41,49]. We classified the children into three categories based on their KIDMED index: [1] high (KIDMED score 8–12), [2] medium (KIDMED score 4–7), and [3] poor adherence (KIDMED score 0–3). The KIDMED index provides an ideal means to assess MD adherence because it is easy to interpret and provides a comprehensive overview of an individual’s dietary intake.
## 2.4. Diet Quality Index-International (DQI-I)
The Diet Quality Index-International [50], represents one of the most thorough and validated instruments to assess diet quality. We used the adapted version for the Mediterranean population [38], which is consistent with other studies based in Italy [51]. The DQI-I explores four factors considered necessary for a healthy diet: variety, adequacy, moderation, and overall balance. Current dietary guidelines encourage adequate consumption of different food groups (variety) and key nutrients for health (adequacy, further suggesting moderation in the intake of certain food elements such as saturated fat, cholesterol, sodium and sugar, and a healthy proportion of macronutrients (overall balance) [2,52].
The four aspects of a child’s diet were evaluated as follow: Variety (0–20 score)—Variety was evaluated both as overall variety among the five food groups (meat/poultry/fish/egg, dairy/beans, grains, fruit, and vegetables) and variety within the protein source groups (meat, poultry, fish, eggs, dairy, beans).
Adequacy (0–40 score)—This category evaluates the intake of dietary elements that must be consumed sufficiently to ensure a healthy diet. Adequacy was assessed considering daily intakes of fruits, vegetables, grains and fiber, protein, iron, calcium and vitamin C. Cut-off values derive from the frequencies of consumption and the daily intake recommended for Italian school children [52,53].
Moderation (0–30 score)—Moderation evaluates the intake of food and nutrients related to chronic diseases and that require a low level of consumption. Modification of the original DQI-I was used, according to Tur et al. [ 38], which suggests an increase optimally to <$30\%$ of total energy/d from fat (instead of $20\%$), due to presence of olive oil as main source of monounsaturated fatty acids (MUFA) in Mediterranean countries. Saturated fat intake was also assessed as the percentage of energy from saturated fat. Cholesterol and sodium intake levels were also calculated. The ‘empty-calorie food’ component assesses how much a person’s energy supply depends on low-nutrient density foods, which provide energy but insufficient nutrients (e.g., sugar, industrial pastries, sweets, and sugary drinks) [38].
Overall balance (0–10)—This category examines the overall balance of diet in terms of proportions of energy sources and fatty acid composition. The proposed cut-off points and corresponding scores reported by Tur et al. [ 38] were considered as more suitable for individuals residing in a Mediterranean country.
The score for each category is the sum of the scores for each component in that category. The total DQI-I score (0–100 scale) is the sum of the scores across the four categories. A score below 60 indicates a poor-quality diet [50].
## 2.5. Parental Nutritional Knowledge
Parental nutritional knowledge (NK) was calculated using seven questions extracted from the ZOOM-8 questionnaire [41]. The questions evaluated whether parents were aware of dietary recommendations for their children regarding breakfast, snack, beverage and vegetable intake (e.g., knowledge of the dietary recommended intake for vegetables, the importance of breakfast, the adequate breakfast and mid-morning snack and the appropriate beverages for school children). Each question was assigned “1” if the answer was correct and “0” if the answer was not correct. The correct answer was the one adhering to dietary recommendations. The final score comprised a unit-weighted index of knowledge ranging from 0 to 7.
## 2.6. Data Analysis
Estimation of the appropriate sample size was previously calculated for the primary analysis [39]. A post-hoc power analysis was conducted to determine if the current sample size was adequate to reliably estimate the multiple regression model presented. Continuous variables are described using mean and standard deviation (±SD) while categorical variables are described through absolute and relative frequencies. Normal distribution of dependent variables was assessed graphically using density graphs and tested with the Shapiro-Wilk test.
The outcome variables investigated in this study included adherence to the MD (KIDMED Index), average daily minutes of organized PA, average daily leisure screen time, and average daily minutes of moderate-vigorous PA (MVPA). The leisure screen time variable was dichotomized using 100 average daily minutes as the cut-off to address its bimodal distribution.
The associations between predictor variables and the designated outcomes were analyzed using Student’s t-test for continuous means or analysis of variance (ANOVA) when three or more levels were statistically compared. Multiple linear regression models with backward stepwise selection were employed to identify efficient predictors associated with the outcome measures. Results from linear regression were reported as unstandardized regression coefficients (Beta) with relative $95\%$ confidence intervals ($95\%$CIs).
The regression models were covariate-adjusted for child’s gender and age and the time of the survey and also included mother’s and father’s education level (high school or lower vs. university degree or higher), parent’s nutrition knowledge (0 to 7 scale), and parents’ PA levels (involved vs. uninvolved). The statistical significance level was set as $p \leq 0.05.$ All analyses were carried out using R version 4.2.2 (R Project for Statistical Computing) [54].
## 3.1. Population Characteristics
A total of 106 questionnaires were available for inclusion in the analyses. The sample included $$n = 53$$ girls ($50\%$) and $$n = 53$$ boys ($50\%$), between the ages of 6 to 10 years (mean 7.92 ± 1.40). Based on Cole cut-off values and also sex and age, more than half of the sample was categorized as normal-weight ($$n = 66$$; $63\%$) and the remaining portion was categorized as overweight/obesity ($$n = 39$$; $37\%$). The average KIDMED score was 4.44 ± 2.28. Specifically, $$n = 35$$ ($33\%$) children reported low adherence to the MD, $$n = 59$$ ($56\%$) intermediate adherence, and $$n = 12$$ ($11\%$) reported optimal adherence. Children were engaged in organized sport activities on average for 22.46 ± 16.91 min daily, while the accelerometers reported 48.25 ± 17.74 min of daily average MVPA. The average daily leisure screen time was 102.31 min (SD = 33.84). Detailed sample characteristics are summarized in Table 1. Males spent more time involved in organized sports (See Supplementary Materials Table S2) and had higher mean levels of vigorous physical activity. The investigated variables were normally distributed except for average daily organized sport (minutes), which was positively skewed.
## 3.2. Regression Models
Table 2 contains the results of the multivariate linear regression models. Father’s educational level was positively associated with the KIDMED index (reference class = “High school or lower” β = 1.0; $95\%$CI 0.10, 1.9). Mother’s education, on the other hand, was negatively associated with leisure screen time (OR = 0.29; $95\%$CI 0.10, 0.81). Parental NK was positively associated with the KIDMED index (β = 0.45; $95\%$CI 0.10, 0.81), and likewise with involvement in sport activities (β = 2.8; $95\%$CI 0.15, 5.4). Parent’s sport engagement was positively related to the KIDMED score (β = 1.0; $95\%$CI 0.17, 1.9).
Older children spent more time engaged in leisure screen time (OR = 1.26; $95\%$CI 1.02, 1.73), were more time engaged in organized sports (β = 3.5; $95\%$CI 1.3, 5.7), and spent less time committed to vigorous physical activity (β = −25; $95\%$CI −42, −8.1). Male children spent more time engaged in organized sports (β = 8.1; $95\%$CI 2.0, 14) and more time in vigorous physical activity (β = 82; $95\%$CI 36, 129).
## 3.3. Diet Quality Index-International (DQI-I) Evaluations
Descriptively, the mean total DQI-I score for the complete sample was 53.89 ± 8.69 (on a scale of 0 to 100). Slightly less than one-quarter ($24\%$, $$n = 27$$) of the children had a total DQI-I score higher than 60 (indicating an intermediate/good diet quality [50]). In terms of subdomains, the better score was for adequacy, followed by variety and moderation. The lowest score was for overall balance (Table 3). In terms of variety, only $4.5\%$ of the children consumed at least one serving from each food type, $26\%$ missed only one food group, and $39\%$ missed two food groups. Additionally, only $1.8\%$ of the sample consumed three or more different sources of protein per day. According to the adequacy category, the majority of the sample reported >$50\%$ of the recommended intake for grains, fiber, protein, iron, calcium, and vitamin C. Most children failed to meet the recommended levels of vegetables and fruit intake (Table 3). In the moderation category, only $5.4\%$ and $0\%$ of adolescents achieved the fat (≤$30\%$ of total energy intake) and saturated fat goals (≤$7\%$ of total energy intake), respectively. Cholesterol intake was ≤300 mg/day in $79\%$ of the sample. A relatively low overall balance score was also found for macronutrients ratio, and among fatty acids ratio.
## 4. Discussion
In the current study, we used cross-sectional data obtained from the first wave of the I-MOVE study [39] in order to examine parental influences on children’s dietary and physical exercise practices. Importantly, we used several novel assessment strategies that provided much richer insight into children’s eating and exercise tendencies and linked this information to parental influences. Included in the assessment was the KIDMED [49] instrument to measure the child’s compliance with the MD, actigraph accelerometers to accurately measure physical activity, the ZOOM-8 [41] to assess nutrient and food intake, lifestyle factors and parents’ characteristics (i.e., nutritional knowledge and their own activity levels). Added to this, we were able to characterize the sample based on DQI-I scores [38] to assess the children’s diet quality and quantify their sedentary behavior. Taken as a whole, this wide array of assessments provides much richer insight into a child’s nutritional practices, their routine physical activity and compliance with the MD.
In terms of diet, DQI-I scores indicated that less than a quarter of the sample would be categorized as having an intermediate/good diet quality. Moreover, less than a quarter could be characterized as having diets that contained adequate balance, and a very small percentage had sufficient exposure to different food groups. The mean DQI-I score was slightly above $50\%$, in line with previous studies on children and adolescents from the Mediterranean region [51,55]. According to DQI-I scores obtained, the children in this sample had diets that lacked moderation in fat intake and were highly unbalanced towards saturated fats. Also, regarding the adequacy of their diets, most of the children did not meet the recommended daily intakes of fruit and vegetables. These findings are consistent with those reported in other Mediterranean populations [56]. These results can be explained by recent change in nutrition patterns, which has witnessed an increase in high-caloric, high fat foods and the worldwide problem of overnutrition [57]. With regard to PA levels, less than half ($39\%$) of our sample met the WHO’s recommended criteria of at least 60 min of MVPA per day [58]. However, the majority of our sample ($89\%$) reported participating in sports activities outside of school hours. In addition, when we examined self-reported sedentary behavior and, more specifically, leisure screen time, $42\%$ of our sample exceeded 100 daily minutes.
To our knowledge only one other Italian study, conducted in Sicily with older youth [51], used the DQI-I index to characterize multiple facets of diet quality in Italian youth. By comparing DQI-I scores of our sample with those obtained from Ferranti et al. [ 51] we found that our sample had lower scores for the variety category including protein sources and fatty acid ratio scores. One explanation for these differences is the large amounts of fish consumed by individuals residing in southern Italy. Fish is an important source of proteins and polyunsaturated fatty acid in Mediterranean countries.
In our study, we found that parents’ education played a role in children’s compliance with the MD and also their leisure time (sedentary behavior). In particular, more educated fathers had children with greater adherence l to the MD and more educated mothers had more active children who engaged in less sedentary behavior.
Other studies, also conducted in Italy, have also found associations between parents’ education and improved adherence to the MD [59]. In particular, Grosso and colleagues [59] found that high socioeconomic status (combining education and occupation) was positively associated with MD adherence in a relatively large sample of adolescents recruited from schools located in the Sicily region (southern Italy). Likewise, additional studies conducted in other European countries [57,60,61,62] reinforce these findings. The connections between parental education and childhood dietary practices are not necessarily clear, but may involve social and cultural factors operating alone or in tandem. Parents with a higher level of education may be more concerned about their children’s weight and more knowledgeable about the benefits of exercise and nutritional eating habits, leading them to offer their children more chances to adopt a healthier way of life [63]. In our case, only fathers’ education was associated with MD adherence (mother’s education was significant in the univariate analyses, but dropped out in the multivariate analyses). Assortative mating may be one reason for these findings, given that educated fathers may select highly educated partners. Given the high magnitude of association between education levels, both scores cannot be efficiently estimated in a single regression model. Notwithstanding, in Italy, which from a culinary standpoint is a heavy matriarchal society, mothers play a prominent role in the lifestyle choices made by children. They are actively involved in food selection and preparation, and they traditionally spend more time with their children [64].
There are other more subtle relations that may lay at the heart of the education–diet and activity relationship. Education and socioeconomic status (SES) are also confabulated to some degree, as education provides a means for higher income. With more money available, parents can afford to enroll their children in afterschool programs including organized sports [65], spend more time with their children [66,67], and provide their children with healthier food choices [68].
A substantial literature has investigated the role of parental modeling on children’s lifestyles [69], with a particular focus on dietary habits and active behaviors [70]. The existing evidence is not definitive, as some previous studies have reported a positive association between the PA of parents and children [71], while others have not found any significant effect of parental PA on children’s physical fitness and engagement in organized sports [72,73]. Our findings align with those of Ruedl and colleagues [72], and support the hypothesis that parental influence on children’s PA may be mediated by the child’s age [74].
Both age and gender were important factors in dietary practices (adherence to MD) and physical activity. Older children and boys spent more time in organized sports, older children spent more time engaged in leisure activity, and accelerometer data showed that older children spent less time engaging in vigorous PA. These results suggest that developmental changes may be related to a child’s lifestyle [75] and are consistent with previous research indicating that older children tend to be more engaged in organized sports [76] and that boys tend to engage in higher levels of PA than girls [77]. Numerous factors may bear on whether a boy or girl plays organized sports or engages in PA. Individual-level factors can include a child’s body weight, overall fitness, boy’s preferences for higher intensity activities, family factors (e.g., parent’s support, gender roles, living conditions), community (e.g., participation in community sport), school (e.g., opportunities to be physically active in school) and environmental considerations (e.g., climate or geography) [78].
Parents with higher nutritional knowledge had children who adhered better to the MD and also spent more time actively involved in organized sports. Previous evidence suggests that parents with high nutritional knowledge are more likely to meet the preventive and health care needs of their children [79]. Furthermore, our findings align with Romanos-Nanclares and colleagues [80] who reported that children with parents having more favorable healthier-eating attitudes were less likely to present micronutrient inadequacy and higher adherence to the MD. Notably, the impact of nutritional knowledge scores on children’s PA was limited to minutes spent in organized sports, while it did not affect their MVPA, the latter assessed using accelerometers. Conceivably, schools might play a role in improving vigorous physical activity by delivering interventions focused on improving students’ physical fitness. The use of school-based interventions also goes a long way toward reducing social inequalities given that schools must offer programs irrespective of race, social class, and other potential factors (economic and cultural) that may serve as barriers to PA and dietary practices [81].
The early formative years of a child’s life are incredibly important toward shaping their future health and well-being. It is during this time of life that children learn how to eat properly, take in sufficient and balanced nutrients, regulate consumption of different food groups and develop an affinity for PA and sport exercise [21]. All of these learning experiences provide a framework for their dietary and fitness habits later in life with tremendous implications for their ability to achieve health and well-being. Children who don’t acquire sound nutritional practices and who don’t practice PA routinely put themselves at risk for a wide range of chronic diseases, many of which can increase their risk of premature mortality [18,20]. Perhaps the greatest influence on a child’s well-being and their pursuit of sound dietary practices and a healthy lifestyle is their immediate family. Parents in particular, are an important influence given their control of food purchasing, mealtime preparation of foods, and because they can role model good eating practices and exercise habits [28,30,32]. Parents are thus the bedrock on which most of a child’s pursuit of healthy eating and living starts.
There are several limitations to the current study that are worth noting. First, the sample was relatively small, drawn from a particular region in Italy, and may not be representative of the larger population of Italian school children. To test this assumption, we compared our sample with a relatively large epidemiological surveillance database for primary school children obtained from the Italian Ministry of Health (OKkio alla SALUTE, 2019). This comparison revealed that the $30\%$ prevalence of overweight or obese conditions in our sample is consistent with the official prevalence of overweight and obesity in the Emilia-Romagna primary school population ($26.4\%$) [26]. In addition, the KIDMED index for our sample was similar to that of other Italian primary school children’s dietary assessments [26]. With regard to PA levels, less than half ($39\%$) of our sample met the WHO recommended criteria of at least 60 min of MVPA per day [58]. However, the majority of children in our sample ($89\%$) reported participating in sports activities outside of school hours. Some of the noted differences in PA levels between our findings and others (including the OKkio alla SALUTE, 2019) may be attributed to measurement as we used actigraph accelerometers to accurately measure PA levels whereas other studies have relied solely on self-reports. Of note, even if the curent study took into account any form of MVPA using Actigraphs, future studies should differentiate between various physical activities (e.g., walking, cycling or gardening). When we examined self-reported sedentary behavior and, more specifically, leisure screen time, slightly under one-half of our sample exceeded 100 daily minutes.
Dietary intake is based on portion size and we are aware that overestimation of portion size can result in an overestimation of calorie and nutrient intake, leading to potential miscalculation in the assessment of diet. Self-reported dietary intake represents a limitation in our study; however for this reason we used a well-validated questionnaire with standardized portion sizes for all food items to ensure consistency in the measurement of dietary intake.
The cross-sectional nature of the data also limits us from making causal inferences regarding parental characteristics as determinants of children’s lifestyle factors. Causality requires establishing temporal relationships where the predictor precedes the outcomes in time and rigorous methodological designs with appropriate statistical controls that permit causal inferences. Notwithstanding, naturalistic observational studies such as the current one provides an important starting point to learn more about associations between variables, and this effort can be followed by prospective longitudinal studies that examine these relations over time and with appropriate statistical controls.
Finally, there are many more measures that could be modeled and that reflect parental socialization that may bear on children’s lifestyle habits. Parent-child dynamics, size of household, parent-child communication, marital status and other historical factors can influence the way parents and children interact. The omission of relevant variables can contribute to mis-specified models or at the very least produce biased parameters.
The results of this study represent a significant contribution to the field of health promotion, particularly with regards to understanding the impact of parental factors on children’s lifestyles. The novelty of our study lies in the fact that few articles have specifically investigated primary school children in Italy, and we have highlighted the unique impact of fathers’ and mothers’ factors on children’s lifestyle and nutritional habits. The results of this study have important implications for public health interventions and can help in targeting the right actors to improve children’s well-being. The insights gained from our study can be used to develop effective strategies to encourage healthy habits and promote a healthy lifestyle in children. These results have the potential to positively impact the daily lives of children and contribute to a more sustainable future for the current and next generations.
## 5. Conclusions
The current study confirms that parental factors, including lifestyle habits, educational level, and nutritional knowledge are related significantly to lifestyle factors in primary school children. This includes their dietary habits, physical activity level, and sedentary behaviors. Clearly, given these relations, it is vital that parents are included as targets in interventions, given that their adoption of healthy lifestyles will trickle down to their children. Importantly, the conduit for this “trickle down” effect can be partly attributed to the parents’ nutritional knowledge as well as their educational background and nutritional awareness. Overall, our study highlights the importance of considering the role of parental factors in shaping children’s lifestyles and suggests that targeted interventions that involve both parents and children may be an effective means of promoting healthy behaviors and reducing social inequalities in this population. Future longitudinal studies should investigate and compare different geographical areas as potential determinants of lifestyles as well as the role of teachers in influencing children’s dietary practices and healthy lifestyles. In light of this, there is a need to continue implementing school-based interventions targeting students, parents, and teachers.
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|
---
title: 'Adopting an Extended Theory of Planned Behaviour to Examine Buying Intention
and Behaviour of Nutrition-Labelled Menu for Healthy Food Choices in Quick Service
Restaurants: Does the Culture of Consumers Really Matter?'
authors:
- Abu Elnasr E. Sobaih
- Mohamed Algezawy
- Ibrahim A. Elshaer
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002157
doi: 10.3390/ijerph20054498
license: CC BY 4.0
---
# Adopting an Extended Theory of Planned Behaviour to Examine Buying Intention and Behaviour of Nutrition-Labelled Menu for Healthy Food Choices in Quick Service Restaurants: Does the Culture of Consumers Really Matter?
## Abstract
This research aims to examine an extended model of the Theory of Planned Behaviour (TPB) to understand the determinants of consumers’ intentions to buy and recommend nutrition-labelled menu (NLM) items for making healthy food choices. The research examines the influence of attitude towards behaviour (ATT), subjective norms (SNs), perceived behavioural control (PBC) and health consciousness on consumers’ intentions to buy and recommend NLM. The research also examines the role of culture in shaping buying and recommendation intentions of NLM by undertaking a comparative study of the extended model among consumers in two different countries that have enough variation based on Hofstede’s cultural dimensions, i.e., the Kingdom of Saudi Arabia (KSA) and the United Kingdom (UK). The results of questionnaire surveys analysed with SmartPLS version 4 showed that ATT, SNs and health consciousness significantly predict intentions to buy NLM items among KSA consumers in quick service restaurants (QSRs). However, PBC did not have a significant influence on KSA consumers’ intentions to buy NLM items. On the other hand, ATT, PBC and health consciousness significantly predict intentions to buy NLM items among UK consumers in QSRs. Nonetheless, SNs did not have a significant influence on UK consumers’ intentions to buy NLM items. The intention to buy NLM significantly predicts the intentions to recommend NLM among consumers in both countries (KSA and UK). The results of a multi-group analysis showed significant differences between the KSA and the UK regarding the influence of both SNs and PBC on consumers’ intentions to buy NLMs as well as on their indirect influence on intentions to recommend NLM items. The results value the role of culture in shaping consumers’ intentions to buy and to recommend NLM items for healthy food choices, which has numerous implications for international QSRs, policy makers, and academics.
## 1. Introduction
Quick service restaurants (QSRs) have been blamed for the introduction of junk and unhealthy food, which has contributed to abdominal and general obesity [1,2,3,4]. The increasing consumption of fast food provided by QSRs was found to be correlated with a high fat diet, a high Body Mass Index (BMI) and weight gain [5]. A recent report issued by the World Health Organisation (WHO) in June 2021 [6] showed that obesity has almost tripled since 1975. Additionally, the report showed that there are about 2 billion overweight adults and about a third of them are obese. Furthermore, rates of obesity among both adults and children are increasing, which contributes to the prevalence of several chronic diseases and health conditions. The report also confirmed that being overweight and obese kills more people than being underweight [6].
In order to manage the consumption of high calories and help consumers make healthier food choices, the governments of many countries, such as the USA and the UK, have forced food providers, e.g., restaurants, to adopt nutrition fact labels [7]. This was executed to help consumers identify the number of calories provided in each served meal. For example, in the USA, recent regulations under the Affordable Care Act, forced restaurants and retail food establishments to provide their consumers with calorie and other nutrition information for each served food item including food on display or self-served food [8]. In the UK, the government has asked all restaurants and cafes to provide calorie information on menus as a part of the government’s strategy to control obesity [9]. Several international QSRs, such as McDonald’s, have provided nutrition-labelled menus (NLM), to support consumers with nutrition information about each item in the menu. This nutrition information is provided at the website and at the point of service on display or on the placemat with each served food or drink item [10].
The growing interest of decision makers and many governments in managing nutrition and calorie labelling for promoting consumer healthy food choices has motivated scholars to examine the effect of nutrition information usage on consumers’ buying intentions and behaviour [7,11]. For example, a relationship between calorie labelling and total calories purchased has been found [12]. Additionally, the introduction of nutrition information and calories has contributed to a reduction in BMI and obesity [13]. However, “the long-term effect of calorie labelling on fast-food purchases is unclear” [10]. There is a paucity of research on the factors that make consumers use nutrition and calorie information to make a buying intentions or behaviour in QSRs [14]. Sobaih and Abdelaziz [14] called for further research, arguing that “future researchers could examine this issue [the culture] by adopting an international comparative study between counties with different cultural dimensions to examine whether culture moderates customer choices of health foods” (p. 13).
This research is a response to the call for more studies for understanding consumers’ buying intentions and behaviour of healthy food choice based on NLMs provided at QSRs, as recommended by recent studies from Petimar et al. [ 10] and Sobaih and Abdelaziz [14]. This research adopted an extended model by investigating the three determinants of the theory of planned behaviour (TPB) [15] along with health consciousness on consumers’ intentions to buy NLMs in QSRs for supporting healthy food choices. The research examines the influence of attitude towards behaviour (ATT), subjective norms (SNs), perceived behavioural control (PBC) and health consciousness (HC) on the intentions to make healthy food choices by considering the nutrition-labelled menu. The research also examines the role of culture on consumers’ intentions to buy and recommend healthy food through an extended model of TPB. More exactly, the research compares between consumers in two different countries, which have enough variation based on Hofstede’s cultural dimensions, i.e., the Kingdom of Saudi Arabia (KSA) and the United Kingdom (UK). Understanding the role of culture is important for international businesses when providing nutrition information to consumers in order to stimulate their intentions and make a decision of healthy food choices.
The following sections of the manuscript start with developing a research conceptual framework and building the research hypotheses (Section 2). Section 3 provides information about the research instrument, research population and sample as well as the data analysis technique adopted in the current research. Section 4 presents the results of the research structural model, its validity, and reliability. This section provides the results of two structural model in the KSA and the UK. Section 5 discusses the findings of the research and the results of two structural models. It compares the results with previous related studies and the implications of the study. Section 6 concludes the research and presents some ventures for future research.
## 2.1. The Influence of TPB Determinants and Health Consciousness on Consumers’ Intentions to Buy and to Recommend NLM Items
This research adopts a TPB framework to understand consumers’ intentions to buy and recommend NLM items for making healthy food choices in QSRs. The TPB framework is an expansion of the Theory of Reasoned Action (TRA) [16]. The TRA structure assumes that behavioural intention is the major antecedent of human behaviour. Furthermore, ATT and SNs are the two main antecedents and predictors of behavioural intention [16]. Attitude refers to an individual’s assessment of a given behaviour favourably or unfavourably, whereas SNs refers to the social influence that makes individuals engage or not in a given behaviour [15]. One of the main weaknesses of TRA is the assumption that individuals have full control regarding the behaviour they intend to practice; however, this may not always be true [15]. Hence, a new variable was added to the TRA called the PBC. The PBC refers to “the perceived ease or difficulty of performing the behaviour” ([15], p. 188). These three antecedents (ATT, SNs and PBC) of behavioural intention encompassed the three main determinants of behavioural intention in the TPB.
The TPB was adopted by several studies to examine consumers’ buying intentions and behaviour in various contexts. For instance, TPB was adopted to understand customers’ intentions to visit green hotels [17]. The results showed that ATT, SNs and PBC positively influence customers’ intentions to stay in a green hotel. Another study by Elshaer et al. [ 18] adopted TPB to examine customers’ intentions to generate food waste. The study showed that determinants of TPB (ATT, SNs and PBC) fully mediate the link between religiosity and food waste intention. Additionally, they partially mediate the link between the food consumption culture and the food waste intention. Shin et al., 2018 [19] examined customers’ intentions and behaviour regarding organic menus through the lens of TPB and a norm activation model. They found that ATT, SNs, PBC and personal norms are all determinants of customers’ intentions to buy organic menu items. Related to this, Shin et al., 2020 [20] found that ATT, SNs, and PBC significantly predict customers’ intentions regarding state-branded products. Additionally, consumers’ purchase intentions predict their actual buying behaviour of state-branded products. A recent study adopted TPB to examine customers’ intentions to buy nutrition-labelled items in fast food operations [14]. The results showed that only SNs and PBC significantly affect consumers’ intentions to buy NLM. However, there was no significant influence of ATT on customers’ behavioural intentions. The intention to buy NLM was found to significantly predict their visit to fast food operations that provide these NLMs and recommend them to others.
The founder of TPB, Ajzen [15], argued that other variables could be added to the TPB framework if they contribute to behavioural intention. Hence, several studies have added some variables to understand consumers’ behavioural intentions. For example, Shin et al. [ 20] added personal norms to the three constructs of TPB (ATT, SNs, PBC) to understand consumers’ intentions and behaviour towards organic menus. Like the study of Shin et al. [ 20], which added health consciousness for understanding customers’ intentions and behaviour regarding state-branded products, the current research extends TPB by adding health consciousness to assess consumers’ intentions to buy and recommend NLMs in QSRs. Health consciousness refers to the integration of health concerns in lifestyles [21]. While Shin et al. [ 20] found no significant influence of health consciousness on consumers’ intentions to purchase state-branded products. Yadav and Pathak [22] confirmed that health consciousness is a significant predictor of the intention to buy organic food. Other studies [23,24] confirmed a relationship between health consciousness and the intention to buy and consume local food items. This research will add to the body of academic literature and examine the effect of health consciousness on consumers’ intentions to buy NLMs.
Drawing on TPB and the above-discussed relationships, this research assumes that ATT, SNs, PBC and health consciousness significantly predict consumers’ intentions to buy MLM items for healthy food choices. Additionally, consumers’ intentions predict consumers’ intentions to recommend NLM items to others (see Figure 1). Therefore, hypotheses (H) 1–5 are proposed:
## 2.2. The Role of Culture
Hofstede [25] defined what causes individuals, groups, and communities to imitate certain attitudes and behaviours as “collective programming”. In that sense, every country has its own culture that drives the behaviours of its people. Recent research [26] on cultural differences among QSR consumers showed that culture plays a prime role in consumers’ perceptions of McDonald’s in four different countries (the US, Egypt, Vietnam and Malaysia) which drive variation in Hofstede’s cultural dimensions [25]. For instance, the research showed that “the US perceived McDonald’s more critically than other countries whereas Egypt and Vietnam viewed it more favourably” ([26], p. 391). The study of Lee and Ulgado [27] showed that international QSRs should consider cultural differences as customers have different expectations for the services based on their culture. For example, while American customers expect low prices in QSRs, Korean customers are focusing on service dimension factors such empathy. Furthermore, Qin et al. [ 28] confirmed that there is a variation in customer’s perceptions of QSRs based their culture. The study of Khan et al. [ 26] showed that American consumers are more concerned about the food quality in MacDonald’s than Vietnamese and Egyptian consumers. Additionally, Malaysian and Egyptian consumers value McDonald’s socially and emotionally whereas Americans see it as a place to consume food. Consumers with Eastern culture, e.g., Malaysian, value McDonald’s as a place for social gathering with family and friends while Americans perceive it as a place for convenience food.
There were several attempts by research to undertake cross-cultural studies for customers’ purchasing intentions and behaviours. Moon et al. [ 29] studied the effect of culture on buying personalized products online and found that individualism significantly influences purchase intentions. The study of Peña-García et al. [ 30] showed that national culture has a moderating role on consumers’ intentions and behaviours online. Another study by Sreen et al. [ 31] showed that collectivism as a dimension of culture significantly affects the three determinants of green purchase intentions (ATT, SNs, PBC). A study on repurchase intentions of fast food meals [32] showed the factors that affect repurchase intentions vary between Americans and Kuwaitis. For example, Kuwaitis often value non-food items such as staff attitude more than Americans who focus on food quality.
Based on these discussions, this research predicts significant differences in ATT, SNs, PBC, health consciousness and consumers’ intentions to buy and recommend NLMs in QSRs between two countries that have variation according to Hofstede’s cultural dimensions (e.g., the KSA and the UK) [25].
## 3. Methods
The approach adopted in the current study was a quantitative cross-sectional research design, where a thorough review of previous studies was carried out to extract the measures that suit the current study and contribute to the design of the theoretical framework. Based on this process, the study hypotheses were created. Subsequently, data were gathered by an instrument uploaded online and analysed employing PLS-SEM bootstrapping and a multi-group analysis method.
## 3.1. Sampling
The study targeted QSR consumers in the UK and the KSA. The survey was distributed through specialized data collection company in each targeted country. The role of the data collection company was to facilitate the process of data collection; however, the research team administered the whole process. Customers were contacted at the point of purchase, e.g., MacDonald’s, to voluntarily participate in the study. Only those who gave consent participated in the current study. To safeguard the privacy of participants, all data that could potentially reveal their identities were removed from the survey’s results. The contribution to fill in the survey was purely voluntary and anonymous. Consumers had the option of providing their name and age. A total of 900 surveys were distributed, with 450 in each country, and 400 valid responses were used for analysis in each country, with a response rate of $88\%$. The sample size of 400 responses in each country used in this study is appropriate for analysis using PLS-SEM. This satisfies the requirements set by Nunnally [33] of having at least ten responses per scale item (as the current study has 20 scale items, the minimum suggested sample size is 200). Additionally, it satisfies Hair et al. ’s [34] standards of having at least 100–150 answers to achieve accurate estimates. According to Krejcie and Morgan’s [35] guidelines, when the population size is above 1,000,000, the minimal required sample size is 384 responses. Based on all these points, it can be determined that the existing sample size of 400 is sufficient for further analysis. The survey was made available in November and December 2022, and participants from each country were asked to provide their perceptions on the same set of questionnaire items.
## 3.2. Development of the Study Measures
Standard psychometric properties were employed to review the literature and choose the research measures. The variables of the theory of planned behaviour (TPB), including intention to buy and recommend, were assessed using a 17-item scale that was adopted from Ajzen [15] and Shin et al., 2019 [19]. This scale included items for attitude (4 items), subjective norms (3 items), perceived behavioural control (4 items), intention to buy (3 items) and intention to recommend (3 items). Additionally, health consciousness was operationalized using a 3-item scale adapted from Shin et al., 2020 [20]. The full measure adopted in the current study is presented in Appendix A. To increase the response rate [34], the items were pre-tested, and the questionnaire was kept as brief as possible. Only key demographic information was collected.
A specialized data collection company was utilized to obtain a high response rate. Consumers were invited to show their agreement on a 5-point Likert scale, going from “strongly disagree” [1] to “strongly agree” [5], instead of 7-point or 10-point Likert scales. The use of 5-point scales requires less time, is easy to answer [36] and enables the use of advanced multivariate statistical analysis methods [37].
As this study used a self-reported online survey, there is a possibility of common method variance (CMV) [33]. To address this potential issue, three techniques were adopted: [1] The dependent questions (intention to buy and intention to recommend) were placed before the independent questions (TPB and health consciousness items) in the survey; [2] Respondents’ private data were retained as confidential; [3] Harman’s single-factor method was used. This method involves subjecting all questions to exploratory factor analysis (EFA) in the SPSS program, with the constraint that just one factor will be retrieved with no rotating of the data. The results of the investigation showed that CMV was not an issue at any point throughout the inquiry, as just one variable explained about $36\%$ (<0.50) of the variance in the data [34,38].
## 3.3. Methods of Data Analysis
The data analysis was performed in four successive stages. The respondents’ characteristics were described in the first stage. The second stage aimed to evaluate the research measurement psychometric properties such as reliability and validity. To achieve this, various statistical techniques were employed including Cronbach’s alpha (α), composite reliability (CR), cross loadings and average variance extracted (AVE). The third stage aimed to test the research model and hypotheses using a partial least squares structural equation modelling (PLS-SEM) approach. Finally, in the last phase (stage four), a multi-group analysis method was tested in PLS-SEM to identify any variance between the UK’s and the KSA’s QSR consumers.
## 4.1. Respondents’ Characteristics (Stage 1)
The demographic characteristics of the targeted consumers such as their age, gender, education degree and diet status were collected through optional questions in the designed survey. As to consumers in the KSA, most respondents, i.e., $65\%$, were in the age range from 21 to <30 years, followed by those between 30 and <40 years, or $25\%$. Additionally, $7\%$ of the respondents were under 21 years and only $3\%$ were between the ages of 40 and 50, indicating that young consumers (usually 40 years or less) are the dominant segment of QSR customers in the KSA ($90\%$). The sample had a similar proportion of female ($52\%$) and male ($48\%$) consumers. With respect to education levels, most the sample had completed a bachelor’s degree ($60\%$) followed by those who had completed a doctoral or master’s degree ($15\%$); $25\%$ were secondary school consumers. In relation to diet condition, only $20\%$ of the consumers were adopting a particular diet program; most were not ($80\%$).
In regard to consumers in the United Kingdom, the majority of respondents in this country, $55\%$, were in the age range between 30 and <40 years followed by those aged between 40 and 50 ($30\%$); $10\%$ of consumers were aged from 21 to <30 years and only $5\%$ of the consumers were <21 years. This shows that $85\%$ of consumers of QSRs in the UK are adults (30 to 50 years old) and are the dominant segment of QSR customers in the UK. The sample had a similar proportion of female ($51\%$) and male ($49\%$) consumers. In relation to their education level, most the sample had completed a bachelor’s degree ($66\%$) followed by those who had completed a doctoral or master’s degree ($20\%$); $14\%$ were secondary school consumers. Concerning diet condition, $40\%$ of the consumers were following a particular diet program but most were not ($60\%$).
## 4.2. Measurement Validity (Stage 2)
The research’s main construct was evaluated for convergent and discriminant validity using various methods such as factor loadings, composite reliability average variance extracted, Cronbach’s alpha, cross-loading, heterotrait–monotrait ratio of correlations and the Fornell–Larcker criterion before testing the proposed model and hypotheses. The factor loadings for all the measured items were calculated and screened to ensure they loaded on the appropriate construct, and all had values higher than the recommended threshold of 0.50 [39]. Table 1 illustrates that the factor loadings calculated for all the measured items in the two countries (the UK and the KSA) were between 0.737 and 0.949 and exceeded the suggested threshold of 0.50 [39].
Table 1 demonstrates that the values for Cronbach’s alpha and Composite Reliability (C.R) in the two groups of interest (the UK and the KSA) for which they were calculated were above the minimum accepted value in similar business research (>0.7) [40]. Additionally, the Average Variance Extracted (AVE) values were computed and compared to the minimum threshold value of 0.50 recommended by Hair et al. [ 39]. As shown in Table 1, the AVE values for all research constructs surpassed this threshold which approves the scale convergent validity in both countries (the UK and the KSA).
The discriminant validity was also evaluated using three criteria: cross-loadings, Fornell–Larcker and the HTMT ratio. In cross-loadings, items are expected to load more strongly to their reflective factor than to any other factor in the scale. The Fornell–*Larcker criteria* state that the correlation coefficient between measured constructs must be lower than the square root value of AVE, and that the HTMT ratio requires the correlation coefficient between constructs to be lower than the recommended level of 0.85 [39]. Table 2 shows the cross-loading metric, where the item loadings are correlated more strongly to their predetermined factors than to any other factor (in both data sets for the UK and the KSA).
Furthermore, the assessment of Fornell–Larcker (Table 3 and Table 4) shows that the value of all squared roots of AVEs, written in bold and located in the diagonal part of the discriminant validity table, are higher than the correlation coefficient between model constructs. Similarly, Table 3 and Table 4 also show that all the HTMT values were lower than the recommended level. Therefore, it can be concluded that the measurement convergent and discriminant validity in both data sets (the UK and the KSA) were achieved, and the collected data were suitable for structural model evaluation.
## 4.3. Structural Model Evaluation and Hypothesis Testing (Stage 3)
The third stage of the analysis included evaluating the structural model of the study using the Partial Least Squares Structural Equation Modelling (PLS-SEM) method. The study’s constructs were then subjected to Smart PLS V.4 software according to the proposed hypotheses, and a path analysis was conducted using the bootstrapping resampling method with 5000 repetitions. All hypotheses were evaluated using the path coefficient (β), and only those with p values ≤ 0.05 were considered significant.
As shown in Figure 2 and Figure 3 and Table 5, the PLS-SEM results revealed that attitude towards behaviour has a positive influence on buying intention in both the data set for the KSA (β = 0.367, $t = 7.530$, $p \leq 0.000$) and for the UK (β = 0.385, $t = 8.184$, $p \leq 0.000$), which support H1 in both groups of interest. On the other hand, subjective norms in the KSA’s data set showed a positive significant influence on buying intention (β = 0.267, $t = 5.385$, $p \leq 0.000$). At the same time, it failed to significantly influence buying intention in the UK’s data set (β = 0.046, $t = 1.226$, $$p \leq 0.220$$), which means that H2 is supported in the KSA but not in the UK. Furthermore, perceived behaviour control failed to significantly influence buying intention in the KSA’s data set (β = 0.086, $t = 1.281$, $$p \leq 0.222$$). At the same time, perceived behaviour control succeeded in influencing buying intention with a significant p value in the UK’s data set (β = 0.327, $t = 6.908$, $p \leq 0.000$), which means that H3 is supported in the UK’s but not in the KSA’s data set. The positive significant effect of health consciousness on buying intention was slightly higher in the UK’s data set β = 0.153, $t = 3.753$, $p \leq 0.000$ than in the KSA’s data set β = 0.141, $t = 3.084$, $p \leq 0.000$, which supports H4 in both groups of interest. Similarly, the positive significant effect of buying intention on recommendation intention was slightly higher in the UK’s data set (β = 0.779, $t = 24.832$, $p \leq 0.000$) than in the KSA’s data set (β = 0.731, $t = 20.746$, $p \leq 0.000$), which supports H5 in both groups of interest.
In relation to the mediating impact of buying intention, the specific indirect effects in the PLS-SEM report were checked. They revealed that buying intention has a significant mediating impact between attitude towards behaviour and recommendation intention in the KSA (β = 0.269, $t = 7.143$, $p \leq 0.000$) and the UK (β = 0.300, $t = 7.639$, $p \leq 0.000$). Similarly, as shown in Table 5, buying intention succeeded in mediating the impact of perceived behaviour control and health consciousness.
On the other hand, buying intention failed to mediate the impact of subjective norms on recommendation intention in the UK’s data set (β = 0.036, $t = 1.222$, $$p \leq 0.222$$), while it succeeded in mediating the same relationships in the KSA’s data set (β = 0.165, $t = 4.847$, $p \leq 0.000$).
## 4.4. Multi-Group Analysis (Stage 4)
In the final stage (Stage 4), we examined the significant differences between the KSA and the UK in terms of the impact of attitude towards behaviour, subjective norms, perceived behaviour control and health consciousness on buying intention. We also checked the differences in the mediation analysis. The two groups’ models (the UK and the KSA) are compared to each other to find out the differences in the causal structure and therefore identify which path causes the variance between the two groups of interest. The findings showed that only two paths in the two groups of interest (the UK and the KSA) were the source of variance in the group analysis. The differences in path coefficients with their corresponding significant p value revealed that the effect of subjective norms on buying intention was stronger in the KSA than in the UK. Similarly, the differences in path coefficients and p value revealed that the impact of perceived behaviour control on buying intention was stronger in the UK than in the KSA. Furthermore, the mediating effects of buying intention on the relationship between subjective norms and recommendation intention, and between perceived behaviour control and recommendation intention, were found to be another two primary sources that can cause the variance between the two groups of interest. The findings of the multi-group analysis are presented in Table 6.
## 5. Discussion
This research has two main objectives. The first objective is to examine the extended model of TPB (Figure 1), which includes the influence of ATT, SNs, PBC and health consciousness on consumers’ intentions to buy and recommend NLMs in QSRs for healthy food choices. The second objective is to examine the effect of national culture on this extended model by undertaking a comparative study between two countries that have enough variation in Hofstede’s cultural dimensions. The results of structural model using Smart PLS v4 showed that ATT and SNs significantly predict intention to buy NLM items in QSRs among KSA consumers. These results are in line with the framework of TRA [16] and partially support TPB [15] in that only two determinants (ATT and SNs) affect the intentions of KSA consumers to buy NLM items. However, PBC did not have a significant influence on consumers’ intentions in the KSA to buy NLM items. This means that Saudi consumers did not find it easy to buy NLM despite their positive attitude and social influence. They do not have enough control on their behaviour, which could be due to lack of time or money [41]. On the other side, the results of UK model showed that both ATT and PBC were found to significantly predict the intention to buy NLM items in QSRs. This result partially supports TPB [15] in that two determinants (ATT and PBC) significantly influence behavioural intention. However, the results showed that SNs did not have a significant influence on consumers’ intention in the UK to buy NLM items. This could be because the UK has an individualist culture [25], hence their buying intentions are less likely to be influenced by their friends and peers.
Unlike the results of Shin et al. [ 20], which found that health consciousness did not affect consumers’ state-branded food purchase intentions and behaviours, the current study found a significant impact of health consciousness on intentions to buy NLMs to make healthy food choices among both KSA and UK consumers. These findings support previous research studies [22,23,24] where health consciousness significantly predicts consumers’ intentions to purchase organic menus or local food items. In addition, the results support the TPB framework [15] and previous research [14] where the intention to buy NLMs significantly predicts intention to recommend NLM among consumers in both countries (the KSA and the UK). Moreover, the results confirmed a mediating role for intention to buy NLM in the relationship between the three antecedents (ATT, SNs, PBC), health consciousness and the intention to recommend NLMs among consumers in the KSA and the UK. Nonetheless, the intention to buy NLM items has no mediation effect between SNs and recommendation intentions for NLM items among UK consumers.
The research adopted a multi-group analysis to investigate the role of culture in the extended model of TPB. Multi-group analysis shows whether there are significant differences between the KSA and the UK in the extended model of TPB, which affect consumers’ intentions to buy and recommend NLM items. The results of the multi-group analysis showed that the differences in culture between the two countries (the KSA and the UK) were significant in four relationships. First, the relationship between SNs and intention to buy NLM items has a significant difference between KSA and UK consumers. KSA consumers are more collective than UK consumers; hence, they are more affected by social influences than by individual societies, i.e., the UK. Second, the relationship between PBC and the intention to buy NLM items has a significant difference between KSA and UK consumers. UK consumers were found to have more control of their behaviour than KSA consumers. Third, SNs had an indirect effect on the intention to recommend NLMs through the intention to buy NLMs. The intention to buy NLMs has a mediating role between SNs and NLM items among KSA consumers with no mediation effect on UK consumers. Fourth, PBC has an indirect effect on the intention to recommend NLM through the intention to buy NLM. The intention to buy NLM has a mediating role between PBC and NLM items among UK consumers with no mediation effect on KSA consumers.
The above results contribute to the literature by adding health consciousness as a significant predictor and antecedent of consumers’ intentions to buy NLM items for healthy food choices. In addition, it has an indirect influence on consumers’ recommendation intentions of NLM items through buying intention. Hence, policy makers and international QSRs should pay high attention to promoting health consciousness among consumers to stimulate their intention to buy and recommend NLM items. The current research supports the work of Huang et al. [ 42] who stressed the role of policy makers in encouraging health consciousness to drive consumer’s healthy food choices. Policy makers should integrate health awareness about consumption of healthy food choices into the healthy lifestyle of consumers. They should undertake a health campaign to encourage consumers to use nutrition information and make healthy choices based on these NLMs provided to them. Legislation and regulations that enforce QSRs to implement NLMs are also important to encourage healthy food choices.
The results of this research confirm that international QSRs should recognize the crucial role of culture and its profound impact on ensuring business success in today’s competitive environment. It is imperative for international businesses to understand the national culture of the country they enter for a global business. Understanding culture could enable QSRs to provide NLMs that meet their needs and expectations. For example, in individualist societies such as the UK, QSRs could make use of media campaigns for increasing social influence on nutrition and calorie information. This will also enable QSRs to meet the needs of the national culture in relation to nutrition and calorie information.
## 6. Conclusions
The current research examined an extended model of TPB, which included three antecedents of behavioural intention (ATT, SNs, PBC) and health consciousness, to better understand consumers’ intentions to buy NLM items for healthy food choices in QSRs. The research compared the extended model between two countries with different cultures (the KSA and the UK) using a structural model of SMART PLS V4 and a multi-group analysis. The results of the structural model confirmed the extended model in both countries, except for the influence of SNs on the intention to buy NLM items among UK consumers and the influence of PBC on the intention to buy NLM items among KSA consumers. This means that consumers’ buying intentions of NLMs in QSRs in the UK are less likely to be affected by their friends and peers, mainly because they are an individualist society. On the other hand, KSA consumers are less likely to control their behaviour, mainly because of time or money. The research showed that health consciousness significantly influences intentions of KSA and UK consumers to buy NLM items, which was found to significantly impact the intention to recommend NLM items to others. The current research adds to academic literature by extending the TPB framework to better understand NLM buying intentions and behaviours. It also highlighted the primary role of culture in QSRs, especially in relation to buying intentions and behaviours towards NLM items, for making healthy food choices. The research was undertaken in two different countries using a self-reporting measure. Future research studies could consider other countries with a deeper analysis of their culture and its relationship with their buying intentions and behaviours.
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---
title: (S)-2-(Cyclobutylamino)-N-(3-(3,4-dihydroisoquinolin-2(1H)-yl)-2-hydroxypropyl)isonicotinamide
Attenuates RANKL-Induced Osteoclast Differentiation by Inhibiting NF-κB Nuclear
Translocation
authors:
- Mina Ding
- Eunjin Cho
- Zhihao Chen
- Sang-Wook Park
- Tae-Hoon Lee
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10002170
doi: 10.3390/ijms24054327
license: CC BY 4.0
---
# (S)-2-(Cyclobutylamino)-N-(3-(3,4-dihydroisoquinolin-2(1H)-yl)-2-hydroxypropyl)isonicotinamide Attenuates RANKL-Induced Osteoclast Differentiation by Inhibiting NF-κB Nuclear Translocation
## Abstract
Osteoporosis is a common skeletal disease; however, effective pharmacological treatments still need to be discovered. This study aimed to identify new drug candidates for the treatment of osteoporosis. Here, we investigated the effect of EPZ compounds, protein arginine methyltransferase 5 (PRMT5) inhibitors, on RANKL-induced osteoclast differentiation via molecular mechanisms by in vitro experiments. EPZ015866 attenuated RANKL-induced osteoclast differentiation, and its inhibitory effect was more significant than EPZ015666. EPZ015866 suppressed the F-actin ring formation and bone resorption during osteoclastogenesis. In addition, EPZ015866 significantly decreased the protein expression of Cathepsin K, NFATc1, and PU.1 compared with the EPZ015666 group. Both EPZ compounds inhibited the nuclear translocation of NF-κB by inhibiting the dimethylation of the p65 subunit, which eventually prevented osteoclast differentiation and bone resorption. Hence, EPZ015866 may be a potential drug candidate for the treatment of osteoporosis.
## 1. Introduction
Osteoporosis is a common skeletal disease that occurs when the bone mineral density (BMD) decreases or the bone structure deteriorates [1]. The development of osteoporosis is primarily caused by the imbalance of bone homeostasis, including bone resorption by osteoclasts and bone formation by osteoblasts [2]. Post-menopause, old age, medications, endocrine disorders, immobilization, inflammatory arthropathy, hematopoietic disorders, and nutrition disorders increase osteoclast activity, leading to osteoporosis [3]. Given that the population’s average age is rapidly increasing worldwide, osteoporosis could become a major health concern that significantly impacts the quality of life of older adults [4]. Currently, the preferred treatment for osteoporosis is pharmacological interventions. Bisphosphonates, denosumab, and strontium ranelate are the main medicines for the treatment of osteoporosis [5]. However, some studies have shown a rapid decrease in BMD and an increased risk of vertebral fractures after the discontinuation of denosumab [6,7]. Additionally, there have been some reported side effects of bisphosphonates in the treatment of osteoporosis [8]. Therefore, this study aimed to find a new pharmacological agent to treat osteoporosis.
Osteoclasts are giant multinucleated cells that have critical roles in the regulation of bone development and bone homeostasis [9]. Osteoclasts are derived from cells of the monocyte/macrophage lineage by the activation of receptors by two factors, the macrophage-colony stimulating factor (M-CSF) and the receptor activator of nuclear factor-kappa B ligand (RANKL). M-CSF primarily regulates the proliferation and survival of osteoclast precursors and mature cells [10]. RANKL is the major osteoclast differentiation factor, and its interaction with RANK recruits tumor necrosis factor receptor-related factors and activates downstream signaling pathways, thereby inducing the nuclear factor of activated T cells 1 (NFATc1) [11,12]. NFATc1 has a major role in regulating several osteoclast-specific genes including matrix metallopeptidase 9 (Mmp9), Cathepsin K (Ctsk), and acid phosphatase 5, tartrate resistant (Acp5) [13,14].
Histone methylation is the modification of certain amino acids in histones, such as lysine, arginine, and histidine, by the addition of one to three methyl groups. Histone methylation is a dynamic process, and methyl groups can be added or removed by histone methyltransferases and histone demethylases [15,16]. These enzymes have been shown to be involved in tumorigenesis [17], angiogenesis [18], and the development of acute myeloid leukemia (AML) [19]. Studies have shown that histone methylation is regulated in bone cell differentiation [20]. The protein arginine N-methyltransferase (PRMT) family is a group of methyltransferases. There are two types of PRMTs: PRMT1, 3, 4, 6, and 8 are type I PRMTs that asymmetrically demethylate arginine, while PRMT5 and PRMT7 are type II PRMTs that symmetrically demethylate arginine [21,22]. PRMT5 is known to play important roles in gene transcriptional regulation and signal transduction [23,24]. Previous research has indicated that PRMT5 protein increases during osteoclastogenesis, and the reduction of PRMT5 via (S)-N-(3-(3,4-Dihydroisoquinolin-2(1H)-yl)-2-hydroxypropyl)-6-(oxetan-3-ylamino)pyrimidine-4-carboxamide (EPZ015666) that inhibits RANKL induced osteoclast differentiation [25]. ( S)-2-(Cyclobutylamino)-N-(3-(3,4-dihydroisoquinolin-2(1H)-yl)-2-hydroxypropyl)isonicotinamide (EPZ015866) is another PRMT5 specific inhibitor, which blocks the enzyme activity of PRMT5 in the proliferation and cell cycle progression of human colorectal cancer cells [26]. This study investigates the underlying molecular mechanisms of EPZ015866 on osteoclast differentiation.
Nuclear factor-κB (NF-κB) is a transcription factor that has an important role in the survival, formation, and functions of osteoclasts [27,28]. The inhibition of NF-κB has been shown to be an efficient method to suppress osteoclast formation and bone resorption [29,30]. Therefore, many studies have focused on NF-κB as a target for the treatment of osteoporosis [28,31]. Studies have confirmed that methylation of lysine and arginine residues in the p65 subunit of NF-κB regulates its activity [32,33]. Additionally, there is evidence that PRMT5 can regulate NF-κB activity through the methylation of p65 [34,35]. The present study demonstrates that EPZ015866, a derivative of EPZ015666, has a better therapeutic effect on osteoporosis than EPZ015666.
## 2.1. EPZ Compounds Attenuates RANKL-Induced Osteoclast Differentiation In Vitro
EPZ015666 is a known PRMT5 inhibitor that suppresses osteoclast differentiation [25]. Since there is a structural similarity (Figure 1A), we compared EPZ015866 with EPZ015666 in a dose-dependent manner to investigate the effect of EPZ015866 as an inhibitor of osteoclast formation. Bone marrow-derived macrophages (BMMs) isolated from the femur and tibia of a mouse were stimulated with RANKL and M-CSF in the absence or presence of EPZ015866 or EPZ015666 at the indicated concentrations for four days. EPZ015866 significantly reduced RANKL-induced tartrate-resistant acid phosphatase (TRAP) positive multinucleated giant cell formation at a low dose, 20 nM, whereas EZP015666 inhibited it at a high dose, 1000 nM (Figure 1B). When we calculated the area of TRAP-positive cells and the number of mature osteoclasts containing more than three nuclei, EPZ015866 dramatically decreased the area and number of osteoclasts at a concentration of 20 nM, the same concentration at which EZP015666 did not significantly work (Figure 1C,D). Therefore, we suggest that EPZ015866 is an effective compound for inhibiting osteoclastogenesis and is better than EPZ015666. Both EPZ compounds were not cytotoxic when the concentration of the compounds was equal to or less than 1000 nM (Figure 1E). These results suggest that the EPZ compounds suppress the RANKL-induced osteoclastogenesis without causing cytotoxicity. Bone remodeling is regulated by the homeostasis between osteoclasts and osteoblasts [36]. Therefore, we indicated whether the EPZ compounds affected osteoblast differentiation. The osteoblast differentiation was analyzed by alkaline phosphatase (ALP) staining after bone morphogenetic protein 2 (BMP2) stimulation. Neither EPZ compound affected BMP2-induced osteoblastogenesis compared with the control (Figure S1A,B). These results suggest that EPZ compounds only suppress osteoclast differentiation and have no effect on osteoblast formation.
## 2.2. EPZ Compounds Suppress F-Actin Ring Formation and Bone Resorption
To determine whether the EPZ compounds inhibited F-actin ring formation, BMMs were treated with or without the EPZ compounds. F-actin ring formation was observed on day 4 after RANKL stimulation in the control group (Figure 2A). However, treatment with EPZ015866 remarkably reduced the size of F-actin ring structures, starting at 20 nM in a dose-dependent manner. Although the EPZ015666 treatment suppressed F-actin ring structures at high doses (500–1000 nM), it did not significantly inhibit them at low doses (Figure 2B). Additionally, we confirmed whether the EPZ compounds suppressed the bone-resorbing activity of osteoclasts. In the control group, bone resorption pits were detected after RANKL stimulation. However, bone resorption pits were decreased by the EPZ015866 treatment starting at 20 nM (Figure 2C). The area of the bone resorption pits was quantified according to the bone resorption assay results (Figure 2D). In the EPZ015666 treatment group, the inhibitory effect of the resorption pits was weaker than EPZ015866. These data indicated that EPZ015866 suppressed the formation of mature osteoclasts and the bone resorption ability better than EPZ015666.
## 2.3. EPZ Compounds Inhibit the Expression of Osteoclast-Specific Genes
To investigate the effect of the EPZ compounds on osteoclastogenesis-associated gene expression, mRNA expression levels were examined by RT-PCR. We found that the mRNA expression of osteoclast-specific genes, including Acp5, Ctsk, Dendritic cell-specific transmembrane protein (Dc-stamp), Osteoclast stimulatory transmembrane protein (Oc-stamp), Atp6v0d2, and Mmp9 were suppressed in a dose-dependent manner by EPZ015866 (Figure 3A–F). However, EPZ015666 only significantly inhibited Acp5 and Atp6v0d2 expression at 1000 nM. These data reveal that EPZ015866 prevents osteoclast differentiation via inhibiting the expression of osteoclast-mediated genes better than EPZ015666 in vitro.
## 2.4. EPZ Compounds Decrease the Expression of the Transcription Factors PU.1 and NFATc1
To demonstrate the molecular mechanisms by which the EPZ compounds may regulate osteoclastogenesis, we examined the expression levels of osteoclast-associated proteins. The protein levels of NFATc1, PU.1, and Ctsk were suppressed in a dose-dependent manner by EPZ015866 (Figure 4A,B). However, NFATc1 expression levels and PU.1 levels were decreased only at 1000 nM EPZ015666. The NF-κB signaling pathway plays a key role in osteoclast differentiation [37]. NF-κB expression levels were not significantly altered by EPZ compound treatment. However, p-NF-κB levels were slightly maintained by EPZ015866 until day 4, although its expression was decreased in the control and by EZP015666 (Figure 4C,D). As a regulator of NF-κB, p-IκBα and IκBα expression levels were not altered between the control and the EPZ compounds (Figure 4C,D). These data indicate that EPZ015866 suppresses osteoclast differentiation by reducing the transcription factors, PU.1 or NFATc1, and altering the expression of p-NF-κB. In addition, the NF-κB gene level in osteoclast was checked after treatment with the EPZ compounds by RT-PCR. The result suggests that the NF-κB gene is not regulated by the EPZ compounds (Figure S2A). Luciferase assay was performed on the NF-κB luciferase activity in the NF-κB reporter HEK293 cell line after treatment with the EPZ compounds. The result showed the NF-κB transcriptional activity was not altered by the EPZ compounds (Figure S2B). These results indicate that the NF-κB transcriptional is not regulated by the EPZ compounds.
## 2.5. EPZ Compounds Downregulate PRMT5 and H3R8me2s/H4R3me2s but Do Not Impact mRNA Levels of Prmt5 during Osteoclastogenesis
To confirm whether the expression and activity of PRMT5 were regulated by the EPZ compounds during RANKL-induced osteoclastogenesis, BMMs were treated with various concentrations of the EPZ compounds (Figure 5). Because PRMT5 regulates the dimethylation of arginine symmetrically [23], we examined the symmetric demethylation of histone H3 arginine 8 (H3R8me2s) and the symmetric demethylation of histone H4 arginine 3 (H4R3me2s) levels to determine the activity of PRMT5 (Figure 5A,B). PRMT5 expression was suppressed in a dose-dependent manner in the cells treated with EPZ015866 or EPZ015666 compared with the control group. Furthermore, the methylation level of H4R3me2s was significantly increased when stimulated with RANKL in the control, but its expression was significantly inhibited in EPZ015866- or EPZ015666-treated groups. However, the methylation level of H3R8me2s was only suppressed by EPZ015866. Interestingly, the Prmt5 mRNA levels were not altered during osteoclastogenesis by the EPZ compounds (Figure 5C). These data indicate that the EPZ compounds suppress PRMT5 expression and suggest their activity in RANKL-induced osteoclast differentiation.
## 2.6. EPZ Compounds Reduce NF-κB Nuclear Translocation by Blocking the Demethylation of p65
To demonstrate PRMT5 activation of NF-κB via the demethylation of the p65 subunit of NF-κB [32], we examined the expression levels of symmetric dimethylarginine (SDMA) in Raw 264.7 cells transfected with a pcDNA3-HA-p65 plasmid vector (Figure 6A). p-p65 expression was suppressed by the EPZ compounds compared to the control. Although non-transfected Raw 264.7 cells expressed p-p65 in response to RANKL stimulation, the expression levels were lower than in the transfected cells. Interestingly, SDMA levels were slightly reduced by the EPZ compounds even though we detected whole SDMA levels. To examine whether the EPZ compounds regulate the activity of p65 by blocking SDMA, immunoprecipitation (IP) was performed with anti-HA−Agarose antibody and then analyzed with anti-SDMA antibodies (Figure 6B). The data show that the SDMA of p65 had a high expression in the control group, which was reduced after EPZ compound treatment. To further investigate the role of methylation in the NF-κB pathway, we examined the translocation of NF-κB. Interestingly, the EPZ compounds inhibited the p65 expression in the nucleus compared to the control. Immunofluorescence was performed to investigate the nuclear translocation of p65 (Figure 6C). p65 was detected in the nucleus by RANKL stimulation on day 2 in control; however, the EPZ compounds effectively inhibited the nuclear translocation of p65. The quantitative analysis results of Figure 6C were shown in Figure 6D. The number of p65 in the cell nucleus was significantly decreased in the EPZ compound treatment groups compared with the control. The nuclear and cytoplasmic fractionation were also separated after RANKL stimulation to determine the expression levels of p65 (Figure 6E). Although nuclear p65 levels were decreased by the EPZ compounds, p65 levels in the cytoplasm were not significantly altered. These results indicate that the inhibitory effect of EPZ015866 on osteoclast differentiation is mediated by reducing the nuclear translocation of NF-κB. These results also indicate that the EPZ compounds inhibit the nuclear translocation of NF-κB by blocking the dimethylation of p65.
## 3. Discussion
In the present study, we demonstrated that EPZ015866 and EPZ015666, known inhibitors of PRMT5, inhibit osteoclast differentiation as promising anti-osteoclastogenesis agents. The inhibition of osteoclastogenesis by EPZ015666 has previously been studied [25], but our current study found a more effective compound, EPZ015866, that could be used at a lower concentration in osteoclastogenesis. Our results indicated that, in the same concentration, EPZ015866 had a more significant inhibitory effect on osteoclast differentiation than EPZ01566 (Figure 1). Based on the area of TRAP-positive cells, the half maximal inhibitory concentration (IC50) values of EPZ015866 and EPZ015666 were around 30 nM and 600 nM, respectively. EPZ015866 reduced RANKL-induced osteoclast differentiation significantly better than EPZ015666 in vitro.
Mature osteoclasts firmly attach themselves to the bone surface using specialized actin rings through cytoskeletal reorganization and cell polarization, ultimately leading to bone resorption [9]. The F-actin ring indicates the fusion state of osteoclasts and is required for osteoclast formation and activation [38]. It has been reported that actin ring formation is a structural factor essential for bone resorption [39]. In our study, EPZ015866 and EPZ015666 inhibited the formation of actin rings in a dose-dependent manner, resulting in reduced formation of mature osteoclasts and marked inhibition of bone resorption.
Osteoclast differentiation is controlled by the transcriptional activation or repression of target genes by transcription factors. NFATc1 and PU.1 play important roles as transcriptional activators in osteoclast differentiation [40,41]. NFATc1 is a major regulator of osteoclast differentiation [42]. NFATc1 regulates the transcription of osteoclast-specific markers, including Acp5, Atp6v0d2, and Ctsk, which are important for the activation of mature osteoclasts [43]. We observed that Acp5, Ctsk, Oc-stamp, Dc-stamp, Atp6v0d2, and Mmp9 mRNA expression levels were significantly inhibited in EPZ015866 treatment, although only Acp5 and Atp6v0d2 levels were reduced at high doses of EPZ015666 (Figure 3). EPZ015866 showed more effective suppression via inhibiting all osteoclast-associated genes at low doses. Some studies have shown that the expression of osteoclast-specific markers is regulated by the transcription factors, NFATc1, PU.1, and NF-κB [44]. Moreover, PU.1 has been reported as a transcriptional activator of NFATc1 involved in the expression of osteoclast-specific genes, including Ctsk, Acp5, and Itgb3 [45]. Our results demonstrated that EPZ015866 more effectively inhibited the protein expression of NFATc1 and PU.1 than EPZ015666, although their regulatory mechanism is unknown or indirect regulation by EPZ compounds. Moreover, there are p65 independent pathways [46], including the alternative NF-κB pathway and direct regulation of NF-κB subunits in osteoclastogenesis [47,48]. These combined results suggest that EPZ015866 suppresses osteoclastogenesis-related genes through the expression of transcription factors PU.1 and NFATc1.
PRMT5 catalyzes the symmetric dimethylation of histone proteins to induce gene silencing by generating repressive histone marks, including H3R8me2s and H4R3me2s [23]. Thus, we checked the expression and activity of PRMT5 during RANKL-induced osteoclast differentiation. Our results suggest that EPZ015866 and EPZ01566 suppress the activation of PRMT5 during osteoclastogenesis. Although EZP015666 did not greatly inhibit H3R8me2s, it did inhibit PRMT5 protein levels.
The NF-κB signaling pathway plays an important role in RANKL-stimulated osteoclast differentiation [37,49]. RANKL-mediated NF-κB activation is further transmitted by inducing the transcription factor NFATc1 in BMMs. Furthermore, during the regulation of NF-ĸB activation by the IκB kinase (IKK) complex, the NF-ĸB/Rel dimer proteins are themselves subject to complex regulation through a series of post-translational modification (PTM) events [50]. Numerous studies have confirmed that PTM on the p65 subunit of NF-κB includes methylation [34], acetylation [51], and ubiquitination [52]. Moreover, Levy. et al. confirmed that the methylation and phosphorylation of p65 are mutually regulated, forming a more complex NF-κB regulatory system [53]. In addition, previous research shows that NF-κB is activated by the dimethylation of arginine 30 of the p65 subunit [54]. PRMT5 regulates the methylation of the arginine residues of p65 [33]. Therefore, we investigated whether the EPZ compounds inhibited p65 methylation in osteoclasts. Interestingly, as shown in Figure 6, pcDNA3-HA-p65 transfected Raw 264.7 cells revealed decreased expression of whole SDMA levels via the EPZ compounds. Additionally, p65-specific SDMA levels were decreased in EPZ treatment, assessed by IP analysis, which is correlated with SDMA regulation by the EPZ compounds [55]. We observed whether a reduction in the SDMA of p65 regulates the subcellular localization of p65. The results of immunostaining and Western blot showed that RANKL stimulation enhanced the translocation of p65 into the nucleus, while the EPZ compounds attenuated NF-κB activation by interfering with the nuclear translocation of p65. Taken together, the EPZ compounds repress p65 nuclear translocation via the inhibition of p65 dimethylation, leading to the prevention of osteoclast formation. It has been studied that the PRMT5-mediated methylation of the p65 subunit of NF-κB at R30 is involved in the regulation of p65 activity [34]. Harris, D.P. et al. [ 2014] indicated that PRMT5 symmetrically methylates R30 and R35 of NF-κB/p65 in TNF-α-activated endothelial cells [35]. In different research, Harris, D.P. et al. [ 2016] also suggested that the PRMT5-mediated methylation of p65 at R174 is required for the induction of CXCL11 in TNF-α-activated endothelial cells [56]. Therefore, we speculate that the role of PRMT5 in the regulation of p65 activity might be by regulating the methylation of R174, R35, and R30 of the NF-κB/p65 subunit in osteoclast differentiation. We will continue to explore in more detail which methylation sites of p65 mediate the suppression of osteoclast differentiation by PRMT5 inhibitors in the future.
Duncan et al. compared the inhibitors of PRMT5 based on their structure and medicinal chemistry optimization [57]. In their results, EPZ015866 shows lower PRMT5 IC50 and lymphoma cell line proliferation IC50 values than EPZ015666, suggesting a greater inhibitory effect on PRMT5. Therefore, EPZ015866 inhibits at lower concentration than EPZ015666 in osteoclast differentiation. However, both EPZ compounds inhibited the methyl status of p65 at a high dose (1000 nM), suggesting that the final inhibitory effect on the substrate may be the same, although further study is necessary to prove their pharmacological inhibitory effect on the substrate.
In conclusion, the EPZ compounds significantly reduced RANKL-induced osteoclast differentiation, F-actin ring formation, and bone resorption. The EPZ compounds were identified as potent inhibitors of NF-κB activity and were shown to inhibit osteoclast differentiation through the inhibition of NF-κB nuclear translocation (Figure 7). Furthermore, we indicated that arginine methylation-mediated NF-κB activity has a critical role in RANKL-induced osteoclast differentiation. Therefore, these EPZ compounds may be suitable for drugs for treating bone diseases characterized by excessive osteoclast activity.
## 4.1. Materials and Reagents
The alpha modification of Eagle’s minimal essential medium (α-MEM) and fetal bovine serum (FBS) were purchased from Thermo Fisher Scientific (Waltham, MA, USA). Recombinant mouse M-CSF and RANKL were procured from Peprotech (Cranbury, NJ, USA). A tartrate-resistant acid phosphatase staining kit was bought from CosmoBio (Tokyo, Japan). Characterized fetal bovine serum (chFBS) was purchased from Hyclone (Logan, UT, USA). Alpha MEM (αMEM, without ascorbic acid) was purchased from Welgene (Taipei, Taiwan). Recombinant human BMP2 was provided by Sino biological (Wayne, PA, USA) and dissolved in distilled water. Phalloidin was bought from Thermos Fisher Scientific (Waltham, MA, USA). 4′,6-diamidino-2-phenylindole (DAPI) stain was purchased from Sigma–Aldrich (St. Louis, MO, USA). Specific antibodies for NFATc1 (#8032s), Ctsk (#48353), PU.1 (#2266), NF-κB (#4764s), p-NF-κB (#3033), IκBα(#9242s), and p-IκBα (#2859s), and secondary antibodies were all purchased from Cell Signaling Technology (Beverly, MA, USA). PRMT5 (ab109451) was purchased from Abcam (Cambridge, UK), and H3R8me2s (A2374) and H4R3me2s (A3159) were purchased from ABclonal Technology (Woburn, MA, USA). EPZ015866 (PubChem CID: 117072552) and EPZ015666 (PubChem CID: 90241673) were purchased from Chemscene and Selleckchem, respectively.
## 4.2. Osteoclast Cell Culture and Viability Assay
Mouse bone marrow cells were obtained from the femur and tibia of 10-week-old C57BL/6J mice, as described previously [58]. Briefly, red blood cells in bone marrow immune cells were lysed with Ammonium-Chloride-Potassium lysing buffer and then cultured in complete medium (α-MEM containing $10\%$ FBS and $1\%$ P/S) at the 37 °C in humidified air with $5\%$ CO2 for 1 day. Non-adherent cells were harvested and cultured in Petri dishes for BMM selection with the complete medium in the presence of 30 ng/mL M-CSF. After three days, adhesion cells (BMMs) were harvested by Enzyme Free Cell Dissociation Solution Hank’s Based. The harvested cells were cultured further in the induction medium to induce the differentiation of osteoclasts.
For the cell viability study, BMMs were cultured at a density of 1 × 104 cells per well in 96-well plates for 24 h. The cells were treated with M-CSF or M-CSF and RANKL (CTRL group) in the presence or absence of the indicated concentrations of EPZ compounds. After 48 h, the cell viability was assessed using an EZ-Cytox Kit. The experiment protocol was conducted following the manufacturer’s manual. Finally, the optical density was measured at 450 nm using a microplate reader (San Jose, CA, USA).
## 4.3. Tartrate-Resistant Acid Phosphatase (TRAP) Staining Assay
For the TRAP staining assay, a TRAP staining kit was obtained from Takara Biotechnology (Shiga, Japan). This kit was used in accordance with the manufacturer’s instructions. BMMs were cultured in 96-well plates in complete α-MEM containing 30 ng/mL M-CSF. After 24 h, the cells were treated with various concentrations of the EPZ compounds that were changed every two days during the experiment period. Afterward, the culture medium was replaced, and cells were fixed in $4\%$ PFA at room temperature for 20 min and then stained for TRAP. Micrographs of cells were captured by the microscope, and the area of TRAP+ multinucleated osteoclasts (≥3 nuclei) was quantified using the Image J software (1.8.0_112 version, National Institutes of Health, Bethesda, MD, USA).
## 4.4. Osteoblast Cell Culture and In Vitro Differentiation
Primary calvarial cells were obtained from three-day-old mice by enzyme digestion. Briefly, the calvarias were cut into pieces and incubated in a digestion solution ($0.1\%$ type I collagenase with $0.2\%$ Dispase II) at 37 °C for 40 min. After digestion, the calvarias were washed twice with a complete culture medium and cultured in a 10 cm dish at 37 °C in $5\%$ CO2 for 3–4 days. Primary osteoblasts growing out of the bone chips were harvested and seeded into the 96-well plate for further experiments. For in vitro osteoblast differentiation, the cells were treated with or without the EPZ compounds in the presence of BMP2 (100 ng/mL) for seven days. Media were refreshed every two days. At the end of differentiation, cells were washed with PBS, fixed in $70\%$ ice-cold ethanol for 30 min, and rinsed with distilled water two times. The cells were stained with BCIP®/NBT Liquid Substrate System for 20 min at room temperature. Images were captured with a microscope, and the intensity of ALP staining was quantified using the Image J software.
## 4.5. Phalloidin Staining and Immunofluorescence (IF) Staining
BMMs were seeded in 12-well plates at a density of 1.5 × 105 per well. The EPZ compounds were added to the wells co-treated with RANKL for five days. After treatment, the cells were fixed with a $4\%$ paraformaldehyde solution for 20 min at room temperature. The fixed cells were permeabilized with 0.1 % Triton-X 100 and blocked with 2 % BSA for 1 h. For immunofluorescence staining, the cells were incubated with a p65 primary antibody at 4 °C overnight. After the cells were washed with PBS, a goat anti-Mouse IgG (H+L) highly cross-adsorbed secondary antibody was incubated for 3 h at room temperature in the dark. After one day, the cells were washed with PBS. Finally, the cells were stained with 4′,6 diamidino2 phenylindole (DAPI).
F-actin ring formation is a critical indicator of the bone resorption activity of osteoclasts and is a characteristic of mature cytoskeletal in osteoclasts [59]. Phalloidin staining was performed on osteoclasts treated with DMSO or EPZ compounds for four days, as described previously [60]. The cells were stained with FITC-conjugated Phalloidin for 45 min. After incubation with Phalloidin, the cells were washed with PBS. Finally, nuclei were visualized with DAPI. Images were captured by fluorescence microscopy.
## 4.6. Bone Resorption Assay
The effect of the EPZ compounds on bone resorption was assessed in accordance with the method of a previous study [61]. To explore the effect of the EPZ compounds on osteoclast-mediated bone resorption, BMMs were seeded into a 48-well bone resorption assay plate (2.5 × 104 cells/well). After 24 h, the cells were treated with DMSO or the EPZ compounds, along with M-CSF and RANKL, for five days further. After cell differentiation, the attached cells were treated with $5\%$ sodium hypochlorite for 5 min. The plates were air-dried at room temperature, and resorption pits were captured using a microscope. The total resorption area was quantified by Image J software.
## 4.7. Western Blot Assay
BMMs were seeded on six-well plates (3 × 105/well) and cultured overnight. Then, the cells were stimulated with RANKL and treated with the indicated concentrations of the EPZ compounds. At the end of the differentiation, the cells were harvested using a plastic cell scraper and lysed with radioimmunoprecipitation assay (RIPA) buffer. The supernatant was collected following sonication and centrifugation. The concentration of proteins was detected by a BCA protein assay following the manufacturer’s protocol. The same amounts of protein (10 μg) were separated by polyacrylamide gel electrophoresis (PAGE) and transferred to polyvinylidene difluoride membranes (Bio-Rad Laboratories, Hercules, CA, USA). The membranes were blocked with $5\%$ non-fat dry milk for 1 h at room temperature, and then incubated with primary antibodies at 4 °C overnight. The next day, the membranes were incubated for 2 h with secondary antibodies at room temperature, and signals were visualized by the enhanced chemiluminescence (ECL) western blotting detection reagent (Cytivalifescences, Marlborough, MA, USA).
## 4.8. Real-Time PCR Assay
Total RNA was extracted from cells with TRIzol reagent (Qiagen Sciences, Valencia, CA, USA), and then reversely transcribed using a PrimeScript RT Reagent Kit following the manufacturer’s instructions (Takara Bio-technology, Shiga, Japan). The cycling conditions were 37 °C for 30 min, 85 °C for 15 s, and storage 4 °C. Quantitative PCR was performed using a QuantStudio 3 real-time PCR system (Applied Biosystems, Foster City, CA, USA) with a Power SYBR Green PCR Master Mix. The mouse glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene was used as the control gene. The primers employed for the amplification are presented in Table S1.
## 4.9. Nuclear and Cytoplasmic Extraction
The cells’ nuclear and cytoplasmic proteins were extracted using the NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Scientific, Waltham, MA, USA) ac-cording to the manufacturer’s instructions. In brief, the cells were seeded in 10 cm dishes (1.5 × 106 cells per dish). After one day, the cells were stimulated with RANKL and co-treated with EPZ015866 or EPZ015666 for 48 h. The cells were lysed with CER I and CER II buffer and centrifuged at 16,000× g for 5 min at 4 °C, and the supernatant (cytosolic protein) was stored at −80 °C. Nuclear pellets were re-suspended with NER buffer and vortexed to extract the nuclear protein, followed by incubation on ice for 40 min. The samples were then centrifuged at 16,000× g for 10 min at 4 °C. The supernatant (nuclear protein) was transferred to microtubes immediately after centrifugation. Finally, the nuclear and cytoplasmic proteins were analyzed by Western blot.
## 4.10. Cell Transfection and Immunoprecipitation (IP)
Constructs were transfected into Raw264.7 cells using the NeonTM transfection system. The pcDNA3-HA-p65 plasmid DNA was a kind gift from Professor Park Jun Soo of the Division of Biological Science and Technology of Yonsei University. Raw 264.7 cells were transiently transfected with pcDNA3-HA-p65 plasmid DNA. The following day, the cells were cultured in complete media for an additional 48 h.
Transfected Raw 264.7 cells were cultured with RANKL in the presence or absence of the indicated concentrations of EPZ compounds for two days. The cells were rinsed with PBS, harvested, and lysed in IP lysis buffer (20 mM Tris–HCl, pH 7.5, 150 mM NaCl, $10\%$ glycerol, and $1\%$ Triton X-100) containing protease inhibitors. The whole cell lysates were incubated on ice for 30 min, collected by centrifugation, and quantified. After the quantification of protein samples, equal volumes of protein samples were incubated with the Monoclonal Anti-HA−Agarose antibody at 4 °C overnight under gentle shaking. The resin was harvested by centrifugation, washed five times with PBS, and re-suspended in an equal volume of 2 × SDS loading buffer. All samples were heated at 95 °C for 5 min. Finally, the protein samples were separated by $10\%$ SDS-PAGE. Western blot assays were performed as described previously.
## 4.11. Statistical Analysis
The results were presented as mean ± SD from three independent experiments in this study. The significant differences between the control and experimental groups were determined by a two-tailed Student’s t-test. Data were analyzed with GraphPad Prism 6.0 (GraphPad Software Inc., San Diego, CA, USA). * $p \leq 0.05$ and ** $p \leq 0.01$ were considered significant.
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|
---
title: Intensity of Depression Symptoms Is Negatively Associated with Catalase Activity
in Master Athletes
authors:
- Larissa Alves Maciel
- Patrício Lopes de Araújo Leite
- Patrick Anderson Santos
- Lucas Pinheiro Barbosa
- Sara Duarte Gutierrez
- Lysleine Alves Deus
- Márcia Cristiane Araújo
- Samuel da Silva Aguiar
- Thiago Santos Rosa
- John E. Lewis
- Herbert Gustavo Simões
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002178
doi: 10.3390/ijerph20054397
license: CC BY 4.0
---
# Intensity of Depression Symptoms Is Negatively Associated with Catalase Activity in Master Athletes
## Abstract
Background: This study examined associations between scores of depression (DEPs), thiobarbituric acid-reactive substances (TBARS), superoxide dismutase (SOD), and catalase activity (CAT) in master athletes and untrained controls. Methods: Participants were master sprinters (MS, $$n = 24$$; 50.31 ± 6.34 year), endurance runners (ER, $$n = 11$$; 51.35 ± 9.12 year), untrained middle-aged (CO, $$n = 13$$; 47.21 ± 8.61 year), and young untrained (YU, $$n = 15$$; 23.70 ± 4.02 year). CAT, SOD, and TBARS were measured in plasma using commercial kits. DEPs were measured by the Beck Depression Inventory-II. An ANOVA, Kruskal-Wallis, Pearson’s, and Spearman’s correlations were applied, with a significance level of p ≤ 0.05. Results: The CATs of MS and YU [760.4 U·μL 1 ± 170.1 U·μL 1 and 729.9 U·μL 1 ± 186.9 U·μL 1] were higher than CO and ER. The SOD levels in the YU and ER [84.20 U·mL−1 ± 8.52 U·mL−1 and 78.24 U·mL−1 ± 6.59 U·mL−1 ($p \leq 0.0001$)] were higher than CO and MS. The TBARS in CO [11.97 nmol·L−1 ± 2.35 nmol·L−1 ($p \leq 0.0001$)] was higher than in YU, MS and ER. MS had lower DEPs compared to the YU [3.60 ± 3.66 vs. 12.27 ± 9.27 ($$p \leq 0.0002$$)]. A negative correlation was found between CAT and DEPs for master athletes [r = −0.3921 ($$p \leq 0.0240$$)] and a weak correlation [r = −0.3694 ($$p \leq 0.0344$$)] was found between DEPs and the CAT/TBARS ratio. Conclusions: In conclusion, the training model of master sprinters may be an effective strategy for increasing CAT and reducing DEPs.
## 1. Introduction
Master athletes are those over 35 years old who maintain a training routine, compete in national and/or international sports championships, and represent a distinct portion of the middle-aged and elderly population [1]. These master athletes undergo intense physical training routines (from 3 to 6 sessions per week, totaling approximately 10 h or more of weekly training). The training programs of elite masters sprinters, for example, are mainly characterized by high-intensity, low-volume sessions based mainly on anaerobic pathways [1,2]. Additionally, the weekly training of such sprinters usually includes two sessions for speed (i.e., short sprints and long sprints for speed endurance), strength (i.e., weight lifting), power (i.e., plyometric exercises), and stretching and flexibility. A few low-volume, moderate-intensity cardiovascular sessions can also be performed between high-intensity anaerobic training sessions. Otherwise, the training programs of elite master endurance athletes are based mainly on low-intensity, high-volume sessions, relying primarily on aerobic pathways. Exercise modes and workloads are selected individually depending on the athlete’s training season [1,2]. Several studies have shown that master athletes have better physical performance, body composition, lipid profile, blood glucose control, and attenuated biological aging when compared to untrained peers, and have thus been referred to as a healthy aging model [3,4].
The healthy aging process of master athletes may be related to several biochemical mechanisms, including lower levels of oxidative stress and increased antioxidant defense [5]. A decreased antioxidant defense, on the other hand, has been identified as a part of the pathogenesis of depression, a multifactorial disease linked to the aging process [6]. In situations in which the antioxidant system is impaired, reactive oxygen species can damage lipids, proteins, and DNA. In addition, pro-inflammatory cytokines (IL-6, TNF-ӱ) increase the activity of indoleamine 2,3-dioxygenase (IDO), an enzyme involved in the synthesis of kynurenine from tryptophan. Kynurenine in turn appears to have potential neurotoxic action, since kynurenine is transformed into 3-monooxygenase (KMO), forming kynurenine into 3-hydroxykynurenine and 3-hydroxyanthranilic, precursors of quinolinic acid. This acid is considered a metabolite that leads to excitotoxicity for the central nervous system and induces oxidative stress. Thus, some studies have shown that catalase seems to block the toxicity generated by 3-hydroxykynurenine [7,8,9].
Antioxidant defense, in turn, protects cells by removing free radicals. This antioxidant system comprises different types of functional components, such as superoxide dismutase (SOD) and catalase (CAT) [10]. SOD acts as a primary cellular defense against free radicals since it catalyzes the reduction of SO to oxygen and hydrogen peroxide. CAT is an antioxidant enzyme present in almost all aerobic organisms. Its function is to break two molecules of hydrogen peroxide into one molecule of oxygen and two molecules of water [10,11]. In our previous studies, we have shown that master athletes have greater catalase activity than their non-athlete peers, in addition to other antioxidant enzymes such as superoxide dismutase (SOD) [3,4].
However, findings on catalase and its relationship with the intensity of depression symptoms (DEPs) are still inconsistent. According to Tsai and Huang [12], catalase activity is increased in patients in the acute phase of depression. On the other hand, in a meta-analysis by Jimenez-Fernandez [13], the differences in catalase levels among depressed and non-depressed people were not significant.
Furthermore, the substance reactive to thiobarbituric acid (TBARS) is an enzyme that is abundant in the depressive process. TBARS is the main method to quantify the end products of lipid peroxidation, being considered a pro-oxidant enzyme used to measure the oxidative stress of tissues and cells [14]. This oxidative stress is defined as an imbalance between pro- and antioxidant molecules.
To the best of our knowledge, no research has been conducted on the relationship between catalase activity, oxidative stress, and the intensity of depression symptoms in people who have followed a training regimen their entire lives, such as master runner athletes. Therefore, we aimed to analyze catalase, oxidative stress, and the intensity of depression symptoms in master athletes, their non-athlete peers, and a young control group. We hypothesized that master athletes have higher catalase activity and lower intensity of depression symptoms when compared to their non-athlete peers and the youth control group. It is also hypothesized that there is a negative correlation between catalase activity and the intensity of depression symptoms.
## 2.1. Ethical Approval
The study was approved by the Human Research Ethics Committee. All procedures were carried out according to the principles of the Declaration of Helsinki ($\frac{466}{2012}$). All subjects who agreed to participate in the study provided written informed consent, which had been clearly explained before participation.
## 2.2. Participants
The total sample ($$n = 63$$) was composed of 35 elite male master athletes at regional, national, and international levels and 28 untrained individuals. The master athletes were subdivided into master sprint athletes (MS, $$n = 24$$) from the 100 m, 200 m, 400 m, and 110 m hurdles, among others, and endurance runners (ER, $$n = 11$$) from 5 km to marathons and triathletes. The control groups consisted of young untrained (UY, $$n = 15$$) and middle-aged untrained controls (CO, $$n = 13$$). The youth sample was entirely collected in Brazil. These were mostly single college students. Master athletes were recruited from participants in the Brazilian Master Athletics Championship (São Bernardo do Campo, Brazil, 2018), Grandprix Del Mercosur (Montevideo, Uruguay, 2019), and World Master Indoor Athletics Championship (Torún, Poland, 2019). The inclusion criteria for master athletes were: [1] systematic training for at least 10 years; and [2] active participation in national and/or international competitions until the date of data collection. The non-athlete subjects of the control group (young and middle-aged) were recruited through pamphlets and electronic advertisements in the city of Brasília-DF, Brazil, and met the inclusion criteria of not being trained and being healthy. The exclusion criteria for all participants were: [1] a history of cardiometabolic diseases; [2] a history of inflammatory disease and cancer; [3] a smoker; and [4] regular drug use, including hormone replacement therapy.
## 2.3. General Procedures
Data were collected in the laboratory between 7 and 9 am, and all volunteers had not exercised in the previous 12 h and had fasted for at least 8 h. The collection protocol consisted of (a) anamnesis, to collect data referring to the health history and history of training and/or physical activity; and (b) an assessment of the intensity of depression symptoms, for which data were collected using the Beck Depression Inventory-II (BDI-II). The instrument has 21 items, and for each of them, there are four response statements, among which the subject chooses the most applicable to describe how she has been feeling in the last two weeks, including the test date [15]. These items refer to levels of intensity of depression symptoms, and the total score is the result of the sum of the individual items, reaching a maximum of 63 points. The final score is classified into minimal, mild, moderate, and severe levels, thus indicating the intensity of depression. The questionnaires were administered one day before the competitions at the athletes′ accommodations, which were usually close to the competition venue. The same researcher performed all of these assessments; and (c) collection of venous blood from the antecubital vein using a 4 mL vacutainer (with EDTA), with blood gradient centrifugation (Sirius 4000, Sieger, Brazil) for 15 min at 3800 rpm for plasma and serum isolation, and storage in a freezer (−80 °C) for further plasma analysis of catalase, superoxide dismutase (SOD), and TBARS.
## 2.4. Antioxidant Parameters
The three antioxidant parameters used in this study were measured using commercial kits and following the manufacturer′s protocol. The SOD activity was measured using the SOD assay kit (Sigma Aldrich®, California, USA), with a final spectrophotometric reading at 450 nm; the CAT activity was measured using the Amplex TM Red Catalase assay kit (Thermofisher Scientific®, California, USA), with a final spectrophotometric reading after one minute of incubation at 560 nm.
## 2.5. Lipid Peroxidation (TBARS)
The protocol used in the present study is adapted from Ohkawa et al. [ 1979]. Briefly, serum samples were diluted in 320 μL MiliQ H2O (1:5) and added 1 mL of trichloroacetic acid (TCA) $17.5\%$, pH 2.0, following the addition of 1 mL of thiobarbituric acid (TBA) $0.6\%$, pH 2.0. After homogenization, the samples were kept in a water bath for 30 min at 95 °C. The reaction was interrupted with the immersion of the microtubes in ice and the addition of 1 mL of TCA $70\%$, pH 2.0, and another incubation for 20 min at room temperature. After centrifugation (3000 rpm for 15 min) the supernatant was removed to new microtubes and taken to spectrophotometry reading at 540 nm. The concentration of lipid peroxidation products was calculated using the molar extinction coefficient equivalent for malondialdehyde (MDA − equivalent = 1.56 × 10 5 M − 1 cm − 1).
## 2.6. Statistical Analysis
The data were analyzed for normality and homogeneity using the Shapiro-Wilk test and the Levene test, respectively. The data were expressed as mean, standard deviation (±), minimum, $25\%$ percentile, median, $75\%$ percentile, and maximum. A one-way ANOVA followed by Tukeys post hoc was applied for comparisons among studied groups for age, catalase and Tbars variables. Kruskal-Wallis with Dunn’s test of multiple comparisons was applied to compare the groups on depression and SOD variables. The Spearman coefficient correlation was used to verify the association between catalase activity and the intensity of depression symptoms. The significance level was set at $5\%$ (p ˂ 0.05), and all procedures were performed using GraphPad Prism (v7.0, California, USA). In order to assess the clinical importance of results, the effect size was calculated and classified either as small ($r = 0.2$ to 0.49), moderate ($r = 0.5$ to 0.79) or large (r ˃ 0.8) [15]. The sample size for a priori statistical power of $80\%$ (1 − β = 0.80) indicated 20 participants for a significance level of $5\%$ (α = 0.05) and small effect size ($f = 0.4$). Thus, we chose a sample of 80 subjects (20 for each studied group) [16].
## 3. Results
The characterization of the sample, the intensity of depression symptoms, the CAT, the SOD level, and the TBARS are expressed in Table 1 as mean and standard deviation. The intensity of depression symptoms in the YU (12.27 ± 9.27) was higher than in the MS group (3.60 ± 3.66; $$p \leq 0.0002$$) and CO (4.61 ± 2.56; $$p \leq 0.002$$). The CAT of MS and YU [760.4U · μL 1 ± 170.1 U·μL−1 and 729.9 U · μL 1 ± 186.9 U · μL 1] were higher than CO and ER [410.3 U · μL 1 ± 67.24 U · μL 1 and 528.8 U · μL 1 ± 103.2 U · μL−1 ($p \leq 0.0001$)]. The SOD level in the YU and ER was higher than CO and MS [84.20 U·mL−1 ± 8.52 U·mL−1 and 78.24 U·mL−1 ± 6.59 U·mL−1 ($p \leq 0.0001$)]. The TBARS in CO [11.97 nmol·L−1 ± 2.35 nmol·L−1 ($p \leq 0.0001$)] was higher than in YU, MS, and ER (Table 1).
Furthermore, a negative correlation was found between CAT and the intensity of depression symptoms for the entire group of master athletes [r = −0.3921 ($$p \leq 0.0240$$)] (Figure 1).
On the other hand, the CAT/TBARS ratio has a negative correlation with symptoms of depression [r = −0.3694 ($$p \leq 0.0344$$)] (Figure 2).
The SOD/TBARS ratio was not correlated with the intensity of depression symptoms [$r = 0.1439$ ($$p \leq 0.4319$$)] (Figure 3).
The relationships between the intensity of depression symptoms and SOD [$r = 0.3320$ ($$p \leq 0.06$$)] and TBARS [$r = 0.0900$ ($$p \leq 0.61$$)] were not statistically significant.
## 4. Discussion
This was the first study to assess the intensity of depression symptoms and their relationship with TBARS, SOD, and CAT activity in master athletes. Our main findings were that: (i) the young control group presented greater intensity of depression symptoms in comparison with both the master athletes from sprints and the middle-aged untrained control group; (ii) the young control group did not differ from the endurance runners in terms of intensity of depression symptoms; (iii) CAT activity was negatively associated with the intensity of depression symptoms in master athletes; (iv) the CAT/TBARS ratio was a negative correlation with symptoms of depression.
There is an increase in the prevalence of depression in young adults (18 to 25 years old), especially during the college period. One of the possible explanations for the phenomenon is that college students, when seeking academic performance, start to neglect their time, their social relationships, and their well-being, and, as a consequence, they also reduce their levels of physical activity [17,18,19,20]. All these changes can generate instability that can, therefore, contribute to the reduction of social support and increase in stress, which are known to contribute to the emergence of mental disorders [21].
On the other hand, while depression is among the most prevalent age-related mental conditions, the literature places the master athlete as a model of healthy aging, as long as he has a balanced lifestyle with healthy eating, stress control, and regular exercise for many years [3,4,22,23]. In this regard, our findings revealed that master sprint athletes have lower levels of intensity depression than untrained young people. Previously, it was evidenced in a meta-analysis that high-intensity neuromuscular training is more effective in reducing the intensity of depression symptoms when compared to aerobic exercise [24]. It is important to note that our master sprint athletes require more intense neuromuscular solicitations than our endurance athletes.
In this regard, neuromuscular/resistance training would increase the release of brain-derived neurotrophic factor (BDNF) from muscle contraction, reaching the brain and activating multiple signaling pathways, starting to regulate the expression of antioxidant molecules [25,26]. In addition, BDNF participates in the pathophysiological mechanism of depression. Since there is signaling for an increase in NF-kB, this would increase oxidative stress, causing an increase in pro-inflammatory cytokines (IL-1 and IL-6) and a decrease in BDNF, resulting in a decrease in brain cell neurogenesis [27].
Furthermore, Schuch et al. [ 2014] demonstrated the effects of 3 weeks of physical exercise in severely depressed hospitalized patients; those who had a decrease in TBARS levels after the exercise protocol was applied [28]. This result is in line with the present, which demonstrated lower TBARS levels in master sprint and endurance athletes when compared to the middle-aged group, confirming a possible adjuvant antioxidant effect in combating the intensity of depressive symptoms in this population.
However, the findings on the activity of antioxidant enzymes and depression are controversial. Increased activities have been detected in some studies, but on the other hand, several studies have published mixed or negative results for catalase activity in depression compared to healthy control groups [12,26,29].
Catalase (CAT) is an enzyme that catalyzes the breakdown of hydrogen peroxide into water and oxygen, mediating signaling in cell proliferation, apoptosis, carbohydrate metabolism, and platelet activation [30]. Humans with low catalase levels are at increased risk for diabetes and altered lipid and carbohydrate metabolism [31]. Some studies that have examined catalase activity in depressed patients have found increased levels of catalase activity during acute episodes of depression compared to healthy volunteers [31]. Szuster-Ciesielska et al. [ 32] also detected increased serum catalase activity in patients with major depression. The increase in catalase activity may reflect a compensatory mechanism since, during depressive disorders, there is an increase in oxidative and nitrosative stress (O&NS) pathways. The catalase would be increased to attenuate the induced O&NS pathways and is congruent with the role of oxidative free radical signaling [32].
On the other hand, some clinical studies reported a decrease in catalase activity during depressive episodes [31]. According to a study by Bhatt et al., mild and chronic stress led to decreased levels of catalase in the brain tissues of stressed mice; however, treatment with antidepressants had beneficial effects and increased catalase levels in these mice [31]. Additionally, catalase overexpression improves memory and reduces anxiety symptoms even in the absence of altered oxidative stress, and antidepressant treatment appears to increase levels of this antioxidant enzyme in patients with depression [29,32]. Correspondingly, the same occurs concerning physical exercise; Sousa et al. [ 33] demonstrated in a meta-analysis that physical exercise seems to promote increased antioxidant defense. Similarly, our study showed an increased activity in antioxidant defenses, mainly catalase, and a negative correlation between the intensity of depression symptoms and catalase activity in master athletes.
## Limitations
Possible limitations of this study may include that we did not measure inflammatory indicators. However, the correlation between depression and inflammation is already well described in the scientific literature. Despite this, we studied a group of high-level master athletes with a track record of long-term sprint and endurance training and success in national and/or international championships. Thus, to the best of our knowledge, this is the first study evaluating and comparing the intensity of depression symptoms and antioxidant parameters in elite master athletes, middle-aged, and young individuals with no lifelong training history.
## 5. Conclusions
In conclusion, master sprinters presented the lowest intensity of depression symptoms, with CAT being higher than CO and ER. CAT and the CAT/TBARS ratio were negatively associated with the intensity of depression symptoms, suggesting that the training model of master sprinters may be more effective in increasing CAT and reducing depressive symptoms. As a general recommendation, the lifestyle of master athletes, which is mainly characterized by, but not limited to, a lifetime of exercise training, seems to promote a better antioxidant defense system, favoring the redox balance. A better antioxidant defense system is related to a lower intensity of depressive symptoms and attenuates the aging process, as documented in several previous studies [3,4,32]. Thus, individuals seeking these benefits should exercise chronically at proper doses according to their preferences, in addition to maintaining a lifestyle with other healthy habits such as a balanced diet, proper sleep, and stress management.
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|
---
title: Oxidized Low-Density Lipoproteins Trigger Hepatocellular Oxidative Stress with
the Formation of Cholesteryl Ester Hydroperoxide-Enriched Lipid Droplets
authors:
- Iku Sazaki
- Toshihiro Sakurai
- Arisa Yamahata
- Sumire Mogi
- Nao Inoue
- Koutaro Ishida
- Ami Kikkai
- Hana Takeshita
- Akiko Sakurai
- Yuji Takahashi
- Hitoshi Chiba
- Shu-Ping Hui
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10002183
doi: 10.3390/ijms24054281
license: CC BY 4.0
---
# Oxidized Low-Density Lipoproteins Trigger Hepatocellular Oxidative Stress with the Formation of Cholesteryl Ester Hydroperoxide-Enriched Lipid Droplets
## Abstract
Oxidized low-density lipoproteins (oxLDLs) induce oxidative stress in the liver tissue, leading to hepatic steatosis, inflammation, and fibrosis. Precise information on the role of oxLDL in this process is needed to establish strategies for the prevention and management of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Here, we report the effects of native LDL (nLDL) and oxLDL on lipid metabolism, lipid droplet formation, and gene expression in a human liver-derived C3A cell line. The results showed that nLDL induced lipid droplets enriched with cholesteryl ester (CE) and promoted triglyceride hydrolysis and inhibited oxidative degeneration of CE in association with the altered expression of LIPE, FASN, SCD1, ATGL, and CAT genes. In contrast, oxLDL showed a striking increase in lipid droplets enriched with CE hydroperoxides (CE-OOH) in association with the altered expression of SREBP1, FASN, and DGAT1. Phosphatidylcholine (PC)-OOH/PC was increased in oxLDL-supplemented cells as compared with other groups, suggesting that oxidative stress increased hepatocellular damage. Thus, intracellular lipid droplets enriched with CE-OOH appear to play a crucial role in NAFLD and NASH, triggered by oxLDL. We propose oxLDL as a novel therapeutic target and candidate biomarker for NAFLD and NASH.
## 1. Introduction
Non-alcoholic fatty liver disease (NAFLD), characterized by the accumulation of fat stored in liver cells, affects people who consume little to no alcohol [1]. These patients are referred to as having simple steatosis (SS). Simple steatosis can progress to non-alcoholic steatohepatitis (NASH) with hepatitis and liver fibrosis when additional oxidative and cytokine stress occurs [2]. Non-alcoholic steatohepatitis is irreversible and progresses to cirrhosis and hepatic carcinoma [3]. Simple steatosis and NASH are collectively defined as NAFLD, which is often observed in patients with metabolic disorders (such as obesity and type 2 diabetes) [4,5]. The global prevalence of NAFLD and NASH in the general population is estimated to be 10–$35\%$ and 3–$5\%$, respectively [2]. In the USA, $34\%$ of the general adult population (or at least 43 million adults) have NAFLD, and $12\%$ have NASH [6,7]. Furthermore, an estimated $20\%$ of patients with NASH develop cirrhosis, and NASH is projected to become the leading indication for liver transplantation in the United States [8,9]. Thus, it is important to prevent the progression of SS to NASH. Currently, the histopathological diagnosis of NASH requires invasive liver biopsies and is clinically problematic because of the lack of useful noninvasive blood testing methods. Therefore, elucidation of NASH pathogenesis is urgently required.
In addition to the fatty liver, NAFLD and NASH are also associated with dyslipidemia [10]. Particularly, patients with NASH have increased plasma oxidized low-density lipoproteins (oxLDL), which contain lipid hydroperoxides [11]. Lipid peroxidation is a leading factor in the development and progression of NASH [12]. Therefore, the oxLDL level is considered a risk factor for NASH.
Our laboratory previously reported a NASH mouse model that was fed a long-term high-fat diet and administered with oxLDL [13]. This suggests that oxLDL is one of the factors involved in the pathogenesis of NASH. Clarifying the association between oxLDL and lipid droplet formation in hepatocytes can contribute to the understanding of NASH pathogenesis. However, only a few studies have focused on oxLDL levels and hepatocytes. There have been no reports on the involvement of oxLDL compared with native low-density lipoproteins (nLDL) in the mechanism of lipid droplet formation, the components of lipid droplets, and the increase of oxidative stress in hepatocytes. Herein, we clarified that the addition of nLDL or oxLDL to human liver-derived C3A cells causes fat accumulation and analyzed the lipid components using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and transcriptional expression analyses to better understand lipid metabolic changes in the cells.
## 2.1. Lipidomic Analysis in LDL
We analyzed the profiles of cholesteryl ester (CE), triacylglycerol (TG), and their hydroperoxides in nLDL and oxLDL (which were added to C3A cells) because the major lipid components in lipid droplets are CE and TG [14]. Six CE molecular species were detected in the LDL particles (Supplementary Figure S1). All molecular species except CE 16:0 decreased with increasing oxidation time. The behavior of the sum of CE was consistent with that of each CE molecular species (Supplementary Figure S2). CE 20:5 was not detected in oxLDL (24 h). In the detection of CE hydroperoxide (CE-OOH), only CE-OOH 18:2, which increased considerably only in oxLDL (2 h), was detected (Supplementary Figure S3).
Furthermore, 41 TG molecular species were detected in the LDL particles (Supplementary Figure S4). Similar to CE, almost all TG molecular species decreased with longer oxidation times. The behavior of the sum of TG was consistent with that of each TG molecular species (Supplementary Figure S5). In the detection of TG hydroperoxide (TG-OOH), 12 TG-OOH molecules were detected (Supplementary Figure S6). Similar to CE-OOH, the sum of the TG-OOH levels was considerably high in oxLDL (2 h) under all conditions (Supplementary Figure S7).
Regarding phosphatidylcholine (PC), 22 PC and 10 PC hydroperoxides (PC-OOH) were detected in LDL (Supplementary Figures S8–S11). Additionally, the sum of PC-OOH was the highest in oxLDL (2 h).
Taken together, oxLDL (2 h) was enriched in CE-OOH, TG-OOH, and PC-OOH compared with LDL at other oxidation times (8, 24 h).
## 2.2. Cell Toxicity Test
To evaluate the toxicity of LDL in C3A cells, lactate dehydrogenase (LDH) in the culture supernatant was analyzed (Figure 1). Under the present ranges of added LDL concentrations, no reduction in cell toxicity was observed in nLDL and any oxLDL groups (2, 8, and 24 h) compared with the control group at 50, 100, and 200 ng protein of LDL/104 cells. Thus, nLDL/oxLDL concentrations of 200 ng protein/104 cells were used to stimulate nLDL/oxLDL in this study.
## 2.3. Fluorescence Imaging of LDL-Induced Lipid Droplets
According to a previous report [15], fluorescence imaging was used for observing nLDL- or oxLDL (2 h)-induced lipid droplets, nuclei (blue), and the accumulation of neutral lipids (red) and lipid hydroperoxides (green) (Figure 2A–C). The locations where neutral lipids and lipid hydroperoxides overlapped are shown in yellow. As a result, their behaviors were different from each other. Native LDL increased the number of non-oxidized lipid droplets (non-oxLDs) and not oxidized lipid droplets (oxLDs), whereas oxLDL increased the number of oxLDs and non-oxLDs compared with the control (Figure 2D,E).
## 2.4. Lipidomic Analysis in LDL-Supplemented C3A Cells
Five CE molecular species were detected in LDL-supplemented cells (Figure 3A). The levels of all molecular species were considerably high in the nLDL group. Similarly, the sum of CE was also increased considerably in the nLDL group (Figure 3B).
Three types of CE-OOH molecules (CE-OOH 18:1, CE-OOH 18:2, and CE-OOH 22:6) were also detected in LDL-treated cells (Figure 3C). The sum of CE-OOH increased considerably only in oxLDL (2 h)-treated cells (Figure 3D).
Regarding the TG profile, 28 TG molecular species were detected in the LDL-supplemented cells (Figure 4A). In addition, 17 TG molecular species showed a considerable decrease in the nLDL group compared with the control. Furthermore, seven TG molecular species were decreased considerably in the oxLDL group compared with the control. The sum of the TG molecular species was reduced considerably in the nLDL and oxLDL groups compared with that in the control group (Figure 4B). Furthermore, three types of TG-OOH molecules (TG-OOH 52:2, TG-OOH 56:7, and TG-OOH 62:12) were detected in LDL-treated cells (Figure 4C). Overall, there was no notable difference in the sum of TG-OOH levels among the three groups (Figure 4D).
Seventeen PC molecular species were detected in LDL-supplemented cells (Supplementary Figure S12A). The levels of all molecular species and the sum of PC were considerably higher in the nLDL-supplemented cells than in the oxLDL-supplemented cells and the control (Supplementary Figure S12B).
Two types of PC-OOH molecules (36:5 and 36:6) were also detected in LDL-supplemented cells (Supplementary Figure S12C). Although there was no notable difference, the sum of PC-OOH showed an increasing trend in the oxLDL-supplemented cells (Con. vs. oxLDL, $$p \leq 0.093$$; nLDL vs. oxLDL, $$p \leq 0.103$$) (Supplementary Figure S12D). The sum of PC-OOH/PC, used as an index of cellular oxidative stress, increased considerably only in oxLDL-supplemented cells (Figure 5).
## 2.5. Expression of Genes in Lipid Metabolism
To investigate the transcriptional changes in lipid metabolism in LDL-supplemented C3A cells, real-time polymerase chain reaction (PCR) was performed under the same conditions as the LC-MS/MS experiments. The expression level of sterol O-acyltransferase 1 (SOAT1), a gene associated with CE biosynthesis, was reduced considerably in the oxLDL group compared with that in the control and nLDL groups (Figure 6A). The expression of lipase E, a hormone-sensitive-type (LIPE) gene associated with the degradation of CE, was markedly reduced in the nLDL- and oxLDL-supplemented cells compared with that in the control (Figure 6A). No considerable differences were observed in the expression of diacylglycerol O-acyltransferase 1 (DGAT1), associated with TG biosynthesis (Figure 6B). The expression level of adipose triglyceride lipase (ATGL), a gene associated with TG degradation, was considerably increased in the nLDL group only (Figure 6B).
The expression level of sterol regulatory element-binding protein 1 (SREBP1), a factor that regulates fatty acid biosynthesis, was decreased considerably in the oxLDL group compared with that in the control group (Figure 6C). The expression level of fatty acid synthase (FASN) was considerably reduced in the nLDL and oxLDL groups compared with that in the control group (Figure 6C). The expression of stearoyl-CoA desaturase (SCD1) for fatty acid unsaturation was considerably reduced in the oxLDL group compared with that in the control group and notably reduced in the nLDL group (Figure 6C). The expression of catalase (CAT), a hepatic antioxidant enzyme, was considerably increased in the nLDL group compared with that in the control and oxLDL groups (Figure 6D).
From the above results, the lipid metabolic changes in nLDL- (Figure 7A) and oxLDL-supplemented C3A cells (Figure 7B) are summarized.
## 3. Discussion
The degree of oxidation varies among oxLDLs in plasma [16]; thus, oxLDL is a heterogeneous particle. Lipid components in LDL become hydroperoxides (-OOH) under early oxidative conditions and aldehydes (-CHO) with increasing degrees of oxidation [17,18]. Mild oxLDL reflects the physiological form of oxLDL and contains lipid hydroperoxides [19]; thus, mild oxLDL is toxic to the body. To determine the optimal oxidative conditions with high levels of lipid hydroperoxides, oxLDL was prepared using different oxidation times (0–24 h). The present experiments showed that the sum of CE decreased with increasing LDL oxidation time and that LDL oxidized for 2 h contained the highest levels of CE-OOH (Supplementary Figures S2 and S3). This suggests that CE may have been reduced by oxidative denaturation and transformed into CE-OOH in LDL, which was oxidized for 2 h. Further oxidation (8 and 24 h) resulted in the reduction of CE-OOH species (Supplementary Figure S3), which may subsequently produce aldehydes and other oxidative compounds. Similar to CE, the TG and PC levels decreased with increasing oxidation time (Supplementary Figures S4, S5, S8 and S9). TG-OOH and PC-OOH were the most abundant in LDL oxidized for 2 h (Supplementary Figures S6, S7, S10 and S11). Based on these results, oxLDL (2 h) with increased CE-OOH, TG-OOH, and PC-OOH was adopted as mildly oxidized LDL for the stimulation condition.
Fluorescence imaging analysis showed that both nLDL and oxLDL accumulated neutral lipids in hepatocytes, suggesting that nLDL or oxLDL was incorporated into the cells and excess lipids were stored in lipid droplets. Furthermore, oxLDL-supplemented cells were observed to have lipid hydroperoxides overlapping with neutral lipids as a reference [15]. This suggests that lipid hydroperoxides of added oxLDL may have accumulated in lipid droplets. CE-OOH levels in the liver tissue of patients with NASH are elevated [20], and the presence of lipid hydroperoxides (-OOH) in hepatocytes is closely associated with NASH [21]. Thus, oxLDL uptake may enhance oxidative stress in the hepatocytes.
Native LDL is taken up by hepatocytes via LDLR [22] and oxLDL via scavenger receptors, such as CD36 and LOX1 [23,24]. Simultaneous analysis of lipids in hepatocytes revealed that CE increased in the nLDL group. Native LDL is a CE-rich lipoprotein (Supplementary Figure S2). The lipid droplets that were stained in the fluorescence microscopy experiment can be derived from the CE in the incorporated nLDL (Figure 2). CE taken into cells can be degraded by hydrolysis [25,26], or CE synthesis can be suppressed by the downregulation of SOAT1 (a gene associated with CE biosynthesis) [27]. In the present study, the expression level of SOAT1 remained unchanged in nLDL-supplemented cells. In contrast, the expression level of LIPE (a gene involved in CE degradation) showed a distinct decrease (Figure 6A). Therefore, CE degradation can be suppressed, and subsequent CE accumulation occurs in hepatocytes. In contrast, CE was at the same level in the oxLDL group as in the control group (Figure 3A,B). This could be reasonable because oxLDL was markedly poor in CE owing to oxidative modifications of CE. The increase in CE-OOH in oxLDL-supplemented cells was smaller than the increase of CE in nLDL-supplemented cells. This might indicate that CE-OOH reacted with numerous other oxidative species (e.g., CE-CHO) that were not targeted in this study.
CE-OOH 18:2 and CE-OOH 22:6 were increased only in oxLDL-supplemented cells (Figure 3C), suggesting a state of increased intracellular oxidative stress. The increase in CE-OOH 18:2 levels in the cells implied that oxLDL (2 h) was rich in CE-OOH 18:2 (Supplementary Figure S3). These acyl chains are polyunsaturated fatty acids (PUFAs). Because PUFAs are susceptible to oxidation, CE with PUFA may be oxidized in oxLDL-supplemented cells. This increase was consistent with the detection of more lipid hydroperoxide using fluorescence staining (Figure 2) and was likely to be attributed to CE-OOH in oxLDL. In contrast, nLDL-supplemented cells showed no increase in CE-OOH (Figure 3), which can be attributed to less CE-OOH in nLDL-supplemented cells. Thus, hepatic antioxidant enzymes may exert inhibitory effects on the excessive oxidation of CE incorporated into the cells. The present transcriptional study revealed an increased expression of the antioxidant enzyme-related gene CAT in nLDL-supplemented cells (Figure 6D). CAT is a family of antioxidant enzymes induced by the activation of the Keap1-Nrf2 pathway [28]. It is most abundant in the liver, kidneys, and erythrocytes and is responsible for degrading most of the hydrogen peroxide [29]. Hydrogen peroxide generates lipid hydroperoxides. Thus, the induction of CAT could eliminate CE-OOH.
We predicted that TG derived from LDL might accumulate via LDL uptake. However, TG levels decreased in both nLDL- and oxLDL-treated cells (Figure 4A,B). An increase in ATGL (a gene involved in TG hydrolysis) promotes a decrease in TG accumulation [30]. Thus, in nLDL, the high expression level of ATGL in nLDL-supplemented cells may have promoted TG degradation and prevented TG accumulation in hepatocytes. However, the effects on the reduction of TG species were weaker in oxLDL-supplemented cells than in nLDL-supplemented cells (Figure 4A,B). This might be due to fewer TG species in the added oxLDL (Supplementary Figures S4 and S5) or few changes in TG species due to little induction of ATGL (Figure 4A and Figure 6B).
The sum of TG-OOH in the cells did not change among the three groups (Figure 4D), despite the addition of oxLDL, including high levels of TG-OOH. Because the amount of TG-OOH was smaller than that of CE-OOH, even in oxLDL (Supplementary Figures S3 and S7), the changes in the cells caused by TG-OOH of oxLDL may have been neglected.
PC-OOH is a primary peroxidative lipid that has been used to monitor hepatocellular damage by lipid peroxidation [31]. Liver PC-OOH levels were higher in NASH model mice than in control mice [21]. Furthermore, PC-OOH/PC has been used as an index of cellular oxidative damage [32]. Thus, we analyzed the PC-OOH and PC-OOH/PC using LC-MS/MS. We found that PC-OOH/PC was increased in oxLDL-supplemented cells compared with that in other groups, which suggests hepatocellular damage due to oxidative stress (Figure 5). In contrast, PC-OOH/PC was unchanged in nLDL, which suggests a protective effect of CAT against oxidation.
In the nLDL and oxLDL groups, the expression levels of genes associated with the synthetic pathway of fatty acids induced by acetyl-CoA were suppressed despite the formation of lipid droplets (Figure 6C). This was consistent with previous reports on mice with fatty livers [33]. This might indicate negative feedback against the accumulation of excessive lipids, possibly suppressing TG accumulation.
## 4.1. Separation of Total Lipoproteins
Blood samples were obtained from healthy participants after overnight fasting. Serum samples were separated by centrifugation at 2200× g for 10 min at 4 °C using a CE16RX (Hitachi Koki Co., Ltd., Tokyo, Japan). As previously reported, total lipoproteins were separated by ultracentrifugation [34]. Briefly, 2.0 mL of serum was adjusted to a density of 1.225 kg/L using potassium bromide (Fujifilm Wako Pure Chemical Corporation, Osaka, Japan) and mixed with 6.0 mL of a specific density solution (density = 1.225). The mixed solution was then ultracentrifuged at 50,000 rpm for 20 h at 4 °C in an Optima MAX Ultracentrifuge (Beckman Coulter Inc., Brea, CA, USA) with a near-vertical rotor MLN-80 (Beckman Coulter Inc., Brea, CA, USA). The total lipoprotein fraction was collected from the top layer.
## 4.2. Separation of LDL Fraction
Gel filtration chromatography was performed to separate LDL and other lipoproteins (very low-density lipoprotein and high-density lipoprotein) [34]. The total lipoprotein fraction was injected into a high-performance liquid chromatograph (HPLC, Shimadzu Corp., Tokyo, Japan) equipped with a Superose 6 column (GE Healthcare, Little Chalfont, UK). The lipoproteins were then eluted with 50 mM phosphate-buffered saline (PBS) (pH 7.4) at a rate of 0.5 mL/min and monitored at OD 280 nm. The LDL fractions were collected at an elution time of 21–27 min. The protein concentrations of these fractions were determined using the Lowry method [35].
## 4.3. Oxidization of LDL
As indicated in previous reports [36], LDL fractions were diluted to a protein concentration of 0.2 mg/mL with phosphate buffer (50 mM PBS, pH 7.4); copper sulfate was added to a final concentration of 0.06 mM and incubated for 2, 8, and 24 h in a thermostatic chamber at 37 °C. Oxidation was stopped by adding ethylenediaminetetraacetic acid (EDTA) to a final concentration of 1.0 mM. To prevent oxidation due to residual copper ions, the solvent was replaced with phosphate buffer (50 mM PBS, pH 7.4) using a 100 kDa filter (Merck Millipore Ltd., Cork, Ireland) [37]. Oxidized LDL solutions were diluted to a protein concentration of 0.2 mg/mL and stored at 4 °C until immediately before use. nLDL was added to equal volumes of water instead of copper sulfate. As with oxLDL, EDTA was added and stored at 4 °C until use.
## 4.4. Lipidomics Using LC/MS
Lipid extraction from each LDL was performed following the procedure described by Folch et al. [ 38] and Hui et al. [ 36] ($$n = 4$$ for each group). nLDL or oxLDL (600 µL) was added to 1.4 mL of distilled water and stirred (3500 rpm, 1 min) using a Multi-Speed Vortex (BIOSAN Ltd., Riga, Latvia). The mixture was transferred to a screw-tip test tube. Internal standards (IS, SPLASH™LIPIDOMIX® Quantitative Mass Spec Internal Standard, Avanti Polar Lipids, Inc., Alabaster, AL, USA) were diluted 50-fold with methanol. Next, 100 µL of diluted IS, 300 µL of methanol, and 2 mL of chloroform were added sequentially, stirred, and centrifuged (2200× g, 4 °C, and 10 min). The lower chloroform layer was collected. Then, 2 mL of chloroform was added to the remaining upper layer, the above collection procedure was repeated, and the lower layer was collected again. The solution was then dried using an evaporator (centrifugal concentrator CC-105; Tomy Industries, Tokyo, Japan). After drying, 300 µL of methanol was added, and the mixture was stirred to collect the total volume. The samples were centrifuged (18,800× g, 4 °C, 10 min), and the supernatant was collected. The samples were stored at −80 °C until immediately before measurement.
For the simultaneous analysis of lipids using Orbitrap LC-MS, cells stimulated with LDL were collected ($$n = 6$$ for each group). C3A cell suspensions ($10\%$ fetal bovine serum, FBS) were seeded in 24-well plates at 2.0 × 105 cells/mL and 1 mL/well, and preincubation and stimulation were performed under the same conditions as for fluorescence staining. The cells were then washed once with PBS. PBS (500 µL) was added, and the cells were collected using a scraper. Fifty microliters of this solution were used to determine the protein concentration of the cells. The cells in the remaining 450 µL were precipitated by centrifugation, and the supernatant was discarded. Next, the cells were washed with PBS, centrifuged again, and the supernatant discarded. The precipitated cell mass was used as a sample and stored at −80 °C until immediately before lipid extraction from the cell mass.
Lipids were extracted from each cell following the procedure described by Hara et al. [ 39] ($$n = 6$$ for each group). Diluted IS (200 µL) and chloroform (400 µL) were added to the collected cell mass and agitated using a Multi-Speed Vortex (BIOSAN, Ltd, Riga, Latvia.). The mixture was then centrifuged (Himac CE15R, Hitachi Koki Co., Ltd., Tokyo, Japan, 18,800× g, 4 °C, 10 min). The supernatant was collected, and the solution was allowed to dry using a centrifugal concentrator (CC-105, Tomy Industries, Tokyo, Japan). Next, 400 µL chloroform was added to the sample before transferring the supernatant and stirring under the same conditions. It was centrifuged, and the supernatant was collected into a sample that had just been allowed to dry. The solution was allowed to dry again, 100 µL of methanol was added, and the mixture was stirred using a Multi-Speed Vortex (BIOSAN Ltd., Riga, Latvia). The mixture was centrifuged, and the supernatant was collected. The samples were stored at −80 °C until immediately before measurement.
Orbitrap LC-MS/MS was used for the simultaneous analysis of lipids in LDL and liver culture cell C3A, based on previous reports [40]. The LC section was performed on a Shimadzu Prominence HPLC system (Shimadzu Corp., Kyoto, Japan), and the analytical column was an Atlantis T3 Column (C18, 2.1 × 150 nm, and 3 µm; Waters Corp., Milford, CT, USA). The column temperature was 40 °C, and the sample injection volume was 10 µL. A gradient elution method was used, and the mobile phase consisted of 5 mM ammonium acetate (A), isopropanol (B), and methanol (C). The flow rate was 0.2 mL/min; the MS section was an LTQ Orbitrap mass spectrometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). Depending on the target molecule, the analysis was performed in the electrospray ionization (ESI)-positive ion mode (Supplementary Table S1). Peak areas were calculated using Xcalibur 2.2 (Thermo Fisher Scientific Inc., Waltham, MA, USA). Based on published data, we identified the peak using the LIPIDMAPS database [21]. The identified species were annotated as class abbreviations: lipid class, total number of carbons in fatty acid moieties:total number of double bonds in the acyl chains (e.g., TG 52:2) (Supplementary Tables S2–S5).
When calculating the lipid concentrations in LDL, the peak areas of the target molecules were corrected using the peak area of the IS. When calculating intracellular lipid concentrations, the peak area was corrected using the peak area of the IS and protein concentration in the hepatocytes. CE, TG, PC, and their peroxides (CE-OOH, TG-OOH, and PC-OOH) were analyzed.
## 4.5. Cell Culture and Toxicity Test
Human liver-derived strain C3A (ATCC) cells were passaged and cultured in MEM supplemented with fetal bovine serum (FBS, Thermo Fisher Scientific Inc., Waltham, MA, USA, final concentration of $10\%$), penicillin-streptomycin (Thermo Fisher Scientific Inc., Waltham, MA, USA, final concentration $1\%$), and GlutaMAX supplement (GlutaMAX, Thermo Fisher Scientific Inc., Waltham, MA, USA, final concentration of $1\%$) at 37 °C and $5\%$ CO2.
To confirm the hepatotoxicity of LDL, a cell toxicity test was performed ($$n = 6$$ for each group): 1.0 × 104 cells/mL of C3A ($10\%$ FBS-containing medium) were seeded in 96-well plates at 100 µL/well and pre-incubated at 37 °C and $5\%$ CO2 for 24 h. For the preparation of stimulants, the supernatant was mixed with Clear MEM ($0\%$ FBS, Thermo Fisher Scientific Inc., Waltham, MA, USA) with LDL solution adjusted to 50, 100, and 200 ng protein per 1 × 104 cells in the nLDL and oxLDL groups. An equal volume of PBS was used instead of LDL for the control group. The supernatant was then replaced with stimulants (100 µL/well), and cells were stimulated at 37 °C and $5\%$ CO2 for 22 h. At the end of stimulation, the supernatant was collected. LDH was measured for toxicity testing according to the manufacturer’s instructions (Takara Bio Inc., Shiga, Japan). Absorbance at 490 nm was measured using a microplate reader (xMark™ Microplate Spectrophotometer, Bio-Rad Laboratories, Inc., Hercules, CA, USA). The values are shown as the percentage of cell toxicity, with the control set to $100\%$.
## 4.6. Fluorescence Microscopy
With reference to Tsukui et al. [ 15], fluorescence staining was performed to quantify the lipid droplets observed when LDL was added. C3A cell suspensions ($10\%$ FBS) were seeded with 2.0 × 105 cells of C3A in glass-bottomed dishes and pre-cultured for 24 h at 37 °C and $5\%$ CO2. The cells were washed once with PBS (Fujifilm Wako Pure Chemical Corporation, Osaka, Japan). For the preparation of the stimulant medium, Clear MEM ($0\%$ FBS) was mixed with nLDL or oxLDL and adjusted to 200 ng protein/104 cells for nLDL, oxLDL, or phosphate buffer (50 mM PBS, pH 7.4) in the same volume as LDL for the control group. Subsequently, the cells were incubated at 2 mL/dish at 37 °C and $5\%$ CO2 for 24 h. At the end of the stimulation, the cell supernatant was discarded. The staining solution was added at 1 mL/dish at 37 °C, $5\%$ CO2, and light-shielded conditions for 30 min. The staining solution was a mixture of Clear MEM, SRfluor (a fluorescence probe for neutral lipids, Molecular Targeting Technologies, Inc., West Chester, PA, USA), Liperfluo (a fluorescence probe for lipid peroxides, Dojin Chemical Laboratories, Kumamoto, Japan), and Hoechst 33342 (a fluorescence probe for nuclei, Fujifilm Wako Pure Chemical Corporation) at a ratio of 1000:5:5:1. After staining, the cells were washed twice with PBS, and the supernatant was replaced with 2 mL/dish of FluoroBrite™ DMEM (Thermo Fisher Scientific Inc., Waltham, MA, USA). Cells were observed under a fluorescence microscope (HS all-in-one fluorescence microscope BZ-9000, Keyence Co. Ltd., Osaka, Japan). Excitation/emission wavelengths for SRfluor, Liperfluo, and Hoechst 33342 were 620 nm/700 nm, 470 nm/525 nm, and 360 nm/460 nm, respectively. Exposure times for SRfluor, Liperfluo, Hoechst 33342, and bright field observations were unified at $\frac{1}{1.5}$, $\frac{1}{2.5}$, $\frac{1}{12}$, and $\frac{1}{120}$ s, respectively.
With reference to Piao et al. [ 41], the images were converted to images suitable for analysis using the HS all-in-one fluorescence microscope BZ-II analysis application (Keyence Co. Ltd., Osaka, Japan) and analyzed for the total number and area of lipid droplets using ImageJ software (NIH, Bethesda, MD, USA) [41,42]. For simplicity, cells in which the entire cell could be identified were included in the analysis (number of counted cells = 53–84 in each group) (Supplementary Figure S13). The images were then converted to 8-bit grayscale images for binarization. A grayscale threshold (0–10) was applied to the images to remove hepatocellular structures that did not exhibit lipid droplet features. All particles with a circularity of 0.00–1.00 and an area of 0.1–50 µm2 were counted. The total number of particles was calculated using this method. The settings of the various analysis parameters were standardized for all images.
## 4.7. Real-Time PCR
To confirm the hepatocellular lipid metabolic changes induced by LDL, transcriptional expression analysis was performed under the same conditions as in LC-MS/MS experiments ($$n = 6$$–8 for each group) [43]. After stimulation for 24 h in an incubator, the cells were washed with PBS, and RNA was extracted from the cells using an RNA extraction kit (PureLink™ RNA Mini Kit, Thermo Fisher Scientific Inc., Waltham, MA, USA) according to the manufacturer’s instructions. RNA concentrations were measured using NanoDrop One (Thermo Fisher Scientific Inc., Waltham, MA, USA). RNA was converted to complementary DNA (cDNA) using ReverTra Ace® qPCR RT Master Mix with gDNA Remover (TOYOBO, Co., Ltd., Osaka, Japan) in a thermal cycler (GeneAmp® PCR System 9700, Applied Biosystems, Foster City, CA, USA). The cDNA was stored at −80 °C. For the PCR reaction, Thunderbird® Next SYBER® qPCR Mix (TOYOBO, Co., Ltd., Osaka, Japan) was mixed with cDNA samples and primers according to the manufacturer’s instructions. *Target* genes included SOAT1, LIPE, DGAT1, ATGL, SREBP1, FASN, SCD1, and CAT, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was the housekeeping gene. Gene-specific primers were used to analyze gene expression (Supplementary Table S6). Gene expression levels were analyzed by the 2−(ΔΔCq) method using a real-time PCR analysis system (CFX Connect Real-Times System, Bio-Rad Laboratories, Inc., Hercules, CA, USA). The expression levels of each target gene were corrected for the expression level of the housekeeping gene GAPDH.
## 4.8. Statistical Analysis
All data obtained were subjected to statistical analysis using the GraphPad Prism V7.0 software (GraphPad Software Inc., San Diego, CA, USA). Statistical analysis was performed using rejection tests to identify outliers where necessary. Then, we performed one-way analysis of variance (ANOVA), followed by Tukey’s multiple comparisons test, or one-way ANOVA, followed by Dunnett’s multiple comparisons test, or followed by Kruskal-Wallis test, or Student’s t-test. The significance level was set at $5\%$. All results are expressed as mean ± standard deviation (SD) or box plots.
## 4.9. Ethics Approval
Ethical approval for blood sampling from healthy subjects was obtained from Hokkaido University (approval number: 19-107-3). Informed consent was obtained from all the subjects.
## 5. Conclusions
The present study demonstrated that nLDL causes the accumulation of CE and the formation of lipid droplets, possibly due to the reduced expression of LIPE in hepatocytes. In contrast, oxLDL appeared to increase lipid hydroperoxide-rich LDs and PC-OOH/PC, mainly CE-OOH and PC-OOH derived from oxLDL. These results suggest that stimulation by oxLDL mediates oxidative stress in the liver and could trigger NASH development. The limitation of the present study is that it is difficult to determine whether this model is closer to NASH or NAFLD because it is a simple cell experiment. Further studies using various cell lines, primary cells, and in vivo experiments are needed to determine the interaction between oxLDL and the liver in detail. However, our demonstration of an association between oxLDL and hepatocytes may lead to new findings in understanding the pathogenicity of oxLDL in NASH. In addition, our data suggest that oxLDL could be established as a novel pharmacological target and candidate biomarker for NAFLD/NASH.
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|
---
title: Participation in the Global Corporate Challenge®, a Four-Month Workplace Pedometer
Program, Reduces Psychological Distress
authors:
- Jessica Stone
- S. Fiona Barker
- Danijela Gasevic
- Rosanne Freak-Poli
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002186
doi: 10.3390/ijerph20054514
license: CC BY 4.0
---
# Participation in the Global Corporate Challenge®, a Four-Month Workplace Pedometer Program, Reduces Psychological Distress
## Abstract
Background: Psychological distress (stress) has been linked to an increased risk of chronic diseases and is exacerbated by a range of workplace factors. Physical activity has been shown to alleviate psychological distress. Previous pedometer-based intervention evaluations have tended to focus on physical health outcomes. This study aimed to investigate the immediate and long-term changes in psychological distress in employees based in Melbourne, Australia after their participation in a four-month pedometer-based program in sedentary workplaces. Methods: At baseline, 716 adults (aged 40 ± 10 years, $40\%$ male) employed in primarily sedentary occupations, voluntarily enrolled in the Global Corporate Challenge© (GCC©), recruited from 10 Australian workplaces to participate in the GCC® Evaluation Study, completed the Kessler 10 Psychological Distress Scale (K10). Of these, 422 completed the K10 at baseline, 4 months and 12 months. Results: Psychological distress reduced after participation in a four-month workplace pedometer-based program, which was sustained eight months after the program ended. Participants achieving the program goal of 10,000 steps per day or with higher baseline psychological distress had the greatest immediate and sustained reductions in psychological distress. Demographic predictors of immediate reduced psychological distress ($$n = 489$$) was having an associate professional occupation, younger age, and being ‘widowed, separated or divorced’. Conclusions: Participation in a workplace pedometer-based program is associated with a sustained reduction in psychological distress. Low-impact physical health programs conducted in groups or teams that integrate a social component may be an avenue to improve both physical and psychological health in the workplace.
## 1. Introduction
Psychological distress represents a combination of nervousness, agitation and psychological fatigue, and is interchangeably referred to as stress [1,2]. Experiencing higher levels of psychological distress may indicate an underlying mental disorder, such as anxiety or depression [3], and has been linked to an increased risk of chronic diseases such as cardiovascular disease, arthritis and chronic obstructive respiratory disease [4]. However, there is a lack of comprehensive data collected on the incidence and prevalence of psychological distress, especially in comparison to physical health [5].
In Canada and Australia, around $10\%$ of people report experiencing high levels of psychological distress, while 15–$20\%$ of workers across Europe and North America report experiencing psychological distress [6,7]. Psychological distress in the workplace is exacerbated by a range of workplace factors including high job demand and low job control, job strain, poor support, poor workplace relationships, low role clarity, poor organisational change management, poor organisational justice, poor environmental conditions, remote or isolated work and violent or traumatic events [8,9,10]. Work-related stress can also increase the risk of chronic disease—a study of 1,592,491 Danish workers concluded that an average of 0.25 years in women and 0.84 years in men were lost due to chronic illness associated with high job demand and low job control [11]. This can partly be explained by findings from a study of 3090 Japanese workers reporting that workers with high job demand, low job control and job strain were more likely to have pre-existing health conditions worsen as workloads and work/family conflicts arose during their employment [10]. Work-related distress is also associated with high levels of unplanned absences, sick leave, staff turnover, withdrawal, presenteeism, poor work and poor product quality [8]. Workers experiencing psychological distress at their workplace emphasise the importance of preventing and managing levels of psychological distress in working populations and identifying interventions that target and reduce psychological distress.
Physical activity has been shown to reduce psychological distress, reduce the risk of chronic disease and increase self-esteem, overall wellbeing, and health-related quality of life [12,13,14,15,16]. Mechanistically, physical activity increases the production of endorphins and neurotransmitters such as serotonin and dopamine, which boost mood and reduce feelings of stress and depression [17]. The benefit of physical activity on reducing psychological distress is irrespective of age, sex, ethnicity or having a medical condition [18,19]. A longitudinal study consisting of 33,918 observations from 17,080 individuals in the Household, Income and Labour Dynamics in Australia (HILDA) Survey over 2007, 2009 and 2011 reported that frequent participation in moderate to vigorous physical activity was associated with lower psychological distress scores [20]. A review to develop new evidence-based Australian guidelines for physical activity for adults concluded that participation in moderate to vigorous physical activity (compared to being inactive or of low levels of physical activity) was associated with a reduction in feelings of psychological distress [13]. Despite these benefits, very few adults undertake the World Health Organisation’s (WHO) recommendation of 150 min of moderate-intensity physical activity and at least 2 days of strength-based muscle training each week [21,22]. Moderate-intensity physical activity is defined as activity that is performed at 3.0–5.9 times the intensity of rest, while vigorous-intensity physical activity is performed at 6.0 or more times the intensity of rest for adults [22].
The increasingly sedentary nature of transport, leisure-time and workplaces contributes to an overall decrease in physical activity worldwide [23]. The WHO has recognised the workplace environment as an important area of action for health promotion and disease prevention [24]. In 2017, $39\%$ of people employed in the European Union worked while sitting [25]. Attempts have been made by workplaces and research groups to reduce sedentary time and increase physical activity at work [20,26,27,28,29,30]. The Toronto Charter, reported by the International Society for Physical Activity and Health (GAPA), calls for physical activity programs that are targeted to all sections of society, including the workplace [31]. The Charter also encourages employers and academia to undertake research to provide evidence for the effectiveness of physical activity programs in work settings and to provide support for employees in workplaces to be physically active [31]. Pedometer-based interventions have been suggested as a simple method for encouraging physical activity in the general and working population [32].
Previous studies investigating the use of pedometers as a physical activity intervention have tended to focus on physical health outcomes rather than psychological health outcomes, and only assessed short term benefits. While there is considerable evidence that physical activity and pedometer interventions in the workplace are effective at improving health outcomes and psychological distress, further clarification is needed in future studies to address the following issues. As identified in the systematic review by Freak-Poli et al., many studies that assessed pedometer interventions and their impact on health outcomes were cross-sectional and only observed the short-term effects of the programs on health [33]. Additionally, although there is an association with lower psychological distress among people who undertake more physical activities and/or are less sedentary, these findings are not validated by changes during physical activity interventions [20,27].
There is also a need for physical activity interventions to assess health outcomes beyond physical health factors. Physical activity interventions primarily focus on improving physical health outcomes linked to chronic disease, but such interventions may have additional benefits. There is a need for evaluations to be expanded to include mental health outcomes as well [33]. Additionally, the Freak-Poli et al. systematic review recommends the use of longitudinal studies to follow participants over a longer period of time to demonstrate sustained long-term effects on physical and mental health outcomes after the intervention has been completed [33].
Furthermore, evidence shows that employees are motivated to engage in pedometer programs, as walking is a low-intensity but sustainable form of physical activity over long periods of time [33]. It is also important to note that the employees most likely to benefit from workplace low-impact walking programs are those in highly sedentary roles, such as office workers and administrative staff [33]. Women, full time workers and individuals that self-reported a healthy weight and high physical activity were more likely to engage and participate in pedometer programs, which indicates that other groups need to be targeted in future studies [33,34,35]. Such interventions may provide the opportunity to negate the negative effects associated with shift work, overtime, and high job stress, as well as improve health outcomes [36].
Our study aims to investigate whether participation in a four-month workplace pedometer program was associated with immediate changes (after the four-month program) and long-term changes (eight months post-program) in psychological distress. Secondly, if changes were observed, we aimed to explore factors associated with change in psychological distress. Based on previous evidence that lower psychological distress is associated with undertaking physical activity, we hypothesize that adults in sedentary occupations will have a reduction in psychological distress after participation in a group-based, low-intensity physical activity workplace program, compared to their baseline measure (pre-post design).
## 2. Materials and Methods
This study involved secondary analysis of an existing, de-identified sample of office workers from Melbourne, Australia who were in predominantly sedentary occupations and enrolled in a group-based, pedometer workplace program.
## 2.1. Global Corporate Challenge®
The Global Corporate Challenge® (GCC®) is an annual pedometer-based, physical activity, four-month workplace health program that is conducted by a corporate organisation. The GCC® is held world-wide through workplaces, which group employees into teams of seven people. In this study, participants were asked to wear the visible pedometers provided by the GCC® on their hip throughout the day, with the exception of swimming and showering (it was removed during sleeping). Each participant aimed to undertake the step goal of 10,000 steps per day, which has been the historical recommended step goal to achieve adequate daily activity [12,14,37,38,39]. Each participant entered their steps into the GCC® website, which was combined to generate a team step count. The team step count was displayed virtually as walking progress around a world map, with information on locations as they arrived. Teams could see their progress, as well as other teams within their company world-wide, providing a competitive edge to the program. For example, an international company can compete with the office on the other side of the world. Additionally, the team or group component of the GCC® provided opportunities to get to know colleagues, external encouragement to achieve the recommended step goal, and increased collegiality among colleagues. Participants were sent weekly encouragement newsletters via email including the participant’s personal best daily step count, health tips from a nutritionist, stories from other participants, a “Dear GCC” section answering participants’ questions, housekeeping and prizes awarded by sponsors of the program. A website was used for logging daily step counts and provided access to additional health information such as the number of steps required to burn off a hamburger, communication among participants and comparing team progress.
## 2.2. Recruitment and Participation
The GCC® Evaluation Study was a prospective longitudinal observational study conducted over a 12-month period in workplaces across Melbourne (Figure 1) [12,14,37,38,39]. Participants were recruited from ten predominantly sedentary workplaces over eight weeks in April and May 2008, and were enrolled in the GCC® program (Appendix A). While 716 participants completed the Kessler Psychological Distress Scale 10-item (K10) [40] at baseline, this study mainly focused on the 422 participants who completed the K10 at baseline and 4- and 12-month follow-ups. Across all variables, there was minimal missing data (Appendix B).
The GCC® Evaluation Study was conducted in accordance with Monash University Human Research Ethics Approval, specifically the Standing Committee on Ethics in Research involving Humans (SCERH); Low Impact Research Project Involving Humans, project number CF$\frac{08}{0217}$-2008000125.
## 2.3. Psychological Distress
Psychological distress was measured using the 10-item Kessler Psychological Distress Scale (K10) [40]. The K10 scale is a short dimensional measure of non-specific psychological distress in the anxiety-depression spectrum [1,41]. Responses to each one of the 10 scale items were scored between 1 and 5. The final scores ranged between 10 and 50, and these were categorised as low (10–15), moderate (16–21), high (22–29) and very high (30–50) psychological distress [1,42] (further detail in Appendix C). There is significant evidence establishing the reliability and validity of the K10 across a number of diverse settings, including both international and Australian contexts, across a range of populations (Cronbach’s alpha coefficient ranges between 0.84–0.94, sensitivity 0.67–0.9 and specificity 0.74–0.81 for cut-offs below 28 [43,44,45,46,47,48,49,50,51]). K10 scores were collected at baseline, 4-months and 12-months via an online self-report survey.
## 2.4. Measures
Daily step count was used as the exposure in this study. Daily step counts were collected using pedometers (GCC® brand) worn on the hip. The pedometer was manufactured by GCC® and internally validated. The 10,000 daily step goal was based on previous evidence from Tudor-Locke that suggested 10,000 daily steps as indicative of active individuals [52]. Alongside the 10,000 daily step goal, we also tested the potentially new threshold of 7500 steps per day [52].
Covariates were assessed alongside psychological distress and step count to assess the health and psychological characteristics of participants in each psychological distress category. Potential confounders were selected a priori [53] (Clayton & Hill,1993) based on their relation with physical activity, aligned with previous papers reporting on the GCC® [12,14,37,38,39]. Demographic information (age, sex, tertiary education, partner status, socio-economic status, occupation), prior participation in the GCC®, motivation for participation (health, to look my best, fitness, colleagues or friends and family) and behavioural measures (fruit and vegetable intake, alcohol intake, smoking status, physical activity, sitting time and takeaway dinner consumption), were collected using the core and expanded options of the WHO STEPwise approach [54] and the WHO mini-STEP [55]. Psychosocial measures of wellbeing were collected using the WHO-5 questionnaire and health-related quality of life was measured using the SF-12 [12]. Locus of control was assessed using the Duttweiler Internal Control Index [56].
Anthropometric measures including blood pressure, heart rate, weight, body mass index (BMI), and waist circumference were measured at baseline, 4 and 12 months. Measurements were conducted by trained staff in the morning at the employees’ workplaces using the following equipment: blood pressure (Omron IA1B Automatic blood pressure intellisense machine), height (stadiometer portable height scale code PE087and step ladder), weight (Salter electronic bathroom scales model 913 WH3R 3007 during baseline and four-month data collection and Seca digital scales model Robusta 813 during twelve-month data collection) and waist and hip measurements (Figure Finder Tape Measure Novel Products Inc. 2005 code PE024 and a mirror) [14].
## 2.5. Data Analysis
The normality of K10 was assessed, with transformation undertaken if required. Baseline characteristics of study participants were stratified by categories of psychological distress and presented as mean (SD) if continuous and counts or percentages if categorical. The mean change in psychological distress in the total sample of participants that completed the K10 at all timepoints and in each psychological distress category ($$n = 422$$) was calculated using linear regression to compare changes from baseline to 4 months and baseline to 12 months. Linear regression exploratory analysis was used to investigate if other subgroups within the study had changes in psychological distress after the program. The mean change in psychological distress in participants that completed the K10 at all timepoints ($$n = 422$$) was stratified by age, sex, education, partner status, socio-economic status, occupation, motivation for participation, locus of control and step data using linear regression. Finally, linear regression analysis was used to determine predictors of immediate change in psychological distress among participants that completed the K10 at baseline and 4 months ($$n = 489$$). Factors associated with change in psychological distress were determined using univariable and multivariable (factors mutually adjusted) linear regression models. The statistical significance level was set at p ≤ 0.05. Data analysis was performed using Stata 16, StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX, USA: StataCorp LLC.
## 3. Results
Of the 716 participants who completed the K10 at baseline, 489 completed the K10 at baseline and 4 months, and 422 completed the K10 at baseline, four and 12 months. The K10 was slightly right-skewed, which was to be expected, as that indicated higher psychological distress (Appendix D). Hence, transformation was not required and assists with the interpretation of the findings as the K10 has prespecified categories. The smoothness of the normality of the data became disjointed with less data points at intervention completion (4 months) and long-term follow-up (12 months). Participants who only completed the K10 at baseline ($$n = 716$$) had a mean age of 40 years, $39.7\%$ were male, and $79.9\%$ had completed tertiary education. Participants that completed the K10 at baseline and 4 months only ($$n = 489$$) had a mean age of 41 years, $40.9\%$ were male, and $80.6\%$ had completed tertiary education. Of the 422 participants that completed K10 at all timepoints, had a mean age of 41, $42\%$ were male, and $81\%$ had completed tertiary education. Participants who remained in the study at four and 12 months ($$n = 422$$) were more likely to eat the recommended daily serving of fruits and vegetables, were more physically active and less sedentary (Appendix E).
Among the 422 participants who completed the K10 at baseline, 4 and 12 months, participants with lower baseline psychological distress (compared to higher baseline psychological distress) were older, had lower health related motivation for participation in the program, met the recommended physical activity guidelines, consumed takeaway dinner less regularly, and had higher scores for wellbeing, the SF-12 mental health component (MCS) and internal locus of control (Table 1).
## 3.1. Immediate and Long-Term Changes in Psychological Distress
Psychological distress decreased by half a unit between baseline and 4 months, which was retained at the 12-month timepoint ($$n = 422$$) (Figure 2 and Appendix F). Participants with higher baseline psychological distress scores had greater reductions in psychological distress after participation in the program, while participants with low baseline psychological distress scores reported increases in psychological distress. Immediate and sustained long term reductions in K10 scores ($$n = 422$$) were observed among those who were aged 30–40, females, had completed tertiary education, were widowed, separated or divorced, associate professionals, reported that they were motivated to participate in the program due to health, to look their best, improve their fitness, or encouragement from colleagues and were more likely to meet the 10,000 daily step goal (Appendix G).
## 3.2. Predictors of a Reduction in Psychological Distress
Univariable analysis of the 489 participants that completed the K10 at baseline and 4 months only were more likely to be younger age, being ‘widowed, separated or divorced’, being an associate professional, and achieving the goal of the program (steps average per day and meeting 10,000 steps average per day) were predictors of reductions in psychological distress after participation in the pedometer program (Table 2). The results of the multivariable analysis, when mutually adjusting for possible predictors of reduction in psychological distress, found that being employed in an associate professional occupation was the predictor with the greatest magnitude of reducing psychological distress from participation in the program ($$n = 453$$, included participants that had data for age, sex, tertiary education, socio-economic status, occupation, partner status and meeting the 10,000 steps daily goal). The associate professional occupation category, compared to the reference category of professional occupation, had the greatest magnitude of reduction in psychological distress. The immediate (4-month) and sustained (12-month) changes in psychological distress within each of these stratum are presented in Appendix H.
## 4. Discussion
Psychological distress among Australian employees in mostly sedentary workplaces was reduced after participation in the four-month workplace pedometer program, which was sustained eight months after the program ended. The reduction in psychological distress was greatest for those experiencing higher levels of stress before participating in the program. Participants achieving the goal of the program of meeting 10,000 steps average per day or with higher baseline psychological distress had the greatest immediate and sustained reductions in psychological distress. At baseline, higher psychological distress was associated with younger age, higher health related motivation for participation in the program, did not meet the recommended physical activity guidelines, consumed takeaway dinner regularly, and had lower scores for wellbeing, the SF-12 mental health component (MCS) and internal locus of control. Demographic predictors of reduced psychological distress were being an associate professional, younger age, and being ‘widowed, separated or divorced’.
## 4.1. Immediate and Long-Term Changes in Psychological Distress
While the importance of physical activity as a factor for reducing psychological distress has been studied many times [12,13,14,15,16,17,20], there is limited evidence for this relationship during participation in a workplace pedometer program. To our knowledge, we are the second study to have assessed long term physical activity interventions that utilise pedometers in terms of psychological distress. Our findings support evidence from a prior study of 1963 Indian and Australian workplaces enrolled in the Stepathlon corporate challenge reporting a benefit in psychological distress of 0.49 (mean change) over the 100-day program period. Interestingly, both our study and the Stepathlon study are opposed to the majority of prior evidence evaluating the effectiveness of workplace physical activity interventions on psychological distress [57,58,59]. This is likely because both our study and the Stepathlon were longer programs, where the interventions were able to form a habit in the participants—a study by Lally et al., 2010 found that it takes on average two months to develop a consistent behaviour [60]. A 2019 systematic review assessing job stress during workplace exercise interventions reported that only two of eight workplace physical activity programs observed a statistically significant reduction in job stress. Another 2018 systematic review concluded that studies assessing workplace physical activity programs were of low quality due to the lack of a control group [61]. In the study by Jindo et al., the participant characteristics were similar to our study and included a lower proportion of male participants to female participants, the mean age was older (around 50 years), and participants were also mainly tertiary educated [62]. The study collected data over six months but did not find improvements in psychological distress with increased compliance in the workplace exercise program. Conversely, participants with low psychological distress at baseline had an increase in psychological distress score during and after the program. Regression to the mean [63] is expected in longitudinal studies, particularly due to the ceiling effects encountered due to the healthy cohort effect [38]. To put this into context, among the healthiest participants (the least psychologically distressed), we observed a slight increase in psychological distress. However, the magnitude of this increase would not impact psychological distress categorization greatly as small increases would shift in an individual’s score to the lower end of the moderate category or remain in the low category. Nonetheless, a bi-directional relationship between physical activity and psychological distress has been observed, where pre-existing higher levels of psychological distress are associated with decreases in physical activity [64]. Further, increases in psychological distress during participation in workplace health programs may be explained by work stressors impacting these participants during the program [10].
Despite the opposing evidence in the above-mentioned systematic reviews, broader literature has shown that physical activity has benefits to psychological distress. Our findings support other prior literature, such as a study by Thogersen-Ntoumani et al., which demonstrated an estimated effect size of −0.31 in enthusiasm, −0.02 in relaxation and 0.05 in nervousness, in stress-related symptoms amongst sedentary British University employees four months post-intervention (note these findings were not statistically significant) [65]. Furthermore, a study by Perales et al. assessing self-reported physical activity data from 2007, 2009 and 2011, showed estimated effects of −0.41 units on the K10 when engaging in moderate to vigorous physical activity less than once a week compared to not at all, −0.83 units for being active once or twice a week, −1.14 units for being active 3 times a week, −1.42 units for being active more than 3 times a week, and −1.79 units for being active every day [20]. This demonstrates that as individuals engaged in frequent physical activity, their psychological distress scores reduced—which aligns with the finding of our study that higher step counts were associated with higher reductions in psychological distress.
## 4.2. Predictors of a Reduction in Psychological Distress
Our study demonstrated that people with a higher step count, higher levels of psychological distress, associate professional occupations, younger age, and being ‘widowed, separated, or divorced’ had the greatest reductions in psychological distress. Our observation that achieving 10,000 steps on average per day was associated with greater reductions of psychological distress supports the prior the Stepathlon corporate challenge study. However, the Stepathlon study also reported a benefit for participants that did not meet the 10,000 step-goal of $5.4\%$ improvement in stress, compared to a $10.1\%$ improvement for those meeting the goal [57]. Our magnitude of benefit was comparatively low, equating to $1.8\%$ improvement in psychological distress among all participants and $4.5\%$ improvement among those meeting the goal. Both our study and the Stepathlon study suggest that greater physical activity has additional benefits for psychological distress. Previous evidence also shows reductions in anxiety and depressive symptoms after moderate to intense physical activity [13]. Furthermore, recent evidence suggests a threshold of 7500 steps reduces mortality risk (hazard ratio [HR] = 0.57, [$95\%$ CI] = 0.38, 0.83), with an $8.5\%$ mean risk reduction for every additional 1000 steps/day [58]. Findings suggest that step counts greater than 7500 daily steps only marginally reduce the magnitude of the risk ($2\%$ mean risk reduction per 1000 steps/day) [58]. However, we did not observe an association between meeting a daily step goal of 7500 steps and a reduction in psychological distress.
Our study supports prior research that identified that people with the higher levels of psychological distress received the most beneficial changes from a walking intervention [66,67]. A review of the literature has concluded that while some studies have shown higher levels of stress decreased participation in exercise and physical activity in employee populations [67], another study reported that individuals experiencing higher levels of stress engaged in higher levels of physical activity [68]. This tends to be the case for those who already engage in physical activity regularly [69] but could also be a result of life events such as new relationships, retirement, changing work conditions, income changes and personal achievements [70].
Being an associate professional was the strongest demographic predictor of benefiting in psychological distress from participation in the program. Job position and having increased autonomy over work has been linked to lower stress [71], however, a study in Japan has reported that professionals and managers have a higher risk of poor health compared to clerks and manual laborers [72]. At baseline, associate professionals were no more likely to be stressed than other occupations in our study, hence, physical activity interventions along with increased job autonomy could greatly benefit this group.
The subgroups of younger age and being ‘widowed, separated or divorced’, could be targeted for low-intensity physical activity interventions to reduce stress. Among 7485 participants aged 20–64 years, higher levels of psychological distress have been observed in younger people that reported work-related stressors [71]. While we also observed a mean difference by age in psychological distress at baseline, there was only a 4-year mean difference between low and very high stress categories among participants aged 37–40 years. In our study, employees who were ‘widowed, separated or divorced’ had greater reductions in psychological distress. Evidence has shown that marriage may benefit mental health by lessening negative effects of chronic stressors, but also suggests that the changing nature of partner status can limit these effects [73]. However, we did not observe any difference in stress by partner status at baseline. Low physical activity interventions, therefore, are effective regardless of partner status, but could be a consideration in accounting for the stressors participants may have in their lives.
There are several possible mechanisms explaining how physical activity could benefit psychological distress. Participation in a physical activity intervention over four months is likely to promote the release of endorphins and be beneficial to psychological distress [17]. We note that a bi-directional relationship may exist with pre-existing higher levels of psychological distress associated with decreases in physical activity [64].
## 4.3. Strengths and Limitations
The main limitation of this study is the lack of a control group, meaning a cause-and-effect relationship could not be established. This study was also undertaken during colder winter months when people are known to be less active [74]. Further, winter has also been shown to have a negative impact on psychological distress [75]. Therefore, participants could have demonstrated greater program benefits if the evaluation was repeated in the warmer months.
Secondly, interventions and research studies typically attract participants who have positive health behaviours and therefore may perform better, known as the healthy cohort effect [38]. This may have been mitigated slightly as the GCC® was available for multiple years in a row. Initial years likely recruited a healthy cohort, but over time, as more and more employees were encouraged to participate, the healthy cohort effect would reduce. Of note, psychological distress at baseline in prior participating GCC® participants was no higher compared to new enrollees, however, a higher proportion of prior GCC® participants completed the K10 at baseline, 4 months and 12 months (data not reported).
Thirdly, the use of pedometers may be outdated and the pedometers are not externally validated [76]. The effect of lack of external validity is likely to be misclassification, and therefore our observed interaction between change in psychological distress and daily step count is likely to be an attenuation of any true effect. Pedometers have generally been found to be correlated with accelerometers, to have concordance with self-reported physical activity, and to have an inverse relationship with time spent sitting [52]. While we could suggest further research be undertaken utilising validated pedometers, this methodology is likely outdated. Pedometers were the device of choice for fitness programs and interventions in the early to mid-2000s. With advancements in technology, there has been a movement towards the use of accelerometers and electronic monitoring [33]. However, our findings of benefits in psychological distress are likely generalisable to studies using other technologies to monitor physical activity. Therefore, our main finding can be more generalisable to indicate that participation in a group-based, low-intensity, physical activity, walking program conducted through the workplace reduced psychological distress. Of note is our generalisation to low-intensity physical activity, as physical activity intensity can have a u-shaped association with mental health [77].
Fourthly, it is possible that participation in this program could have adverse consequences on psychological distress [78]. The competitive component could be experienced as encouragement or psychological distress, likely relating to the individual’s physical activity level, readiness to change, personality, and workplace politics [79]. For example, if a participant has the lowest step count in the team, they may feel pressured or shamed (rather than encouraged) to increase their daily step count.
Further, the workplace has a number of stressors [8,9,10] and participation in a workplace health program could add to these. Despite the program being voluntary, and requiring partial payment by some employees, an employee may find participation in the program overwhelming in terms of the physical activity required or the time commitment. Therefore, the workplace health program may present another competing “job” demand. One way of coping with additional stress is psychological detachment from work, which can have positive or negative outcomes [78]. Potentially a participant may choose to increase their participation in the program as part of psychological detachment from work, thus reducing their psychological distress. Our findings demonstrate that employees with higher psychological distress received the most beneficial effects from participation in the program. Additionally, the workplace environment may provide access to people with high stressors that may not be present in other settings and therefore the effectiveness of the program might be partly attributable to the workplace setting.
Finally, the data were collected in 2008–2009 but have been analysed through a present-day lens. In 2007–2008, around $62\%$ of adults did not meet the recommended physical activity guidelines compared to $55\%$ in 2017–2018 [80]. Despite the increase in meeting physical activity guidelines over time, there has been an overall decrease in manual labour occupations [81] and an increase in digital entertainment during leisure time which means that individuals are continuing to participate in highly sedentary behaviours [23]. We believe that workplaces have not changed significantly over this time and our study findings of an improvement in psychological distress from a low-impact physical activity intervention remains relevant.
The strengths of the study include the large sample size and the use of the K10, which is used by *Australian* general practitioners to assess stress. Our findings are generalisable to tertiary-educated adults employed in sedentary occupations. Our findings, along with prior outcomes from the GCC® Evaluation Study, fills a gap in the literature exploring pedometer-based programs and health outcomes.
## 5. Conclusions
Among 422 predominantly sedentary employees, participation in a group-based, low-intensity, physical activity, walking program conducted in the workplace reduced psychological distress and was particularly beneficial to those with higher levels of psychological distress. Older participants that had a higher daily step count, those in associate professional occupations, and those that were ‘widowed, separated, or divorced’ had the greatest reductions in psychological distress.
A better understanding of the relationship between physical activity and psychological distress can inform health policy. Health promotion programs can be tailored to focus interventions on overall psychological wellbeing (in addition to other health outcomes). It can be difficult to convince workplaces and employees of the value of participation in a workplace-based physical activity program; therefore, workplace policy development should reflect the need to consider the individual characteristics that affect positive health within a workplace, in order to identify and implement an appropriate intervention [82]. While improvements to workplace conditions are much needed, physical activity programs can be a complementary part of longer-term sustainable improvements in employee wellbeing. Policies concerning employee health and stress management should avoid a one-size-fits-all approach, and should focus on creating psychologically safe work environments and strengthening workplace conditions which are shown to be a major driver of employee stress. The opportunity for employees to participate in workplace group based programs that promote small positive health changes, such as the low-intensity walking program evaluated here, can be incorporated into these policies. The COVID-19 pandemic has added another dimension to workplace stress. High job demand, low job control and job strain have been shown to worsen pre-existing health conditions as workloads and work/family conflicts arose during COVID-19 lockdown and stay-at-home orders [83,84]. Low-impact physical activity interventions, such as the one evaluated in this study, can provide a solution to better physical health [38], mental wellbeing [14], and stress.
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|
---
title: 'Factors Related to Physical Activity among Older Adults Who Relocated to a
New Community after the Kumamoto Earthquake: A Study from the Viewpoint of Social
Capital'
authors:
- Yumie Kanamori
- Ayako Ide-Okochi
- Tomonori Samiso
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002188
doi: 10.3390/ijerph20053995
license: CC BY 4.0
---
# Factors Related to Physical Activity among Older Adults Who Relocated to a New Community after the Kumamoto Earthquake: A Study from the Viewpoint of Social Capital
## Abstract
Previous studies have shown an association between social capital and physical activity in older adults. Older adults who relocated after the Kumamoto earthquake may become physically inactive, and the extent of this inactivity may be buffered by social capital. Accordingly, this study applied the social capital perspective to examine factors that affect the physical activity of older adults who relocated to a new community after the Kumamoto earthquake. We conducted a self-administered mail questionnaire survey with 1494 (613 male, 881 female, mean age 75.12 ± 7.41 years) evacuees from temporary housing in Kumamoto City, aged 65 years and above, who relocated to a new community after the earthquake. We performed a binomial logistic regression to examine the factors affecting participants’ physical activity. The results showed that physical inactivity (decreased opportunities for physical activity, decreased walking speed, and no exercise habits) was significantly associated with non-participation in community activities, lack of information about community activities, and being aged 75 years and over. Lack of social support from friends was significantly associated with lack of exercise habits. These findings encourage participation in community activities, alongside giving and receiving social support in health activities that target older adults who relocated to new communities after the earthquake.
## 1. Introduction
The Kumamoto earthquake that occurred in April 2016 registered an intensity value of 6 on the Richter scale throughout Kumamoto City [1]. Located approximately 13 kms from the epicentre, Kumamoto City experienced two earthquakes that resulted in approximately 136,000 damaged dwellings, and up to 11,000 people were forced to relocate to temporary housing [1]. Around 2400 ($21\%$) of the individuals who relocated were older adults. In a survey conducted among those who left the temporary housing four years after the disaster, about $34\%$ had moved from the community where they had lived before the disaster to another community [2].
A survey by Hikichi et al. found that individual relocation was associated with a decline in the daily living activities of disaster victims 2.5–5.5 years after the Great East Japan Earthquake [3]. Tsuji et al. also point out that depressive symptoms increased after the Great East Japan Earthquake [4] and that depression in older adults means that they stay at home [5], which may be a factor in their physical inactivity after the earthquake. They then state that walking and participation in group exercise are necessary to prevent depression in older adults after the earthquake [6]. Furthermore, physical activity is necessary to prevent the development of frailty [7]. However, Murakami et al. found that the physical activity level of evacuees living in temporary housing in Kamaishi, Iwate Prefecture, was lower than that of the national average [8]. Furthermore, Ito et al. showed that a significantly higher percentage of older adults who lived in different rental housing than before the Great East Japan Earthquake were less physically active than those living in the same housing as before the earthquake [9]. In addition, a study comparing pre- and post-earthquake physical activity found that in areas with strong social connections, the decline in daily physical performance after the disaster was about one third [10]. Considering these findings, it can be assumed that older adults who relocated to another community after the earthquake are more likely to be inactive. However, social connections, i.e., social capital, may prevent physical inactivity.
The public health field considers social capital one of the most important factors that influence people’s health and sense of wellness [11]. Especially in public health crisis situations such as natural disasters, it has been demonstrated that the richer the social capital, the more that health damage can be controlled and the faster the recovery [12]. Therefore, we reviewed the previous studies on the relationship between social capital and physical activity, such as those conducted in Korea, Finland, Japan, and other countries [13,14]. Specifically, in a study of Korean adults, physical activity levels, defined by intensity, duration, and frequency, were higher when there was participation in community activities [13]. A study of 8000 adults in Finland also found that those who participated in community activities were more physically active [14]. Furthermore, a study conducted in Japan found that older adults with high social activity levels were more physically active and had shorter passive sitting times [15].
According to Kawachi et al., forms of social capital, such as participation in community organisations and social support, are directly related to health outcomes [16]. Therefore, we considered the relationship between social support and physical activity. In a study by Goodwin et al. that tracked psychological distress after the Great East Japan Earthquake, those with low physical activity levels and no social support after the disaster had lower levels of psychological health [17]. Moreover, Teramoto et al. found that after the Great East Japan Earthquake, social relationships with friends were associated with reduced psychological distress, especially among females aged 65 years and over [18]. Loss of social contact impacts psychological health, which, in turn, decreases physical function [19]. In a study by Sato et al. after the Kumamoto earthquake, the strength of social cohesion contributed to the reduction in depression among women [20]. A previous Kumamoto Prefecture reconstruction survey showed that $43.4\%$ of the population had decreased their opportunities for physical activity after relocating due to the earthquake [21], a situation in which physical activity should be promoted through social capital.
Social support refers to support sought from others to cope with everyday problems [16]. Therefore, social support, defined as having friends and peers with whom one can discuss daily problems, is thought to maintain social contact and psychological health and prevent physical inactivity. Among other things, connections with friends [22], neighbours [23] and district welfare commissioners affect mental health issues [24] such as loneliness, as well as quality of life measures, such as happiness. Furthermore, in a study conducted in Miyagi Prefecture on residents who migrated after the Great East Japan Earthquake, the social capital of individual relocation was lower than that of community-based group relocation [3]. Based on this finding, it is thought that the individuals who relocated may have experienced difficulty in fostering social capital in their new communities after the earthquake, which may have affected their physical activity, especially among older adults.
In Kumamoto City, there was no group relocation by community units after the earthquake. Therefore, it is possible that physical inactivity may be related to social capital among older adults who relocated to a new community after the earthquake in Kumamoto City. However, we did not find any clear findings on this issue in the previous research. Therefore, we examine whether social capital can explain physical inactivity among older adults who relocated to a new community in Kumamoto City after the Kumamoto earthquake. We explore the factors that affect physical activity among the older adults who relocated from the perspective of social capital so as to provide suggestions for future health activities.
## 2.1. Participants
This study followed a cross-sectional design. The sample comprised of 2016 Kumamoto earthquake victims who had moved out of temporary housing by December 2019, were aged 65 years and over, resided in Kumamoto City, and had relocated to a different community after the earthquake. A self-administered questionnaire was mailed to 11,479 households who had relocated to be completed by individuals aged 18 years and over in each household. Completed questionnaires were returned. Of the individuals who responded, those aged 65 years and over who had relocated to another community were included in the study. Of the 8966 collected questionnaires, 1494 persons ($41.0\%$ male, $59.0\%$ female) were selected for analysis; these included adults aged 65 years and over who had relocated to a new community after the earthquake (Figure 1). The data were collected from July to December 2020. In Kumamoto Prefecture, several local authorities have conducted recovery surveys. The recovery survey conducted by Kumamoto Prefecture had already shown that changes in the community after the earthquake reduced opportunities for physical activity. The recovery survey had also been used for research purposes and had shown, for example, mental health. However, this was the first recovery survey to incorporate pieces for older adults. Through this survey, Kumamoto City has ascertained the health status of its citizens. Through this survey, the need for future research and support will be considered [25,26]. The study was reviewed and approved by the Institutional Review Board of Kumamoto University (approval no. 1940, approved on 4 June 2020), Kumamoto, Japan.
In this study, we defined ‘community’ as a social group determined by geographical boundaries, such as administrative areas [27]. We defined ‘physical activity’ as all movements performed in daily life, such as walking, housework, daily activities, and exercise [28]. We define social capital as ‘social connections and the norms and trust that emerge from them, characteristics of social organizations that effectively lead to coordinated action’ [29]. ‘ Older adults’ shall be defined as respondents aged 65 years and over. An ‘Exercise habit’ is defined as performing household chores that replace exercise or physical activity at least once a week.
## 2.2.1. Physical Activity
To understand the changes in opportunities for physical activity before and after the earthquake, we asked the participants, ‘To what extent have the opportunities for physical activity in your daily life changed compared to before the Kumamoto earthquake? Please check all that apply’. The response options were [1] large increase, [2] slight increase, [3] unchanged (active), [4] unchanged (inactive), [5] slight decrease, and [6] large decrease. The questions were set up based on a previous recovery survey conducted by Kumamoto Prefecture, which found that respondents who had a change of address after the earthquake [21] were less likely to be physically active. We also asked, ‘Do you think your walking speed has become slower compared to before? Please check all that apply’. The response options were [1] yes and [2] no. Walking speed was based on the standard questionnaire items during health checks [30] and the Okazaki et al. survey on perceptions of walking speed [31]. To evaluate the participants’ exercise habits, we asked, ‘Do you walk or do household chores (cleaning, gardening, etc.) that constitute exercise at least once per week?’ The response options were [1] yes and [2] no. The questions on exercise habits were set up with reference to the Active Guide [32] based on the Physical Activity Reference for Health Promotion 2013.
## 2.2.2. Attributes
We collected information on participants’ basic attributes, such as sex, age, cohabitants, temporary housing category, and current residence. Age was recorded in a descriptive form by asking the participants to write their current age. Sex was recorded using the Kumamoto City ledger. To assess whether the participants had cohabitants, we asked, ‘Do you have anyone living with you?’ The response options were [1] yes and [2] no. The classification of temporary housing was recorded using the Kumamoto City ledger. We classified the participants’ residence type using the following response options: [1] owned house, [2] houses for rent, [3] public housing, [4] public housing for disasters, [5] hospital and institutions, and [6] other.
## 2.2.3. Social Capital
We classified social capital in terms of participation in community activities and the availability of social support (advisory partners). The types of consulting partners were friends, neighbours, and district welfare commissioners. Participation in community activities was based on the question, ‘Do you participate in events and social gatherings held in your community?’ The response options were [1] I participate, [2] I do not participate, or [3] I do not know about such information (non-information). The question was set based on the fact that the same question had been asked in a previous Kumamoto Prefecture recovery survey; non-participation was found to be $60.2\%$, and non-information was found to be $8.2\%$ [21]. Additionally, in the study by Hikichi et al. which revealed social capital and mental and physical health, the question “Do you participate in any local events?” also referred to this question [3]. To determine participants’ social support, we asked, ‘Who do you consult with about your problems?’ The response options were [1] family; [2] friends; [3] neighbours; [4] co-workers; [5] district welfare commissioners; [6] medical institutions; [7] welfare offices, such as nursing care facilities; [8] city hall/ward office; and [9] no one. As social support, the role of friends [22], neighbours [23] and district welfare commissioners [24] is particularly important. In addition, medical institutions and care providers are involved according to health status [33].
## 2.3. Data Analysis
After calculating the basic statistics, we performed a Chi-square test of independence (Χ2 test) to examine the relationship between physical activity, basic attributes, and the social capital influence variables. Next, we conducted a binomial logistic regression analysis to examine the factors that influenced physical activity. Using decreased opportunities for physical activity after the earthquake as the dependent variable, we examined whether decreased walking speed and lack of exercise habits were applicable.
We created three models for this study (Figure 2). Model I explains how physical activity is affected by the presence or absence of participation in community activities. Model II explains how physical activity is affected by the addition of basic attributes to Model I. Finally, Model III explains the impact on physical activity by adding social support to Model II. Model I utilises a univariate analysis, while Model II and Model III utilise multivariate analyses.
The independent variables were sex, age (whether the person falls into the category of 75 years and over), presence or absence of a cohabitant, temporary housing category, current residence, participation in community activities, and presence or absence of social support (friends, neighbours, or district welfare commissioners). We created dummy variables for the independent variables. Before conducting the logistic regression analysis, we calculated the Spearman’s rank correlation coefficient to confirm multicollinearity and ensure that the correlation coefficient did not exceed 0.8. We selected the variables using the forced entry method and set the statistical significance level as $0.5\%$ (two-sided). We conducted Χ2 and Hosmer-Lemeshow (HL) tests on the models to assess model fit. To assess model fit, we calculated the Nagelkerke R2 values and excluded the missing values for each variable. We used SPSS Statistics 27.0 for Windows as the statistical software.
## 3. Results
There were 613 ($41.0\%$) male and 881 ($59.0\%$) female respondents. The average age of the participants was 75.12 ± 7.41 years (65–105 years); 807 ($54.0\%$) were aged 65–74 years and 687 ($46.0\%$) were aged 75 years and over. The basic attributes of the respondents analysed are shown in Table 1. The participants’ demographics of the analysed participants are shown in Table 1. Table 1 shows that there are 613 ($41.0\%$) males and 881 ($59.0\%$) females. The mean age is 75.12 ± 7.41 years (65–105). Of the total participants, 807 ($54.0\%$) are aged 65–74 years, and 687 ($46.0\%$) are aged 75 years and over. Furthermore, 899 ($60.2\%$) live with a cohabitant, while the largest number of participants [1255 ($84.0\%$)] live in temporary housing in the private sector. At the time of the survey, 209 ($14.0\%$) participants were living in an owned house, 592 ($39.6\%$) were living in a house for rent, and 568 ($38.0\%$) were living in public housing. The results show that 300 ($20.1\%$) participate in community activities, while 960 ($64.3\%$) do not participate in community activities, and 187 ($12.5\%$) have no information about such activities. Regarding social support, 497 ($34.2\%$), 83 ($5.7\%$), and 34 ($2.3\%$) of the participants consult friends, neighbours, and district welfare commissioners, respectively, when they have problems.
Table 2 shows the participants’ physical activity after relocation due to the earthquake. Regarding the changes in opportunities for physical activity after the earthquake, 403 ($27.0\%$) participants state that their opportunities have considerably decreased, while 322 ($21.6\%$) state that their opportunities have somewhat decreased. Moreover, 983 ($65.8\%$) state that their walking speed has decreased and 995 ($66.6\%$) report having exercise habits.
Table 3 shows the results for the cross tabulation of physical activity and the independent variables. Age, type of temporary housing, and social support from friends are significantly associated with decreased opportunities for physical activity after the earthquake. Age, participation in community activities, and social support from district welfare commissioners are significantly associated with decreased walking speed after the earthquake. Sex, age, current residence, participation in community activities, and social support from friends and from neighbours are significantly associated with the presence of exercise habits.
Table 4 shows the results for the binomial logistic regression analysis, with physical activity as the dependent variable and participation in community activities, basic attributes, and social support as the independent variables. Regarding the Χ2 test results for the models, Model I shows that $$p \leq 0.121$$ for decreased opportunities for physical activity after the earthquake. All other results are significant, and the HL test results are p ≥ 0.05. We further checked the Nagelkerke R2 values to select the model with the best fit in each dependent variable. The results show that Model III has the highest values for all three aspects of physical activity, at 0.063 for decreased opportunities for physical activity, 0.118 for decreased walking speed, and 0.110 for no exercise habits. Therefore, from the binomial logistic regression analysis results, we adopted the values of Model III.
Non-participation in community activities (1.38, 1.04–1.83), being unaware of such information (1.71, 1.15–2.56), and being aged 75 years and over (2.11, 1.69–2.65) are more likely to decrease opportunities for physical activity after the earthquake. Non-participation in community activities (1.67, 1.23–2.27), non-information about such activities (3.59, 2.22–5.80), and being aged 75 years and over (3.01, 2.31–3.92) are more likely to be associated with decreased walking speed. Lack of exercise habit is associated with non-participation in community activities (2.42, 1.69–3.47), non-information about such activities (3.05, 1.91–4.86), being male (2.14, 1.67–2.74), being aged 75 years and over (1.55, 1.20–1.99), and having no social support/friends (1.44, 1.09–1.91); thus, participants who fall under these categories are less likely to maintain exercise habits. Conversely, participants living in a house they owned (0.29, 0.12–0.73), houses for rent (0.32, 0.14–0.77), and in public housing (0.34, 0.14–0.82) are less likely to be included in the no exercise habits category.
## 4.1. Participants’ Physical Activity
In 2020, the population of Kumamoto City totalled 738,567 people [34], of which 110,750 people were evacuees (Kumamoto Earthquake Kumamoto City Earthquake Record Magazine, 2016) and approximately 24,000 people were aged 65 years and over. Therefore, the 1494 participants whose data were analysed herein correspond to $6.4\%$ of the total population. A previous study found that $5.3\%$ of the total population had relocated to temporary housing [3]; our study observed a similar trend.
Overall, we found that $48.6\%$ of the participants fell into the category of decreased opportunities for physical activity. In 2020, Kumamoto Prefecture surveyed the health of those affected by the Kumamoto earthquake who were living in temporary housing in 17 municipalities; $32.8\%$ had decreased opportunities for physical activity [21], and the percentage of decliners was lower than $48.6\%$ in this study. In a study of 81 older adults who relocated from their homes to senior housing in Finland, the average walking speed per second decreased by 0.11 s after relocation [35]. However, Okazaki et al. surveyed lifestyle factors related to malnutrition in Fukushima Prefecture after the Great East Japan Earthquake, and found that $55.6\%$ of the participants perceived that their walking speed was not fast enough [31]. Meanwhile, our study found that $65.8\%$ of participants felt that their walking speed had decreased. A previous study found that $37.6\%$ ($41.9\%$ of males and $33.9\%$ of females) of older adults in Japan had exercise habits [36]. Meanwhile, our study found that $66.6\%$ of the participants had exercise habits, which is relatively higher than the general ownership rate. In the aforementioned study by Okazaki et al. in Fukushima Prefecture, $64.6\%$ of the participants had exercise habits [31]. This value is comparable to that of our study. Although Fukushima Prefecture was affected by the Fukushima Daiichi Nuclear Power Plant accident, we used it as a reference to compare the exercise habit rate of older adults living in the area after the earthquake. The results suggest that older adults may have a higher exercise habit rate compared to the general population after the earthquake. Based on the above, we consider our study participants a population that is as inactive as in the previous studies in terms of decreased opportunities for physical activity and decreased walking speed; moreover, their exercise habits are relatively similar to those of older adults after the earthquake.
## 4.2. Factors Associated with Physical Inactivity
We found that social capital was significantly associated with physical inactivity among older adults who relocated to a different community from that of their pre-earthquake community after the Kumamoto earthquake. The positive association between participation in community activities and physical activity has been shown in Kim et al. and Nieminen et al. [ 13,14]. In Korean adults, higher levels of social participation and generalised trust have been found to significantly increase levels of physical activity [13]. In Finnish adults, more social participation and networking have been found to significantly increase physical activity in leisure time [14]. Furthermore, low social capital at the state and county level has been found to be significantly associated with physical inactivity in a multilevel analysis conducted in the United States [37]. In addition, A study after the Armenian earthquake showed that older age significantly decreased physical activity, producing 1.07 times more mobility difficulties and 1.05 times worse usual activity. On the other hand, higher social support scores decreased mobility difficulties and usual activity worse by 0.91 times, indicating the effect of social capital on physical activity [38]. Our results also showed that those who did not participate in community activities had decreased opportunities for physical activity, which was consistent with the results of the previous studies. However, to the best of our knowledge, our study is the first to identify an association between social capital and physical activity in older adults who relocated after the Kumamoto earthquake. Social capital can be reduced through unavoidable migration after a disaster [39]. Moreover, the migration of older adults can result in adaptation problems to their new community, affecting their physical and mental health [40]. Therefore, the decrease in opportunities to go outside after the earthquake [21] may be a trigger for physical frailty. Our study provides valuable insight into the prevention of physical inactivity among earthquake victims.
Simultaneously, we found that lack of information about community activities was associated with more physical inactivity than non-participation. In Kumamoto City, information about community activities is mainly communicated through posts, circulars, verbal communication at meetings, and invitations among residents. Elena et al. indicate that health information may motivate individual health behaviours by turning to it for health-related purposes [41]. Therefore, information about community activities may not catch the attention of those who have no awareness of health issues. Furthermore, there is a risk that the information disseminated to members within a current circle of residents may not reach new residents. Therefore, for the older adults who relocated to a new community after the earthquake, the necessary information should be delivered by professionals and key persons in the community.
Regarding sex, we found that among the older adults who relocated after the earthquake, males showed a significantly higher association with lack of exercise habits than females. To date, there is no study that has identified exercise habits by sex among older adults who relocated to a new community after the earthquake. However, it is noteworthy that even in normal times, males are less likely to maintain exercise habits than females [42], which is consistent with our study results. Thus, in general, males tend to have fewer exercise habits than females. Therefore, we believe that health activities that particularly take males into consideration are required.
Regarding age, our results found that those aged 75 years and over were significantly more likely to have decreased opportunities for physical activity, decreased walking speed, and no exercise habits than those aged 74 years and under. In a study on orthopaedic diseases among the victims of the Great East Japan Earthquake, the prevalence rate for back and joint pain in the limbs was relatively high among those aged 75 years and over [43]. Muscle weakness inevitably occurs with age [44].
Furthermore, as mentioned earlier, preventive care activities in Kumamoto City are conducted on a community basis; hence, older adults who relocated after the earthquake in this study adapted to their new community. Therefore, smooth relationships in a smooth new community make it easier to carry out health promotion activities and increase physical activity [45]. On the other hand, inability to adapt to the community may lead to physical inactivity. The results of this study showed that non-participants in community activities were more physically inactive than participants. This may have led to an inability to adapt to the new community, which may have affected physical inactivity. In addition, the self-restraint on opportunities for people to assemble due to COVID-19 may have also contributed to the difficulty in adapting to the community and the physical inactivity.
Finally, we found that social support from friends was associated with physical activity. We found that the absence of social support from friends was significantly associated with no exercise habits compared to the presence of social support from friends among older adults who relocated after the earthquake. Similarly, a study on middle-aged women in Chicago analysed the correlation between the physical activity of individuals and their friends; those who were continuously active had active friends [46]. Moreover, a systematic review of physical activity among older adults found that unaccompanied companionship was a disincentive for continuous physical activity, including exercise, suggesting the need for companionship [47]. Social support from friends is also believed to contribute towards the protection of psychological health [15,16]. Social cognitive theory states that an important driver of sustained behaviour is high self-efficacy [48]. Therefore, the presence of friends may increase sustained exercise, even after the earthquake. However, self-efficacy has been associated with the maintenance of physical activity for up to 12 months [49], and it is likely that we found a significant association with exercise habits in our study because of this. Therefore, when considering health activities that target older adults who relocated after the earthquake, it is necessary to focus on social support from friends as well. In view of the above, the ideal way to engage in habitual activities such as exercise is not only to participate in community activities, but also to develop friendships with other participants. Therefore, health workers need to pay attention to the frequency of activities and developmental stage of the new conurbations, and to create a familiar environment for older adults who have relocated after the earthquake.
## 4.3. Limitations and Significance
This study has the following limitations. First, this study was limited to the Kumamoto earthquake victims living in Kumamoto City; therefore, the results are not generalisable to all earthquake victims. Second, this was a cross-sectional study that was conducted four years after the Kumamoto earthquake. The survey was conducted when COVID-19 was first beginning to spread, so it is undeniable that the results may have been influenced by social behaviour restrictions. The period from July to December 2020, when the survey was conducted, was the period when measures to prevent the spread of COVID-19 were ordered. Participates were asked to refrain from assembly, although individual activities such as shopping were possible. It is therefore possible that the loss of opportunities for gathering and activity may have caused a low level of physical activity. To differentiate the effects of migration from those of COVID-19, a retrospective longitudinal study is necessary. Longitudinal studies can also confirm that causal relationships between variables cannot be determined. Furthermore, it is possible that there are independent variables that affect physical activity other than those included in our study models. Our survey did not ask if the earthquake caused locomotion, mental or other health problems. However, as previously mentioned, these could be factors influencing physical activity. In the future, it is also necessary to understand the actual situation of older adults with reduced physical activity and the means of activity they are seeking. Under COVID-19, digital media and use of the internet to connect was recommended, but older adults in Japan are not good at communicating using digital devices [50]. Additionally, as this study did not survey the social use of digital media, this needs to be studied in the future. Despite the limitations of our study, it is significant that we were able to clarify a previously unknown finding regarding the physical activity of older adults who relocated to a different community after the earthquake than before the disaster. We also believe that we have identified basic knowledge that is necessary for conducting health activities that target these older adults.
## 5. Conclusions
This study showed that social capital was significantly associated with physical inactivity among older adults who relocated to a different community from their pre- community after the Kumamoto earthquake. Physical inactivity (decreased opportunities for physical activity, decreased walking speed, and lack of exercise habits) was significantly associated with non-participation in community activities, non-information about such activities, and being aged 75 years and over. No social support from friends was also significantly associated with no exercise habits. The study suggests the need to foster social capital among older people who have moved to a new community. Health activities that target older adults who have relocated to a new community after the earthquake should take into account their participation in community activities as well as social support from friends.
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|
---
title: Effects of Virtual Reality Exercise Program on Blood Glucose, Body Composition,
and Exercise Immersion in Patients with Type 2 Diabetes
authors:
- Yu-jin Lee
- Jun-hwa Hong
- Myung-haeng Hur
- Eun-young Seo
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002193
doi: 10.3390/ijerph20054178
license: CC BY 4.0
---
# Effects of Virtual Reality Exercise Program on Blood Glucose, Body Composition, and Exercise Immersion in Patients with Type 2 Diabetes
## Abstract
Background: This study is a preliminary study to examine the effect of a virtual reality exercise program (VREP) on type 2 diabetes patients. Method: *This is* a randomized controlled trial for patients with type 2 diabetes (glycated hemoglobin ≥ $6.5\%$), diagnosed by a specialist. The virtual reality environment was set up by attaching an IoT sensor to an indoor bicycle and linking it with a smartphone, enabling exercise in an immersive virtual reality through a head-mounted display. The VREP was implemented three times a week, for two weeks. The blood glucose, body composition, and exercise immersion were analyzed at baseline, and two weeks before and after the experimental intervention. Result: After VREP application, the mean blood glucose ($F = 12.001$ $p \leq 0.001$) and serum fructosamine ($F = 3.274$, $$p \leq 0.016$$) were significantly lower in the virtual reality therapy (VRT) and indoor bicycle exercise (IBE) groups than in the control group. There was no significant difference in the body mass index between the three groups; however, the muscle mass of participants in the VRT and IBE groups significantly increased compared with that of the control ($F = 4.445$, $$p \leq 0.003$$). Additionally, exercise immersion was significantly increased in the VRT group compared with that in the IBE and control groups. Conclusion: A two week VREP had a positive effect on blood glucose, muscle mass, and exercise immersion in patients with type 2 diabetes, and is highly recommended as an effective intervention for blood glucose control in type 2 diabetes.
## 1. Introduction
Diabetes is one of the major chronic diseases, with 463 million people reportedly affected by it worldwide in 2019, which is expected to gradually increase to 578 million by 2030 and 700 million by 2045 [1]. According to the diabetes fact sheet in Korea, 2020, the prevalence of diabetes in South Korea, in individuals over the age of 30 years, has increased from $11.1\%$ in 2013 to $13.8\%$ in 2018. Patients with diabetes often have concomitant obesity, hypertension, and hyperlipidemia, which increases the socioeconomic burden and lowers the quality of life of the individuals; thus, diabetes management is crucial [2]. The main treatment goals for patients with diabetes are to maintain normal blood glucose levels and prevent acute and chronic complications [3]. Treatment is largely divided into drug therapy and lifestyle modification, among which lifestyle modification can be classified into diet and exercise therapy, allowing patients to self-monitor and prevent complications. However, the practice rate of diet and exercise therapy is still quite low [4]. Exercise therapy increases insulin sensitivity, decreases fasting and postprandial blood glucose levels, reduces cardiovascular risk factors and weight, and ensures the well-being of patients [5,6,7,8].
However, only approximately $36\%$ of patients with diabetes in South Korea engage in regular physical activity [4]. In particular, social distancing and isolation measures, attributed to the recent SARS-CoV-2 virus (COVID-19), have further reduced physical activity [9]. Therefore, a new and safe method for encouraging patients with diabetes to continue performing exercise therapy is warranted. Therefore, this study aimed to devise a new exercise therapy for increasing the exercise practice rate of patients, by rapidly exhibiting the exercise benefits, thereby enhancing exercise immersion and providing a short-term intervention effect.
Cycling is a representative aerobic exercise that can be easily performed. In particular, indoor cycling does not require a lot of space, and can be performed at any time regardless of the weather, time, and season, and can be performed in the current COVID-19 situation. It has the advantage of being able to set an appropriate exercise intensity for each individual, by measuring real-time speed, distance, exercise time, and calorie consumption, and by adjusting the resistance and rotation speed of the wheel [10]. However, indoor cycling can be boring, since it is performed alone in a fixed place, which may hinder engagement in continuous and repeated exercise [11]. Applications using virtual reality (VR) are being developed to compensate for these shortcomings, by increasing the interest and fun of indoor exercise and motivating individuals through a sense of achievement [12].
VR is a technology that ensures realistic real-world experience, by stimulating the individual by creating an environment that is difficult or impossible to obtain in reality, using artificial technology [13]. A head-mounted display (HMD), an immersive VR device, is used in games, movies, education, and training, with images and immersive sound, that provide 360° visual immersion. The sense of reality provided by VR enhances exercise ability by sustaining immersion [14], thereby having a positive effect on participation and learning ability, by improving concentration [15]. Recently, in the health care field, treatment using VR has been reported to be effective in improving motor performance, cognitive function, and fall prevention in patients with Parkinson’s disease and stroke [16,17,18,19,20].
However, among the previous studies that applied VR, a scarcity of studies applying VR to patients with type 2 diabetes exists. Therefore, our study attempted to determine the effects on blood glucose, body composition, and exercise immersion, by applying a 2-week VR exercise program (VREP) in patients with type 2 diabetes.
## 2.1. Study Design
This study was a randomized controlled trial, measuring the effects of a 2-week VREP on the blood glucose, body composition, and exercise immersion in patients with type 2 diabetes (Figure 1).
## 2.2. Participants
Patients were recruited via a recruitment notice at the University of Eulji, University Hospital. The inclusion criteria were, patients between 30 and 65 years of age and diagnosed with type 2 diabetes (glycated hemoglobin ≥ $6.5\%$), who had not participated in any exercise research program in the last 6 months, could use a smartphone, understood the study, and consented to participation. Exclusion criteria were, those with diabetic peripheral neuropathy, diabetic retinopathy, visual impairment, previous lower extremity joint surgery, stroke, severe arthritis, or dizziness.
The sample size was calculated by substituting α value, power, and effect size using the calculation program G-Power 3.1.9.4 (Heinrich Heine University, Germany) [21]. Upon calculating the sample size, through repeated measures analysis of variance (RM ANOVA), with an effect size set at 0.24, significance level at 0.05, power(1-β) at 0.80, number of groups at three each, and correlation coefficient at 0.5, based on a previous study on diabetes [22], the total sample size derived was 39; the study was conducted on 45 participants, considering a dropout rate of $10\%$. The function of the Microsoft Excel program randomly assigned 15 participants to each of three groups. During the study, one participant in the VR therapy (VRT) group refused to participate in the experiment, owing to dizziness during exercise after wearing an HMD. Two participants in the indoor bicycle exercise (IBE) group dropped out, owing to COVID-19 self-quarantine, and one participant in the control group dropped out, due to hospitalization for surgery. Thus, 14, 13, and 14 participants in the VRT, IBE, and control groups, respectively, were included in this study (Figure 2).
## 2.3. Experimental Intervention
The exercise program was developed according to the recommendations of the American College of Sport Medicine [23], based on the type and intensity of exercise, after consulting an internal medicine doctor, a professor of nursing, and a sports therapist. An indoor bicycle with easy access to exercise was selected for this study, and the exercise intensity and duration were changed from low to medium intensity, for 40–60 min, considering patients with type 2 diabetes. Since 3 days of resistance exercise and 3 days of aerobic exercise are recommended per week, and the National Academy of Sports Medicine also recommends at least 2 days of resistance exercise and 3 days of aerobic exercise per week [24], our study prescribed 3 days of exercise per week. When exercising with the indoor bicycle, the exercise intensity was set at a low level (gear two, of gears one to ten), which was adjusted to the intensity that would render participants “slightly out of breath” [25]. The exercise program consisted of warm-up, main, and cool-down exercises. First, warm-up stretching was performed before initiating the exercises, which relieved heart and muscle stimulation and improved the exercise capacity, by improving blood flow. The indoor cycling exercise was performed as the main exercise, followed by cool-down stretching, which accelerated the decomposition of lactic acid accumulated in the blood, after the end of the main exercise, to help recover from fatigue [26].
The exercise program was scheduled at a comfortable time for the participants, to ensure exercise three times a week. After checking the respiratory symptoms and fever of the patients, and disinfecting their hands, the CGM Libre sensor was tagged with a smartphone to check their blood glucose. Thereafter, the participants participated in the exercise program according to the explanation and demonstration provided by the authors. After the exercise program, the CGM Libre sensor was tagged with a smartphone to check participants’ blood sugar.
The VREP was applied to the VRT group for a total of 50 min, which included 10, 30, and 10 min of warm-up, main, and cool-down exercises, respectively; the main exercise consisted of 30 min of VR IBE. The VREP used an indoor bicycle (DP-652-G6, IWHASMP, China), and VR programs and applications.
An internet of things (IoT) sensor was attached to the pedal of an indoor bicycle, converting the indoor bicycle into a VR device. After downloading the VRFit application from Play Store or Apple Store on their smartphone, and upon logging in and connecting to the IoT sensor, the VR background and music were set on the app screen. When the bicycle pedal was turned, following mounting of the mobile phone on the HMD, the set virtual background and music were displayed. The exercise program was applied to the IBE group for a total of 50 min, including 10, 30, and 10 min of warm-up, main, and cool-down exercises, respectively. The control group did not participate in the exercise program, and was allowed to follow their normal daily routine for 2 weeks, without intervention (Figure S1).
## 2.4.1. Mean Blood Glucose (MBG)
In this study, the MBG was obtained by attaching a FreeStyle Libre CGM (Abbott Diabetes Care, Alameda, CA, USA) sensor to the upper arm of the participant, and continuously measuring the glucose level through the interstitial fluid.
## 2.4.2. Serum Fructosamine
For serum fructosamine testing, 3 mL of venous blood was collected, placed in a serum separating tube bottle, and sent to the Green Cross for analysis. The test was conducted by a colorimetric method, using Cobas 8000 (c702, Roche Diagnostics, Mannheim, Germany), which was intended to assess short-term average blood glucose level, using the normal range of 205–285 µmol/L as the standard.
## 2.4.3. Body Composition
Body mass index (BMI) refers to the value obtained by dividing the weight (kg) of the participants by the square of their height, measured using a body composition analyzer (InBody Dial h20b, Seoul, Korea). The muscle mass (kg) of the participants was measured using a body composition analyzer (InBody Dial h20b, Seoul, Korea).
## 2.4.4. Exercise Immersion
Exercise immersion was measured by a sports flow scale, which was developed by modifying the expansion of the Sport Commitment Model scale, developed by Scanlan et al. [ 27]. The scale comprises 12 items in two domains, of cognitive immersion and behavioral immersion, which were scored on a 5-point Likert scale, ranging from 1 point for “strongly disagree” to 5 points for “strongly agree.” The total score could be as high as 60 points; a higher score indicates a higher level of exercise immersion. The reliability of the scale is Cronbach’s alpha 0.86–0.94, while the reliability in this study was 0.90.
## 2.5. Data Analysis
The collected data were analyzed using the IBM SPSS software, version 26.0 (IBM Corp., Armonk, NY, USA). *The* general characteristics of the participants were analyzed by frequency, percentage, and average; the homogeneities of the general characteristics and dependent variables were analyzed by ANOVA and χ2-test. To verify the post-effects, ANOVA and RM ANOVA were used. The post hoc analysis was analyzed by Scheffé and least significant difference (LSD) tests. RM ANOVA was performed to test the difference in the effect according to the time change. When the sphericity test result did not satisfy the sphericity, Wilks’ lambda multivariate test was performed for analysis. Partial eta-squared (η2) between the groups and time was analyzed, to explain the degree of influence between the three groups.
## 2.6. Ethical Considerations
Before conducting this study, the research plan was approved by the Institutional Review Board of the University of Eulji (EU21-002). The research was conducted after registration with the Clinical Research Information Service (CRIS) (KCT0006654). The purpose of the study was fully explained to the participants selected for the experiment, before obtaining written consent for their voluntary participation. The possibility of participation and withdrawal from the experiment, premature abandonment, adverse effects, and treatment for such adverse effects, were described and explained in the informed consent form. It was explained to the participants that the collected data would be ID-coded according to the personal information guidelines, and utilized for approximately a year; thereafter, the data would be wiped out by shredding and permanent deletion from the database, after being stored for years. For the VRT group that participated in the study, a gift, exercise equipment, an M2Me IoT sensor, and an HMD were provided in return for participation in the exercise study. The IBE group was provided with gifts and exercise equipment, and the control group was provided with gifts and exercise equipment after completion of the data collection.
## 3.1. Homogeneity Test for the Participants’ General Characteristics and Previous Dependent Variables
A total of 41 participants were included in the study. The results of one-way ANOVA of the three groups, to verify the previous homogeneity of the general characteristics and dependent variables, are discussed in Table 1. The mean age was 52.93, 49.15, and 53.14 years in the VRT, IBE, and control groups, respectively, indicating no significant difference among the three groups. No significant difference was observed between the three groups based on the length of illness, sex, education level, smoking, and diabetes treatment before the experiment, confirming the homogeneity.
Based on the results of the one-way ANOVA for previous homogeneity of the dependent variables, the homogeneity of the three groups was confirmed, since no significant difference was observed in the MBG measured by CGM, serum fructosamine, BMI, muscle mass, and exercise immersion.
## 3.2. Effects of VREP on the Blood Glucose, Body Composition, and Exercise Immersion
At pre-test (W2), the MBG demonstrated no significant difference. At post-test (W4), MBG was 122.86 mg/dL, 123.54 mg/dL, and 132.43 mg/dL in the VRT, IBE, and control groups, respectively, indicating no significant difference. Upon analyzing this result using RM ANOVA, a significant difference was observed in the interaction between the group and measurement time ($F = 12.001$, $p \leq 0.001$), as shown in Table 2, and the partial η2, which was the effect of the VREP according to the group and time, was 0.387 (Figure 3a).
At baseline (W0) and pre-test (W2), serum fructosamine levels demonstrated no significant difference. At post-test (W4), the serum fructosamine level was 298.79 µmol/L, 307.69 µmol/L, and 311.43 µmol/L in the VRT, IBE, and control groups, respectively, indicating no significant difference. Upon analyzing this result using RM ANOVA, a significant difference was observed in the interaction between the group and measurement time ($F = 3.274$, $$p \leq 0.016$$), as shown in Table 2, and partial η2, which was the effect of the VREP according to the group and time, was 0.147 (Figure 3b).
At baseline (W0) and pre-test (W2), the BMI demonstrated no significant difference. At post-test (W4), the BMI was 24.94 kg/m2, 25.00 kg/m2, and 24.85 kg/m2 in the VRT, IBE, and control groups, respectively, indicating no significant difference (Figure 3c).
At baseline (W0) and pre-test (W2), the muscle mass demonstrated no significant difference. At post-test (W4), the muscle mass was 26.48 kg, 28.31 kg, and 24.87 kg in the VRT, IBE, and control groups, respectively, indicating no significant difference. Upon analyzing this result using RM ANOVA, a significant difference was observed in the interaction between the group and measurement time ($F = 4.445$, $$p \leq 0.003$$), as shown in Table 2, and the partial η², which was the effect of the VREP according to the group and time, was 0.190.
At baseline (W0) and pre-test (W2), the total exercise immersion score demonstrated no significant difference. At post-test (W4), the total exercise immersion score was 35.07, 30.00, and 26.14 points in the VRT, IBE, and control groups, respectively, indicating a significant difference among the three groups. Upon analyzing this result using RM ANOVA, a significant difference was observed in the interaction between the group and measurement time ($F = 4.418$, $$p \leq 0.004$$), as shown in Table 2, and the partial η2, which was the effect of the VREP according to the group and time, was 0.183 (Figure 3d).
## 4. Discussion
This study evaluated the after effects of implementing a 2-week VREP, on the blood glucose, body composition, and exercise immersion of the participants. Considering the characteristics of patients with diabetes, and the COVID-19 situation, an indoor bicycle incorporating the benefits of a combination of both aerobic and resistance exercises was selected for the exercise intervention method. Additionally, VR was used to increase interest and immersion in the exercise.
Following the 2-week long experimental intervention, CGM-measured MBG decreased by 15 mg/dL, 9.08 mg/dL, and 0.93 mg/dL in the VRT, IBE, and control groups, respectively. Serum fructosamine decreased by 14.71 µmol/L, 3.31 µmol/L, and 0.07 µmol/L in the VRT, IBE, and control groups, respectively, indicating a significant decrease in the CGM-MBG and serum fructosamine in the VRT group compared with those of the control group. Thus, the level of blood glucose could be decreased by exercising for only 2 weeks.
Compared to several previous studies evaluating the effect of various types of exercises, such as treadmill and stationary bicycle [28], walking [29], and compound exercises [30,31], as well as a study applying an 8-week long exercise program in patients with type 2 diabetes [32], VREP for 2 weeks appeared to be effective, since the CGM-MBG and serum fructosamine decreased by 15 mg/dL and 14.71 µmol/L, respectively.
*In* general, exercise therapy should be performed continuously, and most of the exercise studies in patients with diabetes have been conducted for more than 6 weeks. However, considering the significant decrease in the CGM-measured MBG and serum fructosamine, compound exercise, including aerobic and resistance exercise, for 2 weeks was effective in controlling the blood glucose levels in patients with type 2 diabetes. This study found that exercise, even for a short period of 2 weeks, had a positive effect on blood glucose control in patients with type 2 diabetes, which may motivate patients to start exercising, thereby serving as an attractive point for emphasizing the importance of exercise.
In the post hoc group analysis, a significant difference was observed in the CGM-MBG in the VRT and IBE groups compared to that of the control group, and in the serum fructosamine between the VRT and control groups. Since CGM-measured MBG is the mean of continuous blood glucose levels, and serum fructosamine is a value that reflects the blood glucose level for 2–3 weeks, VREP was more effective in reducing blood glucose than IBE. Therefore, it is necessary to compare the measurements before and after the experimental intervention for 3 weeks in a future study, to confirm the results of serum fructosamine.
Considering the effect of VREP on body composition, no significant difference was observed in BMI among the three groups, whereas muscle mass increased by 0.31 kg in the VRT group, 0.26 kg in the IBE group, and decreased by 0.22 kg in the control group. Since in previous studies, 8 weeks of aerobic exercise [33], 12 weeks of walking exercise [34], and 12 weeks of compound exercise [35] decreased the BMI by 1.53 kg/m2, 1.75 kg/m2, and 1.55 kg/m2, respectively, the 2-week intervention period of our study seems insufficient to induce a change in the BMI. A longer period of exercise is required for weight loss and BMI reduction.
Compared to the results of previous studies, conducting resistance exercise for 12 weeks [36], and compound exercise for 12 weeks [37], that reported an increase in muscle mass of 3.4 kg and 0.85 kg, respectively, the increase in muscle mass by 0.31 kg in the VRT group suggests that even 2 weeks of exercise could increase muscle mass. A previous study, using a Theraband resistance band [38], reported that the thickness of both the shoulder muscles started to increase after 2 weeks of exercise intervention; thus, muscle growth could occur just by exercising for 2 weeks. *In* general, an increase in muscle mass is crucial for healthy body composition; moreover, these results may motivate patients with type 2 diabetes to start exercising.
In the post hoc group analysis, a significant difference in muscle mass in the VRT and IBE group was observed, compared to that of the control group. It is necessary to reconfirm the experimental intervention in this study, by varying the experimental period, owing to the time-dependent effect.
In this study, owing to the 2-week experimental intervention, exercise immersion in the VRT group was significantly higher than that in the IBE and control groups. This study was conducted among patients with type 2 diabetes, and the symptoms of the participants were checked periodically before and after the experiment, in consideration of the possibility of cybersickness due to the VREP, using an HMD. The average age of the participants in our study was approximately 51.8 years. During the course of this study, a 54-year-old patient in the VRT group dropped out, owing to cyber-sickness, while the remaining participants in the VRT group completed the 2-week experimental intervention. Exercise immersion was much higher in the VRT group than in the IBE group. Therefore, VREP was effective in increasing exercise immersion.
To date, no studies applying an immersive VREP in patients with diabetes exist. However, VREP implemented in other diseases and age groups was effective in improving physical function and muscle strength, by allowing participants to immerse themselves in the fun of exercise [16,17,18,19,20,39].
The use of VR in certain situations reportedly improves calorie consumption and exercise speed, compared with exercise in daily life [40]. Based on the results of such studies, VR increased exercise immersion and improved exercise effectiveness. Further, VR exercise may have a more positive effect on the patients’ health than IBE alone, which would thereby aid in blood glucose control and health management.
Two weeks after the exercise intervention, the exercise immersion score increased by 7.57 points in the VRT group and 2.46 points in the IBE group, and decreased by 0.29 points in the control group. Based on the post hoc group analysis, a significant difference in exercise immersion was observed between the VRT and IBE group, since the VR exercise intervention was very effective in exercise immersion, and a new exercise program with VR stimulated the interest of the participants and induced motivation for exercise. These results suggest that VR, which provides experiences that cannot be experienced in reality, increases engagement and immersion through interaction with participants, thereby improving satisfaction and giving a sense of achievement [41]. It can contribute to increasing the exercise practice rate, by allowing users to continue the exercise by being immersed in it [42]. In a previous study, when VR was applied to a cycle exercise game, the VR group moved longer distances, with improved immersion, and demonstrated increased physical exercise ability [43]. A VR program using sensors designed to capture bodily movements, could reportedly induce users to actively participate in the experience, by allowing them to easily immerse themselves in the VR world.
The limitations of this study include the small number of participants, short period of application, inability to completely restrict dietary and physical activities, and the fact that continuous monitoring of blood glucose by the CGM device may have psychological effects on blood sugar control and exercise. In addition, one of the disadvantages of immersive VREP is that it can cause cybersickness, and in this study, this resulted in participants dropping out in the middle of the study. Therefore, this was a preliminary study, conducted over a short period of time. Based on the results of this study, it is necessary to conduct another study, that considers the number of subjects, diet, and intervention period, in order to ensure generalizability in the future.
## 5. Conclusions
A 2-week VREP application in patients with diabetes decreased their MBG, increased their muscle mass, and increased exercise immersion. Since the VREP is effective in increasing exercise immersion of the participants, and making them exercise, and even a short period of exercise is effective in reducing blood sugar, it could be a very effective exercise program for patients with diabetes, which could further motivate the participants.
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---
title: Knowledge, Awareness, and Attitude of Healthcare Stakeholders on Alzheimer’s
Disease and Dementia in Qatar
authors:
- Pradipta Paul
- Ziyad Riyad Mahfoud
- Rayaz A. Malik
- Ridhima Kaul
- Phyllis Muffuh Navti
- Deema Al-Sheikhly
- Ali Chaari
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002196
doi: 10.3390/ijerph20054535
license: CC BY 4.0
---
# Knowledge, Awareness, and Attitude of Healthcare Stakeholders on Alzheimer’s Disease and Dementia in Qatar
## Abstract
Dementia is characterized by progressive cognitive decline, memory impairment, and disability. Alzheimer’s disease (AD) accounts for 60–$70\%$ of cases, followed by vascular and mixed dementia. Qatar and the Middle East are at increased risk owing to aging populations and high prevalence of vascular risk factors. Appropriate levels of knowledge, attitudes, and awareness amongst health care professionals (HCPs) are the need of the hour, but literature indicates that these proficiencies may be inadequate, outdated, or markedly heterogenous. In addition to a review of published quantitative surveys investigating similar questions in the Middle East, a pilot cross-sectional online needs-assessment survey was undertaken to gauge these parameters of dementia and AD among healthcare stakeholders in Qatar between 19 April and 16 May 2022. Overall, 229 responses were recorded between physicians ($21\%$), nurses ($21\%$), and medical students ($25\%$), with two-thirds from Qatar. Over half the respondents reported that >$10\%$ of their patients were elderly (>60 years). Over $25\%$ reported having contact with >50 patients with dementia or neurodegenerative disease annually. Over $70\%$ had not undertake related education/training in the last 2 years. The knowledge of HCPs regarding dementia and AD was moderate (mean score of 5.3 ± 1.5 out of 7) and their awareness of recent advances in basic disease pathophysiology was lacking. Differences existed across professions and location of respondents. Our findings lay the groundwork for a call-to-action for healthcare institutions to improve dementia care within Qatar and the Middle East region.
## 1. Introduction
Dementia is characterized by acquired, progressive impairment in multiple cognitive domains, including memory, that interfere with independence in social or occupational function [1,2]. It leads to disability and dependability in the elderly and is a paramount source of distress among affected individuals, their families, and caregivers, [3] making it a major public health priority [4]. Worldwide, at least 57.4 million people live with dementia and 1.6 million succumb to it annually [5,6]. While the proportion of the global population aged ≥ 65 years will approximately double between 2019 and 2050, the prevalence of dementia and Alzheimer’s disease (AD), accounting for 60–$70\%$ of dementias, are projected to reach 152.8 million and 106.4 million, respectively [5,7,8]. However, there is marked geographical heterogeneity in these projections of dementia prevalence. While an increase of $53\%$ is expected in the high-income Asia Pacific countries, this rise is expected to be $367\%$ in the Middle East and North Africa (MENA) region, with marked increases in Saudi Arabia ($898\%$), Oman ($943\%$), Bahrain ($1084\%$), and the United Arab Emirates ($1795\%$). Qatar, with a population close to 3 million, seems to be especially at risk; from only 4201 cases of dementia in 2019, the prevalence of dementia is estimated to increase by $1926\%$ to 85, 046 by 2050, largely fueled by aging of the population—the single biggest risk factor for AD [5,9]. Additionally, Qatar also has a high incidence of cardiometabolic risk factors, such as hyperglycemia, smoking, hypertension, and obesity to name a few, all of which are known to increase risk of dementia [10,11,12,13]. Table 1 provides further context regarding dementia care in Qatar [4,7,14,15,16,17,18,19,20,21,22,23].
The increasing prevalence of dementia requires a simultaneous increase in the number of health care professionals (HCPs) providing competent care for this population. Adequate knowledge and competence of HCPs affects timing of diagnosis, implementation of intervention, and quality of care, which impact overall patient outcomes [24,25,26,27,28]. Increased knowledge among HCPs reduces stigma, increases patient quality of life, and reduces caregiver burden, and the converse is also true [29,30,31]. Studies have linked confidence and quality of dementia management ability to not only previous formal dementia education, but also current knowledge, awareness, and attitude of HCPs [32,33,34]. Formally, measuring these factors by surveying this population helps identify gaps in current healthcare delivery and promotes effective resource allocation for professional development via continuing education programs that refresh old knowledge and introduce new advances in the field [35]. To our knowledge, details of the knowledge, attitude, and awareness of HCPs on dementia and AD in the region are not present in literature. This survey has addressed:The gaps in current knowledge on AD and dementia, among major health care stakeholders (physicians, nurses, dentists, pharmacists, allied health professionals, medical students, educators, and researchers) in the region. Their attitude towards addressing knowledge gaps via continuing medical education (CME) webinars.
## 2. Materials and Methods
The current pilot cross-sectional survey targeted various major healthcare stakeholders, including physicians, nurses, dentists, pharmacists, allied health professionals (AHPs), medical students, educators, and researchers, among others. It aimed to assess the knowledge, awareness, and attitude of such individuals towards AD and dementia to determine the needs of a CME webinar series addressing needs-based regional challenges.
## 2.1. Measures
A short, online, self-contained survey in English was designed by the authors in collaboration with local and international researchers in the field of neurodegenerative diseases, locally based physicians who routinely diagnose and manage such patients, and institution-affiliated medical education experts who regularly design and deliver professional development programs and lectures for healthcare professionals in the region. We also adapted components from the published and previously tested Alzheimer’s Disease Knowledge Scale (ADKS) [36], the Alzheimer’s Disease Awareness Scale (ADAS) [37], and the Dementia Attitudes Scale (DAS) [38], in addition to incorporating questions to specifically assess the needs of healthcare practitioners in the region.
The structured questionnaire (Supplementary Material) contained questions to record sociodemographic data including participant’s age, occupation, and their primary country of practice/study/work, questions to assess their experience in caring for elderly and people with AD, and whether they had any recent participation in AD or dementia-related educational training, including webinars, training courses, or grand rounds. Occupation was recorded as follows: physician, nurse, dentist, pharmacist, social worker, AHPs, student, administrator, researcher, educator, insurance representative, and others. To assess current experience and exposure to treating patients with dementia, we inquired about the proportion of the respondents’ current patients who were over 60 years of age, and how many patients with dementia were being seen in a healthcare setting annually.
Additionally, the main questionnaire consisted of seven questions to assess knowledge, nine questions to assess awareness/attitude, and five miscellaneous questions to assess awareness of the pathophysiology and scope of new treatments for dementia. In the 21 questions assessing the main outcome of knowledge, attitude, or awareness, participants selected answers based on a five-point Likert scale ranging from ”strongly disagree” to ”strongly agree” or from “not at all aware” to “extremely aware”. An overall index of knowledge was calculated by summing up the correct answers.
## 2.2. Process
The survey was circulated for four weeks between 19 April and 16 May 2022 via e-mail to over 9742 healthcare practitioners, academics, researchers, and other professionals that were subscribed to the mailing list of the Weill Cornell Medicine-Qatar continuing professional development (CPD) division, which routinely delivers continuing education opportunities to HCPs in the region. It should be noted that professionals based both within and outside Qatar were recipients of this mail and thus survey respondents (Supplementary Material). The survey was hosted on Qualtrics XM software and mass e-mails containing a link to the survey were circulated; follow-up organic advertisement was done via social media platforms and private messaging. Random, lottery-based financial incentives via gift vouchers were promised to five random respondents to increase response rate. Ethical approval for the study was granted by the Weill Cornell Medicine-Qatar Institutional Review Board (IRB#: 22-00013) as a low-risk study. All individual level collected data was confidential and shared only amongst the authors after removal of personal identifiers. We also reviewed the literature for similar quantitative surveys globally and in the MENA region.
## 2.3. Data Analysis
Survey responses were downloaded onto Microsoft Excel sheets and analyzed using IBM SPSS software. Age- and practice-related variables were summarized using frequency distribution. For each of the seven knowledge questions, the percentage of participants who correctly answered “strongly agree” or “moderately agree” was computed for each of the five professional groups. Also, a total of correct answers was computed as a score out of seven with one for correct answer and zero for an incorrect answer. The score was summarized using mean ± standard deviation, along with the median, the interquartile range, and the minimum and maximum values attained by the participants. For each of the “attitudes” questions, participant attitudes were considered positive by combing those who answered “disagree” or “strongly disagree” for some questions and “agree” and “strongly agree” for other questions depending on the wording of the questions. The percentage of participants with positive attitude was computed for each question and stratified by the five profession groups. As for awareness questions, participants were considered aware based on answering “moderately” or “extremely” aware for that question. The proportion of awareness for each question was computed and stratified by the profession groups. No statistical analysis was done to compare the five groups as this is a pilot study, and with such numbers, it is not powered for such comparisons.
## 2.4. Review Methodology
In order to provide context and compare the results of the current study, we finely reviewed existing literature for similar quantitative surveys across the MENA region, with further representation from similar studies outside this region for comparison. We searched PubMed and Google Scholar for terms such as “Middle East”, “dementia”, “Alzheimer’s”, “knowledge”, “attitude”, and “awareness” up to December 2022. We particularly reviewed and summarized studies investigating the knowledge/attitude/awareness/practice of the general population and healthcare professionals towards AD and dementia, whereas we integrated similar studies outside this region into the discussion.
## 3.1. Demographics and Characteristics of Participants
Of 9742 email recipients, 3206 opened the email, 794 clicked on the relevant survey link, and ultimately a total of 229 complete responses (overall response rate of $2.35\%$) to the questionnaires was recorded. Table 2 summarizes the participants’ sociodemographic data and relevant experience in the field. Most respondents primarily identified as either physicians ($20.5\%$), nurses ($21.0\%$), students ($24.5\%$), and educators/researchers ($15.3\%$), while the rest ($18.8\%$) reported being dentists, pharmacists, or allied healthcare professionals, among others. Almost half ($47.6\%$) the respondents were between 31–50 years of age, largely stemming from the large nurse and educator/researcher population ($79.5\%$ and $60.5\%$, respectively) of the same age group. Most physicians ($61.7\%$) tended to be older (41–60 years), whereas all students were between 17–30 years of age. Overall, 154 ($67.2\%$) respondents reported having their primary place of practice/study in Qatar, with $72.9\%$ of nurses and $89.3\%$ of students reporting the same. Regarding their patient characteristics, 102 ($55.6\%$) participants reported that more than $10\%$ of their current patients were elderly (>60 years of age), among whom, 47 ($20.5\%$) reported this proportion to be greater than $50\%$. Over a quarter ($26.7\%$) of participants reported seeing patients with neurodegenerative diseases (≥50 patients annually) in their practice or study. Importantly, however, less than one in three ($29.7\%$) respondents reported having any training in dementia/neurodegenerative disease in the last 2 years.
## 3.2. Knowledge Regarding Alzheimer’s Disease and Dementia
Overall, the respondents demonstrated moderate knowledge regarding Alzheimer’s disease and its manifestations, with the mean (SD) score being 5.3 ± 1.5 out of 7 (median [IQR]: 5.0 [5.0–6.0]; range 0.0–7.0) (Table 3). This average was 5.7 ± 1.3 among physicians, 5.4 ± 1.4 among students, 5.2 ± 1.4 among educators/researchers, 5.0 ± 1.5 among nurses, and 5.0 ± 1.8 among other professions. Most ($72.1\%$) correctly identified that loss of memory and inability to perform daily tasks by the elderly require a medical consultation, whereas only $42.8\%$ identified that changes in executive functioning and balancing finances were not physiologically expected in the elderly. Most ($79.0\%$) did not believe AD to be a result of psychological distress or physical injury, and $88.6\%$ agreed that early AD detection could result in a better response to treatment. Only $68.1\%$ of respondents believed that artificial intelligence may be of benefit in neurodegenerative diseases, whilst most believed that lifestyle shapes the brain ($86.5\%$) and agreed that physical activity, sleep, and cognitive function were related in the elderly ($90.8\%$).
## 3.3. Attitude Regarding Alzheimer’s Disease and Dementia
The awareness/attitude of respondents towards Alzheimer’s disease and its manifestations are displayed in Table 4. Overall, $68.6\%$ of participants disagreed with hiding a diagnosis from relatives with AD and $80.8\%$ did not believe it was best for AD patients to avoid social interactions to avoid embarrassment to themselves; these figures did not vary significantly between responses of those from different professions. On the other hand, only $57.6\%$ of respondents disagreed with turning to alternative medicine when a relative developed signs/symptoms of dementia, with physicians ($72.3\%$), educators/researchers ($62.8\%$), and students ($60.7\%$) being more disapproving compared to nurses ($45.8\%$) and other professions ($46.5\%$). Most believed that when a patient with AD develops difficulty performing everyday tasks, the judiciary should save the patient’s rights, with only $23.6\%$ disagreeing. A total of $77.3\%$ of respondents denied feeling embarrassed if a close relative was diagnosed with AD and an even greater proportion ($85.6\%$) reported that they would not deny a diagnosis of AD in a relative. A total of $63.3\%$ of respondents disagreed with having patients with AD being looked after in state nursing homes rather than at home. Regarding research and expertise of healthcare professions, $87.8\%$ believed in the potential of dementia research to improve the outlook for patients, families, and providers, and $86.9\%$ considered it important that HCPs should be aware of the most recent updates in the field of neurodegenerative disease and dementia.
## 3.4. Awareness of Pathophysiology and Understanding of New Advancements in Neurodegenerative Diseases
Responses to questions on pathophysiology and experimental technologies in dementia are displayed in Table 5. Less than half ($46.7\%$) reported being either moderately or extremely aware of the pathophysiological mechanisms behind the common neurodegenerative diseases, with the lowest awareness among nurses ($37.5\%$), other professions ($41.9\%$), and students ($42.9\%$), whereas physicians ($61.7\%$) and educators/researchers ($51.4\%$) displayed greater confidence. Less than half ($43.2\%$) of participants reported being either moderately or extremely aware of the significance of protein misfolding and amyloid formation, a key hallmark of various neurodegenerative diseases. The greatest awareness was among educators/researchers ($62.9\%$), whilst the least awareness was among nurses ($12.5\%$). Less than one third ($31.9\%$) were either moderately or extremely aware of the applications of artificial intelligence in daily life, whilst a similar proportion ($32.3\%$) reported being either moderately or extremely aware of its potential application in healthcare. Finally, less than one in five ($18.8\%$) had moderate or extreme awareness of the potential of corneal confocal microscopy in the diagnosis of peripheral neuropathies and central neurodegenerative diseases, although his proportion was greater among physicians ($27.7\%$) and educators/researchers ($31.4\%$).
## 3.5. Differences in Responses in Those in Qatar vs. Outside Qatar
Overall, 154 ($67.2\%$) respondents were based in Qatar, with $53.2\%$ of physicians, $72.9\%$ of nurses, $89.3\%$ of students, $42.9\%$ of researchers/educators, and $67.4\%$ of respondents from other professions reporting the same (Table 2). Interestingly, there were a few notable differences (Figure 1). Only $67.5\%$ of those based in Qatar (versus $81.3\%$ of those based outside Qatar) disagreed that symptoms of loss of memory were normal in the elderly and do not require medical attention. Moreover, only $37.7\%$ of Qatar responses (versus $53.3\%$ external) believed that deterioration in daily planning and financial independence is not expected naturally in the elderly. A lesser proportion of respondents in Qatar disagreed that AD may result from black magic or psychological distress ($74.7\%$), and indicated that they would resort to alternative forms of medicine ($51.9\%$), compared to those based outside Qatar ($88.0\%$ and $69.3\%$, respectively). Interestingly, $20.1\%$ of respondents in Qatar did not believe it is necessary for the judiciary to protect AD patients’ rights if they have difficulty performing daily tasks, whereas $30.7\%$ of those based outside Qatar thought so. A higher proportion of Qatar-based respondents ($80.5\%$ vs. $70.7\%$) believed that they would not feel embarrassed if a close relative was diagnosed with AD. Notably, fewer Qatar-based respondents disagreed with having AD patients being admitted to state nursing homes rather than remaining at home ($59.1\%$ vs. $72.0\%$). Other notable differences were in respect to disease pathophysiology and perception of the potential of AI and CCM in medicine. Generally, a lesser proportion of those based in Qatar were either moderately or extremely aware of the mechanisms behind neurodegenerative diseases ($42.9\%$ vs. $54.7\%$), the significance of protein misfolding and amyloid formation ($36.4\%$ vs. $57.3\%$), the current and future role of AI in daily life ($29.2\%$ vs. $37.3\%$) and medicine ($29.3\%$ vs. $37.3\%$), and of CCM in diagnosis of peripheral neuropathies and central neurodegenerative diseases ($14.9\%$ vs. $26.7\%$).
## 3.6. Literature Review of Similar Studies in the MENA Region
Dementia represents a significant challenge not only in Qatar, but also in the rest of the MENA region. Sociocultural and political factors may limit research and dissemination of knowledge. Promoting a knowledge-based culture by imbibing traditional perspectives with evidence-based models across the populous, in contrast to the currently prevalent stigma and lack of awareness, should be a strategy to optimize dementia care [39]. In Table 6 we present the results of a literature review summarizing important findings and conclusions of contemporary studies in the Middle East investigating the knowledge, attitude, or awareness of the general public ($$n = 11$$) and healthcare practitioners ($$n = 8$$) on dementia and Alzheimer’s disease through quantitative survey methods, with one prior study based in Qatar.
## 4. Discussion
As the prevalence of dementia and dementia-causing neurodegenerative disease increases with the continuously aging population, attention is shifting towards early diagnosis to optimize long-term patient outcomes. Challenges in detecting and managing mild cognitive impairment (MCI), often the first presentation of dementia, revolve around a triad of hesitant patients, unprepared providers, and misaligned environments [58]. In order to improve dementia care, addressing simultaneous challenges faced by all involved healthcare stakeholders is necessary and incomplete without the others. This study is the first of its kind in Qatar that has determinedthe the knowledge, awareness, and attitudes of current and future healthcare professionals towards dementia and its associated neurodegenerative diseases. In addition, determining the willingness of HCPs to engage, improve knowledge, and reduce stigma will in turn help us identify effective and efficient areas of intervention, for example, through CME/CPD programs, independent webinars, group discussions, and interactive sessions. This study will help inform future, large-scale studies directed not only at healthcare professionals, but also the general public, in order to address the increasing challenges in dementia care in the region.
## 4.1. Knowledge, Awareness, and Attitude of the General Population
As AD and dementia increasingly contribute to the global burden of disease, researchers have investigated whether public knowledge and beliefs have consistently been followed and kept up in order to promote help-seeking behavior and reduce associated stigma. In Australia, Smith et al. [ 59] showed that many did not hold beliefs or have knowledge that would otherwise reduce dementia risk; only $41.5\%$ of respondents of a large public quantitative survey believed that dementia risk could be reduced. Recent data from Macau in China revealed that although older adults had more dementia knowledge, they had less favorable attitudes when compared to the youth [60]. Wu et al. [ 60] apply the concept of construal level theory to conclude that bridging the existing psychological distance of dementia via intergenerational programs can increase awareness among younger adults. A large cross-sectional investigation of public attitude towards dementia in Bristol and South Gloucestershire in the UK revealed that individuals who were younger, identified themselves as White, and with personal experience of dementia (among close family/friends) had a more positive attitude than their counterparts [61]. A systematic review of 38 studies investigating public awareness about preventative dementia treatment revealed that nearly half considered dementia to be a normal and non-preventable part of aging, with the role of cardiovascular risk factors being poorly understood, although awareness improved over time [62].
Regions with high migrant populations may have more challenges posed by linguistic and cultural differences, translating to obstacles in identification, assessment, and diagnosis in the clinic, which may especially apply to Qatar. Sagbakken et al. [ 63] reveal that two major misconceptions from these patients and their families include considering some symptoms to be attributable to normal aging or something to be ashamed of. Monsees et al. [ 64] show that migrant patients’ willingness to use services increased after incorporating their culture into an aspect of care, which increased comfort, utilization, and satisfaction in this group. In a study from Copenhagen, involving native Danish, Polish, Turkish, and Pakistani immigrants, the latter two groups were more likely to hold normalizing and stigmatizing views of AD which were not significantly influenced by education or acculturation levels [65]. Thus, ethnic background may be strongly associated with wrong or misguided knowledge and perception of dementia and AD, leading to challenges in accessing healthcare services in such populations. Even within a region, knowledge and perceptions regarding dementia/AD may differ between different ethnic groups, which is especially relevant for Qatar with its exceptional diversity [65].
## 4.2. Knowledge, Awareness, and Attitude of Healthcare Professionals and the Effect of Dementia-Specific Updated Training Programs
Family physicians or general practitioners are often the first line of contact for most people with mild cognitive impairment (MCI), an early sign of AD, or other dementias [66]. However, general practitioners are able to identify less than half of all people with MCI and are very poor at recording this in medical notes [67]. Thus, it remains unclear whether general practice physicians and nursing staff are prepared to diagnose and manage patients with dementia, rather than refer to specialists. This may reflect that they are unprepared, unconfident, or reluctant to see such patients in their clinic [68]. In this study, HCPs showed moderate proficiency on seven measures of knowledge regarding AD and dementia, with a mean score of 5.3 ± 1.8 out of 7 (~$75\%$), with physicians displaying the highest proficiency, followed by students, educators, researchers, nurses, and other professions. Differences amongst stakeholders may be attributable to experience in practice and type of dementia-specific training received. Knowledge of the recent advances in basic pathophysiology were generally poor among all groups.
Next, given that there is a gap in knowledge and competence of certain HCPs towards dementia care, it is important to know of their attitude and willingness to improve. In a study of community health service centers in Beijing, China, Wang et al. [ 69] reported that general practitioners demonstrated limited levels of dementia knowledge and skills but expressed positive attitudes [69]. Primary care physicians from Quebec, Canada have displayed positive attitudes towards providing dementia care and expressed interest in more support and staff [68]. Dementia care is the responsibility of a multidisciplinary team of professionals. Bryans et al. [ 70] showed that whilst primary care nursing staff in central Scotland and London had a high level of knowledge on management strategies, they had lower proficiency on the epidemiology and diagnosis of dementia and hence lacked confidence in identifying dementia and managing coexisting behavioral and mental health challenges [70]. Vafeas et al. [ 33] quantitatively surveyed a group of 85 healthcare workers in Australia and found that although the majority have strongly positive views about people with dementia, a large number reported being afraid of such patients. Smyth et al. [ 29] in Queensland, Australia, showed that AD knowledge levels varied significantly between professional groups based on experience of caring for affected patients and having dementia-specific training [29].
These studies highlight the need for dementia-specific updated training programs for primary care practitioners to optimize care outcomes in aging populations. Liu at al. [ 71] report that dementia-trained physicians had significantly greater confidence and less negative views towards dementia care compared to non-trained physicians in Hong Kong, China. Hobday et al. [ 72] from Minneapolis, USA revealed that an online, 4-module dementia training program for nursing assistants and allied hospital workers significantly increased dementia care knowledge and was perceived to be useful, acceptable, feasible, and efficient. Galvin et al. [ 73] showed that an educational program in 540 nurses and other direct-care staff improved knowledge and confidence for recognizing, assessing, and managing dementia for at least four months post-training. Lintern [74] reiterates that nursing and care staff with more positive and “helpful” attitudes towards people with dementia are more likely to engage in social activities with patients and are more likely to use higher quality indicators during physical care tasks with improved staff attitudes and the quality of dementia care [74]. In the present study, only $40\%$ of physicians and $30\%$ of nurses reported having a dementia/neurodegenerative disease-specific training in the last 2 years, which provides an opportunity to introduce effective CME training programs.
## 4.3. Does Lifestyle Shape the Brain?
In our study, around 9 in 10 respondents believed that lifestyle shaped the brain and that there was an association between sleep, physical activity, and cognitive function. This proportion was slightly greater among physicians, possibly reflecting differences in practice experience, or participation in continuing education programs. This translates into providing better recommendations to patients in not only management but also prophylaxis, especially with respect to a disease without many disease-modifying treatments. This is important considering that AD patients and their caregivers consider physical activity to be meaningful and possible despite dementia [75]. Barnes et al. [ 76] estimate that almost half of all AD cases worldwide are attributable to potentially modifiable risk factors, including unhealthy lifestyle and physical inactivity, which has direct relevance to Qatar with its high comorbidity of chronic diseases. Awareness of such risk factors amongst healthcare professionals may allow for more prompt intervention with slower progression of dementia and its complications [77].
In the US, Europe, and the UK, physical inactivity is the highest population-attributable risk factor for AD, attributable for about $21\%$ of the risk and equating to 16.8 million cases. Various prospective studies have shown that even mild to moderate physical activity may reduce the risk of dementia and AD [78,79]. Erickson et al. [ 80] observed that exercise reduces hippocampal cortical decay in the elderly; active individuals had overall better health, larger hippocampi, and better spatial memory. Recent data suggest that higher levels of physical activity in cognitively normal elderly are associated with lower plasma levels of AD-involved biomarkers such as plasma Aβ1−40, Aβ1−42, and APP669−711 in APOE ε4 noncarriers [81]. However, most large-scale trials and prospective studies examining the effects of exercise as a management option for AD are plagued by methodological inconsistencies and bias [82]. Sleep disturbance is associated with an increased risk of cognitive impairment and development of AD pathology. Whilst AD itself may lead to sleep disturbances, modifying the sleep-awake activity has been shown to induce changes in the soluble cerebrospinal fluid Aβ and tau concentrations, suggesting a bi-directional relationship [83]. Physical activity could also moderate the association between sleep and cognitive function and sleep and Aβ, sleep duration and episodic memory, sleep efficiency and episodic memory, sleep duration and Aβ, and sleep quality and Aβ [84].
## 4.4. Recent Advances in Diagnostics and Treatment of Dementia and Alzheimer’s Disease
Approximately $87.8\%$ of all respondents were optimistic that dementia research will improve the outlook for dementia patients, their caregivers, and families. Research on dementia is influenced by the specific regional burden and shaped by the significant variation in population demographics and size, poverty, conflict, culture, land area, and genetics [85]. Innovation in diagnostics and treatment is required to reduce the burden of disease for future healthcare systems. In contrast to clinical diagnoses that are complex and vulnerable to potential errors, detection of dementia using objective biomarkers of bodily processes are currently receiving increased attention and funding [14,86]. Qatar Foundation, through its research funding wing Qatar National Research Fund (QNRF), has funded various biomarker-focused research projects on dementia. Corneal Confocal Microscopy (CCM) is a rapid ophthalmic imaging technique first developed to detect neurodegeneration in diabetic neuropathy [87]. It has recently shown great promise to detect early neurodegeneration in MCI and dementia, even more reliably than magnetic resonance imaging (MRI) [14,88,89] and has been proposed as a tool to longitudinally measure and objectively assess the effectiveness of novel drug treatments in clinical trials [90]. CCM has been shown to have superior diagnostic capability for MCI compared to brain volumetry [91], and can predict progression from MCI to dementia, comparable to hippocampal and whole brain volumetry [92]. In light of these recent promising findings from Qatar, we sought to investigate whether HCPs in the region were aware of this upcoming modality. Only one in five ($18.8\%$) respondents reported at least moderate awareness of CCM and its potential in diagnostics compared to traditional methods. The greatest awareness was among educators and researchers ($31.4\%$), with lowest awareness among nurses ($10.4\%$).
A total of $68.1\%$ of respondents in our study believed that artificial intelligence (AI) has potential to be utilized for the care of patients with neurodegenerative diseases and related disorders such as dementia. However, only $31.9\%$ were at least moderately aware of the applications of AI in daily life and $32.3\%$ were moderately aware about its application in early disease diagnosis. The greatest awareness of AI was among physicians and students, followed by educators and researchers (Table 5). AI in conjunction with CCM has shown considerable promise in diabetic neuropathy [93,94]. Recent advancements have shown the promise of computer-aided diagnostic tools for AD diagnosis; the analysis of large demographic datasets allows for stratification of risk factors and improvement of personalized therapies [95]. In a recent landmark paper published in The Lancet Digital Health, researchers have shown the potential of a deep learning model to effectively detect AD-dementia with accuracies ranging from 79.6–$92.1\%$ based on retinal photographs, a prospect similar to CCM [96].
A total of $86.9\%$ of respondents in our study believed that HCPs from most if not all fields should be aware of latest advancements in dementia research, signaling openness towards learning. Despite a high number of ongoing trials for disease-modifying therapy, between 1988 and 2017, no less than 146 drugs failed in dementia clinical trials [14,97,98]. Therapeutic agents were said to not be able to alter AD-associated dementia disease course despite a temporary improvement in symptoms. However, the phase 3 Clarity AD trial earlier this year showed that lecanemab, a humanized IgG1 monoclonal antibody that binds with high affinity to Aβ soluble protofibrils, reduced amyloid in early AD and slowed down cognitive and functional decline, albeit with greater adverse events compared to placebo [99]. This trial represents the first among a long pipeline of disease- modifying therapies in patients with AD and dementia.
## 4.5. Implications of Present Findings for Dementia in Qatar and the MENA Region
In our secondary comparison of survey responses stratified according to primary location of practice/work/study, we observed that, compared to those based outside Qatar, respondents from within Qatar reported notable beliefs regarding dementia. A higher proportion of Qatar-based participants believed that loss of memory and loss of financial and daily independence were expected in the elderly and did not require medical attention. This is of concern in relation to recognition of early dementia. A lower proportion of Qatar-based respondents also denied that AD may be a result of black magic or psychological distress and that they would not resort to alternative medicine, highlighting lingering superstitious beliefs still present in the region. Qatar-based respondents also reported lower knowledge and awareness of the recent advances in the pathophysiology of dementia, highlighting a perhaps outdated knowledge base. This is of concern in the face of an explosive increase in the population of patients with MCI or dementia in the country.
To provide context to the current findings, we reviewed the contemporary literature on studies in dementia care in Qatar. A 2011 prospective study of 1660 adult Qatari attending primary health care clinics revealed that $1.1\%$ had dementia, but strikingly $52.6\%$ of those aged 50–65 had dementia [100]. Thus, primary care physicians should be expected to detect dementia in every other patient over the age of 50 who walks through the door, which requires appropriate knowledge and attitude [100]. Hamad et al. [ 101] have shown that the major primary causes of dementia in Qatar were AD ($29\%$), vascular dementia ($22\%$), mixed dementia ($15\%$), and Parkinson’s disease ($6\%$). This is important considering that subtype dictates treatment. Another important takeaway from this study was that the primary reason for late help-seeking behavior among families was due to the misconception that forgetfulness and other associated symptoms of cognitive impairment were considered to be part of the physiological aging process [101]. These findings were reiterated in a 2016 study of the first 100 patients referred to a memory clinic in Qatar, where researchers showed that $36\%$, $25\%$, and $23\%$ of referrals were diagnoses of vascular, Alzheimer’s, and mixed dementias, respectively [102]. Furthermore, highly prevalent comorbidities in this cohort were hypertension, diabetes, dyslipidemia, history of stroke, vitamin D deficiency, and anemia. Additionally, compared to non-Qataris, Qataris presented with more severe behavioral and psychological symptoms of dementia [102]. A 2008 retrospective analysis of 50 dementia patients from a cohort of 350 home care patients in Qatar showed that AD and vascular dementia (secondary to stroke) were both equally the most likely pathologies leading to dementia and indeed atherosclerosis and hypertension were significant comorbidities [103]. A 2014 retrospective analysis of 889 elderly patients from major geriatrics facilities in the country showed that more than one in four had dementia, with $72\%$ having some form of vitamin D deficiency, along with other comorbidities [104]. A 2009 study investigating the nutritional status of 130 long- term care Qatari patients found that $20.8\%$ had dementia and more than one in five had lost more than $10\%$ of their admission weight six months into long term care, with around $40\%$ being under the fifth percentile of body mass index, highlighting the absence of appropriate nutritional assessment and nutrition care [105]. In a 2015 study, the rate of potentially inappropriate prescriptions was high among home care elderly patients and this risk was twice as likely in patients with dementia [106]. In a study of 24 COVID-19 positive cases in a long-term care facility in Qatar, $57\%$ of elderly patients had dementia and three patients who died had dementia and diabetes [107]. This is despite the excellent national response of Qatar to geriatric mental health during COVID-19, compared to other Arab countries in the MENA region [108].
Kane et al. [ 109] have recently systematically reviewed major themes surrounding studies reporting on the “invisible” caregivers, namely family members, domestic workers, and private nurses, involved in dementia care. Older persons in Qatari society tend to be cared for by an intergenerational extended family with support from private nurses and domestic helpers. The influential role of religious beliefs [110], family connections, and social cohesion cannot be underestimated with mental health care leading the way for alternate healing practices. A widely documented practice is that of the services of a traditional or religious leader reciting religious texts on the patient’s body to dispel the “evil eye” to relieve symptoms of dementia [111]. Research on informal caregivers with a focus on the specific environment surrounding dementia care within the Arab-Islamic sociocultural context is rare. However, one such study reveals that the care-giving experience intersects with various influences through numerable themes, among which social stigma, personal knowledge of ADRD, and socio-religious attitudes towards caregiving of older persons recurrently influence dementia care [112].
A 2020 collaborative effort of various experts in Qatar summarizes the challenges faced by an aging population with respect to effective mental health as: (a) lack of integration among providers; (b) absence of coordinated data management mechanisms and need for more evidence-based research; (c) lack of specialized human resources in geriatric psychiatry and social care; (d) context-specific sociocultural factors that inhibit help-seeking [113].
## 5. Limitations of the Study
The results of the study cannot be interpreted without understanding the potential limitations with it. One limitation is the low response rate which might not render the results of the study generalizable to all the population of potential participants that it was intended to. However, as a pilot study, its results can help shed light on the topic and results can help in the design of future studies. A second limitation is the small sample size that does not help detect statistically significant difference among groups where observed differences are sizable. A third limitation is that answers are self-reported and might not reflect proper knowledge of some topics. In future studies we recommend using case studies in order to ensure that knowledge might be correctly reflected. Fourth, although the questionnaire utilized in the survey was constructed using components of previously published and tested surveys, as well as collaboration with experts in the field, its reliability as a whole has not been tested, nor was it circulated in the Arabic language, which may have introduced unintended bias or contributed to the low response rate. A fifth limitation is that we utilized a CME distribution list which contained HCPs based both within and outside Qatar, and although this provided opportunity for a secondary analysis based on location, we are unaware of what fraction of Qatar-based HCPs were invited for the survey. These limitations fall under the umbrella of lessons learnt from a pilot study, and future projects are likely to improve on these drawbacks.
## 6. Conclusions
To the best of our knowledge, this is the first study investigating the knowledge, attitude, and awareness of various healthcare stakeholders, ranging from physicians, nurses, medical students, educators, researchers, and allied health professionals in Qatar. The results of this pilot study will be used for a community needs-based assessment of local healthcare professionals to estimate the feasibility, demand, and need for a continuing education program on neurodegenerative diseases, with a focus on dementia and AD. We show that the overall knowledge of HCPs on dementia and AD is moderate, and whilst their attitude is largely positive, their awareness of basic disease pathophysiology and recent research advances is lacking. Notable differences in relation to specific professions both within and outside Qatar merit further study. Measures to improve the care of patients with dementia should include improving knowledge, attitudes, and awareness, while reducing stigma and caregiver burden, by providing specialized education and training. This pilot program will be followed up with a large-scale cross-sectional analysis of both healthcare practitioners and the general public to establish more accurate data in specific groups of practitioners as well as to determine the focus of public awareness campaigns to improve awareness of risk factors, reduce stigma, and improve help-seeking behavior to improve the management of patients with dementia.
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---
title: Adherence to Mediterranean Diet in Individuals on Renal Replacement Therapy
authors:
- Elisabetta Falbo
- Gabriele Porchetti
- Caterina Conte
- Maria Grazia Tarsitano
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002197
doi: 10.3390/ijerph20054040
license: CC BY 4.0
---
# Adherence to Mediterranean Diet in Individuals on Renal Replacement Therapy
## Abstract
Patients on renal replacement therapy are typically subject to several dietary restrictions; however, this approach has been questioned in recent years, with some suggesting that the Mediterranean diet might be beneficial. Data on the adherence to this diet and factors that influence it are scarce. We conducted a web survey among individuals on renal replacement therapy (dialysis or kidney transplant, KT) using the MEDI-LITE questionnaire to assess adherence to the Mediterranean diet and dietary habits in this population. Adherence to the Mediterranean diet was generally low, and significantly lower among participants on dialysis versus KT recipients ($19.4\%$ vs. $44.7\%$, $p \leq 0.001$). Being on dialysis, adopting fluid restrictions, and having a basic level of education were predictors of low adherence to the Mediterranean diet. Consumption of foods typically included in the Mediterranean diet, including fruit, legumes, fish, and vegetables, was generally low, particularly among those on dialysis. There is a need for strategies to improve both the adherence to and the quality of the diet among individuals on renal replacement therapy. This should be a shared responsibility between registered dietitians, physicians, and the patient.
## 1. Introduction
Patients on renal replacement therapy, particularly those on dialysis, are typically subject to several dietary restrictions [1]. Currently available guidelines provide general recommendations on energy intake (e.g., 30–35 kcal/kg body weight), protein (1–1.2 g/kg/day), potassium (1500–2000 mg/day if elevated), phosphorus (800–1000 mg/day if elevated), and sodium intake (<100 mmol/day or <2.3 g/day) and suggest encouraging diets meeting the recommended dietary allowance for adequate intake of all vitamins and minerals [1]; however, they do not recommend specific dietary patterns due to lack of randomized clinical trials in this setting. Use of the Mediterranean diet may be feared by physicians because of its high content in fruits, vegetables, and legumes, which may contain relevant amounts of potassium and phosphate. Dietary restrictions may sometimes be overzealous, leading to deficiencies in important nutrients and poor nutritional status [2]. However, in recent years, this approach has been questioned, both due to the relative lack of evidence supporting such restrictions and to the potential nutritional deficiencies and deprivation of beneficial dietary elements associated with strict dietary regimens [2,3]. The Mediterranean diet is a common eating pattern found in Italy, Greece, Spain, and other countries in the Mediterranean basin. This pattern encompasses nutritious eating habits characterized by a high intake of vegetables, fresh fruits, legumes, and cereals, which serve as major sources of fiber and antioxidants, as well as a moderate intake of alcohol. Fish, nuts, and olive oil also ensure a high intake of mono-unsaturated fatty acids [4]. Adherence to the Mediterranean diet clusters with other healthy behaviors, including physical exercise and abstention from smoking, and is related to the level of education [5] and age [6]. In Italy, the highest adherence has been reported in the central regions, whereas people living in the north seem to have the lowest adherence [7]. The Mediterranean diet was shown to reduce cardiovascular risk [8] and to help prevent [9] or delay the progression [10] of chronic kidney disease (CKD). At present, the National Kidney Foundation’s Kidney Disease Outcomes Quality Initiative (KDOQI) Clinical Practice Guideline for Nutrition in CKD suggests the Mediterranean diet for adults with CKD stage 1–5 not on dialysis or post-transplantation to improve lipid profile [1]. Data on patients on dialysis or kidney transplant (KT) recipients are scarce. The available evidence indicates that in patients on maintenance dialysis, greater adherence to the Mediterranean diet is associated with better cardiac geometry [11] and reduced all-cause mortality [12], although other studies suggest that there is no effect on cardiovascular or total mortality in patients on hemodialysis [13]. In KT recipients, greater adherence to the Mediterranean diet has been associated with lower risk of metabolic syndrome [14], post-transplant diabetes [15], graft failure [16], and mortality [17]. Assessing adherence to the Mediterranean diet, identifying factors that affect it, and detecting potential unhealthy food consumption patterns is therefore important in improving the nutritional care of patients on renal replacement therapy. We sought to assess adherence to the Mediterranean diet among individuals on dialysis or who received a KT and to investigate factors that influence it.
## 2.1. Study Design
This was a secondary analysis of a cross-sectional, observational study aimed at providing information on the burden of obesity and the lifestyle habits of patients on renal replacement therapy in a real-life setting [18]. Data were obtained from an anonymous, open online survey among adult (age ≥ 18 years) individuals on renal replacement therapy (KT or dialysis). The study design has been previously described [18]. Briefly, the survey was published on a dedicated web page on Google Forms between 1 January 2022 and 31 March 2022 and was advertised on social media platforms by the main national associations of patients with polycystic kidney disease or those on renal replacement therapy. All participants who accessed the informed consent page and consented to participate were included in this study. We collected, among other variables, self-reported height and weight to compute body mass index (BMI), age, sex, smoking status, weight changes after KT, and dietary habits (as detailed in the following paragraph). The total number of questionnaire items was 45 on a single web page. Only questionnaires with complete answers were analyzed.
## 2.2. Adherence to the Mediterranean Diet
Adherence to the Mediterranean diet and dietary habits were assessed using the MEDI-LITE questionnaire, a validated questionnaire that assesses the consumption of nine food categories (fruit, vegetables, legumes, cereals, fish, meat and meat products, dairy products, alcohol, and olive oil) [19,20]. The questionnaire is validated for use in the Italian population and yields a score ranging from 0 (minimum) to 18 (maximum). For foods typically part of a Mediterranean diet (fruit, vegetables, cereals, legumes, and fish), 2 points are assigned to the highest (optimal) category of consumption, 1 to the intermediate category, and 0 to the lowest category. For foods not typically part of a MD (meat and meat products, dairy products), 2 points are assigned to the lowest (optimal) category, 1 to the intermediate category, and 0 to the highest consumption category. For alcohol, 2 points are assigned to the middle category (optimal, 1–2 alcohol units/day), 1 to the lowest category (1 alcohol unit/day), and 0 to the highest category (>2 alcohol units/day) of consumption [19]. For olive oil, 2 points are assigned for regular (optimal) use, 1 for frequent use, and 0 for occasional use. Adherence to the Mediterranean diet was considered adequate if the score was >9 [21,22].
## 2.3. Physical Activity
The level of physical activity was assessed using the International Physical Activity Questionnaire (IPAQ) short form, which estimates the level of physical activity (low, moderate, high) based on the subject-reported activities (vigorous/moderate physical activity and walking) relative to the 7 days prior to completion of the questionnaire and is validated for use in the Italian population [23].
## 2.4. Ethical Approval
The study was carried out in accordance with the Declaration of Helsinki and approved by the Ethics Committee of IRCCS San Raffaele Roma (ODIRT protocol, nr. $\frac{21}{29}$). The voluntary questionnaire could be completed only by participants who provided their informed consent to be enrolled in the study. The questionnaire was anonymous, no information that could render the data subject identifiable was collected. The results are reported according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) [24]
## 2.5. Statistical Analysis
Descriptive statistics were obtained for all study variables. Normality was assessed with the Shapiro–Wilk test. Continuous variables were expressed as mean ± standard deviation or median (25th–75th percentile), depending on the data distribution. Categorical variables were summarized as counts and percentages. The Mann–Whitney U test or the Kruskal–Wallis test were used for between-group comparisons for continuous variables. The Fisher exact test or the χ2 test was used to assess the association between categorical variables. Univariable and multivariable logistic regression (including all significant variables at univariable regression) models were used to identify variables associated with inadequate adherence to the Mediterranean diet, adjusting for age and sex. All variables were screened for violations of the assumptions relevant to each of the statistical analyses performed. Multicollinearity was assessed by calculating the variance inflation factors (VIFs, with a threshold of 5). Statistical significance was set at $p \leq 0.05.$ *Statistical analysis* was conducted using IBM SPSS Statistics (IBM SPSS Statistics for Windows, Version 28.0., IBM Corp.: Armonk, NY, USA).
## 3. Results
A total of 333 potential participants accessed the informed consent page, of whom 322 consented to participate in the study and were included in the present analysis. Participant characteristics are presented in Table 1.
The majority of participants were male, median age was 56.0 (48.0; 62.0) vs. 54.0 (45.0; 62.0) years in KT recipients and participants on dialysis, respectively; $$p \leq 0.33.$$ The median BMI was higher in KT recipients than in participants on dialysis (23.9 (21.6; 26.5) vs. 23.6 (20.7; 27.1) kg/m2, respectively; $p \leq 0.001$), with numerically higher proportions of participants with underweight or obesity in the latter group (Table 1). Overall, $44.1\%$ of participants reported having gained weight since the start of dialysis or since KT, the proportion being significantly higher among KT recipients (Figure 1).
In both groups, the majority of participants lived in Northern Italy, had an intermediate level of education (upper secondary or post-secondary non-tertiary education [25]), and were workers. The proportion of participants with comorbidities was similar between the groups, with numerically higher figures for hypertension and dyslipidemia in the KT group. Median time since KT was 6.0 (3.0; 13.0) years. Approximately half ($52\%$) of KT recipients were on steroid therapy.
## 3.1. Adherence to the Mediterranean Diet
The median MEDI-LITE score was 9.0 (8.0; 10.0) in KT recipients and 8.0 (7.0; 9.0) in participants on dialysis ($$p \leq 0.001$$). The proportion of participants with adequate adherence to the Mediterranean diet (MEDI-LITE score > 9) was significantly higher among KT recipients as compared with participants on dialysis ($44.7\%$ vs. $19.4\%$, $p \leq 0.001$). There were no differences in the proportion of participants with comorbidities (diabetes, hypertension, dyslipidemia, vascular disease, obesity) between those with inadequate and adequate adherence to the Mediterranean diet. Overall, $17.4\%$, $29.2\%$, $56.2\%$, and $19.6\%$ of participants reported having to restrict the intake of fluids, potassium, salt, or phosphate, respectively. These restrictions were significantly more common among participants on dialysis (Figure 2).
In participants on dialysis, the MEDI-LITE score did not differ among individuals of different sex ($$p \leq 0.590$$), age ($$p \leq 0.715$$), or BMI ($$p \leq 0.901$$) categories, from northern, central, or southern Italy ($$p \leq 0.408$$), by the level of education ($$p \leq 0.103$$), working status ($$p \leq 0.296$$), or level of physical activity ($$p \leq 0.266$$) (Figure 3).
In KT recipients, the MEDI-LITE score was significantly lower in participants living in southern Italy as compared with those living in central Italy ($$p \leq 0.031$$) (Figure 4). Participants with a basic level of education had the lowest adherence to the Mediterranean diet, with significant differences as compared both to those with an intermediate and advanced level of education ($$p \leq 0.045$$, Figure 4).
There was no difference between sexes ($$p \leq 0.640$$), among age ($$p \leq 0.715$$) or BMI ($$p \leq 0.514$$) categories, by working status ($$p \leq 0.633$$), or the level of physical activity ($$p \leq 0.382$$).
At univariable logistic regression in the whole population, a basic level of education was associated with a nearly 3-fold increase in the odds of inadequate adherence to the Mediterranean diet (OR 2.72, $95\%$ CI (1.27; 5.86); $$p \leq 0.01$$). Dietary limitations such as restricting the intake of fluids (OR 4.14, $95\%$ CI (1.20; 8.84); $p \leq 0.001$), potassium (OR 2.23, $95\%$ CI (1.32; 3.78; $$p \leq 0.003$$)), or phosphate (OR 3.39, $95\%$ CI (1.73; 6.67; $p \leq 0.001$)) were also predictors of inadequate adherence to the Mediterranean diet. No other variables (sex, age, BMI, geographical area, working status, physical activity level, salt restriction) were associated with inadequate adherence to the Mediterranean diet. Assessment of collinearity revealed a VIF = 5.73 for being on dialysis. Therefore, two separate multivariable regression analyses were conducted to assess the role of being on dialysis and to investigate the effect of specific dietary restrictions. At multivariable logistic regression, basic level of education, being on dialysis (Table 2), and fluid restriction retained significance (Table 3).
## 3.2. Consumption of Specific Food Categories
To provide further insight into the dietary habits of individuals on renal replacement therapy, we investigated the proportion of participants scoring 2 (optimal), 1 (intermediate), or 0 (inadequate) on the consumption of the food categories included in the MEDI-LITE questionnaire (Figure 5).
## 3.2.1. Fruit
Fruit consumption patterns were similar between KT recipients and participants on dialysis, with nearly half of them scoring 0 (less than one serving per day) and no significant differences between groups. There were no differences in the proportion of participants with comorbidities (diabetes, hypertension, dyslipidemia, vascular disease, obesity) between those with inadequate fruit consumption and other participants.
## 3.2.2. Vegetables
A significantly greater proportion of participants on dialysis reported consuming low amounts of vegetables (less than one serving per day) as compared with KT recipients, most of whom reported an intermediate consumption of vegetables. There were no differences in the proportion of participants with comorbidities between those with inadequate vegetable consumption and other participants.
## 3.2.3. Legumes
Significant differences were identified between groups with regard to legume consumption. Participants on dialysis reported consuming significantly lower amounts of legumes than KT recipients. However, even in the latter group, more than half of the participants reported a low consumption of legumes (less than one serving per week). There were no differences in the proportion of participants with comorbidities between those with inadequate legume consumption and other participants.
## 3.2.4. Cereals
There were no significant differences in the consumption of cereals between groups. In both groups, approximately $40\%$ of the participants reported consuming less than one serving of cereals per day (0 points). There were no differences in the proportion of participants with comorbidities between those with inadequate cereal consumption and other participants.
## 3.2.5. Fish
The proportion of participants consuming an optimal amount of fish (more than 2.5 servings per week) was similarly low in the two groups. Approximately half of the participants in both groups reported eating less than one serving per week. There were no differences in the proportion of participants with comorbidities between those with inadequate fish consumption and other participants.
## 3.2.6. Meat and Meat Products
The proportion of participants with optimal consumption of meat and meat products, including cured meats (less than one per day), was numerically higher in the KT group. Approximately $15\%$ of the participants in each group reported eating more than 1.5 servings of meat per day. The proportion of participants with obesity was significantly greater among those with excess meat consumption than in other participants ($18.8\%$ vs. $9.1\%$, $$p \leq 0.045$$).
## 3.2.7. Milk and Dairy Products
A high proportion of participants reported consuming less than one serving of milk or dairy products per day. This figure was significantly higher in participants on dialysis, whereas the proportion of participants who reported having more than 1.5 servings per day was significantly greater among KT recipients. The proportion of participants with diabetes was significantly greater among those with excess consumption of dairy products than in other participants ($33.3\%$ vs. $17.0\%$, $$p \leq 0.014$$).
## 3.2.8. Alcohol
Most participants reported drinking less than one alcohol unit per day, this proportion being significantly higher in the dialysis group. No participants in the dialysis group reported inadequate alcohol consumption (more than 2 alcohol units per day), as opposed to $2\%$ in the KT group. The proportion of participants who consumed 1–2 alcohol units per day was numerically higher in the KT group. The proportion of participants with diabetes was significantly greater among those with excess alcohol consumption than in other participants ($60\%$ vs. $18.3\%$, $$p \leq 0.018$$).
## 3.2.9. Olive Oil
The proportion of participants who reported regular consumption of olive oil was similarly high in the two groups. Only $7.5\%$ and $12.9\%$ of participants in the dialysis and KT group, respectively, reported using olive oil only occasionally. The proportion of participants with diabetes ($31.6\%$ vs. $17.3\%$, $$p \leq 0.034$$) or obesity ($21.1\%$ vs. $9.2\%$, $$p \leq 0.043$$) was significantly higher among those with inadequate olive oil consumption than among other participants.
## 4. Discussion
In the present study, we assessed the adherence of individuals on renal replacement therapy to the Mediterranean diet, and investigated what factors influence it. Adherence to the Mediterranean diet was generally lower (median Medi-Lite score 9.0 in KT recipients and 8.0 in participants on dialysis) than in the general Italian population, where the reported median Medi-Lite score is 12 [19]. To the best of our knowledge, no studies used the Medi-Lite questionnaire to assess adherence to the Mediterranean diet in CKD patients 1–5 not on renal replacement therapy. Using the Mediterranean diet serving score, Bučan Nenadić and colleagues found that as low as $9.1\%$ of participants with CKD were adherent to the Mediterranean diet, which is lower than the $44.7\%$ and $19.4\%$ we found in KT recipients and patients on dialysis. The finding that the adherence was lowest in participants on dialysis was, at least in part, due to dietary restrictions, which were significantly more common among participants on dialysis. In fact, being on dialysis increased the likelihood of poor adherence to the Mediterranean diet, possibly due to the need of restricting fluid intake. Basic education (primary or lower secondary education) was also a factor significantly associated with the increased likelihood of inadequate adherence to the Mediterranean diet. The latter association has also been reported in the general Italian population [19]. The association between a lower level of education and unhealthy dietary patterns has been reported in several large studies conducted in different countries [26,27,28] and highlights the need for large-scale educational programs aimed at increasing nutrition literacy, especially among individuals with a lower level of education. Greater nutrition literacy is in fact associated with greater adherence to healthy dietary patterns [29], even in kidney transplant recipients [30], and specifically designed educational programs have been shown to improve adherence to the Mediterranean diet in vulnerable individuals living in extreme poverty [31].
The lack of association with a *Geographical area* is in contrast with previous studies in the general population showing the lowest adherence and consumption of fruit and vegetables in the Northern regions of Italy [7,32]. This discrepancy might be due to a relatively greater proportion of participants from Northern Italy and the generally low adherence to the Mediterranean diet in our study.
Our finding of low adherence to the Mediterranean diet is consistent with the few previous studies conducted in patients on hemodialysis [13] or KT [16], indicating that only a small proportion of patients on renal replacement therapy follow a Mediterranean diet pattern. According to current recommendations, individuals with CKD including those post-transplantation should modulate their dietary intake with the aim of maintaining levels of potassium and phosphate in the normal range [1]. However, very strict dietary limitations are often imposed on these patients, whose appropriateness has been sometimes called into question [2]. It is not surprising that participants on dialysis had the lowest adherence to the Mediterranean diet, which is rich in fruits, vegetables, and legumes that may be relevant sources of potassium and phosphate. Guidelines state that it is reasonable to adjust the dietary intake of these minerals to maintain serum levels within the normal range [1]. This possibly explains why, in our study, adherence to the Mediterranean diet was influenced by dietary restrictions typically adopted in this population.
The benefits and potential risks of adopting the Mediterranean diet in the CKD population have recently been reviewed by the European Renal Nutrition (ERN) Working Group of the European Renal Association–European Dialysis Transplant Association (ERA-EDTA) [33]. The Mediterranean diet is characterized by high consumption of fruits and vegetables, which are high in potassium, and concerns exist that it could predispose CKD patients to hyperkalemia and acid/electrolyte disbalance. Our findings indicate that dietary restrictions such as limiting potassium or phosphate intake and, above all, limiting fluid intake were associated with reduced adherence to the Mediterranean diet. However, the predominance of plant and fish protein versus meat protein might decrease phosphate bioavailability, thus reducing the phosphate load [33]. Regarding dietary potassium, it is not yet clear whether it increases the risk of hyperkalemia, as intracellular/extracellular potassium shifts may be influenced by several factors, including acid–base balance and medications [34]. Furthermore, in patients with end-stage renal disease whose kidneys cannot handle a potassium load, the gut plays an important role [35,36], and constipation could favor hyperkalemia. In this light, providing an adequate amount of fiber as recommended in the Mediterranean diet might help ensure proper bowel movements to counterbalance a relative increase in dietary potassium. However, until more evidence is available in support of or against increasing the consumption of fruit and vegetables in patients at risk of hyperkalemia, choosing fruits and vegetables with low potassium content, implementing strategies to reduce the potassium content in these foods such as leaching vegetables, and careful monitoring of potassium serum levels might be more prudent.
Fluid retention is associated with worse health outcomes in dialysis patients [37,38]; therefore, limiting fluid intake is recommended. However, it has been suggested that the benefit of fluid restriction is achieved only if nutritional status and food intake are adequate [2,37]. In our analysis, fluid restriction, which was adopted almost exclusively by participants on dialysis, was an independent predictor of inadequate adherence to the Mediterranean diet, indicating low consumption of foods characterizing this dietary pattern (mainly fruit, vegetables, legumes, and fish). As discussed further in the following paragraphs, this might lead to a reduced intake of beneficial nutrients.
The analysis of specific food categories revealed that consumption of fruit, vegetables, and legumes was generally inadequate with respect to the Mediterranean diet model, whereas most participants reported optimal consumption of dairy products and olive oil. The use of olive oil, which is a mainstay of the Mediterranean diet, should be encouraged, as it may help counteract constipation in patients on hemodialysis [39], and it exerts anti-inflammatory and anti-atherogenic effects that may be particularly beneficial in patients at high cardiovascular risk such as patients on renal replacement therapy [40].
Dairy products are a source of potassium and phosphorus [1]. However, they also provide calcium, α-linolenic acid, and other heart-healthy nutrients [1,41]. Very little/no information is available on the effects of dairy consumption on health outcomes in patients on renal replacement therapy. A study in CKD patients not on dialysis nor transplanted found that dairy consumption was associated with lower renal function [42]. In the general population, the consumption of dairy is associated with better renal health [43], reduced risk of overweight or obesity (especially milk and yogurt), hypertension (low-fat dairy and milk), and type 2 diabetes (yogurt) [44]. We found that excess consumption of dairy products was significantly associated with having diabetes. It is possible that higher rates of diabetes were related to increased intake of high-fat dairy. Unfortunately, we were not able to discriminate between high-fat and low-fat dairy products.
A high proportion of participants consumed an inadequate amount of fish. Fish is a natural source of long-chain omega-3 polyunsaturated fatty acids, which are known for their cardioprotective properties [45]. Current guidelines on renal nutrition suggest prescribing omega-3 polyunsaturated fatty acids to reduce triglycerides and LDL cholesterol and raise HDL cholesterol levels but do not recommend routine use of these compounds to reduce mortality or cardiovascular risk [1]. To the best of our knowledge, the relationship between fish consumption and health outcomes has not been explored in patients on renal replacement therapy, but it is likely that eating fish carries the same benefits as taking fish oil supplements. Wild (as opposed to farmed) sardine, mackerel, salmon, and other high-content marine-based foods should be chosen to increase blood/tissue levels of eicosapentaenoic and docosahexaenoic acid, although achieving high daily intake may be challenging [1]. Lastly, as already mentioned, fish protein might be more advantageous with regard to the phosphate load as compared to meat protein [33].
When we compared participants on dialysis with KT participants, we found that the consumption of vegetables, legumes, and dairy products was lower in those on dialysis. In a study assessing the association between Mediterranean diet and cardiac geometry in patients with CKD, the consumption of specific food categories, such as vegetables and legumes, was associated with a more favorable cardiac remodeling pattern [11]. According to recent recommendations, decisions about phosphate restriction should consider the bioavailability of phosphorus sources. The phosphate content of vegetables and legumes has low biological availability [46]. Therefore, moderate consumption of these should be considered even for patients on dialysis. Furthermore, vegetable intake has been associated with a reduced risk of post-transplantation diabetes [47]. In a large prospective cohort study of cardiovascular and all-cause mortality, the effect of the Mediterranean diet or dietary approaches to stop hypertension (DASH) on the risk of cardiovascular or all-cause mortality was neutral [13], suggesting that dietary patterns that are beneficial in the general population do not have the same effect in patients on hemodialysis. It is also possible that only some food categories have beneficial effects. In the same cohort, increasing the consumption of fruits and vegetables to approximately 2 to 3 servings per day was associated with a $20\%$ lower risk of all-cause mortality and death from non-cardiovascular causes [48]. These findings indicate that the Mediterranean diet is not harmful in patients on hemodialysis with respect to mortality risk, and that limiting the consumption of fruit and vegetables might even be detrimental due to the deprivation of minerals, vitamins, and plant-derived metabolites with anti-inflammatory and antioxidant properties [49]. This has also been recognized by current guidelines on nutrition in CKD [50]. The use of potassium binding resins may allow for greater consumption of fruit, vegetables, and legumes, although the use of these drugs may be associated with side effects.
The situation might be different in KT recipients. There is evidence that the Mediterranean diet could help reduce the risk of post-transplantation diabetes and metabolic syndrome in this population [14,17], as well as the risk of graft failure and kidney function decline [16]. In Dalmatian KT recipients, adherence to the Mediterranean diet was also associated with greater albumin levels and skeletal muscle mass, indicating better nutritional status [51]. In our cohort, the median BMI was significantly greater in KT recipients than in participants on dialysis. Consistently, more participants reported weight gain among KT recipients. This is a well-known issue, which affects a large proportion of KT recipients. Risk factors for post-transplantation weight gain are several, including female sex, pre-transplant body weight [52], younger age, black ethnicity, lower socioeconomic status, diabetes mellitus, acute rejection, steroids, and antidepressants [53]. Dietary regimens based on the principles of the Mediterranean diet might help maintain a healthy weight [54], and existing barriers to the implementation of the Mediterranean diet in the management of KT recipients should be overcome [55]. Recently, practical suggestions for implementing the principles of the Mediterranean diet in renal nutrition have been published by Perez-Torres and colleagues [56]. These address specific food categories (e.g., fruits, vegetables, legumes, cereals, fish…), suggesting the frequency of consumption for each. As an example, the authors recommend 2–3 servings of legumes per week for patients on hemodialysis, whereas a minimum of 4 servings per week is recommended for KT recipients.
It should be acknowledged that despite the strong rationale supporting fewer restrictions regarding fruit, vegetables, and legumes, we did not find an association between adherence to the Mediterranean diet or the reduced consumption of these foods and the presence of comorbidities, including obesity, diabetes, dyslipidemia, hypertension, and vascular disease. On the other hand, we found that excess consumption of dairy products and alcohol and low intake of olive oil were significantly associated with diabetes. Low consumption of olive oil, as well as excess consumption of meat, was associated with obesity. Larger studies are necessary to investigate the dietary habits of individuals on renal replacement therapy, and how these can affect their comorbidities.
Limitations of the present study include its cross-sectional nature, which does not allow us to establish the direction of the associations described here. We were also unable to rule out the possibility that multiple entries from the same individual were included in the analysis as, to keep the survey completely anonymous, we did not collect information (e.g., IP address, cookies) that could be used to identify duplicate entries. The use of self-reported data, particularly height and weight to calculate BMI, is also a limitation of the present study; BMI values based on self-reported anthropometrics tend to overestimate BMI values at the low end of the BMI scale and underestimate BMI values at the high end [57]. Furthermore, we did not investigate alterations in taste perception, which is another factor that might contribute to poor diet. Dysgeusia is common among patients with CKD, particularly those on hemodialysis, and can be due to nutritional deficiencies, dry mouth brought on by water loss, diabetes neuropathy, side effects of drugs, and uremia [58,59]. Strengths of this study include the use of a questionnaire validated in the Italian population [19], the assessment of dietary restrictions, and the involvement of the main national associations of patients with polycystic kidney disease or those on renal replacement therapy, which we believe helped increase interest and awareness of the Mediterranean diet among patients.
## 5. Conclusions
Adherence to the Mediterranean diet is generally low among individuals on renal replacement therapy; this, at least in part, is due to dietary restrictions, which were significantly more common among participants on dialysis. Participants reported very low rates of fruit, vegetable, and legume consumption. Renal nutrition is complex, and it might be that a “modified” Mediterranean diet, including plant-based proteins, is a good compromise between “too strict” and “no” rules. In light of using food as a therapeutic tool, there is a need for registered dieticians specialized in renal nutrition and of large, well-designed studies addressing these questions.
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|
---
title: 'COVID-19 in the Eastern Brazilian Amazon: Incidence, Clinical Management,
and Mortality by Social Determinants of Health, Symptomatology, and Comorbidities
in the Xingu Health Region'
authors:
- Eric Renato Lima Figueiredo
- Márcio Vinicius de Gouveia Affonso
- Rodrigo Januario Jacomel
- Fabiana de Campos Gomes
- Nelson Veiga Gonçalves
- Claudia do Socorro Carvalho Miranda
- Márcia Cristina Freitas da Silva
- Ademir Ferreira da Silva-Júnior
- João Simão de Melo-Neto
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002208
doi: 10.3390/ijerph20054639
license: CC BY 4.0
---
# COVID-19 in the Eastern Brazilian Amazon: Incidence, Clinical Management, and Mortality by Social Determinants of Health, Symptomatology, and Comorbidities in the Xingu Health Region
## Abstract
This study aims to investigate the relationship between social determinants of health (SDH), incidence, and mortality to verify which sociodemographic factors, symptoms, and comorbidities predict clinical management; second, this study aims to conduct a survival analysis of individuals with COVID-19 in the Xingu Health Region. Consequently, this study adopted an ecological framework, employing secondary data of COVID-19-positive individuals from the Xingu Health Region, Pará State, Brazil. The data were obtained through the database of the State of Pará Public Health Secretary (SESPA) for the period from March 2020 to March 2021. The incidence and mortality were higher in Vitória do Xingu and Altamira. Municipalities with a higher percentage of citizens with health insurance and higher public health expenditure showed a higher incidence and mortality. A higher gross domestic product was associated with a higher incidence. Females were found to be associated with better clinical management. To live in Altamira was a risk factor for intensive care unit admission. The symptoms and comorbidities that predicted worse clinical management were dyspnea, fever, emesis, chills, diabetes, cardiac and renal diseases, obesity, and neurological diseases. There were higher incidence, mortality, and lower survival rates among the elderly. Thus, it can be concluded that SDH indicators, symptomatology, and comorbidities have implications for the incidence, mortality, and clinical management of COVID-19 in the Xingu Health Region of eastern Amazonia, Brazil.
## 1. Introduction
Coronavirus disease 2019 (COVID-19) is a highly contagious infectious disease caused by a new betacoronavirus belonging to the large viral family of Coronaviridae. The first cases of COVID-19 were reported in December 2019 in Wuhan, China [1,2]. Initially, the outbreak of severe acute respiratory syndrome by type 2 coronavirus (SARS-CoV-2) was confirmed in the province of Hubei; however, this virus spread rapidly to several countries, causing a pandemic in 2020 [3,4,5].
Regarding symptoms, the clinical manifestation of COVID-19 virus can range from asymptomatic to severe [6]. The main clinical symptoms of COVID-19 patients include fever, cough, myalgia or fatigue, and dyspnea [7,8]. Additionally, olfactory and gustatory dysfunctions are common clinical findings in these patients [9]. Minor symptoms include sputum production, headache, haemoptysis [8], dizziness, diarrhea, nausea, vomiting [6], and skin lesions [10,11]. The fatality rate is approximately $5\%$ ($95\%$ CI (0.01–0.11)) [12]. The prevalence of comorbidities is considered a risk factor for severe patients. However, the symptoms of infected patients are nonspecific [6], and there is a need to know the characteristics of each population and its implications for clinical evolution.
Since the start of the pandemic till the present, the literature on possible treatments for COVID-19 disease is increasing; however, vitamin supplements, anti-inflammatory agents, and antimicrobial therapy have shown a lack of efficacy in the treatment of patients; the best care strategy throughout the course of the disease remains unknown [13,14].
SARS-CoV-2 has negative effects on clinical practice. Regarding health workers, they are at higher risk of infection due to their efforts to protect the community; consequently, they are exposed to psychological distress, fatigue, and stigma [15]. Furthermore, in the initial phase of the COVID-19 pandemic, the mental health and well-being of the general population were affected, with increasing rates of suicidal thoughts among the population [16]. In addition to host factors, environmental or social factors contribute to the high infection risk, especially factors such as poor living conditions, nutrition, ventilation, sanitation, and overcrowding [17]. Although specific population groups may have higher risk factors, the differences in social-epidemiological patterns, and differences by age and gender have been little studied. Social inequalities due to different living and working conditions and socioeconomic status should be taken into account when assessing the risk to different population groups [18].
The Brazilian Amazon represents approximately $58.9\%$ of Brazil’s national territory, hosts a complex biodiversity which includes diverse cultures, ecosystem services, and human settlements, with various degrees of urbanization and rurality ranging from metropolitan regions such as Belém and Manaus to traditional riverside, indigenous, and quilombola communities [19]. In the eastern Brazilian Amazonia, nine municipalities along the Transamazonian highway comprise the microregion of the middle Xingu. The Xingu River, one of the main right-bank tributaries of the Amazon Basin and the largest fluvial system in the world, runs alongside these municipalities [20]. The population of the state of Pará exceeds 370,000. Altamira is the most populous city in the region, with a population of 116,000 individuals. The other eight municipalities of the state include Anapú, Medicilândia, Vitória do Xingu, Brasil Novo, Senador José Porfírio, Porto de Moz, Pacajá, and Uruará (with populations between 10,000 and 50,000 inhabitants) [21].
The Xingu Health *Region is* one of the more than 400 health regions in Brazil established by states in collaboration with municipalities under the provisions of Decree No. 7508 of 2011 [22]. A health region is a geographic area composed of neighboring municipalities delimited by common characteristics, such as cultural, economic, and social aspects; additionally, in a health region, there is an integration of infrastructure and transportation to conduct health-related actions and deliver services in an integrated and equitable manner [22,23].
The Belo Monte Hydroelectric Power Plant, the largest of its kind in Brazil, is located in this region. This hydroelectric dam was constructed between 2010 and 2017 through the Growth Acceleration Program funding, which led to a transformation in the social and demographic profile of all municipalities in the area, including a population increase, as well as financial and commercial movements, resulting in numerous investments [20,24]. However, simultaneously, there has also been an increase in socioeconomic problems such as violence, exacerbation of agrarian conflicts, relocation of traditional populations, prostitution, and deficiency in educational and health services [25]. Thus, the multiplicity and territorial and cultural complexities of the Xingu region result in a scenario of great social vulnerability, which potentially has a significant influence on the transmission, morbidity, and mortality rates of COVID-19 [26,27].
As humanity’s coexistence with the pandemic evolves, there is a need to understand its legacy across different social organizations and populations affected by the health, environmental, and economic crises it has brought about globally, taking into account the local and regional aspects [28,29].
In this sense, the Brazilian health policy works to improve the living conditions and environment of the population by adopting a technical, operational, and organizational approach to the management of health interventions and services. This approach is based on the conceptual model of the social determinants of health (SDH) theory [30], which suggests that health and disease processes are influenced by factors arranged at different levels, from micro factors such as hereditary factors, age, gender, and lifestyle, to macro factors related to environmental, cultural, and socioeconomic issues [27,31,32].
Additionally, in a pre-vaccination context, understanding the clinical symptoms and comorbidities in each population and their implications for COVID-19 incidence and mortality can contribute to the construction of a more effective and integrated health surveillance model [33,34]. Moreover, the identification of symptoms and comorbidities that are predictors of severe illness and intensive care unit (ICU) admission in these populations can help in the risk stratification of each patient in health services and facilitate more effective planning and mobilization of resources [35,36,37].
This study has the following objectives: [1] to investigate the relationship between SDH, incidence, and mortality; [2] to verify which sociodemographic factors, symptoms, and comorbidities predict clinical management; and [3] to analyze which sociodemographic and clinical factors are associated with lower survival of individuals with COVID-19 in the Xingu Health Region in the eastern Brazilian Amazon. This study hypothesized that poorer indicators of SDH are related to higher rates of incidence and mortality and that variables including advanced age, the female sex, living in cities with poorer social development, and specific symptoms and comorbidities serve as predictors of more severe clinical management, such as medical ward and intensive care, and lower survival in individuals with COVID-19 in the health region of Xingu in the eastern Brazilian Amazon.
## 2.1. Study Design
An observational study design employing an ecological approach with descriptive and inferential analyses was adopted.
## 2.2. Study Population and Period
This study examined the secondary data of individuals diagnosed with COVID-19 between March 2020 and March 2021 in a pre-vaccination scenario in the Xingu Health Region, Pará State, Brazil.
## 2.3. Inclusion and Exclusion Criteria
Diagnosis of SARS-CoV-2 by a reverse transcription-polymerase chain reaction (RT-PCR) or a serological test (rapid test) was defined as the study inclusion criterion. Therefore, only cases that were RT-PCR- or rapid test-positive were included in the study. Cases with missing data (i.e., sociodemographic information) were excluded.
## 2.4. Setting
The data obtained from the nine municipalities of the Xingu Health Region, Pará State, Brazil (Figure 1), were analyzed. To visualize these cities, a map was constructed using QGIS Desktop 3.26.
## 2.5. Assessments
The databases of SESPA, the Brazilian Institute of Geography and Statistics (IBGE), e-Gestor AB, and the National Institute for Space Research (INPE) were utilized. The open-access database maintained by SESPA includes information regarding cases identified as COVID-19-positive, reported daily by municipal health departments and health services as part of the COVID-19 surveillance system in the state of Pará. These data are published daily between 5:00 and 7:00 p.m. on the www.covid-19.pa.gov.br portal; this portal comprises all the data on the COVID-19 pandemic in Pará [36]. The IBGE database includes data on Brazil, its states, and its municipalities; it includes infographics, maps, and other information on topics such as education, labor, the economy, population, health, and territory [21,37]. The e-Gestor AB database [38,39] is a platform that provides access to primary healthcare (PHC) information systems for the management of PHC data by managers and health professionals, facilitating access to data that can be useful in the organization and planning of health services. The INPE database includes environmental information on activities conducted by top research institutes in the country following minimum quality standards to facilitate the understanding and reuse of information [40].
The sociodemographic factors analyzed included age, sex, and municipality of residence. The clinical symptoms evaluated included fever, cough, dyspnea, nausea, headache, runny nose, nasal congestion, sore throat, diarrhea, chills, conjunctivitis, odynophagia, anosmia, ageusia, adynamia, myalgia, and arthralgia. The comorbidities considered included obesity; asthma; diabetes; immunodeficiency diseases; heart disease; pneumopathy; and neurological, renal, hematological, and hepatic diseases. The clinical management analysis included home care and the need for hospitalization, separating those who needed a medical ward from those who were hospitalized in intensive care units (ICUs). The time to symptom onset, date of death, and mortality of individuals were recorded. These data were obtained from the database provided by SESPA [36]. The incidence and mortality rates were calculated subsequently. IBGE [21,37] data were used to estimate the number of individuals in the Brazilian population [2020] in terms of the calculation of incidence and mortality rates, overall and according to sex, per 1000 inhabitants.
The SDH indicators used in the analysis of the Xingu Health Region are provided in Table 1. The SDH indicators analyzed included sociodemographic and habitation factors (population density [21,37]; percentage of elderly in the population [21,37]; percentage of the vulnerable households with an older adult in the population [21,37]; percentage of people in households with walls not made of masonry or wood [21,37]), economic and environmental factors (Gini index [21,37]; gross domestic product (GDP) [21,37]; Human Development Index [21,37]; percentage urbanization of public roads [41]; hotspot concentration [40]), health and resources (percentage of primary health care coverage [38,39]; percentage of people with health insurance [38,39]; number of physicians per 1000 individuals [38,39]; public health expenditure in the municipality, in BRL/inhabitant, per capita), and education and work (schooling sub-index [21,39]; illiteracy rate at age 15 and above [21,37]; unemployment rate at 10 years or older [21,37]). Classification of the indicators was based on the social gradients in health as theorized by Dalgreen and Whitehead [42] with respect to SDH [30].
## 2.6. Statistical Analysis
Descriptive statistical analysis was conducted to compute frequencies (absolute and relative), means, and standard deviations (parametric) or medians with interquartile range (IQR, non-parametric) for each group. The incidence and mortality rates for every 1000 individuals were also calculated. For the spatial representation of incidence and mortality in the Xingu Health Region, the values of the four classes were constructed based on the equal interval technique, which is based on the amplitude of the data.
Bivariate correlation coefficients, Pearson’s r (parametric), and Spearman’s rs (nonparametric), were used to verify the level of correlation between variables. Binary logistic regression analysis was used to establish the determining factors for clinical management and mortality. Initially, univariate analysis was performed considering a p-value of <0.25. To verify multicollinearity, the variance inflation factor (VIF) was calculated, and variables that presented a VIF value above 10 were removed from the final model. Statistical significance was set at $p \leq 0.05.$ An odds ratio (OR) with a $95\%$ confidence interval ($95\%$ CI) was used to quantify the degree of association.
Survival curves were obtained by using the Kaplan–Meier estimator; additionally, log-rank (initial), Breslow (intermediary), and Tarone–Ware (final) tests were used to identify statistically significant differences in the different periods [42].
SPSS Version 26.0 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. IBM Corp., Armonk, NY, USA) was used for the statistical analyses.
## 2.7. Ethical Issues
The open-access database used in this study is maintained by the State of Pará Public Health Secretary (SESPA), a state government agency, and contains consolidated information pertaining to individuals who have sought healthcare services owing to COVID-19-related symptoms. The individuals covered in this database are not identified; hence, according to National Health Council (CNS) Resolution No. 510 of 7 April 2016, evaluation by the relevant research ethics committee was not required [43].
## 3. Results
Overall, 20,296 COVID-19 cases were identified during the study period. However, 117 cases were excluded due to the incomplete sociodemographic information of these patients. Furthermore, 4041 cases were excluded since no other information except the clinical diagnosis was available. The final sample included 16,138 patients (Figure 2).
The incidence rate of COVID-19 per 1000 inhabitants in the Xingu Health Region was 45.59 and the mortality rate was 1.01. In descending order, the highest incidence rate was observed in Vitória do Xingu (90.84), followed by Altamira (56.83), Senador José Porfírio (56.62), Brasil Novo (54.19), Medicilândia (48.07), Anapu (39.75), Pacajá (33.07), Porto de Moz (28.66), and Uruará (26.94). However, most cases were reported in Altamira ($41\%$), Pacajá ($9.9\%$), and Medicilândia ($9.5\%$). It was observed that 359 individuals did not survive being infected with COVID-19. In descending order, Altamira (1.62) showed the highest mortality rate, followed by Brasil Novo (1.47), Vitória do Xingu (1.44), Senador José Porfírio (1.31), Anapú (0.87), Porto de Moz (0.67), Medicilândia (0.63), Uruará (0.42), and Pacajá (0.41). Deaths were concentrated in Altamira ($52\%$), Porto de Moz ($7.8\%$), and Anapú ($7\%$) (Figure 3).
## 3.1. Relationship among the SDH Indicators, Incidence, and Mortality
The incidence and mortality rates according to sex and SDH indicators in all municipalities in the Xingu Health Region are presented in Table 1.
When assessing the correlation of incidence and SDH indicators, correlations with the following variables were observed: GDP (rs = 0.8000, $$p \leq 0.013$$), percentage of people with health insurance (rs = 0.8000, $$p \leq 0.013$$), public health expenditure in the municipality (rs = 0.7667, $$p \leq 0.021$$), and percentage of the population residing in households with walls not made of masonry or wood (r = −0.6764, $$p \leq 0.040$$).
When assessing the correlation of mortality and SDH indicators, correlations with the following variables were observed: percentage of people with health insurance (rs = 0.8333, $$p \leq 0.008$$), public health expenditure in the municipality (rs = 0.7333, $$p \leq 0.030$$), and percentage of the population residing in households with walls not made of masonry or wood (r = −0.6812, $$p \leq 0.040$$). The other SDH indicators were not significant for incidence or mortality.
## 3.2. Sociodemographic Factors, Symptoms and Comorbidities as Predictors of Clinical Management
The sociodemographic factors associated with admission to a medical ward included age and residing in Brasil Novo. However, the female sex, residing in Uruará and Vitória do Xingu, and being locally diagnosed were associated with a lower chance of being admitted to a medical ward. Regarding symptoms, fever, cough, emesis, and dyspnea were associated with admission to a medical ward. Individuals with a headache, sore throat, myalgia, and arthralgia were less likely to be admitted to a medical ward. The predictive comorbidities for admission to a medical ward included diabetes, heart disease, neurological diseases, kidney diseases, and obesity (Table 2).
The sociodemographic factors including advanced age and having an imported case were associated with admission to an ICU. However, the female sex and residing in the municipalities of Brasil Novo, Pacajá, and Vitória do Xingu, compared with Altamira, were protective factors for ICU admission (Table 3).
Emesis, chills, and dyspnea were predictors of ICU admission. However, patients with a headache and sore throat were less likely to be hospitalized. The comorbidities of diabetes, heart disease, and obesity were identified as risk factors for ICU admission (Table 3).
The predictors for admission to ICU were having an imported case, chills, dyspnea, and diabetes. The female sex and residing in the municipalities of Brasil Novo, Medicilândia, Pacajá, and Vitória do Xingu, in comparison with Altamira, were considered protective factors for ICU admission (Table 4).
## 3.3. Sociodemographic Factors, Symptoms and Comorbidities as Predictors of Mortality
A total of 359 individuals (2.2 %) died due to being infected with COVID-19 during the study period. The sociodemographic factors predicting higher mortality included advanced age and residing in the municipality of Anapu. Having an imported case and residing in Pacajá were correlated with lower mortality rates. Individuals with dyspnea and fever had a higher risk of death. However, individuals with a sore throat and anosmia showed less association. Regarding comorbidities, heart disease was correlated with a higher chance of death. According to clinical management, patients admitted to ICU were most likely to die, followed by those admitted to a medical ward. Other variables did not present significant differences (Table 5).
## 3.4. Survival Analysis
Only the variable age group showed an association with COVID-19 survival, and individuals aged 60 years or older exhibited the lowest survival rate. The results of the Kaplan–Meier survival analysis conducted for COVID-19, according to sex, age group, home city, symptom, and commodity, are presented in Figure 4.
## 4. Discussion
Amazonian communities continue to face particular challenges in relation to the COVID-19 pandemic, owing to the fact that each community has a different type of organizational structure and a different type of social and cultural behavior [44]. Moreover, in Brazil, the denialist administration of the federal government, which has operated without a unified policy to combat and control the COVID-19 disease, has reinforced historical structural inequalities and regional vulnerabilities [45]. This has adversely affected vulnerable populations in rural and remote areas as well as traditional peoples (indigenous peoples, forest peoples, quilombolas, and riverine peoples) who reside in the Brazilian Amazon [46]. Additionally, it must be noted that socioeconomic inequalities and limited access to health services can contribute to increased incidence and mortality [47].
The COVID-19 incidence rate distribution per 1000 inhabitants varied in each municipality of the Xingu Health Region, with the highest incidence rate in Vitória do Xingu (90.84) and the lowest incidence rate in Uruará (26.94). Vitória do *Xingu is* a port city, through which a significant number of individuals move to other municipalities of the Xingu region due to its connection with the Transamazonian region, as well as with the Amazon River, which differs from Uruará. This may explain the high incidence in this city. Port areas are common epicenters of disease transmission, as demonstrated by the outbreak of the *Spanish influenza* epidemic in the city of Recife, in northeast Brazil, in the early 20th century; the influenza virus in this case was transmitted via a British ship docked in the city port [48].
Regarding COVID-19 mortality, our study showed that $40.8\%$ of the confirmed COVID-19 cases and $52\%$ of deaths were reported in Altamira, in addition to residence in Altamira being a risk factor for ICU admission. Thus, the confirmed cases were distributed more between cities than mortality. The dependency illustrated the relationship between some Brazilian northern municipalities and highlighted how the virus spread intensely in a local city due to the influence of two other bigger cities [49]. Altamira is the urban center and most populous city of the Xingu Health Region, where the majority of healthcare professionals and specialized services are concentrated. Therefore, it can be supposed that for moderate or severe cases (more likely to evolve to death), these individuals sought healthcare in Altamira. Furthermore, these findings indicate that patients did not stay isolated in their houses but continued to travel between municipalities, especially due to the region’s economic characteristics, of which the export production chain occupies a significant percentage.
Vitória do Xingu and Altamira were the cities with the highest number of foreign cases and highest incidence rates. Similar findings were obtained for mortality, with the highest rates observed in Altamira, Brasil Novo, and Vitória do Xingu. The geography of the Xingu region could explain these findings. As can be seen in Figure 1, both cities are located on the Xingu River, and the existing ports in these cities simultaneously ship a variety of products and people. Despite this economic infrastructure and connection between these cities, wealth is not well distributed [50], and the COVID-19 pandemic started in cities with better socioeconomic conditions and subsequently migrated to more vulnerable local communities [51,52].
Confirming these results, positive correlations between incidence and GDP, the percentage of people with health insurance, and public health expenditure in the municipality were observed in this study. Additionally, a negative correlation was observed between incidence and the percentage of the population residing in households with walls not made of masonry or wood. A positive correlation was observed between the percentage of people with health insurance and public health spending in the municipality. Further, a negative correlation was observed between mortality and the percentage of the population residing in households with walls not made of masonry or wood. Healthcare workers, ICU beds, and mechanical ventilators, which are frequently needed in COVID-19 severe cases, are unequally distributed in Brazil [53,54]. Considering the above, it can be assumed that the spread of COVID-19 and its associated mortality were influenced by high SDH indicators in the economic, health, and resource sectors, as well as by interdependence between cities. Additionally, political and socioeconomic factors were critical to the spatial and temporal dynamics of COVID-19 outcomes in Brazil, especially in the first wave, in which the largest municipalities with a higher socioeconomic profile were the most affected [53]. It was also observed that the municipalities with better coverage indicators and health resources had higher incidence and mortality rates, suggesting misuse of these resources [54].
Considering sociodemographic characteristics, sex-disaggregated mortality and morbidity surveillance data should be a priority in COVID-19 research [55]. Sex differences in viral transmission and disease progression deserve explicit attention due to different levels of exposure between men and women; as comorbidities are usually more prevalent in men, this can be linked with evidence indicating that males are more associated with severely affected with COVID-19 and death [56]. It has also been suggested that the exposure of women to the COVID-19 virus may be higher than that of men, since frontline providers are generally women, comprising $70\%$ of the global health and social care workforce [57]. This study’s results show that females formed the highest proportion in the group that stayed in home care and recovered, were less associated with the need for hospitalization, and were less likely to die. It is not yet well investigated whether biological differences [57] and lifestyle habits, or a combination of these, are the main factors associated with death due to COVID-19 among males.
We also found that older age was associated with the need for hospitalization and death. This can be attributed to the fact that the aging process leads to several changes that increase COVID-19 susceptibility, such as immunosenescence, changes in T-cell diversity, inflammation, a dysregulated renin-angiotensin system, changes in the glycome, advanced biological age, and epigenetic changes [58], that impair the autoimmunity of the individual in addition to the presence of comorbidities [59].
Despite the variety of symptoms investigated, fever, cough, and dyspnea were the most prevalent among patients who required hospitalization; this result is similar to other studies that evaluated symptomatic patients [60,61,62]. We observed that individuals with these symptoms and emesis were more likely to be hospitalized in a medical ward (moderate cases). Regarding ICU admission (severe cases), emesis, dyspnea, and chills remained as risk factors. Systematic and non-systematic reviews have reported that fever, dyspnea, chills, and gastrointestinal symptoms were associated with severe COVID-19 infection and ICU admission [63,64,65,66].
Additionally, headache, sore throat, and myalgia/arthralgia were less associated with the need for hospitalization and were consequently related to a better prognosis. In a systematic review and meta-analysis that investigated predictors and outcomes, an association between headache, myalgia/arthralgia, and COVID-19 severity was not observed [67], even though these were prevalent in many studies. This study shows that these symptoms were more prevalent in homecare cases and did not vary between those that needed hospitalization; therefore, it can be supposed that despite being cited in many studies, these symptoms did not present a risk factor in relation to illness severity.
Diabetes, heart disease, and obesity were consistently associated with the need for hospitalization, either in a medical ward or ICU. The existing literature provides sufficient evidence to support the role of each comorbidity as a risk factor for severe diseases [12,65,66,67,68]. Low-grade chronic inflammation, a compromised immune response, and prothrombotic status are the complications associated with diabetes and obesity [59]. In relation to heart disease, one of the phases of viral action is the inhibition of the Angiotensin 2-Converter enzyme (ECA-2), which could deregulate the renin-angiotensin-aldosterone system, causing local and systemic tissue lesions [68]. Additionally, kidney and neurological diseases were associated with the need for hospitalization in a medical ward. However, there have been limited studies on the association between preexisting kidney diseases and COVID-19, and most studies in the literature have shown that kidney-related conditions are associated with the risk of mortality [60,62,63,66,67,68]. In relation to neurological diseases, a systematic review has pointed out that mental and neurological disorders were associated with COVID-19 disease severity and further with mortality [69].
The literature provides differing evidence with respect to symptoms as predictors of death. However, this study found that individuals with fever or dyspnea were more likely to die. This finding is in line with that of a Nigerian retrospective cohort study [70]. However, systematic reviews have not observed any symptoms to be a factor associated with COVID-19 mortality [63,68]. Regarding baseline characteristics, such as age and the presence of comorbidities, this study found that older individuals with heart disease were more likely to die, and this is well-evidenced in the existing scientific literature [59,68].
By identifying the symptoms and comorbidities that are predictive of serious illness and ICU admission, risk stratification within health services becomes possible. This can lead to management changes and improvements in resource allocation efficiency for patients at a higher risk of serious infection from COVID-19. Such identification would also facilitate more informed discussions regarding the predicted clinical trajectory, allowing for more accurate and timely advanced care planning. Similarly, it would assist the public health response mechanism in controlling the spread of the disease since knowledge about the different prevalence and risks of various conditions can help to focus and adapt public health efforts [71].
The limitations of the present study include the limitations that accompany the process of conducting an ecological study: errors may arise in the filling out of patient data; the underreporting of cases in terms of race or ethnicity, which was not reported in the SESPA database; and a large amount of unknown or unreported data, which affects the reliability of the analysis. Nonetheless, despite these issues, the situation has improved in recent years in Brazil [72]. Another limitation of the ecological design is the acquisition of data on SDH indicators since these data are collected from different sources and different periods [21,36,37,38,39,40]. Although ecological research makes causal inferences about individuals based on group observations, this study can contribute to the evaluation of public health policies in the communities of the Xingu Health Region, especially in the area of clinical management and surveillance of COVID-19, and provide avenues for future studies using other methodological approaches.
## 5. Conclusions
Incidence and mortality were higher in the cities of Vitória do Xingu and Altamira. Additionally, it was found that a higher percentage of people with health insurance and higher public health expenditure in the municipality was associated with higher incidence and mortality. A higher GDP was associated with a higher incidence. The female sex was associated with better clinical management. Residence in Altamira was a risk factor for ICU admission. Symptoms and comorbidities that predicted worse clinical management included dyspnea, fever, emesis, chills, diabetes, cardiac and renal diseases, obesity, and neurological diseases. Headache, anosmia, sore throat, myalgia, and arthralgia were protective factors. With advancing age, patients with COVID-19 face a higher incidence, mortality, and lower survival rates.
Therefore, it can be concluded that SDH indicators, symptomatology, and comorbidities have direct implications for the incidence, mortality, and clinical management of COVID-19 in the Xingu Health Region in eastern Amazonia, Brazil.
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|
---
title: 'A Head-to-Head Comparison of Two Algorithms for Adjusting Mealtime Insulin
Doses Based on CGM Trend Arrows in Adult Patients with Type 1 Diabetes: Results
from an Exploratory Study'
authors:
- Martina Parise
- Sergio Di Molfetta
- Roberta Teresa Graziano
- Raffaella Fiorentino
- Antonio Cutruzzolà
- Agostino Gnasso
- Concetta Irace
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002216
doi: 10.3390/ijerph20053945
license: CC BY 4.0
---
# A Head-to-Head Comparison of Two Algorithms for Adjusting Mealtime Insulin Doses Based on CGM Trend Arrows in Adult Patients with Type 1 Diabetes: Results from an Exploratory Study
## Abstract
Background: Continuous glucose monitoring (CGM) users are encouraged to consider trend arrows before injecting a meal bolus. We evaluated the efficacy and safety of two different algorithms for trend-informed bolus adjustments, the Diabetes Research in Children Network/Juvenile Diabetes Research Foundation (DirectNet/JDRF) and the Ziegler algorithm, in type 1 diabetes. Methods: We conducted a cross-over study of type 1 diabetes patients using Dexcom G6. Participants were randomly assigned to either the DirectNet/JDRF or the Ziegler algorithm for two weeks. After a 7-day wash-out period with no trend-informed bolus adjustments, they crossed to the alternative algorithm. Results: Twenty patients, with an average age of 36 ± 10 years, completed this study. Compared to the baseline and the DirectNet/JDRF algorithm, the Ziegler algorithm was associated with a significantly higher time in range (TIR) and lower time above range and mean glucose. A separate analysis of patients on CSII and MDI revealed that the Ziegler algorithm provides better glucose control and variability than DirectNet/JDRF in CSII-treated patients. The two algorithms were equally effective in increasing TIR in MDI-treated patients. No severe hypoglycemic or hyperglycemic episode occurred during the study. Conclusions: The Ziegler algorithm is safe and may provide better glucose control and variability than the DirectNet/JDRF over a two-week period, especially in patients treated with CSII.
## 1. Introduction
In the last two decades, continuous glucose monitoring (CGM) revolutionized the self-management of diabetes by providing users with near real-time information on their current glucose levels [1,2]. With the ever-increasing accuracy of sensors, more and more systems have been approved for non-adjunctive use by international regulatory agencies, in this way certifying that sensor glucose readings can be safely used for routine diabetes treatment decisions without confirmatory capillary blood testing [3,4].
One of the benefits of CGM is the prediction of future glucose levels with the so-called trend arrows, which indicate both the direction and the rate of change (ROC) of glucose at any given time. However, the meaning of the different arrows may vary depending on the manufacturer [5].
Interpreting trend arrows is a fundamental skill a patient should learn when using CGM. In clinical practice, people with diabetes are told to look at the trend arrows alongside current glucose values before physical activity, driving, bedtime, and before each meal to increase or reduce the calculated meal bolus [6,7]. The changes patients make in mealtime insulin dosage based on trend arrows are largely variable. As illustrated in the survey by Pettus et al., patients would adjust the mealtime dose by an average of $81\%$ and $46\%$ in cases of predicted higher or lower value, respectively [8].
Within the last decade, a number of algorithms have been proposed to determine appropriate dose adjustments based on the trend arrows. However, there is still no consensus due to the lack of robust clinical trials. In two remarkable studies on type 1 diabetes, the Juvenile Diabetes Research Foundation (JDRF) Continuous Glucose Monitoring study and the Diabetes Research in Children Network (DirecNet) Applied Treatment Algorithm study, the ‘$10\%$/$20\%$ rule’ for adjusting bolus insulin dose was evaluated for the first time [9,10]. The results of these studies indicated that the use of the ROC might improve the post-prandial glucose level and the quality of life. Later, other authors proposed different algorithms to encourage people with diabetes to handle CGM data daily. According to Scheiner [11] and Pettus and Edelman [12], a defined value ranging from 25 to 100 mg/dL should be added to or subtracted from the current glucose level based on the trend arrow, and a correction bolus should be calculated according to the patient’s insulin sensitivity factor (ISF). Klonoff and Kerr [13] introduced an easy-to-use formula to adjust the meal bolus dose by adding or subtracting the same amount of insulin for all patients, namely 1, 1.5, or 2 insulin units for the ROC of 1–2, 2–3, and >3 mg/dL/min, respectively. Laffel and Aleppo proposed different trend-informed adjustments of bolus doses depending on the individual ISF (<25 mg/dL, 25 to <50 mg/dL, 50 to <75 mg/dL, or >75 mg/dL), with differences between children [14] and adults [15]. Ziegler and colleagues suggested trend-informed bolus adjustments based on both the ISF (the same strategy proposed by Laffel and Aleppo) and pre-meal glucose levels (<70 mg/dL, 70–180 mg/dL, 180–250 mg/dL, or >250 mg/dL) [16]. In 2021, Bruttomesso et al. modified Ziegler’s slide rule by increasing the number of glucose ranges and insulin sensitivity classes [17].
Taking into consideration the role of the ROC as expressed by the trend arrow before calculating the meal bolus, we designed our research to evaluate the short-term efficacy and safety of two different algorithms for bolus adjustments, namely the earlier and simpler DirectNet/JDRF algorithm and the novel (at the time when this study was conducted) and more sophisticated Ziegler algorithm, in a sample of patients with type 1 diabetes using a CGM device.
## 2.1. Study Design
The current research study is an exploratory single-arm, cross-over study. It was approved by the local Ethical Committee, and performed at the diabetes care center of the University Hospital affiliate of the University Magna Graecia, Catanzaro, Italy. Consecutive patients with type 1 diabetes using the Dexcom G6 (Dexcom, Inc., San Diego, CA, USA) CGM system and regularly attending the center were assessed for eligibility. Those who met the inclusion/exclusion criteria (Table 1) were invited to join the study and were enrolled after signing a written informed consent form. Patients’ characteristics and ongoing treatment were retrieved from the electronic medical record.
The study consisted of two 2-week-long intervention phases to evaluate two different algorithms for adjusting the meal bolus based on trend arrows and a 7-day wash-out period with no trend-informed bolus adjustments between the two phases. Algorithm 1 was the simple DirectNet/JDRF, which suggests increasing or reducing the meal bolus, as was previously calculated according to the insulin:carbohydrates ratio (ICR) and ISF, by $10\%$ in the case of a 1–2 mg/dL/min rise or fall in sensor glucose levels and by $20\%$ in the case of a >2 mg/dL/min rise or fall in sensor glucose levels, respectively [10]. Algorithm 2 was the more sophisticated slide rule by Ziegler et al., which suggests changes in the meal bolus according to the trend arrow, pre-meal glucose level (<70 mg/dL; 70–180 mg/dL; 180–250 mg/dL; >250 mg/dL), and individual ISF (<25 mg/dL; 25–<50 mg/dL; 50–<75 mg/dL; >75 mg/dL). When the glucose is changing at a rate >3 mg/dL/min, insulin doses may vary by ±1–3.5 units; when the glucose is changing at a rate of 2–3 mg/dL/min, insulin doses may vary by ±0.5–2.5 units; when the glucose is changing at a rate of 1–2 mg/dL, insulin doses may vary by ±0.5–1.5 units [16].
In the two weeks before the study initiation, basal insulin doses, ICR, and ISF were optimized. The sequence of Algorithms 1 and 2 was randomly assigned to each participant. All patients received detailed instructions about the study’s protocol and a scorecard with the proposed adjustments. Patients on multiple daily injections (MDI) therapy were suggested to round the final dose to the lower unit for safety reasons. Participants were also invited not to change their lifestyle throughout the study period and to take three meals without snacks when possible. A follow-up phone call on day 3 of both phases was scheduled to ensure that participants were accurately following the study protocol.
## 2.2. Outcome Measures
The outcome measures were CGM-derived glucose metrics as recommended by international consensus [16], including time in the 70–180 mg/dL glucose range (TIR), time below range (TBR), time above range (TAR), mean sensor glucose, the standard deviation of mean glucose (SD), coefficient of variation of mean glucose (CV), and the glucose management indicator (GMI). All the glucose metrics mentioned above were downloaded from the Dexcom Clarity platform for healthcare professionals. During this study, all occurrences of severe hypoglycemia, defined as an event requiring the assistance of another person to actively administer carbohydrates, glucagon, or take other corrective actions and severe hyperglycemia, defined as a hyperglycemic event requiring hospitalization, were recorded.
## 2.3. Statistical Analyses
Statistical analyses were performed using SPSS vers.25.0 (IBM, Armonk, NY, USA). The normal distribution of variables was evaluated using the Shapiro–Wilk test. According to the study design, variables were collected and compared at baseline and after Algorithms 1 and 2 (study phases). Patients were analyzed both as a whole and divided according to the type of treatment (MDI or CSII). The ANOVA and the related-sample Friedman’s two-way ANOVA by ranks were used to compare variables collected at baseline and with Algorithms 1 and 2. The Bonferroni post hoc test and Wilcoxon signed rank test were used to compare the glucose metrics between study phases. The sample size was not driven and was arbitrarily chosen to collect adequate information on protocol efficacy and safety.
## 3. Results
Twenty patients with type 1 diabetes, aged 20–61 years, were recruited and completed this study. No severe hypoglycemic or hyperglycemic episodes occurred during the study. The characteristics of the patients enrolled in this study are illustrated in Table 2.
All patients were adherent and wore the sensor more than $70\%$ of the time during each study phase. Glucose metrics collected at baseline and after using the two algorithms are shown in Table 3. TIR, TAR, and mean glucose significantly differed at the three timepoints of the study, while no difference was detected in TBR, SD, CV, and GMI. The post hoc analysis revealed TIR to be significantly higher and TAR and mean glucose to be significantly lower after Algorithm 2 compared to baseline and Algorithm 1.
Three patients ($15\%$) had a TIR > $70\%$ at baseline, whereas five ($25\%$) and twelve ($60\%$) patients had a TIR > $70\%$ after two weeks of using Algorithms 1 and 2, respectively.
The mean insulin dose injected before meals was 15 ± 5 units with Algorithm 2 and 16 ± 6 units with Algorithm 1 ($$p \leq 0.08$$).
We then divided the participants according to the therapy regimen, MDI or CSII, and again compared the glucose metrics collected at baseline and the end of the two study phases. The results are displayed in Table 4 and Table 5.
In patients treated with MDI therapy, we found a statistically significant difference in TIR and TBR collected at baseline and after the two study phases. However, TBR was no longer significant ($$p \leq 0.076$$) when we excluded one patient with a TBR of $14\%$ at baseline, $7\%$ after Algorithm 1, and $4\%$ after Algorithm 2. Post hoc analyses revealed the TIR to be statistically higher with both algorithms than the baseline.
In patients treated with CSII, TIR, TAR, mean glucose, SD, CV, and GMI were statistically different across the three timepoints of this study. In the post hoc analysis, the same variables differed significantly between Algorithm 2 and the baseline, while Algorithm 1 differed in TIR and TAR when compared to the baseline. Algorithm 1 and Algorithm 2 differed in TIR, TAR, and SD.
## 4. Discussion
Glucose trend arrows add important information for appropriate mealtime insulin dosing in patients with intensive insulin-treated diabetes. However, there is still no consensus on adjusting the scheduled dose according to the upward or downward trend arrows available when using CGM.
In the absence of evidence, healthcare providers recommend increasing or decreasing the amount of insulin injected before a meal by following algorithms proposed by experts or according to self-reported patient experiences.
To our knowledge, this investigation is the first clinical trial evaluating the efficacy and safety of two different algorithms for adjusting mealtime insulin dose based on trend arrows. Among the algorithms available in the literature when the protocol was submitted to the Ethical Committee, we focused on the simple-to-use algorithm adopted in the DirectNet/JDRF study and the more sophisticated algorithm by Ziegler et al.
In our study, using a structured approach as on-top therapy for adjusting mealtime insulin doses based on trend arrows improved CGM-derived glucose control and variability measures, with the best results obtained by using the Ziegler algorithm in the subgroup of patients treated with CSII. We believe that flexible insulin administration with CSII, allowing for fractions of units to be delivered as a bolus, can magnify the fine-tuned dose adjustments with the Ziegler algorithm. Notably, the results were obtained without the occurrence of severe hypo- and hyperglycemia and without appreciable differences in the total daily bolus insulin dose possibly due to a more appropriate within-day distribution of mealtime doses. However, the DirectNet/JDRF algorithm can be regarded as a valid alternative for increasing TIR in patients on MDI therapy.
Several scientific societies recommend using trend arrows for bolus insulin adjustments for diabetes care [7,14,15,18,19]. Unfortunately, the existence of different algorithms and the lack of guidance for choosing between these methods overcomplicate the clinical scenario.
The major strength of our research is the cross-over study design, which eliminates the influence of environmental factors, eating behaviors, and activity level. Notably, the algorithm was added as on-top therapy; that is, patients had adequate glycemic control at baseline, and basal insulin, ICR, and ISF were all optimized before the study.
The current study has some limitations. Firstly, we only included adult patients with type 1 diabetes; therefore, the applicability of our findings to type 2 patients on intensive insulin treatment or to pediatric patients is unknown. However, Ziegler and colleagues propose dedicated tables for insulin-dependent type 2 diabetes and children/adolescents with type 1 diabetes, possibly resulting in more flexible insulin dosing and the better control of prandial glucose excursions in these groups of patients.
Secondly, we only evaluated the short-term safety and efficacy of the two algorithms. Further research is needed to clarify whether the use of the algorithms may be beneficial in the long term and help patients achieve their desired targets of HbA1c.
The interpretation of trend arrows undoubtedly added a layer of complexity when deciding how much insulin to administer before a meal. Any tool facilitating daily dose calculations may result in the more persistent use of dose adjustment algorithms and the long-term improvement of glucose control. In line with this thought, a visual scorecard reporting discrete amounts of insulin units to either add or subtract to the scheduled insulin bolus, such as the Ziegler et al. algorithm, may be more practical than other methods requiring complex calculations (percent dose increase/decrease or recalculation of meal doses based on predicted glucose levels). The development of CGM-informed bolus calculators (CIBC) with automatic trend-based dose adjustments is a further step towards simplifying daily self-management for patients on intensive insulin-based regimens. The feasibility of such an approach has been recently evaluated in a cohort of twenty-five patients with type 1 diabetes on CSII therapy participating in a two-phase, single-arm, prospective, multicenter study conducted in the U.S. At the end of this study, significantly fewer glucose readings of <70 mg/dL at four hours post bolus were found with the CIBC compared to standard bolus calculation without trend-based adjustment ($2.1\%$ ± $2.0\%$ vs. 2.8 ± 2.7, $$p \leq 0.03$$), while the percent of readings >180 and 70–180 mg/dL remained the same with no difference in insulin use or the number of boluses given between the two study phases [20]. However, none of the above-mentioned algorithms have been implemented in the automatic bolus calculators currently available on the market, either as a smart phone application or integrated into insulin pumps.
In recent years, the development of closed-loop systems providing glucose-responsive algorithm-driven insulin delivery revolutionized the treatment of type 1 diabetes, with ever-growing evidence highlighting their value in improving TIR, especially in the overnight period, without causing an increased risk of hypoglycemia [21,22,23,24,25,26,27]. Accordingly, international guidelines recommend that these systems be considered either for treating patients with suboptimal glycemia, significant glycemic variability, or impaired hypoglycemia awareness or to allow for permissive hyperglycemia due to the fear of hypoglycemia [2]. In patients treated with these new generation devices, avoiding insulin dose adjustments based on trend arrows is recommended, as algorithms are designed to automatically correct oscillations without external interference [18]. However, access to these new generation devices is still limited. Regional inequalities exist due to a lack of funding, underdeveloped health technology assessment bodies and guidelines, unfamiliarity with novel therapies, and inadequacies in healthcare system capacities [28]. Therefore, the thoughtful use of real-time glucose information, including insulin dose adjustments based on trend arrows, may help maximize glycemic outcomes in the greater proportion of patients using CGM devices [29,30].
## 5. Conclusions
The appropriate interpretation of trend arrows has the potential to maximize glycemic outcomes and improve engagement with diabetes self-management in patients with type 1 diabetes using CGM devices. We conducted a head-to-head comparison between two different algorithms for trend-informed bolus adjustments, and we have shown that the Ziegler algorithm is safe and provides better glucose control and variability than the DirectNet/JDRF, as measured by CGM over two weeks, especially in patients treated with CSII. Further research is needed to clarify whether these benefits are maintained in the long term and may also apply to type 2 patients on intensive insulin treatments or to pediatric patients.
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|
---
title: A Single Bout of Remote Ischemic Preconditioning Suppresses Ischemia-Reperfusion
Injury in Asian Obese Young Men
authors:
- Min-Hyeok Jang
- Dae-Hwan Kim
- Jean-Hee Han
- Jahyun Kim
- Jung-Hyun Kim
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002219
doi: 10.3390/ijerph20053915
license: CC BY 4.0
---
# A Single Bout of Remote Ischemic Preconditioning Suppresses Ischemia-Reperfusion Injury in Asian Obese Young Men
## Abstract
Remote ischemic preconditioning (RIPC) has been shown to minimize subsequent ischemia-reperfusion injury (IRI), whereas obesity has been suggested to attenuate the efficacy of RIPC in animal models. The primary objective of this study was to investigate the effect of a single bout of RIPC on the vascular and autonomic response after IRI in young obese men. A total of 16 healthy young men (8 obese and 8 normal weight) underwent two experimental trials: RIPC (three cycles of 5 min ischemia at 180 mmHg + 5 min reperfusion on the left thigh) and SHAM (the same RIPC cycles at resting diastolic pressure) following IRI (20 min ischemia at 180 mmHg + 20 min reperfusion on the right thigh). Heart rate variability (HRV), blood pressure (SBP/DBP), and cutaneous blood flow (CBF) were measured between baseline, post-RIPC/SHAM, and post-IRI. The results showed that RIPC significantly improved the LF/HF ratio ($$p \leq 0.027$$), SBP ($$p \leq 0.047$$), MAP ($$p \leq 0.049$$), CBF ($$p \leq 0.001$$), cutaneous vascular conductance ($$p \leq 0.003$$), vascular resistance ($$p \leq 0.001$$), and sympathetic reactivity (SBP: $$p \leq 0.039$$; MAP: $$p \leq 0.084$$) after IRI. However, obesity neither exaggerated the degree of IRI nor attenuated the conditioning effects on the measured outcomes. In conclusion, a single bout of RIPC is an effective means of suppressing subsequent IRI and obesity, at least in Asian young adult men, does not significantly attenuate the efficacy of RIPC.
## 1. Introduction
Remote ischemic preconditioning (RIPC), defined as a set of brief events of ischemia-reperfusion applied in distant tissues or organs from the heart, has been known to protect the cardiovascular systems from subsequent ischemic events in animal [1,2] and human [3] studies. Therefore, ischemic preconditioning has been recognized as one of the non-invasive interventions to prevent ischemic reperfusion injury (IRI) that inevitably occurs during the recovery process after ischemia [4]. Although the signaling mechanism of RIPC remains unclear, the protective effect of RIPC is known to be attributed to both humoral and neural pathways [5].
However, the effectiveness of an acute bout of RIPC is still controversial. For example, a single bout of RIPC was shown to attenuate myocardial ischemic stress through the modification of autonomic nervous system activity in an animal model [6]. In human studies, the RIPC intervention was reported to suppress sympathetic elevation and oxidative stress, together with an improved reactive hyperemic response after IRI in healthy humans [3,7] and attenuated myocardial tissue damage in patients with myocardial infarction [8]. However, others have reported that a single bout of RIPC alters neither autonomic function in young healthy individuals [9], nor cerebrovascular function in the elderly [10]. Further, the efficacy of RIPC was shown to be reduced in some clinical conditions such as type 2 diabetes mellitus [11]. Such inconsistent results, especially in humans, may be partly explained by different health profiles and/or cardiovascular disease risk factors among individuals [12].
Obesity is prevalent worldwide and a strong predictor of ischemic stroke [13] and myocardial infarction regardless of sex, age, and ethnicity [14]; it is also likely to be linked to ischemic diseases [15]. Considering these aspects, the protective effects of RIPC on obesity are worth investigating; however, only a few animal-based studies have been conducted, showing conflicting results. For example, RIPC was shown to offer protective effects on the ischemic livers of obese rats [16], whereas other studies found no meaningful RIPC-induced augmentation of myocardial functions in obese animal subjects [17,18]. Further, to the best of our knowledge, there is no previous study investigating the efficacy of RIPC in obese individuals; therefore, it is uncertain whether obesity influences RIPC outcomes in humans.
For the abovementioned reasons, the purpose of the present study was to investigate whether a single bout of RIPC preserves and/or mitigates vascular function and sympathetic reactivity after induced IRI and to compare the possible differences between obese and normal-weight individuals. We hypothesized that [1] a single bout of RIPC would reduce the degree of IRI and [2] obesity would reduce the degree of RIPC-induced preservation of vascular function after IRI.
## 2.1. Ethical Approval
The current study was conducted after the approval from the Institutional Review Board at Kyung-Hee University (KHGIRB-21-531) and conformed to the standard set by the Declaration of Helsinki. All participants provided written informed consent before their study participation.
## 2.2. Study Design
A total of sixteen male participants (8 normal weight, 8 obese) were recruited for the present study (Table 1). Because all participants in this study were Asian, obesity was defined based on Asia Pacific body mass index criteria equal to or greater than 25 kg/m2 [19]. All participants completed a medical screening questionnaire and those who reported a presence or history of cardiovascular or metabolic disease were excluded from the study.
## 2.3. Experimental Procedure
The schematic view of the study procedure is presented in Figure 1. This study used a cross-over, repeated measures experimental design to test if a single bout of RIPC modifies the degree of IRI in the obese population. All participants visited the laboratory three times at one-week intervals after being abstained from alcohol and caffeine consumption and strenuous physical activity at least 24 h before the scheduled visits. During the first visit, the participants underwent health screening, demographic measurements, and experimental familiarization.
During the remaining two visits, RIPC and SHAM were performed in a counterbalanced order. For the experimental trials, the participants attired medical scrubs upon their arrival at the laboratory and remained in the supine position on a bed for instrumentation. A contoured inflation cuff (18 × 108 cm) was placed on the left proximal thigh for the implementation of RIPC and another cuff (24 × 122.5 cm) was placed on the right proximal thigh to induce IRI. Electrocardiogram standard limb leads (SE-1515, Edan Instruments Inc, Shenzhen, China) were placed onto the torso to monitor heart rate variability (HRV). Cutaneous blood flow was measured (perfusion unit: PU), using laser Doppler flowmetry (LDF, VMS-LDF2, Moor Instruments Ltd., Devon, England) throughout the experiment. The Doppler probe was attached 2 cm from the medial side of the great saphenous vein in the middle of the right leg tibia.
After the completion of the instrumentation, participants rested quietly in a supine position for 20 min followed by the experimental measurements, which were carried out three times throughout the test (e.g., Baseline, Post RIPC/SHAM, and Post IRI). The measurement started with cutaneous blood flow that was continuously monitored throughout the protocol but analyzed at a specific time point followed by HRV measurement for 5 min. Subsequently, blood pressure and pulse rate were measured using a digital sphygmomanometer (BP742N, OMRON Corporation, Kyoto, JAPAN) together with the cold pressor test (CPT) to determine sympathetic reactivity. CPT was carried out by the immersion of participants’ right hand in cold water (at 4–5 °C) for 3 min during which blood pressure and pulse rate were measured at the end of the first and third minutes.
Following the completion of the baseline measurement, RIPC or SHAM was carried out using a rapid cuff inflation system (E20, Hokanson Inc., Bellevue, WA, USA). The RIPC protocol consisted of 3 cycles of ischemia at 180 mmHg for 5 min and reperfusion for 5 min on the left leg. SHAM was performed in the same manner as RIPC; however, the compression intensity was set at each participant’s diastolic blood pressure measured at baseline. Post-RIPC measurement was started immediately after RIPC application followed by induction of the IRI on the right leg for 20 min of ischemia at 180 mmHg and 20 min of reperfusion. We judged that the ischemic stress threshold was reached when the cutaneous blood flow value fell below $20\%$ compared with the baseline [20]. Finally, as soon as the reperfusion period was over, post-IRI measurements were taken in the order described above.
## 2.4. Calculation
Power spectral analysis of HRV (1600 Hz sampling frequency) was conducted using the fast Fourier transform and expressed as the ratio of the low frequency (0.04–0.15 Hz) to high frequency (0.15–0.4 Hz) (LF/HF ratio) to determine an overall balance between the sympathetic and parasympathetic activities. In addition, the lower limb cutaneous vascular conductance (CVC) and cutaneous vascular resistance (CVR) were calculated based on cutaneous blood flow and mean arterial pressure as shown below.
## 2.5. Statistical Analyses
All data in this study were analyzed using SPSS (Ver. 26, IBM, Somers, NY, USA) and presented as mean and standard deviation. A two-way repeated measures ANOVA (2 conditions × 3 time points) with obesity as a between-subject factor was used to compare dependent variables between RIPC and SHAM. When a significant F-value was detected with Greenhouse–Geisser correction for sphericity, a post hoc pairwise comparison with Bonferroni correction was carried out to compare conditions at each time point. The significance level of all statistical analyses was set at α = 0.05.
## 3.1. Heart Rate Variability, Blood Pressure, and Resting Heart Rate
There was a significant interaction for the LF/HF after IRI in the RIPC compared with the SHAM ($F = 4.631$, $$p \leq 0.027$$) (Table 2). Consistently, SBP ($F = 3.423$, $$p \leq 0.047$$) and MAP ($F = 3.488$, $$p \leq 0.049$$) after IRI were significantly lower in RIPC compared with SHAM, but no difference was found for DBP ($F = 1.698$, $$p \leq 0.206$$) and HR ($F = 0.589$, $$p \leq 0.550$$) (Figure 2). However, the existence of obesity did not alter the effect of RIPC on these variables (LF/HF ratio: $F = 0.050$, $$p \leq 0.939$$; HR: $F = 1.260$, $$p \leq 0.301$$; SBP: $F = 0.634$, $$p \leq 0.537$$; MAP: $F = 0.572$, $$p \leq 0.564$$; DBP: $F = 0.572$, $$p \leq 0.564$$).
## 3.2. Sympathetic Reactivity
A significant interaction was found for sympathetic reactivity, where there was an attenuated SBP response to cold in RIPC compared with SHAM after IRI ($F = 5.382$, $$p \leq 0.039$$); however, no significant difference was found in MAP ($F = 3.549$, $$p \leq 0.084$$) or DBP ($F = 1.546$, $$p \leq 0.238$$). Similarly, the existence of obesity did not alter the sympathetic reactivity (SBP: $F = 0.726$, $$p \leq 0.411$$; MAP: $F = 0.012$, $$p \leq 0.913$$; DBP: $F = 0.062$, $$p \leq 0.808$$).
## 3.3. Cutaneous Vascular Responses
A significant interaction was found for CBF, where there was an alleviated reduction in CBF after IRI in the RIPC compared with SHAM ($F = 10.111$, $$p \leq 0.001$$). Consequently, a significantly increased CVC and decreased CVR were found after IRI in the RIPC (CVC: $F = 7.828$, $$p \leq 0.003$$; CVR: $F = 10.576$, $$p \leq 0.001$$). However, RHI did not differ between conditions ($F = 0.716$, $$p \leq 0.474$$) (Figure 2), and the existence of obesity did not influence any of the vascular variables (LDF: $F = 0.231$, $$p \leq 0.784$$; CVC: $F = 0.185$, $$p \leq 0.819$$; CVR: $F = 0.032$, $$p \leq 0.962$$; RHI: $F = 0.973$, $$p \leq 0.381$$).
## 4. Discussion
To our knowledge, this is the first study investigating the effectiveness of RIPC in obese humans. It was found that a single bout of RIPC significantly suppressed the IRI-induced aggravation of vascular function and sympathetic reactivity compared with SHAM. However, there was no difference in any outcome measures between obese and normal-weight individuals. These results suggest that a single bout of RIPC is an effective means of mitigating injuries resulting from a subsequent ischemic event and such modifications were neither blunted nor magnified by obesity, at least in young adult males, in the present study.
The present results showed the significant inhibitory effects of RIPC on the reduction in CBF; in agreement with previous findings, these effects mitigated the IRI-related impairment in CVC and CVR. Kraemer et al. [ 2011] reported that a single bout of RIPC significantly increased blood flow and tissue oxygen saturation during the reperfusion phase in healthy young men [21]. Moreover, Kharbanda et al. [ 2002] showed that blood flow in response to acetylcholine after IRI alone was decreased, whereas a single bout of RIPC suppressed this reduction in healthy humans [1]. These acute preconditioning effects on vascular function, such as reduced coronary resistance and increased cerebral blood flow, have also been reported in animal studies [22,23,24]. Moreover, significantly suppressed elevation in SBP and MAP in RIPC (Figure 2), which might be explained by the augmented cutaneous vasodilation, supports previous findings [25] and implies therapeutic potential for blood pressure management [26].
When considering IRI-induced aggravation in vascular function is attributed to a decline in nitric oxide bioavailability and sympathetic-overactivation-induced vasoconstriction [27,28], a single bout of RIPC in the present study is thought to alter such impairments either singly or in combination. Although previous studies demonstrated RIPC-induced vasodilation originates from both endothelial-dependent and -independent vasodilators [29,30], the ability to explain which of the two vasodilation mechanisms was responsible for improved vascular function in the present experiment is limited. On the other hand, the suppressed vascular impairment in RIPC was accompanied by a significant attenuation in the LF/HF ratio (Table 2) and sympathetic reactivity to a cold stimulation after IRI (Figure 2), similar to the previous results of RIPC-induced improvement in sympathovagal balance in healthy humans [31] and patients with angina pectoris [25].
Both obesity and IRI are hypoxic and share similar inflammatory profiles including excessive production of reactive oxygen species and inflammatory cytokines [32,33]. Therefore, due to increased susceptibility to ischemic injury, we expected a greater degree of IRI together with reduced RIPC-induced preservation of vascular function in obese individuals compared with the normal-weight participants. Contrary to our expectations and previous results from the animal model [17,18], the present results showed the positive effects of RIPC on vascular and autonomic functions in obese participants after IRI, although the obese individuals showed a lesser degree of CBF recovery and maintenance over time compared with the normal-weight individuals in the first 2 min of reperfusion (Figure 3).
This might be due to the characteristics of the obese subjects participating in this study. Activation of phosphatase, known to limit the efficacy of both preconditioning and postconditioning with aging, was more pronounced in obese rats [34,35], but we recruited young healthy subjects without a history of cardiovascular and metabolic diseases. Regardless of the degree of BMI, the longer the period of obesity, the higher the risk of cardiovascular disease [36,37]. Their short obesity period and healthy physical condition most likely offset the adverse effects of obesity, such as cardiovascular disease and impaired physical function.
This study has several limitations. First, the present results and interpretation regarding obesity are limited to Asian men. Secondly, we also excluded female participants to rule out the effect of hormonal changes on measurements such as HRV. Finally, any blood parameters that may have been responsible for explaining the mechanisms and/or effects of RIPC on the outcomes were not included. Therefore, the potential roles of some important markers such as inflammatory cytokines and various vasodilators were limited in the present interpretation.
## 5. Conclusions
A single bout of RIPC was found to be an effective means of reducing impairment in vascular function and hyper-sympathetic nerve activity resulting from an acute ischemic event. Further, based on the present outcome measures, obesity neither significantly aggravated the degree of IRI nor abolished the favorable effects of RIPC in Asian obese young men. Future studies are warranted to investigate how repeated bouts of RIPC could further influence the functioning of the vascular systems in diverse obese populations.
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|
---
title: TCF-1 Is Required for CD4 T Cell Persistence Functions during AlloImmunity
authors:
- Mahinbanu Mammadli
- Liye Suo
- Jyoti Misra Sen
- Mobin Karimi
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10002223
doi: 10.3390/ijms24054326
license: CC BY 4.0
---
# TCF-1 Is Required for CD4 T Cell Persistence Functions during AlloImmunity
## Abstract
The transcription factor T cell factor-1 (TCF-1) is encoded by Tcf7 and plays a significant role in regulating immune responses to cancer and pathogens. TCF-1 plays a central role in CD4 T cell development; however, the biological function of TCF-1 on mature peripheral CD4 T cell-mediated alloimmunity is currently unknown. This report reveals that TCF-1 is critical for mature CD4 T cell stemness and their persistence functions. Our data show that mature CD4 T cells from TCF-1 cKO mice did not cause graft versus host disease (GvHD) during allogeneic CD4 T cell transplantation, and donor CD4 T cells did not cause GvHD damage to target organs. For the first time, we showed that TCF-1 regulates CD4 T cell stemness by regulating CD28 expression, which is required for CD4 stemness. Our data showed that TCF-1 regulates CD4 effector and central memory formation. For the first time, we provide evidence that TCF-1 differentially regulates key chemokine and cytokine receptors critical for CD4 T cell migration and inflammation during alloimmunity. Our transcriptomic data uncovered that TCF-1 regulates critical pathways during normal state and alloimmunity. Knowledge acquired from these discoveries will enable us to develop a target-specific approach for treating CD4 T cell-mediated diseases.
## 1. Introduction
T cell factor-1 (TCF-1, encoded by Tcf7) regulates T cell development, cell fate specification, and maintenance of tissue homeostasis [1]. TCF-1 plays a critical role in T cell responses to viral infection, cancer, and autoimmunity [1,2,3,4]. TCF-1 is a key mediator of both Th1 and Th17 cytokines, and T-bet expression has been shown to be regulated by TCF-1 [5,6]. Studies from germline TCF-1 knock-out mice and enforced expression models have demonstrated that T-bet recruits the transcriptional repressor Bcl6 to the TCF-1 promoter and inhibits TCF-1 gene expression [7]. These data suggest that T-bet can regulate the role of TCF-1 in Th1 differentiation or effector function. A major limitation of these studies was the use of TCF-1 germline knock-out mice or Wnt pathway inhibitors that are not specific. CD4 T cells with a higher TCF-1 expression have been shown to have self-renewing capacity, while CD4 T cells with a lower TCF-1 expression do not, at least in the context of a viral infection [8]. Studies have also shown that both LEF-1 and TCF-1 play a central role in Th2 cell development by suppressing Th2 specific cytokines and the induction of GATA-3 [9]. TCF-1 has been shown to play a critical role in Tfh cells by regulating IL-4/STAT-6 signaling to control the differentiation of CD4 cytolytic cells [9,10,11,12]. TCF-1 has also been shown to inhibit IL-17 during the early stages of development, limiting peripheral Th17 cells [13]. However, to our knowledge, TCF-1 has not been studied in the context of alloimmunity, which is a different process from canonical T cell activation.
To study the role of TCF-1 in CD4 T cells in a clinically relevant model, we utilized an allogeneic hematopoietic stem cell transplantation (allo-HSCT) murine model. Allo-HSCT is a curative option in the treatment of aggressive malignant and non-malignant blood disorders [14,15]. However, the benefits of allo-HSCT can be compromised by graft-versus-host disease (GvHD), a prevalent and morbid complication of allo-HSCT. We used a unique mouse strain that has a deletion of TCF-1 in mature T cells, rather than a global deletion [16,17]. The progeny of a TCF-1 flox/flox mouse was crossbred with a CD4 cre+/+ C57BL/6 mouse experiencing a deletion of TCF-1 in all T cells at the double-positive (DP) phase of development when all T cells express CD4 [18]. This approach allowed us to directly investigate the role of TCF-1 on peripheral CD4 T cells [19]. Our compelling data demonstrate that the loss of TCF-1 in CD4 T cells reduces both the severity and the persistence of GvHD, leading to improved survival of recipient mice following transplantation. We also showed that TCF-1 significantly impacts CD4 T cell activation memory formation. Our data also uncovered that TCF-1 differentially impacts chemokine expression, however, donor CD4 T cells from TCF-1 cKO mice had no impact on donor T cell migration to GvHD target tissues. Several lines of evidence have suggested that CD28 is critical for the CD4 T cell persistence function [20,21]. Our data showed that CD4 T cells from TCF-1 cKO mice expresses significantly less CD28 co-stimulatory molecules. For the first time, we provide evidence that TCF-1 regulates CD28 expression in T cells [17]. The loss of CD28 in CD4 T cells has no impact on CD4 T cell initial cytokine production [22]. We uncovered that mature CD4 T cells from TCF-1 cKO mice produce significantly less Th1 cytokines but show increased production of Th2 cytokines in the in-vivo alloimmune disease model, which was previously unknown. Th1 cytokines have been shown to cause donor T cell proliferation/expansion and to significantly amplify the development of GvHD (more than Th2 cytokines) [23,24,25,26]. Our transcriptomic data uncovered that pre- and post-transplanted CD4 T cells from TCF-1 cKO mice have an altered expression of pathways related to apoptosis and cell death, T cell-mediated processes, cytokine production, and cell adhesion. Overall, our data demonstrate that TCF-1 significantly impacts peripheral CD4 T cell phenotype, cytokine production, chemokine expression, and cell survival, and alters the genetic profile in a clinically relevant murine model. Thus, our findings contribute significantly to understanding the role of TCF-1 in CD4 T cells in alloimmunity.
## 2.1. TCF-1 Regulates CD4 T Cell Phenotype and Memory Formation
Previous research on TCF-1 focused on T cell development and canonical activation [2,27]. These studies primarily used a global TCF-1 knockout, resulting in limited production of mature T cells. We sought to examine the role of TCF-1 in mature T cells, so to overcome this limitation we employed mice with a T-cell-specific deletion of TCF-1 using TCF-1 flox/flox mice bred with CD4 cre+/+ mice [28,29]. This mouse strain has TCF-1 deleted in all CD4 and CD8 T cells at the DP phase, allowing production of mature T cells with a TCF-1 deletion. The CD4 T cell phenotype has been extensively studied in response to viral infections, cancer, and GvHD [30,31,32]. More specifically, CD4 T cell phenotype plays a central role in autoimmune disorders [32,33]. However, whether TCF-1 regulates CD4 T cell activation, effector, and central memory phenotypes in peripheral CD4 T cells is unclear.
To investigate whether TCF-1 regulates these critical functions of CD4 T cells, we examined naïve CD4 T cells from either WT C57Bl/6, CD4 cre+/+, or TCF-1 cKO mice. CD4 T cells were isolated from spleens, and we confirmed by flow cytometry the loss of TCF-1 in CD4 T cells (Figure 1A). Next, we examined the expression of activation markers, such as CD44 and CD122 on CD4 T cells. CD4 T cells expressing these markers have been shown to induce significantly less or no GvHD [32,34]. Our data showed that splenic naïve CD4 T cells from TCF-1 cKO mice expressed significantly more CD44 and CD122 markers, compared to CD4 T cells from control mice including WT and CD4 cre+/+ strains (Figure 1B,C).
These data uncovered that TCF-1 plays a suppressive role in CD4 T cell activation and the loss of TCF-1 affects the CD4 T cell phenotype [17,35]. We also examined whether CD4 T cells from TCF-1 cKO mice might impact the T-box transcription factor family members Eomes and T-bet [36], which are important in anti-tumor responses of T cells and play central roles in T cell mediated GvHD and alloimmunity [37]. The key transcription factor T-bet is known to regulate the balance between the effector and central memory phenotype of T cells [38,39]. Our data showed that CD4 T cells from TCF-1 cKO mice expressed significantly more T-bet than CD4 T cells from control WT, and CD4 cre+/+ mice (Figure 1D). The data with higher T-bet expressions in TCF-1 cKO mice correlate with higher expressions of CD122 and CD44 in CD4 T cells from TCF-1 cKO mice [40,41,42]. However, we did not observe any differences in Eomes expression in splenic naïve CD4 T cells from WT, compared to TCF-1 cKO mice (Supplementary Figure S1A).
Since both effector (EM) and central (CM) memory cells have been shown to play a significant role in CD4 T cell-mediated diseases, including GvHD [32], we wanted to examine the memory phenotype in CD4 T cells from TCF-1 cKO mice. Effector memory cells were defined as CD44high CD62Llow, central memory cells defined as CD44high CD62Lhigh, and naïve CD4 T cells defined as CD44 low CD62Lhigh subgroups. Surprisingly, our data showed that there was a significant increase in both EM and CM cells and significantly fewer naïve cells in TCF-1 cKO mice, compared to WT, and CD4 cre+/+ mice (Figure 1E). These findings shows that TCF-1 does play a significant role in the EM and CM formation in CD4 T cells [17,43,44].
Because ICOS has been shown to play a critical role in effector memory, CD4 T cell survival, and maintenance [45,46,47], we wanted to examine the expression of ICOS in the effector memory of CD4 T cells from TCF-1-deficient and WT, or control and CD4 cre+/+ mice. Our data uncovered that the splenic EM memory of CD4 T cells from TCF-1 cKO mice express ICOS significantly less frequently, compared to CD4 T cells from other control mice, suggesting that even though TCF-1-deficient mice have more effector memory cells, their survival and maintenance may be negatively affected, providing evidence that TCF-1 is required for CD4 T cell persistence functions (Figure 1F).
Published data have shown that CXCR3 expression is directly linked to T-bet expression in EM cells during viral infections, and that TCF-1 controls the chemokine receptor expression in CD4 T cells and their trafficking to GvHD target organs [48,49]. A molecular analysis also showed that TCF-1 has been implicated in T cell migration [50,51]. Our data showed that naïve splenic CD4 T cells from TCF-1 cKO mice express CXCR3 significantly more frequently, which suggests that TCF-1 CD4 T cells might migrate to GvHD target organs more easily than CD4 T cells from WT or CD4cre+/+ control mice (Figure 1G).
The differences we observed in the phenotype of the CD4 T cells between WT and TCF-1 cKO mice could be cell-intrinsic (due directly to gene deletion within the cell) or cell-extrinsic (due to different microenvironments caused by gene deletion) [52,53]. To determine whether the above-mentioned differences of CD4 T cell phenotypes from TCF-1 cKO mice are cell-intrinsic or cell-extrinsic, we mixed TCDBM from WT mice with the congeneric marker CD45.1 along with bone marrow from TCF-1 cKO mice with the congeneric marker CD45.2 at a 1:4 ratio. These ratios were determine based on our previous publication [54].
To determine whether the phenotypic effects we observed were cell-intrinsic or cell-extrinsic, we developed a chimeric mouse model. Briefly, we mixed bone marrow from WT and TCF-1 cKO mice at a 1:4 (WT:TCF) ratio for a total of 50 × 106 BM cells, then used this mixture to reconstitute lethally irradiated into mice with the congeneric marker Thy.1. to generate a mixed chimera model. Nine to 10 weeks post transplantation, we bled the recipient Thy1.1 mice to confirm the successful reconstitution and creation of a chimera model [17]. Ten weeks post-chimerization, we euthanized recipient Thy1.1 mice and used a flow cytometry analysis to examine TCF-1 expression in the WT by CD45.1 or in the TCF-1cKO by CD45.2 bone marrow-derived CD4+ T cells [17]. Our data confirmed that the loss of TCF-1 in the TCF-1 cKO mice is cell-intrinsic, compared to control groups (Supplementary Figure S2A). However, when we examined mixed bone marrow chimera derived CD4 T cells from our chimera model from either WT or TCF-1 cKO mice developed in the same thymus, we did not observe any differences in either CD122 or CD44 expression (Supplementary Figure S2B,C). Next, we examined whether mixed bone marrow derived CD4 T cells from WT that develop in the same thymus as bone marrow derived CD4 T cells from TCF-1 cKO bone marrow derived model developed in the same thymus, we observed no differences in the EM and CM phenotypes (Supplementary Figure S2D,E). We also observed no difference in T-bet expression among the mixed bone marrow derived CD4 T cells from WT and TCF-1 cKO mice (Supplementary Figure S2F). The increase in expression of CD122, CD44, EM, CM, and T-bet were significantly higher in naïve CD4 T cells from TCF-1 cKO mice (Figure 1B–E). However, in a mixed chimera model we did not see any differences. These data suggested that these activating marker CD122 and CD44 expression in a mixed bone marrow chimera become similar to that found in TCF-1 cKO mice. EM, CM, and T-bet expression also become similar to TCF-1 cKO mice. Altogether, these data showed that TCF-1 controls a number of activation markers and memory formation in a cell-extrinsic way.
## 2.2. Loss of TCF-1 in Donor CD4 T Cells Reduces Severity and Persistence of GvHD Symptoms
To investigate whether TCF-1 in mature CD4 T cells contributes to GvHD after allo-HSCT, we employed a murine model of MHC-mismatched allotransplantation. The MHC haplotype mismatch (H2Kb in donors, H2Kd in recipients) results in the alloactivation of the donor T cells, leading to GvHD [35,54,55]. A group of irradiated BALB/c mice were transplanted with 10 × 106 TCDBM cells alone. This group of mice will be considered a control that will not develop GvHD due to the lack of mature T cells. A second group of irradiated BALB/c mice were transplanted with 10 × 106 TCDBM along with 1 × 106 CD4 T cells from WT mice. These groups will develop lethal GvHD, as shown previously [35,43,54,55]. To determine whether CD4cre might contribute to the development of CD4 T cell-mediated GvHD, a third group of irradiated BALB/c mice were transplanted with 10 × 106 TCDBM along with 1 × 106 CD4 T cells from CD4cre mice. Next, we asked whether CD4 T cells from TCF-1Flox/Flox mice with or without CD4cre develop CD4 T cell- mediated GvHD. A fourth group of irradiated BALB/c mice were transplanted with 10 × 106 TCDBM along with 1 × 106 CD4 T cells from TCF-1Flox/Flox mice.
Finally, we examined whether CD4 T cells from mice lacking TCF-1 expression specifically on mature T cells develop GvHD. A fifth group of irradiated BALB/c mice were transplanted with 10 × 106 TCDBM along with 1 × 106 CD4 T cells from TCF-1 cKO mice. Recipient BALB/c mice were monitored for survival (Figure 2A) for up to about 70 days. Recipient mice were examined, weighed, and given a GvHD score (Figure 2B,C) to identify the severity of GVHD for up to about 70 days post-transplant. Recipient mice were scored based on weight loss, fur texture, posture, activity, skin condition, and diarrhea, as previously described [17,35,54,55,56,57].
Recipient BALB/c mice receiving donor CD4 T cells from WT C57Bl/6, CD4 cre+/+ or TCF-1Flox/Flox control donor’s cells experienced a rapid increase in GvHD symptoms and peaked at a very high score, indicating very severe disease (Figure 2C). CD4 T cells are known to cause very severe GvHD symptoms, so this finding was expected [35,53,58]. Over time, these recipient mice continued to show severe symptoms, with consistent scores until death from disease (Figure 2C). Survival was also poor, with most mice in this group dying within 25 days of transplantation (Figure 2A). These mice lost weight due to GvHD and died before they were able to regain much weight (Figure 2B). In contrast, recipient BALB/c mice receiving CD4 T cells from TCF-1 cKO C57Bl/6 mice had significantly better survival (Figure 2A), weight gain following an initial weight loss (Figure 2B) and reduced GvHD scores (Figure 2C). Interestingly, recipient BALB/c mice in the TCF-1 cKO CD4 T cell showed a peak in GvHD score early on around day 5, as was seen in the other control group, but peaked at a much lower score. Additionally, this peak score did not persist over time, as the scores for these mice quickly reduced to the level seen in bone marrow-only transplanted controls (Figure 2C). In addition, this low score remained for an extended period, suggesting that the disease had resolved rather than being delayed (Figure 2C). These data indicated that GvHD symptoms were not only less severe in these mice, but also less persistent over time.
## 2.3. TCF-1 Regulates Chemokine/Chemokine Receptor Expression in Mature CD4 T Cells during Allo-Activation
GvHD involves early migration of alloreactive donor T cells into the target organs, followed by T cell expansion and tissue destruction [23,24]. Modulation of alloreactive T cell trafficking has been suggested to play a significant role in ameliorating experimental GvHD [59]. Therefore, we examined the trafficking of donor T cells to GvHD target tissues, as previously described [60]. To determine whether TCF-1 regulates CD4 T cells trafficking to GvHD target organs, we repeated the short-term experiments, as described. Irradiated BALB/c recipient mice were transplanted with 10 × 106 TCDBM. We mixed CD4 T cells from WT mice with WT B6LY5 (CD45.1+) in a C57Bl/6 background with CD4 T cells from TCF-1 cKO mice with a (CD45.2+) C57Bl/6 background at a 1:1 ratio. Seven days post-transplantation, recipient mice were examined for the presence of donor CD4 T cells in the spleen, lymph nodes, liver, and small intestines. We observed no differences in trafficking of donor CD4 T cells from either WT or TCF-1 cKO mice (Supplementary Figure S2D,E). Chemokines direct cellular infiltration to tissues, and their receptors and signaling pathways represent targets for therapy in multiple disease models, including autoimmunity, cancer, and T cell responses to viral infections [61,62,63,64,65,66].
To determine whether TCF-1 regulates specific chemokine receptors expression, we sorted back donor CD4 T cells from allotransplanted BALB/c mice (using H2Kb to identify donor cells) and performed a qPCR using a 96-well mouse chemokine/chemokine receptor array plate (Thermo Fisher liver pool NY USA). We found that the expression of chemokines and chemokine receptors were upregulated following alloactivation, as expected. However, expression of these markers was consistently higher in TCF-1 cKO CD4 T cells from spleen, both pre- and post-transplant (Figure 3A,B), while these markers were downregulated in TCF-1 cKO CD4 T cells from post-transplanted liver (Figure 3C). These changes also confirmed our observation of CXCR3 expression as increased in splenic cells from TCF-1-deficient mice, compared to WT mice. Our data uncovered that TCF-1 regulates chemokine receptor expression before and after transplantation, with tissue-specific changes.
## 2.4. TCF-1 Regulates CD4 T Cell Damage to GvHD Target Organs
During GvHD, host tissues are damaged by the activity of alloactivated CD4 T cells [53,67,68]. To determine whether damage to the target organs of GvHD (skin, liver, and small intestine) was altered by loss of TCF-1 in donor CD4 T cells, we collected organs from mice allotransplanted, as described above [35,54,55,69]. To induce GvHD, we used MHC-mismatched donors and recipients, with T cell-depleted bone marrow (TCDBM) from WT mice, donor CD4 T cells from either C57BL/6 (B6) WT or TCF-1 cKO mice (MHC haplotype b), and lethally irradiated BALB/c (MHC haplotype d) mice as recipients. Recipient mice were injected intravenously with 10 × 106 wild-type (WT) TCDBM cells along with purified 2 × 106 donor CD4 T cells. To examine the pathological damage to target organs including the liver, small intestine, and skin, tissues from the recipient BALB/c mice and these organs were collected for histology at day 7 and day 21 post-transplantation. Collected organs were fixed, sectioned, hematoxylin and eosin (H&E) stained, and analyzed by a pathologist (L.S.) In the liver, (magnification ×400), much less inflammatory infiltrates in the bile ductal epithelium of the portal triad (black arrows showing the inflammatory cells around interlobular bile ducts) was seen in the recipient mice transplanted with CD4 T cells from TCF-1 cKO cells, compared with recipient mice transplanted with CD4 T cells from WT C57Bl/6 mice, and both euthanized at day 7r: WT (Figure 4A) and TCF-1 cKO (Figure 4B); euthanized at day 21: WT (Figure 4C) and TCF-1 cKO (Figure 4D) post-transplant. In the small intestine (magnification ×400), no apoptotic bodies were seen in the crypts of the small intestine in the recipient mice transplanted with CD4 T cells from TCF-1 cKO C57Bl/6, while many apoptotic bodies with micro abscesses (black arrows and red circle) were present in the small intestine of the recipient mice transplanted with CD4 T cells from WT C57Bl/6 mice and euthanized at day 7: WT (Figure 4E) and TCF-1 cKO (Figure 4F). We observed that fewer apoptotic bodies were present in the small intestine of recipient mice that were transplanted with CD4 T cells from WT C57Bl/6 mice and euthanized at day 21, compared to recipient mice transplanted with CD4 T cells from TCF-1 cKO mice at day 21: WT (Figure 4G) and TCF-1 cKO (Figure 4H). In the skin (magnification ×200), a mild increase of inflammatory cells (red circle) was observed in the dermis of the recipient mice transplanted CD4 T cells from WT C57Bl/6 mice and euthanized on day 7: WT (Figure 4I) and TCF-1 cKO (Figure 4J), and a marked increase of inflammatory cells (red circle) with frequent apoptotic bodies involving both epidermic and dermis was observed in the dermis of the recipient mice transplanted with CD4 T cells from WT C57Bl/6 mice and euthanized at day 21 (Figure 4K), while the dermis of the recipient mice transplanted with CD4 T cells from TCF-1 cKO mice appeared normal at both timepoints (Figure 4L). These findings further support the idea that disease resolves over time and does not persist when recipient mice transplanted with donor CD4 cells from TCF-1 cKO mice. Together, these results indicate that TCF-1 normally contributes to and is indispensable for GvHD damage by T cells, and the loss of TCF-1 reduces its severity and persistence of GvHD.
## 2.5. TCF-1 Regulates CD4 T Cell Survival and Persistence
CD4 T cell survival and persistence functions are critical in both health and disease [70]. The importance of CD4 T cell persistence has been shown in autoimmunity, cancer, viral infection, and several cardiovascular diseases [71,72,73]. Therefore, we sought to examine whether TCF-1 is critical for the survival of peripheral CD4 T cells and for their function. We isolated splenocytes from either control mice, including WT C57Bl/6 or TCF-1 cKO mice and performed an in vitro death and apoptosis assay. These CD4 T cells were either stimulated with 2.5 μg/mL anti-CD3 and 2.5 μg/mL anti-CD28 antibodies for 6, 24, 48, or 72 h in culture or left unstimulated, then were stained for apoptosis and death markers. We did not observe any differences in apoptosis, live cell, or dead cell percentages at 0 h between the strains of mice (Figure 5A). CD4 T cells from TCF-1cKO mice that were stimulated for 6 h or for 24 h had more dead cells (annexin V+, near-IR+) and fewer live cells (annexin V-, near-IR-) than cells from the control WT mice (Figure 5B,C). When stimulated for 48 h, CD4 T cells from TCF-1 cKO mice showed more dead (annexin V+, near-IR+) and apoptotic cells (Annexin+, near-IR-) and fewer live cells (annexin V-, near-IR-), compared to CD4 T cells from WT C57Bl/6 mice (Figure 5D). By 72 h post-stimulation, the frequencies of live, apoptotic, and dead cells from the control mice, WT C57Bl/6, and TCF-1 cKO mice were the same (Figure 5E). These data suggested that TCF-1 is critical for early survival and the persistence function of CD4 T cells.
CD4 T cell exhaustion has been well documented in CD4 T cell responses to viral infections, and the role of TCF-1 in regulating T cell exhaustion during viral infections is also clear [74,75,76]. PD-1 has also been shown to be critical for CD4 T cell function [77]. Thus, we examined whether TCF-1 regulates PD-1 expression on naïve splenic CD4 T cells from WT C57Bl/6 or TCF-1 cKO mice. Splenocytes from TCF-1 cKO or control mice and WT C57Bl/6 mice were isolated and were either stimulated with 2.5 μg/mL anti-CD3 and 2.5 μg/mL anti-CD28 antibodies for 24, 48, or 72 h in culture or were left unstimulated. These cells were stained for PD-1, Ki-67, and TOX. We did not observe any differences in PD-1 expression before or after 24 or 48 h of stimulation (data not shown), but at 72 h post-stimulation, CD4 T cells from TCF-1 cKO mice expressed more PD-1, compared to CD4 T cells from WT C57Bl/6 mice (Figure 5F). This finding suggested that TCF-1 is a critical regulator of PD-1 expression during the late stages of in vitro activation. To examine whether CD4 T cells become exhausted due to the lack of TCF-1 in alloimmunity, as is shown during T cell responses to viral infection, we examined the Ki-67 expression and TOX expression [78,79], in CD4 T cells from WT C57Bl/6 and TCF-1 cKO mice before and after stimulation. Our data uncovered that there were no differences at any timepoint in expression of Ki-67 and TOX among CD4 T cells from WT and TCF-1 cKO mice (Figure 5H,I).
CD28 receptor provides a critical second signal alongside T cell receptor (TCR) ligation for naïve T cell activation. We and others have shown that TCF-1 is critical for TCR stemness [2,17,80]. Published data have also demonstrated that the lack of CD28 significantly weakens TCR stemness [81]. Thus, we examined whether the CD4 T cells from TCF-1 cKO mice also have reduced CD28 expression. We isolated CD4 T cells from either WT or TCF-1 cKO mice. These freshly isolated CD4 T cells were examined for CD28 expression by flow cytometry. Our data uncovered that CD4 T cells from TCF-1 cKO mice have no CD28 expression, compared to that seen in the WT mice (Figure 5J). These findings highlight that TCF-1 has minimal impact on CD4 T cell exhaustion and proliferation in in vivo studies. These findings suggested that TCF-1 regulates CD28 expression required for CD4 stemness.
To understand whether TCF-1 might regulate CD4 T cells differently in vitro than in vivo, we transplanted 1 × 106 purified CD4 T cells from either WT or TCF-1 cKO mice into irradiated BALB/c mice to establish an allo-HSCT model, as described above, to assess these changes in vivo. At day 7 post-transplant, recipient BALB/c mice were euthanized and donor H2Kb+ donor CD4 T cells from the liver and spleen were stained for annexin V and near-IR (apoptotic and dead cell markers). Our data uncovered that there were no differences between the strains in live, apoptotic, or dead CD4 T cells coming from the liver or spleen of the recipients (Figure 6A,B). However, our data demonstrated that there were significantly fewer donor H2kb+ CD4 T cells in the spleen and liver of recipients that were transplanted with CD4 T cells from TCF-1 cKO mice, compared to in those given WT cells (Figure 6C,D). We also wanted to determine the expression of Ki-67 and TOX in the in vivo alloactivated CD4 T cells from TCF-1-deficient and WT mice. Again, we did not find any differences in Ki-67 and TOX expression in CD4 T cells from the liver or spleen between the recipients of the two donor strains (Figure 6E–H). These findings highlight that TCF-1 have minimal impact on CD4 T cells exhaustion and proliferation in in vivo studies. These findings suggested that TCF-1 regulates CD28 expression required for CD4 stemness.
## 2.6. TCF-1 Regulates Serum Levels of Cytokines during Alloimmunity
Production of inflammatory cytokines, eventually culminating in a cytokine storm, is considered a hallmark of CD4 T cell-mediated alloimmunity [25,31,82,83]. Th1 cytokines and cytotoxic mediators are essential for T cells to maintain the GVL effect and kill tumor cells, yet they also lead to the damage of healthy host tissues [84,85,86,87], More specifically INF-γ and TNF-α secretion by donor CD4 T cells are the hall mark of persistence of GvHD mediators [88]. To examine cytokine production by TCF-1 cKO CD4 T cells, we allotransplanted recipient mice, as described above. At day 7 post-transplantation, we also took blood from these recipient mice and obtained serum, which we tested for various cytokines using a LEGEND plex ELISA kit (Biolegend). We uncovered that recipient BALB/c mice transplanted with CD4 T cells from TCF-1 cKO mice had higher serum levels of IFN-γ and TNF-α at day 7 post-transplant than recipient mice transplanted with CD4 T cells from WT mice. However, the level of these proinflammatory cytokines were significantly decreased at day 14 post transplantation (Figure 7A–D). IL-5 and IL-2 have been shown to play critical roles in GvHD [89,90,91]. Thus, we examined whether the levels of IL-2 or IL-5 are impacted by the loss of TCF-1 on mature CD4 T cells. Our data uncovered that recipient BALB/c mice transplanted with CD4 T cells from TCF-1 cKO mice showed increased expression of IL-5 at day 7, compared to recipient mice transplanted with CD4 T cells from WT C57Bl/6 mice. However, at day 14, we did not see significant differences in the serum level IL-5 in recipient mice either transplanted with donor CD4 T cells from WT or TCF-1 cKO mice. Similarly, recipient BALB/c mice transplanted with donor CD4 T cells from TCF-1 cKO mice produced higher serum levels of IL-2 at day 7 post transplantation, however the serum levels dropped at day 14 post transplantation (Figure 7E–H). We observed that recipient mice transplanted with CD4 T cells from TCF-1 cKO mice showed higher serum levels of IL-6 after 7 days post-transplant than mice given WT cells, but the IL-6 expression in these mice later dropped at day 14, such that there was no difference compared to mice given WT cells (Figure 7I,J). These findings provide evidence that recipient BALB/c mice transplanted with CD4 T cells from TCF-1 cKO mice exhibit increased levels of serum cytokines linked to GvHD severity from day 1 to day 14, but these levels reduce to WT levels at day 14 onward. This supports the pattern of resolving the disease severity seen in the in vivo models (Figure 2). Published data have shown that TCF-1 plays a significant role in CD8 T cell-mediated cytokine expression in viral infections [86]. However, the role of TCF-1 in CD4 T cell-mediated Th1 and Th2 cytokine production in alloimmunity has not been defined. Thus, we examined whether TCF-1 regulates CD4 T cell-mediated Th2 cytokines in an allogeneic transplant model. Our data showed that recipient BALB/c mice transplanted with CD4 T cells from TCF-1 cKO mice expressed increased levels of IL-4 and IL-13 at day 7 post transplantation, compared to mice given WT cells. However, levels of both IL-4 and IL-13 in mice given TCF-1 cKO cells decreased at day 14, compared at day 7, while we did not observe any difference in IL-4 and IL-13 levels in between day 7 and day 14 in WT CD4 T cell recipients (Figure 7K–N). This was correlated with the GvHD scores of the same recipient mice as well, suggesting less severe and less persistent GvHD in TCF-1-deficient CD4 T cell transplanted mice. Recipient BALB/c mice transplanted with either TCF-1 cKO or WT cells did not show any differences in IL-10, IL-9, IL-17A, and IL-17F expression (Supplementary Figure S4A–H).
These data suggest that allotransplanted TCF-1 cKO CD4 T cells are more activated early in the response but are less active (or less present) later, suggesting a unique mechanism for how TCF-1 modulates cytokine responses during alloimmunity. We also wanted to determine the cellular cytokine production from the donor cells to correlate with the cellular and serum levels of cytokines at day 7 post-transplant. Splenocytes were obtained from recipient mice and were restimulated to induce cytokine production. Cells were restimulated in culture with anti-CD3/anti-CD28 or left unstimulated for 6 h at 37 °C, and Golgiplug was included in the culture media. Then, cells were stained for H2Kb, CD3, CD4, TNF-α, and IFN-γ markers. Our data showed that the production of TNF-α or IFN-γ did not appear to be affected by the loss of TCF-1 (Figure 7P). The difference between cellular cytokine production and serum levels could be due to the lower numbers of CD4+ H2kb+ donor cells present at day 7 post-transplant in the liver and spleen for mice given TCF-1 cKO cells (Figure 6C,D).
## 2.7. TCF-1 Regulates Key Signaling Pathways in Donor CD4 T Cells
To understand the molecular mechanisms behind the changes we saw in the TCF-1 cKO donor CD4 T cells, and to understand the role of TCF-1 in regulating gene expression in these cells, we employed RNA sequencing. We allotransplanted recipient BALB/c mice with WT or TCF-1 cKO CD3 T cells, as described above. FACS-sorted pre-transplant samples (Pre-Tx) of CD4+ donor cells from WT and TCF-1 cKO mice were taken and stored in TRizol. At day 7 post-transplant, donor T cells were sorted back from the spleen of recipients using H2Kb, CD3, CD4, and CD8 (Post-Tx samples).
A principal component analysis (PCA) of pre-transplant and post-transplant samples showed two clusters of samples, WT and TCF-1 cKO, that were clearly separated by principal component 1 (PC1 $46\%$ for Pre-Tx, $53\%$ for post-Tx), which suggests that the transcriptomic profile of the TCF-1 cKO CD4 T cells differs from the CD4 T cells from WT mice (Figure 8A,B). Further analysis of pre-transplanted samples identified 812 differentially expressed genes (DEGs, defined by FDR < 0.05 and Log FC = 1) in TCF-1 cKO cells, compared to WT cells, of which 220 were downregulated and 592 were upregulated in CD4 T cells from TCF-1 cKO mice (Figure 8C). We identified 839 DEGs (defined by FDR < 0.05) in post-transplanted CD4 T cells from TCF-1 cKO cells, compared to CD4 T cells from WT mice, of which 356 were downregulated and 483 were upregulated in CD4 T cells from TCF-1 cKO mice (Figure 8D). All DEGs were plotted in the heatmap for pre- and post-transplanted samples, and by using the Spearman correlation method, which is associated with hierarchical clustering, the pre-transplanted and post-transplanted samples were categorized into two clusters (WT and TCF-1 cKO). Gene co-regulation was determined by hierarchical clustering by using the Pearson correlation method with a grouping cutoff (k) of two (Figure 8E,F). Module 2 shows all of the upregulated DEGs and module 1 shows all of the downregulated DEGs in pre-transplanted and post-transplanted samples (Figure 8E,F). A gene ontology (GO) analysis of the pre-transplanted and post-transplanted samples revealed that all of the identified DEGs are involved in a number of biological processes, such as cell death, apoptotic processes, T cell-mediated processes, T cell functions, cytokine production, and response to cytokines, among others. We clustered the pathways that related to the cell death and apoptotic processes in a group and listed them based on the adjusted p-value (FDR) for both pre- and post-transplanted samples (Supplementary Figures S2 and S3). Genes that were involved in each pathway are also listed in the tables (Supplementary Figures S2 and S3).
The majority of genes involved in cell death and apoptotic processes were upregulated in the pre- and post-transplanted samples in CD4 T cells from TCF-1 cKO samples, compared to CD4 T cells from WT samples (Figure 8G,H). Interestingly, the β-catenin gene (encoded by Ctnnb1) was significantly upregulated in pre-transplanted samples from donor CD4 T cells from TCF-1 cKO mice, and the WNT-4 gene was significantly upregulated in post-transplanted samples in CD4 T cells from TCF-1 cKO mice, suggesting a compensatory mechanism of upregulation of the β-catenin pathway in the absence of TCF-1 (Figure 8G,H). Even though most of the genes shared were in the cell death and apoptosis-related pathways, some of the genes were unique for each pathway (Supplementary Figures S2 and S3). We also clustered the pathways that related to T cell function and signaling in a group and listed them based on the adjusted p-value (FDR) for both pre- and post-transplanted samples (Supplementary Figures S2 and S3). For pre-transplant samples, even though the majority of genes involved in T cell function and signaling were upregulated in CD4 T cells from TCF-1 cKO mice, compared to CD4 T cells from WT mice, interestingly, LAT, LCK, ZAP70, and CD3e genes (which are downstream of the TCR) were downregulated in CD4 T cells from TCF-1 cKO mice, compared to CD4 T cells from WT mice (Figure 8I). When we analyzed the T cell function and signaling-related genes in post-transplanted samples, we observed that most of the genes were downregulated in CD4 T cells from TCF-1 cKO mice than in CD4 T cells from WT mice (Figure 8J), which suggests that alloactivated CD4 T cells from TCF-1 cKO mice having attenuated T cell signaling and T cell responses, compared to T cells from WT mice. Even though most of the genes were shared between the T cell function and signaling-related pathways, some of the genes were unique for each pathway (Supplementary Figures S2 and S3). Pre-transplanted CD4 T cells from TCF-1 cKO mice showed upregulation of the genes involved in cytokine production and cell response to cytokines (Figure 8K, Supplementary Figure S2) compared to CD4 T cells from WT mice. Post-transplanted CD4 T cells from TCF-1 cKO mice showed downregulation of a number of genes that were involved in cytokine production and responses, such as Ifitm1, JAk3, CD4, CD28, Iκβ, IκG, CD3e, and others, which supported the observed decrease in cytokine production from CD4 T cells from TCF-1 cKO mice (Figure 8L). We also observed that a number of genes involved in chemokine receptor signaling and cell adhesion, such as CCL5, CCL3, CCL4 CXCR5, and CXCR3, are downregulated, and Slit2 is upregulated in CD4 T cells TCF-1 cKO mice, compared to CD4 T cells from WT mice (Figure 8L, Supplementary Figure S3). It has been shown that Slit2 blocks CXCL12/CXCR4-mediated functional effects in T cells, which is important for HIV infection and viral replication. Altogether, the transcriptomic analysis revealed that TCF-1 regulates the CD4 T cell genetic profile, with a loss of TCF-1 directing the cell towards decreased T cell signaling, decreased cytokine and chemokine signaling, and increased apoptosis and cell death, specifically after allotransplantation.
## 3. Discussion
T cell factor-1 (TCF-1) is a T cell transcription factor that is known to be critical for T cell development, activation, and in some cases, responses to pathogens [1,92,93]. The functional and development role of TCF-1 has been extensively studied in CD8 T cell responses to viral infections [2,17,18,76,94,95]. To some extent, the role of TCF-1 has been examined in CD4 T cell development [5,8,11], however, it is unclear whether TCF-1 may regulate alloactivated CD4 T cells during responses to alloantigens. The main significance of our results is that we utilized a clinically relevant model of allo-HSCT, enabling us to study all of the major CD4 T cell functions, as well as phenotypes, clinical outcomes, and gene expression, in a single model. Several publications, including our own, have shown that CD4 T cells with higher CD44, CD122, and Eomes, or T-bet referred to as the innate memory phenotype (IMP) [54,96] T cells with the IMP significantly delayed the development of GvHD, but were able to clear tumors [35,54,55,97]. Our data also showed that CD4 T cells from TCF-1 cKO mice have a significantly higher expression of a IMP phenotype, suggesting the TCF-1 might regulate the IMP phenotype and TCF-1 is considered a repressor factor for IMP cells. Memory phenotypes have been reported to play a significant role in the induction (or lack thereof) of GvHD [44]. Our data showed that CD4 T cells from TCF-1 cKO mice upregulate the effector or central memory phenotypes, and these mice show a decreased naïve cell population, which suggests that TCF-1 regulates the memory formation of CD4 T cells. We and others have shown that the upregulation of EM and CM plays a central role in GvHD development. Studies have shown that T cells with a higher EM and CM do not cause GvHD. [ 35,35,44,54]. Thus, our studies are of great importance because they show that TCF-1 regulates both EM and CM on CD4 T cells.
Another molecule that is critically important for effector memory cell survival and maintenance is ICOS [44,45,98]. The lower expression in ICOS on effector memory CD4 T cells from TCF-1 cKO mice suggest that even though TCF-1-deficient CD4 T cells have more effector memory cells, their survival and homeostasis may be affected by the loss of TCF-1.
Modulation of alloreactive CD4 T cell trafficking has been suggested to play a significant role in ameliorating experimental GvHD [99]. However, we did not observe any differences in the donor CD4 T cell migration to GvHD target organs, including the liver and small intestine. Pro-inflammatory conditioning treatment may promote T cell migration into GvHD target tissues [100,101]. Donor CD4 T cells upregulate the chemokine receptor expression upon alloactivation, which mediates donor T cells migration to the site of inflammation [102]. Since chemokine receptor expression in T cells is central to several T cell-mediated diseases [43,103], determining whether TCF-1 regulates chemokine expression either positively or negatively is critically important. We uncovered that mature splenic CD4 T cells from TCF-1 cKO mice expressed higher levels of chemokine receptors than CD4 T cells from WT mice, both pre- and post-allo-HSCT. However, our data showed that CD4 T cells from the liver in mice given TCF-1 cKO cells showed reduced chemokine receptor expression post-transplantation. Our data showed that CD4 T cells from TCF-1 cKO mice caused significantly less tissue damage. All of our data suggest that CD4 T cells from TCF-1 cKO mice can migrate to GvHD target organs, but also provided stronger evidence that TCF-1 is critical for CD4 T cell stemness, however due to the loss of TCF-1 on CD4 T cells, these CD4 T cells are unable to cause persistence of GvHD symptoms. TCF-1 has been shown to be significantly important in T cell development and survival [104], so we also examined whether TCF-1 is critical for CD4 T cell survival in both in vitro and in vivo models. Our data showed that CD4 T cells from TCF-1 cKO mice developed more rapid cell death and apoptosis in vitro within the first 48 h. We observed increased PD-1 expression on TCF-1 cKO cells versus WT cells only at 72 h of in vitro culture, indicating that the cells that survived after 72 h might cause less severe CD4 T cell-mediated diseases [77,105]. Even though we did not observe any differences in dead, apoptotic, or live cell percentages in in vivo alloactivated CD4 T cells from TCF-1 cKO or WT mice, the frequency of donor CD4+ H2kb+ T cells from TCF-1 cKO mice was significantly less in GvHD target organs, thus supporting our central hypothesis that TCF-1 is indispensable for CD4 T cell stemness. However, our recent findings suggested that TCF-1 is dispensable for anti-tumor response [17]. TCF-1 has been shown to play a critical role in CD4 T cell exhaustion and activation in responding to viral infections [75,76]. Our data, both in vivo and in vitro, showed that activated CD4 T cells from TCF-1 cKO mice had no change in exhaustion. These findings demonstrate that alloactivated CD4 T cells are functioning significantly different to how CD4 T cells from TCF-1 cKO mice function in response to viral infections.
Pro-inflammatory cytokine production by donor cells and host tissues causes damage to nearby healthy host cells [25,84,87]. Our data showed that during initial activation, donor CD4 T cells from TCF-1 cKO mice produce more serum level cytokines, but these drop significantly over time. These findings suggest that despite early increased activation, cytokine production by donor CD4 T cells from TCF-1 cKO mice donor cells quickly reduce post-transplant, allowing the disease to resolve, further confirming that TCF-1 is required for CD4 T cell persistence functions, including cytokine production. This supports our hypothesis that donor CD4 T cells from TCF-1 cKO mice become exhausted and stop proliferating and producing cytokines, allowing for the resolution of the usual persistent disease state. Even though, we initially (day7) observed significant increases in proinflammatory cytokines, including TNF-a and IFN-g from CD4 T cells from TCF-1 cKO mice, this upregulation of TNF-a and IFN-g fails to recruit other inflammatory cells, such as macrophages to the site of inflammation to induce GvHD. These findings suggest that the loss of TCF-1 significantly weakens CD4 T cell persistence during GvHD by the loss of CD28 on CD4 T cells. These findings are supported by our recent publication that the loss of TCF-1 significantly weakens TCR signaling on CD8 T cells [17].
Our transcriptomic data uncovered that TCF-1 regulates several pathways that are critical for CD4 T cell-mediated diseases. Cell death pathways play a central role in CD4 T cell-mediated diseases, including autoimmunity and cancer [17]. We uncovered that TCF-1 significantly and differentially regulates cell death and apoptotic process-related pathways before and after transplantation, which is critical to understand the role of TCF-1 in alloimmunity and fighting infection and cancer. Our data showed that TCF-1 significantly regulates genes in CD4 T cell programmed cell death, including pathways for apoptotic signaling, necrotic signaling and mitochondrial fragmentation. CD4 T cell activation, signaling proliferation, and Th1/Th2/Th17 differentiation are central to both CD4 T cell function and CD4 T cell-mediated diseases, including CD4 T cell responses to viral infection, autoimmunity, cancer, and aging [106].
We also observed that TCF-1 differentially regulates sets of genes in the I-κβ and NF-κβ pathways. This information will enable us to develop target specific approaches to design therapeutic interventions. CD4 T cells primarily function as regulators of other immune cells either through secreted cytokines or by direct cell–cell contact. Inflammatory and anti-inflammatory cytokines production are central to T cell responses to viral infections, autoimmune disorders, cancer, and GvHD [30].
Our data uncovered that CD4 T cells from TCF-1 cKO mice have significantly higher β-catenin expression before transplantation and higher WNT4 expression after transplantation, compared to cells from WT mice. Another key finding of this report is that TCF-1 regulates CD28 expression. CD28 is a key co-stimulatory receptor that plays a central role in T cell receptor stemness [2,80]. Published data has also demonstrated that the lack of CD28 significantly weakens TCR stemness [81]. Therefore, both our in vivo and in vitro data demonstrated that CD4 T cells from TCF-1 cKO mice are prone to activation and cell death. These findings are consisting with transcriptomic data and the development of apoptosis. These findings are also supported our GvHD studies that CD4 T cells from TCF-1 cKO mice showed peak GvHD clinical scores, but that this significantly diminished over time.
Overall, our data uncovered several novel discoveries regarding how TCF-1 differentially regulates CD4 T cell functions, at baseline and during alloactivation. More significantly, how TCF-1 functions during T cell development and on mature peripheral CD4 T cells was not previously known for an alloactivation context. These discoveries will enable us to design target specific approaches in treating CD4 T cell-mediated diseases and alloimmunity.
Limitations of the Study: Currently, the limitation of this study is the use of a mouse model. We are working with structural and medicinal chemists to make specific activators for Wnt/β-catenin pathways. Currently available reagents are Wnt3 ligands [107] or GSK3β-inhibitors. The primary problems with these activators are that either T cells become over activated or there is non-specific activation of several other signaling proteins. Therefore, we are currently working to develop our own specific activators.
## 4.1. Mice
Thy1.1 (B6.PL-Thy1a/CyJ, 000406), B6-Ly5 (CD45.1+, AKA “WT” or B6.SJL-Ptprca Pepcb/BoyJ, 002014), and BALB/c mice (CR:028) were purchased from Charles River or Jackson Laboratory. TCF-1 cKO mice (Tcf7 flox/flox cross bred with CD4cre) [108] were obtained from Dr. Jyoti Misra Sen at the NIH and bred in our facilities. CD4cre [022071]. Using genomic PCR, we confirmed that our newly generated mice are TCF-1 cKO. Eight–12-week-old and sex-matched mice were used for all experiments. Recipient mice for transplant experiments were female BALB/c mice (CR:028 from Charles River, age 8 weeks or older). Recipient mice for the chimera experiments were Thy1.1 mice (B6.PL-Thy1a/CyJ, 000406 from Charles River). Animal maintenance and experimentation were approved by the Upstate Medical University IACUC committee with IACUC #433. All mice used for transplants were female, and flow cytometry experiments were carried out with both male and female mice.
## 4.2. DNA Extraction and PCR
Donor mice were genotyped using PCR. Ear punches were taken from each mouse at 4 weeks of age, DNA was extracted, and run in a PCR reaction using the Accustart II mouse genotyping kit (95135-500 from Quanta Biosciences). Standard PCR reaction conditions and primer sequences from Jackson Laboratories were used for Eomes, T-bet, and CD4 cre+/+. For TCF-1, primer sequences and reaction conditions were obtained from Dr. Jyoti Misra Sen of NIH.
Primers used for CD4 cre+/+ genotyping are: Common primer: 5′-GTTCTTTGTATATATTGAATGTTAGCC; WT reverse primer: 5′-TATGCTAGGACAAGAATTGACA; and Mutant reverse primer: 5′-CTTTGCAGAGGGCTAACAGC. PCR conditions: Step 1. 94 °C for 2:00 min; Step 2. 94 °C, 20 s; Step 3. 65 °C, 15 s; Step 4. 68 °C, 10 s; Step 5. Go to Step 2, 10×; Step 6. 94 °C, 15 s; Step 7. 60 °C, 15 s; Step 8. 72 °C, 10 s; Step 9. Go to Step 6, repeat 28×; Step 10. 72 °C, 2 min; Step 11. 10 °C, infinite hold.
Primers used for TCF-7 genotyping: Forward primer: 5′-AGCTGAGCCCCTGTTGTAGA, Reverse primer #1: 5′-TTCTTTGACCCCTGACTTGG, Reverse primer #2: 5′-CAACGA GCTGGGTAGAGGAG. PCR conditions for TCF-7 are: Step 1. 94 °C, 2 min; Step 2. 55 °C, 30 s; Step 3. 72 °C, 1 min; Step 4. Go to Step 2. repeat 38×; Step 5. 72 °C, 10 min, 12 °C infinite hold.
## 4.3. Flow Cytometry, Sorting, and Phenotyping
For phenotyping experiments, splenocytes were obtained from WT C57Bl/6 and CD4 cre+/+ C57Bl/6 control mice and TCF-1 cKO mice. For all other experiments, cells were obtained from transplanted recipients. Cells were incubated with RBC lysis buffer (00-4333-57 from eBioscience) to remove red blood cells when necessary. Following processing, cells were stained in MACS buffer (1× PBS with EDTA and 4 g/L BSA) with extracellular markers and were incubated for 30 min on ice. Cells were then washed and run on a BD LSRFortessa flow cytometer to collect data. If intracellular markers were used, cells were washed after extracellular staining, then fixed overnight using buffers from the Fix/Perm Concentrate and Fixation Diluent from FOXP3 transcription factor staining buffer set (eBioscience cat. No. 00-5523-00). The following day, cells were washed in Perm buffer from the same kit and were stained with intracellular markers in Perm buffer for 40 min at room temperature. Stained cells were resuspended in FACS buffer (eBioscience cat. No. 00-4222-26) and transferred to flow tubes. All antibodies were used at 1:100 dilution and were purchased from Biolegend or eBioscience. The cells were then washed and run on a BD LSRFortessa. For cell sorting, cells were stained in the same manner and run on a BD FACSAria, equipped with cold-sorting blocks. Cells were sorted into sorting media ($50\%$ FBS in RPMI) for maximum viability, or TRizol for RNAseq/qPCR experiments. All flow cytometry data was analyzed using FlowJo software v9 (BD). Depending on the experiment, the antibodies used were: anti-CD4 (FITC, PE, BV785, BV510, BV421), APC, PerCP, Pacific Blue, PE/Cy7), anti-CD3 (BV605 or APC/Cy7), anti-H2Kb-Pacific Blue, anti-H2Kd-PE/Cy7, anti-CD122 (FITC or APC), anti-CD44 (APC, PercP or Pacific Blue), anti-CD62L-APC/Cy7, anti-ICOS-PE, anti-CXCR3-Percp-Cy5.5, anti-TNF-α-FITC, anti-IFN-γ-APC, anti-Eomes (AF488 or PE/Cy7), anti-T-bet-BV421, anti-CD45.2-PE/Dazzle594, anti-CD45.1-APC, anti-Ki67 (PE or BV421), anti-PD-1-BV785, anti-annexin V-FITC, LIVE/DEAD near IR, and anti-TOX-APC.
## 4.4.1. Allo-HSCT and GVHD Studies
For GvHD experiments, we utilized the MHC-mismatch mouse model of allo-HSCT (WT C57Bl/6 ➔ BALB/c, i.e., H2Kb+ ➔ H2Kd+). BALB/c recipient mice were irradiated twice with 400 cGy x-rays (total dose 800 cGy), with a rest period of at least 12 h between doses, and 4 h of rest prior to transplantation. Lethally irradiated BALB/c mice were transplanted with 10 × 106 T cell-depleted bone marrow (TCDBM) cells. Briefly, bone marrow T-cells were depleted using CD90.2 MACS beads (130-121-278 from Miltenyi) and LD columns (130-042-901 from Miltenyi) [35,54]. T cells CD4+ were separated from donor mice spleens using CD90.2 or CD4 microbeads and LS columns (Miltenyi, CD4: 130-117-043, CD90.2: 130-121-278, LS: 130-042-401) [17,35]. We used purified donor CD4 T cells from WT, CD4cre, or Tcf7 flox/flox mice as controls and TCF-1 cKO mice as the experimental group. The cells were then IV injected into the tail vein in PBS. The recipient mice received 1 × 106 T cells per mouse, along with 10 × 106 T cell-depleted bone marrow cells (TCDBM) collected from WT C57BL/6J mice. Recipient mice were evaluated for clinical signs of GvHD and weight loss for more than 70 days [17,35]. Clinical presentation of the mice for each experiment was assessed 2–3 times per week by a scoring system that summed the changes in 6 clinical parameters: diarrhea, posture, activity, fur texture, weight loss, and skin integrity (Cooke et al., 1996). Mice were euthanized when they lost ≥ $30\%$ of their initial body weight [17,35,56].
## 4.4.2. Allo-HSCT Sort Term Experiments
Lethally irradiated BALB/c mice were transplanted, as described above. At day 7 or day 14, the recipients were euthanized, and serum, spleen, small intestine, skin, or liver was collected, depending on the experiment.
## 4.4.3. Bone Marrow Chimera Model
For the mixed bone marrow chimera experiments, a 1:4 ratio of WT (CD45.1) to TCF-1 cKO (CD45.2) bone marrow was injected into Thy1.1 mice, and the mice were rested for 9 weeks. This ratio was chosen based on our previous publication [54]. At 9 weeks post-transplant, tail vein blood was collected and stained with anti-CD45.1 and anti-CD45.2 to detect the two donor cell types. At 10 weeks, these chimeras were euthanized, and their spleens were processed and stained for phenotyping by flow cytometry.
## 4.5. qPCR Analysis
Mice were short-term transplanted, as described above (1 × 106 donor CD4 T cells and 10 × 106 TCDBM) cells, and at day 7, recipient mice were humanely euthanized. Splenocytes were obtained from pre- and post-transplanted mice, and FACS sorted, as described above to obtain donor cells. These cells were all sorted into TRizol, then RNA was extracted using chloroform phase separation protocols. The extracted RNA was eluted using the Qiagen RNAEasy Minikit (74104 from Qiagen Germantown MD USA) and run on a spectrophotometer to determine concentration. RNA was then converted to cDNA with the Invitrogen Super-Script IV First Strand Synthesis System kit (18091050 from Invitrogen) and run on a spectrophotometer to determine the concentration. The master cocktail, including 10 ng/μL cDNA and Taqman Fast Advanced Master Mix (4444557 from Invitrogen), was prepared for each sample, and 20μL was added to each well of a 96 well custom TaqMan Array plate with chemokine/chemokine receptor primers (Thermo Fisher, Mouse Chemokines & Receptors Array plate, 4391524). The plates were run on a Quantstudio 3 thermocycler, according to manufacturer’s instructions for the TaqMan assay, and data were analyzed using the Design and Analysis software v2.4 (provided by Thermo Fisher). Five separate recipient mice were sorted, and cells were combined to make one sample for qPCR testing per condition/organ.
## 4.6. Histopathological Evaluation
Lethally irradiated recipient mice were transplanted with 10 × 106 TCDBM) cells, 1 × 106 WT CD4+ T donor cells and at day 7 and day 21 post-transplant, the organs were removed from WT or TCF-1 cKO T cell-transplanted recipient mice. The spleen, liver, small intestine, and skin from the back and ear were removed and fixed in $10\%$ neutral buffered formalin. Tissue pieces were sectioned and stained with hematoxylin and eosin (H&E) by the Histology Core at Cornell University. Stained slides were then imaged and analyzed by a pathologist at SUNY Upstate (L.S.) who was blinded to the study conditions and slide identity.
## 4.7. Isolation of Lymphocytes from the Liver and Small Intestine
To isolate the lymphocytes from the liver, they were perfused with 5–10 mL of ice-cold PBS to remove red blood cells (RBCs) before removal. Livers were then ground through a 70 mm filter with PBS, centrifuged to remove debris, and lymphocytes were isolated by a 22-min spin in $40\%$ Percoll in RPMI/PBS (22 °C, 2200 rpm, no brake, no acceleration). Isolated lymphocyte pellets were washed, cells were briefly incubated with red lysis buffer to remove remaining RBCs, and resuspended with PBS or MACS buffer (BSA in PBS). To isolate lymphocytes from the small intestine, the intestine was removed and put into an ice cold MACS buffer, opened lengthwise, washed with MACS, and epithelial cells were stripped off by a 30 min shaking incubation (37 °C) in strip buffer (1× PBS, FBS, EDTA 0.5 M, and DTT 1 M). The guts were then cut into small pieces and digested by a 30 min shaking incubation (37 °C) in digestion buffer (collagenase, DNAse, and RPMI). The tubes were then vortexed, and liquid and solid gut pieces were filtered through a 70 mm filter to obtain a cell suspension. Percoll was then used to isolate lymphocytes, as was carried out for liver, with no RBC lysis afterwards. The gut cells were then placed in MACS buffer for further use.
## 4.8. Cellular Level Cytokine Production Assay
Mice were short-term transplanted, as described above (1 × 106 donor CD4 T cells), and at day 7 post-transplant, recipient mice were humanely euthanized and splenocytes were obtained. The splenocytes were cultured for 6 h at 37 °C and $7\%$ CO2 with GolgiPlug (1:1000) and PBS or anti-CD3 (1 μg/mL)/anti-CD28 (2 μg/mL) to restimulate them. Then, after 6 h, the cells were removed from the culture, stained for surface markers, fixed and permeabilized, then stained for the cytokines IFN-g and TNF-a using the BD Cytokine Staining kit [555028], and run on a flow cytometer.
## 4.9. Serum Level Cytokine Production Assay
Mice were short-term transplanted, as described above with 10 × 106 TCDBM) cells, along with 1 × 106 WT CD4+ T donor T cells. At day 7, the recipient mice were euthanized, and serum was obtained from cardiac blood. The serum was collected from recipient mice in the cytokine experiment and analyzed using the Biolegend LEGENDplex Assay Mouse Th Cytokine Panel kit [741043] [35,54]. This kit quantifies serum concentrations of: IL-2 (T cell proliferation), IFN-g and TNF-a (Th1 cells, inflammatory), IL-4, IL-5, and IL-13 (Th2 cells), IL-10 (Treg cells, suppressive), IL-17A/F (Th17 cells), IL-21 (Tfh cells), IL-22 (Th22 cells), IL-6 (acute/chronic inflammation/T cell survival factor), and IL-9 (Th2, Th17, iTreg, Th9—skin/allergic/intestinal inflammation). Data were collected on a BD LSR Fortessa cytometer, and data were analyzed using the LEGENDplex software (provided with kit via Biolegend).
## 4.10.1. In Vitro
We obtained splenocytes from WT and TCF-1 cKO mice and either activated them with 2.5 μg/mL anti-CD3 (Biolegend #100202) and anti-CD28 antibodies (Biolegend #102115) for 6, 24, 48, or 72 h in culture, or left them unstimulated. Annexin V-FITC (V13242 from Invitrogen) and LIVE/DEAD near IR (L34976 from Invitrogen) were used to identify dead (Ann. V+NIR+), live (Ann. V-NIR-), and apoptotic (Ann. V+NIR-) T cells
## 4.10.2. In Vivo
Mice were short-term transplanted, as described above (1 × 106 CD4 donor T cells), and at day 7 post-transplant, cells from the spleen and liver were stained with annexin V-FITC (V13242 from Invitrogen) and LIVE/DEAD near IR (L34976 from Invitrogen). Annexin V and NIR were used to identify dead (Ann. V+NIR+), live (Ann. V-NIR-), and apoptotic (Ann. V+NIR-) cells. Donor T cells were identified by H2Kb, CD3, and donor CD4 T cells.
## 4.11. RNA Sequencing
Freshly isolated CD4T cells were FACS sorted in TRizol for pre-transplanted (Pre-Tx) samples. For post-transplanted (post-Tx) samples, recipient mice were transplanted with 1 × 106 CD3 donor T cells, and at day 7 post-transplant, donor CD8 T cells were FACS-sorted back from the recipient spleen of TCF-1 cKO and WT transplanted mice and sorted into TRizol. RNA was extracted from all of the pre- and post-transplanted samples and prepped by the Molecular Analysis Core (SUNY Upstate, https://www.upstate.edu/research/facilities/molecular-analysis.php (accessed on 21 November 2022)). Paired end sequencing was carried out with an Illumina NovaSeq 6000 system at the University at Buffalo Genomics Core (http://ubnextgencore.buffalo.edu (accessed on 21 November 2022)). The statistical computing environment R (v4.0.4), the Bioconductor suite of packages for R, and Rstudio (v1.4.1106) were used for transcriptomic analysis. Kallisto (version 0.46.2) was used for transcript abundance determination and performing the pseudoalignment. Calculated transcript per million (TPM) values were normalized and fitted to a linear model by the empirical Bayes method with the Voom and Limma R packages to identify differentially expressed genes (DEGs) for both pre- and post-transplanted samples. For pre-transplant samples, DEGs filtered by adjusted p-value (FDR) < 0.05, log fold change = 1, and for post-transplant samples by adjusted p-value (FDR) < 0.05. DEG’s were used for hierarchical clustering and heatmap generation in R. A gene ontology enrichment analysis was conducted using the g: Profiler toolset; g:GOSt tool. Data will be deposited in the Gene Expression Omnibus (GEO) database for public access (https://www.ncbi.nlm.nih.gov/geo (accessed on 21 November 2022)). With accession number GSE204747.
## 4.12. Statistical Analysis
All numerical data are reported as means with standard deviation unless otherwise noted. Data were analyzed for significance with GraphPad Prism v9. Differences were determined using one-way ANOVA and Tukey’s multiple comparisons tests for three or more groups, or with a Student’s t-test when only two groups were used. A Kaplan–Meier survival analysis was used for survival experiments. All tests were two-sided. p-values less than or equal to 0.05 were considered significant. All transplant experiments were carried out with $$n = 3$$–5 mice per group and repeated at least twice. Ex vivo experiments were carried out two to three times unless otherwise noted with at least three replicates per condition each time. RNAseq was carried out once with three replicates per group. qPCR was completed once with one sample per condition, and 5 mice combined to make the one sample. Sorting was carried out once for each of these two experiments, and data were recorded for Figure 3 and Figure 8. Data in the figures are presented as mean and SD unless otherwise noted.
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|
---
title: Discovery-Based Proteomics Identify Skeletal Muscle Mitochondrial Alterations
as an Early Metabolic Defect in a Mouse Model of β-Thalassemia
authors:
- Patricia Reboucas
- Carine Fillebeen
- Amy Botta
- Riley Cleverdon
- Alexandra P. Steele
- Vincent Richard
- René P. Zahedi
- Christoph H. Borchers
- Yan Burelle
- Thomas J. Hawke
- Kostas Pantopoulos
- Gary Sweeney
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10002226
doi: 10.3390/ijms24054402
license: CC BY 4.0
---
# Discovery-Based Proteomics Identify Skeletal Muscle Mitochondrial Alterations as an Early Metabolic Defect in a Mouse Model of β-Thalassemia
## Abstract
Although metabolic complications are common in thalassemia patients, there is still an unmet need to better understand underlying mechanisms. We used unbiased global proteomics to reveal molecular differences between the th3/+ mouse model of thalassemia and wild-type control animals focusing on skeletal muscles at 8 weeks of age. Our data point toward a significantly impaired mitochondrial oxidative phosphorylation. Furthermore, we observed a shift from oxidative fibre types toward more glycolytic fibre types in these animals, which was further supported by larger fibre-type cross-sectional areas in the more oxidative type fibres (type I/type IIa/type IIax hybrid). We also observed an increase in capillary density in th3/+ mice, indicative of a compensatory response. Western blotting for mitochondrial oxidative phosphorylation complex proteins and PCR analysis of mitochondrial genes indicated reduced mitochondrial content in the skeletal muscle but not the hearts of th3/+ mice. The phenotypic manifestation of these alterations was a small but significant reduction in glucose handling capacity. Overall, this study identified many important alterations in the proteome of th3/+ mice, amongst which mitochondrial defects leading to skeletal muscle remodelling and metabolic dysfunction were paramount.
## 1. Introduction
Thalassemia comprises a group of monogenetic disorders that result from the impairment of α- or β-globin expression due to mutations in the α- or β-globin genes [1,2]. The imbalance of α/β-globin chains in the hemoglobin tetramer leads to the formation of hemichromes, reduction in the lifespan of red blood cells and the increased apoptosis of erythroid progenitor cells, which, in turn, triggers extramedullary erythropoiesis and hepatosplenomegaly. The degree of anemia and the overall clinical phenotype vary among patients carrying homozygous or compound heterozygous globin gene mutations. In the most severe form of the disease, known as thalassemia major (TM), frequent blood transfusions are required for sufficient tissue oxygenation and survival [3]. This treatment corrects anemia and attenuates extramedullary erythropoiesis. However, red blood cells contain high amounts of iron (200–250 mg per unit), and, therefore, blood transfusions lead to iron overload. This is further aggravated by increased dietary iron absorption due to the suppression of the iron hormone hepcidin, mainly by erythroferrone and possibly also by additional bone marrow-derived factors, which are induced in response to ineffective erythropoiesis [4]. Indeed, hereditary hemochromatosis is a frequent genetic disorder of iron overload that is caused by the inactivation of hepcidin regulators [5].
Large increases in iron levels cannot be fully controlled by iron chelation therapy [6]; therefore, this imbalance can cause cardiomyopathy, diabetes and liver diseases [7]. As many as $50\%$ of TM patients die before the age of 35 [8]. Heart failure, arrhythmia and myocardial infarction are responsible for ~$70\%$ of all thalassemic patient deaths [9]. Diabetes, a known driver of cardiovascular disease, also occurs frequently [10,11,12], with one meta-analysis finding the prevalence among TM patients to be $9\%$, with around $12\%$ having impaired fasting glucose and glucose tolerance [13]. In the early stages of the condition, patients are asymptomatic, and there is often no correlation between serum ferritin levels and the development of cardiac and metabolic dysfunction. Thus, understanding early events that act as drivers of complications in thalassemia is an important research question. Skeletal muscle is the major site of fatty acid catabolism, plays a key role in mediating whole-body glucose homeostasis and is a major determinant of insulin sensitivity. Hence, it is important to consider that impairments to muscle health/metabolism could expedite the progression of complications in persons with thalassemia.
Due to iron overload conditions, the tissues of TM patients experience increased oxidative damage to lipid membranes, proteins and DNA [14]. They have also been shown to exhibit mitochondrial damage, which results in decreased mitochondrial potential [15] and a reduced mitochondrial copy number compared with normal healthy controls [16,17]. Additionally, studies in animal models of thalassemia have demonstrated significant cardiac mitochondrial dysfunction, a contributor to cardiomyopathy [18,19]. Previous studies investigating thalassemia-related alterations in skeletal muscle have documented numerous findings relevant to this work. For example, a study examining muscle biopsies from a small number of α-thalassemia patients indicated a higher capillary tortuosity and unchanged capillary density and diameter [20]. It was concluded that the increased capillary tortuosity would promote oxygen supply to muscle tissues in order to compensate for the lower hemoglobin in those subjects [20]. Ultrastructural changes in the heart of a mouse model and patients with thalassemia included mitochondrial swelling, loss of myofilaments and the presence of lipofuscin, related to the high tissue iron content [21]. Thus, there is increasing evidence that mitochondrial dysfunction is a major manifestation of iron overload in thalassemia, although the temporal and mechanistic aspects of these changes remain to be resolved.
In this study, we used a thalassemic mouse model (th3/+) and wild-type control animals at 8 weeks of age. We performed a proteomic analysis of skeletal muscle to discover differentially expressed proteins. Data input into an Ingenuity pathway analysis allowed for the identification of key pathways, which differed between genotypes. String analysis was used to identify major alterations in protein interactomes. Key data were further verified via standard histological, Western blotting and qPCR approaches. The impact on metabolic genotype was determined using glucose and insulin tolerance tests.
## 2.1. Hematological and Iron Phenotype of 8-Week-Old th3/+ Mice
Hematological analysis validated the thalassemia phenotype of th3/+ mice at the age of 8 weeks. When compared with the wild-type controls, the th3/+ animals exhibited reduced hemoglobin (HGB) content (Figure 1A), hematocrit (HCT) (Figure 1B), red blood cell (RBC) count (Figure 1C), mean corpuscular volume (MCV) (Figure 1D) and mean corpuscular hemoglobin (MCH) (Figure 1E). Additionally, they manifested increased red cell distribution width (RDW) (Figure 1F), white blood cell (WBC) count (Figure 1G), platelet (PLT) count (Figure 1H) and mean platelet volume (MPV) (Figure 1I). This typical hematological phenotype of thalassemia in th3/+ mice was accompanied by systemic iron overload, as indicated by the increased liver iron content (LIC) (Figure 1J) and serum ferritin (Figure 1K), a reflection of LIC. There was no genotype-specific difference in the body weight of the female animals, which was lower compared with that of the males; however, the body weight of the th3/+ males was significantly reduced vs. the wild-type (Figure 1L).
## 2.2. Proteomics Analysis of Skeletal Muscle
In order to investigate potential alterations to the skeletal muscle proteome of th3/+ mice, we conducted an unbiased quantitative mass spectrometry-based proteomics study using label-free quantitation. We measured differences in protein expression relative to control mice. Using this approach, we were able to quantify 891 proteins with a minimum of 1 protein-group-unique peptide ($80\%$ having ≥2 protein-unique peptides). Of these quantified proteins, 97 showed statistically significant differential expression based on an FDR-adjusted p-value of <0.05 and a fold change cut-off of twofold. Based on these proteins, we performed hierarchical clustering, which revealed several clusters of regulated networks (Figure 2A). Clusters 4–10, generally representing proteins with decreased expression in the thalassemic group vs. controls (Figure 2B,C), and clusters 1–3, representing proteins with generally increased expression (Figure 2D).
Ingenuity pathway analysis (IPA) demonstrated functional enrichment for components involved in cardiomyopathy signalling, protein ubiquitination and mitochondrial dysfunction, among others (Figure 3A). The further functional enrichment of protein–protein interaction networks using StringDB indicated that regulated components were enriched for members of the proteasome complex, ribonucleoproteins, mitochondrial proteins involved in oxidative phosphorylation and electron transport, as well as myofibril proteins and components of the troponin complex (Figure 3B).
## 2.3. Partial Deletion of β-Globin Gene Results in Altered Phenotype in the Gastrocnemius Muscle of Thalassemia Mouse Model
Gastrocnemius muscles from th3/+ mice had a significantly lower proportion of type IIA fibres vs. WT ($$p \leq 0.0003$$) and a significant increase in the proportion of type IIB fibres vs. WT ($$p \leq 0.0066$$) (Figure 4A,B). In gastrocnemius muscles, cross-sectional area (CSA) differences between fibre types were present. A trend toward a greater CSA for type I fibres existed in th3/+ compared with the WT mice ($$p \leq 0.68$$). The CSA of the type IIa fibres was significantly greater in th3/+ compared with WT mice ($p \leq 0.0001$). The CSA of the type II a/x hybrid fibres was also significantly greater in th3/+ vs. WT mice ($$p \leq 0.021$$) (Figure 4C). To observe the influence of the thalassemia phenotype on muscle capillarization, we investigated the proportion of capillaries in regions of interest with an alkaline phosphatase stain. There was an increase in capillary density in gastrocnemius muscles from th3/+ mice shown by a significant increase in alkaline phosphatase-stained areas compared with WT mice ($$p \leq 0.0003$$) (Figure 4D,E). Figure 4F shows representative images of oxidative and glycolytic muscle samples (the red-highlighted area shown at higher magnification), although no overt differences were observed between genotypes.
## 2.4. Mitochondrial Content Is Changed in Skeletal Muscle of Thalassemia Model
Mitochondrial DNA (mtDNA) quantification has been used as a reliable indicator of mitochondrial quantity, as mtDNA levels remain almost constant in healthy organisms. Analysis of the 8-week-old tissue data showed significantly reduced mitochondrial content in the skeletal muscle of the th3/+ mice compared with the controls (Figure 5A). Skeletal muscle from the th3/+ mice had significantly decreased content for mitochondrial markers, 16S rRNA and ND1, whereas, in heart tissue, there was no significant difference in mtDNA markers (Figure 5A). Similarly, when we examined the content of OXPHOS complex I-V, there was a significant decrease in the skeletal muscle, but not the hearts, from th3/+ mice (Figure 5B). In addition, we observed a decrease in the expression of the mitochondrial proteins TOM20, MitoNEET, DRP1 and OPA1 (Figure 5C–F) in skeletal muscle from th3/+ mice. Interestingly, there was no significant difference in ferritin or mitoFerritin expression between genotypes when adjusted to the GAPDH and TOM20 controls, respectively (Figure 5G,H).
## 2.5. Metabolic Analysis of 8-Week-Old th3/+ Mice
To determine how these changes in skeletal muscle are linked to diabetes, a glucose tolerance test (GTT) was performed. No significant difference between wild-type and thalassemia mice was observed regardless of sex (Figure 6A or B). A significant increase in blood glucose was, however, observed during the first 5 min of the GTT in both male and female th3/+ mice (Figure 6C). There was a higher insulin level (indicative of reduced insulin sensitivity) in 8-week-old th3+/− mice at the first 5 min post-glucose injection timepoint (Figure 6D).
## 3. Discussion
Studies with thalassemia patients have demonstrated an increased risk of diabetes, heart disease and metabolic syndrome [22,23]. Interestingly, approximately $30\%$ of metabolic syndrome patients exhibit iron overload, and the term dysmetabolic iron overload syndrome (DIOS) has been coined to describe this population [24]. Interventions to reduce iron excess, such as via venesection or the use of chelators, have been shown to improve insulin sensitivity and delay the onset of type 2 diabetes and heart failure [25,26], although this approach has not always been successful [27]. Based on the overall knowledge derived from studies to date, we postulated that iron overload itself may be a primary driver of impairments in the metabolic health of skeletal muscle. This work is of clinical significance, as additional insight into mechanisms underlying the development of metabolic complications in thalassemia is needed, particularly with a view to early intervention. Thus, we used an established mouse model of thalassemia and a proteomics-driven approach, which directed our analyses to mitochondrial alterations in skeletal muscle.
Here, we show that the thalassemia phenotype is associated with the substantial remodelling of the skeletal muscle proteome. Based upon our standard analytical criteria, in this study, there were 97 quantified proteins that showed statistically significant differential expression. Hierarchical clustering revealed several clusters of regulated networks. Broadly speaking, the most prominent changes were observed in proteins related to mitochondrial pathways and protein ubiquitination. Accordingly, additional functional enrichment of protein–protein interaction networks using StringDB indicated that regulated components were enriched for members of the proteasome complex and mitochondrial proteins involved in oxidative phosphorylation and electron transport. In this study, we focused mainly on further investigating the proteomics signature for mitochondrial dysfunction. The association between mitochondrial defects and thalassemia has been indicated by various previous studies [28,29]. In erythroblasts from thalassemia patients versus controls, decreased mitochondrial oxidative phosphorylation was observed [17]. Impaired fatty acid oxidation in mitochondria was suggested to be related to decreased carnitine levels found in the circulation of individuals with thalassemia [30]. A study using reticulocytes showed the increased expression of mitochondrial ferritin in patients with α-thalassemia, suggesting that iron excess in mitochondria occurs, and this may be important in mitochondrial dysfunction [31]. There may be many contributors to skeletal muscle mitochondrial remodelling in thalassemia, one of which may be the direct effects of labile iron excess, in particular, intramitochondrial iron overload [28]. This is known to attenuate mitochondrial respiration by causing a decrease in cytochrome C oxidase [32], a finding we observed here in our study. Interestingly, targeting improved cytochrome c oxidase content via in vitro-transcribed (IVT)-mRNA delivery has been proposed as a therapeutic approach applicable to thalassemia [33]. In addition to mitochondrial metabolic dysfunction, our data also indicated potential defects in antioxidative mechanisms, thus exacerbating the impact of mitochondrial damage. For example, predominant in our dataset was decreased NADPH dehydrogenase content. It has been suggested that the peroxiredoxin-2-mediated induction of NADPH dehydrogenase quinone-1 is an important adaptive response to counteract oxidative stress [34], and it is likely that lack of this in the muscle of th3/+ mice correlates with the development of metabolic dysfunction. It should be noted that, while the proteomics analysis presented here indicates many interesting findings, one potential limitation is that the number of animals used is relatively small.
One of the primary drivers for the changes in proteome observed here may be the need for skeletal muscles to adapt to reduced (due to anemia) oxygen delivery. Indeed, the idea that anemia may be modifying the morphology of skeletal muscle is consistent with the present results (increased capillary density) and previous work showing an increased capillary tortuosity in the muscles of thalassemia patients, a phenomenon the authors speculated would promote an increased oxygen supply to muscle tissue [20]. In this study, we observed a shift away from oxidative and mitochondrial-dependent fibre types toward more glycolytic and less mitochondrial-dependent fibre types. This response is consistent with the compensation of reduced oxygen delivery to skeletal muscles with glycolytic metabolism, yet it comes at the cost of inefficiency. This increase in the glycolytic phenotype of the muscle is also in agreeance with a reduction in the mitochondrial capacity in thalassemia mice, which we observed based on the proteomics profile; reduced mitochondrial DNA markers and Western blotting showing reduced OXPHOS expression. Based on glucose tolerance tests, we observed a mild insulin resistance phenotype in th3/+ mice at 8 weeks of age. The development of insulin resistance in muscle is expected to occur following mitochondrial dysfunction [35] or iron overload [36,37]. It is likely that as the mice age this defect would become more prominent [38].
Another aspect of the proteomic signature we found in this study that is likely of great significance in the context of the related literature is altered protein homeostasis pathways, both proteasome- and lysosome-mediated. It is well established that the ubiquitin–proteasome system and autophagy both play an important role in thalassemia via the excess amounts of free α-globin being processed via these protein quality control mechanisms [39,40]. Elevated proteasome activity was found in a previous study using red blood cell units from fourteen β-thalassemia donors versus sex- and aged-matched controls [41]. A study of the platelet proteome of X-linked thrombocytopenia with thalassemia patient pathway analysis revealed protein ubiquitination as a principal alteration [42]. Many and varied connections of autophagy with thalassemia have been reported, particularly the regulation of β-thalassemia erythropoiesis [43]. However, it is also likely that altered autophagy at the tissue level may have an important role in disease pathogenesis. Specifically, altered skeletal muscle autophagy, particularly mitophagy, can certainly contribute to muscle mitochondrial damage and metabolic dysfunction [44]. Excess labile iron is also likely to be a direct driver of the changes seen here, as persistent high levels of iron attenuate skeletal muscle autophagy by inhibiting autophagosome lysosome regeneration (ALR) [36]. Muscle can normally initiate endogenous mechanisms to protect itself from slightly elevated iron levels, and autophagy is a critical part of this response [45]. However, our data indicated this may not occur in th3/+ mice muscle, rendering them more susceptible to cellular damage. Excess iron has also been shown to attenuate the ubiquitin–proteasome protein quality control system in various cell types [46,47].
As far as we are aware, this is the first study of the skeletal muscle proteome in either human patients or a mouse model of thalassemia. Nevertheless, other proteomics-based studies have been conducted [48]. Analysis of the plasma proteome of patients with β-thalassemia versus healthy controls identified 13 potential biomarkers [49]. Interestingly global correlation analysis identified some pathways that can be cross-referenced with findings from our study; for instance hypertrophic/dilated cardiomyopathy signature being prominent and altered lysosomal function, especially cathepsins. In keeping with a cardiomyopathy signature, serum lipidomics analysis indicated that transfusion-dependent thalassemia patients had elevated triacylglycerols and long-chain acylcarnitines, with lower ether phospholipids or plasmalogens, sphingomyelins and cholesterol esters, reminiscent of what has been previously characterized in cardiometabolic diseases [5]. Untargeted metabolomics studies have also begun to characterize the metabolic defects in thalassemic individuals and how this can be altered upon intervention with hydroxyurea [50,51]. In summary, various types of omics-driven studies have established defective metabolic pathways in thalassemia that must now be further investigated. The most appropriate targets need to be defined and interventions tested.
Overall, in 8-week-old th3/+ mice, we identified alterations in the proteome that point toward early mitochondrial alterations and dysfunction. A lack of hemoglobin-mediated oxygen delivery to muscle, in concert with this reduced mitochondrial capacity, correlated with a switch toward glycolytic muscle fibres and an increase in capillarization. These remodelling events are logical in terms of maximizing the use of available oxygen. Thus, we identified many important alterations in the proteome of th3/+ mice, among which, mitochondrial defects associated with skeletal muscle remodelling and metabolic dysfunction were paramount. From this and various other proteomics studies related to β-thalassemia, an important insight into disease pathogenesis is beginning to emerge, with mitochondrial function and protein degradation (lysosome and proteasome) being two prominent cellular processes that are perturbed.
## 4.1. Experimental Animals, Serum and Tissue Collection
Eight-week-old mice heterozygous for the β-globin gene deletion (th3/+; also known as Hbbth3/+) on C57BL/6 background were used as a model of thalassemia and compared with wild-type littermates [52]. This mouse model is most akin to human thalassemia intermedia, as these mice do not require blood transfusions. The animals, both male and female, were housed in macrolone cages (up to 5 mice/cage, 12:12 h light–dark cycle: 7 am–7 pm; 22 ± 1 °C, 60 ± $5\%$ humidity) and were allowed ad libitum access to chow and drinking water. Experimental procedures were approved by the Animal Care Committee of McGill University (protocol 4966). At the endpoint, blood was collected via cardiac puncture following anesthesia under isoflurane; tissues were then rapidly collected and snap-frozen in liquid nitrogen followed by storage at −80 °C until further use.
## 4.2. Hematological Analysis, Serum Biochemistry and Quantification of Liver Iron
Hematological parameters were determined with the Scil Vet-ABC hematology analyzer. Serum was prepared by using micro-Z-gel tubes with a clotting activator (Sarstedt) and kept frozen at −20 °C until analysis. Serum ferritin was determined at the Biochemistry Department of the Montreal Jewish General Hospital using a Roche Hitachi 917 Chemistry Analyzer. Tissue iron was quantified with a ferrozine assay, as previously described [53].
## 4.3. Global Proteome Analysis of WT and Thalassemic Mouse Muscle Tissues
Gastrocnemius muscle tissue samples from wild-type and th3/+ mice (total, $$n = 10$$) were lysed in buffer containing $5\%$ sodium dodecyl sulphate (SDS) and 100 mM TRIS pH 7.8. Samples were subsequently heated to 99 °C for 10 min and subjected to probe-based sonication using a Thermo Sonic Dismembrator at $25\%$ amplitude for 3 cycles × 5 s. Remaining debris was pelleted by centrifugation at 20,000× g for 5 min. An aliquot of the supernatant was diluted to <$1\%$ SDS and used for estimation of protein concentration via bicinchoninic acid assay (BCA) (Pierce, cat# 23225). Lysates were clarified by centrifugation at 14,000× g for 5 min and transferred to a new reaction tube; disulphide bonds were reduced by the addition of tris(2-carboxyethyl)phosphine (TCEP) to a final concentration of 20 mM and incubated at 60 °C for 30 min. Free cysteines were alkylated using iodoacetamide at a final concentration of 30 mM and subsequent incubation at 37 °C for 30 min in the dark. An equivalent of 200 µg of total protein was used for proteolytic digestion via suspension trapping (S-TRAP) [54]. Proteins were acidified by adding phosphoric acid to a final concentration of $1.3\%$ v/v. Samples were then diluted 6-fold in STRAP loading buffer (9:1 methanol:water in 100 mM TRIS, pH 7.8) and loaded onto an S-TRAP Mini cartridge (Protifi LLC, Huntington NY) prior to centrifugation at 2000× g for 2 min. Samples were washed three times with 350 µL of STRAP loading buffer and proteolytically digested using trypsin (Sigma, Toronto, Canada) at a 1:10 enzyme-to-substrate ratio for 16 h at 37 °C. Peptides were sequentially eluted in 50 mM ammonium bicarbonate, $0.1\%$ formic acid in water and $50\%$ acetonitrile. Peptide-containing samples underwent solid phase extraction using Oasis HLB, 30 mg, 1CC cartridges (Waters). Peptide samples were dried and reconstituted in $0.1\%$ trifluoro acetic acid (TFA) prior to analysis using mass spectrometry.
## 4.4. LC-MS/MS Acquisition and Data Analysis
Samples were analyzed via data-dependent acquisition (DDA) using an Easy-nLC 1200 online coupled to a Q Exactive Plus (both Thermo Fisher Scientific). Samples were first loaded onto a pre-column (Acclaim PepMap 100 C18, 3 µm particle size, 75 µm inner diameter × 2 cm length) in $0.1\%$ formic acid (buffer A). Peptides were then separated using a 100 min binary gradient ranging from 3–$40\%$ B ($84\%$ acetonitrile, $0.1\%$ formic acid) on the analytical column (Acclaim PepMap 100 C18, 2 µm particle size, 75 µm inner diameter × 25 cm length) at 300 nL/min. MS spectra were acquired from m/z 350–1500 at a resolution of 70,000, with an automatic gain control (AGC) target of 1 × 106 ions and a maximum injection time of 50 ms. The 15 most intense ions (charge states +2 to +4) were isolated with a window of m/z 1.2, an AGC target of 2 × 104 and a maximum injection time of 64 ms and fragmented using a normalized higher-energy collisional dissociation (HCD) energy of 28. MS/MS spectra were acquired at a resolution of 17,500, and the dynamic exclusion was set to 40 s. DDA MS raw data were processed with Proteome Discoverer 2.5 (Thermo Scientific) and searched using Sequest HT against the canonical mouse SwissProt FASTA database downloaded from UniProt. The enzyme specificity was set to trypsin with a maximum of 2 missed cleavages. Carbamidomethylation of cysteine was set as the static modification and methionine oxidation as the variable modification. The precursor ion mass tolerance was set to 10 ppm, and the product ion mass tolerance was set to 0.02 Da. The percolator node was used, and the data were filtered using a false discovery rate (FDR) cut-off of $1\%$ at both the peptide and protein level. The Minora feature detector node of Proteome Discoverer was used for precursor-based label-free quantitation.
## 4.5. Analysis of Proteomics Data
Proteins quantified by at least one protein-unique peptide were further filtered based on a minimum of >$50\%$ valid values in at least one of the two sample groups. Remaining missing values were imputed by low abundance sampling within Proteome Discoverer 2.5. LFQ abundances were scaled (normalized) based on the total amount of quantified peptides, and abundance ratios were calculated as the ratio of grouped protein abundances. Statistical significance was determined using background-adjusted t-tests and adjusted for false discovery rate (FDR) using the Benjamini–Hochberg method within Proteome Discoverer 2.5. Regulation was defined on the basis of having an adjusted p-value of less than 0.05 and an expression ratio cut-off of 2-fold. Dimensional reduction via principal component analysis and hierarchical clustering was conducted using the normalized protein and phosphopeptide LFQ abundances in Instant Clue (http://www.instantclue.uni-koeln.de/). Functional enrichment analysis of protein–protein interaction networks was performed using STRINGDB analysis [55] with the StringApp for visualization within Cytoscape (v 3.9.1) [56].
## 4.6. Fibre Typing
Muscle fibre typing was performed according to the protocol by Bloemberg and Quadrilatero [57]. Briefly, 10 μm of gastrocnemius muscle sections were cut with the Leica CM1850 Cryostat, maintained at −20 °C. The muscle sections were blocked with $10\%$ goat serum (Vector Laboratories, S-1000) for 60 min followed by incubation with a primary antibody cocktail (Developmental Springs Hybridoma Bank, Iowa City, IO; BA-F8 1:50, SC-71 1:600, BF-F3 1:100) against three myosin heavy chain (MHC) isoforms (type I, type IIA and type IIB) for 120 min. Sections were washed with PBS and then incubated with the appropriate secondary antibodies (Alexa Fluor 350 IgG2b 1:500, Alexa Fluor 488 IgG1 1:500, Alexa Fluor 555 IgM 1:500) for 60 min.
## 4.7. Alkaline Phosphatase Stain
Gastrocnemius (10 μm) sections were cut and stained for alkaline phosphatase with SIGMAFAST (BCIP/NBT (B5655-25TAB)) dissolved in 10 mL of dH₂O to determine capillary density. Muscle sections were incubated with the alkaline phosphatase solution for 15 min at 37 °C, whereafter, they were rinsed with dH₂O and counterstained with eosin ($0.5\%$ v/w in dH₂O).
## 4.8. Transmission Electron Microscopy (TEM)
Gastrocnemius muscle was dissected in 1 mm3 for analysis via TEM. After two washes with ice-cold 0.2 M sodium cacodylate buffer containing $0.1\%$ calcium chloride, pH 7.4, samples were fixed overnight at 4 °C in $2.5\%$ glutaraldehyde and washed 3× with washing buffer. Pellets were post-fixed with $1\%$ aqueous OsO4 + $1.5\%$ aqueous potassium ferrocyanide for 1 h and washed 3× with washing buffer. Specimens were dehydrated in a graded alcohol series, infiltrated with graded epon:alcohol and embedded in epon. Sections were polymerized at 58 °C for 48 h. Ultrathin sections (90–100 nm thick) were prepared with a diamond knife using a Reichert Ultracut E-ultramicrotome, placed on 200 mesh copper grids and stained with $2\%$ uranyl acetate for 6 min and Reynold’s lead for 5 min. Grids were then examined with transmission electron microscopy.
## 4.9. PCR Analysis
Samples were prepared and processed following the methods and protocol, as described before [58]. The primers used were 16S rRNA: Forward: 5′-CCGCAAGGGAAAGATGAAAGAC-3′ Reverse: 5′-TCGTTTGGTTTCGGGGTTTC-3′, ND1: Forward: 5′-CTAGCAGAAACAAACCGGGC-3′ Reverse: 5′-CCGGCTGCGTATTCTACGTT-3′, HK2: Forward: 5′-GCCAGCCTCTCCTGATTTTAGTGT-3′ Reverse: 5′-GGGAACACAAAAGACCTCTTCTGG-3′.
## 4.10. Western Blotting
Skeletal muscle, gastrocnemius and heart tissues were chop-frozen or pulverized with mortar and pestle in liquid nitrogen. The powdered tissue was then suspended in lysis buffer and homogenized with beads and then incubated on a rotating rocker for 1 h at 4 °C, followed by centrifuging samples at 1000 rpm for 10 min at 4 °C. The supernatant was transferred to a new Eppendorf tube, concentration was measured using BCA and samples were kept at −80 °C. Samples were then centrifuged at 10,000 rpm for 5 min at 4 °C and denatured at 95 °C for 5 min. Samples were run on $8\%$, $12\%$ and $15\%$ SDS-PAGE gels conducted at approximately 90 V for 2 h, followed by transfer to a polyvinylidene difluoride (PVDF) membrane at 120 V for 1.5 h. Membranes were blocked in $3\%$ bovine serum albumin (BSA) blocking solution for 1 h at room temperature, followed by incubation in a 1:1000 dilution of the primary antibody overnight. The next day, membranes were washed and incubated with a secondary antibody in 1:5000 dilution for 1 h at room temperature. Membranes were activated using Clarity Western ECL Substrate solution and visualized using X-ray film development techniques. Western blot (WB) band intensity was quantified using ImageJ software and normalized to specific loading control. The following primary antibodies were used in this study: TOM20 (Cat#42406), CISD1/mitoNEET (Cat#83775), total DRP1 (Cat#8570), OPA1 (Cat#80471), GAPDH (Cat#2118). They were purchased from Cell Signalling Technology, Beverley, MA. Ferritin (Cat#PA1-29381) was purchased from Invitrogen, Toronto, Canada. Mitochondrial ferritin (Cat#ab66111) and total OXPHOS (Cat#ab110413) were purchased from Abcam. The following secondary antibodies were used: anti-rabbit immunoglobulin G horseradish peroxidase-linked antibody (Cat#7074) and anti-mouse immunoglobulin G horseradish peroxidase-linked antibody (Cat#7076), from Cell Signalling Technology.
## 4.11. Glucose Tolerance Test (GTT) and Analysis of Insulin
Wild-type and th3/+ mice littermates fasted for 5 h before the experiment. Subsequently, the animals were injected with 100 g/kg glucose ip. Blood glucose levels were measured by using the OneTouch Verio Flex glucose meter.
## 4.12. Statistical Analysis
Data are presented as mean ±SEM. Statistical significance between treatment groups was calculated using an unpaired Student’s t-test when comparing two groups. One-way ANOVA and two-way ANOVA were used for the comparison of more than two groups. A p-value of <0.05 was considered statistically significant, and it was plotted with GraphPad Prism 9.
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|
---
title: 'Fatty Acid Sensing in the Gastrointestinal Tract of Rainbow Trout: Different
to Mammalian Model?'
authors:
- Jessica Calo
- Sara Comesaña
- Ángel L. Alonso-Gómez
- José L. Soengas
- Ayelén M. Blanco
journal: International Journal of Molecular Sciences
year: 2023
pmcid: PMC10002231
doi: 10.3390/ijms24054275
license: CC BY 4.0
---
# Fatty Acid Sensing in the Gastrointestinal Tract of Rainbow Trout: Different to Mammalian Model?
## Abstract
It is well established in mammals that the gastrointestinal tract (GIT) senses the luminal presence of nutrients and responds to such information by releasing signaling molecules that ultimately regulate feeding. However, gut nutrient sensing mechanisms are poorly known in fish. This research characterized fatty acid (FA) sensing mechanisms in the GIT of a fish species with great interest in aquaculture: the rainbow trout (Oncorhynchus mykiss). Main results showed that: (i) the trout GIT has mRNAs encoding numerous key FA transporters characterized in mammals (FA transporter CD36 -FAT/CD36-, FA transport protein 4 -FATP4-, and monocarboxylate transporter isoform-1 -MCT-1-) and receptors (several free FA receptor -Ffar- isoforms, and G protein-coupled receptors 84 and 119 -Gpr84 and Gpr119-), and (ii) intragastrically-administered FAs differing in their length and degree of unsaturation (i.e., medium-chain (octanoate), long-chain (oleate), long-chain polyunsaturated (α-linolenate), and short-chain (butyrate) FAs) exert a differential modulation of the gastrointestinal abundance of mRNAs encoding the identified transporters and receptors and intracellular signaling elements, as well as gastrointestinal appetite-regulatory hormone mRNAs and proteins. Together, results from this study offer the first set of evidence supporting the existence of FA sensing mechanisms n the fish GIT. Additionally, we detected several differences in FA sensing mechanisms of rainbow trout vs. mammals, which may suggest evolutionary divergence between fish and mammals.
## 1. Introduction
In mammals, there is unequivocal evidence that the gastrointestinal tract (GIT) is critically involved in the homeostatic control of feeding and energy balance through the so-called gut-brain axis [1]. For this, the GIT contains intestinal cells able to sense the presence of nutrients (carbohydrates, fatty acids/lipids, and amino acids/proteins) in the lumen by specific “taste” receptors or transporters and respond to such information by releasing signaling molecules [2]. While three types of intestinal cells (enterocytes, brush cells, and enteroendocrine cells (EECs)) have been associated with nutrient sensing, the main chemosensory cells within the GIT are EECs [3]. When EECs sense nutrients, multiple regulatory peptides, mainly ghrelin (GHRL), cholecystokinin (CCK), peptide tyrosine-tyrosine (PYY), and glucagon-like peptide-1 (GLP-1), are released. These peptides can act paracrinally on neighboring cells, but their main role is to serve as signaling molecules for gut-brain communication, which can take place either by transmission through the vagus nerve or systemic circulation [2,3,4]. Information derived from the GIT reaches the central nervous system (CNS), where it is integrated, ultimately resulting in changes in the production of key hypothalamic factors that govern food intake [5].
Over the last decades, the mechanisms underlying nutrient sensing in the GIT have become an area of increasing scientific interest, and, to date, several carbohydrate, fatty acid, and amino acid sensing systems have been described in the mammalian EECs [2,3,4]. For the purpose of the present research, only sensing mechanisms involving lipids/fatty acids will be further described. The GIT is exposed to high levels of lipids derived from diet (mainly triglycerides, TGs), which, after lipase digestion in the small intestine, are cleaved to release free fatty acids (FFAs) that are sensed by different G protein-coupled receptors (GPCRs). The main GPCRs sensing FFAs are referred to as free fatty acid receptors (FFARs), and they respond to FFAs depending on the length of their aliphatic chain [6]. Thus, medium-chain (6–12 carbons) and long-chain (13–21 carbons) fatty acids (MCFAs and LCFAs, respectively) are detected by FFAR1 (previously termed as GPR40) and FFAR4 (or GPR120), both primarily located in I- and L-cells [6,7,8]. In contrast, FFAR2 and FFAR3 (previously named GPR43 and GPR41, respectively) are responsive to short-chain fatty acids (SCFAs; <6 carbons) such as butyrate, propionate or acetate, which can be acquired from food but predominantly derive from the metabolism of non-digestible carbohydrates by gut microbiota in the distal intestine; because of this, FFAR2 and FFAR3 are expressed at large amounts in colonic L-cells [6,9]. Apart from these major receptors, GPR84 has been later discovered to bind MCFAs [10], although available evidence indicates that this receptor is not very abundant in the mammalian GIT and that it is not expressed in EECs. Thus, its role as a fatty acid sensor appears to be secondary; instead, its major role seems to be to enhance pro-inflammatory signaling and macrophage effector functions [11]. Finally, the receptor GPR119 has been considered an intestinal lipid sensor, although its natural ligands are not typically FFAs but endogenous lipid derivatives such as oleoylethanolamide (OEA) [12]. Nevertheless, a recent study has reported GPR119 activation in response to FFAs such as palmitoleic acid in human islet EndoC-betaH1 cells [13]. GPR 119 is also activated by dietary TG-derived 2-monoacylglycerols (2-MAG) [14]. Indeed, GPR119 is at least as important as FFAR1 in mediating the TG-induced secretion of gastrointestinal incretins in the small intestine, co-acting in synergy with FFAR1 [14]. Finally, in the colon, GPR119 is activated by microbiota-derived metabolites [15]. Besides GPCRs, several carriers have been associated with fatty acid sensing in the mammalian GIT. These include the fatty acid transporter CD36 (FAT/CD36), the fatty acid transport protein 4 (FATP4), and the monocarboxylate transporter isoform-1 (MCT-1) [2,3,4]. Fatty acid carriers are typically located on the apical membrane of enterocytes, where they facilitate FFA uptake. FAT/CD36 and FATP4 appear to be involved in LCFA translocation along the intestine, while MCT-1 participated in the absorption of SCFAs in the colon. MCFAs are absorbed by passive diffusion [2,4]. Despite its predominant enterocyte location, some studies have demonstrated the presence of some fatty acid transporters (at least FAT/CD36) in EECs, where they contribute to lipid-derived gastrointestinal hormonal release [16].
Gut nutrient sensing mechanisms remain almost unexplored in fish. In a previous recent study from our research group, we identified that the rainbow trout (Oncorhynchus mykiss) genome contains 10 different isoforms of ffar genes, of which only ffar1 seems clearly homologous to its mammalian counterpart. By contrast, the remaining isoforms identified appear to have evolved independently and it is not clear whether they are homologous to mammalian genes. In addition, we observed that a gene encoding a Ffar4 receptor subtype is missing in the rainbow trout. These observations allow us to suggest functional differences in gut fatty acid sensing between mammals and rainbow trout. However, as far as we are aware, there is no information in the literature regarding gut fatty acid sensing mechanisms in fish. Besides all the general roles of lipids in vertebrates [17], this nutrient type is particularly relevant for fish because the major aerobic fuel source for energy metabolism of fish muscle is FFAs derived from triglycerides (as those in diet) [18], and the main source of energy in aquaculture nutrition is lipids [19]. Additionally, it is important to note that fish and mammals differ importantly in terms of lipid metabolism (e.g., fish have the ability to produce long-chain polyunsaturated fatty acids (PUFAs), essential for multiple physiological processes, endogenously, while most mammals have a very low capacity for PUFA synthesis [20]), thus being of enormous interest to study whether evolutionary variations in terms of sensing mechanisms may exist between two groups derived by such differences. With this background, the present study aimed to identify and characterize fatty acid sensing mechanisms in the GIT of a fish model with a great interest in aquaculture, the rainbow trout. These carnivorous species have a better ability to digest lipids compared to herbivorous and omnivorous species, which appears to be attributed to their more specific and higher lipase activity and/or their genetic potential to store lipids [21]. Therefore, the comparison between rainbow trout and the known mammalian models provide two different frames, mostly unknown: evolutionary trends within vertebrates and putative differences between carnivore species and the omnivore models assessed so far in mammals.
## 2.1. Fatty Acid Receptors and Transporters mRNAs Are Differentially Expressed along the Rainbow Trout Gastrointestinal Tract
As shown in Figure 1, mRNAs encoding different Ffar isoforms, Gpr84, Gpr119, Fat/cd36, Fatp4, and Mct-1 (a/b), are found, at different abundance levels, in almost all of the regions of the rainbow trout GIT studied, i.e., stomach, pyloric caeca, proximal intestine, middle intestine, and distal intestine. Specifically, ffar1 mRNAs were more abundantly expressed in the pyloric caeca and proximal intestine, followed by the rest of the intestinal sections, with undetected expression in the stomach (Figure 1A). The abundance of ffar2b1.1 mRNAs was higher in the pyloric caeca compared with the rest of the tissues analyzed, but quantifiable levels were also observed in the proximal and middle intestine; however, expression levels were extremely low in the stomach and hindgut, thus hampering gene expression quantification in these regions (Figure 1B). ffar2b1.2 mRNAs were more abundant in the pyloric caeca, followed by the proximal and distal intestine, although low levels were detected in all gastrointestinal regions (Figure 1C). mRNAs encoding Ffar2b2a and Ffar2b2b were the most abundant of all receptor mRNAs studied (Ct values ≈28) and were detected along the entire GIT, with the highest levels found in distal intestine (Figure 1D,E). ffar2a1b mRNAs were higher in the intestine compared to the stomach, and pyloric caeca, with the highest levels detected in the proximal and distal regions, (Figure 1F). The expression of ffar2a2 mRNAs was high in the distal intestine, low in the rest of the intestinal regions, and almost undetected in the stomach (Figure 1G). Both gpr84 and gpr119 mRNAs were predominantly found in the distal intestine, although quantifiable expression levels were observed in all gastrointestinal regions (Figure 1H,I). It should be noted that the abundance of all receptors mRNAs throughout the gastrointestinal, yet quantifiable, was rather low, as indicated by high Ct values in the real-time PCR runs (≈28–34). In contrast, lower Ct values (≈25–29), and therefore greater expression levels, were observed for mRNAs encoding the fatty acid transporters Fat/cd36, Fatp4, and Mct-1a. All three were abundantly expressed throughout the entire GIT, although some differences in expression levels were detected among regions for fatp4 (lower relative expression in the stomach) and slc16a1a (higher relative expression in the distal intestine) (Figure 1J–M). The expression of slc16a1b was high in the stomach, with levels compared to the rest of transporters throughout the GIT (Ct values ≈25), but very low in pyloric caeca and all intestinal regions (Ct values ≈30–31) (Figure 1M).
## 2.2. Luminal Fatty Acids Modulate Fatty Acid Receptors and Transporters mRNA Expression in the Gastrointestinal Tract
Fish fasted for 48 h were intragastrically administered with octanoate, oleate, ALA, or butyrate, and samples of different regions of the GIT were collected at 20 min and 2 h post-administration to assess different parameters related to fatty acid sensing and appetite regulation (Figure 2A). Figure 2B–M shows the effects of intragastrically administered fatty acids on the mRNA expression of fatty acid receptors and transporters along the rainbow trout GIT at 20 min and 2 h post-administration. In a short time, treatment with octanoate led to a significant upregulation of ffar1 and ffar2b1.2 in the middle intestine (Figure 2B,D), ffar2a1b in the proximal and middle intestines (Figure 2G), ffar2a2 in the stomach and middle intestine (Figure 2H), and slc16a1a (encoding Mct-1a) in the middle intestine (Figure 1M). Oleate induced ffar2b1.1, ffar2b1.2, ffar2a1b, ffar2a2, gpr84 and gpr119 in proximal intestine (Figure 2C,D,G–J), and also increased ffar1 in middle intestine (Figure 2B), and ffar2b1.2 in stomach (Figure 2D), while it decreased gpr84 in the distal intestine (Figure 2I) and fatp4 in the proximal intestine (Figure 2L). In addition, both octanoate and oleate significantly increased gpr119, cd36, and fatp4 mRNAs in the distal intestine (Figure 2J–L). Administration of ALA resulted in increased levels of ffar2b1.1 and ffar2b2a in the proximal intestine (Figure 2C,E), ffar1 and ffar2b1.2 in the proximal and middle intestines (Figure 2B,D), cd36 and fatp4 in the distal intestine (Figure 2K,L), and gpr119 in all regions of the GIT analyzed, except for the stomach (Figure 2J). On the contrary, significant ALA-induced downregulations of stomach slc16a1a (Figure 2M) and distal intestine ffar2a1b and gpr84 (Figure 2G,I) was detected. Finally, significantly higher levels of gpr84 and gpr119 in the stomach (Figure 2I,J), ffar2a1b, gpr84, cd36, fatp4, and slc16a1a in the proximal intestine (Figure 2G,I,K–M), gpr84, gpr119, and cd36 in the middle intestine (Figure 2I–K), and ffar2b2b and slc16a1a in the distal intestine (Figure 2F,M) were observed in fish administered with butyrate compared to control fish. Butyrate treatment also led to decreased slc16a1b mRNAs in the stomach (Figure 2N), ffar2b1.1 in the middle intestine (Figure 2C), and fatp4 mRNAs in the stomach (Figure 2L).
At 2 h post-administration, a significant increase in the mRNA levels of ffar2b2a, ffar2a1b, and gpr84 in the stomach (Figure 2E,G,I), cd36 in the proximal and middle intestine Figure 2K, and fatp4 in the stomach and proximal intestine (Figure 2L), was observed in response to octanoate. Oleate up-regulated the expression of ffar2b2a, gpr84, and cd36 in the stomach (Figure 2E,I,K), of ffar1 in the proximal intestine (Figure 2B), and of ffar1, ffar2b1.2, and gpr84 in the distal intestine (Figure 2B,D,I). On the contrary, it down-regulated the mRNA abundance of gpr119 in the distal intestine (Figure 2J). Treatment with ALA resulted in significantly higher levels of ffar1 and ffar2b2a in the stomach (Figure 2B), ffar2b1.2 in the proximal, middle, and distal intestine (Figure 2D), ffar2a1b in the distal intestine (Figure 2G), gpr84 in the stomach, proximal intestine, and middle intestine (Figure 2I), fatp4 in the stomach and proximal intestine (Figure 2L), and slc16a1a in the distal intestine (Figure 2M). Lastly, increased levels of ffar2b1.2 and ffar2b2b in the middle and distal intestine (Figure 2D,F), ffar2a1b in the stomach and proximal intestine (Figure 2G), ffar2a2 and slc16a1a in proximal intestine (Figure 2H,M), gpr119 in the distal intestine (Figure 2J), fatp4 and slc16a1b in the proximal and middle intestine (Figure 2L,N), and cd36 in all gastrointestinal regions analyzed (Figure 2K), were detected upon butyrate administration.
## 2.3. Gastrointestinal mRNA Expression of Intracellular Signaling Molecules Is Altered by the Luminal Presence of Fatty Acids
Considering that gustducin is the major G protein activated in response to FFAR activation in the mammalian GIT [2] and that the phospholipase C (PLC)- inositol triphosphate (IP3) and adenylate cyclase (AC)-cAMP-protein kinase A (PKA) pathways are the main intracellular signaling cascades triggered as a consequence [3,4,5,6], in this study we measured the gastrointestinal mRNA expression of a putative G protein involved in nutrient signaling in fish (Gnai1) as well as the mRNA levels of key elements of both the PLC-IP3 and AC-cAMP-PKA pathways in response to fatty acid administration to study whether the same mechanisms may operate in rainbow trout. The changes in mRNA abundance of such parameters at 20 min and 2 h after intragastric administration of fatty acids are shown in Figure 3 and Supplementary Table S1. Increased gnai1 mRNAs were observed upon octanoate treatment at 20 min in the middle and distal intestines, upon oleate treatment at 2 h in proximal and middle intestines, upon ALA treatment at 20 min in proximal and distal intestines, and at 2 h in the stomach and all intestinal regions, and upon butyrate treatment at 20 min in the stomach. Expression of plcβ1 was found to be up-regulated by butyrate in the stomach, proximal and middle intestines at 20 min, while at 2 h, only a significant upregulation was detected in the stomach. Oleate and ALA also caused significant increases in plcβ1, as well as plcβ3, mRNAs, especially in the proximal and middle intestines. On the contrary, the expression of both genes remained unaltered or even down-regulated in response to octanoate. Except for the middle intestine, all fatty acids tested led to significantly lower levels of plcβ4 mRNAs compared to the control group in all or most regions tested and at 20 min and/or 2 h. As for itpr1, we found increased mRNA levels at 20 min in the proximal intestine in response to butyrate, in the middle intestine in response to all fatty acids, and in the distal intestine in response to octanoate and ALA, as well as at 2 h in the proximal intestine in response to ALA and butyrate, middle intestine in response to ALA and distal intestine in response to butyrate. The expression of itpr3 was, in general, down-regulated in response to the luminal presence of fatty acids, with the major changes found in the stomach. Finally, ac mRNA levels were observed to be downregulated by butyrate treatment in the proximal intestine at 20 min and in the middle intestine at 20 min and 2 h. However, increased ac mRNAs were found in the middle intestine 2 h after oleate administration and in the distal intestine 20 min after ALA administration.
## 2.4. Abundance of Gastrointestinal Hormones Responds to Luminal Fatty Acids
The luminal presence of fatty acids modulates mRNA and protein levels of gastrointestinal hormones, as shown in Figure 4. The abundance of ghrl mRNAs was observed to be up-regulated by ALA and butyrate in the stomach, proximal intestine (only butyrate), and middle intestine at 20 min post-treatment and by oleate in the stomach and ALA in the proximal and middle intestine at 2 h. No significant differences in stomach Ghrl levels were detected in response to any of the fatty acids (Figure 4B,H). Levels of cck/Cck were unaltered by luminal fatty acids at 20 min (Figure 4C,D,H). At 2 h, oleate and ALA led to a significant upregulation of cck and/or Cck levels in the proximal intestine, while the opposite effect was observed for butyrate (Figure 4C,D,H). In addition, oleate and butyrate led to increased cck mRNAs in the distal intestine (Figure 4C). Treatment with octanoate, oleate, and ALA resulted in a general tendency to increase the abundance of pyy/Pyy, especially at 2 h (Figure 4E,F,H). Butyrate, on the other hand, reduced pyy mRNA levels in both the proximal and middle intestine at 20 min and 2 h (Figure 4E), although a significant increase in Pyy protein levels was observed in the proximal intestine at 2 h (Figure 4F,H). Finally, gcg (proglucagon, gene encoding Glp-1) mRNAs were found to be up-regulated by octanoate, oleate, and ALA treatment at 20 min in the proximal intestine, while down-regulated by the former two at the same time point in the distal intestine (Figure 4G). Due to technical difficulties with finding a suitable Glp-1 antibody, we were not able to detect levels of this protein by Western blot in the present study.
## 3. Discussion
Great interest in elucidating the mechanisms by which the gut senses luminal nutrients and how this sensing impacts the homeostatic control of feeding has been taking place over the last few years. Gut nutrient sensing relies on the presence of specific receptors and transporters located mainly in the luminal surface of enteroendocrine cells and enterocytes, respectively, which are able to respond to variations in the luminal levels of nutrients [7,8,9]. Studies on this topic, however, have focused mainly on mammalian models, and whether equivalent mechanisms operate in other vertebrate groups remains practically unknown, particularly in fish. This research aims to address this scarcity of information in the fish literature and provides the first evidence on the presence and functioning of fatty acid sensing mechanisms in the GIT of a carnivore fish species, the rainbow trout. We focused on lipids because of three reasons: (i) they are the main energy source in aquaculture nutrition [11], (ii) they are the major aerobic fuel source for energy metabolism of the fish muscle [10], and (iii) there are several key differences between fish and mammalian lipid metabolism [12].
## 3.1. Fatty Acid Transporters and Carriers in Rainbow Trout GIT and Their Involvement in Sensing Different Types of FAs
The first objective of our research was to study whether the main fatty acid receptors and transporters described in the mammalian GIT (i.e., FFAR$\frac{1}{2}$/$\frac{3}{4}$, GPR$\frac{84}{119}$, FAT/CD36, FATP4 and MCT-1; [7,8,9]) are present in rainbow trout. A previous in silico study from our research group, together with a study by Roy and coworkers (Roy et al., under review), described that some genes encoding such mammalian receptors (particularly Ffars) are not present within the rainbow trout genome (ffar4), some are duplicated (as expected due to whole-genome duplications events during evolution), and some appear not to be orthologous to mammalians. In the same previous study, we showed that most of these ffar genes identified within the rainbow trout genome (specifically, ffar1, ffar2b1.1, ffar2b1.2, ffar2b2a, ffar2b2b, ffar2a1b, and ffar2a2) are expressed at a smaller or greater extent in the stomach, anterior intestine and/or posterior intestine. In the present study, we carried out PCRs targeting these genes to confirm previous observations. However, the putative presence of the rest of the fatty acid receptors and fatty transporters remains unknown. Fatty acid transporter genes are pretty well conserved throughout evolution, and sequences encoding Fat/cd36, Fatp4, and Mct-1 (with two copies for the latter) can all be found within the rainbow trout genome [14,15]. A high degree of conservation is also observed for genes encoding Gpr84 and Gpr119 [16]; thus, their presence in the rainbow trout genome is also evident. PCR and RT-qPCR analyses targeting all these genes indicated the expression of mRNAs encoding Gpr84, Gpr119, Fat/cd36, Fatp4, and Mct-1 (a and b isoforms) in the GIT of rainbow trout. In the mammalian gut, fatty acid receptors (except for GPR84, [17]) and transporters are located in the apical membrane of different cell types, with receptors being typically found in enteroendocrine cells while transporters in enterocytes [7,8,9]. Experimental approaches used in the present research do not allow us to discriminate the cell type location of receptors or transporters, so we will discuss obtained results considering gastrointestinal cells in general. However, the fact that the mRNA abundance of receptors was low (very high Ct values) and that of transporters considerably high might be an indirect indicator of their cell type location. Thus, considering that enterocytes are the most abundant epithelial cells in the GIT, and EECs represent only $1\%$ of them [18], we might suggest receptor presence in EECs and transporter in enterocytes. Future lines of research will focus on the sorting of rainbow trout intestinal epithelial cells by type using flow cytometry and the study of nutrient-sensing mechanisms taking place in each individual cell type.
Based on the GIT distribution study, we observed that except for ffar1 and ffar2b1.1, whose transcripts were not quantifiable in the stomach, and also hindgut in the case of the latter, all fatty acid receptors and transporters detected are found at quantifiable levels in the stomach, pyloric caeca, and along the entire intestine, thus showing a widespread distribution within the GIT. This differs from the mammalian model for some receptors/transporters. For instance, any receptor was observed to be almost exclusively expressed in the distal intestine of the rainbow trout, as FFAR2 and FFAR3 are in the case of mammals [19,20]. It is also worth pointing out the case of MCT-1. Two isoforms of this transporter (a and b) have been described in rainbow trout [21]. These forms were here observed to show a very different expression profile along the GIT, with slc16a1a (encoding Mct-1a) mRNAs expressed in the whole GIT but most importantly in the hindgut, and slc16a1b (encoding Mct-1b) almost exclusively detected in the stomach. In mammals, studies in mice and rats have shown that the single MCT-1 isoform found in these vertebrates is poorly expressed in the stomach but abundant in the colon [22], as expected considering that MCT-1 is involved in SCFA uptake and that the colon is the predominant location for SCFA synthesis. However, interestingly, a high expression of this transporter was reported in both the caprine stomach and large intestine [23], which appears to be related to the fact that ruminants also produce large amounts of SCFAs in the rumen. Indeed, SCFAs constitute the major fuel source in ruminants, providing up to $80\%$ of their energy requirements [24]. The physiological significance of the distinct expression profile of the two Mct-1 isoforms along the rainbow trout GIT observed in this study requires additional investigation. However, we could hypothesize that each isoform, predominant at each end of the GIT, might be involved in the uptake of different SCFAs. This could relate to the previous report that the microbiota of the rainbow trout stomach and intestine shows considerable differences [25].
The next step of our study was to characterize the response of the identified receptors to the luminal presence of fatty acids of different lengths and degrees of unsaturation [i.e., octanoate (8-carbon saturated FA), oleate (18-carbon monounsaturated FA), ALA (18-carbon PUFA), and butyrate (4-carbon saturated FA)]. For this, we intragastrically administered fatty acids into fasted rainbow trout and assessed the abundance of mRNA encoding the target receptors in the GIT. The most important changes include increases in the mRNA abundance of ffar2a1b and ffar2a2 in response to octanoate, ffar1, ffar2b1.1 and ffar2b1.2 in response to oleate, ffar1, ffar2b1.2 and gpr119 in response to ALA, and ffar2b2b, and gpr84 in response to butyrate, particularly in anterior regions of the GIT (importantly involved in nutrient sensing in mammals). Some of the fatty acids tested, mainly oleate and ALA, also led to increased expression of other types of fatty acid receptors (e.g., ALA up-regulated the expression of ffar2b1.1, ffar2a and gpr119, while oleate that of the ffar2a); however, these increases were, in general, less pronounced than the formers. This suggests that the different fatty acid receptors appear to be more responsive to specific ligand/s (i.e., Ffar1:oleate and ALA, Ffar2b1 (1 and 2): oleate and ALA, Ffar2b2b: butyrate, Ffar2a1b: octanoate and butyrate, Ffar2a2: octanoate, Gpr84: butyrate, Gpr119: ALA), although they may also be activated by other types of fatty acids. While we only measured mRNA abundance here, and experiments testing the ligand affinity of each receptor are required, the activation profile of fatty acid receptors in the rainbow trout GIT that can be suggested from our experiment points out important putative differences with regard to gut fatty acid sensing mechanisms in mammals, in which FFAR1 is only activated by MCFAs and LCFAs, FFAR2 and FFAR3 are only activated by SCFAs, GPR84 mainly by MCFAs, GPR119 by lipid derivatives (e.g., OEA) [7,8,9]. These differences strengthen our hypothesis of rainbow trout not having clear orthologous receptors to mammalian FFFAR2 and FFAR3. Although a deeper understanding of the mechanisms underlying fatty acid sensing in the rainbow trout GIT is required, present observations establish a basis in favor of the existence of major functional (maybe evolutionary) differences between gut nutrient sensing mechanisms between fish and mammals.
An interesting observation from our intragastric experiment is that there is a clear differentiation in the activation of receptor mRNA abundance in response to the luminal presence of fatty acids depending on the region of the GIT and time. Such a differentiation also applies to the mRNA abundance of the fatty acid transporters tested, i.e., Fat/cd36, Fatp4, and Mct-1a/b (Figure 5). *In* general terms, we can distinguish between one type of response in the anterior region of the GIT (including stomach, proximal intestine, and likely middle intestine) and another type of response in the distal intestine. In addition, results obtained point towards comparable mechanisms of action for octanoate, oleate, and ALA, while butyrate displayed clear differences. With this in mind, major results from our study allow us to hypothesize that the presence of octanoate, oleate, ALA, or butyrate in the intestinal lumen would be first sensed by specific membrane receptors located in the anterior regions of the GIT. As mentioned earlier, although functional studies on ligand affinity are needed, we propose that Ffar2a (1b and 2) could be more activated by octanoate, Ffar1 and Ffar2b1 (1 and 2) by oleate, Ffar1, Ffar2b1 (1 and 2) and Gpr119 by ALA, and Ffar2a1b and Gpr84 by butyrate, although less pronounced activations with other fatty acids may occur. Besides receptors, the butyrate-induced increased expression of cd36, fatp4 and slc16a1a in the proximal intestine suggests the transporters Fat/cd36, Fat/p4, and Mct-1a as additional important sensors of butyrate in the rainbow trout GIT at a short-time. This observation differs from the mammalian model, in which only MCT-1, and not FAT/CD36 or FATP4, plays a role in the intestinal transport of SCFAs like butyrate [26]. Interestingly, our results demonstrated the increased abundance of mRNAs encoding Mct-1a not only in response to butyrate but also to octanoate in the proximal and middle intestine, which points towards this fatty acid as an additional activator of Mct-1a in the rainbow trout GIT. Except for this, treatment with octanoate, oleate, and ALA led to a general inhibition of the transporter’s mRNA abundance in the stomach and proximal intestine. Since we measured mRNA abundance only, this downregulation does not discard Fat/cd36, Fatp4, and Mct-1a/b as putative sensors for octanoate, oleate, and ALA, but could be the result of another response (e.g., negative feedback), although further studies are required for a certain explanation.
In the distal intestine, unlike what has just been stated, we observed that octanoate, oleate, and ALA increased cd36 and fatp4 mRNA abundance. This response can be attributed to the fact that the number of fatty acids in the distal vs. proximal intestine is likely considerably lower and/or that the mRNA levels of both cd36 and fatp4 are lower in the distal vs. proximal intestine/stomach (as observed from the GIT distribution study). Therefore, an increase in transporter expression in response to fatty acids may be related to transporter sensitivity increase. In any case, all three fatty acids (not only oleate) increased cd36 and fatp4 mRNA abundance (and considering that this observation might be an indicator of increased transport activity) is different from the mammalian model, in which both FAT/CD36 and FATP4 are in charge of LCFA translocation, whereas MCFAs are absorbed by passive diffusion [7,9]. However, again, this observation is just based on mRNA abundance data, and further research devoted to the study of transporter activity in response to different fatty acids is needed to confirm that both FAT/CD36 and FATP4 would be translocating fatty acids of different lengths (LCFAs, MCFAs, and PUFAs) in the rainbow trout distal intestine. The translocation model for SCFAs would likely occur according to the mammalian model [26], with MCT-1 (specifically, Mct-1a isoform in rainbow trout) being responsible for such an action, as suggested for the increased slc16a1a mRNA abundance and unaltered/decreased cd36 and fatp4 mRNA abundance in the distal intestine in response to butyrate. As for the receptors, Gpr119 and Ffar2b2b appear to be the only receptor types detecting the luminal presence of fatty acids (octanoate, oleate, and ALA in the case of the former, and butyrate in the latter) in the distal intestine at a short-term, as depicted by increased gpr119 (and not other receptors) expression in response to octanoate, oleate, and ALA, and increased ffar2b2b in response to butyrate.
Over a long-time, major results demonstrated that all fatty acids up-regulated the mRNA abundance of cd36 and/or fatp4 in proximal areas of the GIT. This result seems controversial when compared with the octanoate/oleate/ALA-induced down-regulation of the mRNA abundance of both transporters in the proximal intestine at 20 min. However, as discussed for the distal intestine, such up-regulation after 2 h could increase the sensitivity of the transporters in response to low luminal levels of fatty acids (as there would likely be compared to 20 min). In any case, these results support the wider affinity of FAT/CD36 and FATP4 to fatty acids of different lengths in rainbow trout vs. the restricted affinity in mammals [7,9]. In contrast, the induced expression of slc16a1a and slc16a1b in the proximal and/or middle intestine in response to butyrate (and not to other fatty acids) argues in favor of Mct-1 being more devoted to the translocation of SCFAs rather to other fatty acid types. Unlike proximal gastrointestinal regions, no major fatty acid-induced changes in the transporter mRNA abundance (except for a butyrate-induced increase in cd36 mRNAs) were detected in the distal intestine, which may indicate that transport of at least octanoate, oleate, and ALA into distal intestinal cells occur at a shorter time. Regarding receptors, we observed a general attenuation in expression activation of fatty acid receptors mRNA abundance compared to 20 min, as observed, for instance, in the cases of ffar2b1.1 (unaltered upon all treatments) and gpr119 (only activated in response to butyrate in the distal intestine). Other receptors, such as ffar1, showed a similar induction in expression to that observed at 20 min, i.e., mainly in response to oleate and ALA in the proximal and middle intestine. Expression of ffar2b1.2 was also mainly induced by the same ligands (oleate and ALA) but at more distal regions of the GIT. Finally, we can highlight the case of gpr84, which was observed to be increased in the stomach 2 h after intragastric fatty acid administration regardless of the fatty acid assessed. It might be possible that these observations respond to an increase in receptor sensitivity over time. Altogether, results from the present research clearly suggest a differential activation profile of fatty acid receptors along the rainbow trout GIT depending on the time after nutrient administration.
## 3.2. Intracellular Mechanisms Triggered and Hormone Release as a Consequence of Gastrointestinal Fatty Acid Receptor Activation
Fatty acid receptors, as classical GPRs, respond to fatty acid binding with structural changes that lead to the activation of intracellular guanine nucleotide-binding proteins (G proteins) and the subsequent triggering of diverse signaling pathways. The major G protein coupling FFAR activation to hormonal release in the mammalian GIT appears to be gustducin (initially found in taste cells) [2]. Nevertheless, it appears that no ortholog of the mammalian gustducin gene (gnat3, guanine nucleotide-binding protein g (t) subunit alpha-3) is present in teleost fish; instead, other G proteins (e.g., Gnai1) appear to participate in the signaling of gut sensing [27,28]. *The* general up-regulation of gnai1 mRNA abundance in response to intragastrically administered fatty acids observed in this study argues in favor of this G protein being activated as a consequence of fatty acid binding to FFARs in the rainbow trout GIT. In mammals, different signaling pathways appear to be triggered upon G protein activation depending on the receptor. Thus, the major effector for FFAR2 and FFAR3 seems to be PLC, whose activation results in increased production of IP3, which in turn binds to its receptor (ITPR3) located at the endoplasmic reticulum, releasing Ca2+ into the cytoplasm [3]. GPR119 operates mainly through the AC-Camp-PKA pathway: its activation results in the activation of AC, responsible for converting ATP to the second messenger Camp, thus leading to Camp accumulation and, thereby, activation of PKA [4]. In the case of GPR84, signaling pathways downstream of its activation have been well studied regarding its pro-inflammatory nature. Considering that elevated intracellular Camp levels suppress innate immune functions, it has been proposed that GPR84 exerts its pro-inflammatory actions by inhibiting AC and thereby suppressing intracellular Camp [5,6]. Additionally, other signaling pathways, such as the ERK cascade, have been associated with GPR84 signaling in immune functions [29]. Nevertheless, no information is available on the specific intracellular cascades in charge of coupling GPR84 and gastrointestinal hormone release. With this background, and considering that no evidence in this respect is available in fish literature, we investigated in the present study whether the gastrointestinal abundance of mRNAs encoding key elements within the PLC-IP3 and AC-Camp-PKA pathways is affected by the luminal presence of fatty acids. The results demonstrated increased levels of plcβ1, plcβ3, and itpr1 mRNAs in anterior regions of the rainbow trout GIT in response to oleate and ALA, which may indicate that these two fatty acids could possibly signal through the PLC-IP3 pathway, although some important differences, such as the involvement of the Plcβ1 and 3 (instead of PLCβ2) and Itpr1 (instead of ITPR3), may exist with respect to the mammalian model. The PLC-IP3 pathway (specifically involving the isoforms Plcβ1 and Itpr1) may also participate in mediating butyrate actions in anterior regions of the rainbow trout GIT. As for the AC-cAMP-PKA signaling cascade, it might mediate at least some ALA responses in the distal intestine, maybe by acting through GPR119, as suggested by increased ac mRNA levels upon treatment with this fatty acid in the mentioned region. We also observed an interesting down-regulation of ac mRNAs in the proximal and middle intestine upon butyrate treatment, which, considering the role herein proposed for GPR84 in the mediation of butyrate responses, may match the intracellular signaling cascade proposed to this receptor in mammals [5,6]. Notably, no major changes occurred in the mRNA abundance of the intracellular signaling elements tested in response to octanoate, suggesting that other signaling pathways different from PLC-IP3 and AC-cAMP-PKA likely mediate octanoate actions. It has to be taken into consideration, however, that all these observations are based on gene expression data only, and future studies measuring the levels of second messengers should be performed in order to confirm present results.
In mammals, the triggering of intracellular signaling cascades in response to the sensing of luminal fatty acids and leads to the release of gastrointestinal hormones [8,9]. In the case of FFAR2 and 3, such a release occurs as a consequence of the rise in intracellular Ca2+, which activates the fusion machinery of the secretory granules containing hormones, thus triggering their release by exocytosis. Ca2+-triggered exocytosis likely operates for GPR119 as well, with PKA acting as a regulator of such a process [30]. Experimental approaches included in this study do not allow to describe the triggering process underlying hormonal release, but both qPCR and Western blot analysis demonstrated increased mRNA/protein levels of major gastrointestinal hormones (Ghrl, Cck, Pyy, and/or Glp-1) in the rainbow trout GIT upon fatty acid intragastric treatment, suggesting that hormone release is a consequence of gut fatty acid sensing in rainbow trout, as is the case in mammals [8,9]. Major increases in gastrointestinal hormone levels occurred in anterior regions of the GIT (stomach, proximal, and, to a lesser extent, middle intestine), suggesting these regions are primarily involved in appetite regulation. We observed a differential modulation of the Ghrl, Cck, and Pyy mRNA and/or protein level abundance depending on the fatty acid. *In* general, both octanoate and oleate led to increased Glp-1 levels at a short time-post administration, while at a longer time, they led to increased Pyy and also Cck in the case of oleate. All these hormones are of anorexigenic nature [31,32]; thus, their release in response to octanoate and oleate would be in agreement with an inhibitory role in feed intake for these two fatty acids. In the case of oleate, present observations regarding the hormonal release are in accordance with mammalian studies, which reported that LCFAs trigger CCK, GLP-1, and PYY secretion and suppress ghrelin release [33,34]. However, this response was not seen with fatty acids of 11 carbon atoms or fewer [35]; thus, results here observed for octanoate support a different model than that known in mammals. In mammals, both octanoate and oleate are primarily sensed by FFAR1 and FFAR4, and these two receptors are, therefore, related to hormone release. MCFAs are also detected by GPR84, but this receptor in mammals appears not to be expressed in EECs; thus, it would not be involved in the release of gastrointestinal hormones. In rainbow trout, we proposed that Ffar4 is absent, and thus the receptor binding these two fatty acids in this species (apparently n-Ffar5b (1b and 2a) in the case of octanoate and Ffar1 and n-Ffar2a (1 and 2) in the case of oleate) would be associated with octanoate- and oleate-evoked hormone release. FFAR2 and FFAR3 in mammals are responsive to SCFAs (such as butyrate), and they are believed to induce the release of PYY [36,37] and GLP-1 [38]. However, a later study using isolated rat colons suggested that the release of colonic PYY/GLP-1 in response to the presence of luminal SCFAs does not involve FFAR2/FFAR3; it rather occurs in response to the metabolization of SCFAs and subsequent function as a colonocyte energy source [39]. Results from the present study using butyrate demonstrated, in general lines, increased levels of Ghrl and a decrease in those of Cck and Pyy in response to this SCFA, hormonal responses that would presumably occur upon activation of n-FFar2b2b, n-Ffar5b1b, and/or GPR84. Contrary to octanoate and oleate, increased levels of Ghrl (orexigen; [31,32]) and decreased levels of Cck and Pyy (anorexigens; [31,32]) would suggest a stimulatory role in feed intake. Finally, the release of Ghrl and Glp-1 at a short time and of Cck and Pyy at a longer time would likely be responses occurring upon activation of gastrointestinal sensors of luminal PUFAs, such as ALA. In this case, hormonal release (especially at a short time) would suggest a contradictory effect on feed intake. It must be highlighted that the observation of hormonal release in response to ALA administration in rainbow trout indicates an important difference compared to mammals, in which n-3 PUFAs (such as ALA) do not seem to activate fatty acid sensors [40]. Future studies should focus on the determination of feed intake levels upon fatty acid intragastric administration to confirm changes in the abundance of gastrointestinal hormones observed in the present study.
In summary, the present study offers the first set of evidence supporting the presence of mechanisms able to sense fatty acids in the GIT lumen of rainbow trout. The data presented here show clear similarities to the widely accepted mammalian model of fatty acid gut sensing and its involvement in food intake regulation but also suggest several important differences (Figure 6). The most notorious of such differences is probably the lack within the rainbow trout genome of one of the main sensors of MCFAs and LCFAs in mammals, i.e., FFAR4, which appears to be compensated by other receptors binding and responding to these types of fatty acids. Another important difference lies in the ALA-induced modulation of fatty acid sensors and putative response triggered; this observation differs from mammals, in which no activation of fatty acid sensors seems to occur in response to n-3 PUFAs [41,42,43]. The differences between rainbow trout and mammalian fatty acid gut sensing mechanisms may be due to phylogenetical reasons (divergence between mammals and fish) and/or to the different dietary habits between carnivore (rainbow trout) and omnivore (mammalian models assessed so far) species of vertebrates. Further studies are required to study the basis for these differences.
## 4.1. Animals
Rainbow trout (body weight (bw) = 90 ± 20 g) were obtained from a local fish farm (A Estrada, Spain) and maintained in 100 L tanks ($$n = 40$$ fish/tank) with dechlorinated and aerated tap water (15 ± 1 °C) in an open circuit. The photoperiod was set to 12 h light:12 h darkness (12L:12D, lights on at 08:00 h). Fish were fed with a commercial dry pellet diet (proximate analysis: $44\%$ crude protein, $21\%$ crude fat, $2.5\%$ carbohydrates, and $17\%$ ash; 20.2 MJ kg−1 of feed; Biomar, Dueñas, Spain) daily at 11:00 until apparent visual satiety. All studies adhered to the ARRIVE Guidelines, were performed following guidelines of the European Union Council ($\frac{2010}{63}$/UE) and the Spanish Government (RD $\frac{53}{2013}$) for the use of animals in research and were approved by the Ethics Committee of the Universidade de Vigo (00013-19JLSF).
## 4.2. Expression and Distribution of Fatty Acid Receptors and Transporters mRNAs along the Rainbow Trout Gastrointestinal Tract
Three 48 h-fasted fish were anesthetized in water containing 2-phenoxyethanol ($0.02\%$ v/v; Sigma-Aldrich, St. Louis, Missouri, USA) and euthanized by decapitation. Samples from the stomach, pyloric caeca, and intestine (proximal, anterior middle, intermediate middle, posterior middle, and distal; see Supplementary Figure S2 for graphical details) were collected, snap-frozen in dry ice and stored at −80 °C until quantification of the mRNA abundance of fatty acid receptors and transporters by RT-qPCR as described in Section 4.5. This experiment was repeated twice. Following RT-qPCR, representative samples of each tissue were run on $1.5\%$ agarose gels, and single bands for each PCR were purified using QIAquick Gel Extraction Kit (Qiagen, Hilden, Germany) and sent for sequencing (CACTI, University of Vigo, Vigo, Spain). The specificity of the nucleotide-deduced sequences was analyzed using the BLAST tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome; accessed on 3 February 2023).
## 4.3. Characterization of the Response of Gastrointestinal Fatty Acid Sensing Mechanisms to the Luminal Presence of Fatty Acids
This experiment was performed on two consecutive days. For both days, fish scheduled for use in the experiment (maintained in acclimation tanks) fasted for 48 h so that intestinal emptying and basal levels of hormones involved in the metabolic control of food intake were achieved. On day 1, 30 fish were captured in batches of 6 ($$n = 6$$ per treatment) and slightly anesthetized with 2-phenoxyethanol ($0.02\%$ v/v). Then, intragastric administration of 1 mL. 100 g−1 bw of vehicle (distilled water containing $5\%$ EtOH) alone (control) or containing 50 μmol.mL−1 of octanoate/octanoic acid (Sigma-Aldrich, Cat # C-2875), oleate/oleic acid (Sigma-Aldrich, Cat # O-1008), α-linolenate (ALA, Sigma-Aldrich, Cat # L2376) or sodium butyrate (Sigma-Aldrich, Cat # B5887) was performed. We selected octanoate, oleate, and ALA as representative MCFA, LCFA, and PUFA, respectively, because previous experiments from our research group demonstrated their effectiveness as feed intake modulators and/or modulators of related parameters in rainbow trout or Senegalese sole [44,45]. No available previous studies show a role for SCFAs in the control of feed intake in fish; butyrate was selected as representative in this study among the main SCFAs. To calculate the dose of fatty acid, we based on a typical amount of oleate (selected because we previously reported important effects of this fatty acid on feed intake in rainbow trout [44,46,47]) ingested daily by a trout fed with a standard commercial diet [48]). We then used an equimolar dose for the remaining fatty acids. Administration of treatments was carried out with a 13 cm-long cannula attached to a blunt-tip syringe. Putative regurgitation was checked visually, and we did not observe any during treatment administration. After intragastric treatments, fish from each experimental group were placed in individual tanks for recovery. After 20 min, they were again anesthetized to collect blood samples and, subsequently, plasma, which was used to determine the circulating levels of glucose, lactate, triglyceride, and free fatty acid (see Section 4.4). Then, fish were sacrificed by decapitation, and stomach and intestine (proximal, middle, and distal) samples were collected (see Supplementary Figure S2 for a graphical description of the regions sampled) for RT-qPCR or Western blot analysis; see below). We selected 20 min as sampling time based on preliminary experiments demonstrating this time to be adequate for a dye-containing saline solution to reach the middle/distal intestine after intragastric administration. On day 2, 30 fish per day were captured and intragastrically administered as described above, but sample collection was carried out 2 h post-administration.
## 4.4. Assessment of Plasma Metabolite Levels
Plasma levels of lactate, glucose, triglyceride, and free (non-esterified) fatty acid were assessed as indicators of the metabolic status of fish during experiments. Levels of all metabolites were assessed enzymatically using commercial kits adapted to a microplate format (For glucose, lactate, and triglyceride: Spinreact, Barcelona, Spain; for fatty acid: Fuji, Neuss, Germany). Results from these analyses are included in Supplementary Table S2. Levels of all parameters tested showed values comparable to those previously detected in healthy, unstressed individuals of the same species, with no considerable significant differences observed among groups, allowing us to consider that fish used for experiments have an adequate metabolic status and that fish were not exposed to major stress during experiments.
## 4.5. Quantification of mRNA Abundance by Reverse Transcription—Quantitative Polymerase Chain Reaction (RT-qPCR)
Isolation of total RNA from tissues and DNase treatment ($$n = 6$$ fish) were carried out using Trizol reagent (Life Technologies, Grand Island, Nebraska, USA) and RQ1-DNAse (Promega, Madison, Wisconsin, USA), respectively, as directed by the manufacturers. Optical density (OD) absorption ratio (OD 260 nm/280 nm) was used as an indicator of RNA purity, and it was determined using a NanoDrop 2000c (Thermo, Vantaa, Finland); only samples with an OD 260 nm/280 nm ratio > 1.8 were used for analysis. Following DNase treatment, 2 μg of total RNA was reverse transcribed into cDNA using Superscript II reverse transcriptase (Promega) and random hexamers (Promega) in a final volumen reaction of 20 μL, following manufacturer’s guidelines. Finally, using specific forward and reverse primers, mRNA abundance was quantified by RT-qPCR using MAXIMA SYBR Green qPCR Mastermix (Life Technologies). Specific primers to ffar1, ffar2b1.1, ffar2b1.2, ffar2b2a, ffar2b2b, ffar2a1b, and ffar2a2 were designed based on rainbow trout cDNA sequences obtained in a previous study of our research group. Among the 10 ffar isoforms described in such a study to be present in the rainbow trout genome, we selected the 7 mentioned above because they are the most abundantly expressed in the trout intestine. Primers to fatp4 were designed from the nucleotide sequence of Salmo salar (GenBank ID: XM_014138749.1) and positively checked for specificity within the rainbow trout genome using Genoscope (https://www.genoscope.cns.fr/trout/; accessed on 3 February 2023). Primers to gpr84, gpr119, cd36, and slc16a1 (gene encoding Mct-1; two isoforms, a and b), as well as those to intracellular signaling elements and gastrointestinal hormones, were designed from rainbow trout nucleotide sequences available on GenBank, using Primer-BLAST online tool (https://www.ncbi.nlm.nih.gov/tools/primer-blast/; accessed on 3 February 2023). All primers used are included in Table 1 and were ordered from IDT (Leuven, Belgium). PCRs were performed in 96-well plates using 1 µL cDNA (replaced by water and RNA for controls) and 500 nm of forward and reverse primers in a final volume of 10 µL. Each sample was run in duplicate wells. All qPCRs were carried out in an iCycler iQ (Bio-Rad, Hercules, California, USA). Cycling conditions for qPCRs consisted of an initial step at 95 °C for 10 min, followed by 40 cycles at 95 °C for 30 s and 60 °C (except for gcg and itpr3, whose annealing temperature is 59 °C, and ffar2b1.1 and ffar2a1b, with an annealing temperature of 62 °C; see Table 1) for 30 s. We included a melting curve (temperature gradient at 0.5 °C/5 s from 65–95 °C) at the end of each run to ensure that a single amplicon was being amplified. R2 of all reactions was 0.97–1, and efficiency was 95–$100\%$. Following PCRs, resulting products were run on $1.5\%$ agarose gels to confirm that a single product of the expected size was being amplified. The relative abundance of target transcripts was calculated using the 2-ΔΔCt method [49], using actb (gene encoding β-actin) and ef1a (gene encoding elongation factor 1α) as reference genes. These two genes were both stably expressed in this experiment.
## 4.6. Analysis of Protein Levels by Western Blot
Western blot analysis was performed from tissue samples from 6 fish. Extraction and quantification of protein were carried out as previously described [27]. Then, 50 µg protein was mixed with 4x Laemmli buffer containing $0.2\%$ 2-mercaptoethanol (Bio-Rad) and denatured at 95 °C for 10 min. Then, samples were electrophoresed in Stain-Free $20\%$ acrylamide gels (Bio-Rad) and transferred to a nitrocellulose membrane (0.2 µm pore-size; Bio-Rad) with the use of the Trans-Blot Turbo transfer system (Bio-Rad). After 60 min-blocking using Pierce Protein-Free T20 (PBS) Blocking Buffer (ThermoFisher), a specific primary antibody was added to the membrane and allowed to incubate overnight. Primary antibodies used for detecting gastrointestinal hormones in the stomach and intestine were custom synthesized as rabbit-raised polyclonal antibodies against synthetic peptide synthesized based on rainbow trout sequences (GenScript, Piscataway, NJ, USA). The exact antigen peptide sequences used are as follows: Ghrl: SQKPQVRQGKGKPPC (UniProtKB: Q76IQ4), Cck: CRPSHSQDEDKPEPP (UniProtKB: Q9YGE3), and Pyy: YPPKPENPGEDAPPC (UniProtKB:A0A060X2J5). All antibodies were diluted 1:500. After washing, membranes were incubated with secondary antibody (goat anti-rabbit IgG (H + L) HRP conjugate; Cat # ab205718, Abcam, Cambridge, United Kingdom) diluted to 1:5000. Clarity Western ECL substrate (Bio-Rad) was used to visualize proteins in a ChemiDoc Touch imaging system (Bio-Rad). We quantified protein bands by densitometry using Image Lab software and expressed results relative to the amount of total protein.
## 4.7. Statistical Analysis
All data were first checked for homogeneity of variance and normality, and, in case of failure of any of these requirements, they were log-transformed and re-assessed. Then, statistical differences among groups were assessed by one-way ANOVA followed by Dunnett’s test (for the in vivo intragastric experiment) or the Student-Newman-Keuls test (for plasma metabolite levels). Significance was considered when $p \leq 0.05.$ SigmaPlot version 12.0 (Systat Software Inc., San Jose, CA, USA) statistical package was used to carry out all analyses.
## References
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|
---
title: 'Risk Factors of Microalbuminuria among Patients with Type 2 Diabetes Mellitus
in Korea: A Cross-Sectional Study Based on 2019–2020 Korea National Health and Nutrition
Examination Survey Data'
authors:
- Eun Sook Bae
- Jung Yi Hur
- Hyung Soon Jang
- Jeong Suk Kim
- Hye Seung Kang
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002237
doi: 10.3390/ijerph20054169
license: CC BY 4.0
---
# Risk Factors of Microalbuminuria among Patients with Type 2 Diabetes Mellitus in Korea: A Cross-Sectional Study Based on 2019–2020 Korea National Health and Nutrition Examination Survey Data
## Abstract
Diabetes mellitus is a chronic disease with high economic and social burdens. This study aimed to determine the risk factors of microalbuminuria among patients with type 2 diabetes mellitus. Microalbuminuria is predictive of early-stage renal complications and subsequent progression to renal dysfunction. We collected data on type 2 diabetes patients who participated in the 2019–2020 Korea National Health and Nutrition Examination Survey. The risk factors for microalbuminuria among patients with type 2 diabetes were analyzed using logistic regression. As a result, the odds ratios were 1.036 ($95\%$ confidence interval (CI) = 1.019–1.053, $p \leq 0.001$) for systolic blood pressure, 0.966 ($95\%$ CI = 0.941–0.989, $$p \leq 0.007$$) for high-density lipoprotein cholesterol level, 1.008 ($95\%$ CI = 1.002–1.014, $$p \leq 0.015$$) for fasting blood sugar level, and 0.855 ($95\%$ CI = 0.729–0.998, $$p \leq 0.043$$) for hemoglobin level. A significant strength of this study is the identification of low hemoglobin level (i.e., anemia) as a risk factor for microalbuminuria in patients with type 2 diabetes. This finding implies that the early detection and management of microalbuminuria can prevent the development of diabetic nephropathy.
## 1. Introduction
Microalbuminuria is defined as the persistent elevation of albumin excretion (30–300 mg/day) in urine. This range is higher than that of normoalbuminuria (<30 mg/day) but lower than that of albuminuria (<300 mg/day) [1]. Microalbuminuria is a known early predictor of kidney and cardiovascular diseases as well as diabetes and hypertension [2,3,4,5], and an increased albumin concentration in the urine is a result of kidney disease [6]. The kidney is mainly comprised of microvessels, and diabetic nephropathy occurs due to defects in the glomerular filtration barrier caused by damage in the renal microvasculature of the glomerulus; the lack of moderate regulation of blood pressure or blood glucose levels in patients with hypertension or diabetes, respectively, could accelerate this condition [7]. Diabetic nephropathy is initially characterized by a drop in the glomerular filtration rate, followed by a period of microalbuminuria. In a considerable number of diabetic patients, the proportion of those with microalbuminuria increases by $20\%$ each year; then, they are eventually diagnosed with diabetic nephropathy when albuminuria is detected [8,9]. Approximately $20\%$~$40\%$ of diabetic patients develop diabetic nephropathy; it is a major complication of diabetes that reduces the patient’s quality of life. Hence, the early diagnosis of diabetic nephropathy is critical to enable active treatment [10,11].
The prevalence of diabetes mellitus continues to increase due to advances in medical technology, longer life expectancy, and changes in diet and lifestyle. As a result, the rate of diabetic nephropathy has steadily increased and it is currently the most common cause of end-stage renal failure worldwide [12,13]. Microalbuminuria is the most important indication of diabetes-related kidney complications, as it appears in the early phase and predicts the progression of complications [2,3,4]. Moreover, the risk of cardiovascular complications increases in patients with diabetes with microalbuminuria; therefore, early screening and prevention are important [5]. Hence, The American Diabetes Association has recommended that a microalbuminuria test be performed following diagnosis and annually thereafter [14].
Abnormal glucose homeostasis in diabetic patients results in various complications as it is accompanied by hyperglycemia caused by a lack of insulin secretion, hypertension, and metabolic disorders [15,16]. Diabetes can induce macrovascular complications that affect the brain, heart, and peripheral vessels and microvascular complications that damage the eyes, kidneys, and nerves [17,18]. Diabetic nephropathy is the most common diabetes-related complication of the microvasculature—it accounts for 30–$40\%$ of chronic kidney disease (CKD) cases as the main cause and $45\%$ of end-stage renal disease (ESRD) cases [19]. The prevalence of ESRD in diabetic patients is 10 times higher than that in non-diabetic patients [20]; over the past decade, the incidence of ESRD has rapidly increased as a result of the high incidence of diabetes [20]. Diabetic nephropathy is a severe complication detected in patients with diabetes; it is associated with mortality and increased risks of cardiovascular diseases and ESRD. Hence, renal replacement therapy such as dialysis or transplantation is required [21]. This leads to social burdens and enormous economic costs. Therefore, the risk factors of microalbuminuria should be identified at the early stages of diabetes to prevent the occurrence of complications [22,23].
Related studies have reported that microalbuminuria is an integrated index for renal and cardiovascular risk reduction in patients with type 2 diabetes (T2DM) [24], and that hypertension in patients with diabetes accelerates the onset of diabetic nephropathy in the presence of microalbuminuria [25,26]. Microalbuminuria in patients with diabetes is associated with, among others, diabetes duration, blood pressure, fasting blood sugar (FBS) level, glycosylated hemoglobin (HbA1c) level, serum insulin concentration, dyslipidemia, smoking, and body mass index (BMI) [27,28,29,30,31,32].
However, most studies among patients with diabetes have been conducted in a clinical setting. This study aims to discover appropriate management measures to decelerate the occurrence of complications in consideration of the general characteristics of community-dwelling patients with diabetes. Therefore, we conducted this study to reduce the socioeconomic burden caused by diabetes complications and contribute to public health by identifying the risk factors related to microalbuminuria in patients with T2DM living in the community.
## 2.1. Data Collection and Study Population
The study included adults aged ≥30 years who participated in the eighth Korea National Health and Nutrition Examination Survey (KNHANES) in 2019–2020 [33]. As a nationwide cross-sectional study conducted by the Korea Centers for Disease Control and Prevention based on Article 16 of the National Health Promotion Act, the KNHANES provides reliable statistics that can be used to assess the health and nutritional status of the Korean population [34]. The KNHANES data are useful in the development of health policies that reflect the current health status of people in South Korea. In accordance with the Korean Bioethics and Safety Act, the KNHANES is a government-run research project for public welfare and has been conducted with Institutional Review Board exemption since 2015. The requirement for informed consent was also waived.
Among the participants of the 2019–2020 KNHANES, 11,093 adults aged ≥30 years were selected for this study; among them, 1737 patients diagnosed with T2DM by a physician, receiving hypoglycemic drugs, or with an FBS level of ≥126 mg/dL or HbA1c level of ≥$6.5\%$ were selected. In contrast, patients diagnosed with T1DM (including those receiving insulin injection monotherapy), renal disease (CKD or ESRD), cardiovascular disease, or hypertension prior to the diagnosis of diabetes, which could thus affect the level of microalbuminuria, were excluded. Overall, only 539 patients were included in the subsequent analyses.
## 2.2. Assessment of Microalbuminuria Using the ACR Index
In microalbuminuria, the amount of albumin excreted in the urine is 30–300 mg (or 30–300 μg/mg creatinine) per 24 h [1]. Microalbuminuria cannot be detected using the standard urine dipstick test. Microalbuminuria was determined based on the levels of albumin and creatinine in urine using a turbidimetric assay. Therefore, the albumin-to-creatinine ratio (ACR), which is estimated using a random urine sample, is used to screen for microalbuminuria. In this study, a spot urine sample was used to estimate the ACR.
## 2.3. Anthropometric and Biochemical Data
For the blood pressure data, the final systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurements were used. The blood pressure was measured using a manometer (Baumanometer Wall Unit 33, Baum, Methuen, MA, USA) with the arm elevated above the level of the heart after a 5 min rest. To obtain the anthropometric data to estimate the BMI, standard devices and measurement methods were used; the height and weight were measured to the nearest 0.1 cm and 0.1 kg, respectively, using portable measurement equipment (Seca 225, Seca Deutschland, Hamburg, Germany; GL-6000-20, G-Tech, Uijeongbu, Republic of Korea). Obesity was determined based on the BMI, which was calculated by dividing the weight in kilograms by height in meter squared. The BMI was categorized based on the World Health *Organization criteria* for the Asia-Pacific region. A BMI of <18.5 kg/m2 is categorized as underweight, a BMI of 18.5≥–<23 kg/m2 is categorized as normal, a BMI of ≥23 kg/m2 but <25 kg/m2 is categorized as overweight, and a BMI of ≥25 kg/m2 is categorized as obese. The waist circumference (WC) cutoff points for Korean individuals were determined according to the criteria suggested by the Korean Society for the Study of Obesity: 90 cm for men and 85 cm for women.
For blood testing, blood samples were collected in the morning after fasting for at least 8 h. The Hitachi Automatic Analyzer 7600-210 (Hitachi, Japan) was used to obtain the measurements. The following biochemical data were collected: FBS, HbA1c, hemoglobin (Hb), serum lipid (triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)), serum albumin, and creatinine levels.
## 2.4. Demographic Characteristics
The obtained demographic characteristics included age (≤50, 50–59, 60–69, and ≥70 years), marital status (single, married, separated, or divorced), and economic status (low, middle–low, middle–high, or high) which was also divided into quartiles based on the average monthly household income. The household income was divided by the square root of the number of household members, which is the standard method recommended by the Organization for Economic Cooperation and Development. In terms of smoking status, the patients were categorized as never, ex-, and current smokers.
## 2.5. Statistical Analysis
The collected data were statistically analyzed using R software version 4.1.1 (R Foundation, Vienna, Austria) and according to the guidelines of the 2019–2020 KNHANES for complex sample design. All values in the sample data are expressed as mean and standard error, and the level of significance was set at a p-value of <0.05. *The* general characteristics of the study population were compared using Pearson’s chi-square test for categorical variables and the t-test for continuous variables according to the microalbuminuria status. Logistic regression analysis was performed to assess the risk factors of microalbuminuria in the study population. Using a logistic regression model incorporating the significant independent variables based on the chi-square test and t-test results, the odds ratio (OR) and $95\%$ confidence interval (CI) were obtained through the exponentiation of the estimated regression coefficients. In the logistic regression analysis, the model fitness and significance of each variable were tested to determine the significant variables.
## 3. Results
Among the 539 patients with T2DM in this study, $17.63\%$ had microalbuminuria, $3.71\%$ had albuminuria, and $78.66\%$ had normoalbuminuria. The patients’ mean ages were 64.68 years in the microalbuminuria group and 63.76 years in the normoalbuminuria group. The incidence of microalbuminuria was the highest in individuals aged ≥70 years and in the low-income group. The groups with <10 years and >20 years of diabetes duration had the highest proportions of patients. The microalbuminuria and normoalbuminuria groups did not vary significantly in terms of age, sex, economic status, BMI, WC, or smoking status, although they varied significantly in the duration of diabetes and medication for hypertension (Table 1).
Regarding the health-related characteristics, the mean ACR values were 9.94 mg/g creatinine in the normoalbuminuria group and 96.25 mg/g creatinine in the microalbuminuria group. The between-group variation based on microalbuminuria status was significant for HbA1c, FBS, Hb, TG, HDL-C, LDL-C, serum blood urea nitrogen (BUN), serum creatinine levels, and WC, but not for BMI, TC level, or DBP (Table 2).
When logistic regression was performed to identify significant variables, factors associated with the determination of microalbuminuria were unnecessarily included. To compensate, we excluded diabetes duration, BUN, and creatinine from the significant variables. This improved the fit of the model and the significance of each variable.
Regarding the logistic regression analysis, the ORs were 1.036 ($95\%$ CI = 1.019–1.053, $p \leq 0.001$) for SBP and 0.966 ($95\%$ CI = 0.941–0.989, $$p \leq 0.007$$), 1.008 ($95\%$ CI = 1.002–1.014, $$p \leq 0.015$$), and 0.855 ($95\%$ CI = 0.729–0.998, $$p \leq 0.043$$) for HDL-C, FBS, and Hb levels, respectively (Table 3). For the independent variables in the final model, the OR and $95\%$ CI were visualized in a decreasing order; the results are shown in Figure 1.
## 4. Discussion
Based on the results of this study, the significant risk factors of microalbuminuria in patients with T2DM were SBP and HDL-C, FBS, and Hb levels, in a decreasing order, with the level of significance set at <0.05. The discussion is as follows.
First, as the SBP level increased, the risk of microalbuminuria increased 1.036-fold. Microalbuminuria is an important risk factor that predicts the occurrence of diabetic nephropathy and CKD [2,3,4] and a risk factor of cardiovascular disease associated with hypertension [5]. According to a previous study, the risk of diabetic nephropathy in patients with hypertension and T2DM could be reduced via strict regulation of blood pressure levels [35]; the incidence of microalbuminuria was higher in patients with T2DM with hypertension compared with that in patients with T2DM alone. In the Kidney Disease Improving Global Outcomes guideline [1], the recommended target blood pressure levels to suppress the development of nephropathy and reduce cardiovascular-disease-related mortality are <$\frac{140}{90}$ mmHg in those with an excretion of <30 mg/g creatinine in the urine and <$\frac{130}{80}$ mmHg in those with an excretion of ≥30 mg/g creatinine in the urine or a high risk of cardiovascular disease [36]. Thus, for patients with T2DM, blood pressure regulation combined with periodic microalbuminuria tests could be a preventive measure against the development of diabetic nephropathy as well as microalbuminuria.
Second, as the HDL-C level increased, the risk of microalbuminuria decreased 0.996-fold. This finding agrees with the results of a previous study [22] which reported the correlations of microalbuminuria with hypertension, hyperglycemia, a low HDL-C level, and a high TG level, and with the study conducted by Sun et al. [ 37], which reported a decrease in microalbuminuria caused by a high HDL-C level. A typical patient with T2DM exhibits dyslipidemia with a characteristically low HDL-C level; meanwhile, HDL-C plays a role in the reverse transport of cholesterol, anti-inflammation, and anti-oxidation [38]. The process of reverse cholesterol transport is inhibited by the lack of HDL-C or a related dysfunction, while glomerulosclerosis and tubulointerstitial injury are induced [36]. The reduced anti-oxidation capacity of HDL-C also increases systemic oxidative stress and oxidized LDL levels in the circulation [39]; as the low HDL-C level decreases the glucose absorption in the skeletal muscles and induces the dysfunction of pancreatic β cells, the resulting hyperglycemia and metabolic disorder can damage the glomerular endothelial and tubulointerstitial cells [40]. Through these mechanisms, a low HDL-C level promotes microalbuminuria, hyperglycemia, and diabetic nephropathy [41]. In addition, dyslipidemia is closely associated with an increased risk of diabetes due to the changes in dietary habits and lifestyle; these changes have been correlated with being overweight, insufficient physical activity, smoking, hypertension, and cholesterol levels [39]. For patients with T2DM, increasing the HDL-C level could be a preventive measure against diabetic nephropathy and microalbuminuria.
Third, as the FBS level increased, the risk of microalbuminuria increased 1.008-fold. This finding agrees with the results of a previous study which reported that the risk factors for microalbuminuria were FBS level, blood pressure, old age, TG level, and duration of diabetes [22,23]. Hyperglycemia is a risk factor of the complications of diabetes that play a key role in the onset and development of diabetic nephropathy; hence, patients with T2DM who required strict regulation of blood sugar levels had a significant association with microalbuminuria [42]. Another study supported this association by reporting a correlation between increased excretion of microalbumin and increased levels of blood glucose and insulin [28]. In T2DM, the strict regulation of blood glucose in the early stages and before the onset of diabetic nephropathy prevents the development of diabetic nephropathy [1,14]. Other studies reported that HbA1c, FBS, SBP, and blood lipid levels were risk factors of microalbuminuria [28,29]. In our study, HbA1c was not a significant risk factor for microalbuminuria, but FBS was significant, which means that stricter daily FBS confirmation is required for the long-term glycemic management of HbA1c. The current study showed that the microalbuminuria group had higher FBS levels compared with the normoalbuminuria group (155 vs. 134 mg/dL). This is thought to reflect the importance of FBS in daily glycemic management as a risk factor for microalbuminuria, which implies that hyperglycemia or the inadequate regulation of blood glucose level could cause diabetic nephropathy with microalbuminuria.
As such, the ultimate goals of T2DM treatment are to prevent potential diabetic complications and maintain a healthy life through the regulation of blood glucose, blood pressure, and blood cholesterol levels [43]. To achieve these goals, palliative therapy such as blood glucose and blood pressure control, treatment of dyslipidemia, lifestyle modification, and regulation of dietary sodium intake are critical. Hence, the early detection of microalbuminuria and public health education and management are necessary to prevent diabetic nephropathy. Thus, a mobile health program for the systematic management of patients with T2DM should be developed; moreover, e-learning education and notifications regarding the probability of developing microalbuminuria and related complications should be provided as part of an integrated healthcare service.
Finally, as the Hb level increased, the risk of microalbuminuria decreased 0.885-fold. These results show that the hemoglobin level was associated with low HDL-C and high FBS and SBP levels which is considered to be significant due to vascular damage. According to a previous study into diabetes, a lower hemoglobin level was a risk factor of the progression of diabetic nephropathy [44], and it was increased in the prevalence of anemia in microalbuminuria compared to normoalbuminuria [45]. Anemia was a risk factor for albuminuria and kidney damage in patients with T2DM [46]. Furthermore, albuminuria is a risk factor for anemia in the CKD [47]; this is mainly due to reduced erythropoietin formation in the kidneys. Renal anemia appears early in the CKD process and worsens as it progresses. Given that the signs and symptoms of anemia in diabetes depend on the period in which Hb reduction is advanced and begin slowly, anemia associated with CKD is often asymptomatic and is only detected via routine blood tests. Other studies have shown that Hb, albuminuria, and kidney function are also strongly associated with cardiovascular risk [48]. This is important because delayed diagnosis and treatment of anemia associated with kidney disease may increase the risk of cardiovascular complications. Therefore, when microalbuminuria occurs in patients with T2DM, follow-up with anemia testing may help to prevent diabetes complications.
In addition, we found that the incidence of microalbuminuria was highest in patients aged ≥70 years and was higher in patients with a duration of diabetes of <10 years or >20 years compared with that in patients with a diabetes duration of 10–20 years. These results indicate that patients with a 10–20 year duration of diabetes had a greater interest in undergoing diabetes management as their diabetes had already progressed. In patients with a <10 year duration of diabetes, periodic tests could be inadequate or their diabetic management could have been neglected, thus suggesting a need for the intensive prevention and management of diabetic complications. In particular, it is worth noting that the incidence of microalbuminuria was the highest in patients with T2DM with low income, which coincided with a study on the differences in the incidence of microalbuminuria according to socioeconomic status [49,50]. This finding suggests the need for active public health strategies and support regarding the early detection of microalbuminuria and the prevention of diabetic nephropathy in patients with T2DM with low income.
The strength of this study is that the sample population was selected from the participants of KNHANES, an extensive national survey, which supports the possibility of generalizing the results of the study. Meanwhile, this study has several limitations. The cause–effect relationships across factors involved in microalbuminuria could not be determined as the study was cross-sectional in nature. Second, the classification of microalbuminuria could have been inaccurate as the microalbuminuria test was performed only once. Third, the effects of drugs on diabetes, hypertension, and hyperlipidemia could not be taken into account. Lastly, transient false-positive results were possibly obtained due to the performance of intense physical exercise, the occurrence of fever, or the development of urinary tract infection.
## 5. Conclusions
The risk factors of microalbuminuria in patients with T2DM were SBP and HDL-C, FBS, and Hb levels, the most important finding being that the Hb level was identified as a risk factor. Nonetheless, verification is required through subsequent studies on the association between microalbuminuria and anemia. In addition, the need for public health strategies that consider age and income level in community-dwelling patients with T2DM was confirmed.
Based on the results of this study, the authors would like to make the following suggestions. First, efforts should be made to reduce the inequalities in healthcare for patients with T2DM and low socioeconomic status and the use of healthcare services for the aged population. Second, further studies should develop a digital health program to reduce the risk factors of microalbuminuria in patients with T2DM.
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|
---
title: 'When Students Patronize Fast-Food Restaurants near School: The Effects of
Identification with the Student Community, Social Activity Spaces and Social Liability
Interventions'
authors:
- Brennan Davis
- Cornelia Pechmann
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002251
doi: 10.3390/ijerph20054511
license: CC BY 4.0
---
# When Students Patronize Fast-Food Restaurants near School: The Effects of Identification with the Student Community, Social Activity Spaces and Social Liability Interventions
## Abstract
US schools have fast-food restaurants nearby, encouraging student patronage, unhealthy consumption, and weight gain. Geographers have developed an activity space framework which suggests this nearby location effect will be moderated by whether people perceive the location as their activity space. Therefore, we study whether students perceive a fast-food restaurant near school as their activity space, and whether social marketing messages can change that perception. We conducted six studies: a secondary data analysis with 5986 students, a field experiment with 188 students, and four lab experiments with 188, 251, 178, and 379 students. We find that students who strongly identify with their student community patronize a fast-food restaurant near school (vs. farther away) because they view it as their activity space, while students who weakly identify do not. For example, in our field experiment, $44\%$ vs. $7\%$ of students who strongly identified with the student community patronized the near versus farther restaurant, while only $28\%$ versus $19\%$ of students who weakly identified patronized the near and farther restaurants comparably. We also find that to deter the strong identifiers, messages should convey that patronage is a social liability, e.g., portray student activism against fast food. We show that standard health messages do not change perceptions of restaurants as social activity spaces. Thus, to combat the problem of fast-food restaurants near schools causing unhealthy consumption, policy and educational interventions should focus on students who strongly identify with their student community and find ways to weaken their perceptions that fast-food restaurants near schools are their activity spaces.
## 1. Introduction
Research finds that retail nearness relates to retail patronage and product consumption [1] which we will refer to as the nearby location effect. Much of this work has studied the effects of near retail locations that sell unhealthy or risky products, such as fast food [2,3,4] or alcohol or tobacco [5,6,7]. In the US, 1 in 3 students are overweight and 1 in 5 are obese [8]. Strikingly, the majority of schools have a fast-food restaurant within a 1-mile radius [9,10], and $40\%$ of students eat fast food daily [11,12]. Nearby location effects have frequently been found; students who have fast food near school (vs. not) have poorer diets [1,2,13,14,15] and are more likely to be overweight or obese [2,3,9,14,15,16]. Virtually all studies have been observational, not experiments, but the robust results are compelling. The problem with fast-food restaurants contributing to obesity is now a global one. US fast-food ad spending is increasing in non-US markets [17], and in China, fast-food sales and obesity rates are concurrently increasing [18].
In the US, high school students’ fast-food consumption is rising because there are more open campuses, meaning students can leave school for lunch, not just eat fast food before or after school [12,19,20]. Approximately $50\%$ of California high schools have open campuses [21], and $67\%$ in Oregon [12]. Moreover, research indicates that fast-food restaurants near schools have a disproportionate impact on minority and low-income students [3,14,15], because fast-food restaurants are more often situated by their schools [22,23], a situation that has worsened over time [23]. Low-income adults are also disproportionately affected by fast-food proximity [24].
The most common policy solution in the US has been to try to ban fast-food restaurants near schools [25,26,27] and otherwise restrict land use for fast-food restaurants [1]. Small affluent communities have had some success with locational bans, but urban, racially diverse communities where fast-food restaurants already abound have faced fierce business opposition dooming their efforts to limit fast food [25,26]. Very little research has tried to identify other solutions to the problem [28]. Moreover, research on fast-food proximity has lacked a unifying framework that, for instance, identifies relevant moderators and mediators.
In this research, we borrow a unifying framework from the geography literature which posts that the most fundamental predictor of any nearby location effect relates to whether people perceive it as their activity space [29,30]. According to the activity space literature, an unsupervised location near adolescents will emerge as their risky social activity space if their own peer group congregates there [31,32,33,34]. So, we asked the following question: When might a fast-food restaurant near school emerge as a social activity space for students, encouraging patronage?
We reasoned that a nearby fast-food restaurant could become a social activity space for students who strongly identify with their student community. Due to their activities in and around school and their identification with their schoolmates, it could become a popular destination for these students to meet up. When students are strongly identified, this means they feel that they share beliefs, interests, and values with other students at their school, and feel accepted and liked by them [35,36,37,38,39]. Strong identification is often ignited when students engage in school-sponsored extracurricular activities such as sports, clubs, or the arts [40,41,42]. US schools provide wide access to extracurricular activities, and thus building strong identification with the student community is not limited to white or wealthy students [38,39,43].
Many US schools use surveys to measure students’ level of identification with the student community because of its predictive value [38,39,43]. Strong identification has been found to relate to many positive behaviors and to protect against numerous negative behaviors, from the teenage years through college [36,42,44]. Students who strongly identify with their student community tend to be more committed to academic goals [39,40,42] and less likely to use cigarettes, marijuana, or cocaine [41,42], though more likely to drink alcohol [40,41,45,46].
We posit that when a fast-food restaurant is located near school (vs. farther away), it will be perceived by high identifiers as their social activity space, attracting them there and promoting unhealthy eating. We will measure students’ perception of the location as their social activity space by asking them whether they go there to see friends. Strong identifiers should agree; weak identifiers should not. If the nearby fast-food restaurant is a draw for the strongly identified students, it is unlikely to attract the weakly identified, because different peer groups tend to hang out in separate places [29,30,31,32,34]. To summarize, we test the following hypothesis.
If students who are strongly identified with their student community think the fast-food restaurant near school is their social activity space, how might they be dissuaded? Studies have examined social marketing messages to deter students from unhealthy eating [47,48], alcohol use [49,50], and drug use [51,52,53]. Messages countering activity spaces have not been studied. However, the activity space framework posits that those spaces attract people by providing social benefits such as seeing friends [33,34,54]. Because the attraction is a social benefit, reducing its attraction will likely require reversing that perception to one of a social liability. Stating that the fast-food restaurant’s food is unhealthy is unlikely to be effective because it does not address students’ perception that it is a social activity space. An analogous situation occurs with smoking; adolescents start smoking for social acceptance [55,56]. To reduce its attraction, the opposite message must be conveyed: smoking is a social liability [53,57]. It is generally ineffective to convey to adolescents that smoking is a health liability [58].
It will be challenging to reverse adolescents’ perception that a previously acceptable hangout has become socially unacceptable among their peers. How can they be persuaded to see it differently? An emerging approach is to educate adolescents that marketers target them for unhealthy products and encourage student activism against being so targeted [59,60,61]. Sometimes students even engage in major activism, by which we mean they actively protest or boycott a product. A student-run product boycott is highly likely to make the product socially unacceptable to use among their peers. The US “truth” campaign against big tobacco did this effectively [61,62]. We tested activism messages and hypothesized the following.
## 2.1. Overview
In Study 1, we used Geographic Information System (GIS) data on fast-food restaurant locations combined with California’s Healthy Kids student survey to study whether a fast-food restaurant near school (vs. farther away) increased students’ fast-food restaurant patronage. We sought to determine whether a nearby location effect mainly occurred among students who strongly identified with their student community.
## 2.2. Participants
The participants were 5986 eleventh grade students who completed the long form of the California Healthy Kids Survey. They were the oldest respondents, most likely to have off-campus lunchtime privileges and make their own food decisions. Most participants were aged 16 ($50\%$) or 17 ($43\%$) and half were female ($53\%$). They were ethnically diverse; $18\%$ were Non-Hispanic White, $61\%$ Hispanic, $29\%$ Asian, $11\%$ Black, $3\%$ Hawaiian, and $8\%$ Native American (2+ ethnicities could be chosen). Additionally, $59\%$ were socioeconomically disadvantaged, eligible for free or reduced-price school meals due to low family incomes.
## 2.3. Measures
To determine if at least one fast-food restaurant was near schools, we merged two types of GIS data: [1] the locations of all California high schools from the state’s Department of Education, and [2] the locations of all California fast-food restaurants from the GIS firm ESRI’s Business Analyst product using NAICS code 722513 [63,64]. The restaurant-to-school distance was the traversable distance, considering roads [65]. Research shows fast-food restaurants tend to be clustered in a one-mile radius around schools in the US [9,10], so we coded whether there was 1+ restaurant within one mile of each school.
To measure students’ fast-food restaurant patronage and identification with the student community, we used the California Healthy Kids school survey administered to public school students by the state’s Department of Education. Schools were sampled to represent the school district populations. Students were required to complete the survey if selected unless a parent actively withheld consent. We used surveys from 2011–2012 and 2013–2014, obtained GIS data for the same years, and verified result consistency across years. We used the long-form survey that was administered in 27 randomly selected public high schools ($$n = 222$$ students per school in average), because it asked: “How many times did you eat fast food in the past 24 hours?” ( 0 = 0 times; 5 = 5 or more times). It also included a measure of identification with the student community: “I feel like I am part of this school.”, “ I feel close to the people at this school.”, and “I am happy to be at this school.” ( 1 = strongly disagree, 5 = strongly agree, averaged, α = 0.83).
## 2.4. Analyses
We estimated a hierarchical ordinary least square regression model of fast-food restaurant patronage, relating it to a fast-food restaurant near school, identification with the student community, and their interaction [2]. We used a hierarchical model to account for student observations being non-independent [3]. This allowed us to test our main hypothesis (H1) at the individual level while controlling for some students being from the same county or the same school within the county. To assess the interaction between nearness and identification, we used floodlight analysis [66]. Though we used ordinary least squares because the dependent variable was a scale (e.g., 5 = 5 times or more), all models were re-estimated using Poisson regressions for count variables with similar results.
## 2.5. Results
In our sample of predominantly urban, ethnically diverse, and economically disadvantaged high school students, $94\%$ ($$n = 5627$$) had a fast-food restaurant near school; $6\%$ did not ($$n = 359$$). They reported consuming fast food 0.83 times (SD = 1.19) in the past 24 h, and their mean identification with the student community was 3.40 (SD = 0.94, 1–5 scale). Whether these students had a fast-food restaurant near their school, as opposed to all fast-food restaurants being relatively far from school, did not relate to their fast-food restaurant patronage as a main effect ($b = 0.10$, df = 5980, $z = 0.85$, $$p \leq 0.40$$). Students’ identification with their student community related negatively to their fast-food restaurant patronage as a main effect, indicating that strong identification generally had a protective effect (b = −0.16, df = 5980, $z = 3.79$, $p \leq 0.001$). Finally, as hypothesized (H1), there was an interaction between fast-food restaurant nearness to school and identification with the student community on restaurant patronage ($b = 0.23$, df = 5980, $z = 2.81$, $p \leq 0.01$). See Table 1.
In supplemental analyses, we looked at whether students’ ethnicity or income (free or reduced-price meal eligible) related to identification with their student community. Hispanic ($r = 0.03$, $$p \leq 0.06$$) and White ($r = 0.08$, $p \leq 0.001$) correlated positively with identification. Black (r = −0.06, $p \leq 0.001$), Asian (r = −0.03, $p \leq 0.01$), and low income (r = −0.03, $p \leq 0.05$) correlated negatively. Native American ($r = 0.005$, $$p \leq 0.72$$), Pacific Islander (r = −0.01, $$p \leq 0.61$$), and mixed (r = −0.007, $$p \leq 0.58$$) were uncorrelated. However, all correlations were weak. We included ethnicity and income as covariates in our model, but the results were unaffected (see Appendix A).
We also conducted a floodlight analysis to understand the interaction effect we had observed [66]. Among students who strongly identified with their student community, fast-food restaurant patronage was higher if a restaurant was near school compared to farther away (right side of graph, solid line > dotted line). This nearby location effect was significant at an identification level of 4.25 or more on a 1–5 scale, $p \leq 0.05$ (shaded area on graph). Among students who weakly identified with their student community, fast-food restaurant patronage was higher if a restaurant was farther from school compared to nearby (left side of graph, dotted line > solid line). However, this effect only became significant at a very low identification level of 1.25 or less on a 1–5 scale, nearly at the scale endpoint. See Figure 1, which illustrates why the negative main effect for identification was qualified by the two-way interaction. The stronger the identification, the less the fast-food patronage if it was far from school (steep negative slope for dotted line); this was much less so if the fast food was near school (slight negative slope for solid line).
## 2.6. Discussion
In Study 1, we analyzed data from a large statewide survey of high school students. We found that, overall, strong identification with the student community reduced the risk of fast-food restaurant patronage, consistent with other protective effects of strong identification. However, while the strongly identified students tended to avoid unhealthy fast food, when a fast-food restaurant was located near (vs. farther from) school, their patronage increased, indicating it was a social activity space for them. Weakly identified students showed elevated patronage overall, but no more so when a restaurant was near (vs. farther from) school and tending toward the reverse. Identification with the student community was not highly related to ethnicity or income, and our results were confirmed even when these variables were controlled. However, these data were observational, so unobserved variables could have affected the results. Additionally, while students reported how many times they ate fast food, we could not verify where the food came from or whom they were with. Additionally, the data were skewed toward fast-food being near schools due to our sample.
## 3.1. Overview
For Study 2, we conducted a behavioral field experiment. Each student received a money-saving promotional coupon for the same fast-food restaurant (e.g., McDonalds) redeemable either at a location near school or, alternatively, farther away but still reachable. We then monitored actual coupon redemption at both locations. To manipulate identification, students completed an essay eliciting either strong or weak identification.
## 3.2. Design and Participants
The design was a 2 (restaurant nearness to school) × 2 (identification with the student community) between-subjects factorial with both factors manipulated. Participants were 153 older adolescents, university students, with a mean age of 20.5 years, $44\%$ female, $75\%$ White Non-Hispanic, $10\%$ Hispanic, $9\%$ Black, $12\%$ Asian, and $3\%$ other. Respondents could select more than one ethnicity. We recruited 188 but dropped 35 because they had dietary restrictions precluding fast food or did not complete the identification essay (Nnear, strong = 30; Nnear, weak = 43; Nfar, strong = 37; Nfar, weak = 43).
## 3.3. Manipulations
Students participated for partial course credit. First, we manipulated their identification with the student community using an essay task [35]: “Please write for a few minutes (about 1 paragraph). In what ways do you think you are similar to (different from) other students here at University X? Consider attributes, interests, beliefs, values, etc. Try to recall some specific experiences that made you feel a part of (different from) the University X student community.” University X was named as their university in this and all studies.
Next, fast-food restaurant nearness to school was manipulated by giving students one of two promotional coupons for the same fast-food restaurant, redeemable at one of its two locations, one near the school, the other farther away but still accessible because most students had cars, or their friends did. Each promotional coupon offered “$5 off any food item” and showed the location. The nearby (farther) location was described as “2 [20] minutes away” and was in fact about 0.5 [5] miles away, but we referred to drive time rather than miles because travel time more meaningfully conveys distances [67]. The coupon also showed the address and a small map and stated the $5 off promotion could only be used at that location on that day by 7 PM (see Appendix B). We asked participants not to share or discuss their coupon with others.
## 3.4. Measures
Research assistants were stationed at the two restaurant locations and collected the promotional coupons at the end of the redemption period. A subtle mark on each coupon identified each participant’s identification condition. We collected the promotional coupons from the cash registers, but we could not obtain the sales receipts. Therefore, we could not determine what participants bought or whom they were with if anyone. While having each individual sales receipt would have been more informative, the restaurants did not allow this, as it would have been obtrusive and slowed down their processes, which depend on speed.
The next day, participants completed an online survey with a restaurant nearness manipulation check which displayed their promotional coupon and asked: “How spatially close or far does this restaurant seem to you?” ( very far to very close, very distant to very near, and very large travel time to very small travel time, 1–7, α = 0.91) [68]. A product attitude covariate was measured: “I like [restaurant X, named]” with 1 = strongly disagree and 100 = strongly agree, to control for product disinterest [69]. Demographics were measured in all studies.
To check the identification essay manipulation, two raters blinded to condition read each essay and answered [35]: “To what extent does this individual … seem to identify with University X?” “… discuss themselves as a part of the University X community?” “… discuss themselves as similar to other University X students?” “… discuss themselves as a prototypical University X student?” “… seem to feel that being a part of University X is important to them?” ( 1 = not at all, 6 = a great deal). Inter-rater reliability was high (α = 0.84).
## 3.5. Analyses
Restaurant patronage data were analyzed using 2 (nearness) × 2 (identification) logistic regressions as the outcome was binary (1 = redeemed coupon, 0 = did not redeem) and interactions were assessed using planned pairwise comparisons. Manipulation check data were analyzed similarly but using ANOVAs. In this and all lab studies, we included the product attitude covariate in our models and report covariate adjusted values.
## 3.6. Manipulation Check Results
Students who received the promotional coupon for the fast-food restaurant location that was near versus farther from school reported its location as nearer (F[1, 148] = 71.49, $p \leq 0.001$; $M = 5.42$ vs. 3.49), with no main effect for identification ($$p \leq 0.28$$), no interaction ($$p \leq 0.53$$), and no effect for the product attitude covariate ($$p \leq 0.35$$). The raters judged the essays designed to elicit strong as compared to weak identification as more strongly identifying with the student community (F[1, 148] = 108.64, $p \leq 0.001$; $M = 5.40$ vs. 2.66), with no main effect for restaurant nearness ($$p \leq 0.43$$), no interaction ($$p \leq 0.89$$), and no effect for the product attitude covariate ($$p \leq 0.58$$).
## 3.7. Main Results
On fast-food restaurant patronage, while there was no main effect for identification (b = −0.12, z[148] = 0.53, $$p \leq 0.60$$), there was a main effect for restaurant nearness ($b = 0.77$, z[148] = 3.33, $$p \leq 0.001$$), but it was qualified by an interaction between restaurant nearness and identification ($b = 0.50$, z[148] = 2.18, $$p \leq 0.03$$), and the product attitude covariate also related to patronage ($b = 0.04$, z[148] = 2.85, $$p \leq 0.004$$). Students who strongly identified with the student community patronized the near versus farther restaurant more ($44\%$ vs. $7\%$; $b = 1.27$, z[148] = 3.46, $$p \leq 0.001$$), while students who weakly identified patronized the near and farther restaurants comparably ($28\%$ vs. $19\%$; $b = 0.26$, z[148] = 0.94, $$p \leq 0.35$$). See Figure 2.
## 3.8. Discussion
In Study 2, we gave students a promotional coupon for a fast-food restaurant that was redeemable at only one location, either near or farther from school, and we observed coupon redemption. We also manipulated their identification with the student community. We found direct behavioral evidence that, among those who felt strongly identified with the student community, a fast-food restaurant near versus farther from school was a significant draw. Students who felt weakly identified with the student community were equally likely to redeem the coupon irrespective of restaurant location.
## 4.1. Design and Participants
In Study 3, we investigated the underlying mediating process that may have caused students who were strongly identified with their student community to patronize a nearby fast-food restaurant. The posited mediator was the perception of it being a social activity space, i.e., where friends could be found. The design was a 2 (restaurant nearness to school) × 2 (identification with the student community) between-subjects factorial with nearness manipulated and identification measured. Participants were 188 older adolescents, university students, with a mean age of 19.5 years, $54.8\%$ female, $63\%$ White Non-Hispanic, $4\%$ Hispanic, $16\%$ Asian, $1\%$ Black, and $16\%$ other (2+ ethnicities could be selected). We recruited 198 but dropped 10 with dietary restrictions precluding fast food (Nnear = 94; Nfar = 94).
## 4.2. Manipulations and Measures
Students completed a study “about consumer response to retailers” for partial course credit. Restaurant nearness was manipulated as follows: “Imagine you are just leaving the University X campus. You receive a text from a new donut shop at least 2 [20] minutes away from campus offering you an attractive promotional discount for donuts today only. Picture this place and the people there in your mind.” Then, we asked their restaurant patronage intent: “Would you redeem this promotional coupon to eat at the restaurant?” ( 1 = definitely not, 7 = definitely yes). Next, we performed a nearness manipulation check: “How spatially close or far does this restaurant seem to you?” ( very far to very close, very distant to very near, and very large travel time to very small travel time, 1–7, α = 0.99). Then, we measured the mediator, the perception the fast-food restaurant was a social activity space to see friends: “Please indicate which items were salient to you when you decided whether to go eat at the restaurant”: “See friends” “Going to a place for people like me” “Be with people with whom I identify” (1 = strongly disagree to 7 = strongly agree; α = 0.73).
After this, we measured identification with the student community (see Appendix C). We showed increasingly overlapping circles labeled “You” and “University X” (Tropp and Wright 2001) and asked: “Please click on the picture below that best describes how much you happily feel a part of your University X student community” (0 = no overlap, 7 = complete overlap).” Finally, we measured the product attitude covariate: “I like donuts” (1 = strongly disagree to 5 = strongly agree). The data were analyzed using 2 (nearness) × 2 (identification) ANOVAs, with interactions assessed using spotlight analysis [66]. Moderated mediation models used Hayes model 8 [70] with 5000 replications.
## 4.3. Manipulation Check Results
Students in the near versus farther restaurant condition reported the restaurant was nearer (F[1, 183] = 273.26, $p \leq 0.001$; $M = 5.58$ vs. 3.05). There was no main effect for identification ($$p \leq 0.56$$), no interaction ($$p \leq 0.27$$), and no effect of the product attitude covariate ($$p \leq 0.53$$).
## 4.4. Main Results
We observed the hypothesized interaction between restaurant nearness and identification on restaurant patronage intent (F[1, 183] = 4.87, $$p \leq 0.03$$) which qualified the main effect for restaurant nearness that favored the near versus farther restaurant (F[1, 183] = 18.94, $p \leq 0.001$; $M = 4.17$ vs. 2.95), with no main effect for identification (F[1, 183] = 0.18, $$p \leq 0.67$$). Students strongly identified with their student community (mean + 1 SD) were more likely to intend to patronize the near versus farther restaurant ($M = 4.54$ vs. 2.58; t[183] = 5.32, $p \leq 0.001$). Students weakly identified with their student community (mean—1 SD), were indifferent to the near versus farther restaurant ($M = 3.80$ vs. 3.32; t[183] = 0.97, $$p \leq 0.33$$). The product attitude covariate also related to patronage (F[1, 183] = 25.08, $p \leq 0.001$).
## 4.5. Results on Mediation
We observed a marginal interaction between restaurant nearness and identification on the posited mediator: students’ perception of the restaurant as their social activity space (F[1, 183] = 2.87, $$p \leq 0.09$$), with no main effect for nearness (F[1, 183] = 0.78, $$p \leq 0.38$$) but a main effect for identification (F[1, 183] = 5.17, $$p \leq 0.02$$). Students who strongly identified with their student community (mean + 1 SD) were more likely to perceive the near versus farther restaurant as their social activity space ($M = 4.21$ vs. 3.66; t[183] = 2.21, $$p \leq 0.03$$); while students who weakly identified (mean—1 SD) perceived the near and farther restaurants comparably ($M = 3.83$ vs. 4.04; t[183] = 0.64, $$p \leq 0.52$$). The product attitude covariate was unrelated to this perception (F[1, 183] = 1.53, $$p \leq 0.22$$).
In a direct test of mediation, among students who strongly identified with their student community (mean + 1 SD), the effect of the near versus farther restaurant on patronage intent was mediated by the perception the restaurant was their social activity space (indirect effect = 0.18, SE = 0.11, $95\%$ CI = 0.01, 0.46). Among students who weakly identified with their student community (mean—1 SD), there was no such effect (indirect effect = −0.01, SE = 0.09, $95\%$ CI = −0.22, 0.16). See Figure 3.
## 4.6. Discussion
In Study 3, we manipulated fast-food restaurant nearness to school, and we measured students’ identification with their student community and the theorized mediator. We found direct evidence of mediation. Students who strongly identified with their student community said they decided to patronize the nearby (vs. farther away) fast-food restaurant to see friends, indicating they perceived it to be their social activity space. Students who weakly identified with their student community did not perceive it this way or patronize it.
## 5.1. Design and Participants
Study 4 tested a mild form of student activism: a disparaging social media post from a student at the high school, indicating it would be a social liability to be seen at a fast-food restaurant. The design was a 2 (restaurant nearness to school) × 2 (identification with the student community) × 2 (social liability vs. control message) between-subjects factorial, with all three factors manipulated. Participants were 251 older high school students from MTurk, screened to be in high school but over the age of 17 to exclude minors as mandated by our human subjects review board. Virtually all were aged 18, $35\%$ were female, $67\%$ White Non-Hispanic, $15\%$ Asian, $11\%$ Hispanic, $8\%$ Black, and $3\%$ other (permitting 2+ ethnicities). We recruited 273 but dropped 22 who did not complete the identification manipulation (Nnear_strong_control message = 31; Nnear_strong_social liability message = 41; Nfar_strong_control message = 28; Nfar_strong_social liability message = 31; Nnear_weak_control message = 34; Nnear_weak_social liability message = 20; Nfar_weak_control message = 30; Nnear_weak_social liability message = 36).
## 5.2. Manipulations and Measures
The social liability message, described as a social media post from a student at their high school, stated: “Students at this school would never be seen by friends at fast-food restaurants.” The control message was likewise described as a social media post from a student at their high school: “The school library is now open on weekends.” These social media posts were displayed on mobile phones (see Appendix D). Then, we used our prior methods to manipulate identification via an essay task, manipulate fast-food restaurant nearness using a burger restaurant (2 vs. 20 drive-time minutes) and measure restaurant patronage intent (“Would you redeem this promotional coupon…”).
We used our prior nearness manipulation check (α = 0.95). We used an identification manipulation check with increasingly overlapping circles “You” and “High School X” [37] that asked: “Please click on the picture below that best describes how much you feel part of [or close to, or happily part of] your High School X student community” (0 = no overlap, 7 = complete overlap, α = 0.94). Our manipulation check of the social liability message measured seeing that post (1 = strongly disagree, 5 = strongly agree), e.g., “Students at my high school would not like to be seen by friends at fast-food restaurants” (3 items, α = 0.82). Finally, we measured the product attitude covariate (“I like fast food” 1 = strongly disagree, 5 = strongly agree; 4 missing responses). The data were analyzed using 2 (nearness) × 2 (identification) × 2 (message) ANOVAs and interactions were assessed using planned pairwise comparisons.
## 5.3. Manipulations Check Results
Students in the near versus farther condition reported the fast-food restaurant was nearer to them (F[1, 238] = 32.35, $p \leq 0.001$; $M = 5.16$ vs. 4.02). Those in the strong versus weak identification condition reported more identification (F[1, 238] = 46.93, $p \leq 0.001$; $M = 4.00$ vs. 2.74). Those seeing the social liability versus control message reported what it said; students would not like to be seen at fast-food restaurants (F[1, 238] = 12.92, $p \leq 0.001$; $M = 3.12$ vs. 2.97). There were no other effects.
## 5.4. Restaurant Patronage Intent
As hypothesized (H2), there was a three-way interaction on restaurant patronage intent (F[1, 238] = 6.89, $$p \leq 0.009$$). There were no other effects except a main effect for the product attitude covariate (F[1, 238] = 22.70, $p \leq 0.001$). With the control message, strongly identified students increased their intent to patronize a fast-food restaurant if near versus farther from school (t[238] = 10.51, $$p \leq 0.009$$; $M = 5.48$ vs. 4.10); weakly identified students did not (t[238] = 0.07, $$p \leq 0.79$$; $M = 4.85$ vs. 4.95). With the social liability message, strongly identified students no longer increased their intent to patronize if near versus farther (t[238] = 0.91, $$p \leq 0.34$$; $M = 4.87$ vs. 5.24), and weakly identified students remained indifferent to nearness (t[238] = 0.57, $$p \leq 0.45$$; $M = 4.94$ vs. 4.60). See Figure 4.
## 5.5. Discussion
We tested a mild form of student activism; a student posted that going to a nearby fast-food restaurant was a social liability. Others saw a control post. The effects were, again, limited to the strongly identified students. If they saw the control post, they were attracted to a nearby (vs. farther) fast-food restaurant; if they saw the social liability post, they were not.
## 6.1. Design and Participants
We tested a stronger student activism message; students announced a boycott of nearby fast-food restaurants. Student activism of this type is increasingly prevalent; thus, the message was realistic [71]. The design was a 2 (restaurant nearness to school) × 2 (social liability vs. control message) between-subjects factorial with both factors manipulated. All participants were manipulated to feel strongly identified with their student community. We studied 178 older adolescents, university students, with a mean age of 19.7 years, $68\%$ female, $60\%$ White Non-Hispanic, $6\%$ Hispanic, $1\%$ Black, $20\%$ Asian, and $14\%$ other (2+ ethnicities allowable). We recruited 193 but dropped 15 because of dietary restrictions precluding fast food or the identification manipulation not being done (Nnear, control message = 51; Nnear, social liability message = 41; Nfar, control message = 40; Nfar, social liability message = 46).
## 6.2. Manipulations and Measures
The social liability message was a color poster of students stating: “The University X student community boycotts fast food near campus.” The visually identical control message stated: “The University X student community boycotts tobacco shops near campus.” ( See Appendix E). We used our prior strong identification essay and manipulated nearness as 2 vs. 20 drive-time minutes. We used prior measures of restaurant patronage intent, the nearness manipulation check (α = 0.98), identification with the student community (α = 0.86), and the product attitude covariate. The social liability manipulation check asked whether “the poster encouraged the University X student community to boycott fast-food restaurants” (1 = strongly disagree to 5 = strongly agree). Data were analyzed using 2 (nearness) × 2 (message) ANOVAs and interactions using planned pairwise comparisons.
## 6.3. Manipulations Check Results
Students in the near versus farther condition reported the restaurant was nearer (F[1, 173] = 65.34, $p \leq 0.001$; $M = 5.62$ vs. 3.39). Students who saw the social liability versus control message reported the content correctly (F[1, 173] = 341.84, $p \leq 0.001$; $M = 4.30$ vs. 1.33). Identification was strong as manipulated ($M = 4.33$ out of 5). There were no other effects.
## 6.4. Main Results
Restaurant nearness and message interactively affected restaurant patronage intent (F[1, 173] = 3.89, $p \leq 0.05$) which qualified main effects for near versus far (F[1, 173] = 17.20, $p \leq 0.001$, $M = 4.16$ vs. 3.11) and social liability versus control message (F[1, 173] = 11.81, $p \leq 0.001$, $M = 3.18$ vs. 4.10), with product attitude covariate having no effect (F[1, 173] = 2.08, $$p \leq 0.15$$). When the strongly identified students saw the control message, as before, they reported higher intent to patronize the near versus farther fast-food restaurant (t[173] = 19.03, $p \leq 0.001$; $M = 4.75$ vs. 3.32), but when they saw the social liability message, this effect was nullified (t[173] = 2.32, $$p \leq 0.130$$; $M = 3.44$ vs. 2.91; see Figure 5).
## 6.5. Discussion
In Study 5, we showed strongly identified students a forceful activism message: a boycott against nearby fast-food restaurants, implying that going there would be a social liability. The strong identifiers who saw the control message were attracted to the nearby (versus farther) fast-food restaurant; those who saw the social liability message no longer were.
## 7.1. Design and Participants
Study 6 tested a health liability message stressing that fast food was unhealthy. The design was a 2 (restaurant nearness to school) × 2 (identification with the student community) × 2 (health liability versus control message) between-subjects factorial, with all three factors manipulated. Participants were 379 older adolescents, university students, with a mean age of 20.3 years, $48\%$ female, $64\%$ White Non-Hispanic, $12\%$ Hispanic, $21\%$ Asian, $1\%$ Black, and $3\%$ other. We recruited 425 but dropped 46 who had dietary restrictions precluding fast food; all completed the identification manipulation (Nnear, strong, control message = 46; Nnear, strong, health liability message = 45; Nfar, strong, control message = 40; Nfar, strong, health liability message = 57; Nnear, weak, control message = 59; Nnear, weak, health liability message = 45; Nfar, weak, control message = 46; Nfar, weak, health liability message = 41).
## 7.2. Manipulations and Measures
The health liability message was visually similar to the liability message used in Study 5. This message showed a similar group of students who proclaimed: “Students at this school do not like unhealthy fast-food restaurants.” Thus, the main emphasis was unhealthy food. The control message was: “The school library is now open on weekends.” ( See Appendix F). We used our prior manipulations of identification and nearness.
Next, we asked: “Would you make a purchase at this fast-food restaurant?” ( 1 = extremely unlikely, 7 = extremely likely). We checked our nearness manipulation as before (α = 0.98), and our identification manipulation (“I identify with the University X student community.” 1 = strongly disagree, 7 = strongly agree). We checked our message manipulation: “This study showed me a poster discouraging University X students from going to fast-food restaurants” (1 = strongly disagree to 5 = strongly agree). We measured the product attitude covariate as before. Data were analyzed using 2 (nearness) × 2 (identification) × 2 (health liability versus control message) ANOVAs and interactions were assessed using planned pairwise comparisons.
## 7.3. Manipulations Check Results
Students in the near versus farther condition reported the restaurant was nearer (F[1, 370] = 323.83, $p \leq 0.001$; $M = 5.34$ vs. 3.21). Those in the strong versus weak identification condition reported stronger identification (F[1, 370] = 6.54, $$p \leq 0.01$$; $M = 5.10$ vs. 4.83). Those shown the health liability versus control message reported the content correctly (F[1, 370] = 353.23, $p \leq 0.001$; $M = 3.59$ vs. 1.50). There were no other effects.
## 7.4. Main Results
There was a three-way interaction for nearness, identification, and health liability message on restaurant patronage intent (F[1, 370] = 4.97, $$p \leq 0.03$$), a main effect for nearness (F[1, 370] = 49.48, $p \leq 0.001$), a main effect for the product attitude covariate (F[1, 370] = 40.55, $p \leq 0.001$), but no other effects. The control message results replicated what we saw earlier. Those strongly identified with the student community reported higher intent to patronize the fast-food restaurant when near versus farther from school (t[370] = 4.03, $p \leq 0.001$; $M = 4.48$ vs. 3.10); weakly identified students did not (t[370] = 1.67, $$p \leq 0.10$$; $M = 4.04$ vs. 3.53). The health liability message results were different. This message failed to lower the attraction of nearby fast food among strong identifiers, and it increased fast-food attraction among weak identifiers. After seeing the health liability message, the strong identifiers continued to report higher intent to patronize the near (vs. farther) fast-food restaurant (t[370] = 3.35, $p \leq 0.001$; $M = 4.31$ vs. 3.26). The weak identifiers did likewise, primarily because they became attracted to the near restaurant (t[370] = 4.85, $p \leq 0.001$; $M = 4.62$ vs. 2.96; see Figure 6).
## 7.5. Discussion
Study 6 tested a health liability message, stressing that fast food was unhealthy. Showing it to strong identifiers had no effect; a fast-food restaurant near (vs. farther from) school continued to increase patronage intent. Showing it to weak identifiers was counterproductive; now even they were attracted to the nearby (vs. farther) restaurant. Conceivably, the weak identifiers experienced reactance when told the food was unhealthy, and so they decided to patronize the nearby restaurant.
## 8.1. Contributions
Fast-food restaurants near schools are problematic, contributing to poor diet, weight gain and obesity. Our novel hypothesis, supported by detailed data analysis, is that teenagers who have a strong sense of identity with their student community, although their risk is usually low, face the greatest risk of a fast-food restaurant near school, because they think the restaurant is their social activity place. Advocating policy and educational interventions to change this view has important practical significance for solving the problem of unhealthy consumption caused by fast-food restaurants near schools.
Our findings suggest new policy approaches to addressing the problem of unhealthy fast-food restaurants near schools, that are not reliant on zoning restrictions that have been tried in the past [1]. Zoning restrictions have largely been unsuccessful in the US, especially at protecting disadvantaged students [25,26]. We advocate the use of school policies, social marketing messages, and educational efforts targeted at students that seek to change their perception of fast-food restaurants near school from socially beneficial spaces to social liability spaces.
Specifically, we recommend that teachers use their educational toolbox to encourage student activism, e.g., boycotts against local fast-food restaurants. Local activism is an increasingly popular strategy for promoting social change in the US and abroad, used by students [71], employees [72,73], even corporations [74]. For instance, social studies, nutrition, or language teachers might encourage students to think critically about whether and how they have been targeted by unhealthy fast-food restaurants. If students understand they have been targeted by fast-food marketers who have encouraged them to eat unhealthy food since they were small children unable to think critically, they may want to do something, perhaps start a boycott.
## 8.2. Links to Past Literature
Our research complements past work that discovered that student demographics moderate their vulnerability to fast-food restaurants near schools [3,14,15]. We study a different moderating variable, not a demographic variable, but rather strong identification with the student community [38,39,43]. Strong identification generally protects students from risk [36,42,44], but in the case of fast food it elevates their risk because they perceive a fast-food restaurant near school as a social activity space where they can derive social benefits, i.e., see friends. Policymakers should adopt educational and messaging strategies to change this perception, so that going to a fast-food restaurant is a social liability. They should not stress the health liability, i.e., unhealthy food, as we found this to be ineffective.
Our work supports the geographers’ activity space framework which indicates that the most fundamental moderator of any nearby location effect relates to whether people perceive that location as their activity space [29,30,31,32]. However, we add to the work in geography by showing that students’ identification with their school community affects their activity space perceptions.
We also contribute to past work in marketing on social influences that often adversely affect food consumption. Studies have found that people tend to match others’ portion sizes despite their hunger [75], match others’ menu selections despite their preferences [76] and use food to signal preferred identities independent of other considerations [77]. We demonstrate another adverse social influence on food consumption by showing that adolescents will go to a fast-food restaurant, despite its unhealthy food, to see friends.
## 8.3. Theoretical and Methodological Contributions
Our work makes a theoretical contribution by showing that an individual difference variable, student identification with their student community, moderates their perception of whether they will see peers at a nearby fast-food restaurant and want to go there. Past research tells us that adolescents’ perceptions of peers strongly influence their use of drugs and alcohol [55,78,79], elevating social concerns over health ones [58,80,81]. We add the insight that peers also matter with fast food. While this may seem to be a logical extension, the focus of past fast-food research has been on restaurant location not peer perceptions. Activity space geographers have challenged the narrow focus on location, noting that perceptions of locations as activity spaces also matter [31,32,33,34]. However, we are the first to identify an individual difference variable, adolescent identification with the student community, which affects activity space perceptions. Moreover, we demonstrate how to measure activity space perceptions as a mediating variable, and how to test for mediation.
## 8.4. Limitations
Limitations of our research include that we focused on fast-food restaurant patronage not consumption. We do not know what students might have eaten at the restaurants and, thus, it is conceivable some might have chosen the relatively healthier items. We did not study socializing at the restaurants, only whether students decided to go to see friends. Only one of our studies (study 3) measured the theorized mediating process, about the nearby restaurant being a social activity space or hangout for friends. We used high school students in Studies 1 and 4, but otherwise used college students. Our findings replicate with both groups, consistent with extensive research indicating that adolescence extends from the teenage years through to about age 24 [79]. The entire period of adolescence is characterized by highly salient social goals and affiliation needs, and a tension between necessary dependence on parents versus independence from them, e.g., with respect to cars, meals, and privileges [82]. However, as the younger adolescents are understudied, more research should be done on them.
We also recommend studies of other risky locations near schools, to ascertain if students who are strongly identified with their student community are especially vulnerable. What about nearby liquor, tobacco, nicotine vape or pot (cannabis) retailers; do they attract students who are strong identifiers? What about nearby fitness centers or fresh produce markets (farmers markets); do they attract strong identifiers but elicit positive behaviors? In addition, researchers should examine adults with workplaces nearby fast-food restaurants who vary in workplace identification, to see if the results replicate. Activity space researchers have replicated their findings among adolescents and adults, and replication work would be beneficial here too. Researchers should study other negative behaviors that might be evoked by strong identification with a student community, e.g., aggressive behavior at intercollegiate sports events. Among geography researchers, it would be useful to study other individual difference variables that may affect perceptions of locations as social activity spaces. “ Location, location, location” is indeed important, but social perceptions of locations matter too and should be investigated further.
## 9. Conclusions
Fast-food restaurants near schools are problematic, contributing to poor diet, weight gain, and obesity. Adolescents who strongly identify with their student community, while generally at lower risk, face the greatest risk from fast-food restaurants near school because they perceive the restaurants as their social activity spaces. Education and policy should be directed at changing that perception. Students must perceive the restaurants differently, as well as adults.
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---
title: The Influence of Hip and Knee Joint Angles on Quadriceps Muscle-Tendon Unit
Properties during Maximal Voluntary Isometric Contraction
authors:
- Alessandra Martins Melo de Sousa
- Jonathan Galvão Tenório Cavalcante
- Martim Bottaro
- Denis César Leite Vieira
- Nicolas Babault
- Jeam Marcel Geremia
- Patrick Corrigan
- Karin Grävare Silbernagel
- João Luiz Quaglioti Durigan
- Rita de Cássia Marqueti
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002253
doi: 10.3390/ijerph20053947
license: CC BY 4.0
---
# The Influence of Hip and Knee Joint Angles on Quadriceps Muscle-Tendon Unit Properties during Maximal Voluntary Isometric Contraction
## Abstract
Determining how the quadriceps femoris musculotendinous unit functions, according to hip and knee joint angles, may help with clinical decisions when prescribing knee extension exercises. We aimed to determine the effect of hip and knee joint angles on structure and neuromuscular functioning of all constituents of the quadriceps femoris and patellar tendon properties. Twenty young males were evaluated in four positions: seated and supine in both 20° and 60° of knee flexion (SIT20, SIT60, SUP20, and SUP60). Peak knee extension torque was determined during maximal voluntary isometric contraction (MVIC). Ultrasound imaging was used at rest and during MVIC to characterize quadriceps femoris muscle and tendon aponeurosis complex stiffness. We found that peak torque and neuromuscular efficiency were higher for SUP60 and SIT60 compared to SUP20 and SIT20 position. We found higher fascicle length and lower pennation angle in positions with the knee flexed at 60°. The tendon aponeurosis complex stiffness, tendon force, stiffness, stress, and Young’s modulus seemed greater in more elongated positions (60°) than in shortened positions (20°). In conclusion, clinicians should consider positioning at 60° of knee flexion rather than 20°, regardless if seated or supine, during rehabilitation to load the musculotendinous unit enough to stimulate a cellular response.
## 1. Introduction
The quadriceps musculature weakness has been associated with the initiation, progression and severity of knee osteoarthritis [1]. Thus, strengthening exercise has been identified as a powerful intervention to treat knee injuries, including following surgery. However, pain and arthrogenic muscle inhibition are significant barriers to the generation of adequate stimulus for improvement in muscle function [2,3]. Among the strategies to fasten recovery, isometric training is often used in rehabilitation programs because it may increase muscle force faster than dynamic exercise, with the benefit of lower joint shear stress [4] but the adaptations according to joint angle (i.e., muscle-tendon unit length) raise questions regarding the most appropriate lower limb position according to training objectives.
The quadriceps femoris musculature is mainly responsible for the knee extension torque. The aponeuroses of its four constituents are joined distally to form the quadriceps tendon, and, lastly, the patellar tendon, as the final force-transmission structure [5,6]. Due to origin and insertion characteristics of the quadriceps femoris musculature, changes in both hip and knee joint angles have implications on force production [7] *There is* a general consensus on the influence of knee joint angle on knee extensor torque during maximum voluntary isometric contraction (MVICs), where knee extension torque is commonly greater at knee flexion angles closer to 60° [7,8,9]. However, exercise prescription for knee extension requires a full reporting of body mechanics, mainly the lower limbs, but the combined effect hip and knee joint angles on force production is still a matter of debate [10]. When comparing the supine and seated hip positions, voluntary torque may be reduced or not change in supine for several knee joint angles (20°–90°) [11,12].
Although positions closer to 60° knee flexion are beneficial for greater force production [8,10], patellofemoral and tibiofemoral compressive forces increase as the knee flexes [13]. In contrast, in supine with the knee fully extended, there is minimal contact between the femur and the patella [14]. Therefore, investigating lower limb positions that reduce joint stress, without reducing force production may be helpful in cases of painful knee, risk of accelerated knee osteoarthritis, or for bedridden patients. Considering that several physiological parameters are involved in muscle force production and the adaptations to exercise, the neural (electrical activity) and morphological (muscle-tendon structure) adaptations must also be elucidated in the context of lower limb position [15].
In shortened positions, the quadriceps femoris musculature has greater activation than in elongated positions [16] and a higher value for activation could be expected in the seated versus supine position for the superficial quadriceps: vastus medialis (VM), vastus lateralis (VL) and rectus femoris (RF) muscles [11] Compared to shortened, mid-range and elongated positions allow greater fascicle length (Lf) [17] and lower pennation angle (θp) [17]. These alterations directly imply that, under isometric conditions, a muscle with longer fascicles may be expected to develop torque more quickly (i.e., higher shortening velocity); and the decrease of the pennation angle, at the elongated positions, implies a mechanical advantage for force generation [17].
Tendon mechanical properties are also of paramount importance for muscle function and integrity [18]. Thus, muscle work can be affected by reduced tendon function due to connective tissue disorders [19], periods of insufficient tendon loading [18,19], or positioning of the joint [20]. Tendon stiffness is greater at longer muscle length than at shorter muscle length, after isometric training [20] and resistance training increase force production capacity, stiffness, and resistance to stress [21], which is beneficial for tissue remodeling. The tendon aponeurosis complex (TAC) stiffness shows the relationship between elongation of the deep aponeurosis to the distal free tendon in response to muscle force to the bones [22]. It is well known that joint angles in elongated positions remove the looseness of the TAC (increasing its stiffness), and optimize muscle length for greater force production, seeming ideal for speeding up adaptation [23,24]. In addition, the tension of the TAC is enlarged in strained situations allowing less muscle work due to better force transmission [25,26,27].
Therefore, we aimed to investigate the influence of hip (0° or 85°) and knee (60° or 20°) joint angles on the knee extensor MVIC, along with surface electromyography (EMG), neuromuscular activity, muscle architecture, TAC stiffness of the four quadriceps femoris constituents, and patellar tendon properties in healthy male subjects. Specifically, we hypothesized that: [1] peak knee extension torque during MVIC would be greater at 60° of knee flexion compared to 20°, as well as greater in seated compared to supine; [2] greater quadriceps femoris muscle activity at 20° of knee flexion compared to 60°; [3] a higher neuromuscular efficiency on 60° positions; [4] at rest and during MVIC, the Lf would be greater, and the θp would be lower when the knee is at 60°; and [5] the TAC stiffness and the patellar tendon stiffness can be higher at more elongated positions in comparison to shortened positions. This study is important because determining how the neuromuscular and muscle-tendon unit acts according to lower limb position may help researchers and exercise prescribers in the rationale for their choices.
## 2.1. Trial Design
This was a study with randomized, single blinded, repeated measures. This is a sub-study to a larger trial that is aimed at gaining a better understanding of muscle-tendon adaptation based on hip and knee joint angles. The full protocol is available on ClinicalTrials.gov (Identifier: NCT03822221). Participants were guided regarding the purposes, benefits, and risks before recruitment, and all afforded written consent. Consent was received (protocol number 94388718.8.0000.8093) from the Research Ethics Committee at the University of Brasília/Faculty of Ceilândia following the Helsinki Declaration of 1975. This study was stated according to the Consolidated Standards of Reporting Trials (CONSORT) Statement for Randomised Trials of Nonpharmacologic Treatments [28]. All the procedures were performed in the Laboratory of Strength of the Faculty of Physical Education at the University of Brasília.
## 2.2. Participants
Twenty male participants (age 24 ± 4.6 years; height 177 ± 6.3 cm, and body mass 77 ± 9.3 kg) were recruited through flyers and oral invite. They also had expected values of quadriceps muscle thickness and subcutaneous tissue thickness on the anterior thigh (RF: 24.8 ± 3.97 mm; VL 22.6 ± 2.80 mm; VM 23.9 ± 4.5; vastus intermedius [VI] 19.6 ± 2.5 mm; subcutaneous tissue: 4.32 ± 0.02 mm), which were obtained in supine with 20° of knee flexion. The inclusion criteria were: healthy male, aged 18 to 30 years, and physically active. The exclusion criteria were: involved in regular lower limb strengthening or sports competitions in the prior six months, any musculoskeletal abnormality (including reduced lower limb range of motion, deformity or amputation in any part of the lower limbs; history of patellar dislocation or trauma to limbs or trunk that may interfere in the results), motor control disorder, or systemic diseases that could affect performance or safety on tests.
## 2.3. Randomization, Allocation Concealment, and Blinding
Testing was performed in four positions (Figure 1). Supine and seated were considered 0° and 85° of hip flexion, respectively. A fully extended knee was considered as 0° of knee flexion. Order of the testing positions was randomized for each participant. Randomization was guaranteed by having participants blindly remove four small square paper sheets from an opaque envelope. Participants were also blinded to study aims and hypotheses to prevent expectancy affecting performance. Nevertheless, researchers and volunteers could not be blinded to the assessment positions.
## 2.4. Experimental Procedures
The protocol consisted of five laboratory visits (Figure 1), including a familiarization visit and four experimental visits. Each visit lasted 2–3 h and was performed seven days after the previous visit. We instructed participants to abstain from alcohol and stimulants (e.g., caffeine, chocolate, and performance supplements) for at least 24 h before visits, avert hard exercise 36 h ahead of the visits, and keep their common diet. We obtained anthropometrics (body mass and height) during the familiarization visit, and participants practiced ramped MVICs in each position. The positions were tried apart in each experimental session and composed of 12 MVICs to complete all ultrasound imaging and electromyography exams (two for EMG, two for patellar tendon properties, and eight for muscle architecture).
## 2.5. Outcomes
We measured the peak knee extension torque during MVIC in each position, along with Root Mean Square (RMS) by EMG of the quadriceps musculature. Moreover, the tendon-aponeurosis complex (TAC) stiffness and the muscle architecture (θp and Lf) were measured from the four quadriceps femoris musculature, and the morphological (cross-sectional area (CSA) and resting length) and, mechanical (stiffness, force, and elongation), and material properties (Young’s Modulus and stress, strain,) were measured from the patellar tendon.
## 2.5.1. Torque Evaluation
A computerized dynamometer (System 4; Biodex Medical Systems, Shirley, New York, USA) was applied to collect knee extension torque during MVICs of the dominant limb (i.e., the preferred limb to kick a ball). The mechanical axis of the dynamometer was visibly in line with the flexion-extension axis of the knee and hip angles, which were adjusted with a goniometer. The lever arm of the dynamometer transducer was attached 2–3 cm superior the lateral malleolus with a girdle. Subjects were stabilized in the chair using belts on the chest and pelvic girdle to reduce body movement. Seat height was adjusted to each subject’s height to ensure a comfortable fit. Contact of the volunteer’s lumbar spine with the back support was confirmed. A bench was provided to support the non-tested leg during rest periods to avoid excessive hip flexor stretching in supine position and discomfort while seated. A warming-up of submaximal isometric contractions was carried out for muscle and tendon pre-conditioning: $50\%$: 3 contractions; $75\%$: 2 contractions; and $90\%$: 1 contraction. Rest for 10 s was provided between submaximal contractions [8]. Following the warm-up, participants completed 12 MVICs and were encouraged verbally to cross their arms with hands on shoulders and to perform maximum strength on ramping contraction for 6–10 s and obtained visible feedback of the torque generated. A 2-min rest was provided between MVICs.
## 2.5.2. EMG
The EMG of the VL, VM, RF, and most lateral portion biceps femoris was recorded bipolarly with a sampling frequency of 1000 Hz by the data accession device New Myotool (Miotec—Biomedical Equipment, Porto Alegre, Brazil®). The device was synchronized with the dynamometer, and electrical activity was recorded. Passive electrodes (circular silver-silver chloride electrodes with a 20 mm diameter) were positioned on the belly of the muscles [29] with an inter-electrode range (center to center) of 20 mm. A referred electrode was attached on the patella of the ipsilateral limb [30] Impedance reduction between the two electrodes was achieved through trichotomy and cleaning with alcohol. Raw EMG signal was band-pass filtered (20–500 Hz) to remove artifacts, and a notch filter of 60 Hz was applied. The raw RMS values were calculated within a 500 ms period in the most stable part of the torque trace (the MVIC plateau). Neuromuscular efficiency was calculated by dividing the peak knee extension torque by the RMS of the knee extensors [31] and then transformed into a percentage multiplied by 100.
## 2.5.3. Muscle Architecture Assessment
We used an ultrasound system (M-turbo, Sonosite, Washington, USA) connected to a linear transducer (40 mm, 7.5 MHz, depth 6.0 cm, acquisition frame of 30 Hz). A water-based gel served as a coupling mean between the transducer and the skin surface. The muscle fibers were visualized at their longitudinal plane, being the transducer in a right angle with the skin at $50\%$ (RF), $60\%$ (VL), $75\%$ (VM), and $80\%$ (vastus intermedius [VI]), from cranial to distal, considering the thigh length (between the medial landmark of the anterior superior iliac spine and the patella base), as adjusted from prior comments [32,33]. These regions were chosen to allow a homogeneous muscle sonography, i.e., minimal fiber and constraints [32]. The RF and VI were scanned on the anterior thigh, while the VL and VM were scanned on the lateral and medial thigh surface, respectively. A customized *Styrofoam apparatus* retained the transducer avoiding undesired movement. The transducer alignment was also manually corrected to keep the superficial and deep aponeuroses in parallel, so several fascicles could be observed [34,35]. With the ultrasound set to record a 15-svideo, two recordings were made for each quadriceps femoris component during MVIC. The resting state was also recorded prior and after the MVIC trial. The recording with the best visualization of multiple fascicles was used for the measurement of Lf and θp. The video files were transferred to a computer for processing. Frames were selected at rest and at the MVIC plateau and stored as image files to be analyzed in ImageJ software (v. 1.46; National Institutes of Health, Bethesda, USA) (Figure 2). The fascicle that could be evidently delineated from the attachment point on the deep aponeurosis to the transducer field-of-view limits was used for the measurements [35]. The θp is the angle formed by the deep aponeurosis and the fascicle. The *Lf is* obtained by following the fascial from the superficial to the deep aponeurosis. When the fascicle was greater than the field-of-view boundaries, the insertion on the deep aponeurosis was kept, and the remaining portion up to the superficial attachment was estimated by [36]. For all ultrasound imaging results, the average of three measurements were used. A researcher with extensive experience in ultrasonography performed all measurements. In addition, we synchronized the MVIC and ultrasonographic recordings with a data acquisition device, New Miotool (Miotec Biomedical Equipment Ltd., POA, Brazil®; sampling rate: 2000 Hz, A/D converter: 14 bits, common rejection mode: 110 db at 60 Hz). The device was interfaced with the computerized dynamometer, and with a high-definition camera positioned to record the ultrasound system display. When the evaluator started the video ultrasound video recording, a visual indicator appeared on the ultrasound screen, which allowed the synchronization of all data on a torque-time trace generated on the New Miotool software [37].
## 2.5.4. TAC Stiffness
The TAC displacement of RF, VL, VM, and VI were assessed using the same video recordings obtained for the muscle architecture variables. During data collection, a custom-made device held the probe, preventing it from moving. As mentioned above, care was taken to avoid the slide of the transducer on the skin surface. However, if sliding occurred, the TAC displacement was adjusted considering a hypoechoic shadow from an adhesive tape. Moreover, ultrasonographic recordings obtained (two for each muscle belly) during passive motion at 10°/s of the knee from 60° to 0° in both seated and supine positions were used to correct displacement overestimation due to any undesired angular rotation of the knee. Only the corrected values were used to calculate each constituent’s stiffness [22].
The Tracker 4.87 software allowed the manual tracking of the fascicle-deep aponeurosis attachment while it was displaced from rest to MVIC. If the deep insertion started outside the probe’s field-of-view, we made a linear extrapolation as previously described [21,25]. Quadriceps femoris muscle force was obtained by dividing the knee extensor torque by the patellar tendon moment arm, which was a fixed value according to the knee angle (60°: 0.056 m; 20°: 0.0475 m) [38]. To obtain a quadriceps femoris TAC stiffness of all quadriceps femoris constituents, we used the delta force from $50\%$ to $100\%$ of the MVIC divided by the mean delta displacement of each quadriceps femoris constituent also at $50\%$ and $100\%$ [33].
## 2.5.5. Patellar Tendon Properties
For all analysis of patellar tendon properties, participants then performed six 5-s submaximal isometric knee extension MVICs for tendon pre-conditioning [39], which was mentioned above in the torque assessment section. Following the submaximal MVICs, the four MVIC were randomly performed to assess the patellar tendon with 120 s of rest between each. For patellar tendon variables, two volunteers were excluded due to technical problems in the analysis.
## Morphological Properties
The same ultrasound system, settings, and synchronization method used for muscle architecture were also used for the assessment of the patellar tendon properties. The resting length was obtained with the ultrasound probe positioned longitudinally along the tendon, from the patella’s apex to the deep insertion to the tibial tuberosity [40]. If the size of the transducer did not allow the complete visualization of the patellar tendon, then it was obtained using an overlapping images method adopted by [41] (Figure 3). The length from the marker to each anatomical structure was measured with Tracker 4.87 software (www.physlets.org/tracker/ (accessed on 13 December 2018)) and summed to determine the patellar tendon length, according to [42].
Patellar tendon CSA was obtained with the ultrasound probe positioned perpendicular to the long-axis of the tendon. The mean value from three images was obtained at three locations ($25\%$, $50\%$, and $75\%$ of the tendon length) [43] to allow patellar tendon CSA to be measured from these axial images using Image J software (v. 1.46; National Institutes of Health, Bethesda, Maryland).
## Mechanical Properties
Patellar tendon force was defined by the torque obtained during MVIC divided by the patellar tendon moment arm, determined from previous literature as 0.056 m and 0.0475 m at 60° and 20° of knee flexion, respectively [38]. Patellar tendon force was determined at $10\%$ intervals of the MVIC (from 0 to $100\%$). The elongation was measured with cine-loop ultrasound imaging during MVIC using the same landmarks described above for the patellar tendon rest length. Patellar tendon elongation was defined as the length change between the patellar tendon proximal and distal insertions. The patella’s apex and the tendon’s deeper insertion to the tibial tuberosity were determined by manual tracking using Tracker 4.87 (www.physlets.org/tracker/ (accessed on 13 December 2018)). Patellar tendon strength and elongation were synchronized using the same technique mentioned above in the muscle architecture assessment section, as proposed by Bojsen-Moeller, 2003 [37]. Force-elongation plots were fitted with a second-order polynomial forced through zero. The slope of the stress-strain curve was used to calculate stiffness, based on the chosen force levels for measuring tendon stiffness. Linear regression was employed to derive the slopes of both the force-elongation and stress-strain curves, which were calculated from $50\%$ to $100\%$ MVIC [44].
## Material Properties
Stresses and strains were obtained at $10\%$ torque steps throughout the MVIC to assess the patellar tendon stress-strain relationship and estimate patellar tendon material properties for each condition [44]. The patellar tendon stress was calculated by dividing tendon force by CSA, and tendon strain was calculated by dividing tendon elongation by patellar tendon resting length. Stress-strain plots were fitted with a second-order polynomial forced through zero. Using the associated quadratic equations, Young’s modulus was determined as the stress-strain relationship using the same relative (50–$100\%$) force levels as selected for determining tendon stiffness force.
## 2.6. Statistical Analysis and Sample Size
All outcomes are reported as geometric mean and $95\%$ confidence intervals ($95\%$ CI). To compare peak knee extension torque, RMS, efficiency neuromuscular, tendon-aponeurosis complex stiffness, and patellar tendon properties between the different positions, we used repeated-measures one-way analysis of variance (ANOVA). For θp and Lf, once we had valued at rest and during MVIC, a repeated measure two-way ANOVA [position by condition (rest and MVIC)] was used. When a significant difference was detected, a Tukey post-hoc test was applied to identify the differences. Effect sizes and statistical power were calculated. The effect size was determined using partial eta squared (ηρ2), according to the following classification: small (ηρ2 = 0.01), medium (ηρ2 = 0.06), and large (ηρ2 = 0.14) effects [45].
For reliability assessment, the intra-class correlation (ICC) of torque (all eight MVIC performed during muscle ultrasound imaging for each position) was obtained using a mean of multiple measurements, absolute agreement, 2-way mixed-effects model. The purpose of this ICC was to guarantee that all of the muscle structure assessments were performed under stable conditions of contraction intensity. Moreover, a single-measurement, absolute-agreement, 2-way mixed-effects model was used for the interrater ICC of muscle architecture and the TAC displacement (two repeated analyses, seven to 14 between-days, of 25 recordings for each quadriceps femoris constituents). To determine the reliability of measuring tendon elongation, two repeated measurements of 25 random points (i.e., at any force level) were obtained for each condition from the force-elongation curve and used to calculate the ICC using a single-measurement, absolute-agreement, 2-way mixed-effects model. Reliability was classified as: poor (<0.5), moderate (0.5–0.75), good (>0.75–0.9), and excellent (>0.9). All statistical analyses used a significance level at p ≤ 0.05. All analyses were performed using STATISTICA 23.0 (STATSOFT Inc., Tulsa, Oklahoma, USA), and the software GraphPad PRISM 8.4.1 (San Diego, CA, USA) was used for graphic design.
The sample size was determined a priori using G*Power (version 3.1.3; University of Trier, Trier, Germany) with the level of significance set at $$p \leq 0.05$$ and power (1-β) = 0.80 to detect a large effect size (ηρ2 = 0.45). Based on Lanza et al. [ 8], we expected means and standard deviations from knee extension torques to be approximately 125.93 ± 31.81 Nm, 249.3 ± 30.13 Nm, 267.1 ± 32.26, and 216.3 ± 36.25 Nm for knee flexion angles of 25°, 50°, 80°, and 106°, respectively. Based on these values, we found a combined standard deviation of 63.62 Nm with a sample size of 20 participants.
## 3.1. Reliability of Measurements
High test-retest reliability was observed from the ICC values for torque at SUP60 (0.92), SIT60 (0.94), SUP20 (0.92), and SIT20 (0.93). We obtained good reliability for the θp of RF (0.75), VL (0.78), VM (0.82), and VI (0.77), Lf of RF (0.81), VL (0.80), VM (0.77), VI (0.79), and tendon-aponeurosis complex displacement for RF (0.98), VL (0.95), VM (0.95), and VI (0.86) and for maximal elongation of the patellar tendon (0.98).
## 3.2. MVIC, Raw RMS, and Quadriceps Femoris Neuromuscular Efficiency
A significant main effect of position was found for peak knee extension torque (F 3, 57 = 87.57, $p \leq 0.001$, ηρ2 = 0.82, power = 1.0). The post-hoc analysis showed that knee flexed at 60° (SUP60 and SIT60) had higher MVIC ($p \leq 0.001$ for all analyses) than SUP20 and SIT20 (Figure 2). There was a non-significant main effect of position for raw RMS (F 3, 57 = 0.87, $$p \leq 0.460$$, ηρ2 = 0.04, power = 0.22) (Figure 4). A significant main effect of position was found for quadriceps femoris neuromuscular efficiency (F 3, 57 = 22.32, $p \leq 0.001$, ηρ2 = 0.54, power = 1.0). The post-hoc analysis showed that knee flexed at 60° (SUP60 and SIT60) had higher values ($p \leq 0.001$ for all analyses) than SUP20 and SIT20 (Figure 4).
For RF (Figure 5A,B), there was interaction between position and condition for the θp (F 3, 57 = 3.65, $$p \leq 0.017$$, ηρ2 = 0.16, power = 0.77). The post-hoc analysis showed that both at rest and during contraction, SUP60 had lower θp compared to SIT60, SUP20, and SIT20 ($p \leq 0.001$–0.036), with no differences between other comparisons ($$p \leq 0.15$$–0.99). There was no interaction of factors for Lf (F 3, 57 = 1.87, $$p \leq 0.140$$, ηρ2 = 0.089, power: 0.46), but the effect of position was significant (F 3, 57 = 24.89, $p \leq 0.001$, ηρ2 = 0.56, power = 1.00), where the post-hoc analysis showed greater Lf for SUP60 ($p \leq 0.001$; Figure 5B) than all positions, with no differences between other comparisons ($$p \leq 0.46$$–0.97).
The VL (Figure 5C,D) presented a significant interaction between positioning and condition for θp (F 3, 57 = 3.48, $$p \leq 0.021$$, ηρ2 = 0.15, power = 0.75). The post-hoc analysis showed that, at rest, there was lower θp for SUP60 compared to SUP20 ($$p \leq 0.012$$; Figure 5C) and SIT20 ($p \leq 0.001$), and at SIT60 compared to SIT20 ($$p \leq 0.033$$), with no differences between other comparisons ($$p \leq 0.31$$–0.97). Furthermore, during MVIC, θp was lower ($p \leq 0.001$) at SUP60 and SIT60 compared to SUP20 and SIT20. No significant differences were observed between SUP60 and SIT60 ($$p \leq 1.0$$), nor between SUP20 and SIT20 ($$p \leq 0.16$$). Position factor was significant for the θp (F 3, 57 = 13.66, $p \leq 0.001$, ηρ2 = 0.41, power = 0.99). The post-hoc analysis indicated lower θp ($p \leq 0.001$) for SUP60 and SIT60 compared to SUP20 and SIT20. No significant differences were observed between SUP60 and SIT60 ($$p \leq 0.90$$), nor between SUP20 and SIT20 ($$p \leq 0.37$$). There was no interaction for Lf (F 3, 57 = 0.56, $$p \leq 0.064$$, ηρ2 = 0.02, power = 0.15), but there was a significant main effect of positioning (F 3, 57 = 14.10, $p \leq 0.001$, ηρ2 = 0.42, power = 0.99), with post-hoc analyses showing higher Lf at SUP60 compared to SUP20 and SIT20 ($p \leq 0.001$), respectively. The same was true at SIT60 when compared to SUP20 and SIT20 ($p \leq 0.001$), respectively.
Only for VM (Figure 5E,F), there was no significant interaction between position and condition for θp (F 3, 57 = 0.31, $$p \leq 0.812$$, ηρ2 = 0.01, power: 0.10) and for Lf (F 3, 57 = 0.85, $$p \leq 0.46$$, ηρ2 = 0.043, power: 0.22). However, position factor was significant for both θp (F 3, 57 = 37.40, $p \leq 0.001$, ηρ2 = 0.66, power: 1.00) and Lf (F 3, 57 = 13.06, $p \leq 0.001$, ηρ2 = 0.40, power: 0.99), with post-hoc analysis indicated lower θp ($p \leq 0.001$) and greater Lf ($$p \leq 0.002$$–0.037) for SUP60 and SIT60 compared to SUP20 and SIT20.
Regarding VI (Figure 5G,H), there was a significant interaction effect between position and condition for both θp (F 3, 57 = 2.82, $$p \leq 0.046$$, ηρ2 = 0.12, power = 0.64) and Lf (F 3, 57 = 6.24, $p \leq 0.001$, ηρ2 = 0.24, power = 0.95). The post-hoc analysis showed that, at rest, there was a lower θp for SUP60 compared to SUP20 ($$p \leq 0.003$$) and SIT20 ($$p \leq 0.02$$). During MVIC a lower θp was found for SUP 60 and SIT60 when compared to SIT20 ($$p \leq 0.008$$; $$p \leq 0.027$$, respectively). Other pairwise comparisons at rest and during MVIC were not significant ($$p \leq 0.26$$–0.99). A greater Lf was found at rest only for SUP60 and SIT60 ($p \leq 0.0001$ for all analyses) when compared to SUP20 and SIT20. For Lf, other pairwise comparisons during rest were not significant ($$p \leq 0.56$$–0.99). However, during MVIC, Lf was greater only at SUP60 compared to SIT20 ($$p \leq 0.005$$). The main effect of position was also significant for both θp (F 3,57 = 4.40, $$p \leq 0.007$$, ηρ2 = 0.18, power = 0.85) and Lf (F 3, 57 = 16.16, $p \leq 0.001$, ηρ2 = 0.45, power = 0.99). The θp was lower at SUP60 compared to SIT20 ($p \leq 0.005$) and, Lf was greater for SUP60 and SIT60 compared to SUP20 and SIT20 ($p \leq 0.001$ for all analyses).
## 3.3. TAC Stiffness
The TAC stiffness of quadriceps femoris is presented in Table 1. A significant main effect of position was found for TAC (F 3,57 = 7.84, $$p \leq 0.001$$, ηρ2 = 0.29, power = 0.98) and the post-hoc analysis showed that TAC stiffness was greater in SUP60 ($$p \leq 0.001$$), SIT60 ($$p \leq 0.0004$$) and SUP20 ($$p \leq 0.01$$) compared to SIT20.
## 3.4. Tendon Properties
The patellar tendon (morphological, mechanical, and material) properties for each position are presented in Table 1. The tendon force-elongation (A) and stress-strain relationships (B) are shown in Figure 6.
## 3.4.1. Morphological Properties
No changes were found in the patellar tendon resting length ($$p \leq 0.186$$) and CSA ($$p \leq 0.563$$) for all conditions.
## 3.4.2. Mechanical Properties
The mechanical properties of the patellar tendon are shown in Table 1 and Figure 4A. A significant main effect was found for patellar tendon force (F 3,51 = 33.90; $p \leq 0.01$; ηρ2 = 0.66; power = 1.00). In the post-hoc analysis both SUP60 and SIT60 showed a greater force ($p \leq 0.001$) than SUP20 and SIT20, with no differences between positions with the same knee angle: SUP60 vs. SIT60: $$p \leq 0.057$$; SUP20 vs. SIT20: $$p \leq 0.93.$$ Maximal tendon elongation presented main effect (F 3,51 = 3.29; $$p \leq 0.027$$; ηρ2 = 0.16; power = 0.71) and the post-hoc analyses showed SIT20 significantly higher than SUP20 ($$p \leq 0.022$$), but there were no significant differences in comparison to SUP60 and SIT60 ($$p \leq 0.10$$, $$p \leq 0.19$$), respectively. Significantly greater stiffness at SIT60 was found compared to SUP20 and SIT20 (F 3,51 = 6.88; $p \leq 0.01$; ηρ2 = 0.28; power = 0.96) with post-hoc analysis $p \leq 0.001.$
## 3.4.3. Material Properties
The material properties of the patellar tendon are shown in Table 1 and Figure 6B. The stress at SUP60 and SIT60 was significantly higher than at SUP20 and SIT20 (F 3,51 = 30.10; $p \leq 0.01$; ηρ2 = 0.63; power: 1.00). However, no differences were found in tendon stress at the same knee angle (SUP60 vs. SIT60 and SUP20 vs. SIT20). The tendon strain was not changed ($$p \leq 0.057$$). We found a significant main effect for Young’s modulus (F 3,51 = 7.01; $p \leq 0.01$; ηρ2 = 0.29; power: 0.97). In the post-hoc analysis, SIT60 was higher than SUP20 ($$p \leq 0.001$$) and SIT20 ($$p \leq 0.001$$).
## 4. Discussion
To the best of our knowledge, this is the first study to assess different hip and knee joint angles on torque generation, RMS activity, neuromuscular efficiency, muscle architecture, and tendon-aponeurosis complex stiffness of the quadriceps muscle constituents and patellar tendon properties in healthy adults. *In* general, we found: [1] higher torque and neuromuscular efficiency at 60° of knee flexion compared to 20°, regardless of hip position; [2] no differences for RMS between positions; [3] RF showed a lower pennation angle and greater fascicle length at SUP60 compared to all other positions, while VL, VM, and VI showed lower pennation angle, and greater fascicle length at 60° of knee flexion when compared to 20°; [4] the TAC stiffness was greater at the more elongated position; and [5] tendon force, stiffness, stress and ‘Young’s modulus were greater with the knee flexed at 60°, compared to 20°.
## 4.1. MVIC, RMS, and Quadriceps Neuromuscular Efficiency
We found greater MVIC at 60° of knee flexion compared to 20°. According to Lanza et al. 2017, the differences in torque production are due to the force-length relationship of the muscle, in which changes in the joint’s angle and the muscle’s length affect the extent of force generation [8]. Thus, the knee extensor torque reduction on positions closer to the full extension could be partly attributed to mechanical factors, such as the reduced number of cross-bridges attached subsequently to sarcomere beyond the optimal actin-myosin overlap [6,14].
Our results demonstrated no differences in MVIC torque between supine and seated positions and corroborated Bampouras et al. [ 2017] [46]. In contrast, Maffiuletti and Lerpes, [2003] [11], and Ema et al. [ 2017] [12], found higher torque values in the seated position, which may be due to the difference in the operated region of the force-length relationship of RF between the two hip positions [12]. However, the choice of knee angle for Maffiuletti and Lerpes, [2003] [11], and Ema et al. [ 2017] were 90° and 70°, respectively. It is possible that we did not find any differences in our study since 60° of knee flexion may not have been enough to lengthen the RF and generate a considerable effect on torque output, showing a disadvantage from one position to the other [12].
We demonstrated no differences in RMS activity between positions. Babault et al. [ 2003] found higher activation values at short (i.e., 35° knee flexion) compared with long (i.e., 75° knee flexion) quadriceps muscle length [16]. With a shortened position, lesser muscle activation was expected [47,48]; higher activation was observed that would compensate for the weaker torque observed at higher degrees of knee flexion [16]. Maffiuletti and Lerpes, [2003] demonstrated greater activation in the seated position in comparison with the supine position for VM, VL, and RF muscles [11]. However, it is noteworthy that Maffiuletti and Lerpes, [2003], found the greatest neural activation of the knee extensors with the knee positioned at 90°, which may reflect a neurophysiological mechanism as compensation for the neuromuscular transmission-propagation deficiency and/or mechanical disadvantage of RF in a shortened position [11]. These results still fluctuate widely between these two positions because the lack of significant effect of the hip joint angle on agonist and antagonist muscle activations found by Ema et al. [ 2017] [12] suggests that neural factors may not have a substantial effect on the difference in knee extension torque and need to be further investigated.
Neuromuscular efficiency could be shown in several in vivo human studies, indicating optimized muscle function [49]. Aragão et al. [ 2015] consider those individuals as sufficiently capable of producing greater strength with a lower magnitude of muscle activation [50]. We found greater efficiency for the quadriceps femoris muscle in positions with the knee flexed at 60°. Although the RMS did not present differences between the positions, 60° positions indicate an economic and efficient mechanism since it was not necessary to increase muscle activation to generate greater torque, demonstrating the mechanical advantage of this joint angle.
## 4.2. Muscle Architecture
The observable adaptations in the muscular architecture during a contraction are the increase of the muscle thickness and the pennation angle and the decrease of the fascicle length, which are determinants in the generation of strength, range of motion and velocity of muscular shortening [17,51,52,53]. We found an increase in pennation angle of quadriceps femoris constituents from rest to contraction, consistent with previous studies [33]. Therefore, fascicle length and pennation angle change depending on the shortening or lengthening of the sarcomeres and the response to variations in tendon slack and total muscle length. As a result, these changes have important functional relevance concerning the production of force that is modified by the sarcomere and changes in whole muscle length [54].
We demonstrated an apparent effect of the hip angle on RF architecture, as expected for the quadriceps femoris’ biarticular constituent. The pennation angle was lower, and fascicle length was higher at SUP60 than in all other positions. The VL, VM, and VI operated with lower pennation angle when the knee was flexed at 60° compared to 20°. Placing the quadriceps femoris in a better physiological architectural configuration for generating torque favors a better transmission of muscle strength to the tendon and the ideal length of the sarcomere/fiber [55,56]. Furthermore, our findings demonstrate that fascicle length was shorter during VI contractions at SIT20, and the larger shortening would have been caused by taking up the elongated series elastic component [17]. Therefore, positions at 60° set the quadriceps femoris in a better architectural configuration, leading to a neuromuscular economy. This can be included in the proposals for strength rehabilitation programs, since an improvement was observed in the neuromuscular transmission of muscle strength to the tendon.
## 4.3. TAC Stiffness
The TAC stiffness index of quadriceps femoris on SUP60 was higher than on all other positions, similar to other studies [42], indicating an increased passive tension that limited tendinous elongation during contraction [24]. Shortened positions limit the mechanical stress and consequently lead the muscle to bear less force and generate less stress on the tendon. The increased tension of the TAC in stretched conditions is known to allow stronger contractions with less effort due to better force transmission [26,27].
## 4.4.1. Morphological Properties
Patellar tendon resting length and CSA did not differ between conditions. Similar results were previously observed considering the changes in knee angle [57,58]. We showed that the hip angle, from 85° of flexion to 0°, also did not provide any lengthening of the patellar tendon. This probably occurred because tendons designed to withstand high forces should not suffer significant length change between relatively close knee angles (60° and 20°), even with the additional stretch promoted by the hip extension [42]. The lack of changes in patellar tendon length may reflect biomechanical implications since it is not likely to attribute differences in stiffness to appreciable changes in the resting length, but possibly to collagen molecule coiling/uncoiling [59], associated with crimp pattern, which may imply transmission of force and load in tendons [60].
## 4.4.2. Mechanical and Material Properties
Stiffness presented higher values at SIT60 in comparison with SUP20 and SIT20 positions. It is probably due to higher levels of force being applied to the tendon and, consequently, the higher level of tendon stiffness presented. It is possible to notice that a longer position generates greater tendon stiffness, in agreement with Kubo et al. [ 2006] [22] during an isometric training protocol. According to these results, it seems preferable to load the patellar tendon at voluntary contractions using the knee at 60°. Simultaneously, the hip angle variation may affect how tensile loads are transmitted through the tendon. The increase of stiffness in positions at 60° can provide an advantage in rehabilitation since it promotes more significant tension generation in the muscle-tendon unit. As tendon stiffness increases with high-intensity training [56,61], the high load achieved by muscle contractions in this angle can lead to higher tendon adaptations than training programs with lower loads. A remarkable finding was that patellar tendon stress supports the force results (i.e., significant stress with the knee at 60° without the hip’s influence). Therefore, we cannot attribute these results to differences in the CSA average, but rather to the different higher strength levels in positions at 60° of knee flexion. Stress and Young’s modulus were greater with the knee flexed at 60° compared to 20°.
A potential limitation of this study was the lack of estimated contributions of each muscle for the total quadriceps muscle force. Therefore, tendon-aponeurosis complex stiffness of each quadriceps muscle constituent was calculated considering the total force. These calculations may lead to errors due to changes in contribution according to both force and muscle length levels. However, if we do not perform comparisons between the constituents, our values may be useful as a snapshot. Another limitation was restricted to the healthy young male population used in this study. More broadly, research is also needed to determine these properties in clinical populations and other muscles and tendons. Finally, although the participants were carefully examined (verbal interview, visual inspection, palpation, passive and active movement) before inclusion in the study to check for any visible musculoskeletal abnormality, we did not perform objective measures that could better inform the volunteers’ physical characteristics, such as the Q angle, somatotype, and arches of the foot. This information may be important for future research to better qualify the healthy populations and improve understanding of their specificities.
## 5. Conclusions
*Torque* generation, neuromuscular efficiency, a greater fascicle length and lower pennation angle, the patellar tendon force, stiffness, stress and ‘Young’s Modulus were higher with the knee flexed at 60° compared to 20°. Elongation was higher at SIT20 compared to the SUP20 position. All quadriceps femoris constituents presented higher tendon-aponeurosis complex stiffness in more elongated positions, indicating a higher capacity to support tension and expose the tendon to greater stress. In this way, our results suggest the superiority of the knee angle at 60° for isometric contractions compared to 20° comes with significant physiological and structural characteristics, which may be important factors guiding the adaptation to regular training/rehabilitation on the muscle-tendon unit. Furthermore, the hip angle was involved in changes in the quadriceps muscle (not only the rectus femoris) which may be explored in further studies. These findings are essential for understanding the quadriceps femoris muscle-tendon unit’s behavior in detrimental hip-knee angle positions and should be brought to the attention of rehabilitation programs since they could be related to force transmission. It is possible to suggest that clinicians preferably use SUP60 or SIT60 conditions for strengthening and remodeling purposes since these positions seemed to provide a mechanical advantage for generating greater strength. Gaining a better understanding of the possible physiological mechanisms that underlie muscle and tendon efficiency can provide a framework to develop strengthened protocols to produce more effective contractions and improve the outcomes of rehabilitation programs.
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|
---
title: Effects of Trail Running versus Road Running—Effects on Neuromuscular and Endurance
Performance—A Two Arm Randomized Controlled Study
authors:
- Scott Nolan Drum
- Ludwig Rappelt
- Steffen Held
- Lars Donath
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002259
doi: 10.3390/ijerph20054501
license: CC BY 4.0
---
# Effects of Trail Running versus Road Running—Effects on Neuromuscular and Endurance Performance—A Two Arm Randomized Controlled Study
## Abstract
Running on less predictable terrain has the potential to increase the stimulation of the neuromuscular system and can boost aerobic performance. Hence, the purpose of this study was to analyze the effects of trail versus road running on neuromuscular and endurance performance parameters in running novices. Twenty sedentary participants were randomly assigned to either a trail (TRAIL; $$n = 10$$) or road running (ROAD; $$n = 10$$) group. A supervised and progressive, moderate intensity, and work-load-matched 8 wk endurance running program on TRAIL or ROAD was prescribed (i.e., randomized). Static balance (BESS test), dynamic balance (Y-balance test), gait analysis (RehaGait test, with regard to stride time single task, stride length dual task, velocity single task), agility performance (t-test), isokinetic leg strength (BIODEX), and predicted VO2max were assessed in pre- and post-tests. rANOVA analysis revealed no significant time–group interactions. Large effect sizes (Cohen’s d) for pairwise comparison were found for TRAIL in the BESS test ($d = 1.2$) and predicted (pred) VO2max ($d = 0.95$). Moderate effects were evident for ROAD in BESS ($d = 0.5$), stride time single task ($d = 0.52$), and VO2max predicted ($d = 0.53$). Possible moderate to large effect sizes for stride length dual task ($72\%$), velocity single task ($64\%$), BESS test ($60\%$), and the Y-balance test left stance ($51\%$) in favor of TRAIL occurred. Collectively, the results suggested slightly more beneficial tendencies in favor of TRAIL. Additional research is needed to clearly elucidate differences between TRAIL and ROAD, not only in novices but also in experienced exercisers.
## 1. Introduction
Regular physical activity, such as running, enhances cardiorespiratory and neuromuscular performance and is associated with a delay in all causes of mortality and morbidity [1,2,3,4]. Lee et al. [ 5] found that minimal running training volumes of 30–59 min a week, or 5–10 min a day are associated with lower risks of all-cause and cardiovascular mortality. Despite proven health benefits of physical exercise, the number of sedentary people worldwide is large and steadily growing [6,7,8] in both sexes and with increasing age [7,9]. Physical inactivity accelerates aging-induced functional decrements and compromises physical performance which can lead to impairments in activities of daily living [3,10,11].
At approximately 30 years of age, muscle mass and muscle strength begin to decrease gradually by 10–$15\%$ each decade [3]. Progressive skeletal muscle atrophy is accompanied by a loss in muscle coordination and a decline in balance [11], which can already be evident in individuals of ≥40 years of age [12]. Balance impairments and related spatiotemporal gait deficits both represent crucial risk factors for falls and fall-related injuries [13,14,15]. Falls and fall-related injuries as well as general health impairments not only occur in the elderly but are a frequent problem in middle-aged and young people [16,17]. Few studies have investigated falls and the frequencies of falls in young and middle-aged individuals [16]. In a longitudinal study by Niino et al. [ 17], the prevalence of falls among middle-aged individuals (40–59 years) was $12.9\%$, compared to $16.5\%$ among the elderly group (60–79 years). Talbot et al. [ 16] observed a prevalence of one or more reported falls within a two-year period in $18.5\%$ of young adults, $21\%$ of middle-aged adults and $35\%$ of older adults. In addition to the direct consequences of falls, many people develop a fear of falling after such an event which often leads to a vicious cycle of reduced physical activity, decreased mobility and muscle strength, and a subsequent higher risk for future falls [14,18,19].
To refute the natural decline in neuromuscular properties with aging and augment spontaneous balance and maintenance of strength, our main study objective was to determine the effectiveness of exercising on uneven surfaces (i.e., dirt trails) vs. familiar (or predictably even road) surfaces in a younger adult population on the prior mentioned variables (e.g., neuromuscular or gait training, balance, strength). For instance, running has been shown to improve or amplify several task-specific, metabolic, and neuromuscular factors [20]. However, few studies have focused on neuromuscular variables (e.g., gait parameters via a wearable gait analysis system) resulting from endurance training on distinctly different surfaces [20]. As a suggestion, future researchers should theoretically look at the protective effects of frequent running on uneven surfaces related to unexpected falls, especially in the elderly. Ultimately, the impact of trail running, which is attracting an increasing number of recreational and competitive runners [21,22], compared to road running, has not been extensively compared.
In the present project, we hypothesized trail running would lead to more pronounced improvements in neuromuscular and endurance performance than road running. These assumptions are based on the different characteristics of surface type and gradients between the two conditions. Trail running tends to invoke higher challenges for the neuromuscular system, especially regarding involved muscle coordination, proprioception, and activation [23,24,25,26] compared to road running. Furthermore, since uphill running is an effective stimulus for improving endurance running performance [27,28] and submaximal running economy [27,29] we expected a more pronounced performance at posttest in the submaximal incremental treadmill test for TRAIL.
## 2.1. Participants and Experimental Setting
This pilot study adheres to CONSORT guidelines [30]. Participants were recruited via flyers, posters, word-of-mouth, and local advertisement as well as via “batch” emails among faculty and staff at the university where the project was conducted. Inclusion criteria [31] for participation were: (i) 18–59 years of age; (ii) currently sedentary or not exercising more than twice a week for the last three months; (iii) free from any injury or illness and currently no intake of any medication; (iv) and non-smoker. Importantly, according to ACSM, sedentary, healthy (e.g., free of disease, non-smoker, uninjured) individuals will showcase a greater physiological change from pre to post exercise intervention.
To ensure that participants met the inclusion criteria, all subjects were asked to complete several physical activity questionnaires. The questionnaires included: (a) International Physical Activity Questionnaire—Short Form (IPAQ-SF) [32], (b) the Physical Activity Readiness Questionnaire (PAR-Q&YOU) [33], and the (c) American College of Sports Medicine (ACSM) Risk Stratification [31] to assess individual current health and activity levels. If a participant reported two risk factors related to cardiovascular diseases, he/she had to consult a physician for medical clearance to participate in moderate to vigorous exercise.
The study was conducted according to the Code of Ethics for Human Experimentation of the World Medical Association and the Declaration of Helsinki [34]. Participants were informed in detail about the design of the study, including the potential risks and benefits of included procedures, before providing their informed written consent to participate. The study protocol was approved by the Institutional Review Board of the Northern Michigan University (Trial registration number: ID Proposal Number HS16-786; Date of registration: September, 2017).
Participants were anonymously assigned by the researcher via simple randomization using a random number generator to either TRAIL ($$n = 20$$) or ROAD ($$n = 19$$) and entered into an endurance exercise program. The program consisted of 8 weeks of gradually increasing running workouts with a total amount of 29 training sessions. This randomized controlled pilot trial compared two training groups (i.e., TRAIL vs. ROAD) in terms of balance, gait, agility, along with strength and endurance performance measures in a pre- and post-intervention testing format. Participants in the TRAIL group ran outdoors on uneven and soft trails with varying gradients and under-foot terrain (e.g., rocks, roots, more consistent undulating routes). Participants from the ROAD group ran on predictable terrain or roads with asphalt, concrete or paved surfaces exhibiting no or infrequent gradients. An adherence rate of a minimum of $80\%$ (24 runs) was required for inclusion in the final analysis.
To confirm, a total of 39 healthy adults were initially assigned, whereof 6 subjects did not start the program; 5 participants dropped out during the intervention due to injuries; 3 participants did not meet the required $80\%$ adherence rate and 1 participant was not available for post testing. Additionally, 2 participants (i.e., “4” total) from each group were excluded from analysis due to other exclusion criteria—not following the prescribed training load and for participating in additional training during the period of the study. Then end total of analyzed participants equaled 20.
Demographic data at baseline for all participants who received the allocated intervention are depicted in Table 1.
## 2.2. Experimental Design
Qualifying participants were asked to report to an Exercise Science Laboratory for pre- and post-intervention testing. Post-testing sessions were scheduled at a similar time of the day as pre-testing and within a week upon completion of the training program in November and December 2017, depending on pre-testing dates. Testing order, as well as the examiner were kept constant for each participant.
Finally, ten participants in each group were included in the statistical analysis. The study flow is depicted following the CONSORT criteria, which is easily referenced [30]. Notably, 10 participants in each group provided significant differences (alpha error probability: 0.05) and notable study power (i.e., 1-beta error probability: 0.9) when moderate to large effects size differences between group were presumed for balance performance as the primary outcome.
Lastly, mandatory running meetings were held twice a week and coaching appointments were scheduled as required. Furthermore, participants were contacted by email or phone once a week for feedback. As an additional motivation, a final joint 5k running event was held upon completion of the intervention.
## 2.3. Heart Rate and Blood Pressure
Prior to baseline testing, a blood pressure cuff (Adcuff™, Hauppauge, NY, USA) and stethoscope (Littmann, St. Paul, MN, USA) were employed for blood pressure measures; then, pre-exercise resting heart rate (Polar monitor and watch, Lake Success, NY, USA), as well as body height (Seca stadiometer, Chino, CA, USA) and weight (Health O Meter scale, Mccook, IL, USA) were measured. Maximal heart rate (HRmax) in beats per minute (bpm) was predicted using the following formula according to Tanaka et al. [ 35]: 207—(age × 0.7) for men and 206—(age × 0.88) for women. The lateral preference inventory for measurements of footedness [36] was used to evaluate leg dominance. Limb length was measured from the umbilicus to the medial malleolus of the right leg using a tape measure [37]. Blood pressure, pre-exercise resting heart rate, as well as body height and weight measurements were repeated before post-testing as well.
## 2.4. Warm-Up
Warm-up consisted of walking on a treadmill for 5 min at a rate of perceived exertion (RPE) of 3 on the Borg CR-10 scale [38], followed by dynamic stretching and muscle activation (Knee Hug to Forward Lunge–Elbow to Instep, Heel to Butt Moving Forward with Arm Reach, Handwalk, Lateral Squat Low).
## 2.5. Static Balance Testing
Static balance was tested with the Balance Error Scoring System (BESS) [39], which evaluates 3 stance variations in the following order: [1] double leg, [2] single leg, and [3] tandem or feet in line with one another. The test takes place on 2 different surfaces, starting on firm for all “3” conditions and ending on foam for all “3” conditions while wearing no shoes. Each trial lasts 20 s, during which the number of deviations from the proper testing position were counted. Deviations from the proper testing position in the BESS test are: (a) moving hands off the hips; (b) opening the eyes; (c) step, stumble or fall; (d) abduction or flexion of the hip beyond 30°; (e) lifting the forefoot or the heel off of the testing surface; and (f) remaining out of proper testing position for more than 5 s. Proper position consists of the hands on the iliac crest, eyes closed, and consistent foot position. For the double leg stance, feet need to touch and remain flat on the testing surface. For the single leg stance, the participant stands on the non-dominant leg with the other leg held in approximately 20° of hip flexion, 45° of knee flexion, and neutral position in the frontal plane. For the tandem stance, one foot is placed in front of the other with the heel of the anterior foot touching the toes of the posterior foot, and the non-dominant leg in the posterior position. The maximum amount of errors for any single condition was set at 10. If multiple errors were committed simultaneously, only one was recorded. To improve reliability, the test was repeated 3 times by the same examiner [39] and the mean score of the three trials was calculated for final analysis.
## 2.6. Dynamic Balance Testing
The Y Balance test (YBT) was performed to evaluate dynamic postural stability and functional symmetry during single leg stance in three (anterior, posteromedial, posterolateral) directions [40]. In a Y pattern, each posterior line was marked with tape 135° from the anterior line and 90° apart from one another. Subjects performed a practice trial followed by three test trials for each direction and each leg and were instructed to reach as far as possible, thereby pushing a pen held by the examiner to mark the reaching distance. The testing order started with standing on the left foot and reaching in the anterior direction followed by the trials standing on the right foot for the same direction. This procedure was repeated for all directions. Trials were considered invalid and were repeated if the participant either made a heavy touch or rested the reaching foot on the ground, could not return in a controlled way to the starting position, raised or moved the stance foot, or kicked the marker with the reaching foot to gain more distance [40]. Results were calculated as a composite score with the help of following formula:(((anterior length + posteromedial length + posterolateral length)/3 × leg length) × 100).[1]
## 2.7. Gait Analysis
Spatiotemporal gait parameters (stride time [s], stride length [m], and stride velocity [m/s]) were measured during 20 m (65.6 feet) of level walking at self-selected habitual walking speed by using the portable gait analysis system RehaGait® (Hasomed GmbH, Magdeburg, Germany). The RehaGait® system consists of two mobile sensors which are attached to the lateral part of each shoe to measure linear acceleration, angular velocity, and the magnetic field of the foot at a sampling rate of 500 Hz [41]. Each participant performed a familiarization trial followed by 2 trials with single task condition and 2 trials with dual task condition. For dual task trials, participants were asked to perform a double-digit subtraction task while walking. The combination of gait analysis with cognitive interference tasks was applied to distract participants and limit the cognitive resources for gait control. The mean score for each condition was included in further analysis. For all trials, the phases of gait initiation and deceleration at the end of the walkway were excluded from analysis. For both pre- and post-testing, participants were wearing their running shoes.
## 2.8. Agility Testing
The t-test evaluates the subjects’ agility, leg power and leg speed [42]. Four cones are set out in a T pattern. The test starts at the first cone with a forward sprint of 9.14 m to the second cone, continues with shuffling sideways for 4.57 m to another cone on the right, then 9.14 m to the one on the left, and again 4.57 m back to the middle, before ultimately running backwards 9.14 m to return to the starting point. The base of the cone always has to be touched with the hand further away from the cone when performing the test. The fastest out of 3 trials was used for analysis.
## 2.9. Strength Testing
Unilateral isokinetic concentric leg strength was assessed for the dominant leg using the BIODEX Multi-Joint System 4 Pro (Biodex Medical Systems, New York, NY, USA). Knee extension and flexion as well as ankle plantar- and dorsi-flexion were tested for peak torque (PT) and total work (TW). Subjects were seated with chair and dynamometer position at 90° and the dynamometer positioned outside the testing leg. The anatomical axis rotation (lateral femoral condyle on a sagittal plane for the knee and through the body of talus, fibular malleolus, and tibial malleolus for the ankle) was in alignment with the dynamometer shaft for both knee and ankle attachment, ensuring that the testing pattern was consistent with the proper biomechanics of the joint. Body parts on either side of the tested joint were firmly secured with straps, in order to restrict motion as much as possible to the area of interest. Range of motion was set for each subject and joint individually. After a 12-repetition warm-up trial at 180°/s and low effort, participants performed two sets of 5 repetitions at 60°/s and maximal effort with a 60 s break between sets.
## 2.10. Aerobic Endurance Testing
Oxygen consumption was measured by indirect calorimetry on a treadmill during the walking-based Pepper protocol [43] with the Parvo Medics TrueOne 2400 automated gas analysis system (Sandy, UT, USA). The Pepper protocol is an incremental submaximal test starting at an inclination of $0\%$ and a velocity of 2.5 mi (4 km) per hour. Intensity increases each minute by elevating either inclination or velocity. The test is ended at $85\%$ of predicted HRmax [35]. Gas exchange variables (VO2 and VCO2, RER), RPE on the Borg CR-10 scale [38] and HR were monitored and averaged to 15s time-intervals. Finally, maximal oxygen consumption (VO2max) was predicted from the highest value recorded at HR$85\%$ using the formula VO2max pred = VO2max at HR $85\%$/85 × 100. Prediction was used to minimize cardiovascular risk of pushing to maximum in this mixed age group [2].
## 2.11. Training Program
The training program started with 3 training sessions per week in weeks 1–3. Each training session had a duration of 22–36 min (which was the standard range throughout most of the 8 wk intervention) of running interspersed with 2 min walk rest periods. Novice participants progressed to 4 running (with prescribed intermittent walking) sessions per week in weeks 4–6 with 2-min walk breaks before gradually omitting the walk breaks in week 7 and finishing the program at the end of week 8 with a 45 min continuous run (i.e., their 4th run of week 8). Exercise training started for each participant after the pretest and was performed individually on self-selected outdoor trails (i.e., TRAIL) and roads (i.e., ROAD) at a perceived exertion of 3–4 on Borg CR-10 (although the average RPE approached “5” for both groups upon end analysis). Each participant was provided with a running log in which they recorded training duration, perceived exertion levels, location, and estimated percentage of each session on TRAIL or ROAD. Actual training loads for both groups are summarized in Table 2.
## 2.12. Statistical Analysis
Group means of all variables for all pre- and post-test data were calculated based on individual test scores in order to compare changes between groups. All data are presented as means with standard deviations (SD). Data analysis was computed using the statistical software program SPSS for Windows V.14.0 (SPSS Inc., Chicago, IL, USA). After adjustment for baseline scores (note, baseline values were added as covariate in order to adjust for potential baseline differences), repeated-measures ANOVA procedures were conducted to determine significant between-group differences. Group (TRAIL and ROAD) served as the between-subject factor, and time (pre- and post-test) as the within-subject factor. Statistical significance level was set at $p \leq 0.05.$ Because of the small sample size, partial eta squared (ηp2) and Cohen’s d (d < 0.2 = trivial effect; d ≥ 0.2 = small effect; d ≥ 0.5 = moderate effect; d ≥ 0.8 = large effect), as the standardized mean difference, were calculated to estimate effect sizes from pre- to post-testing for all ANOVAs. The probability for an effect being practically worthwhile in favor of either TRAIL or ROAD was calculated according to the magnitude-based inference (MBI) approach (25–$75\%$, possibly; 75–$95\%$, likely; 95–$99.5\%$, very likely; >$99.5\%$, most likely) using the Hopkins [44] spreadsheet for analysis of controlled trials with adjustments for a predictor in Microsoft® excel.
## 3. Results
In review, of the 33 subjects that received the allocated intervention, 5 people (4 in TRAIL; 1 in ROAD) ended the program prematurely due to injuries and/or pain. A total of 3 people (2 in TRAIL; 1 in ROAD) did not meet the required attendance rate and 1 person from ROAD never reported to the post-testing. Two more subjects of each group were excluded from further evaluation based on exclusion criteria (age, amount of previous physical activity, adherence rate, ≤2 risk factors according to the ACSM Risk Stratification). A total of 10 participants from each group were included in the final analysis. Higher baseline test scores and differences between the two groups were seen for leg strength in knee flexion PT ($19.9\%$ higher in TRAIL) and ankle plantar flexion PT ($18.5\%$ higher in TRAIL), and for VO2max pred ($24.3\%$ higher in ROAD).
The mean overall attendance rate for the intervention was $93.8\%$ or 27.2 ± 2.3 out of 29 total trainings; $91.4\%$ (26.5 ± 1.7) for TRAIL and $96.2\%$ (27.9 ± 2.6) for ROAD.
## 3.1. Static and Dynamic Balance
The repeated-measures ANOVA revealed no statistically significant differences between groups for any balance measures. However, for the BESS test, a significant time-effect between pre-and post-testing was noted ($$p \leq 0.001$$, ηp2 = 0.46) and large and moderate effect sizes according to Cohen’s d for TRAIL ($d = 1.2$) and ROAD ($d = 0.5$), respectively. Results for static and dynamic balance testing are presented in Table 3.
## 3.2. Gait
The spatiotemporal gait analysis rANOVA showed no notable improvements over time in any parameter for either TRAIL or ROAD, as displayed in Table 4. According to Cohen’s d, a moderate effect size for stride time ST in ROAD ($d = 0.52$) as well as small effects for velocity DT in TRAIL ($d = 0.32$), velocity ST in ROAD ($d = 0.23$), and for stride time DT in both groups ($d = 0.43$ in TRAIL; $d = 0.45$ in ROAD) were calculated.
## 3.3. Agility
Both groups improved their t-test performance by $4.6\%$ (TRAIL) and $6.8\%$ (ROAD), respectively. Yet, no significant change over time or between groups was observed. Effects from the intervention on agility are shown in Table 5.
## 3.4. Strength
Gains in isokinetic concentric leg strength were only recorded in knee extension TW ($8.2\%$) and knee flexion TW ($11.8\%$) for TRAIL, and knee extension TW ($1.6\%$) as well as ankle dorsi flexion TW ($1.9\%$) for ROAD. Thereof, only knee flexion TW in favor of TRAIL resulted in a close to significant between-group difference over time ($$p \leq 0.06$$; ηp2 = 0.19; $d = 0.25$). This finding was reinforced by a $76\%$ likely probability of a substantial worthwhile effect according to the MBI approach. A significant negative time-effect in ankle plantar flexion PT ($$p \leq 0.02$$; ηp2 = 0.29) was recorded for ROAD. All other strength measures showed small declines between pre- and post-testing, as shown in Table 6.
## 3.5. VO2max
The results of the aerobic endurance testing (VO2max pred) show the greatest probability for a substantial beneficial effect between pre- and post-testing with $97\%$ in favor of TRAIL. These findings are supported by the calculated Cohen’s d effect sizes ($d = 0.95$ in TRAIL; $d = 0.53$ in ROAD). Time-effect ($$p \leq 0.14$$) and between-group differences ($$p \leq 0.13$$) did not reach statistical significance. Results for VO2max pred are depicted in Table 7.
## 4. Discussion
This is the first study that comparatively investigated the impact of trail running versus road running on neuromuscular performance parameters in healthy adults. We hypothesized that running on natural trails would lead to more pronounced improvements in static and dynamic balance, gait patterns, agility, and leg strength between pre- and post-testing compared to road running. This assumption was based on previous findings which have shown that the navigation of the body on varying surface densities, inclines and speeds evoked higher muscle activation and coordination as opposed to moving on more firm and flat terrain [23,24,25,26,45,46]. Greater physiological strain on softer terrain is associated with a greater degree of energy absorption by the training surface that results in a loss of elastic energy, followed by greater concentric work and overload stimulus in the lower-limb muscles [26,45]. Against this background, we expected gains in concentric quadriceps and hamstring muscle strength as well as in ankle strength and stability in favor of TRAIL from navigating in uneven terrain. However, according to the BIODEX isokinetic concentric leg strength testing, knee flexion TW was the only parameter that resulted in close to significant improvements. On the other hand, for ankle dorsi flexion PT, a significant negative time-effect was recorded. A possible explanation for this decrease could be found in a reduction in ankle work and range of motion that has been seen when running on uneven and unpredictable terrain in order to stabilize the joint [26]. The fact that all other strength measures showed small declines between pre- and post-testing might be attributed to fatigue as a result of the newly increased exercise routine. It is also possible that the reduced strength outcomes especially for PT values are a consequence of endurance training-specific adaptations. When interpreting leg strength results, baseline differences and high standard deviations in both groups should be taken into account. Especially in TRAIL, large discrepancies in strength scores among subjects in pre- and post-testing were observed. Another factor that added to these inconsistencies is the fact that most participants from both groups had no experience in resistance training, much less with the applied strength-testing device. The lack of experience might have influenced the test performances.
We found no statistically significant differences in the rANOVA analysis between TRAIL and ROAD for static and dynamic balance measures. But a significant time-effect between pre-and posttest was calculated ($$p \leq 0.001$$, ηp2 = 0.46) for the BESS test. In addition, large ($d = 1.2$) and moderate ($d = 0.5$) effect sizes for Cohen’s d for TRAIL and ROAD respectively indicate potential balance improvements from running, especially on trails. In a review on sports participation and balance performance, Hrysomallis et al. [ 47] stated that athletes generally have a superior balance ability compared to control subjects as a result of repetitive experience and improved motor responses to proprioceptive and visual cues. Additionally, the same authors observed improved coordination, strength and range of motion. However, it remains unclear whether proprioception can actually be improved by exercise or if athletes just become more skilled at reacting to sensory cues. In a study on functional fitness gains through various types of exercise in older adults, Takeshima et al. [ 48] reported improvements in dynamic balance (functional reach test) in all intervention groups (balance, aerobic, and resistance training). They also predicted that training on unstable surfaces not only leads to improvements in balance but also in lower-body strength due to greater muscle activation when counteracting increased sway following unexpected perturbations. A few other studies report improvements in locomotion in older adults after aerobic training interventions involving walking, treadmill walking, jogging, and step aerobics [19]. The results of the BESS test in this pilot study support previous findings that physical exercise, specifically running, may have a positive influence on balance. Nevertheless, benefits from running for dynamic and functional balance could not be proven with the administered tests for the lack of significant results in the Y-Balance test and gait analysis.
Despite the close relationship between balance and gait performance in regards to fall- and injury-risk factors [14,19,26,47,48,49], the spatiotemporal gait analysis in this study showed no notable characteristics or changes in any parameter for either TRAIL or ROAD. rANOVA, Cohen’s d, as well as MBI calculations show inconsistent results and no conclusions can be drawn about the influence of trail or road running on gait stability. Likewise, no statistically significant differences for time or between groups were recorded in agility performances. Nevertheless, most participants achieved faster T-test times after the intervention and demonstrated noticeably increased confidence and security levels in their sprint performances. Increased confidence levels and sprint ability might result in an overall increased gait stability and thus reduce fall risk. When discussing the lack of evidence for gait and agility in this study, testing devices and procedures need to be considered. More task-specific trials might elicit more pronounced changes.
Aerobic endurance testing showed the highest probability for a substantial worthwhile effect in favor of TRAIL ($97\%$, very likely) together with a large Cohen’s d effect size ($d = 0.95$). Relative VO2max outcomes from the gas analysis test improved by $23.1\%$ and $13.7\%$ from pre- to post-testing for TRAIL and ROAD, respectively. Still, time-effect ($$p \leq 0.14$$; ηp2 = 0.12) and between-group differences ($$p \leq 0.13$$; ηp2 = 0.13) did not reach statistical significance. Moreover, big baseline differences ($24.3\%$ higher in ROAD) need to be considered when interpreting predicted maximal oxygen consumption. Lower baseline values in TRAIL might have facilitated the larger responses to the training intervention in that group. Even so, it is probable that trail running may elicit greater benefits for cardiovascular fitness. Several studies [26,50,51,52,53] documented that running on natural surfaces such as irregular trails required a higher energy expenditure and metabolic cost, which translated to a higher training intensity and higher aerobic training adaptations. However, recorded RPE from the running logs revealed no group differences (4.6 ± 1.1 for TRAIL; 4.9 ± 0.8 for ROAD), an interesting finding if greater energy expenditure is realized on TRAIL versus ROAD without a concurrent rise in RPE. Therefore, TRAIL could be a strategy or modality for advanced energy output and weight loss, leading to better motor control at a lower perceived exertion.
To date, a lot of research regarding neuromuscular adaptations from running has focused on different types of footwear or foot strike patterns and related kinematic, metabolic, and biomechanical parameters of the lower limb, as well as running-related injuries [20,24,54]. Various research groups examined the effects of training on different outdoor terrain, mainly focused on grass or sand surface [23,45,55,56,57], or defined trail running as an ultra-endurance activity. In this understanding, Easthope et al. [ 58] analyzed performance levels between young and older master runners in a 55-km ultra-endurance trail run. They observed equal performances in both groups despite structural and functional age-related alterations and confirmed that the decline in physical performance can be prevented with regular endurance training such as running. In a study that compared the different effects of concrete road, synthetic track, and woodchip trail on dynamic stability and loading in runners, Schütte et al. [ 22] revealed significant performance differences from a biomechanical perspective. Running on woodchip trail altered measures of dynamic stability and lower-limb musculature compared to running on concrete road due to compression and displacement of the woodchips under the foot causing destabilization and directional shift with each stride. Similarly, Boey et al. [ 59] looked at running on concrete, synthetic running track, grass, and woodchip trail at two different speeds and the different vertical impacts on the lower leg. Their results showed that running on woodchip trails and at a slower speed, reduced the injury risk at the tibia.
Running related injuries (RRI) of the lower extremities are a common negative side effect in runners [60,61]. The prevalence is usually higher for overuse musculoskeletal injuries than for acute injuries [21,60,62]. There is a large heterogeneity of injuries that originates from different methods and definitions when evaluating RRI [21,60]. Among the most commonly reported RRIs in the literature are to the Achilles tendon, plantar fascia, calf muscle, knee, meniscus, shin, foot, ankle, hip/pelvis, lower back, hamstring, and thigh [21,60,63,64]. Risk factors for RRI appear to be previous injuries to the same anatomical area, high training loads and little running experience [64,65].
In the current study, 5 out of 33 people reported an injury during the 8-week intervention that prevented them from completing the training program. Affected body sites and type of injuries are all in line with the formerly reported common injury types and risk factors. Two participants from TRAIL developed reoccurring overuse injuries (i.e., knee and lower back) that had probably not been fully and appropriately cured. The other participants suffered from tibial stress syndrome (1 in TRAIL) and ankle sprains (1 in TRAIL; 1 in ROAD). The recorded amount and type of injuries in this study seem to reinforce the fact that previous injuries, little running experience, and an increase in training load within a relatively short amount of time may be risk factors for RRI. Meanwhile, as stated by Taunton et al. [ 64], previous activity, cross-training and running surface appear to be non-significant injury risk factors for either gender. Interestingly, 4 of the 5 injured subjects in this study were part of the trail running group, which contrarily seems to imply a connection between surface and injury prevalence. Trail running might be more strenuous for physiological parameters due to its specific surface characteristics and the resulting challenges for involved muscle groups and the metabolic system. Therefore, running on natural and more compliant trails may be more likely to cause overuse injuries in an untrained population. Despite the mentioned risk factors, authors agree that health benefits from running outweigh the related risks and costs of RRI [21].
Limitations to this study are the small group sizes and baseline differences between groups in VO2max pred and certain strength parameters, as well as the fact that the running intervention itself was not supervised and subjects performed most of the training units individually. Consequently, even though participants were instructed to exercise at a comfortable, moderate to somewhat hard intensity (3–4 on the Borg CR-10 scale), it is possible that some trained at intensities that were too high for their level of fitness. Additionally, the program was based on running time and not distance, which may have resulted in a different training volume dependent on different training pace among individuals. The training log was a way of controlling for these interferences. Regarding adherence, a slightly lower attendance rate in the trail running group was expected since trails require more effort and planning to access and may become impassable in bad weather or darkness. As a final point, MBI’s should be interpreted carefully, especially implications drawn from them, and one should be mindful of how the performed tests may have related to the intervention.
## 5. Conclusions
The results of this training intervention show no statistically significant between-group differences. This suggests that benefits derived from running on uneven and soft natural terrain as opposed to a more flat and concrete road surface in respect to static and dynamic balance, gait, agility, and lower limb strength should not be overrated. Based on current knowledge and the outcomes of this study, no well-founded recommendations for an integrative training approach in regard to trail running and the prevention of falls and fall-related injuries can be given. More research is needed on the influence of running on trails or similar natural surfaces on different neuromuscular performance parameters.
Nevertheless, the findings of this intervention indicate slightly more beneficial tendencies for balance and leg strength improvements when running on trails as opposed to road; and, therefore, potential benefits for the prevention of falls and fall-related injuries. While a significant time-effect between pre- and post-testing in static balance was recorded for both groups ($$p \leq 0.001$$, ηp2 = 0.46), the trail running group also showed large effect sizes ($d = 1.2$) for static balance, compared to only moderate effect sizes ($d = 0.5$) in the road running group. Trail running also seems to have positive impacts on upper leg strength performance, which is indicated by gains in knee extension ($8.2\%$) and flexion ($11.8\%$) total work and a close to significant between-group difference over time ($$p \leq 0.06$$; ηp2 = 0.19; $d = 0.25$) in knee flexion TW.
For more detailed and specific results, future studies should target larger group sizes of recreational runners within smaller age ranges and in a longitudinal approach over a longer time period. Moreover, the scope of the intervention should be limited to one particular neuromuscular parameter. Thereby, the combined effects for cardiovascular and neuromuscular performance factors from running on different surfaces might be disentangled more clearly. Finally, repeating this study in an older, untrained population and tracking at-home falls throughout a pre-determined follow-up period (e.g., over 5-years) post intervention could yield more precise commentary regarding TRAIL’s effectiveness in or lack of promoting better neuromuscular coordination.
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|
---
title: “What If Others Think I Look Like…” The Moderating Role of Social Physique
Anxiety and Sex in the Relationship between Physical Activity and Life Satisfaction
in Swiss Adolescents
authors:
- Silvia Meyer
- Christin Lang
- Sebastian Ludyga
- Alexander Grob
- Markus Gerber
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002265
doi: 10.3390/ijerph20054441
license: CC BY 4.0
---
# “What If Others Think I Look Like…” The Moderating Role of Social Physique Anxiety and Sex in the Relationship between Physical Activity and Life Satisfaction in Swiss Adolescents
## Abstract
Background: Physical activity has been shown to have a positive effect on life satisfaction in adolescents. Despite these benefits, physical activity levels constantly drop during adolescence, suggesting potential interfering factors in this link. Since worries about physical appearance are an important issue at this age, this study aims to examine the relationship between physical activity and life satisfaction in adolescents and explores possible moderating effects of social physique anxiety and sex. Methods: We used data from a longitudinal study with $$n = 864$$ vocational students (mean age = 17.87 years, range: 16–25, $43\%$ female) from Switzerland. To test our hypotheses, we used multiple hierarchical regression analyses as well as simple slope analyses. Results: We did not find a significant direct effect of physical activity on life satisfaction. However, we found a significant two-way interaction between physical activity and social physique anxiety. An additional significant three-way interaction occurred, indicating that a positive effect of physical activity on life satisfaction holds only for female adolescents with low social physique anxiety levels. Conclusions: This study highlights the importance of developing a healthy relationship with one’s body to fully benefit from physical activity, especially for female adolescents. Taken together, these results reveal important considerations for physical activity educators.
## 1. Introduction
Life satisfaction plays a crucial role during adolescence in reaching developmental milestones and in ensuring a healthy transition into adulthood [1]. Among adolescents, life satisfaction represents a key indicator for psychological health and psychological wellbeing [2]. Therefore, it is not surprising that well-being, in general, is associated with a variety of positive personal, behavioral, psychological, and social factors at this age [3].
Life satisfaction is defined by a global evaluation of one’s life and represents the cognitive–evaluative component of subjective well-being (SWB) [4]. It is well-known that life satisfaction levels decrease during adolescence, particularly between the ages of 11 and 16 years [5,6,7]. This decrease can be explained by the various challenges that adolescents face during puberty and the tempo at which these changes come along [8]. Further, decreases in domain-specific life satisfaction (family, hobbies/leisure time, social life) have been observed across adolescence, which may contribute to a decrease in global life satisfaction [5,6].
Life satisfaction is an important factor for psychological functioning and plays a crucial role in the risk of development of emotional and behavioral problems, such as depression or anxiety [9,10]. Accordingly, low levels of life satisfaction increase other behavioral health risk factors, such as substance abuse, which potentially increase the risk of developmental problems [2]. It is therefore crucial to consider potential explaining factors to better understand the decrease in life satisfaction during this age.
## 1.1. The Role of Physical Activity
Physical activity has gained a lot of attention as a protective factor during adolescence [11]. Physical activity is not only beneficial for adolescents’ physical health [12] but also has a positive impact on psychological factors such as self-esteem, emotions, or mood [13,14]. A link between engagement in regular physical activity and better psychosocial health and well-being in adolescents has repeatedly been confirmed (for reviews see: [15,16,17]). As part of psychosocial health, life satisfaction is linked to physical activity as well as to different forms of physically active behaviors, such as exercising or participating in a sports club [18]. Several reviews have shown that higher levels of life satisfaction (or happiness) are associated with higher levels in various physical activity outcomes, such as higher exercise levels or more active lifestyles [17,19]. Additionally, a recent study with a large representative sample of adolescents in 44 countries reported a significant association between physical activity and life satisfaction [20].
The relevance of this relationship becomes even more clear by looking at the impact of a lack of physical activity. Thus, physical inactivity during adolescence can track into adulthood [21] and increase the risk of obesity [22]. Moreover, adolescents who do not engage in vigorous physical activity show significantly lower levels of life satisfaction [23]. To avoid these possible negative consequences associated with a lack of physical activity, it seems crucial to focus on and obtain a better understanding of the mechanisms and possible amplifying factors of regular physical activity.
Most researchers agree on the fact that it is probably a combination of both psychological and neurobiological factors that lead to benefits of physical activity on psychosocial health and well-being [24,25]. In their recent review, Rose and Soundy [15] proposed that physical activity enhances autonomy development. Autonomy then, on the one hand, has a direct effect on adolescent’s well-being and, on the other hand, increases intrinsic motivation to spend more time in physical activity. In an earlier review, Paluska and Schwenk [25] also discussed theory-based mechanisms; in line with the self-efficiency theory, they suggested that exercise can lead to feelings of self-confidence and a sense of success, which directly improves mood and also leads to more confidence to use available resources important for mental health. Finally, another review concluded that the strongest evidence was found for the physical self-perception mechanism; thereby, physical activity leads to improvements in physical self-perception, which enhances self-esteem and thus well-being of adolescents [11,26].
## 1.2. The Role of Social Physique Anxiety
Despite the potential beneficial effects of physical activity on adolescents’ life satisfaction and well-being, physical activity levels tend to decline during adolescence [27]. Accordingly, several interpersonal as well as structural factors exist that drive adolescents to quit or avoid physical activity [28]. For instance, research has shown that girls reported losing interest, insufficient time, and a perceived lack of competence as the main reasons for quitting sports [29].
As highlighted in the previous chapter, improved self-perceptions are one of the strongest explanatory factors for the relationship between physical activity and life satisfaction [11]. In line with this, social physique anxiety could be an important contributing factor to explain why some adolescents do not sufficiently engage in physical activity [30]. Social physique anxiety is defined as the anxiety of being evaluated negatively by others based on one’s physique and physical appearance (e.g., body fat, muscularity, tone, body proportions) and therefore can be seen as a connection between a person’s image of his/her body and the satisfaction or dissatisfaction with this image [31]. This notion was supported by empirical evidence showing that significant associations exist between body dissatisfaction and social physique anxiety [32] and that especially weight- and muscular-related social comparisons are predictive of the development of social physique anxiety in college students [33].
During adolescence, the body undergoes a series of significant changes [34], including changes in body proportions or body fat distribution [35]. At the same time, physical attractiveness becomes more important for adolescents [36]. Due to the high importance of physical appearance and the changes that come with puberty, there is an increased risk of developing a negative body image during adolescence [37]. This could also be due to the fact that, during this developmental stage, social comparison is an important tool for adolescents to develop their self-concept [38]. Combined with the insecurities that come along with puberty-related body changes, adolescents are usually more sensitive about the way their bodies are evaluated by others [39,40]. Girls seem to be at particularly high risk of social comparison as well as negative self-perceptions of their bodies. They show a significant difference between their perceived and desired body perceptions, independent of their BMI [41].
Negative body perceptions in adolescents are not only frequent but also correlated with high social physique anxiety levels [42]. Due to adolescents’ strong focus on social comparisons and more specifically on how their body is seen or evaluated by others, social physique anxiety has been related to participation motives, engagement, and avoidance of physical activity in adolescents [43]. Due to their lower body image, teens might be anxious about others thinking negatively about their bodies or may have low perceptions of their physical competence and therefore may stop engaging in physical activities such as exercise and sport [28,39]. Further, many adolescents with social physique anxiety may have had negative experiences related to past physical activities (which may be perpetuated by their anxiety); as the literature shows, it is how physical activity is perceived that motivates or discourages an individual from engaging in further physical activity [44].
There are several further theories that explain why people may stop engaging in regular physical activity [45]. One of these theories is the theory of impression management and self-presentation [46]. The theory assumes that if people are motivated to make certain impressions but doubt they will be successful (such as the fear of being negatively judged in terms of their body), they usually experience anxiety and behave rather shyly. More importantly, they use strategies to minimize the likelihood of being evaluated in an unflattering way. Therefore, not engaging in physical activity can be understood as a coping strategy to avoid potential negative impressions [46].
Studies have shown that adolescents use both behavioral and cognitive avoidance of physical activity to cope with social physique anxiety, with girls also using more emotion-focused coping strategies (e.g., behavioral avoidance or appearance management) compared to boys [39,47]. Apart from leading to reduced physical activity levels, social physique anxiety is also directly linked to mental health and life satisfaction levels [48]. A study with male recreational cyclists and triathletes (aged 18–60 years) showed that social physique anxiety negatively impacted their life satisfaction through a decrease in perceived satisfaction with basic psychological needs [49]. In summary, since social physique anxiety leads to avoidance of physical activity and is also directly linked to lower life satisfaction, it could be reasoned that adolescents with social physique anxiety might have had negative experiences while participating in physical activity [44] and might therefore not benefit from it in the same way as adolescents who have healthy relationships with their bodies.
## 1.3. The Role of Sex
Evidence suggests that girls and boys show significant differences in how they see and evaluate their physical self-concepts [50]. For instance, girls seem to be more concerned about being overweight and show a higher desire for thinness, while boys often aim to be more muscular, even though this might lead to gaining weight [51]. Male adolescents usually have more positive self-perceptions regarding their bodies as well [42,52]. In line with this, female adolescents score higher on social physique anxiety than their male counterparts across the entire lifespan (for a review see: [43]).
Moreover, there is ample evidence that girls have lower levels of physical activity in general and also show a stronger decrease in physical activity from childhood to adulthood [53], whereas boys show higher and more constant levels of physical activity during this transition [42]. Moreover, boys also show higher levels in general and health-related life satisfaction than girls [5,54].
Besides these differences in physical activity, life satisfaction, and social physique anxiety itself, there are indicators that there are sex differences in the associations. Thus, the negative relationships between physical activity and mental health as well as physical activity and life satisfaction have been found to be stronger in girls [55,56]. However, it remains largely unknown whether other relationships between these constructs, especially potential moderating effects, also differ between boys and girls.
## 1.4. Purpose of This Study
As summarized above, past research has shown different relationships between physical activity and life satisfaction in adolescents and connected social physique anxiety to both physical activity and life satisfaction separately. Sex differences were also investigated before but focus mainly on the differences in the investigated constructs itself. Consequently, little is known of how these different effects are all connected. The present study aims to better understand the mechanisms involved in the relationship between physical activity and life satisfaction in adolescence and therefore investigates if this relationship is moderated by social physique and sex.
Given the literature presented above, we hypothesized that physical activity is related to higher life satisfaction, both at present and 10 months later (Hypothesis 1) (e.g., [19]). We also expected this effect to be only significant if social physique anxiety levels are low (Hypothesis 2) [43,49]. Finally, we assumed that, for girls, the moderating effect of social physique anxiety is stronger than for boys (Hypothesis 3) [43,53,56].
## 2.1. Participants
We used data from the EPHECT study (Effects of a Physical Education-based Coping Training), a cluster randomized trial. Vocational students from two schools in Switzerland were recruited and asked to complete a battery of questionnaires during a physical education lesson at the beginning (t1: September/October 2010) and at the end of the academic year (t2: May/June 2011). During the data assessment, two research assistants were present. Students were assured confidentiality and provided informed written consent. In addition, minors (students below 18 years of age) provided written parental informed consent. The study was approved by the local ethics committee and was conducted in line with the guidelines set forth in the Declaration of Helsinki. Data from this study have been published previously, mainly focusing on the effect of stress in combination with physical activity and mental health outcome [57,58,59].
A total of $$n = 1242$$ students participated in the baseline (t1) data assessment. The dropout rate from baseline (t1) to follow-up (t2) was $30.4\%$ ($$n = 378$$). However, we did not find any statistically significant differences in physical activity, life satisfaction, social physique anxiety, or the distribution of sex between participants who dropped out and peers who completed both data assessments. The final sample with complete longitudinal data was composed of $$n = 864$$ participants (comprising $43\%$ female; which is representative of the sex distribution among students in vocational training in Switzerland ($41\%$ female students in 2020, see: [60])).
## 2.2.1. Physical Activity
A short form of the International Physical Activity Questionnaire (IPAQ-SF) [61] was used to assess physical activity. Questions included the number of days in the last week the students were vigorously or moderately physically active for at least 10 minutes. Vigorous physical activity was defined as movements that cause one to work up a sweat, such as jogging, biking, or playing soccer. Moderate physical activity was defined as movements causing one to be just a little short of breath, such as dancing, table tennis, or cycling at an easy pace. We summed these two items up to an overall moderate-to-vigorous physical variable, representing the days on which participants were physically active. The IPAQ has shown good validity for adolescents above 15 years of age and has shown to predict physical activity levels comparably to the accelerometer [62,63,64].
## 2.2.2. Life Satisfaction
Three items (those with the highest factor loadings in the initial studies, see [65,66]) of the Satisfaction With Life Scale (SWLS) [67] were used to measure life satisfaction. Students were asked to evaluate the following questions: (a) “In most ways, my life is close to my ideal.” ( b) “I’m satisfied with my life.” ( c) “So far I have gotten the important things I want in life.” Answers were given on a 7-point Likert scale ranging from 1 (completely incorrect) to 7 (completely correct). The three items showed good internal consistency in the present sample (Cronbach’s α = 0.77) and were combined to build a mean score. The validity of the SWLS score has been demonstrated repeatedly in previous studies [67,68]. Specifically, the three-item scale of the SWLS has shown similar validity to the original five-item scale [69].
## 2.2.3. Social Physique Anxiety
Social physique anxiety was measured with the Social Physique Anxiety Scale (SPAS) [31]. We used the 9-item version of the scale, which has shown acceptable validity [70,71], where students had to evaluate questions such as “I worry that there are parts of my body that people may not like.” Answers were given on a 5-point Likert scale ranging from 1 (not at all) to 5 (extremely). Two items were reverse coded and had to be recoded before calculating the mean score. In the present sample, the index showed good internal consistency (Cronbach’s α = 0.82).
## 2.3. Statistical Analysis
Descriptive and bivariate correlation analyses with all the main study variables were calculated in the first step. Body mass index (BMI) was measured as weight in kilograms divided by height in meters squared. Students were asked to self-report their height (without shoes) in cm and weight (without clothes) in kilograms. With these answers, the BMI was calculated. We included age, body mass index (BMI), and group (intervention vs. control condition) as covariates to ensure the results would be independent of participants’ age, BMI, or whether they participated in the intervention or not. We then also controlled for baseline life satisfaction levels in the prospective analyses. Further analyses were controlled for socio-economic status, assessed via self-reported financial situation of the adolescents (see Table S1 in the Supplemental Materials). Since these covariate analyses did not change overall effects, we decided to report these data as supplemental online material only.
To test the moderating effect of social physique anxiety and sex on the relationship between physical activity and life satisfaction, we used conditional process modeling to examine three-way interactions. In the first step, we analyzed the data at the first measurement point in a cross-sectional way to detect the proposed associations. For these analyses, the full sample (students with complete baseline data) was used. In the second step, we then analyzed the longitudinal data by adding the data from the second measurement point to investigate a potential direction and the robustness of the effects. For these analyses, only students with complete data at baseline and follow-up were considered. We calculated a moderated moderation (Model 3) using the PROCESS macro developed by Hayes [72] in SPSS. PROCESS estimates the best fitting ordinary least squares regression model and examines the interactions. We first performed cross-sectional and then longitudinal analyses. Therefore, we defined life satisfaction as the outcome variable and added physical activity as the predictor variable. Social physique anxiety and sex were then added as moderator variables. The Johnson–Neyman technique was used to derive regions of significance for the two-way interaction (PA × SPA) at a value of the third variable (sex).
We mean-centered the independent variable (PA) and the linear moderator variable (SPA) before entering them into the regression model. The results of the regression models are presented as unstandardized regression coefficients (b), standard errors, and p values. To further interpret the slopes of the three-way interaction effects and plot the interactions, we used simple slope analyses and the plotting procedures [73]. To this end, the moderator variable “sex” was divided into binary categories: boys [1] and girls [2]. The level of significance was set at $p \leq 0.05$ across all analyses.
## 3.1. Descriptive Statistics and Bivariate Correlations
Table 1 provides an overview of the sample characteristics and descriptive statistics of the main study variables as well as the main covariates. Statistics are presented separately for the full sample (with complete baseline data) and the prospective sample (participants with complete data at baseline and follow-up). Means, standard deviations, minimal and maximal values, as well as skewness and kurtosis are presented.
The bivariate correlations between the investigated variables are presented in Table 2. Again, correlations are presented separately for the full sample with valid cross-sectional data at baseline (above diagonal) and those participants with both complete baseline and follow-up data (below diagonal). Physical activity was positively correlated with life satisfaction, whereas a negative correlation occurred between physical activity and social physique anxiety. Social physique anxiety was negatively correlated with life satisfaction. Boys reported significantly higher levels of physical activity and lower levels of social physique anxiety than girls. Age was significantly and negatively correlated to physical activity and positively correlated with BMI. Finally, BMI was positively correlated with social physique anxiety.
## 3.2. Two- and Three-Way Interactions
To examine interaction effects, we calculated a cross-sectional process model (Table 3). We first calculated the analyses with the full sample which included complete baseline data (Sample 1: $$n = 1242$$), and then with those participants who had full baseline and follow-up data (Sample 2: $$n = 864$$). In the cross-sectional analyses, after adjustment for age, BMI, and group, a significant main effect appeared for social physique anxiety (Sample 1: b = −0.466, $p \leq 0.001$, Sample 2: b = −0.372, $p \leq 0.001$) on life satisfaction in both samples. In contrast, no significant main effect was found for physical activity (Sample 1: $b = 0.029$, $$p \leq 0.094$$, Sample 2: $b = 0.027$, $$p \leq 0.197$$). In the larger sample, a significant two-way interaction was found between physical activity and social physique anxiety (b = −0.045, $$p \leq 0.029$$). In the smaller sample, this two-way interaction effect did not reach statistical significance, although the sign of the interaction pointed in the same direction (b = −0.023, $$p \leq 0.298$$). No significant three-way interaction between physical activity, social physique anxiety, and sex was found in either sample. The variables included in the cross-sectional model explained a total of $11\%$ (Sample 1) and $10\%$ (Sample 2) of variance in life satisfaction.
To further explore the potential three-way interaction, we calculated a prospective conditional process model. As shown in Table 4, students with higher baseline life satisfaction and participants assigned to the control group reported higher life satisfaction at follow up. No statistically significant main effects were found for age, sex, and BMI.
After controlling for baseline life satisfaction and covariates, there were no significant main effects for physical activity or for social physique anxiety. However, there was a statistically significant (negative) two-way interaction between physical activity and social physique anxiety (b = −0.052, $$p \leq 0.013$$), indicating that only adolescents with low social physique anxiety benefitted from higher physical activity in relation to their life satisfaction (see Figure 1). In the longitudinal analyses, a significant three-way interaction between physical activity, social physique anxiety, and sex appeared ($b = 0.080$, $$p \leq 0.014$$). The Johnson–Neyman technique showed that the relationship between physical activity and life satisfaction was only significant for girls who had low social physique anxiety (1 SD below the mean). The variables included in the prospective model explained a total of $27\%$ of variance in life satisfaction at follow-up.
## 3.3. Simple Slope Analyses
Simple slope analyses were used to interpret and to compare the slopes of the three-way interaction of physical activity, social physique anxiety, and sex when predicting adolescents’ life satisfaction at follow-up. As displayed in Figure 2, different pattern occurred for boys and girls. Girls with low social physique anxiety reported higher life satisfaction when they were more physically active ($b = 0.071$, $$p \leq 0.003$$). For girls with high social physique anxiety, no significant effect of physical activity on life satisfaction occurred (b = −0.005, $$p \leq 0.984$$). For boys, the results point in a different direction, but the slopes for boys were not statistically significant, indicating that physical activity had no significant effect on life satisfaction among boys, independent of their social physique anxiety levels.
Slope difference tests further pointed toward significant differences between the girls with high and the girls with low social physique anxiety slope (t[864] = −2.662, $$p \leq 0.008$$). These results indicate that if social physique anxiety levels are high, girls seem to not fully benefit from physical activity.
## 4. Discussion
The goal of this study was to investigate the role of social physique anxiety and sex on the relationship between physical activity and life satisfaction in adolescents. In the first step, we analyzed the data cross-sectionally with a larger sample. In the second step, we analyzed the data longitudinally to get a better understanding of the potential direction of the effects.
Contrary to our first hypothesis, we did not observe a significant effect of physical activity on life satisfaction, neither cross-sectionally nor longitudinally. One reason for the statistically non-significant finding could be that physical activity impacts rather emotional components of well-being, such as positive affect and happiness, than the cognitive components reflected by life satisfaction. This is in line with a recent large international study with young adolescents (aged 10 to 12 years), which showed a stronger correlation between physical activity and positive affect than with cognitive measures of subjective well-being, including life satisfaction [74]. Another assumption is that the link between physical activity and life satisfaction only becomes relevant in adulthood. This notion is in line with that of Maher et al. [ 75], who reported that physical activity and life satisfaction were correlated in middle and late adulthood, but not in young adulthood, which the authors explained could be due to an increased perception of control. However, the fact that the effects were not significant can as well be explained by the existence of other moderating factors. Therefore, we postulated with our second hypothesis that social physique anxiety moderates the effect between physical activity and life satisfaction.
Our data support our hypothesis, as shown with a significant two-way interaction between physical activity and social physique anxiety. The effect was found in the cross-sectional analyses and was corroborated in the longitudinal analyses. Simple slope analyses revealed that the effect of physical activity on life satisfaction was only significant when social physique anxiety levels were low. In other words, engagement in physical activity had no positive effect on life satisfaction among adolescents who were challenged with social physique anxiety. This result is in line with a study of 298 young adolescents (mean age: 11.6 years) showing that social physique anxiety was associated with lower enjoyment in sports as well as reduced leisure-time physical activity [76]. A study with 245 female college students (mean age: 19.9 years) further found that enjoyment of sports is crucial to benefit from psychological effects, for example, gaining a higher self-esteem through sporting activities [77]. Moreover, another study with 1492 adolescents showed that enjoyment of sports is even more important than the frequency of the physical activities to increase self-esteem [78]. Yet, self-esteem is subsequently linked to higher life satisfaction levels in adolescents [54]. In contrast, concerns about physical appearance or the judgment of others on their bodies have been shown to reduce participation in sports among adolescents [79,80]. At the same time, adolescents with low levels of physical activity and sedentary behavior report lower levels of life satisfaction [16].
Another assumption for the moderating effect of social physique anxiety on the link between physical activity and life satisfaction is that for adolescents with high social physique anxiety, situations surrounding physical activity, for example, changing clothes or a shared shower, may constitute a source of distress [81]. These and other forms of stress then lead to a decrease in adolescents’ life satisfaction [30,82].
Finally, the question arises whether this moderating effect presents differently depending on the sex of the adolescents. In line with our third hypothesis, a significant association between physical activity and life satisfaction was only present if participants were female and social physique anxiety levels low. Several reasons exist to explain this three-way interaction. First, girls tend to be more concerned about people staring or even laughing at their appearance during sports or of being called names that refer to their weight [79]. Therefore, girls may experience more distress while participating in physical activities, as they perceive higher levels of teasing from peers and show higher withdrawal rates in sports clubs than boys [79,83]. Second, boys and girls may use different coping strategies when experiencing social physique anxiety. Eklund and Bianco [81] argued that social physique anxiety can have controversial effects on physical activity, as it can lead to avoidance because of the fear of being evaluated or increased levels through the motivation of changing their physical appearance (for example, to become more muscular or to lose body fat). It is also possible that boys who have high levels of social physique anxiety use physical activity to come closer to the image they want others to have of their bodies. This has also been confirmed in previous research showing that a higher percentage of girls use behavioral avoidance to cope with social physique anxiety compared to boys. In turn, the percentage of boys using physical activity as a coping strategy was higher [47]. In addition, among boys, contrary to girls, a significant correlation between physical activity and perceived body attractiveness was found, indicating that physical activity may improve their physical self-perception [84]. Our results point in a similar direction, as life satisfaction was slightly higher among boys with social physique anxiety if they were highly engaged in physical activities (see Figure 2). However, simple slope effects were not significant for boys, so the findings have to be interpreted with caution.
This study has several strengths such as the use of longitudinal data, the large sample size, and instruments that all showed satisfactory internal consistency. Moreover, we used two- and three-way interactions as well as simple slope analyses to determine the relationship between physical activity, life satisfaction, social physique anxiety, and sex, which allowed us to examine moderating effects. Despite these strengths, some shortcomings should not remain unmentioned. Limitations include the exclusive focus on self-reported data during a physical education lesson, which could have added stress to students who already have social physique anxiety or could have led to social desirability or recall bias; the fact that we only focused on frequency when assessing physical activity; and the focus on adolescents in vocational education and training, which makes it difficult to generalize the findings to a broader student population. It is also noteworthy that in the longitudinal analyses, we found a main effect for group showing that students assigned to the control group reported higher life satisfaction at follow-up. The reasons for this unexpected result are not entirely clear, particularly as the intervention had no impact on stress and coping, the key outcome variables of the intervention study [59].
Further research should focus on representative samples of adolescents to gain more information about the generalizability of these effects. More than two measurement points would allow one to figure out how the measured variables change over time and how these changes predict each other. From a methodological point of view, it would be interesting to include other physical activity measures beyond self-reported data such as pedometers or actigraphs. Furthermore, this study highlights the importance of getting a better understanding of adolescents’ relationships with their bodies when examining the health-enhancing potential of physical activity. Future replication studies should examine whether different constructs such as body image, weight perception, or body satisfaction moderate or have differential effects on the association between physical activity and life satisfaction. For intervention studies, it seems important to consider potential social physique anxiety levels beforehand and test intervention effects in this topic for girls as well. Lastly, the increasing presence of social media could also be an important factor to consider while helping students dealing with body dissatisfaction. Social media use has shown to be associated with body dissatisfaction in adolescents; however, it was also found that positive parent relationships can prevent adolescents from those effects [85]. On the other hand, approaches such as the body positivity movement on social media, where mainly pictures that might be different from societal beauty ideals were posted, have also been shown to help adolescents to develop a healthy relationship with their own bodies [86]. Further studies therefore could include the use of social media as an additional moderating factor.
## 5. Conclusions
The present study gives new insights into the moderating role of social physique anxiety on the relationship between physical activity and life satisfaction in adolescents. Specifically, our data suggest that physical activity-based interventions are beneficial for promoting adolescent’s physical and mental health, yet only if they do not struggle with high levels of social physique anxiety. Thus, it seems crucial for adolescents to develop a more positive relationship with their body, particularly girls with social physique anxiety.
Moreover, our findings highlight the fact that adolescent girls and boys cope differently with body-related anxieties or worries. Coaches and teachers who interact with adolescents during physical activity should take these differences into consideration. For instance, by addressing and discussing the concerns and coping strategies students may have regarding body dissatisfaction as well as the potential effects of social media.
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|
---
title: Importance of Communication Skills Training and Meaning Centered Psychotherapy
Concepts among Patients and Caregivers Coping with Advanced Cancer
authors:
- Normarie Torres-Blasco
- Lianel Rosario-Ramos
- Maria Elena Navedo
- Cristina Peña-Vargas
- Rosario Costas-Muñiz
- Eida Castro-Figueroa
journal: International Journal of Environmental Research and Public Health
year: 2023
pmcid: PMC10002270
doi: 10.3390/ijerph20054458
license: CC BY 4.0
---
# Importance of Communication Skills Training and Meaning Centered Psychotherapy Concepts among Patients and Caregivers Coping with Advanced Cancer
## Abstract
Latinos are more likely to be diagnosed with advanced cancer and have specific existential and communication needs. Concepts within Meaning-Centered Psychotherapy (MCP) interventions and Communications Skills Training (CST) assist patients in attending to these needs. However, Latino-tailored MCP interventions have yet to be adapted for advanced cancer patients and caregivers. A cross-sectional survey was administered to Latino advanced cancer patients and caregivers where participants rated the importance of the goals and concepts of MCP and CST. Fifty-seven ($$n = 57$$) Latino advanced cancer patients and fifty-seven ($$n = 57$$) caregivers completed the survey. Most participants rated MCP concepts as extremely important, ranging from $73.75\%$ to $95.5\%$. Additionally, $86.8\%$ favored finding meaning in their life after a cancer diagnosis. Participants ($80.7\%$) also selected the concept of finding and maintaining hope to cope with their cancer diagnosis. Finally, participants found CST concepts and skills acceptable, ranging from $81.6\%$ to $91.2\%$. Results indicate the acceptability of Meaning-Centered Therapy and Communication Skills Training among Latino advanced cancer patients and caregivers coping with advanced cancer. These results will inform the topics to be discussed in a culturally adapted psychosocial intervention for advanced cancer patients and their informal caregivers.
## 1. Introduction
Foreign-born Latinos, from countries such as Cuba, Puerto Rico, Mexico, and Central and South America, are more likely to be diagnosed with cancer at an advanced stage when compared to non-Latino whites [1,2,3]. In addition, foreign-born Latinos have specific existential [4] and communication needs [5,6]. An advanced cancer diagnosis can cause physical, emotional, psychosocial, and existential stress not only for the patient but also for the caregiver [7,8,9,10]. Cancer as a significant stressor has been treated with several psychotherapeutic interventions designed to address this existential suffering and communication need. Specifically, Meaning-Centered Psychotherapy (MCP) and Communication Skills Training (CST) have shown an effect by targeting the specific psycho-spiritual needs of patients with advanced cancer and enhancing a sense of meaning, peace, and purpose as they face an advanced diagnosis [11,12,13], while CST targets communication skills with patients and caregivers coping with cancer [14].
William Breitbart developed Meaning-Centered Psychotherapy (MCP) as an intervention to address the existential distress often experienced by patients with advanced cancer [13]. What differentiates MCP from other types of psychotherapy is its direct approach to identifying sources of meaning in the patient’s life through a set list of strategies grounded in the work of Viktor Frankl [13]. A clinical trial comparing Individual Meaning-Centered Psychotherapy (IMCP), Supportive Psychotherapy (SP), and Enhanced Usual Care (EUC), or standard care, showed IMCP had significant treatment effects compared to EUC and some modest differences when compared to SP [13]. Patients with advanced cancer are not the only ones who could benefit from MCP. Informal caregivers are also at risk of suffering distress from anxiety, depression, and existential concerns, including “guilt, issues with role changes, sense of identity, and responsibility to the self [15,16]”. A caregiver-focused MCP intervention addresses existential burdens [15,16] and has been found to be feasible and acceptable [17].
Communication between patients and their caregivers is crucial after a diagnosis of advanced cancer, as themes regarding the patient’s values and end-of-life care may surface [18], leading to issues involving death [19], which is still taboo in Hispanic/Latino society [20] and distressing for patients. Given that Hispanics/Latinos are a heterogeneous culture, there are many reasons why death is a taboo subject: fear of expediting the process [21], denial [18], religious matters [21], and sociocultural factors [22]. Skillfully navigating this initial conversation requires a high dexterity in communication on behalf of the provider [23,24]. Some recent studies have focused on communication coaching for patients before appointments with providers [25,26,27]. However, a lesser-studied element is the communication between the patient and their family caregiver. Patient-family communication is an integral part of adapting to the new diagnosis, as family members may take on new roles as informal caregivers and patients adjust to a newly uncertain future. Because dysfunctional communication can be a source of distress for both members of this unique dyad, recent studies have focused on communication skills between patient and caregiver [28,29], and interventions that address communication are being developed [14].
A meta-analytic review has shown that culturally adapted treatments tailored for a specific cultural group are four times more effective than interventions provided to participants from a variety of cultural backgrounds, and those conducted in Latino participants’ native language are twice as effective as interventions conducted in English [30]. Though several psychotherapeutic interventions are designed for advanced cancer patients [11,12,13,31,32,33,34,35,36], only one has been adapted for Latino patients. Nevertheless, interventions have yet to be explicitly adapted for Latino patients and caregivers.
Literature underscores the importance and impact of MCP and communication for advanced cancer patients and their caregivers. Moreover, it highlights the need for culturally adapted interventions. The team used a quantitative approach with patients and caregivers coping with advanced cancer to identify the accepted concepts of Meaning-Centered Psychotherapy and Communications Skills Training. This paper aims to evaluate the importance of MCP concepts and communication concepts among Latino patients and caregivers coping with cancer. The results of this study will be used to inform the topics to be discussed in the psychosocial intervention for advanced cancer patients and their informal caregivers.
## 2. Materials and Methods
Meaning-Centered *Psychotherapy is* grounded in the work of Dr. Breitbart and aims to target the specific psycho-spiritual needs of patients with advanced cancer [11]. Its primary goal is to help patients enhance a sense of meaning, peace, and purpose as they approach the end of life. The intervention focuses on meaningful concepts such as: maintaining hope, making sense of the cancer experience, having a purpose in life, reflecting on their heritage, having a purpose in life after a cancer diagnosis, changing their attitude, and being responsible for themselves and others after the cancer diagnosis. Moreover, the intervention addresses experiential sources of meaning, such as love, humor, and beauty.
Using the Communication Skills Training approach [14], the team hypothesizes that the MCP intervention will be enhanced by/including taught coping skills. The coping skills training approach was adapted for non-spousal patients’ caregivers by eliminating spousal terms (e.g., taking care of your partner–spouse) and using general caregiving terms (e.g., taking care of your significant other). The concepts related to CST involve learning how to share thoughts about cancer, express feelings regarding a cancer diagnosis, learn strategies to accept others’ perspectives, and acquire communication strategies to accept and validate others [14].
Participants for this study comprised patients and caregivers who were recruited as dyads from an oncology clinic in the southern area of Puerto Rico between October 2020 and September 2021. The Ponce Research Institute Institutional Review Board (IRB) and Ethical Committee approved all the study procedures. An IRB-approved introductory letter to familiarize potential participants with the study. Patients’ inclusion criteria included: [1] patients with solid stage III or IV tumors, [2] age 21 or older, and [3] self-reported Latino. Eligible family caregivers included those who were: [1] a caregiver with a family member diagnosed with solid stage III or IV tumors referred by the advanced cancer patient, [2] age 21 or older, and [3] self-reported Latino. Patients’ exclusion criteria included: [1] diagnosed with a major disabling medical or psychiatric condition, [2] unable to understand the consent procedure, or [3] too ill to participate, reported by the patient and determined by the PI’s judgment. After completing the screening process, those eligible and interested were consented and scheduled to complete the questionnaire. Following informed consent, patients, and family caregivers (FCs) were assigned a subject number and administered the survey and self-report assessment to evaluate the patients’ and FCs’ perspectives and psychosocial needs.
The cross-sectional survey in Spanish included rating the importance of the goals and concepts of MCP and CST. In addition, the survey included general demographic questions (age, education, and gender) and a series of standardized scales described in the protocol paper [37]. Participants were given USD 15 as compensation for their time and effort.
All analyses were conducted using IBM SPSS Statistics 21. The database was checked for coding errors and missing data using descriptive statistics. The analyses included descriptive statistics and frequency analysis for the survey to rate the importance of the goals and concepts of MCP and CST. The study was properly powered to use the findings in this formative work. The G Power statistical program [38] was used to determine the sample size. The study had a power of 0.80 ($p \leq 0.05$) to detect a medium-sized effect (Cohen’s $d = 0.50$) [38]. Based on the analysis, the team recruited a sample of 114 participants (57 advanced cancer patients; 57 family caregivers).
## 3. Results
Fifty-seven ($$n = 57$$) Latino cancer patients with stages III ($38.5\%$) and IV ($61.4\%$) cancer participated. Most patients ($57.9\%$) and caregivers ($71.9\%$) were married. On average, patients were 63 and caregivers were 56 years old. Most patients were male ($57.9\%$), and most caregivers were female ($67.9\%$). The predominant cancer diagnoses were cervical ($17.5\%$), breast ($14.0\%$), and prostate ($14.0\%$). The sociodemographic and diagnostic characteristics are included in Table 1.
## 3.1. Meaning Centered Psychotherapy Concepts: Dyads
When asked about MCP concepts, the majority of participants rated the concepts as extremely important, ranging from 73.75 to $95.5\%$. Participants ($95.5\%$) ranked the concept of their “love for loved ones” as extremely important when coping with a cancer diagnosis. Participants ($91.2\%$) valued the concept of “maintaining hope” as extremely important. Many participants ($89.5\%$) ranked the concept of “being responsible for themselves after cancer diagnosis” as extremely important. Most participants ($89.5\%$) rated the concept of their “love for life” extremely important when coping with a cancer diagnosis. Participants ($87.6\%$) ranked the concept of “finding beauty in music, nature, and other life experience” as extremely important. Regarding the concept of the “care of others after a cancer diagnosis”, $86\%$ of participants rated it as extremely important. Most participants ($81.6\%$) ranked the concept of “understanding their life’s purpose after a cancer diagnosis” as extremely important. Additional concepts were ranked as extremely important: ”reflecting or thinking about their changes after a cancer diagnosis” ($79.8\%$), “changing or adjusting their attitude when circumstances are out of their control” (77.25), “creating meaning in life or thinking about their purpose” ($78.1\%$), “reflecting on heritage and thinking about their life’s contributions” ($75.4\%$), and “making sense of the cancer experience” ($73.7\%$) (see Table 2 for more details). Most participants ($86.8\%$) chose the concept of “finding meaning in their life after a cancer diagnosis”. Moreover, $80.7\%$ of participants selected the concept of “finding and maintain hope to cope with their cancer diagnosis” (see Table 3).
## 3.2. Meaning Centered Psychotherapy Concepts: Patients
When asked about MCP concepts, most patients rated the concepts as extremely important, ranging from $75.4\%$ to $94.7\%$. Patients ($94.7\%$) ranked the concept of their “love for loved ones” as extremely important when coping with a cancer diagnosis. Patients ($93\%$) assessed the concept of “maintaining hope” as extremely important. Many patients ($91.2\%$) ranked the concept of “persevering a sense of humor has helped them cope with their cancer diagnosis” as extremely important. Most patients ($89.5\%$) rated the concepts of their “love for life”, “being responsible for themselves after cancer diagnosis”, and “finding beauty in music, nature, and other life experience” as extremely important when coping with a cancer diagnosis Regarding the concept of the “care of others after a cancer diagnosis”, $82.4\%$% of patients rated it as extremely important. Additional concepts were ranked as extremely important:” create meaning in life or think about their purpose in life” ($78.9\%$), “changing or adjusting their attitude when circumstances are out of their control” ($78.9\%$), “understand their life’s purpose after being diagnosed with cancer” ($77.2\%$), “making sense of the cancer experience” ($77.2\%$), “reflect or think about how they have changed after a cancer diagnosis” ($75.4\%$), and “reflect on their heritage or thinking about what they have contributed with their life” ($75.4\%$) (see Table 4 for more details). Most patients ($84.2\%$) chose the concept of “Find meaning in life after a cancer diagnosis”. Moreover, $80.7\%$ of patients selected “finding and maintaining hope to cope with their cancer diagnosis” (see Table 5).
## 3.3. Meaning Centered Psychotherapy Concepts: Caregivers
When asked about MCP concepts, the majority of caregivers ranked the concepts as extremely important, ranging from 70.2 to $96.4\%$. Caregivers ($96.4\%$) rated the concept of their “love for loved ones” as extremely important when coping with a cancer diagnosis. Caregivers ($89.5\%$) also rated the concepts of “maintaining hope”, “being responsible for themselves after a cancer diagnosis”, “love for life has helped them cope after a cancer diagnosis”, and “taking care of others after a cancer diagnosis” as extremely important. Most caregivers ($86\%$) ranked the concept of “understanding their life’s purpose after a cancer diagnosis” as extremely important. Caregivers ($84.2\%$) ranked “finding beauty in music, nature, and other life experience” as extremely important. Regarding the concept of “reflect or think about how they have changed after a cancer diagnosis”, $84.2\%$ of caregivers rated it as extremely important. Many caregivers ($83.9\%$) rated “preserving a good sense of humor” as extremely important when coping with a cancer diagnosis. Additional concepts were ranked as extremely important: “creating meaning in life or thinking about their purpose” ($77.2\%$), “changing or adjusting their attitude when circumstances are out of their control” ($75.4\%$), “reflecting on heritage and thinking about their life’s contributions” ($75.4\%$), and “making sense of the cancer experience” ($70.2\%$) (see Table 6 for more details). Most caregivers ($89.5\%$) chose the concept of “finding meaning in their life after a cancer diagnosis”. Moreover, $80.7\%$ of caregivers selected “finding and maintaining hope to cope with their cancer diagnosis” (see Table 7).
## 3.4. Communication Skills Training Concepts:Dyads
When asked about communication skills training concepts and skills, most participants wanted to learn more, ranging from $81.6\%$ to $91.2\%$. Most participants ($91.2\%$) chose that they would like to acquire problem-solving skills, and $90.4\%$ favored the concept of “wanting to learn that they worry about each other”. A large portion ($89.5\%$) favored the concept of “learning ways to show they are accompanying each other in the process”. Participants ($86.6\%$) also favored the concept of “learning to talk about their thoughts regarding cancer”. Eighty-six percent ($86\%$) of participants favored the concept of “acquiring communication strategies to accept and validate others”. Furthermore, $85.8\%$ of participants chose the concept of “wanting to express their feelings about cancer” and $85.1\%$ selected the concept of “acquiring communication skills to accept other people’s feelings”. Many participants ($82.5\%$) favored the concept of “learning communication strategies to accept other people’s perspectives”. Finally, $81.6\%$ of participants selected the concept of “reviewing their life and considering their heritage” (see Table 8 for more details).
## 3.5. Communication Skills Training Concepts: Patients
When asked about communication skills training concepts and skills, most patients wanted to learn more, ranging from $80.7\%$ to $91.2\%$. Most patients ($91.2\%$) selected that they would like to acquire problem-solving skills. A large portion ($89.5\%$) favored the concepts of “wanting to learn that they worry about each other”, “learning ways to show they are accompanying each other in the process”, “learning to talk about their thoughts regarding cancer”, and “wanting to express their feelings about cancer”. Patients ($87.7\%$) also favored the concepts of “acquiring communication strategies to accept and validate others” and “acquiring communication skills to accept other people’s feelings”. Furthermore, $84.2\%$ of patients chose the concept of “learning communication strategies to accept other people’s perspectives” Finally, $80.7\%$ of patients selected the concept of “reviewing life and considering their heritage” (see Table 9 for more details).
## 3.6. Communication Skills Training Concepts: Caregivers
When asked about communication skills training concepts and skills, most caregivers wanted to learn more, ranging from $80.7\%$ to $91.2\%$. Most caregivers ($91.2\%$) chose that they would like to acquire problem-solving skills and favored the concept of “wanting to learn that they worry about each other”. A large portion ($89.5\%$) favored the concept of “learning ways to show they are accompanying each other in the process”. Caregivers ($87.7\%$) also favored “learning to talk about their thoughts regarding cancer”. Additionally, caregivers ($84.2\%$) selected “acquiring communication strategies to accept and validate others”. Moreover, $82.5\%$ of caregivers chose the concepts of “acquiring communication skills to accept other people’s feelings” and “reviewing their life and considering their heritage”. Many ($82.1\%$) favored the strategy of “wanting to express their feelings about cancer”. Finally, $80.7\%$ of caregivers selected the concept of “learning communication strategies to accept other people’s perspective” (see Table 10 for more details).
## 4. Discussion
When patient and caregiver dyads were asked about the concepts of MCP and CST, the majority of participants favorably rated all of the concepts. The acceptance of MCP concepts ranged from $73.75\%$ to $95.5\%$, while CST ranged from $81.6\%$ to $91.2\%$. Comparable results were seen in the adaptation of MCP for a Latino population, where patients expressed a need to integrate communication skills as well as accepted MCP concepts in the process of adapting to their cancer diagnosis [37,39]. Some of the many MCP concepts included finding meaning in family and loved ones, maintaining hope, taking responsibility to care for oneself, finding meaning in life after a diagnosis, maintaining a love for life, and preserving a sense of humor. Moreover, the literature acknowledges the efficacy of interventions designed to improve dyadic communication among cancer patients and caregivers [40]. However, studies with Latino patient-caregiver dyads are lacking. A portion of CST concepts includes having problem-solving skills, worrying about each other, demonstrating companionship through the journey, learning to talk about a cancer diagnosis, and acquiring communication strategies.
Results indicate that participants favored love for their loved ones to cope with their diagnosis, which is consistent with studies that underscore how many patients lean on family for support as a coping mechanism during a cancer diagnosis [41]. Additionally, family is an important value to Latinos [42], which could explain why many participants consider it important to take care of others after a cancer diagnosis. Latino patients have reported the desire for assistance in finding hope and meaning in life [43]. Given that participants were also caregivers, many regarded maintaining hope as essential. These results are congruent with literature where caregivers used hope and prayer while caring for a family member with cancer [20]. However, while patients may use hope as a coping mechanism, it can become a difficult topic when discussing end-of-life [19]. The current literature regarding Latino cancer patients and meaning highlights the use of positive reframing and meaning to cope with a cancer diagnosis [41]. Additionally, in the same study, some of the participants integrated the value of life with purpose into their experience with cancer. These results are congruent with the participants’ selection of the concepts of finding meaning, creating meaning, and finding purpose in their life. MCP attempts to assist participants in the search for meaning and purpose through experiential sources of meaning. Moreover, participants favor the discussion of “making sense”, which is seen in advanced cancer patients as an attempt to make sense of and understand the terminality of an advanced cancer diagnosis experience [44].
Many of the participants indicated that reflecting on the changes in one’s life after receiving a cancer diagnosis was important. Even though the MCP concept of change after a diagnosis focuses on general life, hope, and experiences, Latino participants might also reflect on changes attributed to physical changes [45,46], sexuality [46], work [47], or overall quality of life [48]. Regarding responsibility for oneself after a cancer diagnosis, many participants found this to be required within the cancer trajectory. These results are seen in the literature where Latino cancer patients take responsibility for their part in the cancer trajectory [49]. Concerning humor as a coping mechanism, a study with Hispanic male cancer survivors yielded how the survivors used humor as one of many coping mechanisms during their diagnosis and treatment process [50]. Even though our sample includes men, women, patients, and caregivers, most selected humor to cope with their diagnosis.
Some Latinos would rather not discuss the end-of-life stage [18] or death [19]; however, dyads within this study ranked different communication skills as essential. These results could be attributed to integrating cultural factors and values in adapting interventions [51]. The integration of cultural values within interventions has been shown to be successful [52]. For instance, communication interventions aimed at Latinos and their caregivers with a chronic illness (diabetes) yield positive results. Firstly, dyads with good relationships had better care routines, considered the program successful in managing the disease at home, and had better social support [53]. Couples’ CST results underscore the benefits of communication between the advanced cancer patient and partner. Some benefits include: the desire not to be seen as a “patient” and “caregiver,” symptom management, support for a partner, decision making, conflict resolution, and preparation for death [14]. These results are seen within the team’s sample, with participants favoring problem-solving skills and companionship throughout the process. Delivering tailored communication interventions proves to be acceptable and beneficial for the patient and caregiver [54]. Thus, it is highly imperative that caregiver–patient dyads are provided with the necessary skills to discuss thoughts about cancer, express their feelings about cancer, and acquire the necessary communication skills they might need in their daily lives.
## 5. Conclusions
Results within this study show how advanced cancer patients and caregivers favor Meaning-Centered Psychotherapy and Communication Skill training concepts. Existing literature also aids in showing how patients favor these concepts independently and when integrated into an MCP intervention or a communication-based intervention. These results highlight the importance of integrating both patient and caregiver perspectives into the development and application of a culturally adapted psychosocial intervention.
## 6. Limitations
The instrument used in the study, which contained Meaning-Centered Concepts and Communication Skills Training, was developed in a questionnaire manner; therefore, analyses were limited to a descriptive approach. As a result, inferential analyses cannot be measured. If a scale were to be devised, the analysis could explore statistical differences between patients and caregivers, as well as sex, income, and clinical variables.
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