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title: 'Emergency department use and Artificial Intelligence in Pelotas: design and
baseline results'
authors:
- Felipe Mendes Delpino
- Lílian Munhoz Figueiredo
- Ândria Krolow Costa
- Ioná Carreno
- Luan Nascimento da Silva
- Alana Duarte Flores
- Milena Afonso Pinheiro
- Eloisa Porciúncula da Silva
- Gabriela Ávila Marques
- Mirelle de Oliveira Saes
- Suele Manjourany Silva Duro
- Luiz Augusto Facchini
- João Ricardo Nickenig Vissoci
- Thaynã Ramos Flores
- Flávio Fernando Demarco
- Cauane Blumenberg
- Alexandre Dias Porto Chiavegatto
- Inácio Crochemore da Silva
- Sandro Rodrigues Batista
- Ricardo Alexandre Arcêncio
- Bruno Pereira Nunes
journal: Revista Brasileira de Epidemiologia (Brazilian Journal of Epidemiology)
year: 2023
pmcid: PMC10000014
doi: 10.1590/1980-549720230021
license: CC BY 4.0
---
# Emergency department use and Artificial Intelligence in Pelotas: design and baseline results
## RESUMO
### Objetivo:
To describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different machine learning algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas, Southern Brazil.
### Methods:
The study is entitled “Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). Between September and December 2021, a baseline was carried out with participants. A follow-up was planned to be conducted after 12 months in order to assess the use of urgent and emergency services in the last year. Afterwards, machine learning algorithms will be tested to predict the use of urgent and emergency services over one year.
### Results:
In total, 5,722 participants answered the survey, mostly females ($66.8\%$), with an average age of 50.3 years. The mean number of household people was 2.6. Most of the sample has white skin color and incomplete elementary school or less. Around $30\%$ of the sample has obesity, $14\%$ diabetes, and $39\%$ hypertension.
### Conclusion:
The present paper presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents of Pelotas, in Rio Grande do Sul state.
## Objetivo:
Descrever os resultados iniciais da linha de base de um estudo de base populacional, bem como um protocolo para avaliar o desempenho de diferentes algoritmos de aprendizado de máquina, com o objetivo de predizer a demanda de serviços de urgência e emergência em uma amostra representativa de adultos da zona urbana de Pelotas, no Sul do Brasil.
## Métodos:
O estudo intitula-se “Emergency department use and Artificial Intelligence in PELOTAS (RS) (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). Entre setembro e dezembro de 2021, foi realizada uma linha de base com os participantes. Está previsto um acompanhamento após 12 meses para avaliar a utilização de serviços de urgência e emergência no último ano. Em seguida, serão testados algoritmos de machine learning para predizer a utilização de serviços de urgência e emergência no período de um ano.
## Resultados:
No total, 5.722 participantes responderam à pesquisa, a maioria do sexo feminino (66,$8\%$), com idade média de 50,3 anos. O número médio de pessoas no domicílio foi de 2,6. A maioria da amostra tem cor da pele branca e ensino fundamental incompleto ou menos. Cerca de $30\%$ da amostra estava com obesidade, $14\%$ com diabetes e $39\%$ eram hipertensos.
## Conclusão:
O presente trabalho apresentou um protocolo descrevendo as etapas que foram e serão tomadas para a produção de um modelo capaz de prever a demanda por serviços de urgência e emergência em um ano entre moradores de Pelotas, no estado do Rio Grande do Sul.
## INTRODUCTION
Chronic diseases affect a large part of the population of adults and older adults, leading these individuals to seek urgent and emergency care. The implementation in 1988 of the Unified Health System (SUS) resulted in a model aimed at prevention and health promotion actions based on collective activities 1 – starting at Basic Health Units (UBS). There is also the National Emergency Care Policy, which advanced in the construction of the SUS, and has as guidelines universality, integrity, decentralization, and social participation, alongside humanization, the right of every citizen 2.
In a study that evaluated the characteristics of users of primary health care services in a Brazilian urban-representative sample, it was found that the vast majority were women and part of poorer individuals, in addition to almost $\frac{1}{4}$ of the sample receiving the national income distribution program (family allowance) 3. Brazil is a country highly unequal in socioeconomic terms; approximately $75\%$ of the Brazilian population uses the SUS and depends exclusively on it, and do not have private health insurance 4,5.
Individuals with multimorbidity are part of the vast majority who seek urgent and emergency services 6. Multimorbidity is a condition that affects a large part of the population 7, especially older adults 7. In addition, the association of multimorbidity with higher demand for emergency services is a challenge to appropriately manage and prevent these problems 8,9.
Innovative approaches may allow health professionals to provide direct care to individuals who are more likely to seek urgent and emergency services. The use of artificial intelligence can make it possible to identify and monitor a group of individuals with a higher probability of developing multimorbidity. In this context, machine learning (ML), an application of artificial intelligence, is a promising and feasible tool to be used on large scale to identify these population subgroups. Some previous studies have demonstrated that ML models can predict the demand for urgent and emergency services 10,11. Besides, a systematic review showed that ML could accurately predict the triage of patients entering emergency care 12. However, in a search for studies in Brazil, we found no published article on the subject.
In Brazil, urgent and emergency services are a fundamental part of the health care network, ensuring timely care in cases of risk to individuals’ lives 9. Urgent and emergency services are characterized by overcrowding and high demand. In addition, with the current pandemic of COVID-19, updated evidence on the characteristics of the users seeking these services is timely and necessary. The objective of this article was to describe the initial baseline results of a population-based study, as well as a protocol in order to evaluate the performance of different ML algorithms with the objective of predicting the demand for urgent and emergency services in a representative sample of adults from the urban area of Pelotas.
## METHODS
The present cohort study is entitled “Emergency department use and Artificial Intelligence in PELOTAS-RS (EAI PELOTAS)” (https://wp.ufpel.edu.br/eaipelotas/). The baseline was conducted between September and December 2021, and a follow-up was planned to be conducted 12 months later. We utilized the cross-sectional study to measure the prevalence of urgent and emergency care and the prevalence of multimorbidity, in addition to other variables and instruments of interest. The prospective cohort design intends to estimate the risk of using and reusing urgent emergency services after 12 months. Contact information, collected to ensure follow-up, included telephone, social networks, and full address. In addition, we also collected the latitude and longitude of households for control of the interviews.
## Study location and target population
The present study was conducted in adult households in the Pelotas, Rio Grande do Sul (RS), Southern Brazil. According to estimates by the Brazilian Institute of Geography and Statistics (IBGE) in 2020, Pelotas had an estimated population of 343,132 individuals (https://cidades.ibge.gov.br/brasil/rs/pelotas/panorama). Figure 1 shows the location of the city of Pelotas in Brazil.
**Figura 1.:** *Map of Brazil highlighting the city of Pelotas (RS).*
Pelotas has a human development index (HDI) of 0.739 and a gross domestic product per capita (GDP) of BRL 27,586.96 (https://www.ibge.gov.br/cidades-e-estados/rs/pelotas.html). The municipality has a Municipal Emergency Room that operates 24 hours a day, seven days a week, and serves about 300 patients a day, according to data provided by the unit.
## Criteria for inclusion and exclusion of study participants
We included adults aged 18 years or older residing in the urban area of Pelotas. Children and individuals who were mentally unable to answer the questionnaire were not included in the sample.
## Sample calculation, sampling process, and data collection
The sample size was calculated considering three objectives. First, to determine the sample size required to assess the prevalence of urgent and emergency services use, it was considered an estimated prevalence of $9\%$, with±two percentage points as a margin of error and a $95\%$ confidence level 13, concluding that 785 individuals would be necessary. Second, for multimorbidity prevalence, an estimated prevalence of $25\%$, with ± three percentage points as a margin of error and a confidence level of $95\%$ was used 14,15; reaching again, a total of 785 individuals needed. Finally, for the association calculations, similar studies in Brazil were assessed, and the following parameters were considered: significance level of $95\%$, power of $80\%$, exposed/unexposed ratio of 0.1, percentage of the outcome in the unexposed $20\%$, and a minimum prevalence ratio of 1.3. With these parameters, 5,104 individuals would be necessary to study the proposed associations. Adding 10 to $20\%$ for losses and/or refusals, the final sample size would be composed of 5,615–5,890 participants.
The process to provide a population-based sample was carried out in multiple stages. The city of Pelotas has approximately 550 census tracts, according to the last update estimates provided by IBGE in 2019. From there, we randomly selected 100 sectors. Since the sectors vary in size, we defined a proportional number of households for each.
Thus, it was estimated that, in total, the 100 sectors had approximately 24,345 eligible households. To interview one resident per household, we divided the total number of households by the sample size required, which resulted in 4.3. Based on this information, we divided each of the 100 sectors by 4.3 to reach the necessary number of households for each sector. One resident per household was interviewed, resulting in a total of 5,615 households. If there was more than one eligible resident, the choice was made by a random number generator application. Residents were placed in order, a number was assigned for each one, and one of them was selected according to the result of the draw. The first household interviewed in each sector was selected through a draw, considering the selected jump (4.3 households). Trades and empty squares were considered ineligible, and thus, the next square was chosen. Due to a large number of empty houses, it was necessary to select another 50 sectors to complete the required sample size. The additional households were drawn according to the same methodological criteria as the first draw to ensure equiprobability.
## Data collection instrument
We collected the data with the Research Electronic Data Capture (REDCap), a data collection program using smartphones 16,17. Experienced and trained research assistants collected the data. The questionnaire from EAI PELOTAS was prepared, when possible, based on standardized instruments, including questions about chronic diseases, physical activity, food security, use of urgent and emergency services, functional disability, frailty syndrome, self-perception of health, COVID-19, in addition to sociodemographic and behavioral questions. Supplementary Table 1 shows the instruments utilized in the present study.
**Table 1.**
| Characteristics | EAI PELOTAS* | EAI PELOTAS*.1 | PNS 2019† |
| --- | --- | --- | --- |
| Characteristics | Crude % (95%CI) | Survey design % (95%CI) | % (95%CI) |
| Mean age, years | 50.3 (49.9–50.8) | 46.2 (45.5–47.0) | 46.7 (45.9–47.5) |
| Mean number of household people | 2.6 (2.5–2.7) | 2.7 (2.6–2.8) | 3.0 (2.9–3.1) |
| Female (%) | 66.8 (65.6–68.0) | 54.2 (52.4–55.6) | 54.1 (51.7–56.4) |
| Skin color (%) | Skin color (%) | Skin color (%) | Skin color (%) |
| White | 78.2 (77.1–79.2) | 77.3 (74.9–79.5) | 76.8 (74.6–78.7) |
| Black | 15.0 (14.1–16.0) | 15.3 (13.5–17.3) | 8.3 (7.0–9.8) |
| Brown | 6.1 (5.5–6.7) | 6.7 (5.7–7.9) | 14.5 (12.9–16.3) |
| Other | 0.7 (0.5–1.0) | 0.7 (0.4–1.1) | 0.4 (0.2–0.8) |
| Schooling (%) | Schooling (%) | Schooling (%) | Schooling (%) |
| Incomplete elementary school or less | 35.7 (34.5–37.0) | 31.3 (28.6–34.2) | 30.2 (28.1–32.4) |
| Complete elementary school/incomplete high school | 16.2 (15.3–17.2) | 16.4 (15.1–17.7) | 15.7 (14.0–17.5) |
| Complete high school/incomplete higher education | 33.5 (32.3–34.7) | 37.6 (35.6–39.6) | 36.9 (34.6–39.2) |
| Complete higher education or more | 14.6 (13.7–15.5) | 14.7 (12.4–17.4) | 17.2 (15.7–18.9) |
## Dependent variables
The use of urgent and emergency services was assessed on a baseline using the following question: “In the last 12 months, how many times have you sought urgent and emergency services, such as an emergency room?”. This was followed by the characterization of the service used, city of service, frequency of use, and referral after use. One year after the study baseline, we will contact again the respondents to inquire about the use of urgent and emergency care services (number of times and type of service used).
## Independent variables
We assessed multimorbidity as the main exposure using a list of 22 chronic diseases and others (asthma/bronchitis, osteoporosis, arthritis/arthrosis/rheumatism, hypertension, diabetes, cardiac insufficiency, pulmonary emphysema/chronic obstructive pulmonary disease, acute kidney failure, Parkinson’s disease, prostate disease, hypo/hyperthyroidism, glaucoma, cataract, Alzheimer’s disease, urinary/fecal incontinence, angina, stroke, dyslipidemia, epileptic fit/seizures, depression, gastric ulcer, urinary infection, pneumonia, and the flu). The association with urgent and emergency services will be performed with different cutoff points, including total number, ≥2, ≥3, and combinations of morbidities. We will also perform network analyzes to assess the pattern of morbidities.
Other independent variables were selected from previous studies in the literature 18-21, including demographic, socioeconomic information, behavioral characteristics, health status, access, use and quality of health services.
## Data analysis
We will test artificial intelligence algorithms, ML, to predict the use of urgent and emergency services after 12 months. The purpose of ML is to predict health outcomes through the basic characteristics of the individuals, such as sex, education, and lifestyle. The algorithms will be trained to predict the occurrence of health outcomes, which will contribute to decision-making. With a good amount of data and the right algorithms, ML may be able to predict health outcomes with satisfactory performance.
The area of ML in healthcare has shown rapid growth in recent years, having been used in significant public health problems such as diagnosing diseases and predicting the risk of adverse health events and deaths 22-24. The use of predictive algorithms aims to improve health care and support decision-making by health professionals and managers. For the present study, individuals’ baseline characteristics will be used to train popular ML algorithms such as Support Vector Machine (SVM), Neural Networks (ANNs), Random Forests, Penalized Regressions, Gradient Boosted Trees, and Extreme Gradient Boosting (XGBoost). These models were chosen based on a previous review in which the authors identified the most used models in healthcare studies 25. We will use the Python programming language to perform the analyzes.
To test the predictive performance of the algorithms in new unseen data, individuals will be divided into training ($70\%$ of patients, which will be used to define the parameters and hyperparameters of each algorithm) and testing ($30\%$, which will be used to test the predictive ability of models in new data).
We will also perform all the preliminary steps to ensure a good performance of the algorithms, especially those related to the pre-processing of predictor variables, such as the standardization of continuous variables, separation of categorical predictors with one-hot encoding, exclusion of strongly correlated variables, dimension reduction using principal component analysis and selection of hyperparameters with 10-fold cross-validation. Different metrics will evaluate the predictive capacity of the models, the main one being the area under the receiver operating characteristic (ROC) curve (AUC). In a simplified way, the AUC is a value that varies from 0 to 1, and the closer to 1 the better the model’s predictive capacity 26. The other metrics will be F1-score, sensitivity, specificity, and accuracy. As measures of model fit, we will perform hyperparameters and balancing fit, as well as K-fold (cross-validation).
## COVID-19
The current pandemic, caused by the SARS-CoV-2 virus, has brought uncertainty to the world population. Although vaccination coverage is already high in large parts of the population, the arrival of new variants and the lack of other essential measures to face the pandemic still create uncertainty about the effects of the pandemic on people. General questions about symptoms, tests, and possible effects caused by coronavirus contamination were included in our baseline survey. We will also use SARS-CoV-2-related questions to evaluate the performance of ML algorithms. In September 2021, restrictive measures were relaxed due to a decrease in COVID-19 cases in Pelotas, allowing the study to begin. A vaccination passport was required from the interviewers to ensure the safety of both participants and interviewers. In addition, all interviewers received protective equipment against COVID-19, including masks, face shields, and alcohol gel. Finally, the interviewers were instructed to conduct the research in an open and airy area, ensuring the protection of the participants.
## Quality assurance and control
The activities to allow for control and data quality were characterized by a series of measures aimed at ensuring results without the risk of bias. Initially, we developed a research protocol, followed by an instruction manual for each interviewer. Thereafter, interviewers were trained and standardized in all necessary aspects.
REDCap was also important to garanteee the control and quality of responses as the questions were designed using validation checks according to what was expected for each answer. Another measure that ensured the control of interviews was the collection of latitude and longitude of households, which was plotted by two members of the study coordination weekly on maps, to ensure that the data collection was performed according to the study sample. With latitude and longitude data, it is also intended to carry out spatial analysis articles with techniques such as sweep statistics and Kernel.
The database of the questions was checked daily to find possible inconsistencies. Finally, two members of the study coordination made random phone calls to $10\%$ of the sample, in which a reduced questionnaire was applied, with the objective of comparing the answers with the main questionnaire.
## Ethical principles
We carried out this study using free and informed consent, as determined by the ethical aspects of Resolution No. $\frac{466}{2012}$ of the National Council of the Ministry of Health and the Code of Ethics for Nursing Professionals, of the duties in Chapter IV, Article 35, 36 and 37, and the prohibitions in chapter V, article 53 and 54. After identifying and selecting the study participants, they were informed about the research objectives and signed the Informed Consent Form (ICF). The project was referred to the Research Ethics *Committee via* the Brazilian platform and approved under the CAAE 39096720.0.0000.5317.
## Schedule
Initially, we conducted a stage for the preparation of an electronic questionnaire at the beginning of 2021. In February 2021, we initiated data collection after preparing the online questionnaire. The database verification and cleaning steps occurred simultaneously with the collection, and continued until March 2022. After this step, data analysis and writing of scientific articles began.
## First descriptive results and comparison with a population-based study
Of approximately 15,526 households approached, 8,196 were excluded — 4,761 residents were absent at the visit, 1,735 were ineligible, and 1,700 were empty (see Figure 2). We identified 7,330 eligible participants, of which 1,607 refused to participate in the study, totalizing 5,722 residents. Comparing the female gender percentage of the refusals with the completed interviews, we observed a slightly lower prevalence with $63.2\%$ ($95\%$CI 60.7–65.5) among the refusals, and $66.8\%$ ($95\%$CI 65.6–68.0) among the complete interviews. The mean age was similar between participants who agreed to participate (50.3; $95\%$CI 49.9–50.8) and those who refused (50.4; $95\%$CI 49.0–51.9).
**Figura 2.:** *Flowchart describing the sampling process.*
To evaluate the first descriptive results of our sample, we compared our results with the 2019 Brazilian National Health Survey (PNS) database. The PNS 2019 was collected by the IBGE in partnership with the Ministry of Health. The data are in the public domain and are available in the IBGE website (https://www.ibge.gov.br/). To ensure the greatest possible comparability between studies, we used only residents of the urban area of the state of Rio Grande do Sul, aged using the command svy from Stata, resulting in 3,002 individuals (residents selected to interview).
We developed two models to compare our data with the PNS 2019 survey: Crude model (crude results from the EAI PELOTAS study, without considering survey design estimates); Model 1 using survey design: primary sampling units (PSUs) using census tracts as variables and post-weight variables based on estimates of Pelotas population projection for 2020 (Table 1). We evaluated another model using individual sampling weight (i.e., the inverse of the probability of being interviewed in each census tract). These models are virtually equal to the above estimates (data not shown).
The mean age of our sample was 50.3 years (Table 1), 46.2 for model 1, which was similar to PNS 2019 (46.7 years). Our weighted estimates presented a similar proportion of females compared to the PNS 2019 sample. The proportions of skin colors were similar in all categories and models. Our crude model presented a higher proportion of participants with incomplete elementary school or less compared to model 1 and PNS 2019.
Table 2 describes the prevalence of chronic diseases and lifestyle factors in our study and the PNS 2019 sample. Our prevalence of diabetes was higher in the crude model compared to weighted estimates and PNS 2019 sample. In both models, we had a higher proportion of individuals with obesity and hypertension than in PNS 2019. Asthma and/or bronchitis presented similar proportions in our results compared to PNS 2019; the same occurred for cancer. Our study presented a higher proportion of smoking participants in both models than in the PNS 2019 sample.
**Table 2.**
| Chronic diseases and lifestyle factors | EAI PELOTAS* | EAI PELOTAS*.1 | PNS 2019† |
| --- | --- | --- | --- |
| Chronic diseases and lifestyle factors | Crude | Survey design 1 | PNS 2019† |
| Chronic diseases and lifestyle factors | % (95%CI) | % (95%CI) | % (95%CI) |
| Diabetes | 14.2 (13.3–15.1) | 11.5 (10.6–12.4) | 9.0 (8.9–11.1) |
| Obesity | 30.4 (29.2–31.7) | 29.2 (27.7–30.8) | 24.8 (22.6–27.1) |
| Hypertension | 39.0 (37.7–40.3) | 32.4 (31.0–33.9) | 28.1 (25.9–30.5) |
| Asthma or chronic bronchitis | 9.3 (8.6–10.1) | 9.3 (8.4–10.4) | 8.7 (7.3–10.3) |
| Cancer | 4.2 (3.7–4.7) | 3.4 (2.9–4.0) | 3.8 (2.9–4.9) |
| Current smoking | 20.6 (19.6–21.7) | 20.4 (18.9–22.0) | 16.3 (14.6–18.1) |
## DISCUSSION
We described the initial descriptive results, methodology, protocol, and the steps required to perform the ML analysis for predicting the use of urgent and emergency services among the residents of Pelotas, Southern Brazil. We expect to provide subsidies to health professionals and managers for decision-making, helping to identify interventions targeted at patients more likely to use urgent and emergency services, as well as those more likely to develop multimorbidity and mortality. We also expect to help health systems optimize their space and resources by directing human and physical capital to those at greater risk of developing multiple chronic diseases and dying. Recent studies in developed countries have found this a feasible challenge with ML 21,27. If our study presents satisfactory results, we intend to test its practical applicability and acceptance to assist health professionals and managers in decision-making in emergency services among residents of Pelotas.
The baseline and methods used to select households resemble the main population-based studies conducted in Brazil, such as the Brazilian Longitudinal Study of Aging (ELSI-Brazil) 28, the EPICOVID 29, and the PNS. The applicability of ML requires suitable predictive variables. Our study included sociodemographic and behavioral variables related to urgent and emergency services, and chronic diseases. EAI PELOTAS study also includes essential topics that deserve particular importance during the COVID-19 pandemic, such as food insecurity, decreased income, physical activity, access to health services, and social support.
We also presented one weighting option in order to obtain sample estimates considering the complex study design. All estimates have their strength and limitation. Each research question answered through this study may consider these possibilities and choose the most suitable one. The estimates were similar without weighting and those considering the primary sampling unit (PSU) and sampling weight. Using the census tract in the PSU is fundamental to consider the sampling design in the estimates of variability (standard error, variance, $95\%$CI, among others). In addition, due to the possible selection bias in the sample, which contains more women and older people than expected, the use of a post-weighting strategy becomes necessary to obtain estimates adjusted for the sex and age distributions of the target population (due to the lack of census data, we used population projections). However, it should be noted that this strategy can produce estimates simulating the expected distribution only by sex and age. Still, we do not know how much this strategy can distort the estimates since the demographic adjustment cannot reproduce adjustment in all sample characteristics, especially for non-measured variables that may have influenced the selection of participants. Thus, we recommend defining the use of each strategy on a case-by-case basis, depending on the objective of the scientific product. Finally, we suggest reporting the different estimates according to the sample design for specific outcomes (e.g., the prevalence of a specific condition) that aim to extrapolate the data to the target population (adults of the city of Pelotas).
In conclusion, the present article presented a protocol describing the steps that were and will be taken to produce a model capable of predicting the demand for urgent and emergency services in one year among residents in Pelotas (RS), Southern Brazil.
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---
title: Alterations in Fecal Microbiota Linked to Environment and Sex in Red Deer (Cervus
elaphus)
authors:
- Yue Sun
- Yanze Yu
- Jinhao Guo
- Linqiang Zhong
- Minghai Zhang
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000040
doi: 10.3390/ani13050929
license: CC BY 4.0
---
# Alterations in Fecal Microbiota Linked to Environment and Sex in Red Deer (Cervus elaphus)
## Abstract
### Simple Summary
The gut microbiota forms a complex microecosystem in vertebrates and is affected by various factors. Wild and captive red deer currently live in the same region but have vastly different diets. In this study, the 16S rRNA sequencing technology was performed to evaluate variations in the fecal microbiota of wild and captive individuals of both sexes of red deer. It was found that the composition and function of fecal microbiota in wild and captive environments were significantly different. As a key intrinsic factor, sex has a persistent impact on the formation and development of gut microbiota. Overall, this study reveals differences in the in the fecal microbiota of red deer based on environment and sex. These data could guide future applications of population management in red deer conservation.
### Abstract
Gut microbiota play an important role in impacting the host’s metabolism, immunity, speciation, and many other functions. How sex and environment affect the structure and function of fecal microbiota in red deer (Cervus elaphus) is still unclear, particularly with regard to the intake of different diets. In this study, non-invasive molecular sexing techniques were used to determine the sex of fecal samples from both wild and captive red deer during the overwintering period. Fecal microbiota composition and diversity analyses were performed using amplicons from the V4–V5 region of the 16S rRNA gene sequenced on the Illumina HiSeq platform. Based on Picrust2 prediction software, potential function distribution information was evaluated by comparing the Kyoto Encyclopedia of Genes and Genome (KEGG). The results showed that the fecal microbiota of the wild deer (WF, $$n = 10$$; WM, $$n = 12$$) was significantly enriched in Firmicutes and decreased in Bacteroidetes, while the captive deer (CF, $$n = 8$$; CM, $$n = 3$$) had a significantly higher number of Bacteroidetes. The dominant species of fecal microbiota in the wild and captive red deer were similar at the genus level. The alpha diversity index shows significant difference in fecal microbiota diversity between the males and females in wild deer ($p \leq 0.05$). Beta diversity shows significant inter-group differences between wild and captive deer ($p \leq 0.05$) but no significant differences between female and male in wild or captive deer. The metabolism was the most important pathway at the first level of KEGG pathway analysis. In the secondary pathway of metabolism, glycan biosynthesis and metabolism, energy metabolism, and the metabolism of other amino acids were significantly different. In summary, these compositional and functional variations in the fecal microbiota of red deer may be helpful for guiding conservation management and policy decision-making, providing important information for future applications of population management and conservation.
## 1. Introduction
Red deer (Cervus elaphus), which belong to the family Cervidae, order Artiodactyla, distributed in Asia, Europe, North America, and North Africa [1]. The red deer is a typical forest-inhabiting mammal in northeast China and has an important ecological status in the forest ecosystem [2]. Owing to habitat fragmentation, the populations of red deer in the wild are currently in sharp decline [2]. Using captive populations as reintroduction resources is an effective strategy to restore the populations of wild red deer [3].
The complex gut microbiota systems in the mammalian gut are composed of large fractions of microbes [4]. The gut microbiota are a complex product of the long-term evolution of hosts and microbes [4]. Recent studies have shown that not only are gut microbiota a part of the host, but they also have a significant impact on the health of the host, such as promoting immunity, digestion, metabolism, and intestinal endocrine hormones, among others [5,6,7]. Simultaneously, the complex and flexible gut microbiota can be affected by multiple environmental and host genotypes [8]. Many studies have shown that diet is an important factor that affects the structure and function of the fecal microbiota [9,10,11]. For example, changes in diet alter the function and diversity of fecal microbiota as well as the relative abundance of some microorganisms [12]. Moreover, diet-induced loss of microbial function and diversity will increase the risk of diversity loss and extinction through generational amplification [13]. It was necessary to investigate the gut microbiome by comparing differences between wild and captive red deer. However, to date, there has been a lack of studies comparing the gut microbiota between wild and captive red deer [11]. Because of sex differences in behavior and physiology, sex as an important intrinsic factor leads to differences in gut microbiota among individuals within species [14,15,16]. Although the results are inconsistent, animal species with significant sexual dimorphism and human studies have shown sex-related differences in gut microbiota. In mice (Mus musculus), poultry, and forest musk deer (Moschus berezovskii), the composition of the gut or fecal microbiota shows sex differences [17,18,19]. At present, few studies have analyzed the sexual dimorphism of fecal microbiota in red deer.
In order to save endangered populations, artificial breeding of wild populations is carried out. The food types and nutrient intake ratios obtained in captivity and wild environments are very different, especially for endangered cervidae [20]. Therefore, monitoring the digestive system of captive animals and identifying standardized levels of nutritional requirements and fiber composition is critical for captive wild animals to determine whether they have acclimated to artificially provided food and new environments—a part of wildlife conservation’s main problem [21]. Using captive populations as reintroduction resources is an effective strategy to restore the populations of wild red deer. The composition of gut microbiota in wild populations can be a good indicator of the breeding direction of the captive population [9]. Therefore, understanding the impact of dietary differences between wild and captive red deer on the fecal microbiota can help to assess and ensure the long-term viability of this species [9]. At present, the research methods for fecal microbiota have also shifted from traditional methods to 16S rRNA gene sequencing technology, from simple microbial composition, community structure, and core microbiota research to microbial function research, which has become a hot frontier in ungulate research today [22].
The main goal of this study was to characterize the composition of the fecal microbiota of red deer of different sex and feeding plus environment. We used high-throughput 16S rRNA sequencing technology to comprehensively analyze. Thus, we hypothesized that: [1] the fecal microbiota composition and function are different between wild and captive deer; and [2] under the wild or captive environment, the microbiota diversity and evenness are different between females and males.
## 2.1. Study Site, Subjects, and Sample Collection
This study was conducted at the Gaogestai National Nature Reserve in Chifeng, Inner Mongolia (119°02′30″, 119°39′08″ E; 44°41′03″, 45°08′44″ N). The total area is 106,284 hm2. It is a typical transition zone forest-steppe ecosystem in the southern foothills of Greater Khingan Mountains, including forests, shrubs, grasslands, wetlands, and other diverse ecosystems. In February 2019, 75 line transects were randomly laid in the Gogestai protection area. Positive and reverse footprint chain tracking was carried out after the foodprints of red deer were found through line transect investigation. Disposable PE gloves were worn to collect red deer feces. While tracking the footprint chain, set 2 m × 2 m plant quadrate every 200 m to 250 m along the footprint chain, and collect all kinds of plant branches eaten by deer in the quadrate as far as possible [23]. A total of 162 fecal samples were collected and stored at −20 °C within 2 h. The feces of red deer from different areas of the Reserve were identified as coming from different individuals, and 43 feces were identified individually in the laboratory.
In February 2019, the HanShan Forest Farm in Chifeng City, Inner Mongolia, China (adjacent to the Gaogestai Nature Reserve) had a total of 11 healthy adult red deer of similar age and size. Ear tags were used to differentiate each individual red deer. Through continuous observation, feces were collected immediately after excretion by different red deer individuals and stored at −20 °C. We measured crude protein, energy, neutral detergent fiber (NDF), and total non-structural carbohydrates in red deer diets.
## 2.2. Individual Recognition and Sex Identification
We used a qiaamp DNA Fecal Mini-kit (QIAGEN, Hilden, Germany) to extract host deoxyribonucleic acid (DNA) from the fecal samples of red deer as previously described [24]. Microsatellite PCR technology was used with nine pairs of microsatellite primers (BM848, BMC1009, BM757, T108, T507, T530, DarAE129, BM1706, and ILST0S058) [25,26] with good polymorphism that were selected based on the research results of previous studies. These nine pairs of primers can amplify fecal DNA stably and efficiently. A fluorescence marker (TAMRA, HEX, or FAM) was added to the 5′ end of upstream primers at each site (Supplementary Table S1). Primer information, PCR amplification, and genotype identification procedures are described in the literature [27]. Multi-tube PCR amplification was used for genotyping [28], and 3–4 positive amplifications were performed for each locus to determine the final genotype [29]. The excel microsatellite toolkit [30] was used to search for matching genotypes from the data. Samples are judged to be from the same individual if all loci have the same genotype or if only one allele differs at a locus. The microsatellite data were analyzed by Cervus 3.0 software, and the genotyping was completed [31].
Male and female individuals were identified by detecting the existence of genes after the individual identification of red deer was completed. *Sry* gene primers (F:5′-3′ TGAACGCTTTCATTGTGTGGTC; R:5′-3′ GCCAGTAGTCTCTGTGCCTCCT) were designed, and the amplification system was determined. To minimize the occurrence of false positives or false negatives that could affect results, the *Sry* gene was repeated three times to expand and increase during the experiment, and samples with target bands that appeared on the second and third occasions were determined to be male [32].
## 2.3. Fecal Microbiota DNA Extraction, Amplification, and Sequencing
The total microbial DNA of fecal samples was extracted using an E.Z.N.A® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA). The DNA integrity of the extracted samples was determined by $1\%$ agarose gel electrophoresis. Targeting a 420 bp fragment encompassing the V4-V5 region of the bacterial 16S ribosomal RNA gene was amplified by PCR using primers 515F (5′-GTG CCA GCM GCC GCG GTA A-3′) and 907R (5′-CCG TCA ATT CMT TTR AGT TT-3′). NEB 154 Q5 DNA high-fidelity polymerase (NEB, Ipswich, MA, USA) was used in PCR amplifications (Supplementary Table S1). A 1:1 mixture containing the same volume of 1XTAE buffer and the PCR products were loaded on a $2\%$ agarose gel for electrophoretic detection. PCR products were mixed in equidensity ratios. Then, the mixture of PCR products was purified using the Quant-iTPicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). Sequencing libraries were generated using the TruSeq Nano DNA LT Library Prep kit (Illumina, San Diego, CA, USA) following the manufacturer’s recommendations, and index codes were added. The library’s quality was assessed on the Agilent 5400 (Agilent Technologies Co. Ltd., Santa Clara, CA, USA). At last, the library was sequenced on an Illumina NovaSeq 6000 platform, and 250 bp paired-end reads were generated.
Microbiome bioinformatics were performed with QIIME2 2019.4 [33] with slight modification according to the official tutorials (https://docs.qiime2.org/2019.4/tutorials/ (accessed on 30 September 2022)). Briefly, raw data FASTQ files were imported into the format that could be operated by the QIIME2 system using the qiime tools import program. The DADA2 [34] process is to obtain amplified variant sequences through de-duplication. In the process, clustering is not carried out based on similarity, but only de-duplication is carried out. Demultiplexed sequences from each sample were quality filtered and trimmed, de-noised, merged, and then the chimeric sequences were identified and removed using the QIIME2 DADA2 plugin to obtain the feature table of amplicon sequence variants (ASV) [34]. The QIIME2 feature-classifier plugin was then used to align ASV sequences to a pre-trained GREENGENES 13_8 $99\%$ database (trimmed to the V4V5 around a 420bp region bound by the 515F/907R primer pair) to generate the taxonomy table [35]. In order to unify the sequence effort, samples were rarefied at a depth of 25,318 sequences per sample before alpha and beta diversity analysis. Rarefaction allows one to randomly select a similar number of sequences from each sample to reach a unified depth.
## 2.4. Bioinformatics and Statistical Analyses
Sequence data analyses were mainly performed using QIIME2 and R software (v3.2.0). ASV-level alpha diversity indices, such as the Chao1 richness estimator and Pielou’s evenness, were calculated using the ASV table in QIIME2 [36,37], and visualized as box plots (R software, package “ggplot2”). Beta diversity analysis was performed to investigate the structural variation of microbial communities across samples using weighted or unweighted UniFrac distance metrics [38,39] and visualized via principal coordinate analysis (PCoA) (R software, package “ape”). The significance of differentiation of microbiota structure among groups was assessed by PERMANOVA (permutational multivariate analysis of variance) [40]. Random forest analysis (R software, package “randomForest”) was applied to sort the importance of microbiota with differences in abundance between groups and screen the most critical phyla and genera that lead to microbial structural differences between groups using QIIME2 with default settings [41,42]. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (Picrust2) [43] is software that predicts the functional abundance from the sequencing data of marker genes (typically 16S rRNA). An ASV’s abundance table is used for standardization, and the corresponding relationship of each ASV is compared with the Kyoto Encyclopedia of Genes and Genomes (KEGG) library to obtain the functional information and functional abundance spectrum.
## 3.1. Identification of Individuals and Sex
A total of 22 red deer individuals were identified from 43 fecal samples, including 12 males and 10 females (Supplementary Table S2). The female captive deer were CF1, CF2, CF3, CF4, CF5, CF6, CF7, and CF8. The male captive deer were CM1, CM2, and CM3. We divided all the red deer (22 wild and 11 captive) into four groups: wild females (WF) ($$n = 10$$), wild males (WM) ($$n = 12$$), captive females (CF) ($$n = 8$$), and captive males (CM) ($$n = 3$$). The information about identification, location, sex, and diet is summarized in Supplementary Table S2.
## 3.2. Diet Composition and Nutritional Composition of Wild and Captive Red Deer Winter Diets
The wild red deer were fed on 16 species of plants in the winter. The edible plants belonged to 16 species of 16 genera and 9 families. Since the frequency of occurrence of other edible plants in red deer, such as Mongolian oak (Quercus mongolica) and Chinese maple (Acer sinensis), was less than $7\%$, the nutrient content of these plants was not measured. In addition, we hypothesized that they had little influence on the nutritional strategy of red deer. Therefore, the primary nutrient contents of 14 types of edible plants were determined. The food and nutritional composition of wild red deer are shown in Supplementary Table S3. When the captive red deer were fed, each type of food was fed separately at different times. The nutritional content of the primary food of captive red deer from the farm (adjacent to the Gaogestai Nature Reserve) in winter is shown in Supplementary Table S4. Only one kind of diet were provided to captive deer at each feeding time with all captive deer feeding together. Captive red deer feed on leaves and high protein given by artificial feeding. Compared with captive red deer, wild deer have a wider feeding range and no dietary limitations. Substantial differences exist between these two feeding methods.
## 3.3. Sequencing Analysis and Clustering
A total of 1,561,654 high-quality sequences were obtained from the fresh winter feces of 22 wild deer and 11 captive deer. Rarefaction curves based on the Chao1 diversity index reached asymptotes at 22,500. The results showed that with the increase in amount of sequencing, the curve tended to be flat and no longer changed, indicating that the amount of sequencing in this study basically reflected the diversity of red deer fecal microbiota in this study (Supplementary Figure S1). A total of 15,228 ASVs were obtained using a $100\%$ similarity clustering method. The WF, WM, CF, and CM groups included 3056 ASVs, 3924 ASVs, 6661 ASVs, and 1587 ASVs, respectively.
## 3.4. Microbial Composition and Diversity by Environment and Sex
We found significant differences in fecal microbial composition between wild and captive red deer based on environment. The fecal microbial communities of four groups (WF, WM, CF, and CM) were dominated by the phyla Firmicutes and Bacteroidetes (Figure 1A). The phylum Firmicutes was the most abundant in WF (81.12 ± $2.87\%$), followed by WM (79.03 ± $2.19\%$), CF (58.24 ± $3.17\%$), and CM (59.66 ± $0.47\%$). Secondly, Bacteroidetes was abundant in WF (15.19 ± 2.09), WM (16.89 ± $2.08\%$), CF (33.02 ± 5.48), and CM (31.55 ± $1.61\%$). At the genus level, the genera from the four groups with abundance > $1\%$ were Oscillospira, a candidate genus 5-7N15 from the family Bacteroidaceae, Ruminococcus, Roseburia, Clostridium, and Prevotella (Figure 1B and Table 1).
The chao1 diversity indices demonstrate a significant difference between the WF and WM groups ($p \leq 0.01$). There was no statistically significant difference between the CF and CM groups ($p \leq 0.05$). Pieluo’s diversity index showed that no significant differences occurred between WF and WM groups ($p \leq 0.05$) or CF and CM groups ($p \leq 0.05$) (Figure 2).
Wild and captive red deer also differed in beta-diversity. An PCoA plot based on the Unweighted Unifrac and Weighted Unifrac distance matrix revealed clear separation of the fecal microbiota between wild and captive red deer (Figure 3A). The results of a PCoA analysis showed that the fecal microbial structures of the CF and CM groups were more similar than those of the WF and WM communities ($F = 13.82$, $$p \leq 0.001$$; and unweighted: $F = 5.983939$, $$p \leq 0.001$$; Figure 3A; Supplementary Table S5).
A random forest analysis showed that Firmicutes and Bacteroidetes were the primary microorganisms that had differences between the wild and captive populations by (an importance > 0.1) (Figure 3C, D). This analysis indicated that there were significant differences in the abundances of Firmicute and Bacteroidetes between the four groups (an importance > 0.1), which were the primary phyla that caused differences in the microbial communities between groups (Figure 3C). Ruminococcus, Treponema, Akkermansia, a candidate genus 5-7N15 belonging to family Bacteroidaceae, and a candidate genus rc4-4 belonging to family Peptococcaceae were the main genera that caused differences in microbial communities between sex and environment (importance > 0.04; Figure 3D).
## 3.5. Functional Modules of Fecal Microbial Communities
Metabolism was found to be the most common function prediction performed on fecal microbial communities and included the most important pathways for microbial clustering ($76.67\%$). The second pathway of metabolism included amino acid metabolism ($17.26\%$), carbohydrate metabolism ($17.85\%$), metabolism of cofactors and vitamins ($16.57\%$), and metabolism of terpenoids and polyketides ($12.66\%$) (Figure 4A). A PCoA analysis showed that the WF and WM groups had more similar microbial function clusters (Figure 4B).
It was found that there were significant differences in the three metabolic pathways of glycan biosynthesis and metabolism (GBM), energy metabolism (EM), and metabolism of other amino acids (MAA) ($p \leq 0.05$) (Figure 5).
## 4. Discussion
This is the first study to apply high-throughput sequencing to describe the fecal bacterial microbiota of wild and captive red deer by sex. Analysis of the differences in fecal microbiota is a key step in releasing captive red deer to help expand the wild population. *In* general, the fecal bacterial microbiota of red deer was similar to that of other cervidae, such as elk (Cervus canadensis), white tailed deer (Odocoileus virginianus) [38], and white-lipped deer (Cervus albirostris) [39], at least at the bacterial phylum level, with high proportions of the phyla Firmicutes and Bacteroidetes. In the digestive tract of herbivores, the role of *Firmicutes is* mainly to decompose cellulose and convert it into volatile fatty acids, thereby promoting food digestion and host growth and development. The enrichment of Firmicutes plays an important role in promoting the ability of red deer to obtain abundant nutrients from food and, at the same time, affects the metabolic function of the fecal microbiota. Bacteroidetes can improve the metabolism of organisms, promote the development of the gastrointestinal immune system, participate in the body’s bile acid, protein, and fat metabolisms, and also have a certain regulatory effect on carbohydrate metabolism. It can also produce special glycans and polysaccharides, which have a strong inhibitory effect on inflammation [43]. Differences in microbiota may be explained by changes in diet. Data from previous local and overseas studies have shown that diet is the main factor affecting the gut microbiota in mammals [40]. It is likely that wild deer have a more varied diet, more than captive deer. These phyla, Firmicutes and Bacteroidetes, are involved in important processes such as food digestion, nutrient regulation and absorption, energy metabolism, and host intestinal defense against foreign pathogens [40,41,42].
Alpha diversity alterations may be attributed to differential diet or hormonal influences on the gut microbiota. Fecal microbiota richness in wild populations is higher than that in captive animals, such as the Tibetan wild ass (Equus kiang), bharal (Pseudois nayaur), Tibetan sheep (Ovis arise), and yak (Bos mutus) [44,45,46,47,48]. Nevertheless, other studies also found that captivity might increase the alpha diversity of fecal microbiota in most Cervidae compared with other animals, for example, sika deer (Genus Cervus), Père David’s (Elaphurus davidianus), and white-tailed deer (Odocoileus virginianus) [49,50]. It may be that some environmental stresses in the wild or the special structure of the stomach and intestines in these deer lead to decreased alpha diversity of fecal microbiota in wild deer [50]. This phenomenon needs further research to determine its cause. Our results showed that the richness of the fecal microbial community in wild red deer differed by sex (Figure 2). In wild deer, the microbiota diversity was higher for females than males. Microbial community alterations by sex could be attributed to hormonal [51]. The sampling time was during the gestation period of red deer. Levels of female growth hormone during pregnancy may affect the fecal microbiota. Reproductive hormones have also been associated with sex and gut microbial changes in wild animals [17,52,53]. Increased evidence indicates that sex steroid hormone levels are associated with the human gut microbiota [54,55]. Futher, Edwards et al. reported that estrogen and progesterone had an impact on gut function [56]. The captive deer also had the smallest sample size ($$n = 3$$ males and 8 females), which limited our ability to detect these differences.
In this study, the functional pathway composition of wild red deer is more similar (Figure 5B), which is completely opposite to the microbial structure (Figure 3A). The change in microbial structure does not necessarily lead to the change in function, which may be due to the same function in different microbial communities [57]. In recent years, studies have shown that gut microbiota are involved in various metabolic processes such as amino acids, carbohydrates, and energy, confirming their primary role in assisting host digestion and absorption [58]. It has also been found to be involved in environmental information processing, suggesting that the gut microbiota plays an important role in facilitating acclimation to changing environments [59]. The metabolism of gut microbiota is closely related to the feeding habits of the host. In the long-term evolution process, the gut microbiota will respond to changes in diet types or specific diets by adjusting the content of certain digestive enzymes [4,60]. Studies have shown that the decrease of fecal microbial diversity can lead to a reduction in the functional microbiota, in the efficiency of the microbiota, and in the resistance to pathogen invasion [61]. The decrease in fecal microbial diversity in captive populations resulted in a decrease in functional microbiota [61]. Ruminococcaceae and Lachnospiraceae are two of the most common bacterial families within the *Firmicutes phylum* [62]. It has been hypothesized that they have an important role as active plant degraders [63,64]. According to our results, the level of Ruminococcaceae in the captive groups is significantly lower than that in the wild group, which could suggest that the fiber-reduced diet in captivity is modifying the ability of the fecal microbiota to degrade recalcitrant substrates such as cellulose, hemicellulose, and lignocellulose, among others, that are commonly found on the main resources of the wild red deer diet. The captive deer’s consequent reduction of diet resources might trigger the decline of important metabolic pathways associated with nutrient use [64].
16S rRNA analysis constitutes a valuable and cost-efficient approach for surveillance and monitoring wild populations as well as captive individuals. Picrust2 prediction accuracy is dependent on the availability of closely related annotated bacterial genomes in the database and the phylogenetic distance from the reference genome. However, the prediction results are still uncertain, which does not mean that the correlation between the predicted genes and the real metagenome of the microbiota is $100\%$ [65]. At present, due to the difficulty of cultivation, the mechanism by which some functional bacteria exert their effects remain unclear. Therefore, in the follow-up work, it is necessary to repeatedly cultivate the conditions of some intestinal anaerobic bacteria, the most extensive of which are Firmicutes and some Bacteroidetes. The microbiota was cultured in vitro by simulating the gut environment, and its functions were speculated and further verified in combination with multiple groups of studies (metagenomics, meta transcriptome, and proteome, etc.). At the same time, the unknown functional microbiota and its genome sequence information can be explored and studied. These works will help to understand the metabolic activities of the complex microbiota and further explore the host physiological processes involved in gut microbiota.
## 5. Conclusions
In conclusion, our study provided information on the structure and function of the fecal microbiome of red deer through the 16S rRNA gene of fecal samples. Comparing analyses identified significant variations of fecal microbiota composition and functions between captive and wild populations and also indicated that environment and sex have a great influence on these variations. These findings were of great significance for the reintroduction of captive red deer, given that the differences in fecal microbiota composition and functions between captive and wild red deer would greatly impact the ability of captive red deer to adapt to the wild environment. For further study, incorporating novel methods (e.g., transcriptome) to study the functional annotation of gene content and the functional traits of the host would be essential for better understanding the physiology and immunology of red deer.
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|
---
title: Rumen-Protected Lysine and Methionine Supplementation Reduced Protein Requirement
of Holstein Bulls by Altering Nitrogen Metabolism in Liver
authors:
- Songyan Zou
- Shoukun Ji
- Hongjian Xu
- Mingya Wang
- Beibei Li
- Yizhao Shen
- Yan Li
- Yanxia Gao
- Jianguo Li
- Yufeng Cao
- Qiufeng Li
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000044
doi: 10.3390/ani13050843
license: CC BY 4.0
---
# Rumen-Protected Lysine and Methionine Supplementation Reduced Protein Requirement of Holstein Bulls by Altering Nitrogen Metabolism in Liver
## Abstract
### Simple Summary
Excessive protein intake causes dietary nitrogen to be excreted through urine nitrogen and fecal nitrogen, reducing nitrogen use efficiency. The main way to reduce dietary nitrogen loss is to reduce dietary protein content, as well as to meet the nutritional needs of ruminants. Therefore, reducing crude proteins while adding rumen amino acids can achieve a reduction in nitrogen emissions. The results showed that adding RPLys (55 g/d) and RPMet (9 g/d) to the bull diet and low protein diet ($11\%$) could improve the growth performance, increase the level of nitrogen metabolism, and enhance the expression of genes related to nitrogen metabolism.
### Abstract
The aim of this study was to investigate the effect of low-protein diets supplemented with rumen-protected lysine (RPLys) and methionine (RPMet) on growth performance, rumen fermentation, blood biochemical parameters, nitrogen metabolism, and gene expression related to N metabolism in the liver of Holstein bulls. Thirty-six healthy and disease-free Holstein bulls with a similar body weight (BW) (424 ± 15 kg, 13 months old) were selected. According to their BW, they were randomly divided into three groups with 12 bulls in each group in a completely randomized design. The control group (D1) was fed with a high-protein basal diet (CP$13\%$), while bulls in two low-protein groups were supplied a diet with $11\%$ crude protein and RPLys 34 g/d·head + RPMet 2 g/d·head (low protein with low RPAA, T2) or RPLys 55 g/d·head + RPMet 9 g/d·head (low protein with high RPAA, T3). At the end of the experiment, the feces and urine of dairy bulls were collected for three consecutive days. Blood and rumen fluid were collected before morning feeding, and liver samples were collected after slaughtering. The results showed that the average daily gain (ADG) of bulls in the T3 group was higher than those in D1 ($p \leq 0.05$). Compared with D1, a significantly higher nitrogen utilization rate ($p \leq 0.05$) and serum IGF-1 content ($p \leq 0.05$) were observed in both T2 and T3 groups; however, blood urea nitrogen (BUN) content was significantly lower in the T2 and T3 groups ($p \leq 0.05$). The content of acetic acid in the rumen of the T3 group was significantly higher than that of the D1 group. No significant differences were observed among the different groups ($p \leq 0.05$) in relation to the alpha diversity. Compared with D1, the relative abundance of Christensenellaceae_R-7_group in T3 was higher ($p \leq 0.05$), while that of Prevotellaceae _YAB2003_group and Succinivibrio were lower ($p \leq 0.05$). Compared with D1 and T2 group, the T3 group showed an expression of messenger ribonucleic acid (mRNA) that is associated with (CPS-1, ASS1, OTC, ARG) and (N-AGS, S6K1, eIF4B, mTORC1) in liver; moreover, the T3 group was significantly enhanced ($p \leq 0.05$). Overall, our results indicated that low dietary protein ($11\%$) levels added with RPAA (RPLys 55 g/d +RPMet 9 g/d) can benefit the growth performance of Holstein bulls by reducing nitrogen excretion and enhancing nitrogen efficiency in the liver.
## 1. Introduction
Protein, as typically the most expensive macronutrient of diets, plays critical roles in the health, growth, production, and reproduction of animals. However, protein ingredient shortages and nitrogen pollution challenge the livestock farming worldwide, albeit these problems have been alleviated in recent decades due to an increase in demand for animal source food from a fast-growing population with rising incomes [1,2]. Therefore, enhancing the utilization efficiency of dietary protein and reducing excretory losses would be alternative strategies to solve these problems [3].
Low-protein diets have been proven to enhance nitrogen utilization [4,5]. However, restricting N intake also sacrificed the growth performance and productivity of animals [6,7], which has been attributed to limiting amino acid deficiency in low-protein diets [8]. Lysine (Lys) and methionine (Met) are the top two limiting amino acids (LAA) for ruminants [9,10]. Adding rumen-protected Lys and Met in low-protein diets was considered an efficient way to the meet animal amino acids requirement, as they could escape from rumen degradation and increase the supply of amino acids to the intestines, thus improving the N utilization [11]. Incorporating rumen-protected Lys and (or) Met into low-protein diets was reported to increase dry matter intake in transition cows [12,13]. Previous studies also suggested that rumen-protected Lys and (or) Met in low-protein diets promoted milk protein yield in high-producing dairy cows [14,15] and maintained milk production and milk protein yield while reducing the N losses in urine in dairy cows [16]. The question of how to reduce nitrogen emissions of ruminants without affecting their production performance has always been the focus of scholars, and the research in this area has mostly been focused on dairy cows; however, there have been few studies conducted on Holstein bulls.
Nitrogen recycling contributes to effective N utilization in ruminants [17], and ruminal microbiota and the liver play important roles in this nitrogen metabolism [4]. Therefore, the aim of this study was to investigate the effect of low-protein diets supplemented with rumen-protected lysine (RPLys) and methionine (RPMet) on growth performance, rumen fermentation, blood biochemical parameters, nitrogen metabolism, and gene expression related to N metabolism in the livers of Holstein bulls.
## 2. Materials and Methods
This study was conducted between March 2016 and June 2016 at Hongda an animal husbandry in Baoding, P. R. China. The experimental protocol (YXG 1711) was approved by the Institutional Animal Care and Use Committee of Hebei Agricultural University.
## 2.1. Animals, Experimental Design, and Diets
Thirty-six healthy and disease-free Holstein bulls with a similar body weight (BW; 424 ± 15 kg, aged 14 months old) were selected. According to their BW, they were randomly divided into 3 groups with 12 bulls in each group in a completely randomized design. The control group (D1) was fed with a high-protein basal diet (CP$13\%$), while bulls in two low protein groups were supplied diet with $11\%$ crude protein and RPLys 34 g/d·head + RPMet 2 g/d·head (low protein with low RPAA, T2) or RPLys 55 g/d·head + RPMet 9 g/d·head (low protein with high RPAA, T3). Basic diets were prepared according to Japanese feeding standard [2008] for beef cattle [18] (Table 1). The RPAA (Hangzhou Kangdequan Feed Limited Company, Hangzhou, Zhejiang, China) feed was used with a rumen protection rate of $60.0\%$ and was premixed with 100 g of grounded corn which, was used as a carrier for the supplement and was the same amount of grounded corn as that supplied to bulls in the D1 group. All animals were fed ad libitum the basic diets and with free access to clean water. All the experimental animals were housed in tie stalls according to the groups and were fed twice daily at 06:00 and 18:00 h following the removal of the feed refusals before morning feeding. The experiment consisted of 3 periods: a 14-day adaptation period, a 2-month feeding period, and a 7-day sample collection period. Holstein bulls were weighted before morning feeding at the beginning and end of every feeding period.
## 2.2. Sample Collection
The diet offered and refused for individual bulls was weighed every day throughout the trial to average daily dry matter intake (ADMI). Samples of individual feed ingredients, orts, and diets were collected weekly during the experimental period and stored at −20 °C [19]. At the beginning of the experiment, all Holstein bulls were weighed before feeding in the morning to obtain their initial weight. Similarly, at the end of the trial, all Holstein bulls were weighed before morning feeding to obtain the final weight, and the average daily gain (ADG) was calculated as (final weight–initial weight)/test days. Based on the ADMI and ADG, the feed weight ratio (F/G) was calculated. At the end of the feeding period, four Holstein bulls in each group were randomly selected, and a 10-mL blood sample was collected via jugular venipuncture from each bull before morning feeding. The samples were immediately centrifuged at 3000 rpm for 15 min, and the serum samples were collected and stored at −20 °C for further analysis. After 2 h of morning feeding at the end of the feeding period, the ruminal fluid samples of four bulls were collected via an oral stomach tube equipped with a vacuum pump. We discarded the first 100 to 200 mL of fluid collected to reduce the chance that the stomach tube rumen samples were contaminated with saliva. Once again, approximately 200 mL of rumen fluid was collected, and about 20 mL was taken, filtrated with four layers of sterile cheesecloth, and then transferred to 2-mL sterile tubes and stored in liquid nitrogen for further analysis.
Three bulls in each group were randomly selected and euthanized at the end of the feeding experiment after 2 h of morning feeding. The middle part of liver tissue was immediately collected after animal sacrifice and cut into 5-mm fragments; the tissue sample was then placed into sterile tubes and stored in liquid nitrogen for further analysis.
Another three bulls in each group were randomly selected after the feeding period and were transferred to metabolic cages. After a 5-day adaption period, feces and urine were collected during the next 3 days. Total feces and urine were respectively collected daily before morning feeding. The feces of each bull were weighted, mixed, subsampled (100 g/kg), and stored at −20 °C. Each bull fecal sample was evenly divided into two parts, one with $10\%$ (10:1) sulfuric acid solution and the other without acid, before being dried, crushed, sifted, and stored at room temperature for the determination of nutrient content. The urine of each bull was collected using a plastic container with 10 mL of $10\%$ sulfuric acid to prevent the loss of ammonia; then, after the volume was measured, the urine was filtered with four layers of gauze filter, and subsamples (100 mL/individual) were stored at −20 °C for urine nitrogen measurement.
## 2.3. Laboratory Analysis
Offered and refused feed and feces were dried at 55 °C for 48 h, ground to pass through a 1-mm screen (Wiley mill, Arthur H. Thomas, Philadelphia, PA, USA), and stored at 4 °C for analysis of chemical composition. The dry matter (DM, method 934.01), ash (method 938.08), crude protein (CP, method 954.01), ether extract (EE, method 920.39), Ca (method 927.02), and P (method 965.17) contents of the samples were determined according to the procedures of the AOAC [20], and NDF (amylase) and ADF content was analyzed using the methods of Van Soest et al. [ 21]. Lysine and methionine content in the feed was analyzed using an automatic AA analyzer (Hitachi 835, Tokyo, Japan).
Serum alanine transferase (ALT), aspartate transferase (AST), albumin (ALB), total protein (TP), glucose (GLU), and blood urea nitrogen (BUN) were analyzed using an automatic biochemical analyzer (Hitachi 7020, Tokyo, Japan). Serum growth hormone (GH) and insulin-like growth factor-1 (IGF-1) contents were measured with enzyme-linked immunosorbent assay (ELISA) kits according to the manufacturer’s specifications (HZ Bio. CO., Shanghai, China).
The pH value of the rumen fluid was measured immediately by using a digital pH analyzer (PHS-3C, Shanghai, China), and ammonia nitrogen (NH3-N) and microbial protein (MCP) were determined following recommendations provided in previous studies [22]. Volatile fatty acid (VFA) concentrations in rumen fluid were analyzed using gas chromatography (TP-2060F, Tianpu. Co., Ltd., Beijing, China).
The DNA in rumen fluid was extracted using the CTAB method using a commercial kit (Omega Bio-Tek, Norcross, GA, USA), and, after DNA was purified with $1\%$ agarose gel electrophoresis, the library was constructed using a TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, Inc., San Diego, CA, USA). Then, the constructed library was quantified using HiSeq2500 PE250 (Illumina, Inc., San Diego, CA, USA). Sequences data were analyzed using the QIIME2 pipeline according to a previous study [23] and submitted to NCBI with project ID P2016030502-S2-3-1.
The primer of target genes (Table 2) was designed according to the bovine gene sequences reported in NCBI and synthesized by the Shanghai Biotedchnology Technology Corporation Limited Company. The total amount of ribonucleic acid (RNA) was extracted from the liver tissue of Holstein bulls with a miRNeasy kit (Qiagen, Hilden, Germany); then, RNA quality was determined using NanoDrop 2000 (NanoDrop Tec, Rockland, DE) with OD260/OD280 ranging between 1.9 and 2.1. Real-time polymerase chain reaction (PCR) was performed to quantify the expression of target genes, using an SYBR Green PCR Master mix (Takara bio-Co., Shiga, Japan) and following the manufacturer’s protocols. *The* gene expression of liver tissue was calculated using the method of 2-ΔΔCt, where the expression of ACTB was used as referenced D1.
## 2.4. Statistical Analysis
The data management was performed using a spreadsheet program with Excel, and statistical analysis was carried out using R software (version 3.6.3, R Foundation for Statistical Computing, Vienna, Austria.) with a one-way analysis of variance (ANOVA) model: Y = α + Xi + ei, where Y is the observed parameters, α is the overall mean, *Xi is* the ith treatment effect, and ei is the residual error. All data were shown using least squares means, and significant differences among treatments were declared at $p \leq 0.05$ and a tendency if 0.05 < p ≤ 0.10.
## 3.1. Growth Performance
There was no significant difference ($p \leq 0.05$) in ADG, ADMI, and F/G among different groups; however, the F/G in the T2 and T3 groups decreased by $8.45\%$ and $6.67\%$, respectively, compared with D1 (Table 3).
## 3.2. Nitrogen Metabolism
Compared with the D1 group, the intake of nitrogen and the amount of nitrogen excretion by feces and urine were significantly lower in the T2 and T3 groups ($p \leq 0.05$). The ratio of nitrogen excretion by feces and nitrogen intake (FN/IN) was lower in T3 compared with the D1 and T2 groups, while the ratio of nitrogen excretion by urine and nitrogen intake (UN/IN) was lower in the T2 and T3 groups compared to the D1 group. Thus, a significantly higher nitrogen utilization rate was observed in both T2 and T3 groups compared with the D1 group ($p \leq 0.05$; Table 4).
## 3.3. Serum Biochemical Index
Low-protein diet with RPAA supplementation had no effect on concentrations of ALT, AST, ALB, TP, GLU, and GH in serum ($p \leq 0.05$). Concentration of serum BUN significantly decreased; however, the concentration of serum IGF-1 significantly increased in the T3 group compared with the D1 group ($p \leq 0.05$; Table 5).
## 3.4. Rumen Fermentation
No significant difference was detected in the rumen pH, concentration of NH3-N, MCP, propionate, and butyrate, and in the ratio of acetate/propionate among different groups ($p \leq 0.05$). The concentration of acetate in the T3 group was significantly higher than that in D1 and T2 ($p \leq 0.05$; Table 6).
## 3.5. Rumen Microbiota
No significant difference was observed in alpha diversity among the different groups ($p \leq 0.05$; Table 7). The relative abundance of the highest 16 abundant bacteria at the genus level was compared among the different groups. However, the relative abundance of Ruminococcaceae_NK4A214 in the T3 group was lower than that in the D1 group ($p \leq 0.05$), and the abundance of Christensenellaceae_R-7_group in the T3 group was lower than that in both D1 and T2 groups ($p \leq 0.05$). Meanwhile, the relative abundance of Prevotellaceae_YAB2003_group in T3 was higher than that in the D1 group ($p \leq 0.05$), and the relative abundance of Succinivibrio in T3 was higher than that in both the D1 and T2 groups ($p \leq 0.05$; Table 8).
## 3.6. Gene Expression in Liver Tissue
The expression of the CPS-1, ASS, ARG, OTC, and N-AGS genes, which relate to nitrogen metabolism or urea metabolism in liver tissue, are shown in Figure 1. The expression of CPS-1, ARG, and N-AGS was significantly upregulated in the T3 group ($p \leq 0.05$), although no significant difference was observed between the rT2 and D1 groups ($p \leq 0.05$). The expression of CPS-1, ARG, and N-AGS increased by $25\%$, $18\%$, and $13\%$ in the T2 group compared with D1. The expression of ASS and OTC was upregulated in both the T2 and T3 groups compared with D1 ($p \leq 0.05$).
The expression of the SLC3A2, IRS1, PDK, P13K, TSC1, TSC2, mTORC1, eIF4EBP1, S6K1, and eIF4B genes, which are related to the nitrogen metabolism in liver tissue, are shown in Figure 2. The low-protein diet with RPAA supplementation did not affect gene expression of SLC3A2, P13K, TSC2, and eIF4EBP1 ($p \leq 0.05$); however, the expression of IRS1, PDK, S6K1, and eIF4B genes in liver tissue increased significantly ($p \leq 0.05$), and the expression of the mTORC1 gene also increased ($$p \leq 0.09$$), while the expression of TSC1 gene decreased significantly ($p \leq 0.05$).
## 4. Discussion
Protein is one major factor that affects the health, growth, and production of ruminants. Moreover, although people tend to formulate high-protein diets to achieve a better production of ruminants, the global protein shortage is increasing [1], and high-protein diets overload the environment by increasing nitrogen (N) excretion through urine and feces [3], which is harmful for the sustainability of the livestock industry.
By providing bulls with a low-protein diet ($11\%$ CP) supplemented with rumen-protected lysine and methionine, our findings indicate that, compared with a high-protein diet ($13\%$ CP) group which followed the recommended Japanese feeding standard for beef cattle [18], our low-protein diet supplemented with RPAA increased ADG and N utilization and decreased N excretion through urine and feces. These findings were comparable with previous studies in which the feeding of rumen-protected Lys and (or) Met to castrated cattle increased daily gain [24] and reduced urinary nitrogen and urea nitrogen in urine [25]. The World Health Organization (WHO) proved that the addition of RPAA to a low-protein diet increases N utilization, reduces N emission and environmental pollution, and promotes the growth performance of dairy cows [12,14].
Blood biochemical parameters are sensitive to animal health and nutrient condition [26,27]. The serum content of ALT, AST, ALB, TP, GLU, BUN, GH, and IGF-1 was used to assess the nutrient condition of bulls with different treatment groups. From this, we observed that BUN content decreased, and IGF-1 content increased, in bulls provided with a low-protein diet supplemented with RPAA, while other indexes were not affected. The serum BUN content reflects the nitrogen balance of ruminants and negatively correlated with N utilization [17]. When ruminants were provided with low-dietary protein with a higher N utilization, serum BUN decreased [4,28]. The main function of IGF-1 relates to the inhibiting of protein degradation and the promoting of protein synthesis to maintain nitrogen balance and to improve the growth performance of animals [29,30]. These observations further explained the improvement in N utilization and growth performance of bulls on a low-protein diet supplemented with RPAA.
When cattle are fed with low-protein diets, urea N recycling can be considered a high-priority metabolic function because a continuous N supply for microbial growth in the rumen is a strategy for animal survival [31]. The abundance of the microflora reflects its ability to adapt to a particular environment and compete for available nutrients; moreover, it indicates its importance to the overall function of the microbiome as a whole [32]. The ACE (reflecting the richness of bacteria in the sample), Shannon, and PD-whole-tree (reflecting the microbial diversity in feces) indexes were used to assess the alpha diversity of rumen microbiota. Previous studies have demonstrated that rumen fermentation and microbiota are sensitive to protein levels [33,34] or feed ingredients [35] in ruminants, which were also sensitive biomarkers of N utilization [36]. By monitoring the rumen fermentation and microbiota, we observed an increase in the acetate content of rumen; however, other parameters including NH3-N and MCP content were not significant affected, which is similar to the results of a study by Martin et al. [ 37]. The addition of methionine analogue 2-hydroxy-4-methylthiobutyric acid (HMB) and esterified 2-hydroxy-4-methylthiobutyric acid (HMBi) to the diet of dairy cows significantly increased the content of rumen total volatile fatty acids (TVFAs) [37]. Some studies have shown that methionine hydroxy analogue (MHA) can increase the ratio of acetic acid and butyric acid in rumen content [38]. Research has showed that $0.52\%$ of methionine could increase the content of butyric acid in rumen, while $0.26\%$ methionine did not affect the content of VFA [39]. The above results show that the effect of methionine on rumen VFA content is unpredictable. The alpha diversity of microbiota in rumen was not affected by treatment, and only a small portion of bacteria at the genus level (~$5\%$ in abundance) was determined to be significantly different between groups with a decreased relative abundance of Ruminococcaceae_NK4A214_group and Christensenellaceae_R-7_group and increased Prevotellaceae_YAB2003_group and Succinivibrio in bulls on a low-protein diet supplemented with RPAA. These findings hinted that bulls on a low-protein diet supplemented with RPAA would maintain the rumen fermentation and maintain ruminal microbiota homeostasis compared with that from D1.
The liver plays important roles in the utilization efficiency of recycled N. The excess nitrogen in the rumen is usually inhaled into the animal’s blood in the form of ammonia, which is then metabolized by the liver to synthesize urea. All the urea synthesized by the liver, some of which is secreted via saliva into the rumen and intestines of animals, are reused by bacteria, protozoa, and other microorganisms; the other part is filtered by the kidneys and excreted with the urine [28]. The urea cycle plays a key role in maintaining a positive balance of nitrogen in anima, especially at low dietary nitrogen levels. S6K1 and eIF4EBP1 are genes that regulate protein translation downstream of mTORC1. The S6K1 gene can promote protein translation by stimulating the phosphorylation of downstream eIF-4B, RPS6, eIF-2, and PAPB [40], and the SLC3A2, IRS1, PDK, P13K, TSC1, TSC2, mTORC1, eIF4EBP1, S6K1, and eIF4B genes are related to nitrogen metabolism in the liver; moreover, these genes would become overexpressed when blood ammonia increased to increase urea synthesis and balance the blood ammonia [41]. However, unexpected results were observed in the current experiment: when feeding bulls with a low-protein diet supplemented with RPAA, we observed that the serum BUN decreased but the expression of genes associated with urea synthesis in liver increased. This finding can explain why the low-protein diet supplemented with RPAA induced an increase in N efficiency; however, the mechanism behind these upregulated genes in the liver was unclear. Previous studies have demonstrated that AA in diets not only provide animal nutrition but also act as a functional regulator and have ability to stimulate expression altering in multiple tissue cells such as mammary tissue [42], polymorphonuclear cells [43], and adipose tissue [44], as well as liver tissue [45,46]. The influence of RPLys and RPMet on liver genes’ expression requires further study. As the number of samples selected in this study is limited, it is necessary to further test the current data in the future research.
## 5. Conclusions
In summary, providing low dietary protein ($11\%$) with RPLys (55 g/d) and RPMet (9 g/d) to bulls could increase their nitrogen utilization rate, serum IGF-1 content, ruminal acetate content, and expression genes associated with urine metabolism and nitrogen metabolism in liver compared to that with high protein ($13\%$). Our findings indicate that providing a low-protein diet supplemented with RPAA could benefit bulls mainly by increasing liver nitrogen metabolism and utilization; however, the RPAA’s affecting of liver gene expression at a nutrition level or as a signal molecule still requires further study.
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|
---
title: Epithelial-to-Mesenchymal Transition and Phenotypic Marker Evaluation in Human,
Canine, and Feline Mammary Gland Tumors
authors:
- Alessandro Sammarco
- Chiara Gomiero
- Giorgia Beffagna
- Laura Cavicchioli
- Silvia Ferro
- Silvia Michieletto
- Enrico Orvieto
- Marco Patruno
- Valentina Zappulli
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000046
doi: 10.3390/ani13050878
license: CC BY 4.0
---
# Epithelial-to-Mesenchymal Transition and Phenotypic Marker Evaluation in Human, Canine, and Feline Mammary Gland Tumors
## Abstract
### Simple Summary
In this study we addressed the analysis of human breast cancer and canine and feline mammary tumors with regard to the expression, at either gene or protein level, of some molecules that are related to the capacity of an epithelial cell to become mesenchymal (epithelial-to-mesenchymal transition), acquiring higher ability to metastasize. In our samples, some typical markers of this transition were not higher at mRNA levels in tumors than in healthy tissues, indicating that some other markers should be investigated. Instead, at protein levels, some molecules such as vimentin and E-cadherin were indeed associated with higher aggressiveness, being potential useful markers. As already described in the literature, we also demonstrated that feline mammary tumors are close to an aggressive subtype of human breast cancer called triple negative, whereas canine mammary tumors are more similar to the less aggressive subtype of human breast cancer that expresses hormonal receptors.
### Abstract
Epithelial-to-mesenchymal transition (EMT) is a process by which epithelial cells acquire mesenchymal properties. EMT has been closely associated with cancer cell aggressiveness. The aim of this study was to evaluate the mRNA and protein expression of EMT-associated markers in mammary tumors of humans (HBC), dogs (CMT), and cats (FMT). Real-time qPCR for SNAIL, TWIST, and ZEB, and immunohistochemistry for E-cadherin, vimentin, CD44, estrogen receptor (ER), progesterone receptor (PR), ERBB2, Ki-67, cytokeratin (CK) $\frac{8}{18}$, CK$\frac{5}{6}$, and CK14 were performed. Overall, SNAIL, TWIST, and ZEB mRNA was lower in tumors than in healthy tissues. Vimentin was higher in triple-negative HBC (TNBC) and FMTs than in ER+ HBC and CMTs ($p \leq 0.001$). Membranous E-cadherin was higher in ER+ than in TNBCs ($p \leq 0.001$), whereas cytoplasmic E-cadherin was higher in TNBCs when compared with ER+ HBC ($p \leq 0.001$). A negative correlation between membranous and cytoplasmic E-cadherin was found in all three species. Ki-67 was higher in FMTs than in CMTs ($p \leq 0.001$), whereas CD44 was higher in CMTs than in FMTs ($p \leq 0.001$). These results confirmed a potential role of some markers as indicators of EMT, and suggested similarities between ER+ HBC and CMTs, and between TNBC and FMTs.
## 1. Introduction
Mammary gland cancer is the most common tumor in women [1] and in female dogs [2], and the third most common neoplasia in cats [3]. Human breast cancer (HBC) is classified into four main subtypes according to the expression of estrogen receptor (ER), progesterone receptor (PR), and epidermal growth factor receptor ERBB2, as follows: (i) Luminal A tumors (ER+ and/or PR+, ERBB2-); (ii) Luminal B tumors (ER+ and/or PR+, ERBB2+); (iii) ERBB2-overexpressing tumors (ER-, PR-, ERBB2+); and (iv) triple-negative (ER-, PR-, ERBB2-) breast cancer (TNBC) [4]. TNBCs are typically high-grade carcinomas characterized by an aggressive behavior and a poor prognosis, with high risk of distant metastasis and death [5]. Canine mammary tumors (CMTs) are classified based on morphologic features [6]. Fifty per cent of CMTs are malignant with a $20\%$ risk of metastasis [7]. The majority (80–$90\%$) of feline mammary tumors (FMTs) are characterized by a highly aggressive behavior that leads to rapid progression and distant metastasis development [8,9]. Typically, FMTs lack the expression of ER, PR, and ERBB2, and have been considered a remarkable spontaneous model for TNBC [10,11,12,13,14,15,16]. In all three species, mammary tumors exhibit both inter- and intra-tumor heterogeneity as a consequence of genetic and non-genetic aberrations [17].
Over the past 20 years, the investigation of cell differentiation/phenotypic markers has been used in both human and veterinary medicine, primarily to improve our knowledge of the histogenesis of mammary tumors [18]. In the normal human, canine, and feline mammary gland, two cell subpopulations are present: luminal epithelial cells, positive for cytokeratin (CK) 7, CK8, CK18, and CK19; and basal/myoepithelial cells, variably positive for CK5, CK6, CK14, CK17, SMA, calponin, vimentin, and p63 [19]. In HBC, the evaluation of cell differentiation proteins is frequently performed in association with routine diagnostic markers (ER, PR, ERBB2, and Ki-67) to better classify this tumor. The identification of HBC subtypes has a diagnostic, prognostic, and therapeutic value, and is associated with the cell differentiation and epithelial-to-mesenchymal transition (EMT) status of the neoplastic population according to a hierarchical model [20].
EMT is a key event that neoplastic epithelial cells use to acquire a mesenchymal phenotype [21]. As a result, tumor cells obtain the ability to detach from the primary tumor mass, invade the surrounding tissue, migrate throughout the body, and eventually give rise to metastases in distant organs [22]. The classical EMT is characterized by a decreased expression of epithelial markers and a complementary upregulation of mesenchymal markers. Classical EMT transcription factors, namely snail family transcription repressor $\frac{1}{2}$ (SNAIL), TWIST, and zinc-finger-enhancer binding protein $\frac{1}{2}$ (ZEB) are known to orchestrate EMT by regulating cell adhesion, migration, and invasion, also interacting with different signaling pathways and microRNAs [22,23]. Although this is a well-de-scribed process that promotes metastasis formation, accumulating evidence suggests the existence of an intermediate state called partial EMT or hybrid E/M, whereby both epithelial and mesenchymal markers are co-expressed in cancer cells [23,24,25].
The aim of this study was to investigate the mRNA expression of classical EMT-related transcription factors SNAIL, TWIST, and ZEB in human, canine, and feline mammary tumors. Additionally, we studied the expression of key proteins involved in the EMT process, including E-cadherin and vimentin, and of proteins related to the tumor phenotype, such as ER, PR, ERBB2, Ki-67, cytokeratin (CK) $\frac{8}{18}$, CK$\frac{5}{6}$, CK14, and CD44.
## 2.1. Tissue Collection
Human samples were collected from the Istituto Oncologico Veneto (IOV, Padua, Italy), whereas canine and feline samples were collected from local veterinary clinics. The human sample collection was approved by the IOV Ethics Committee. All patients or patients’ owners provided informed, written consent to use their samples for this study. Specifically, samples from 5 healthy human mammary gland tissues (MGTs), 5 ER+ HBCs, 5 TNBCs, 4 healthy canine MGTs, 10 canine mammary tumors (CMTs) (5 grade I and 5 grade II), 6 healthy feline MGTs, and 6 grade III FMTs were collected. In this study, to avoid contaminations with other tumor cell subpopulations, we selected only simple tubular carcinomas (STC), which are composed of only one tumor cell subpopulation (luminal epithelial cells) [6]. Healthy MGTs were collected from tumor-bearing patients during the therapeutic/diagnostic surgical procedures, with no additional sampling performed only for the study. Sampling was performed by surgeons. At the time of sampling, most of the tissue was fixed in $4\%$ formaldehyde for histopathology and immunohistochemistry, whereas a peripheral small portion of tumor and normal tissues (approx. 0.5 cm2 each) was collected and preserved in RNALater (Ambion, Austin, TX, USA), according to manufacturer’s instructions. In the lab, before RNA extraction, a small portion of each RNALater-preserved sample was fixed in $4\%$ formaldehyde and embedded in paraffin to check the content of the samples themselves. Four-μm tissue sections were stained with hematoxylin and eosin, and slides were visualized under the microscope to further confirm the presence of healthy tissue in the samples labelled as “healthy” and of tumor tissue in the samples labelled as “tumor”.
## 2.2. RNA Extraction and Real-Time Polymerase Chain Reaction
*For* gene expression analysis, a small portion of each tissue sample preserved in RNALater was used for RNA extraction using Trizol Reagent (Invitrogen, Carlsbad, CA, USA), following the manufacturer’s protocol. The extracted RNA was treated with RNAse-free DNAse I (New England Biolabs, Ipswich, MA, USA). Five-hundred ng of total RNA from each sample was reverse transcribed using the RevertAid First Strand cDNA Synthesis Kit (Invitrogen). The cDNA was then used as a template for quantitative real-time PCR using the ABI 7500 Real-Time PCR System (Applied Biosystem) to evaluate the mRNA expression of the following EMT-related genes: SNAIL1, SNAIL2, TWIST1, TWIST2, ZEB1, ZEB2. All the samples were tested in triplicate. ACTB was used as a house-keeping gene. The primer sequences are reported in Table 1. The primers were designed using NCBI Primer-BLAST. To examine primer specificity, the dissociation curves of qPCR products were assessed to confirm a single amplification peak. The qPCR reactions were then purified using the ExoSAP-IT PCR product cleanup (Applied Biosystems) and sequenced at the BMR Genomics (Padua, Italy). The sequences were then verified using the NCBI BLAST database. For data analysis for each sample, the ΔΔCt value was calculated and expressed as a relative fold change (2−ΔΔCt), as described in [16]. Real-time PCR efficiency was calculated by performing a dilution series experiment and applying the following formula to the standard curve: efficiency = 10(−1/slope) − 1 [26,27]. Real-time PCR efficiency was between 90 and $100\%$ for all the samples.
## 2.3. Immunohistochemistry
Immunohistochemistry (IHC) was performed on the above-mentioned samples as well as on additional human breast tissue samples from the Division of Anatomic Pathology archive of the University of Padua Hospital, and on additional canine and feline mammary tissue samples from the anatomic pathology archive of the Department of Comparative Biomedicine and Food Science of the University of Padua. Specifically, IHC was per-formed on the following tissue samples: 10 ER+ HBC, 11 TNBCs, 11 CMTs grade I, 11 CMTs grade II, 12 FMTs grade III. Sections (4 μm) were processed with an automatic immunostainer (BenchMark XT, Ventana Medical Systems), as previously described [11]. Briefly, the automated protocol included the following steps: a high-temperature antigen unmasking (CC1 reagent, 60 min), primary antibody incubation (1 h at RT, see below for dilutions), an ultrablock (antibody diluent, 4 min), hematoxylin counterstain (8 min), dehydration, and mounting. Negative controls omitted the primary antibody, whereas adnexa, epidermis, and non-tumor mammary gland, when present, were used as positive controls for CK$\frac{8}{18}$, CK$\frac{5}{6}$, CK14, E-cadherin, vimentin, and Ki-67. For ERBB2, an additional technical external positive control was used (ERBB2 3+ HBC), whereas the species-specific cross-reactivity was previously tested in dogs and cats [10,28]. For ER and PR, feline and canine uterus as well as ovary were also stained as positive controls. For CD44, the lymph node was used as positive control. Positive control tissues, typically collected from necropsies, were derived from the same archive as the canine and feline mammary tumor samples. The following antibodies were tested: anti-ER alpha (anti-ERα) (NCL-ER-6F11 1:40, Novocastra in human and feline species—NCL-ER-LH2 1:25, Novocastra in canine species); anti-PR (NCL-PGR-312 1:80, Novocastra in human and feline species); an-ti-ERBB2 (A0485 1:250, Dako in canine and feline species); anti-CK$\frac{8}{18}$ (NCL-L-5D3 1:30, Novocastra); anti-CK$\frac{5}{6}$ (D$\frac{5}{16}$ B4 1:50, Dako); anti-CK14 (NCL-LL 002 1:20, Novocastra); anti-E-cadherin (610182 1:120, BD Biosciences); anti-CD44 (550538 1:100, BD Biosciences); anti-vimentin (M0725 1:150, Dako); and anti-Ki-67 (M7240 1:50, Dako). In the human species, ERBB2 immunolabeling was performed with Bond Oracle HER2 IHC System for BOND-MAX (Leica Biosystems), containing the anti-ERBB2 antibody (clone CB11, ready-to-use). IHC positivity was semi-quantitatively and separately evaluated by ECVP-boarded (V.Z.) and experienced (L.C.) pathologists. Specifically, cytoplasmic and nuclear positivity were measured as a percentage of positive cells for all markers (100 cells per field in 10 high-power fields were counted). ERBB2 was scored as 0, 1+, 2+, and 3+ according to the American Society of Clinical Oncology (ASCO) 2018 recommendations [29] ($10\%$ cut-off), with 2+ and 3+ cases considered weakly and strongly positive for complete membrane immunolabeling, respectively. The protein expression of the studied markers was evaluated in the epithelial/luminal component. Additionally, immunolabeling was observed in healthy/hyperplastic adjacent mammary tissue, and in this case normal basal/myoepithelial cells were also evaluated.
## 2.4. Statistical Analysis
Statistical analyses were performed using Prism version 9.3.1 (GraphPad Software, San Diego, CA, USA). To verify mean differences among groups, either the Student’s t-test or the one-way ANOVA with Tukey’s multiple comparison test was used, when values were normally distributed. A Mann–Whitney test or Kruskal–Wallis test were used when values were not normally distributed. Normality was tested using the Shapiro–Wilk test. The Spearman’s rank correlation analysis was used to analyze associations between variables. The level of significance was set at $p \leq 0.05.$
## 3.1. Gene Expression
We sought to investigate the mRNA expression of the EMT transcription factors SNAIL, TWIST, and ZEB in mammary tumors compared with healthy tissue. In HBC (Figure 1), SNAIL1 showed a higher mRNA expression in TNBCs when compared with ER+ ($p \leq 0.05$). Conversely, the mRNA expression of TWIST1, TWIST2, and ZEB1 in ER+ and TNBCs was significantly lower than in healthy MGTs ($p \leq 0.05$). Additionally, TNBCs had a significantly lower mRNA expression of SNAIL2 and ZEB2 when compared with healthy MGTs ($p \leq 0.05$).
In CMTs (Figure 2), SNAIL1 showed a higher mRNA expression in STC II when compared with healthy MGTs ($p \leq 0.01$) and STC I ($p \leq 0.001$). The mRNA expression of SNAIL2, ZEB1, and ZEB2 was lower in tumors than healthy MGTs, although not statistically significant.
In FMTs (Figure 3), tumors showed a lower mRNA expression of SNAIL1, SNAIL2, TWIST1, TWIST2, ZEB1, and ZEB2 when compared with healthy MGTs, which was significant only for ZEB1 ($p \leq 0.05$).
## 3.2. Immunohistochemistry
Next, we aimed to study the expression of key proteins involved in the EMT process. The expression of the studied markers was evaluated in the tumor epithelial luminal cell population.
CD44 and ERBB2 staining was membranous, whereas CK$\frac{8}{18}$, CK$\frac{5}{6}$, CK14, and vimentin staining was cytoplasmic. E-cadherin staining was present in either or both membrane and cytoplasm and it was separately evaluated. Ki-67, ER, and PR staining was nuclear. As expected, epithelial luminal cells of healthy MGT in all three species were diffusely positive for CK$\frac{8}{18}$, membranous E-cadherin, ER, PR, and occasionally positive for CK$\frac{5}{6}$, CK14, and CD44. The basal/myoepithelial cells of healthy MGT in all three species were diffusely positive for CK$\frac{5}{6}$, CK14, CD44, and vimentin, and occasionally also positive for ER and PR.
Results for the human, canine, and feline mammary tumors are summarized in Table 2, Table S1 and are graphically represented in Figure 4.
In HBC (Figure 4A), ER+ tumors had a high protein expression (roughly $100\%$) of CK$\frac{8}{18}$, whereas they were negative for basal cytokeratins CK$\frac{5}{6}$ and CK14. In TNBCs, the protein expression of CK$\frac{8}{18}$, although fairly heterogeneous, was lower than in ER+ ($p \leq 0.001$) and the protein expression of CK$\frac{5}{6}$ was higher than in ER+ ($p \leq 0.05$). In ER+ tumors the protein expression of E-cadherin was predominantly membranous (Figure 5A), whereas in TNBCs E-cadherin protein expression was often lost from the membrane and pre-dominantly cytoplasmic (Figure 5B). Membranous E-cadherin protein expression was higher in ER+ than in TNBCs ($p \leq 0.001$), whereas cytoplasmic E-cadherin protein ex-pression was higher in TNBCs when compared with ER+ ($p \leq 0.001$) (Figure 4A). Overall, the expression of this protein was quite heterogeneous across the samples. Interestingly, a strong negative correlation between membranous and cytoplasmic E-cadherin protein expression was found in ER+ (r = −1, $p \leq 0.001$) (Figure 4B) and in TNBCs (r = −0.9, $p \leq 0.001$) (Figure 4C). CD44 protein expression was lower in ER+ (Figure 5C) than in TNBCs (Figure 5D), although not statistically significant. Notably, in TNBCs, a strong positive correlation between CK$\frac{5}{6}$ and CK14 expression ($r = 0.8$, $p \leq 0.01$), and a moderate positive correlation between CD44 and vimentin ($r = 0.6$, $$p \leq 0.05$$), were found.
All CMTs (Figure 4D) were positive (>$1\%$) for ER and, therefore, classified as ER+. ER protein expression was lower in STC II than in STC I ($p \leq 0.01$). The protein expression of E-cadherin was quite heterogeneous across the samples. As in HBC, a strong negative correlation between membranous and cytoplasmic E-cadherin protein expression was found in the CMTs (r = −0.974, $p \leq 0.001$) (Figure 4E). In addition, in STC II, a strong positive correlation between CK$\frac{8}{18}$ and membranous E-cadherin ($r = 0.8$, $p \leq 0.01$) and a strong negative correlation between CK$\frac{8}{18}$ and cytoplasmic E-cadherin (r = −0.8, $p \leq 0.01$) were found. Interestingly, in STC II, Ki-67 expression was positively correlated with CK$\frac{8}{18}$ ($r = 0.7$, $p \leq 0.05$) and membranous E-cadherin ($r = 0.8$, $p \leq 0.01$) expression, and negatively correlated with cytoplasmic E-cadherin expression (r = −0.7, $p \leq 0.05$).
All FMTs (Figure 4D) were negative for ER (<$1\%$), PR (<$1\%$), and ERBB2 (either 0 or 1+), and were therefore classified as triple negative. E-cadherin protein expression was quite heterogeneous. As in the HBCs and CMTs, a strong negative correlation between membranous and cytoplasmic E-cadherin protein expression was found (r = −0.984, $p \leq 0.001$) (Figure 4F). In addition, a strong negative correlation between CK$\frac{5}{6}$ and vimentin expression was found ($r = 0.8$, $p \leq 0.01$).
CD44 protein expression was higher in the CMTs (Figure 5E) than in the FMTs ($p \leq 0.001$) (Figure 5F). Vimentin and Ki-67 protein expression was lower in the CMTs than in the FMTs ($p \leq 0.001$) (Figure 6).
The expression of the studied markers was not associated with other histopathological features, such as vascular invasion or regional lymph node metastases (data not shown). Moreover, no significant correlations were found between gene and protein expression of the analyzed markers.
## 4. Discussion
In this study, we investigated the expression of genes and proteins involved in one of the processes thought to play a major role in cancer progression: epithelial-to-mesenchymal transition [22].
EMT is an evolutionally conserved morphogenetic program during which epithelial cells undergo a series of changes allowing them to acquire a mesenchymal phenotype [21]. During classical EMT, epithelial cells lose the expression of tight junction molecules such as membranous E-cadherin and acquire mesenchymal properties such as migration, invasiveness, and elevated resistance to apoptosis. Transcription factors like SNAIL, TWIST, and ZEB regulate this process and are activated by a variety of signaling pathways, including TGF-α, Notch, and Wnt/β-catenin [30,31,32,33].
SNAIL is a classical regulator of EMT that represses E-cadherin transcription in both mouse and human cell lines [34]. In HBC, it has been associated with tumor recurrence and metastasis [35], and with poor patient prognosis [36]. In contrast to the findings of other authors [37], we found that the mRNA expression of SNAIL2 was significantly lower in TNBCs than in healthy MGTs. In CMTs, SNAIL1 expression was higher in STC II when compared with healthy MGTs and STC I, indicating a possible association of EMT with a higher aggressiveness of these tumors. SNAIL2 in CMTs did not show any difference between healthy MGT and tumor tissue, confirming what other authors have also found [38,39,40]. Conversely, in FMTs, there was a trend such that STC III had a lower mRNA expression of SNAIL1 and SNAIL2 when compared with healthy MGTs. To the best of our knowledge, SNAIL has never been investigated in feline tumors.
It is believed that TWIST plays an essential role in cancer metastasis [33]. In HBCs and FMTs, the mRNA expression of TWIST1 and TWIST2 was lower in tumors than in healthy MGTs, which differs from what some authors have found in HBC [41], but is similar to what other authors have found in HBC [42] and in FMTs [43].
ZEB1 has been implicated in carcinogenesis in breast tissue [44] because it enhances tumor cell migration and invasion [45]. In our samples, ZEB1 mRNA expression was lower in tumor than in healthy MGTs, as previously reported by other authors in HBC [42]. Although one study examined the expression of ZEB1 and ZEB2 in five canine mammary carcinoma cell lines [46], to the best of our knowledge, ZEB mRNA expression has never been studied in CMT and FMT tissues.
Overall, our data suggest that these transcriptional factors are often downregulated in tumors compared with healthy MGTs, except for SNAIL1 in TNBCs and in CMTs STC II. The RNA isolated from healthy tissues came from the whole mammary gland, which is composed of different cell populations, namely epithelial cells, connective tissue, and fat. Although these transcription factors are barely detectable in normal mesenchymal cells of adult tissues [47], adipose tissue expresses these genes variably [48]. As a result, the mRNA levels of these genes in healthy samples can be dramatically influenced by the presence of non-mammary gland tissues, such as fat.
Moreover, it is possible that the number of cells undergoing classical EMT is low when compared with the tumor bulk, which is known to be characterized by a remarkable intra-tumor heterogeneity [22]. Furthermore, some authors believe that these genes are regulated post-transcriptionally [35,49,50,51]. Furthermore, accumulating evidence suggests the existence of cell populations with a hybrid E/M state, which exhibit increased plasticity and metastatic potential, characterized by the co-expression of epithelial and mesenchymal markers [23,24,25,52]. However, the expression of some of these markers may be associated with a complete EMT status, whereas others may be associated with a partial EMT status. For example, it is believed that SNAIL1 is a stronger inducer of complete EMT than SNAIL2, which is rather associated with a hybrid E/M state [53,54]. This suggests that the choice of the markers to be analyzed is fundamental and may help in identifying intermediate EMT states more precisely. In addition, in order to study the EMT process, it would be interesting in the future to investigate the expression of these markers at a single cell level, using single-cell omics approaches such as Laser Capture Microdissection or single-cell RNA sequencing.
In the present study, we also assessed the protein expression of several phenotypic as well as EMT-related markers, such as ER, PR, ERBB2, CK$\frac{8}{18}$, CK$\frac{5}{6}$, CK14, E-cadherin, CD44, vimentin, and Ki-67, in a subset of HBCs, CMTs, and FMTs.
The HBC ER+ samples showed a high expression of luminal CK$\frac{8}{18}$, and a negative expression of basal CK$\frac{5}{6}$ and CK14, confirming the strong association between ER+ tumors and highly differentiated glandular cells (CK$\frac{8}{18}$+), as well as null expression of basal CKs (CK$\frac{5}{6}$, CK14). In the TNBCs, the protein expression of CK$\frac{8}{18}$ was highly heterogeneous, whereas the expression of CK$\frac{5}{6}$ and CK14 was low in most of the samples. This result, in concordance with another study [55], supports the idea that the terms “basal-like cancer” and “triple-negative breast cancer” are not interchangeable. Indeed, only a small percentage of TNBCs are basal-like [56]. The CMTs were positive for ER, whereas the FMTs were negative for ER, PR, and ERBB2. Despite only a few samples being analyzed, these data suggest, as already proposed by other authors [11,57], a similarity between CMTs and HBC ER+ and between FMTs and TNBCs. In CMTs and FMTs, the protein expression of CK$\frac{8}{18}$, CK$\frac{5}{6}$, and CK14 was highly heterogeneous, confirming the high inter- and intra-tumor heterogeneity [16,57]. Basal CK14 protein expression was higher in FMTs than in CMTs, confirming that FMTs are more “basal-like” when compared with CMTs [11,12].
E-cadherin is a cellular adhesion molecule, and its disruption may contribute to the enhanced migration and proliferation of tumor cells, leading to invasion and metastasis [58,59,60,61,62]. In our samples, E-cadherin protein expression was evaluated in the membrane and in the cytoplasm of tumor cells, separately. Overall, the expression of E-cadherin was highly heterogeneous across the samples of the three species, confirming once more the high inter-tumor heterogeneity of mammary cancer in the three species. In human ER+ tumors, E-cadherin protein expression was predominantly membranous, whereas in TNBCs it was predominantly cytoplasmic, confirming that the delocalization of the protein is associated with increased tumor aggressiveness [56,63]. These results confirm that it is not only the loss of E-cadherin that correlates with increased tumor aggressiveness, but also the protein translocation from the membrane to the cytoplasm, as already described [64,65,66,67].
Together with E-cadherin, CD44 has been extensively studied in tumor cell differentiation, invasion, and metastasis, and is thought to be involved in the EMT process in HBC [68,69]. Although a few studies on HBC have shown that protein overexpression of CD44 is associated with poor prognosis and metastasis [70], others have shown that downreg-ulation of its expression is correlated with an adverse outcome [68,71]. For this reason, the role of CD44 in the behavior and prognosis of HBC is controversial [71,72]. In our study, CD44 expression was heterogeneous and lower overall in ER+ tumors compared with TNBCs. This trend agrees with study findings by Klingbeil and collaborators, who found high levels of CD44 expression in tumors with a basal-like or triple-negative phenotype, suggesting an association of this protein with an aggressive phenotype in HBC [73]. CD44 was highly expressed (roughly $85\%$) in our CMT samples, regardless of the tumor grading, as well as in the healthy mammary gland tissues. Moreover, other authors found no differences between benign CMTs, malignant CMTs, and normal mammary gland tissues, suggesting that CD44 is not associated with aggressiveness in canine mammary tumors [74,75,76,77,78]. In FMTs, the expression of CD44 was low overall (approximately $5\%$). Sarli and collaborators evaluated the intramammary/intratumoral and extramammary/extratumoral expression of CD44 in feline normal mammary tissues, benign tumors, and malignant tumors in relationship to lymphangiogenesis [79]. They found that CD44 had a significantly higher expression in intramammary/intratumor areas compared with extramammary/extratumor areas in both benign and malignant tumors. Additionally, no statistically significant differences in CD44 expression between normal mammary gland, benign tumors, and malignant tumors were found. To the best of our knowledge, no other studies on CD44 expression in FMT tissues are present within the literature. These data, together with our findings, suggest that CD44 is not a useful marker of malignancy in cats.
Another protein that is well-studied and plays a central role in the EMT process, and therefore in tumor invasion and metastasis, is vimentin [51]. Vimentin is one of the major intermediate filament proteins and is ubiquitously expressed in normal mesenchymal cells [80]. Recent studies have reported that vimentin knockdown causes a decrease in genes linked to HBC metastasis, such as the receptor tyrosine kinase Axl [81]. In our study, we also evaluated the expression of vimentin in HBCs, CMTs, and FMTs. We found a higher expression of vimentin in TNBCs compared with ER+, although not statistically significant. This result suggests that vimentin expression is associated with the triple-negative subtype, aggressive behavior, and a poor prognosis of HBC, as previously reported by many authors [82,83,84,85]. In CMTs, vimentin expression is low (approximately $15\%$), con-firming the low aggressiveness of mammary tumors in dogs, which is in concordance with the findings of other authors [86]. Conversely, in FMTs, the expression of vimentin, although heterogeneous, was quite high (approximately $70\%$), suggesting the high aggressiveness of mammary tumors in this species [9], as well as their similarities with TNBCs [11].
Unfortunately, as a limitation of this study, only grade I and II CMTs were included. No RNALater-sampled canine tumors were diagnosed as grade III. For possible IHC analyses in our archive of paraffin-embedded tissues, a very limited number of grade III simple CMTs were found (14 cases over five years) that were often already vascular/lymph node invasive ($\frac{10}{14}$). This study would not benefit much from adding only IHC analysis of grade III CMTs that already have invaded the vascular system or with metastases. We still believe that the study allowed the collection of some new data on the most frequent FMTs and CMTs in comparison with HBC samples assessing both gene and protein expression.
## 5. Conclusions
In summary, this study showed that most of the classical EMT-related transcription factors SNAIL, TWIST, and ZEB are downregulated in tumor tissues compared with healthy tissues, although additional analyses should be performed to better investigate them in neoplastic clones and in a larger set of samples. IHC analyses indicated a potential role of some markers, namely vimentin and E-cadherin, but not of others (i.e., CD44) as indicators of EMT (including loss of cell differentiation and increased malignancies). Moreover, all the IHC data seem to support the already proposed similarities between FMTs (grade III) and TNBCs, as well as between CMTs (grade I and II) and ER+ HBCs. The two species are widely discussed as potential spontaneous models of specific HBC subtypes [11,12,15,16,57,87,88,89,90].
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---
title: Effects of Dietary Alpha-Lipoic Acid on Growth Performance, Serum Biochemical
Indexes, Liver Antioxidant Capacity and Transcriptome of Juvenile Hybrid Grouper
(Epinephelus fuscoguttatus♀ × Epinephelus polyphekadion♂)
authors:
- Guanghai Ou
- Ruitao Xie
- Jiansheng Huang
- Jianpeng Huang
- Zhenwei Wen
- Yu Li
- Xintao Jiang
- Qian Ma
- Gang Chen
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000056
doi: 10.3390/ani13050887
license: CC BY 4.0
---
# Effects of Dietary Alpha-Lipoic Acid on Growth Performance, Serum Biochemical Indexes, Liver Antioxidant Capacity and Transcriptome of Juvenile Hybrid Grouper (Epinephelus fuscoguttatus♀ × Epinephelus polyphekadion♂)
## Abstract
### Simple Summary
As global demand for animal protein increases, the entire animal production system is gradually moving towards intensification. The aquaculture industry is growing rapidly, but it is vulnerable to disease and environmental stress, resulting in aquaculture losses. Antioxidant supplementation in diets can improve the resistance of fish to environmental stress, which is an important measure to reduce the loss of the aquaculture industry. Alpha-lipoic acid (α-LA) is considered to be a “general antioxidant” or “ideal antioxidant” which has a strong antioxidant capacity. In this study, juvenile hybrid groupers were fed a diet supplemented with α-LA for 56 days. The results indicated that the addition of 0.4 and 0.6 g/kg α-LA to the diet inhibited the growth performance of juvenile hybrid groupers. Furthermore, 1.2 g/kg α-LA could reduce the blood lipid level, improve hepatocyte damage, and increase the antioxidant enzyme activity of the liver. In addition, transcriptome results indicated that dietary α-LA significantly affected the pathway related to immune function (the JAK/STAT signaling pathway, prolactin signaling pathway, and antigen processing and presentation) and glucose homeostasis (glycolysis/gluconeogenesis).
### Abstract
We aimed to investigate the effects of dietary alpha-lipoic acid (α-LA) on the growth performance, serum biochemical indexes, liver morphology, antioxidant capacity, and transcriptome of juvenile hybrid groupers (Epinephelus fuscoguttatus♀ × Epinephelus polyphekadion♂). Four experimental diets supplemented with 0 (SL0), 0.4 (L1), 0.6 (L2), and 1.2 (L3) g/kg α-LA were formulated and fed to three replicates of juvenile hybrid grouper (24.06 ± 0.15 g) for 56 d. The results indicated that dietary 0.4 and 0.6 g/kg α-LA significantly decreased the weight gain rate in juvenile hybrid groupers. Compared with SL0, the content of total protein in the serum of L1, L2, and L3 increased significantly, and alanine aminotransferase decreased significantly. The content of albumin in the serum of L3 increased significantly, and triglyceride, total cholesterol, and aspartate aminotransferase decreased significantly. In addition, the hepatocyte morphology in L1, L2, and L3 all showed varying degrees of improvement, and the activities of glutathione peroxidase and superoxide dismutase in the liver of L2 and L3 were significantly increased. A total of 42 differentially expressed genes were screened in the transcriptome data. KEGG showed that a total of 12 pathways were significantly enriched, including the pathway related to immune function and glucose homeostasis. The expression of genes (ifnk, prl4a1, prl3b1, and ctsl) related to immune were significantly up-regulated, and the expressions of gapdh and eno1 genes related to glucose homeostasis were significantly down-regulated and up-regulated, respectively. In summary, dietary supplementation of 0.4 and 0.6 g/kg α-LA inhibited the growth performance of juvenile hybrid groupers. A total of 1.2 g/kg α-LA could reduce the blood lipid level, improve hepatocyte damage, and increase the hepatic antioxidant enzyme activity. Dietary α-LA significantly affected the pathway related to immune function and glucose homeostasis.
## 1. Introduction
As global demand for animal protein increases, the entire animal production system is gradually moving towards intensification [1]. The aquaculture industry is growing rapidly, but it is vulnerable to the interactions between the animals themselves, diseases, and the environment. Therefore, a new model of aquaculture management strategy has emerged as a result of a growing understanding of animal nutrition and feed. The core objectives of this model are to minimize the effects of stressors by neutralizing free radicals, repairing oxidative damage to biological macromolecules and membrane systems, enhancing immunity, and maintaining normal physiological homeostasis. The key points of this model are antioxidant supplementation and increasing endogenous cellular antioxidants [1]. Supplementation of diets with antioxidants can improve the resistance of fish to environmental stresses and is an essential measure to reduce losses in the aquaculture industry [2]. Alpha-lipoic acid (α-LA), also known as 1,2-dithiolane-3-valeric acid, with the molecular formula C8H14O2S2, was first isolated from pig liver by Lester J. Reed in 1951 [3]. α-LA is a naturally occurring compound found in microorganisms, plants, and animals, and is considered to be an “ideal antioxidant” or “universal antioxidant” because of its strong antioxidant capacity [4,5].
Studies have shown that α-LA can improve the survival rate, growth performance, and immunity of fish, and also improve the nutritional value of fish, which makes α-LA suitable for application in aquaculture [6]. For example, dietary supplementation with an appropriate amount of α-LA could promote growth, fatty acid β-oxidation, and lipolysis of grass carp (Ctenopharyngodon idellus; Cuvier et Valenciennes, 1844), increase protein deposition, enhance immunity and antioxidant capacity, alleviate the inflammatory response, and reduce lipid oxidative damage. It also could promote the expression of peripheral anorexia factor mRNA and reduce the expression of peripheral appetite factor mRNA, thus, reducing the intake and body weight of grass carp [7,8,9,10]. The enhancement of growth performance has also been found in other aquatic organisms with moderate amounts of α-LA in their diets, such as *Nile tilapia* (Oreochromis niloticus; Linnaeus, 1758) [11], African catfish (Clarias gariepinus; Burchell, 1822) [2], giant gourami (Osphronemus goramy; Lacepède, 1801) [12], Chinese mitten crab (Eriocheir sinensis; H. Milne Edwards, 1853) [13], and northern snakehead (Channa argus; Cantor, 1842) [14]. In addition, dietary supplementation with moderate amounts of α-LA could promote the expression of gluconeogenesis-related genes induced by a high-fat diet in fish, reduce lipid accumulation under high-fat conditions [15], and enhance starch utilization in carp (Cyprinus carpio; Linnaeus, 1758) [16].
The hybrid grouper (Epinephelus fuscoguttatus♀; Forsskål, 1775 × Epinephelus polyphekadion♂; Bleeker, 1849) is an important mariculture fish in southern China, with the characteristics of rapid growth and strong stress resistance, and it has a high economic value in China [17,18,19,20]. Although α-LA has been studied for over 70 years and there have been numerous studies on its addition as an antioxidant of aquatic animal diets, there have been few studies of α-LA in terms of the supplementation of marine fish diets. There are no reports of α-LA being added to the diet of groupers. Therefore, the purpose of this experiment was to research the effects of diet supplementation with α-LA on the growth performance, serum biochemical indexes, hepatic morphology, antioxidant capacity, and transcriptome of juvenile hybrid grouper fish, and to expand theoretical knowledge for the application of antioxidants in the hybrid grouper diet.
## 2.1. Preparation of Diets and Testing of Nutritional Levels
Four isonitrogenous diets (SL0, L1, L2, and L3) were prepared with 0, 0.4, 0.6, and 1.2 g/kg of α-LA ($99\%$ purity, Yingbo biotechnology Co., Ltd.), respectively. The α-LA content was referenced from previous studies [2,7,12]. Referring to the study on experimental diet formulation and nutrient levels of hybrid groupers by Xie et al. [ 20]. The experimental diet formulations, as well as nutrient levels, are shown in Table 1. All feed raw materials were crushed and sieved through a 60-mesh sieve. We weighed the ingredients accurately according to the feed formula and mixed them well, then added the fish oil and soybean lecithin, rubbed the powdered ingredients and oil together manually, then added the right amount of water to knead all the ingredients into a dough. Finally, the raw material was processed into pellets with a particle size of 2.5 mm using a twin-screw extruder, air-dried, and stored in a −20 °C refrigerator. The nutritional levels of the diets were tested according to the AOAC standard method [21], and specific detection methods refer to An et al. [ 22].
## 2.2. Experiment Design
The experiments were conducted in the Zhanjiang Marine Hi-tech Park of Guangdong Ocean University (Zhanjiang, China). The water used for the culture experiments was natural seawater treated by sand filtration and sedimentation with uninterrupted aeration. The water temperature was kept at 28.5 ± 2.0 °C, pH was maintained at 7.6–8.2, dissolved oxygen was kept above 6 mg/L, total nitrite and ammonia content was kept below 0.04 mg/L, and the photoperiod adopted a natural day–night cycle (12 h of light, 12 h of darkness). The experimental juvenile hybrid groupers were purchased from a grouper hatchery at the southeast quay of Zhanjiang City, Guangdong Province, China, and were temporarily reared in an indoor cement pond (2.0 m × 4.0 m × 2.0 m) for 14 days after being transported back to the base. At the end of temporary rearing, the fish were starved for 24 h. A total of 360 fish (24.06 ± 0.15 g) with similar sizes, intact scales, and normal diet were randomly allotted to 12 fiberglass tanks (0.5 m3). Twelve fiberglass tanks were divided into four groups (SL0, L1, L2, and L3) with three replicates per group. The experiment was carried out in an indoor flowing water aquaculture system for 56 days. During the experiment, the corresponding feed was fed at 8: 30 and 17: 00 every day at 5–$8\%$ of their body weight. The water was changed as necessary to maintain superior water quality.
## 2.3. Sample Collection
After the end of the experiment, all the experimental fish were fished out and weighed after 24 h starvation treatment. Six fish were randomly fished out from each fiberglass tanks and anesthetized with eugenol (each 100 mg of eugenol is dissolved in 1 L of seawater, Shanghai Yuanye Biotechnology Co., Ltd., Shanghai, China). The body length and body weight were measured to calculate morphological indices. Subsequently, blood was collected from the tail vein of the fish using a 1 mL needle tube, and the needle tube was washed with heparin sodium before blood collection. After standing for 12 h at 4 °C, the blood was centrifuged using a refrigerated high-speed centrifuge (4 °C, 3500 rpm,10 min). The supernatant (serum) was collected and placed in a 2 mL centrifuge tube and stored at −80 °C for biochemical testing. The abdomen was dissected using sterilized scissors and tweezers, the visceral mass and liver were separated and weighed, and the liver was then washed with saline to remove other impurities. A liver tissue (5 mm × 5 mm × 5 mm) was cut from the center of the liver and placed in a 2 mL centrifuge tube containing $4\%$ paraformaldehyde. After 24 h of fixation, the liver tissue was washed with $70\%$ ethanol solution and stored in $70\%$ ethanol solution for HE sections. About 1.0 g of liver tissue was cut from the remaining liver tissue and placed in a 2 mL cryopreservation tube, frozen in liquid nitrogen, and transferred to a −80 °C refrigerator for antioxidant capacity testing and transcriptome sequencing.
## 2.4. Growth Performance Index Measurement
In this study, the calculation methods of the growth performance indexes of juvenile hybrid groupers are as follows:[1]Weight gain rate (WGR,%)=[final body weight (g) − initial body weight (g)] / initial body weight (g)×$100\%$; [2]Survival rate (SR, %)= The number of final fish / The number of initial fish×100; [3]Feed conversion ratio (FCR)= feed intake (g) / [final body weight (g) − initial body weight (g)]; [4]Specific growth rate (SGR,%/d)={Ln[final body weight (g)] − Ln[initial body weight (g)]} / Number of days (d)×$100\%$; [5]Condition factor (CF, g/cm3)=[final body weight (g) / Final body length (cm)3]×$100\%$; [6]Feed intake (FI,%)=total feed weight (g){[initial body weight (g)+final body weight (g)]/2}×Number of days (d)×100 [7]Hepatosomatic index (HSI, %)=[liver weight (g)/ weight of this fish (g)]×$100\%$; [8]Viscerosomatic index (VSI, %)=[viscera weight (g)/ weight of this fish (g)]×$100\%$
## 2.5. Determination of Serum Biochemical Indexes and Liver Antioxidant Parameters
The serum biochemical indexes included triglyceride (TG, GPO-PAP enzymatic method), total cholesterol (TCHO, COD-PAP method), total protein (TP, BCA microplate method), albumin (ALB, bromocresol green method), low-density lipoprotein cholesterol (LDL-C, dual reagent direct method), high-density lipoprotein cholesterol (HDL-C, dual reagent direct method), aspartate aminotransferase (AST, Lai’s method), and alanine aminotransferase (ALT, Lai’s method). Antioxidant parameters of the liver included glutathione peroxidase (GSH-Px, colorimetric method), catalase (CAT, ammonium molybdate method), superoxide dismutase (SOD, WST-1 method), and malondialdehyde (MDA, TBA method). The above indicators were determined using kits produced by Nanjing Jiancheng Bioengineering Institute (Jiangsu, China). Experiments were conducted in strict accordance with the instructions, and all instructions can be found and downloaded at http://www.njjcbio.com (accessed on 15 January 2023).
## 2.6. Preparation and Observation of Hematoxylin-Eosin Stained Liver Sections
The fixed liver tissue was taken out and repaired with a scalpel and placed in a dehydration box. The liver tissue was dehydrated by gradient alcohol using a dehydrator (Donatello, DIAPATH) and then embedded in paraffin. The paraffinized liver tissue was embedded in an embedding machine (JB-P5, Wuhan Junjie Electronics Co., Ltd., Wuhan, China) to form a tissue block. After cooling, 4 μm thick sections were cut out in a paraffin sectioning machine (RM2016, Shanghai Leica Instrument Co., Ltd., Shanghai, China). After deparaffinization, the sections were stained with hematoxylin and eosin, and finally dehydrated and fixed in a glass slide. The morphology of liver cells was observed using an upright fluorescence microscope (Nikon ECLIPSE Ni-E, Tokyo, Japan).
## 2.7.1. RNA Extraction and Detection, Library Construction and High-Throughput Sequencing
Total RNA from SL0 and L3 hepatic tissues was extracted using a Trizol kit (Invitrogen, Carlsbad, CA, USA). The total RNA quality and integrity were examined using an Agilent 2100 biological analyzer (Agilent Technologies, Palo Alto, CA, USA) and RNase-free agarose gel electrophoresis, respectively. The library construction and high-throughput sequencing were completed by Genedenovo Biotechnology Co., Ltd. (Guangzhou, China), and high-throughput sequencing was performed using an Illumina NovaSeq 6000.
## 2.7.2. Data Quality Control, De Novo Assembly and Unigene Basic Annotation
The raw data were quality-controlled using the quality control software fastp (version 0.18.0) to filter low-quality raw sequencing data. De novo assembly was performed using the short reads assembling the program, Trinity. The unigene sequence was compared with the SWISS-PROT protein database, NCBI non-redundant protein (Nr) database, Kyoto Encyclopedia of Genes and Genome (KEGG) database, and COG/KOG database using the BLASTx program, and then the protein function annotation was obtained according to the best alignment results.
## 2.7.3. Differentially Expressed Genes Analysis
The analysis was performed using DESeq2 software. First, we normalized the read counts, then calculated the probability of hypothesis testing (p-value) based on the model, and, finally, we performed multiple hypotheses testing corrections to obtain the FDR value (false discovery rate). Based on the results of differential analysis, the genes of FDR < 0.05 and |log2(Fold Change)| > 1 were screened as differentially expressed genes (DEGs). Volcano plot analysis, KEGG pathway enrichment analysis, and GO functional enrichment analysis were performed according to the DEGs.
## 2.7.4. Real-Time Quantitative PCR (RT-qPCR) Validation
*Five* genes, namely phosphatase inhibitor-1 (i-1), alpha-enolase (eno1), thioredoxin-interacting protein (txnip), parvalbumin (pvalb), and dual specificity phosphatase 1 (dusp1), were randomly selected from DEGs for RT-qPCR to verify the reliability of RNA-Seq data. Total RNA extraction, cDNA synthesis, and RT-qPCR detection of SL0 and L3 liver tissues were performed using TransGen Biotech kits (Beijing, China). The specific primers (Table 2) were designed by Primer Premier 5.0 software and was synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). According to the RT-qPCR kit instructions, qualified cDNA and primers were tested using a LightCycler 96 real-time fluorescent quantitative PCR instrument (Roche, Basel, Switzerland) with four replicates for each sample. The reaction procedures were as follows: 94 °C for 30 s (1 cycle); 95 °C for 5 s, 60 °C for 15 s and 72 °C for 10 s (40 cycles); 95 °C for 10 s, 60 °C for 60 s and 95 °C for 1 s (1 cycle); 37 °C for 30 s (1 cycle). According to the measured Ct value, the relative expression of each gene was calculated using the 2−ΔΔCt method [23].
## 2.8. Data Statistical Analysis
One-way ANOVA was performed on the experimental data using SSPS 21.0. For WGR, SR, SGR, VSI, and HSI, data transformation was required to remove the % before performing ANOVA, and, after completing ANOVA, data transformation was performed and the % was added. Tukey’s test was used for multiple comparisons if there were significant differences between groups. The experimental data were expressed as mean ± standard deviation (mean ± SD). Here, $p \leq 0.05$ represents a significant difference between groups.
## 3.1. Growth Performance
The effects of dietary α-LA supplementation on the growth performance, morphology, and feed utilization of juvenile hybrid groupers are shown in Table 3. The WGR was significantly lower in the L1 and L2 groups than in SL0 and L3, and the FCR was significantly higher in the L2 group than in the other groups ($p \leq 0.05$). There was no significant difference in other indicators.
## 3.2. Serum Biochemical Indexes
The effects of dietary α-LA supplementation on the serum biochemical indexes of juvenile hybrid groupers are shown in Table 4. The TG level of L3 was significantly lower than that of SL0 and L1, and the TCHO level of L3 was significantly lower than that of SL0 ($p \leq 0.05$). The TP content of SL0 was significantly lower than the other three groups ($p \leq 0.05$). The ALB level of L3 was significantly higher than that of SL0 and L1 ($p \leq 0.05$). The LDL-C level of L2 was significantly higher than the other three groups, and the LDL-C level of L3 was also significantly higher than SL0 and L1 ($p \leq 0.05$). The AST level of L3 was significantly lower than the other three groups, while the ALT level of SL0 was significantly higher than the other three groups ($p \leq 0.05$).
## 3.3. Hepatic Morphology
The effect of dietary α-LA supplementation on the hepatic morphology of juvenile hybrid groupers is shown in Figure 1. It was observed that the hepatic cells of the SL0 (control) group showed serious cell vacuolation, swelling, disordered arrangement, and nuclear migration. Compared with SL0, L1, L2, and L3 liver cells were slightly vacuolated, the phenomenon of nuclear migration was reduced, and cell morphology was more regular.
## 3.4. Antioxidant Capacity of Liver
The effect of dietary α-LA supplementation on the hepatic antioxidant capacity of juvenile hybrid groupers is shown in Table 5. The GSH-Px activity of L3 was significantly higher than that of the other three groups, and the GSH-Px activity of L1 and L2 was also significantly higher than that of SL0 ($p \leq 0.05$). The activity of SOD in L2 and L3 was significantly higher than that in SL0 and L1 ($p \leq 0.05$). There was no significant difference in CAT activity and MDA content ($p \leq 0.05$).
## 3.5.1. Transcriptome Sequencing Results
The transcriptome data are shown in Table 6. A total of 37760784900 bp of RawData was obtained. After data quality control and filtering low-quality data, a total of 37,229,629,218 bp of CleanData was obtained. Base quality and composition analysis showed that the GC content range in each liver tissue sample was 49.58–$50.10\%$, the percentage of Q20 bases was higher than $98.13\%$, and the percentage of Q30 bases was higher than $94.62\%$.
## 3.5.2. DEGs Analysis
A total of 42 DEGs were identified using DEseq2 software under FDR < 0.05 and |log2(Fold Change)| > 1. Compared with the SL0, 31 DEGs were up-regulated and 11 DEGs were down-regulated in L3 liver tissue (Figure 2). A part of the DEGs is shown in Table 7.
## 3.5.3. GO Function Analysis of DEGs
GO functional enrichment analysis was performed on DEGs. Based on sequence homology, all DEGs were classified into the following three major branches of GO: molecular function, biological process, and cellular component, including 40 functional subcategories, involving 7 molecular functions, 12 cellular components, and 21 biological processes (Figure 3). Among them, the biological process is mainly composed of the single organism process, metabolic process, and cellular process. Cellular components were mainly the membrane, organelle, cell part, and cell. The main molecular functions were molecular function regulation, catalytic activity, and binding.
## 3.5.4. KEGG Pathway Enrichment Analysis of DEGs
In the KEGG pathway database, biological metabolic pathways are divided into six categories, namely human diseases, organismal systems, cellular processes, environmental information processing, genetic information processing, and metabolism. In this experiment, a total of 17 DEGs were annotated into these six categories. DEGs were mostly enriched in the two KEGG main classes of biological systems and human diseases; they were also enriched in the overall and overview maps, signal transduction, endocrine system, immune system, cardiovascular disease, and infectious disease KEGG subclasses (Figure 4).
When performing KEGG pathway enrichment analysis on DEGs, the top 20 pathways with the smallest p-value were used to make KEGG enrichment bubble maps, and the results were shown in Figure 5. DEGs are significantly enriched in the JAK/STAT signaling pathway, glycolysis/gluconeogenesis, amino acid biosynthesis pathways, cytokine–cytokine receptor interaction, lysosomes, and so on. However, each KEGG significantly enriched pathway contained no more than three DEGs.
## 3.5.5. Validation of RNA Sequencing Data
To verify the accuracy of RNA-Seq results, five DEGs (two down-regulated and three up-regulated genes) were randomly selected, and their expression levels were detected by RT-qPCR. The results are shown in Figure 6. The results of the gene expression obtained were consistent with the trend of the results obtained from RNA-Seq, indicating that the RNA-*Seq data* had a certain feasibility.
## 4. Discussion
α-LA is a multifunctional antioxidant that can promote growth performance as a feed additive for poultry animals [24]. However, α-LA could also inhibit AMPKα in the hypothalamuses of chickens (Gallus; Linnaeus, 1758) to reduce food intake [25], and could activate AMPKα in the liver to inhibit the synthesis of glycogen synthase in the liver, resulting in a decrease in glycogen synthesis, thereby changing energy homeostasis and delaying the growth of chicken weight [26]. Therefore, the growth-promoting effect of α-LA needs to be analyzed specifically in combination with the amount of α-LA added. In the study of α-LA as a diet supplement for aquatic animals, more studies have shown that with the increase in α-LA dose, the growth performance of aquatic animals exhibited a trend of increasing first and then decreasing, and high doses of α-LA were still able to improve the growth performance, such as in catfish [2], giant gourami [12], northern snakehead [14], and Chinese mitten crab [27]. However, some studies have suggested that high doses of α-LA had an inhibitory effect on the growth of aquatic animals, such as in *Nile tilapia* [11] and oriental river prawn (Macrobrachium nipponense; de Haan, 1849) [28]. The recommended addition amounts were 439–528 mg/kg and 1354.8 mg/kg, respectively, but their growth performance was inhibited at 2400 mg/kg and 4000 mg/kg, respectively. In the present study, dietary supplementation with low doses of α-LA (0.4 and 0.6 g/kg) significantly reduced the WGR of juvenile hybrid groupers. Similar to the experimental results of Zhang et al. [ 29], the addition of lower α-LA to the diet reduced the WGR of abalone (*Haliotis discus* hannai; Ino, 1952), which may be the result of α-LA increasing energy consumption in juvenile hybrid groupers [30]. However, the addition of 1.2 g/kg α-LA had no significant effect on the WGR of juvenile hybrid groupers, which may be the result of a high dose of α-LA promoting lipolysis to consume energy by activating the AMPKα-ATGL pathway without causing weight loss [9]. In addition, Ding et al. [ 31] found that α-LA in diets could reduce the growth rate of oriental river prawn fed with low carbohydrate diet but had no significant effect on the growth rate of oriental river prawn fed with high carbohydrate diet. This indicates that the composition of the diet may affect the mechanism of α-LA. Huang et al. [ 7] discovered that dietary supplementation of 1.2 g/kg α-LA could inhibit the growth performance of grass carp. In this experiment, dietary supplementation of 1.2 g/kg α-LA had no significant effect on the WGR of juvenile hybrid groupers, indicating that different species had different sensitivities to α-LA. At present, the optimal α-LA addition amount for juvenile hybrid groupers with WGR as a reference still needs further study, and 1.2 g/kg α-LA has a certain reference value.
Serum biochemical indexes can reflect the overall health status, physiological stress response, and nutritional status of fish [32,33]. TG and TCHO are the main components of blood lipids [34,35]. The contents of TG and TCHO in serum are important indicators to measure lipid metabolism in fish [36,37]. Studies have shown that α-LA has the effect of lowering blood lipids and could reduce the content of TG and TCHO in mice and rats [38,39,40]. Samuki et al. [ 12] reported that dietary supplementation of 0.3, 0.6, and 0.9 g/kg α-LA reduced the content of TG in the serum of giant gourami; similarly, Siagian et al. [ 2] also reported that 1.0 and 1.5 g/kg α-LA reduced the content of TG in the serum of African catfish. In this experiment, α-LA not only reduced the content of TG in L3 serum but also reduced the content of TCHO. Butler et al. [ 41] suggested that α-LA could reduce the TG content in blood and the liver by inhibiting the expression of liver lipogenic genes, reducing hepatic TG secretion, and stimulating the clearance of TG-rich lipoproteins. Zulkhairi et al. [ 42] believed that α-LA may reduce the TCHO content in the blood by cholesterol metabolism or lipoprotein lipase activity in the liver. TP and ALB are important indicators of protein synthesis and metabolism and immune function [20,43,44]. Shi et al. [ 9] discovered that α-LA regulates the AMPKα-CPT-1α pathway to reduce protein consumption in grass carp to increase protein deposition. In addition, Liu et al. [ 8] found that α-LA could enhance the immune function of the grass carp skin, spleen, and head kidney. In this experiment, the contents of TP and ALB in the serum of L3 were significantly increased, but the growth performance of L3 did not change significantly. Therefore, the increase in TP and ALB may be the result of α-LA enhancing the immune function of juvenile hybrid groupers.
ALT and AST are low in serum and are mainly distributed in liver cells. When liver cells are damaged, they can release ALT and AST to increase their activity in serum, which is consistent with the extent of hepatic cell damage [45]. In this experiment, the levels of ALT and AST in the serum of L3 decreased significantly, which was similar to the experimental results of adding α-LA in grass carp [10] and African catfish [2]. This indicated that the degree of hepatic damage of L3 was lower than that of SL0, i.e., dietary supplementation of α-LA could improve the damage of liver cells in juvenile hybrid groupers. At the same time, by observing the morphology of hepatic tissue cells, it was found that compared with SL0, the morphology of hepatic tissue cells of juvenile hybrid groupers fed with α-LA was improved to varying degrees. This further confirmed that dietary supplementation of α-LA can improve hepatic cell damage.
The antioxidant system can protect fish from oxidative stress and is essential for fish health [46]. Antioxidant enzymes (GSH-Px, CAT, and SOD) can scavenge free radicals to reduce oxidative stress, and their activities can reflect the health status of aquatic animals [47]. GSH-Px can remove hydrogen peroxide and lipid peroxide in the body [48]. SOD is a common antioxidant enzyme in the body and can remove superoxide anions [49]. α-LA is considered to be an “ideal antioxidant” or “general antioxidant”, which can reduce oxidative damage and enhance the antioxidant defense systems of fish by scavenging excessive ROS and regenerating other antioxidants [50,51]. In this experiment, the activity of GSH-Px in the livers of juvenile hybrid groupers fed with α-LA was increased to varying degrees, and the activity of SOD in L2 and L3 was significantly increased. At present, many studies have reported similar results, and α-LA could improve the antioxidant capacity of aquatic animals. Xu et al. [ 11] found that 0.3 g/kg α-LA significantly increased the activities of SOD and GSH-Px in the liver of Nile tilapia. Li et al. [ 14] discovered that 600, 900, and 1200 mg/kg α-LA significantly increased the activities of SOD and GSH-Px in the liver, head kidney, and spleen of northern snakehead. Zhang et al. [ 29] found that 800 mg/kg α-LA significantly increased the activities of SOD and GSH-Px in abalone. In summary, the results of this experiment showed that an appropriate amount of α-LA could increase the activity of antioxidant enzymes in the livers of juvenile hybrid groupers, thereby enhancing antioxidant capacity.
The transcriptome includes RNA transcripts expressed in a specific cell or tissue types under environmental conditions or specific developmental conditions [52]. In recent years, transcriptome analysis has been widely used in aquaculture, and can be used for effective identification and expression analysis of candidate genes, such as growth, development, reproduction, disease, immunity, stress, and toxicology genes [53]. In this experiment, according to serum biochemical indicators and liver antioxidant capacity, liver samples of SL0 and L3 were selected for transcriptome sequencing analysis. Functional analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that a total of 10,810 unigenes were annotated into 48 KEGG pathways, of which 12 pathways were significantly enriched, including the JAK/STAT signaling pathway, prolactin signaling pathway, antigen processing and presentation, glycolysis/gluconeogenesis, and so on.
The JAK/STAT signaling pathway is a common pathway for signal transduction of many cytokines, which is closely related to apoptosis, cell proliferation, inflammatory response, and differentiation. It is very important for coordinating adaptive immune mechanisms, initiating innate immunity, and inhibiting inflammatory responses [54]. In this experiment, the JAK/STAT signaling pathway included three genes: ifnk, prl4a1, and prl3b1. In addition, the prolactin signaling pathway also includes prl4a1 and prl3b1 genes. The ifnk gene is a new type I interferon subclass [55]. Interferons are proteins that are crucial to the human immune system. They are formed in various cells of fish, mammals, reptiles, and amphibians. IFN type I and IFN type II are found in ray-finned fish (Actinopterygii), and IFN type III is also found in phylogenetically older cartilaginous fishes. IFN type I in ray-finned fish (Actinopterygii) can activate the JAK/STAT signaling pathway and induce the expression of IFN-stimulated genes containing IFN-stimulated response elements complexes and, thus, possessing antiviral activity. In addition, in Perciformes, IFN I has been shown to exert antibacterial effects through macrophage phagocytosis [56]. The grouper belongs to Osteichthyes, Actinopterygii, and Perciformes. Both prl4a1 and prl3b1 are members of the prolactin family, and prolactin is a multifunctional polypeptide hormone with immunomodulatory and protective effects [57]. Studies have shown that prolactin can induce the expression of genes encoding major phagocytic NADPH oxidase components and ROS production in fish macrophages through the JAK2/Stat/IRF-1 signaling pathway [58]. Antigen processing and presentation is the mechanism by which the entire antigen is degraded and loaded onto MHC molecules (class I and II) to display on the cell surface of T cells [59]. Zhang and Chen [60] found that a novel CC chemokine may be involved in the adaptive immune response by regulating MHC class I antigen processing and presentation in large yellow croaker (Pseudosciaena crocea; Richardson, 1846). In this experiment, only the expression of the ctsl gene was significantly up-regulated in antigen processing and presentation. Cathepsin L (ctsl) is a member of the papain family of cysteine proteases [61] which plays an important role in the biological activities of fish, including antigen processing [62], antigen presentation [63], protein degradation [64], and anti-microbial invasion [65]. Recently, the key role of ctsl in the innate immune system of many fish species has been further revealed [66]. In summary, combined with the significant increase in TP and ALB in the serum of L3 and the significant up-regulation in ifnk, prl4a1, prl3b1, and ctsl in the JAK/STAT signaling pathway, prolactin signaling pathway, and antigen processing and presentation in liver, it is speculated that dietary supplementation of α-LA can enhance the immune function of juvenile hybrid groupers by regulating the JAK/STAT signaling pathway, prolactin signaling pathway, and antigen processing and presentation.
Glycolysis/gluconeogenesis is an opposing metabolic pathway involved in carbohydrate degradation and synthesis and plays an important role in maintaining glucose homeostasis [67]. In this experiment, the glycolysis/gluconeogenesis pathway included two genes, gapdh and eno1. Glyceraldehyde-3-phosphate dehydrogenase (gapdh) plays a key role in the glycolytic pathway. It can catalyze the formation of glyceraldehyde-3-phosphate to 1,3-bisphosphoglycerate, which produces NADH. NADH can synthesize ATP through the electron transport chain in mitochondria [68]. α-enolase (eno1) plays a functional role in glycolysis/gluconeogenesis. It can catalyze the conversion of 2-phosphate-D-glycerate to phosphoenolpyruvic acid during glycolysis and phosphoenolpyruvic acid to 2-phosphate-D-glycerate during glycogen synthesis [69]. Huang et al. [ 15] showed that the addition of α-LA to the diet could enhance the expression of glycolysis, gluconeogenesis, and glucose transport-related genes in zebrafish (Danio rerio; Hamilton, 1822) livers. In this experiment, the expression of gapdh gene was significantly down-regulated, and the expression of eno1 gene was significantly up-regulated. Therefore, it is speculated that α-LA can maintain the glucose homeostasis of juvenile hybrid groupers by regulating the expression of gapdh and eno1 genes in the glycolysis/gluconeogenesis pathway.
Therefore, the optimal addition of α-LA in the diet of hybrid groupers needs further study. However, this experiment showed that without affecting the growth of hybrid groupers, it could reduce the blood lipid level of hybrid groupers, improve the damage of liver cells, and increase the activity of antioxidant enzymes in the liver. This indicates that an appropriate amount of α-LA can be used as an additive to improve fish health in actual production. In addition, the transcriptome results provide some theoretical knowledge for the further study of α-LA in immune and glucose homeostasis.
## 5. Conclusions
In summary, in this experiment, 0.4 and 0.6 g/kg α-LA inhibited the growth performance of juvenile hybrid groupers. Although 1.2 g/kg α-LA had no significant effect on the growth performance, it could reduce the blood lipid level of juvenile hybrid groupers, improve hepatocyte damage, and increase the antioxidant enzyme activity of the liver. Transcriptome analysis showed that dietary α-LA supplementation significantly affected the pathway related to immune function (JAK/STAT signaling pathway, prolactin signaling pathway, and antigen processing and presentation), and significantly up-regulated the expression of genes related to immunity (ifnk, prl4a1, prl3b1, and ctsl). In addition, α-LA also changed the pathway related to glucose homeostasis (glycolysis/gluconeogenesis), significantly down-regulated the expression of the gapdh gene, and up-regulated the expression of the eno1 gene.
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|
---
title: Relationships between the Content of Micro- and Macroelements in Animal Samples
and Diseases of Different Etiologies
authors:
- Marina V. Stepanova
- Larisa F. Sotnikova
- Sergei Yu. Zaitsev
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000063
doi: 10.3390/ani13050852
license: CC BY 4.0
---
# Relationships between the Content of Micro- and Macroelements in Animal Samples and Diseases of Different Etiologies
## Abstract
### Simple Summary
Macro- and microelements (MMEs), simply called as “minerals”, are essential biogenic elements for animals and humans. These two MME groups are very important and differ only in that the first group) macroelements (Ca, P, K, Na, S, Cl, Mg) are containing in concentrations more than $0.01\%$ and required by the body in doses from a few grams to several milligrams per day, whereas the second group) microelements (Fe, Zn, Cu, Mo, Mn, I, etc.) – from ten to hundred times less. MMEs are distributing in all the tissues and organs, playing the key roles in their functions. The numerous enzymes (as protein biocatalysts) are using MMEs as essential elements (“cofactors” or “coenzymes”) for metabolic reactions in all cells. Some researchers, including us, are mentioning toxic heavy metals (Arsenic, Cadmium, Lead, Mercury, etc.) in the MME general concept, but as the third special group, because these metals are quite dangerous and have the ability to cause various diseases (cardiovascular, metabolic, nervous, oncological diseases, etc.). It is important to monitor the MME-status of the animals regularly by using non-invasive biological materials (such as hair, fur, etc.).
### Abstract
Many of the micro- and macro-elements (MMEs) required by the body are found in environmental objects in concentrations different from their original concentration that can lead to dangerous animal diseases (“microelementoses”). The aim was to study the features of MME (accumulating in wild and exotic animals) in connection with particular diseases. The work using 67 mammal species from four Russian zoological institutions was completed in 2022. Studies of 820 cleaned and defatted samples (hair, fur, etc.) after “wet-acid-ashing” on an electric stove and in a muffle furnace were performed using a Kvant-2A atomic absorption spectrometer. The content of zinc, copper, iron, cadmium, lead, and arsenic was assessed. The level of MME accumulation in the animal body contributes not only to the MME status and the development of various concomitant diseases, but the condition itself can occur by intake of a number of micronutrients and/or drugs. Particular correlations between the accumulation of Zn and skin, oncological diseases, Cu—musculoskeletal, cardiovascular diseases, Fe—oncological diseases, Pb—metabolic, nervous, oncological diseases, and Cd—cardiovascular diseases were established. Therefore, monitoring of the MME status of the organism must be carried out regularly (optimally once every 6 months).
## 1. Introduction
Many “micro- and macro-elements” (MMEs) are found at different concentrations in environmental objects (especially in areas with a high anthropogenic load) that can lead to animal diseases [1,2,3,4,5,6]. The supply of microelements to all organisms should occur only in optimal amounts [1,7,8,9,10]. This is well-known for all domestic [11,12,13,14] and farm [15,16,17,18,19,20,21,22,23,24] animals, as well as for humans [25,26,27,28]. That is why here we would like to point out only a few recent reviews concerning the comparison of inorganic and chelate forms of MMEs for broilers’ feeding [29] and some requirements of MME diets for two commercial broiler strains [30]. For example, the numerous data of the most important MMEs (such as iron, copper, zinc, manganese, selenium) indicated that the chelate forms of these MMEs (i.e., complexes of MME with organic ligands) provided better “protection” for broilers, for “the environment and also improve egg quality” [29].
A substantial comparison of the 9th and 10th versions of nutrient requirements of poultry (NRC 1994 and 2017) with the particular recommendations of “the trace element requirements for commercial broiler strains” (Ross 308 and Cobb 500, since 2007 and 2008, respectively) was provided by Iran scientists [30]. In the “Conclusion” part of this paper [30], they wrote “the iron requirements of broilers have been increased and the requirements of copper, manganese, zinc, selenium and iodine have decreased compared with the NRC [1994] recommendations”. However, all data (in the paper [30]) presented the opposite tendencies. The iron requirements have been decreased from 80 mg/kg (NRC 1994 for broiler chickens) to 40 mg/kg (for broiler strains Ross 308, since 2007 and Cobb 500, since 2008 or 2013) or even to 20 mg/kg (for broiler strain Ross 308, since 2014). The requirements of copper, manganese, zinc, selenium, and iodine have been increased from 8, 60, 40, 0.15, and 0.35 mg/kg (NRC 1994 for broiler chickens) to 16, 120, 100–110, 0.30, and 1.25 mg/kg (for broiler strain Ross 308, since 2007–2014) or 15, 100, 100, 0.30–0.35, and 1.0 mg/kg (for broiler strain Cobb 500, since 2008–2013) [30].
*In* general, less is known about MMEs in the tissues of the wild and exotic animals compared to those for the farm animals and birds. It is especially important now because of the high technological activity all over the world that results in the introduction of the increasing amounts of various “trace elements into biogeochemical cycles” [25].
In recent decades, wild animals have been successfully and increasingly used as “bioindicators of environmental pollution” [31,32]. Due to their wide distribution and often high trophic levels, birds and mammals are the most suitable indicators of pollution, and therefore the determination of trace element concentrations, including heavy metals, in various tissues of different bird and mammal species (for example, in blood, feathers, liver, kidneys, muscles, eggs, feces, etc.) is widely used in “biomonitoring studies” [33]. However, the most prominent studies [19,20,21,22,23,24] have been connected with the exposure of animals to pollution in heavily polluted areas or aquatic ecosystems, whereas the relatively moderately polluted urban and suburban environments have been less studied, despite their ecological significance [34]. Most studies are aimed at elucidating the effect of one pollutant or elevated doses of several substances [35,36], but there are still no clear patterns concerning the effects of trace elements in animals.
A number of Polish authors [24,34,37] revealed significant differences in the level of accumulation of microelements in animals living in nature and kept in captivity (i.e., in urban facilities). Therefore, the study of the features of their accumulation in animal zoological institutions, taking into account the characteristics of their maintenance and nutrition, is significant [37].
It is important to highlight that a deviation from the optimal level can lead to the animal diseases (“microelementoses”) with various degrees of severity [1,27,28].
## 1.1. MMEs in Animal Tissues and Organs
Animals need MMEs in certain concentrations for the implementation of optimal metabolism level in their tissues and organs, as well as in the whole body. The clinical picture of the pathology occurrence (taking into account the complex interaction between individual microelements, the physical and chemical properties of their compounds) leads to difficulties of data interpretation due to the presence of direct and indirect (not always explicit) effects [1,9,10,13]. For these purposes, it is of great importance to choose an adequate (“correct”) marker, i.e., “biosubstrate”, and comprehensive monitoring of the MMEs level, which will allow their accurate quantitative analysis [1,31,38,39].
Each MME has its own optimal level of content depending on the animal breed. If this level deviates towards an increase or decrease in the concentration of an element in the body, a biological effect occurs. Moreover, the degree of bioeffect development depends on the microelement dose. Acute poisoning develops when the body absorbs or accumulates relatively large doses of toxicant MME. Chronic poisoning develops with prolonged, cumulative, action of chemicals. In this case, the increase in symptoms and the clarity of the manifestation of the clinical picture occurs gradually. The contact of chemical compounds with the epithelium and skin leads to their entry into the blood and lymph. Absorption is accompanied by special “transformation” of compounds, distribution and accumulation of microelements by organs and tissues [40].
Four major and minor zoos in Russia were selected for our research.
## 1.2. The Moscow Zoo
The Moscow zoo is the first among the major zoos in Russia and one of the oldest zoos in Europe, which was opened on February 13 (January 31), 1864. Now, it has the largest zoological collection in Russia and is the leading methodological center in the system of zoos in our country. At the moment, this zoo includes: a center for the reproduction of rare species of animals (zoo nursery) of the Moscow State Zoological Park, a branch of the zoo in the Estate of Father Frost (“Veliky Ustyug”), and the main exposition parts in the center of Moscow city. In total, this zoo contains 1340 species of various breeds and exhibits 14,105 wild and exotic animals, birds, etc., in total [41]. The research facility of this institution specializes in breeding rare species of predatory mammals, birds of prey and waterfowl, cranes, poisonous snakes, etc. [ 41,42].
There are about $15\%$ of mammals in the total number of animals in the institution collection and 24–$26\%$ of birds (depending on the studied period) [41,42]. The change in the number of animals is associated mainly with the acquisition and exchange of species between zoological institutions and the reproduction of formed pairs. During the studied period, the total collection of mammals increased by $19\%$ of species (from 174 to 207) and $8.7\%$ in total head numbers (from 1462 to 1589 items) mammals; birds increased by $12.3\%$ of species (from 292 to 328), but the total head numbers decreased by $0.2\%$ (from 620 to 612 items) [41,42,43].
## 1.3. The Ivanovo Zoo
Ivanovo zoo is a rather small institution located in the capitol of the textile regional center (officially opened in 1994). Today, the territory of the zoo is about 3.4 hectares. In total, this zoo contains 790 animals of 173 species. The zoological institution specializes in breeding birds of prey. The collection of the institution is represented mainly by birds (60.8–$67.6\%$) and mammals (22.0–$28.9\%$) [41,42]. In this zoo, the species diversity of mammals decreased by $25.5\%$ (from 51 to 38 species) and by $13.7\%$ in numbers (from 256 to 221 items). The number of bird species and heads increased by $9.3\%$ and $12\%$ (from 107 to 117 species and from 475 to 532 items, respectively) [41,42,43].
## 1.4. The Yaroslavl Zoo
Yaroslavl zoo is the first landscape-type zoo in Russia, where animals are kept in conditions as close to natural as possible. Today, the territory of the zoo is about 123 hectares (under the exposition—58 hectares). In total, 1791 animals of 441 species live in this zoo. The institution specializes in keeping and breeding animals of the central Russia population. The collection of the institution is represented by birds (18.3–$20.8\%$) and by mammals (13.0–$14.9\%$), depending on the year of the study [41,42]. The species diversity of the mammalian exposition decreased by $11.5\%$ species (from 104 to 92), but slightly increased by $0.5\%$ in total head numbers (from 440 to 442). The number of bird species and heads decreased by $1.3\%$ (from 160 to 158 and from 620 to 612 items, respectively). The change in the number of livestock is associated mainly with the physiological conditions of some old animals [41,42,43].
## 1.5. The Uglich Zoo Station
The Uglich zoo station is one of the smallest in Russia and located in a historical building since 1936. During the last year, more than 5000 people from the region and city guests came to the station for excursions and public events. In total, this station contains 305 individuals belonging to 52 animal species. The institution specializes in keeping and exhibiting exotic and domestic animals (especially in the contact mode). The collection of the institution, depending on the year of the study, is represented by birds (18.2–$28.8\%$) and by mammals (25.0–$31.9\%$) [41,42]. The species diversity of the mammal exposition remained at the level of 13 species, but total head numbers increased by $33.7\%$ (from 89 to 119 items, due to exchange and birth) [41,42]. The number of bird species and heads increased by $87.5\%$ and $77.3\%$, respectively (from 8 to 15 species and from 22 to 39 heads) [41,42]. The change in the number of livestock is associated with the organization of regular monitoring and veterinary care of animals [41,42,43].
The aim of this study was to evaluate the features of the MME accumulation in the biological samples (hair, fur) of wild and exotic animals from these four zoos during some diseases of various etiologies.
## 2.1. Living Creatures as Objects
The studies were carried out based on the Moscow, Ivanovo, Yaroslavl, and Uglich zoos. The objects were wild and domestic animals of different taxonomic groups.
The studies were carried out during the whole year. The level of microelements, including heavy metals, in biological media was analyzed based on the results of our original research. The following species of wild, domestic, and exotic mammals kept in zoological institutions in territories with different anthropogenic pressure in the Central Federal District of Russia were selected for study: Moscow, Ivanovo, and Yaroslavl zoos. In particular: bristly armadillo—Chaetophractus (Euphractus) villosus ($$n = 15$$), globular armadillo—*Tolypeutes matacus* ($$n = 9$$), Egyptian flying dog—*Rousettus aegyptiacus* ($$n = 15$$), hare—*Lepus timidus* ($$n = 9$$), European hare—*Lepus europaeus* ($$n = 6$$), yellow pied—*Eolagurus luteus* ($$n = 15$$), Mongolian (clawed) gerbil—*Meriones unguiculatus* ($$n = 36$$), Bush-tailed gerbil—*Sekeetamys calurus* ($$n = 18$$), Eastern mole vole—Ellobius tancrei ($$n = 6$$), Cactus hamster—*Peromyscus eremicus* ($$n = 9$$), golden (Syrian) hamster—*Mesocricetus auratus* ($$n = 30$$), Djungarian hamster—*Phodopus sungorus* ($$n = 15$$), Baraba hamster (Chinese hamster)—*Cricetulus barabensis* griseus ($$n = 9$$), acacia rat—Thallomys loringi ($$n = 15$$), spiny mouse—*Acomys cahirinus* ($$n = 30$$), dwarf mouse—Mus minutoides ($$n = 9$$), gray rat—*Rattus norvegicus* ($$n = 18$$), house mouse—*Mus musculus* ($$n = 18$$), multi-mother mouse—*Mastomys natalensis* ($$n = 9$$), chinchilla (home form)—Chinchilla laniger var. dom. ( $$n = 18$$), degu—*Octodon degus* ($$n = 24$$), guinea pig—*Cavia porcellus* ($$n = 27$$), Indian porcupine—*Hystrix indica* (leucura) ($$n = 12$$), red fox—Vulpes vulpes ($$n = 18$$), Fennec fox—Vulpes (Fennecus) zerda ($$n = 6$$), Arctic fox—*Alopex lagopus* var. dom. ( $$n = 12$$), Alaskan Malamute—*Canis familiaris* ($$n = 12$$), polar wolf—*Canis lupus* tundrorum ($$n = 6$$), wolf—*Canis lupus* ($$n = 39$$), raccoon dog—Nyctereutes procyonoides ($$n = 15$$), raccoon—Procyon lotor ($$n = 6$$), nosoha—*Nasua nasua* ($$n = 9$$), domestic ferret (furo, ferret)—*Mustela putorius* var. Dom. ( $$n = 15$$), common genet—*Genetta* genetta ($$n = 9$$), brown bear—Ursus arctos ($$n = 12$$), Ussuri white-breasted (Himalayan) bear—Selenarctos (Ursus) thibetanus ussuricus ($$n = 6$$), lynx—Felis (Lynx) lynx ($$n = 15$$), puma—Puma (Felis) concolor ($$n = 9$$), snow leopard (Irbis)—Uncia (Panthera) uncial ($$n = 6$$), Far Eastern (Amur) leopard—*Panthera pardus* orientalis ($$n = 9$$), Amur tiger—*Panthera tigris* altaica ($$n = 12$$), white lion—Pantera leo var. alba ($$n = 6$$), lion—Panthera leo ($$n = 6$$), Bruce’s hyrax—Heterohyrax brucei ($$n = 3$$), alpaca—Vicugna pacos ($$n = 12$$), bactrian camel—*Camelus bactrianus* (ferus) dom. ( $$n = 21$$), camel—*Camelus dromedarius* ($$n = 6$$), reindeer—*Rangifer tarandus* ($$n = 28$$), spotted deer—Cervus nippon ($$n = 9$$), European fallow deer—Dama (Cervus) dama ($$n = 9$$), European elk—Alces alces ($$n = 18$$), musk ox—*Ovibos moschatus* ($$n = 6$$), domestic yak—*Bos mutus* (grunniens) var. dom. ( $$n = 12$$), bison—*Bison bonasus* ($$n = 9$$), Dagestan tur—*Capra cylindricornis* ($$n = 9$$), Sichuan takin—Budorcas taxicolor tibetana ($$n = 6$$), blue sheep—Pseudois nayaur ($$n = 3$$), Cameroonian goat—*Capra hircus* hircus ($$n = 12$$), domestic horse—*Equus caballus* ($$n = 33$$), Grant’s zebra—Equus burchelli boehmi ($$n = 6$$), Przewalski’s horse—*Equus przewalskii* ($$n = 9$$), domestic donkey—*Equus asinus* dom. ( $$n = 12$$), common marmoset (Wistity)—*Callithrix jacchus* ($$n = 6$$), ring-tailed lemur—*Lemur catta* ($$n = 6$$), mandrill—Mandrillus (Papio) sphinx ($$n = 6$$), rhesus monkey—*Macaca mulatta* ($$n = 6$$), and lapunder (pig-tailed macaque)—*Macaca nemestrina* ($$n = 9$$).
## 2.2. Methods
The studies were carried out on a “Kvant-2A” atomic absorption spectrometer. The selection of biological samples (hair, fur, etc.) of all types was carried out from the whole body with a total sample weight of about 1–3 g. The samples were cleaned and degreased with acetone and bidistilled water for two days. Then, “wet-acid-ashing” was carried out on an electric stove, and then in a muffle furnace with a gradual increase in temperature from 250 to 450 °C with a half-hour exposure. The samples were assessed for the level of trace elements—zinc, copper, iron, cadmium, lead, and arsenic.
The results obtained were processed statistically. Arithmetic mean values (M), mean errors (m), and standard deviation (δ) were determined. To identify statistically significant differences in the compared groups and the contingency between the signs, the nature of the distribution of compatibility data, the nonparametric criterion (W criteria, Shapiro–Wilk test), Student’s t-test, and Spearman’s correlation coefficient were used. Databases were formed in the programs “Microsoft Office Excel” 2010 and “Statistica” version 10.0 (Windows XP).
## 3. Results and Discussion
As a first part of this work, a study of the nosological profile of diseases of wild and exotic birds and mammals of zoos (the Yaroslavl, Moscow, Ivanovo, and Uglich zoos) was carried out.
## 3.1. The Moscow Zoo
The annual mortality in the institution was about $0.3\%$ of all animal specimens: birds—0.4–$0.5\%$; mammals—$0.2\%$ [41,42]. In the studied period, parasitic diseases were recorded. Most enclosures with predatory mammals are permanently unfavorable for ascariasis and toxocariasis. It is impossible to carry out a complete devastation of open enclosures, because a part of the development cycle of ascariasis and toxocariasis occurs in the soil, where eggs can be stored for months. For young and healthy animals, carriage is not dangerous and, as a rule, does not affect their condition. However, in aging or chronically ill animals, the presence of parasites can cause various clinical symptoms. A similar situation develops with helminths in ungulates—”strongyloidosis” and “ascariasis” were recorded. Animals are constantly dewormed and the degree of infection is maintained at a “safe level”. Various types of nematodes were observed in almost all groups of birds living in outdoor enclosures, which is associated with the presence of synanthropic birds in enclosures. Some populations of parrots turned out to be a reservoir of megabacteriosis. There was a trend towards an increase in the number of gerontological diseases (chronic diseases of the musculoskeletal system, cardiovascular system, chronic kidney diseases, and tumors for aged animals), which is associated with the aging of the collection. It should be noted that one of the systemic problems is obesity, especially for mammals (diagnostic studies) [41,42,43].
## 3.2. The Ivanovo Zoo
During the year, up to $9.5\%$ of the animals from the total number of livestock were exposed to diseases. The survival rate of animals (after past diseases) was $94.7\%$ in all cases. Of the total number of established diseases of animals, $50.7\%$ were injuries, $17.3\%$—diseases of the respiratory system, and $16.0\%$—diseases of the digestive system. Then, other diseases were observed in $8.0\%$ cases, metabolic disorders in $6.7\%$ cases, and diseases of the reproductive organs in $13\%$ cases [41,42,43].
## 3.3. The Yaroslavl Zoo
About $12.8\%$ of animals from the total number of livestock were exposed to diseases during the year [41,42]. There is a regular increase in registered diseases by 34.2–$76.3\%$, which was associated with an increase in the number of livestock and an improvement in the results of diagnostic tests due to the purchase of laboratory equipment. The effectiveness of the therapeutic measures taken was confirmed by the increase in the survival rate of animals after diseases from $55.4\%$ of cases to $79.3\%$ in recent years. Of the total number of established diseases of animals—$31.2\%$ were diseases of the digestive system, then we observed lesions of the musculoskeletal system—$17.3\%$ and the cardiovascular system—$10.1\%$, diseases of the hearing organs and the nervous system are sporadically noted—$1.1\%$ and $1.3\%$, respectively [41,42]. Cancer diseases were detected in $5.05\%$ of individuals during the year [41,42]. The main causes of diseases of non-contagious etiology in wild animals in zoos were the factors that limit their active movement and constant stress factors due to the specifics of the institution [41,42,43].
## 3.4. The Uglich Zoo Station
During the year, up to $37.4\%$ of animals from the total number of livestock were exposed to diseases. The percentage of survival of animals (after past diseases) was about $41.9\%$ in all cases [41,42]. Of the total number of established animal diseases, $45.3\%$ occurred against the background of physiological conditions of some old animals (since most often individuals came to the institution from visitors), $24.4\%$ were diseases of the digestive system, $11.08\%$ were lesions of the musculoskeletal system, in $7.2\%$ had diseases of the respiratory system [41,42,43].
## 3.5. A General Description of Animal Diseases
It was found that up to $12.8\%$ of animals from the total number of livestock were subjected to various diseases. Of the total number of established animal diseases, the main share was diseases of the digestive system, then lesions of the musculoskeletal system and the cardiovascular system. Moreover, diseases of the hearing organs and the nervous system were sporadically noted [41,42]. During the research, an increase in the proportion of oncological diseases in all the studied institutions was established [41,42,43].
Based on a retrospective analysis of records entered in the register of sick animals, 1208 heads of wild animals were treated, i.e., about $12.9\%$ of animals from the total number of livestock. Clinical examination was carried out as follows: history taking, general clinical examination, laboratory tests. The information obtained was recorded in the medical history of each animal.
Skin diseases (dermatitis, urticaria, fungal infections) accounted for 2.3–$2.5\%$ of the total number of non-communicable diseases. A significant increase in the number of animals with pathology of the hearing organs (otitis media, injuries) was observed, amounting to $2.5\%$, diseases of the musculoskeletal system in wild animals—$21.6\%$. Most often during the study period, various traumatic injuries were noted that occurred in animals and birds as a result of intraspecific and interspecific aggression, a lot of injuries were observed in mixed species exposures. Some injuries that the animals inflicted on themselves on the structural elements of the enclosures were revealed.
Respiratory system diseases were observed in $6.2\%$ of cases, among them inflammatory pathologies were noted—acute and chronic pneumonia and bronchopneumonia (of unknown etiology), rhinitis, tracheitis.
At the same time, diseases of the digestive system of non-contagious etiology were registered in $25.6\%$ of the total number of non-contagious animal diseases. The main disorders of the gastrointestinal tract, as a rule, were interconnected with a violation of the functional state of the liver; hepatosis (fatty, granular, cholestatic) was established in $33.1\%$ of cases of diseases, then enteritis ($17.5\%$ of diseases) and poisoning, $6.8\%$; intestinal obstruction was established in isolated cases only.
The level of metabolic diseases during the research period was found between 4.8 and $4.9\%$ of cases, including exhaustion and impaired calcium-phosphorus metabolism. During the research period, diseases of the cardiovascular system accounted for $10.2\%$, detected mainly at the autopsy of animals, and a high percentage of pathologies of the heart muscle was noted—$25.8\%$. Diseases of this system were represented by atherosclerotic changes in the coronary arteries, myocardial infarction, hypertrophic cardiomyopathy, and myocardial dystrophy. Inflammatory diseases were represented by chronic lymphocytic pericarditis and chronic focal myocarditis (complicated by thrombosis of the right ventricle—in the fluffy-tailed gerbil), cardiomegaly myocardial dystrophy, as well as a case of hemopericardium of unknown etiology. The reasons may be inadequate feeding with a deficiency in the diet of carbohydrates and minerals, autointoxication from the liver and kidneys.
Eye diseases were established in $8.0\%$ of cases. Basically, conjunctivitis was observed, which is associated with a violation of the conditions of detention, for example, abundant contamination of the litter, strong dust formation due to too small a fraction of sawdust. The decrease in the level of morbidity was associated with the formation of new expositions, the operating time of suppliers, and the improvement of the skill level of service personnel.
Among the diseases of the musculoskeletal system, the most common were arthritis, bursitis, discopathy, myositis, sprain, inflammation of the jaw bone plate, and various kinds of injuries.
The incidence of the reproductive system was detected in $6.3\%$ (dystocia, ovarian cyst complicated by secondary infection and abscess formation, follicular stasis). Often there was a pathology of labor and postnatal activity in ungulates, a delay in the placenta was observed. Predisposing factors for this may be multiple pregnancies (twins), long, difficult births, and lack of vitamins and minerals in the concentrated type of feeding. The increase in the number of diseases was associated with the formation of breeding pairs in the institution and the entry of young animals into the sexually mature stage.
Insignificant fluctuations in the incidence of the nervous system during the study period from 1.4 to $1.6\%$ were noted. The low level of diseases of the nervous system was explained by the presence of large open-air cages, the formation of species groups, mixed species exposures, and regular enrichment and diversity of the animal habitat.
During the research period, diseases of the excretory system were detected in $9.5\%$ of the total number of diseases (renal failure was due to various causes: chronic glomerulonephritis with outcome in nephrosclerosis, hydronephrosis, pyonephrosis, acute interstitial nephritis; unspecified nephropathy, ascites). The cause of kidney pathology may be an unbalanced diet, the presence of mycotoxins in feed.
Oncological diseases accounted for $13.8\%$ of the total number of diseases and $19.5\%$ of the number of non-communicable diseases. The most common malignancies were carcinomas and adenocarcinomas.
Based on the results obtained, a model of the nosological profile of non-communicable diseases (of exotic, wild, and ornamental animals kept in captivity) was obtained. Based on the nosological profile of non-communicable diseases, one can conclude that the most often there are the following diseases: of the digestive system—in $31.2\%$ of cases, of the musculoskeletal system—$17.3\%$, of the cardiovascular system—$10.1\%$, of the nervous system—$1.3\%$, and of the hearing organs—$1.1\%$ for exotic, wild, and decorative animals.
To establish the relationship between the MME content in samples of biological media of animals with general morbidity during the study period, a correlation-regression analysis was carried out, the results of which are presented in Table 1. The diagnosis was established based on the study of individual animal maps, pathological anatomical acts, and registers of veterinary procedures that was proposed before [44,45,46,47,48,49,50,51].
A significant relationship was established between the accumulation of Zn and the following diseases: skin, digestive, and vision systems, as well as oncological diseases, Cu—with diseases of the musculoskeletal and the cardiovascular system, oncological diseases, Fe—with diseases of the cardiovascular system, Pb—with metabolic diseases, nervous and excretory systems, oncological diseases, Cd—with diseases of the cardiovascular and nervous system, and As—with diseases of the excretory systems (Table 2), which corresponded to some other data [1,42,52,53].
## 3.6. Zinc
Pairwise statistical analysis of the data (Table 2) revealed a significant ($p \leq 0.05$) decrease in the level of Zn in animals with diseases of the organs of vision, digestive system, and skin diseases. There was also a tendency of a decrease in the zinc concentration in the case of metabolic disorders and diseases of the reproductive system [53].
Zinc is believed to be essential for optimal retinal cell metabolism, modification of photoreceptor plasma membranes, regulation of the light-rhodopsin response, and modulation of synaptic transmission. In mice, the ZnT3 and ZnT7 transporters present in different layers of the retina, along with MME, provide zinc homeostasis and are involved in the functions of the eye [54]. Moreover, zinc is required to maintain the level of taurine in the retina by acting on the transporter responsible for the movement of taurine into tissues [55]. In animals, the effect of low zinc levels on abnormalities in the functioning of photoreceptors, the onset of primary glaucoma, disorganization of the ultrastructure, and loss of color sensitivity has been shown [56,57].
A decrease In the level of Zn in the digestive system may be associated with a decrease in food intake due to a decrease in animal weight or the development of various infections, diarrhea, which prevents MME’s reabsorption [58,59].
Skin diseases with a decrease in zinc levels may be associated with a violation of its homeostasis with a decrease in the zinc transporter present in epidermal skin cells, etc. ( Figure 1) [60,61,62,63].
For example, ZIP4 and especially ZIP2 are highly expressed in keratinocytes and are involved in their proliferation [61]. Moreover, ZIP10 is highly expressed in epidermal progenitor cells located in the outer root sheath of hair follicles and plays an important role in the regulation of zinc to maintain the skin epidermis [62]. In vitro studies have shown that ZIP2 is activated by the induction of differentiation in cultured keratinocytes and that when this transporter is disabled, differentiation is inhibited [63].
The effect of Zn on the excretory system may be due to the fact that the metal mainly accumulates in the liver [8,13,64,65], while the biliary system is the main pathway zinc excretion and enterohepatic circulation [66]. A number of authors note a decrease in the level of the microelement in pathological lesions [13] associated with the presence of metallothionein [66], which is regulated by a decrease in the level of intake of food and water in the body with low body weight caused by liver disease, cytokines and inflammatory mediators, general inflammation, stress, and medications such as glucocorticoids [66].
The importance of zinc for reproductive function in males has been documented [67]. Zinc ions in the seminal fluid play both structural and regulatory roles in the activity of prostate-specific arginine esterase, which maintains normal prostate and sperm function [68]. For females, data on the effect of Zn on reproductive function are practically absent, there are only indications of its significance in the formation and development of pregnancy and fetal size [69,70]. There are observations of a significant increase in the concentration of Zn in both malignant and benign tumor skin tissues [71]. This was due to two possible reasons: intensive metabolic processes in neoplastic cells and increased activity of intracellular enzymes that require intracellular zinc for proper functioning, or an increase in intracellular Zn, which inhibits tumor cell apoptosis [71]. In contrast, zinc was significantly lower and copper significantly higher in neoplastic tissue with hepatocellular carcinoma [72] and females with mammary tumors [73] compared to healthy tissues. This can be explained by zinc chelation, which is especially important in this context, since copper is essential for angiogenesis, and by reducing tissue copper levels, zinc can limit tumor growth [74]. In the study, there were no significant differences in the accumulation of MME in the case of some oncological diseases.
## 3.7. Copper
During the statistical analysis of the data, a significant ($p \leq 0.05$) decrease in the Cu level for animals with a disease of the musculoskeletal system was found. In contrast, an increase in the Cu level by oncological disorders and diseases of the cardiovascular system was established.
Although a deficiency of copper, which is involved in normal iron transport, may be accompanied by the development of iron deficiency anemia [75], its excess can lead to antagonism between the both metals due to the presence of common transport pathways, primarily the DMT-1 protein (divalent metal transporter 1, Figure 2) [76].
In particular, an increase in the level of copper in the hair of patients with iron deficiency anemia has been noted [77].
It has been established that copper plays an important role in cancer cell proliferation and metastasis [78], since cancer cells are characterized by a high demand for copper [79]. The impact on the mobility of copper ions can be one of the tools of anticancer therapy [79]. At the same time, an increased concentration of copper in the blood was found in patients with pancreatic cancer [80]. There are indications that high levels of copper in hair are associated with the risk of developing prostate cancer [81], as well as neoplasms in children [82]. At the same time, it is noted that under the conditions of the physiological level of copper intake into the body (0.6–3 mg/day), the existence of a relationship between copper in the body and oncology is unlikely [83].
These results, in relation to Cu, can be explained by the fact that the deficiency of this MME leads to deficient collagen synthesis, accompanied by skeletal deformity, and changes in elastic fibers, which further leads to the occurrence of orthopedic diseases or Cu deficiency in the body is a consequence of such a disease [1].
## 3.8. Iron
A significant decrease in the level of iron was found by development of cardiovascular diseases. Dysregulation of Fe homeostasis, increased uptake, and accumulation of MME in the reticuloendothelial system leads to the removal of the element from the blood into the cells of the reticuloendothelial system following a decrease in the availability of iron for erythroid progenitor cells and iron-limited erythropoiesis [84]. The acute phase protein hepcidin plays a key role in the development of anemia due to its ability to inhibit Fe absorption in the intestine. In addition, at the same time, there is an increase in the uptake of iron by macrophages and blocking of the export of iron from macrophages, mainly to the bone marrow. As a result, serum iron concentration decreases (with normal total body iron), which slows down erythropoiesis and causes anemia [85]. However, sometimes such a drop in serum iron concentration can be beneficial, as it makes iron less available to micro-organisms that inhibit their growth [86]. Regulation of systemic iron metabolism, including organs and cell types involved in systemic iron balance are discussed in details [86].
For example, cells (such as enterocytes) are absorbing “dietary iron” by divalent metal transporter 1 “located on the apical surface upon reduction of Fe3+ to Fe2+” by ferrireductases such as duodenal cytochrome B [86]. These cells in addition to the macrophages (“spleenic reticuloendothelial”) that are recycling iron from “senescent red blood cells” [86], finally “release iron via ferroportin with the aid of hephaestin, which oxidizes of Fe3+ to Fe2+” [86]. Circulating plasma transferrin (which represents “the most dynamic body iron pool”) is transferring this metal all around the animal body. Another conditions “that affect iron metabolism indirectly” are the following: “inflammation, ER stress, erythropoiesis, and hypoxia” [86].
## 3.9. Lead (Plumbum)
A significant increase in the content of Pb in animals with lesions of the cardiovascular system and a tendency to an increase in the content of metals in lesions of the excretory system and reproductive organs were revealed. Lead has a negative impact on health [47], which is associated with a pronounced toxicity of the metal for a number of systems and organs [87] and primarily for the nervous system [88]. The influence of lead on the maternal organism and the development of congenital heart anomalies in newborns [89], as well as congenital neural tube defects [90], has been established, which is associated with the ability of MME to disrupt the regulatory mechanisms of DNA methylation [91,92]. Scientists note the role of Pb in the development of oncological diseases, but these data were not confirmed in our study [93]. It is well-known [84,94] that *Pb is* able to influence metabolic processes.
## 3.10. Cadmium
The sample found a significant increase in the content of cadmium in animals with diseases of the cardiovascular and circulatory systems. The hematopoietic system is one of the targets of the toxic action of Cd. It has been shown that an increase in the level of the xenobiotic in the blood is caused by a decrease in the concentration of hemoglobin [95] and increases the likelihood of developing iron deficiency anemia [96,97]. Experiments have shown that exposure to cadmium is accompanied by a shift in the blood formula towards myelopoiesis [98], as well as hemolysis and insufficient production of erythropoietin [99,100].
## 3.11. Arsenic
A significant increase in the content of As was found in the presence of diseases of the excretory system. Arsenic induces the formation of oxidized lipids, which in turn generate several bioactive molecules (ROS, peroxides, and isoprostanes), the main end products of which are aldehydes. There is an indication of chronic and acute exposure to As in the etiology of cancer, cardiovascular disease (hypertension and atherosclerosis), neurological disorders, gastrointestinal disorders, liver and kidney disease, reproductive health effects, skin changes, and other health disorders. The role of antioxidant defense systems against arsenic toxicity is also discussed in detail [101].
## 4. Conclusions
The level of MME accumulation in the body of animals can be the key reason to the occurrence of microelementoses, i.e., the development of various diseases. A study of the “nosological profile” of diseases was carried out and it was found that up to $12.9\%$ of the animals from the total number of livestock (from four Russian zoological institutions) were exposed to diseases. The following tendency of disease frequency for all animals in our research was obtained: digestive system > musculoskeletal system ≥ cardiovascular system > hearing organs ≥ nervous system. During the research, an increase in the proportion of oncological diseases in all the studied institutions was established. A significant relationship was established between the accumulation of Zn and the following diseases: skin, digestive, and vision systems, as well as oncological diseases, Cu—with diseases of the musculoskeletal and the cardiovascular system, oncological diseases, Fe—with diseases of the cardiovascular system, Pb—with metabolic diseases, nervous and excretory systems, oncological diseases, Cd—with diseases of the cardiovascular and nervous system, and As—with diseases of the excretory systems. There was a tendency of a decrease in the concentration of MMEs in case of metabolic disorders and diseases of the reproductive system. Therefore, monitoring of the MME status of the organism of all animals must be carried out regularly (optimally—once every 6 months).
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|
---
title: Effect of Maternal Gradient Nutritional Restriction during Pregnancy on Mammary
Gland Development in Offspring
authors:
- Xusheng Dong
- Xueyan Lin
- Qiuling Hou
- Zhiyong Hu
- Yun Wang
- Zhonghua Wang
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000074
doi: 10.3390/ani13050946
license: CC BY 4.0
---
# Effect of Maternal Gradient Nutritional Restriction during Pregnancy on Mammary Gland Development in Offspring
## Abstract
### Simple Summary
The embryonic period, together with puberty and pregnancy, are known as the three main stages of mammary gland development. The development of the mammary glands is slowed during the embryonic period due to factors such as inadequate nutrition, which directly affect the development of the mammary glands and lactation after birth. However, the impact of embryonic nutrition on fetal mammary gland development is often unnoticed. We investigate the effect of nutritional intake on embryonic mammary gland development by administering different levels of nutritional restriction to female mice during gestation. Contrary to common belief, we found that mild maternal nutritional restriction contributes to mammary gland development in the offspring. Mammary gland dysplasia is not obvious until maternal nutritional restriction reaches $70\%$ of the normal intake. Further embryonic mammary gland development studies can be performed based on our level of maternal nutritional restriction. In addition, the use of mice as model animals can also provide a reference for dairy farming, where nutrition should not be excessive during the gestation period of the cow; otherwise, it affects the mammary gland development of the offspring.
### Abstract
We aimed to investigate the effect of different levels of nutritional restriction on mammary gland development during the embryonic period by gradient nutritional restriction in pregnant female mice. We started the nutritional restriction of 60 female CD-1(ICR) mice from day 9 of gestation based on $100\%$, $90\%$, $80\%$, $70\%$ and $60\%$ of ad libitum intake. After delivery, the weight and body fat of the offspring and the mother were recorded ($$n = 12$$). Offspring mammary development and gene expression were explored by whole mount and qPCR. Mammary development patterns of in offspring were constructed using Sholl analysis, principal component analysis (PCA) and regression analysis. We found that: [1] Mild maternal nutritional restriction (90–$70\%$ of ad libitum intake) did not affect offspring weight, while body fat percentage was more sensitive to nutritional restriction (lower at $80\%$ ad libitum feeding). [ 2] A precipitous drop in mammary development and altered developmental patterns occurred when nutritional restriction ranged from $80\%$ to $70\%$ of ad libitum intake. [ 3] Mild maternal nutritional restriction ($90\%$ of ad libitum intake) promoted mammary-development-related gene expression. In conclusion, our results suggest that mild maternal nutritional restriction during gestation contributes to increased embryonic mammary gland development. When maternal nutritional restriction reaches $70\%$ of ad libitum intake, the mammary glands of the offspring show noticeable maldevelopment. Our results help provide a theoretical basis for the effect of maternal nutritional restriction during gestation on offspring mammary development and a reference for the amount of maternal nutritional restriction.
## 1. Introduction
Nutritional challenges that occur during gestation, a critical period for embryonic growth and development, may lead to alterations in the physiological development and metabolism of the offspring after birth [1]. The most possible nutritional challenges during gestation are undernutrition and overnutrition, which can affect the health of both the fetus and the maternal body [2]. In particular, malnutrition during pregnancy, which still exists in underdeveloped regions, as a global problem, has important implications for the healthy development of the mother and the newborn [3]. These nutritional damages can cause permanent adjustments in the embryonic physiological state and organ development by inducing genetic changes in the proliferation/differentiation pathways during embryonic development [4,5]. Current research on nutritional restriction during pregnancy has focused on the placenta, brain and other organs that affect fetal survival [6,7], with little attention paid to fetal mammary gland development.
The embryonic period, puberty and pregnancy are known as the three main stages of mammary gland development. The embryonic development of the mammary gland begins in many mammals at mid-gestation [8]; for mice, with a gestation period of 19–21 days, the mammary gland initiates development on day 10 of embryonic life [9]. The embryonic mammary glands are formed by a bilaterally multilayered ectodermal stripe from the forelimb bud to the hindlimb bud on the ventral surface of the embryo, referred to as the mammary line [10]. At day 11.5 of the mouse embryonic stage, these milk lines form five visible pairs of placebos. These placebos then become embedded in the mammary mesenchyme. At day 15–16 of the mouse embryonic stage, primary bud formation invades the secondary mammary mesenchyme and begins to develop a branching morphology [11]. Before birth, the mammary gland consists of a small ductal tree with a dominant duct and 10–15 branches embedded in the nascent fat pad [12]. The basic mammary duct system that forms at this time arises in the absence of hormonal input and remains essentially quiescent until puberty [10]. This basic ductal system forms the framework from which the mammary glands develop further during puberty and pregnancy to form the mature mammary glands [13]. If the development of the embryonic mammary glands is slowed at this period due to nutritional deficiencies and other factors, it directly affects the development of the mammary glands after birth and may even affect the amount of milk produced during lactation [14,15].
Although the embryonic stage is the initiation of mammary gland development, nutritional regulation has remained less studied for this stage of mammary gland development. Since puberty is considered a critical window for nutritional regulation, most nutrition-related research has focused on this period [16,17]. The impact of embryonic nutrition on fetal mammary gland development is often unnoticed. Although the mammary gland that develops during the embryonic period is considered a basic ductal system, it has the ability to produce milk, known as neonatal milk or switch’s milk [10]. This indicates that the mammary gland is already equipped with basic lactation functions after birth, and if there are problems with the development of the mammary gland during the embryonic period, these basic functions are affected. Terminal end buds, an important structure in the extension of the mammary ducts during puberty, form only at the tips of the elongated ducts which are based on branches generated during the embryonic period [9,10]. Multipotent mammary stem cells (MASCs) from embryonic mammary gland formation are the source of MASCs and progenitor cells required for mammary duct development during puberty and alveolar luminal formation during pregnancy [9]. These results all suggest that there is a connection between embryonic, pubertal and gestational mammary development, and that mammary gland damage caused by nutritional fluctuations received during the embryonic period further affect mammary gland development after birth.
The embryonic stage is the period of initial mammary gland formation, when the mammary gland gradually begins to expand through proliferation and differentiation in multipotent MASCs [18]. *Many* genes associated with mammary stem cells during the embryonic period have been shown to influence the future developmental fate of the mammary gland. The *Axin2* gene was found to have the ability to allow cell regeneration in mammary gland transplantation assays, and the expression of the *Axin2* gene during the embryonic period has been shown to be associated with future development in the ductal cell lineage [18]. During this period, Wnt5a has also been shown to be required for normal development of the mammary ducts [19]. In addition, MASCs marker genes such as Sox10, Procr, ELF5 and Aldh1a1 were identified by knockout studies and regulate key functions of mammary gland development [20,21,22,23]. After birth, MASCs become lineage-restricted with some becoming progenitor cells and contributing to the development of the mammary gland base or lumen [9]. Thus, nutritionally induced changes in embryonic mammary development may continue to affect mammary development in adulthood. The effect of altered nutrient levels on mammary stem cells has been demonstrated in previous studies with cells and adult mice [24,25]. Maria Theresa E. Montales et al. found that angiotensin in food affects the number of mammary cell-like/progenitor cells [24]. Omar M. Rahal et al. speculated that diet-regulated hormonal signaling could influence MASC self-renewal [25]. Studies on the effects of nutritional restriction on mammary stem cells have had mixed results, with one study suggesting that nutritional restriction attenuates mammary stem cell viability and inhibits mammary gland development [26], while another study suggests that nutritional restriction induces the self-renewal of mammary stem cells [27]. These results may be due to differences in the amount of nutrient limitation. Mild nutrient limitation mediates the restoration of stem cell self-renewal capacity through nutrient and energy-sensing pathways [27]. When nutrient limitation exceeds the regulatory level of the cells, apoptosis and necrosis of stem cells can occur due to nutrient deficiency.
Compared to nutritional treatment after birth, nutritional treatment for the embryonic period is more difficult and requires nutritional interventions for the maternal body. The most common approach in studies of fetal undernutrition is accomplished through food or caloric restriction of the mother during gestation [28]. The mammalian placenta has evolved mechanisms that help buffer the fetus from short-term fluctuations in maternal diet and energy status [29]. In order to avoid this buffering mechanism, most of the studied protocols reduce maternal nutritional intake to 50–$60\%$ of the normal amount, exerting a significant impact on fetal growth and development through high levels of food restriction [7]. Moderate or low levels of food restriction may better mimic the clinical features of malnourished women, but few studies have investigated the effects of moderate food restriction during pregnancy on embryonic development.
In addition to maternal nutritional interventions, the smaller size of the embryonic mammary gland presents challenges for the study of mammary gland development. The most visual method of viewing mammary gland development is the whole mount, a method of viewing a three-dimensional overview of the mammary gland, which provides a dense ductal epithelial structure within the complete mammary gland [30]. The whole mount requires the complete mammary gland to be isolated from the skin of the mouse and spread out as naturally as possible, which is more challenging for embryonic and newborn mice. In earlier studies, the results of the whole-mount analysis were difficult to quantify and were only used as a display image in the studies [31]. The complex ducts of the mammary gland in puberty can be evaluated in terms of the area covered and the denseness of the ducts observed visually. However, this unquantifiable observation is difficult to evaluate in the primary mammary gland, which has only 10–15 branches at birth. Jason P. Stanko et al. reported the use of Sholl analysis, an ImageJ plug-in for neuronal analysis, to quantify whole-mount results of the mammary gland [32]. The *Sholl analysis* creates a series of concentric rings based on a custom center (origin of the mammary duct) and extends to the most distal portion of the branch (enclosing radius). The *Sholl analysis* plug-in calculates the number of intersections that occur on each ring and then returns a Sholl regression coefficient (k), which is a measure of the rate of decay of the epithelial branches. In Sholl analysis, the sum inters (N) is the number of intersections of multiple concentric circles centered on the primary ducts with the ducts, reflecting the complexity of the mammary gland. The sholl regression coefficient (k) is a measure of the distal mammary branch complexity, which is close to 0, indicating more complex and well-developed distal mammary branches. Branch density is calculated using the formula N/MEA. Sholl analysis provides a valid quantitative measure of mammary branch complexity and has become a reliable method for studying mammary gland development. Mammary gland development in embryonic mice can be evaluated through a combination of fine dissection and whole-mount and Sholl analysis.
Different levels of maternal nutritional restriction may have different effects on embryonic mammary gland development due to different maternal nutritional buffering and stem cell responses to nutrition. To investigate this, we established a pattern of nutritional restriction on mammary gland development during embryonic period by setting $100\%$, $90\%$, $80\%$, $70\%$ and $60\%$ diet intakes for female mice during pregnancy. The objective of our study was to investigate the effect of maternal gradient nutritional restriction on mammary gland development in offspring and provide a reference for the amount of maternal nutritional restriction.
## 2.1. Animals and Experimental Design
Sixty female 8-week-old CD-1(ICR) mice were provided by Vital River Laboratory Animal Technology Co., (Beijing, China) and mated with males of similar age. Each male mouse was put in a cage with 1 female mouse. Mating of mice was demonstrated by the presence of vaginal plugs. Female mice were individually housed after the discovery of the vaginal plugs and recorded as day 0 of gestation. All mice in our study were fed commercially available irradiated sterile growth and reproduction diets for experimental mice (SFS9112, Xietong Biotechnology, Yangzhou, China). To reduce the impact of nutritional restriction on early embryonic growth, it began on the ninth day of pregnancy. Pregnant mice were divided into five groups ($$n = 12$$): the $100\%$ group was fed ad libitum (control group), and the $90\%$, $80\%$, $70\%$ and $60\%$ groups were fed $90\%$, $80\%$, $70\%$ and $60\%$ of the ad libitum food weight daily, respectively. The ad libitum group was mated one day earlier and their intake was used as the basis multiplied by $90\%$, $80\%$, $70\%$ and $60\%$ as the feed intake for the gradient nutrient restriction. The weight of the mice was recorded daily. The number of litters as well as the weight of the female mice and offspring were recorded on the day of delivery.
## 2.2. Body Fat Percentage Assay
On the day of delivery, whole body image and body fat percentage were evaluated in vivo using dual-energy X-ray absorptiometry (DEXA) on an InAlyzer (Medikors Co., Seongnam, Republic of Korea). Female mice and female offspring were anesthetized using isoflurane (RWD, Shenzhen, China) and placed on a scanner bed and operated according to the instructions. After in vivo imaging, female offspring mice were euthanized using CO2.
## 2.3. Collection and Preservation of Mammary Glands
The mammary glands were removed immediately after euthanasia, the #4 inguinal mammary glands were placed on slides and immersed in Carnoy’s solution for whole mount, and the other mammary glands were stored at −80 °C for real-time quantitative polymerase chain reaction (qPCR).
## 2.4. Mammary Whole Mount
The mammary glands were fixed in Carnoy’s solution ($60\%$ absolute ethanol, $30\%$ chloroform, $10\%$ glacial acetic acid) for 4 h and then placed sequentially in ethanol at $100\%$, $70\%$, $50\%$ and $10\%$ concentrations for 15 min each. After soaking in deionized water for 5 min, the mammary glands were stained using carmine alum solution (1 g carmine alum, 2.5 g aluminum potassium sulfate in 500 mL dH2O) for 4 h. The stained mammary glands were soaked for 5 min using distilled water, then sequentially soaked in $70\%$, $95\%$ and $100\%$ alcohol, each concentration for 15 min. The mammary glands were placed in xylene for 12 h for transparency and then sealed with neutral resin. Whole-mount slices of mammary glands were sectioned for image acquisition using an upright microscope (Nikion, Japan).
## 2.5. RNA Extraction and qPCR
RNA from offspring mammary glands was extracted using RNA-easy Isolation Reagent (R701-01, Vazyme Biotech, Nanjing, China) according to the instructions. RNA quality was evaluated by $1\%$ agarose gel electrophoresis, while the purity of the total RNA was determined by NanoDrop 2000 (NanoDrop, ThermoFisher Science, Waltham, MA, USA). The genomic DNA was removed from each RNA sample and reverse-transcribed into cDNA using an Evo M-MLV Mix Kit (Accurate Biology, AG11728, Hunan, China). Then qPCR was performed using a SYBR Green Premix Pro Taq HS qPCR Kit (Accurate Biology, AG11701, Hunan, China) with a LightCycler 96 Instrument (Roche, Basel, Switzerland). The reaction program was set to pre-denaturation at 95 °C for 30 s, followed by denaturation at 95 °C for 5 s and extension at 60 °C for 30 s, for a total of 40 cycles, with each reaction repeated 3 times. The primer sequences are shown in Table 1. The amplification efficiency and the specificity of the amplified products of each primer pair were verified using standard curves and melting curves, respectively. The mRNA expression of each sample was normalized relative to the expression of glyceraldehyde 3-phosphate dehydrogenase (GAPDH). *Relative* gene expression levels of each target gene were analyzed using the 2−ΔΔct method.
## 2.6. Statistical Analysis
We performed *Sholl analysis* on mammary whole-mount results according to the method described in a previous study [32]. Mammary gland whole-mount analysis was performed using ImageJ 2.1 software, and the *Sholl analysis* plugin 4.0.1 for ImageJ was used for Sholl analysis. The distance from the primary ducts to the most distal end of the mammary epithelium (enclosing radius) and the mammary epithelial area (MEA) were measured using ImageJ. The *Sholl analysis* was performed with the primary duct as the center, the enclosing radius as the ending radius and a radius step size of 0.02 mm. Since the mammary ducts in newborn mice are less developed and farther away from the mammary lymph nodes, the area occupied by the mammary lymph nodes was not calculated in Branch density.
Body weight, body fat, litter size, *Sholl analysis* results and gene expression were analyzed using one-way ANOVA in the ad libitum feeding, $90\%$, $80\%$, $70\%$ and $60\%$ groups. One-way ANOVA was performed using IBM spss 25 (Armork, NY, USA), with Sidak correction for multiple testing. Body weight, body fat, litter size and gene expression data were presented as the mean ± the standard deviation (SD). Principal component analysis (PCA) was performed on enclosing radius, MEA, sum inters and k from the results of *Sholl analysis* in all groups. PCA was performed using the FactoMineR and factoextra packages in R4.2.1, and PCA biplot figures were generated. The enclosing radius of each group in the *Sholl analysis* results were regressed against MEA, sum inters and k. Linear regression analysis of the mammary *Sholl analysis* was performed using simple linear regression in GraphPad Prism software 9.1.0 (San Diego, CA, USA).
## 3. Results
After nutrient restriction management, a significant difference in body weight was observed in mice from day ten of pregnancy, and the difference persisted until the end of gestation ($p \leq 0.05$; Figure 1A). After parturition, the adult female mice showed a significant decrease in body weight compared to the control group ($p \leq 0.05$), except for the $90\%$ group ($p \leq 0.05$; Figure 1B). However, there was no significant difference in body fat percentage in adult female mice after parturition ($p \leq 0.05$; Figure 1C).
When the nutritional intake was only $60\%$ of the normal intake, a significant decrease in litter size was observed compared to the control group ($p \leq 0.05$; Figure 2A), while the individual offspring weight was significantly lower than that of the other groups ($p \leq 0.05$; Figure 2B). The body fat percentage of the offspring was significantly higher in the control group than in the $80\%$, $70\%$ and $60\%$ groups ($p \leq 0.05$; Figure 2C,D).
The mammary whole-mount images are shown in Figure 3A. For the enclosing radius, a significant increase was observed in the control group compared to the $70\%$ and $60\%$ groups ($p \leq 0.05$), while a significant increase was observed in the $90\%$ group compared to the $60\%$ group ($p \leq 0.05$; Table 2). MEA did not differ in the control, $90\%$ and $80\%$ groups ($p \leq 0.05$), while it was significantly lower in the $70\%$ and $60\%$ groups than in the former three groups ($p \leq 0.05$; Table 2). Sum inters were significantly higher in the control, $90\%$ and $80\%$ groups than in the other two groups ($p \leq 0.05$; Table 2). The k of $70\%$ and $60\%$ were significantly higher than the other three groups ($p \leq 0.05$; Table 2). Branching density was not significantly different among the groups ($p \leq 0.05$; Table 2). Consistent with the results in Table 2, an identifiable change in mammary gland development was observed from the $80\%$ group to the $70\%$ group in Figure 3B. Figure 3C shows the number of intersections of each concentric circle with the mammary ducts in the Sholl analysis. The control group had the longest duct extension distance. At a radius of 0.5 mm, the control, $90\%$ and $80\%$ groups reached the highest number of intersections with a similar peak, all higher than the $70\%$ and $60\%$ groups.
In order to further investigate the reasons for the dramatic decline in offspring mammary development from the $80\%$ group to the $70\%$ group, we performed a PCA (Figure 3D) of the mammary whole-mount results (enclosing radius, MEA, sum inters and k). After dimensionality reduction, the data points in the control, $90\%$ and $80\%$ groups were nearer to each other, forming visible distance differences with the $70\%$ and $60\%$ groups, indicating that a massive reduction in mammary gland development in the offspring occurs when the maternal nutritional limit is reduced from $80\%$ to $70\%$. The PCA bipartite plot shows the scores and loadings of the first two components (dim1 and dim2), revealing the projection of the observed indicators on a space with dim1 and dim2 as axes. In our study, the indicators of mammary gland development were explained by $79.1\%$ of dim1 and $10.2\%$ of dim2, respectively. The variable with the highest weight in the first principal component is the enclosing radius, indicating that the main reason for the difference in distance from the $80\%$ to the $70\%$ group in the mammary glands was the change in enclosing radius. A positive correlation between enclosing radius and MEA and sum inters and a negative correlation with k are presented in the PCA biplot.
To analyze the effect of the enclosing radius, which has the highest weight in PCA, on the pattern of mammary gland development in the offspring, we performed a regression analysis of the whole-mount results (Figure 4). In the regression analysis of the enclosing radius with MEA, the $90\%$ and $60\%$ groups had larger slopes compared to the control group, while the $80\%$ and $70\%$ groups had smaller slopes. In the regression analysis of the enclosing radius versus sum inters, as maternal nutritional restriction increased, the slope first increased in the $90\%$ group, then gradually decreased in the $80\%$ and $70\%$ groups and then showed an increase in the $60\%$ group. In the regression analysis of the enclosing radius versus k, the slope of each group is less than the control group, with the $60\%$ group having the lowest slope.
We analyzed the expression of development-related genes (Sox10, Axin2, Elf5, Lgr5, Wnt5a, Aldh1a1, Procr), mammary basal cell marker genes (K5), mammary luminal cell marker genes (K18), estrogen (ERα,ERβ) and progesterone receptor (PR) genes in the mammary glands (Figure 5) by one-way ANOVA followed by a Sidak multiple-comparison test. In the $90\%$ group, Sox10 expression was significantly higher than in the other four groups ($p \leq 0.05$), and Elf5 was significantly higher than in the control and $60\%$ groups ($p \leq 0.05$). Sox10 was significantly lower in the control group than in the $90\%$, $70\%$ and $60\%$ groups ($p \leq 0.05$), and Axin2 was significantly higher in the control group than in the $60\%$ group ($p \leq 0.05$). Aldh1a1 was significantly higher in the $80\%$ group than in the $60\%$ group ($p \leq 0.05$). The expression of K5 was significantly higher in the control group than in the $80\%$, $70\%$ and $60\%$ groups ($p \leq 0.05$). In the $60\%$ group, ER1 was significantly lower than in the $90\%$ group ($p \leq 0.05$) and ER2 was significantly lower than in the control group ($p \leq 0.05$). The expression of other genes did not differ significantly among the groups ($p \leq 0.05$).
## 4. Discussion
Nutritional deficiencies during gestation cause irreversible effects in fetal organs [33], but nutritional deficiency research on embryonic mammary gland development remains vacant. The impairment of mammary gland development at this phase may directly lead to delayed fetal mammary gland development in adulthood [9]. The small size of the mammary gland, which is difficult to observe, and the buffering through the placenta, which reduces the impact of nutritional fluctuations in the embryo, present challenges for the study of mammary gland development during this period. We performed a quantitative study of the mammary glands using whole mount combined with *Sholl analysis* and further analyzed the developmental pattern of the mammary gland by PCA and regression analysis. Through maternal gradient nutrient limitation, we established a pattern of offspring mammary gland development and revealed stem cell-related gene expression through a gradient reduction in maternal nutrient intake from $100\%$ to $60\%$ during gestation. The main findings of the study were: [1] Mild maternal nutritional restriction (90–$70\%$ of ad libitum intake) did not affect offspring weight, while body fat percentage was more sensitive to nutritional restriction (lower at $80\%$ ad libitum feeding). [ 2] A precipitous drop in mammary development and altered developmental patterns occurred when nutritional restriction ranged from $80\%$ to $70\%$ of ad libitum intake. [ 3] Mild maternal nutritional restriction ($90\%$ of ad libitum intake) promoted mammary development-related gene expression.
Inadequate nutrition during pregnancy can have an impact on maternal and fetal health, most notably in the form of weight loss [34]. In our study, differences in body weight of female mice emerged from the tenth day of gestation after nutritional restriction. After delivery, maternal mice in the $80\%$ group lost significant body weight, while body fat percentage was not affected. For offspring, body fat percentage decreased first when nutritional intake was $80\%$ of ad libitum, and weight loss occurred when it was $60\%$. Our result is similar to a previous study, which found no significant change in offspring birth weight during gestation for a maternal restriction to $75\%$ ad libitum feeding [28]. A reduction in offspring body weight occurs when nutritional restriction reaches $60\%$ or less of the ad libitum intake [34,35]. It seems that embryonic body fat percentage is more susceptible than body weight when faced with nutritional constraints. Mammary ducts and epithelium need to be embedded in the mammary stroma for growth, which is composed of homogeneous adipose tissue [36]. In studies on obesity, there is a strong association between mammary fat pads and obesity [37]. Although mammary fat pad and body fat have not been studied in studies on nutritional restriction, the possibility exists that a decrease in whole body fat percentage may affect mammary fat pad development.
To assess mammary gland development, we performed a *Sholl analysis* on the mammary glands of offspring with different levels of nutritional restriction. Based on the *Sholl analysis* reported in the previous study [32], we innovatively performed PCA analysis and regression analysis on the results of the *Sholl analysis* to explore the developmental pattern of mammary glands. We found a dramatic decrease in mammary gland development when nutritional restriction was dropped from $80\%$ to $70\%$ of ad libitum intake. In contrast, there was no significant difference in the effect of normal feeding versus $90\%$ and $80\%$ of ad libitum feeding on mammary gland development. We hypothesize that the dramatic delay in mammary gland development may be due to the buffering of embryonic nutrients by the placenta as a “nutrient sensor” [29]. For maternal nutritional restriction to $90\%$ and $80\%$ of normal intake, the buffering mechanism in the maternal body mitigates the effect of nutrition on fetal mammary development, and as the intake decreases to $70\%$, the maternal buffering limit is exceeded, resulting in delayed mammary development. Inconsistently with our results, the nutritional intake of sows being restricted to $70\%$ of ad libitum intake had no effect on the weight of mammary parenchyma, fat content, protein content and DNA content of the offspring [38]. The weight, DNA content and other methods used in their study to evaluate the mammary glands do not provide a complete view of the development of the mammary ducts compared to the whole mount. In addition, the species may also be responsible for the discrepancy between their results and our findings.
From the results of the PCA analysis, we determined that the variable with the greatest weight is the enclosing radius. This suggests that the dramatic decrease in mammary development from the $80\%$ to the $70\%$ group was mainly caused by changes in the enclosing radius. To analyze the developmental pattern of the mammary glands, we performed a regression analysis of the enclosing radius with MEA, sum inters and k. Our study found a positive regression relationship between the enclosing radius and MEA and sum inters and an inverse regression relationship with k. This suggests that as the distance of the terminal duct from the primary duct increases, the mammary gland will cover a larger area with more complex branching, while the terminal decay will be slower. Similar to our results, the similar trend of the mammary longitudinal extension distance with the mammary epithelial area was found in a previous study [39].
We found that compared to controls, mild nutritional restriction ($90\%$ of ad libitum intake) had larger regression coefficients in regression analyses with MEA and sum inters and smaller regression coefficients with k. This suggests that offspring with mild maternal nutritional restriction have better potential for mammary gland development. When nutrition was restricted to $80\%$ of ad libitum feeding, the regression coefficients of the enclosing radius and MEA reflected reduced mammary epithelial area growth potential, despite no difference in mammary gland developmental indicators compared to the control group. Interestingly, the regression coefficients of MEA, sum inters and k all showed greater absolute values when the nutritional restriction was $60\%$ of the ad libitum intake. At this level of nutritional restriction, the enclosing radius already showed a significant shortening and, therefore, mammary gland development slowed down.
Sox10, Axin2 and Elf5 have been shown to function as key genes in embryonic mammary gland development. Sox10 is expressed in fetal mammary gland stem cells during embryonic mammary gland development and plays a central role in mammary gland development [40,41]. Axin2, a target gene of the Wnt/β-catenin pathway, has been used as a marker of functional stem cells in the mammary gland in a lineage-tracing approach [18]. Elf5 is required for the proliferation and differentiation of mammary epithelial cells in embryonic mouse mammary glands [42]. We found that mild nutritional restriction ($90\%$ of ad libitum intake) increased the gene expression of Sox10 and Elf5, suggesting a positive effect on mammary gland development. In a previous study on dietary control, alternate-day nutritional restriction was proven to increase the activity of tissue-specific stem cells and had positive implications for life extension [43]. Combined with our regression analysis of whole-mount results, our results suggest that mild maternal nutritional restriction does not impair offspring mammary development and may even increase offspring mammary growth potential by increasing the expression of stem cell-related genes. In addition, $60\%$ of ad libitum feeding reduced Axin2 expression, suggesting that high levels of nutritional restriction inhibit mammary stem cell development and mammary gland development. Consistent with our results, in a study of high levels of maternal gestational nutritional restriction ($50\%$ of ad libitum feeding), the ability to differentiate neural progenitor cells was decreased [44]. These results suggest that stem cell activity in the embryonic mammary gland is related to the level of maternal nutritional restriction, with mild nutritional restriction contributing to stem cell-associated gene expression and high nutritional restriction inhibiting them. K5 is a known marker gene in the myoepithelial/basal layer of the mammary gland [45]. Our study shows that a decrease in K5 gene expression occurs in the basal lamina of the mammary glands when nutrition is restricted to $80\%$ of ad libitum feeding. Combined with regression analysis, our results showed that the expansion potential of mammary basal and mammary gland area was affected by maternal nutritional restriction up to $80\%$ of ad libitum feeding, despite no significant difference in the results of whole-mount analysis.
Embryonic mammary gland development is considered to be hormone-nondependent, and previous studies have demonstrated that embryonic mammary glands are able to develop in mice lacking estrogen (ER-α and ER-β) and progesterone receptors [9,46,47]. After birth, especially during puberty, the mammary glands are stimulated by these hormones to develop rapidly. Estrogen is required for the branching of the mammary ducts during puberty, and estrogen and progesterone are required for lobuloalveolar development during pregnancy. In our study, ER-β receptor expression appeared to be reduced when nutritional restriction reached $60\%$ of the ad libitum intake. This suggests that high levels of maternal nutritional restriction may affect the development of offspring mammary estrogen receptors whose impairment may have further effects on mammary development during puberty.
## 5. Conclusions
In conclusion, our results suggest that mild maternal nutritional restriction ($90\%$ of ad libitum intake) during gestation contributes to increased embryonic mammary gland development. When nutritional restriction ranges from $80\%$ to $70\%$ of ad libitum intake, mammary gland development decreases dramatically, and changes in developmental patterns occur.
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|
---
title: Effects of Different Phospholipid Sources on Growth and Gill Health in Atlantic
Salmon in Freshwater Pre-Transfer Phase
authors:
- Renate Kvingedal
- Jannicke Vigen
- Dominic Nanton
- Kari Ruohonen
- Kiranpreet Kaur
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000100
doi: 10.3390/ani13050835
license: CC BY 4.0
---
# Effects of Different Phospholipid Sources on Growth and Gill Health in Atlantic Salmon in Freshwater Pre-Transfer Phase
## Abstract
### Simple Summary
Optimal nutrition is important for Norwegian-farmed Atlantic salmon in the challenging early seawater phase, which shows a higher mortality leading to significant economic losses. Phospholipids are reported to enhance growth, survival, and health in the early stages of the fish life. Atlantic salmon (74 to 158 g) were fed six test diets to evaluate alternative phospholipid (PL) sources in freshwater and were transferred to a common seawater tank with crowding stress after being fed the same commercial diet up to 787 g. Krill meal (KM) was evaluated using dose response with the highest $12\%$ KM diet compared against $2.7\%$ fluid soy lecithin and $4.2\%$ marine PL (from fishmeal) diets, which were formulated to provide the same level of added $1.3\%$ PL in the diet similar to base diets with $10\%$ fishmeal in the freshwater period. A trend showing increased weight gain with high variability was associated with an increased KM dose in the freshwater period but not during the whole trial, whereas the $2.7\%$ soy lecithin diet tended to decrease growth during the whole trial. No major differences were observed in liver histology between the salmon that were fed different PL sources during transfer. However, a minor positive trend in gill health based on two gill histology parameters was associated with the $12\%$ KM and control diets versus the soy lecithin and marine PL diets during transfer.
### Abstract
Growth and histological parameters were evaluated in Atlantic salmon (74 g) that were fed alternative phospholipid (PL) sources in freshwater (FW) up to 158 g and were transferred to a common seawater (SW) tank with crowding stress after being fed the same commercial diet up to 787 g. There were six test diets in the FW phase: three diets with different doses of krill meal ($4\%$, $8\%$, and $12\%$), a diet with soy lecithin, a diet with marine PL (from fishmeal), and a control diet. The fish were fed a common commercial feed in the SW phase. The $12\%$ KM diet was compared against the $2.7\%$ fluid soy lecithin and $4.2\%$ marine PL diets, which were formulated to provide the same level of added $1.3\%$ PL in the diet similar to base diets with $10\%$ fishmeal in the FW period. A trend for increased weight gain with high variability was associated with an increased KM dose in the FW period but not during the whole trial, whereas the $2.7\%$ soy lecithin diet tended to decrease growth during the whole trial. A trend for decreased hepatosomatic index (HSI) was associated with an increased KM dose during transfer but not during the whole trial. The soy lecithin and marine PL diets showed similar HSI in relation to the control diet during the whole trial. No major differences were observed in liver histology between the control, $12\%$ KM, soy lecithin, and marine PL diets during transfer. However, a minor positive trend in gill health (lamella inflammation and hyperplasia histology scores) was associated with the $12\%$ KM and control diets versus the soy lecithin and marine PL diets during transfer.
## 1. Introduction
Farmed salmon are typically transferred from early phase production in tanks on land to seawater cages that constitutes a challenging environment, where fish can experience significant mortality before reaching harvest size. For example, mortality in Atlantic salmon ranged from 15 to $16\%$ from 2017 to 2021 in Norway, with approximately $35\%$ of sea cage mortality occurring in the first 0–3 months at sea for the 2010–11 salmon generations in the Norwegian-farmed Atlantic salmon [1]. This mortality in the early sea cage phase leads to significant economic loss [2]. Thus, research on optimal nutrition to produce robust smolts for improved survival and growth after transfer to the sea cage is of interest to the aquaculture industry [3]. Fish meal (FM) and fish oil (FO) dominated early commercial salmon feed formulations and provided essential nutrients, but usage of these marine ingredients has declined over time as they are limited resources at generally higher prices compared to alternative ingredients where sustainability measures are also considered [4]. Antarctic krill meal (KM; Euphausia superba) is a commercially known ingredient in salmon feeds, with potential benefits toward enhancing growth and health in salmonids [5]. The krill fishery in the Antarctic Southern *Ocean is* considered highly regulated and sustainable [6,7]. KM provides a range of nutrients including proteins (similar amino acid profile to FM); water soluble nitrogenous components (free amino acids, peptides, nucleotides, and trimethylamine N-oxide), which can act as potential feed attractants; astaxanthin; marine omega-3 fatty acids (eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)); and phospholipids (PLs) [5]. Substantial evidence exists showing that dietary PL can improve growth, survival, and health (reduced intestinal steatosis and deformities) in the larval and early juvenile stages of the fish [8,9,10,11]. In addition, KM and krill oil (KO) reduced fat accumulation in the hepatocytes in comparison to soybean lecithin as the PL sources in the diet of seabream larvae [10,12,13]. In addition, there was an indication that seabream juveniles that were fed a diet with $9\%$ KM had lower hepatocyte vacuolization (fat storage) versus a control diet without KM that was higher in fishmeal [12,13], and a non-significant trend for lower hepatocyte vacuolization was indicated for seabream larvae that were fed a diet with krill oil versus soybean lecithin as the PL source [10]. PLs from different sources can have different properties. KM has approximately $40\%$ PL consisting of the total lipid with phosphatidylcholine (PC) at >$80\%$ of the total PL and ca. $18\%$ EPA + DHA of the total lipid [14]. In comparison, fluid soy lecithin can have approximately $46\%$ PL of product (does not include glycolipids and complex sugars) with ca. $35\%$ PC of the total PL and ca. $55\%$ 18:2n-6 of the total FA as the major FA with no EPA + DHA [15]. KM has been documented in the diet of seawater salmon [16,17,18], however, only KO has been documented in the diet of freshwater salmon during the pre-transfer to the seawater phase [19]. The objective of the present study was to document the effect of the KM dose as a source of PL and compare it against other PL sources in the feed of freshwater Atlantic salmon during the pre-transfer phase followed by the early seawater phase by evaluating the growth and histological health parameters. A four-level graded dose response for KM up to $12\%$ of the diet along with a comparison of alternative PL sources (soy lecithin and marine PL from fishmeal) formulated to provide the same level of added $1.3\%$ PL in the diet as $12\%$ KM was evaluated in freshwater diets for salmon during the pre-transfer phase. Fish identified by pit tag with this pre-transfer freshwater feeding history were then transferred to a common seawater tank with crowding stress after transfer and a drop in water temperature at transfer (crowding and water temperature drop can be experienced at transfer commercially) and then were fed the same commercial feed. Gill and liver histology were also compared for salmon that were fed the alternative PL source diets at the end of the freshwater pre-transfer period.
## 2.1. Feed Formulation and Composition
Three different sources of PL were tested in pre-transfer freshwater feeds: (i) krill meal (QrillTM Aqua; Aker BioMarine Antarctic ASA) at four levels for dose response ($4\%$, $8\%$, and $12\%$ of diet), (ii) fluid soy lecithin as a vegetable PL source, and (iii) marine phospholipid-rich oil sourced from North Atlantic fish species from Triple 9 (TripleNine, Trafikhavnskaj 9, DK-6700 Esbjerg, Denmark))., and a control diet. The trial diets are referred to as Control, KM4, KM8, KM12, VegPL, and MarPL, respectively. Trial feeds were formulated using a commercial formulation program with external oil mix calculations and produced by extrusion at Cargill Innovation Center (Dirdal, Norway) for ca. 74 g fish with lipid nutrients and then adjusted for purposes of the trial. The 4-mm pre-transfer freshwater trial feeds were formulated and analyzed to have similar digestible energy (22.1–23.6 MJ/kg gross energy), protein (46–$49\%$ range), and fat (22–$24\%$ range) (Table 1) and with similar calculated $1.1\%$ EPA + DHA in diet, 15–$16\%$ saturated in total FA, and 1.3 n-6/n-3 fatty acid (FA) ratio across trial feeds. Protein was analyzed by the Dumas principle using the Elementar Rapid Max N system. Fat was analyzed by low-field nuclear magnetic resonance scan using the NMR Analyzer Bruker minispec mq10 system (Cargill Innovation Center, Dirdal, Norway). Gross energy was analyzed by the Leco gross energy bomb calorimetry system (Cargill Innovation Center, Dirdal, Norway). Moisture was predicted by the NIR FOSS DS2500 system (Cargill Innovation Center, Dirdal, Norway) by using the feed model at Cargill. A similar $1.3\%$ PL in diet across pre-transfer freshwater diets was calculated from the addition of $12\%$ krill meal, fluid soy lecithin, and marine PL test ingredients to base formulations with the same $10\%$ fishmeal level across the diets. There was variation in the other ingredients (added oil, plant ingredients, and micronutrients) needed for balancing or reaching nutrient targets. The choline level was formulated to be the same for control and VegPL diet with MarPL and KM12 providing additional choline to these diets in the form of phosphatidylcholine (PC). However, formulated choline levels for control diet and fluid soy lecithin diets were in excess of the NRC 2011 requirements for salmonids and in excess of the lowest choline level used by Hansen and coworkers [20] with no growth differences observed (1340 to 4020 mg choline/kg diet dose response trial for 456 g initial weight salmon). Lipid accumulation in the gut was reduced for salmon (456 g initial weight) at increased choline levels [20]. The formulation and composition of feeds are given in Table 1.
## 2.2. Fish Trial Conditions
The experiment was performed according to the guidelines and protocols approved by the European Union (EU Council $\frac{86}{609}$; D.L. 27.01.1992, no. 116) and by the National Guidelines for Animal Care and Welfare published by the Norwegian Ministry of Education and Research.
Atlantic salmon (Salmo salar) with an initial weight of ca. 67 g were used for the trial. The fish were pit-tagged and randomly distributed into 24 freshwater flow-through tanks (1 m diameter and 0.45 m3 volume) to have 40 fish per tank at the start of trial diet feeding. These fish after 15 days of tank acclimation were 74 ± 12 g (average ± SD for all 960 fish in 24 tanks at the start of trial feeding) and then were fed the freshwater pre-transfer trial diets (Table 1) over a 53-day period. Water temperature averaged 14.3 °C (13.3–15.3 °C range) with $107\%$ average oxygen saturation at the inlet and $90\%$ oxygen saturation at the outlet during the freshwater acclimation and trial diet feeding period. Fish were fed the six trial diets to four replicate tanks during the 53-day freshwater pre-transfer period using an automatic belt feeder with continuous feeding for 20 h per day in excess of satiation level. Feed intake was calculated on a weekly basis by collecting and weighing uneaten pellets as well as by weighing the amount fed. There was a 12 h light: 12 h dark photoperiod regime from Day 0 at freshwater tank acclimation to Day 33 after which a 24 h light regime was used to initiate smoltification. After this freshwater pre-transfer feeding period, fish from all the tanks (17–20 fish per tank from the 24 freshwater tanks) were transferred to a larger common seawater flow-through tank (5 m diameter and 21.6 m3 volume with 28.5 ppt salinity, and no acclimation time from 0 ppt freshwater to 28.5 ppt seawater) with a water temperature drop at transfer (ca. 14 to 9 °C) and crowding stress after transfer (lowered water level to ca. 20 cm for one hour with supplemental oxygen for all 459 fish of ca. 167 g within a ca. 0 to 30 h period after transfer) in the common seawater tank after all 17–20 fish per tank from the 24 freshwater tanks fish were transferred over and then were fed a common commercial extruded salmon diet (EWOS AS) for a further 98 days. Daily water temperature was lower during the seawater phase averaging 9.4 °C (8.5–11.1 °C range).
## 2.3. Fish Growth
The 40 fish per tank were weighed individually with pit-tag identification on acclimation to the freshwater tanks (Day 0), at the start of trial diet feeding (Day 15), at intermediate weighing (Day 33), and after 53 days of trial feeding in the freshwater (Day 68). The fish weight gain in the freshwater pre-transfer period from Day 15 (start of freshwater trial diet feeding) to 68 were compared statistically between diets. A total of 17–20 fish from each of the 24 freshwater tanks were transferred to the common seawater tank on Day 68 with fish weighing performed on Days 35, 73, and 98 after transfer to seawater. There were 9 to 17 fish representing the original tanks in the freshwater period with 50 to 58 fish representing each of the test diets from the freshwater period at final weighing in seawater at 98 days after transfer to the common seawater tank. The fish weight gain over the whole trial period in freshwater and seawater from Day 15 to 166 days were statistically compared between diets.
## 2.4. Hepatosomatic Index
Hepatosomatic index (HSI) is the liver weight percent of the whole body weight. HSI was measured on 10 fish randomly sampled per tank (four tank replicates per diet) to study 40 fish per diet at the end of the freshwater pre-transfer period when fed test diets and 40 fish per diet (identified by pit-tag) at the end of the seawater phase when fed the common commercial diet.
## 2.5. Histology
Gill and liver histology were performed on the fish involved in the dietary phospholipid source comparison (KM12, VegPL, and MarPL) and on fish fed the Control diet at the end of the freshwater pre-transfer period. Liver (half tissue section) and gill (left gill arch 2) tissues were randomly sampled from five fish per tank to give a total of 20 liver and 20 gill tissues per diet group for histological analysis. The tissues were fixed in formalin ($4\%$ formaldehyde) and stored at room temperature until sent to Pharmaq Analytiq AS (Harbitzallée 2A, 0275 Oslo, Norway) for histological analysis.
## 2.6. Statistical Analysis
The weight gain for the different periods was modelled by computing the weight gain of each tagged individual and then using a hierarchical generalized additive model (GAM) with the spline function to describe the possibly non-linear dose-response. A random effect of tank was added to the model to account for the multiple individual observations per experimental unit. The total feed intake over the periods of interest was modelled with a single level GAM with a spline function describing the dose-response function. Hepatosomatic index (HSI) was modelled by a hierarchical GAM model using a spline function to describe the dose-response function, mean-centered round weight of the fish as a covariate, and a random effect of tank to account for the multiple individual observations per tank. From this model the expected liver weight was solved for an average-sized sampled fish and expressed as HSI by dividing the expected liver weight with the mean round weight of the sample. Gill and liver histology scores are ordinal variables for which common arithmetic operations, such as sum or mean, are not defined and therefore scores require an ordinal model returning the score probability for evaluation. A hierarchical GAM for ordinal data was set up by using a spline function to describe the dose-response function, and a random effect of tank was included to account for multiple individuals observed per tank. The models for weight gain, feed intake, and HSI assumed the error distribution is the normal distribution, and the model for gill and liver scores assumed the model is ordinal and the errors followed the ordered categorical family. All data processing and statistical modelling was conducted with the R language [21]. The GAMs were estimated with the “gam” function of the R language add-on package “mgcv” [22].
The outcomes from the fitted statistical models are presented graphically by showing the mean response and the $95\%$ credible intervals. The mean (median) response and the $95\%$ credible intervals were computed with the help of a parametric bootstrap (with 10,000 random draws per parameter) by taking the $25\%$, $50\%$, and $97.5\%$ quantiles of the computed response vector. In the case of a categorical predictor variable (for comparing the different PL sources), the graphs show the mean and an error bar of the $95\%$ credible interval. In the case of a continuous predictor (for the dose-response of krill meal inclusion), the mean response is shown as a median dose-response curve and the $95\%$ credible interval is shown as a confidence band around the mean curve. This way both the magnitude of any potential effect (biological significance) and the uncertainty of any effect estimate (statistical significance) can be shown in the same graph for all the results independent of the response following the normal, binomial, or ordered categorical distribution.
## 3.1. Growth Performance
Atlantic salmon of 74 g (overall tank average) were fed the six test diets up to 158 g (overall tank average), growing 2.1-times the initial fish weight to the end of the freshwater pre-transfer period. There was no clear trend for increased feed intake with KM dose in the FW pre-transfer phase (Figure 1). A trend for increased feed intake was indicated for the Control and KM12 diets compared to the MarPL and VegPL diets in the PL source comparison for the FW pre-transfer phase (Figure 2). There was overall high variability for the feed intake comparisons. A trend for increased fish weight gain with high variability was indicated with increased KM dose in the FW phase (Figure 3). There was similar weight gain during the whole trial with feeding the KM dose in the FW pre-transfer phase followed by feeding the same commercial diet in a common tank for the SW phase (Figure 4). Fish fed the KM12 diet had increased weight gain compared to the VegPL diet with the MarPL and Control diets having intermediate weight gains in the PL source comparison for the FW pre-transfer phase (Figure 5). Weight gain was similar for the fish that were fed KM12, MarPL, and Control diets, with a trend for higher indicated weight gain than the VegPL group during the whole trial, with feeding the KM dose in the FW pre-transfer phase followed by feeding the same commercial diet in a common tank for the SW phase (Figure 6, Tables S1 and S2).
## 3.2. Hepatosomatic Index
A trend for decreased hepatosomatic index (HSI; liver% of fish weight) was indicated for the fish that were fed increased KM dose from 0 to $12\%$ of diet at the end of the freshwater pre-transfer feeding phase (Figure 7). There was no decrease in HSI with feeding KM dose at the end of the whole trial after the FW pre-transfer phase followed by feeding the same commercial diet in a common tank for the SW phase (Figure 8). A lower HSI was indicated for the fish that were fed the KM12 diet compared with the fish that were fed the MarPL, VegPL, and Control diets at the end of the freshwater pre-transfer feeding phase (Figure 9) with a similar minor HSI trend observed over the whole trial (Figure 10).
## 3.3.1. Gill Histology
An increased probability for very mild to mild gill lamella inflammation and hyperplasia score was indicated for the salmon that were fed the VegPL and MarPL diets compared to the Control and $12\%$ KM diets at the end of the freshwater pre-transfer phase after 53 d of feeding the trial diets (Figure 11a,b). Other following gill histology responses were evaluated with no major differences between the diets: vascular lesions, filament inflammation, necrosis of respiratory epithelium, necrosis affecting deeper tissues, fusion of lamella,and other lesions noted as present or absent.
## 3.3.2. Liver Histology
No major differences were observed in liver histology between the control, $12\%$ KM, soy lecithin, and marine PL diets at the end of the FW pre-transfer phase after 53 d of feeding the trial diets (data not shown). The following liver histology responses were evaluated: total amount of abnormal tissue, inflammation, necrosis, inflammation in liver tissue or capsule (peritonitis), peribiliary or perivascular inflammation, neoplasia, fibrosis, lipid deposition, other degenerative changes, vascular lesions, and other lesions noted as absent or present.
## 4. Discussion
The present study evaluated the effect of different phospholipid sources fed over 53 d in the freshwater pre-transfer phase followed by feeding the same commercial diet over 98 d in a common seawater tank on growth performance and health parameters of Atlantic salmon. KM was evaluated in dose response ($4\%$, $8\%$, and $12.0\%$ of diet), and diets with $2.7\%$ fluid soy lecithin (VegPL) and $4.2\%$ MarPL as alternative PL sources were formulated to provide the same level of added $1.3\%$ PL in diet as $12\%$ KM. All the test diets contained $10\%$ fishmeal in the FW phase. A trend was indicated for increased fish weight gain (high variability) with increased KM dose in the FW pre-transfer phase but a carry-over effect on growth was not observed for the same salmon fed the same commercial diet after seawater transfer. Salmon (104 g initial weight) that were fed krill meal at 7.5 and $15\%$ of diet for higher fishmeal diets (40–$52\%$ of diet range) than the current trial had increased growth after transfer to sea cage [16]. Fishmeal provides PL, so higher fishmeal diets may reduce the need for KM as a PL source [23]. However, KM also provides amino acids (protein), water-soluble nitrogenous components (potential feed attractants), astaxanthin, and EPA + DHA, hence, it is more than a PL source. KM feeding may need to continue after sea water transfer to have a positive effect on growth at the end of the trial, noting the positive effects of KM on salmon growth observed in other but not all trials, which can depend on life stage and challenges, diet composition, KM refining (de-shelling etc.), and inclusion level [5].
A trend for decreased fish weight gain was indicated for the VegPL diet in the FW phase and over the whole trial compared with the control diet, whereas the MarPL diet showed more similar growth to the control diet over the whole trial, noting that only one PL level tested for MarPL and fluid soy lecithin matched that provided by KM12, so optimal dose was not evaluated. The choline level was formulated to be the same for the control and VegPL diets with KM12 and MarPL providing additional choline to these diets in the form of phosphatidylcholine (PC). Formulated choline levels for the control diet and fluid VegPL diets were in excess of the NRC 2011 requirements for salmonids and in excess of the lowest choline level used by Hansen et al. in 2020 with no growth differences observed (1340 to 4020 mg choline/kg diet dose response trial for 456g initial weight salmon) [20]. Lipid accumulation in the gut was reduced for these salmon (456 g initial weight) at increased choline levels [20]. Effects of increased choline with KM inclusion cannot be ruled out and further research would be needed to separate choline from PL effects for these smaller pre-transfer salmon (74 to 158 g fish weight) that were fed lower fat pre-transfer diets (22–$24\%$ fat) than during the seawater growth with choline requirements for reducing the lipid accumulation in the intestine, potentially dependent on dietary fat level [20]. Higher growth was generally observed for PL provided by KO over soy lecithin at various PL doses for the first-feeding stage of salmon, but this growth trend was not consistent at various PL doses over the whole trial from the first-feeding to smolt [19]. PL from KO was indicated to be more effective than fluid soy lecithin for reducing intestinal steatosis in smaller salmon (2.5 g salmon, but no steatosis observed across diets for 10–20 g salmon) and low level of vertebral deformities [19]. Marine PL sources (FM and KO) were also compared against soy lecithin at a similar ca. $3.5\%$ PL of diet level for the first-feeding Atlantic salmon (0.14 g initial weight) with these PL sources, giving similar growth to ca. 2.4 g final fish weight with no conclusive mortality or intestinal histology differences between PL sources but these parameters were generally improved for the PL source diets with higher PL compared to the control diets with lower PL. An uncertain observation of higher average growth was indicated for the marine PL sources over soy lecithin at intermediate weighing for salmon at ca. 0.6 g [24]. Effects of PL cannot be isolated from KM but the increased growth for KM12 over the VegPL diet in the pre-transfer phase may be due to PL, choline, water soluble nitrogenous components, etc., noting that there was also an indicated trend for decreased growth of VegPL versus the control diet in the pre-transfer phase.
Addition of KM did not give a clear increase in feed intake compared to the control diet and there was an indicated trend of decreased feed intake for the MarPL and VegPL diets, but strong conclusions cannot be made due to the high variability. Feed intake can only be measured on a tank basis, so it was not possible to estimate feed intake of fish with different pre-transfer freshwater feeding histories in a common tank that were fed the same diet in the seawater phase.
A trend for decreased hepatosomatic index (HSI) was indicated with increased KM inclusion and for the $12\%$ KM diet versus the other PL sources added to provide the same PL level in the pre-transfer phase, but the effect of KM on decreasing HSI was not carried over into the seawater phase with fish that were fed the same diet in a common tank (Figure 7, Figure 8, Figure 9 and Figure 10). There was no difference in the liver lipid droplet accumulation based on histology (normal scores only) for salmon that were fed the diets containing different PL sources at the end of the freshwater pre-transfer period. The lower HSI in KM12 could be due to the positive effects from krill PL (and choline) on the lipid transport and deposition in organs, with this effect of feeding $12\%$ KM to Atlantic salmon documented by [17] with less pale livers and reduced liver fat. The authors further supported this observation with a significantly higher expression of the cadherin 13 (Chd) gene in the $12\%$ KM group associated with circulating levels of the adipocyte-secreted protein adiponectin that has potential anti-inflammatory effects and plays an important role in metabolic regulation and is associated with the fatty liver index in humans [25]. However, Chd expression was not studied in the current study, and hence, further studies are warranted to explore the association between Chd expression, his, and absolute fat accumulation in the liver in salmon. Increased choline, which KM provided in this trial, was shown to reduce fat accumulation in the intestine of Atlantic salmon [20]. Choline supplementation was also indicated to reduce HSI in Atlantic salmon, but this was not reflected in lower liver fat or histological vacuolization, noting that there are variable trends of dietary choline deficiency on the liver fat level of fish reported in the literature [26]. PL from KO was indicated to be more effective than fluid soy lecithin for reducing intestinal steatosis in smaller salmon (2.5 g salmon but no steatosis observed across diets for 10–20 g salmon). Further studies are required to associate higher liver fat with welfare in salmon.
Gills are one of the most vital organs of fish, due to their function in respiration, osmoregulation, excretion of nitrogenous waste, pH regulation, and hormone production [27]. Gill health has become one of the most significant health and welfare challenges in the salmon aquaculture industry in Norway, Scotland, and Ireland [28,29,30]. The gill disorders are generally complex and multifactorial and are related to both biological factors, such as parasites and pathogens, handling stress, treatments, or due to the environmental factors, such as temperature, salinity, algal blooms, etc. Hence, the gill diseases are challenging to prevent and control and lead to high mortality, reduced production performance, and impaired fish welfare, cumulating in huge economic losses [31]. There were no differences reported for histological parameters investigated except in the presence of ectopic epithelial cells containing mucus in the lamina propria in the hindgut (potential inflammatory marker) of salmon (grown from 2.3 to 3.9 kg in sea cages) that were fed $15\%$ fishmeal diet but not for $12\%$ KM of diet in a $5\%$ fishmeal diet, which may suggest anti-inflammatory effects of KM [17]. KM provides astaxanthin (166 mg/kg in the KM used for the present study) to the diet as a natural antioxidant with potential anti-inflammatory properties [32]. KM and MarPL also provide EPA + DHA attached to PL, which may affect bioavailability of EPA + DHA for use in cell membranes and inflammatory response [33] but this is not documented in fish. In the current study, there was decreased probability for very mild to mild gill lamella inflammation and hyperplasia scores indicated in salmon that were fed $12\%$ KM compared to the soy lecithin and marine PL diets but gill histology for salmon that were fed the $12\%$ KM diet was similar to the control diet without KM (Figure 5).
## 5. Conclusions
Overall, increased KM tended to increase growth (high variability), whereas the VegPL diet tended to decrease growth compared to the control diet in the FW pre-transfer phase. The positive growth trend indicated for KM fed pre-transfer was not carried over into the seawater phase for fish fed the same diet. A minor positive trend in gill health (lamella inflammation and hyperplasia histology scores) was indicated for the $12\%$ KM and Control diets compared with the VegPL and MarPL diets in the FW pre-transfer phase. Hepatosomatic index tended to decrease with KM fed in the pre-transfer phase, noting that all livers evaluated by histology were considered normal for lipid droplet accumulation. Only one VegPL and MarPL dose was tested, so dose effect of these PL sources and comparison with krill oil to better isolate the PL effect from other nutrients in KM as well as a post-transfer feeding comparison of these PL sources could be areas to research further in transfer diets for salmon.
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|
---
title: Effects of Bacillus licheniformis and Combination of Probiotics and Enzymes
as Supplements on Growth Performance and Serum Parameters in Early-Weaned Grazing
Yak Calves
authors:
- Jia Zhou
- Kaiqiang Zhao
- Lisheng Shao
- Yuhong Bao
- Dundup Gyantsen
- Chenglong Ma
- Bai Xue
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000113
doi: 10.3390/ani13050785
license: CC BY 4.0
---
# Effects of Bacillus licheniformis and Combination of Probiotics and Enzymes as Supplements on Growth Performance and Serum Parameters in Early-Weaned Grazing Yak Calves
## Abstract
### Simple Summary
This study was conducted to investigate the effects of dietary supplementation with *Bacillus licheniformis* and a combination of probiotics and enzymes on the growth and blood parameters of grazing yak calves. The body weight, body size, serum biochemical parameters, and growth hormone levels of grazing yaks were assessed. We found that supplementation with probiotics alone or with a combination of probiotics and enzymes significantly increased the average daily gain, compared to the controls, and the combination of probiotics and enzymes showed a better performance. Supplementation with the complex of probiotics and enzymes significantly increased the concentration of serum growth hormone, insulin-like growth factor-1, and epidermal growth factor, which may be the main reason for the higher daily weight gain. The findings of this study may help improve the growth efficiency of yak calves on the Qinghai–Tibetan Plateau.
### Abstract
Early weaning is an effective strategy to improve cow feed utilization and shorten postpartum intervals in cows; however, this may lead to poor performance of the weaned calves. This study was conducted to test the effects of supplementing milk replacer with *Bacillus licheniformis* and a complex of probiotics and enzyme preparations on body weight (BW), size, and serum biochemical parameters and hormones in early-weaned grazing yak calves. Thirty two-month-old male grazing yaks (38.89 ± 1.45 kg body weight) were fed milk replacer at $3\%$ of their BW and were randomly assigned to three treatments ($$n = 10$$, each): T1 (supplementation with 0.15 g/kg Bacillus licheniformis), T2 (supplementation with a 2.4 g/kg combination of probiotics and enzymes), and a control (without supplementation). Compared to the controls, the average daily gain (ADG) from 0 to 60 d was significantly higher in calves administered the T1 and T2 treatments, and that from 30 to 60 d was significantly higher in calves administered the T2 treatment. The ADG from 0 to 60 d was significantly higher in the T2- than in the T1-treated yaks. The concentration of serum growth hormone, insulin growth factor-1, and epidermal growth factor was significantly higher in the T2-treated calves than in the controls. The concentration of serum cortisol was significantly lower in the T1 treatment than in the controls. We concluded that supplementation with probiotics alone or a combination of probiotics and enzymes can improve the ADG of early-weaned grazing yak calves. Supplementation with the combination of probiotics and enzymes had a stronger positive effect on growth and serum hormone levels, compared to the single-probiotic treatment with Bacillus licheniformis, providing a basis for the application of a combination of probiotics and enzymes.
## 1. Introduction
Yaks (Bos grunniens) occur on the Qinghai–Tibet Plateau at high altitudes and with long cold seasons and limited pasture resources. This species is a unique product of long-term natural selection, providing local herders with the most basic living materials and livelihood resources, such as meat, milk, shelter (hides and furs), and fuel (dung), and is an indispensable part of the ecology and economy of the Qinghai–Tibetan Plateau [1]. However, the low reproductive rate of yaks seriously restricts their production and utilization. The cold season on the Tibetan Plateau lasts for eight months (October to the following May), during which time the quantity and quality of pasture decrease below the nutritional requirements of lactating yaks [2]. The deficiency of feed intake results in a negative body energy balance and metabolic stress [3]. On the other hand, under traditional grazing management, plateau-grazing yak calves are weaned naturally or artificially under various conditions at an age of 18–24 months [4], rather than the weaning age of domestic beef cattle (<6 months). The slow recovery itself and the late weaning of yak calves, which result in a poor postnatal physical condition, severely affect the onset of the next estrous cycle in the cow. Most yaks exhibit a long postpartum anestrous period and calve twice every 3 years or once every 2 years [5]. Therefore, the early weaning of yak calves may help mitigate these adverse effects.
Early weaning has become more popular in recent years for various reasons, including the better use of limited feed resources and alleviating grazing pressure on pastures by reducing the nutritional needs of cows [6]. Weaning calves before the start of the breeding season improves the reproductive performance of cows [7,8] because the cows can regain their weight faster, thus accelerating the onset of postpartum estrus. The use of milk replacer in early weaning is common in livestock production [9,10]. The milk replacer has demonstrated positive benefits in animal experiments, such as improved immunity and relieved weaning stress response [11]. Increasing evidence suggests that enhanced milk replacer feeding is beneficial for improving gut microbial development and growth performance in early-weaned lambs [12,13].
Over the past few decades, probiotics have been widely used in livestock and poultry production for their ability to enhance animal disease resistance, improve feed utilization, and improve growth performance [14]. In ruminants, yeasts and bacteria, including Lactobacillus, Bifidobacterium, Bacillus, Propionibacterium, and Enterococcus, alone or in combination, are used as additives in diets [15,16]. Probiotics can decrease diarrhea, improve production and feed utilization efficiency, and strengthen the immunity system in young ruminants [17,18,19]. Moreover, supplementation with probiotics improves the rumen and intestinal epithelial cell growth, which enhances the gastrointestinal tract development and health status of calves [17,20,21]. Oral administration of *Bacillus licheniformis* can increase ruminal digestibility and total volatile fatty acid concentrations in Holstein cows [22] and growth performance in Holstein calves [23]. In vitro inoculation with *Bacillus licheniformis* also improves ruminal fermentation efficiency of forage of various qualities [24]. However, no information is currently available on the effect of *Bacillus licheniformis* on the growth performance of yak calves.
Compound enzyme preparations are produced from one or more preparations containing a single enzyme as the main entity, which is mixed or fermented with other single enzyme preparations to form one or more microbial products [25], including saccharylases, amylases, cellulases, proteases, phytases, hemicellulases, and pectinases. Depending on the differences in digestive characteristics and diet composition, specific enzyme preparations can be used for livestock [26]. Specific enzyme complex preparations can degrade multiple feed substrates (antinutrients or nutrients), and different types of enzymes can work synergistically to maximize the nutritional value of feed [27]. In buffalo calves, cellulase and xylanase are more effective with regard to average daily weight gain (ADG) and feed efficiency [28]. Further, the addition of exogenous fibrolytic enzymes to wheat straw has no effect on starter feed intake and increases nutrient digestibility and recumbency, but decreases the ADG of weaned Holstein dairy calves [29].
The effects of probiotics or compound enzyme preparations on the production performance and biochemical blood indexes of calves are not consistent [29,30,31,32,33]. The respective discrepancies may be due to differences in the amounts of added probiotics and exogenous enzymes, the strains of probiotics, diets, and animal management strategies. Therefore, this study was conducted to compare the effects of *Bacillus licheniformis* and a combination of probiotics and enzymes on the growth performance and serum parameters in yak calves, so as to provide a theoretical basis for the application of probiotics in grazing yak calves.
## 2.1. Animals and Treatment
This study was performed in accordance with the Chinese Animal Welfare Guidelines, and the experimental protocols were approved by the Animal Care and Ethics Committee of the Institute of Animal Husbandry and Veterinary Medicine, Tibet Academyof Agriculture and Animal Husbandry Science (No. # TAAAHS-2016–27).
The feeding trial was conducted at Damxung Co., (Lhasa, China; 30.5° N, 91.1° E) from July to October. The average altitude was 4200 m, the average annual temperature was 1.3 °C, and the average annual precipitation was 456.8 mm. Thirty two-month-old male yaks (38.89 ± 1.45 kg body weight (BW)) were fed milk replacer solution at $3\%$ of their BW every day and were randomly assigned to three dietary supplementation treatments ($$n = 10$$, each), according to BW and age, as follows: T1, supplemented with 0.15 g/kg *Bacillus licheniformis* (2 × 1010 CFU/g); T2, supplemented with a 2.4 g/kg combination of probiotics and enzymes (containing 0.4 g/kg Bacillus licheniformis, 2 × 1010 CFU/g; 1.0 g/kg yeast, 1 × 1010 CFU/g; 1.0 g/kg mixture of xylanase, cellulase, and glucanase in a 1:1:1 ratio, xylanase, 20,000 U/g, cellulase, 1500 U/g, glucanase, 6000 U/g); and a control treatment. The milk replacer, probiotics, and enzyme preparations were provided by the Chinese Academy of Agricultural Sciences (Beijing, China). All yak calves were allowed to graze on an alpine meadow during daytime for the 60-day trial, and they were individually fed milk replacer before and after grazing (0800 and 2000 h, respectively). The forage of the alpine meadow was mainly composed of Kobresia tibetica, and the nutrient composition (dry matter basis) was analyzed in our previous study [34], i.e., $10.4\%$ crude protein, $2.1\%$ ether extract, $67.8\%$ neutral detergent fiber, $34.2\%$ acid detergent fiber, and $4.6\%$ ash. The powdered milk replacer was weighed and mixed with warm water (approximately 40 °C) at a ratio of 1:7 (w/v) to obtain milk replacer solution, according to our previous study [35]. Based on preliminary assessments, the feeding amount of milk replacer was calculated so that all yak calves were able to feed without surplus [35]. The nutrient composition of the milk replacer is shown in Table 1.
## 2.2. Sample Collection and Analysis
The BW of each yak calf was recorded before morning feeding on d 0, 30, and 60 using a platform scale, and the ADG was calculated accordingly. The body size indexes of all yak calves were determined using a linen tape at the beginning (d 0) and end (d 60) of the experiment, as previously described [36].
Blood samples (approximately 10 mL) were collected from the jugular vein of the yak calves using a vacuum tube before morning feeding on d 0 and 60. The blood samples were centrifuged at 1100× g for 10 min to obtain serum, which was then aliquoted in 1.5 mL centrifuge tubes and stored at −20 °C.
The serum biochemical parameters, including blood urea nitrogen (BUN), globulin (GLB), blood glucose (GLU), and non-esterified fatty acids (NEFAs), were analyzed using an automatic biochemical analyzer 7020 (Hitachi, Tokyo, Japan). Metabolic hormones in the serum, including insulin-like growth factor-1 (IGF-1), epidermal growth factor (EGF), cortisol, insulin (INS), and growth hormone (GH), were determined using commercial ELISA kits (Jiahong Technology Co., Ltd., Beijing, China) according to the manufacturer’s instructions. Briefly, 50 μL of each five-fold diluted serum sample was added to each well of a 96-well ELISA plate. After 30 min of incubation at 37 °C, the plate was washed five times using PBS (Servicebio, Wuhan, China) to remove unbound proteins. Then, 50 μL of HRP-conjugated antibodies was added to allow them to bind with their corresponding antigens. The 3,3′,5,5′-tetramethylbenzidine working solution was added to each well, followed by stop solution. Absorbance was measured using a multi-plate reader (Varioskan LUX, Thermo Fisher Scientific, Waltham, MA, USA) at a wavelength of 450 nm.
## 2.3. Statistical Analysis
All experimental data of this study were statistically analyzed using a one-way analysis of variance followed by Duncan’s post hoc test with SPSS 26.0 software (SPSS Inc., Chicago, IL, USA). Each yak calf was considered an experimental unit. Data are expressed as means ± standard error. $p \leq 0.05$ was considered statistically significant.
## 3.1. Body Weight
The three treatments did not differ significantly in terms of BW on d 0, 30, and 60 (Table 2). The ADG was higher ($p \leq 0.05$) in the calves under T2 treatment than those under the control treatment, from d 0 to 30, d 30 to 60, and d 0 to 60, and higher ($p \leq 0.05$) than that of those calves under the T1 treatment from d 0 to 60, indicating that the supplementation of *Bacillus licheniformis* and the combination of probiotics and enzymes could improve the growth performance of early-weaned grazing yak calves. The ADG of calves under T1 treatment was higher ($p \leq 0.05$) than that of those under the control treatment from d 0 to 60.
## 3.2. Body Size
The body size parameters did not differ significantly among the three treatments on d 0 and 60 (Table 3), indicating that the supplementation of *Bacillus licheniformis* and the combination of probiotics and enzymes did not affect the body size of yak calves within 60 d.
## 3.3. Serum Biochemical Parameters
The concentrations of serum GLB, BUN, GLU, and NEFAs did not differ significantly among the three treatments on d 0 and 60 (Table 4).
## 3.4. Serum Hormone
As shown in Table 5, the concentrations of serum IGF-1 on d 60 were higher in T2-treated calves than in the T1- and control-treated calves ($p \leq 0.05$, each). The concentrations of serum EGF and GH on d 60 were higher in the T2-treated calves than in the controls ($p \leq 0.05$). The concentration of serum COR on d 60 was higher in the control calves than those under the T1 treatment ($p \leq 0.05$).
## 4. Discussion
Early weaning may have various benefits for cows; however, early weaned calves generally perform poorly compared to naturally weaned calves [37]. Early weaned calves without breastfeeding grew at a lower rate and subsequently took longer to reach their target weight than breastfed calves [38]. To improve the growth performance of early-weaned calves, several improvements were made to the composition of milk replacer or additional feeds were added [39,40,41]. Moreover, the addition of probiotics to the diets of calves significantly improved the ADG [29,30,33]. Dietary supplementation with compound enzyme preparations also improved growth performance in weaned piglets [42,43] and growing-finishing pigs [44]. However, previous studies also reported that supplementation with probiotics, yeast cultures or enzymes had no effect on the growth performance of calves [31,32,45]. In the current study, the addition of *Bacillus licheniformis* alone or a complex of probiotics and compound enzyme preparations to the milk replacer significantly improved the performance of grazing yaks and calves compared with milk replacer alone. Further, the addition of probiotics is beneficial for the regulation of the intestinal microbiota community structure, improving intestinal health and fecal consistency, and reducing diarrhea prevalence [19,31,46,47,48]. The supplementation of fibrolytic enzyme to the diet of crossbred calves improved their nutrient digestibility with a positive effect on daily gain [49]. Calves typically exhibit high metabolism and fast growth; however, their growth performance is susceptible to environmental stress and nutrient absorption and digestive problems, especially in the period after weaning [50]. Under natural grazing conditions on the Qinghai–Tibet Plateau, due to the long-term lack of pasture and harsh environmental conditions, the normal growth of yak calves is severely restricted [48]. In the present study, none of the study animals died, which may be attributed to the supplementation with milk replacer. Therefore, the addition of probiotics and compound enzyme preparations was beneficial for the growth of grazing yak calves.
In most cases, calf weight is positively correlated with body length, and body length can be used to predict calf live weight [51,52]. Supplementation with *Bacillus subtilis* results in an increased body length and BW in Barki lambs at the third and fourth week, as observed in a four-week continuous feeding trial [53]. In the present study, neither body size nor BW differed among the treatments, which may be due to insufficient trial duration and individual differences in animals. Therefore, more time may be required to elucidate whether the probiotic and compound enzyme preparations affected the calves’ body size.
To a certain extent, blood biochemical parameters reflect the metabolism and the acid–base balance of the animal body, and they vary within a certain range [54,55]. The results of the current study revealed that supplementation with *Bacillus licheniformis* and the complex of probiotics and enzyme preparations had no effect on the blood biochemical parameters of grazing yak calves, which is consistent with previously reported results in crossbred and Holstein calves [56,57]. The blood biochemical values of calves vary with the growing stage and are strongly influenced by weaning [58,59], and these possible factors may be stronger than the influence of diet on blood biochemical indicators.
Insulin-like growth factors (IGFs) are small polypeptide hormones mainly synthesized and secreted from the liver, and they are structural homologs of insulin, with similar activities. These consist in binding to specific carrier proteins in the blood to form a composite factor that stimulates systemic body growth and has growth-promoting effects on almost every cell in the body [60,61]. As mediators of GH action, the synthesis of IGFs is also affected by the blood level of GH [62]. EGF is a member of the growth factor family, a single polypeptide of 53 amino acid residues that is involved in regulating cell proliferation [63]. We found that the addition of probiotics and a combination of probiotics and enzymes significantly increased the concentration of serum IGF-1, EGF, and GH, whereas supplementation with *Bacillus licheniformis* alone did not achieve this effect. These results are consistent with the ADG results. GH and IGF-1 are important controllers in regulating amino acid metabolism in calves, where GH promotes the entry of amino acids in muscle tissue into cells and increases protein synthesis, and IGF-1 increases protein deposition by promoting protein synthesis [63,64]. Cortisol is commonly used as a marker of stress responses (such as weanling stress) in animals, and it occurs at high serum levels for a period of time after calves are weaned [65]. In line with our results, oral supplementation with probiotics markedly decreases the concentrations of serum cortisol in neonatal and weaned calves [66,67]. Interestingly, we found that the concentrations of serum cortisol were lower in the T1 than in the T2 group, which was, however, not statistically significant. This suggested that the addition of *Bacillus licheniformis* alone may better alleviate weaning stress in grazing yak calves. However, the respective mechanisms remain to be resolved in more detail.
A limitation of this study is that the T2 group did not strictly control a single variable compared to the T1 group, and the factors (yeast or xylanase, cellulase and glucanase) that contributed to the difference were unclear. This was due to the initial intention of this study to improve the milk replacer by adding probiotics or compound enzyme preparations, and ultimately promote the growth performance of yak calves on the Qinghai–Tibet Plateau. Further, we were unable to collect data on diarrhea and determine nutrient digestibility in grazing calves, which would have further improved our understanding of the weight gain of yaks under the various treatments.
## 5. Conclusions
Our results suggest that supplementation with *Bacillus licheniformis* alone or with a complex of probiotics (*Bacillus licheniformis* and yeast) and compound enzyme preparations (xylanase, cellulase, and glucanase) can improve the ADG of grazing yak calves, and the complex had a better effect on the ADG. The addition of the complexes of probiotics and complex enzyme preparations also increased the concentrations of serum GH, IGF-1, and EGF, which may have led to a higher ADG. Thus, the addition of a combination of probiotics and enzymes to milk replacer may serve as an effective strategy to improve the production of yak calves.
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|
---
title: Growth Performance, Antioxidant and Immunity Capacity Were Significantly Affected
by Feeding Fermented Soybean Meal in Juvenile Coho Salmon (Oncorhynchus kisutch)
authors:
- Qin Zhang
- Fanghui Li
- Mengjie Guo
- Meilan Qin
- Jiajing Wang
- Hairui Yu
- Jian Xu
- Yongqiang Liu
- Tong Tong
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000117
doi: 10.3390/ani13050945
license: CC BY 4.0
---
# Growth Performance, Antioxidant and Immunity Capacity Were Significantly Affected by Feeding Fermented Soybean Meal in Juvenile Coho Salmon (Oncorhynchus kisutch)
## Abstract
### Simple Summary
Fish meal has been the main aquatic feed protein source for aquaculture. However, global fish meal is lacking, and the price of fish meal continues to rise, which has been unable to meet the needs. Soybean meal is currently recognized as the best choice to replace fish meal in aquatic feed, but soybean meal contains anti-nutritional factors which can affect the health of aquatic animals. Microbial fermentation is a commonly used biological method for treating soybean meal antigens and palatability. In this study, juvenile coho salmon were fed a diet with replaced $10\%$ fish meal protein with fermented soybean meal protein supplementation for 12 weeks. The results indicated that the diet with replaced $10\%$ fish meal protein with fermented soybean meal protein supplementation could significantly ($p \leq 0.05$) influence the expression of superoxide dismutase, catalase, glutathione peroxidase, glutathione S-transferase, nuclear factor erythroid 2-related factor 2, tumor necrosis factor α and interleukin-6 genes, the growth performance, the serum biochemical indices, and the activity of antioxidant and immunity enzymes.
### Abstract
This study aims to investigate the effects of partial dietary replacement of fish meal with unfermented and/or fermented soybean meal (fermented by Bacillus cereus) supplemented on the growth performance, whole-body composition, antioxidant and immunity capacity, and their related gene expression of juvenile coho salmon (Oncorhynchus kisutch). Four groups of juveniles (initial weight 159.63 ± 9.54 g) at 6 months of age in triplicate were fed for 12 weeks on four different iso-nitrogen (about $41\%$ dietary protein) and iso-lipid (about $15\%$ dietary lipid) experimental diets. The main results were: Compared with the control diet, the diet with replaced $10\%$ fish meal protein with fermented soybean meal protein supplementation can significantly ($p \leq 0.05$) influence the expression of superoxide dismutase, catalase, glutathione peroxidase, glutathione S-transferase, nuclear factor erythroid 2-related factor 2, tumor necrosis factor α and interleukin-6 genes, the growth performance, the serum biochemical indices, and the activity of antioxidant and immunity enzymes. However, there was no significant effect ($p \leq 0.05$) on the survival rate (SR) and whole-body composition in the juveniles among the experimental groups. In conclusion, the diet with replaced $10\%$ fish meal protein with fermented soybean meal protein supplementation could significantly increase the growth performance, antioxidant and immunity capacity, and their related gene expression of juveniles.
## 1. Introduction
Coho salmon (Oncorhynchus kisutch) has become one of the most promising fish in China because of its fast growth rate, high economic value, rich nutrition, containing a variety of minerals, and delicious meat [1,2,3]. At present, the feed needed by the salmon aquaculture industry is mainly fish meal, and fish meal has been the main aquatic feed protein source for aquaculture because of its high protein content, balanced amino acid composition and rich nutrition [4]. However, due to the continuous growth of the modern aquaculture industry, global fish meal is lacking, and the price of fish meal continues to rise, which has been unable to meet the needs [5]. Therefore, it is urgent to find a suitable protein source to replace fish meal in the aquaculture industry.
Soybean meal is a plant protein with high digestive protein content, wide source, and low price, so it is currently recognized as the best choice to replace fish meal in aquatic feed [6]. However, the soybean meal contains unbalanced amino acids and soybean antigen protein, urease, trypsin inhibitor, soybean lectin, phytic acid, saponins, phytoestrogens, anti-vitamins and allergens, and other anti-nutritional factors [7,8,9], which can affect the palatability, and inhibit the digestion and absorption of nutrients, and cause the damage of tissue and organ, and seriously affect the health of aquatic animals [10,11]. Microbial fermentation is a commonly used biological method for treating soybean meal antigens and palatability, and soybean meal after microbial fermentation can reduce most of the anti-nutritional factors, produce carbohydrates, digestive enzymes and other nutrients, degradation of macromolecular protein, produce small active peptides, organic acids, thereby enhancing its nutritional value and enhance the digestion and absorption of nutrients [12,13,14]. In addition, fermented soybean meal can also provide animals with probiotics, prebiotics and flavonoids and other active substances [15,16] and increase the antioxidant properties of free amino acid content and the concentration of phenolic compounds [17].
At present, there are relatively few studies on the replacement of fish meal with fermented soybean meal in coho salmon. The antibacterial substances produced by *Bacillus cereus* have the effects of promoting growth, regulating immune function, and treating diseases in livestock and poultry [18]. Therefore, coho salmon was selected as the research object, and *Bacillus cereus* was used as a fermentation strain to explore the effects of replacing part of fish meal with fermented soybean meal on the growth performance, muscle composition, antioxidant and immunity capacity, and their related gene expression of juvenile coho salmon in this study. The results provide a theoretical basis for the development and optimization of coho salmon compound feed and the healthy development of the artificial breeding industry.
## 2.1. Experimental Diets
Four different iso-nitrogen (about $41\%$ dietary protein) and iso-lipid (about $15\%$ dietary lipid) experimental diets were designed and based on the references [19,20,21], in which the soybean meal could replace $10\%$ fish meal protein. The G0 diet contained $28\%$ fish meal protein (control group). Three other diets (G1, G2 and G3) were replaced $10\%$ fish meal protein with unfermented and/or fermented soybean meal: The G1 diet replaced by $10\%$ unfermented soybean meal protein, the G2 diet replaced by $5\%$ unfermented soybean meal protein and $5\%$ fermented soybean meal protein, and the G3 diet replaced by $10\%$ fermented soybean meal protein, based on per kg of dried feed, as shown in Table 1.
All the feed materials were provided by Conkerun Ocean Technology Co., Ltd. in Shandong, China, and they were animal food-grade. The soybean meal was fermented by Bacillus cereus, and the bacterial strain was collected from mangrove root soil in Maowei Sea, Qinzhou, Guangxi, China (21°81′66″ N, 108°58′46″ E). The experimental strains and fermentation conditions were derived from preliminary experiments in our lab. The inoculation amount of *Bacillus cereus* was $10\%$ (v/m), the ratio of material to water was 1:1.4, and the fermentation was cultured at 37 °C for 60 h. The fermented soybean meal was dried for 24 h in a blast drying baker at 37 °C. A hammer mill was used to grind raw all the dry materials into a fine powder (80-μm mesh), then all the dry materials were mixed in a roller mixer for 15 min and added some water to make a hard dough. Floating pellets with a diameter of 2.0 × 3.0 mm were obtained by a single screw extruder, and they were dried in the air flow at 37 °C until the water content was below 100 g/kg. Then the dry floating pellets were sealed in plastic bags and stored at −20 °C until use.
## 2.2. Experimental Fish and Culture
Six hundred juvenile coho salmon at the age of 6 months were from a hatchery located in Benxi rainbow trout breeding farm in Liaoning, China. Outdoor feeding and breeding experiments of juvenile coho salmon were carried out at a rainbow trout breeding farm in Nanfen District, Benxi City, Liaoning, China.
After being disinfected using a concentration of $\frac{1}{100}$,000–$\frac{1}{50}$,000 potassium permanganate, the juveniles were acclimatized for 14 days, using water temperature at 10–18 °C, water intake ≥ 100 L/s, surface velocity ≥ 2 cm/s, dissolved O2 ≥ 6.0 mg/L, pH 7.8–8.3 and natural light. The juveniles were fed three times a day at 08:00, 12:00 and 16:00 h, using a control diet ($28\%$ fish meal protein), and the daily feeding quantity was fed until the fish was no feeding behavior at the feeding time.
After being acclimatized for 14 days, 390 juvenile coho salmon (initial weight 159.63 ± 9.54 g) were selected for the formal experiment, and 30 of the selected juveniles were freely taken for initial samples. The remaining 360 of them were assigned randomly into 4 groups in triplicate, making a total of 12 net cages (1.0 × 1.0 × 0.8 m, L × W × H) with 30 fish in each net cage. The juveniles were cultured in the same breeding environment, and they were fed for 12 weeks using one of the 4 diets above (Table 1) and the daily feeding quantity was fed until the fish was no feeding behavior at the feeding time.
## 2.3. Sampling
The juvenile coho salmon were sampled at day 0 and the end of 12 weeks, respectively, after being starved for 24 h. All sample fish were separately anesthetized using 40 mg/L of 3-aminobenzoic acid ethyl ester methane sultanate (MS-222, Adamas Reagent, China). Then, their body weight and length were individually measured. At day 0, 20 juveniles were taken for dissecting liver samples and the other 10 juveniles for the sampling of whole fish. At the end of 12 weeks, 9 fish per net cage were randomly taken for the samples, 3 of which were for whole fish samples and 6 for the samples of serum, viscera mass, and liver.
A sterile syringe was used to collect blood from the tail vein of juvenile coho salmon; then, the blood was transferred to a 2 mL sterile enzyme-free centrifuge tube. At 3000× g and 4 °C, the blood was centrifuged in a centrifuge for 15 min, and the supernatant was serum. The liver weight and visceral mass weight were weighed and recorded separately for analysis of the growth performance. All the experimental samples were stored at −80 °C for subsequent analysis.
## 2.4.1. Growth Performance
The survival rate, weight gain rate, specific growth rate, condition factor, hepatosomatic index, viscerosomatic index, feed conversion ratio, and protein efficiency ratio are calculated according to the following formulas. Survival rate (SR, %)=100 ×final amount of fishinital amount of fish Weight gain rate (WGR, %)=100 ×final body weight (g) − initial body weight (g)initial body weight (g) Specific growth rate (SGR, %/d)=100 ×ln(final body weight (g)) − ln(initial body weight (g))days Condition factor (CF, %)=100 × body weight (g)(body length (cm))3 Hepatosomatic index (HSI, %)=100 ×liver weight (g) body weight (g) Viscerosomatic index (VSI, %)=100 ×viscera weight (g) body weight (g) Feed conversion ratio (FCR)=total diets weight (g) final body weight (g) − initial body weight (g) Protein efficiency ratio (PER, %)=100 ×final body weight (g) − initial body weight (g) total intake of crude protein weight (g)
## 2.4.2. Determination of Feed and Whole Fish Composition
The compositions of feed and whole fish were analyzed following the standard methods of the Association of Official Analytic Chemists (AOAC, 2005) [22]. The samples were dried at 105 °C until constant weight in an oven to determine moisture content. The muffle furnace at 550 °C for 24 h was used to determine ash. Kjeldahl method was used to determine crude protein. Soxhlet method by ether extraction was used to determine crude lipid.
## 2.4.3. Determination of Serum Biochemical Parameters
The indicators in serum were measured using the kit produced by Nanjing Jiancheng Bioengineering Institute (Nanjing, China) and referred to the instructions in the kit for specific operation steps. All the instructions can be found and downloaded at http://www.njjcbio.com (accessed on 1 March 2023). The total protein (TP) content was determined by the Coomassie brilliant blue method. The glucose (GLU) content was determined by the glucose oxidase method. The total cholesterol (T-CHO) content was determined by the cholesterol oxidase (COD-PAP) method. The albumin (ALB) content and alkaline phosphatase (AKP) vitality were determined by the microplate method.
## 2.4.4. Determination of Liver Antioxidant Capacity
The indicators in the liver were measured using the kit produced by Nanjing Jiancheng Bioengineering Institute (Nanjing, China) and referred to the instructions in the kit for specific operation steps. All the instructions can be found and downloaded at http://www.njjcbio.com (accessed on 1 March 2023). The superoxide dismutase (SOD) was determined by the water-soluble tetrazole salt (WST-1) method. The catalase (CAT) was determined by the visible light method. The malondialdehyde (MDA) was determined by the thiobarbituric acid (TBA method). The total antioxidant capacity (T-AOC) was determined by the ferric-reducing ability of plasma (FRAP) method. The glutathione peroxidase (GSH-PX), glutathione S-transferase (GST), hydroxyl radical clearance ratio (OH·-CR) and superoxide radical clearance ratio (O2·-CR) were determined by the colorimetric method. The reduced glutathione (GSH) was determined by the microplate method.
## 2.4.5. Expression of Antioxidant and Immunity Genes
The method of Ding et al. [ 23] was applied to determine the expression of sod, cat, gsh-px, gst, nrf2, tnf-α and il-6 mRNA in the liver of the juvenile coho salmon. Briefly, the Steady Pure Universal RNA Extraction Kit and the Evo M-MLV reverse transcription kit (Accurate Biology Biotechnology Engineering Ltd., Changsha, China) were used to extract 500 ng of total RNA from samples and reverse-transcribe it into cDNA. The polymerase chain reaction (PCR) conditions were 50 °C for 30 min, 95 °C for 5 min, and 5 °C for 5 min.
The forward and reverse primers of sod, cat, gsh-px, gst, nrf2, tnf-α and il-6 genes for reverse transcription were designed by referencing the corresponding genomic sequences of coho salmon in the National Center for Biotechnology Information (NCBI) database. The primers were synthesized by Sangon Biotech (Shanghai) Co., Ltd. (Shanghai, China). The primers were shown in Table 2, and β-actin was chosen as the nonregulated reference gene.
The real-time quantitative polymerase chain reaction (RT-qPCR) was conducted using an RT-qPCR System (LightCycler® 96, Roche, Switzerland) and SYBR Green Pro Taq HS qPCR kit (Accurate Biology Biotechnology Engineering Ltd., Changsha, China). The RT-qPCR conditions were as follows: initial denaturation at 95 °C for 30 s, 40 cycles of denaturation at 95 °C for 5 s, annealing at 60 °C for 30 s and extension at 72 °C for 20 s.
The 2−ΔΔCT method [24] was applied to calculate the relative expression levels of sod, cat, gsh-px, gst, nrf2, tnf-α and il-6 mRNA.
## 2.5. Statistical Analysis
All the data were analyzed using IBM SPSS Statistics 25 (Chicago, IL, USA) and one-way analysis of variance (ANOVA) and tested for normality and homogeneity of variance. Duncan’s test was used for multiple comparison analysis when it was significantly different ($p \leq 0.05$). Statistics are expressed as means ± standard deviation (SD).
## 3.1. Effect of Replacing a Portion of Fish Meal with Unfermented and/or Fermented Soybean Meal on the Growth Performance of Juvenile Coho Salmon
The WGR, SGR, CF, and PER of the juveniles in G3 and the HSI, VSI, and FCR of the juveniles in G1 and G2 were significantly higher ($p \leq 0.05$) than those of the juveniles in G0. The HSI, VSI, and FCR of the juveniles in G3 and the WGR, SGR, CF, and PER of the juveniles in G1 and G2 were significantly lower ($p \leq 0.05$) than those of the juveniles in G0. However, there was no significant difference in the SR of the juveniles between the groups ($p \leq 0.05$), as shown in Table 3.
## 3.2. Effect of Replacing a Portion of Fish Meal with Unfermented and/or Fermented Soybean Meal on the Whole-Body Composition of Juvenile Coho Salmon
No significant difference ($p \leq 0.05$) was found in the moisture, crude protein, crude lipid, and ash of juvenile coho salmon fed diets of replacement of fish meal with unfermented soybean meal and/or fermented soybean meal, as shown in Table 4.
## 3.3. Effect of Replacing a Portion of Fish Meal with Unfermented and/or Fermented Soybean Meal on the Physiological and Biochemical Indices in Serum of Juvenile Coho Salmon
The TP, GLU, ALB, AKP, and T-CHO of the juveniles in G3 were significantly higher ($p \leq 0.05$) than those of the juveniles in G0. The TP, GLU, ALB, AKP, and T-CHO of the juveniles in G1 and G2 were significantly lower ($p \leq 0.05$) than those of the juveniles in G0, as shown in Table 5.
## 3.4. Effect of Replacing a Portion of Fish Meal with Unfermented and/or Fermented Soybean Meal on the Antioxidant Capacity in the Liver of Juvenile Coho Salmon
The SOD, CAT, GSH-PX, GSH, GST, OH·-CR, O2·-CR, and T-AOC of the juveniles in G3, and the MDA of the juveniles in G1 and G2 were significantly higher ($p \leq 0.05$) than those of the juveniles in G0. The MDA of the juveniles in G3 and the SOD, CAT, GSH-PX, GSH, GST, OH·-CR, O2·-CR, and T-AOC of the juveniles in G1 and G2 were significantly lower ($p \leq 0.05$) than those of the juveniles in G0, as shown in Table 6.
## 3.5. Effect of Replacing a Portion of Fish Meal with Unfermented and/or Fermented Soybean Meal on the Expression of Antioxidant and Immune Genes in the Liver of Juvenile Coho Salmon
The expression of the sod, cat, gsh-px, gst, and nrf2 genes in the liver of the juveniles in G3 and the expression of the il-6 and tnf-α genes in the liver of the juveniles in G1 and G2 were significantly higher ($p \leq 0.05$) than those of the juveniles in G0. The expression of the il-6 and tnf-α genes in the liver of the juveniles in G3 and the expression of sod, cat, gsh-px, gst, and nrf2 genes in the liver of the juveniles in G1 and G2 were significantly lower ($p \leq 0.05$) than those of the juveniles in G0, as shown in Figure 1.
## 4. Discussion
The growth performance of fish can be used to reflect growth and health status, and it is affected by many factors, such as fish species, growth stage, nutrient deficiency, metabolic disorders, anti-nutritional factors, and toxic and harmful substances [25]. The results of this study showed that partial replacement of fish meal with fermented soybean meal could significantly increase the growth performance of juvenile coho salmon. However, partial replacement of fish meal with unfermented soybean meal could significantly decrease the growth performance of juvenile coho salmon. The reasons are supposed to be: First, unfermented soybean meal had adverse factors such as poor palatability, essential amino acid imbalance, low phosphorus utilization, high anti-nutritional factors, and easily cause lipid metabolism disorder, which will lead to decreased growth performance [26]. Second, fermented soybean meal could reduce and even eliminate anti-nutrient factors, and the protein could be degraded into easily digestible peptides or amino acids; thus, fermented soybean meal could improve the nutritional quality of feed and the digestibility of fish [27]. Third, the active bacteria, organic acids, and vitamins in fermented soybean meal would also play a positive role in growth performance [28]. Similar studies had shown that feeding largemouth bass (Micropterus salmoides) [21] and Macrobrachium nipponense (Macrobrachium nipponense) [29] with the diet with partial replacement of fish meal with fermented soybean meal significantly improved their growth performance.
Serum biochemical indexes of fish are closely related to metabolism, nutrient absorption, and health status. They are important indexes to evaluate physiology and pathology and are widely used to measure metabolism and health status [30,31]. TP and ALB in the blood are synthesized by the liver, and the increase of TP and ALB content indicates that the ability of the liver to synthesize protein is enhanced. AKP is one of the important indicators of fish physiological activity and disease diagnosis, which can reflect the anti-stress ability of biological organisms [32]. T-CHO is an important index to reflect the body’s lipid metabolism [33]. GLU is the main functional substance of the body, and its content is affected by nutrition and feed intake [34]. The results of this study showed that partial replacement of fish meal with fermented soybean meal could significantly increase the serum biochemical indexes of juvenile coho salmon, indicating that fermented soybean meal could be used as a protein substitute for fish meal to improve the health of juvenile coho salmon. The reasons are supposed to be: First, fermented soybean meal could improve the intestinal structure and function of fish, increase the activity of digestive enzymes, and increase the absorption and utilization of dietary proteins and lipids [35]. Second, compared with macromolecular proteins, the small peptides in fermented soybean meal are more easily absorbed by fish, which could improve the diet protein utilization rate, consequently enhancing the serum protein content of fish [12]. Third, fermented soybean meal could decrease the content of soybean saponins, increase the activity of α-glucosidase, and improve the absorption of glucose [36]. Fourth, fermented soybean meal could not only reduce the inhibitory effect of soy isoflavones on serum T-CHO levels but also stimulate the antioxidant system of the body, thereby inhibiting the process of lipid oxidation and increasing the content of T-CHO in the serum [37]. In addition, bioactive peptides during fermentation can act as immune stimulants to enhance AKP activity [38].
Nuclear factor erythroid 2-related factors (nrf2) is an important nuclear transcription factor and can be involved in a variety of cellular processes, including maintaining intracellular redox balance, cell proliferation/differentiation, metabolism, protein homeostasis and inflammation regulation, and disease development [39,40]. The activation of the nrf2 signaling pathway can initiate the expression of multiple downstream target proteins, such as SOD, CAT, GPX, glutathione ligase (γ-GCS), glutathione catalase (GR), glutathione S-transferase (GST) and glucose-6-phosphate kinase (G-6-PDH) [41]. The expression of these genes is an important way for the body to resist oxidative stress damage [42]. Nrf2 signaling pathway can negatively regulate various cytokines (TNF-α, IL-1 and IL-6), chemokines, cell adhesion factors, matrix metalloproteinases, cyclooxygenase-2, inducible nitric oxide synthase, and other inflammatory mediators, which plays a protective role in the dysfunction caused by inflammation [43]. IL-6 and TNF-α are often used as indicators of the inflammatory response [44]. MDA content has been used by many researchers to evaluate the effect of protein replacement sources on the antioxidant capacity of fish, which can be used as an important marker of endogenous oxidative damage in organisms [45]. The results of this study showed that partial replacement of fish meal with fermented soybean meal could significantly increase the antioxidant capacity and the expression of their related gene in the liver and significantly decrease the expression of il-6 and tnf-α gene in the liver of juvenile coho salmon. However, partial replacement of fish meal with unfermented soybean meal could significantly decrease the antioxidant capacity and the expression of their related gene in the liver and significantly increase the expression of the il-6 and tnf-α genes in the liver of juvenile coho salmon. The reasons are supposed to be: First, the soybean globulin and β-conglycinin in soybean meal could destroy the antioxidant system of fish and cause oxidative damage [46]. Previous studies have shown that soybean meal in feed may cause oxidative stress in fish such as gilthead sea bream (Sparus aurata) [47]. Second, a high concentration of soybean peptides and phenols in fermented soybean meal could up-regulate nrf2 gene expression, induce the expression of the sod, cat, gsh, and gsh-px genes, and improve the antioxidant ability of the body [48,49]. Lee et al. found that an appropriate proportion of fermented soybean meal in a diet can increase the activities of SOD, GSH-Px, and GSH in the liver [50]. Third, *Bacillus could* stimulate the production of antioxidant enzymes and antioxidants, thereby scavenging free radicals, maintaining homeostasis, improving antioxidant capacity, and activating the Nrf2 pathway [51]. Fourth, the replacement of fish meal protein with $10\%$ fermented soybean meal protein was insufficient for causing a change in the body’s ability to recognize foreign bodies and did not lead to an inflammatory reaction [52]. In addition, after soybean meal fermentation, a unique fragrance could be formed, which can promote the feeding of aquatic animals and increase their immunity [53].
However, the results of this study showed that partial replacement of fish meal with unfermented and/or fermented soybean meal had no significant effect on the survival rate and whole-body composition of juvenile coho salmon. The reasons are supposed to be: First, the energy required by fish to maintain normal life activities mainly depends on the breakdown of protein and fat, and fish meal contains a complete set of essential amino acids that meet the protein requirements of most aquatic animals [54,55]. Second, the crude protein and crude fat contents of the four diets in this study were the same and were enough to satisfy the daily needs of juvenile coho salmon. Third, fish body composition is affected by external conditions such as feed nutrients, food composition, aquaculture water environment and season, but fish body composition was not affected by plant protein levels [56]. Similar results were obtained in pompano (Trachinotus ovatus) [53] and Florida pompano (Trachinotus carolinus) [56] fed with fermented soybean meal partially replacing fish meal. However, studies have shown that a high proportion of fermented soybean meal instead of fish meal significantly increased the whole-body moisture and reduced crude protein and crude lipid content of Japanese seabass (Lateolabrax japonicus) [57]. In giant grouper (Epinephelus lanceolatus), high levels of fermented soybean meal replacement also significantly increased whole-fish moisture and decreased crude protein and crude lipid content [58]. The above inconsistent results might be related to the strains of fermented soybean meal, the basic feed formula, the substitution ratio of fermented soybean meal, the types of aquatic animals, the breeding cycle, and the growth stage.
## 5. Conclusions
In conclusion, the diet with replaced $10\%$ fish meal protein with fermented soybean meal protein supplementation can significantly influence the expression of superoxide dismutase, catalase, glutathione peroxidase, glutathione S-transferase, nuclear factor erythroid 2-related factor 2, tumor necrosis factor α and interleukin-6 genes, the growth performance, the serum biochemical indices, and the activity of antioxidant and immunity enzymes of juvenile coho salmon. The results provide a theoretical basis for the development and optimization of coho salmon compound feed and the healthy development of the artificial breeding industry.
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---
title: Beef Nutritional Characteristics, Fat Profile and Blood Metabolic Markers from
Purebred Wagyu, Crossbred Wagyu and Crossbred European Steers Raised on a Fattening
Farm in Spain
authors:
- Juan M. Vázquez-Mosquera
- Aitor Fernandez-Novo
- Eduardo de Mercado
- Marta Vázquez-Gómez
- Juan C. Gardon
- José L. Pesántez-Pacheco
- Ángel Revilla-Ruiz
- Raquel Patrón-Collantes
- Maria L. Pérez-Solana
- Arantxa Villagrá
- Daniel Martínez
- Francisco Sebastián
- Sonia S. Pérez-Garnelo
- Susana Astiz
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000121
doi: 10.3390/ani13050864
license: CC BY 4.0
---
# Beef Nutritional Characteristics, Fat Profile and Blood Metabolic Markers from Purebred Wagyu, Crossbred Wagyu and Crossbred European Steers Raised on a Fattening Farm in Spain
## Abstract
### Simple Summary
Beef cattle production has improved to achieve consumers’ preferences, including meat quality and human-health-related indexes. Wagyu (WY) breed is Japanese cattle with high intramuscular fat infiltration and rich in unsaturated fatty acids. Most Wagyu beef cattle are raised in Japan. Our objective was to describe Wagyu, Wagyu-by-Angus (Wangus, WN), and Angus-by-Charolaise-Limousine (ACL) beef produced in a Spanish fattening system with high-olein diets, regarding the fat profile, health-related indexes and the metabolic biomarkers prior to slaughtering. Blood lipid-related metabolites, except for non-esterified fatty acids (NEFA) and low-density level cholesterol (LDL), were higher in WY and WN than in ACL, while glucose was lower in WY and WN. Leptin was higher in WN than in ACL. Nutritional analyses showed higher fat infiltration in WY and WN steers than ACL animals for both meat cuts (sirloin and entrecote), including three-fold higher content. Wagyu beef had the highest intramuscular fat in sirloin ($51.5\%$ vs. $21.9\%$) and entrecote ($59.6\%$ vs. $27.6\%$) vs. ACL animals. Wagyu entrecote contained more unsaturated fatty acids ($55.8\%$ vs. $53.0\%$) and more oleic acids ($47.5\%$ vs. $43.3\%$) than ACL’ beef. Wagyu and WN entrecote showed better atherogenic (0.6 and 0.55 vs. 0.69), thrombogenicity (0.82 and 0.92 vs. 1.1), and hypocholesterolemic/hypercholesterolemic index (1.9 and 2.1 vs. 1.7; all $p \leq 0.001$) than ACL’s beef. In brief, the fat profile and nutritional characteristics of beef depend on the fattening period, breed/crossbred, and cut of meat, with Wagyu and Wangus beef showing a healthier fat profile than ACL animals.
### Abstract
A high intramuscular fat content characterizes Wagyu (WY) cattle breed. Our objective was to compare beef from WY, WY-by-Angus, or Wangus (WN) steers with European, Angus-by-Charolais-Limousine crossbred steers (ACL), considering metabolic biomarkers pre-slaughtering and nutritional characteristics, including health-related indexes of the lipid fraction. The fattening system with olein-rich diets and no exercise restriction included 82 steers, 24 WY, 29 WN, and 29 ACL. The slaughter ages and weights were (median and interquartile range) 38.4 mo.-old (34.9–40.3 mo.) and 840 kg (785–895 kg) for WY; for WN, 30.6 mo. ( 26.9–36.5 mo.) and 832 kg (802–875 kg), and for ACL steers, 20.3 mo.-old (19.0–22.7 mo.) and 780 kg (715–852 kg). Blood lipid-related metabolites, except for non-esterified fatty acids (NEFA) and low-density level cholesterol (LDL), were higher in WY and WN than in ACL, while glucose was lower in WY and WN. Leptin was higher in WN than in ACL. Pre-slaughtering values of plasma HDL underscored as a possible metabolic biomarker directly related to beef quality. The amino-acid content in beef did not differ among experimental groups, except for more crude protein in ACL. Compared to ACL, WY steers showed higher intramuscular fat in sirloin (51.5 vs. $21.9\%$) and entrecote (59.6 vs. $27.6\%$), more unsaturated fatty acids in entrecote (55.8 vs. $53.0\%$), and more oleic acid in sirloin (46 vs. $41.3\%$) and entrecote (47.5 vs. $43.3\%$). Compared to ACL entrecote, WY and WN showed better atherogenic (0.6 and 0.55 vs. 0.69), thrombogenicity (0.82 and 0.92 vs. 1.1), and hypocholesterolemic/hypercholesterolemic index (1.9 and 2.1 vs. 1.7). Therefore, beef’s nutritional characteristics depend on breed/crossbred, slaughtering age and cut, with WY and WN entrecote samples showing a healthier lipid fraction.
## 1. Introduction
The Black-Japanese breed, also called Wagyu, is known worldwide because of the outstanding quality and organoleptic characteristics of its meat due to its extensive fat infiltration of muscle [1,2,3]. Gotoh et al. [ 4] determined a very high level of intramuscular fat content (IMF) in the *Longissimus thoracis* et lumborum muscle of 24 months old Wagyu steers ($23\%$), while German Angus, Belgian Blue, and Holstein Friesian bulls showed lower IMF (<$5\%$). Wagyu cattle have not only higher IMF [5] but also a different fatty acid composition (FA) with a larger oleic acid (OA) concentration ($52.9\%$ of total fatty acids) when compared to Hanwoo steers ($47.3\%$) or corn-fed Angus ($39.8\%$) [6]. Meat chemical composition and the FA profile are directly linked to beef quality [7], with an increased crude fat content (range 23.8–$48.6\%$) improving tenderness, juiciness, and fattiness [5] and thereby its acceptability by the consumers in Japan [2].
Consumer concerns are also linked to issues related to the health and convenience of beef intake [8] and the advantages and disadvantages of red beef consumption [9]. Although beef is a nutrient-dense protein source, the saturated fat content has been associated with an increased risk of cardiovascular disease [10], diabetes type 2 [11], and mortality due to non-communicable diseases [12]. Contrary to these associations, clinical trials have demonstrated that the inclusion of beef in a healthy and balanced diet does not negatively influence disease risk factors [13]. Furthermore, a recent meta-analysis shows that out of the top 15 dietary components related to non-communicable disease risk, a diet high in red meat was ranked as the lowest dietary factor related to increased risk [14]. Due to the high OA content, Wagyu beef has even been related to a reduction in the blood cholesterol level in human consumers [15]. Therefore, besides the quantity, the lipid composition of meat is of increasing interest not only due to its effect on the organoleptic characteristics of the meat [16] but to rate how healthy beef is [17,18]. In fact, cardiovascular disease and coronary heart disease risk are reduced when saturated fatty acids (SFA) are replaced by monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA) [13]. These effects can be achieved by modifying the profile of the ingested fat [19] and the kind of beef consumed [20,21,22].
The effects of the diets may vary depending on the bovine breed [23]. Key fatty acid metabolism-associated genes with upstream regulators (SREBF1) are related to a higher FA index in beef [24]. In the Wagyu breed, condensed barley distillers soluble promoted growth, a higher glycolytic and lower oxidative muscle metabolism [25], with this breed steers producing higher MUFA percentage in carcasses than Angus steers when fed high-roughage diets [26]. Increasing the concentration of oleic acid in diet, Wagyu beef increased the OA level in IMF [6], but there is no evidence about Wagyu- and Angus-crossbreed managed under Spanish farming conditions. The evidence suggests that depending on the breed, the nutritional system can induce relevant improvement in the beef fatty acid profile.
Nutritional and management systems trigger changes in the homeostasis of the fattened animals, inducing blood metabolite changes [27]. These changes may be the link between management and final beef characteristics. Indeed, metabolite concentrations are commonly used to assess health and nutritional status in cattle [28,29]. In Wagyu cattle, metabolic markers show different patterns than in another leaner breeds [30,31]. The main metabolic parameters related to marbling are cholesterol, glucose, and urea, among others [31,32,33,34,35,36]. Although these parameters may change with fed diets, the relationship among metabolic parameters, beef composition, and fatty acid profile remains unclear.
A previous study of our group described healthy growing purebred Wagyu, crossbred Wagyu-by-Angus (Wangus), and Angus-by-European crossbreeds, such as Charolais and Limousine (ACL) steers, in a Spanish fattening system involving olein-rich diets, no exercise restriction and high animal welfare [37]. We hypothesize that this production system results in different beef products depending on the experimental group, in fact, on the breed/crossbred. Furthermore, the aim of the study was to describe the beef characteristics of these groups raised in this Spanish fattening system and to explore the relationship between blood metabolic parameters prior to slaughtering and beef characteristics, possibly associated with the group and age at slaughtering. This information is highly relevant to choose the optimal system to produce a more appreciated, high-marbled beef with Wagyu animals efficiently in other world regions outside Japan.
## 2.1. Animals and Farm Management
This study was conducted under commercial farming practices on the farm “Mudéjar-Wagyu”, located in the North-Centre of Spain, with the appropriate farming measures and clinical activities [38]. All available, healthy steers slaughtered during the study time were included in the study. A total of 82 steers were studied: 24 purebred Black-Japanese or Wagyu, (WY); 29 crossbred Wagyu-by-Angus or Wangus (WN); and 29 Angus-by-European Charolais and Limousine crossbred steers (ACL). Data were recovered at once, at the end of the fattening period, without repeated measurements. A different objective-slaughtering age was intended by experimental group, following the usual slaughtering times. This guaranteed an adequate carcass quality, with an earlier expected slaughtering age in European crossbreds, to avoid excessive fattening (WY ≈ 37 mo., WN ≈ 31 mo., and ACL ≈ 22 mo.-old), assuring a comparable and marketable beef after slaughtering the steers from the three experimental groups. The actual slaughter ages, expressed as median and interquartile range, were 38.4 mo.-old (34.9–40.3 mo.) for WY; for WN, 30.6 mo. ( 26.9–36.5 mo.) and for ACL steers 20.3 mo.-old (19.0–22.7 mo.).
The animals were raised as described previously [37]. Castrated male calves were housed during the last phase of fattening in groups of 10 animals/pen (20 m2/animal). All barns were open (natural ventilation), with anti-slip, concrete or soil floors and chopped straw bedding, and access to open areas. Brushers and sprinklers were available in the pens assuring a high level of animal welfare. The nutritional management of the animals included ad libitum water and diets adjusted to their requirements [39]. From 10 to 22 mo. of age, the diet was a wet total mixed ration (wet TMR), and afterward, the diet was a dry TMR enriched in oleins. ACL animals received the finalization diet at least 2 months before slaughtering, independently of age. Further details on diets and management are described in the previous study on productive and health parameters [37], and the detailed composition of this finalization diet, enriched in oleins, is resumed in Table 1 and Table 2.
Animals were slaughtered according to European Union regulation $\frac{1099}{2009.}$ Carcasses were aired for temperature decrease up to 4 °C in less than 24 h and kept at 2 °C for 72 h. At the meat processing facility, 200 g fresh meat samples of the *Longissimus lumborum* muscle, between the lumbar ribs (sirloin) and *Longissimus thoracis* between the 6 and 7th ribs (entrecote or ribeye steak) were taken from each carcass on the 5th day after slaughtering. Samples were adequately identified, vacuum-packed, and sent under a stable temperature of 4 °C to the diagnostic laboratory (Labocor Analítica, Colmenar Viejo, Madrid, Spain), operating under the Spanish National Accreditation Body (ENAC; complying the applicable requirements and conditions of the standard GMP+ B10 Laboratory Testing of the GMP+ FC scheme (based on GMP+ C6) of GMP+ International and the UNE-EN ISO/IEC 17025:2017).
## 2.2. Description of the Slaughtered Animals: Weight, Height and In Vivo Fatness
Animals that could enter the weighing booth during the last week before slaughtering ($$n = 15$$/24 WY, $\frac{14}{29}$ WN, and $\frac{29}{29}$ ACL) were weighted (BW, Kg) at the farm before slaughtering using an electronic balance (TRU–TEST, Auckland, New Zealand; accuracy of ±0.5 Kg), while its height (cm) was determined with the animal standing, using a measuring stick, from a point directly over the hip bones (hocks) to the floor. In vivo evaluation of the body-fattening stage was performed with ultrasound using the PieQuip technology and a MyLab One® ultrasound system (Esaote, Barcelona, Spain) with an animal science probe (ASP) of 18 cm length and a frequency of 3.5 Mhz linear transducer [40,41]. Measurements were made at the closest moment before slaughtering when images could be processed. As previously determined [37], in Wagyu and Wangus animals older > 22 mo. of age, the ultrasound images could not be recovered due to excessive dorsal fat layer (>20 mm). Therefore, the total of animals scanned near slaughtering with valid figures was 9 purebred Wagyu, 19 Wangus, and 29 ACL steers (Table 3).
Ultrasound scanning traits related to growth and fat deposition [41] were:Rump fat thickness (RF, mm), at the “P8” rump site. Measured at the level of the rump, at the intersection of the gluteus medius and biceps femoris muscles. Depth of the gluteus medius muscle (GMD, in mm). Measured at point P8.*Ribeye area* (REA, in cm2) of the *Longissimus thoracis* muscle measured between the 12–13th ribs. Back fat (BF, mm). It was 12–13th rib fat thickness. Percentage of intramuscular fat estimation (marbling; IMF) of the *Longissimus lumborum* muscle was measured between the 12–13th ribs as a numerical value given by the ultrasound equipment software and positively correlated to intramuscular fat.
## 2.3. Metabolic Status of the Animals
Before slaughtering, blood samples were extracted for sanitary reasons by the official veterinarians, and the authors received an aliquot for metabolic assessment. Once obtained, plasma was separated by centrifugation at 4500× g for 15 min and stored in polypropylene vials at −80 °C until later analysis. Parameters of plasma total cholesterol (TC, mg/dL), triglycerides (TG, mg/dL), high- (HDL, mg/dL) and low-density lipoprotein cholesterol (LDL, mg/dL), glucose (GLU, mg/dL), fructosamine (FRU, mg/dL), lactate (LAC, mg/dL), β-hydroxy butyrate (BHB, mmol/L), non-esterified-fatty acid (NEFA mmol/L) and urea (UR, mg/dL) were assessed with clinical chemistry analyzer (Konelab 20; Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer’s instructions. Plasma leptin concentration (LEP, µg/mL) was determined with a multispecies ELISA kit (Demeditec Diagnostics GmbH, Kiel, Germany; assay sensitivity 0.25 µg/mL and intra-assay variation coefficient < $15\%$).
## 2.4. Meat Nutritional Analyses
Meat analyses were performed immediately after reception at the laboratory Labocor Analitica on fresh samples. Samples were minced and homogenized. Crude protein content was determined by Kjeldahl [42] with a conversion factor of 6.25, and total fat was determined by ethyl ether extraction with previously acid-hydrolyzed samples (Soxhlet technique [43]). Moisture was gravimetrically measured by drying at 103 ± 2 °C [44], and ash was gravimetrically assessed after combustion at 550 °C in a muffle furnace [45] to a constant weight. Energy content (kcal) was calculated based on 100 g portion using according to the equation of Merrill and Watt [46] as the following: energy value (kcal/100 g) = (Protein% × 4) + (Fat% × 9) + (Carbohydrate% × 4). Cholesterol in meat was assessed with the enzymatic method (Kit Boehringer Mannheim) and read by spectrophotometry. pH- were quantified with a glass electrode (FC2323) specifically designed for meat, introduced 1 cm into the meat, performing 3 measurements/piece, and recalibrated by each sample.
Fatty acid content of beef was determined by extracting the fat fraction, following the method of ISO [47] for determining FA methyl esters by gas chromatography, set up, and verified. Only cis-isomers of fatty acids were detected. Fatty acids were expressed as a concentration in fresh meat and/or as a percentage of the total amount of the identified fatty acids. The total contents of saturated fatty acids (SFAs), unsaturated fatty acids (UFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), and ω–3 and ω–6 PUFAs were calculated.
In addition, the health-related indexes of the lipid fraction were assessed by the following calculations:ω–6/ω–3 ratio: ω–6 to ω–3 fatty acids ratio (optimum value ≈< 4; although it is a discussed cut-off) [48]. Σω–6 PUFA/Σω–3 PUFAAtherogenic index (AI [49]; the lower the healthier). AI = [12:0 + (4×14:0) + 16:0]/(Σω–3 PUFA + Σω–6 PUFA + ΣMUFA)Thrombogenicity index (TI [49]; the lower the healthier). TI = (14:0 + 16:0 + 18:0)/[(0.5 × ΣMUFA) + (0.5 × Σω–6 PUFA) +(3 × Σω–3 PUFA) + (Σω–3 PUFA/Σω–6 PUFA)]Hypo-/hypercholesterolemic ratio (h/H; modified from Fernández et al. [ 50]; the higher the healthier). h/H = [(C18:1 + C18:2n–6 + C18:3n–6 + C18:3n–3 + C20:3n–6 + C20:4n–6 + C20:5n–3 + C22:4n–6 + C22:5n–3 + C22:6n–3)/(C14:0 + C16:0)].
## 2.5. Amino Acids Assessment
A representative subset of the entrecote samples of each breed group was randomly selected ($$n = 13$$/24 WY, $\frac{12}{29}$ WN and $\frac{8}{29}$ ACL). Samples were hydrolyzed with HCL 6N, at the Laboratory of the Veterinary Faculty at the Universidad Católica of Valencia (Valencia, Spain), by high-performance liquid chromatography (HPLC), with the derivatization method (Waters AccQ-Tag reagent kit). Then, the amino acids were separated on a reverse phase C18 column (AccQ Tag, 3.9 × 150 mm, Waters Co, Milford, CT, USA) using a gradient of two eluents from the kit as mobile phase. Three µL of sample were injected for each analysis carried out in 50 min.
The detection of the amino acids was carried out with a fluorescence detector set up to work at 250 and 395 nm (excitation and emission wavelengths, respectively). Amino acids were identified by retention times and quantified by the external standard technique using an amino acid standard [α–aminobutyric acid (αAba)] and processed with the EZChromeElite program (Agilent, Santa Clara, CA, USA). Results were expressed in g/100 g of dry matter.
Tryptophan content was determined following the protocol described by Yust et al. [ 51], in which alkaline hydrolysis of samples of meat and ashes was performed. In brief, tryptophan was extracted from other amino acids and assessed by reverse phase HPLC and its detection by absorbance set up with 280 nm wavelength. The same reverse phase C18 column was used for separating derivatized amino acids according to the Waters AccQ–Tag method described above. Tryptophan was quantified using a standard curve with different concentrations of tryptophan standard. Results were expressed in g/100 g of dry matter.
## 2.6. Statistical Analyses
Data were analyzed using SPSS® v.25 (IBM, Armonk, NY, USA). Normality of variables was assessed with Kolmogorov–Smirnov and Shapiro–Wilk tests. Most of the variables showed a skewed distribution, and all the variables were reported as median and range instead of average and standard deviation as best measures of centrality and variability for skewed distributions; therefore, non-parametric tests were used for assessing intergroup differences such as Kruskal–Wallis test for independent samples. The comparisons were made among experimental groups (three groups, one full breed: WY and two crossbreeds: WN and ACL), separated by beef cut (sirloin or entrecote) or between cuts (sirloin or entrecote) separated by experimental groups. Potential pairwise relationships among numerical variables of steers characteristics prior slaughtering, blood metabolites, meat nutritional parameters, and amino acid values were assessed with Spearman tests. Differences associated with p-values ≤ 0.05 were considered significant.
## 3.1. Pre-Slaughter Metabolic Data
WY and WN steers showed levels of metabolites in plasma that were not statistically different (Table 4). Concentrations of all lipid-related metabolites, except NEFA and LDL, were higher in WY and WN steers than in ACL animals. Conversely, glucose values were lower in WY and WN steers. Levels of the hormone leptin, which are related to the lipid profile, were higher in WN than in ACL steers.
## 3.2. Nutritional, Fatty Acid, and Amino Acid Profiles in Beef Samples
Nutritional analyses showed that for both meat cuts, WY and WN steers showed higher fat infiltration than ACL animals, including 2 to 2.5-fold higher IMF (in the fresh matter; Table 5). However, ACL meat had a higher content of protein and lower content of saturated fatty acids in both cuts of meat. Entrecote samples showed more IMF and less SFA in all breeds (Table 5).
The relative content of saturated fatty acids, expressed as the percentage of the total amount of identified fatty acids, varied depending on the cut of meat. Only in ACL animals, the amount of PUFA and the ratio ω–6/ω–3 were significantly higher in sirloin than in entrecote (Table 6). Entrecote samples from ACL steers contained more saturated fatty acids than the corresponding cut from the other two breeds, with the exception of stearic acid (C18:0, Table 7). However, this pattern was absent in sirloin samples, where only stearic acid values differed, with WN steers presenting the lowest level.
The relative content of unsaturated fatty acids was higher in sirloin from WN steers and lower in entrecote from ACL steers (Table 6), with WY and WN samples showing the highest concentrations of oleic acid (C18:1; Table 7) and linoleic acid (C18:2n–6). Conversely, the content of linoleic acid (C18:2n–6) was highest in ACL steers. C17:1 was the most abundant fatty acid in sirloin from WN animals.
Next, we correlated the fat characteristics of meat with healthiness indexes for humans. The ratio of monounsaturated to saturated fatty acids (MUFA/SFA) was higher in WY and WN than in ACL meat (Table 6). The ACL entrecote showed the lowest hypocholesterolemic/hypercholesterolemic index, indicating less healthy meat, while sirloin did not differ substantially in this index across the breeds. Moreover, for both cuts of meat, ACL steers showed the highest thrombogenicity index, indicating less healthy meat, and their entrecote showed the highest atherogenic index. Consistently, ACL meat showed the highest ratio of ω–6 to ω–3 than Wagyu in both cuts, and Wangus in sirloin, which ideally should be <4.
The amino acid content of entrecote samples did not differ among breed/crossbreed (Table 8), except that ACL steers showed higher absolute levels of arginine and threonine than the other two breeds and higher leucine content than Wagyu due to the higher crude protein content.
## 3.3. Relationships between Variables
In sirloin, the IMF content samples correlated positively with the total content of monounsaturated fatty acids due to the oleic acid content and inversely with the content of polyunsaturated fatty acids or PUFAs (Figure 1). In entrecote and sirloin, the human health-related indexes AI and TI correlated negatively with unsaturated fatty acids but positively with saturated fatty acids, while the opposite was true for the h/H ratio.
Among blood metabolites, IMF correlated positively with total cholesterol and high-density lipoprotein cholesterol, regardless of the meat cut (Table 9). β-hydroxybutyrate correlated with moisture (negatively) and IMF (positively), but only in sirloin, while the negative correlation between the total content of PUFAs and urea was found only in entrecote. Plasma total cholesterol and HDL were revealed to be positively associated with the IMF in beef in both cuts and negatively with the amount of ω–6 PUFAs in entrecote and sirloin, independently of breed/crossbreed.
Figure 2 summarizes the significant pairwise correlations between variables by breed/crossbreed. In WY meat, moisture and IMF did not correlate with health-related indexes. In WN or ACL meat, increased moisture and decreased IMF correlated with better health-related indexes. In meat from WN and ACL steers, but not WY animals, moisture and IFM correlated negatively with the atherogenic index and positively with the ratio h/H.
Content of specific fatty acids correlated with the corresponding total amounts. In all breeds, higher content of palmitic acid (C16:0), the most frequent saturated fatty acid, was associated with worse health-related indexes. The content of other fatty acids correlated differently with health-related indexes depending on the experimental group. In WY and ACL meat, the content of stearic fatty acid (C18:0) was positively correlated with the TI index. In WY meat, the content of linoleic acid [C18:2n–6] and linolenic acid [C18:3n–3] did not correlate with h/H, TI, or AI. However, a robust positive correlation with health-related indexes was observed in WN and ACL steers for the linoleic acid, while such a relationship was observed for linolenic acid, but only in WN meat. The stearidonic fatty acid [C18:4n–3] correlated negatively with TI in WY and WN steers, while it was positively related to h/H and AI in ACL steers.
More pre-slaughter metabolic parameters correlated with fatty acid composition and leptin content in meat from WN animals than in meat from WY or ACL steers (UREA, TC, and HDL positively correlated to saturated fatty acids). Leptin levels, although highest in WN animals, significantly correlated with NEFA and glucose metabolic parameters in purebred WY steers, while leptin correlated positively in ACL animals only with triglycerides (Figure 2).
## 4. Discussion
Wagyu and Wangus steers are of high commercial interest worldwide because of their high beef quality, due in part to high IMF content. The impact of different fattening systems on beef quality and fat profile is poorly understood. Therefore, we aimed to describe the pre-slaughter metabolic parameters, beef quality, and fat profile of the final marketable meat samples from three high-marbling bovine breeds/crossbreeds raised under the same Spanish fattening system but slaughtered at different ages. We also analyzed pairwise relationships among variables. As we hypothesized, pure and crossbred WY steers presented higher IMF content, lower crude protein content, and a healthier fatty acid profile than the European ACL crossbred steers.
Regarding the pre-slaughter metabolic biomarkers, ACL animals showed lower levels of blood plasma metabolites related to lipid metabolism, including leptin, than WY and WN animals. The exception was LDL. These differences may be due to the experimental group but also to the different slaughter ages (ACL animals were slaughtered with an age of at least 10 months younger than WY and WN steers), a factor demonstrated to alter the metabolic status of cattle [28]. The only metabolic parameter that was higher in ACL animals than in the other was plasma glucose concentration, which supports previous findings demonstrating higher insulin levels and lower plasma glucose in WY steers than in European crossbreeds, and a positive correlation between IMF and insulin in cattle [30,52]. We found that pre-slaughtering blood plasma BHB correlated negatively with beef moisture and positively with IMF, independently of breed/crossbreed. The production of BHB inhibits some class I histone deacetylases [53], reducing lipolysis in adipocytes. The availability of fatty acids in the liver, which is strongly regulated by insulin, is a critical determinant of ketogenesis, so the reduction of lipolysis may act as a feedback mechanism to limit BHB production [54]. Such feedback may explain the positive correlation between BHB and IMF: increased BHB reduces lipolysis and therefore increases IMF. It may also explain the negative correlation between BHB and moisture: moisture correlates inversely with IMF. Pre-slaughtering values of HDL related to lipid beef characteristics, underscoring a possible metabolic biomarker directly related to beef quality, i.e., foreseeing a high level of IMF and ω–6 PUFA in beef, result highly interesting. Moreover, the correlations between metabolites and fat profiles differed clearly among experimental groups. *In* general, WN meat showed stronger correlations between metabolic parameters and fatty acids than WY meat (Figure 2). This may indicate that the metabolism of WY animals is better adapted to high body fat than the metabolism of WN animals.
As expected, WY and WN steers showed more fat infiltration in both meat cuts due to their typical Wagyu beef characteristics, and accordingly, the energy content of Wagyu and Wangu beef was significantly higher than that of ACL steers, and the absolute amount of SFA per 100 g of meat was the lowest in the ACL beef samples. We did not observe differences in IMF between purebred WY and crossbred WN animals. Previous work [55] confirmed that crossbreds with WY show stronger IMF content and greater tenderness and marbling score [56], which would explain the high IMF values in our WN steers. Therefore, our work illustrates that the relationships among the meat characteristics that we examined depend on the experimental group (i.e., on breed/crossbreed), even when diet and management are the same. These differences could also relate to the different slaughtering age (linked, in turn, to the group) effects that we cannot separate in this study.
Regarding the assessment of the lipid fraction in beef, among the three experimental groups in our study, ACL steers showed the lowest levels of MUFAs. Previous work found higher MUFA values in subcutaneous fat from WY steers ($60.7\%$) and crossbred WN steers ($60.1\%$) than from Holstein animals ($57.6\%$) [57]. This result reflects the higher ratio of C18:1 to C18:0 desaturases in the WY breed, which leads to higher oleic acid content [58]. Interestingly, our WN crossbred showed even higher MUFA values than WY in sirloin; the WY values were similar to those in a previous study [57]. On the other hand, other authors did not observe such differences when they compared WY animals with the genetically similar Hanwoo and Jeju Black breeds [59]. In our work, this specific high-olein diet may have promoted a higher content of MUFAs in WY and WN steers. Similar results have been reported in steers fed with diets rich in oleic acid, which increased the oleic acid content in meat fat from 1.72 to $4.22\%$ [60]. This may have important implications for the marketability of this meat since consumption of Hanwoo beef from animals with high oleic acid content might reduce the risk of cardiovascular disease [61]. The link between a grain-fed diet and higher, healthier levels of MUFAs in beef was also observed previously in various breeds, such as Angus, Hereford, and Hanwoo, among others [62].
Angus-by-Charolais or Limousine beef in our study showed the highest content in PUFAs and the highest ω–6/ω–3 ratio across all three breed/crossbreds, with nearly doubling the content of linoleic acid (C18:2n-6). In contrast, the entrecote pieces of ACL showed the highest content of saturated fatty acids, similar to that reported in former reports [63,64]. In the current study, a high-olein diet appeared to induce high levels of PUFAs in all three breeds/crossbreds. These values were like those previously described in beef cattle of other breeds (4–$5\%$) [65,66] and higher than those reported in grain-fed WY steers (2.6–$3.5\%$) [56,59]. It seems, therefore, that a high-olein diet improves beef quality more intensively, leading to levels of PUFAs and ω–6/ω–3 ratio healthier for humans in WY steers than in European crossbred animals.
The health-related indexes AI and TI in our work correlated negatively with the content of unsaturated fatty acids (improving the effect for healthiness in humans) and positively with the content of saturated fatty acids (worsening the effect for healthiness in humans). These relationships seem logical given that the indexes are calculated in a way that saturated fatty acid content is penalized due to its assumed adverse effects on human health. One study has linked worse AI and TI indexes to higher saturated fatty acid content in beef as well as a greater risk of cardiovascular disease in humans who consume that beef [10]. However, the inclusion of beef in a healthy, balanced diet does not necessarily increase the risk of cardiovascular disease [13], especially if the fatty acids in the meat are predominantly unsaturated.
Although the two cuts of meat in our study are similarly highly regarded on the market, we found interesting differences in nutritional parameters and in the lipid fraction between cuts from the same breed/crossbreed, some of them previously found [67], as well as in correlations between those variables and health-related indexes. Independently of the experimental group, entrecote showed more IMF than sirloin pieces and, accordingly, more energy content and SFA in g/100 g of meat. In the lipid fraction, the main difference between sirloin and entrecote differed by group, with ACL animals showing higher PUFA and lower SFA contents in sirloin than in entrecote. The relationship among fatty acids also varied by meat cut. The higher content of total fat and total saturated fatty acids in entrecote probably weakened some relative relationships among specific MUFAs, PUFAS, and health-related indexes, indeed observed in sirloin. For example, content in ω–6 fatty acids correlated negatively with palmitic acid and positively with linolenic acid in sirloin but not in entrecote. Only in entrecote did we find correlations of lower values of AI with higher values of linoleic acid and higher values of h/H, indicating healthier beef, with linoleic acid being a potential marker. In fact, linoleic acid is known to be beneficial to human health [68], and it is valued by beef consumers [69]. Therefore, the cut should always be taken into account when analyzing the beef quality and healthiness of humans.
Congruently with a lower absolute content in intramuscular fat, Angus-by-Charolais or Limousine beef samples had more crude protein per 100 g of entrecote and a higher absolute content of several amino acids. Unfortunately, none of the three types of beef showed higher content of essential amino acids. Moreover, a higher protein level may not always be desirable: for example, higher levels of leucine and methionine in ACL beef can lead to a bitter taste in consumers [57]. Wagyu and Wangus beef may be superior to ACL beef in this regard. In addition, WY and WN beef showed a higher ratio of MUFAs to saturated fatty acids, indicating better organoleptic qualities [70]. The three kinds of beef should be compared in taster panel studies.
Finally, the health-promoting value of meat can be assessed based on indexes of its various long-chain fatty acids [71]. As we hypothesized, the different groups in our study showed different indexes linked to different intramuscular lipid fractions. Congruently with the higher content in PUFAs and higher ω–6/ω–3 ratio in beef, ACL entrecote showed the lowest h/H index and the highest atherogenic index, and both meat cuts from ACL steers showed the highest thrombogenicity index. These results support the idea that beef quality strongly depends on the breed/crossbreed [72,73]. In fact, specific genes in WY steers have been positively linked to high fat deposition [74], and the maternal gametic effect may also contribute to fat deposition [75]. This study implies that the fat composition of beef influences many aspects of its quality and health value [4,5,6]. In a previous study, adding olive oil to the diet of Blonde d’Aquitaine steers led to a similar atherogenic index as that in our ACL bulls (0.6), which further improved when soy oil was used instead of olive oil [27]. In the current study, a high-olein diet led to better entrecote fat in WY steers than in other groups, as well as lower AI and TI (Table 6). *In* general, lower atherogenic and thrombogenicity indexes, a higher h/H ratio, and a ω–6/ω–3 ratio lower than four are desirable [48]. This kind of fat profile lowers LDL [19], increases HDL, and decreases triglycerides in humans [20], although the magnitude of the health benefit requires further study [76]. Moreover, based on the lipid fraction, beef fat from WY may be healthier for humans than beef fat from European crossbreeds raised under the same conditions. These considerations also suggest that raising WY steers on pasture or feeding them forage may further improve the health-related indexes of WY beef, as suggested before [77], which is an interesting question for further research [78].
The information about the influence of breed/crossbreed and management systems on the lipid fraction of meat is further scarce, and although recent efforts in “foodomics” have brought some light to this topic [79], further research is required.
## 5. Conclusions
Differences among experimental groups in certain metabolic parameters were found, probably linked not only to the breed or crossbreed but also to slaughtering age. Pre-slaughtering values of plasma HDL underscored as a possible metabolic biomarker directly related to beef quality and concretely to intramuscular fat. Although the fat profile and nutritional content of beef depend on the cut, on the breed/crossbreed, and on the slaughtering age, purebred WY and their crossbred WN, raised in Spain under fattening conditions characterized by olein-rich diets, no exercise restriction, and high animal welfare, achieve high IMF content and quality that are similar to those of animals fattened in Japan.
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|
---
title: The Impact of Biotechnologically Produced Lactobionic Acid in the Diet of Lactating
Dairy Cows on Their Performance and Quality Traits of Milk
authors:
- Diana Ruska
- Vitalijs Radenkovs
- Karina Juhnevica-Radenkova
- Daina Rubene
- Inga Ciprovica
- Jelena Zagorska
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000126
doi: 10.3390/ani13050815
license: CC BY 4.0
---
# The Impact of Biotechnologically Produced Lactobionic Acid in the Diet of Lactating Dairy Cows on Their Performance and Quality Traits of Milk
## Abstract
### Simple Summary
The European food industry creates millions of tons of waste products annually that are discarded or utilized inefficiently. The goals set in European legislation have been pivotal drivers in enhancing waste management and stimulating innovation in recycling. Without innovations in processing technologies, the quantity of waste will steadily rise. Considering the evidence of lactobionic acid’s (Lba) health-promoting benefits and already established protocol for whey lactose conversion via microbial cultures developed by a group from Latvia University of Life Sciences and Technologies LBTU, the current study aimed to elucidate the effect of the supplementation of dairy cows’ diets with biotechnologically obtained Lba-rich whey on animals’ performances and milk quality traits. The acquired results revealed that produced Lba could be deemed an alternative to sugar beet molasses to supplement the diet of dairy cows and positively influence the composition of essential amino acids and polyunsaturated fatty acids. The use of Lba in the diet of dairy cows during the lactation period equal to molasses affected the cows’ performances and milk quality traits, especially fat composition.
### Abstract
Dairy processing is one of the most polluting sectors of the food industry as it causes water pollution. Given considerable whey quantities obtained via traditional cheese and curd production methods, manufacturers worldwide are encountering challenges for its rational use. However, with the advancement in biotechnology, the sustainability of whey management can be fostered by applying microbial cultures for the bioconversion of whey components such as lactose to functional molecules. The present work was undertaken to demonstrate the potential utilization of whey for producing a fraction rich in lactobionic acid (Lba), which was further used in the dietary treatment of lactating dairy cows. The analysis utilizing high-performance liquid chromatography with refractive index (HPLC-RID) detection confirmed the abundance of Lba in biotechnologically processed whey, corresponding to 11.3 g L−1. The basic diet of two dairy cow groups involving nine animals, Holstein Black and White or Red breeds in each, was supplemented either with 1.0 kg sugar beet molasses (Group A) or 5.0 kg of the liquid fraction containing 56.5 g Lba (Group B). Overall, the use of Lba in the diet of dairy cows during the lactation period equal to molasses affected cows’ performances and quality traits, especially fat composition. The observed values of urea content revealed that animals of Group B and, to a lesser extent, Group A received a sufficient amount of proteins, as the amount of urea in the milk decreased by $21.7\%$ and $35.1\%$, respectively. After six months of the feeding trial, a significantly higher concentration of essential amino acids (AAs), i.e., isoleucine and valine, was observed in Group B. The percentage increase corresponded to $5.8\%$ and $3.3\%$, respectively. A similar trend of increase was found for branched-chain AAs, indicating an increase of $2.4\%$ compared with the initial value. Overall, the content of fatty acids (FAs) in milk samples was affected by feeding. Without reference to the decrease in individual FAs, the higher values of monounsaturated FAs (MUFAs) were achieved via the supplementation of lactating cows’ diets with molasses. In contrast, the dietary inclusion of Lba in the diet promoted an increase in saturated FA (SFA) and polyunsaturated FA (PUFA) content in the milk after six months of the feeding trial.
## 1. Introduction
Data published by the EUROSTAT disclose that in 2020 European union farms produced 160.1 million tons (Mt) of raw milk, $1.1\%$ more than in 2019 [1]. Of the total milk obtained, $96.3\%$ was used to produce a range of processed dairy products and fresh products. Among other products, the dairy industry generates a substantial amount of whey which in 2020 corresponded to 55.5 Mt [1]. The relative abundance of water and the high ratio of lactose to protein in dairy whey makes further processing challenging. The report of Ozel et al. [ 2] reveals that from the total amount of whey generated, only $50\%$ is being processed for the production of high added value products. Whey drying technologies using conventional approaches such as spray dryers are sometimes risky to manufacturers due to extensive fouling and blocking of the production equipment. These challenges increase with higher whey protein levels and temperatures, leading to protein denaturation [3]. Manufacturers are forced to use ultrafiltration and/or reverse osmosis to reduce the risk of equipment clogging along with making whey more solid [4]. However, these approaches challenge small enterprises and are not economically feasible [5], explaining the relatively high price for whey protein isolates. While considering the high biochemical oxygen consumption (BOD) of 50 g L−1 and chemical oxygen (COD) of 65 g L−1 values, the direct disposal of raw whey is strongly prohibited in many EU countries as it creates serious environmental problems, leading to changes in soil’s physical condition, chemical indicators, and microbiota, thus affecting the yield of crops to be planted [6]. Therefore, it is paramount to find new and cost-effective processing technologies that could stimulate the reuse of whey in many economic sectors, including animal husbandry and food production, and foster a circular economy toward the sustainable development of high added value products from by-products.
The chemical composition of whey is discussed as it varies considerably depending on the milk source and the production process used. However, per 100 g−1, it contains an average of 6.5 g of total solids, which includes 5.0 g of lactose, 0.6 g of protein, 0.6 g ash, 0.2 g of non-protein nitrogenous substances, and 0.1 g of fat [7]. Given the composition of whey, especially the high content of lactose, whey represents interest to biotechnologists as a potential source of carbon-containing molecules suitable for being used as nutrients for microbiological cultures. Cutting-edge biotechnology research came with the discovery of functional compounds derived from dairy whey with antioxidant, antimicrobial, antiaging, and immunomodulation activities, such as α-lactalbumin and β-lactoglobulin [8], glycomacropeptide [9], and lactoferrin [10]. Lactobionic acid (Lba) is another functional product that, for the first time, was synthesized chemically by Fischer and Meyer by oxidizing lactose with bromine [11]. To date, the production of Lba has been accomplished via enzymatic biocatalytic [12], electrocatalytic [13], or heterogeneous [14] oxidation and is widely used in some pharmaceutical products as an excipient agent [15]. More recently, a microbially synthesized Lba was obtained under optimized fermentation conditions of cheese whey with *Pseudomonas fragi* [16]. Meanwhile, the ability of P. taetrolens to produce enzymes involved in the oxidation of acid whey lactose to Lba was highlighted by Sarenkova et al. [ 17].
Due to intrinsic properties, e.g., the calcium delivery vehicle, acidity regulator, and free radical chelating agent, Lba may represent interest to stockbreeders. Recently, our group achieved encouraging results on the elaboration of Lba from acid whey through a biotechnological approach via lactose oxidation enzymes produced by P. taetrolens [18]. Furthermore, the potential utilization of synthesized, isolated, and purified Lba has been demonstrated by Zagorska et al. [ 19], indicating the ability of Lba to contribute to pig growth performance and enhance the nutritional value of meat proteins. Furthermore, the ability of fermented acid whey permeate to act as a prebiotic while positively affecting the growth and development of normal intestinal microflora of lactating cows was highlighted in an in vivo study performed by Lakstina et al. [ 20]. Moreover, the inclusion of Lba in laying hens’ diets contributed to the eggshell thickness and their strengthening, as reported in the patent application [21].
These observations, along with limited information regarding the influence of Lba on the productivity of lactating cows and the quality traits of milk, have promoted the design of this study which focuses on the evaluation of the influence of biotechnologically obtained Lba used as a feed supplement in the diet of lactating cows on their performance and the quality traits of milk.
## 2.1. Experimental Design
The study was conducted at the Farm Ruki (Latvia, Vidzeme) and lasted from November 2020 to April 2021 (six months). Two groups of lactating dairy cows were included, i.e., control (Group A) and experimental (Group B). Each group included in the current study was composed of nine animals of two breeds, i.e., Holstein Black and White and Red dairy cows. The animals were up to 60 days in lactation (DIM), representing different lactations (from 1 to 8) divided proportionally between the groups. The basic feed for both groups was prepared directly on the farm as a partial mixed ration (PMR). The composition for one animal included grass silage 41 kg, hay 0.5 kg, rapeseed cakes 3.6 kg, grain flour 8.3 kg, and premix 0.6 kg (JOSERA Cami 0.25 kg, sodium bicarbonate 0.15 kg, DairyPilotFlavoVital® 0.1 kg, lime 0.1 kg, and salt 0.02 kg). Group A was additionally fed 1.0 kg sugar beet molasses, while the diet of Group B was supplemented with 5.0 kg Lba-rich whey. The amount of Lba supplemented into the basic diet was estimated based on the carbohydrate content of molasses. The proximate composition of the liquid fraction rich in Lba and molasses is given in Table 1. Dairy cows were housed under tie stalls and individually fed and watered ad libitum. Cows were milked twice daily.
## 2.2. Dairy Lactating Cows’ Performances and Quality Traits of Milk Samples
Milk productivity traits: milk yield kg d−1 and sampling for testing were conducted twice per month during the experiment. Milk composition and quality indices were determined at the beginning and end of the experiment. Raw milk samples were taken during morning milking and divided into two parts: one part was preserved using Broad Spectrum MicroTabs II (BSM II) and immediately delivered to the Dairy Laboratory, Ltd. The analysis of raw milk quality traits, i.e., fat, protein, casein, and urea content, was performed using the MilkoScan FT6000 (FOSS, Hilleroed, Denmark) mid-infrared spectroscopic approach following the guidelines outlined in the ISO 9622|IDF 141:2013. The estimation of somatic cells was performed using an instrumental flow cytometry method by Fossomatic™ (FOSS, Hilleroed, Denmark) according to the ISO standard 13366-2|IDF 148-2:2006. The second part of the raw milk samples devoted to the analysis of fatty acids (FAs) and amino acids (AAs) was kept at a temperature of −20 ± 1 °C until further processing and analysis, a maximum of two weeks.
The somatic cell count (SCC) per 1 mL of milk was converted to standardized units, i.e., somatic cell score (SCS), by using the following equation [22]:[1]SCS=log2SCC100+3 where SCC is somatic cells per mL of milk.
The SCS in milk was used as a quality and indirect animal health indicator.
In order to evaluate results among groups and study phases (beginning and end of the experiment), the milk yield and its composition were transformed to energy-corrected milk (ECM), which indicates the amount of energy in the milk considering the values of milk, fat, and protein yield (ICAR, 2017). The ECM was determined following the equation [2]ECM=FY×38.3+PY×24.2+MY×0.78323.14 and was expressed in kg d−1, where ECM is energy-corrected milk, FY is fat yield in kg, PY is protein yield in kg, and MY is milk yield in kg.
The predicted milk protein efficiency ratio (PER) was calculated according to three equations proposed by Alsmeyer, Cunningham, and Happich [1974], taking into account the values of AAs:[3]PER1=-0.684+0.456LEU-0.047PRO where LEU and PRO are the content of leucine and proline, respectively. [ 4]PER2=-0.468+0.454LEU-0.105TYR where LEU and TYR are the content of leucine and tyrosine, respectively. [ 5]PER3=-1.816+0.435MET+0.780LEU+0.211HIS-0.944TYR where MET, LEU, HIS, and TYR is the content of methionine, leucine, histidine, and tyrosine, respectively.
The ratio of essential AAs (E) to the total AAs (T) of the protein was calculated based on the equation provided by Chavan et al. [ 23]:[6]ET=∑EAA∑TAA×$100\%$ where E/T is a ratio of essential amino acids (E) to the total amino acids (T); ∑EAA is the sum of essential amino acids; and ∑EAA is the sum of total amino acids.
The index of atherogenicity (IA) which characterizes the atherogenic potential of fatty acids, the index of thrombogenicity (IT), a ratio of hypocholesterolemic to hypercholesterolemic (HH) values, and the health-promoting index (HPI) were calculated according to Equations (7–10) proposed by Chen and Liu [2020]. [ 7]IA=C12:0+4×C14:0+C16:0∑UFA where IA is the index of atherogenicity and ∑UFA is the sum of unsaturated fatty acids. [ 8]IT=C14:0+C16:0+C18:00.5×∑MUFA+0.5×∑n-6PUFA+3×∑n-6PUFA+∑n-3n-6 where IT is the index of thrombogenicity; ∑MUFA is the sum of monounsaturated fatty acids; and ∑PUFA is the sum of polyunsaturated fatty acids. [ 9]HH=cis-C18:1+∑PUFAC12:0+C14:0+C16:0 where HH is a ratio of hypocholesterolemic to hypercholesterolemic values; cis implies the isomeric form of C18:1 fatty acid; and ∑PUFA is the sum of polyunsaturated fatty acids. [ 10]HPI=∑UFAC12:0+4×C14:0+C16:0 where HPI is the health-promoting index and ∑UFA is the sum of unsaturated fatty acids.
## 2.3. Chemicals, Standards, and Reagents
A mixture of C4-C24 fatty acid methyl esters (FAMEs) and amino acids (AAs) with a purity of ≥$99.0\%$ were acquired from Sigma-Aldrich Chemie Ltd. (St. Louis, MO, USA). Acetonitrile, methanol, n-hexane, and formic acid (puriss p.a., ≥$98.0\%$) of liquid chromatography–mass spectrometry (LC-MS) grade were purchased from the same producer. Lactobionic acid (Lba) with purity ≥$97.0\%$ was obtained from Acros Organics (Geel, Belgium). Ammonium hydroxide solution ($25\%$ v/v) and diethyl ether (puriss p.a., ≥$99.5\%$) were obtained from Chempur (Piekary Śląskie, Silesia, Poland). Hydrochloric acid ($37\%$ v/v) was purchased from VWR™ International, GmbH (Darmstadt, Germany). Sodium hydroxide, potassium hydroxide, phenolphthalein, and 0.5 M trimethylphenylammonium hydroxide solution (TMPAH) in methanol for GC derivatization were of reagent grade and were obtained from Sigma-Aldrich Chemie Ltd.
## 2.4. Production of Lactobionic Acid from Pre-Concentrated Whey
In this study, pre-concentrated whey obtained from a local producer Jaunpils Ltd. (Jelgava, Latvia), with a proximate composition depicted in Table 2, was used as a carbon source for P. taetrolens DSM 21104 during the production of Lba. The description of the operational and process conditions for the production of Lba was provided in detail in our previous study [26].
## 2.5. The HPLC-RID-DAD Analytical Conditions for Lactobionic Acid and Lactose Determination
Lba was analyzed using a Shimadzu series LC-20 high-performance liquid chromatography system equipped with the SPD-M20A photodiode-array detector (Shimadzu Corporation, Tokyo, Japan). All samples before HPLC analyses were centrifuged at 14,200× g for 10 min to remove cell debris and other water-insoluble substances. The LBA was determined using a refractive index detector RID-10A (Shimadzu Corporation, Tokyo, Japan). Chromatographic separation of Lba was carried out using a hybrid silica-based YMC-C18 column (4.6 mm × 250 mm, 5 µm; YMC, Kyoto, Japan) operating at 40 °C and a flow rate of 1.0 mL min−1. The separation of Lba was conducted using an isocratic mobile phase with 2 L elution containing 1.15 mL H3PO4, 14.36 g KH2PO4, and 20 mL acetonitrile. The detection wavelength was set at 210 nm. The injection volume was 10 μL.
The quantitative analysis of lactose was performed using the same system while utilizing a refractive index detector RID-10A (Shimadzu Corporation, Tokyo, Japan). Chromatographic separation was conducted using an Altima Amino (4.6 × 250 mm; 5 μm; Grace™, Columbia, MD, USA) column. The temperature of the column and flow cell was maintained at 30 °C. A mixture of H2O and MeCN (75:25, v/v) was used as the mobile phase in the isocratic mode. The flow rate of the mobile phase was 1.0 mL min−1. The injection volume was 10 μL. System control, data acquisition, analysis, and processing were performed using Empower 3 Chromatography Data Software version (build 3471) (Waters Corporation, Milford, MA, USA).
## 2.6. Acid Hydrolysis of Milk for Amino Acid Determination
Before acid hydrolysis, each milk sample was defatted and freeze-dried to obtain protein isolates. Then, the prepared isolates 200.0 mg ± 0.1 were subjected to acid hydrolysis with 5.0 mL of 6M HCl solution according to the ISO 13903:2005 standard with modifications. The hydrolysis was undertaken in 22.0 mL glass Headspace chromatography bottles (PerkinElmer, Inc., Waltham, Massachusetts, USA) with screw caps and silicone seals in a drying cabinet of Pol-Eko Aparatura SP.J. (Wodzislava Slonska, Poland) at a temperature of 110 °C for 24 h. Before hydrolysis, to slow down the oxidation–reduction reaction of the compounds of interest, the stabilizing reagent phenol was added directly to the sample in the amount of $0.02\%$ (w/w). After hydrolysis, the volume of hydrolysate was adjusted to 7.0 mL with H2O, and it was normalized to 6.5–6.8 using 2.18 mL of $25\%$ NH4OH solution. The final volume was 10.0 mL. The obtained hydrolysate was subjected to 1 min intensive Vortexing using the “ZX3” vortex mixer (Velp® Scientifica, Usmate Velate, Italy), followed by centrifugation at 16,070× g for 10 min at 19.0 ± 1 °C in a “Hermle Z 36 HK” centrifuge (Hermle Labortechnik, GmbH, Wehingen, Germany). Before LC-MS analysis, the collected supernatant was filtered using a 0.22 µm hydrophilized polytetrafluoroethylene (H-PTFE) membrane filter (Macherey-Nagel GmbH & Co. KG, Dueren, Germany).
## 2.7. The HPLC-ESI-TQ-MS/MS Analytical Conditions for Amino Acids
The chromatography analysis of AA was conducted using a “Shimadzu Nexera UC” series liquid chromatography (LC) system (Shimadzu Corporation, Tokyo, Japan) coupled to a triple quadrupole mass-selective detector (TQ-MS-8050, Shimadzu Corporation, Tokyo, Japan) with an electrospray ionization interface (ESI). A sample of 3 µL was injected onto a reversed-phase “Discovery® HS F5-3” column (3.0 µm, 150 × 2.1 mm, Merck KGaA, Darmstadt, Germany) operating at 40 °C with a flow rate of 0.25 mL min−1. The mobile phases used were acidified H2O ($1.0\%$ HCOOH v/v) (A) and acidified MeCN ($1.0\%$ HCOOH v/v) (B). The program of stepwise gradient elution of the mobile phase B for 20 min was implemented as follows: T0 min = $5.0\%$, T5.0 min = $30.0\%$, T11.0 min = $60.0\%$, T12.0 min = $80.0\%$, and T12.1 min = $5.0\%$. Finally, re-equilibration for 3 min was conducted after each analysis following the initial gradient conditions. The MeCN injections were included as a blank run after each sample to avoid the carry-over effect. Data were acquired using “LabSolutions Insight LC-MS” version 3.7 SP3, which was also used for instrument control and processing. Ionization in the positive ion polarity mode was applied in this study. At the same time, data were collected in profile and centroid modes, with a data storage threshold of 5000 absorbance for MS. The operating conditions were as follows: detector voltage 1.98 kV, conversion dynode voltage 10.0 kV, interface voltage 4.0 kV, interface temperature 300 °C, desolvation line temperature 250 °C, heat block temperature 400 °C, nebulizing gas argon (Ar, purity $99.9\%$) at a flow rate of 3.0 L min−1, heating gas carbon dioxide (CO2, purity $99.0\%$) set low at 10.0 L min−1, and drying gas nitrogen (N2, separated from air using a nitrogen generator system from “Peak Scientific Instruments Ltd.” (Inchinnan, Scotland, UK), purity $99.0\%$) at flow 10.0 L min−1. All AAs were observed in the programmed and optimized multiple reaction monitoring (MRM) mode. Quantitative analysis of AAs was performed by injecting 3.0 μL of calibration solution at 15 °C with the range of 0.075–2.5 μM L−1. The working solution was prepared immediately before being used. Representative chromatographic separation of 18 AAs is given in Figure 1.
## 2.8. Preparation of Lipid Fraction via Alkaline-Assisted Hydrolysis with Subsequent Liquid–Liquid Extraction
The isolation of lipophilic fraction from dry milk cream (please refer to Section 2.6. Acid Hydrolysis of Milk for Amino Acid Determination) was performed following the procedure described by Radenkovs et al. [ 27] with minor modifications. For the release of bound forms of fatty acids (FAs), $10\%$ (w/v) KOH dissolved in $80\%$ MeOH (MeOH:H2O ratio 80:20 v/v) was applied. Briefly, duplicate samples of 3.0 ± 0.1 g of freeze-dried cream obtained after milk separation were weighed in 50 mL reagent bottles with screw caps. For the hydrolysis of FAs, 30 mL of freshly prepared methanolic KOH was added to each cream sample, and the mixture underwent incubation in a water bath “TW8” (Julabo®, Saalbach-Hinterglemm, Germany) at 65 °C temperature for 3 h. After hydrolysis, the cleavage of bonds present in the potassium salts of FAs was obtained by adjusting the pH of the solution to pH 2.0 ± 0.2 by adding 3.3 mL HCl (6M). The extraction of FAs was implemented via liquid–liquid phase separation using n-hexane as the sole solvent. After hydrolysis, samples were cooled to room temperature (22 ± 1 °C) and quantitatively transferred to Falcon 50 mL conical centrifuge tubes (Sarstedt AG & Co. KG, Nümbrecht, Germany). Afterwards, 10 mL of n-hexane was added to each tube, followed by vortex-mixing for 1 min. Finally, the layers were separated via centrifugation at 3169× g for 10 min in a “Sigma, 2-16KC” centrifuge (Osterode near Harz, Germany). The top n-hexane layer was decanted and collected. The extraction procedure was repeated three times. First, the resulting lipid fraction (30 mL) was evaporated using a “Laborota 4002” rotary evaporator (Heidolph, Swabia, Germany) at 65 °C, and the dry residues were then reconstituted in 5 mL of n-hexane and filtered through a polytetrafluoroethylene hydrophobic (PTFE) membrane filter with a pore size of 0.45 µm. The filtrates were quantitatively transferred to 20 mL scintillation glass vials and subjected to drying under a gentle stream of N2 to complete dryness. Prepared samples were kept at a temperature of −18 ± 1 °C until further analysis and were used within a maximum of two weeks. Before GC-MS analysis, obtained dry lipid fractions were reconstituted in 2 mL pyridine.
## 2.9. Preparation of Fatty Acids for GC-MS Analysis
The TMPAH reagent was used as a methylation agent of the functional groups to obtain volatile FAMEs derivatives. The methylation procedure was performed following the methodology ensured by Radenkovs et al. [ 27].
## 2.10. The GC Conditions for FAMEs Analysis
The analysis of FAMEs was performed using a “Clarus 600” system PerkinElmer, Inc. (Waltham, MA, USA) coupled with a quadrupole “Clarus 600 C” mass-selective detector (Waltham, MA, USA). The conditions were adopted from Radenkovs et al. [ 27].
## 2.11. Statistical Analysis
The obtained data were analyzed using descriptive statistics, and the differences between the study groups and phases of the study were assessed using ANOVA with Student’s t-test correction, setting the confidence level at p ≤ 0.05. Statistical processing of the data was carried out using the MS Office program Excel version 2016 (Microsoft Corporation, Redmond, Washington, USA).
## 3.1. Animals’ Performances and Quality Traits of Milk
Balanced feeding for lactating cows, especially at the beginning of lactation, is crucial [28] as it influences cows’ performances, overall health, and milk quality traits. The selection of a supplement to compensate for the lack of energy in feed depends on factors such as feeding technology, supplement availability in the market, and costs. In the current study, sugar beet molasses for Group A and Lba for Group B were selected as supplements to compare the effect on cows’ performances and milk quality traits. The productivity results were analyzed for each group at the study’s beginning and end (see Table 3). As seen at the beginning of the experiment, the milk yield values were not significantly different (p ≥ 0.05) between the study groups. However, a substantial difference (p ≤ 0.05) was observed comparing milk yield within the study phase between initial and final values. It was observed that at the end of the experiment passing six months, the milk yield in Group A decreased by $19.3\%$, while in Group B, the decrease amounted to $23.0\%$, which was $3.7\%$ higher in Group A (Table 3). No apparent influence of dietary treatment on lactation performance was found; this observation is in line with Penner and Oba [28]. The decrease in milk yield is explained by the lactation phase, which directly influences the milk yield as the number of lactation days increased during the study [29,30]. A similar observation was made by Vijayakumar et al. [ 31], indicating that cows with the second lactation produced $24.18\%$ greater milk than the first lactation cows, while the fourth lactation cows showed a decreased milk yield by $16.04\%$ from the third lactation. According to a study reported by the National Research Council [32], fat content in milk is the most varying value, while lactose is the least, and this observation was also reinforced in this study. As seen, the fat content between the groups within the beginning phase of the experiment varied significantly (p ≤ 0.05), corresponding to $23.5\%$ (Table 3). The percentage difference between the groups at the end of the experiment amounted to $15.4\%$, which was $8.1\%$ lower than at the beginning. Such a difference could be the case of the animals’ physiological states, e.g., the availability of hormones such as adrenaline and noradrenaline that are reported to be responsible for lipolytic activity in adipose tissue [33]. At the end of the experiment, the most apparent increase in fat content was found in Group A, corresponding to a $14.7\%$ increase compared with the initial value. A similar increase in fat content was also observed in Group B, though this value corresponded to $7.1\%$. It was reported that the supplementation of molasses in dairy cows’ diets substantially contributes to a higher fat yield and concentration of fatty acids in primiparous cows [34]. The increase in fat content of Group A fed with a basic diet supplemented with molasses can be explained by an enhanced rumen fermentation process moderated by pH, thus promoting mammary de novo fatty acid synthesis [35,36]. This statement was further reinforced by [37], indicating the increase in effective ruminal degradability (ERD) of dry matter in an in situ ruminal study. It is worth noting that the supplementation of lactating cows’ diets with Lba could be considered a promising carbon-containing alternative to molasses, ensuring an increase rather than a decrease in fat in milk without affecting acidosis.
Multiple pieces of scientific evidence have revealed that milk composition, especially crude protein and fat content, strongly depends on the milk yield [38]. Hence, highly productive dairy cows will provide a lower protein yield in milk than those with low productivity [39]. However, one of the critical factors determining the amount of protein in milk is the availability of nutrients, especially those rich in proteins, that the animal ingests in the feed [40]. The results of this study imply that the content of protein in milk from the dairy cows of Group A who received a high feed diet rich both in carbohydrates and protein (Table 1) was found to be significantly (p ≤ 0.05) higher compared with the initial value, corresponding to a $9.1\%$ increase.
Previous studies also observed an increase in protein content, reflecting a higher nutritional value of milk from low-productivity dairy cows [41]. However, in Group B, in which the feed of animals was supplemented with biotechnologically produced Lba, the protein content, considering the lower milk yield at the end of the experiment, remained intact, corresponding to $3.8\%$. The observed values are consistent with those reported by Murphy [42]. A plausible explanation for obtaining higher values of protein in milk from Group A relies upon the availability of readily digestible compounds present in molasses, such as sucrose, fructose, and glucose [24,40], while in Lba-rich whey solution, the main representative is lactose.
Casein and whey protein are milk’s major proteins, and casein corresponds to roughly $80\%$ of the total protein in bovine milk [43]. The initial values of casein in milk samples fluctuated from 2.6 to $3.0\%$, with Group B having the highest content and Group A having the lowest (Table 3). The observed values are consistent with those of Guo and Wang [44]. As with protein, the content of casein was affected by feeding. The highest content was found in Group A at the end of the experiment, corresponding to an increase of $7.7\%$. In turn, no changes were observed in Group B after six months of the feeding trial. The results indicate that optimizing feed intake with ingredients rich in carbohydrates and organic acids such as molasses or Lba can ensure the necessary energy level to retain milk’s nutritional value (casein in particular) during lactating. This observation is in line with those proposed by Emery as far back as four decades ago, in 1978 [45].
The SCC is a direct marker of mastitis infection for individual cows and within herds and therefore was evaluated critically as an indirect indicator of cow udder health [46]. To better reflect the state of animal health, the SCS values were calculated in this study and are depicted in Table 3. According to Shook and Schutz [47], having these numbers allows for achieving genetic improvement and better results in controlling mastitis resistance. As seen, the initial values of SCS varied from 3.2 and 2.3, with Group A at the initial stage of the experiment having the highest value and Group B having the lowest, respectively (Table 3). The estimation made available by Smith et al. [ 48] was that cows with an SCS of 0–3 are generally considered to be uninfected. At the end of the experiment, the SCS values varied in the range from 3.0 to 3.5, with Group A showing the highest value while Group B showed the lowest. The most apparent increase in SCS values was found in Group B. Although, such an increase in SCS only marginally contributed to the reduction in milk yield, as proposed by Smith et al. [ 48].
It has been proposed that the content of urea in milk can be utilized as a non-invasive input to a system to monitor the crude protein status in dairy cows on a regular basis [49]. Therefore, urea content in milk samples was used in this study as a biomarker to estimate the availability of AAs in the diet of animals. The initial values of urea content varied from 23.3 to 23.5 mg dL−1. The observed values were consistent with those reported by Rzewuska and Strabel [50] for the dairy cow in the first phases of lactation (Table 3). However, after six months of the experiment, following the developed dietary treatment schedule, the amount of urea in the milk of Group A and Group B decreased by $35.2\%$ and $21.7\%$, respectively. Nevertheless, the observed values comply with data reported by Duinkerken et al., 2011 [51], indicating the range of urea in milk from 15.0 to 30.0 mg dL–1 as being optimal. Since the composition of molasses is mainly represented by carbohydrates while lacking essential AAs such as methionine, histidine, and lysine [52], a relatively higher value of urea content in Group B can be explained. Incorporating biotechnologically produced Lba into the diet of dairy cows resulted in ensuring the availability of readily digestible AAs essential for animals [53,54].
Since the ECM is a generic productivity indicator that provides a clue on the value of milk based on the milk yield, fat, and protein content, this value is widely used to assess the overall quality of obtained milk as a function of dietary treatment [39]. As seen, the ECM values at the initial stage of the experiment were significantly different (p ≤ 0.05) between the study groups (Table 3). The observed values were considerably higher than those reported by Guinguina et al., 2020, [55] for dairy cows with a basic-feed diet while they were significantly lower than for dairy cows fed rumen-protected lysine as a supplement to the basic diet [56]. The ECM values changed significantly (p ≤ 0.05) at the end of the study, corresponding to a percentage reduction of $11.4\%$ and $23.2\%$ for Group A and Group B, respectively. The main factor contributing to the decrease in ECM values was milk yield, which dropped the most in Group B fed with Lba. The report of Miller et al., 2021, [34] indicates that molasses with $34\%$ sucrose positively contributed to dairy cows’ performances during the postpartum period, along with improved milk quality traits, by stimulating ruminal butyrate production and papillae development. Moreover, Ravelo et al. [ 57] also concluded that by-products rich in sucrose or lactose in the diet of dairy cows promoted ruminal microbial fermentation, encouraging digestibility and increasing the pH of rumen fluids.
Overall, the use of Lba in the diet of dairy cows during the lactation period favorably affected the performance and quality traits of milk; however, to achieve better results, the optimization of feed intake with ingredients rich in carbohydrates and proteins such as molasses and biotechnologically produced Lba, respectively, can deliver the necessary energy levels for increased milk production and its quality.
## 3.2. The Changes in Amino Acids and Their Quality Indices in Relation to Feeding Trial
Met, Lys, and His have been identified as the most limiting AAs for lactating dairy cows [53], and their lack in the diet of animals leads to limited milk protein, fat production, and milk yield [58]. Therefore, it has been proposed that supplementing the diet of lactating cows with rumen-protected AAs may be a prosperous approach for improving animals’ performances and the quality traits of milk [59].
The AA profile was analyzed via the HPLC-ESI-TQ-MRM-MS/MS approach, by-passing the derivatization step to elucidate the quality of proteins obtained in produced milk. The selective analysis confirmed the presence of 17 AAs in all milk samples except tryptophan due to its high oxidative degradation (Table 4). Furthermore, it was observed that Glu, Leu, Pro, and Lys were the prevalent representatives of AAs in milk protein. The observed values are consistent with those reported by Landi, Ragucci, and Di Maro [60]. In the course of further study of the content of total AAs in milk protein, no significant difference between Group A and Group B was found at the beginning of the experiment. However, statistically significant differences (p ≤ 0.05) were established between Group A and Group B in the content of individual AAs such as Ile, Lys, and Val. After six months of the feeding trial, a significantly higher concentration of Ile and Val was detected in Group B, and the percentage increase corresponded to $5.9\%$ and $3.3\%$, respectively. An increase in Tyr content by $6.5\%$ and $4.3\%$ was also observed in Group A and Group B, corresponding to the value of 4.9 g 100 g−1 in both groups. It is worth noting that the most apparent increase in the content of Ala was observed in Group B, indicating that cows responded favorably to Lba rather than to molasses. The study also noted a significant decrease (p ≤ 0.05) in Gly content by $10.5\%$ and $5.3\%$ in Group A and Group B, respectively. A similar observation was made by Li et al. 2019 [61], performing the metabolic profiling of yak mammary gland tissues and speculating that the decrease in Gly was related to the negative energy balance in yaks. The most pronounced decrease in Thr content was found in the milk of Group B, corresponding to $4.7\%$, while in Group A, a reduction amounted to $2.3\%$. A plausible explanation for having a reduction in Thr has been given by Tang et al., 2021 [62], indicating that the presence of this essential AA in high concentrations is vitally important to newborns since its primary function is to provide antimicrobial activity against pathogenic bacteria and to modulate the immune system response to viruses, while its gradual decrease takes place as the calf grows.
Branched-chain AAs (BCAAs, valine, leucine, and isoleucine) belong to the group of exogenous AAs that must be supplied to the body through the diet [63]. Multiple beneficial effects of BCAAs have repeatedly been proven [64,65], so the importance of these AAs in human nutrition is undebatable. The results of this study revealed a relatively similar sum of BCAAs in the milk sample at the beginning of the experiment with the dietary treatment. The content varied from 20.2 to 20.7 g 100 g−1, with Group A having the lowest value and Group B having the highest value. The observed values are consistent with those of Hulmi et al., 2010 [66]. It is worth noting that the content of BCAAs in Group A after six months of the feeding trial decreased by $0.5\%$, while in Group B it increased by $2.4\%$.
The predicted protein efficiency ratio (PER) is a valuable method providing crucial information on the quality of proteins in food systems. However, utilizing in vivo models to estimate PER is considered time-consuming and expensive [67]. In this study, we attempted to predict the PER values based on mathematical equations using the information on amino acids from the milk samples. According to these models, the PER1 (Leu and Pro), PER2 (Leu and Tyr), and PER3 (Met, Leu, His, Tyr) values as functions of the AAs selected were estimated and they are depicted in Table 4. The results of the present study showed that PER1 values for protein isolates obtained from Group A and Group B before the feeding trial lay within the range between 3.1 and 3.2, respectively. Based on Friedman’s classification, a PER < 1.5 is to be considered poor, from 1.5 to 2.0 is considered to be moderate, and > 2.0 is considered to be superior [68]. Based on this proposal, the protein isolates can be classified as highly digestible, close to the values reported by Lee et al. [ 69] for proteins of deboned chicken meat. In addition, Sarwar [70] and Dupont and Tomé [71] support our observation, indicating that nearly $95\%$ of milk protein is readily digestible within in vivo gastrointestinal tract models. It is worth noting that the observed values were far from those indicated for extruded, cooked, and baked yellow and green split pea flour [72]. Since the PER1 values for Group A and Group B were close to each other after a feeding trial of six months, it is believed that animals received a balanced diet and even energy distribution within the entire experimental period.
However, for calculation using Leu and Tyr, relatively higher values were determined for the PER2 index. The observed values after the feeding trial ranged from 3.2 to 3.3 for Group A and Group B, respectively. A relatively high value of Tyr can explain the difference between PER1 and PER2, reported to have roughly $99.0\%$ true ileal digestibility [73]. Group B tended to show a higher value after a feeding trial with Lba than Group A who were fed a high sucrose diet. The higher PER2 values compared to PER1 were mostly Leu-concentration-dependent.
The predicted PER3 values were found to be different from those of PER1 and PER2 due to the inclusion of additional AAs, which were speculated to be more accurate. The assessed values after the feeding trial for both groups were statistically similar, corresponding to 2.7. However, after the feeding trial, the digestibility rate worsened despite the increase in Tyr and Ile, estimated by a percentage reduction of $11.1\%$ and $3.7\%$ for Group A and Group B, respectively. On the other hand, the increase in Phe and Ala concentrations and the rearrangement of AAs were the main factors that did affect the lowering of the digestibility rate of milk proteins. However, a decrease in PER3 values observed by Pastor-Cavada et al. [ 74] seemed to be Met-content-dependent. According to PER3 values, the predicted digestibility of milk protein isolates above the standardized PER value for casein of 2.5 indicates its high bioavailability with a digestibility rate close to bean proteins, as Mecha et al. reported [75]. Finally, the ratio of essential AAs to the total AAs at the beginning and end of the study was found in the range from 44.9 to $45.5\%$ and from 45.6 to $45.8\%$ for Group A and Group B, respectively.
The observed ratio values of essential to total AA comply with the quality criteria outlined by the FAO/WHO [76]. Given these numbers, it is attainable to state that milk obtained following lactating cows’ dietary treatments should be considered to be an excellent source of amino acids that could provide the body with all essential AAs.
## 3.3. The Changes in Fatty Acids and Their Nutritional Indexes of Milk Lipids in Relation to Feeding Trial
The composition of FAs in lipids recovered from milk cream is depicted in Table 5. In total, 29 FAs were identified and quantified, among which the dominance of palmitic acid (C16:0) from 31.0 to $34.7\%$, followed by oleic acid (C18:1n9c) from 18.4 to $20.9\%$, myristic acid (C14:0) from 11.8 to $12.9\%$, stearic acid (C18:0) from 8.5 to $10.1\%$, linolelaidic acid (C18:2n6t) from 1.5 to $2.3\%$, linoleic acid (C18:2n6c) from 1.6 to $1.8\%$, and behenic acid (C22:0) from 1.4 to $2.1\%$ was found. The results are consistent with those of Månsson [77], indicating a similar descending order of FA content recovered from bovine milk. The results of the present study indicated that a high feed diet rich in sucrose (Group A) negatively affected the amount of individual FAs in the milk. The most apparent decline in the content of major FAs was observed for linoleic acid (C18:2n6t), corresponding to a $21.6\%$ loss. In contrast, no changes for this polyunsaturated FA were found in Group B fed with biotechnologically obtained Lba. Similar changes were observed for behenic acid (C22:0), corresponding to a $21.6\%$ loss and a $21.4\%$ increase for Group A and Group B. A positive influence of biotechnologically obtained Lba was identified for FAs such as lauric (C12:0), tridecanoic (C13:0), myristoleic (C14:1), pentadecanoic (C15:0), and pentadecanoic (C15:1) acids.
The negative effect of molasses supplementation on dairy cows’ performances and milk composition by reducing milk yield, milk protein, lactose yield, and the composition of unsaturated FAs, in particular, was reported by Torres et al. [ 78]. In some cases, there was a marked decrease in oleic acid after using molasses as an additive to feed lactating cows due to the interaction of molasses with buffers that negatively affected ruminal fermentation and consequently led to a loss in milk quality [79]. However, after animals received a molasses diet, this experiment observed an increase rather than a decrease in oleic acid (C18:1n9c) concentration in milk. The content of CLA is feed-type-dependent since its concentration greatly varies from report to report [80]. However, the CLA values found are in direct agreement with those reported by Brito et al. [ 81] for cows fed chiefly grass. The CLA concentration in milk varied from 0.6 to $1.0\%$, with Group A fed with molasses having the highest value and Group B supplied with Lba having the lowest. After six months of the feeding trial, a significant reduction (p ≤ 0.05) in CLA was noted in Group A, corresponding to a $30.0\%$ loss. On the other hand, the Lba positively affected CLA in Group B since as much as a $33.3\%$ increase was observed. Relatively higher values of CLA in Group B can be explained presumably by the chemical composition of a liquid fraction rich in Lba, particularly the availability of linoleic acid that promoted the synthesis of CLA as reported by Gómez-Cortés et al. [ 82].
Overall, the content of FAs in milk samples was affected by feeding. Without reference to the decrease in individual FAs, the higher values of MUFAs were achieved by the supplementation of lactating cows’ diets with molasses (Group A). In contrast, the dietary inclusion of biotechnologically produced Lba in the diet promoted the increase in the content of SFAs and PUFAs in the milk.
In the present study, health-related lipid indexes IA, IT, HH, and HPI were established to better reflect the health-promoting properties of milk lipids (Table 5).
It was highlighted that IA and IT are great tools for assessing the potential contribution of FAs to human health [83]. The lower IA and IT values, the less risk of developing cardiovascular diseases caused by blood vessel clogging. Performing mathematical analysis, the following IA indices were obtained from 2.8 to 3.6 and from 2.4 to 3.4 for Group A and Group B at the beginning and end of the study, respectively. The observed values are consistent with those of Lobos-Ortega et al. [ 84] for fresh bovine milk estimated using near-infrared spectroscopy. IA values were statistically different (p ≤ 0.05) between Group A and Group B at the study’s beginning and end. Up to $28.6\%$ and $41.7\%$ increases in IA values were observed for Group A and Group B, passing six months of the feeding trial, respectively. The lower IA value in Group B is explained by the statistically higher concentrations of individual MUFAs. Additionally, statistically higher CLA values in Group B reinforce this speculation as their superior anti-atherogenic, anti-platelet, and antioxidant properties have been reported multiple times by [85,86].
However, the opposite results were obtained concerning the IT index, indicating statistically lower values for Group A than for Group B, corresponding to 3.0 and 3.5, respectively. To a greater extent, the observed IT values in both groups corresponded to those reported by Silva et al. [ 87] and Sharifi et al. [ 88] for milk from crossbred cows subjected to feed ad libitum and from high forage and nitrate-fed Holstein lactating cows, respectively. The enhancement of the rumen fermentation process and pH optimum achieved by supplementing the diet of Group A with readily digestible mono and disaccharides present in molasses perhaps promoted mammary de novo FAs rather than those with anti-atherogenic activity synthesis as proposed by Palmquist, Beaulieu, and Barbano [35]. Further analysis revealed no statistically significant differences (p ≥ 0.05) between the HH values, indicating equal values for all samples investigated, corresponding to 0.5.
For the first time, the HPI was proposed by Chen et al. [ 89] as a quality marker of dietary fat, which is presently widely used in the analysis of dairy products. Furthermore, it is believed that products with higher HPI values are supposed to be more beneficial to human health [19]. The HPI values were found in the range from 0.3 to 0.4, and no statistically significant differences (p ≥ 0.05) for both groups were revealed at the end of the feeding trial (Table 5). The observed values are in line with those reported by Kasapidou et al. [ 90] for sheep fed in confinement with no access to grazing and by Chen and Liu [91] for the cream of Holstein cows.
## 4. Conclusions
The abundance of Lba in the whey fraction obtained by taking advantage of optimized fermentation conditions developed by the group of LBTU utilizing P. taetrolens DSM 21104 was confirmed chromatographically, corresponding to 11.3 ± 0.3 g L−1. A substantial yield of functional Lba made it attainable to enrich the diet of lactating cows (Group B) with the functional component, which has been used as an alternative to sugar beet molasses (Group A). The results of this study revealed an equally effective contribution of the Lba supplementation on dairy cow performance compared with the molasses. Milk quality indicators, e.g., protein and casein content, remained unaffected after six months of the feeding trial. Similar to molasses, the Lba contributed to the improvement in lipid synthesis as $7.1\%$ and $14.7\%$ higher lipid content was observed in the milk of Group B and Group A compared with the initial values, respectively. The content of essential AAs such as isoleucine and valine was significantly higher in the experimental group fed with Lba rather than molasses. A similar trend of increase was found for branched-chain AAs, indicating an increase of $2.4\%$ compared with the initial value. It was found that Group B tended to show higher PER1, PER2, and PER3 values after feeding with Lba than Group A fed with a high sucrose diet. The content of FAs in milk samples was affected by feeding. The highest content of MUFAs was observed in Group A, which received molasses. In contrast, the dietary inclusion of Lba promoted the increase in SFA and PUFA content in the milk after six months of the feeding trial.
To summarize, the use of Lba in the diet of dairy cows favorably affected the performance and quality traits of milk; however, to achieve better results, the optimization of feed intake with ingredients rich in carbohydrates and proteins such as molasses and biotechnologically produced Lba, respectively, can deliver the necessary energy levels for increased milk production and its quality.
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|
---
title: Association of Computed Tomography Measures of Muscle and Adipose Tissue and
Progressive Changes throughout Treatment with Clinical Endpoints in Patients with
Advanced Lung Cancer Treated with Immune Checkpoint Inhibitors
authors:
- Azim Khan
- Christopher J. Welman
- Afaf Abed
- Susan O’Hanlon
- Andrew Redfern
- Sara Azim
- Pedro Lopez
- Favil Singh
- Adnan Khattak
journal: Cancers
year: 2023
pmcid: PMC10000131
doi: 10.3390/cancers15051382
license: CC BY 4.0
---
# Association of Computed Tomography Measures of Muscle and Adipose Tissue and Progressive Changes throughout Treatment with Clinical Endpoints in Patients with Advanced Lung Cancer Treated with Immune Checkpoint Inhibitors
## Abstract
### Simple Summary
The impact of sarcopenia (i.e., progressive and generalised loss of skeletal muscle mass) and obesity on survival are substantially investigated in cancer patients. However, the relationship between sarcopenia and mortality is quite unclear in patients with lung cancer treated with immunotherapy, while the prognostic value of obesity remains controversial. These issues are potentially related to the obesity paradox and lack of precise measures of body composition on survival. As a result, we aimed to explore the associations between measures of skeletal muscle mass and adiposity (i.e., intramuscular, visceral and subcutaneous adipose tissue) and changes during treatment with disease progression and overall survival in patients with advanced lung cancer receiving immunotherapy. Our results demonstrated that rather than sarcopenia, higher intramuscular and subcutaneous adipose tissue are associated with better prognosis during immunotherapy. These findings are of great importance for clinical practice and may inform specific and tailored therapies to improve immunotherapy prognosis.
### Abstract
To investigate the association between skeletal muscle mass and adiposity measures with disease-free progression (DFS) and overall survival (OS) in patients with advanced lung cancer receiving immunotherapy, we retrospectively analysed 97 patients (age: 67.5 ± 10.2 years) with lung cancer who were treated with immunotherapy between March 2014 and June 2019. From computed tomography scans, we assessed the radiological measures of skeletal muscle mass, and intramuscular, subcutaneous and visceral adipose tissue at the third lumbar vertebra. Patients were divided into two groups based on specific or median values at baseline and changes throughout treatment. A total number of 96 patients ($99.0\%$) had disease progression (median of 11.3 months) and died (median of 15.4 months) during follow-up. Increases of $10\%$ in intramuscular adipose tissue were significantly associated with DFS (HR: 0.60, $95\%$ CI: 0.38 to 0.95) and OS (HR: 0.60, $95\%$ CI: 0.37 to 0.95), while increases of $10\%$ in subcutaneous adipose tissue were associated with DFS (HR: 0.59, $95\%$ CI: 0.36 to 0.95). These results indicate that, although muscle mass and visceral adipose tissue were not associated with DFS or OS, changes in intramuscular and subcutaneous adipose tissue can predict immunotherapy clinical outcomes in patients with advanced lung cancer.
## 1. Introduction
In recent years, immune checkpoint inhibitors (ICIs) or immunotherapies, such as nivolumab, pembrolizumab and ipilimumab, have evolved rapidly in medical oncology. The utilisation of ICIs has become a key component for managing a variety of malignancies including lung cancer, resulting in an unprecedented survival advantage over standard therapies such as radiation therapy and chemotherapy. While chemotherapy acts directly on cancer cells inhibiting the cell cycle, ICIs are antibodies targeting the programmed death 1 (PD-1), programmed death-ligand (PD-L1) or cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), blocking key regulatory signals that dampen immune responses in the tumour microenvironment. As a result, ICIs counteract immune suppression allowing for tumour reactive T cells to mount an antitumour response utilising the patient’s immune system to target the malignancy [1]. These therapies have shown promising effects in the treatment of lung cancer, as well as a selection of other solid tumours and haematologic malignancies [2,3,4,5].
Several studies have pointed out a significant relationship between immunotherapy and multiple variables on overall survival. Among potential factors, sarcopenia (i.e., progressive and generalised loss of skeletal muscle mass [6]) has emerged as an important prognostic factor in different groups of cancer patients [7]. However, the relationship between sarcopenia and overall survival in patients treated with immunotherapy is still unclear [8,9]. While studies present a significant association between sarcopenia and shorter overall survival [8], others have no significant relationship [9]. For example, in a previous study with small-cell lung cancer patients receiving salvage anti-PD-1 immunotherapy ($$n = 105$$), patients presenting with low levels of muscle mass (i.e., sarcopenic patients) had a ~$200\%$ greater risk of all-cause mortality compared to those with higher levels of muscle mass [8]. In contrast, there was no difference in overall survival between sarcopenic and non-sarcopenic patients with solid metastatic tumours treated with ICIs ($$n = 261$$) [9]. Moreover, the prognostic value of obesity in various malignancies is unknown and remains controversial for the survival of various malignancies [10]. Although previous studies indicated a potential association between body mass index (BMI) and overall survival in advanced cancer patients treated with immunotherapy [11,12], others have demonstrated no significant association between BMI and clinical endpoints [13]. These conflicting results, potentially related to the obesity paradox (i.e., inconsistency concerning the role of obesity on survival), preclude us from understanding the role of fat mass components (i.e., visceral adipose tissue or subcutaneous adipose tissue) on survival in this population [14,15]. For example, while visceral adipose tissue (VAT) secretes various cytokines and cytokine-like factors, which potentially enhance cancer progression [16,17], derived factors from the subcutaneous adipose tissue can increase insulin sensitivity and lipid metabolism potentially resulting in an improved survival [18]. Therefore, although BMI is a much simpler and widely used tool in clinical practice, it does not reflect individual components of body weight such as fat distribution or muscle quantity and quality.
As a result, this study aims to investigate the associations between measures of skeletal muscle mass, intramuscular adipose tissue, subcutaneous adipose tissue, visceral adipose tissue and visceral-to-subcutaneous adipose tissue index and changes throughout treatment with disease progression and overall survival in patients with advanced lung cancer receiving immunotherapy.
## 2.1. Study Population
Retrospective analyses of computerised tomography (CT) imaging and electronic medical record data were performed for all patients treated with immunotherapy who presented to Fiona Stanley Hospital, Western Australia between March 2014 and June 2019. A total of 124 patients with lung cancer were identified on immunotherapy. Patients without CT imaging data were excluded from the final cohort, resulting in a total of 97 patients included for further analyses. Demographic, pathological and survival information were obtained via electronic medical record review. The duration of follow-up was 60 months from the first presentation to the date of death for deceased patients or the date of last documented encounter for surviving patients. Demographic and clinical data such as sex, age, BMI, smoking habits, Eastern Cooperative Oncology Group (ECOG) performance status (PS), distant metastases, cancer type, treatment regimens, progression-free survival (PFS) and overall survival (OS) were collected by self-report and medical records, respectively. Our study was approved by the Hospital Ethics Committee (RGS0000003289) and conducted in compliance with the Helsinki Declaration.
## 2.2. Assessment of Muscle Mass and Fat Mass Parameters
CT scans were at a median of 20 [interquartile range (IQR): 8 to 31] days before commencing immunotherapy treatment. CT scans of the abdomen/pelvis were performed as part of recommended staging pathway and retrieved from the hospital imaging PACS/RIS system (version 6.7.0.6011; Agfa, Mortsel, Belgium). A single 3 mm axial slice through the middle of the L3 vertebral body was retrieved using the sagittal reformatted images with the morphologic L5/S1 junction as reference. These images were imported into SliceOmatic (version 5.0 Rev 12; TomoVision, Magog, QC, Canada) and analysed using the ABACS mode (version 6 Rev-7b; Voronoi Health Analytics, Coquitlam, BC, Canada). If there was an artifact at this level, the nearest artifact-free contiguous slice above or below this level was utilised. A visual colour-coded overlay was reviewed to assess for correct segmentation; any errors were manually corrected using Edit mode and following standard anatomic boundaries. Area measurements (cm2) were obtained by auto-segmentation using the default Hounsfield unit (HU) thresholds and skeletal muscle was determined in the range of −29 to 150 HU, including the skeletal muscle compartment of psoas, paraspinal and abdominal wall musculature. Intramuscular adipose tissue (IMAT) was determined in the range of −190 to −30 HU, visceral adipose tissue (VAT) in the range of −150 to −50 HU and subcutaneous adipose tissue (SAT) in the range of −190 to −30 HU. Visceral-to-subcutaneous adipose tissue ratio was defined as the ratio between VAT and SAT values. Values were normalised to height squared (m2) to derive skeletal muscle, IMAT, VAT, SAT and VAT/SAT indexes.
For further analysis, the skeletal muscle index was analysed as a categorical variable with two levels corresponding to sarcopenia (skeletal muscle index < 43 cm2·m−2 and BMI < 25 kg·m−2, or skeletal muscle index < 53 cm2·m−2 and BMI ≥ 25 kg·m−2) and non-sarcopenia (skeletal muscle index ≥ 43 cm2·m−2 and BMI ≥ 25 kg·m−2, or skeletal muscle index ≥ 53 cm2·m−2 and BMI < 25 kg·m−2), as previously established [19]. Considering the lack of cut-off values for adiposity measures, median values based on our sample were used to categorise patients with higher and lower levels of IMAT, VAT, SAT and VAT/SAT indexes. Relative changes (%) were calculated as indexfollow−upindexbaseline∗$100\%$, with a threshold of $10\%$ utilised to categorise groups with the lowest and highest index changes throughout treatment.
## 2.3. Assessment of Outcomes
The primary outcome was overall survival, defined as deaths as a result of any cause, while disease progression defined as an increase in the size of the tumour by $20\%$ was secondary. Vital causes and causes of death were obtained via electronic medical record review. Follow-up time for overall mortality was calculated as the time from CT scans to death from any cause or the end of follow-up (i.e., 60 months following the time of the first scan).
## 2.4. Statistical Analyses
Analyses were performed using SPSS v.27 (Armonk, IBM Corp., NY, USA) and R Core Team [2013]. Differences in overall mortality between groups based on sarcopenia, IMAT, SAT, VAT and VAT/SAT variables were assessed using the Kaplan–Meier method and the log-rank test. Paired-sample t-test was used to compare values between the first and second CT scans during immunotherapy. The hazard ratios (HRs) for the associations of skeletal muscle index, IMAT, SAT, VAT and VAT/SAT ratio indexes with overall mortality and disease progression were estimated in separate models using Cox proportional hazards regression. Logistic regression was used to determine the impact of body composition components on the occurrence of adverse events ≥ grade 2. Odds ratios (ORs) and $95\%$ CIs were reported. Models were adjusted for age, BMI, cancer type and stage. A p-value of ≤0.05 was considered statistically significant and point estimates were presented with $95\%$ confidence interval.
## 3.1. Patient Characteristics
Patient characteristics are presented in Table 1. Patients were 67.5 ± 10.2 years of age (mean ± standard deviation) with a BMI of 26.1 ± 4.9 kg·m−2. Most patients were overweight/obese ($60.8\%$). The majority of patients had adenocarcinoma ($62.9\%$), followed by squamous cell carcinoma ($29.9\%$). Most patients were treated with second line immunotherapy ($75.3\%$). A total of 81 patients were stage IV ($84.4\%$) and had metastatic disease present in more than two sites ($22.9\%$), bone ($17.1\%$), lymph node ($8.6\%$), liver ($5.7\%$), adrenal ($2.9\%$) and brain ($2.9\%$). In this cohort, the most common immunotherapy agent was Nivolumab ($58.8\%$), followed by Pembrolizumab ($24.7\%$) and Atezolumab ($16.5\%$). A total number of 96 patients had disease progression and died during follow-up ($99.0\%$), with median disease progression of 11.3 (IQR: 4.9 to 20.4) months and 15.4 (IQR: 7.2 to 24.0) months, respectively.
## 3.2. Association of Body Composition Components with Disease Progression and Overall Survival
The median IMAT, SAT, VAT and VAT/SAT ratio index values were 3.85, 55.43, 41.90 and 0.74 cm2·m−2, respectively. Multivariable models indicated no significant associations of sarcopenia, IMAT, SAT, VAT and VAT/SAT ratio indexes at baseline with 5-year disease progression (HR: 0.69–1.25, $$p \leq 0.199$$–0.877) and 5-year overall survival (HR: 0.69–1.34, $$p \leq 0.123$$–0.724) in patients with advanced lung cancer undergoing immunotherapy (Table 2). Kaplan–Meier analyses stratifying patients according to body composition components cut-off values on 5-year disease progression and overall survival are presented in Figure 1 and Figure 2, respectively ($$p \leq 0.061$$–0.606).
A second CT scan was performed in 88 patients as presented in Table 3. Changes in skeletal muscle, IMAT, SAT, VAT and VAT/SAT ratio indexes were not statistically significant following a median time of 15.4 months after the first CT scan (IQR: 7.1 to 26.5 days). Although changes in sarcopenia, VAT and VAT/SAT ratio indexes were not associated with 5-year disease progression (HR: 0.63–1.24, $$p \leq 0.064$$–0.484), >$10\%$ increases in IMAT (HR: 0.60, $95\%$ CI: 0.38 to 0.95) and SAT indexes (HR: 0.59, $95\%$ CI: 0.36 to 0.95) were associated with improved 5-year disease progression ($$p \leq 0.028$$ and 0.029; Table 4). Patients with a >$10\%$ increase in IMAT index presented a median disease progression of 15.9 (IQR: 8.8 to 24.6) months vs. 11.7 (IQR: 5.5 to 19.0) months in patients with a ≤$10\%$ decrease in IMAT index (Kaplan–Meier Log-Rank, χ2 = 4.2, $$p \leq 0.042$$). Likewise, patients who had a >$10\%$ increase in SAT index presented a median disease progression of 16.9 (IQR: 10.8 to 29.6) months vs. 10.2 (IQR: 4.7 to 18.8) months of patients who had a decrease in SAT index (Kaplan–Meier Log-Rank, χ2 = 5.3, $$p \leq 0.022$$). Kaplan–*Meier analysis* on 5-year disease progression is presented in Figure 3.
Regarding overall survival, a >$10\%$ increase in IMAT was associated with improved 5-year overall survival (HR: 0.60, $95\%$ CI: 0.37 to 0.95, $$p \leq 0.031$$; Table 5). Patients who had an increase of $10\%$ in IMAT presented a median overall survival of 17.8 (IQR: 9.6 to 27.9) months vs. 15.5 (IQR: 8.5 to 23.2) months of patients who had a decrease in this outcome (Kaplan–Meier Log-Rank, χ2 = 3.4, $$p \leq 0.067$$). Kaplan–*Meier analysis* on 5-year overall survival is presented in Figure 4.
## 3.3. Association of Body Composition Components with Immune-Related Adverse Events
Thirty-six adverse events ($43.4\%$) were observed during immunotherapy. Of these, a total of 11 grade 2 ($13.3\%$) and 5 grade 3 events ($6.0\%$) were observed. No associations were observed between sarcopenia, IMAT, SAT, VAT and VAT/SAT ratio indexes with high-grade adverse events during immunotherapy (OR: 0.95–2.00, $$p \leq 0.279$$–0.947).
## 4. Discussion
The present study reported the associations between radiological measures of muscle and adipose tissue with disease progression and overall survival in patients with advanced lung cancer receiving immunotherapy. The main findings were: (i) muscle mass index at the time of or during immunotherapy was not associated with disease progression or overall survival; and (ii) patients with lung cancer presenting with increases of $10\%$ in intramuscular and subcutaneous adipose tissue following treatment were at a ~$40\%$ decreased risk of disease progression and overall survival compared to those presenting with lower levels, regardless of age, BMI, cancer type and stage.
The significant association of sarcopenia with poor disease prognosis has been observed in several papers across different types of cancer [20,21,22]. Interestingly, the majority of studies reporting such findings in the field of immunotherapy were undertaken in patients with lung cancer [8,20,23,24,25,26]. As far as we know, this is one of the few studies [25] undertaken in patients with lung cancer mainly with adenocarcinoma and squamous cell carcinoma (~$93\%$ of the sample). Our study indicates that sarcopenia is not significantly associated with disease progression or overall survival in this population with advanced cancer receiving immunotherapy. As observed in our results, the presence of sarcopenia at the start of immunotherapy or a reduction of $10\%$ in skeletal muscle mass index were not associated with disease progression and mortality. However, this result disagrees with previous studies undertaken in patients mainly with non-squamous lung cancer [8,26], which indicate that tumour histology may affect the interaction between sarcopenia and immunotherapy in patients with advanced lung cancer. Nevertheless, lower levels of muscle mass may still affect other important components of immunotherapy such as inflammation, cachexia and physical disability. Consequently, more research is required to elucidate the importance of sarcopenia for other important clinical measures.
The investigation of obesity in immunotherapy is challenging given the confounding factors associated with the obesity paradox [15] and its role in cancer dynamics [27,28]. We observed that intramuscular and subcutaneous adipose tissue could be a predictive marker for improved survival when increased throughout the treatment course. The subcutaneous adipose tissue derives a range of factors such as leptin that could act to improve insulin sensitivity and lipid metabolism [17,18,29]. As a result, this could potentially increase overall survival in this group of patients and represent an important measure during the cancer survivorship. However, the result that an increase in intramuscular adipose tissue could improve survival was unexpected. While previous studies identified a significant association of intramuscular adipose tissue with shorter survival in women with non-metastatic breast cancer [30] and men with hormone-sensitive prostate cancer [31], others did not observe a significant association in metastatic breast cancer [32] or advanced non-small-cell lung cancer treated with immunotherapy [33,34]. Moreover, previous studies have demonstrated that increased intramuscular fat is related to increased frailty and sarcopenia [35] and impaired physical function [36]. In addition, others indicate that increased intramuscular fat is associated with poor survival and increased risk of hospitalisation in older adults or critically ill patients [37,38]. Therefore, the interaction between intramuscular fat and immunotherapy is yet to be determined in this setting.
Interestingly, we also observed an unexpectedly longer 5-year disease progression compared to other large immunotherapy randomised controlled studies [39,40,41,42]. While we observed a median disease progression time of 11.3 months, a range of 3.0 to 5.0 months was reported in these trials [39,40,41,42]. The reasons are likely multifactorial and related to our smaller sample size and retrospective nature compared to these large randomised controlled trials [39,40,41,42]. Additionally, we observed high PDL$1\%$ values in our sample (median of $60\%$) and this may also account to a long disease progression as PDL$1\%$ is associated with improved survival even when using monotherapy agents in advanced non-small-cell lung cancer. Other factors such as mixed cancer stages (~$16\%$ stage III) and treatment line (~$25\%$ first treatment line) are different than these previous immunotherapy trials [39,40,41,42] and may affect disease progression. Our cohort also presented more favourable histology (i.e., adenocarcinoma) and tumour burden may be different as $40\%$ did not present distant metastasis. These factors may play a role in disease progression.
Some limitations are worthy of comment. The retrospective nature of the study and the heterogeneity of CT scans may limit our ability to extrapolate our findings to a large scale. Future studies should undertake prospective models to assess the influence of body composition changes on clinical endpoints, as well as reporting the time of body composition assessment. In addition, the lack of standardisation (i.e., cut-off values), and this is due to variability in underlying technique without clear standardisation, makes comparison difficult to assess radiological measures of muscle and adipose tissue and affects our ability to provide more meaningful recommendations based on our findings. Although the use of body composition is promising, critical and technical studies are required to understand the relationship of sarcopenia with clinical endpoints and to inform specific and tailored interventions in patients treated with immunotherapy. Finally, we could not estimate the impact of sarcopenic obesity in our sample. This is an emergent topic in oncology given the high risk of mortality and severe complications experienced by patients during systemic and surgical cancer treatments. Future studies are required to investigate the impact of sarcopenic obesity in lung cancer patients during immunotherapy and identify clinical management strategies for this population.
## 5. Conclusions
In conclusion, our findings are that rather than muscle mass and visceral adipose tissue, changes in intramuscular and subcutaneous adipose tissue can predict immunotherapy clinical outcomes regardless of age, BMI, cancer type and stage. This result provides new insights into the assessment of body composition in patients with advanced lung cancer undergoing immunotherapy. Consequently, future research should seek to assess a larger sample size of patients undergoing immunotherapy to further elucidate the influence of body composition, specifically monitoring intramuscular and subcutaneous adipose tissues.
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|
---
title: Comparison of Fecal Microbiota Communities between Primiparous and Multiparous
Cows during Non-Pregnancy and Pregnancy
authors:
- Xianbo Jia
- Yang He
- Zhe Kang
- Shiyi Chen
- Wenqiang Sun
- Jie Wang
- Songjia Lai
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000135
doi: 10.3390/ani13050869
license: CC BY 4.0
---
# Comparison of Fecal Microbiota Communities between Primiparous and Multiparous Cows during Non-Pregnancy and Pregnancy
## Abstract
### Simple Summary
An imbalance of the gut microbiota composition may lead to several reproductive disorders and physiological diseases during pregnancy. This study investigates the fecal microbiome composition between primiparous and multiparous cows during non-pregnancy and pregnancy to analyze the host-microbial balance at different stages. The results indicate that host-microbial interactions promote adaptation to pregnancy and will benefit the development of probiotics or fecal transplantation for treating dysbiosis and preventing disease development during pregnancy.
### Abstract
Imbalances in the gut microbiota composition may lead to several reproductive disorders and diseases during pregnancy. This study investigates the fecal microbiome composition between primiparous and multiparous cows during non-pregnancy and pregnancy to analyze the host-microbial balance at different stages. The fecal samples obtained from six cows before their first pregnancy (BG), six cows during their first pregnancy (FT), six open cows with more than three lactations (DCNP), and six pregnant cows with more than three lactations (DCP) were subjected to 16S rRNA sequencing, and a differential analysis of the fecal microbiota composition was performed. The three most abundant phyla in fecal microbiota were Firmicutes ($48.68\%$), Bacteroidetes ($34.45\%$), and Euryarchaeota ($15.42\%$). There are 11 genera with more than $1.0\%$ abundance at the genus level. Both alpha diversity and beta diversity showed significant differences among the four groups ($p \leq 0.05$). Further, primiparous women were associated with a profound alteration of the fecal microbiota. The most representative taxa included Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG_003, Christensenellaceae_R_7_group, Ruminococcaceae UCG-005, Ruminococcaceae UCG-013, Ruminococcaceae UCG-014, Methanobrevibacter, and [Eubacterium] coprostanoligenes group, which were associated with energy metabolism and inflammation. The findings indicate that host-microbial interactions promote adaptation to pregnancy and will benefit the development of probiotics or fecal transplantation for treating dysbiosis and preventing disease development during pregnancy.
## 1. Introduction
Pregnancy is a wonderful and complex physiological process. In order to adapt to the growth and development of the fetus, drastic changes occur in maternal hormones, immunity, and metabolism before and after pregnancy. For mammals, progesterone (P4), estradioal (E2), follicle stimulating hormone (FSH), luteinizing hormone (LH), and Prolactin (PRL) are the main reproductive hormones to maintain and evaluate maternal pregnancy [1]. Growth hormone, thyroid hormone, and sex hormones could also change with maternal pregnancy [2]. The maternal immune system undergoes significant adaptations during pregnancy to avoid harmful immune responses against the fetus and to protect the mother and her future baby from pathogens [3]. For example, the number of T cells during pregnancy is lower than before pregnancy [4]. More nutrients are needed to be stored and consumed during pregnancy to meet the nutritional demands of the mother and fetus. Maternal metabolism changes to meet the nutritional requirements during pregnancy, the most obvious being the decrease in insulin sensitivity [5,6]. Additionally, compared to multiparous women, primiparous women have more exaggerated physiological responses, resulting in higher weight gain and body fat gain than that of multiparous women during pregnancy [7]. There are also many differences between primiparous and multiparous cows, including productivity, reproductive ability, energy balance, immune, metabolic, and hormonal responses [8,9].
Gut microbiota can produce a variety of nutrients, such as amino acids, fatty acids, and vitamins, which play an important role in regulating host metabolism, energy balance, and immune response [10,11,12,13]. With the changes of maternal hormones, immunity and metabolism during pregnancy, the composition and abundance of gut microbiota also shifted. The relative abundance of 21 genera of gut microbiota showed significant differences between non-pregnant and pregnant mice fed a standard diet. There were 4 abundant genera (present at greater than $1\%$) significantly increased and 5 rare taxa (present at lower than $0.5\%$) reduced during pregnancy compared to non-pregnant mice [14]. For dairy cows, the fecal microbial communities change dramatically in bacterial abundance at different taxonomic levels among the 12 distinctly defined production stages in a modern dairy farm, especially between virgin cows and parous cows [13]. Information on host-microbial interactions during pregnancy is emerging [15]. Recent studies showed that gut microbiota can impact the synthesis and metabolism of a variety of substances during pregnancy, regulating body weight, blood pressure, blood sugar, blood lipids, and other physiological indexes, and even leading to some pregnancy complications [16,17,18]. Parity has also been identified as one of the key determinants of the maternal microbiome during pregnancy. The difference in microbiome trajectories among different parities was significant in sows, with the greatest difference between zero parity and low parity animals. It was suggested that there are dramatic differences in the microbial trajectories of primiparous and multiparous animals [19]. Compared to multiparous sows, primiparous sows had a lower gut microbiota richness and evenness during the periparturient period [20]. Primiparous cows have different uterine and rumen microbiome compositions compared to multiparous cows [21,22]. However, it is still unclear if parity impacts the maternal cow’s gut microbiome during both non-pregnancy and pregnancy.
In this study, the gut microbiome composition was investigated in fecal samples from primiparous and multiparous cows during non-pregnancy and pregnancy. It confirmed that there is an inherent shift in gut microbiota associated with pregnancy and differences in gut microbiota composition between primiparous and multiparous animals. The results will help develop strategies to improve the reproductive management of cows.
## 2.1. Ethics Statement
The collection of biological samples and experimental procedures carried out in this study were approved by the Institutional Animal Care and Use Committee in the College of Animal Science and Technology, Sichuan Agricultural University, China (DKY20210306).
## 2.2. Sample Collection
A total of 24 healthy Holstein cows were selected from one dairy herd under the same conditions in southwestern China, with the same feeding processes, similar body conditions, and similar body weight. According to their reproductive stages, the cows were divided into four groups: the cows before their first pregnancy (13 months, $$n = 6$$, BG); at their first pregnancy (the 4th month of pregnancy, 18 months, $$n = 6$$, FT); open cows with more than three lactations (30 days after parturition, 57 months, $$n = 6$$, DCNP); and pregnancy cows with more than three lactations (the 4th month of pregnancy, 60 months, $$n = 6$$, DCP). Animals were fed the total mixed ration (TMR) made according to NRC [2012] with the same feed raw material. None of the cows had received antibiotics in the last 3 months. All 24 fecal samples were obtained once from cow rectum content on the same day, transferred to separate sterilized 2 mL tubes, and stored immediately in liquid nitrogen. All samples were then transported to the laboratory and stored at −80 °C for further analysis.
## 2.3. DNA Extraction, PCR Amplification and Gene Sequencing
Total genome DNA was extracted from fecal samples, the negative control (DNA free water), and the positive control (16S Universal E29), using a BIOMICS DNA Microprep Kit (Zymo Research, D4301, Irvine, CA, USA) according to the manufacturer’s instructions. DNA concentration and purity were tested on $0.8\%$ agarose gels. DNA yield was detected with a Tecan Infinite 200 PRO fluorescent reader (Tecan Systems Inc., San Jose, CA, USA). The 16S rRNA amplification covering the variable region V4-V5 was carried out using the primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 915R (5′-GTGCTCCCCCGCCAATTCCT-3′) by a Thermal Cycler PCR system (Gene Amp 9700, ABI, Foster City, CA, USA). PCRs were performed in triplicate in a 25 µL mixture. The PCR products were diluted six times, quantified with electrophoresis on $2\%$ agarose gel, and then purified by the Zymoclean Gel Recovery Kit (Zymo Research, D4008, Irvine, CA, USA). About 100 ng of DNA were used for library preparation. The library was prepared using the TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA), followed by quality evaluation on the Qubit@ 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) and Agilent Bioanalyzer 2100 system (Agilent, Santa Clara, CA, USA). Library was finally paired-end sequenced (2 × 300) on an Illumina MiSeq PE300 platform (Illumina, San Diego, CA, USA).
## 2.4. Data Analysis
The raw fastq files were merged using FLASH [23]. The raw tags were analyzed using the QIIME (v1.9.0) pipeline [24]. All tags were quality filtered. Sequences shorter than 200 nt with an average quality value less than 25, and those containing two or more ambiguous bases, were discarded. The clean tags were then mapped to the Gold database (http://drive5.com/uchime/uchime_download.html (accessed on 5 May 2021)) using UCHIME algorithm, followed by removal of the chimera sequences to identify the effective tags [25]. The operational taxonomic units (OTUs) table was created at $97\%$ similarity using the UPARSE pipeline [26]. Representative sequences from each OTU were aligned to 16S reference sequences with PyNAST [27]. The phylogenetic trees were drawn using FastTree [28]. Annotation analysis was performed using the UCLUST taxonomy and the SILVA database [29,30].
The abundance of OTUs was normalized using a standard sequence number corresponding to the sample with the least sequence. The comparison of OTU numbers used a one-way analysis of variance (one-way ANOVA), followed by the Bonferroni multiple comparisons test. The alpha diversity was calculated to analyze the complexity of species diversity in the sample, including observed species, Chao1, Shannon, Simpson, coverage, and Faith’s PD. The beta diversity, weighted Unifrac and unweighted UniFrac, was calculated to evaluate the differences of samples in species complexity. Principal coordinate analysis (PCoA) was used to visualize differences in bacterial community composition among groups. The linear discriminant analysis coupled with effect size (LEfSe) was performed to identify the differentially abundant taxa between different groups. Pairwise comparisons were made using metagenomSeq.
## 3.1. Sequencing Information
In order to evaluate the effect of reproductive status on the cow fecal microbiota, the V4–V5 hypervariable regions of the 16S rRNA gene were sequenced in the microbial communities of 24 samples. A total of 705,988 raw PE reads were generated from these 24 samples (average: 29,416 ± 4914, range: 21,956–36,765). After quality control, 632,192 effective tags were obtained from 24 samples (average: 26,341 ± 4408, range: 19,472–32,926), with an average of 407.67 ± 0.92 bps per tag after the merging of overlapping paired-reads, quality filtering, and removing of chimeric sequences. By the $97\%$ sequence similarity, 6842 OTUs were computationally constructed with 1727.38 ± 405.39 (range: 999–2788) as the mean number of OTUs per sample, and the mean number of OTU in DCNP group was significantly lower than that of BG and FT group ($p \leq 0.01$) (Figure 1).
## 3.2. Microbial Ecology of the Fecal Microbiome
These 6842 OTUs taxonomically assigned to microbial 2 Kingdom, 17 phyla, 25 classes, 38 orders, 67 families, 168 genera, and 117 species. According to OTUs’ number, the average abundance of each group at different category levels was evaluated (Figure 2). The fecal microbial communities were dominated by bacteria ($84.58\%$), and archaea were only $15.42\%$ abundant. The most abundant phyla across all 24 metagenomic libraries were Firmicutes ($48.68\%$), followed by Bacteroidetes ($34.45\%$), and Euryarchaeota ($15.42\%$). Other less abundant phyla were Spirochaetes ($0.85\%$), Tenericutes ($0.42\%$), Proteobacteria ($0.07\%$), Actinobacteria ($0.06\%$), Fibrobacteres ($0.02\%$), Cyanobacteria ($0.02\%$), and Planctomycetes ($0.01\%$) (Figure 3). At the genus level, there are 11 genera with more than $1.0\%$ abundance, including Ruminococcaceae UCG-005 ($21.91\%$), Methanobrevibacter ($13.28\%$), Rikenellaceae RC9 gut group ($10.13\%$), [Eubacterium] coprostanoligenes group ($7.10\%$), Prevotellaceae UCG-004 ($6.47\%$), Alistipes ($5.52\%$), Ruminococcaceae UCG-013 ($4.89\%$), Prevotellaceae UCG-003 ($4.61\%$), Ruminococcaceae UCG-014 ($1.78\%$), Methanocorpusculum ($1.42\%$), Christensenellaceae R-7 group ($1.12\%$) (Figure 4).
## 3.3. Microbial Diversity of the Fecal Microbiome
The alpha diversity indexes, including observed species, Chao1, Shannon, Simpson, coverage, and Faith’s PD, for four groups were calculated to estimate species richness and diversity (Figure 5). Compared to the BG and FT groups, the observable species, Chao1, and Faith’s PD were significantly lower, and coverage was significantly higher in the DCNP group ($p \leq 0.05$, Kruskal–Wallis test), but without statistical significance in the DCP group ($p \leq 0.05$, Kruskal–Wallis test). Further, no statistically significant difference was shown among the four groups in Shannon and Simpson ($p \leq 0.05$, Kruskal–Wallis test).
Based on the Jaccard and Bray–Curtis methods, principal coordinated analysis (PCoA) of beta diversity was further used to analyze compositional differences in fecal microbiota among four groups (Figure 6). The samples in the BG, FT, and DCP groups were clustered together according to their particular groups, while the samples in the DCNP group were spread out. The samples in the BG and FT groups tended to cluster together in accordance with PCoA results. Both Jaccard and Bray-Curtis distances showed significant differences among the four groups (ANOSIM, $p \leq 0.01$), except between groups DCP vs. DCNP (ANOSIM, $p \leq 0.05$).
## 3.4. Microbial Taxonomy and Function Analysis
Linear discriminant analysis effect size (LEfSe) was used to discover the differential microbiota and estimate their effect size. Based on LEfSe, it restrictively analyzed the successfully annotated species and detected 60 taxa significantly different in abundance among four groups. There were 7 taxa significantly more abundant in the BG group, 17 in the FT group, 8 in the DCNP group, and 28 in the DCP group (Figure 7). The most representative taxa were Rikenellaceae and Rikenellaceae_RC9_gut_group in the DCP group, Prevotellaceae and Prevotellaceae_UCG_003 in the FT group, Christensenellaceae_R_7_group in the DCNP group, and Firmicutes, Clostridia, Clostridiales, and Ruminococcaceae in the BG group.
The metagenomeSeq was further used to compare the abundance of OTUs between each group. The abundance of 4, 12, and 23 OTUs was significantly increased, while that of 1, 2, and 17 OTUs was significantly reduced in the FT, DCNP, and DCP groups compared with the BG group, respectively. In the three comparison groups, the abundance of six common genera (>$1\%$), namely Prevotellaceae UCG-003, Ruminococcaceae UCG-013, [Eubacterium] coprostanoligenes group, Rikenellaceae RC9 gut group, Methanobrevibacter, and Ruminococcaceae UCG-005, was identified as a significant difference (Figure 8). There were 16 and 21 OTUs that were significantly increased, and 2 and 19 OTUs that were significantly reduced, in the DCNP and DCP groups compared with the FT group, respectively. A total of 8 common genera, such as the Christensenellaceae R-7 group, Ruminococcaceae UCG-014, Prevotellaceae UCG-003, Ruminococcaceae UCG-013, [Eubacterium] coprostanoligenes group, Rikenellaceae RC9 gut group, Methanobrevibacter, and Ruminococcaceae UCG-005, were observed to have significant differences (Figure 8). Furthermore, in the DCP group, the abundance of 4 OTUs decreased compared with the DCNP group. The relative abundance of 2 common genera, Methanobrevibacter and Prevotellaceae UCG-003, in the DCNP group was higher than that in the DCP group.
## 4. Discussion
The reproductive efficiency and health of cows have always been priorities. The gut microbiota composition plays an important role in the reproductive performance throughout a female’s lifetime. In humans, the gut microbiome has been considered to affect every stage and level of female reproduction, including follicle and oocyte maturation in the ovary, fertilization and embryo migration, implantation, the whole pregnancy, and parturition [31,32,33,34]. The gut microbial communities can influence reproductive success from mate choice to healthy pregnancy and successfully producing offspring in animals [35,36]. Recent studies reported that bovine vaginal and fecal microbiome associated with differential pregnancy outcomes [37,38]. The fecal microbiome predicted pregnancy with a higher accuracy than that of the vaginal microbiome [38]. In this study, the fecal microbiota were investigated in 4 different reproductive stages and revealed the dramatic changes in fecal microbiota diversity and composition among 4 groups using the sequencing of the 16S rRNA gene.
In this study, Firmicutes, Bacteroidetes, and Euryarchaeota were the three most dominant phyla, and Ruminococcaceae UCG-005, Methanobrevibacter, and Rikenellaceae RC9 gut group were the three most dominant genera in the cow fecal samples. They were consistent with several earlier studies [39]. In previous studies, Bacteroidetes (51.6~$59.74\%$) and Firmicutes (27.6~$38.74\%$) together comprised up to 81.6~$93.20\%$ of the cow fecal bacterial abundance [13,40,41]. The phylum Euryarchaeota was predominant within the Archaea and accounted for around $0.25\%$ of the cow fecal microbiota abundance [41,42]. Ruminococcaceae UCG-005, Methanobrevibacter, and Rikenellaceae RC9 gut groups predominate in the Firmicutes, Euryarchaeota, and Bacteroidetes phyla, respectively. Ruminococcaceae UCG-005 and Rikenellaceae RC9 gut group usually had a relative abundance >$8\%$ of fecal microbiota in dairy cows. The genus Methanobrevibacter comprised more than $80\%$ of the phylum Euryarchaeota in cow fecal Archaea [13,43].
The age and pregnancy are two important factors contributing to the species richness and diversity of fecal microbiota. The alpha diversity index, observed species, Chao 1, coverage, and Faith’s PD were significantly different among the BG, FT, and DCNP groups in this study. However, the cluster among four groups was significant, separating BG and FT groups from DCNP and DCP groups by PCoA based on Jaccard and Bray–Curtis distances. These also showed that the greatest differences in microbiome trajectories occurred between nulliparous and primiparous animals [19]. Nulliparous animals had higher gut microbial diversity than that of primiparous animals, and pregnancy could increase gut microbial diversity [19,20]. The effect of age is more related to calving. The increase in alpha diversity during pregnancy could be due to an increase in nutrient requirements during lactation. The first birth is the most important physiological change in a cow’s life, and pregnancy increases metabolism.
In order to further identify important taxa differed among groups, LEfse and metagenomeSeq analyses were conducted. LEfse analysis is helpful to discover the important differential taxa (biomarkers) and estimate their effect sizes. The LEfSe analysis revealed that the most differentially abundant taxa were in DCP, followed by FT, DCNP, and BG. The metagenomeSeq analyses showed that the comparisons with the most significant differences in microbial taxa are BG vs. DCP and FT vs. DCP, followed by FT vs. DCNP, BG vs. DCNP, BG vs. FT, and DCNP vs. DCP. These suggested that parturition experience is one of the most important factors to impact cattle gut microbiome trajectory. Previous study also reported that the most difference in microbiome trajectory occurred between nulliparous and low parity sows [19]. There was significant difference between multiparous and primiparous cows on vaginal and uterine microbiotas [44,45]. The most representative taxa were associated with energy metabolism and inflammation. Mice fed with high-fat diet increased the richness of gut microbial Rikenellaceae_RC9_gut_group. The high-fat diet also increased the risks of intestinal pathogen colonization and inflammation [46]. Supplementation of probiotics increased the relative abundance of Prevotellaceae_UCG_003, which improved the energy status of the beef steers [47]. Fibrolytic enzyme increased the relative abundance of Christensenellaceae_R_7_group, which improve the average daily gain and feed conversion ratio of lambs [48]. The ruminococcaceae family is the predominant acetogen in the cattle rumen, which is related to cellulose and hemicellulose degradation [49]. The carbohydrate resource and the fiber decomposition process in diet contribute to the different abundances of Ruminococcaceae UCG-005, Ruminococcaceae UCG-013, Ruminococcaceae UCG-014, and other Ruminococcaceae in cattle feces [49,50]. Methanobrevibacter is another common inhabitant of the cattle rumen, which can reduce CO2 with H2 to form methane [51,52].*The serum* cholesterol concentration tended to be lower after feeding Eubacterium coprostanoligenes to germ-free mice [53]. Thus, gut microbes are involved in changes in energy intake and immunity during cattle adaption to pregnancy.
## 5. Conclusions
In conclusion, this study investigated the difference in fecal bacterial communities between primiparous and multiparous cows during non-pregnancy and pregnancy. The results revealed that pregnancy increased the relative abundance and diversity of fecal microbiota, while aging reduced those traits. In addition, primiparous were related to a profound alteration of the fecal microbiota. The most representative taxa included Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG_003, Christensenellaceae_R_7_group, Ruminococcaceae UCG-005, Ruminococcaceae UCG-013, Ruminococcaceae UCG-014, Methanobrevibacter, and [Eubacterium] coprostanoligenes group, which were associated with energy metabolism and inflammation. In the future, further functional studies will be able to treat dysbiosis and prevent disease development during pregnancy by using probiotics or fecal transplantation.
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|
---
title: Bamboo Plant Part Preference Affects the Nutrients Digestibility and Intestinal
Microbiota of Geriatric Giant Pandas
authors:
- Ying Yao
- Wenjia Zhao
- Guilin Xiang
- Ruiqing Lv
- Yanpeng Dong
- Honglin Yan
- Mingxi Li
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000146
doi: 10.3390/ani13050844
license: CC BY 4.0
---
# Bamboo Plant Part Preference Affects the Nutrients Digestibility and Intestinal Microbiota of Geriatric Giant Pandas
## Abstract
### Simple Summary
Bamboo part preference and a panda’s age have been shown to shift the gut microbiota composition of the giant panda, thus eliciting changes in their nutrient utilization capacity. The present study compared the differences in nutrient digestibility and fecal microbiota composition between adult and geriatric captive giant pandas when fed exclusively with a diet comprising of either bamboo shoots or leaves. Bamboo part preference exerted a significant effect on nutrient digestibility and fecal microbiota composition in both adult and aged giant pandas. Bamboo part dominated over age in shaping the nutrient digestibility and gut microbiota composition of giant pandas.
### Abstract
Bamboo part preference plays a critical role in influencing the nutrient utilization and gastrointestinal microbiota composition of captive giant pandas. However, the effects of bamboo part consumption on the nutrient digestibility and gut microbiome of geriatric giant pandas remain unknown. A total of 11 adult and 11 aged captive giant pandas were provided with bamboo shoots or bamboo leaves in the respective single-bamboo-part consumption period, and the nutrient digestibility and fecal microbiota of both adult and aged giant pandas in each period were evaluated. Bamboo shoot ingestion increased the crude protein digestibility and decreased the crude fiber digestibility of both age groups. The fecal microbiome of the bamboo shoot-fed giant pandas exhibited greater alpha diversity indices and significantly different beta diversity index than the bamboo leaf-fed counterparts regardless of age. Bamboo shoot feeding significantly changed the relative abundance of predominant taxa at both phylum and genus levels in adult and geriatric giant pandas. Bamboo shoot-enriched genera were positively correlated with crude protein digestibility and negatively correlated with crude fiber digestibility. Taken together, these results suggest that bamboo part consumption dominates over age in affecting the nutrient digestibility and gut microbiota composition of giant pandas.
## 1. Introduction
The giant panda (Ailuropoda melanoleuca) is a highly specialized herbivorous species of ursid that consumes bamboo as the primary and almost exclusive diet. Unlike most herbivores, the giant panda has no apparent internal gastrointestinal adaptions to its bamboo-dominated diet, and exhibits a short digestive tract with a rapid passage of digesta, which is similar to the gastrointestinal tract morphology of most carnivores [1]. The extremely high amount of bamboo consumption each day and low energy expenditure can partly explain how giant pandas persist solely on bamboo, a high fibrous plant with low nutritional value and digestibility [2]. However, the giant panda has been shown to lack homologs of the enzymes needed for the degradation of structural carbohydrates, the key component of bamboo [3]. It has thus been believed that the utilization and extraction of nutrients from the bamboo diet largely depends on the gut microbiome of the giant panda, as the giant panda gut microbiome has been found to exhibit a high abundance of putative genes involved in carbohydrate degradation, suggesting high utilization potential of structural polysaccharides [1,4].
Both wild and captive pandas exhibit seasonal changes in bamboo part preference, with shoots consumed in spring and summer, leaves in autumn and winter, and culms in the transition period, namely later winter and early spring [5,6]. Dietary changes are an important factor influencing the composition and function of the gut microbiome [7]. Evidences have been accumulated to show the giant panda’s gut microbiota are shaped by the seasonally-driven shifts in bamboo part preference, as the nutrient content in different parts of bamboo varies significantly, with higher cellulose, hemicellulose, and starch, as well as lower proteins, in the leaves and culms than in shoots [3,8,9]. Gut microbiota has been shown to significantly affect the nutrient utilization capacity and health status of the host [10]. In captive giant pandas, the apparent digestibility of bamboo parts differed significantly, resulting in different degrees of nutrient retention used by gut microbes in the hindgut [8]. Therefore, the changes in gut microbiome elicited by different bamboo part consumption would significantly affect the nutrient digestibility of the giant pandas.
Aging is an inevitable biological process in an organism that leads to an increased risk of many diseases [11]. In terms of longevity, captive giant pandas generally have a lifespan of almost 30 years, and individuals older than 20 are considered to be “geriatric” because the reproduction process of the giant panda generally ends after this age [12]. Aging has been proven to significantly shape the structure of gut microbiota and affect the immune and metabolic functions of giant pandas [13]. Likewise, impaired digestive function and higher risk of gastrointestinal disorders have been recognized in aged giant pandas [12]. The seasonal variation in bamboo part consumption has been shown to significantly affect the nutrient digestibility of captive giant pandas [6]. However, little is known about the effects of bamboo part preference on aged giant pandas, especially the changes of gut microbiome and nutrients digestibility. To address this issue, the nutrients digestibility and gut microbiota composition were compared between adult and older captive giant pandas when fed exclusively with a diet comprising of either shoots or leaves.
## 2.1. Ethics Statement
All protocols for the present study that involved animal care and treatment were approved by the Institutional Animal Care and Use Committee of Chengdu Research Base of Giant Panda Breeding (No. 2020010).
## 2.2. Study Subjects and Animal Husbandry
A total of 11 adult (aged 9–17 years, average age was 13) and 11 geriatric (aged 20–37 years, average age was 25) captive giant pandas were the subjects of the present study. All subjects were singly housed at the Chengdu Research Base of Giant Panda Breeding (CRBGPB, Chengdu, Sichuan, China), and all were considered healthy and were not under any medical treatment during the study period. The ambient temperature was maintained at 15 °C–22 °C, and the air humidity was 65–$75\%$. All giant pandas were fed according to the normal husbandry practices of the CRBGPB as described in Wang et al. [ 6]. Bamboo was provided to giant pandas three times each day (08:00, 14:00, and 20:00). In the present study, giant pandas were given free access to bamboo and water, and the specific bamboo part was offered according to the seasonal shifts. In CRBGPB, bamboo shoots of *Phyllostachys nidularia* Munro were consumed by pandas in autumn and bamboo leaves of *Bashania fargesii* were provided to pandas in winter. In addition to the supply of bamboo parts, dietary supplements were provided daily and of the same mass to all subjects. In this study, both adult and geriatric pandas were provided with bamboo shoots for 3 months and bamboo leaves for 3 months: bamboo shoot-fed adult (AS), bamboo leaf-fed adult (AL), bamboo shoot-fed old (OS), and bamboo leaf-fed old (OL) giant pandas.
## 2.3. Sample Collection
At the last day of each period during which pandas were offered the corresponding bamboo part, fecal samples were collected from each giant panda. For each panda, the spontaneous excreted fecal samples were collected within 10 min of defecation after the feeding in the morning. To avoid contamination, samples were collected only after the floor was cleaned and disinfected. Furthermore, the outer layer of feces that contacted the floor was discarded and only fecal parts that did not touch the floor were kept and stored at −80 °C pending further analysis.
## 2.4. Apparent Nutrient Digestibility Measurement
During the last three days of each single-bamboo-part consumption period, the apparent nutrient digestibility of the corresponding bamboo part was determined in both adult and older giant pandas. The amount of ingested food and excreted feces of each individual giant panda was weighed. The bamboo samples that pandas consumed and fecal samples were collected twice a day, weighed, and immediately stored at 4 °C. During the next day, corresponding proportions of fecal samples were kept and mixed according to the amount of daily excreted feces. Finally, about 1 kg of bamboo leaves and 1.5 kg of the corresponding fecal samples, as well as 5 kg of bamboo shoots and the corresponding fecal samples, were kept at −80 °C for long-term storage. The bamboo and fecal samples were dried, ground, and sieved through a 0.45 mm sieve, then mixed, sampled, and stored at −20 °C. The chemical components of the bamboo and fecal samples were determined according to the AOAC analysis method [14]. An oven drying method was adopted to measure the dry matter (DM) content, the Kjeldahl method was used to determine the crude protein (CP) content, the Soxhlet extraction method was applied to evaluate the ether extract (EE) content, the continuous extraction of samples by dilute acids and bases was used to measure crude fiber (CF), and lastly, the oxygen bomb calorimeter calorimetric method was used to analyze the gross energy (GE) concentration of bamboo and fecal samples.
The calculation equation of apparent nutrient digestibility was as follows:Apparent digestibility= Daily intake × Nutrient substance (Bamboo)− Daily feces × Nutrient substance (Feces)Daily intake × Nutrient substance (Bamboo)
## 2.5. Genomic DNA Extraction from Feces and Sequencing
The genomic DNA of each fecal sample was isolated with the QIAamp Fast DNA Stool Mini Kits (Qiagen, Beijing, China) following the manufacturer’s instructions. The integrity and concentration of obtained DNA samples were assessed visually by agarose gel electrophoresis or measured using a NanoDrop ND-1000 device. Sterilized water was used as a negative control sample, and was included in the DNA isolation process, which showed no detectable PCR product. The common primers 515F and 806R were used to amplify the V4 region of the bacterial 16S rRNA gene, and the resulting PCR products were pooled and purified by using the Agencourt AMPureXP beads (Beckman Coulter, Brea, CA, USA) along with the MinElute PCR Purification Kit (Qiagen, Beijing, China). After pooling and purification, these amplicons were then used to construct Illumina libraries with the Ovation Rapid DR Multiplex System 1-96 (NuGEN, San Carlos, CA, USA). All of the sample libraries were sequenced on the Illumina MiSeq platform with a PE250 sequencing strategy (Novogene, Beijing, China). The raw data were deposited in the NCBI BioProject database with the accession number PRJNA916390.
## 2.6. Fecal Microbiota Analysis
The raw *Illumina data* were processed by Mothur software v1.3.6 (MI, USA) [15]. The high-quality paired-end sequences, which were obtained by removing the primer and barcode sequence, and also the low-quality reads, were assembled into tags with overlapping relationships. The library size of each sample was randomly subsampled into the minimum sequencing depth to minimize the biases caused by sequencing depth between samples. The USEARCH v7.0.1001 [16] was applied to cluster tags into OTUs based on $97\%$ cut-off. The representative sequence of each OTU cluster was used for taxonomic classification against the Ribosomal Database Project database with RDP v2.6 [17]. The OTU abundance table and the OTU taxonomic assignment table laid out from the Mothur software were processed with R studio v3.4.1 [18] to calculate alpha diversity indexes of communities, as well as the beta diversity index and the Bray–Curtis distance [19]. The structural dissimilarity of the microbiota communities across the samples were visualized by non-metric multidimensional scaling (NMDS) analysis based on the Bray–Curtis distance matrix.
## 2.7. Statistical Analysis
For nutrient digestibility parameters, the statistical analysis was performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Giant panda was considered the experimental unit for all analyses ($$n = 11$$ per treatment), and the results were expressed as means and SEM. The main effects of bamboo part and age, and the interaction between bamboo part and age were determined via two-way ANOVA. After transforming non-normal distributed data to approximately conform to normality by SAS software, the alpha indexes [20] including Observed species, Chao 1, Shannon and Simpson index as well as the relative abundance of top 10 phyla and top 30 genera were tested for significance with the one-way ANOVA, followed by Tukey’s test to evaluate the differences between treatments. Data were presented as mean ± SE. The intragroup statistic differences in beta diversity based on the Bray–Curtis distance were assessed using the one-way ANOSIM test with 10,000 permutations. Spearman’s correlation between the gut microbiota composition and nutrient digestibility parameters were calculated by the ggcor package within R software version 3.6.1 [18]. Only correlations with Spearman’s coefficient r > 0.5 and $p \leq 0.05$ were used to generate the network graph, which was visualized and manipulated by Gephi version 9.2 [21]. The differences were considered statistically significant when the p values were less than 0.05.
## 3.1. Bamboo Part and Age Affect Apparent Nutrient Digestibility of Giant Pandas
A significant effect of age ($F = 4.86$, df = 1, $$p \leq 0.04$$) on the dietary gross energy utilization efficiency was observed showing that aged giant pandas had weaker energy extraction capacity from their diet compared to their younger counterparts (Table 1). There was a significant effect of bamboo part (($F = 203.23$, df = 1, $p \leq 0.001$) for crude protein digestibility, indicating that bamboo shoot ingestion increased the crude protein digestibility of both adult and aged giant pandas (Table 1). There was a significant effect of bamboo part ($F = 13.65$, df = 1, $$p \leq 0.001$$) and age ($F = 11.44$, df = 1, $$p \leq 0.002$$) as well as a significant bamboo part × age interaction ($p \leq 0.05$) for ether extract digestibility (Table 1). This demonstrates that bamboo shoot feeding increased ether extract digestibility of aged rather than adult giant pandas when compared to bamboo leaf ingestion. Results indicated that bamboo shoot-fed giant pandas had lower crude fiber digestibility than bamboo leaf-fed counterparts ($F = 16.06$, df = 1, $p \leq 0.001$, Table 1).
## 3.2. Bamboo Part and Age Affect Fecal Microbial Profiles of Giant Pandas
After the pre-processing of raw reads, high-quality tags were generated from all samples ranging from 57,136 to 91,531, which were subsampled to 57,136 to avoid the bias induced by the sequencing depth between samples. A total of 3,728 OTUs were obtained by clustering these tags at a $97\%$ similarity cutoff. The fecal microbiome of the bamboo shoot-fed giant pandas exhibited greater observed species ($F = 4.65$, df = 3, $$p \leq 0.01$$), Chao1 ($F = 56.08$, df = 3, $p \leq 0.001$), Shannon ($F = 62.11$, df = 3, $p \leq 0.001$), and Simpson index ($F = 5.01$, df = 3, $$p \leq 0.005$$) values than the bamboo leaf-fed counterparts regardless of age (Figure 1). The inter-group Bray–Curtis distance was significantly higher than the intra-group when giant pandas were fed with different bamboo parts independent of age ($F = 25.49$, df = 5, $p \leq 0.001$), otherwise there was no difference in the inter-group and intra-group Bray–Curtis distances (Figure 2A). The NMDS-based map also showed that the fecal microbiome of giant pandas could be sorted into two clusters by bamboo part consumption rather than age (Figure 2B), indicating the dominant role of bamboo part consumption in shaping the fecal microbiome of both adult and old giant pandas.
The predominant phyla in feces of AS, AL, OS, and OL pandas were Firmicutes and Proteobacteria (Figure 3A, Table S1). Bamboo shoot feeding was found to decrease the relative abundance of Firmicutes and increase the relative abundance of Proteobacteria in adult giant pandas rather than old giant pandas compared to bamboo leaf consumption (Figure 3B). Additionally, bamboo shoot feeding increased the relative abundance of Acidobacteriota, Actinobacteria, and Chloroflexi as well as decreased the relative abundance of Bacteroidetes in both adult and old giant pandas compared to bamboo leaf feeding (Figure 3C). At the genus level, Escherichia-Shigella and Clostridium_sensu_stricto_1 were the two most abundant bacteria in feces of all four groups (Figure 4A, Table S2). The relative abundance of Cellulosilyticum, Citrobacter, Enterococcus, Lactococcus, Pantoea, Ralstonia, Raoultella, Acinetobacter, Bradyrhizobium, Leuconostoc, Massilia, and Providenicia were higher in feces of bamboo shoot-feeding giant pandas than the bamboo leaf-feeding group regardless of age (Figure 4B,C). Bamboo shoot intake was found to decrease the relative abundance of Streptococcus, Lachnospiraceae_NK4A136_group, and Terrisporobacter in feces of both adult and old giant pandas compared to bamboo leaf consumption (Figure 4B,C). Bamboo shoot feeding increased the relative abundance of *Helicobacter and* decreased the relative abundance of Clostridium_sensu_stricto_1 in feces of adult giant pandas rather than the old group (Figure 4B,C). Compared to bamboo leaf consumption, the decreased abundance of Escherichia-Shigella and increased abundance of Turicibacter, Hafnia-Obesumbacterium, and Weissella were observed in bamboo shoot-fed old giant pandas rather than the adult group (Figure 4B,C).
## 3.3. The Correlation between Fecal Microbiota and Nutrient Digestibility in Giant Pandas
The genus *Streptococcus and* Lachnospiraceae_NK4A136_group were significantly positively correlated with crude fiber digestibility, whereas the genus Lactococcus, Turicibacter, Raoultella, Citrobacter, Enterococcus, Pantoea, Cellulosilyticum, Weissella, Providencia, and Hafnia-Obesumbacterium were significantly negatively correlated with crude fiber digestibility ($p \leq 0.05$, Figure 5). The genus Streptococcus, Terrisporobacter, and Lachnospiraceae_NK4A136_group were significantly negatively correlated with crude protein digestibility, whereas the genus Lactococcus, Turicibacter, Raoultella, Citrobacter, Enterococcus, Ralstonia, Pantoea, Cellulosilyticum, Weissella, Providencia, Helicobacter, Hafnia-Obesumbacterium, Massilia, Bradyrhizobium, Leuconostoc, and Acinetobacter were all significantly positively correlated with crude protein digestibility ($p \leq 0.05$, Figure 5). The genus Providencia was significantly positively correlated with ether extract digestibility ($p \leq 0.05$, Figure 5).
## 4. Discussion
Despite exhibiting a carnivore’s characteristic simple gastrointestinal tract, giant pandas acquire the majority of the required nutrients from bamboo. Because of the limited digestibility of plant cellulose by the giant panda genome, it was suggested that the gut microbiome may play a vital role in the digestion of this highly fibrous bamboo diet [22]. Seasonal dietary shifts in bamboo part selection have been observed in both wild and captive giant pandas, and have been shown to extensively shape the host microbiome [5]. The bamboo part preference during different seasons has been shown to significantly influence the nutrient digestibility of adult captive giant pandas, which is associated with changes in the gut microbiota composition [6]. Owing to the improvements in husbandry and veterinary care, the number of geriatric pandas in zoological institutions has increased in recent years. The aging process in giant pandas elicits a significant change in the gut microbiome, indicating that geriatric pandas exhibit a different gut microbiota composition than younger pandas [12]. While studies in humans and other animals have shown that there may exist an interaction between diet and aging in regulating host phenotype and shaping gut microbiota composition [23,24], such information in different bamboo part-fed geriatric and adult pandas remains unknown.
Unlike studies in other animals showing similar nutrient digestibility between adult and senior individuals [25,26], lower energy digestibility was found in aged giant pandas compared to the adults in the present study, indicating the declined energy extraction capacity from food in aging giant pandas. Giant pandas feed almost exclusively on bamboo, of which the different plant parts exhibit significantly different nutrient compositions [4]. Wang et al. [ 6] showed that the bamboo part exerted a significant effect on nutrient digestibility in giant pandas. Bamboo shoots consumption has been shown to increase the crude protein digestibility and decrease the crude fiber digestibility of giant pandas [6]. Consistently, higher crude protein digestibility and lower crude fiber digestibility were observed in bamboo shoot-fed adult and geriatric giant pandas compared to those fed with bamboo leaves in the present study, which might be attributed to the inhibition of crude protein utilization induced by the higher level of fiber in bamboo leaves [27]. In rodent models, the aging process was found to decrease lipid absorption through reducing the pancreatic lipase activity [28]. In this study, bamboo shoot consumption increased the ether extract digestibility in aged giant pandas rather than in adults compared to bamboo leaf feeding. This finding might be related to the lower lipase activity in the small intestine of senior giant pandas and the higher ether extract content in bamboo leaves. Compared with adults, the ether extract in bamboo leaves was too high for aged giant pandas to fully digest, resulting in the lower digestibility of ether extract in senior pandas fed with bamboo leaves than those fed with bamboo shoots [6].
Accumulated evidences have demonstrated the possible role of the gut microbiota in the regulation of nutrient harvest in humans and monogastric animals [29,30]. More typically, as the giant panda lacks enzymes for the digestion of bamboo, it has thus been suggested that the giant panda appears to have no alternative but to rely on symbiotic gut microbes to extract nutrients from its highly fibrous bamboo diet [31]. A previous study contended that dietary shifts induced changes in nutrient digestibility in captive giant pandas and were associated with the alteration of the microbiota composition [6]. Both bamboo plant part and age have been shown to play a critical role in shaping the gut microbiota profile in captive giant pandas [7,8,12], however the interaction between bamboo plant part and age on intestinal microbiota composition, as well as the relationship between the interaction-induced gut microbiota shifts and nutrient digestibility of the captive giant pandas, remains unknown. Consistent with the previous study showing a more diverse gut microbiome in bamboo shoot-fed giant pandas than their counterparts [8], we found that bamboo shoot feeding increased the observed species, Chao1, Shannon, and Simpson indexes in both adult and old giant pandas. This indicates that there is a more abundant and diverse microbiome in bamboo shoot-fed giant pandas.
Research showed that the elderly pandas exhibited lower bacterial species richness and diversity than the younger individuals [12,22]. However, in this study, the main effect of age on the alpha diversity indices of microbiome in giant pandas was not observed, which is inconsistent with findings in rodents in which the microbial composition was generally affected by age rather than diet [32]. This indicates the predominant role of dietary shifts rather than age in shaping the gut microbiota of giant pandas. The dissimilarity distance analysis in the present study also confirmed that the fecal microbiota of giant pandas could be sorted into two clusters by bamboo part independent of age. It has been demonstrated that phyla Firmicutes and Proteobacteria were the most predominant bacteria in the fecal microbiome of giant pandas [3,4]. In the present study, bamboo shoot feeding decreased the abundance of Firmicutes and increased the abundance of Proteobacteria in the adult group rather than the geriatric group compared to bamboo leaf feeding. This is contradictory with the previous finding that the relative abundance of Proteobacteria was the highest in the bamboo-leaf fed giant pandas [8]. However, in vivo studies in rodents revealed that bamboo shoot-derived components promoted the colonization of bacteria belonging to Proteobacteria and decreased the abundance of *Firmicutes bacteria* in the gut [33,34]. The contradictory results might stem from the different study subjects or use of different bamboo species. Previous studies in monogastric animals showed that the relative abundance of Acidobacteriota was positively correlated with the intake amount of dietary protein and the relative abundance of Bacteroidetes was negatively correlated with dietary protein level [35,36]. In the present study, the higher abundance of Acidobacteriota and lower abundance of Bacteroidetes were observed in bamboo shoot-fed giant pandas regardless of age, which might be attributed to the higher amount of protein in bamboo shoots than bamboo leaves [6]. Consistent with the previous findings [4], the genera Escherichia-Shigella and Clostridium_sensu_stricto_1 were predominantly present in the fecal microbiome of giant pandas in this study. Bamboo shoot consumption has been shown to decrease the abundance of Escherichia-Shigella and increase the abundance of Weissella in the feces of giant pandas [8]. Our study further revealed that the bamboo shoot feeding-induced changes in Escherichia-Shigella and Weissella abundances were only observed in aged giant pandas. In addition, the decreased abundance of Clostridium_sensu_stricto_1 was observed in bamboo shoot-fed adults rather than geriatric giant pandas compared to the bamboo leaf group. This finding was consistent with the previous study showing the higher abundance of Clostridium_sensu_stricto_1 in the bamboo leaf consumption stage versus bamboo shoot consumption stage [3]. The inconsistent findings demonstrate that the genus Clostridium_sensu_stricto was not significantly enriched in the bamboo leaf stage and showed low sensitivity to the host’s seasonal dietary changes [1]. These contradictory results regarding the effects of bamboo part consumption on predominant genera abundance in giant pandas further suggest that the distribution of bacteria at the genus level in giant pandas might be dependent on the interaction effect of dietary shifts and age of the host.
Seasonal variations in bamboo part selection has been shown to shape the bacteria distribution at the genus level of giant pandas [1,3]. The abundances of genera Cellulosilyticum, Lactococcus, and *Streptococcus were* significantly affected by the consumption of different bamboo parts [8]. Consistently, in this study, bamboo shoot feeding significantly increased the abundance of Cellulosilyticum, Lactococcus and other genera as well as decreased the abundance of *Streptococcus in* feces of both adult and aged giant pandas compared with bamboo leaf ingestion. In monogastric animals, the shifts in gut microbiota composition were found to closely correlate with nutrient digestibility [37]. The genus *Streptococcus was* positively related to crude fiber digestibility in pigs [38]. In this study, the genera *Streptococcus and* Lachnospiraceae_NK4A136_group were positively correlated with crude fiber digestibility in giant pandas, indicating the critical role of these two genera in the utilization of crude fiber of bamboo. High protein diets and ingredient consumptions have been shown to increase the abundance of the genera Turicibacter and Lactococcus in rodents [39,40]. In the present study, the genera Turicibacter, Lactococcus, and other genera were positively correlated with the crude protein digestibility of giant pandas, which indicates that these bacteria may be important for the protein utilization of the bamboo parts. Taken together, the gut microbiota composition of giant pandas was mainly shaped by bamboo part consumption rather than age.
## 5. Conclusions
In conclusion, bamboo shoot feeding increased the crude protein digestibility and decreased the crude fiber digestibility of giant pandas regardless of age. Bamboo part consumption dominated over age in shaping the gut microbiota composition of giant pandas. The shifts in taxa distribution at genus level might be responsible for the bamboo part-induced nutrient extraction alterations.
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|
---
title: Systematic Identification and Comparison of the Expressed Profiles of Exosomal
MiRNAs in Pigs Infected with NADC30-like PRRSV Strain
authors:
- Feng Cheng
- Hui Wang
- Lei Zhou
- Ganqiu Lan
- Hanchun Yang
- Lixian Wang
- Ligang Wang
- Jing Liang
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000162
doi: 10.3390/ani13050876
license: CC BY 4.0
---
# Systematic Identification and Comparison of the Expressed Profiles of Exosomal MiRNAs in Pigs Infected with NADC30-like PRRSV Strain
## Abstract
### Simple Summary
Exosomes play a unique role in virus infection, antigen presentation, and suppression/promotion of body immunity. Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most damaging pathogens in the pig industry. Here, we used the PRRSV NADC30-like CHsx1401 strain to artificially infect 42-day-old pigs, isolate serum exosomes, and identify 33 significantly differentially expressed (DE) exosomal miRNAs between infection and control groups, and 18 DE miRNAs associated with PRRSV infection and immunity were screened as potential functional molecules involved in the regulation of PRRSV virus infection by exosomes.
### Abstract
Exosomes are biological vesicles secreted and released by cells that act as mediators of intercellular communication and play a unique role in virus infection, antigen presentation, and suppression/promotion of body immunity. Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most damaging pathogens in the pig industry and can cause reproductive disorders in sows, respiratory diseases in pigs, reduced growth performance, and other diseases leading to pig mortality. In this study, we used the PRRSV NADC30-like CHsx1401 strain to artificially infect 42-day-old pigs and isolate serum exosomes. Based on high-throughput sequencing technology, 305 miRNAs were identified in serum exosomes before and after infection, among which 33 miRNAs were significantly differentially expressed between groups (13 relatively upregulated and 20 relatively downregulated). Sequence conservation analysis of the CHsx1401 genome identified 8 conserved regions, of which a total of 16 differentially expressed (DE) miRNAs were predicted to bind to the conserved region closest to the 3′ UTR of the CHsx1401 genome, including 5 DE miRNAs capable of binding to the CHsx1401 3′ UTR (ssc-miR-34c, ssc-miR-375, ssc-miR-378, ssc-miR-486, ssc-miR-6529). Further analysis revealed that the target genes of differentially expressed miRNAs were widely involved in exosomal function-related and innate immunity-related signaling pathways, and 18 DE miRNAs (ssc-miR-4331-3p, ssc-miR-744, ssc-miR-320, ssc-miR-10b, ssc-miR-124a, ssc-miR-128, etc.) associated with PRRSV infection and immunity were screened as potential functional molecules involved in the regulation of PRRSV virus infection by exosomes.
## 1. Introduction
Porcine reproductive and respiratory syndrome virus (PRRSV) is a single-stranded positive-strand RNA virus with an envelope structure belonging to the order Nidovirales, family Arteriviridae, genus Betaarterivirus [1,2]. It is spherical or ellipsoidal with a diameter of 50–65 nm under a freezing electron microscope [3,4]. The PRRSV genome is about 15 kb in length with a 5′ cap and a 3′ polyA-tail and contains at least 10 open reading frames (ORFs) flanked by untranslated regions (UTRs) at both the 5′ and 3′ termini [5,6], and is wrapped by nucleocapsid protein, with lipid double-layer coating to form virus particles.
Exosomes belong to vesicles with monolayer membrane structures and have the same topological structure as cells [7]. The shape is “cup-shaped” or “disc-shaped” under an electron microscope [8,9]. Exosomes can exist in the circulatory system for a long time, and substances in exosomes can be absorbed by adjacent cells or distant receptor cells and then regulate the receptor cells to participate in the exchange of genetic materials between cells [10,11]. They are mainly composed of membrane surface substances and carried contents, including cell surface receptors, membrane proteins, soluble proteins, lipids, RNA (mRNA, miRNA, lncRNA, and viral RNA, etc.), genomic DNA, mitochondrial DNA [12,13,14]. MicroRNAs (miRNAs) are a class of 18–25 nucleotides (nt) evolutionarily conserved endogenous non-coding single-stranded small RNAs, which inhibit the translation process by inducing the degradation of target mRNA or by binding with 3′ UTR of target mRNA, leading to post-transcriptional gene silencing, then regulating the gene expression at the post-transcriptional level [15,16,17]. It is estimated that miRNAs regulate more than $60\%$ of mammalian genes post-transcriptionally [18,19]. MiRNAs play an important role in intercellular communication and can also be used as a potential functional molecule for disease and virus infection, transmission, and defense [20]. A growing number of studies have shown that miRNAs can be present in body fluids, such as saliva, urine, breast milk, and blood, and act through the body’s fluid circulatory system [21,22]. Exosomal miRNAs are considered to be endogenous regulators of gene expression and metabolism and can indicate various pathological conditions [23,24].
Over the past two decades, it has been shown that miRNAs have crucial roles in the regulation of immune cell development, innate immune responses, and acquired immune responses. Some other miRNAs are reported to impair PRRSV infection through the following ways, directly target the PRRSV genome or PRRSV receptor, or play a role by regulating the host’s innate immune response. The miR-26 family can significantly damage virus replication, and miR-26a can inhibit the replication of type 1 and type 2 PRRSV strains in porcine alveolar macrophages (PAMs) by regulating the type I interferon (IFN) pathway, which is more efficient than miR-26b [25,26]. miR-30c and miR-125b are identified to modulate host innate immune response by targeting the type I IFN pathway and NF-κB pathway, respectively [27,28,29]. MiR-23, miR-378, and miR-505 are antiviral host factors targeting PRRSV and have conservative target sites in type 2 PRRSV strains [30]. At the same time, host miR-506 has been identified to inhibit PRRSV replication by directly targeting PRRSV receptor CD151 in MARC-145 cells [31]. miR-181 also can indirectly inhibit PRRSV replication by down-regulating PRRSV receptor CD163 in blood monocytes and PAMs [32]. In addition, miRNAs can promote PRRSV replication by interfering with basic cell physiology. MiR-24-3p and miR-22 directly target 3′UTR of HO-1 during PRRSV infection to escape the inhibition of heme oxygenase-1 (HO-1), a heat shock protein (also known as HSP32) on PRRSV [33,34].
Pigs are known to be more susceptible to PRRSV and less able to defend themselves against the entry of this pathogen into the organism [35]. In the present study, the innate immunity and acquired immunity of pigs infected with this virus were studied at the molecular level using a strain prevalent in the field. A serum exosome isolation kit, transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and Western blot (WB) were used to isolate and identify serum exosomes before and after infection with PRRSV, followed by small RNA sequencing analysis, identification, and analysis of differential expression results using bioinformatics methods to obtain a number of PRRSV-associated serum exosome miRNAs, followed by identification of data results using quantitative real-time PCR (qRT-PCR).
## 2.1. Animal Experiments
Six PRRSV antigen and antibody double-negative healthy 42-day-old large white pigs were placed in the pig clean feeding system for isolation, healthcare, and environmental adaptation. All pigs were free to eat and drink without restrictions. When they were familiar with the conditions in the isolator, the pigs were nasally inoculated with 2 mL 105 TCID50/mL PRRSV NADC30-like CHsx1401, which was mentioned by predecessors [36,37]. The blood of the pigs before (control group, $$n = 6$$) and 7 days after (treatment group, $$n = 6$$) virus inoculation was collected from the anterior vena cava for serum isolation. The cellular debris in the serum was removed by centrifugation at 3000 g for 15 min. All animal experiments in our study were approved by the Animal Ethics Committee of the Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS) (Beijing, China), IAS2022-130.
## 2.2. Isolation and Purification of Serum Exosomes
Exosome isolation and purification were carried out using the exoEasy Maxi kit (QIAGEN, Hilden, Germany, cat. no. 76064) according to the manufacturer’s protocol.
## 2.3. Transmission Electron Microscopy (TEM)
Extracted exosome suspensions were spotted onto the formvar carbo-coated copper mesh, and the exosomes were rinsed with PBS and subjected to standard uranyl acetate staining for 3 min at room temperature. After drying for several minutes at room temperature, the grid was visualized and photographed at 100 kV by transmission electron microscope (HT-7700, Hitachi-High Tech, Tokyo, Japan).
## 2.4. Nanoparticle Tracking Analysis (NTA)
Extracted exosomes were diluted with 1 × PBS by changing the volume from 10 to 30 μL. After the sample was tested, the concentration and size of serum exosomes were analyzed by an N30E flow nano-analyzer following the manufacturer’s instructions (NanoFCM, Xiamen, China).
## 2.5. Western Blot
The extracted exosome samples were added to RIPA lysate mixed with protease inhibitor (Invitrogen, Waltham, MA, USA) and phenylmethylsulfonyl fluoride (PMSF) to extract the exosome protein, which was lysed on ice for 30 min. Then, according to the instructions of the Bradford kit, we quantified the concentration of serum exosome protein. Exosome proteins underwent thermal denaturation. The same amount of protein was separated on $12\%$ SDS-PAGE gel and then transferred to a polyvinylidene fluoride (PVDF) membrane (Millipore, Burlington, MA, USA). It was soaked in TBST containing $5\%$ skimmed milk powder and sealed for 1 h at room temperature. We soaked the membrane in the diluted primary antibody (anti-CD9 antibody, Abcam, Boston, MA, USA, #ab92726; anti-CD81 antibody, Abcam, Boston, MA, USA, #ab109201) overnight at 4 °C, and recovered the primary antibody. We soaked the membrane in the diluted secondary antibody, incubated it at room temperature for 1 h, and recovered the secondary antibody. We laid the washed film of PBST on the fresh-keeping film, added equal volume mixed ECL a/b chromogenic solution, and placed it in the chemiluminescence imager.
## 2.6. Exosomal Small RNA Sequencing and Data Analyses
Total RNA from the exosomes was extracted with Trizol according to the manufacturer’s instructions. We then detected the RNA concentration and optical density (OD) value and detected the degradation and purity of RNA with $1\%$ agarose gel electrophoresis. Meanwhile, Agilent Bioanalyzer 2100 was used to detect the integrity of RNA. We used the total RNA of exosomes after quality inspection. According to the manufacturer’s instructions, we used NEB NEXT multiplex small RNA library prep set for Illumina® (Illumina, San Diego, CA, USA). The kit prepared a small RNA cDNA library and sequenced it to produce 50 nt single-end reads by the Illumina Novaseq 6000 platform. All the procedures for small RNA library preparation were accomplished by Novogene (Beijing, China).
The data after quality control were aligned to the porcine reference genome (*Sus scrofa* 11.1) using bowtie. Known miRNAs were identified by the miRbase (v22.0) database [38] (https://www.mirbase.org, accessed on 14 January 2022), miRdeep2 (v0.0.5) [39], and miRevo (v1.1) [40] and were used to predict new miRNAs. At the same time, the differential expression analysis for miRNAs was performed by DESeq (v1.24.0) [41], requiring |fold change| > 1.6 and $p \leq 0.05.$ Alignment was performed using MEGA (V11) [42] followed by single base scoring using PHAST (v1.6.9) [43] and evaluation of the most conserved regions of 10 virus genes, including WUH3 (GenBank accession no. HM853973), VR2332 (GenBank accession no. U87392), JXA1 (GenBank accession no. EF112445), CH-1a (GenBank accession no. AY032626), NADC30 (GenBank accession no. HN654459), HUN4 (GenBank accession no. EF635006), HLJZD22-1812 (GenBank accession no. MN648450), SC/DJY (GenBank accession no. MT075480), and Lelystad (GenBank accession no. M96262.2). RNAhybrid (V2.0) [44] was used to predict the binding of the identified miRNA sequence to the 3′ UTR of the CHsx1401 virus genome. MiRanda (v3.3a) and RNAhybrid were used to target gene prediction. The clusterProfiler [45] R package was used for GO (Gene Ontology) functional enrichment analysis of target genes and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis.
## 2.7. Validation of miRNA Expression by RT-qPCR
Total RNA was isolated from serum exosomes using Trizol (Invitrogen, Shanghai, China) according to the manufacturer’s protocol. The isolated RNA was verified by RT-qPCR on samples ($$n = 6$$ per group). cDNA was synthesized according to the instructions of miRNA 1st strand cDNA synthesis (by stem-loop) kit (Vazyme, Nanjing, China), and the fluorescence quantification was performed using ABI 7500 according to the instructions of miRNA universal SYBR qPCR master mix (Vazyme, Nanjing, China). The thermal cycle parameters used were as follows: the first stage: 95 °C for 30 s; Stage 2: 95 °C for 5 s, 60 °C for 34 s, and 40 cycles; Stage 3: 95 °C for 15 s, 60 °C for 1 min, and 95 °C for 15 s. Primer sequences of miRNAs, the U6 gene, were used as a reference [46] and listed in Supplementary Table S1. All qRT-PCR verifications were performed using three biological replicates and with three replicates for each sample. The relative abundance of transcripts was calculated by the 2−ΔΔCt method, and SPSS (v22.0) and GraphPad Prism (v8.0) were used for data analysis and mapping, respectively. $p \leq 0.05$ means the difference is statistically significant.
## 3.1. Relative Value of Antigen and Antibody after Virus Inoculation
The results of PRRSV antigen and antibody tests before (day 0) and after the (day 7) challenge are shown in Table 1. The serological detection of the PRRSV antigen and antibody before the challenge was negative, and the antigen was positive after the challenge, indicating that the pigs were successfully infected with CHsx1401.
## 3.2. Isolation and Identification of Serum Exosomes
The vesicles isolated from serum were discovered by TEM. Most vesicles can clearly see the concave saucer- or disc-shaped exosomes in the middle. The membrane edge of exosomes is clearly visible, and the morphology is relatively complete (Figure 1A,B). The nanoparticle tracking analysis showed that $95.73\%$ of the exosomes had a diameter of 30–150 nm, mainly around 72.25 nm, with an average diameter of 76.22 nm, which was consistent with the size characteristics of exosomes (Figure 1C). This size range was similar to that detected by TEM and further confirmed the identity of these vesicles as exosomes. Western blot analysis showed that the vesicles isolated from the serum samples were positive for CD9 and CD81 proteins (Figure 1D). The above characteristics conform to the exosome identification standards formulated by the international society for extracellular vesicles (ISEV) in MISEV2018 [47].
## 3.3. Small RNA Sequencing of Serum Exosomes
For each sample, the clean data reached 0.5 Gb, and the Q30 base percentage was above $96.20\%$. The clean reads of each sample were aligned with the pig reference genome. Among the 12 samples, the control group obtained 10,920,887, 10,248,696, 10,109,117, 10,655,494, 9,217,285, and 9,782,523 reads, respectively. The treatment group obtained 11,889,518, 10,593,504, 10,593,504, 12,846,080, 10,105,325, 11,729,451, and 9,789,542 reads, respectively. On average, $77.96\%$ of the total clean reads comprised 19–22 nucleotides (nt) in length (Figure 2A). The reads after quality control accounted for more than $92.59\%$ of the total reads. The processed clean reads were aligned to the porcine reference genome, and the mapped rate of 12 libraries on the genome was more than $92.30\%$, and the mapped rate was $94.98\%$ (Figure 2B). It indicated that the constructed serum exosomal miRNA library was of high quality and suitable for further analysis. Details are listed in Supplementary Table S2.
## 3.4. Differentially Expression Analysis of miRNAs
After quantitative analysis of the identified miRNA expression, miRNAs were screened by the thresholds described previously in Section 2.6. A total of 305 miRNAs were obtained before and after inoculation of the CHsx1401 strain (control, $$n = 6$$; treatment, $$n = 6$$). A total of 33 differentially expressed (DE) miRNAs were identified between the two groups, 13 DE miRNAs were upregulated, and 20 DE miRNAs were downregulated in the treatment group (Figure 3 and Supplementary Table S3).
## 3.5. Functional Enrichment Analysis of miRNA Target Genes
A total of 7283 target genes were predicted by 33 DE miRNAs, and the functions of target genes were mainly concentrated in the positive regulation of MAPK cascade, lipid metabolism process, regulation of intracellular signal transduction, ERK1 and ERK2 cascade, etc. ( Figure 4A). In terms of molecular functions, the differentially expressed miRNAs target genes mainly focus on GTP-enzyme regulatory activity, kinase activity, nucleoside triphosphatase regulatory activity, and other functions related to signal transduction and energy metabolism (Figure 4B). In addition, among the cell components, the target genes mainly participate in the biological functions of supramolecular polymers, Golgi, autophagosomes, cell surface, early endosomes, etc. ( Figure 4C). The functions of these components are closely related to the formation of exosomes, which also explains the accuracy of the sequencing.
KEGG pathway enrichment analysis showed that the target genes were significantly enriched in endocytosis, the MAPK signaling pathway, the Rap1 signaling pathway, the sphingolipid signaling pathway, and the PI3K Akt signaling pathway ($p \leq 0.05$) (Figure 5A). At the same time, the enriched pathways were classified and analyzed. The results showed that the KEGG pathway of the target gene was mainly enriched in environmental information processing, human diseases, and biological systems (Figure 5B).
## 3.6. Targeting Prediction of Serum Exosomal miRNA and PRRSV CHsx1401 Genome
According to the phastCons score of a single base after alignment by PHAST, a total of eight most conserved segments (black bands above the peak map) were obtained among the viral genomes (Figure 6). A total of 31 DE miRNAs were found to bind to the conserved segment by predicting the miRNAs bound to the conserved segment. Among them, in the conserved region (14,644–15,020 nt) closest to the 3′ UTR (14,870–15,020) of CHsx1401 genome, 16 DE miRNAs are predicted to bind to it, including 5 miRNAs (ssc-miR-34c, ssc-miR-375, ssc-miR-378, ssc-miR-486, and ssc-miR-6529) that can bind to the 3′ UTR of CHsx1401. Among these miRNAs, only ssc-miR-223 was upregulated after infection, and other miRNAs were downregulated after infection. See Supplementary Table S4 for details.
## 3.7. Screening DE miRNAs Related to Exosome Function and PRRSV
A variety of differentially expressed miRNAs related to the function of exosomes and PRRSV were found by functional enrichment analysis of target genes. Among them, 11 DE miRNAs such as ssc-miR-4331-3p, ssc-miR-744, and ssc-miR-320 are involved in exosome uptake, and their target genes are mainly concentrated in the *Ras* gene family, annexin family, and ADP ribosylation gene family. Eighteen DE miRNAs, including ssc-miR-10b, ssc-miR-124a, and ssc-miR-128, participate in immune-related pathways, and their target genes are mainly concentrated in the MAPK gene family, PIK3 gene family, and protein phosphatase gene family. While 11 DE miRNAs are involved in virus invasion, the related target genes are mainly concentrated in the MAPK gene family and protein phosphatase gene family. Furthermore, multiple differentially expressed miRNAs, such as novel_102. Six DE miRNAs, including ssc-miR-320, ssc-miR-423-5p, ssc-miR-4331-3p, ssc-miR-7137-3p, and ssc-miR-744, are co-expressed in exosome function, PRRSV virus invasion, and immune-related pathways, as shown in Figure 7. Details are shown in Supplementary Table S5.
## 3.8. QRT-PCR Assay of DE miRNAs between the Two Groups
Five DE miRNAs were randomly selected for verification. According to the qRT-PCR results, the expression of ssc-miR-19a and ssc-miR-32 increased in the treatment group, while ssc-miR-124a, ssc-miR-375, and ssc-miR-34c showed higher expression in the control group, consistent with the sequencing data (Figure 8).
## 4. Discussion
PRRSV is still a stubborn pathogen in the global pig industry, causing huge economic losses in the world. At present, vaccination is mainly used to prevent and control PRRSV, among which the modified live (MLV) virus vaccine is the most widely used [48]. Although this vaccine was effective in reducing PRRS outbreaks and incidence, it also greatly increased genetic variation and diversity of the virus and led to viral recombination between wild and live vaccine viruses in the field [49,50]. In recent years, the spread and prevalence of the recombinant virus NADC30-like PRRSV strain have caused multiple outbreaks of porcine reproductive and respiratory syndrome in China. The similarity between CHsx1401 and NADC30 used in this study remained at 92.2–$99.1\%$. Since then, it has become an epidemic strain in China. Exosomes, as mediators of cell communication, are widely found in various body fluids and have unique advantages in disease diagnosis and treatment [51,52]. According to previous reports, exosomes play an important communication role in antigen presentation [53], immune response [53,54], virus replication [54], cancer [55], neurodegenerative diseases [56], angiogenesis [57], tumor cell migration [58] and invasion [59], and have high research value.
In this study, high-throughput sequencing technology was used to construct the miRNA expression profile of serum exosomes, and 33 DE miRNAs were identified. As we all know, the host-encoded miRNA can bind with the viral genome and then regulate the replication, synthesis, and release of the virus to limit infection and affect the pathological process [15]. Studies of miRNAs targeting the viral genome have also been repeatedly reported in animals. gga-miR-454 and gga-miR-130b in chicken infectious bursal disease can target the viral genome to inhibit viral replication, while gga-miR-21 directly targets the viral protein VP1 to inhibit viral protein translation [60,61]. In PRRSV studies, ssc-miR-181 specifically binds to a highly conserved region downstream of the viral genome ORF4 and strongly inhibits PRRSV replication [62]. In this study, the expression difference of ssc-miR-181 between the two groups did not reach a significant level. In our study, the genomes of nine different PRRSV viruses were compared with those of the CHsx1401 strain, and the eight most conserved segments were identified. It was predicted that 31 DE miRNAs could bind to the 8 most conserved segments of CHsx1401, and 16 DE miRNAs could bind to the conserved sequences close to the 3′ UTR of CHsx1401. Among them, 5 DE miRNAs (ssc-miR-34c, ssc-miR-375, ssc-miR-378, ssc-miR-486, and ssc-miR-6529) can simultaneously bind to the CHsx1401 3′ UTR. In addition, the upregulated expression of ssc-miR-223 was predicted to bind to the 3′UTR target of the PRRSV genome. The results showed that the conserved sequences of the virus genome might play a key role in its pathogenicity, and the miRNAs that can bind to the conserved sequences between the genomes of different PRRSV strains may have important significance in controlling the pathogenicity of the virus. Some differentially expressed miRNAs have been proven to be related to PRRSV by previous studies and even directly involved in the regulation of PRRSV, including ssc-miR-10b [63], ssc-miR-378 [30], ssc-miR-124a [64], let-7f-5p [65], ssc-miR-744 [66], and ssc-miR-19a [67].
PRRSV can evade host defense by interfering with innate immune response. This process is regulated by many signaling pathways, including the MAPK signaling pathway, PI3K Akt signaling pathway, autophagy, chemokine, and TNF signaling pathway. At present, the MAPK signaling pathway includes three main pathways: ERK$\frac{1}{2}$, JNK, and p38 pathway. Activation of the MAPK cascade can promote host cell apoptosis, assist the virus in escaping the host immune defense response and promote PRRSV replication [68]. Moreover, the activation of c-Jun N-terminal kinases (JNKs) and p38 can also promote the release of the inflammatory factor IL-10 [68,69,70] and enhance the inflammatory effect. In addition to inducing apoptosis, PRRSV can also induce autophagy, which can promote PRRSV replication. The activation of PI3K/*Akt is* necessary for virus entry and promotion of virus replication, and PRRSV-activated Akt inhibits host cell apoptosis by negatively regulating the JNK pathway [71]. TNFα It can play an important role in the induction and regulation of inflammatory response together with other inflammatory factors, but TNF α *Expression is* affected by the negative regulation of PRRSV replication [72]. In the present study, miRNAs (ssc-miR-10b, ssc-miR-122-5p, ssc-miR-124a, ssc-miR-128, ssc-miR-129a-5p, etc.) enriched in these pathways are involved in PRRSV-induced apoptosis, autophagy, and inflammation and are closely associated with viral immune response, immune evasion, and replication.
The cell plasma membrane is rich in a variety of lipid rafts, and sphingolipid- and cholesterol-rich in sphingolipids (sphingomyelin and glycosphingolipids) are key molecules of lipid rafts. The recognition of lipids by some proteins of the virus may be a necessary condition for the entry of the virus [73]. Envelope viruses insert viral envelope glycoproteins into lipid rafts at the stage of virus entry, interact with receptors located in lipid rafts, or change from their natural state to activated form to initiate or promote viral internalization/fusion, such as HSV, SARS coronavirus, and piglet epidemic diarrhea virus [73,74]. Previous studies found that the removal of cholesterol from the surface of MARC-145 cells significantly reduced PRRSV infection, demonstrating that inhibition of PRRSV infection was specifically mediated by the removal of cellular cholesterol. Depletion of cell membrane cholesterol significantly inhibited virus entry, particularly virus attachment, and release [75]. Obviously, sphingolipid metabolism can regulate membrane structure and adhesion, which is of great significance in PRRSV virus invasion.
Endocytosis was the most significant enrichment in this study. Endocytosis is an important mechanism of exosome uptake by target cells. Previous studies have shown that exosome uptake is an energy-demanding and cytoskeleton-dependent process, which highlights the potential role of endocytosis in this process [76]. It has been proved that there are several pathways that can mediate this process, including phagocytosis, macropinocytosis, clathrin, etc. [ 77,78], which led to different classifications and roles of endocytosed substances. The enrichment of differentially expressed exosomal miRNAs in this pathway indicates that exosomes play an important role in PRRSV infection, and the regulation of content transport and uptake in exosomes may lead to pathophysiological changes in target cells and organs.
## 5. Conclusions
Through the identification and bioinformatics analysis of serum exosomal miRNAs from PRRSV-infected pigs, a variety of PRRSV-related pathways and differentially expressed miRNAs were obtained in this study, such as ssc-miR-4331-3p, ssc-miR-744, ssc-miR-320, ssc-miR-10b, ssc-miR-124a, ssc-miR-128, etc., which play potential functional roles in PRRSV-induced immune response, invasion, and exosome uptake. In addition, because a single miRNA can target multiple genes and a single gene is also regulated by multiple miRNAs, there are a number of miRNAs that perform multiple functions in the above pathways. Some miRNAs have been verified to regulate PRRSV infection by acting on key receptors or directly targeting the virus genome, such as ssc-miR-10b, ssc-miR-378, miR-124a, let-7f-5p, ssc-miR-744, ssc-miR-19a, etc. Meanwhile, the present study also predicted a variety of miRNAs that can bind to the most conserved fragment of the 3′ UTR of the CHX1401 virus genome, including ssc-miR-34c, ssc-miR-375, ssc-miR-378, ssc-miR-486, and ssc-miR-6529, which may be important for regulating viral pathogenicity.
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---
title: Fecal Microbiota, Forage Nutrients, and Metabolic Responses of Horses Grazing
Warm- and Cool-Season Grass Pastures
authors:
- Jennifer R. Weinert-Nelson
- Amy S. Biddle
- Harini Sampath
- Carey A. Williams
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000167
doi: 10.3390/ani13050790
license: CC BY 4.0
---
# Fecal Microbiota, Forage Nutrients, and Metabolic Responses of Horses Grazing Warm- and Cool-Season Grass Pastures
## Abstract
### Simple Summary
Incorporating warm-season grasses into traditional cool-season grass equine rotational grazing systems can increase pasture availability during hot, dry months and bridge the “summer slump” forage gap. The objective of this study was to evaluate the impacts of this pasture management practice on the equine microbiome and to explore relationships between the fecal microbiota, forage nutrients, and metabolic responses of grazing horses. Results of this study indicate that distinct changes in microbial community structure and composition occur as horses adapt to different forages and that shifts in the microbial community were most influenced by forage non-structural carbohydrates and crude protein, rather than fiber. Interrelationships were found between these nutrients, glycemic responses, and Akkermansia and Clostridium butyricum. These bacteria were also found to be enriched in horses adapted to warm-season grasses. While the results of this study suggest that integrating warm-season grasses may not offer substantial metabolic benefits in healthy adult horses, this study did reveal new insights and targets for future research necessary to better understand the function of Akkermansia and *Clostridium butyricum* in the hindgut microbiome of grazing horses and possible roles in modulation of equine metabolic health.
### Abstract
Integrating warm-season grasses into cool-season equine grazing systems can increase pasture availability during summer months. The objective of this study was to evaluate effects of this management strategy on the fecal microbiome and relationships between fecal microbiota, forage nutrients, and metabolic responses of grazing horses. Fecal samples were collected from 8 mares after grazing cool-season pasture in spring, warm-season pasture in summer, and cool-season pasture in fall as well as after adaptation to standardized hay diets prior to spring grazing and at the end of the grazing season. Random forest classification was able to predict forage type based on microbial composition (accuracy: 0.90 ± 0.09); regression predicted forage crude protein (CP) and non-structural carbohydrate (NSC) concentrations ($p \leq 0.0001$). Akkermansia and *Clostridium butyricum* were enriched in horses grazing warm-season pasture and were positively correlated with CP and negatively with NSC; *Clostridum butyricum* was negatively correlated with peak plasma glucose concentrations following oral sugar tests (p ≤ 0.05). These results indicate that distinct shifts in the equine fecal microbiota occur in response different forages. Based on relationships identified between the microbiota, forage nutrients, and metabolic responses, further research should focus on the roles of Akkermansia spp. and *Clostridium butyricum* within the equine hindgut.
## 1. Introduction
Integrating warm-season grasses into traditional cool-season grass rotational grazing systems can provide productive pasture for grazing horses during the hot, dry months of the “summer slump” period [1,2]. Despite reported benefits for pasture yield [2,3] the impacts of this practice on equine metabolic health and the hindgut microbiome have not been previously investigated.
Warm- and cool-season grasses have different mechanisms for storage of soluble carbohydrates [4], with non-structural carbohydrate (NSC = sugars + starch + fructans) concentrations in cool-season grasses typically greater than that of warm-season grasses [1,5]. These differences in NSC content are of interest in equine management as current feeding recommendations for horses with existing metabolic dysfunction include limiting dietary NSC concentrations [6,7,8]. Thus, lower-NSC warm-season grasses have been suggested as an alternative forage source [1,9]. Feeding supplemental concentrate higher in NSC has been shown to lower insulin sensitivity in horses [10,11,12], but over the relatively smaller range of NSC concentrations observed in forages, potential benefits of limiting NSC intake are less clear [13,14,15]. However, glycemic and insulinemic responses of horses grazing low-NSC warm-season grasses have not been extensively evaluated.
In addition to the potential metabolic effects, transitioning horses between forage types may also have implications for equine gastrointestinal health. Diet has been identified as a dominant factor shaping the community structure of the gut microbiota both in humans and across animal species including horses [16,17]. However, prior studies on the influence of diet on the equine hindgut microbiome have focused primarily on concentrate vs. concentrate or concentrate vs. forage diets [18,19,20,21]. Recent studies have demonstrated that type of hay (alfalfa vs. grass) and transitions between hay and pasture grass can impact equine cecal or fecal microbial community composition [22,23]. Overall, few studies have evaluated the hindgut microbiome of grazing horses and only one previous study has been conducted in horses grazing cool- vs. warm-season pasture grasses [24].
A number of studies have begun to explore associations between the equine hindgut microbiota and metabolic health [25,26,27,28], but there is a lack of information on the interplay between forage nutrients, the hindgut microbiome, and metabolism of grazing horses. Biddle et al. [ 28] explored relationships between abundance profiles of fecal microbial taxa, feed types (pasture, hay, or hay supplemented with concentrate), and blood analytes including circulating glucose and insulin, finding negative correlations between insulin and over 50 taxa. Studies conducted in mouse models have conclusively demonstrated that changes in diet influence host metabolism in a microbiome-dependent manner [29,30]. Thus, there is a need for future research to better understand potential roles of the gut microbiota in modulating metabolism of grazing horses and the influence of specific forage nutrients on these interactions. While shifts in hindgut microbial communities have been documented in pastured horses over time, little is known regarding relationships between these changes in bacterial composition and forage nutrient profiles or metabolic responses of grazing horses. Therefore, the aims of this study were to characterize shifts in glucose metabolism and the fecal microbiota of horses adapted to different forage types and to explore relationships between forage nutrients, microbial composition, fecal metabolites, and metabolic responses of grazing horses.
## 2. Materials and Methods
Research was conducted in 2018 at the Ryders Lane Environmental Best Management Practices Demonstration Horse Farm (Rutgers, The State University of New Jersey; New Brunswick, New Jersey, NJ, USA). Weather data for the study period and historical averages are presented in Table S1 [31].
## 2.1. Grazing Systems
Two separate 1.5 ha integrated warm- and cool-season rotational grazing systems were utilized in this study. Grazing system design and management were as published in previous companion studies [2,24]. In brief, warm-season grass pasture sections contained either Wrangler bermudagrass [BER; Cynodon dactylon (L.) Pers.; Johnston Seed Company, Enid, OK, USA] or Quick-N-Big crabgrass [CRB; *Digitaria sanguinalis* (L.) Scop.; Dalrymple Farms, Thomas, OK, USA]. Mixed cool-season grass sections contained Inavale orchardgrass [*Dactylis glomerata* (L.)], Tower tall fescue (endophyte-free) [*Lolium arundinaceum* (Schreb.) Darbysh.], and Argyle Kentucky bluegrass [*Poa pratensis* (L.)] (DLF Pickseed, Halsey, OR, USA). Grazing management was guided by established best management practices [32].
## 2.2. Animal and Grazing Management
Use of animals in this study was approved by the Rutgers University Institutional Animal Care and Use Committee protocol #PROTO201800013. Eight adult Standardbred mares (age: 18 ± 0.71 yr; body weight (BW): 537 ± 17 kg; body condition score (BCS): 5–7)) were used in this study, with horses grouped by BW and BCS (Henneke Body Condition Score scale [33]. Horses were then randomly assigned to each system ($$n = 4$$ horses system−1). Prior to the study, oral sugar tests (OST) were administered to screen horses for impaired insulin sensitivity [34]. Insulinemic responses of all horses were found to be normal (peak insulin ≤45 mIU/L) [7]. Animal care and management have been previously detailed in companion studies [1,24]. Horse condition measures over the course of the study can be found in Table S2.
## 2.3. Fecal and Blood Sample Collection
Manual grab fecal samples were collected (0800 h) rectally from horses after 21 d adaptation to the initial hay diet in the spring (HAY-SP), cool-season grass pasture in the spring (CSG-SP), warm-season grass (WSG)—either BER or CRB during the “summer slump” period, and again following a return to cool-season grass in the fall (CSG-FA) and a final cool-season grass hay at the end of the grazing season (HAY-FA). See Figure S1 for a diagram of experimental design and sampling protocol. Due to delayed establishment and grazing of BER, only 17 days of grazing were possible prior to sample collection. Upon collection, samples were immediately placed on ice, transported to the laboratory, and then stored at −80 °C.
To determine impact of forage type within integrated systems on glycemic and insulinemic responses of grazing horses, OST [34] were conducted following adaptation to CSG-SP, WSG, and HAY-FA. On the evening prior to each collection period, horses were confined to dry lots at 2000 h with no access to either hay or pasture. Following the overnight fast, horses were moved into the barn facility at 0800 h. Two consecutive baseline blood samples were collected (15 min apart) by venipuncture at 0800 and an oral dose of Karo Syrup (0.25 mL/kg BW; modified dosing as utilized by Jacob et al. [ 12]) was immediately administered after the second baseline sample. Subsequent blood samples were collected at 30, 60, 90, 120, 180, and 240 min following Karo Syrup administration. Whole blood was collected into sodium heparin Vacutainer tubes, which were inverted several times before being placed on ice. Due to logistics of distance and travel time (~10 min from the barn to the laboratory), samples were transported to the laboratory after the 120, 180, and 240 min samples. In the laboratory, whole blood was centrifuged at 3700 rpm for 7 min. Following centrifugation, plasma was harvested from the Vacutainer tubes and aliquoted into microcentrifuge tubes, which were then stored at −80 °C.
## 2.4. Forage Sampling
Representative hand-clipped forage samples were also collected (0800–1000 h) on three days per period for analysis of nutrient composition. Pasture samples were collected according to previously published procedures [1,24,32] and dried (60 °C; 36 h minimum) in a Thelco oven (Precision Scientific, Chicago, IL, USA). Samples were ground (1 mm) and submitted to a commercial laboratory (Equi-Analytical Laboratories, Ithaca, NY, USA) for analysis by near-infrared spectroscopy. The mean nutrient composition of hay diets and pasture forages are shown in Table 1.
## 2.5. Analysis of Plasma Glucose and Insulin
Plasma glucose was analyzed by colorimetric assay (Glucose C-2, Wako Chemicals, Richmond, VA, USA), with the commercial kit adapted for microplate assay following manufacturer instructions. Plasma insulin was evaluated using an enzyme-linked immunoassay (Mercodia Equine Insulin ELISA, Mercodia, Winston-Salem, NC, USA) previously validated in horses [35]. Inter-assay and intra-assay coefficients of variation for glucose were $4.0\%$ and $2.9\%$, respectively. Inter-assay and intra-assay coefficients of variation for insulin were $7.8\%$ and $3.4\%$, respectively.
## 2.6. Fecal Sample Analyses
Fecal pH was measured in duplicate with a handheld Accumet pH meter (Fisher Scientific; Waltham, MA, USA) using a previously published protocol for preparation of fecal slurries [24,36]. Short-chain and branched-chain fatty acid (SCFA and BCFA, respectively) concentrations were determined by GC-MS analysis of fecal samples. The SCFA analyzed included acetate, propionate, butyrate, and valerate. The BCFA analyzed included isobutyrate, isovalerate and isocaproate. Sample preparation was performed according to a previously published protocol [37]. In brief, frozen fecal samples were weighed and deposited in bead tubes over dry ice. Feces were then resuspended in 1 mL of $0.5\%$ phosphoric acid per 0.1 g of sample and tubes were beaten for 5 min at 22.5 rpm in a cold case. Samples were again frozen at −80 °C until analyzed by Gas Chromatography and Mass Spectrometry system GC-MS (Agilent Technologies, Santa Clara, CA, USA). Prior to GC-MS analyses, thawed fecal suspensions were re-homogenized and centrifuged (10 min at 17,949× g), with the aqueous phase extracted using diethyl ether in a 1:1 volume to volume ratio. Before analysis, 2-methyl hexanoic acid (Thermo Fisher Scientific, Dallas, TX, USA) was added to the organic phase extract as an internal standard. Samples were analyzed in duplicate, with independent extractions for each replicate. The specifications of the GC-MS system and analyses as well as data acquisition and procedures for SCFA/BCFA quantitation have been previously described by Honarbakhsh et al. [ 37] and Garcia-Villalba et al. [ 38].
Quick-DNA Fecal/Soil Microbe Kits (Zymo Research; Irvine, CA, USA) were used for DNA extraction (in triplicate). The highest yielding replicate (quantified with a Qubit 2.0 Flourometer [Invitrogen; Carlsbad, CA, USA]) was submitted to a commercial laboratory (RTL Genomics; Lubbock, TX, USA). Amplification of the V4-V5 region of the 16S rRNA gene was conducted using region specific primers (515F/926R) [39]; sequencing was conducted by Illumina MiSeq.
## 2.7. Sequence and Statistical Analysis
Sequence and statistical analyses were performed in QIIME 2 (Quantitative Insights into Microbial Ecology, v. 2020.8) [40] and R (v. 4.0.2) [41]. Network mapping was conducted in Cytoscape (v. 3.8.0) [42]. Animal was considered the experimental unit.
Quality and chimera filtering of forward reads was conducted using DADA2 (read length = 185) [40,43,44]. Mafft and FastTree (q2-phylogeny plugin) were used to create trees for diversity analyses [45,46,47]. The lower quartile of Amplicon Sequence Variants (ASV) based on absolute abundance were removed (minimum frequency = 16; minimum samples = 4). The feature table was rarefied to a minimum sampling depth of 10,600 prior to α- and β-diversity analyses. The α-diversity metrics were analyzed by Kruskal–Wallis tests [48,49,50,51,52]. The β-diversity metrics analyzed included Weighted and Unweighted UniFrac by permutational ANOVA (PERMANOVA) [53,54,55,56,57,58,59]. Benjamini and Hochberg FDR adjustments for multiple pairwise comparisons were applied for all diversity analyses. Permutational multivariate analysis of dispersion (PERMDISP) was used to test homogeneity of dispersion [60].
To further explore differential abundances, ASV were then grouped into bacterial co-abundance groups (BCG) based on abundance profiles using Sparce Cooccurrence Network Investigation for Compositional Data (SCNIC) [61,62]. A random forest classifier with nested cross validation was applied to determine if forage type could be predicted based on BCG composition [63,64]. Features (BCG) were removed from the model based on importance scores generated by the random forest classifier in an iterative process (serial reduction with increments of 5) until the point at which model accuracy began to decline [65]. Relative abundances of remaining BCG and any uncorrelated ASV retained in the model following feature reduction processes were then analyzed by linear discriminant analysis effect size (LEfSe) to identify BCG specific to each forage type, with significance set at an LDA score >2.0 [66]. Taxonomy was then assigned using the latest SILVA database (SSU 138) [63,67,68,69,70].
Glucose and insulin response variables as well as SCFA/BCFA concentrations and fecal pH were analyzed by mixed model ANOVA in R, with grazing system, forage type and their interactions set as fixed factors and horse as the random factor in the initial model. The area under the curve (AUC) was calculated based on the trapezoid rule as the positive incremental AUC utilizing a published macro [71] in SAS (v.9.4 SAS Institute, Cary, NC, USA). Proxies for insulin sensitivity (fasting glucose-to-insulin ratio (FGIR); reciprocal of the square root of insulin (RISQI)) and insulin secretory response (modified insulin-to-glucose ratio (MIRG)) were calculated using baseline (fasting) values as previously described [72,73].
The relationship between forage nutrients and SCFA/BCFA fermentation metabolites (as well as pH) and fecal microbial community composition was then explored using random forest regression with nested cross validation to determine if nutrient concentrations and/or metabolite concentrations could be predicted based on bacterial abundance profiles. Spearman correlations between BCG and forage nutrients and fecal metabolites were analyzed in R. Relationships between forage nutrients/fermentation metabolites and BCG were then visualized in Cytoscape. Spearman correlations between metabolic variables and the BCG, as well as between forage nutrients and metabolic responses were also evaluated in R.
For all variables analyzed by mixed model, model residuals were analyzed for normality using the Shapiro–Wilk test. Log, square root, or inverse data transformations were applied where appropriate for non-normal data. Means were separated using Tukey’s method. When analyzing pairwise comparisons of transformed data, means and standard errors were back-transformed to the original variable scale following application of Tukey’s method with the delta method. For all analyses which generated p-values, results were considered significant at p ≤ 0.05, with trends considered at p ≤ 0.10. Data for variables analyzed by mixed model are presented as means ± SEM. Overall analysis of microbiome, fecal metabolite, and glucose/insulin data did not reveal differences by grazing system. Therefore, grazing system was removed from models and results for combined data are presented with $$n = 8$.$
## 3.1. Initial 16s rRNA Sequence Analysis
A summary of 16S rRNA gene sequencing reads before and after quality and chimera filtering as well as after filtering of low abundance features in the 40 samples analyzed for this study is shown in Table 2. There were 1264 distinct ASV in the final dataset (taxonomy can be found in Table S3).
## 3.2. Diversity Analyses
All α-diversity metrics evaluated, including the Shannon Diversity Index, Faith’s Phylogenetic Diversity, Pielou’s Evenness, and Observed ASVs, differed by forage (Kruskal–Wallis tests with Benjamini and Hochberg FDR adjustments; $p \leq 0.03$; Figure 1a–d). Shannon Diversity was greater when horses were adapted to WSG than CSG-SP ($p \leq 0.05$), and there was a trend for greater diversity when adapted to HAY-SP compared to CSG-SP ($p \leq 0.08$) and WSG vs. CSG-FA ($$p \leq 0.09$$). However, the only differences in evenness (Pielou’s Evenness) were trends for greater evenness in WSG and CSG-SP than in CSG-FA ($p \leq 0.06$). Conversely, richness (Observed ASVs) was greater in horses adapted to WSG, CSG-FA, and HAY-FA than CSG-SP ($p \leq 0.02$), but did not differ between horses adapted to HAY-SP and CSG-SP. Similarly, phylogenetic differences (Faith’s Phylogenetic Diversity) were also found between CSG-SP and subsequent forages (WSG, CSG-FA, HAY-FA; $p \leq 0.02$).
Principal coordinate analysis of β-diversity metrics including Weighted and Unweighted UniFrac did not reveal distinct clustering by forage (Figure 2a,b). However, statistical analysis by PERMANOVA (with Benjamini and Hochberg FDR adjustments) found significant differences in these measures (p ≤ 0.02). Subsequent PERMDISP analysis confirmed that these differences were not due to differences of variance or dispersion within groups. Unweighted UniFrac differed for all pairwise comparisons of forages ($p \leq 0.02$), with the exception of WSG vs. CSG-FA and WSG vs. HAY-FA for which there were trends for differences ($p \leq 0.10$). In contrast, there were no significant differences in any pairwise comparisons of forages for Weighted UniFrac. The percent of variation explained by PC1 was almost 3 times greater for Weighted than for Unweighted UniFrac, indicating an influence of abundance profiles in addition to phylogenetic differences.
## 3.3. Differential Abundance
Application of SCNIC identified 333 BCG, with 224 individual ASV remaining ungrouped. Iterative reduction of features (BCG) based on random forest model importance scores revealed that model accuracy increased through reduction to the top 65 features (0.90 ± 0.09). Further feature reduction to the top 25 features (based on importance scores) did not impact random forest model accuracy (0.90 ± 0.09 with the top 25 features retained). All retained features were BCG; no ungrouped ASV remained in the reduced feature set. These 25 BCG were retained for further analysis, with $11.54\%$ of the total microbial community abundance represented by the reduced 25 BCG feature set. The strength of the random forest model accuracy score indicated that forage type could be predicted based on bacterial composition, and that, conversely, bacterial community composition was influenced by forage.
Subsequent Linear discriminant analysis Effect Size (LEfSe) analysis conducted on features retained from the random forest classification modelling identified 6 BCG as markers of HAY-SP, 3 BCG enriched in CSG-SP, 5 BCG for WSG, 5 BCG for CSG-FA, and 5 BCG for HAY-FA (LDA > 4.0; $p \leq 0.03$). Forage-specific BCG markers as well as taxonomic classifications of subsequent linear discriminant analysis Effect *Size analysis* conducted on features retained from the random forest classification modelling identified 6 BCG as markers of HAY-SP, 3 BCG enriched in CSG-SP, 5 BCG for WSG, 5 BCG for CSG-FA, and 5 BCG for HAY-FA (LDA > 4.0; $p \leq 0.03$). Forage-specific BCG markers as well as taxonomic classifications of individual ASV within each BCG are presented in Table 3, Table 4 and Table 5.
Numerous taxa were represented in BCG markers for multiple forages. At the family level, the BCG markers for HAY-SP contained twice as many ASV mapped to the Lachnospiraceae family as any other forage (CSG-SP: 0; WSG: 2; CSG-FA: 3; HAY-FA: 2 ASV). The family Oscillospiraceae also had ASV members of BCG markers for all forages but CSG-SP (HAY-SP: 3; WSG: 3; CSG-FA: 2; HAY-FA: 2 ASV). At the genus level, ASV assigned to Christensenellaceae R-7 group were present in BCG markers of both hay diets (HAY-SP: 2; HAY-FA:1 ASV) and WSG (2 ASV); ASV assigned to the NK4A214 group of Oscillospiraceae were among members of BCG identified as markers of HAY-SP (2 ASV), WSG (1 ASV), and CSG-FA (1 ASV). The BCG markers of both hay diets and CSG-FA each included an ASV mapped to Rikenellaceae RC9 gut group, and BCG markers of HAY-SP and WSG each contained ASV assigned to Fibrobacter and Papillibacter. The BCG markers of HAY-SP and CSG-FA contained ASV within Catenisphaera and Lachnospiraceae UCG-009. Amplicon sequence variants mapped to the genus *Clostridium sensu* stricto 1 were found in BCG markers of CSG-SP and WSG. The BCG markers of WSG and CSG-FA included ASV assigned to Bacteriodales RF16 group and the Family XIII AD3011 group of Anaerovoracaceae. The BCG markers of CSG-FA and HAY-FA each contained an ASV assigned to Coprostanoligenes group, Lachnospiraceae XPB1014 group and Marvinbryantia, and ASV assigned to Treponema were assigned to BCG markers of WSG and HAY-FA.
Amplicon sequence variants assigned to Ruminococcus, Pseudobutyvibrio, Anaerovibrio, probable genus 10 of Lachnospiraceae, and the p-251-o5 genus and family of the order Bacteroidales were only present in BCG markers identified for HAY-SP; no other ASV assigned to these taxa were found in BCG markers of other forages. An ASV within Bacteroidales BS11 gut group was the only taxa specific to BCG markers of CSG-SP. Amplicon sequence variants mapped to the genera Akkermansia, Mogibacterium, and the Hallii group of Lachnospiraceae as well as UCG-005 metagenome of Oscillospiraceae and *Clostridium butyricum* were only found in BCG markers of WSG. The BCG markers of CSG-FA contained ASV assigned to Alloprevotella, Erysipelatoclostridium, Bacteroidales UCG-001, the WCHB1-41 genus, family and class within the class Kiritimatiellae, and Denitrobacterium detoxificans; these taxa were not identified in BCG markers of other forages. Taxa specific to only BCG markers of HAY-FA included ASV from the Incertae *Sedis genus* of Ethanoligenenaceae as well as Streptococcus.
## 3.4. Fecal pH and Fermentation Metabolites
Fecal pH differed by forage type (mixed model ANOVA with Tukey’s post hoc adjustment; $p \leq 0.0001$). Fecal pH was greater in horses adapted to WSG (7.56 ± 0.18) and HAY-FA (7.57 ± 0.18) than in HAY-SP (6.73 ± 0.18), CSG-SP (6.58 ± 0.18), or CSG-FA (6.53 ± 0.18; p ≤ 0.02; Figure 3). Fecal concentrations of short-chain fatty acids (SCFA) and branched-chain fatty acids (BCFA) also differed by forage (mixed model ANOVA with Tukey’s post hoc adjustment; $p \leq 0.001$; Table 6).
Unlike fecal pH, differences in SCFA and BCFA were primarily between pasture forages and the hay diets, with horses adapted to WSG often intermediate (numerically) between cool-season pasture and hay diets. Total BCFA were greater in CSG-SP, WSG, and CSG-FA than for either HAY-SP or HAY-FA ($p \leq 0.05$). Total SCFA were greater in CSG-SP and CSG-FA than HAY-SP or HAY-FA; WSG did not differ from CSG-SP and CSG-FA or HAY-SP, but was greater in comparison to HAY-FA ($p \leq 0.002$). Similarly, acetate was greater in CSG-FA than HAY-SP or HAY-FA, while WSG only differed from HAY-FA ($p \leq 0.02$). Acetate concentrations for CSG-FA also differed with HAY-FA ($$p \leq 0.0003$$), but there was only a trend for a difference between CSG-SP and HAY-SP ($$p \leq 0.09$$). Fecal butyrate was lower for HAY-FA than when horses were adapted to any of the pasture forages ($p \leq 0.03$), but there was no difference between HAY-SP and any other forage. Fecal propionate was lowest in horses adapted to HAY-FA ($p \leq 0.002$), but propionate concentrations were also lower for HAY-SP than either CSG-SP or CSG-FA ($p \leq 0.03$). Propionate did not differ between horses adapted to WSG and all other forages. Fecal valerate was lower for horses adapted to HAY-FA than CSG-SP or CSG-FA ($p \leq 0.03$) but did not differ from WSG or HAY-SP. Horses adapted to HAY-SP also had fecal valerate concentrations lower than CSG-SP or CSG-FA (p ≤ 0.0002). Isobutyrate was greater for all pasture forages in comparison to both HAY-SP and HAY-FA ($p \leq 0.02$). Isovalerate was greater in CSG-SP and CSG-FA vs. HAY-SP and HAY-FA (p ≤ 0.002), while concentrations in horses adapted to WSG were once again intermediate. Isocaproate was detected in all fecal samples, but concentrations were negligible and below the limit of quantification (<1.0 ug g feces−1).
## 3.5. Relationships between Fecal Microbiota and Metabolites
Random forest regressors were applied to determine if fecal pH and metabolite concentrations could be predicted based on microbial composition. Random forest regression did not support a strong influence of bacterial composition of the full microbial community on these fecal variables. Relatively weak predictive accuracy was found for isovalerate, valerate, hexanoate, and heptanoate (model R2 and p-values are shown in Table 7). Major SCFA including acetate, butyrate, and propionate could not be predicted based on BCG abundance profiles.
However, when random forest regressors were applied to only the top 25 BCG identified as most predictive of forage type, model accuracies improved for all fecal variables with the exception of valerate (Table 7). While predictive accuracy was still relatively weak, all metabolites (and pH) could be predicted with statistically significant accuracy (p ≤ 0.001).
Individual correlations were found between 19 of these BCG and at least one fecal metabolite or pH (Spearman correlation; rs ≥ |0.30|; p ≤ 0.05; Figure 4), with most BCG correlated with multiple fecal variables. There were positive correlations between BCG_300 and total SCFA and total BCFA as well as with acetate, butyrate, propionate, valerate, isobutyrate, and isovalerate, while this BCG was negatively correlated with hexanoate, heptanoate, and pH. The ASV members of this BCG were assigned to the genera Lachnospiraceae XPB1014 and Streptococcus. There were also positive correlations between both BCG_9 and BCG_259 and total SCFA and BCFA in addition to acetate, butyrate, propionate, valerate, isobutyrate, and isovalerate; these BCG were negatively correlated with heptoanate. These BCG included multiple ASV assigned to Lachnospiraceae as well as ASV within the genera Christensenellaceae R-7 group, Rikenellaceae RC9 gut group, Papillibacter, and the NK4214 group of Oscillospiraceae. Positive correlations were also found between BCG_173 and BCG_201 and total SCFA and BCFA, acetate, propionate, valerate, isobutyrate, and isovalerate, with negative correlations between these BCG and both hexoanate and heptoanate. These BCG included ASV assigned to Treponema, Rikenellaceae RC9 gut group, the NKA214 group of Oscillospiraceae, and the p-251-o5 and F082 genera and families of Bacteroidales. Total SCFA and BCFA as well as acetate, butyrate, propionate, isobutyrate, and isovalerate were positively correlated with BCG_113, while hexanoate was negatively correlated. This BCG contained ASV assigned to taxa including Synergistaceae, Bacteroidales RF16 group, Papillibacter, Christensenellaceae R-7 Group, Mogibacterium, and Clostridium butyricum.
Seven BCG were positively correlated with total BCFA and some combination of propionate, valerate, isobutyrate, and isovalerate, while negative correlations were found between these BCG and hexanoate and/or heptanoate. These BCG included ASV members assigned to the family level for Anaerovoracaceae, Oscillospiraceae, and Lachnospiraceae. Other ASV within these BCG were assigned to Lactobacillus equigenerosi and the genera Akkermansia, Fibrobacter, Treponema, Sphaerocheata, Phasolarctobacterium, Christensenellaceae R-7 group, Lachnoclostridium, Cellulosilyticum, Marvinbryantia, Coprostanoligenes group, Lachnospiraceae XPB1014, Lachnospiraceae UCG-009, Erysipelatoclostridium, *Clostridium sensu* stricto 1, and Bacteroidales BS11 gut group as well as Family XIII AD3011 group of Anaerovoracaceae, the UCG-004 genus within Erysipelatoclostridiaceae, the UCG-002 and UCG-005 genuses of Oscillospiraceae, and the UCG-010 genus and family of Oscillospirales.
Fecal pH was positively correlated with five BCG (in addition to BCG_300) (rs ≥ |0.30|; p ≤ 0.05), which contained ASV from taxa including Anaerovoracaceae, the Family XIII AD3011 group of Anaerovoracaceae, Akkermansia, Christensenellaceae R-7 group, Fibrobacter, Treponema, Catenisphaera, Alloprevotella, Bacteroidales RF16 group, Marvinbryantia, Coprostanoligenes group, Bacteroidales UCG-001, the NK4A214 group of Oscillospiraceae, and the WCHB1-41 genus, family, and order within Kiritimatiellae. Fecal pH was negatively correlated with BCG_94. This BCG included ASV assigned to Prevotella, Sarcina, and *Clostridium sensu* stricto 1.
## 3.6. Relationships between Fecal Microbiota and Forage Nutrients
Application of random forest regressors demonstrated that forage nutrient concentrations could be predicted based on bacterial community composition (BCG composition of the full microbial community), including water-soluble carbohydrate (WSC) and NSC at a predictive accuracy > R2 = 0.50 and crude protein (CP) with a predictive accuracy of R2 = 0.41 ($p \leq 0.0001$; Table 7). Weaker, but still statistically significant, predictive accuracy was found for digestible energy (DE), acid detergent fiber (ADF), neutral detergent fiber (NDF), ethanol-soluble (ESC), and starch ($p \leq 0.04$).
Similar to results for fecal metabolites, when random forest regressors were applied only to the top 25 BCG identified as most predictive of forage type, model accuracies improved (Table 7). Forage CP, NSC, and WSC remained as the nutrients most accurately predicted by the bacterial composition of this subset of BCG, all with R2 > 0.60 ($p \leq 0.0001$). Model predictive capacity with the reduced feature set was also above R2 = 0.40 for starch; weaker, but statistically significant, predictive accuracy was found all other nutrients ($p \leq 0.0001$).
Subsequent correlation analysis found 23 BCG (of the 25 in the reduced feature set) were correlated with at least one forage nutrient (Spearman correlation; rs ≥ |0.30|; p ≤ 0.05; Figure 5). Nine BCG were correlated with ADF, NDF, CP, and DE. In all cases, opposite correlations were seen between ADF/NDF and CP/DE (i.e., if ADF and NDF were positively correlated with a BCG, CP and DE were negatively correlated). There were distinct ASV from the taxa Christensenellaceae R-7 group and NK4A214 group of Oscillospiraceae that were members of separate BCG which were positively and negatively responding to these nutrients (i.e., these taxonomic classifications were found across all positive and negative responder groups). The BCG negatively correlated with ADF/NDF, and thus positively correlated with CP/DE included ASV members assigned to *Clostridium butyricum* and genera Papillibacter, Lachnoclostridium, Cellulosilyticum, Fibrobacter, Treponema, Catenisphaera, Bacteroidales RF16 group, Synergistaceae, Mogibacterium, Rikenellaceae RC9 gut group, the Hallii group of Lachnospiraceae, the p-251-o5 genus and family of Oscillospiraceae, and the F082 genus and family of Bacteroidales, as well as multiple ASV assigned only to the family level for Lachnospiraceae. The BCG positively correlated with ADF/NDF and negatively correlated with CP/DE included ASV within Sphaerochaeta, Phascolarctobacterium, Bacteroidales UCG-001, Rikenellaceae RC9 gut group, Coprostanoligenes group, the UCG-002 genus within Oscillospiraceae, and the UCG-010 genus and family within Oscillospirales. Four additional BCG were correlated with some combination of ADF/NDF and CP/DE. The BCG negatively correlated with ADF/NDF, and thus positively correlated with CP/ DE contained ASV members assigned to additional taxa including Ruminococcus, Anaerovibrio, Pseudobutyvibrio, Lachnospiraceae UCG-009, probable genus 10 of Lachnospiraceae, and the WCHB1-41 genus, family and order of Kiritimatiellae. The BCG positively correlated with ADF/NDF, and negatively correlated with CP/DE included ASV within Bacteroidales BS11 gut group and *Clostridium sensu* stricto 1. Forage ADF and NDF were also negatively correlated with BCG_300 (*Streptococcus and* Lachnospiraceae XPB1014), but this BCG was not correlated with either CP or DE. Forage CP was also positively correlated with BCG_35, for which there was no relationship with ADF, NDF, or DE, but for which there was also a negative correlation with NSC and WSC. The ASV members of BCG_35 were assigned to taxa including Akkermansia, Fibrobacter, Treponema, Christensenellaceae R-7 group, and Family XIII AD3011 within Anaerovoracaceae.
Forage NSC and WSC were correlated with 13 and 12 BCG, respectively (rs ≥ |0.30|; p ≤ 0.05). In addition to the above-mentioned taxa from ASV within BCG_35, ASV within BCG negatively correlated with both NSC and WSC were assigned to Lactobacillus equigenerosi and genera including Lachnoclostridium, Cellulosilyticum, Catenisphaera, Marvinbryantia, Erysipelatoclostridium, Mogibacterium, Lachnospiraceae XPB1014 group, Lachnospiraceae UCG-009, Coprostanoligenes group, UCG-005 metagenome within Oscillospiraceae, the Hallii group within Lachnospiraceae, the NK4A214 group of Oscillospiraceae, the Incertae *Sedis genus* of Ethanoligenenaceae, the WCHB1-41 genus, family, and order within Kiritimatiellae, as well as Denitrobacterium detoxificans. Additional ASV were assigned only to the family level for Synergistaceae, Lachnospiraceae, Oscillospiraceae, and Anaerovoracaceae. Forage NSC and WSC were positively correlated with BCG_94 (*Clostridium sensu* stricto 1, Prevotella, and Sarcina). Forage ESC was correlated with 9 of the same BCG as NSC and WSC, but also had a negative correlation with BCG_173, which contained multiple ASV assigned to Lachnospiraceae, probable genus 10 within Lachnospiraceae, Pseudobutyvibrio as well as to the p-251 genus and family of Bacteroidales. Starch was positively correlated with BCG_113 and BCG_300 and negatively correlated with two BCG, BCG_43 and BCG_48, containing ASV from Sphaerochaeta, Phascolarctobacterium, Christensenellaceae R-7 group, Coprostanoligenes group, Bacteroidales UCG-001, Rikenellaceae RC9 gut group, the NK4A214 group of Oscillospiraceae, the UCG-002 genus within Oscillospiraceae, and the UCG-010 genus and family within Oscillospirales; these BCG were also among those positively correlated with ADF/NDF and negatively correlated with CP/DE.
Relationships between fecal variables and forage nutrients were also evaluated through correlation analysis (Figure 6). Acetate, butyrate, propionate, valerate, isobutyrate, isovalerate and total SCFA and BCFA were positively correlated with DE (Spearman correlation; rs ≥ 0.62) and CP (rs ≥ 0.41), but were negatively correlated with NDF (rs ≤ −0.48) and ADF (rs ≤ −0.57; p ≤ 0.008). There was also a weaker positive correlation between these fecal metabolites and starch (rs ≥ 0.39; p ≤ 0.01). Total BCFA, isobutyrate, and isovalerate were negatively correlated with NSC, WSC, and ESC (rs ≤ −0.37; p ≤ 0.02), with a weaker negative correlation between valerate and NSC and WSC (rs ≤ −0.34; p ≤ 0.03). Hexanoate and heptanoate were negatively correlated with DE, CP, and starch (rs ≤ −0.39; p ≤ 0.01), but were positively correlated with NDF, ADF, NSC, WSC, and ESC (rs ≥ 0.37; p ≤ 0.02). Conversely, fecal pH was positively correlated with ADF (rs = 0.47; $$p \leq 0.002$$), but negatively correlated with DE, NSC, WSC, ESC, and starch (rs ≤ −0.32; p ≤ 0.04).
## 3.7. Blood Samples and Glycemic/Insulinemic Responses
Plasma glucose responses to OST administration differed by forage (mixed model ANOVA with Tukey’s post hoc adjustment; p ≤ 0.01). The AUC for glucose was lowest when horses were adapted to WSG (42.4 ± 6.8 mg/dL*h) vs. CSG-SP (70.0 ± 6.8 mg/dL*h) and HAY-FA (76.3 ± 6.8 mg/dL*h; p ≤ 0.03; Figure 7a). Peak plasma glucose was also lower for WSG (110 ± 3 mg/dL) in comparison to HAY-FA (123 ± 3 mg/dL; $$p \leq 0.01$$), and there was a trend for lower peak plasma glucose for WSG than for CSG-SP (120 ± 3 mg/dL; $$p \leq 0.06$$; Figure 7b).
Forage did not, however, impact OST insulin responses. Neither AUC (CSG-SP: 36.1; WSG: 29.1; HAY-FA: 32.7 ± 6.4 mIU/L*h) or peak plasma insulin (CSG-SP: 28.8; WSG: 25.5; HAY-FA: 26.6 ± 3.3 mIU/L) varied by forage (Figure S2). Fasting plasma insulin (CSG-SP: 3.69; WSG: 5.04; HAY-FA: 5.20 ± 0.64 mIU/L) and glucose (CSG-SP: 81.4; WSG: 78.7; HAY-FA: 81.3 ± 1.4 mg/dL) also did not differ by forage (Figure S3).
There were trends for differences by forage in proxies for insulin sensitivity including the fasting glucose-to-insulin ratio (FGIR) and reciprocal of the square root of insulin (RISQI; mixed model ANOVA with Tukey’s post hoc adjustment; p ≤ 0.10; Figure S4). Differences in FGIR and RISQI were limited to WSG (FGIR: 25.2 ± 16.4; RISQI: 0.58 ± 0.04) vs. HAY (FGIR: 16.4 ± 2.1; RISQI: 0.46 ± 0.04; $$p \leq 0.08$$). However, the modified insulin-to-glucose ratio (MIRG), a proxy for the insulin secretory response, did not vary by forage (Figure S4).
## 3.8. Relationships between Glucose Metabolism and the Fecal Microbiota
Glucose and insulin dynamics were only assessed after three of the forages (CSG-SP, WSG, and HAY-FA), and this smaller dataset precluded the use of random forest regression modelling for these variables. As the primary metabolic differences in grazing horse metabolism were AUC and peak plasma glucose in response to OST administration, correlation analysis was conducted to explore relationships between these metabolic variables and BCG. Only one BCG (of the 25 BCG in the reduced feature set) was correlated with AUC (rs = −0.48; $$p \leq 0.04$$), with members of BCG_124 including ASV within Papillibacter, Christensenellaceae R-7 group, and Synergistaceae. Four additional BCG were correlated with peak plasma glucose. Positive correlations were found between peak glucose and BCG_51 and BCG_305 (rs ≥ 0.43; p ≤ 0.04). These BCG included ASV mapped to genera including Lachnospiraceae XPB1014, Lachnospiraceae UCG-009, Erysipelatoclostridium, Catenisphaera, and Fibrobacter as well to the family level for Anaerovoracaceae. Conversely, BCG_113 (Clostridium butyricum, Papillibacter, Bacteroidales RF16 group) and BCG_259 were negatively correlated with peak glucose (rs = −0.44; $$p \leq 0.03$$). Members of BCG_259 included ASV assigned at the family level to Lachnospiraceae in addition to the Hallii group within Lachnospiraceae.
While relationships between AUC and peak plasma glucose and forage nutrients were identified through correlation analysis (Spearman correlation; rs ≥ |0.41|; p ≤ 0.04; Figure 7c), these metabolic variables were not correlated with any fecal variables including SCFA, BCFA, and pH. Forage NSC, WSC, and ESC were positively correlated with AUC (rs ≥ 0.53; p ≤ 0.007) and peak glucose (rs ≥ 0.41; p ≤ 0.04). There was also a negative correlation between CP and glucose responses to OST administration (rs ≤ −0.50; p ≤ 0.01). Forage DE, NDF, ADF, and starch, however, were not correlated with AUC or peak glucose.
## 4.1. Forage Type and the Microbiome
Warm-season grasses can be utilized to bridge the “summer slump” forage gap in cool-season grass grazing systems [1,2], and differences in NSC between these forage types could have implications for equine metabolic health [1,5,9]. However, there is limited information on the impacts of grazing warm-season grasses on the equine hindgut microbiome as well as potential associations between forage nutrients, the hindgut microbiome, and equine metabolic responses. Therefore, this study aimed of to characterize the fecal microbiota of horses adapted to different forage types and to explore relationships between forage nutrients, microbial composition, fecal metabolites, and glycemic responses of grazing horses. Results of this study clearly demonstrated that shifts in fecal microbiome structure and species composition occur as horses are adapted to different forages within an integrated warm- and cool-season grass rotational grazing system. This was supported by statistical differences in both α- and β-diversity. Furthermore, random forest classification modelling was able to predict forage type based on microbial community composition, indicating the influence of forage type on the fecal microbiome. Finally, forage-specific BCG were identified through LEfSe analysis, but the 25 BCG identified as most predictive of forage type through random forest modeling and subsequently analyzed by LEfSe represented only ~$10\%$ of the total fecal microbiota across all forages. This indicates that distinct and identifiable shifts in microbial composition do occur as horses adapt to different forage types. However, the majority of the microbiome (~$90\%$ in the current study) is resistant and/or resilient to potential perturbations induced by transitioning among forage types with different physical and chemical properties, including between cool-season and warm-season grass pastures.
The BCG identified as markers specific to HAY-SP included multiple ASV assigned to taxa to which fibrolytic and butyrate-producing functions have been previously ascribed [74,75,76]. The BCG specific to this forage contained a heavy representation of ASV within the Lachnospiraceae family as well as Fibrobacter, Ruminococcus, and Pseudobutyvibrio. Prior studies have also reported increased prevalence of Lachnospiraceae in horses fed hay vs. pasture [19,77]. Conversely, ASV assigned to Anaerovibrio were also identified in BCG markers of HAY-SP. Increases in this genus in response to abrupt inclusion of dietary starch have been documented [78] as well as in temporal proximity to oral administration of oligofructose in experimental laminitis induction models [79,80]. The co-occurrence of bacteria in these taxonomic groups could potentially be reflective of the nutrient composition of HAY-SP, which was the highest in both fiber and NSC of all forages. Co-occurrence of these bacteria may also reflect cross-feeding relationships between bacteria and/or metabolic plasticity of bacterial populations. These factors may also account for unexpected associations revealed by analysis in the current study, such as ASV assigned to *Streptococcus within* BCG markers of HAY-FA, despite the relatively low NSC content of this forage. However, Zhu et al. [ 77] also reported a lower abundance of species within Streptococcaceae in horses maintained on pasture vs. horses fed hay or silage [77].
Overall, the fecal microbiota of horses adapted to cool-season pasture was characterized by bacteria capable of utilizing rapidly fermentable fibers, which could explain the greater fecal SCFA concentrations for CSG-SP and CSG-FA. Capacity for hemicellulose fermentation and SCFA production have been previously documented in the Bacteroidales BS11 gut group [81], which was identified only in BCG markers of CSG-SP. Equine studies have found increased relative abundance of Bacteroidales BS11 gut group in horses fed barley vs. a hay diet [82] and in horses presenting with colic [83,84]. For CSG-FA, BCG markers included genera such as Alloprevotella and Erysipelatoclostridium, which are also associated with degradation of fermentable fibers [85,86,87]. The BCG markers of CSG-FA also included ASV assigned WCHB1-41 clade within Kiritimatiellae. Prior studies have found enrichment of the phylum Kiritimatiellaeota in the fecal microbiota of horses with equine metabolic syndrome and insulin dysregulation [26,27]. In contrast to results of the present study, Fitzgerald et al. [ 27] reported increased abundance of this phylum in response to a change from pasture to a hay diet in both healthy and insulin dysregulated ponies, and Ericsson et al. [ 88] similarly found increased abundance of the class Kiritimatiellae when horses with equine metabolic syndrome were transitioned from pasture to a hay diet. These conflicting results reinforce that nutritional composition of specific forages, rather than form of forage alone, need to be considered when evaluating interstudy effects of diet on the equine microbiome.
A number of interesting relationships were found between BCG identified as markers specific to WSG and fecal variables, forage nutrients, and horse glucose metabolism. Warm-season grasses are characteristically lower in soluble carbohydrates than cool-season grasses [1,5], and of all forages evaluated in the current study, NSC and WSC were lowest in WSG. Four of the five BCG markers of WSG were negatively correlated with forage NSC and WSC, including BCG_35, which contained ASV assigned Akkermansia as well as Fibrobacter, Treponema, Christensenellaceae R-7 group, and the Family XIII AD3011 group of Anaerovoracaceae. Akkermansia has been linked to metabolic health via regulation of inflammatory responses and has also been explored for probiotic use in animal species [89,90,91,92]. Lindenberg et al. [ 93] recently demonstrated immune (and inflammatory) modulation by specific hindgut microbiota in horses, including Akkermansia spp. Furthermore, supplementation with mannanoligosaccharides and fructooligosaccharides led to increased abundance of *Akkermansia muciniphilia* in a subsequent study [94], and improvements in insulin sensitivity have been previously documented in horses supplemented with low doses of fructooligosaccharide [95,96].
Markers of WSG also included BCG_113, which contained an ASV assigned to Clostridium butyricum. Clostridium butyricum is a prolific butyrate producer that has also been investigated for probiotic use due to its capacity to promote anti-inflammatory responses and improve gut barrier function and metabolic health [97,98,99]. BCG_35 was positively correlated with CP as well as isobutyrate and total BCFA, which reflects the proteolytic capacity of this BCG. BCG_113 was also positively correlated with total SCFA and all three major SCFA (acetate, butyrate, and propionate) in addition to isobutyrate, isovalerate, and total BCFA. This BCG was also negatively correlated with peak plasma glucose. These findings suggest that this bacterial group including *Clostridium butyricum* could play a role in modulation of equine metabolic health. While limited connections between the gut microbiota and metabolic responses were observed in the current study, three of the five BCG correlated with AUC and peak plasma glucose were WSG-specific BCG markers. Further research is necessary to determine if other WSG species or varieties would produce similar effects as those observed in the current study.
While Akkermansia and *Clostridium butyricum* have been investigated in mice and other species, comparatively little research has been conducted to understand the function of these bacteria in the equine hindgut. The relationships found between ASV in these taxa and forage nutrients, fecal metabolites, and equine glycemic responses in addition to associations with low-NSC warm-season grasses in the current study support further research to determine the role of these bacteria, factors including dietary interventions that can promote prevalence in the hindgut, and potential probiotic applications.
## 4.2. Forage Nutrients and the Microbiome
Results of this study also confirmed the strong influence of dietary nutrients on the equine microbiome and provided insights into the complex relationships between forage nutrients and the gut microbiota. The concentrations of several nutrients could be predicted based on microbial community composition. Furthermore, regression model accuracy improved when the reduced feature set identified through prior forage classification modeling was utilized for prediction of nutrient concentrations. This indicates that differences in forage nutrients were driving the shifts in equine fecal microbial communities as horses adapted to various forages within the integrated grazing system. Results of this study revealed the influence of soluble carbohydrates including NSC and WSC on fecal microbial community composition. In contrast, fiber was not identified as a key nutrient shaping differences in microbial communities. Forage NSC and WSC concentrations could be predicted based on microbial community composition with an accuracy of R2 = 0.61 and R2 = 0.67, respectively, while predictive accuracy for ADF and NDF was < 0.40 (R2). Random forest modeling also revealed that in addition to soluble carbohydrates, the gut microbiota were also influenced by CP (R2 = 0.62). The impact of CP on the gut microbial community was interesting, but unsurprising, as two-thirds to three-fourths of total tract nitrogen digestion and absorption occurs in the equine hindgut [100], and many microbial species are capable of proteolytic fermentation [101].
Prior studies in horses have found distinct effects of high-fiber vs. lower-fiber diets on the hindgut microbial community, with benefits of increased fiber including greater microbial diversity [102], reduction of lactic-acid producing bacteria [101,103], and less-acidic hindgut pH [104,105]. However, these previous studies have been primarily conducted in horses fed concentrate vs. forage-based diets in which there was broad variation in fiber concentrations between treatments. In the current study, horses were maintained on a forage-only diet throughout, and there were relatively high concentrations of NDF and ADF found across all forages. It is possible that only minimal shifts occur in bacterial populations most responsive to fiber above a certain concentration threshold, and that these populations remained relatively stable throughout the study (and thus fiber concentration was not able to be predicted with strong accuracy by random forest modeling). However, it should be noted that the concentrations of NSC, WSC, and CP in forages is low in comparison to that of fiber. Additionally, in contrast to fiber, these nutrients are all subject to digestion in the equine foregut, and only a fraction of the total soluble carbohydrates and CP ingested would be available for bacterial fermentation in the hindgut. Thus, only a small amount of these nutrients were capable of exerting influence on gut microbial communities. Digestibility was not evaluated in the current study, however, and a more in-depth analysis of total digestibility and digestibility of specific nutrients would be necessary to fully understand the interactions between forage nutrients and the gut microbiota.
## 4.3. Fecal pH and Fermentation Metabolites
Fecal pH is commonly utilized as a marker of microbial activity in the equine hindgut [20,103]. Differences in fecal pH in the current study indicated that functional changes occur in the equine gut microbiota, in addition to shifts in microbiome structure and composition, as horses adapt to different forages. Fecal pH was highest in horses adapted to WSG and HAY-FA, which were lower in NSC than CSG-SP, CSG-FA. Accordingly, fecal pH was negatively correlated with forage NSC. Lower fecal pH is broadly associated with hindgut dysfunction in horses [106,107], and thus the higher fecal pH in horses adapted to WSG in comparison to cool-season pasture could suggest some benefit to grazing horses on warm-season grass pastures. However, mean pH was above 6.5 for CSG-SP, CSG-FA, and HAY-SP, which is within ranges previously documented in healthy forage-fed horses [33,108]. Therefore, while statistically significant, the difference in fecal pH between horses adapted to WSG vs. cool-season pasture may not be of physiological relevance.
Differences in fermentation metabolites across forages did not mirror results for fecal pH, as differences between forages for SCFA and BCFA were primarily between pasture forages and the hay diets. Numerically, SCFA and BCFA concentrations in horses adapted to WSG were intermediate between the cool-season pasture (greatest) and hay (lowest), but these differences were not statistically significant. Furthermore, while statistically significant, the predictive accuracy of random forest regressors for fecal metabolites and pH were lower in comparison to those found for forage nutrients. Numerous studies have noted that while the gastrointestinal tract harbors a phylogenetically diverse community, many unrelated microorganisms are capable of performing similar functions [109,110,111,112,113]. The relatively poor predictive accuracy of regressors for fecal metabolites in the current study is likely due to this functional redundancy within the equine microbial community. Functional redundancy has been previously suggested as a contributing factor in stability of the gut microbial communities [111,113] including within the equine microbiome during transitions between warm- and cool-season grasses [24].
## 4.4. Glucose and Insulin Metabolism
While there were distinct shifts in the fecal microbiota across forages in the integrated system, forage type exerted minimal effects on glucose metabolism. A large body of research, primarily conducted in mouse models, has established that diet (and dietary intervention) modulates metabolic health in a microbiome-dependent manner [29,30,114]. However, shifts in gut microbial structure and function may precede changes in metabolic outcomes [115]. The adaptation period utilized in the present study was of a similar duration as used in prior studies evaluating the microbiome of forage-fed horses [22,23,116], and is considered sufficient for stabilization of microbial communities [95]. However, longer treatment periods are often required to assess metabolic adaptations to diet [13,103,117,118]. The lower AUC and peak plasma glucose in horses adapted to WSG in the current study does suggest that glucose may be cleared more rapidly in horses adapted to this forage, but a longer adaptation period would be necessary to confirm these results. Regardless, glucose and insulin responses to the OST for all forages evaluated in the current study were within normal and previously reported ranges [12,34], indicating that substantial metabolic changes are unlikely within the context of integrated rotational grazing management even with the observed changes in the fecal microbiota, fecal metabolites, and pH. Additionally, the lack of correlation between any fermentation metabolites and glucose responses, as well as the relatively small number of BCG correlated with AUC and peak plasma glucose, suggest that any difference in glycemic responses of horses in the current study may not have been heavily dependent on shifts in the hindgut microbiome.
## 4.5. Additional Considerations
Seasonal and environmental factors should be considered when interpreting results of this study. Prior studies have reported seasonal changes in microbial diversity and species composition [119,120,121] but also lacked a true seasonal control, and thus, the effect of seasonality on the equine hindgut microbiome requires further investigation. Seasonal variance in insulin sensitivity has been documented in grazing horses [15,122], but when fed controlled diets without nutrient fluctuations seen in pasture forages, minimal seasonal differences in circulating glucose and insulin and insulin sensitivity have been found in healthy horses [15,123,124]. Environment and management factors beyond season can also impact pasture forage nutrient composition. Characterizing the microbiome of a larger number of grazing horses over multiple years and in multiple locations/regions would allow a more robust evaluation of the impacts of cool- versus warm-season grasses on the equine hindgut microbiome.
Finally, it should be noted that the analytical approach in this study differs from more conventional taxon-based analysis. Rather, this study implemented a guild-based approach, grouping individual microbial ASV by co-abundance to subsequent analysis of abundance profiles [123,124,125,126,127]. This strategy has been previously utilized as an alternative to grouping bacteria by taxonomy [24,29,30,92,121]. *Substantial* genetic variation is possible within taxa, even at the species level, and therefore bacteria with similar taxonomic assignments may not represent a functionally homologous group [29,92,127]. Results of the current study also illustrate this concept, as ASV with the same assigned taxonomy were found in BCG characteristic of different forages as well as in separate BCG that positively and negatively responded to forage nutrients and with both positive and negative relationships with fecal metabolites and grazing horse metabolism.
## 5. Conclusions
In conclusion, distinct shifts in equine fecal microbial community structure and composition occur as horses adapt to different forages within an integrated warm- and cool-season grass rotational pasture system, but a substantial impact of this management practice on glucose metabolism in healthy adult grazing horses is unlikely. Forage NSC, WSC, and CP were the most influential nutrients driving these shifts in microbial composition. Results of this study underscore the potential for relatively small amounts of NSC to influence hindgut microbial composition and also that protein utilization may be an important ecological niche within the microbiome of forage-fed horses. Fecal BCFA and SCFA concentrations were higher in horses adapted to all pasture forages versus hay, but in comparison to forage nutrients, bacterial community composition did not have as strong an impact on fermentation metabolites, likely reflecting functional redundancy of the microbial community. The guild-based analytical approach utilized in this study also identified key relationships between specific bacterial groups associated with adaptation to warm-season grass pasture and forage nutrients, fecal metabolites, and equine glycemic responses to administration of oral sugar tests. Relationships identified in this study revealed new insights and targets for future research necessary to better understand the function of Akkermansia spp. and *Clostridium butyricum* in the hindgut microbiome of grazing horses, as these bacteria may play a role in modulation of equine metabolic health.
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---
title: 'Two Worlds in One: What ‘Counts’ as Animal Advocacy for Veterinarians Working
in UK Animal Research?'
authors:
- Renelle McGlacken
- Alistair Anderson
- Pru Hobson-West
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000174
doi: 10.3390/ani13050776
license: CC BY 4.0
---
# Two Worlds in One: What ‘Counts’ as Animal Advocacy for Veterinarians Working in UK Animal Research?
## Abstract
### Simple Summary
The concept of advocacy is of increasing importance to the veterinary profession internationally, yet there are concerns about what it means in practice. This paper explores what ‘animal advocacy’ involves for veterinarians working in an area where its performance may appear particularly challenging, that of animal research. Based on an analysis of interviews with veterinarians working in UK animal research facilities, we aim to demonstrate both what ‘counts’ as veterinary animal advocacy in this domain and illustrate some of the tensions that may arise in its performance. Focusing on the themes of ‘mitigating suffering’, ‘speaking for’, and ‘driving change’ as three central ways in which veterinarians working in animal research facilities act as animal advocates, we draw out some of the complexities of advocacy for veterinarians working in areas where animal care and harm coexist. Finally, we conclude by calling for further empirical exploration of animal advocacy in other veterinary domains and for more critical attention to the wider social systems which produce the need for such advocacy.
### Abstract
The concept of advocacy is of increasing importance to the veterinary profession internationally. However, there are concerns around the ambiguity and complexity of acting as an advocate in practice. This paper explores what ‘animal advocacy’ involves for veterinarians working in the domain of animal research, where they are responsible for advising on health and welfare. In focusing on the identity of veterinarians working in an arena of particular contestation, this paper provides empirical insights into how veterinarians themselves perform their role as an ‘animal advocate’. Analysing interview data with 33 UK ‘Named Veterinary Surgeons’, this paper therefore examines what ‘counts’ as animal advocacy for veterinarians, considering the way their role as animal advocate is performed. Focusing on the themes of ‘mitigating suffering’, ‘speaking for’, and ‘driving change’ as three central ways in which veterinarians working in animal research facilities act as animal advocates, we draw out some of the complexities for veterinarians working in areas where animal care and harm coexist. Finally, we conclude by calling for further empirical exploration of animal advocacy in other veterinary domains and for more critical attention to the wider social systems which produce the need for such advocacy.
## 1.1. Animal Advocacy and the Veterinary Profession
Acting as an ‘advocate’ for animals is framed as a key principle for the modern veterinary profession. For example, in their 2016 animal welfare strategy report entitled ‘Vets speaking up for animal welfare’, the British Veterinary Association (BVA) highlight the importance of advocacy to the veterinary profession, claiming that ‘Our opportunity to be advocates for animals may be the greatest of all and we have clear social, professional and legal responsibilities to do so’ [1] (p. 9). Also cited here is the World Organization for Animal Health’s assertion that veterinarians should provide leadership on ethical issues and ‘should be the leading advocates for the welfare of all animals’ (ibid), illustrating the global proliferation of the language of ‘advocacy’. Relatedly, in their ‘recalibration’ of the veterinary profession for the 2020s, De Paula Vieira and Anthony [2] argue that ‘society expects veterinarians to be animal advocates’ (p. 7), adding that ‘veterinarians will need to utilise their voice more’ when called upon to provide societal leadership on moral and welfare issues involving animals (ibid, p. 2).
Despite this shared language of veterinary ‘advocacy’, the concept of advocacy is complex and eludes a single definition, encompassing multiple, sometimes incongruent, values and necessitating different approaches in its practice. Capturing this ambiguity in their review of the theory and practice of advocacy in relation to social work, Forbat and Atkinson [3] (p. 322) observe that ‘At its simplest, advocacy means ‘speaking up’ for oneself or others. However, it is rarely that simple’. In the nursing profession, the nurse has long been theorised as a ‘patient advocate’, representing and intervening on behalf of those who are vulnerable [4]. Indeed, Gadow [5] (p. 81) put forward the influential concept of ‘existential advocacy’ as core to the nurse’s role, with nursing described as a process of participating with the individual’s unique experience of health and illness. For Gadow, existential advocacy is ‘based upon the principle that freedom of self-determination is the most valuable human right’ (ibid, p. 84).
Such humanistic conceptualisations of advocacy rely on principles of self-determination and ‘Promoting and protecting patients’ rights to be involved in decision-making and informed consent’ [6] (p. 35). These ideas are not easily transferable to non-human animal advocacy, given communication barriers between humans and animals. A possible parallel is paediatrics. Here, Waterston [7] (p. 155) summarises that ‘Paediatricians advocate for children because they are vulnerable and not usually able to speak for themselves’ and claims that such advocacy is importantly embedded in the principle of partnership with the family [8] (p. 587). However, this differs with animal patients, as Ashall, et al. [ 9] (p. 255) affirm that ‘Whilst medical consent protects a patient’s rights to make autonomous decisions concerning their own body, veterinary informed consent aims to protect an owner’s right to make autonomous decisions concerning their legal property’. This imperfect comparison illustrates the complexity of applying a concept such as advocacy across medical and veterinary contexts.
Indeed, existing veterinary literature confirms the challenges of advocacy in the veterinary context, including the question of balancing what the vet and the client (owner) may desire. For example, Morgan [10] (p. 116) distinguishes between the ‘animal advocate’ and the ‘client advocate’ professional model, with the former prioritising the interests of the animal and the latter prioritising those of the client, though they acknowledge that such models ‘do not have firm boundaries’ in practice. Others have utilised ideas of partnership similar to that raised by Waterston. For example, Gray and Fordyce [11] (p. 10) suggest that both the veterinarian and owner should act as advocates for the animal patient but add that this framework ‘requires elevation of the status of the animal patient to more than just ‘property’ or legal ‘object’’. Yet, if it is the case that the principle of autonomy has been applied to owners rather than animal patients, Hiestand [12] (p. 6) claims that ‘this misstep has serious consequences for both the integrity of the profession and animal patients’. While the ‘opportunity’ for veterinarians to be ‘advocates for animals’ may be great as the BVA urges, there is evident complexity for the individual professional in performing this advocacy on the ground.
In thinking about the current veterinary profession and the extent to which principles of advocacy inform the veterinary role, Main [13] has claimed that despite evidence of public trust in the veterinary profession, the profession needs to work harder to actually fulfil societal expectations regarding their advocacy role. Main adds that although clinicians are justified in their daily ambition to promote animal interests, ’a minimal scratch below the surface reveals obvious tensions in this well-intentioned mantra within the profession’. Furthermore, there is no ‘one’ singular veterinary profession, and particular challenges exist in different contexts. For example, in companion animal medicine, Kipperman and German [14] (p. 5) argue that failing to address problems such as obesity represents an abdication of their animal advocate role. Both Coghlan [15] and Hernandez et al. [ 16] promote a strong and active approach to patient advocacy in the veterinary profession. However, as the latter indicates, ‘Veterinarians embedded within certain animal production industries may find it particularly challenging to separate their ethical obligations to animals from their professional responsibilities to the corporation within which they are employed’ [16] (p. 6).
Another domain in which veterinarians are tasked with managing multiple responsibilities which may challenge their ethical obligations to animals is in animal research. In the UK, ‘Named Veterinary Surgeons’ (henceforth ‘NVSs’) advise on the welfare of animals kept at scientific establishments. As will be discussed, these veterinarians are involved in a contested practice in which animal care often coalesces with harm [17]. Attending to how such veterinarians perform their role through the ‘animal advocate’ identity stands to offer important empirical insights into some of its key characteristics, complexities, and limits in practice.
## 1.2. Animal Research, the NVS, and Animal Advocacy
In the UK, the presence of a veterinarian in all research facilities using animal models is mandated by law under the Animals (Scientific Procedures) Act 1986—‘ASPA’—which governs UK scientific animal use [18]. Being ‘nominated by the establishment license holder and specified in the establishment license’, the NVS is ‘responsible for, monitors and provides advice on the health, welfare and treatment of animals, and should help the establishment license holder to fulfil his/her responsibilities’ [19] (p. 150).
Working under the ASPA, the NVS is also required to uphold the principles of the 3Rs (Russell and Burch 1959). This requires that researchers must aim for ‘the replacement of animals with alternative mechanisms, where possible; the reduction of the number of animals required for a given procedure through statistical or other improvements; and the refinement of experimental procedures to minimise suffering and improve animal welfare’ [20] (pp. 605–606). Crucially, in addition to these core requirements under the ASPA, the NVS is also governed by their professional accreditation and have ‘professional responsibilities to the animals under their care, to other veterinary surgeons, to the public, and to the Royal College of Veterinary Surgeons (RCVS) (under the VSA (Veterinary Surgeons Act 1966))’ [21] (p. 72). This means that the NVS must ‘actively navigate the boundary between these two pieces of legislation, exercising professional judgement to reconcile potentially conflicting tensions arising from multiple professional accountabilities within the laboratory’ [22].
Existing empirical literature on the role of the NVS is sparse, but has focused, for example, on their route into the role from clinical practice [23]; their relationship with the 3Rs [24,25,26]; geographical dimensions of their ethical boundary work [27] and their role in promoting standardisation and regularisation [28,29]. Historians have argued that animal welfare groups were the first to propose the role, imagining the veterinarian to act as ‘the animal’s friend’ [30] (pp. 117–118). Today, the NVS is claimed to be an ‘advocate of the animal’ [31] (p. 26) and, indeed, acting as an advocate is described as the role’s ‘principal reward’ [32] (p. ii). However, existing literature also highlights some of the complexities of performing this advocacy role in practice. For example, Brouwer-Ince [32] (p. ii) has neatly summarised some of these challenges, which involve: This list of challenges is evidence of the ethical complexity of the NVS role, with multiple factors complicating the veterinarian’s responsibilities for the welfare of research animals. In short, the NVS must balance multiple care obligations: caring for research animals, with the welfare of each individual being interwoven with the cohort, caring for the specific and broader scientific goals, and caring for patients and publics invested in both the expected scientific outputs and the protection of animal welfare.
Given these complexities, it is arguably unsurprising that some critics have voiced concern about the extent to which the veterinary profession should even be involved in the practice of animal research. Indeed, during the formalisation of the NVS role through A(SP)A [1986], there was some dissensus within the veterinary profession. As historians Kirk and Myelnikov [30] (pp. 117–118) observe, although the establishment of a veterinarian in the animal research domain was viewed as a compromise between welfare and scientific interests, the prioritisation of the veterinarian’s ‘duty of care for the animal made experiments a difficult sell to many veterinarians’. This is part of a critical debate beyond animal research, which continues today, about whether and how veterinarians are complicit in profiting from the bodily labours of animals [33], their commodification in both life [34] and afterlife [35], and whether or not the profession itself is compatible with ideas of animal advocacy [36].
With this wider context in mind, it becomes increasingly urgent to understand more about how the identity of the veterinarian as an animal advocate is performed by those working within the complex practice of animal research, in which animal care coincides and converges with deliberate harm. In this paper, we therefore examine the performance of veterinary advocacy within animal research facilities through analysis of interviews with NVSs. Our analytical approach draws on the literature already discussed in both the human and veterinary fields. In particular, being inspired by Friese [25], who, following Despret, asks ‘what counts’ to animal technicians ‘in the doing of their work’, we are similarly keen to explore what ‘counts’ as animal advocacy for NVSs. More specifically, we anticipate that the insertion of a veterinarian into the animal research facility as an animal advocate creates the potential for dissensus, establishing ‘the presence of two worlds in one’, to briefly borrow terminology from the political philosophy of Rancière [37] (p. 43), in which the logics of veterinary advocacy within the laboratory to ‘count’ the animals in one way exists alongside the logics of science, which include the requirement to ‘count’ the animals in another.
## 2. Methods
This study focusing on NVSs is part of a broader constellation of work under the Animal Research Nexus Programme (Animal Research Nexus Programme 2019; Davies et al. 2020). The aim of this specific study was to understand more about the role of veterinarians as key and underexplored actors in animal research facilities. Ethical approval for data collection was granted by the School of Veterinary Medicine and Science at the University of Nottingham (approval number 1800160608), and data collection took place in 2018. An interview guide was developed and discussed with an expert advisory panel of three NVSs and was trialled during two pilot interviews with NVSs. The interview format and question focus were subject to revision as the data collection progressed. NVS participants were contacted through snowball sampling initiated via personal networks and a call out during a specialist conference. Thirty-three interviews were carried out in person at a location chosen by the participant or over the telephone. Interviews were transcribed by a third-party under a confidentiality agreement. Transcripts were anonymised and decontextualised and each transcript was assigned a random but gender-specific pseudonym beginning with the letters M, N, O, or P.
An initial inductive thematic analysis [38] of the transcripts was originally undertaken by the second author, using the qualitative data analysis software NVivo 12. This aimed to create analytic themes that reflect meaning-based patterns across the interview dataset. The use of this inductive approach was particularly important as NVSs are an understudied group, and there was minimal prior research on this group to draw upon to inform the study and analysis. Through two coding cycles, this phase of analysis concentrated on drawing out participants’ physical and conceptual actions using their own phrasing and language to prioritise their voices in driving the analysis [39] and then worked to broaden and deepen these categories, focusing on the patterns underpinning them [40]. This coding process was not linear, and in developing the second cycle of codes, the first cycle categories were sometimes modified in title or content as the analyst clarified the key themes.
Following the initial phase of coding, a second phase of thematic analysis was undertaken by the first author. Working with the second author’s coding structure in NVivo 12, the first author created subcodes within the thematic category of ‘Advocate for the animals’. With animal advocacy already identified as an important theme in the dataset, this round of coding was guided by a broad questioning of what ‘animal advocacy’ means to Named Veterinary Surgeons. In doing so, the first author sought to examine how the advocate identity might be configured through particular orientations, enactments, and understandings of their role. These issues formed the basis of discussions between the authors. In drawing on this analysis, this paper therefore explores how the enactment of animal advocacy was characterised by Named Veterinary Surgeons, focusing on the three key aspects of mitigating suffering; speaking for; and driving change that were prominent throughout many of the interviews. Though overlapping in many ways, these themes are organised separately for clarity.
This analysis aims to elucidate how veterinarians working in the ethically complex and contested arena of animal research perform ‘advocacy’ and draw out the tensions that may exist within this. Such tensions have the potential to complicate the dominant image of the veterinarian as ‘the kindly, trained person who knows how to take care of [one’s] companion animal’ [41] (p. 69). In other words, focusing on how veterinarians frame their advocacy for research animals within complex structures in which care and harm coalesce [17] may offer important insights into the reality of the veterinary profession, which is ‘considerably more murky’ than the dominant image [41] (p. 69). As will be returned to in the discussion, such analysis may reveal the limits of veterinary animal advocacy in the research context, how it might be expanded, better supported, and what can be expected of Named Veterinarians as advocates for research animals.
## 3.1.1. Preventing and Limiting ‘Unnecessary’ Suffering
A key way in which participants described enacting their role as an ‘animal advocate’ was through preventing and limiting suffering via the safeguarding of animal welfare. This may sound simple initially, but the analysis reveals how complex this can be in practice. As the following examples illustrate, this can mean drawing lines between ‘necessary’ and ‘unnecessary’ suffering [42,43,44]: As both participants indicate, some amount of animal suffering may be an accepted part of a study and so the NVS must work, as Owen states, ‘to mitigate it as much as possible’. However, at certain points, the NVS may call for an end to an animal’s use within a study, meaning the possible disruption of research aims, as Obadiah notes ‘even despite millions of pounds invested and you have to say, ‘Look…’’. To give another example, the extract below from Mia’s interview demonstrates their desire to ensure that the animals are ‘looked after properly’ and that research does not go ‘too far’ in compromising animal welfare. This is also linked, however, with apparent acceptance of the value of animal research: Through their focus on animal welfare, NVSs can therefore be seen to articulate the importance of preventing and limiting ‘unnecessary’ suffering to their performance of the animal advocate role. However, the analysis reveals that there are limits to this in practice and that, sometimes, the NVSs’ animal advocacy may require ‘ending’ suffering.
## 3.1.2. Ending Suffering
In some cases, preventing or minimising suffering is not deemed possible or sufficient and thus NVSs report the use of ‘euthanasia’ to end the suffering of particular animals. For instance, Oliver indicates that if measures to make animals more comfortable are not working then, as in general practice, the NVS will recommend euthanasia: In animal research, such decision making reflects the guidance against using death as an endpoint for an animal’s use in a study. Rather, researchers are encouraged to implement ‘humane endpoints’. This involves defining ‘clear, predictable and irreversible criteria which substitute for more severe experimental outcomes such as advanced pathology or death’, and, when these are reached, the animal is humanely killed [45].
However, decision-making around euthanasia or humane killing can involve more than assessments of individual animal welfare when deciding what counts as ‘too much’ suffering. *In* general veterinary practice, it may be complicated by the human–animal bond [46,47], the client’s judgment of the animal’s health [48], their wider interests, and their finances [49]. In animal research, with the ‘two worlds’ of veterinary professionalism and scientific practice existing ‘in one’, decisions around killing an animal may also be complicated by the competing interests of researchers and other institutional actors, obligations towards the research aims and helping to ensure meaningful outputs from the experiences of animals, and the statistical value of each animal which interweaves their individual welfare. Indeed, as Michelle discusses, this can create areas of conflict: In describing a situation in which the NVS’s call to remove or discount an animal from a study may meet challenge from researchers, Michelle states that ‘you have to stand up for the animal’. In this example, this can mean ending their life. This extract raises difficult questions, discussed in the wider literature, about the extent to which euthanasia should be understood as a ‘gift’ [50], or whether it should be identified as a harm [51,52,53]. For example, Coghlan [15] (pp. 360–361) contends that ‘The denial that animals can be harmed by death is often connected with a quasi-technical notion of animal ‘welfare’ and ‘Since veterinarians have power over animal life and death, this denial is significant’. In the animal research context, one could also question whether the relative absence of opportunities for rehoming research animals ([54,55]) could be seen to reinforce decisions around humane killing. In other words, the wider context of animal research needs to be appreciated, within which the advocacy of NVSs is articulated.
Overall, we have demonstrated that the NVS’s work to prevent, limit, and end animal suffering is an important part of their advocacy. However, this is arguably premised on a professional acceptance of ‘necessary’ suffering. Critics have argued that such a conceptualisation of ‘necessary’ suffering is itself morally flawed. For instance, Fox [44] (p. 30) has argued that ‘if animals’ lives have value independent of their interests to others, all of their suffering is morally unjustified’. Likewise, in challenging the ‘necessity’ of scientific animal use more broadly, Francione [43] (p. 248) asserts that, although ‘problematic in a number of respects’, the ‘use of nonhumans in biomedical research may involve a plausible claim of necessity […] But such a claim, even if justified, cannot serve to provide a satisfactory moral basis for this use of animals’. Such contestation around the premise of ‘necessary’ suffering which is seen to inform how the welfare of research animals is conceived therefore highlights tensions within the identity of the NVS as an animal advocate within the worlds of scientific practice.
## 3.2.1. Speaking for Research Animals
As well as constructing the performance of advocacy for research animals as intervening in suffering, the NVSs interviewed also emphasised the representational dimensions of their role, describing their work to ‘speak for’ the animals and act on behalf of their best interests. As Margaret discussed: This ‘speaking for’ is here intended to safeguard animals from the harms of the research and the researchers. Margaret clarifies that the harms posed by the latter are not intentional but rather result from a lack of certain knowledge. Indeed, as she puts it, researchers ‘can look at an animal and they won’t see what I will see or the technician would see’. With their embodied expertise and the identification of welfare impacts and diagnosis of illness and disease this enables, Margaret’s description portrays the NVS as thus able to count the animals as part of an ‘intervention of the visible and sayable’ [37] (p. 45). This means representing the interests of research animals, speaking up for them on issues that may be missed by researchers who lack the same levels of knowledge of or familiarity with the presentation of animal suffering.
Similarly framing their advocacy for research animals as juxtaposed to the interests of researchers, Owen describes how they are responsible for speaking up for the research animals ‘against’ the positions of the researchers. In this extract, the initial ‘NACWO’ refers to the Named Animal Care and Welfare Officer: Defining the NVS role as a ‘care person’, Owen here identifies their role and that of the NACWO as posing a challenge to the scientists, whose interests concern the animal’s use as an experimental model. Perhaps half-joking in their description of the moral character and interests of scientists, portraying an oppositional relationship between an animal-loving veterinary surgeon [56] and ‘wicked scientists’, Owen illustrates the conflict that can be expected as part of their role. Yet, later in the interview, Owen discusses how their advocacy can be enacted in conjunction with rather than against scientists if a working relationship has been built from the project’s nascent stages: Owen’s claim here is that it is good practice in regard to animal welfare if the NVS is involved in a project from its inception. In this case, the NVS’s’ advocacy can take the form of sharing expertise, with the NVS advising how best the research aims can be met whilst in-keeping with animal welfare and meaning that all actors are ‘on the same side’. Other participants described how an embracing of the 3Rs by scientists can have a significant impact in changing the NVS’s role from one of opposition to one of collaboration.
Relatedly, in representing and speaking up for the interests of research animals, some participants framed independence as a crucial part of the NVS role, as Nadir expresses: With the NVS’s responsibility to speak for research animals often necessitating challenging the decisions and interests of others, their independence from the interests of researchers and the institution is significant for maintaining appropriate levels of impartiality. However, as Nadir implies, such independence relates not only to the research infrastructure, but also wider political agendas. In being seen as uninvested not only in the research outcomes and institutional goals but also in relation to other stakeholder agendas, Nadir discusses how they aim to present their role to others in the facility as an ‘honest broker’.
Interestingly, in Pielke’s [57] influential typology of the roles that scientists may play in political issues, the ‘honest broker’ is distinct from the role of ‘issue advocate’. Indicating the key difference between the two, Pielke observes that the broker seeks to ‘expand (or at least clarify) the scope of choice’ whilst the advocate ‘seeks to reduce the scope of available choice’ (ibid, p. 18). In advocating on behalf of the research animals’ interests, the NVS may be seen as carrying ‘political baggage’ of a kind. However, Nadir’s excerpt suggests that, to perform their role effectively, it is important to be perceived as helping and not hindering researchers, providing impartial insights into how animals can be used and counted within welfare parameters instead of working to discount or limit their use altogether.
## 3.2.2. Speaking for Society
The advocacy element of the NVS role may be framed, not only as representing the interests of research animals within a facility, but also representing wider society’s interests in animal welfare. As Nicholas suggests: As well as caring for the research animals in their charge, Nicholas indicates that an important responsibility of the NVS is to act as ‘the people’s representative of the animals’, representing the wider public interest in the protection of animal welfare. This perspective emphasises that the NVS is more than ‘just’ a representative for the animal, they also perform a social role mediating between science and society. This reflects the importance of public opinion in shaping the governance of animal research, a topic that has been itself explored in some depth [58,59,60,61]. In this case, public concern for animal welfare and trust in the veterinarian to embody ‘the side of the animals’ is considered to have created a professional niche for veterinary surgeons in the establishment of the NVS role.
Other participants invoked wider publics in their claims about their complex professional responsibilities: Here, publics are figuratively present in the form of taxpayers who contribute to the funding of scientific research and as patients who are expected to ultimately benefit from the research. In this framing, the NVS is constructed as responsible for assisting the return on this financial and affective investment by ensuring that animal research is conducted to appropriate standards that facilitate the materialisation of promised and expected outputs. Publics are thus enrolled and counted here as one of the ‘objects of care’ [62] (p. 60) for the veterinarian, alongside the animals and the scientific output.
In summary, NVSs in our study articulate obligations for multiple objects of care, with their relationship to animal welfare being entangled with the project’s scientific aims and also the wider interests of publics invested in both the research outputs and the protection of animals. Indeed, Ashall and Hobson-West [63] (p. 292) argue that ‘NVS responsibilities to a scientific establishment under A(SP)A should be viewed as additional to the multiple responsibilities which are faced by all veterinary surgeons, and which have previously been identified as a potential source of ethical conflict’. The plurality of the NVS’s care obligations suggests that although their independence from the research itself is important, they must negotiate logics that count the animal as an experimental model, shaping the NVS’s advocacy for research animals.
## 3.3.1. Changing from the ‘Inside’
Thus far, this analysis has highlighted the tensions involved in the NVS’s performance of the animal advocate role. Given the limits of advocacy in the scientific context within which NVSs operate, it is perhaps understandable that some participants connected their role as an animal advocate to driving change in research practice. Indeed, prompted by the interviewer’s discussion of the potential conflict in constructing the NVS as an animal advocate whilst their work can be seen as facilitating uses of animals that contravene their interests, Margaret argues that by being on the ‘inside’, the NVS role provides an opportunity to affect change: The NVS here is framed as an agent of change within animal research facilities, able to initiate difficult conversations with scientists and organisational actors which would not be possible if they were ‘standing outside’. This was contextualised differently by Nadir, who contrasted professional responsibilities towards animals with ethical responsibilities to advancing human medicine and preventing human illness and deaths. Nadir listed multiple relatives and members of their local community who had died of various diseases or lived with chronic conditions, and having justified animal research in this context echoed Margaret’s sentiment: Nadir draws a clear distinction here between human and non-human animals, having a ‘foot in both camps’ that contextualises their ethical and professional responsibilities towards the two groups. However, following Clarke and Knights [64] (p. 269), this assumption that animals are needed to address historic failures in human medicine is to reproduce narratives of ‘anthropocentric masculinities’ which are common in veterinary practice, whereby ‘men seek to transform animals and nature into orderly, predictable and serviceable objects of human(istic) desire’. Interesting here, is the way in which, Nadir works through the tension they articulate between their ethical and professional responsibilities by being on the ‘inside’ of animal research. Such justifications of the NVS role and the possibilities they provide to advocate for research animals, rather than ‘standing on the outside griping’, raise broader questions around the lack of opportunities for both publics and professionals outside of organisational frameworks to participate in decision-making processes and influence change within animal research.
## 3.3.2. Changing Practice
In discussing their desire to affect change, several participants referred to the implementation of the 3Rs as a key responsibility of the NVS. In working to implement the 3Rs, the NVS’ animal advocacy is framed as necessitating an approach which goes beyond individual animal welfare and is also concerned with driving best practice. As Parker discussed: Parker differentiates between the core work of a veterinarian who is ‘there for the animals’ and the work of the NVS who is ‘employed by the institute […] not only to tick boxes’, describing the latter as going further than caring for animal health and welfare and also working to drive uptake of 3Rs approaches and techniques. In particular, Parker characterises their work around replacement as especially difficult, with their role embedded in the day-to-day animal use.
Similarly, other participants highlighted the important yet tricky nature of the NVS’s advocacy for the replacement of animal models. As Maeve describes in this interview exchange: Interviewer: NVSs should?
In this excerpt, Maeve makes a distinction between ‘giving independent advice in a welfare situation’ and challenging decisions which may impact on animal welfare (e.g., single housing of animals). Such a differentiation suggests that for the NVS to be effective in their animal advocacy, they must recognise the appropriate moments when challenge is called for and appreciate the equal importance of advice and education. Indeed, in the context of paediatric medicine, Waterston [7] (p. 156) states that ‘Advocacy does not mean storming the White House or number 10 Downing Street; instead, it is using the wherewithal of professional expertise, credibility and experience to draw attention to issues and execute beneficial changes’.
Yet, the specific challenges that surround the NVS’s work in the area of replacement mean that the ways they can advocate is more easily directed at improving the ways in which animals are used (in terms of getting the best scientific output from the animals and safeguarding animal welfare) rather than preventing their use at all. Such barriers to advocating for replacement may hint at the pitfalls of becoming professionalised within systems one may aim to ultimately transform or disrupt. Hence, as some extracts included earlier in this section suggest, although being on the ‘inside’ may be said to enable veterinarians to question scientists on their animal use and hold challenging but important conversations aimed at stimulating better practice, the orientation of the NVS role towards animal use may impede their advocacy around the ways in which animals might be replaced.
## 4. Discussion
In seeking to enrich understandings of what ‘animal advocacy’ means for veterinarians working in animal research, this paper has focused on three key ways in which Named Veterinary Surgeons enact their role as an ‘animal advocate’, that is, through mitigating suffering, speaking for, and driving change.
The first thematic section analysed the link between the NVS’s advocacy and their work to mitigate animal suffering and considered how the requirements of the research context may orient the advocacy of NVSs towards protecting animals from the impacts of a study, rather than cultivating lives ‘worth living’ [65] per se. This section also highlighted how the mobilisation of distinctions between necessary and unnecessary involves some acceptance of degrees of animal suffering; in other words, NVSs bring together logics of veterinary professionalism and scientific practice. Finally, this section touched on how the work of NVSs to limit unnecessary suffering plays out within the confines of the research infrastructure when an animal’s suffering cannot be controlled, with euthanasia or ‘humane killing’ discussed by several participants as an important part of how their advocacy is practiced. Given existing challenges around the rehoming of research animals [54,55], advocating for the humane killing of animals may often be one of the only options available to NVSs when advocating for the removal of an animal from a study.
Scholars are increasingly recognising the way in which care, harm, and killing coalesce in animal research [17,66] and in wider areas of veterinary practice. Indeed, Venkat [67] (p. 1) considers that cruelty ‘might be figured as an unavoidable aspect of the relation of dependency between animals and their human caretakers’ and suggests that in and through veterinary medicine ‘humans and their particular forms of cruelty are perhaps unavoidably implicated in the deaths of animals’ (ibid, p. 14). This reminds us that the case of veterinary involvement in animal research is not the only example of how veterinary animal advocacy may be conflicted or contested. Our analysis thus might contribute to these wider debates by serving to highlight the ways in which veterinary animal advocacy is inherently complicated and open to contestation and involves a negotiation of multiple and often divergent interests within broader paradigms of anthropocentricism.
The second thematic section, that of ‘speaking for’ research animals and wider society, illustrated how many NVSs framed their relationship with researchers as often being one of challenge, with the professional dynamics of their two different worlds associated with a common occurrence of conflict when they exist together. However, some participants described how when the NVS’s involvement from the formation of a study and the prioritisation of animal welfare and the 3Rs is evident throughout, then the NVS’s advocacy can more often take the form of advising rather than challenging researchers. Although the participation of the NVS in their institution’s Animal Welfare and Ethical Review Body (AWERB) is mandatory in the UK, this analysis suggests the importance of relational dimensions of cooperation, beyond the formal institutional processes of ethical review. However, we also considered how presenting themselves as an impartial mediator may stir tensions with their identification as animal advocates, with their advocacy framed here as assisting with the scientific use of animals within acceptable welfare parameters, rather than advocating for their interests outside of this paradigm.
This last point raises questions beyond the identity of veterinarians and encourages us to consider which versions of the animal are being counted or represented within animal advocacy. Here we find Weich and Grimm’s [68] analysis useful in arguing for more ‘critical scrutiny of the normative social structure’ in which an animal patient’s interests emerge and an ‘interrogation of animals’ activities within that structure’. With understandings of research animals fluctuating between the naturalistic and the analytical [69], model and pet [70,71], individual and colony, wild and domestic, this provocation pushes us to consider what kinds of patients are produced through animal research for which the veterinarian is responsible. Future research might therefore focus more directly on how Named Veterinarians and other professionals who advocate for the welfare of research animals construct their animal patients, the meanings they make of their health and illness, what these involve, and where they begin and end. Further work could also explore where this differs internationally (as called for by Anderson and Hobson-West [27]). Future studies might therefore also question the extent to which animal advocacy in the research context is complicated by conceptualisations of the animal as both patient and scientific tool, and how their other roles and accordant interests may or may not be given opportunities to flourish in research facilities.
The final theme explored the way in which NVSs discuss advocacy as partly about driving change in practice. That some participants described the NVS role as enabling them to advocate for research animals by permitting them entry inside the research infrastructure arguably draws attention to the current lack of opportunity for veterinary professionals outside of animal research to participate in driving good practice and inform policymaking on scientific animal use. This point relates to broader calls to open up this arena to more actors, including publics [61]. The current paper also highlights how the construction of the role of the NVS as an ‘independent insider’ might generate both opportunities and limitations for veterinary animal advocacy for research animals. For example, this analysis has pointed to the ways in which a focus on the day-to-day use of animals may restrict the NVS’s animal advocacy around the replacement of animals. Given the relationship between the NVS and wider publics discussed in the first analytical section, and evidence that publics are invested in full replacement of animals with alternative models [72,73], this may prove problematic in the longer term.
## 5. Conclusions
This paper began by demonstrating the way in which the veterinary identity is closely associated with acting as an animal advocate. However, in doing so, we have also argued that definitions of advocacy are problematic and illustrated that transferring the concept from the human to the animal patient is not straightforward. We then introduced the role of the NVS and the ways in which assumptions about advocacy are built into this role. Whilst the role of the NVS is quite specific, we hope that this analysis will serve to enrich the definition of ‘animal advocacy’ in veterinary practice more broadly. Indeed, as Anderson and Hobson-West [23] (p. 6) have previously argued, ‘Focusing on the experiences of Named Veterinarians may therefore help us grapple with some of these fundamental questions about the future of the profession and the role of veterinary expertise in society’. This is arguably particularly urgent given the veterinary profession finds itself at ‘somewhat of a crossroads’ regarding the nature of its professionalism and the future of the profession in the UK as it addresses a number of concurrent crises [23].
Returning to the BVA’s proclamation that veterinarians’ ‘opportunity to be advocates for animals may be the greatest of all and we have clear social, professional and legal responsibilities to do so’ [1] (p. 9), this paper has illustrated the trickiness in defining veterinary advocacy for animals, demonstrating the tensions surrounding the veterinarian’s advocacy for animals whilst working within arenas governed by both care and harm. This description not only applies to the animal research context, but arguably pertains to the majority of human–animal relations and the veterinarian’s work as a, not independent, but thoroughly partial mediator of them [74]. After all, veterinarians are not external to the cultures which radically shape their patients’ experiences of health and illness. Given this broader context, we would encourage more practical support for veterinarians working in all fields, to scrutinise the specific structures and practices of power which produce animals as patients in the first place.
This argument has implications for the social scientific study of veterinarians more broadly, signalling the need for further research which focuses, not just on the experiences or perspectives of veterinarians, or the interactions between veterinarian and client, but which critically examines the relationship between the veterinary profession and wider society. This in turn would demand further exploration of the kinds of veterinary patients that come into being through human–animal relations and allow consideration of the extent to which the animal as more-than-patient might be included in veterinary animal advocacy.
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---
title: Metacarpophalangeal Joint Pathology and Bone Mineral Density Increase with
Exercise but Not with Incidence of Proximal Sesamoid Bone Fracture in Thoroughbred
Racehorses
authors:
- Kira J. Noordwijk
- Leyi Chen
- Bianca D. Ruspi
- Sydney Schurer
- Brittany Papa
- Diana C. Fasanello
- Sean P. McDonough
- Scott E. Palmer
- Ian R. Porter
- Parminder S. Basran
- Eve Donnelly
- Heidi L. Reesink
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000193
doi: 10.3390/ani13050827
license: CC BY 4.0
---
# Metacarpophalangeal Joint Pathology and Bone Mineral Density Increase with Exercise but Not with Incidence of Proximal Sesamoid Bone Fracture in Thoroughbred Racehorses
## Abstract
### Simple Summary
Identifying imaging features associated with racehorse catastrophic musculoskeletal injuries could improve both jockey and racehorse welfare. Bone mineralization and fetlock joint pathology have been hypothesized to correlate with catastrophic proximal sesamoid bone (PSB) fracture. This study compared bone mineral properties in cadaver distal limb specimens obtained from horses sustaining catastrophic PSB fracture and controls using multiple imaging modalities, including computed tomography (CT), dual-energy X-ray absorptiometry (DXA), and Raman spectroscopy. Fetlock joint pathology was compared between fracture and control groups using CT. Few differences were observed between PSB fracture and control groups; however, total high-speed furlong exercise was strongly predictive of third metacarpal bone mineral density and pathologic features, including palmar osteochondral disease (POD), condylar sclerosis, and condylar lysis. Total high-speed furlong exercise was also predictive of increased radiographic bone density in the subchondral region of PSBs.
### Abstract
Proximal sesamoid bone (PSB) fracture is the leading cause of fatal musculoskeletal injury in Thoroughbred racehorses in Hong Kong and the US. Efforts are underway to investigate diagnostic modalities that could help identify racehorses at increased risk of fracture; however, features associated with PSB fracture risk are still poorly understood. The objectives of this study were to [1] investigate third metacarpal (MC3) and PSB density and mineral content using dual-energy X-ray absorptiometry (DXA), computed tomography (CT), Raman spectroscopy, and ash fraction measurements, and [2] investigate PSB quality and metacarpophalangeal joint (MCPJ) pathology using Raman spectroscopy and CT. Forelimbs were collected from 29 Thoroughbred racehorse cadavers ($$n = 14$$ PSB fracture, $$n = 15$$ control) for DXA and CT imaging, and PSBs were sectioned for Raman spectroscopy and ash fraction measurements. Bone mineral density (BMD) was greater in MC3 condyles and PSBs of horses with more high-speed furlongs. MCPJ pathology, including palmar osteochondral disease (POD), MC3 condylar sclerosis, and MC3 subchondral lysis were greater in horses with more high-speed furlongs. There were no differences in BMD or Raman parameters between fracture and control groups; however, Raman spectroscopy and ash fraction measurements revealed regional differences in PSB BMD and tissue composition. Many parameters, including MC3 and PSB bone mineral density, were strongly correlated with total high-speed furlongs.
## 1. Introduction
The metacarpophalangeal joint (MCPJ) is the most common site for fracture in Thoroughbred racehorses [1,2], and proximal sesamoid bone (PSB) fracture is the leading cause of fatal musculoskeletal injury in Hong Kong and the US [1,2,3,4]. Prior studies have evaluated exercise history and PSB morphology [5,6,7,8,9] using radiography [7,9,10] and computed tomography (CT) [9,11,12,13,14] to identify risk factors and imaging features associated with PSB fracture. Bone volume fraction [11,14], bone mineral density [8,9,12], sclerosis [9,15], and focal osteopenic lesions [8,9] have been investigated as potential imaging features associated with catastrophic PSB fracture [8,9,11,12,14,15]. Here, we investigated multiple modalities for assessing metacarpophalangeal joint pathology and bone mineralization in Thoroughbred racehorses sustaining catastrophic PSB fracture and controls, including dual-energy X-ray absorptiometry (DXA), computed tomography (CT), Raman spectroscopy, and ash fraction measurements.
In humans, bone mineral density (BMD) is the most important quantitative predictor of fracture risk [16]. UK racehorses sustaining lateral condylar fracture of the third metacarpal bone (MC3) had increased MC3 BMD adjacent to the fracture site as compared to the non-fractured condyle or non-fracture racehorse control MC3 [17]. Conversely, in New Zealand racehorses, MC3 BMD did not differ between horses with condylar fractures and non-fracture controls but was greater in horses with more exercise [18,19]. In New York racehorses, microCT-derived PSB bone volume fraction (BVF) was greater in horses sustaining catastrophic PSB fracture as compared to controls [11]. Similarly, voxel-based morphometry revealed increased PSB BMD in Hong Kong racehorses with catastrophic PSB fracture as compared to controls [12]. In California racehorses, a focal subchondral region of osteopenia and decreased tissue mineral density was identified in the abaxial, subchondral region of medial PSBs from racehorses that sustained catastrophic PSB fracture which was hypothesized to predispose to PSB fracture [9].
Imaging modalities capable of quantifying bone mineral content in vivo include dual energy X-ray absorptiometry (DXA) and quantitative computed tomography (CT). DXA is the most common osteoporosis screening method in women [20,21], and DXA has demonstrated comparable accuracy to ash fraction measurements for quantifying bone mineral density of the mid-MC3 in an equine cadaver study [22]. Likewise, a portable DXA device was used to measure BMD in the live, standing horse, suggesting that DXA could be adapted to equine practice for in vivo detection of BMD changes [22]. Raman spectroscopy is being investigated for clinical use to predict age-related fragility fractures in humans [23,24] and has been used to evaluate the equine MC3 in cadaveric tissues [25] as well as in vivo [26].
Radiography has been used to evaluate equine MCPJ pathology, including changes associated with palmar osteochondral disease (POD) [27] and fetlock fractures [28]. Prior studies have identified gross [6,9,11], radiographic [9,15,28], and histopathologic [6,9,11] evidence of osteoarthritic changes associated with PSB fracture, including PSB discoloration; PSB radiolucency and subchondral bone osteopenia, osteophytosis, and enlarged vascular channels; and PSB articular cartilage fibrillation and chondrone formation. Interestingly, a cadaveric magnetic resonance imaging (MRI) study revealed that horses sustaining PSB fracture were more likely to have evidence of orthopedic disease in the contralateral limb compared to non-fracture controls, including subchondral bone densification, disruption of the subchondral bone plate or articular cartilage of the palmar distal MC3 condyle, and intraarticular osteochondral fragmentation [15].
The objectives of this study were to investigate PSB density and mineral content using multiple modalities, including dual-energy X-ray absorptiometry (DXA), computed tomography (CT), Raman spectroscopy, and ash fraction measurements; and to investigate bone quality and MCPJ pathology in Thoroughbred racehorses with and without catastrophic PSB fracture. We hypothesized that horses sustaining PSB fracture would have greater bone mineral content and inferior bone quality as compared to controls and that cumulative high-speed furlong exercise would be predictive of bone volume fraction and bone mineral density measurements.
## 2.1. Study Population
Forelimbs from Thoroughbred (TB) racehorses that died or were euthanized on New York racetracks were ethically obtained and chronologically enrolled in the study from 2019 to 2020. The study population consisted of 29 animals, including cases that sustained unilateral PSB fracture ($$n = 14$$, Supplemental File S1: Table S1A) and controls that died or were euthanized due to reasons unrelated to MCPJ fracture ($$n = 15$$, Supplemental File S1: Table S1B). Horses with bilateral fractures and horses with concurrent fracture of the distal MC3 or proximal P1 were excluded from the study.
The fracture group consisted of 14 horses, including 8 females, 3 castrated males, and 3 intact males which ranged in age from 2 to 6 years with a median age of 3 years. The median age at death was 3 years and ranged from 2 to 6 years. The fracture group included horses that sustained a fracture of one or both PSBs in one forelimb, while bones in the contralateral limb remained intact ($$n = 14$$, 8 left forelimb fractures, 6 right forelimb fracture). The control group consisted of 15 horses, including 5 females, 7 castrated males, and 3 intact males which ranged in age from 2 to 6 years, with a median age of 4 years. The median age at death was 4 years and ranged from 2 to 6. The control group contained horses that were euthanized or died due to disease or injury unrelated to the fetlock joint including: cardiovascular collapse ($$n = 4$$), laminitis ($$n = 4$$), head trauma ($$n = 2$$), colic ($$n = 2$$), sudden death ($$n = 1$$), carpal fracture ($$n = 1$$), humeral fracture ($$n = 1$$).
For the epidemiologic portion of the study, the mean percentage of exercise-related fatalities for 2-year-old horses during the 6-year period leading up to the 2020 Saratoga race meet was compared with that for the 2020 Saratoga race meet.
## 2.2. Training and Racing Exercise History
For each fracture case and control, training and racing exercise history was curated using information provided by the New York State Gaming Commission (NYSGC). Data collected included total high-speed furlong workouts, defined as any session in which an animal runs one furlong in 14 s or faster (~14.3 m/s or faster). Total furlongs is a race training factor that can be altered in future training methods and has previously been used as a measure of exercise history for evaluating catastrophic PSB fracture [6,11]. Additionally, from the exercise history provided, career duration (weeks), total weeks of rest, total weeks of work, total career furlongs, total number of races, number of breaks greater than 8 weeks of no work, and career work to rest ratio were calculated. The exercise-related fatality rate at Saratoga racetrack during the 2020 race meet was sorted by age and compared to the mean exercise-related fatality rate sorted by age from the 2014–2019 Saratoga race meets, using the New York State Gaming Commission Equine Breakdown, Death, Injury and Incident Database.
## 2.3. Imaging Acquisition and Processing
All limbs were transected at the level of the mid-radius for imaging and stored at 4 °C for <24 h while awaiting imaging and dissection.
## 2.3.1. Dual-Energy X-ray Absorptiometry
All limbs were analyzed using a Discovery A/216 DXA scanner (Hologic Canada ULC, Mississauga, ON, Canada). Images were acquired within a 42 cm sagittal plane that included the distal MC3, both PSBs, and the proximal first phalanx (P1). Images were analyzed using onboard software. Measurements included the area and radiographic bone density (g/cm2) of the distal MC3 condyle, the palmarodistal MC3 condyle, and the PSBs.
## 2.3.2. Computed Tomography Acquisition
Images of all forelimbs (total $$n = 58$$: fracture $$n = 14$$, fracture contralateral $$n = 14$$, control $$n = 15$$, and control contralateral $$n = 15$$) were acquired with the limb’s long axis perpendicular to the scan axis. The image acquisition was centered on the PSBs and extended approximately 30 cm proximally (distal $\frac{1}{3}$ of MC3) and distally (proximal $\frac{1}{3}$–$\frac{1}{2}$ of the proximal phalanx, P1) from the metacarpophalangeal joint using 16-slice helical computed tomography (Toshiba/Canon Aquilion Large-Bore, Toshiba/Canon America Medical Systems, Tustin, CA, USA). Images were acquired using 135 kVp with a scan field of view 240 mm and a standard reconstruction kernel. Images were reconstructed using 0.5 mm slice thickness at a 0.3 mm slice interval, reconstruction field of view 240 mm, pixel dimensions ranging from 0.296 to 0.468 mm, and 512 × 512 display matrix. All images were reconstructed in a bone filter, displayed with window width 4500, window center 1100, and variable tube current (SURE ExposureTM).
## 2.3.3. Computed Tomography Manual Analysis
Images were exported and analysed using HorosTM (Nimble Co. LLC d/b/a Purview, Annapolis, MD, USA). The proximal first phalanx (P1), PSBs, and distal MC3 were assessed for signs of pathological changes, including osteophytes, osteochondral fractures or fragmentation, enthesiophytes, subchondral lysis, and subchondral cyst-like lesions. Distal MC3 measurements included areas of the medial and lateral condyles, depth, and area of sclerosis of the condyles, radiographic bone density as measured in Hounsfield Units (HU) of the complete condyle and of the sclerotic region, and standard deviation of the radiographic bone density of the distal condyle and sclerotic region in a sagittal plane centered on each condyle. All intact PSB bones were measured for maximum height, width, and depth. Area, radiographic bone density, and standard deviation of radiographic bone density were measured for all intact PSBs in the following planes: apical, mid-body, basilar, axial, mid-sagittal, abaxial, sub-condylar, medullar, and flexor (Supplemental File S2). Fractured PSBs were not analyzed. Proximal P1 measurements included the depth of subchondral bone sclerosis measured in the frontal plane at the level of the sagittal grove and the medial and lateral thirds of the bone.
## 2.3.4. Computed Tomography Radiomics Analysis
DICOM images were exported to 3D Slicer (v4.11) (Irvine, CA, USA) for subsequent segmentation and analysis (“3D Slicer”, n.d.). For each intact forelimb, four regions of interest (ROIs) were drawn on each forelimb scan: the visible MC3 within the image volume, the visible portion of proximal P1, and the medial and lateral PSBs. Because many CT scans did not include the entire MC3 or P1, two additional regions of interest were created for the MC3 and P1 bones that extended from the subchondral surface of the bone to approximately 5 cm either proximally or distally to the joint. Finally, a 15 mm spherical volume entirely contained within the PSBs was drawn, resulting in a total of eight ROIs analyzed. These ROIs are hereafter referred to as PSBs, MC3-Full, MC3-Partial, P1-Full, P1-Partial, and PSBs-15 mm.
Bone mineral density (BMD), bone volume fraction (BVF), and total mineral density (TMD) were extracted from the CT data with scripts written in Matlab, using Otsu’s method for thresholding image data [29]. The conversion of CT Hounsfield Units (HU) to K2HPO4 density was performed by sampling the voxel intensities over a 1 cm diameter by 2 cm long cylinder within the 2 cm diameter phantom plugs, and an average HU to K2HPO4 density (mg·cm−3) calibration curve was applied to the CT data. Tabularized data of features were exported and analyzed in JMP.
## 2.4. Dissection and Gross Examination
Following imaging, distal limbs from both fracture and control groups were dissected by a board-certified pathologist (SPM). Pathology descriptions included macroscopic observations of joint disease, including articular cartilage damage to the MC3, PSBs, and proximal P1 (none, mild, moderate, or severe) and the presence of osteochondral fragmentation. For fracture limbs, cartilage damage that was believed to be the result of trauma from the acute sesamoid fracture fragment was excluded to describe the health of the joint prior to fracture. Medial and lateral metacarpal condyles were assigned a grade for palmar osteochondral disease (POD), based on the degree of discoloration of the subchondral bone and disruption of overlying articular cartilage. Grades were assigned as follows: grade 0, no evidence of POD; grade 1, discoloration or subchondral bone without disruption of overlying articular cartilage; grade 2, discoloration of subchondral bone plus mild to moderate disruption of overlying articular cartilage; and grade 3, discoloration of subchondral bone plus collapse of overlying articular cartilage. As previously reported, the mean POD grade for each horse was calculated and reported as a categorical outcome ranging from 0 to 3 [30]. Following necropsy, PSBs were analyzed for fracture configuration and abaxial subchondral bone lesions characterized by discoloration or an articular cartilage or subchondral defect as recently described by Shaffer et al. [ 9]. The distal MC3, PSBs, and P1 were stored in saline-soaked gauze at −20 °C until further processing.
## 2.5. PSB Sectioning and Processing
The medial sesamoid of the contralateral limb of the fracture group ($$n = 8$$) and the medial sesamoid of a randomly assigned limb in the control group ($$n = 8$$) were selected for Raman spectroscopy. Samples were cut into five parasagittal sections using a low-speed saw (Buehler IsoMet, Buehler, Lake Bluff, IL, USA) and stored at −20 °C until further processing. A 2 to 4-mm-thick section just axial to the mid-sagittal section was selected for Raman spectroscopy to maximize the surface area. These sections were adhered to a metal sample holder using mounting wax (Allied High Tech Products Inc., Rancho Dominguez, CA, USA) and polished on an automated/semi-automated polishing system (MultiPrepTM, Allied High Tech Products, Inc., Rancho Dominguez, CA, USA), using decreasing particle sizes of diamond wafer paper (30-micron to 3-micron, Allied High Tech Products, Inc., Rancho Dominguez, CA, USA), followed by 0.3-micron alumina slurry (Allied High Tech Products, Inc., Rancho Dominguez, CA, USA). Samples were sonicated in isopropyl alcohol to remove abrasive particles, removed from the sample holder, wrapped in lens paper, and stored at −20 °C in saline-soaked gauze until the time of characterization with Raman spectroscopy.
## 2.6. Raman Spectroscopy
Raman spectra were acquired from the polished surfaces of mid-sagittal sections of the PSB. A confocal Raman microscope (WITec Alpha300R, WITech Instruments Corp., Ulm, Germany) with a 785 nm diode laser; 20×, 0.5 N.A. objective (Zeiss, Jena, Germany) and software that enables imaging guided by surface topography (TrueSurface Microscopy, WITech Instruments Corp., Ulm, Germany) was used to collect the spectra. The spot size was ~1900 nm. Nine rectangular regions in a 3 × 3 grid were identified on the sample surface (Figure 1A) and grouped for subsequent analysis both in the proximal-distal direction, to form the subchondral, medullary and flexor regions (Figure 1B) and in the dorsal-palmar direction to form the apical, midbody and basilar regions (Figure 1C). Three spectra were collected in each region in a line along the proximal-distal direction with a 1000 µm spacing between each collection point. Thus, a total of 27 spectra were collected from each sample. For each spectrum, the total acquisition time was 30 s.
Background fluorescence was subtracted from the *Raman spectra* using commercial software (WITec Project FIVE, WITech Instruments Corp., Ulm, Germany). All spectra were normalized to the ν1PO4 peak (960 cm−1). Raman spectra (Supplemental File S3: Figure S1A) were analyzed using custom code (Matlab, Mathworks, Natick, MA, USA) developed to calculate Raman compositional parameters (Supplemental File S3: Table S1) from integrated peak areas that characterize the bone mineral and matrix (Supplemental File S3: Table S2). The positions of several features that characterize the collagen matrix were determined through second derivative spectroscopy, including glycosaminoglycans (GAGs, derived from CH3 deformation [1365–1390 cm−1]) [31], extracellular matrix (derived from methylene side chains [CH2] deformation (1450 cm−1) [32,33], the mature trivalent enzymatic crosslink Pyridinoline (PYD, 1660 cm−1) [34,35], the immature divalent enzymatic crosslink dehydrodihydroxylysinonorleucine (de-DHLNL, 1690 cm−1) [35], and the AGE pentosidine (PEN, 1495 cm−1) [35,36,37]. After initial peak positions were determined from the second derivative spectra, Gaussian functions were fit to the *Raman spectra* using spectroscopy software (GRAMS/AI, Thermo Galactic, Waltham, MA, USA) (Supplemental File S3: Figure S1B,C). A non-linear least squares method was used to find the final peak position of each peak with a ±5 cm−1 window [35].
## 2.7. Ash Fraction
Three to four mm sagittal sections of the medial PSB from each case ($$n = 29$$, $$n = 14$$ fracture, and $$n = 15$$ control) were further sectioned into nine sub-regions: apical flexor, apical medullary, apical subchondral, mid-body flexor, mid-body medullary, mid-body subchondral, basilar flexor, basilar medullary, and basilar subchondral using a low-speed saw (IsoMet, Buehler, Lake Bluff, IL, USA). Each sub-region was individually placed into an Eppendorf microcentrifuge tube and stored at −70 °C prior to ash fraction measurements.
Initial weights for each of the nine PSB sections were recorded. New microcentrifuge tubes were obtained for each bone section and filled with acetone such that the volume was approximately 10× the volume of the bone. Bone sections were individually submerged, and microcentrifuge tubes were sealed with laboratory film (Parafilm). The tubes were placed within a fume hood in end-over-end rockers for 72 h, and the acetone in the microcentrifuge tubes was replaced every 24 h. Bone sections were subsequently removed from acetone and placed in individual porcelain crucibles (High-Form Porcelain Crucibles, Fisherbrand, Leicestershire, UK) using thumb forceps and dried in an oven (Horizontal Air Flow 1370 FM, VWR Scientific Products, Radnor, PA, USA) at 60 °C for 23 h, followed by measurement of dry weight (scale description and manufacturer here). The sections were ashed in a muffle furnace (Type 1300 Furnace, Barnstead Thermolyne Corp., Dubuque, TX, USA) at 600 °C for a period of approximately 18 h. After cooling in a desiccator (Pyrex, Corning, NY, USA) for approximately one hour, a final ash weight was recorded. Ash fraction measurements were calculated by dividing weight by ash weight. Percent mineral by weight was calculated by dividing ash weight by dry weight and multiplying by 100.
## 2.8. Statistical Analysis
To compare exercise variables between fracture and control groups, a paired t-test was performed for career duration, weeks of rest, weeks of work, total furlongs, total number of races, number of training breaks of ≥8 weeks, and the ratio of work weeks to rest weeks. To determine associations between age and exercise variables, Pearson’s correlation coefficients were calculated for age and these same exercise variables. To validate the repeatability of the measurements manually acquired from CT images by a boarded radiologist (IRP) and an equine veterinarian (KJN), an intra-class correlation coefficient (ICC) analysis was performed.
All nominal and ordinal data were assessed for normality. Nominal data were analyzed for differences between fracture and control groups using Fisher’s Exact tests, and ordinal data were analyzed using ChiSquare likelihood ratios. Continuous data obtained from DXA, CT, and ash fraction measurements were compared using linear mixed-effects models. The fixed effects consisted of group (fracture vs. control), sex, and high-speed furlongs (HSF). The random effects included horse, limb (left vs. right), and, when appropriate, laterality (medial vs. lateral). Radiomic features for the medial and lateral PSB were examined together when exploring differences in the case and control image sets, as previously described [9,11].
Linear mixed-effects models were also used to compare Raman parameters across the fracture and control groups and across the anatomic regions (Supplemental File S4). Comparisons across anatomic regions were performed using three models: a model that included all nine anatomic regions (Figure 1A) and two additional, simplified models that included three anatomic regions grouped in the dorsal-palmar direction (creating subchondral/medullary/flexor regions) (Figure 1B) or in the proximal-distal direction (creating apical/midbody/basilar regions) (Figure 1C). Fixed effects included group (fracture vs. control), sex, total high-speed furlong workouts (HSF), region and the interaction between region and group; random effects included the sample to account for multiple spectra collected within each animal. A Tukey posthoc test was used for multiple comparisons of the estimated marginal means of the regions. The significance level was set to $$p \leq 0.05.$$ Raman analyses were performed using R studio (R Studio, PBC, Boston, MA, USA).
Finally, a multivariate analysis with pairwise correlations was performed to compare between modalities and reported using Pearson’s correlation coefficients. This included a comparison between DXA radiographic bone density of the sesamoids in the lateral projection, CT-derived Hounsfield Units of the mid-sagittal PSB, radiomics derived bone mineral density of the mid-sagittal PSB, and ash fraction. To maintain independence of observations and assess correlations across modalities, mean values for combined left and right forelimbs were calculated as appropriate. All analyses were performed in JMP Pro 16 (Cary, NC, USA) unless stated otherwise.
## 3.1. Study Population
Within the fracture group, 13 of 14 horses ($92.9\%$) sustained fractures to both the medial and lateral proximal sesamoid bones, while one horse fractured only the medial PSB. Of the fractured sesamoids, 12 were fractured in the basilar region, 7 in the mid-body region, and 1 in the apical region.
For the 2014 through 2019 Saratoga race meets, 2-year-old horses represented a mean of $29\%$ of the total number of exercise-related fatalities. During the 2020 Saratoga race meet, 2-year-old horses represented $82\%$ of the total number of exercise-related fatalities. This represents a $183\%$ increase in 2-year-old horse exercise-related fatalities in 2020.
## 3.2. Exercise Comparisons between Fracture and Control Groups
Exercise histories were available for 28 of the 29 horses in this study; training data were not available for one case except for cumulative total furlongs. Exercise history data were similar for fracture cases and controls (Table 1).
## 3.3. Association between Age and Exercise
Exercise variables, including career duration, total weeks of rest, total weeks of work, total furlongs, and the total number of races, were strongly positively correlated with horse age (Supplemental File S5). As a result of the strong correlation between exercise variables and age, the total furlongs measure was selected as the variable to analyze in regression models [6,11].
## 3.4. Intra-Class Correlation Coefficients
The majority of intraclass correlation coefficients (ICC) for manual CT parameters identified moderate (0.5 to 0.75 ICC) to good (0.75 to 0.9 ICC) correlation between the two observers. For example, ICCs ranged from a high of 0.91 for frontal plane measurements of the PSB midbody area to a low of 0.35 for the mean area of sclerosis of the MC3 condyle believed to be due to the subjective nature of this area measurement. Complete ICC values are reported in Supplemental Materials (Supplemental File S6).
## 3.5.1. Pathologic Features
Palmar osteochondral disease (POD), defined as a gross POD score of ≥1, was detected in 19 out of 22 ($86.4\%$) horses in which necropsy POD score data were available. Of these 22 horses, 12 were fracture cases and 7 were controls. Grossly, no differences were noted for POD scores between fracture and control cases ($$p \leq 0.20$$). Discoloration of PSB articular cartilage was present in $\frac{7}{14}$ fracture cases and $\frac{8}{15}$ control cases ($$p \leq 1.0$$).
## 3.5.2. Imaging Features
DXA bone mineral density measurements of the distal MC3 condyle, palmar distal region of MC3, and the PSBs did not identify any differences between fracture and control groups. However, the CT radiographic density of the basilar PSB region was more homogenous in fracture cases as compared to controls (fracture: 1211.7 ± 61.9, control: 1184.8 ± 70.7 HU; $$p \leq 0.049$$) which could indicate sclerosis. Dorsal cavitation of MC3 at the level of the proximal MCPJ was identified in 7 out of 15 control horses. Interestingly, this feature was not observed in any horses sustaining PSB fracture ($p \leq 0.0001$) (Figure 2A,E). No differences in osteophytes, enthesiophytes, or subchondral lysis of MC3, P1, or the PSBs were observed between fracture and control groups (Supplemental File S6).
## 3.5.3. Ash Fraction
Whole bone PSB percent mineral was greater than tuber coxae percent mineral (60.2 ± $1.3\%$ versus 53.4 ± $3.9\%$ (Supplemental File S7). The PSB basilar medullary region had $0.48\%$ less mineral in fracture cases as compared to controls ($$p \leq 0.02$$). Percent mineral measurements for the remaining eight sections did not differ between groups ($$p \leq 0.07$$ to 0.96). Regional comparison of all PSB mid-sagittal sections combined identified $2.44\%$ great mineral in the medullary region compared to subchondral region ($p \leq 0.001$) and $4.01\%$ greater mineral that the flexor region ($p \leq 0.001$). Additionally, the midbody region had $2.48\%$ greater mineral compared to the apical region ($p \leq 0.001$) and $0.96\%$ greater mineral than the basilar region ($p \leq 0.05$) (Supplemental File S9). Tuber coxae samples were available for 26 of the 29 horses, including $\frac{13}{14}$ fracture cases and $\frac{13}{15}$ controls. The percent mineral of the tuber coxae did not differ between cases and controls ($$p \leq 0.94$$).
## 3.5.4. Raman Spectroscopy
When the effect of group (fracture vs. control) was analyzed, all Raman spectroscopic parameters were similar across groups (Figure 3). The absence of differences between groups was consistently observed across all statistical models regardless of how the region was defined (9 sub-regions, 3 dorsal-palmar regions, or 3 proximal-distal regions), ($p \leq 0.05$), (Figure 4, Supplemental File S4).
## 3.6.1. Gross Pathologic Features
On gross examination, POD lesions were more severe in horses with more accrued total furlongs, with an increase in 1.0 grade per 100 furlongs ($p \leq 0.0001$) (Figure 2B,F). POD score data were not recorded in the necropsy reports of the remaining seven horses, including two fracture cases and eight controls.
## 3.6.2. Imaging Features
Both DXA (Figure 5) and CT (Figure 6) identified increased BMD in horses with greater total furlongs. Distal MC3 condyle bone mineral density was greater by 0.1 g/cm2 per 100 total furlongs, and the palmar distal region of MC3 was greater by 0.2 g/cm2 per 100 total furlongs ($p \leq 0.0001$) (Figure 5A, Supplemental File S8). Similarly, increased CT radiographic bone density was detected in the lateral condyle of MC3, with an increase of 2.0 HU per 100 total furlongs accrued ($p \leq 0.0001$). Radiomics CT analysis identified that distal MC3 condyle BMD was greater by 0.10 g/cm2 per 100 total furlongs ($$p \leq 0.005$$), and the PSB 15 mm bone mineral density was greater by 0.19 g/cm2 per 100 total furlongs ($$p \leq 0.03$$) (Figure 6A,B). CT identified pathological features of the MC3 condyle associated with more total furlongs, including condylar sclerosis ($p \leq 0.0001$) (Figure 2C,G) and subchondral lysis ($p \leq 0.0001$) (Figure 2D,H). The subchondral lysis score was greater by 0.9 per 100 total furlongs ($p \leq 0.0001$). A non-significant trend for greater PSB BMD with increased total furlongs on DXA was observed ($$p \leq 0.054$$) (Figure 5B). CT identified increased radiographic bone density in the subchondral region of the PSBs by 16.4 HU per 100 total furlongs accrued ($$p \leq 0.04$$).
## 3.6.3. Ash Fraction
Subchondral mineral content (percent mineral) was greater in PSBs from horses with more accrued total furlongs (Supplemental File S7). The midbody subchondral PSB region had $0.47\%$ more mineral for each additional 100 total high-speed furlongs. No other regional differences in mineral content were detected as an effect of group or exercise, though there was a trend for increased mineral content in the mid-body region as an effect of total furlongs ($$p \leq 0.07$$, Supplemental File S7). Tuber coxae percent mineral did not differ as a function of high-speed furlongs ($$p \leq 0.43$$).
## 3.6.4. Raman Spectroscopy
The carbonate:phosphate ratio ($$p \leq 0.054$$, 9-subregion; $$p \leq 0.055$$, dorsal-palmar; $$p \leq 0.053$$, proximal-distal, Supplemental File S4) demonstrated a trend towards significance as a function of total high-speed furlongs. Specifically, a one-unit increase in high-speed furlongs resulted in a $0.04\%$ increase in carbonate:phosphate ratio. However, the effect of high-speed furlongs was not significant for any other Raman parameters (Supplemental File S4).
## 3.7. Correlation Statistics
To assess correlation metrics of PSB bone mineral density across modalities, Pearson’s correlation coefficients were calculated. Ash fraction and DXA ($r = 0.24$), ash fraction and CT ($r = 0.23$), manual CT and DXA ($r = 0.06$), and radiomics CT and DXA ($r = 0.14$) data were only weakly correlated. Manual and radiomics CT values ($r = 0.58$) were highly correlated.
## 4. Discussion
Cumulative high-speed furlong exercise was strongly predictive of several bone mineral measurements and metacarpophalangeal joint pathologic changes across all modalities, including DXA, CT, Raman spectroscopy, and ash fraction measurements. BMD in the MC3 condyles and PSBs and percent mineral in the subchondral region of PSBs were greater in horses with more total furlongs. MCPJ pathology, including POD scores, MC3 condylar sclerosis, and MC3 subchondral lysis, was greater in horses with more total furlongs. Contrary to our hypothesis, there were no differences in BMD or Raman parameters between fracture and control groups. MC3 dorsal cavitation was detected in $\frac{7}{15}$ control cases but was not detected in horses sustaining PSB fracture. Raman spectroscopy and ash fraction measurements revealed regional differences in PSB BMD and tissue composition, including greater mineral-to-matrix ratios in the subchondral and basilar PSB regions and greater carbonate-to-phosphate ratios in the flexor and apical PSB regions. Taken together, these data suggest that cumulative high-speed furlong exercise is a strong predictor of bone mineralization and joint pathology and that these effects predominate over differences between PSB fracture cases and controls.
Prior studies have demonstrated that MC3 [38,39,40] and PSB [5,6,8,11,41] morphologic features and fracture risk vary as a function of exercise [6,11]. For example, BMD was greater in the MC3 condyles in 2-year-old Thoroughbreds with more exercise [19], and bone volume fraction of the parasagittal groove of MC3 was greater in Thoroughbreds in training than those in rest [42]. Some differences in morphologic features, especially those related to bone mineral content, could be due to adaptive responses to exercise and may not be pathologic, although the degree that these responses are considered adaptive versus maladaptive is currently unknown. In both humans and horses, bone is highly responsive to exercise [43], with changes occurring as early as 8 weeks of training [44]. Noble et al. identified differences in volumetric bone mineral density and subchondral bone thickness of the proximal phalanx in racehorses with catastrophic proximal phalanx fractures as compared to both raced and unraced controls, including greater variance in bone mineral density, which may indicate a maladaptive response [45]. More advanced osteoarthritic changes and greater POD scores were associated with more accrued total furlongs in New York racehorses [6]. California racehorses sustaining PSB fracture spent more time in active racing and training, exercised for longer periods prior to their last rest period, exercised at higher intensities during the 12 months prior to their death, and accumulated greater distances in their career than non-fracture racehorse controls [5]. In the UK, PSB fractures were also more common in experienced racehorses with more starts than in those in their first season of racing [46].
In the current study, horses sustaining PSB fracture were approximately evenly divided between ≤3-year-old horses and >3-year-old horses, with 3 out of 14 horses ($21\%$) sustaining catastrophic PSB fracture prior to their first race. In addition, there were no differences in measures of total exercise between fracture and control horses. While these findings suggest that exercise history may account for some PSB and MCPJ pathologic changes, they suggest that PSB fracture is not always an injury associated with excessive work. It is likely that some PSB fractures occur in horses that have not sufficiently modeled their PSBs prior to their first race. Of significance, in 2020, New York Thoroughbred racetracks were closed due to the COVID-19 pandemic from the first week in March until the first week in June. During this time, horses were not able to complete their regular training schedules in preparation for the 2020 Saratoga race meet (July 16–August 6). This disruption of high-speed exercise training was associated with a disproportionate increase in exercise-associated catastrophic fractures among 2-year-old Thoroughbred racehorses during the 2020 Saratoga race meet compared with that of previous years. This pandemic-associated anomaly in PSB fracture demographics could potentially account for the absence of differences in bone mineralization parameters identified in this cohort of NY racehorses as compared to prior NY studies [47].
Consistent with prior studies [19,42,48,49], BMD was greater in MC3 condyles from horses with more total furlongs as measured via DXA, CT, Raman spectroscopy, and ash fraction. In addition, quantitative CT bone mineral density measurements were greater in PSBs from horses with more total furlongs. Since high-speed exercise is known to be a strong stimulus for bone remodeling, it is not surprising that total high-speed furlongs was the predominant explanatory variable for MC3 and PSB BMD in our models. Interestingly, no differences were noted in tuber coxae BMD as a function of group or exercise, suggesting that the effects of high-speed exercise are less pronounced at this axial skeletal site. Within PSBs, increased BMD was most notable in the subchondral region as evaluated by CT and ash fraction. However, no differences were found in whole PSB BMD between fracture cases and controls. These results, combined with previous findings of focal decreased bone mineral density in the abaxial subchondral region of racehorses sustaining catastrophic PSB fracture [9], suggest that regional differences in bone and mineral properties may be better predictors of fracture risk than whole PSB changes.
Palmar osteochondral disease (POD) commonly affects Thoroughbred racehorses, with a reported prevalence as high as $80.4\%$ [50]. In the present study, POD was detected on gross examination at necropsy in 19 out of 22 ($86.4\%$) horses, with greater POD scores in horses with more total furlongs. POD scores have previously been associated with greater cumulative racing and training, suggesting that POD is a progressive disease that results from bone fatigue with repetitive loading [50]. POD lesions have previously been associated with microcracks in MC3 condyle subchondral bone [40,51] and PSB fracture [11] in racehorses. The only gross pathologic finding that differed between fracture and controls in the current study was dorsal MC3 cavitation, which was present in 7 of 15 controls and no fracture cases. Davis et al. previously described MC3 dorsal cavitation as a reliable radiographic marker of POD [27]; however, in the present study, dorsal cavitation did not correlate with POD scores nor total high-speed furlong exercise.
PSB mineral content and crystallinity did not differ as a function of total furlongs or group. Prior studies with higher-resolution micro-CT suggested that horses sustaining PSB fracture had increased bone volume fraction, leading to the hypothesis that tissue mineralization would also be increased in horses with PSB fracture [11]. Contrary to our hypothesis, bone tissue material properties were similar between fracture and control PSBs, consistent with a previous study that revealed similar BMD in whole bone and axial sub-regions of PSBs between fracture and control groups [12].
When examining the effect of region on material properties within all PSBs (fractures and controls combined), lower mineral content, lower advanced glycation end products (AGEs, including pentosidine and carboxymethylysine), and greater carbonate substitution were observed in the flexor region. In PSBs, the flexor region is subjected to high tensile forces, which may promote remodeling activity and decrease average tissue mineralization and AGEs concentration [52]. Lower tissue mineral and AGEs content are both indicators of younger tissue, while greater carbonate:phosphate ratios may be attributed to the loss of mineral or the increase of younger mineral with more substitution [53]. Lower tissue mineral and AGEs content also may contribute to greater toughness in flexor region, which would enable this region to be more durable under tension [54,55]. The subchondral region had the lowest carbonate:phosphate ratio, an intermediate mineral:matrix ratio, and trended toward the highest collagen maturity. These observations suggest that the subchondral region may have older tissue [55], consistent with reduced remodeling activity. Focal lesions [9] and microcracks [14,56] are more frequently observed in subchondral region and may predispose this site to fatigue fractures.
Correlation between modalities was considered weak, whereas CT manual and radiomics BMD measurements were highly correlated. CT provides better morphological and texture resolution than DXA or radiography, and access to standing equine CT units is increasing; however, challenges still exist, including image acquisition at high resolution and unifying image analysis across different platforms. Advances in quantitative analyses of large imaging data sets using radiomics [57] hold significant promise to mine the substantial amounts of data provided by volumetric imaging. Raman spectroscopy is not currently employed in vivo for clinical diagnostics, but several groups are investigating the possible future in vivo use of Raman for bone quality assessment in both humans and horses [24,58]. Ash fraction is the gold standard for measuring bone mineral content and is considered a superior marker of bone strength compared to radiographic measured bone volume fraction in humans [59]; however, ash fraction measurements are destructive and require an invasive bone biopsy. Here, ash fraction measurements served as a gold standard for comparison to DXA, CT, and Raman measurements of bone mineral content.
## Study Limitations Paragraph
Limitations of this study included the sample size of 29 horses (58 PSBs). While high-speed furlong exercise data were available for horses training and racing on NY racetracks, exercise history was unavailable for time periods where horses were not training at a NY racetrack facility. This study utilized clinical CT with an imaging resolution ranging from 0.296 mm to 0.468 mm pixels rather than micro-CT with resolutions ranging from 2.8 μm to 80 μm. However, clinical CT has practical application in live horses, especially with the increasing availability of standing CT in practice. Of the total 29 samples, Raman spectroscopy was only performed in 16 horses, and both Raman spectroscopy and ash fraction measurements were performed on a single parasagittal section of the proximal sesamoid bone rather than the whole bone. Despite these limitations, our analyses combined complementary analyses across multiple levels of bone structural hierarchy to clinical specimens. The novel application of Raman spectroscopy to study bone tissue from horses with PSB fractures provided information unobtainable from existing clinical imaging modalities, including mineral properties such as carbonate substitution and matrix properties such as AGEs and glycosaminoglycan (GAGs) content which impact bone mechanical properties and are associated with fracture incidence in humans [36,37,60].
## 5. Conclusions
Cumulative high-speed furlong exercise was strongly predictive of several bone mineral measurements and metacarpophalangeal joint pathologic changes across multiple modalities. However, few differences were detected between fracture and control groups, suggesting that whole bone mineral properties or metacarpophalangeal joint pathology are not alone sufficient for identifying horses at risk of PSB fracture. Recent work suggests that focal or regional changes in bone mineral content, such as the presence of osteopenic subchondral PSB lesions, may be associated with fracture risk [9]. Unfortunately, current equine standing imaging modalities may lack the resolution required to identify changes at this length scale. Functional imaging assessments, such as positron emission tomography (PET), may hold more potential for identifying these small defects through metabolic changes which often precede structural changes observed with CT [61]. PET scanning has been shown to detect radiopharmaceutical uptake in the dorsoaxial articular surface and abaxial border of PSBs and palmar subchondral bone of the lateral MC3 condyles in racehorses which was undetectable by CT or MRI [61]. Nonetheless, PET scanning in horses is recommended to be used in conjunction with CT [61], and identifying parameters correlated with PSB fracture using both modalities could improve the accuracy of identification of horses at risk for fracture.
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|
---
title: Dietary Fermentation Product of Aspergillus Oryzae Prevents Increases in Gastrointestinal
Permeability (‘Leaky Gut’) in Horses Undergoing Combined Transport and Exercise
authors:
- Melissa McGilloway
- Shannon Manley
- Alyssa Aho
- Keisha N. Heeringa
- Lynsey Whitacre
- Yanping Lou
- E. James Squires
- Wendy Pearson
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000214
doi: 10.3390/ani13050951
license: CC BY 4.0
---
# Dietary Fermentation Product of Aspergillus Oryzae Prevents Increases in Gastrointestinal Permeability (‘Leaky Gut’) in Horses Undergoing Combined Transport and Exercise
## Abstract
### Simple Summary
Equine leaky gut syndrome is characterized by gastrointestinal hyperpermeability and may be associated with adverse health effects in horses. The purpose was to evaluate the effects of a prebiotic *Aspergillus oryzae* product (SUPP) on the stress-induced leakiness of the gut. For 28 days, 8 horses received a diet containing the prebiotic or an unsupplemented diet (CO). On Days 0 and 28, horses were dosed with a compound (iohexol) that should only leak out of the gastrointestinal tract if the gut walls become leaky. Immediately following iohexol administration, four horses from each feeding group underwent 60 min of transport immediately followed by a moderate-intensity exercise bout of 30 min (EX), and the remaining horses were maintained as sedentary controls (SED). Blood was sampled before iohexol, immediately after trailering, and at 0, 1, 2, 4, and 8 h post-exercise. Blood was analyzed for iohexol, as well as lipopolysaccharide (a compound found in the gastrointestinal tract that can leak out) and serum amyloid A (a marker of inflammatory response). EX resulted in a significant increase in plasma iohexol in both CO and SUPP groups on Day 0; this increase was not seen in SED horses. On Day 28, EX increased plasma iohexol only in the CO feeding group; this increase was completely prevented by the provision of SUPP. It is concluded that combined transport and exercise induce leaky gut. Dietary SUPP prevents this and therefore may be a useful prophylactic for pathologies associated with gastrointestinal hyperpermeability in horses.
### Abstract
Equine leaky gut syndrome is characterized by gastrointestinal hyperpermeability and may be associated with adverse health effects in horses. The purpose was to evaluate the effects of a prebiotic *Aspergillus oryzae* product (SUPP) on stress-induced gastrointestinal hyperpermeability. Eight horses received a diet containing SUPP (0.02 g/kg BW) or an unsupplemented diet (CO) ($$n = 4$$ per group) for 28 days. On Days 0 and 28, horses were intubated with an indigestible marker of gastrointestinal permeability (iohexol). Half the horses from each feeding group underwent 60 min of transport by trailer immediately followed by a moderate-intensity exercise bout of 30 min (EX), and the remaining horses stayed in stalls as controls (SED). Blood was sampled before iohexol, immediately after trailering, and at 0, 1, 2, 4, and 8 h post-exercise. At the end of the feeding period, horses were washed out for 28 days before being assigned to the opposite feeding group, and the study was replicated. Blood was analyzed for iohexol (HPLC), lipopolysaccharide (ELISA), and serum amyloid A (latex agglutination assay). Data were analyzed using three-way and two-way ANOVA. On Day 0, the combined challenge of trailer transport and exercise significantly increased plasma iohexol in both feeding groups; this increase was not seen in SED horses. On Day 28, EX increased plasma iohexol only in the CO feeding group; this increase was completely prevented by the provision of SUPP. It is concluded that combined transport and exercise induce gastrointestinal hyperpermeability. Dietary SUPP prevents this and therefore may be a useful prophylactic for pathologies associated with gastrointestinal hyperpermeability in horses.
## 1. Introduction
Leaky gut syndrome (LGS) is characterized by gastrointestinal hyperpermeability and increased accessibility of the systemic environment to compounds that are normally sequestered within the gastrointestinal lumen [1]. The contribution of LGS to equine disease is poorly understood, and its mitigation by dietary interventions has not been described in the literature. An MSc thesis from Michigan State University [2] describes a study in which oral phenylbutazone contributed to the development of gastrointestinal hyperpermeability in 18 Arabian horses, suggesting that gastric ulceration, phenylbutazone administration, or both, contribute to the development of LGS in horses. Evidence also implicates diets high in starch as complicit in gastrointestinal hyperpermeability [3]. Exercise is another likely candidate as an LGS risk factor but has not been clearly described in horses. Research in humans, however, provides evidence for a positive correlation between exercise intensity/duration and hyperpermeability of the gastrointestinal tract [4,5,6]. A recent study in eight horses reports that the combination of exercise and trailer transport induces an increase in gastrointestinal permeability, as well as increased serum amyloid A and lipopolysaccharide [7]. Whilst the pathophysiological consequences of LGS are as vaguely characterized as its triggers, there is evidence that, depending on the degree of inflammatory response to luminal toxins, LGS may impair skeletal muscle metabolism [8], and contribute to metabolic dysfunction [9,10], allergies [11,12], and inflammatory diseases such as arthritis [13]. Dietary interventions with evidence for an ability to protect against the development or clinical consequences of LGS will make an important contribution to preserving robust equine health.
Perhaps due (at least in part) to the incomplete picture defining the cause-and-effect of LGS, interventions tend to rely heavily on the management of downstream clinical consequences. To the authors’ knowledge, there are currently no feed supplements or pharmaceutical drugs that have been evaluated against the gastrointestinal hyperpermeability that is the cornerstone of LGS. A commonly reported feature of LGS in non-equine species is gastrointestinal dysbiosis, and there is evidence that this dysbiosis contributes to the development of hyperpermeability [14,15,16,17]. Dysbiosis is likely in horses receiving a high-starch diet [3,16], and in horses experiencing physiological stress [16]. Thus, interventions with potential to stabilize gastrointestinal microbiota may protect against the development of hyperpermeability under conditions of stress. Aspergillus oryzae is a filamentous fungus, which has demonstrated the ability to amplify the abundance of probiotic microbes (particularly Bifidobacterium pseudolongum) whilst protecting DSS-challenged mice against colitis [18]. The fermentation product of A. oryzae also promotes fiber-degrading bacteria in the rumen and hindgut when fed to lactating dairy cows [19]. In addition to evidence for a prebiotic-like effect, A. oryzae also exerts a marked anti-inflammatory effect in LPS-stimulated polymorphonuclear cells and improves the structure of gastrointestinal lumen (i.e., villus height–crypt ratio) in broiler chickens [20]. Furthermore, the administration of a postbiotic from A. oryzae to calves prevented the increase in intestinal permeability associated with exposure to high ambient temperature [21]. These data support the hypothesis that A. oryzae protects against stress-induced hyperpermeability by amplifying the abundance of a healthy gastrointestinal microbiome. Accordingly, the purpose of the current study was to evaluate the effects of a fungal prebiotic produced through a proprietary fermentation process with A. oryzae (SUPP; BioZyme Inc.; St. Joseph, MO, USA) on equine gastrointestinal hyperpermeability induced by a combination of trailer transport and moderate-intensity exercise horses. The objectives were to characterize the effect of a dietary A. oryzae prebiotic on the appearance and disappearance of an oral permeability marker (iohexol) in the blood of horses challenged with combined transport and exercise stress, and to correlate observed effects with those on downstream evidence of inflammation (serum amyloid A (SAA)) and translocation of enteric endotoxin (lipopolysaccharide (LPS)).
## 2. Materials and Methods
Care and use of animals was reviewed and approved by the University of Guelph Animal Care Committee in compliance with the guidelines published by the Canadian Council on Animal Care (Approval Number 3800).
## 2.1. Horses
Eight [8] healthy mares (Age: 14.2 ± 3.7 years; body weight: 570 ± 47.4 kg) from the Arkell Equine Research Station, University of Guelph, were included in the randomized, partial cross-over trial. The horses were group-housed in an open turnout area, with unrestricted access to a large covered shelter bedded with straw, 1st cut Timothy hay, water, and trace mineral salt. Two hundred and fifty [250] g of a $12\%$ maintenance pellet rationa was provided once per day (morning) (Table 1). Horses were all accustomed to a lifestyle that did not include forced exercise.
At the beginning of the study, all 8 horses were randomized into one of two feeding groups ($$n = 4$$ per group): Group A: unsupplemented control diet (CO); Group B: diet containing A. oryzae prebioticb (SUPP; 0.02 g/kg BW). SUPP was a textured, unpelleted product and was top-dressed onto the horse’s individual pelleted feed once per day. Horses consumed their pelleted ration with or without SUPP once per day in individual stalls. Once their feed was completely consumed, they were returned to the outdoor turnout area. Within each feeding group, horses were further divided into stress-challenged (EX—see below for details) or non-challenged sedentary controls (SED) ($$n = 2$$ per group per replicate). Horses received their assigned diet for 28 days. On Days 0 and 28, one SED and one EX horse were evaluated in the morning, and a second SED and second EX horse were evaluated in the afternoon. At the end of the 28-day feeding period, horses were washed out for 28 days, and then assigned to the opposite feeding group for an additional 28 days. The trial was then repeated, for a final ‘n’ of 8 per feeding group (i.e., 4 × EX and 4 × SED per feeding group). Horses were tested at the same time of day (morning or afternoon) in both study periods.
On study days, horses remained in their turnout area with unrestricted water access, but from which all feed had been removed. Following 12 h of fasting, horses were stalled and administered via nasogastric tube an indigestible marker of gastrointestinal permeability (iohexolc; $5.6\%$ solution, 1.0 mL/per kg BW; 56 mg/kg BW) by a licensed veterinary professional [7]. The procedure was conducted in the absence of any sedation, so as not to interfere with normal gastrointestinal motility [22].
## 2.2. Stress Challenge
Horses were challenged with combined trailer transport and exercise, which we have previously demonstrated to produce a measurable and significant increase in gastrointestinal hyperpermeability [7]. Briefly, following the administration of iohexol, one EX horse was walked onto a 2-horse trailer for a 60 min drive to the Equine Sports Medicine and Reproduction Centre, University of Guelph. Once at the facility, a heart rate (HR) monitord was attached to the horse using a flexible belly-band, and the horse was free-lunged around an indoor arena (5 min’ walk, 10 min trot (left), 10 min trot (right), and 5 min’ walk) on a sand footing for 30 min. Horses were encouraged to achieve an exercise intensity that resulted in a HR of approximately 150 bpm during the trot, in order to encourage the horse to work at or beyond the anaerobic threshold [23]. At the cessation of exercise, EX horses returned to the group housing yard directly and were turned out with unrestricted access to hay and water. This challenge has previously been demonstrated to produce gastrointestinal hyperpermeability in horses [6].
Following the application of topical lidocaine at the jugular groove, blood was sampled from the jugular vein immediately before iohexol administration (P1), immediately after trailering (P2), immediately after exercise (P3), and then 1 (P4), 2 (P5), 4 (P6), and 8 h (P7) post-exercise. Blood samples were cooled on ice, centrifuged within 2 h of collection, and the recovered plasma was frozen (−20 °C) until analysis.
Manure samples were collected within 2 min of voiding before the horse walked into the trailer, at the end of 60 min of transport, and the first manure after exercise.
## 2.3. Non-Challenged Controls
SED horses received iohexol at the same time as the EX horses, and blood was sampled at the same time as the EX horses. After receiving iohexol they were returned to the group housing area with free access to water. Hay was provided upon return of the EX horse from transport and exercise.
## 2.4. Sample Analysis
All chemicals and reagents were purchased from Sigma Aldrichf, unless otherwise stated. Plasma samples were analyzed for systemic inflammation (serum amyloid A and lipopolysaccharide (LPS)) biomarkers, and an exogenous marker of gastrointestinal permeability (iohexol).
Plasma iohexol was determined via HPLC (Agilent 1200 series HPLC gradient system), which was used to quantify plasma iohexol (μ g/mL) with UV detection at 254 nm, as previously described [7] (intra- and inter-assay CV: 3.106 and $4.217\%$, respectively).
SAA was determined by Eiken Serum Amyloid A latex agglutination assay at a commercial laboratory (Animal Health Laboratory, University of Guelph).
Plasma samples, acclimated at room temperature, were analyzed in duplicate for LPS (pg/mL) using an equine-specific quantitative sandwich ELISA kit according to manufacturerh instructions (inter- and intra-assay coefficient of variability: 1.5 and $1.6\%$, respectively). A standard curve was used to generate a linear regression equation, which was used to calculate LPS concentrations in each sample.
## 2.5. Data Analysis
Data analysis was conducted using SigmaPloti (Version 14.2). Data are presented as mean ± SD unless otherwise indicated. Normality of data was determined using the Shapiro–Wilk test. Three-way ANOVA was used to detect interactions between feeding groups, stress challenge, and time after iohexol administration. Two-way ANOVA was used to identify significant differences between feeding groups in SED and EX horses on Day 0 and Day 28 with respect to stress challenge and time after iohexol administration. The Holm–Sidak post-hoc test was used to identify significantly different means when a significant F-ratio was calculated. Significance was accepted at $p \leq 0.05.$
## 3.1.1. Control Diet (Figure 1)
Day 0: In SED horses receiving the CO diet, there was no significant change in plasma iohexol at any time between P1 (0.56 ± 0.02 ug/mL) and P7 (0.69 ± 0.04 ug/mL) ($$p \leq 0.26$$). EX horses demonstrated a significant increase in plasma iohexol between P1 (0.52 ± 0.03 ug/mL) and P3 (1.14 ± 0.08 ug/mL) ($$p \leq 0.02$$). Plasma iohexol was significantly higher in EX horses than in SED horses at P2 (SED: 0.71 ± 0.06 ug/mL; EX: 1.02 ± 0.18 ug/mL) ($$p \leq 0.04$$) and P3 (SED: 0.75 ± 0.09 ug/mL; EX: 1.14 ± 0.08 ug/mL) ($$p \leq 0.01$$) (Figure 1).
Day 28: In SED horses receiving the CO diet, there was no significant change in plasma iohexol at any time between P1 (0.48 ± 0.04 ug/mL) and P7 (0.60 ± 0.06 ug/mL) ($$p \leq 0.44$$). EX horses demonstrated a significant increase in plasma iohexol between P1 (0.58 ± 0.09 ug/mL) and P3 (1.07 ± 0.06 ug/mL) ($$p \leq 0.006$$). Plasma iohexol was significantly higher in EX horses than in SED horses at P2 (SED: 0.54 ± 0.06 ug/mL; EX: 1.01 ± 0.12 ug/mL) ($p \leq 0.001$), P3 (SED: 0.56 ± 0.07 ug/mL; EX: 1.07 ± 0.12 ug/mL) ($p \leq 0.001$) and P4 (SED: 0.59 ± 0.04 ug/mL; EX: 1.00 ± 0.10 ug/mL) ($p \leq 0.001$) (Figure 1).
Day 0 vs. Day 28: In SED horses, plasma iohexol was significantly higher on Day 0 than on Day 28 at P3 and P5 ($$p \leq 0.04$$ and 0.05, respectively). There were no significant differences between Day 0 and Day 28 in EX horses ($$p \leq 0.23$$) (Figure 1).
## 3.1.2. Supplemented Diet (Figure 2)
Day 0: In SED horses receiving the SUPP diet, there was a significant increase in plasma iohexol between P1 (0.51 ± 0.03 ug/mL) and P2 (0.87 ± 0.04 ug/mL) ($$p \leq 0.005$$), P3 (0.82 ± 0.06 ug/mL) ($$p \leq 0.02$$) and P4 (0.97 ± 0.09 ug/mL) ($p \leq 0.001$). EX horses demonstrated a significant increase in plasma iohexol between P1 (0.70 ± 0.15 ug/mL) and P3 (1.75 ± 0.19 ug/mL) ($$p \leq 0.01$$). Plasma iohexol was significantly higher in EX horses than in SED horses at P3 (SED: 0.82 ± 0.06 ug/mL; EX: 1.75 ± 0.19 ug/mL) ($p \leq 0.001$) (Figure 2).
Day 28: In SED horses receiving the SUPP diet, there was no significant change in plasma iohexol at any time between P1 (0.49 ± 0.05 ug/mL) and P7 (0.70 ± 0.05 ug/mL) ($$p \leq 0.43$$). There was also no significant increase in plasma iohexol in EX horses at any time between P1 (0.87 ± 0.23 ug/mL) and P7 (0.56 ± 0.12 ug/mL) ($$p \leq 0.36$$)(Figure 2).
## 3.1.3. Day 0 and Day 28 in Supplemented and Control Diets
On Day 0, iohexol tended to be higher in SUPP than CO horses ($$p \leq 0.053$$). Overall iohexol was significantly elevated in EX horses at P2, P3, ($p \leq 0.001$) and P4 ($$p \leq 0.02$$) compared with P1, but there were no differences between treatment groups (Figure 2) On Day 28, iohexol was significantly higher overall in CO horses compared with SUPP horses ($$p \leq 0.008$$). Overall, iohexol was significantly higher at P3 than P1, but there were no significant differences between treatment groups (Figure 2).
## Control Diet
Day 0: In SED horses receiving the CO diet, there was no significant change in SAA at any time between P1 (0.10 ± 0.1 μg/mL) and P7 (0.10 ± 0.1 μg/mL) ($$p \leq 0.78$$). There was also no significant change in EX horses in SAA between P1 (0.22 ± 0.16 μg/mL) and P7 (0.86 ± 0.56 μg/mL) ($$p \leq 0.70$$). Overall, SAA was significantly higher in EX than in SED horses ($$p \leq 0.01$$), but there were no significant differences between groups at any specific time point (Table 2).
Day 28: In SED horses receiving the CO diet, there was no significant change in SAA at any time between P1 (0.0 ± 0.0 μg/mL) and P7 (0.10 ± 0.10 μg/mL) ($$p \leq 0.92$$). There was also no significant change in EX horses in SAA between P1 (0.15 ± 0.15 ug/mL) and P7 (0.20 ± 0.20 μg/mL) ($$p \leq 0.96$$). In horses receiving the CO diet, SED horses had significantly lower SAA than EX horses overall ($$p \leq 0.04$$), but there were no significant differences at individual time points (Table 2).
Day 0: In SED horses receiving the CO diet, there was no significant change in LPS at any time between P1 (2.10 ± 0.09 pg/mL) and P7 (2.13 ± 0.12 pg/mL) ($$p \leq 0.71$$). There was also no significant change in EX horses in LPS between P1 (2.18 ± 0.06 pg/mL) and P7 (2.21 ± 0.10 pg/mL) ($$p \leq 0.99$$). Overall, LPS was significantly higher in EX than in SED horses ($$p \leq 0.02$$), but there were no significant differences between SED and EX at any specific time point (Table 2).
Day 28: In SED horses receiving the CO diet, there was no significant change in LPS at any time between P1 (2.1 ± 0.09 pg/mL) and P7 (2.1 ± 0.05 pg/mL) ($$p \leq 0.94$$). There was also no significant change in EX horses in LPS between P1 (2.14 ± 0.03 pg/mL) and P7 (2.10 ± 0.08 pg/mL) ($$p \leq 0.94$$). Overall, LPS was significantly higher in EX than in SED horses ($$p \leq 0.004$$), but there were no significant differences between groups at specific time points (Table 2).
## Supplemented Diet
Day 0: In SED horses receiving the SUPP diet, there was no significant change in SAA at any time between P1 (0.33 ± 0.33 μg/mL) and P7 (0.15 ± 0.15 μg/mL) ($$p \leq 0.71$$). There was also no significant change in EX horses SAA between P1 (0.08 ± 0.08 μg/mL) and P7 (0.30 ± 0.30 μg/mL) ($$p \leq 0.70$$). There were no significant differences between SED and EX at any specific time point on Day 0 (Table 2).
Day 28: In SED horses receiving the SUPP diet, there was no significant change in SAA at any time between P1 (0.17 ± 0.17 μg/mL) and P7 (0.35 ± 0.15 μg/mL) ($$p \leq 0.59$$). There was also no significant change in EX horses in SAA between P1 (0.35 ± 0.25 μg/mL) and P7 (1.00 ± 0.53 μg/mL) ($$p \leq 0.96$$). Overall, SAA was significantly higher in EX than in SED horses ($$p \leq 0.02$$), but there were no significant differences between groups at specific time points (Table 2).
Day 0: In SED horses receiving the SUPP diet, there was no significant change in LPS at any time between P1 (2.15 ± 0.04 pg/mL) and P7 (2.17 ± 0.04 pg/mL) ($$p \leq 0.91$$). There was also no significant change in EX horses LPS between P1 (2.06 ± 0.04 pg/mL) and P7 (2.13 ± 0.01 pg/mL) ($$p \leq 0.98$$). LPS was significantly higher in SED than EX horses ($$p \leq 0.03$$), but there were no significant differences between groups at specific time points (Table 2).
Day 28: In SED horses receiving the SUPP diet, there was no significant change in LPS at any time between P1 (2.20 ± 0.08 pg/mL) and P7 (2.18 ± 0.07 pg/mL) ($$p \leq 0.90$$). There was also no significant change in EX horses in LPS between P1 (2.06 ± 0.04 pg/mL) and P7 (2.06 ± 0.05 pg/mL) ($$p \leq 0.97$$). LPS was significantly higher in SED than EX horses overall ($p \leq 0.001$), as well as at P5 ($$p \leq 0.01$$) and P6 ($$p \leq 0.05$$) (Table 2).
## Day 0 and Day 28 in Supplemented and Control Diets
On Day 0, there were no differences in SAA between SUPP and CO horses ($$p \leq 0.257$$). Overall, SAA was significantly higher in EX than SED horses ($$p \leq 0.015$$), primarily owing to significantly higher SAA in EX than SED horses in CO horses ($$p \leq 0.002$$) that was not observed in SUPP horses ($$p \leq 0.826$$) (Table 2).
On Day 28, SAA was significantly higher overall in SUPP horses compared with CO horses ($$p \leq 0.01$$). There was no significant difference in SAA between EX and SED horses overall, but SAA was significantly higher in SED horses than EX horses in horses receiving the supplemented diet ($$p \leq 0.05$$) (Table 2).
On Day 0, there were no differences in LPS between SUPP and CO horses ($$p \leq 0.346$$). There was also no significant difference between EX and SED horses overall ($$p \leq 0.268$$). LPS was significantly higher in EX than SED horses in the CO group ($$p \leq 0.003$$), but there were no significant differences in LPS between EX and SED horses in the SUPP group ($$p \leq 0.068$$) (Table 2).
On Day 28, there were no differences in LPS between SUPP and CO horses ($$p \leq 0.674$$). There was also no significant difference between EX and SED horses overall ($$p \leq 0.392$$). LPS was significantly higher in EX than SED horses in the CO group ($$p \leq 0.004$$) and significantly lower in EX than SED in the SUPP group ($p \leq 0.001$) (Table 2).
## 4. Discussion
The purpose of the current study was to quantify the effect of a dietary A. oryzae prebiotic on gastrointestinal permeability in horses challenged with combined transport and exercise stress. The main finding was that 28 days of supplementation with the A. oryzae prebiotic completely eradicated stress-induced gastrointestinal permeability in this group of horses.
We have previously demonstrated that the combination of transport and exercise stress model utilized in the current study produces gastrointestinal hyperpermeability and an increase in blood biomarkers that evidence transient, low-grade systemic inflammation [7]. Like our previous study, we report herein that 60 min of trailer transport immediately preceding half an hour of moderate-intensity exercise is a clear, reproducible model of gastrointestinal hyperpermeability. On Day 0 for both feeding groups, the stress model resulted in a significant uptick in the systemic appearance of orally administered iohexol that was not seen in unstressed controls. That this spike in the systemic appearance of iohexol was absent in stressed horses in the SUPP feeding group on Day 28 provides strong evidence for the role of A. oryzae prebiotic in protecting gastrointestinal barrier function in horses during stress. The mechanism for this blockade is not known but may be associated with an effect of A. oryzae prebiotic on the enteric microbiome. A. oryzae strongly increases the relative abundance of anti-inflammatory bacterial strains such as Bifidobacterium [18,24] and important fiber-degrading bacteria such as Ruminococcaceae [19]. Dietary provision of Bifidobacterium-based probiotics to obese humans results in a marked decrease in gastrointestinal hyperpermeability [25], which provides support for the hypothesis that A. oryzae prebiotic protects the enteric barrier from stress-induced hyperpermeability via its modulation of the gastrointestinal microbiome. This hypothesis should be tested in future studies.
When dietary groups were combined, there was an overall increase in SAA in response to our stress challenge, consistent with our previous study [7], but this effect was not observed when analyzing dietary groups individually. SAA is the major acute phase protein in the horse. While it is a highly sensitive indicator of an inflammatory event, it is not specific, and its production can be markedly increased in the presence of almost any inflammatory challenge [26]. The vast majority of SAA is produced by hepatocytes, but small amounts may also be produced by enterocytes [27]. Our small sample size, together with SAA fluctuations in both EX and SED groups that were unrelated to our stress challenge, likely contributed to the lack of statistical increase in SAA within groups. Consequently, the effect of A. oryzae prebiotic on this biomarker remains unknown. Owing to the highly plastic nature of SAA in vivo, future studies to evaluate the effects of the A. oryzae prebiotic on this outcome measure may benefit from controlled in vitro assessment of enterocyte-specific production of SAA [27].
The marked gastrointestinal hyperpermeability that was observed in the current study in EX horses in the control feeding group on Days 0 and 28 was not associated with a significant time-dependent increase in circulating LPS, and like SAA, this may have been due, at least in part, to our small sample size. But the overall serum LPS concentration of EX horses was significantly higher than SED horses. Surprisingly, however, serum LPS was significantly lower in EX than in SED horses for the A. oryzae feeding group. This result is probably not associated with the supplement because it was observed both on Day 0 (prior to beginning supplementation) and on Day 28, so instead is more likely an artifact of randomizing a small number of animals to the feeding groups. Furthermore, our maximum LPS concentration of 2.24 pg/mL in either feeding group is well within the reference interval for the normal flux of systemic LPS in healthy horses [26]. Future studies designed to detect the effect of the dietary A. oryzae prebiotic on the translocation of enteric LPS at levels expected to be associated with disease will require a stronger stress challenge such as non-steroidal anti-inflammatory drugs [2,27].
The current study had fewer animals in each treatment group than our previous study, which may have resulted in the current study being underpowered to detect the effects of stress and/or diet on SAA and LPS.
## 5. Conclusions
In conclusion, the data presented herein provide compelling evidence for a protective effect of A. oryzae prebiotic on stress-induced gastrointestinal hyperpermeability. This supplement may be a useful dietary ingredient for horses undergoing combined transport and exercise stress as a prevention for gastrointestinal hyperpermeability. Future studies should explore the effects of A. oryzae prebiotic on the equine gastrointestinal microbiome as a potential mode of action.
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|
---
title: 'Neuropathology of Central and Peripheral Nervous System Lymphoma in Dogs and
Cats: A Study of 92 Cases and Review of the Literature'
authors:
- Niccolò Fonti
- Francesca Parisi
- Çağla Aytaş
- Sara Degl’Innocenti
- Carlo Cantile
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000237
doi: 10.3390/ani13050862
license: CC BY 4.0
---
# Neuropathology of Central and Peripheral Nervous System Lymphoma in Dogs and Cats: A Study of 92 Cases and Review of the Literature
## Abstract
### Simple Summary
Nervous system lymphoma (NSL) is reported to be uncommon in dogs and rare in cats. The literature on this topic is fragmentary with heterogeneous results. Current knowledge is based on a few case series and mostly on single case reports. Therefore, the aim of our study was to retrospectively analyze 92 cases of canine and feline NSL, collecting data on breed, age, gender, clinical signs, type, neurolocalization, and assessing pathological patterns and phenotype. Finally, our results were compared with previously published studies and an extensive review of the literature was provided.
### Abstract
The literature about nervous system lymphoma (NSL) in dogs and cats is fragmentary, based on a few case series and case reports with heterogeneous results. The aim of our study was to retrospectively analyze 45 cases of canine and 47 cases of feline NSL and compare our results with previously reported data, also providing an extensive literature review. Breed, age, gender, clinical signs, type, and neurolocalization were recorded for each case. The pathological patterns and phenotype were assessed by histopathology and immunohistochemistry. The occurrence of central and peripheral NSL was similar between the two species in both primary and secondary types. NSL occurred with a slightly higher prevalence in Labrador Retrievers, and spinal cord lymphoma (SCL) was associated with young age in cats. The most frequent locations were the forebrain in dogs and the thoracolumbar segment in cats. Primary central nervous system lymphoma (CNSL) in cats most frequently involved the forebrain meninges, particularly as a B-cell phenotype. Peripheral NSL mostly affected the sciatic nerve in dogs and had no preferred location in cats. Nine different pathological patterns were identified, with extradural as the most prevalent SCL pattern in both species. Finally, lymphomatosis cerebri was described for the first time in a dog.
## 1. Introduction
Primary central nervous system lymphoma (CNSL) is a relatively uncommon form of tumor in dogs and is rare in cats, accounting for approximately $4\%$ of all canine lymphomas [1] and less than $3\%$ of cats with primary CNS tumors [2]. Lymphoma within the CNS develops most commonly as part of a multicentric process in both species with a reported prevalence up to $12\%$ [3] or even nearly $30\%$ [4] in dogs, and $7.8\%$ [2] to $14.4\%$ [5] in cats.
CNSL is believed to be more common in adult dogs with a mean age of onset of 7.4 years (range 3–11). Although there is no breed predisposition, Rottweiler dogs seem to have a slightly higher risk of developing CNS lymphoma [3,6]. The mean age of cats with prevalent location in the spinal cord is reported to be 6.3 years (range 2–8) [7] and no breed prevalence is recognized, with domestic shorthair cats being the most represented [2,5,8].
Peripheral nervous system lymphoma (PNSL) is less commonly encountered than CNSL. In dogs, it is mostly secondary [9,10,11,12,13], whereas in cats the primary type is most frequently recorded [2,8,14,15,16,17,18] and is considered the most common secondary tumor involving the PNS [15,19]. However, the incidence of both CNS and PNS lymphoma may vary depending on the tumor location in the neuraxis and spinal and cranial nerves [20,21].
The phenotype in canine CNSL is almost equally represented between B-cell [4,20,22,23,24,25,26,27] and T-cell [4,20,24,26,27,28,29,30,31,32,33,34], and less than $4\%$ are reported as non-B non-T cell. B-cell lymphoma, especially diffuse large B-cell lymphoma, predominates in secondary types [26]. In cats, B-cell lymphoma [2,8,18,21,24,35,36,37,38,39,40] is more frequently detected than T-cell lymphoma [2,8,17,18,21,41,42,43].
Studies reporting a consistent number of cases with detailed neurolocalization and lesion distribution, as well as pathological patterns and immunophenotyping, are limited, with most of the existing literature being represented by case reports with fragmented information and therefore not always comparable. Given the lack of consistency in the evaluation of nervous system lymphoma in previous studies, the aims of this retrospective study were to determine the specific location, pathological patterns, phenotype, and prevalence of primary versus secondary, central and peripheral nervous system lymphomas in dogs and cats, evaluating 92 cases and comparing the results with the available literature.
## 2.1. Caseload
Ninety-two cases of central and peripheral nervous system lymphoma diagnosed between 1994 and 2022 (45 in dogs and 47 in cats) were retrieved from the archive of the Neuropathology Laboratory of the Department of Veterinary Sciences of Pisa University. Signalments including breed, age, gender, main neurological signs, and tissue sample origin (biopsy or necropsy) are reported in Table 1. Specimens were obtained either from surgical biopsy or post-mortem examination and routinely formalin-fixed and paraffin wax-embedded and processed for histology. For each case, the type (primary or secondary/multicentric), the anatomical sites (intracranial, intraspinal, cranial, and spinal nerves), and the precise location and distribution were also recorded. In primary neoplasia cases, the extra-neural system involvement was assessed clinically or after complete post-mortem examination in case of death, as previously reported by other authors [2,8].
## 2.2. Histopathology and Immunohistochemistry
Four-µm tissue sections were stained with hematoxylin and eosin (H&E), Luxol fast blue, and immunohistochemical methods. The histological classification and grading of the tumors were assessed according to the World Health *Organization criteria* [44]. Immunoperoxidase was performed using a rabbit polyclonal antibody anti-human-CD20 (1:200, Thermoscientific, Fremont, CA, USA) and a rat monoclonal antibody anti-human-Paired Box 5 (PAX5, 1:250, Abcam, Cambridge, MA, USA) as B-cell markers, and a rabbit polyclonal antibody anti-human-CD3 (1:200, Dako, Glostrup, Denmark) as T-cell marker. Antigen retrieval for CD3 and PAX5 was performed by heat-induced epitope retrieval in citrate buffer at pH 6.0. No retrieval was performed for anti-CD20 antibody. Sections were pretreated with $1\%$ H2O2 in PBS for 10 min to quench endogenous peroxidase activity, then rinsed with $0.05\%$ Triton-X (TX)-100 in phosphate-buffered saline (PBS) (3 × 10 min) and blocked for 1 h with $5\%$ normal horse serum (PK-7200, Vector Labs, Burlingame, CA, USA) in PBS. The sections were then incubated overnight at 4 °C in a solution of the antibodies with $2\%$ normal horse serum and $0.05\%$ TX-100 in PBS. Sections were then rinsed in PBS (3 × 10 min), incubated for 20 min with universal biotinylated anti-mouse/rabbit IgG (Vectastain Kit, PK-7200, Vector Labs), and then with ABC reagent (Vectastain Kit, PK-7200, Vector Labs). Sections were again rinsed in PBS (3 × 10 min). The immunoreactivity was detected by the streptavidin-biotin peroxidase method (Streptavidin Peroxidase, ThermoFisher Scientific, Fremont, CA, USA), using 3,3′-diaminobenzidine as chromogen. Negative controls were obtained by replacing the primary antibody with an irrelevant, isotype-matched antibody and with an anti-serum. Canine and feline normal lymph nodes were used as positive control.
## 2.3. Pathological Patterns
Based on the histopathological location and distribution, the tumors were divided into the following pathological patterns: intraparenchymal mass (IP) (or intramedullary when in the spinal cord; IM), extraparenchymal (comprising both meningeal masses and leptomeningeal lymphomatosis; MM and LL, respectively), intravascular (IVL), lymphomatosis cerebri (LC), extradural (ED), intradural-extramedullary (ID-EM), and involving cranial and/or spinal nerves. In this latter condition, the term neurolymphomatosis (NL) was adopted, based on the presence of an infiltrating monomorphic population of malignant lymphoid cells within the endoneurium and perineurium of peripheral nerves [10,11]. To compare our results with the literature, we grouped the affected nerves in four compartments: [1] cranial nerves; [2] brachial plexus and/or its branches; [3] spinal roots, spinal nerves, and spinal ganglia; and [4] sciatic plexus and/or its branches. Additional localization of neoplastic cells included choroid plexus, pituitary gland, and surrounding soft tissue of spinal cord and nerves.
## 3. Results
Dogs affected by lymphoma had a mean age of 6.8 ± 3.8 years (ranging from 10 months to 14 years). There were $\frac{24}{45}$ ($53.3\%$) males and $\frac{21}{45}$ ($46.7\%$) females. Ten dogs were mixed breed, while the remaining were pure breed, namely Labrador Retriever ($$n = 5$$), Boxer ($$n = 4$$), German Shepherd ($$n = 3$$), Cane Corso ($$n = 3$$), Border Collie ($$n = 2$$), Hound ($$n = 2$$), Rottweiler ($$n = 2$$), and one of each of the following breeds: Epagneul Breton, Golden Retriever, Shih-tzu, Bulldog, West Highland White Terrier, Yorkshire Terrier, Dachshund, Beagle, Staffordshire Terrier, Bergamasco Shepherd, English Cocker Spaniel, Amstaff, Great Dane, and Pointer.
Cats affected by lymphoma had a mean age of 8.3 ± 4.4 years (ranging from 3 months to 15 years). There were $\frac{22}{47}$ males ($46.8\%$) and $\frac{25}{47}$ females ($53.2\%$). Most of the cats were European Shorthair ($$n = 40$$), while the remaining seven cats belonged to the following breeds: Persian, Ragdoll, Maine Coon, Norwegian Forest Cat, Carthusian, Siamese, and European Longhair.
The type, anatomical site, location, distribution, pathological pattern, nuclear size, grading, and immunophenotype of the lymphomas affecting the 92 animals are summarized in Table 2.
In dogs, $\frac{22}{45}$ ($48.9\%$) lymphomas were intracranial, $\frac{14}{45}$ ($31.1\%$) were located within the spinal canal, and $\frac{9}{45}$ ($20\%$) were limited to the peripheral nervous system (PNS). In four cases, a simultaneous CNS and PNS involvement was observed. In cats, $\frac{24}{47}$ ($51\%$) lymphomas were located intracranially, $\frac{21}{47}$ ($44.7\%$) in the spinal canal, and $\frac{7}{47}$ ($14.9\%$) in the PNS. Within these cases, in three cats lymphoma was detected both in the brain and spinal cord, and in another two cats in the brain together with cranial and spinal nerves. A total of $\frac{24}{45}$ ($53.3\%$) canine lymphomas were classified as primary forms, and $\frac{21}{45}$ ($46.7\%$) had multicentric involvement. In cats, $\frac{27}{47}$ ($57.4\%$) and $\frac{20}{47}$ ($42.6\%$) lymphomas were classified as primary and multicentric forms, respectively. Regarding the phenotype, the majority ($\frac{24}{45}$; $53.3\%$) of canine lymphomas had a B-cell phenotype, while $\frac{15}{45}$ ($33.3\%$) a T-cell phenotype, and $\frac{6}{45}$ ($13.4\%$) were non-B non-T cell lymphoma. In feline lymphomas, $\frac{25}{47}$ ($53.2\%$), $\frac{18}{47}$ ($38.3\%$), and $\frac{4}{47}$ ($8.5\%$) had B-cell, T-cell, and non-B non-T cell phenotypes, respectively.
## 3.1. Intracranial Lymphoma
The $27\%$ ($\frac{6}{22}$) of canine and $20.8\%$ ($\frac{5}{24}$) of feline intracranial lymphomas consisted of an intraparenchymal neoformation primarily but not exclusively located in the forebrain in both species (Figure 1A), and mostly as part of multicentric lymphoma in both dogs (five out of six; $83.3\%$) and cats (three out of five; $60.0\%$).
Grossly, focal or multifocal, whitish-grey masses accompanied by swelling of the peritumoral parenchyma and occasionally hemorrhagic areas were observed. The histopathological pattern was characterized by dense sheets of perivascularly arranged malignant cells that invaded the surrounding neuroparenchyma, creating highly cellular areas (Figure 1B). In one cat (#46), the neoplastic infiltration involved both the optic nerves and extended into the diencephalon. B-cell, T-cell, and non-B non-T phenotypes were observed in both species. In dogs, four out of six ($66.7\%$) cases were B-cell, one out of six ($16.7\%$) was T-cell, and one out of six ($16.7\%$) was non-B non-T cell lymphoma. In cats, three out of five ($60.0\%$) were T-cell and two out of five ($40.0\%$) were B-cell lymphomas.
In one dog (#39), there was a grayish discoloration of large areas of the telencephalic white matter associated with bilateral loss of distinction between gray and white matter (Figure 2A). Moreover, there was a blurring appearance of the hippocampal layers and cerebral sulci. No space-occupying lesions were detected in any areas of the brain. Histopathologically, the neoplastic cells diffusely infiltrated the white matter tracts and the perivascular spaces (Figure 2B) and showed large, round nuclei with a thin rim of slightly eosinophilic cytoplasm. Anisokaryosis was moderate with two to three mitoses for high power field (HPF, 400×, 2.37 mm2), and apoptotic figures were observed. Frequently, neurons were normal, even if extensively surrounded by neoplastic cells. Most malignant cells were positively immunolabelled with anti-CD3 marker and a small number of CD20-positive B-lymphocytes were observed within the perivascular cell infiltrate (Figure 2C,D). Within the most severely affected areas, there was activation and proliferation of microglial cells. The morphological features of this case were consistent with lymphomatosis cerebri (LC).
Almost half of the canine intracranial lymphomas ($\frac{10}{22}$; $45.4\%$) occurred as intravascular lymphoma (IVL), characterized by aggregation of neoplastic cells within the blood vessel lumen and scant invasion of the surrounding neuroparenchyma (Figure 3A). Multifocal thrombosis, hemorrhage, and necrosis of different regions of the brain were consistently associated with IVL. Detailed features of these 10 cases were reported in a previous study [45].
Intracranial lymphomas were characterized in many cases by prominent meningeal involvement, resulting in an extra-axial location (extra-axial lymphoma, EAL). Meningeal lymphoma was detected in $\frac{15}{24}$ ($58.3\%$) cats and in $\frac{2}{22}$ ($9.1\%$) dogs. These two canine tumors were multicentric, while $\frac{9}{15}$ ($60.0\%$) of feline cases were primary. This pathological pattern appeared as a well-defined, focal, irregular mass within the leptomeninges, resulting in a space-occupying lesion with compression of the underlying nervous tissue (Figure 3B). The cells exhibited mild to severe pleomorphism and typical lymphoid appearance. In most cases, despite well-defined margins, the meningeal masses were found in association with perivascular aggregates of neoplastic lymphoid cells within the neuroparenchyma, showing an intraparenchymal pattern. Both B-cell and T-cell phenotypes were observed in the two canine tumors. In cats, $\frac{9}{15}$ of lymphomas had a B-cell phenotype ($60.0\%$), whereas $\frac{4}{15}$ ($26.7\%$) and $\frac{2}{15}$ ($13.3\%$) showed a T-cell and non-B non-T phenotype, respectively. With regard to location, all feline tumors involved the forebrain, mainly the leptomeninges of the frontal lobes. Additionally, in two feline cases, the meningeal masses were associated with an intradural-extramedullary (#77) and an extradural (#79) spinal cord lymphoma.
Four cases of leptomeningeal lymphomatosis (LL) were observed; two were in dogs and two were in cats. In this pattern, neoplastic lymphocytes showed widespread diffusion predominantly within the subarachnoid space, infiltrating in some cases the subpial neuroparenchyma and perivascular spaces (Figure 4A). The canine forms were a multicentric T-cell lymphoma affecting the brain (#40) and a multicentric B-cell lymphoma restricted to the spinal cord (#35). The feline forms were a primary T-cell intracranial neoplasia (#88) and a multicentric B-cell lymphoma affecting both the brain and the spinal cord (#49, Figure 4B).
## 3.2. Intraspinal Lymphoma
Extradural (ED) and intramedullary (IM) pathological patterns of spinal cord lymphoma (SCL) were observed in both species. A total of $81\%$ ($\frac{17}{21}$) of all feline SCL described in this study were ED lymphomas, represented by $47\%$ ($\frac{8}{17}$) primary lymphomas and $53\%$ ($\frac{9}{17}$) multicentric. The thoracolumbar segment was most affected (10 cases), including one cat (#79) with multifocal meningeal involvement of the brain and thoracic spinal cord. Canine ED lymphoma was recorded in $\frac{9}{14}$ ($64.3\%$) of all canine SCLs. A total of $33.3\%$ (three out of nine) were primary, while $66.7\%$ (six out of nine) were multicentric.
The lesions were usually non-encapsulated, poorly defined, soft, grayish masses within the epidural fat, often with secondary severe spinal cord compression (Figure 5A). In some cases, the tumor infiltrated and invaded the adjacent vertebral bodies or soft tissues. The meninges and spinal cord parenchyma were focally invaded. Most of the diffuse neoplastic infiltration in the epidural fat was composed of large, rounded to oval lymphoblastic cell sheets (Figure 5B). B-cell lymphoma was the most frequently observed phenotype in primary types, both in dogs (three out of three; $100\%$) and in cats (six out of eight; $75.0\%$). As for secondary extradural lymphomas in dogs, four out of six ($66.7\%$) were B-cell and two out of six ($33.3\%$) were T-cell lymphomas. In cats, five out of nine ($55.6\%$) were B-cell and four out of nine ($44.4\%$) were T-cell.
Intradural-extramedullary lymphoma was observed in only three ($\frac{3}{21}$; $14.3\%$) feline cases, either as a primary or multicentric form. They were all T-cell lymphomas located in the thoracolumbar segment. One of them was associated with a cerebral meningeal mass (#77). In one dog (#35), diffuse intradural-extramedullary neoplastic infiltration was observed throughout the entire spinal cord, showing a pattern consistent with LL.
Intramedullary lymphoma was recorded in $\frac{4}{14}$ ($28.6\%$) of canine cases. Two intramedullary tumors were primary lymphoma B-cell and non-B non-T cell phenotypes, and two multicentric lymphomas showed sciatic nerve involvement and lumbosacral intra-medullary invasion (#5 and #9). A primary, non-B non-T cell lymphoma was observed in only one cat (#65).
## 3.3. Peripheral Nervous System Lymphoma
Lymphoma of the peripheral nervous system (or neurolymphomatosis, NL) was observed in $\frac{13}{45}$ dogs ($28.9\%$), in two of which it was associated with an intracranial mass and one with concurrent spinal cord neoplasia. Of these cases, $\frac{7}{13}$ ($53.8\%$) were males and $\frac{6}{13}$ ($46.2\%$) were females. The mean age of dogs with NL was 7.4 ± 3.1 years (ranging from 2.5 to 14 years). There was no breed prevalence: four dogs were mixed breed, and the remaining dogs belonged to the following breeds: Great Dane, Hound, Golden Retriever, Bulldog, Staffordshire Terrier, Amstaff, Cane Corso, Rottweiler, and Labrador Retriever. Eight out of 13 cases ($61.5\%$) were primary lymphomas, while the remaining $\frac{5}{13}$ ($38.5\%$) were multicentric. As for neurolocalization, in $\frac{7}{13}$ ($53.8\%$) cases the tumor involved the sciatic nerve and/or its branches, in $\frac{4}{13}$ ($30.8\%$) cases the spinal roots, spinal nerves and spinal ganglia (i.e., L1, L5, L6 nerves and one case multiple nerves), while in the remaining two cases ($15.4\%$) it involved the trigeminal nerves (Figure 6A,B). Histopathologically, NL was characterized by widespread infiltration of malignant lymphoid cells within the endoneurium and perineurium (Figure 6C). By immunohistochemistry, $\frac{6}{13}$ ($46.2\%$) cases were T-cell lymphomas and $\frac{7}{13}$ ($53.8\%$) were B-cell lymphomas.
In the feline cases, NL was detected in $\frac{7}{47}$ ($14.9\%$) cases, two of which were associated with an intracranial mass. Of these cases, three out of seven ($42.9\%$) were males and four out of seven ($57.1\%$) were females. The age of cats with NL varied widely, ranging from 3 months to 14 years; there was no breed prevalence, as four cats were European Shorthair and the remaining were a Ragdoll, a Carthusian, and an ELH cat. Five out of seven cases ($71.4\%$) were primary lymphomas. As regards the localization, in two cases (#82 and #89) the neoplasm involved both the cranial and spinal nerves. Looking at each compartment individually, in three cases the neoplasm involved the sciatic plexus and/or its branches, and the same occurrence was recorded for spinal roots, spinal nerves, and spinal ganglia. In two cases, NL was observed in cranial nerves, and in another two cases the brachial plexus and/or its branches were involved. By immunohistochemistry, four out of seven ($57.1\%$) cases were B-cell and three out of seven ($42.9\%$) were T-cell lymphomas.
## 4. Discussion
In our study, nervous system lymphoma occurred at any age in dogs and cats, from 3 months to 15 years, but was most common in middle-aged animals, as previously described [5,8,18,26]. Similarly, our results support that there is no breed or gender predisposition, except in dogs, where five ($11\%$) Labrador Retrievers were affected. Neurological signs were present in each patient and were highly variable, depending on the anatomical location of the tumor [20,21]. Both peripheral and central nervous system involvement were identified, and nine different pathological patterns of nervous system lymphoma (intraparenchymal mass, lymphomatosis cerebri, intravascular lymphoma, meningeal mass, leptomeningeal lymphomatosis, extradural, intradural-extramedullary, intramedullary, and neurolymphomatosis) were recorded and will be discussed in this section.
## 4.1. Intracranial Lymphoma
Intracranial lymphoma can involve the brain parenchyma and/or meninges as focal, multifocal, diffuse, and intravascular lesions, showing different histopathological patterns.
## 4.1.1. Intraparenchymal Patterns
Intraparenchymal lymphomas (IPLs) are commonly reported in feline nervous system lymphomas [2,5,8,24,35,36,37,39,40,46,47,48] and less frequently in the canine counterpart as either primary or multicentric types [3,24,25,26,28,48]. There are currently insufficient data to support a different localization between primary and multicentric tumors; therefore, a thorough post-mortem examination remains mandatory to distinguish these two forms [2]. Rostrotentorial regions such as the cerebral cortex [2,25,36,48,49], diencephalon [1,5,8,28,37], and the olfactory bulb [5,8,48] are the most targeted brain sites. Although considered rarer, cerebellar location has also been described [1,2,4,20,27,40,50].
In our study, canine IPL was observed in the occipital lobe, ponto-cerebellar angle, basal nuclei, thalamus, and parieto-occipital lobe with trigeminal nerve involvement. In cats, no preferred sites were identified, with both the forebrain and hindbrain regions involved. IPLs were mainly multicentric, and no breed or gender predisposition was identified in both species. The mean age of the dogs of our study was 6.8 years, in accordance with previously reported data (from 5.5 to 7.4 years) [1,48]. As noted in this study, where the mean age at diagnosis was 10 years, affected cats are usually reported to be adult or elderly [2,37,48,51].
The distribution of neoplastic cells and the invasion of the neuroparenchyma were similar to those reported in the literature [24,25,28,49]. Lymphoma is usually composed of heterogeneous soft tissue with a whitish-gray appearance and is occasionally associated with small malacic foci [36,49]. Adjacent tissue may be swollen, and multifocal lesions are described [5,48]. Both T-cell [2,24,26,28] and B-cell [8,24,25,35,36,37,40,46] CNSLs have been described in the literature as in this study.
## 4.1.2. Lymphomatosis Cerebri
The term “lymphomatosis cerebri” (LC) is used to describe an atypical neuroanatomical pattern of lymphoma cell spread [52]. This variant is not so uncommon in the feline species [5,49]. The absence of a cohesive mass is the most notable histological feature of LC. Lymphoid cells spread widely and diffusely mainly within the cerebral white matter from the frontal lobe to the brainstem and spinal cord [3,49,53,54]. Focal leptomeningeal congestion and dilation of the lateral and third ventricles and central canal have also been described [42,54]. Feline LC is always described as a T-cell primary form, although large granular T-cell lymphoma, presumably originating in the alimentary tract with a secondary diffuse infiltrate in the white matter of the brain, has been reported [43]. No breed, age or gender predisposition has been identified [42,54]. In human medicine, no differences in the distribution ratio between sexes have been reported and B-cell LC account for most cases [55,56].
There are no reports of LC in dogs in the veterinary literature. In our case series we described a novel finding of a primary and diffuse infiltration of neoplastic T-cells within the white matter and leptomeninges in one dog that was consistent with LC. No cases of LC in cats were observed in our study.
## 4.1.3. Intravascular Lymphoma
Intravascular lymphoma (IVL), also known as “angiotropic lymphoma”, is a rare type of lymphoma that has been reported in both humans [57,58] and animals [26,29,34,45,59,60]. This form is characterized by the predominant growth of neoplastic cells within the lumen of blood vessels with little or no extension into the neuroparenchyma. Progressive occlusion of blood vessels with neoplastic cells leads to thrombosis, hemorrhage, and infarction. Various organs could be affected [61,62], but nervous system involvement is the most frequently reported in the literature, both in humans and domestic animals [26,29,34,45,49,60,63]. In dogs, IVL has been described in 10 reports including a total of 51 dogs [26,29,34,45,59,60,63,64,65,66]. In those IVL cases, there was either a restricted involvement of the brain or it was part of a multiorgan/multicentric IVL form. In our study, 10 cases of IVL were observed, representing almost half of the canine intracranial lymphomas. Affected animals were middle-aged dogs with a mean age of 8 years (ranging from 2.5 to 13 years), slightly above what was previously described in two studies (mean age of 7.25 and 6, respectively) [34,59]. In accordance with the literature, no breed or sex predilection was observed in our cases. Although reported in previous studies [29,34,59,66], no spinal cord involvement was detected in our IVL cases, where the lesions were restricted to the brain. The histopathological lesions observed in the affected brain regions were similar to the ones described in previous studies on canine IVL [29,34,59,66]. In our cases, the non-B non-T phenotype was identified in four IVL cases with a prevalence similar to that reported in a previous study [59]. Although the T-cell IVL phenotype was the most prevalent in previous studies [29,59,63], in our remaining six cases there was an equal distribution of T-cell and B-cell IVL.
There were four reports of IVL in cats [8,61,67]. In two of them, the vascular lesions were limited to the brain [8], in one case to the brain and kidneys [67], and in only one case as systemic disease. No gender or breed predisposition for feline IVL was reported in those studies. No cases of feline IVL were recorded in our series.
## 4.1.4. Extraparenchymal Patterns
Extraparenchymal (or extra-axial) lymphoma (EAL) may appear as a well-defined focal mass involving the meninges but may also diffusely invade the leptomeninges and choroid plexuses, resulting in “leptomeningeal lymphomatosis” and “lymphomatous choroiditis”, respectively [8]. Meningeal masses (MM), unlike intraparenchymal ones, are usually well-defined. They may be irregularly shaped with a whitish-gray appearance and soft texture. Sporadic malacic and hemorrhagic foci are mentioned [49,68].
In this report, intracranial extraparenchymal lymphomas in cats were mainly characterized by meningeal location, usually accompanied by secondary infiltration of the neuroparenchyma. This is a frequent condition in cats, accounting for 30 to $100\%$ of intracranial lymphomas [5,18,21,48], as a primary or multicentric type [48]. Feline EAL was diagnosed more commonly than IPL in our series, primarily as primary types. This pathological pattern is less frequent in dogs and has been reported in a few cases [31,48]. Additionally, in our series only two dogs had an occipito-temporal meningeal lymphoma and a multifocal meningeal infiltration also involving the thalamus, cerebellum, medulla oblongata, choroid plexus, and pituitary gland.
In cats, most tumors described by Mandara et al. [ 2022] [8] were within the cranial fossa. Interestingly, all feline MM in our study were in the forebrain, with the frontal lobe being the most common site. The median age of cats was 11 years (ranging from 4 to 17), with ESH as the main represented breed in accordance with the existing literature [5,8,38]. Based on the immunohistochemistry results, most intracranial meningeal masses in our feline cases were B-cell lymphomas, as reported in previous studies [8,38].
EAL may also develop in the choroid plexus (CP) and either grow to a recognizable mass [27,68] or lead to diffuse neuroparenchymal infiltration [8,18,26,30]. In both human and veterinary medicine, the latter variant, known as “lymphomatous choroiditis,” is sporadically documented [27,68].
In our series, four and two cases with CP involvement were observed in dogs and cats, respectively. However, neoplastic lesions within the CP were always associated with lymphoma spread to other CNS or PNS locations, both as primary and multicentric forms.
An additional neuroanatomical growth pattern of extraparenchymal lymphoma is intradural lymphoma. In humans, an uncommon subtype of primary CNSL that develops from the dura mater and differs considerably from other CNSLs is called primary dural lymphoma. This tumor is classified as a mucosa-associated lymphoid tissue (MALT) lymphoma and is typically indolent [69,70]. However, secondary involvement of the dura and leptomeninges is reported [71]. In veterinary medicine, pachymeningeal lymphoma is poorly documented, and Mello et al. [ 18] reported the lone occurrence of intradural lymphoma in cats. In our case series this pathological pattern was not observed.
## 4.1.5. Leptomeningeal Lymphomatosis
When neoplastic lymphocytes show prevalent diffuse infiltration of the subarachnoid space, this pathological pattern is referred to as leptomeningeal lymphomatosis (LL), or lymphomatous meningitis, and has been reported in dogs and cats [8,49,72]. In some reports it has been considered as a secondary evolution of an extra-nervous lymphoma [32,72], or as a primary form [22,23,30,41,73]. Neoplastic lymphocytes are thought to directly extend from pre-existing primary or metastatic CNS tumors, from an extra-CNS mass invading the PNS, or through hematogenous dissemination into the leptomeningeal structures [72,74]. However, since primary forms have been reported, the etiopathogenesis of LL remains unknown. Being frequently described alongside neoplastic infiltrates occurring in the nervous tissue, such as neurolymphomatosis [2,8] or parenchymal and periventricular infiltrates [43,54], this form represents a diagnostic challenge [72].
In the present study, two cases of canine LL ($\frac{2}{45}$; $4.4\%$) were detected as a multicentric form: one was found in the cerebral and cerebellar lobes, and the other involved the meninges of the entire spinal cord. Canine LL is reported to be composed of T-cell [30,31] or B-cell [23,26] phenotypes. Both T-cell and B-cell phenotypes were represented in our study.
Recently, Mandara et al. [ 2022] [8] described three cases of primary feline LL: one case with involvement limited to the brain, one to the spinal cord, and one with a pathological continuum between brain LL and neurolymphomatosis of the optic chiasm, all affecting DSH adult/elderly cats. Feline LL was also described in primary [2] and in secondary brain lymphoma [18] as the most frequent pathological intracranial pattern. In our study, two cases of LL were observed in cats with occasional leptomeningeal thickening. One of these was primary and limited to the brain meninges, while the second was a multicentric type affecting both the brain and spinal cord.
The feline primary form described in our series was T-cell lymphoma. Notably, five out of seven primary LL described in the literature were classified as T-cell lymphoma [2,8,41]. The two B-cell primary LL described in the literature showed rare oculo-cerebral involvement [8,73]. Further studies are needed to assess whether there is an association between the phenotype and this location. Regarding LL as part of multicentric lymphoma in cats, both B-cell and T-cell phenotypes have been reported [17,18,72].
## 4.2. Intraspinal Lymphoma
Lymphoma within the vertebral canal (or intraspinal lymphoma, SCL) can be categorized into three main forms with distinct biological characteristics based on anatomical location within the vertebral canal and spinal cord involvement [7,49]. Extra-axial lesions may be external to the dura mater (extradural; ED) or internal to the dura mater but external to the pia and spinal parenchyma (intradural-extramedullary; ID-EM). The SCL may also be located within the spinal cord parenchyma (intramedullary; IM) [14,75,76,77,78,79].
Most frequently, SCLs are reported as secondary [7,14,18]; however, primary forms are also described [2,7,8,14,17]. The mechanism of primary SCL formation is not fully understood; hematopoietic tissues of extramedullary or vertebral bone have been suggested as the site of origin of spinal lymphoma [7,14,80,81,82]. Spinal cord secondary lymphomas are thought to result from direct extension from the paravertebral regions through the vertebral foramen, or from hematogenous dissemination through the epidural venous system [83].
Canine SCL occurs almost as frequently as in cats. The most common location is extradural [20,22,32,33,48,79,80,81,84,85,86], followed by intramedullary [20,48,75,76], and less commonly intradural-extramedullary location [20,48]. In our study, SCL was more frequently detected in cats than in dogs, with the thoracolumbar location over-represented in both species.
Cats with spinal lymphoma are often younger than those with other spinal cord tumors [7]. In cats, the mean age at the time of diagnosis was highly variable, with 4.5 years (ranging from 8 months to 7 years) and 4 years (ranging from 1 to 11 years) for primary and secondary forms, respectively [14,77,87]. Similarly, age at diagnosis was variable in our study, with a comparable mean age between primary (5 years; range 0.7–12 years) and secondary (6 years; range 0.7–13 years) types. No gender or breed predisposition was found in our study, as in the series reported in the literature.
Clinical and pathological features of SCL may vary. It can manifest as a focal mass [14,32,33,77], multifocal masses, or extensively disseminated tumor [8,14]. The lumbosacral and thoracic segments are the preferential site of onset [32,33], but lymphoma has the potential to involve any segment of the spinal cord [8,14,18,20,79,87]. Furthermore, SCL is often reported in association with cranial [7,88] or spinal nerves [2,21,68,77] involvement, depending on the anatomical location.
## 4.2.1. Extradural Lymphoma
Our findings on ED lymphoma are comparable to those of previous studies, where ED lymphoma was the most frequently reported pattern of SCL, described as both primary and secondary type [7,14,20,22,32,33,48,68,77,80,81,84,85,86,89]. Both T-cell and B-cell phenotype were found, as previously reported [2,8,17,18,20,32,33,81,90,91]. Most of the primary ED lymphoma in our cats and all primary types in dogs were B-cell lymphomas, whereas the B and T phenotypes were equally represented in the multicentric types. Further studies on feline SCL phenotyping and its possible association with neuroanatomical patterns are warranted. Based on the limited data available, B-cell lymphoma seems to be the most prevalent phenotype in cats. Although numerous cases have been reported in dogs, immunophenotyping was performed in only a few of them [20,32,33,81], showing that the most frequent phenotype was T-cell.
## 4.2.2. Intradural-Extramedullary Lymphoma
In the present study, only one case of canine ID-EM lymphoma was found in a dog and three in adult cats. ID-EM has been reported in both species [7,20,88] as an irregular mass that fills the subdural and subarachnoid spaces and tends to infiltrate contiguous segments of the spinal cord or nerve roots [49,87]. In two primary intradural-extramedullary lymphomas, T-cell phenotypes were identified [2]. In our case series, all tumors were T-cell, and in one case it was associated with intracranial meningeal involvement.
## 4.2.3. Intramedullary Lymphoma
Intramedullary pattern is occasionally reported in dogs and cats, mainly as secondary SCL [7,20,76]. The tumor may appear as a soft, poorly defined mass or may produce neuraxis enlargement, altering the parenchymal architecture [2,7]. Hemorrhagic myelomalacia due to compression injury has been documented in cats in both intramedullary and extramedullary lymphomas [91,92].
In our study, four tumors in dogs and only one tumor in a cat were classified as intramedullary SCL. Given the few cases, no differences between primary and secondary forms were identified in both species. The feline tumor was a non-B non-T lymphoma. Notably, no intramedullary B-cell lymphomas have been reported in cats [8,17].
## 4.3. Peripheral Nervous System Lymphoma
Peripheral nerve lymphoma (or neurolymphomatosis, NL) is uncommon in dogs and rare in cats [12,15,51,93]. They can involve any cranial nerve, spinal nerve roots, somatic, or autonomic nerves [78,94,95,96]. The most frequently affected nerves are the trigeminal, those within the cervico-thoracic segment (C6-T2), and, more rarely, nerves of the lumbar intumescence [93]. The neoplasms may be confined to or extend along the nerves, resulting in an intradural-extramedullary location with spinal cord compression [97,98,99]. Multicentric NL is rarely reported in both human and animal species [12,100,101,102,103,104].
Macroscopically, the affected peripheral nerve may be normal or homogeneously thickened [105], soft, and have yellowish discoloration [18], mimicking inflammatory infiltration or the less common chronic hypertrophic neuritis [106]. Less frequently, a mass that focally deforms the nerve trunk can be observed, whereas in other cases, a large tumor mass is easily identifiable [16,107]. These masses are commonly described as solid, whitish, irregularly bosselated, and non-symmetric [103]. Complete nerve loss and replacement with a well-circumscribed white mass has also been reported [16]. In several cases, moderate atrophy of the muscles innervated by the affected nerves was the most significant gross lesion associated with NL [15,16,105,107].
In our study, the higher prevalence in dogs than in cats is in contrast with the data reported in the literature [2,8,9,10,11,12,13,14,15,16,18,101,105,107]. Our results showed no breed or gender predisposition in either dogs or cats and that NL typically affected adults. The mean age of dogs and cats with NL was similar to that reported in the literature. In dogs, the prevalence of the primary type was higher than the secondary, unlike what was reported in other studies, in which all cases were secondary types. Regarding the feline species, NL was primary in most of the cases, accordingly with previous studies [2,8,15,16,18,101]. The increase in the number of primary lymphoma diagnoses may be related to the development of veterinary neurology and neurosurgery referral services [8].
In our dogs, the sciatic nerve and/or its branches was the most affected of the compartments, followed by the spinal roots, spinal nerves and spinal ganglia, and the trigeminal nerve. Similar findings have been reported in the literature [10,11,12,13]. Results on the feline population showed that there was no predominance of one compartment over the others [2,8,14,15,16,18,101,103,105,107]. Optic nerve involvement may be more frequent in cats [8]. Moreover, PNS involvement with and without concurrent CNS involvement are findings similar to those reported in the literature [2,8,9].
Data on phenotype are still fragmentary in veterinary medicine, as the only data are single case reports or small cohorts. Our results showed that there is no predominance of one phenotype over the other, but B- and T-cells lymphomas are equally distributed among population in both dogs and cats. These results are in line with the literature [2,8,14,15,16,18,101,103,105,107].
## 5. Conclusions
In this retrospective study we evaluated 92 cases of central and peripheral nervous system lymphoma in dogs and cats, which were studied on the basis of consistent histopathological and immunohistochemical examination. An extensive literature review was performed to collate available information on reported cases and reconcile fragmented clinical and pathological data so that our results could be compared with the available literature.
In our study, no age, breed, or gender predisposition related to specific CNSL subtypes was seen in both species, even if in Labrador Retriever dogs it occurred with a slightly higher prevalence. The lone exception was the occurrence of SCL in juvenile cats, as previously reported. The overall anatomical locations of CNSL vs. PNSL, type (primary vs. secondary/multicentric), and phenotypes were similar across the species.
Regarding CNSL, the forebrain was the most frequent site of lymphoma in dogs and the thoracolumbar segment was the most frequent site in cats. Primary CNSL in cats most frequently involved the forebrain meninges and, to a lesser extent, the optic nerves, especially as a B-cell phenotype. Extradural location was the most prevalent pathological pattern of SCL in both species. The B-cell phenotype predominated in primary lymphoma, whereas the B-cell and T-cell phenotypes were equally represented in the multicentric type.
PNSL most frequently affected the sciatic nerves and its branches in dogs, whereas no prevalent compartment was recognized in cats. B-cell and T-cell phenotypes were equally represented in primary and multicentric PNSL in both species.
Canine intravascular lymphoma mimicked a neurovascular condition rather than space-occupying or inflammatory lesions. Lymphomatosis cerebri can also occur in dogs.
Clinicopathological features were variable in both species, particularly in neurolymphomatosis, due to different combinations of central and peripheral nervous system involvement.
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|
---
title: The Gut Microbiota of Young Asian Elephants with Different Milk-Containing
Diets
authors:
- Chengbo Zhang
- Junmin Chen
- Qian Wu
- Bo Xu
- Zunxi Huang
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000238
doi: 10.3390/ani13050916
license: CC BY 4.0
---
# The Gut Microbiota of Young Asian Elephants with Different Milk-Containing Diets
## Abstract
### Simple Summary
Insufficient maternal milk is one of the important reasons for the low survival rate of young Asian elephants. Finding the optimal break milk supplementation for young Asian elephants is a matter of urgency. In our study, we investigated the microbiomes of young Asian elephants on different milk-containing diets (elephant milk only, elephant milk–plant mixed feed, and goat milk–plant mixed feed). Our results suggested that goat milk is not suitable for young elephants, and yak milk may be an ideal source of supplemental milk for Asian elephants.
### Abstract
Evaluating the association between milk-containing diets and the microbiomes of young Asian elephants could assist establishing optimal breast milk supplementation to improve offspring survival rates. The microbiomes of young Asian elephants on different milk-containing diets (elephant milk only, elephant milk–plant mixed feed, and goat milk–plant mixed feed) were investigated using high-throughput sequencing of 16S rRNA genes and phylogenetic analysis. Microbial diversity was lower in the elephant milk-only diet group, with a high abundance of Proteobacteria compared to the mixed-feed diet groups. Firmicutes and Bacteroidetes were dominant in all groups. Spirochaetae, Lachnospiraceae, and Rikenellaceae were abundant in the elephant milk–plant mixed-feed diet group, and Prevotellaceae was abundant in the goat milk–plant mixed-feed diet group. Membrane transport and cell motility metabolic pathways were significantly enriched in the elephant milk–plant mixed-feed diet group, whereas amino acid metabolism and signal transduction pathways were significantly enriched in the goat milk–plant mixed-feed diet group. The intestinal microbial community composition and associated functions varied significantly between diets. The results suggest that goat milk is not suitable for young elephants. Furthermore, we provide new research methods and directions regarding milk source evaluation to improve elephant survival, wellbeing, and conservation.
## 1. Introduction
The Asian elephant (Elephas maximus) is a large phytophagous mammal that is mainly found in the Xishuangbanna region of Yunnan Province, China, south of 24.6° north latitude, and in parts of south and southeast Asia [1]. The Asian elephant is a Class I protected wildlife species in China and is listed as endangered by the International Union for Conservation of Nature Red List of Threatened Species™ [2,3]. Furthermore, these elephants are in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora [4]. There are only approximately 300 Asian elephants left in China [5].
Although the Asian elephant population has rebounded after years of effort, its survival rate still requires improvement. Approximately $25.6\%$ of elephant calves in Myanmar reportedly die before they reach 5 years of age, with a quarter of these deaths attributed to insufficient maternal milk or the inability of the calves to receive the milk properly [6]. Similarly, in wild African elephant populations, an average of $19\%$ of young elephants die before 5 years of age, with a proportion of these deaths attributed to maternal difficulties regarding meeting nursing needs [7]. During droughts, maternal elephants struggle to maintain milk production, when the metabolic demands of young male elephants are greater, making it difficult for maternal elephants to meet their needs. Thus, young male elephants are more likely to die [8]. A major reason for the high mortality rate of elephant calves in zoos, especially in Asia, is the refusal of mothers to nurse their young, resulting in the need for manual intervention to feed the young [9,10]. Inadequate maternal milk in Asian elephants results in the poor survival rate of young elephants, and currently, staff at the Xishuangbanna Asian Elephant Sanctuary are using goat milk to supplement the feeding of rescued infants and young elephants.
The large number of microbial communities present in the gastrointestinal tract of animals constitute the microbiota, which contribute to host nutrient acquisition and immune regulation [11,12] and assist in maintaining host homeostasis in response to environmental changes [13,14,15]. Diet, especially early nutrition, influences the composition and metabolic activity of the gut microbial community and is a key factor in the growth and healthy development of newborn elephants [16,17]. Breastfeeding is considered an influential driver of the gut microbiota composition during infancy, potentially affecting the function thereof [18]. The gut microbiota early in life is associated with physiological development, and early gut microbiota is involved in a range of host biological processes, particularly immunity, cognitive neurodevelopment, metabolism, and infant health [19,20].
Early foods can promote the survival rate of infant and young elephants; therefore, it is vital to study the effects of different foods, especially different kinds of milk on the gut microbiota of infant and young elephants. In this study, the gut microbiota composition and function of young elephants fed an elephant milk-only diet, elephant milk–plant mixed-feed diet, and goat milk–plant mixed-feed diet were analyzed using 16S rRNA gene high-throughput sequencing technology. Although there have been studies regarding the use of non-breast milk dairy products for feeding endangered wildlife (e.g., Siberian tigers [16]), only few studies on the gut microbiota of Asian elephants on diets containing goat milk exist. To the best of our knowledge, this study is the first to describe the composition and function of the gut microbiota of young elephants fed a goat milk diet.
## 2.1. Fecal Sample Collection
In March 2019, we collected fresh feces from eight young Asian elephants with different milk-containing diets at Wild Elephant Valley in Xishuangbanna: three in the elephant milk diet-only group (BF1, BF2, and BF2; they are healthy, aged about 6 months, and can freely shuttle below the abdomen of adult female elephants); three in the elephant milk-plant mixed feeding group (BPM1, BPM2, and BPM3; they are healthy, more than one year old, and tall to the base of the forelegs of adult female elephants); and two in the goat milk–plant mixed feeding group (GPM1 and GPM2; they are healthy, more than three year old, and height slightly higher than the previous group). The detailed sampling method was as follows [21]: young elephants were accompanied by the breeder until defecation, samples were collected immediately from the center of fresh feces with sterile tweezers, placed in sterile centrifuge tubes, and stored in liquid nitrogen. Samples were transported in liquid nitrogen, and then stored at −80 °C until DNA extraction.
## 2.2. Genomic DNA Extraction, Gene Amplification and High-Throughput Sequencing
*Microbial* genetic DNA was extracted from eight fecal samples using the EZNA® Soil DNA Kit (Omega, GA, USA) following the steps in the kit instructions. DNA quality and quantity were assessed using a $1\%$ agarose gel and a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with the primer pair 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) using an ABI GeneAmp 9700 PCR thermal cycler (Appliedbiosystems, Foster City, CA, USA). The PCR mix consisted of 4 μL of 5× TransStart FastPfu buffer, 2 μL of 2.5 mM dNTP, 0.8 μL each of 5 μM forward and reverse primers, 0.4 μL of TransStart FastPfu DNA polymerase, 10 ng of template DNA and ddH2O up to 20 μL. PCR amplification was performed in triplicate under the following conditions: 95 °C for 3 min, followed by 30 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s, and a final extension at 72 °C for 10 min. Purified amplicons were pooled in equimolar aliquots and then sequenced on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) to obtain paired-end reads [22].
## 2.3. Sequencing Data Processing
Raw 16S rRNA gene sequencing reads were demultiplexed and quality-filtered using fastp version 0.20.0 [23] and then merged using FLASH version 1.2.7 [24]. Stringent criteria were established for quality. Three hundred-base pair reads were truncated at any site that received an average quality score <20 over a 50 bp sliding window. Truncated reads shorter than 50 bp and reads with ambiguous characters were discarded. Sequences required an overlap larger than 10 bp for assembly, and the maximum mismatch ratio of the overlap region was 0.2. Reads that could not be assembled were discarded. Samples were distinguished by barcodes and primers, and the sequence direction was adjusted accordingly. Exact barcode matching was required, and a mismatch of two nucleotides in primer matching was allowed.
Operational taxonomic units (OTUs) with a $97\%$ similarity cutoff [25,26] were clustered using UPARSE version 7.1 [25]; chimeric sequences were identified and removed. Taxon assignments for each representative OTU sequence were determined using RDP Classifier version 2.2 [27] with the 16S rRNA gene database (Silva v138) with a confidence threshold of 0.7.
## 2.4. Data Analysis and Statistical Methods
To investigate the similarity and difference relationship of microbial community structure among different milk-containing diet groups, sample-level clustering analysis was performed using UPGMA method based on the average Bray_curtis distance matrix among groups. Alpha diversity indices including Chao1 index, Shannon index, and Pielou index were calculated using software mothur (version 1.30.2, http://www.mothur.org/wiki/Schloss_SOP#Alpha_diversity, accessed on 23 April 2019), and difference tests between multiple groups were performed using Welch’s t-test. The Kruskal–Wallis H test was applied to detect species that exhibited differences in abundance in the microbial communities between groups. In addition, functional prediction results were obtained using PICRUSt2, and the difference significance was detected using the Kruskal–Wallis H test.
## 3.1. Unweighted Pair Group Method with Arithmetic Mean Hierarchical Clustering Analysis
At the family and genus levels, the samples were analyzed using hierarchical clustering based on the unweighted pair group method with arithmetic mean (UPGMA) cluster analysis method (Figure 1), which indicated that the samples were clearly clustered into two groups: the elephant milk diet group (BF1, BF2, and BF2) and the milk–plant mixed-feed diet group (remaining samples). The milk–plant mixed-feed diet group was clearly further divided into two groups according to the type of supplemented milk: the elephant milk–plant mixed-feed diet group (BPM1, BPM2, and BPM3) and the goat milk–plant mixed-feed diet group (GPM1 and GPM2). These results exhibited that the gut microbial community composition of young elephants in the elephant milk-only diet group and that of young elephants in the milk–plant mixed-feed diet groups differed clearly. Moreover, the gut microbial community composition of young elephants in the elephant milk-only diet group and that of young elephants in the goat milk–plant mixed-feed diet group also differed significantly.
## 3.2. Alpha Diversity Analysis
An α-diversity test was performed to evaluate the differences in the gut microbial community between the three groups at the family level (Figure 2). Consequently, the richness index (Chao1) and diversity index (Shannon) were significantly different between the three groups ($p \leq 0.05$, Figure 2A,B). The richness and diversity indices of the milk–plant mixed-feed diet groups were significantly higher than those of the elephant milk-only diet group ($p \leq 0.05$), which was consistent with the richness of dietary diversity in the milk–plant mixed-feed diet groups. In addition, the Shannon and Pielou indices were significantly higher in the elephant milk–plant mixed-feed diet group than in the goat milk–plant mixed-feed diet group ($p \leq 0.05$, Figure 2B,C). These findings suggested that supplementation with elephant milk in young elephants resulted in a more diverse and homogeneous gut bacterial community than supplementation with goat milk, and supplementation with goat milk may lead to a highly dominant bacterial taxon in the gut environment of young elephants.
## 3.3. Community Composition
Firmicutes and Bacteroidetes represented the dominant phyla in young elephant guts, which was consistent with the dominant phyla in the gut microbiota of adult Asian elephants (Figure 3) [28]. The young elephant intestinal microbiota in the elephant milk-only diet group (BF1, BF2, and BF3) contained a high abundance of Proteobacteria, averaging around approximately $17.3\%$ (Figure 3). The elephant milk–plant mixed-feed diet group (BPM1, BPM2, and BPM3) had a higher abundance of Spirochaetae (approximately $8.8\%$), Fibrobacteria (approximately $3.8\%$), and Verrucomicrobia (approximately $3.6\%$) compared to the elephant milk-only diet group (Figure 3). The BPM1 group had a relatively higher intake of elephant milk and, correspondingly, higher abundance of Proteobacteria, while BPM2 and BPM3, which had lower intakes of elephant milk, had an extremely low abundance of Proteobacteria, indicating that elephant milk is closely related to Proteobacteria levels. The goat milk–plant mixed-feed diet group (GPM1 and GPM2) contained nearly no Proteobacteria, Spirochaetae, and Fibrobacteria (Figure 3), and the considerably low abundance of Proteobacteria indicated that elephant milk is closely related to the abundance of this bacterium. In addition, Synergistetes were abundant in the intestinal microbiota of young elephants in the goat milk–plant mixed-feed diet group compared to the other groups (Figure 3).
At the family level, the intestinal bacteria of young elephants in the elephant milk-only diet group consisted mainly of Bacteroidaceae, Enterobacteriaceae, Ruminococcaceae, and Lachnospiraceae, accounting for >$75\%$ of intestinal bacteria (Figure 1A). The intestinal bacteria of young elephants in the elephant milk–plant mixed-feed diet group consisted mainly of Lachnospiraceae, Ruminococcaceae, Rikenellaceae, Spirochaetaceae, and Prevotellaceae, accounting for >$70\%$ of intestinal bacteria (Figure 1A). BPM1, who consumed a large amount of elephant milk, had an abundance of Enterobacteriaceae, suggesting that Enterobacteriaceae levels are closely related to the elephant milk consumed by young elephants. The intestinal bacteria of young elephants in the goat milk–plant mixed-feed diet group consisted mainly of Ruminococcaceae, Lachnospiraceae, Prevotellaceae, and Synergistaceae, accounting for approximately $60\%$ of intestinal bacteria (Figure 1A).
## 3.4. Differential Microbiota Analysis
At the family level, differential microbiota analysis of young elephants (Figure 4) revealed that Rikenellaceae, Spirochaetaceae, Fibrobacteraceae, and Bacteroidales_UCG-001 were significantly enriched in the elephant milk–plant mixed-feed diet group ($p \leq 0.05$). These bacterial taxa belong to the lignocellulose-degrading bacterial phyla commonly encountered in the gastrointestinal tracts of animals, such as Bacteroidetes, Spirochaetes, and Fibrobacteres, suggesting that elephant milk enriches lignocellulose-digesting bacterial groups in the intestinal tract of young elephants, facilitating the transition from an elephant milk diet to a plant-based diet. Prevotellaceae, Synergistaceae, and Christensenellaceae were significantly enriched in the goat milk–plant mixed-feed diet group ($p \leq 0.05$). This indicated that there was a significant difference in the effect of elephant and goat milk supplementation in the diet on the intestinal microbiota of young elephants.
## 3.5. Function Predictive Analysis
Predictive analysis of the intestinal microbiota function in young elephants revealed differences in microbial community functions between different milk-containing diet groups (Figure 5). Carbohydrate and cofactor metabolism, vitamins, and glycan biosynthesis and metabolism were significantly more enriched in the elephant milk-only diet group compared to the mixed-feed diet group ($$p \leq 0.044$$). These function enrichments were beneficial to infant elephant growth and development. The enrichment of nucleotide metabolism ($$p \leq 0.044$$) and biosynthesis of other secondary metabolites ($$p \leq 0.044$$) were significantly higher in the goat milk–plant mixed-feed diet group compared to that of the elephant milk-only diet group, indicating that secondary metabolic pathways occurred during food digestion in the goat milk–plant mixed-feed diet group. The other amino acid metabolic ($$p \leq 0.030$$), transformation ($$p \leq 0.046$$), transcriptional ($$p \leq 0.020$$), replication and repair ($$p \leq 0.030$$), endocrine system ($$p \leq 0.044$$), and cell growth and death metabolic ($$p \leq 0.030$$) pathways were also significantly more enriched in the elephant milk–plant mixed-feed diet group than in the elephant milk-only diet group. The significant enrichment of these functions reflected strong metabolism and good growth and development of the young elephants in this group, indicating that the elephant milk–plant mixed-feed diet promoted the transition of young elephants from an elephant milk-based diet to a plant-based diet. In the elephant milk–plant mixed-feed diet group, enrichment of the membrane transport pathway ($$p \leq 0.044$$) and cell motility pathway ($$p \leq 0.044$$) was significantly higher in the elephant milk–plant mixed-feed diet group than in the goat milk–plant mixed-feed diet group. Meanwhile, the energy metabolic ($$p \leq 0.044$$), amino acid metabolic ($$p \leq 0.044$$), and signal transduction ($$p \leq 0.025$$) pathways were significantly more enriched in the goat milk–plant mixed-feed diet group than in the elephant milk–plant mixed-feed diet group. These results suggested that supplementation of the host’s diet with milk from different sources led to changes in the functional structure of the gut microbiota in Asian elephants.
## 3.6. Composition Comparison of Different Kinds of Milk
There were significant differences in the composition and function of the gut microbiota between the elephant milk diet groups and the goat milk diet group of young elephants (Figure 4 and Figure 5). Moreover, there was a close correlation between the host’s diet and their gut microbiota [29,30], where diet may have represented the main reason for these differences. Previous studies have shown significant differences in the nutrient composition of Asian elephant milk [6,10,31,32] compared to goat milk [33,34]. In the Asian elephant milk, the total solids (17.56–$19.60\%$), protein (3.30–$5.23\%$), and milk fat (7.70–$8.30\%$) content were significantly higher than those in the goat milk (11.53–$13.00\%$, 3.17–$3.75\%$, and 3.95–$4.25\%$ for total solids, protein, and fat contents, respectively), while the water content (81.90–$82.44\%$) was significantly lower than that of the goat milk ($88.00\%$) (Table 1). The differences in the gut microbiota composition and function between the mixed-feed diet groups in this study may be mainly due to the differences in the nutrient composition and content between elephant milk and goat milk.
Comparisons of the nutrient composition and content of different kinds of milk and Asian elephant milk have been conducted in previous studies [35,36,37,38]. The nutritional composition and content of yak milk [35,36] was similar to that of Asian elephant milk (Table 1). Water, total solids, protein, milk fat, ash, and lactose accounted for $83.74\%$, 16.60–$18.52\%$, 4.68–$5.41\%$, 6.72–$8.18\%$, 0.72–$1.19\%$, and 4.40–$5.10\%$ of yak milk, respectively (Table 1). Although there has been no study on the intestinal microbiota of Asian elephants supplemented with yak milk, the similarity between the composition and content of yak milk and Asian elephant milk suggests that yak milk may represent a viable choice of milk compared to goat milk for the supplementation of rescued young Asian elephants.
## 4. Discussion
Asian elephants are endangered wild animals, and there are few milk-drinking young elephants. Although the number of samples in each group in this study is insufficient, this is all the samples that could be collected in Xishuangbanna region at that time. Here, the diversity of gut microbial communities of young elephants differed significantly between different milk-based diet groups, reflecting the various effects that these diets may have on the growth and development of young elephants. The richness (Chao 1 index) and diversity (Shannon index) of human intestinal microbiota are crucial indicators of health [39]. Claesson et al. [ 40] reported that preterm infants with necrotizing colitis have a significantly lower diversity of fecal microbiota compared to those without the disease, and young children with lower gut microbiota diversity are at higher risk of developing allergic diseases later in life. Thus, the greater the gut microbiota richness and diversity, the more likely it is that the nutritional status and health of the host will be good. In this study, the elephant milk–plant mixed-feed diet group had higher intestinal microbiota diversity compared to the goat milk–plant mixed-feed diet group; therefore, although it is feasible to feed goat milk to young elephants, these results suggest that more suitable milk sources should be identified to serve as appropriate elephant milk supplementation for Asian elephants.
Firmicutes and Bacteroidetes were the dominant phyla in all three groups, which is consistent with the results of Ilmberger et al. [ 41], and are also the dominant phyla in the adult Asian elephant gut microbiota [21]. Intestinal Firmicutes have many genes encoding fermentable dietary fiber proteins, which can also interact with the intestinal mucosa, contributing to the stability of the host’s internal environment [42]. Bacteroidetes are the main drivers of plant biomass degradation in Asian elephants [21,28,41]. These two bacterial taxa are indispensable for Asian elephants, as they assist plant digestion for energy acquisition. In the goat milk–plant mixed-feed diet group, the dominant phyla in the gut remained Firmicutes and Bacteroidetes, indicating that the use of goat milk to feed young Asian elephants could maintain the stability of the dominant phyla in the intestinal microbiota, allowing digestion and energy acquisition from food. The abundance of Spirochaetae in the intestinal microbiota of young Asian elephants was higher in the elephant milk–plant mixed-feed diet group compared with the goat milk–plant mixed-feed diet group. Spirochaetae are associated with the cell motility pathway, which is required by intestinal microbiota to actively contact their substrates and facilitate the biochemical reactions of the substrates [43,44]. This suggests that goat milk is not the most suitable supplement for elephant milk. In addition, Lachnospiraceae were more abundant in young Asian elephants in the elephant milk–plant mixed-feed diet group compared to in the goat milk–plant mixed-feed diet group, and are closely associated with host mucosal integrity, bile acid metabolism, and polysaccharide catabolism [45]. The low Lachnospiraceae abundance in the goat milk–plant mixed–feed diet group further suggested that goat milk may not be the best choice for feeding young Asian elephants. The abundance of Prevotellaceae and Rikenellaceae was higher in the mixed-feed diet groups than in the elephant milk-only diet group. A low abundance of Rikenellaceae and a high abundance of Prevotellaceae have been associated with obesity [46,47]. Therefore, the lower abundance of Rikenellaceae and higher abundance of Prevotellaceae in the goat milk–plant mixed-feed diet group compared to the elephant milk–plant mixed-feed diet group suggest that goat milk–plant mixed feeding may cause obesity in Asian elephants. In turn, this could lead to a potential risk of obesity-related diseases in Asian elephants. Synergistaceae encode multiple pathways that may be associated with the metabolism of diet-generated compounds [48], and these are predicted to be key factors in dietary detoxification in herbivores. In this study, Synergistaceae were significantly enriched in the goat milk–plant mixed-feed diet group, which was consistent with the significant enrichment of biosynthesis of other secondary metabolites in this group. This was likely due to the excess of secondary metabolism occurring during food digestion in this group. Meanwhile, the reason behind excess secondary metabolism, caused by the supplementation of goat milk or the presence of specific components in the foraged plants, requires further elucidation.
Recent studies on the relationship between breast milk and the gut microbiota have revealed a correlation between milk composition and gut microbiota in infants [31], and that milk composition varies by mammalian species [49,50]. The composition and content of Asian elephant [5,10,31,32] and goat milk [33,34] differ significantly. Asian elephant milk is richer in nutrients than goat milk, which may have been the main reason for the difference in the composition and function of the gut microbiota between the elephant milk–plant mixed-feed diet group and the goat milk–plant mixed-feed diet group. Nutrient composition analysis and the content of yak milk [35,36] indicates that it is similar to Asian elephant milk. Furthermore, through the study of yak milk on retinoic acid-induced osteoporosis in mice, it was found that yak milk could improve bone quality and microstructure to promote bone health [51]. The study of Zhang Wei et al. showed that yak milk could improve endurance capacity and relieve fatigue [52]. It is reported that yak dairy products seem to be particularly rich in functional and bioactive ingredients, which may play a role in maintaining the health of nomadic peoples [53]. Nutritional composition analysis of yak milk and its advantages in other animals suggested that yak milk may be an ideal source of supplemental milk for Asian elephants, compared to goat milk.
## 5. Conclusions
By studying the gut microbiome of Asian elephants on different milk-containing diets, it revealed the fact that the diet supplemented with goat milk diet seems not to be the most indicated to young elephants, and the composition and function of the gut microbiota of young elephants on a supplemented goat milk diet were also revealed for the first time, which were compared with those on an elephant milk diet only and an elephant milk–plant mixed-feed diet. This study presents a breakthrough in a new research area, the gut microbiome, regarding the serious problem of a low survival rate of infant and young elephants due to insufficient breast milk. Furthermore, we demonstrate the importance of finding a more suitable supplemental or alternative source of breast milk for Asian elephants. We believe that, in the future, with the help of wildlife gut microbiome analysis, the best supplemental or alternative sources of milk can be identified for other endangered wildlife infants and young to enhance the wellbeing of wildlife and relieve the threat to survival caused by insufficient breast milk.
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|
---
title: Physicochemical and Biochemical Properties of Trypsin-like Enzyme from Two
Sturgeon Species
authors:
- Abbas Zamani
- Maryam Khajavi
- Abdolmohammad Abedian Kenari
- Masoumeh Haghbin Nazarpak
- Atefeh Solouk
- Mina Esmaeili
- Enric Gisbert
journal: 'Animals : an Open Access Journal from MDPI'
year: 2023
pmcid: PMC10000239
doi: 10.3390/ani13050853
license: CC BY 4.0
---
# Physicochemical and Biochemical Properties of Trypsin-like Enzyme from Two Sturgeon Species
## Abstract
### Simple Summary
Beluga and sevruga are two highly valuable sturgeon species from the Acipenseride family in Iran. In recent years, research has been focused on commercial rearing of these species. A very important aspect in the sturgeon farming industry is the development of formulated compound diets for promoting growth. However, the ability of fish to digest compound diets is mostly related to the existence of the digestive enzymes in different parts of the gastrointestinal tract. In gastric species, protein digestion is conducted along the gastrointestinal tract by several proteases such as pepsin, trypsin, and chymotrypsin. Trypsin, as an alkaline protease, is able to hydrolyze protein residues and peptides to release free amino acids and small peptides for intestinal absorption; therefore, the activity of trypsin has been widely used as a valuable indicator of digestive capacity in fish. In this work, we aimed to characterize trypsin from beluga and sevruga for the first time. The results of our study show that the physicochemical and biochemical properties of trypsin from beluga and sevruga were in agreement with data reported in bony fish and may be considered a preliminary step to design in vitro tests for the assessment of protein digestibility in these primitive species.
### Abstract
This work aimed to determine the physicochemical and biochemical properties of trypsin from beluga Huso huso and sevruga Acipenser stellatus, two highly valuable sturgeon species. According to the results obtained from the methods of casein-zymogram and inhibitory activity staining, the molecular weight of trypsin for sevruga and beluga was 27.5 and 29.5 kDa, respectively. Optimum pH and temperature values for both trypsins were recorded at 8.5 and 55 °C by BAPNA (a specific substrate), respectively. The stability of both trypsins was well-preserved at pH values from 6.0 to 11.0 and temperatures up to 50 °C. TLCK and SBTI, two specific trypsin inhibitors, showed a significant inhibitory effect on the enzymatic activity of both trypsins ($p \leq 0.05$). The enzyme activity was significantly increased in the presence of Ca+2 and surfactants and decreased by oxidizing agents, Cu+2, Zn+2, and Co+2 ($p \leq 0.05$). However, univalent ions Na+ and K+ did not show any significant effect on the activity of both trypsins ($p \leq 0.05$). The results of our study show that the properties of trypsin from beluga and sevruga are in agreement with data reported in bony fish and can contribute to the clear understanding of trypsin activity in these primitive species.
## 1. Introduction
Beluga (Huso huso) and sevruga (Acipenser stellatus) are among the most important species of sturgeon fish (Acipenseridae) inhabiting the Caspian Sea with a high demand for products such as caviar, meat, skin, and cartilage [1,2]. Today, sturgeons are considered a vulnerable group of fish species for different reasons such as overfishing for production of meat and caviar, water pollution, and destruction of their natural habitats [3,4]. Therefore, in recent years, researchers have focused their studies on restocking and commercial rearing purposes. According to the Iranian Fisheries Organization report, the aquaculture production of sturgeon has increased from 363 t in 2009 to 2516 t in 2020 [5]. A very important aspect in sturgeon farming industry, affecting its production efficiency and long-term sustainability, is the development of formulated compound diets for promoting growth and product quality. However, the ability of fish to digest compound diets is mostly related to the existence of the digestive enzymes in different parts of the gastrointestinal tract [6]. Digestive enzymes reflect the capability of digestion in the organism under study and thus indicate the nutritional status at different stages of growth [7,8]. Herein, analysis of digestive enzymes activity is regarded as a biochemical procedure which may contribute to generate valuable information for understanding the physiology of digestion in fish [9,10]. This important issue can also help to define the requirements of fish for essential nutrients such as proteins, lipids, or carbohydrates [11]. Among macronutrients, dietary proteins are key nutrients for fish growth, since proteins are the building blocks of muscle cells and organs. They occur in a great array of forms, and their nutritional value depends on their amino acid composition. As Moraes and Almeida, 2020 [12] reviewed, the use of dietary proteins depends on a wide array of functional, biochemical, and genetic species-specific characteristics such as the age of organisms; the range of environmental factors (pH, dissolved oxygen, and ammonia levels); the amino acid profile of dietary protein; the digestible usable dietary energy levels; and the presence of antinutritional factors, among others.
Protein digestion is conducted along the gastrointestinal tract by several proteases, with specific actions on the polypeptide chain. In gastric species, pepsin, trypsin, and chymotrypsin are the most important proteolytic enzymes in fish [13,14,15]. As shown by Nolasco-Soria, 2021 [16], trypsin in combination with other alkaline proteases and peptidases such as chymotrypsin, aminopeptidases, and carboxypeptidases complete the acid predigestion conducted in the stomach, hydrolyzing protein residues and peptides to release free amino acids and small peptides for intestinal absorption; therefore, the activity of trypsin has been widely used as a valuable indicator of digestive capacity in fish, as well as a useful biomarker for its nutritional and physiological condition [17]. According to surveys conducted in various species of fish, trypsin participates in activating trypsinogen and other zymogens in the intestine and plays an effective role in protein degradation of the consumed diet in the carnivorous fish up to 40–$50\%$ [14,18,19,20,21]. Furthermore, trypsin quantification is essential for the design of in vitro digestibility protocols of feed ingredients and for the formulation of high digestible compound feeds for aquaculture fish species [22].
Hence, a better understanding of the properties of trypsin is necessary to generate valuable information for protein degradation in the fish digestive tract. The characterization of trypsin, especially its physicochemical and biochemical properties, has been thoroughly studied from the intestine of various fish including grass carp (Ctenopharyngodon idellus), spotted goatfish (Pseudupeneus maculatus), grey triggerfish (Balistes capriscus), skipjack tuna (Katsuwonus pelamis), smooth hound (Mustelus mustelus), and Brazilian flounder (Paralichthys orbignyanus) [23,24,25,26,27,28]. The activity of trypsin among several sturgeon species has been mostly studied during larval ontogeny in the members of the genus Acipenser such as A. transmontanus [29], A. fulvescens [30], A. oxyrinchus [31,32], A. baerii [33], A. persicus [34], A. nacarii [35,36], A. stellatus [37], and genus Huso such as H. huso [38]. However, there are no studies evaluating the physicochemical and biochemical characteristics of trypsin in sturgeons; thus, this study attempts to characterize trypsin from beluga and sevruga, as two of the main sturgeon species from the Caspian Sea, for the first time.
## 2.1. Fish Samples
Viscera from five specimens of beluga and sevruga (8.0 ± 0.4 kg; 95 ± 8 cm) were obtained from Saei sturgeon rearing center, Sari, Mazandaran, Iran. Fish were fed a commercial diet (crude protein $46\%$, crude lipid $16\%$, ash $8.5\%$, crude fiber $2.5\%$, and moisture $9\%$, Mazandaran Animal & Aquatic Feed Company, Semeskandeh Olya, Iran) and kept in fasting condition for 72 h before sampling. Those samples were packed in polyethylene bags, placed in ice with the sample/ice ratio of approximately 1:3 (w/w), and directly transported to the laboratory. Upon arrival, the intestine was separated from the rest of the collected viscera, washed with cold distilled water (4 °C), pooled, and stored at −80 °C for further analysis.
## 2.2. Preparation of Intestinal Crude Extracts for Trypsin Characterization
The frozen intestine of beluga and sevruga was partially thawed in the refrigerator at 4 °C for 2 h. The samples were then cut into small pieces and homogenized in 50 volumes of 50 mM Tris–HCl buffer (pH 7.5, 10 mM CaCl2, 0.5 M NaCl) by a tissue homogenizer (Heidolph Diax 900, Sigma Co., St. Louis, MO, USA) at 4 °C for 2 min. The homogenate was then filtered with a cheese cloth to separate the floating fat phase and centrifuged for 45 min at 4 °C at 14,000× g by a refrigerated centrifuge (Hettich Benchtop Centrifuge Rotina 420R, Berlin, Germany). The resulting supernatant from each sample was collected, defined as intestinal crude extract (ICE), and then used throughout this study.
## 2.3. Reagents
EDTA (Ethylenediaminetetraacetic acid), Pepstatin A, PMSF (phenylmethanesulfonyl fluoride), and sodium cholate were obtained from Molekula Co (Gillingham, UK). BAPNA (Nα-benzoyl-DL-arginine-ρ-nitroanilide hydrochloride), ß-mercaptoethanol, BSA (bovine serum albumin), iodoacetic acid, saponin, SBTI (soybean trypsin inhibitor), TLCK (N-ρ-tosyl-L-lysine-chloromethyleketone), and TPCK (N-tosyl-L-phenylalanine chlorom ethyleketone) were purchased from Sigma Chemical Co (St. Louis, MO, USA). Molecular weight marker (PM 2700) was obtained from SMOBIO Technology, Inc. (Hsinchu, Taiwan).
## 2.4. Trypsin Assay
To measure the enzyme activity in ICE, BAPNA was used as a substrate at a concentration of 1 mM in 50 mM Tris–HCl, 20 mM CaCl2 (pH 8.5) according to the method of Erlanger et al., 1961 [39]. Each ICE (25 μL) was mixed with the prepared substrate (1250 μL) and incubated at 55 °C for 20 min. The reaction was terminated by adding $30\%$ (v/v) acetic acid (250 μL) to the mixture and followed by measuring the trypsin activity at an absorbance of λ = 410 nm using a spectrophotometer (UV-1601, Shimadzu, Kyoto, Japan). One unit of activity was defined as 1 μmol of ρ-nitroaniline released per min and calculated with the following equation [40]:Trypsin activity unit/mL=Absorbance 410 nm ×1000× mixture volume mL8800× reaction time min×0.025 where 8800 (cm−1 M−1) is the molar extinction coefficient of ρ-nitroaniline measured at λ = 410 nm.
## 2.5. Protein Assay
The concentration of protein in both ICEs was determined at λ = 750 nm by using BSA (1 mg mL−1 as a standard) and Folin–Ciocalteau reagent according to the Lowry et al., 1951 [41] method.
## 2.6. Characterization of Trypsin by SDS-PAGE Electrophoresis
SDS-PAGE electrophoresis was performed to determine the protein pattern in ICEs from both sturgeon species [42]. Each ICE was mixed at 2:1 (v/v) ratio with sample buffer (62.5 mM Tris–HCl pH 6.8, $2\%$ SDS (w/v), $10\%$ (v/v) glycerol, $0.3\%$ (w/v) bromophenol blue and $5\%$ (v/v) ß-mercaptoethanol) and boiled for 10 min. Thereafter, the ICEs (with protein concentration of 15 µg) were loaded onto the gel made of $4\%$ stacking gel and $12\%$ separating gel and the electrophoresis was run at a constant current of 15 mA using a vertical electrophoresis system (Bio-Rad Laboratories, Inc., Hercules, CA, USA). After the run, protein bands present in the gel were stained with $0.1\%$ Coomassie Brilliant Blue (G-250) in methanol ($35\%$) and acetic acid ($7.5\%$) and unstained in methanol ($35\%$) and acetic acid ($7.5\%$).
Casein-zymography was performed after electrophoresis for detection of proteases in both ICEs as described by Garcia-Carreno et al., 1993 [43]. Both ICEs were submitted to native-PAGE electrophoresis in a same manner of SDS-PAGE where samples were not boiled, and SDS and reducing agent were removed. After the run, the gel was immersed in 50 mL of a casein solution (20 mg mL−1 in 50 mM Tris–HCl, pH 7.5) for 1 h at 4 °C with gentle agitation to allow diffusion of the casein into the gel. Thereafter, the gel was transferred to another solution (50 mL) containing casein (20 mg mL−1 in 50 mM Tris–HCl, pH 8.5, 10 mM CaCl2) for 20 min at 55 °C with continuous agitation. The gel was then stained with $0.1\%$ Coomassie Brilliant Blue (R-250) in methanol ($35\%$) and acetic acid ($7.5\%$) and unstained in methanol ($35\%$) and acetic acid ($7.5\%$). The presence of proteolytic activities in both ICEs was indicated by the appearance of clear zones on the blue background of the gel, which meant that casein was digested by the targeted protease in these areas.
To reveal the trypsin present in both ICEs, the inhibitory activity staining was used after the submission of both ICEs to native-PAGE electrophoresis as described by Ahmad and Benjakul [44] with a slight modification. After the run, the gel was immersed in 30 mL of an SBTI solution (1 mg mL−1 in 50 mM Tris–HCl, pH 8.5, 10 mM CaCl2) for 30 min at 4 °C to allow diffusion of SBTI into the gel. Thereafter, the incubation of gel was performed for 40 min at 55 °C and followed by washing in cold distilled water and staining with $0.05\%$ Coomassie Brilliant Blue (R-250) to appear inhibitory zones, indicating the presence of the trypsin in both ICEs. The molecular weight of the trypsin that appeared in both ICEs was estimated using wide-range molecular weight markers (PM2700, SMOBIO, Hsinchu, Taiwan) by calculating the trypsin Rf in comparison with those of protein markers.
## 2.7. Optimum Temperature and Thermostability
To determine the optimum temperature for trypsin activity, the activity of this alkaline protease was measured in both ICEs at different temperatures, including 10, 25, 35, 45, 50, 55, 60, 65, and 70 °C after 20 min of incubation at pH 8.5, using 1 mM BAPNA as a substrate. For the thermostability test, both ICEs were incubated at the above-mentioned temperatures for 30 min and then cooled in an ice bath for assay of residual activity of the enzyme at pH 8.5 as described by Zamani et al., 2014 [40].
## 2.8. Optimum pH and Stability
Different buffers in the pH range of 4.0 to 11.0 (50 mM acetic acid–sodium acetate for pHs 4–6; 50 mM Tris–HCl for pHs 7–9 and 50 mM glycine–NaOH for pHs 10–11) were used for determining the optimum pH for trypsin activity. Both ICEs were used, and they were incubated using1 mM BAPNA as a substrate after 20 min of incubation at 55 °C at different pHs. For the pH stability test, the remaining activity of the trypsin from each ICE was measured using 1 mM BAPNA as a substrate at 55 °C after being incubated at the above-mentioned pHs for 30 min [40].
## 2.9. Effect of Inhibitors
Several protease inhibitors (0.01 mM pepstatin A, 0.05 mM SBTI, 1 mM iodoacetic acid, 2 mM EDTA, 5 mM TLCK, 5 mM TPCK, 5 mM ß-mercaptoethanol, and 10 mM PMSF) were prepared in the relevant solvents and incubated with an equal volume of each ICE at room temperature for 15 min. The remaining activity of the enzyme was then measured by 1 mM BAPNA as a substrate (at 55 °C, pH 8.5) and the percent inhibition was calculated according to the method of Khantaphant and Benjakul, 2010 [45]. The trypsin activity of control was measured in the same manner without the presence of inhibitors and scored to $100\%$.
## 2.10. Effect of Metal Ions
To investigate the effect of metal ions (5 mM) on the trypsin activity of both ICEs, univalent (K+, Na+) and divalent (Ca2+, Cu2+, Zn2+ and Co2+) cations were dissolved in 50 mM Tris–HCl (pH 8.5) and then incubated with an equal volume of each ICE for 30 min at room temperature. The residual activity of the enzyme was determined using 1 mM BAPNA as a substrate at 55 °C and pH 8.5 [40]. The enzymatic activity of control was assayed without the presence of metal ions and taken as $100\%$.
## 2.11. Effect of Surfactants and Oxidizing Agents
The effect of surfactants (anionic: SDS and sodium cholate; non-ionic: saponin and Triton X-100, all at $1\%$) and oxidizing agents (sodium perborate at a concentration of $1\%$ and H2O2 at three concentrations of $5\%$, $10\%$, and $15\%$) on the trypsin activity was measured by incubation of the above-mentioned surfactants and oxidizing agents with an equal volume of each ICE for 1 h at 40 °C. The residual activity of the enzyme was then determined at 55 °C and pH 8.5 using 1 mM BAPNA as a substrate. The assessment of control enzymatic activity was conducted in a similar condition in the absence of chemicals and scored to $100\%$ [25].
## 2.12. Statistical Analysis
This study was conducted on the basis of a completely randomized design, and a one-way ANOVA was used for data analysis using SPSS package 22.0 (SPSS Inc., Chicago, IL, USA). All experimental assessments were performed in triplicate, and data was expressed as the mean ± standard deviation (SD). The comparison of means was carried out by Duncan’s multiple range tests with a statistical significance at $p \leq 0.05.$
## 3. Results and Discussion
Fish proteases such as trypsin have been the main objective of many of studies, but it is difficult to compare the results obtained in different species because the data are affected by the use of many different methodologies, the state of feeding of experimental animals (fed vs. starved fish), and the type of enzyme preparation (intestinal tissues alone vs. intestinal extracts with the intestinal content) [46].
## 3.1. Protein Pattern, Casein-Zymographyand Inhibitory Activity
Protein pattern of ICE from beluga and sevruga is depicted in Figure 1a. Based on results obtained from the SDS-PAGE, a number of proteins with different molecular weights were shown in the ICE of both sturgeon species. The major bands of each ICE appeared between molecular weights comprised between 10 and 60 kDa.
Casein-zymogram can be used as a highly sensitive and fast assay method for detecting nanograms of protein. The protease activity of both ICEs was demonstrated by casein-zymography as illustrated in Figure 1b. The clear bands, showing the presence of protease, appeared on the gel with different molecular weights. Based on the zymogram pattern, proteases present in ICE from beluga were observed in the range of molecular weights between 19 to 35 kDa, while those in the ICE of sevruga ranged from molecular weights of 19 to 45 kDa.
Inhibitory activity staining for detection of trypsin in ICEs is depicted in Figure 1c. Results showed that a single band for each ICE clearly appeared on the gel with a molecular weight of 27.5 and 29.5 kDa for sevruga and beluga, respectively. *In* general, trypsins have molecular weights in the range of 20–30 kDa [47]. In particular, different molecular weights for trypsins have been reported in various fish species such as 21.7 kDa for mrigal carp [48], 23.2 kDa for common kilka [40], 23.5 kDa for pirarucu [49], 24 kDa for small red scorpion fish [50], 21 and 24 kDa for liver of albacore tuna [51], 24 kDa for catfish [52], 24.4 kDa for gulf corvina [53], 25 kDa for Monterey sardine [54], 26 kDa for common dolphinfish [55], 27 kDa for zebra blenny [56], 28.8 kDa for sardinelle [57], 29 kDa for Atlantic bonito [58], 38.5 kDa for tambaqui [59], and 42 kDa for skipjack tuna [60]. However, several reasons such as different habitat and climate, autolytic degradation, and genetic variation among fish species may explain why trypsins from various sources have different molecular weights [60,61].
## 3.2. Optimum Temperature and Thermostability
Enzymes are one of the main biological macromolecules and their maximum activity depends on an optimum temperature to make them functional. Figure 2a revealed that optimum temperature of the trypsin in ICE prepared from beluga and sevruga was found to be 55 °C, although $92.70\%$ of the maximum activity of the enzyme was still maintained at 60 °C for both sturgeon species. However, an obvious decrease in the trypsin activity of both ICEs was observed at temperatures above 60 °C, probably due to thermal inactivation of this enzyme caused by protein unfolding [40]. Similar optimum temperature (55 °C) was recorded for trypsins in skipjack tuna [26], gilthead seabream [46], sardinelle [57], and silver mojarra [62]. Optimum temperature of trypsin for both sturgeon species was higher than values reported for cold-water fish such as Atlantic cod (Gadus morhua) [63], grey triggerfish [25], lane snapper (Lutjanus synagris) [64], and *Japanese sea* bass (L. japonicus) [65] indicating optimum temperatures over a range of 40–45 °C. These differences could be attributed to the temperature of the fish habitat or experimental conditions used in assessments [66]. The optimum temperature for enzyme maximum activity may be interesting for comparative physiological studies, even though such data offer limited information on enzyme activity under normal rearing conditions. Although fish trypsins are mostly unstable at temperatures higher than 40–50 °C, their thermal stability is well known to be at temperatures below 40 °C [67]. Trypsin thermal stability from ICE of beluga and sevruga is displayed in Figure 2b. As can be observed in this figure, the stability of both trypsins was highly maintained up to 50 °C with a remaining activity of $90.2\%$ and $91.7\%$ for sevruga and beluga, respectively. A gradual decrease in the activity of both trypsins was recorded at 55 °C, whereas enzymatic activity sharply decreased at 60 °C. After heating the ICEs at 70 °C, the relative activities for both trypsins were only about $0.9\%$ and $1.6\%$ of their initial activity for sevruga and beluga, respectively. These results were in accordance with those of sardinelle, common kilka, mrigal carp, and pirarucu, which were exhibited to be stable up to 50 °C [40,48,49,57]. The trypsins from beluga and sevruga showed to be more stable at high temperatures in comparison with those reported for the Monterey sardine, chinook salmon, bluefish, Tunisian barbell, and common dolphinfish that the enzymatic activity was rapidly lost at temperatures above 40 °C [54,55,58,68,69]. *In* general, thermostability of the trypsin enzyme might vary by some factors such as fish species and experimental conditions [23,70]. From a biological point of view, it is difficult to deduce any advantage for beluga and sevruga in possessing proteases showing different resistances to heating, since the normal temperature of water rarely exceeds 21–24 °C. Nevertheless, from a biotechnological perspective, it may be interesting to have information about active and easily denaturalizable proteases potentially useful in the feed industry [71].
## 3.3. Effect of pH on Trypsin Activity and Stability
The results observed from the effect of pH on the activity and stability of trypsin from beluga and sevruga are illustrated in Figure 3. Trypsins from both species had a maximal activity at pH 8.5 (Figure 3a). Trypsin activity was dramatically reduced at pH values ranging from 4.0 to 5.0. Our results showed that the stability of trypsin from both sturgeon species was highly preserved at pH values comprised between 5.0 and 11.0 with activity values of $75\%$ for beluga and $80\%$ for sevruga. The high ranges of pH may change the net charge and conformation of an enzyme and inhibit to bind to the substrate properly, resulting in the abrupt loss of enzymatic activity [60,72]. Trypsins are mainly known to have more activity within a range of pH values comprised between 7.5 and 10.5 [46,73]. The optimum pH (8.5) recorded for trypsin in both sturgeon was similar with results reported for trypsins from the brownstripe red snapper viscera and the albacore tuna hepatopancreas [45,51], whereas both trypsins indicated the lower optimum pH than those recorded for the *Japanese sea* bass, gilthead seabream and common dentex, and the pirarucu [46,49,65]. However, optimum pH may differ depending upon the experimental conditions such as concentration and type of substrate, temperature, and type of metal ions [60]. For instance, Martinez et al., 1988 [74] showed that the trypsin from pyloric caeca of the anchovy had the optimum pHs of 8.0 and 9.5 for the hydrolysis of BAPNA and casein, respectively.
The effect of pH on the trypsin stability in both ICEs is displayed in Figure 3b. The stability of both enzymes was considerably retained between pH 6.0 to 11.0. Trypsin activity was lost about $63.15\%$ and $69.26\%$ at pH 4.0 in sevruga and beluga, respectively, while the loss of the enzyme activity at pH 5.0 was recorded by $34.13\%$ and $35.84\%$ for sevruga and beluga, respectively. However, trypsin in ICE of sevruga and beluga lost only $1.19\%$ and $1.51\%$ of its activity at pH 8.5, respectively. Similar behavior was reported for trypsins from common kilka [40], common dolphinfish [55], and zebra blenny [56] which retained 80–$100\%$ of the activity at pH ranges of neutral and alkaline. The high catalytic activity of the trypsin is observed in alkaline pHs, and its stability at a particular pH may be linked to the net charge of the enzyme at that pH [75].
## 3.4. Effect of Inhibitors on Trypsin Activity
The sensitivity of protease enzymes to various inhibitors is a valuable tool for their proper functional characterization [55]. Based on their nature, inhibitors can be classified into two classes: chemical inhibitors and protein inhibitors [76]. A trypsin inhibitor is a type of serine protease inhibitor that reduces the biological activity of trypsin thereby rendering it unavailable to bind with proteins for the digestion process [77,78]. Therefore, it can be considered important to characterize the effect of different inhibitors on the trypsin activity.
Table 1 shows the effect of different inhibitors on the activity of trypsin from beluga and sevruga. As it is shown in this table, a serine protease inhibitor such as PMSF inhibited $39.11\%$ and $36.29\%$ of the trypsin activity in sevruga and beluga ICE samples, respectively. Both enzymes were completely inhibited by trypsin specific inhibitors such as SBTI and TLCK, while a chymotrypsin-specific inhibitor (TPCK) did not show any inhibitory effect on their enzymatic activity ($p \leq 0.05$). Furthermore, a metalloproteinase inhibitor (EDTA) and a disulfide bond reducing agent (ß-mercaptoethanol) had a partial inhibitory effect on trypsin activity in both sturgeon species, although the inhibition rates varied between both species. In particular, trypsin from sevruga was inhibited by ß-mercaptoethanol ($25.33\%$) and EDTA ($23.55\%$) more than those in beluga (22.84 and $21.06\%$, respectively) where no significant difference was shown between both species ($p \leq 0.05$). Results from EDTA indicate the high dependence of trypsin activity from both sturgeon species on divalent cations [46]. However, an aspartic proteinase inhibitor (Pepstatin A) and a cysteine proteinase inhibitor (iodoacetic acid) exhibited a negligible inhibitory effect on the trypsin activity of both species. Similar results have been observed in other fish species [40,51,56]. For instance, Khangembam and Chakrabarti, 2015 [48] reported that trypsin activity from the digestive system of mrigal carp was inhibited by SBTI and TLCK. SBTI is a single polypeptide chain that acts as a reversible competitive inhibitor of trypsin and forms a stable, enzymatically inactive complex with trypsin, resulting in reduction of the enzyme availability [79]. TLCK is an irreversible inhibitor of trypsin and trypsin-like serine protease that deactivates these enzymes through the formation of a covalent bond with histidine residue in the catalytic site of the enzyme and blocks the active center of the enzyme for binding to substrate [80]. As reported by several authors [40,49,56], PMSF strongly inhibited the activity of trypsin from viscera of common kilka, pirarucu, and zebra blenny, respectively, whereas TPCK had no effect on the enzyme activity from common kilka [40]. The trypsin activity from the intestine of common dolphinfish was partially inhibited by β-mercaptoethanol and EDTA [54], while the trypsin activity from liver of albacore tuna was not reduced in the presence of pepstatin A and iodoacetic acid [51].
## 3.5. Effect of Metal Ions
Metal ions have a key role in the activity regulation of many enzyme-catalyzed reactions [81]. Our results on the effect of metal ions on the trypsin activity in beluga and sevruga are detailed in Table 2. No significant effect on the activity of both enzymes was found in the presence of univalent cations Na+ and K+ ($p \leq 0.05$). The enzymatic activity of trypsin in both species was significantly reduced by divalent cations Cu2+, Zn2+ and Co2+, whereas Ca2+ significantly enhanced the activity of both trypsins ($p \leq 0.05$). Similar results on the effect of Ca2+ on the trypsin activity were also observed in common kilka, common dolphinfish, and zebra blenny [40,55,56]. The attachment of Ca2+ to the active site of serine proteases such as trypsin not only increases the stability of the enzyme structure, but it also protects the enzyme from self-digestion [66,69,82]. The enzymatic activity of trypsin in common dolphinfish was reduced by $82\%$ and $81\%$ by Zn2+ and Cu2+, respectively [55], while $100\%$ of enzymatic activity of tryspin from zebra blenny was lost in the presence of Zn2+ and Cu2+ [56]. In common kilka [40], no inhibition was observed in the trypsin activity in presence of Na+ and K+. Differences in percent inhibition might be linked to species diversity, environmental adaptations and feeding habits of fish [83].
## 3.6. Effect of Surfactants and Oxidizing Agents
Surfactants are the most widely used groups of compounds today, with wide application in industry and household. These are unique substances that contain hydrophobic and hydrophilic moieties within their molecule and find enormous applications in biology. Oxidizing agents such as sodium perborate and H2O2 are also used in the detergent industry as bleaching agent. Surfactants and oxidizing agents may be harmful or even toxic to aquatic organisms. These compounds can penetrate through tissues and bind to biomolecules, such as enzymes, causing changes in cellular activity [84]. As reviewed by Rubingh, 1996 [85], surfactants can influence the activity of enzymes in two ways. Firstly, by binding to the enzyme, surfactants can influence intrinsic enzyme properties such as the secondary and tertiary structure or flexibility, and thereby, affect its ability to serve as a catalyst. A less direct, but equally important, way in which surfactants affect enzyme activity is by changing the environment in which the enzyme functions. It is well-known that SDS disrupts non-covalent bonds within and between enzymes, denaturing them, and resulting in the loss of their native conformation and function [86], whereas saponin, Triton X-100, and sodium cholate are the non-denaturing surfactants [87,88,89]. Hence, characterizing the effect of these chemical compounds on trypsin activity is of relevance for proper characterizing its activity.
The results on the effect of various surfactants and oxidizing agents on the trypsin activity in sevruga and beluga are shown in Table 3. A significant increase in the activity of both trypsins was observed after incubation for 1 h at 40 °C in the presence of surfactants tested, including saponin, sodium cholate, and Triton X-100 at final concentrations of $1\%$ ($p \leq 0.05$). Both trypsins were highly unstable against sodium dodecyl sulfate (SDS), in which trypsins from sevruga and beluga significantly lost about $94\%$ and $97\%$ of their activity in the presence of $0.1\%$ SDS, respectively ($p \leq 0.05$). Similar results were found in trypsins of other fish species in the presence of saponin, sodium cholate, Triton X-100 and SDS [40,56]. The obtained results on the effect of oxidizing agents on both trypsins showed that the enzymatic activity was reduced in the presence of sodium perborate ($1\%$) in sevruga and beluga by $22.23\%$ and $24.37\%$, respectively. The activity of both enzymes was also decreased significantly with an increase in H2O2 concentrations from $5\%$ to $15\%$, as described in Table 3 ($p \leq 0.05$). Trypsin from sevruga showed significantly higher activity than trypsin from beluga in the presence of H2O2 ranging from $5\%$ to $15\%$, indicating that trypsin from sevruga was more tolerant to H2O2 than trypsin from beluga. The biochemical and structural properties of enzyme can affect its ability as a catalyst in presence of oxidizing agents [85]. These results showed that trypsins from sevruga and beluga were more stable against H2O2 than trypsins from grey triggerfish and zebra blenny [25,56], whereas most proteases have shown to be unstable in the presence of oxidizing agents like hydrogen peroxide [25].
## 4. Conclusions
The results of our study indicated that trypsin from intestine of beluga and sevruga had similar properties to trypsins from bony fish. The enzyme had an optimum temperature of 55 °C and thermal stability was maintained over $90\%$ up to 55 °C. This alkaline protease had an optimum pH of 8.5 and showed to be tolerant in the pH range of 6.0 to 11.0 in both studied sturgeon species. The molecular weight of trypsin for sevruga and beluga was estimated to be 27.5 and 29.5 kDa, respectively, as data from inhibitory activity staining indicated. Both trypsins were inhibited by main specific inhibitors, SBTI and TLCK. Additionally, the enzymatic activity of trypsins was still detected after 1 h in the presence of surfactants and oxidative agents. Information provided in this manuscript related to trypsin activity for beluga and sevruga based on changes in the activity of this alkaline protease based on a tested range of pH and temperature values, and presence of potential inhibitors, ions and cations may be considered a preliminary step to design in vitro tests for the assessment of protein digestibility in these species.
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---
title: Characteristics of circulating small noncoding RNAs in plasma and serum during
human aging
authors:
- Ping Xiao
- Zhangyue Shi
- Chenang Liu
- Darren E. Hagen
journal: Aging Medicine
year: 2023
pmcid: PMC10000275
doi: 10.1002/agm2.12241
license: CC BY 4.0
---
# Characteristics of circulating small noncoding RNAs in plasma and serum during human aging
## Abstract
Human aging is associated with increased susceptibility to age‐related diseases due to alteration of biological processes. Here we identified changes in extracellular small noncoding RNA (sncRNA) expression with age from plasma and serum samples. A machine learning‐based aging clock was developed using age‐related sncRNAs and is capable of predicting individual age information. As a result of profiling the circulating sncRNA transcriptome we identified putative core biomarkers linked to the aging process.
### Objective
Aging is a complicated process that triggers age‐related disease susceptibility through intercellular communication in the microenvironment. While the classic secretome of senescence‐associated secretory phenotype (SASP) including soluble factors, growth factors, and extracellular matrix remodeling enzymes are known to impact tissue homeostasis during the aging process, the effects of novel SASP components, extracellular small noncoding RNAs (sncRNAs), on human aging are not well established.
### Methods
Here, by utilizing 446 small RNA‐seq samples from plasma and serum of healthy donors found in the Extracellular RNA (exRNA) *Atlas data* repository, we correlated linear and nonlinear features between circulating sncRNAs expression and age by the maximal information coefficient (MIC) relationship determination. Age predictors were generated by ensemble machine learning methods (Adaptive Boosting, Gradient Boosting, and Random Forest) and core age‐related sncRNAs were determined through weighted coefficients in machine learning models. Functional investigation was performed via target prediction of age‐related miRNAs.
### Results
We observed the number of highly expressed transfer RNAs (tRNAs) and microRNAs (miRNAs) showed positive and negative associations with age respectively. Two‐variable (sncRNA expression and individual age) relationships were detected by MIC and sncRNAs‐based age predictors were established, resulting in a forecast performance where all R 2 values were greater than 0.96 and root‐mean‐square errors (RMSE) were less than 3.7 years in three ensemble machine learning methods. Furthermore, important age‐related sncRNAs were identified based on modeling and the biological pathways of age‐related miRNAs were characterized by their predicted targets, including multiple pathways in intercellular communication, cancer and immune regulation.
### Conclusion
In summary, this study provides valuable insights into circulating sncRNAs expression dynamics during human aging and may lead to advanced understanding of age‐related sncRNAs functions with further elucidation.
## INTRODUCTION
Heterogeneity of human lifespan and health outcomes occurs due to differential aging process. 1, 2, 3 Organismal aging is often accompanied by dysregulation of numerous cellular and molecular processes that triggers age‐related pathologies such as tissue degradation, 4 tissue fibrosis, 5 arthritis, 6 renal dysfunction, 7 diabetes, 8 and cancer. 9 The highly proactive secretome from senescent cells, termed the senescence‐associated secretory phenotype (SASP), is one of main drivers that cause age‐related pathogenesis through intercellular communication. 10 The classical SASP includes secretome of soluble factors, growth factors, and extracellular matrix remodeling enzymes, 11 and it can transmit age‐related information to the healthy cells via cell‐to‐cell contact.
As one of the emerging SASP components protected by extracellular vesicles (EVs), ribonucleoprotein (RNP) complexes, and lipoproteins, 12 extracellular RNAs (exRNAs) are found in many biological fluids 13 and can bridge the communication between “donor” and “recipient” cells through endocytosis, inducing paracrine senescence and pro‐tumorigenic processes. 14, 15 Deep sequencing of human plasma exRNA revealed more than $80\%$ of sequencing reads mapped to small noncoding RNAs (sncRNAs) in human genome, including microRNAs (miRNAs), PIWI‐interacting RNAs (piRNAs), transfer RNAs (tRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs). 16 Extracellular miRNA expression in plasma of mice changes with age and cellular senescence can affect age‐related homeostasis throughout the body by circulating miRNA. 17 Other studies uncovered the roles of circulating miRNAs in age‐related dysfunction such as osteogenesis imperfecta, 18 decreased myelination, 19 tumorigenesis, 20 and cardiovascular disease. 21 However, the molecular function of other circulating sncRNAs in aging and age‐related diseases has been overlooked, and their expression profiles during human aging process must be further characterized.
In this study, we determined the extracellular sncRNAs landscape during healthy human aging. Furthermore we generated an aging clock based on dynamic changes in extracellular sncRNAs and identified putative core sncRNAs with larger contribution weights in machine learning models for age‐related risks prediction. To achieve this, we used 446 pre‐selected small RNA‐seq data from plasma and serum samples (age: 20–99 years) and employed differential expression analysis and linear or nonlinear association measurements to determine age‐related sncRNAs as primary inputs for comprehensive machine learning modeling. Based on supervised machine learning models, aging estimators were created in high accuracy and sncRNAs candidates with top importance values in built models were considered as final age‐related biomarkers. Additionally, pathway enrichment of targets of core miRNAs strengthens our viewpoint that extracellular sncRNAs change with age‐related processes.
## Overview of integrated human small RNAs dataset
To profile sncRNAs features during human healthy aging, we obtained small RNA‐seq datasets from the Extracellular RNA (exRNA) *Atlas data* repository (https://exrna‐atlas.org). 22 This work includes the studies for which information on age, health status, and gender, but only individuals having healthy aging process were retained for analysis. For datasets meeting the quality control standards established by the Extracellular RNA Communication Consortium (ERCC) (see experimental procedures), we created a bioinformatics procedure for reads mapping, processing, normalizing, categorizing, and modeling (Figure 1A). As a result of these criteria, 302 plasma and 144 serum samples (Figure 1B) were used in this study, with a similar number of samples representing each gender ranging from 20–99 years old (Figure 1C, Table S1). As these datasets originate from distinct studies with multiple sampling and library preparations, there are clear batch effects after Counts Per Million (CPM) normalization (Figure S1A,B). The ComBat function from the R package sva (v3.40.0) in Bioconductor 23 was employed to reduce or eliminate batch effect that may deviate from actual cross‐study results (Figure S1C,D). These corrected data were used for correlation measurements and machine learning training described below.
**FIGURE 1:** *Identifying practical computational models of healthy aging via plasma and serum small noncoding RNAs (sncRNAs). (A) Flow chart of data preprocessing, normalizing, batch effect correcting, and analyses of 446 blood samples. (B) Proportion of plasma and serum samples from healthy donors. (C) Distribution of age and gender in plasma and serum*
## Identification of expressed sncRNAs in plasma and serum
To determine sncRNAs expressed during aging, we considered sncRNAs with ≥1 CPM in at least $30\%$ of individuals within an age group (young (20–30), adult (31–60), and aged (61+) groups) as expressed sncRNAs. As a result, there were 7953 and 6476 sncRNAs observed in plasma and serum samples respectively (Figure 1A). Further, we identified highly expressed sncRNAs by increasing minimal CPM to 10, resulting in 1243 and 1139 sncRNAs retained in plasma and serum samples respectively (Figure 1A, Table S2). In terms of distribution of sncRNAs subtypes in three age groups, miRNAs account for a high proportion ($26.5\%$–$63.4\%$) of all sncRNAs in both plasma and serum, and their abundance consistently decreased with age (Figure 2A,B). tRNAs increased and became the dominant sncRNA in aged group while expression of miRNAs were reduced in older individuals (Figure 2A,B). The corresponding mapped reads are proportional to the number of each highly expressed subtype, even though miRNA showed relatively more sequencing reads than others in both plasma and serum (Figure 2C,D).
**FIGURE 2:** *Highly expressed sncRNAs in plasma and serum. Subtype distribution of highly expressed sncRNAs, which meet the expression cutoff (≥10 CPM in ≥30% of samples) among young (20–30 years), adult (31–60 years), and aged individuals (≥61 years) in plasma (A) and serum (B). Total sequencing reads of highly expressed sncRNAs among three age groups in plasma (C) and serum (D)*
## Exploring the correlation between sncRNAs and human aging
We calculated the maximum information coefficient (MIC) (D. N. 24) to investigate both linear and nonlinear associations between sncRNAs expression and corresponding individual age. By employing batch‐corrected data of expressed sncRNAs, we identified 364 and 1941 age‐related sncRNAs from plasma and serum respectively (Figure 3A,B, Table S3). Intriguingly, piRNAs became the most abundant sncRNAs in MIC measurement, with the number of snRNAs representing the second largest (Figure S2A,B). Similarly, the over‐represented biological processes of miRNA targets were identified, and cellular response and epigenetic modification were enriched in plasma (Figure 3C), while biosynthetic processes were significantly observed in serum samples (Figure 3D).
**FIGURE 3:** *Identification of age‐related sncRNAs. MIC‐based age‐related sncRNAs in plasma (A) and serum (B), identified by both MIC and total information coefficient (TIC) values ≥0.7. Over‐representation analysis of biological process of MIC‐based age‐associated miRNAs targets in plasma (C) and serum (D) (p‐adjusted value <0.05)*
## Core feature selection of age‐related sncRNAs
As the expression of sncRNAs changes with age, further data‐driven analysis was conducted to construct a human aging clock. MIC‐based age‐correlated sncRNAs were used as inputs to train regression models in plasma and serum samples. Compared to the linear models, such as Linear Regression (without feature selection) and Elastic Net (feature selection through regularization), the tree‐based ensemble machine learning methods (including Adaptive Boosting, Gradient Boosting, and Random Forest regressors) showed stronger power of prediction with better performance in accuracy (Figure 4) since its great capability of learning the underlying nonlinear patterns. With stably ideal performance in test subsets (Table S4), all models inputting age‐correlated sncRNAs (MIC_plasma and MIC_serum) accurately predicted the ages of corresponding individuals in test sets, with average R 2 values greater than 0.96, root mean squared error (RMSE) values less than 3.7 years and mean absolute error (MAE) values less than 1.9 years (Figure 4A–C).
**FIGURE 4:** *Performance evaluation of sncRNAs based aging clocks built by linear regression, elastic net, Adaptive Boosting, Gradient Boosting, and Random Forest approaches. Summary of R
2 value (A), root mean squared error (RMSE) (B), and mean absolute error (MAE) (C). (D) Model fit based on plasma MIC‐based associated sncRNAs. (E) Model fit based on serum MIC‐based associated sncRNAs. All model fits were constructed using Adaptive Boosting method.*
Due to the strong generalization ability in all ensemble learning methods, core sncRNAs associated with aging processes were determined by combined statistics and sum of importance ranks in the three methods was used as the criteria for core sncRNAs identification. As a result, there were 222 and 321 core sncRNAs overlapped in all three methods with MIC_plasma and MIC_serum as the inputs respectively (Table S5). Particularly, four snRNAs, three piRNAs, two small cytoplasmic RNAs, and one miRNA were identified as top core sncRNAs in plasma (Table 1). In serum samples, seven snRNAs, two tRNAs, and one small cytoplasmic RNA identified as top core sncRNAs in serum samples (Table 2).
Notably, we also observed a gender‐specific model performance. When male‐only samples were used as training set for predicting female‐only test sets or vice versa, there were core sncRNAs unique to one gender (Figure S3A,B and Table S6), with slightly lower performance in R 2 and RMSE values compared to the models trained in gender‐mixed data (Figure S3C,D).
## Core miRNAs are involved in aging‐related processes
To gain further insight into extracellular sncRNAs potential functions in a microenvironment, we focused on miRNAs, which are well characterized in post‐transcriptional gene regulation. The most ranked miRNA with the largest importance score in plasma and serum, hsa‐miR‐11,181‐3p and has‐miR‐7845‐5p (Table S5), were selected and their targets were separately predicted via the integration of eight miRNAs databases. The expressional profile of these two miRNAs in three age groups is in Figure S4 and corresponding targets are included in Table S7. As expected, these miRNA targets are enriched in canonical cell–cell communication pathways such as Sulfur relay system and Endocytosis pathways, as well as Immune development, Asthma and Ras signaling pathways that closely related to immune dysfunction and tumorigenesis during aging process (Figure 5A).
**FIGURE 5:** *Top core miRNAs are associated with human aging and aging‐related disease. (A) KEGG pathway enrichment analysis of core miRNA targets. Pathway terms are ranked by combined score in Erichr.
73
(B) Interaction network among core miRNAs (in red), targets (in blue), and corresponding regulatory proteins (in purple). Only targets and interacted proteins have validated function in cell senescence, human aging, and longevity (information from HAGR) are shown*
We also investigated the association between miRNA targets and protein coding genes previously validated in the human aging process from Human Aging Genomic Resources (HAGR), 25 and we found targets, including DDIT3, HLA‐DQA1, PTK2B, TTR, and YWHAG, were experimentally identified to be associated with cancer progression, senescence, aging, and longevity (Table S8). Based on protein–protein interaction enrichment analysis, these targets were demonstrated to have regulatory relationship with hallmark proteins, such as PIK3R1, STAT3, IL7R, and JAK2 (Figure 5B and Table S9), which have function in cancer, immune response, and intercellular transduction, bolstering the probability that other non‐miRNA sncRNAs also have functions in aging and aging‐related diseases.
## DISCUSSION
Our study comprehensively profiled the relationship of extracellular sncRNAs with age in blood and built an aging clock of healthy individuals using sncRNAs linear and nonlinear correlated with age. Previously, age predictors were developed through DNA methylation sites, 26 transcriptome expression, 27, 28 repeat elements, 29 microRNAs, 2 and protein abundance. 30 This study provides the first detailed analysis of relationship between circulating sncRNAs and age based on regression models and core sncRNAs whose expression changes with age, allowing reliable age prediction.
From previous human biofluids studies, differential composition of small RNA has been reported in multiple biofluids. Godoy et al. 31 used 12 normal human biofluids including plasma and serum in their study and for mapping reads of corresponding RNA sequencing (RNA‐seq), miRNA showed relative high fraction ($63.8906\%$, median) in adult plasma compared to serum ($36.0154\%$, median). However, the percentage of tRNA mapped reads in serum increased ($42.2067\%$, median) and became the most abundant RNA biotype, while median value was $0.7759\%$ in adult plasma. One study determined the diversity of small RNA in different biofluids, and tRNA showed the largest percentage of mapped reads ($39.7\%$) in serum compared to plasma ($5.8\%$) and whole blood ($2.1\%$). 32 Also, in the Max et al. study, 33 they characterized extracellular RNAs (exRNAs) from both plasma and serum samples of the same healthy volunteers, and interestingly they showed substantial differences of small RNA composition, with higher proportion of miRNA in plasma and more tRNA reads in serum. We have some serum and plasma samples from the same individuals (Table S1) and consistent results were observed (Figure 2). Max et al. 33 also concluded that different biofluid types, even though they come from the same origin, plasma and serum show significant variable that impact exRNA profile. One of the reasons is that additional absorption and continuous degradation of exRNAs by retained blood clot will reduce exRNA abundance. 33 So proper exRNA isolation is essential and immediate platelet and cell debris depletion for plasma collection may avoid losses of exRNA characteristics as much as possible.
It is of interest to identify a detectable increase of highly expressed tRNAs in aged individuals, and it has been reported that spleen and brain had the highest tRNA expression, 34 which may indicate unique and differential biological process happen as individuals age. A previous report similarly finds tRNAs were the second most abundant sncRNAs in healthy adults (20–40 years) when small cytoplasmic RNA was not mentioned. 35 Unlike tRNAs driving protein synthesis, tRNA‐derived small RNAs (tsRNAs), including tRNA‐derived fragment (tRF) and stress‐induced tRNA halves (tiRNA), have been uncovered as aging process related sncRNAs. 36 Similar as human studies, the expression of tsRNAs increased during aging in Drosophila, 37 C. elegans, 38 and mouse brain cells. 39 Compared with healthy controls, differential expression of tsRNAs in age‐related diseases has been employed in disease prediction such as Alzheimer's disease and Parkinson's disease, 40 ischaemic stroke, 41 and osteoporosis. 42 tsRNAs have roles not only in potential biomarkers, but also in expressional regulation of age‐related mRNAs. 36 For example, 5′‐tRFTyr from tyrosine pre‐tRNA can silence PKM2, which is the inhibitor of p53, to cause p53‐dependent neuronal death. 43 The number of highly expressed miRNA in our study displayed a decreased tendency in older group, and it has been observed in both plasma and serum. Both core miRNAs identified by machine learning models were found to have reduced expression as age increased, similar to decreased expression of a majority of age‐associated miRNAs in whole‐blood, 2 serum, 44 and peripheral blood mononuclear cells. 45 It has been previously demonstrated that circulating sncRNAs from serum samples show strong association with human aging, 46 while the human aging modeling based on regression relationship was not yet built. In our study, potential function of core sncRNAs was predicted via miRNA target prediction, and these targets showed enrichment in cancer, cell cycle, and longevity regulating pathways. There are overlapping genes included in both cancer and longevity regulation pathways, and this result was consistent with early study that profiled miRNAs expression between young and old individuals. 45 For example, increased PIK3R1 expression has been identified to impair anti‐tumor effect through PI3K‐Akt activation in breast and ovarian cancer chemotherapy. 47, 48 Previous research determined that protein level of p85α, which is the subunit of PIK3R1, was elevated with age, and age‐associated miRNAs that potentially target PIK3R1 were downregulated. 45 Studies in human aging also show that sequence variations within PIK3R1 gene are significantly correlated with longevity, 49 and individuals with different genotypes of PIK3R1 were associated with longevity through reduced mortality risk in cardiovascular disease. 50 Interestingly, both core miRNAs (hsa‐miR‐11,181‐3p and has‐miR‐7845‐5p) that are potentially involved in PIK3R1 regulation (Figure 5B) showed lower expression in aged individuals (Figure S4). The hsa‐miR‐11,181‐3p has been used as biomarker for identification of glioma brain tumors from other brain tumor types. 51 By suppressing Wnt signaling inhibitor APC2, overexpression of hsa‐miR‐11,181‐3p can promote Wnt signaling pathway and increase cell viability in colon malignant tumor cell line. 52 For has‐miR‐7845‐5p, its expression in serum has been applied in constructing diagnostic classifier of ovarian cancer, 53 and higher expression was also observed in serum of patients with persistent atrial fibrillation. 54 Some direct targets of core miRNAs have been determined as drivers of age‐related process. For example, protein tyrosine kinase 2β (PTK2B) is a tyrosine kinase activated by angiotensin II through Ca2+‐dependent pathways to mediate ion channels as well as map kinase signaling pathway. 55 PTK2B is involved in cell growth, inflammatory response, and osmotic pressure regulation after activation and mutated PTK2B is statistically associated with hypertension in Japanese population. 56 PTK2B has also been reported in memory formation and corresponding protein variants can trigger cognitive dysfunction and higher prevalence of Alzheimer's disease. 57 *As a* nuclear protein that activated by DNA damage, DNA‐damage inducible transcript 3 (DDIT3) shows increased expression and prevents gene transcription by dimerizing with transcription factors. 58 Specifically, DDIT3 plays role in endoplasmic reticulum (ER) protein processing and resulted ER stress promotes cardiomyocyte senescence in mouse hearts. 59 The function of most of age‐associated sncRNAs identified in this study is unknown and further investigation into their function may provide meaningful results.
We also observed the mild sex‐dependent differences in the aging clock modeling. Similarly, a previous study indicated that sncRNAs differences between genders were minor 33 and sex‐specific training sets have relatively low performance score in prediction compared to the gender‐mixed training sets. During this process, some gender‐dependent core sncRNAs were identified, including male‐specific sncRNAs piR‐31,143 and piR‐48,977 in plasma, male‐specific sncRNAs piR‐33,527 and piR‐57,256 in serum, female‐specific sncRNAs hsa‐miR‐3789 and U5‐L214 in plasma and female‐specific sncRNAs U6‐L989 and piR‐30,597 in serum (Table S6). Further mechanistic study is needed to uncover their prospective role in aging and aging‐related disease.
A major limitation of our current study is the corresponding datasets utilized were developed by researchers for different, unique projects and with multiple RNA extraction protocols, which may bias extracellular RNA abundance. 35 Furthermore, trait information such as ethnicity, body mass, and smoking habits were not considered in our study due to the lack of information, and a more sophisticated and systematic sample processing and recording would help future research on big data‐based human aging modeling.
In conclusion, we provide a novel insight into the circulating sncRNAs profile of human aging. We developed predictive models in uncovering core sncRNAs and estimated age by utilizing meta‐analysis based correlation measurement and machine learning modeling. The sncRNA dynamics with age provide valuable references for extracellular RNA study in aging, and the potential mechanisms of age‐related intercellular communication by sncRNAs need further investigation.
## Data acquisition and filtration
Human small RNA‐Seq datasets in the extracellular RNA (exRNA) *Atlas data* repository (https://exrna‐atlas.org) 22 were queried with studies filtered using the following requirements: [1] data were sequenced from plasma/serum samples; [2] samples have definitive age and gender information within each study; and [3] the donor of corresponding samples should have a healthy status and was sampled as a control individual for the study. As a result, two studies (Accession ID: EXR‐MTEWA1ZR3Xg6‐AN and EXR‐TTUSC1gCrGDH‐AN) were included in both plasma and serum studies, and two studies (Accession ID: EXR‐TPATE1OqELFf‐AN and EXR‐KJENS1sPlvS2‐AN) were obtained with only plasma and serum samples respectively and 366 plasma and 188 serum samples passed preliminary filtration. To avoid genes' expressional bias due to the low sequencing reads and host genome contamination, we only retained samples that met the quality control (QC) standards developed by Extracellular RNA Communication Consortium (ERCC). Briefly, individual dataset should have a minimum of 100,000 reads that aligned to annotated RNA transcript (including miRNAs, piRNAs, tRNAs, snoRNAs, circular RNAs, protein coding genes, and long noncoding RNAs), and ratio of transcriptome reads over total sequencing reads should be more than 0.5. Consequently, 302 plasma and 144 serum samples (Table S1) were retained for further analysis.
## Quantification and batch effect removal
*To* generate expression matrices of sncRNAs, read adaptors and low quality bases were removed using the Trim Galore (v0.6.5) wrapper. 60 Clean reads were aligned and quantified with bowtie2 (v2.4.4) 61 and samtools (v1.1.4) 62 through miRNAs and other sncRNAs annotation file from miRBase (Release 22.1) and the DASHR (v2.0) 63 database, respectively. The raw sncRNAs expression results were integrated and processed in R (v4.1.1) computational environment for identifying age‐related sncRNAs after preprocessing. To correct for actual expression characteristics masked by sequencing depth variability, gene read counts were transformed into CPM values after measuring normalized library sizes by edgeR (v3.14) package. 64 Since there were still obvious batch effects observed via principal component analysis (Figure S1), we conducted batch removal using the ComBat function in sva package (v3.40.0) 23 and processed CPM‐based data showed improved sample clustering by age (Figure S1). Batch‐effect corrected data were used for identifying maximum information coefficient and constructing machine learning models described below.
## Identification of association between sncRNAs and age
To select the sncRNAs representative of the age prediction model, the maximal information coefficient (MIC), 24 which permits the identification of important, difficult‐to‐detect associations, 65 was used to identify and screen the linear or nonlinear correlations between each sncRNA expression (X) and the individual's chronological age (Y). Reshef et al. 24 reported that MIC − ρ2 to be near zero for linear relationships and MIC − ρ2 > 0.2 for nonlinear relationships, where ρ2 is the coefficient of determination (R 2). We also employed total information coefficient (TIC) to evaluate the power of independence testing between X and Y. 66 The sncRNAs having both MIC and TIC values greater than 0.7 with actual age were retained for building models.
## Comprehensive machine learning modeling
The corrected expression data of sncRNAs selected from differential expression analysis and MIC‐based correlation measurement were used for machine learning modeling. Since sncRNAs expression inputs could be seen as the explanatory variable X, which is a high dimensional vector, the modeling process was performed as a regression analysis problem and was formularized as: [1] y=f^X where X denotes the sncRNA inputs, y denotes individual's age, and f^ denotes the fitted mapping function. Ensemble learning including Adaptive Boosting, Gradient Boosting, and Random Forest were leveraged in this study, taking advantage of their strong generalization ability achieved by multiple weak learners combination. 67 Based on manual parameter tuning, the parameter “number of estimators,” which is the number of weak learners (i.e., the regression tree in this study) to be integrated in model fitting, was determined in each specific model based on the overall performance (RMSE, R 2, and MAE, showed in Table S10). The performance of ensemble learning is compared with linear regression and elastic net. The corresponding importance of each sncRNA was calculated as impurity‐based feature score (sum to 1), which can be used to determine the fraction of sncRNA that it makes contribution to distinguish. 68 Potentially core sncRNAs were determined by sorting the corresponding sum of ranks of their importance values in each ensemble learning model.
Since the number of samples is different in each age group (young, adult, and aged), simple k‐fold cross‐validation may cause uneven sampling and then trigger bad model performance due to over‐fitting. Therefore, stratified k‐fold cross‐validation is a better option to avoid this issue by selecting approximately the same proportions of samples in each pre‐set age group to the training set (Figure S5). In this study, we stratified fivefold cross‐validation based on the overall sample size. The regression modeling was conducted under Python 3.8.8 and scikit‐learn 0.24.1. 69
## Targets prediction of age‐related miRNAs
To better understand the potential function of circulating sncRNAs changing with age, we primarily predicted the targets of miRNA candidates by using multiMiR R package (V3.14), 70 which integrates eight microRNA‐target databases (DIANA‐microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA, and TargetScan).
## Functional enrichment analyses
Functional enrichment analyses of genes targeted by age‐related miRNAs performed through *Enrichr* gene list‐based enrichment analysis tool. 71 We used the combined score, which is a combination of the P value and z‐score, to offset the false positive rate caused by the different length of each term and input sets. For direct miRNAs functional enrichment, an over‐representation analysis was performed via miRNA Enrichment Analysis and Annotation Tool (miEAA 2.0), 72 with expressed miRNA sets as the background set and P values were adjusted using Benjamini‐Hochberg (BH) procedure.
## AUTHOR CONTRIBUTIONS
PX performed the experiments and contributed to project design, data collection, execution of machine learning modeling and analysis, and manuscript writing. ZS and CL contributed to experimental design and execution of machine learning modeling and analysis. DEH contributed to data collection, analysis, and manuscript writing.
## FUNDING INFORMATION
Not applicable. This research did not receive external funding.
## CONFLICT OF INTEREST
The authors have no conflicts of interest to declare.
## DATA AVAILABILITY STATEMENT
All of the small RNA‐Seq raw data (FASTQ) files and corresponding metadata are available directly from Extracellular RNA (exRNA) *Atlas data* repository with study ID (EXR‐MTEWA1ZR3Xg6‐AN, EXR‐TPATE1OqELFf‐AN, and EXR‐TTUSC1gCrGDH‐AN), or from the database of Genotypes and Phenotypes (dbGaP) with accession ID phs000727.v1.p1 for study EXR‐KJENS1sPlvS2‐AN.
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|
---
title: Bone health in ambulatory male patients with chronic obstructive airway disease
– A case control study from India
authors:
- Mohammad Sadiq Jeeyavudeen
- Samuel George Hansdek
- Nihal Thomas
- Thangakunam Balamugesh
- Mahasampath Gowri
- Thomas V. Paul
journal: Aging Medicine
year: 2023
pmcid: PMC10000280
doi: 10.1002/agm2.12239
license: CC BY 4.0
---
# Bone health in ambulatory male patients with chronic obstructive airway disease – A case control study from India
## Abstract
Chronic obstructive airway disease (COPD) is a multimorbid disorder with two thirds affected have at least one extra‐pulmonary complication. Bone health in COPD is least studied in developing nations and, in our study, we have reported that osteoporosis is twice more common in COPD than in healthy individuals and with a significant number demonstrating at least one parameter of adverse metabolic bone health on assessment.
### Objective
Chronic obstructive airway disease (COPD) is characterized by airflow limitation due to airway and/or alveolar abnormalities with significant extra‐pulmonary manifestations. Bone health impairment is an extra‐pulmonary complication of COPD which is less well studied in India. Moreover, it can contribute to significant morbidity and mortality. Hence, we aim to estimate the prevalence of osteoporosis and metabolic parameters of adverse bone health in patients with COPD.
### Methods
In this case control study, male subjects aged 40–70 years with COPD attending the respiratory outpatient clinic in a tertiary care hospital were recruited over a period of 2 years and the control population were derived from the historical cohort who were apparently healthy with no obvious diseases. Metabolic parameters of bone health measured from fasting blood samples were calcium, albumin, alkaline phosphatase, phosphorous, parathormone, creatinine, 25‐hydroxy vitamin D, and testosterone. Bone mineral density (BMD) was estimated using DXA scan and the World Health Organization (WHO) criteria was used to categorize into osteoporosis, osteopenia, and normal BMD based on the T‐score at femoral neck, lumbar spine and distal forearm. Pulmonary function tests and 6 minute walk test were performed if they had not been done in the previous 3 months. The associations of COPD with osteoporosis were analyzed using linear regression analysis and effect size are presented as beta with $95\%$ confidence interval.
### Results
Of the 67 participants with COPD enrolled in the study, osteoporosis was present in $61\%$ ($\frac{41}{67}$) and osteopenia in an additional $33\%$ ($\frac{22}{67}$) of the cases, which was higher when compared to the control population (osteoporosis $20\%$ [$\frac{50}{252}$] and osteopenia $58\%$ [$\frac{146}{252}$]). In regression modeling, there was a trend toward adverse bone health with advanced age, low body mass index, low forced expiratory volume in 1 second and testosterone deficiency in COPD.
### Conclusion
Individuals with COPD have a substantially higher prevalence of osteoporosis and osteopenia, up to almost twice that of the general population, with a significant number demonstrating at least one parameter of adverse metabolic bone health on assessment. Hence, bone health assessment should be a part of comprehensive COPD care to prevent adverse consequences due to poor bone health.
## INTRODUCTION
Global Initiative for Chronic Obstructive Lung Disease (GOLD), defines chronic obstructive pulmonary disease (COPD) as a progressive disease characterized by persistent airflow limitation. 1 COPD is a preventable and treatable disease; however, it contributes to significant morbidity in affected individuals due to its pulmonary and extra‐pulmonary effects. The burden of COPD is steadily increasing both in developed and developing countries. The recent World Health Organization (WHO) report estimates that around 328 million people around the world are living with moderate to severe COPD and more than 3 million deaths in 2005 were attributed to COPD or its systemic complications. 2 This corresponds to $5\%$ of deaths reported globally, although this number may be higher given that $90\%$ of deaths occurred in developing countries where the reporting systems are suboptimal. COPD is the second leading cause of disease burden in India, contributing to $8.7\%$ of the total deaths and $4.8\%$ of the total disability adjusted life years (DALYs). 3, 4, 5 Death due to COPD is higher in male patients, and people with longer disease duration, frequent exacerbations, and significant extrapulmonary complications. 6 With advances in the treatment of COPD over the last 2 decades, people live longer, with more than two thirds affected by at least one extrapulmonary complication. 6, 7 Cardiovascular comorbidity is one of the most feared extra pulmonary complications, characterized by increased incidence of systemic and pulmonary arterial hypertension, congestive cardiac failure, and arrhythmias. 8 *In a* study by De Luise et al, there was a significant increase in the 30‐day mortality after a hip fracture in patients with COPD when compared with patients without COPD. 9 This additional risk extends well beyond the immediate postoperative period with the mortality rate reaching nearly three folds even after a year. Hence, non‐communicable diseases, like osteoporosis, has emerged to significantly contribute to the disease morbidity and mortality. The increased risk of osteoporosis in patients with COPD has been attributed to the systemic nature of the disease and its treatment, which requires glucocorticoids, especially with those with frequent exacerbation. 10 Major societal guidelines do not recommend COPD as risk factor for osteoporosis screening. 11, 12 Fracture Risk Assessment (FRAX), one of the most popular assessment tools, does not include COPD as a risk factor in its assessment algorithm but has current smoking and glucocorticoid use as factors contributing to higher risk score. 13 QFracture, another commonly used risk assessment tool, includes COPD as a risk factor for major osteoporotic fracture. 14 Both these risk scores do not take into account factors like dose and repeated exposure to oral steroid and high dose inhaled glucocorticoids, which are commonly used for exacerbation in patients with uncontrolled COPD, and can independently predispose them to increased risk of fracture and added morbidity. There is also paucity of data on bone health in patients with COPD in developing countries like India. Hence, we have designed this study to estimate the prevalence of osteoporosis and other metabolic bone health indices in this cohort of patients.
## SUBJECTS AND METHODS
This was a case control study conducted between September 1, 2012, and June 30, 2014. The study was approved by the institutional review board. The cases were consecutive male patients with COPD between 50 and 70 years of age attending the Respiratory Medicine outpatient services were screened, and those with known COPD, or newly diagnosed to have COPD as per the GOLD criteria, were enrolled into the study. 1 Subjects of this age and gender were selected to homogenize the study population and to minimize the influence of hormonal changes affecting bone health seen in the extreme of ages, particularly in women. Subjects with hyperthyroidism, hyperparathyroidism, Cushing's syndrome or any other severe systemic illness, immobilization, and those who were already on calcium and vitamin D were excluded from the study. The control population was derived from the cluster random sampling of 242 individuals from the community who were apparently healthy without COPD and were of similar age and gender to the cases. 15 They were also from the same region, and this was done to avoid the confounding effect of ethnicity influencing bone health. The prevalence of osteoporosis in the control population at any site was $20\%$ ($15\%$ at the lumbar spine and $10\%$ at the femoral neck), and further details of this study can be found elsewhere. 15 Written informed consent was obtained from all subjects. Data were obtained regarding age, symptoms, exacerbation triggers of COPD, and the severity of the disease. A detailed medication history, including oral and inhaled glucocorticoid frequency, dose, and duration were documented along with the presence of pre‐existing comorbidities (eg, diabetes, hypertension, and dyslipidemia). The doses of inhaled glucocorticoids were calculated for the budesonide equivalent dose. Patients were then categorized into high dose and less than high dose based on the cumulative daily inhaled glucocorticoids dose. The high dose category patient received a cumulative dose of budesonide > 800 μg/day and the latter received less than 800 μg/day. The cumulative dose of oral glucocorticoids was calculated for the prednisolone equivalent dose. A validated semiquantitative food frequency questionnaire (FFQ) was used to calculate the dietary calcium intake by 24‐hour dietary recall method. 16 Sunlight exposure was calculated from the duration for which the patient's body surface area was directly exposed to the sunlight such that when the shadow formed is smaller than the real image. 17 All subjects underwent spirometry using the Jaeger spirometer and a 6‐minute walk test to make assessments as per the American Thoracic Society Guidelines. 18 The GOLD criteria were used to categorize patients into the various disease stages. 1 The body mass index, airflow obstruction, dyspnea, and exercise (BODE) index, which is a composite marker of disease severity that takes into consideration of the systemic nature of the disease, was calculated for all patients. 19 The mortality risk according to the BODE index is as follows: a score greater than 7 is associated with a $30\%$ 2‐year mortality, a score of 5–7 is associated with a $15\%$ 2‐year mortality and < 5 is associated with $10\%$ 2‐year mortality, respectively. 20 Assessment of bone mineral density (BMD) was performed using the Hologic DXA Discovery QDR 4500 at lumbar spine, femoral neck, and distal forearm by the same technician. The reference standard consisted of healthy young White subjects used by the manufacturer's database with precision of $2\%$ and the WHO criteria for osteoporosis based on T‐score were used to categorize the patients. 21 Early morning fasting blood samples were collected in order to assess the following metabolic bone and other biochemical parameters: serum calcium (normal [N]: 8.3–10.4 mg/dL), phosphorus (N: 2.5–4.6 mg/dL), albumin (N: 3.5–5.0 g/dL), alkaline phosphatase (ALP; N: 40–125 U/L), creatinine (N: 0.5–1.4 mg/dL), 25‐hydroxyvitamin D3 (25[OH]D; N: 30–70 ng/mL), intact parathyroid hormone (iPTH; N: 8–50 pg/mL) and C‐reactive protein (CRP; N: < 6 mg/L), total testosterone (N: 300–1030 ng/dL), and cortisol (N: 7–25 μg/dL). The biochemical variables, such as calcium, phosphorus, creatinine, albumin, and ALP were measured in a fully automated computerized microanalyzer (Hitachi model 911; Boehringer Mannheim). The intra‐assay and inter‐assay coefficients of variation of the variables being studied from these machines were $1\%$–$5\%$. Intact PTH, testosterone, and 25(OH)vitamin D were measured by a chemiluminescence immunoassay using an Immulite analyzer 2000. Vitamin D level was defined as sufficient for 25 (OH) D levels more than 30 ng/mL and deficient for levels < 20 ng/mL. CRP was estimated by immunonephelometry (BN ProSpec; Dade Behring) according to the manufacturer protocol using the CardioPhase highly sensitive CRP reagents. Hypogonadism was defined as 8 am total serum testosterone < 300 ng/dL.
## SAMPLE SIZE CALCULATION AND STATISTICAL ANALYSIS
The sample size was calculated using prevalence data from a previously published study from India. 14 A sample size of 64 subjects was required to study the prevalence of low bone density (osteoporosis and osteopenia) assuming a prevalence of $80\%$ based on the previous Indian study using the equation 4 pq/d2 with a precision of $10\%$. The continuous variables were described using means and standard deviations or median and interquartile range (IQR) depending on normality. All categorical variables were summarized by using frequencies and percentages. Association for continuous variables with low bone density was done using Independent t test and for categorical associations chi‐square test was used. The T‐scores of each region were considered as continuous outcome as the larger percent of the cohort has either osteopenia or osteoporosis. Linear regression model was used to determine significant predictors. Univariate model was used to define the individual effect of each predictor. Multivariate model was constructed adjusting for variables with entry criteria of P value < 0.20. The effect sizes were presented with beta (and $95\%$ confidence interval [CI]). For all analyses, the significance level was determined for $P \leq 0.05.$ The results of this study were compared with a historical cohort of previously published subjects from the same ethnicity without COPD. 15 All statistical analyses were done using STATA/IC version 16.0.
## RESULTS
This study included 67 male subjects diagnosed with COPD based on the GOLD criteria. The mean (±SD) age group of the study population was 60 (±6) years, and the mean duration of COPD was 48 months (Table 1).
**TABLE 1**
| Unnamed: 0 | Overall (n = 67) | Normal (n = 6) | Osteopenia (n = 33) | Osteoporosis (n = 28) | P valued |
| --- | --- | --- | --- | --- | --- |
| Age (y) a | 60.2 ± 6.9 | 59.5 ± 6.8 | 59.2 ± 7.3 | 61.6 ± 6.4 | 0.176 |
| Current smokers c | 7 (10) | 0 (0) | 2 (6.1) | 5 (17.9) | 0.093 |
| No. of pack years b | 30 (20, 46.5) | 30 (28, 40) | 24 (15, 44.5) | 36 (25, 50) | 0.176 |
| Duration of COPD in months b | 48 (24, 72) | 18 (12, 39) | 60 (36, 84) | 54 (24, 72) | 0.673 |
| 6 MWD (meters) a | 348 ± 92.1 | 318.9 ± 84.3 | 370.2 ± 97.1 | 328 (84) | 0.134 |
| FEV1 a | 42.2 ± 18.6 | 51.9 ± 21.8 | 44.3 ± 19.6 | 37.5 ± 16 | 0.085 |
| FVC a | 61.3 ± 17.2 | 71.8 ± 14.9 | 61 ± 18.8 | 59.5 ± 15.4 | 0.464 |
| FEV1/FVC a | 67.6 ± 17.5 | 70.3 ± 20.9 | 72.2 ± 18.8 | 61.7 ± 13.4 | 0.017 |
| Oral steroid dose b | 0 (0, 0) | 0 (0, 0) | 0 (0, 0) | 0 (0, 20) | 0.287 |
| Oral steroid duration in the last 1 y b | 0 (0, 0) | 0 (0, 0) | 0 (0, 0) | 0 (0, 5) | 0.282 |
| Dietary calcium intake b | 1156.3 ± 264.2 | 1048.3 ± 231.4 | 1157.9 ± 291.6 | 1177.5 ± 238.5 | 0.581 |
The majority of the patients were distributed equally in stages II, III, and IV, there was only one patient with stage I disease. The frequency of patients in three BODE categories‐ < 5, 5–7 and more than 7 were 8, 7 and 52 patients, respectively. Nine of the study participants received high dose inhaled glucocorticoids of which one had osteoporosis and the rest had osteopenia. Seven patients received oral glucocorticoids in the last 2 years. As expected, these patients were in stages III and IV disease category and had a high BODE index score. The prevalence of vitamin D deficiency was $52\%$ (N: $\frac{35}{67}$). Biochemical hypogonadism was seen in $31\%$ (N: $\frac{21}{67}$). Duration of sunlight exposure was equal in all the groups.
The prevalence of osteoporosis at any one site in this study was found to be $61\%$ ($\frac{41}{67}$). The prevalence of osteoporosis at the lumbar spine and femoral neck were almost equal with $24\%$ ($\frac{16}{67}$) at the lumbar spine and $25\%$ ($\frac{17}{67}$) at the femoral neck. The prevalence of osteopenia at the lumbar spine and femoral neck was found to be $47\%$ ($\frac{31}{67}$) and $53\%$ ($\frac{36}{67}$), respectively. There was an increased prevalence of osteoporosis of $33\%$ ($\frac{22}{67}$) and osteopenia $33\%$ ($\frac{22}{67}$) at the distal forearm compared to the other sites (Figure 1).
**FIGURE 1:** *Prevalence of osteoporosis between cases and controls across different sites.*
In the univariate regression model, at least one site of lower T‐score for osteoporosis in male patients with COPD were significantly associated with age, body mass index (BMI), smoking status, forced expiratory volume in 1 second (FEV1) and FEV1/FVC (Table 2). BMI remained significantly associated with lower T‐score even in the multivariate analysis (Table 3).
The mean BMD in the present study was compared with age and gender‐matched controls without COPD or other chronic disease affecting bone health (Table 4). 15 The mean BMD at the femoral neck for patients with COPD (0.692 kg/m2) was significantly lower when compared with healthy subjects of similar age group, ethnicity, and gender (0.761 kg/m2, $P \leq 0.001$). A similar finding was also found in the lumbar spine region (mean BMD patient with COPD: 0.906 kg/m2 vs. normal subject 0.943 kg/m2, $$P \leq 0.024$$).
**TABLE 4**
| Parameters | COPD (n = 67) Mean (SD) | Non‐COPD 15 (n = 252) Mean (SD) | Unpaired t test P value |
| --- | --- | --- | --- |
| Serum calcium (mg/dL) | 9.32 (0.56) | 8.82 (0.43) | < 0.001 |
| Serum PO4 (mg/dL) | 3.65 (0.75) | 3.9 (0.5) | 0.001 |
| Serum iPTH (pg/mL) | 57.11 (28.59) | 44.5 (25.6) | < 0.001 |
| Serum alkaline PO4 (U/L) | 83.84 (28.42) | 73.5 (21.4) | 0.001 |
| Serum 25 OH vitamin D (ng/mL) | 25.25 (16.50) | 20.4 (8.3) | < 0.001 |
| Serum testosterone (ng/dL) | 381.15 (173.71) | 620 (124) | < 0.001 |
| ESR (mm/h) | 17.37 (12.93) | – | |
| CRP (mg/L) | 11.04 (13.89) | – | |
| Bone mineral density | Bone mineral density | Bone mineral density | Bone mineral density |
| Femoral neck (g/cm2) | 0.692 (0.130) | 0.761 (0.124) | < 0.001 |
| Lumbar spine (g/cm2) | 0.906 (0.145) | 0.943 (0.111) | 0.024 |
| Distal forearm (g/cm2) | 0.588 (0.089) | – | |
## DISCUSSION
In the current study, the prevalence of osteoporosis in men with COPD was $61\%$, and hypovitaminosis D was seen in $52\%$ of the study subjects. These results along with the previously published data confirms that people with COPD have weaker bone mass, and prevalence of osteoporosis is nearly doubled when compared with healthy men in the same community (Table 4). 15, 22, 23, 24 *The osteoporosis* prevalence from our study matches data from two other previously published reports from India. The first was published by Bhattacharya et al who measured BMD using calcaneal ultrasound. 22 In the second study by Hattiholi et al, the prevalence of osteoporosis and osteopenia were $66.7\%$ and $19.6\%$, respectively. 23 However, other parameters relating to adverse bone health were not reported in both these studies. The prevalence of osteoporosis reported in these Indian studies were more when compared with Western studies. 25, 26 In the multicentric TOwards A Revolution of COPD Health Study (TORCH trial), the prevalence of osteoporosis and osteopenia were $18\%$ and $41\%$, respectively. 27 The reason for an increased prevalence of osteoporosis in our study and other studies reported from India may be due to an increased community prevalence of osteoporosis and vitamin D deficiency, an advanced stage of the disease, and a higher dose of glucocorticoids used for treatment. 28 The increased risk for osteoporosis in patients with COPD is due to the systemic nature of the disease, glucocorticoid intake, change in body composition and weight, decreased activity, reduced exercise reserve, and reduced sunlight exposure due to dyspnea associated with mobility during advanced stages of the disease. What causes this systemic dysfunction is not clearly understood, but there are some hypotheses that are postulated and tested. The two important ones are a systemic spillover theory and a compartment model. In the systemic spillover hypothesis, it is assumed that there is a spillover of the cytokines and inflammatory mediators due to chronic inflammation in the lungs into the systemic circulation. 29, 30 The compartment model states that there are two or more compartments where the disease process is ongoing simultaneously. 31, 32 The distant organ or systems affected, as mentioned earlier, were the cardiovascular system, adipose tissue, and bone, and the primary organs are the lungs.
The mean BMI of our study population was 23 kg/m2 (2 SD ± 5.06). BMI in our study population is similar to that seen in the other two studies reported from India as compared to the Western study population who have a much higher BMI. 22, 23 In our study, BMI was positively correlated with the BMD. Mechanical bone loading increases the bone strength and remodeling but it also ultimately depends on the fat free mass that contributes to this increased effect. 33 Fat free mass in patients with COPD has been reported to be low and this depends on the severity of disease category with a decrease of $20\%$ in a clinically stable patient with COPD to $41\%$ in severe cases those requiring pulmonary rehabilitation when compared to the age and gender matched general population. 34 Leptin, an adipocyte derived hormone has a biphasic effect on bone modeling and re‐modeling. At low concentration, it promotes proliferation and differentiation of osteoblasts but at high concentration it inhibits the bone formation both through central and peripheral effects. 35 Moreover, this effect of leptin is more pronounced in obese women with COPD, who have high circulating leptin levels. 36 Hence, body weight and BMI have a complicated relationship with bone health.
The other parameters that were significant in the regression modeling were testosterone deficiency and FEV1 level. It is well‐established that testosterone has positive effects on bone formation by its direct action and indirect action through aromatization to estrogen. 37 Testosterone exerts its direct effects by binding to androgen receptors expressed on the pre‐osteoblast and helps its maturation whereas estrogen influences bone formation and inhibits resorption through its action on the estrogen receptor. 21 FEV1 had a positive effect on the bone health and is likely related to the systemic state of the patient, as a higher FEV1 indicates better lung function. Hence, this will make the individual mobilize better for proper bone loading, sunlight exposure, and lower steroid requirements for disease control. The inflammatory markers, erythrocyte segmentation rate (ESR) and CRP were elevated in our study population. Suppression of bone formation and an increase in osteoclastogenesis in chronic inflammatory disease has been shown to induce proteins, such as Dickopf 1 and sclerostin. 38 By inhibition of the Wnt pathway, these proteins along with several other cytokines, such as IL‐15, interferon gamma, IL‐17 MCP‐4 (monocyte chemoattractant protein), and TNF‐α blunt the bone formation there by leading to osteoporosis and its sequelae. 39, 40 Regular use of oral glucocorticoids significantly increases the risk of osteoporosis. 41 *This is* due to the uncoupling of bone formation as well as due to the direct toxic effect of steroids on the osteoblast. High dose inhaled glucocorticoids are known to have systemic effects with adverse bone effects and dose‐related adrenal suppression. 42 Our study had only nine participants ($14\%$ percent) on high dose inhaled glucocorticoid and this did not achieve statistical significance with adverse bone health, potentially due to the reduced sample size. But this finding is similar to TORCH trial, which did not show an increase in bone loss in people taking inhaled glucocorticoids when compared with those on placebo. 27 Although the study population resides in and around Vellore (Vellore, 12 degrees55′N, longitude 79 degrees11′E) where there is abundant sunlight throughout the year, only $13\%$ had adequate exposure to sunshine. Sunlight is an abundant source for vitamin D, which in turn is an intermediate factor contributing to the bone health. 43 Exposure to the sunlight should be at the time when the vitamin D synthesis is at its peak, and this usually happen at early noon when the ultraviolet B component of the sunlight is at its maximum. The surrogate marker for this in practical sense would be when the length of the shadow formed is less than the individual's height and the recommended duration of exposure is for at least 30 minutes. 28 Because of restriction to outdoor activity, due to dyspnea, and in the late stages due to the requirement of oxygen therapy, this can be limited in patients with COPD. The dressing pattern among Indian men exposes only face and feet to sunlight when involved in outdoor activities. Hence, only $23\%$ of our study population had sufficient 25(OH)D levels, which is less than community prevalence in a healthy individual. To our knowledge, we do not know any other study from India which has reported the prevalence of vitamin D deficiency in patients with COPD. Comparing our prevalence data with Western studies would be inappropriate, as the vitamin D synthesis due to sunlight exposure depends on the solar zenith angle, minimal erythema dose, duration of sunlight exposure, and dressing pattern. 44, 45 The limitation of our study is the small sample size which precludes the possibility of making comparison across different stages of COPD. However, this is the first study from India, to our knowledge, to assess other parameters other than BMD to examine bone health in a male patient with COPD. It may be prudent to conduct similar studies in groups of premenopausal and postmenopausal women with COPD on a separate basis to understand the profile of their bone health.
## CONCLUSION
Osteoporosis and an abnormal bone health profile is highly prevalent among patients with COPD. Differences in the patient characteristics and diagnostic tools account for the varied prevalence across studies, in any case, it is much higher than the general population. Higher prevalence of osteoporosis in the past was solely attributed to the increased glucocorticoid exposure but parameters for adverse bone health were seen even in steroid naive patients suggestive of a more complex underlying mechanism. Osteoporosis and osteoporotic fracture related morbidity and mortality will add to the already existing disease burden in those affected by COPD. But these can be prevented with proper screening and intervention, including lifestyle changes (increasing calcium intake in the diet and adequate sunlight exposure), vitamin D, calcium supplementation, and bisphosphonates when needed. This should be included in the comprehensive COPD care plan and modified to suit each individual patients’ needs.
## AUTHOR CONTRIBUTIONS
Research and study design: Jeeyavudeen, Hansdek, Thomas, Balamugesh, Gowri, and Paul. Data collection: Jeeyavudeen, Hansdek, Gowri, and Paul. Data analysis: Balamugesh, Gowri, and Paul. Interpretation and conclusion: Jeeyavudeen, Hansdek, Thomas, and Paul. Preparation of manuscript: Jeeyavudeen, Hansdek, and Paul. Review of manuscript: Jeeyavudeen, Hansdek, Thomas, Balamugesh, Gowri, and Paul. Critical revision: Jeeyavudeen, Hansdek, and Paul. Guarantors for the study: Jeeyavudeen.
## FUNDING INFORMATION
The protocol was approved by the institutional review board (IRB) of Christian Medical College, Vellore, and the funding was provided by the FLUID grant of the IRB. There was no involvement of the funding source in study design, in the collection, analysis, and interpretation of data, in the writing of the report, and in the decision to submit the paper for publication.
## CONFLICT OF INTEREST
The authors report no conflicts of interest for this study.
## ETHICAL APPROVAL
This study was approved by Office of Research, Institutional Review Board, Christian Medical College, Vellore, India IRB Min No: 7996 [Dated] February 12, 2013.
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|
---
title: Circadian dysfunction and Alzheimer's disease – An updated review
authors:
- Faizan Ahmad
- Punya Sachdeva
- Jasmine Sarkar
- Raafiah Izhaar
journal: Aging Medicine
year: 2022
pmcid: PMC10000289
doi: 10.1002/agm2.12221
license: CC BY 4.0
---
# Circadian dysfunction and Alzheimer's disease – An updated review
## Abstract
Alzheimer's disease (AD) is considered to be the most typical form of dementia that provokes irreversible cognitive impairment. Along with cognitive impairment, circadian rhythm dysfunction is a fundamental factor in aggravating AD. A link among circadian rhythms, sleep, and AD has been well‐documented. The etiopathogenesis of circadian system disruptions and AD serves some general characteristics that also open up the possibility of viewing them as a mutually reliant path. In this review, we have focused on different factors that are related to circadian rhythm dysfunction. The various pathogenic factors, such as amyloid‐beta, neurofibrillary tangles, oxidative stress, neuroinflammation, and circadian rhythm dysfunction may all contribute to AD. In this review, we also tried to focus on melatonin which is produced from the pineal gland and can be used to treat circadian dysfunction in AD. Aside from amyloid beta, tau pathology may have a notable influence on sleep. Conclusively, the center of this review is primarily based on the principal mechanistic complexities associated with circadian rhythm disruption, sleep deprivation, and AD, and it also emphasizes the potential therapeutic strategies to treat and prevent the progression of AD.
Amyloid beta plaques and accumulation of tangles are two major pathological hallmarks of Alzheimer's disease. Due to cholinergic disturbance, HPA axis dysfunction, neuronal loss, and retinal ganglion loss there is disturbance in circadian rhythm which leads to Alzheimer's disease dysfunction.
## INTRODUCTION
Alzheimer's disease (AD) is the most common type of neurodegenerative disorder, which largely causes dementia and mainly affects older aged people. By the year 2050, around 12 million cases will be reported. 1, 2 In AD, accumulation of amyloid beta and hyperphosphorylated tau are microscopic pathologies, whereas reduction in hippocampal volume, frontotemporal, and associated cortical atrophy with ventricular enlargement are macroscopic findings. 3, 4, 5 To rule out AD, multiple biomarkers are available, like cerebrospinal fluid (CSF) molecules (for example, amyloid and tau), and to see atrophy in the brain, various neuroimaging techniques, such as computed tomography, magnetic resonance imaging, or positron emission tomography (PET). Current pharmacological treatments include donepezil, galantamine, and rivastigmine, which work as cholinesterase inhibitors. Memantine works as an N‐methyl D‐aspartate antagonist and Abun approved this in 2021. 6, 7 Most current studies focus on the molecular aspect of AD, which mainly focuses on neuroinflammation, mitochondrial dysfunction, and glial cell activation. 8 Currently, researchers focus on circadian rhythms, which help the researchers to understand AD pathophysiology in a relatively comprehensive and satisfactory way and also help to address or develop therapeutic targets of AD. Sleep disruptions and circadian disorders are quite common; around $45\%$ of patients face problems with sleep. 9, 10 These symptoms are present for several patients with AD even before the final medical diagnosis of AD. Based on multiple studies, it is seen that sleep disturbances can lead to neurodegeneration and even cognitive impairment. In the future, it can be utilized as a biomarker for neurodegeneration. In one study, it is seen that older women with diminished and irregular circadian rhythms have a higher risk of developing one of the types of impairments of AD, such as mild cognitive impairment and dementia. Various studies suggest that $25\%$–$66\%$ of patients with AD face sleep disruption, which can be easily noticeable. 11, 12, 13, 14, 15, 16, 17 Melatonin (N‐acetyl 5–methoxytryptamine) is a hormone regulated by the circadian rhythms, and it plays a vital role in the neurodegenerative event of AD. 18 The primary source of melatonin is the brain's pineal gland, but other organs like the retina, bone marrow, kidney, pancreas, skin, and glial cells are also involved. Melatonin is a multifunctional hormone that regulates circadian rhythm and shows anti‐inflammatory, cytoprotective, and anti‐oxidant properties. The circadian clock regulates melatonin and during a study in rat and mice models, melatonin shows the highest plasma melatonin level at midnight. 19, 20 Melatonin production decreases with aging which can be considered a critical factor for the onset of AD. When impairment or disruption is seen in the suprachiasmatic nucleus (SCN), melatonin levels are reduced, resulting in circadian rhythm disruption. 21, 22, 23 Even reduction in CSF is linked with melatonin, and, finally, melatonin progresses AD by causing oxidative damage in the AD brain. Patients with AD have a low level of melatonin as compared with healthy patients. Melatonin can be a promising therapeutic approach to inhibit AD progression as it has free radical scavenging properties as well as anti‐amyloidogenic properties. Melatonin also inhibits the secretion process of soluble amyloid precursor protein (APP) in various cell lines through APP maturation. Melatonin administration attenuates amyloid beta generation and deposition in vitro and in vivo models. 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 A sundowning phenomenon enhances mental health decline, confusion, and agitation in patients with AD, whereas melatonin reduces the symptoms of sundowning and enhances cognition. In this review, we discuss the association of circadian dysfunction with AD pathology as well as a few pharmacological and non‐pharmacological interventions for sleep disruption in patients with AD. 35, 36, 37, 38, 39
## CIRCADIAN BIOLOGICAL CLOCK MECHANISM IN THE BRAIN
A core gene of the circadian clock, the Period (PER) gene, was the first clock gene to be discovered by Jeffrey C. Hall and Michael Rosbach. The (PER protein is produced mainly at night and broken down during the day, and this whole cycle is regulated with the help of a negative feedback loop where PER protein blocks its production. 40, 41 This protein is encoded by the PER gene. Recently, a new gene which is known as the double‐time (DBT) gene, has been discovered to encode DBT protein. The DBT protein averts the PER accumulation, proving that rhythm can be flagged according to the 24 hour biological clock. Circadian rhythm regulation is observed both at the central and peripheral levels. In 2017, Jeffrey C. Hall, Michael Rosbash, and Michael Wyong uncovered the molecular mechanisms regulating circadian rhythm and received the Nobel Prize in physiology or medicine. This mechanism demonstrates that mammals have a central pacemaker called the SCN in the hypothalamus. When the retina gets photic input, it transmits information to the SCN. This central clock regulates the circadian rhythm throughout all body functions through the peripheral autonomic nervous system and hormonal factors. The circadian system is a web of interlinked feedback loops and oscillators across all organisms. The Period (PER 1–3), Cryptochrome (CRY1 and 2), and Reverb (NR1D1 and NR1D2) genes are negative feedback regulators which suppress the positive limb. The SCN helps in the synchronization of cellular oscillators across organs in humans. The retina sends light and dark signals to the SCN, which further regulates it. It synchronizes the core clock oscillations in neurons, ultimately translated into oscillatory synaptic output, which transfers the signals to the multiple nuclei in the hypothalamus. All these patterns in neuronal activity, and behavioral and physiological arrhythmicity can be lost post ablation of the SCN. 40, 41, 42, 43, 44, 45 The circadian clock system is shown in Figure 1, and relationship between circadian rhythm and AD is shown in Figures 2 and 3.
**FIGURE 1:** *Twenty‐four hour biological clock in the human brain and its circadian disruption* **FIGURE 2:** *Crosstalk between sleep deprivation and Alzheimer's disease. Aβ, amyloid beta* **FIGURE 3:** *Linkage between circadian rhythm and Alzheimer's disease. Aβ, amyloid beta; EEG, electroencephalogram; nREM, non‐rapid eye movement; SCN, suprachiasmatic nucleus*
## CHOLINERGIC DISTURBANCES AND CIRCADIAN DYSFUNCTION IN AD PATHOLOGY
Neurodegeneration can also be seen in the basal cholinergic forebrain. Disruption in circadian rhythm can also occur due to cells of the nucleus basalis magnocellularis, which projects to the SCN. Enrhardth reported that in rats, there are increased phase delays in response to lights when the cholinergic basal forebrain projects to the SCN. This study suggests a relationship between AD neurodegeneration and the circadian clock's signal entrainment ability. 46, 47, 48
## NEURONAL LOSS IN THE SCN AND CIRCADIAN DYSFUNCTION IN AD
During the autopsy of patients with AD, it was seen that there is a neuronal loss in the SCN, which is related to loss of amplitude in the circadian rest‐activity pattern. Apart from MT1, melatonin receptor expression was disturbed, which resulted in the SCN responding to the phase resetting signal and generating daily rhythms. 49, 50
## RETINAL GANGOLIAN CELL LOSS AND CIRCADIAN DYSFUNCTION IN AD
A particular type of subset of retinal ganglion cells (RGCs) known as Melanopsin expressing RGCs (mRGCs) was discovered in 2002. These cells are photoreceptors inside the retina, which help in the photoentrainment of circadian rhythms by projecting light to the SCN. Melanopsin expressing mRGCs constitutes $1\%$–$2\%$ of all RGCs, but they can direct signals to the SCN through the retinal hypothalamic tract. In patients with AD, mRGC loss can be seen, which can cause amyloid beta deposition, and lead to impairment of the entire RGCs even though there is a deposition of amyloid beta in mRGCs. The Toronto study shows interesting results involving retinal amyloid beta deposition in patients with AD. These findings will help better understand the pathology of retinal amyloid beta deposition in patients with AD. Amyloid beta deposition in mRGCs can lead to instability in transmitting the circadian signal of light from the retina to the SCN. 51, 52, 53, 54, 55
## CIRCADIAN GENE DELETION AND CIRCADIAN DYSFUNCTION IN AD
Deletion mutations in the circadian clock gene cause neuronal injury. Core circadian clock disruption is directly linked to neurodegeneration in AD. BMAL1 is considered to be one of the core genes of the master clock, and a study conducted in mice has shown the deletion of BMAL1 in the hippocampus and cortex. In mice, we observe normal behavioral rhythms and normal sleep wake cycles assessed by wheel running actigraphy and electroencephalogram, respectively, in the presence of severe cortical astrogliosis, synaptic degeneration, and oxidative brain region damage in specific BMAL1 knockout mice. These mice are closely related to transcription multiple redox defenses linked with circadian impairment. Low levels of BMAL1 in the brain also lead to neurodegeneration caused by mitochondrial toxin B nitropropionic acid. The data suggest that decreased BMAL mediated transcriptional exacerbate neurodegeneration in AD. Clock‐gene regulation and better insight into the linkage of clock genes and neurodegeneration require further research and a deeper understanding to examine such regulations. 56, 57, 58, 59 The effect of different clock genes on animal models is shown in Table 1.
**TABLE 1**
| Subject no. | Different models | Effect of clock genes on different circadian models | References |
| --- | --- | --- | --- |
| 1.0 | APP‐PS1 mouse model | Casein kinase 1 isoforms ε and δ with inhibitor PF‐670462 reduce amyloid and plaque size as well reduce Aβ signal in the prefrontal cortex and hippocampus, which proves chronotherapy as a promising tool to improve behavior in mice | 103 |
| 2.0 | Two‐month‐old female APPSwe/PS1dE9 mice | Female APPSwe/PS1dE9 mice show abnormal locomotor activity in which clock gene expression of clock genes Per 1, Per 2, Cry 1, and Cry 2 was increased during night time compared to day type in wild type control mice as Cry 1 and Cry2 expression was low in APPSwe /PS1dE9 mice. This study proves APPSwe /PS1dE9 mice as a most promising AD model to test therapeutic agents related to behavioral and circadian rhythm changes. | 104 |
| 3.0 | Cultured fibroblasts and brain samples | BMAL1 is a positive regulator of the circadian clock, and in cultured fibroblasts, DNA methylation regulates BMAL1 rhythms which is linked to circadian alteration in AD | 105 |
| 4.0 | Tg 4510 mice | In Tg4510 mice, it is seen that there is tauopathy in SCN and even disruption in PER2 and BMAL1 in the hypothalamus of Tg4510 mice. This study proves that tauopathy can lead to normal circadian clock function disruption. | 106 |
| 5.0 | AD brain | In this study, the glial fibrillary acid protein in human astrocytes is suppressed as there is an elevation in CLOCK and BMAL, which cause functional impairment by inhibition of aerobic glycolysis in AD | 107 |
| 6.0 | 5XFAD mouse model | Rev‐erbα, a circadian repressor, decreases amyloid plaque number and size in the 5XFAD AD mouse model. Even Rev‐erbα show a neuroinflammatory effect, which proves Rev‐erbα as a novel therapeutic target. | 108 |
| 7.0 | APP/PS1dE9 mice | In APP/PS1dE9 mice, there is an alteration of rhythmic expression patterns of BACE 1 and ApoE in the hippocampus, which is activated by E4BP4 and BMAL1, respectively. So, finally, study suggests that hippocampal clock and circadian oscillation of AD risk gene are regulated by orexin signaling. | 109 |
## MICROGLIA, ASTROCYTE, AND CIRCADIAN DYSFUNCTION IN AD
Activation of microglia and astrocyte leads to neuroinflammation, which ultimately causes neurodegeneration. Astrocyte activation can be observed to model clock gene deletion in the in vitro model. Even the inflammatory response of microglia leads to variation in the functional circadian clock. Rev‐*Erb alpha* regulates pro‐inflammatory cytokine production in macrophages. Finally, inflammation shows the effect of the circadian clock as both Rev‐*Erb alpha* suppressing BMAL1 levels in macrophages in response to lipopolysaccharides. Therefore, the BMAL1 expression in the surrounding glia and neurons can be suppressed by cortex inflammation causing impairment of BMAL1‐associated genes, ultimately leading to neurodegeneration. 56, 60
## OXIDATIVE STRESS AND CIRCADIAN DYSFUNCTION IN AD PATHOLOGY
Numerous studies support the presence of augmented oxidative stress in AD. Less concentration of glutathione and catalase with higher consumption of oxygen ($20\%$–$30\%$) and a higher amount of polyunsaturated fatty acids make the brain a highly vulnerable target for lipid peroxidation. 61, 62, 63 Lipids peroxidation interrupts cellular functions, followed by neuronal membrane destruction, and the production of highly reactive electrophilic aldehydes, including acrolein, malondialdehyde, and 4 hydroxy 2‐nomial (elevated in AD brains). 64, 65, 66 Oxidative stress also damages nucleic acid and proteins. The role of oxidative stress etiology in AD pathogenesis is still unknown. In 1985, the activity of antioxidants, like superoxide dismutase and glutathione peroxidase with oxidative damage in the day‐night cycle in the rat cerebral cortex, whereas in humans, anti‐oxidants and circadian rhythmicity protect cells from oxidative damage. 67, 68, 69, 70 The levels of glutathione reductase, glutathione peroxidase, superoxide dismutase, catalase, uric acid, and peroxiredoxin are high in the morning. In contrast, ascorbic melatonin and plasma level are high in the evening or night. This proves that oxidative stress leads to oxidative damage with the progression of AD, which is ultimately regulated by circadian dysregulation. 71
## ERK/MARK AND CIRCADIAN DYSFUNCTION IN AD
Cognitive impairment is the first symptom observed in AD. Impairment, such as memory, is enhanced by short‐term stress and impaired by long‐term stress, and the number of dendritic synapses decreases due to high cortisol levels during chronic stress. 72 The pathway primarily revolves around memory consolidation, and the level of phosphor‐ERK CAMP, phosphor CREB, and activity of PKA and MEK are associated with a circadian rhythm. Moreover, the SCN regulates the hippocampus' Camp/PKA/ERK/CREB signaling pathway. 73, 74, 75 The CREB/ERK/PKA/CAMP signaling pathway increases during rapid eye movement sleep. They are even ablating the BMAL1 gene results in reduced Per1 and PERK levels. A study reported that ERK appears overactivated and memory is improved by pharmacological inhibition of ERK in an AD mouse model, whereas memory impairment is seen due to reduction of pCREB level downstream of the ERK pathway. 76 ERK signaling pathway is disrupted in AD due to amyloid beta 1, 2, 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, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42 bind injury. Finally, ERK/MAPK signaling pathway is a common pathway that causes stress as circadian rhythm even plays a role in memory consolidation. 77
## HPA AXIS AND CIRCADIAN DYSFUNCTION IN AD
HPA axis activation promotes AD pathogenesis. Even reducing cortisol levels by taking dexamethasone does not show positive results in patients with AD; instead of cortisol levels, few approaches to decrease and modulate HPA axis activity can be a promising avenue for treating AD. Even amyloid beta promotes HPA axis activity and increases corticosterone. The HPA axis is one of the common pathways by which SCRD and stress increase amyloid beta production, leading to AD. 78
## HIPPOCAMPAL VOLUME AND CIRCADIAN DYSFUNCTION IN AD
Reduced hippocampal volume was observed in AD and different neurodegenerative and psychiatric disorders. It is hypothesized that prolonged sleep restriction or sleep disruption can cause a decrease in hippocampal neuronal cell proliferation and neuronal cell survival. Few preliminary clinical trials and observational studies suggest that regular physical exercise, cognitive stimulation, and general medical conditions can reduce hippocampal volume or atrophy, reverse hippocampal atrophy, or even expand the hippocampal size. 79, 80
## GLYMPHATIC SYSTEM AND CIRCADIAN DYSFUNCTION IN AD
The glymphatic system was first described in 2012, which consists of intestinal fluid that regulates brain amyloid clearance by the perivascular space surrounding blood vessels. Glymphatic system dysfunction also plays a vital role in the severity of AD. To date, no clinically approved system has been developed to evaluate the functionality of the glymphatic system in humans. Recently, the glymphatic system has even played a role in glaucoma pathogenesis, characterized by progressive degeneration of RGCs and amyloid beta accumulation. This activity is higher during sleep and low during wakefulness. Even body posture during sleep, especially lateral body position, may increase the rat's glymphatic transport. Further studies need to be done to see the relation of the glymphatic system with patients with AD. 11, 81, 82
## PROTEOSTATIS AND CIRCADIAN DYSFUNCTION IN AD
Amyloid beta and tau are specific protein hallmarks seen in AD. Heat shock factor 1 is a type of factor in which deletion alters circulation clock oscillation. Proteasomal degeneration of proteins display oscillations in circadian patterns and expected circadian clock timing requires an understanding of the proteasome function. It is still unknown how the circadian clock controls rhythmic protein degradation in the brain. 83
## VASCULAR AND CIRCADIAN DYSFUNCTION IN AD
Microvascular change is considered an essential factor in the development of AD. Cerebral vascular perfusion is also under the control of the circadian system. According to PET scans and simple‐photon emission computed tomography, people with moderate cognitive impairment and an increased risk of developing AD exhibit hypometabolism and cerebral hypoperfusion. Antihypertensive treatment has also been shown to reduce the risk of AD. Brain microvascular changes are critical to AD development, both pathologically and clinically. The circadian system regulates cerebral vascular circulation as well. 84, 85, 86 Conroy et al investigated the daily regularity of cerebral blood flow velocity (CBFV) across 30 hours of continuous awake time. The findings of this study suggested that human CBFV probably follows an endogenous circadian rhythm, which will be investigated further in the context of cerebrovascular/cardiovascular events and cognitive function deterioration. 87, 88, 89 Laser‐Doppler flowmetry revealed similar results in rats. The cerebral blood flow has a diurnal periodicity independent of locomotor activity and blood pressure changes. The effect of the circadian rhythm on brain metabolism and perfusion should be carefully considered in future studies on the role of vascular function in AD etiopathogenesis. 90, 91, 92
## METABOLIC CHANGES AND CIRCADIAN DYSFUNCTION IN AD
Circadian/sleep disruption may be mediated by metabolic changes in neurodegenerative disorders, particularly AD. Insulin resistance has been linked to an increased risk of AD in clinical studies, and childhood obesity can also cause cognitive impairment later in life apart from diabetes. Apolipoprotein E (APOE) is a key regulator of lipid metabolism found primarily in brain astrocytes. The APOE 4 allele can cause mitochondrial dysfunction, leading to insulin resistance and metabolic defects as a major risk factor for AD. 93, 94, 95, 96, 97, 98 A recent study suggests that peripheral metabolic dysfunction plays a role in the development of AD‐related neuropathology. The clock regulates the majority of metabolic activity, and the loss of circadian clocks has been linked to cellular and system‐wide metabolic deficits. Sleep deprivation significantly impacts metabolism, including an increase in insulin resistance markers. Based on these findings, it is enticing to believe that sleep disruption increases the risk of AD by disrupting metabolism. 99, 100, 101, 102
## MELATONIN AS A PROMISING THERAPEUTIC TARGET FOR AD
In AD, melatonin has shown multiple beneficial effects, like prevention of mitochondrial dysfunction, inhibition of amyloid beta toxicity, free radical scavenging, and even circadian dysregulation like sundowning and sleep disturbances. 110 Melatonin even has blood–brain barrier crossing capacity, anti‐oxidant properties, as well as balanced amphiphilicity. Amyloid beta peptides are mainly produced with the help of amyloidogenic beta‐amyloid precursor protein (beta APP). Amyloid beta 42 is the most neurotoxic form of amyloid beta. This beta pleated sheet peptide ultimately forms an aggregation of senile plaques in the brain in the form of amyloid fibrils that disrupts synaptic communications leading to abnormal function of neurons and neuronal death. As melatonin has anti‐oxidant, neuroprotective, and anti‐amyloidogenic properties, it might help in decreasing amyloid beta formation. Melatonin has shown effects on both in vivo and in vitro models. 111, 112, 113, 114, 115 Hyperphosphorylated tau plays a crucial role in dealing with memory and cognitive impairment in AD. Neurodegeneration happens due to tau hyperphosphorylation. This tau phosphorylation and protein kinase A (PKA) overactivation in the isopropanol‐induced rat brain can be attenuated by melatonin. This process is followed in the neuroblastoma SHSY5Y cell line and N2a induced by calyculin A, okadaic acid, and wortmannin. Melatonin shows neuroprotective effects in the degeneration of the hippocampus and enhances cognitive effects. These effects are displayed through regulating GSK3 and CDK5 activities in hippocampal neurons. Melatonin inhibits the expression level of caspase 3, prostate apoptosis response 4 (Par4), and Bcl2 associated BAX, reducing neuronal death. 116, 117, 118, 119, 120, 121 Melatonin has an anti‐oxidant property that reduces oxidative stress. In an experimental study, it was observed that NF‐KB commenced IL‐6 in amyloid beta treated brain slices can be inhibited by melatonin in a concentration‐dependent fashion. Melatonin injection (ie, 5 mg/kg, 0.1 to 10 mg/kg, and 10 mg/kg) in the rat in which melatonin shows anti‐inflammatory effects and reduces neuroinflammation by increasing ATP production, stimulating GPX activities, and even enhances SOD activity. 122 Therefore, this evidence shows the anti‐neuroinflammatory effects of melatonin on AD.
## RELATION AMONG EXERCISE, CIRCADIAN RHYTHM, AND AD
Various animal models show exercise chronobiotic properties. It is difficult to identify whether exercise has chronobiotic properties in humans because it is quite hard to differentiate the range of effects shown by exercise from multiple other factors, like food, social influences, and light. 123 Non‐photic stimuli, on the other hand, appear to be capable of synchronizing circadian rhythms in people who are blind who lack sensitivity to light, and this helps them entrain to routine schedules without utilizing exogenous melatonin. A recent study related to circadian rhythms and AD has shown that when a person exercises just before habitual sleep, it accelerates circadian rhythm and if it is performed during habitual sleep time, it delays circadian rhythms. 124, 125, 126 Exercise also affects the hippocampus, which plays a role in affecting sleep quality. It has also been reported that people who do exercise regularly on a daily basis have better sleep quality as well as less daytime sleepiness when compared to people who are inactive and do not exercise. As a result, it is still possible that exercise has a greater impact on older adults who face difficulty in sleeping. Exercises also enhance the cognitive part and show neural plasticity which is effective in normal aging as well as a treatment for AD. 127, 128, 129, 130, 131, 132 Sleep after exercise has a well‐known effect on cognitive performance. According to the recent study findings, physical activity plays a huge role in diminishing the effects of poor sleep quality on cognitive functioning in older adult women. As a result, more research is needed to understand the mechanisms underlying exercise, sleep, and cognitive function that are linked in older adults. 133, 134, 135, 136, 137, 138
## CURRENT THERAPIES AND FUTURE IMPLICATIONS
Unfortunately, at present, we have limited pharmacological and non‐pharmacological interventions to manage sleep disturbance in patients with AD. In AD, current behavioral practices include limited caffeine and alcohol intake, regular exercise, and maintaining regular bed and wake times with ample light exposure upon waking. 60 Sufficient daytime light exposure is crucial for patients with AD, mainly for institutionalized patients. Consistent light exposure may bring changes in dysfunctional circadian rhythms in AD and reduce the “sundowning.” Patients with moderate‐to‐severe AD were included in the melatonin and trazodone trials, but only patients with mild‐to‐moderate AD were included in the ramelteon study. Melatonin is considered a part of various clinical manifestations and treatment strategies of AD. 139, 140, 141 *Actigraphy is* used to measure all primary sleep outcomes. Despite the absence of severe side effects, we still have no evidence to suggest that melatonin and trazodone improve sleep quality. More comprehensive clinical trials are desperately needed in this area, particularly those focusing on sleep and cognitive or pathological outcomes in AD. Suvorexant is the first US Food and Drug Administration (FDA)‐approved orexin receptor antagonist which can show effects on amyloid deposition and cognitive end points in early‐stage or presymptomatic AD. Melatonin supplementation on a regular basis may help patients with mild cognitive impairment improve their cognitive performance slightly. However, there appears to be conflicting evidence in mice regarding the effectiveness of melatonin supplementation in reducing amyloid plaques and other AD correlates. Ramelteon has been approved for insomnia, whereas tasimelteon is for the treatment of non‐24 hour sleep–wake disorder in the blind. Until now, these two drugs have not been tested for AD but can be more effective than melatonin. Researchers are trying to develop a drug that can directly target the circadian clock, although they are still in the early stages of development. Small molecules that can alter circadian oscillations' amplitude, frequency, and period have been discovered through high throughput screening. RevErb is a small molecule agonist of the nuclear receptor that can improve metabolic function in mice by directly affecting circadian rhythms. Finally, the right targeting of the circadian clock could be a promising remedial option for treating AD. 33, 34
## CONCLUSION
The pathology of AD (amyloid and tau) has been linked to circadian dysfunctions, and sleep disruptions are very common in patients with Alzheimer's disease that play an important role in disease succession and pathology. Moreover, circadian rhythms communicate with nearly all systems and risk factors involved in the growth and progression of AD. Recognizing early signs of AD, such as changes in sleep patterns and rest‐activity rhythm anomalies, could be useful in identifying early biomarkers for interference to prevent the formation of amyloid‐beta, neurofibrillary tangles and the succession of neurodegeneration. In patients with advanced AD, bright light therapy combined with chronobiotics is effective in treating sundowning characteristics and other cognitive symptoms. Future research into the function of circadian misalignment in the initial stages of AD could lead to new preventive and therapeutic approaches. As a result, circadian rhythms are an excellent target for combating pathology.
## AUTHOR CONTRIBUTIONS
Manuscript writing and drawing figures: Faizan Ahmad. Manuscript writing, reviewing, and editing: Punya Sachdeva. Editing: Jasmine Sarkar. Reviewing: Rafiah Izhaar.
## FUNDING INFORMATION
No funding was received for this study.
## CONFLICT OF INTEREST
The authors declare they have no conflict of interest.
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|
---
title: 'Apocynum venetum, a medicinal, economical and ecological plant: a review update'
authors:
- Tian Xiang
- Longjiang Wu
- Murtala Bindawa Isah
- Chen Chen
- Xiaoying Zhang
journal: PeerJ
year: 2023
pmcid: PMC10000306
doi: 10.7717/peerj.14966
license: CC BY 4.0
---
# Apocynum venetum, a medicinal, economical and ecological plant: a review update
## Abstract
Apocynum venetum L. is an important medicinal perennial rhizome plant with good ecological and economic value. Its leaves have many pharmacological effects such as anti-inflammatory, anti-depression, anti-anxiolytic, etc., while its fibers have the title of “king of wild fibers”. Furthermore, it was suitable for the restoration of degraded saline soil in arid areas. An increasing studies have been published in the past years. A scientometric analysis was used to analyze the publications of *Apocynum venetum* L. to clearly review the pharmacology, fiber application of *Apocynum venetum* L. and the potential value with its similar species (*Apocynum pictum* Schrenk) to the environment.
## Introduction
Apocynum venetum L. (A. venetum), commonly known as “Luobuma” in Chinese and “Rafuma” in *Japanese is* a perennial herbaceous shrub (Fig. 1) widely distributed in the temperate regions of Asia, Europe and North America, especially in saline-alkali land, river-banks, fluvial plains and sandy soils (Grundmann et al., 2007; Jiang et al., 2021b; Xie et al., 2012). The species *Apocynum venetum* L. (Apocynaceae) currently includes 9 subspecies documented on World Flora Plant List (Table S1) (World Flora Online, 2022) A. venetum can adapt to extreme conditions where the surface salinity is up to $20\%$ and the annual average precipitation is more than 250 mm, making the plant of high ecological value for the transformation of coastal saline and barren lands (Thevs et al., 2012; Yuan, Li & Jia, 2020a). A. venetum leaves has been used to produce herbal drugs and tea (Chinese Pharmacopoeia, 2020). Furthermore, since 2002 luobuma tea has been included in the list of health-care food in China (National Health Commission of the People’s Republic China, 2002). Lau et al. [ 2012] confirmed that A.venetum leaf extract could stimulate vascular receptor (alpha-adrenergic and angiotensin II receptors) and inhibit vasoconstriction, suggesting antihypertensive properties of the plant. Modern pharmacological investigations confirmed that A. venetum has, among other effects, anti-inflammatory, anti-depression, anti-anxiolytic, anti-ageing, antioxidants, cardiotonic and hepatoprotective effects (Du et al., 2020; Grundmann et al., 2007; Xie et al., 2015; Xie et al., 2012). A. venetum fiber, known as the “king of wild fibers”, is receiving increasing attentions in the apparel industry owing to its additional advantage of possessing antibacterial properties (Han et al., 2008; Wang, Han & Zhang, 2007; Xu et al., 2020a).
**Figure 1:** *Apocynum venetum ssp. Tauricum.Image credit: Roman, https://www.inaturalist.org/photos/21168806.*
Alongside the rapid increase in A. venetum-related studies, systematic and comprehensive analyses on A. venetum publications is essential. We have previously reviewed the traditional uses, phytochemistry and pharmacology of A. venetum (Xie et al., 2012). As a timely update, this article aims to respond to the rapidly increasing literature on A. venetum studies by: (i) conducting scientometric analysis of the publications on A. venetum and (ii) reviewing the progress recorded on the exploration of the medicinal, economical and ecological benefits of the plant from 2012 to date. For the scientometric analysis, we used Citespace, which is a specifically designed to facilitate the detection of emerging trends and mutations in the scientific literature (Chen et al., 2012). Web of Science Core Collection (WoSCC; Clarivate Analytics, London, UYK) is the premier resource on the Web of Science platform. It is considered as the most trusted citation index on many research topics (Wu, Yakhkeshi & Zhang, 2022). This work can provide researchers and readers with a comprehensive information on A. venetum, covering the areas of phytopharmacy and pharmacology, functional food, ecology, and applications in textile and fiber industry.
## Survey Methodology
Data were collected from WoSCC with the following search strategy: Topical Subject = (“Apocynum venetum” OR “Luobuma”) OR Title = (“Apocynum venetum” OR “Luobuma”) OR Abstract = (“Apocynum venetum” OR “Luobuma”). The searched time spans 1987–2022, the type of literature was article and review, and the language was English. Our search strategy did not limit the impact factor of journals and the affiliation of authors. A total of 200 publications were obtained, including 190 articles and 10 reviews, and their full record with the cited references was exported in plain text format. CiteSpace 6.1.3 was used to analyze keywords of the literatures, with the time partition set to 1987–2022, the time slice set to 1, the node types set to keyword, G-index set to 25, and the pathfinder, pruning sliced networks and pruning the merged network were used to trim the atlas. Based on the result of keywords analysis of CiteSpace, the chapter topics were divided into the pharmacological effects and related components of A. venetum, A. venetum fiber, other Apocynum species similar to A. venetum: *Apocynum pictum* Schrenk, and the ecological value of A. venetum and A. pictum, and the topics were discussed. The discussion on the bioactive components cover the period 2012–2022.
## Keywords analysis of CiteSpace
Keywords represent the core content of an article and provide information on the topic or the important category to which an article belongs. The keywords with high frequency and highly mediated centrality were analyzed and presented in the form of a visual mapping through the Citespace software (Fig. 2). The most frequent keywords from 1987–2022 were *Apocynum venetum* L. [111], *Apocynum venetum* leaves [52], *Apocynum venetum* leaf extract [31]. The keywords with the highest centrality before 2018 include *Apocynum venetum* L. (0.49), component (0.28), hepatoprotective activity (0.27), identification (0.25) and antioxidant (0.23) (Fig. 2A, Table 1). However, after 2018, among the top ten keywords showing the highest centrality, two words that are poorly correlated with *Apocynum venetum* leaves appeared: *Apocynum venetum* fiber (0.28) and *Apocynum pictum* schrenk (0.27). These results implied that the studies before 2018 mainly focused on the components and the pharmacological effects of *Apocynum venetum* leaves while *Apocynum venetum* fiber and *Apocynum pictum* Schrenk have also attracted the attention of researchers in recent years.
**Figure 2:** **Keywords analysis* of A. venetum.(A) Nodes in the network represent keywords. Node size represents the number of keyword occurrences. Node color: average time to appear, color from white to red, time from 1987 to 2022. (B) Top 16 keywords with the strongest citation bursts. The grey line represents time interval, the yellow line indicates time period in which a keyword was found to have a burst.* TABLE_PLACEHOLDER:Table 1 Based on the keyword co-linear graph (Fig. 2A), the parameter of “burstiness” was set to γ = 0.5, minimum duration = 1. Sixteen burst entries were generated. Among them, the words that have kept the outbreak status were oxidative stress (3.93), *Apocynum venetum* fiber (3.26), identification (2.68) and tolerance (2.0) (Fig. 2B). These data confirmed that apart from the further in-depth pharmacological investigations, the fiber of this plant has recieved attention in recent years. In addition, the ecological value of *Apocynum venetum* L and *Apocynum pictum* Schrenk L has attracted increasing attentions.
## Flavonoids
With the deepening of research and the technological improvement in high performance liquid chromatography, mass spectrometry etc., many phytochemicals of A.venetum have been identified and isolated. Some of these phytochemicals were flavonoids such as hyperoside and isoquercetin, which bioactivities have been comprehensively reviewed previously (Xie et al., 2016a; Xie et al., 2016b; Xie et al., 2012). Since then, more studies have reported on the isolation and bioactivities of known and novel flavonoids from A.venetum. The flavonoids isolated from A. venetum since 2012 are listed in Table 2 and their structures shown in Fig. 3. Kaempferol, quercetin, isoquercitrin (quercetin-3-O-β-D-glucose) and astragalin (kaempferol-3-O-β-D-glucose) isolated from A. venetum leaves have significant anti-depressant activities in mice (Yan et al., 2016). Hyperoside isolated from the leaves of A. venetum showed antidepressant-like effect in P12 cell lines which could improve neuronal viability by protecting neurons from corticosterone damage (Zheng et al., 2012). Hyperoside had protective effect on H2O2-induced apoptosis of human umbilical vein endothelial cells (Hao et al., 2016). For acetaminophen-induced liver injury, both hyperoside and isoquercetin exerted hepatoprotective effect by upregulating the expression and activity of detoxifying enzymes such as sulfotransferases (hyperoside could also increase activities of UDP-glucuronosyltransferase) in liver microsomes and inhibited the activity of cytochrome P450 2E1, accelerating the harmless metabolism of acetaminophen. Additionally, isoquercetin could significantly inhibit acetaminophen induced oxidative stress and nitrosative stress (Xie et al., 2016a; Xie et al., 2016b). Isoquercitrin, isolated from the A. venetum leaf aqueous extract exerted anti-obesity effect in high fat diet induced obese mice by inhibiting adenosine 5′-monophosphate-activated protein kinase (AMPK)/sterol regulatory-element binding protein (SREBP-1c) signaling pathway, glucose uptake, and glycolysis flux. C-1-tetrahydrofolate synthase, carbonyl reductase, and glutathione S-transferase P are potential target proteins of isoquercitrin (Manzoor et al., 2022). 8-O-methylretusin (Fig. 3) isolated from A venetum leaves showed antifouling activity (Kong et al., 2014). On the other hand, 4′,7-dihydroxy-8-formyl-6-methoxyflavone isolated from A venetum leaves showed high anti-inflammatory activity via significant inhibitory effect on the production of nitric oxide (NO) and tumor necrosis factor-α (TNF-α) (IC50 values were 9.0 ± 0.7 and 42.1 ± 0.8 µM, respectively) in lipopolysaccharide-induced mouse peritoneal macrophages (RAW 264.7) (Fu et al., 2022).
Wang et al. [ 2020] investigated the absorption and metabolism of quercetin-3-O-sophoroside, isolated from the leaves of A. venetum, in rats. The results indicated that quercetin-3-O-sophoroside was completely absorbed in the small intestine and metabolized in the jejunum to sulfated quercetin-3-O-sophoroside, methylated quercetin-3-O-sophoroside, and methylated quercetin-3-O-sophoroside sulfate. Quercetin-3-O-sophoroside was deglycosylated to aglycones by the cecal microbiota to form derivatives of benzoic, phenylacetic and phenylpropionic acids (Wang et al., 2020).
To obtain larger amounts of flavonoids, A. venetum hairy roots were induced with Agrobacterium rhizogenes strain Ar.1193, and 117 kinds of flavonoids were detected in the roots. The flavonoid content and antioxidant activity of the roots were significantly increased as compared to field-planted roots, therefore, this technique could be used for large-scale production of flavonoids from A. venetum (Zhang et al., 2021).
## Polysaccharides
Natural polysaccharides have been proved to possess, among other effects, immune regulatory, anti-oxidative and anti-inflammatory activities, as well as having the advantages of being safe and non-cytotoxic (Liu et al., 2022). Zhou et al. [ 2019] used different concentrations and kinds of solvents (HCl, H2O, NaOH) to extract polysaccharides from A. venetum leaves. The results showed that the polysaccharide yield was the highest with $21.32\%$ (w/w), 0.5 M NaOH at 90 °C, and the bioactivity of the alkaline extracted polysaccharides was the strongest, which was reflected in the antioxidant capacity (DPPH and ABTS radical scavenging activities) and α-glucosidase and lipase inhibitory activities. The 0.5 M NaOH extracted polysaccharides showed a strong inhibitory activity on α-glucosidase (IC50 value of 16.75 µg/mL), which was better than the positive control, acarbose (IC50 value of 1,400 µg/mL). In addition, the alkaline polysaccharide-rich extracts were proved to possess hypoglycemic and hypolipidemic effects on mice with high fat diet induced and streptozotocin-induced type 2 diabetes. Moreover, the extract reversed intestinal dysbiosis by increasing the abundance of Odoribacter, Anaeroplasma, Muribaculum, Parasutterella and decreasing the abundance of Enterococcus, Klebsiella, Aerococcus in diabetic mice (Yuan et al., 2020b).
Some polysaccharides were also isolated from various parts of A. venetum and validated for bioactivity. These are summarized in Table 3. ALRPN-1 and ALRPN-2 exerted a significant anti-inflammatory activity in lipopolysaccharide-induced macrophages by regulating the levels of pro-inflammatory mediators (NO) and cytokines (TNF-α, interleukin-6, interleukin-1 β) and the mechanism may involve, in part, extracellular signal-related kinase (ERK)/mitogen-activated protein kinases (MAPKs) signaling pathway (Liu et al., 2022). Vp2a-II and Vp3 obtained from the flowers of A. venetum showed anticoagulant activity and immunoregulation. The anticoagulant activities of Vp2a-II and Vp3 were assayed in vitro by plasma coagulation parameters (activated partial thromboplastin time (APTT), thrombin time (TT), prothrombin time (PT), fibrinogen). The results showed that Vp3 significantly prolonged TT and PT, while Vp2a-II significantly prolonged APTT and TT, indicating that the two polysaccharides could inhibit blood coagulation (Wang et al., 2019b). In addition, the polysaccharides could exert immunomodulatory effects by promoting phagocytic activity, enhancing NO secretion and mRNA expression of inducible nitric oxide (iNO) synthase, interleukin-6 and TNF-α which activate RAW264.7 cells. Vp2a-II might activate the MAPK signaling pathway, which then induce the nuclear translocation of NF-κB p65 (Wang et al., 2022).
**Table 3**
| Name | Average molecular weight | Monosaccharide | Bioactivity | Mechanism | Plant part | Reference |
| --- | --- | --- | --- | --- | --- | --- |
| ALRPN-1 | 1. 542 ×104 Da | Glucose | Anti-inflammatory | ALRPN-1 and ALRPN-2 exert significant anti-inflammatory activity in LPS-induced macrophages by regulating the levels of pro-inflammatory mediators (NO) and cytokines (TNF- α, IL-6, IL-1 β) and activating the ERK/MAPKs signaling pathway. | A. venetum root | Liu et al. (2022) |
| ALRPN-1 | 1. 542 ×104 Da | Galactose | Anti-inflammatory | ALRPN-1 and ALRPN-2 exert significant anti-inflammatory activity in LPS-induced macrophages by regulating the levels of pro-inflammatory mediators (NO) and cytokines (TNF- α, IL-6, IL-1 β) and activating the ERK/MAPKs signaling pathway. | A. venetum root | Liu et al. (2022) |
| ALRPN-1 | 1. 542 ×104 Da | Arabinose | Anti-inflammatory | ALRPN-1 and ALRPN-2 exert significant anti-inflammatory activity in LPS-induced macrophages by regulating the levels of pro-inflammatory mediators (NO) and cytokines (TNF- α, IL-6, IL-1 β) and activating the ERK/MAPKs signaling pathway. | A. venetum root | Liu et al. (2022) |
| ALRPN-2 | 5.105 × 103 Da | Glucose | Anti-inflammatory | ALRPN-1 and ALRPN-2 exert significant anti-inflammatory activity in LPS-induced macrophages by regulating the levels of pro-inflammatory mediators (NO) and cytokines (TNF- α, IL-6, IL-1 β) and activating the ERK/MAPKs signaling pathway. | A. venetum root | Liu et al. (2022) |
| ALRPN-2 | 5.105 × 103 Da | Galactose | Anti-inflammatory | ALRPN-1 and ALRPN-2 exert significant anti-inflammatory activity in LPS-induced macrophages by regulating the levels of pro-inflammatory mediators (NO) and cytokines (TNF- α, IL-6, IL-1 β) and activating the ERK/MAPKs signaling pathway. | A. venetum root | Liu et al. (2022) |
| ALRPN-2 | 5.105 × 103 Da | Mannose | Anti-inflammatory | ALRPN-1 and ALRPN-2 exert significant anti-inflammatory activity in LPS-induced macrophages by regulating the levels of pro-inflammatory mediators (NO) and cytokines (TNF- α, IL-6, IL-1 β) and activating the ERK/MAPKs signaling pathway. | A. venetum root | Liu et al. (2022) |
| Vp2a-II | 7 ×103 Da | – | Anticoagulant activity | Vp2a-II could inhibit blood coagulation through exogenous pathways and endogenous coagulation pathways. | A. venetum flower | Wang et al. (2022); Wang et al. (2019a); Wang et al. (2019b) |
| Vp2a-II | 7 ×103 Da | – | Immunoregulatiory | Vp2a-II and Vp3 could activate RAW264.7 cells by promoting cell viability phagocytosis, and enhancing the NO secretion and mRNA expression of iNOS, IL-6 and TNF- α. Moreover, Vp2a-II and Vp3 could trigger the MAPK signaling pathway and then induce the nuclear translocation of NF- κB p65. | A. venetum flower | Wang et al. (2022); Wang et al. (2019a); Wang et al. (2019b) |
| Vp3 | 9 × 103 Da | – | Immunoregulatiory | Vp2a-II and Vp3 could activate RAW264.7 cells by promoting cell viability phagocytosis, and enhancing the NO secretion and mRNA expression of iNOS, IL-6 and TNF- α. Moreover, Vp2a-II and Vp3 could trigger the MAPK signaling pathway and then induce the nuclear translocation of NF- κB p65. | A. venetum flower | Wang et al. (2022); Wang et al. (2019a); Wang et al. (2019b) |
| Vp3 | 9 × 103 Da | – | Anticoagulant activity | Vp3 could inhibit blood coagulation mainly through exogenous pathways and coagulation pathways. | A. venetum flower | Wang et al. (2022); Wang et al. (2019a); Wang et al. (2019b) |
| ATPC-A mixture (the polysaccharide conjugates contained three components) | 5.50 × 104 Da 5.38 × 104 Da 5.67 × 103 Da | Mannose | Emulsifying properties | – | A. venetum tea (made of A.venetum leaves) residues | Chen et al. (2022a), Chen et al. (2022b) |
In addition to the pharmacological effects of A. venetum polysaccharides, researchers have also began exploring their other properties. The polysaccharide conjugates (ATPC-A) extracted from A. venetum tea residues with an alkaline solution (0.10 M NaOH) had emulsifying properties and stabilized the emulsion which comprised of amphipathic polysaccharides covalently bound to proteins. The stability of the neat ATPC-A emulsions with a concentration equal to or greater than 1.00 weight % was higher than 5.00 weight % gum arabic during storage at different temperatures and pH values (Chen et al., 2022b).
## Other phytochemical components of A. venetum
Many studies have reported other phytochemicals from A. venetum leaf extracts and their pharmacological effects. The ethanol extract of A. venetum leaf possesses anti-cancer activity. A fraction separated from the extract could inhibit the proliferation of Human PCa cells tumor cells. Lupeol accounted for approximately one-fifth ($19.3\%$ w/w) of the components of the fraction and was implicated for the induced cytotoxicity against PCa cells. The fraction and lupeol elicited similar anti-proliferative mechanisms, involving: regulating apoptosis signal molecules (P53, cytochrome c, Bcl-2, and caspase 3 and 8), promoting G2/M arrest through impairing the DNA repair system via downregulating the expression of uracil-DNA glycosylase, as well as downregulating the expression of β-catenin (Huang et al., 2017). In preventing D-galactose-induced oxidative damage in mice, the polyphenol extract of A. venetum was superior to the antioxidant vitamin C (Guo et al., 2020). Within its safe concentration range (0–100 µg/ml), the polyphenol extract of A. venetum inhibited U87 glioma cell proliferation and caused cell apoptosis by affecting NF- κB and genes of other relevant pathways (Zeng et al., 2019). Additionally, A. venetum leaf extract inhibited doxorubicin induced cardiotoxicity through (protein kinase B) Akt/(B-cell lymphoma-2) Bcl-2 signaling pathway (Zhang et al., 2022). The efficacy and mechanism of action of individual chemical components, as well as their possible synergistic effects, of A. venetum leaf extract need to be further investigated.
In addition to flavonoids, polysaccharides and polyphenols, sterols (β-sitosterol, sitgmasterol), triterpenoids (lupeol, uvaol), glycolipids (apocynoside I), natural lignan glycoside (alloside of benzyl alcohol) and amino acids have been isolated from A. venetum (Huang et al., 2017; Sun et al., 2022). A. venetum flowers are rich in free amino acids, accounting for about $3\%$ of the total dried weight, including leucine (13.71 µg/mg), isoleucine (7.86 µg/mg), lysine (2.22 µg/mg), tryptophan (1.67 µg/mg) and valine (1.20 µg/mg) (Jin et al., 2019). Uvaol from A. venetum leaves had potent anti-inflammatory effects on dextran sulfate sodium-induced experimental colitis and lipopolysaccharide-stimulated RAW264 cells (Du et al., 2020). Validation of the activities of other components in A. venetum should be the focus of future studies.
## A. venetum fiber
The fiber of A. venetum has been used in textile and paper industries with superior properties compared to other commonly used fibers. Fiber from Apocynum species has a higher average length to diameter ratio (up to 1219) compared to kenaf [209], another natural plant fiber (Liu et al., 2020; Wang, Han & Zhang, 2007; Xie et al., 2012). Another reason for the popularity of A. venetum fabric is the antibacterial effect that A. venetum fiber naturally possesses (Li et al., 2012; Song et al., 2019). Such antibacterial activity might be because: (i) A. venetum fiber has small openings between microstructures, which improve the breathability of the A. venetum fabric, which subsequently destroy the environment for bacterial growth (Han et al., 2008); (ii) the A. venetum stem cells contain tanning agents, which is resistant to microbial decomposition (Thevs et al., 2012); (iii) the presence of water-insoluble polyphenol derivatives confers antimicrobial properties to the fabric (Xu et al., 2020a).
A. venetum is rich in cellulose, but impurities such as pectin, lignin, and waxes must be removed to produce clean fibers (Lou et al., 2019). In the direction of environmental safety and high efficiency, various degumming methods have been proposed, including chemical degumming, biological degumming and microwave-assisted ultrasonic degumming. A study revealed that microwave-assisted ultrasonic degumming showed the advantages of requiring less chemical reagents during degumming (1 kg raw A. venetum bast needed 0.6 kg of reagents while the chemical degumming treatment required 1.34 kg) and shorter time, as well as higher quality (low residual gum content of $5.15\%$; lignin content less than $3\%$; whiteness more than $80\%$ in the refined A. venetum fibers) (Li et al., 2020). Degumming methods and the fiber quality of A. venetum reported from 2012 to 2022 are listed in Table 4.
**Table 4**
| Degumming type | Processing method | Fiber quality | Impact on the environment | Reference |
| --- | --- | --- | --- | --- |
| Bio-chemical combined degumming process | Apocynum fibers > > Boiling (12 g/L pectinase, Material: Liquor (M: L)-1:30, time: 2 h, temperature: 50 °C, PH8-10) > > washing > > boiling (12 g/L NaOH, M: L-1:30, time: 1.5 h) > > washing > > bleaching (20 g/L H2O2, M: L-1:30, time: 1.5 h, temperature: 95 °C) > > washing > > oven-dried (temperature: 80 °C) | Fiber breaking strength: 22.84 cN/dtex; Whiteness: 73.9; Fineness:4.97 dtex; Crystallinity: 74.5%; Moisture regain: 7.7380%. | This method could reduce the pollution caused by chemicals. | Chen et al. (2022a), Chen et al. (2022b) |
| Biodegumming (Bacterial strain Pectobacterium wasabiae) | Oscillating fermentation (fermentation time: 12 h, inoculum size: 2%, M: L -1:10, temperature: 33 °C, shaking rate:180 rpm) > > boiling (temperature: 100 °C, time: 20 min) > > washing by machine | Residual gum content: 12.57%; Percentage of raw material weight loss: 30.05%; The fiber counts:1,002 m/g | Chemical Oxygen Demand: 3,119 mg/L | Duan et al. (2021) |
| Microwave-assisted ultrasonic degumming | Sample > > Microwave pretreatment (10 g/L NaOH, M: L-1:20, time: 20 min, temperature:120 °C, power: 600W) > > rinsing > > drying > > ultrasonic degumming > > soaking (10 g/L NaOH and 1 g/L H2O2, M: L-1:20, time: 60 min, temperature:50 °C, power: 800W, frequency: 28 Hz | Residual gum content: 5.15%; Fiber breaking strength: 7.67 cN/dtex; Fiber length:32.5mm; Whiteness: 83%; Fineness: 4.05 dtex; | For degumming 1 kg of raw AV bast needed 0.6 kg of chemical reagents | Li et al. (2020) |
| Chemical degumming | Stripped bast by machine > > pretreatment (0.2%Al2(SO4)3, room temperature, M: L- 1:15, time: 7h) > > fiber washing > > cooking (1%NaOH, 0.25% thiourea, M: L- 1:15, temperature:95 °C, time intervals:2, 3, 5 h) > > washing > > acid soaking (2% CH3COOH, room temperature, M: L- 1:15, time: 2 min) > > washing > > bleaching (2% H2O2, 0.1% tween-80 surfactant, temperature: 94 °C, M: L- 1:15, time: 1 h) > > washing > > drying (oven-dried at 105 °C). | Moisture regain: 7.0%; The cooking processes of three different time intervals: Residual gum content: 3.64, 3.03, 2.70%, respectively; Crystallinity: 81.14, 78.80 73.75%, respectively; Tenacity: 8.63, 7.00, 6.93 cN/dtex, respectively; Fiber diameter: 2.52, 2.37, 2.14 dtex, respectively. | The method uses metal salts of aluminum for pretreatment, which is more sustainable. | Halim et al. (2020) |
| Deep eutectic solvents (DES) with the assistance of microwave | DES Configuring (choline chloride and car bamide-1:2 molar ratio (w/w) > > oil bathing (temperature: 80 °C, M: L- 1:20, time: 1 h) > > immersing with microwave oven (temperature:110 °C, M: L- 1:20, time: 1 h ) > > washing > > cooking (1%NaOH, time: 1 h) > > washing > > oven-dried | Residual gum content: 6.54%; Fiber breaking strength:14.14 cN/dtex; Crystallinity: 77.92%. Average fiber fineness: 4.05 dtex. | DES reagent selected for this method is biodegradable | Song et al. (2019) |
| Degumming with Ionic Liquid (IL:1-butyl-3-methylimidazolium acetate-water mixtures.) Pretreatment | A.venetum fibers > > pretreatment > > water boiling (temperature: 70 °C, M: L- 1:20, time: 3 h) > > rinsing with hot water (60 °C) > > rinsing with tap water > > degumming with IL-water mixtures (80% IL-water mixtures, temperature: 90 °C M: L- 1:20, time: 4 h) > >chemical degumming (10 g/L NaOH and 2% Na3P3O10, M: L- 1:20 temperature: 95 °C, time: 2 h) > > acid rinsing (1.5 g/LH2SO4, room temperature, M: L- 1:20, time: 5 min) > > washing with tap water > > drying | Residual gum content: 3.90%; Fiber breaking strength: 452.7 cN/dtex; Fineness: 0.7 um Crystallinity:76.62% | Mild conditions and low toxicity. | Yang et al. (2019) |
| Chemical degumming | Pre-acid treatment (2% H2SO4, temperature: 60 °C, M: L- 1:15, time: 1 h) > > washing > > first-cooking (5% NaOH, 3% Na2SiO3, 2.5% Na2SO3, temperature: 100 °C, M: L- 1:10, time: 2.5 h) > > washing > > second-cooking (15% NaOH, 3% Na2SiO3, 2% sodium tripolyphosphate, temperature: 100 °C, M: L- 1:10, time: 2.5 h) > > washing > > acid rinsing (1 g/L H2SO4) > > washing > > dewatering > > shaking > > drying | Fiber breaking strength:401.56 cN/dtex; The average length:29.68 mm; Fineness:4673.25 nm; Color: reddish yellow; Moisture regain: 8.70%; Crystallinity:70.36%; | – | Lou et al. (2019) |
| Bio-degumming (Pectobacterium sp. DCE-01) | Machine rolling preprocessing > > bacteria culture (Pectobacterium sp. DCE-01, temperature: 34 °C, time: 6 h, speed: 180rpm, culture medium: 1.0% glucose, 0.5% NaCl, 0.5% beef extract, 0.5% peptone, and 100 mL water, pH 6.5–7.0.) > > Bacterial liquid preparation (water containing: 0.05% NH4H2PO4 and 0.05% K2HPO4, pH 6.5–7.0) > > fermentation and degumming (temperature: 33 °C, M: L- 1:15, bacterial solution: fermentation water-2:100, time: 16 h, speed: 180 rpm) > > boiling (temperature: 33 °C, time: 20 min) > > washing by a fiber washer > > drying | Residual gum content: 12.22%; Fiber breaking strength: 5.47 cN/dtex; | Chemical Oxygen Demand: 3,245 mg/L | Duan et al. (2017) |
| A novel ionic liquid degumming | Boiling (1 g/L H2SO4, temperature: 50 °C, M: L- 1:20, time: 2 h ) > > washing (until the washings were neutral) > > degumming (80% 1-butyl-3-methylimidazolium acetate, temperature: 130 °C, M: L- 1:20, time: 3 h ) > > washing > > drying | Residual gum content: 9.80%; Fiber breaking strength: 4.64 cN/dtex; Length:24.44 mm Fineness: 4.10 dtex; Crystallinity:78.66% | The degumming process was mild compared to the traditional chemical process. | Yang et al. (2015) |
In addition to the textile industry, A. venetum fiber also has many potential applications in medicine as well as in the construction industry. Microcrystalline cellulose (MCC-N) from A. venetum fibers was shown to have a rougher structure and less macrostructure than commercially available microcrystalline cellulose (MCC-C). MCC-N had a crystallinity of up to $78.63\%$ and a thermal stability comparable to that of MCC-C, which made it suitable as a load-bearing material for composite structures, and could be used in polymer composites with high temperature resistance (Halim, 2021). Furthermore, cellulose nanofibers (CNFs) from A. venetum straw were added into poly lactic acid (PLA), and the prepared PLA/CNFs film did not only improve the wettability and permeability of PLA, but also had superior antibacterial properties (the antibacterial growth inhibition rate on *Escherichia coli* and *Staphylococcus aureus* were $96.31\%$ and $92.83\%$ at PLA/$6\%$ (w/w) CNFs film, respectively). Then, polyvinyl pyrrolidone was added to this film to form a sustained-release nanofiber membrane (PLA/drug-loaded PVP nanofiber membranes), and a purified sea buckthorn was embedded in the drug-loaded film to evaluate its performance. The nanofiber membrane extended and sustained the release of purified sea buckthorn, and the cumulative release reached a maximum of $75.41\%$. It showed the advantage of a profile with a high initial release followed by a slow diffusion phase (Wang et al., 2021b; Wang et al., 2019a). In addition, when the hydrogel was prepared with chitosan as the matrix, the addition of CNFs improved the mechanical properties and swelling rate of the chitosan-based hydrogel. As the CNFs was $1.5\%$, the compressive strength of the hydrogel increased by nearly $20\%$, the swelling capacity reached $140\%$. In this form, the antibacterial efficacy against *Escherichia coli* and *Staphylococcus aureus* were $98.54\%$ and $96.15\%$, respectively (Wang et al., 2021a). See Abubakar, Gao & Zhu [2021] for further details on the composition, properties and degumming methods of A. venetum fiber.
## Other Apocynum species similar to A. venetum: Apocynum pictum Schrenk
Due to excessive exploitation, wild A. venetum has declined in recent years. A similar species, *Apocynum pictum* Schrenk (*Apocynum hendersonii* Hook) is often used in the market as a substitute for A. venetum due to their similarity in morphological characteristics and geographical distribution. The incorporation of A. pictum may affect the safety and effectiveness of A. venetum (An et al., 2013; Chan et al., 2015; Zheng et al., 2022). Although A. pictum has not been included in the Chinese Pharmacopoeia (Chinese Pharmacopoeia, 2020), some studies have reported that it is an important medicinal plant (Gao et al., 2021; Jiang et al., 2021a). For the quality control of A. venetum and to explore the potential application of A. pictum, some studies compared the similarities and differences between the two species in terms of genome size, flavonoid content, chemical composition and biological activity. The whole genomes of the two species were both small and similar, with 232.80 megabase (A. venetum) and 233.74 megabase (A.pictum). The contents of quercetin, hyperoside and total anthocyanin in A. venetum were much higher than those of A. pictum, which was considered to be the reason for the difference in color between the two species (Gao et al., 2019). Hyperoside could be a suitable chemical marker to distinguish between the two species (Gao et al., 2019). In addition, A. venetum has a better antioxidant activity than A. pictum (Chan et al., 2015). However, recent studies have shown that the flavonoids from A. pictum (quercetin-3-sophoroside, isoquercetin, quercetin-3-O-(6-O-malonyl)-galactoside) and A. venetum (hyperoside, isoquercetin, quercetin-3-O-(6-O-malonyl)-galactoside, quercetin-3-O-(6-O-malonyl)-glucoside, and quercetin-3-O-(6-O-acetyl)-galactoside) both exhibited significant antimicrobial activity against methicillin-resistant Staphylococcus aureus, *Pseudomonas aeruginosa* and the fungus, Aspergillus flavus, but A. pictum was superior to A. venetum in terms of antimicrobial capacity (Gao et al., 2021). Apart from the pharmacological value, in recent years, A. pictum is often studied together with A. venetum because of its high ecological value.
## The ecological value of A. venetum and A. pictum
Phytoremediation is one of the appropriate ways to deal with land problems such as drought, salinity and metal pollution (Pilon-Smits, 2005). Apocynum spp. were selected to stabilize sands and restore the degraded saline lands due to their advantages of easy propagation, resistance to harsh environment, and high economic value (Jiang et al., 2021a; Jiang et al., 2021b). The matured seeds of A. venetum appeared to possess higher drought tolerance than seeds of A. pictum. The simulation of the critical values of Apocynum spp. seeds under PEG-6000 simulated drought conditions are summarized in Table 5. Different PEG-6000 concentrations ($0\%$–$35\%$) was used to simulate natural drought conditions to study the effect of drought stress on the germination of Apocynum spp. seeds. The results showed that low concentrations PEG (0–$20\%$) had no significant impact on the germination rate of Apocynum spp. seeds. However, when the concentration was more than $20\%$, the germination rates of the seeds were reduced, and the negative impact on A. pictum seeds was higher than that on A.venetum. In addition, after the drought stress was alleviated, the seeds were able to germinate under appropriate conditions (Han et al., 2021; Jiang et al., 2021a). Moreover, the membership function (A mathematical tool for representing fuzzy sets) was used to comprehensively evaluate the drought resistance of A. venetum and another desert economic plant, Lycium ruthenicum, by analyzing the physiological and biochemical indices (the content of chlorophyll a, chlorophyll b, proline and soluble sugar, antioxidant enzyme activity, etc.). The results showed that when the soil moisture content was $9.70\%$, $6.89\%$ and $5.54\%$, the drought resistance of A. venetum was stronger than that of *Lycium ruthenicum* (Wang, 2017).
**Table 5**
| Tolerance value | A. venetum | A. pictum | Reference |
| --- | --- | --- | --- |
| Simulated critical value (PEG concentration) | 29.56% | 26.58% | Jiang et al. (2021a); Jiang et al. (2021b) |
| Simulated limit value (PEG concentration) | 40.16% | 39.81% | Jiang et al. (2021a); Jiang et al. (2021b) |
| Simulated critical value (NaCl concentration) | 431 mM | 456 mM | Jiang et al. (2021a); Jiang et al. (2021b) |
| Simulated limit value (NaCl concentration) | 653 mM | 631 mM | Jiang et al. (2021a); Jiang et al. (2021b) |
| Simulated critical value (LiCl concentration) | 196 mM | 235 mM | Jiang, Wang & Tian (2018a); Jiang et al. (2018b) |
| Simulated limit value (LiCl concentration) | 428 mM | 406 mM | Jiang, Wang & Tian (2018a); Jiang et al. (2018b) |
Low concentration of salt solution (0–200 mM NaCl) had no significant effect on the germination rate of current season mature seeds the two species (Jiang et al., 2021a; Shi et al., 2014). However, another study showed that under 200 mM NaCl stress, the growth and development of A. venetum seedlings were inhibited, the phenotypic characteristics (plant height, root length, leaf length, leaf width) were damaged, and the total flavonoid content decreased. However, salt stress increased the content of quercetin and kaempferol in seedlings (Xu et al., 2020b). In addition, the seeds of Apocynum spp. both exhibited high tolerance to lithium salts during germination, particularly LiCl (Table 5) (Gao et al., 2020; Jiang, Wang & Tian, 2018a; Jiang et al., 2018b). The simulated critical value of A. venetum was as high as 196 mM (Jiang, Wang & Tian, 2018a). To put the salt tolerance of A. venetum into perspective, Brassica carinata, another heavy metal tolerant plant with phytoremediation potential, has a germination rate of less than $50\%$ at LiCl concentration above 120 mM (Li et al., 2009). Notably, the addition of lithium in soil did not reduce the concentrations and antioxidant capacity of total flavonoids, rutin and hyperoside in A. venetum leaves (Jiang et al., 2019). Therefore, Apocynum spp. are suitable for the restoration of degraded saline soil in arid areas, and are promising species in the remediation of lithium pollution in the environment (Jiang et al., 2021a; Jiang et al., 2021b; Rouzi et al., 2018).
## Conclusions
Looking back on the research history of A. venetum, the research focuses mainly on the components and pharmacological effects of A. venetum leaves. At present, many of the pharmacological effects are attributable to flavonoids, however these active components and their synergistic mechanism need to be further studied. In addition to flavonoids, some polysaccharides (Vp2a-II, Vp3) and triterpenoid (uvaol) from A. venetum have also shown pharmacological effects. However, the current research in this area is still lacking. In recent trends, the fiber of A. venetum have attracted attention. Apart from its textile value, the potential application of the fiber in other industries needs further exploration in future studies. The ecological value of Apocynum spp. is gradually being revealed by multiple research.
This study provided rich and rigorous CiteSpace analysis on A. venetum. However, as a limitation, we analyzed only the papers written in English, and within the WoS database, therefore it may not be comprehensive enough to reflect the entire research status. For example, we searched a major Chinese scientific literature database, the China National Knowledge Infrastructure (CNKI), and more than 2,000 Apocynum related publications were retrieved, although these were not within the analysis scope of the current study. This further attests to the interest Apocynum species have received from the scientific community over the past decades.
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|
---
title: 'Efficacy of silymarin in patients with non-alcoholic fatty liver disease —
the Siliver trial: a study protocol for a randomized controlled clinical trial'
authors:
- Camila Ribeiro de Avelar
- Beatriz Vieira Coelho Nunes
- Betina da Silva Sassaki
- Mariana dos Santos Vasconcelos
- Lucivalda Pereira Magalhães de Oliveira
- André Castro Lyra
- Allain Amador Bueno
- Rosângela Passos de Jesus
journal: Trials
year: 2023
pmcid: PMC10000352
doi: 10.1186/s13063-023-07210-6
license: CC BY 4.0
---
# Efficacy of silymarin in patients with non-alcoholic fatty liver disease — the Siliver trial: a study protocol for a randomized controlled clinical trial
## Abstract
### Background
Non-alcoholic fatty liver disease (NAFLD) is one of the most prevalent liver diseases globally. Pharmacological treatments for NAFLD are still limited. Silymarin, a compound extracted from Silybum marianum, is an herbal supplement traditionally used in folk medicine for liver disorders. It has been proposed that silymarin may possess hepatoprotective and anti-inflammatory properties. The present trial aims to assess the efficacy of silymarin supplementation in the adjuvant treatment of NAFLD in adult patients.
### Method
This is a randomized double-blind placebo-controlled clinical trial recruiting adult NAFLD patients in therapy on an outpatient basis. Participants are randomized to an intervention (I) or control (C) group. Both groups receive identical capsules and are followed for 12 weeks. I receives 700mg of silymarin + 8mg vitamin E + 50mg phosphatidylcholine daily, while C receives 700mg maltodextrin + 8mg vitamin E + 50mg phosphatidylcholine daily. Patients undergo a computerized tomography (CT) scan and blood tests at the beginning and end of the study. Monthly face-to-face consultations and weekly telephone contact are carried out for all participants. The primary outcome assessed will be change in NAFLD stage, if any, assessed by the difference in attenuation coefficient between liver and spleen, obtained by upper abdomen CT.
### Discussion
The results of this study may provide a valuable opinion on whether silymarin can be used as adjuvant therapy for the management or treatment of NAFLD. The data presented on the efficacy and safety of silymarin may provide more foundation for further trials and for a possible use in clinical practice.
### Trial registration
This study has been approved by the Research Ethics Committee of the Professor Edgard Santos University Hospital Complex, Salvador BA, Brazil, under protocol 2.635.954. The study is carried out according to guidelines and regulatory standards for research involving humans, as set out in Brazilian legislation. Trial registration - ClinicalTrials.gov: NCT03749070. November 21, 2018
## Strengths and limitations of this study
Placebo-controlled randomized double-blind clinical trial employing silymarin extract with greater bioavailability. The effectiveness of silymarin supplementation will be assessed by CT without contrast as reference standard for the detection and assessment of liver steatosis [1].Participants are recruited from a Nutrition and Hepatology Clinic of a single tertiary referral hospital.
## Background and rationale {6a}
NAFLD is one of the most prevalent liver diseases worldwide. With a continuously increased incidence and level of complications, NAFLD has become a major public health concern worldwide [2–4], with approximately $20\%$ to $30\%$ of the general adult population affected. Men appear to show a higher prevalence for NAFLD than women in all age groups [5]. Based on risk factors, NAFLD is manifested in approximately $50\%$ of overweight individuals and in approximately $80\%$ to $90\%$ of obese individuals. Individuals diagnosed with metabolic syndrome (MS) are approximately twice as likely to develop NAFLD [6]. The main risk factors associated with NAFLD overlap with those of metabolic syndrome, including central obesity, type 2 diabetes (T2D), dyslipidemia, and insulin resistance (IR). NAFLD has been associated with a pro-inflammatory background and is considered a hepatic manifestation of obesity and MS [7, 8].
In its first stage, NAFLD patients show lipid inclusion in the liver parenchyma without evident signs of inflammation or hepatocellular necrosis. At this stage, NAFLD management is focused on improving IR, body fat reduction, as well as MS and T2D prevention and management. Body weight reduction combined with amelioration of metabolic disarrangements can prevent the progression of steatosis to non-alcoholic steatohepatitis (NASH), cirrhosis, and cellular hepatocarcinoma [9, 10].
Despite our understanding of the epidemiological and pathophysiological aspects of NAFLD, the main and by far most successful treatment option available is a positive lifestyle change. The use of pharmacological agents is still limited and requires stronger evidence regarding safety and efficacy [11]. As successful long-term adherence to positive lifestyle changes is not always achieved in full, researchers have investigated supporting pharmacotherapeutic strategies, such as herbal medicines, to ameliorate NAFLD. A few studies have suggested that silymarin supplementation may induce beneficial effects for NAFLD patients, including amelioration of biochemical markers associated with inflammation and NAFLD progression [12–15].
Silymarin is a flavonoid extracted from Silybum marianum, one of the most used medicinal herbs by individuals with liver diseases [16]. Silybum marianum has shown good tolerance and safety, with limited adverse effects reported in liver disease patients. The hepatoprotective, anti-inflammatory, antioxidant, and anti-fibrotic effects of silymarin have been studied in patients with cirrhosis associated with viral hepatitis, exposure to environmental toxins, alcoholic steatosis and NASH [17–20].
A few studies [13, 16, 18] have pointed out to a beneficial effect of silymarin therapy upon the evolution of NAFLD, but significant variability and methodological differences across available studies prevent the establishment of robust conclusions. A systematic review with meta-analysis [16] including six clinical trials showed that silymarin reduced serum levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in NAFLD patients, but the studies appraised in that meta-analysis had a high degree of heterogeneity and low methodological quality. The scarcity of clinical trials employing silymarin as adjuvant therapy for liver disease and NAFLD, with a particular attention to methodological design, planning, and execution stages, has encouraged us to propose and execute the Siliver trial. Our initial hypothesis is that silymarin supplementation will improve the metabolic status and reduce liver fat content in NAFLD patients. Our hypothesis will be tested by following the protocol detailed below.
## Objectives {7}
The present study aims to investigate the efficacy of silymarin supplementation as an adjunctive treatment for adult patients suffering with NAFLD. The primary outcome assessed will be change in NAFLD stage, if any, assessed by the difference in attenuation coefficient between liver and spleen, obtained by upper abdomen CT scan without contrast. The attenuation coefficient between the liver and spleen is a reference standard for the assessment of liver steatosis.
As secondary objectives, we will assess body mass index (BMI) and waist circumference (WC); glucose metabolism biomarkers including blood glucose, insulin, glycated hemoglobin (HbA1C), and Homeostasis Model Assessment-Insulin Resistance Index (HOMA-IR); blood ferritin levels; and biomarkers of liver damage, including transaminases, gamma-glutamyl transferase (γGT), and alkaline phosphatase (AP).
## Trial design {8}
As the trial hypothesis is to investigate whether silymarin supplementation is better than placebo for NAFLD amelioration, a framework of superiority was adopted for this double-blind randomized placebo-controlled clinical trial. Patients will be supplemented for 12 weeks (Fig. 1). The planning of this trial follows the guidelines of the Consolidated Standards of Reporting Trials (CONSORT) and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines. Fig. 1Study flowchart
## Study settings {9}
All participants had a confirmed NAFLD diagnosis prior to participation in this trial. NAFLD patients were referred to the trial by local health centers located in the neighborhoods of the city of Salvador BA Brazil, in partnership with our outpatient recruitment unit at the Nutrition and Hepatology Outpatient Clinic of the Professor Edgard Santos University Hospital Complex (NHOC), located in the city of Salvador BA, Brazil, where the trial took place.
## Inclusion criteria
Eligibility criteria for the Siliver trial include consenting adult patients aged between 20 and 60 years of both sexes. All patients had a medically confirmed diagnosis of NAFLD prior to participation in this trial.
## Non-inclusion criteria
Patients who meet any of the following criteria have not been included lactating or pregnant women, and women during their menacme, except for those who underwent definitive contraception such as hysterectomy or tubal ligation; patients with a previously established diagnosis of chronic disease including congestive heart failure, decompensated or severe lung disease, neoplasms, kidney disease, carriers of the human immunodeficiency virus (HIV), and advanced chronic liver diseases (Child-Pugh classification B and C) caused by any causal agent unrelated to NAFLD; recreational drug users; patients with an average intake of more than 20 g of alcohol per day and or a history of alcoholic disease in abstention for less than 6 months; use of prescribed medication including steroids, oestrogens, amiodarone, warfarin, anticonvulsants, antipsychotics, or chemotherapy drugs in the past 6 months; infections, surgery, trauma, or hospitalization in the past 30 days; chronic degenerative diseases of non-hepatic origin; and unavailability of previously obtained imaging tests with a diagnosis of NAFLD in its screening phase.
## Exclusion criteria
The following patients will be excluded patients who at the recruitment stage do not show confirmation of NAFLD by CT scan; patients who missed any phase of the trial; and patients who during the trial were diagnosed with a condition listed in the non-inclusion criteria above.
## Who will provide informed consent? {26a}
Patients will provide informed consent themselves. All eligible participants have received information about the study and had the opportunity to have their questions answered to their satisfaction. The blinded evaluator has obtained written informed consent (IC) from all participants prior to assessment.
## Additional consent provisions for collection and use of participant data and biological specimens {26b}
The blood samples obtained, once analyzed for the specific purpose of this trial, are discarded following the Teaching Hospital Standard Operating Procedures (SOPs) on disposal of biological waste. Data collected from participants withdrawn from the study or lost on follow-up will be excluded and deleted, as the aim of the trial has not been completed for those participants. All participants have given their consent to the research team to share relevant data with researchers taking part in the research, as well as regulatory authorities. This set of information was explained to participants and made available in the consent form. All participants have agreed to the above.
## Explanation for the choice of comparators {6b}
Participants are randomized to groups intervention (I) or control (C). They receive identical capsules and follow the trial instructions for 12 weeks. All participants are instructed to take two capsules per day soon after a meal. Considering the pathophysiology of NAFLD and pharmacotherapies currently available for its treatment or management, there are no specific pharmacological agents currently approved for NAFLD specifically that could be compared to the test item. Therefore, the research team adopted a methodological design using a placebo.
## Intervention description {11a}
I participants receive 700 mg silymarin + 8 mg vitamin E + 50 mg phosphatidylcholine, daily. C participants receive 700 mg maltodextrin + 8 mg vitamin E + 50 mg phosphatidylcholine, daily.
Each I capsule contains 350 mg silymarin + 4 mg vitamin E + 25 mg phosphatidylcholine. Each C capsule contains 350 mg maltodextrin + 4 mg vitamin E + 25 mg phosphatidylcholine. All participants are instructed to take two capsules per day soon after a meal.
We have carefully defined the capsule composition for both groups to achieve a planned daily dosage. We employed phosphatidylcholine and vitamin E in the composition to increase silymarin bioavailability, which is based on a protocol described previously and evaluated in a systematic review with meta-analysis published by our group [15].
Microcrystalline cellulose, corn starch, and colloidal silicon dioxide were used as standard excipients in all capsules of both I and C. The capsule composition did not include dyes, preservatives or additives in I or C, guaranteeing the standardization of their appearance. The capsule weight, size, shape, and coating were identical between I and C, and have been designed to ensure the same ease of swallowing, to minimize risks of gastric discomfort for the participants, and to ensure similar disintegration time and propensity for swelling. The capsules are visually unidentifiable between I and C. All I and C capsules were manufactured and kindly donated by Singular Pharma (Salvador, Brazil). The expiry date is three months after production.
## Criteria for discontinuing or modifying allocated interventions {11b}
Participants are free to withdraw from the trial at any time and for any reason without penalty. Participants are withdrawn from the study if they start a different treatment elsewhere.
## Strategies to improve adherence to interventions {11c}
Participants are monitored weekly by telephone calls to collect information on adherence. They are given the opportunity to ask the research team any question and receive reminders of upcoming appointments. In addition, participants are instructed to bring along the latest flasks with the capsules received at each appointment, favoring greater adherence and commitment to the treatment.
## Relevant concomitant care permitted or prohibited during the trial {11d}
During the intervention and follow-up, participants are advised not to use any medication, supplement, tea or herbal supplement without prior medical and nutritional advice, and without prior notice to the research team. If they do, they are asked to inform the research team immediately, informing what was ingested and the date and dosage taken.
## Provisions for post-trial care {30}
All study participants have the right to medical care and follow-up, and if they experience a worsening of their condition. In the final consultation, participants who no longer show fatty infiltration receive guidance on how to prevent the recurrence of NAFLD. Participants who show evidence of NAFLD, regardless of the degree of steatosis, are referred to the outpatient follow-up clinic for the continuation of their care provision with a hepatologist consultant and a nutritionist (Fig. 1).
## Primary outcome
The primary outcome will be the assessment of NAFLD resolution, or change in its grade, as assessed by the difference in the attenuation coefficient between liver and spleen, obtained by CT of the upper abdomen, at the end (after) compared to beginning (before) the trial. In summary, the primary outcome is to investigate whether silymarin supplementation can reduce liver fat content in NAFLD patients, measured by a CT scan.
## Secondary outcomes
The secondary outcomes investigated in this trial include:Differences in ALT, AST, γGT, and AP levels after versus before;Difference in ferritin levels after versus before;Difference in fasting glucose, insulin, HbA1C, and HOMA-IR levels after versus before; andDifference in BMI and WC after versus before.
## Participant timeline {13}
Table 1 shows the stages of the study and which assessments will be performed throughout the study period. Table 1Schedule of enrolment, interventions, and assessmentsSchedule of activitiesEnrolmentAllocationPost-allocationClose-outTime PointWeek 1Week 2Week 3–5Week 6Week 7–10Week 11Week 12–15Week 16Week 17Enrolment Screening• Eligibility screen• Informed consent• Computed tomography (CT) and collection of laboratory tests• Baseline consultation (allocation)•Interventions Intervention start• Telephone follow-up••• Return consultation 1• Return consultation 2• Return consultation 3•Assessments Computed tomography (CT) and laboratory tests• Final consultation•
## Sample Size {14}
The sample size was determined through an accuracy analysis of the primary outcome. A significance level of 0.05 and a power of $80\%$ were adopted, which resulted in a minimum sample size of approximately 132 participants.
## Recruitment {15}
Patient recruitment started in February 2019 and ended in May 2022. All participants have been diagnosed with NAFLD prior to participation in this trial. All participants were recruited directly from the NHOC, or they have been referred to the NHOC by local health centers located in the neighborhoods of the city of Salvador. The NHOC provides specialist care for patients with liver diseases, including NAFLD. However, only patients diagnosed with NAFLD have been approached to discuss their participation in the trial.
All patients received in May 2022 or onwards a phone call with an invitation for a face-to-face consultation, in which their individual results will be discussed with a clinician researcher. Any current or new need for medical care will be discussed at that consultation, and a new referral will be made if necessary.
The burden of this randomized controlled trial was not assessed by the patients themselves or their families. Prior to signing the consent forms, all patients were reassured that their participation in this trial was entirely voluntary. All patients were also reassured that they could withdraw at any time without giving a reason and reassured that withdrawing would not result in any penalty to them and would not interfere with any current or future medical or healthcare provision to them.
## Sequence generation {16a}
Patients who agreed to take part in the study and met all the study criteria were assigned to I or C groups by computer-generated randomization in blocks with the aid of a spreadsheet to ensure a random but uniform variation in sizes of each I and C blocks. Randomization and record keeping of all participants were carried out by a statistician researcher external to the research group. The statistician is not involved in patient clinical assessment.
## Concealment mechanism {16}
The allocation sequence was concealed from the researchers assessing participants and their clinical outcomes. The allocation sheet hardcopies are kept in envelopes in a locked drawer of a secure filing cabinet located at the NHOC administrative office.
## Implementation {16c}
All patients who give consent for participation and who fulfill the inclusion criteria are randomly assigned to I or C. The Principal Investigator (PI) will open the envelopes containing the allocation sequence only after the last participant included completes the trial.
## Who will be blinded {17a}
All clinical assessments were conducted by evaluators blinded to treatment allocation. Participants were blinded to the study hypothesis and their allocation group, but were advised of the overall aspects involved in the treatment of both groups. I and C capsules were dispensed by a registered pharmacist at the clinical research pharmacy of the Teaching Hospital where the trial took place. The pharmacist was responsible for receiving and storing all batches of I and C capsules manufactured by Singular Pharma, as well as identifying the correct capsule to be dispensed according to the specific numbering of each participant.
## Procedure for unblinding if needed {17b}
The trial design is open-label with outcome assessors being blinded. Only the pharmacist in charge of dispensing the trial capsules and the statistician have access to group allocation; neither, however, are involved in clinical assessment or data collection.
## Data collection and management {18a}
The research team involved in this trial underwent training prior to commencement of the study regarding the protocol to be followed, to avoid biases and errors in data collection. The clinical dietitians (CRA, BVCN, BSS, MSV) in charge of seeing patients at the NHOC performed a triage of eligible patients, and the more experienced dietitian running the clinic (CRA) was responsible for double-checking that all the inclusion and non-inclusion criteria had been fully met for each patient. CRA was also responsible for explaining the study in further detail and taking verbal and written consent. The clinical trial is structured as presented in Table 1 and detailed below.
## Screening
Patients who meet the eligibility criteria are referred to the screening consultation to receive information about the clinical trial and are given the opportunity to ask questions to the research team. Consenting participants are invited to sign the free and informed consent form. All participants are informed that they can withdraw at any time without giving reasons and that they will not be penalized for withdrawing. After signing the consent form, all participants answer questions on a standardized form filled in by the researcher. Information on sociodemographics, clinical history and clinical presentation, lifestyle data, diet intake, and feeding patterns are collected. The first anthropometric assessment is completed at that point.
At the end of the screening consultation, participants receive general nutritional guidelines and a study identification card with the contact details of the researchers. Participants are encouraged to make contact should they have any late questions about the trial. Following the first appointment, participants are referred to an upper abdomen CT scan before the intervention begins.
## CT scans
Participants undergo a scheduled upper abdomen CT scan at the Radiology Unit of the Professor Edgard Santos University Hospital Complex. NAFLD is confirmed by a radiologist consultant. Patients who do not present NAFLD at the CT scan, even if they have historical imaging tests diagnosing the disease at an earlier period, are excluded from this trial and referred to a consultation with a registered nutritionist to receive specific guidelines on how to prevent NAFLD recurrence,
## Blood tests
After the CT scan, participants are taken a 12-h fasting venous blood sample. They are given a breakfast meal after the blood sample collection and are given the details of their next consultation.
## Allocation consultation
At this appointment, participants receive their CT scan and blood test results and are submitted to a second anthropometric assessment. At this consultation, a new form with nutritional and dietary data is completed by the researcher, and patients are given additional nutritional guidelines to complement the advice offered in the screening appointment. Subsequently, participants are sent to the clinical research pharmacy of the Hospital Complex for capsule collection according to their randomization.
## Monthly follow-up visits (return visits 1, 2, and 3)
All participants were followed up through face-to-face individual consultations in 4-week intervals during the 12-week supplementation period. The Return Visits were scheduled as detailed in Table 1. At these appointments, patients were asked about adherence to the study. They were asked to bring along to each consultation the flasks they receive containing the capsules from the previous 4-week period, so that the research team can more accurately estimate adherence to the protocol.
At each monthly follow-up visit participants had a nutritional and dietary assessment undertaken by a registered nutritionist member of the research team. The researcher completed a form with dietary intake and dietary habits over the last 4-week period and evaluated adherence to nutritional guidelines, ingestion of capsules as prescribed, tolerance, and the occurrence of any adverse effects.
## Telephone follow-up
except for the weeks when participants had their scheduled Return Visits, all participants were contacted weekly by telephone to monitor adherence to the protocol, clarify any questions, and report any occurrence or adverse reactions.
At Return Visit 3, which took place at the end of the 12th week of intervention, patients were referred to a second CT scan and blood tests following the same protocols as the baseline tests. A final consultation was scheduled for the test results to be delivered (Table 1).
In the final consultation, participants who no longer showed fatty infiltration received guidance on how to prevent the recurrence of NAFLD. Participants who showed evidence of NAFLD, regardless of the degree of steatosis, were referred to the outpatient follow-up clinic for continuation of their care provision with a hepatologist consultant and a registered nutritionist (Fig. 1).
## Plans to promote participant retention and complete follow-up {18b}
Participants received information about the study design and the importance of completing the final follow-up. The research team contacted the participants via telephone calls, and in case of not reaching, a voice message or text message 24 h before the scheduled appointments and blood tests, aiming to minimize non-attendance.
## Data management {19}
The data were collected in paper form and stored in binders, which are kept in a locked drawer of a secure filing cabinet located at the NHOC administrative office. Only the PI has access to the documents.
After data collection, all forms will be checked by two members for data quality and missing information. The data will be entered manually into an electronic spreadsheet and subsequently checked by two researchers, one at a time.
The database and electronic analyses will be stored on a secure computer server with personal login access authorized by the PI. After completion of the study, all data and study documents will be archived and stored by the PI. The data is not public and remains in the possession of the PI. Individual unidentifiable data can be made available upon reasonable request.
## Confidentiality {27}
The data will be treated anonymously and confidentially and at no time will the personal details of the participants be disclosed at any stage of the study.
## Plans for collection, laboratory evaluation, and storage of biological specimens for genetic or molecular analysis in this trial/future use {33}
Blood tests were performed following standardized laboratory protocols for medical diagnosis in humans. Blood samples were not stored for genetic or molecular analysis in the current study or any other future use. Blood samples were discarded following standardized hospital protocol. Blood test results were collected from the clinical analysis laboratory of the Institute of Pharmacy of the Federal University of *Bahia via* a secure hospital intranet.
## Statistical methods for primary and secondary outcomes {20a}
Statistical analyses will firstly be performed using descriptive analysis to characterize the distribution of the events studied. Categorical variables will be investigated using simple absolute frequencies. Continuous variables will be investigated by measures of central tendency and dispersion. Parametric or non-parametric tests will be used considering the distribution nature of the variables studied. A significance level of $5\%$ will be adopted for all statistical tests. Tests to verify variable behavior and comparisons of proportions analyses, such as the application of the chi-square test or Fisher’s exact test, will be discussed later with a qualified statistician. Tests for comparison of means between groups, analysis of correlations between continuous variables and logistic regression analysis, will also be discussed with the statistician. Data will be tabulated and analyzed using the R Project for Statistical Computing software (R-3.2.4 for Windows).
## Interim analyses {21b}
This is a low-risk intervention, as silymarin at the dosage adopted in this trial has been considered safe and well-tolerated. All patients were carefully followed up in person and by telephone, aiming to identify any adverse event. Only patients who successfully complete the trial will have their data included in the final data analyses.
## Methods for additional analyses (e.g., subgroup analyses) {20b}
No additional analysis will be performed.
## Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c}
Only patients who successfully completed the trial will have their data included in the final trial data analyses, and there will be no imputations for missing data. The data will be assessed by intention-to-treat, in which all participants who completed the trial are included in the statistical analyses and analyzed according to the group they were originally assigned, regardless of which group they were assigned to. Participants who drop out of the study due to illness, moving to another city, or inability to attend appointments or perform tests will be considered as protocol deviations and will be excluded from the data analyses.
## Plans to give access to the full protocol, participant-level data, and statistical code {31c}
The full protocol, participant-level data, and statistical code generated in this trial will be available from the corresponding author in electronic format on reasonable request. Identifiable information such as full name, address, and date of birth will not be shared for confidentiality purposes.
## Composition of the coordinating center and trial steering committee {5d}
The PI as a blind evaluator and a coordinator assistant will coordinate all phases of the study, the randomization, and record-keeping of I and C participants. Patient and Public Involvement Groups (PPIG) were not involved in the design, recruitment, or execution of this trial, nor will they be involved in the reporting and dissemination of this research trial, with the exception of dissemination through their own social media channels if they wish.
## Composition of the data monitoring committee, its role and reporting structure {21a}
There will be no data monitoring committee since only the primary evaluator, the research team, and the coordinator assistant will have access to the clinical trial data. Additionally, this trial is a low-risk intervention, and participants are advised to report any unexpected or adverse effects to the research team.
## Adverse event reporting and harms {22}
The capsules provided to patients in both groups were produced and kindly donated by Singular Pharma (Salvador BA, Brazil). Singular Pharma have contractually undertaken not to interfere in any stage of the trial and have allowed the dissemination of any trial results, even if such results do not confirm the hypothesis that silymarin may be a beneficial adjuvant compound for the treatment or management of NAFLD. The donation document was submitted to, and approved by, the Ethics Committee.
Silymarin is commonly prescribed by clinicians and nutritionists and its use is considered safe [15]. According to data available from studies included in a previously published meta-analysis [15], there have been no reports of serious adverse events categorized as frequent or uncommon. However, a few studies have reported episodes of nausea, vomiting, and abdominal discomfort as rare adverse events [16, 17, 20]. Interestingly, a few clinical trials and meta-analyses that have investigated the effects of supplements containing silymarin did not record adverse events or complications associated with supplementation, suggesting good tolerability and safety to participants [16, 17, 20].
In the current trial, participants were advised to immediately stop taking the capsules and contact the research team as soon as possible in the event of any unexpected or adverse effect, or any discomfort supposedly associated with the capsule intake. All participants were followed up and monitored every 4 weeks at the NHOC, where the data collection and weekly telephone follow-up calls took place. All possible adverse events or complications observed were recorded, evaluated, and reported in the study.
## Frequency and plans for auditing trial conduct {23}
Fortnightly meetings were held with the group of researchers involved in the study to discuss development and question clarification. An independent researcher external to the research team will verify the data collected during the study. If any documents are missing or information is inconsistent, the Ethics Committee will be notified. Lastly, if there is any change in the study, the ethics committee, the journal, and ClinicalTrials will be notified immediately.
## Plans for communicating important protocol amendments to relevant parties (e.g., trial participants, ethical committees) {25}
This study has been approved by the Research Ethics Committee of the Professor Edgard Santos University Hospital Complex under application protocol 2.635.954. The research must be undertaken as set out in the approved documents for the approval to be valid. The Research Ethics Committee will be contacted should the research team intend to make any amendments to the approved research.
Patients eligible to join the trial were invited to sign the consent form after receiving all the information regarding the trial, including potential risks, and after having their questions answered in full. The consent form was developed in compliance with guidelines and regulatory standards for research involving human beings. During all stages of this trial the standards set out in the Brazilian Resolutions 196 and $\frac{466}{2012}$, approved by the National Health Council, were followed. The Good Clinical Practices of the Document of the Americas of 2008 were also followed.
All participant information is kept confidential. At the end of the trial, all participants were advised to maintain their clinical and nutritional follow-up appointments at the NHOC of the University Hospital Complex according to their health needs.
## Dissemination plans {31a}
The results of this randomized controlled clinical trial are expected to be disseminated through presentations at conferences and publications in peer-reviewed journals.
## Discussion
This clinical trial aims to assess the efficacy of silymarin in adult patients diagnosed with NAFLD. The prevalence and incidence of NAFLD are expanding at an accelerated pace and currently pose a burden to public health systems. There are currently limited pharmacotherapeutical options for NAFLD [3, 4].
Silymarin has been hypothesized as a useful adjuvant therapeutic resource in clinical practice for NAFLD therapies, as beneficial hepatoprotective properties attributed to silymarin have been reported [21]. A few clinical studies have observed improvement in liver function and injury biomarkers after silymarin supplementation in various liver diseases [14, 22–25]. Liver biomarker levels are a true representation of the progression of liver disease and their levels are strongly associated with greater morbidity. Amelioration of liver biomarkers attributed to silymarin therapy is of great benefit for sufferers and a promising tool worth of further investigation.
Several studies investigating the effects of silymarin in patients with liver diseases unfortunately feature some methodological biases. A meta-analysis of clinical trials published by our research team [15] found a high degree of heterogeneity and low methodological quality in the qualitative assessment of the clinical trials included in the meta-analysis. Lastly, only very few published studies recruiting reasonable sample sizes have evaluated the efficacy of silymarin supplemented as a single test item in adult patients with NAFLD; we have addressed such a problem in our study by supplementing silymarin only.
The lack of robust evidence, as well as inconsistencies identified in existing publications, reinforce the need for additional clinical trials with stronger methodological designs to further elucidate the roles of silymarin, if any, in the treatment and management of NAFLD. The results of the present clinical trial may contribute to the dissemination of reliable outcomes that may or may not support the recommendation of silymarin adjuvant therapy for NAFLD patients.
## Trial status
Patient recruitment started in February 2019 and ended in May 2022. A significant delay in study progression and data collection was attributed to the SARS-CoV-2 pandemic and subsequent lockdowns.
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---
title: Factors Associated with Lack of Health Screening among People with Disabilities
Using Andersen’s Behavioral Model
authors:
- Ye-Soon Kim
- Seung Hee Ho
journal: Healthcare
year: 2023
pmcid: PMC10000362
doi: 10.3390/healthcare11050656
license: CC BY 4.0
---
# Factors Associated with Lack of Health Screening among People with Disabilities Using Andersen’s Behavioral Model
## Abstract
People with disabilities often have poorer health than the general population, and many do not participate in preventive care. This study aimed to identify the health screening participation rates of such individuals and investigate why they did not receive preventive medical services based on Andersen’s behavioral model, using data from the Survey on Handicapped Persons with Disabilities. The non-participation health screening rate for people with disabilities was $69.1\%$. Many did not in health screening because they showed no symptoms and were considered healthy, in addition to poor transportation service and economic limitations. The binary logistic regression result indicates that younger age, lower level education, and unmarried as predisposing characteristics; non-economic activity as the enabling resources; and no chronic diseases, severe disability grade, and suicidal ideation as need factor variables were the strongest determinants of non-participation health screening. This indicates that health screening of people with disabilities should be promoted while takings into account the large individual differences in socioeconomic status and disability characteristics. It is particularly necessary to prioritize ways to adjust need factors such as chronic disease and mental health management, rather than focusing on uncontrollable predisposing characteristics and enabling resources among barriers to participation in health screening for people with disabilities.
## 1. Introduction
Health screening aims to detect and treat diseases at an early stage, thereby reducing the burden of medical expenses and ensuring a healthy life [1]. In Korea, health screening services are divided into national and private health screenings, which differ in terms of screening items and cost burdens. National health screening mainly provides basic and essential health screening items, with little financial burden on individuals. In a private health screening, although various health screening items can be selected according to the individual’s characteristics and preferences, the economic burden is high because it is fully borne by the individual [2].
Korea’s national health screening aims to detect obesity, dyslipidemia, high blood pressure, and diabetes, which are risk factors for cardiovascular and cerebrovascular diseases, early and improve quality of life through treatment or lifestyle improvement. The Korean national health screening is aimed at checking health conditions and preventing and detecting diseases at an early stage. Health screening consists of examination and consultation, physical examination, diagnostic examination, pathology examination, radiological examination, etc., through health screening institutions [3,4].
The most representative health screening in *Korea is* that of the National Health Insurance Service. National health screenings have expanded in subjects and examination items since medical insurance health screening for public servants and teachers began in 1980. The national health screening participation rate in Korea in 2019 was $74.1\%$ [5]. However, the health screening participation rate of people with disabilities was $64.6\%$ [6]. Since the introduction of the national health screening, the increased rate of health screening participation and preparation strategies for health promotion shows its success. However, it was found that the health screening participation rate of people with disabilities was not only low, but this group also suffers from many chronic diseases [6]. Because of this, it is important to determine the cause of this reduced rate and take countermeasures. Although the rate of health screenings for people with disabilities is reported steadily, it is clear that there are deficiencies in implementing national policies and health promotion services for people with disabilities.
There are still no general or specialized health screening systems for people with disabilities to detect or prevent secondary diseases at an early stage. Article 7 of the Guarantee of the Right to Health and Medical Accessibility of Persons with Disabilities (Act on the Right to Health of Persons with Disabilities), enacted in December 2015 stipulates the “health screening project for persons with disabilities”; efforts were made at the national level to ensure customized health screening for people with disabilities [7]. Health screening items suitable for characteristics such as gender, sex, and life cycle should be designed. To do so means that it is necessary to identify the influencing factors related to the health screening of people with disabilities.
Previous studies related to health screening for people with disabilities have been reported by Park et al. [ 8], Yoon [9], Kim et al. [ 10], and the National Rehabilitation Center [11]. According to a study on the health screening rate of people with disabilities, screenings were lower among women with disabilities, those of an older age, and those receiving medical aid; the higher the income, the lower the health screening rate, and there are differences in the health screening participation depending on the type and grade of disability. In particular, it is reported that the screening rate decreases as the degree of disability increase from mild to severe and if the mobility disability is greater. A study in the United States also reported that the higher the degree of disability, the lower the screening rate for diseases such as cervical cancer [12]. In addition, the screening rate of people with disabilities is lower than that of the general population [13]. People with disabilities have the same rights to healthcare as the general population. To improve the health screening participation rate, which is also emphasized in The 5th Policy Plan for people with disabilities in South Korea [14], it is necessary to identify related factors. For this study’s purpose, health screening is also applied as part of medical utilization and Anderson’s behavioral model of health service utilization is applied. We looked at the actual health screening participation behavior and tried to predict the factors that caused this behavior.
Therefore, in this study, we tried to identify the status of health screening of people with disabilities and the factors affecting health screening by using the disability status survey, which provides sample statistical data for people with disabilities. The findings can help identify factors that affect the health screening of people with disabilities, as well as factors needed to improve the health screening rate. In addition, by identifying and addressing the factors influencing health screening by predisposing characteristics, enabling resources, and need factors, it is possible to grasp the current status of health screening for people with disabilities and re-examine it, providing evidence for follow-up tasks and research in the field of health for people with disabilities. This study aimed to examine the health screening rates of people with disabilities and the characteristics of those who did not undergo health screenings, and identify factors that affect health screening for people with disabilities. The specific research objectives were as follows: first, the sociodemographic characteristics of the people with disabilities were identified. Second, the general health screening rate of people with disabilities and reasons for not taking the examination were identified. Third, the characteristics of the predisposing characteristics, enabling resources, and need factors for general health screenings for people with disabilities and those who did not undergo health screenings were identified. Fourth, factors affecting general health screening of people with disabilities were analyzed.
## 2. Materials and Methods
This analytical study used the 2020 Survey of People with Disabilities, (as secondary data) to identify factors that affect the health screenings for people with disabilities based on Andersen’s behavioral model (Figure 1) [15]. Andersen’s behavioral model is a conceptual model aimed at demonstrating factors that lead to the use of health services. According to the model, usage of health services (including inpatient care, etc.) is determined by three dynamics: predisposing characteristics, enabling resources, and need factors. Predisposing characteristics can be factors such as sex, age, and health beliefs. Need factors represent both perceived and actual need for health care services. The original model was expanded through numerous iterations and its most recent form models past the use of services to end at health outcomes and includes health screening [16].
## 2.1. Participants and Analysis Data
This study used data from the 2020 Insolvency Survey conducted by the Ministry of Health and Welfare and the Korea Institute for Health and Social Affairs [17]. This is reflected in Korea’s Social Welfare Act, which has been renewed every three years since the 2007 legal system. The 2020 Survey on Handicapped Persons with Disabilities comprises data on contact disabilities obtained by surveying 11,210 registered persons across 248 survey areas in Korea. It is representative data that used two-stage cluster sampling considering type, degree of disability, and age of the target disabilities group. A total of 7025 people participated in this survey, of which 365 people under the age of 19 were excluded, and 6660 people were finally analyzed.
## 2.2.1. Dependent Variable
Among the survey items for people with disabilities in 2020, based on the question “Have you had a health screening in the past two years (2018–2020)?” was used [17]. This survey included comprehensive health examinations paid for by the individual, special health examinations at industrial sites (for workers exposed to hazardous substances), health examinations from the National Health Insurance Service (for the workplace or regional subscribers and medical benefit recipients), and free health examinations (including health screening by local governments other than the National Health Insurance Corporation).
## 2.2.2. Independent Variable
The predisposing factors included sociodemographic variables such as sex and age, and social structural variables such as occupation and education, which the individual already possesses, regardless of his or her will. Education level was divided into elementary school, middle school, high school, and university graduation. Marital status was divided into married (having a spouse) and other categories (single, widowed, divorced, separated, single mother/unmarried father, etc.).
Enabling factors satisfy the need for medical services by enabling individuals to use medical services, such as income and medical security benefits. The enabling resources in this study were subjective economic house status, national health insurance, and economic activity. In the case of economic activity, “Did you work for income? “ was identified through questions.
Necessary factors are the pursuit of medical service because of the condition of the disease; in this study, the variables were disability type and grade, chronic disease, stress levels in daily life, feelings of sadness or despair, suicidal ideation, and suicide attempt. Concerning disability types, 15 categories were investigated in the survey: physical function disability, disability with a brain lesion, visual impairment, hearing impairment, speech impairment, intellectual disability, autistic disorder, mental disorder, kidney dysfunction, cardiac dysfunction, respiratory dysfunction, liver dysfunction, facial dysfunction, intestinal or urinary fistular, and epilepsy. However, these 15 disability types were adjusted to five considering the proportion: physical function disability, disability with a brain lesion, visual impairment, hearing impairment, and others considering the specific gravity. The ratings for each type of disability ranged from 1 to 6. Grade 1 refers to the most severe disability, while Grade 6 refers to the least severe disability. Usually, grades 1 to 3 represent people with severe disabilities, and grades 4 to 6 represent people with mild disabilities.
## 2.3. Data Analysis
We used SPSS Window 26.0 for data analysis, and the significance level was set at 0.05. *The* general and disability-related characteristics of people with disabilities were analyzed by frequency, percentage, mean, and standard deviation. The relationship between the predisposing characteristics, enabling resources, and need factors of the participants and the health examination for people with disabilities were verified using a chi-square test. To identify the factors that affect health screenings of people with disabilities, a multiple logistic regression analysis was performed, which included predisposing characteristics, enabling resources, and need factors as independent variables.
## 3.1. General Characteristics
Regarding the general characteristics of the participants, $59.1\%$ were male and $40.9\%$ were female, with a male-to-female ratio of 6:4. Regarding age groups, $8.7\%$ were aged 20–39 years, $28.8\%$ were aged 40–59 years, $48.3\%$ were aged 60–79 years, and $14.2\%$ were aged 80 years or older. Regarding education level, $38.9\%$ graduated from elementary school or less, $19.6\%$ graduated from middle school, $36.2\%$ graduated from high school, and $5.3\%$ graduated from college or higher (including junior college). Regarding marital status, $50.7\%$ were married and $49.3\%$ were in “other”.
Regarding national health insurance, $71\%$ were enrolled in health insurance, $27.1\%$ in medical aid, and $1.8\%$ in others. Regarding subjective house economic status, $70.2\%$ of the participants belonged to “lower level”, $28.9\%$ to the middle level, and $0.9\%$ to the upper level, which showed that people with disabilities generally experience economic difficulties. Of the participants, $24.7\%$ said they were engaged in economic activities, and $75.3\%$ were not. Chronic diseases were present in $75.6\%$ of the participants and absent in $24.4\%$. The disability types were physical function disability ($26.6\%$), brain lesions ($11.9\%$), vision impairment ($11.7\%$), hearing impairment ($14.6\%$), developmental issues ($7.6\%$), and others (language, mental, and height problems; $27.6\%$). Disability grades were severe (grades 1–3; $49.4\%$) and mild (grades 4–6; $50.6\%$). The degree of stress in daily life was slight ($14\%$), moderate ($50.5\%$), and high ($35.5\%$). Of the participants, $19.8\%$, $12.3\%$, and $0.7\%$ people experienced sadness or hopelessness, suicidal thoughts, and suicide attempts, respectively; $80.2\%$, $87.7\%$, and $99.3\%$ did not experience sadness or hopelessness, suicidal thought, and suicidal attempts, respectively (Table 1).
## 3.2. Health Screening Participation Rates and Reasons for Not Participation Health Screening
It was found that $69.1\%$ of people with disabilities underwent health screening. The main reasons for not undergoing health screening were “lack of symptoms and being considered healthy” ($32.9\%$), “convenience of transportation” ($20.4\%$), “others reasons” ($12.4\%$), “economic reasons” ($8.2\%$), and “lack of time” ($6.2\%$). In addition, there were opinions that responded: “Anxiety regarding health screening results”, “difficulty in communication”, “insufficient knowledge regarding health screening”, “insufficient facilities for people with disabilities in medical institutions”, “not having someone for the company when visiting a health screening institution.” There were also reasons such as “there is no reason” and “it is difficult to make a reservation for a screening institution” (Table 2).
## 3.3. Comparison of Factors According to Health Screening Status
There were significant differences in health screening rates related to age, education level, marital status, subjective house economic status, chronic diseases, health insurance, economic activity, disability type and grade, depressive symptoms, suicidal ideation, and suicide attempts. Regarding age groups, 60–80-year-old ($52.8\%$) and 40–60-year-old ($28.9\%$) participants showed higher health screening rates than those aged 80 ($12.9\%$) and 20–40 years ($5.4\%$). The age groups reported elsewhere were 20–39, 40–59, 60–79, ≥80 years. Elementary school graduates ($37.7\%$) showed higher health screening rates than middle school ($20.9\%$), high school ($36.4\%$), or college ($5.1\%$) graduates. Health screening rates were higher for those with spouses ($56.6\%$) than those without a spouse ($43.4\%$), and the health screening rate was high in the group with low subjective house economic status. Regarding the existence of national health insurance, the health insurance group ($75.3\%$) had a higher health screening rate than those with medical aid ($22.8\%$), and the non-economically active group ($70.5\%$) had a higher screening rate than the economically active group ($29.5\%$).
The health screening rate of those with chronic diseases ($77\%$) was higher than that of the group without chronic diseases ($23\%$), classified by disability type [physical disability ($29.1\%$); brain lesion disorder ($10.5\%$); visually impaired disability ($12.9\%$); hearing impairment disability ($15.6\%$)]. The screening rate for mild level ($56.7\%$) was higher than that for severe level ($43.3\%$) of people with disabilities. The health screening participation rate was high for people with disabilities that had relatively good mental health conditions, such as no depression ($82.3\%$), no suicidal ideation ($89.7\%$), and no suicide attempts ($99.4\%$) (Table 3).
## 3.4. Analysis of Influencing Factors Related to Non-Participating in Health Screening
The results of the multi-logistic regression analysis on the nonparticipation of people with disabilities in health screening showed that age, education, marital status, type of medical insurance, economic activity, chronic diseases, degree of disability, and suicidal ideation were statistically significant at a significance level of 0.5 (Table 4). In terms of age, compared to those aged ≥80 years, the health screening rate in individuals in their twenties or thirties was approximately 2.1 times ($95\%$ CI = 1.4 to 2.9) lower. In terms of education, the probability of participation in health screening was 1.4 times lower for those with a lower education than for those with a higher education degree. The probability of not taking a health screening was approximately 1.3 times higher for people with disabilities without a spouse than for those with a spouse. Compared to national health insurance, the health screening participation rate of the medical aid group was approximately 1.2 times higher among those enrolled in health insurance schemes, and the rate of non-examination was twice as high among those who were not engaged in economic activities. Compared to those with physical disabilities, those with brain lesions and developmental disabilities were 1.6 times more likely to miss a health screening. The rate of non-examination for health screening was 1.4 times higher in cases of both no chronic diseases and severe disabilities. Those with suicidal ideation were 1.3 times more likely to fail health screening.
## 4. Discussion
Research on health screening rates for people with disabilities is often conducted sporadically. In this study, factors affecting the nonparticipation rate in health screening for people with disabilities were classified into predisposing characteristics, enabling resources, and need factors. The study aimed to provide basic data for establishing programs and policies that can improve the rate of health screenings for people with disabilities by analyzing the factors that affect non-participation in health screening for people with disabilities.
In this study, the health screening participation rate for adults with disabilities was $69.1\%$. Similar results were reported by Kim et al. which revealed a $70.2\%$ health screening rate for people with disabilities [10]. In addition, the results of this study were $4.5\%$ higher than the $64.6\%$ health screening rate of people with disabilities in the 2019 health statistics for people with disabilities published by the National Rehabilitation Center [18], which reflected the results of the national health screening. Because this study included private health screenings in addition to national examinations, the results were higher than those of the National Rehabilitation Center. However, in 2019, the health screening rate for people without disabilities in Korea was $74\%$ [18]. Therefore, the health screening participation rate of people with disabilities which was somewhat lower than that of the people without disabilities.
A study in the United States also reported that people with disabilities had lower screening rates than those without disabilities [13,19]. Few studies have quantitatively and qualitatively identified health screening rates of people with disabilities; therefore, comparison with existing studies is limited, making health screening an urgent task for people with disabilities. The first reason people with disabilities do not participate in health screening is that they have no other symptoms and think they are healthy. The prevalence of chronic diseases with disabilities is reported to be $86.4\%$ [6]. Rather than waiting until the reason for visiting the hospital, it is necessary to detect and treat the disease early in an asymptomatic state and inform them of the need to improve their lifestyle. It has been found that uncomfortable transportation is a major barrier for people with disabilities, leading to non-participation in health screening. The government needs to establish a transportation system by expanding convenient mobility equipment in means of transportation, passenger facilities, and on the roads, and by improving the pedestrian environment, so that people with disabilities may travel safely and conveniently. In addition, a lack of information on health screenings, absence of guardians, and communication difficulties were found to be barriers to participation in health screenings for people with disabilities. For people with disabilities who have difficulty moving, policies such as ‘moving health screening service’ and ‘visiting health screening center’ are required for improvement.
In this study, the health of people with disabilities was analyzed according to age groups identified in previous studies [18,20], subjective economic status, economic activity, and degree of disability [8,20,21]. There was a difference in health screening participation rates. Although not significant in this study, there was a sex-based difference in the health screening rates of people with disabilities [21,22] Compared to men, women with disabilities had a lower health screening rate, meaning that their health is more vulnerable. In addition, a health screening strategy for people with low gross house income and severe disabilities is required. The results of the logistic regression analysis to understand the influence of variables that affect the health screening participation of people with disabilities showed that age group, subjective economic status, economic activity, and degree of disability had a statistically significant effect on the health screening rates. Older age, better subjective economic status, and milder symptoms were found to have a positive effect on the health screening participation rate. On the contrary, health screening rates were low for those with younger age, poor subjective economic status, and severe disabilities. In addition, of non-participation rate in health screening was 1.2 times higher for those without a spouse (unmarried, widowed, divorced, separated, single mother/unmarried father, etc.) than for those with a spouse.
This study has some limitations First, the survey data on the actual condition of people with disabilities depended on the participants’ responses to the question, “Have you had a health screening in the past two years?” In addition, it was not useful to segment and analyze various types of examinations, such as national general examinations, life transition period examinations, and cancer screening. Therefore, in the future, research identifying related factors with more diverse forms of examinations, such as health screenings during the transition period of life and cancer screenings, are required. Second, because the survey respondents were home-based people with disabilities, there could be limitations in representing all people with disabilities. Third, we cannot rule out that the critical variables of the factors affecting health screenings for people with disabilities are omitted because of the limiting variables. Various important variables, such as chronic disease status, region, and individual private insurance should be included. In this study, to increase the health screening participation rates for people with disabilities, age should be considered as a predisposing factor, economic level as an enabling factor, and severity of disability as a need factor. Based on these results, it is possible to improve the health screening rates of people with disabilities and establish health management and promotion policies to improve the health and happiness of people with disabilities, detect diseases early, and improve and promote current health conditions. Therefore, social and institutional support measures are required. In addition, appropriate rehabilitation services for people with disabilities are also required.
## 5. Conclusions
This study identified the factors affecting the health screening of 6660 people with disabilities aged 20 years or older who responded to the 2020 Survey on People with Disabilities. It is commonly known that people with disabilities have poor access to medical services compared to people without disabilities, considering their poor health and low economic status. Therefore, although the need for preventive medical services, such as health screening, is much higher for people with disabilities, its current provision is lower than that for people without disabilities. This inevitably leads to an increase in medical expenses [23,24]. Thus, the government requires active planning and design. Recently, the government invited people with disabilities to undergo health screening without any inconvenience, but the response rate was low. *In* general, for people with disabilities to receive health screening, facilities, equipment, and time must be customized. Accordingly, the government is building customized screening centers for people with disabilities. In addition to providing basic health screening services for people with disabilities through health screening centers, specialized health screening items should be developed and disseminated. Health promotion and disease prevention for people with disabilities can be achieved through the provision of customized health screening services for each life cycle considering the characteristics of people with disabilities, and more active and voluntary participation by the concerned people in the health screening to monitor their health at the national level. Considered that continuous efforts are also necessary to achieve a more suitable screening system for people with disabilities.
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---
title: Micrandilactone C, a Nortriterpenoid Isolated from Roots of Schisandra chinensis,
Ameliorates Huntington’s Disease by Inhibiting Microglial STAT3 Pathways
authors:
- Minhee Jang
- Jong Hee Choi
- Dae Sik Jang
- Ik-Hyun Cho
journal: Cells
year: 2023
pmcid: PMC10000367
doi: 10.3390/cells12050786
license: CC BY 4.0
---
# Micrandilactone C, a Nortriterpenoid Isolated from Roots of Schisandra chinensis, Ameliorates Huntington’s Disease by Inhibiting Microglial STAT3 Pathways
## Abstract
Huntington’s disease (HD) is a neurodegenerative disease that affects the motor control system of the brain. Its pathological mechanism and therapeutic strategies have not been fully elucidated yet. The neuroprotective value of micrandilactone C (MC), a new schiartane nortriterpenoid isolated from the roots of Schisandra chinensis, is not well-known either. Here, the neuroprotective effects of MC were demonstrated in 3-nitropropionic acid (3-NPA)-treated animal and cell culture models of HD. MC mitigated neurological scores and lethality following 3-NPA treatment, which is associated with decreases in the formation of a lesion area, neuronal death/apoptosis, microglial migration/activation, and mRNA or protein expression of inflammatory mediators in the striatum. MC also inhibited the activation of the signal transducer and activator of transcription 3 (STAT3) in the striatum and microglia after 3-NPA treatment. As expected, decreases in inflammation and STAT3-activation were reproduced in a conditioned medium of lipopolysaccharide-stimulated BV2 cells pretreated with MC. The conditioned medium blocked the reduction in NeuN expression and the enhancement of mutant huntingtin expression in STHdhQ111/Q111 cells. Taken together, MC might alleviate behavioral dysfunction, striatal degeneration, and immune response by inhibiting microglial STAT3 signaling in animal and cell culture models for HD. Thus, MC may be a potential therapeutic strategy for HD.
## 1. Introduction
Huntington’s disease (HD) is a genetic disorder that causes the progressive degeneration of brain cells, particularly in the basal ganglia and cerebral cortex. HD typically causes a combination of chorea, cognitive impairment, and psychiatric symptoms in patients [1,2]. Neurodegeneration in HD is caused by an expansion of a CAG trinucleotide repeat in the huntingtin (Htt) gene. The CAG repeat encodes an abnormally long polyglutamine (PolyQ) tract in the huntingtin protein, specifically, striatal medium spiny neurons [1,2]. The abnormal aggregation of mutant huntingtin (mHTT) protein may produce multiple pathological features, including neuronal loss, neuronal toxicity, excitotoxicity, mitochondrial dysfunction, transcriptional dysfunction, changes in axonal transport, and synaptic dysfunction within various brain areas such as the striatum [1,2].
Despite there being many promising theories about the pathological mechanisms underlying HD, there are few pharmacotherapies that have been proven to effectively target these mechanisms and improve symptoms (chorea and psychosis) in clinical trials [3]. Tetrabenazine (Xenazine®) is currently the only medication approved by the US Food and Drug Administration for the treatment of HD, and some newer antipsychotic agents (olanzapine and aripiprazole) might have adequate efficacy with a more favorable adverse-effect profile than older antipsychotic agents for treating chorea and psychosis. However, they might produce serious adverse effects such as akathisia, depression, dizziness, and fatigue [3]. Nonetheless, the exact mechanism underlying neuronal death in HD has not been fully elucidated yet.
As a metabolite of 3-nitropropanol, 3-nitropropionic acid (3-NPA) is a naturally occurring toxin that has been found in various fungal species, including Aspergillus flavus, Astragalus, and Arthrinium [4,5]. It can irreversibly inhibit the activity of mitochondrial complex II, also known as succinate dehydrogenase, which is an essential component of both the electron transport chain and the tricarboxylic acid cycle in mitochondria [4,5,6]. Systematically administering 3-NPA into experimental rodent models can cause striatal toxicity. It closely mimics and reproduces behavioral (hyperkinetic and hypokinetic movement), histopathological, and neurochemical pathology features seen in HD [4,5]. Thus, 3-NPA has been used as an efficient chemical to induce HD-like symptoms and pathological features in animal models to study HD [4,5]. The STHdhQ$\frac{111}{111}$ cell line is a striatal cell line derived from a knock-in transgenic mouse containing homozygous huntingtin (HTT) loci with a humanized Exon 1 with 111 polyglutamine repeats. The STHdhQ$\frac{111}{111}$ cell line is a well-known and commonly used model to study molecular aspects of HD [7].
Schisandra (S.) chinensis, commonly known as ‘Omija’ in Korean and ‘Wǔ wèi zi’ in Chinese, meaning five-flavor berry, is a plant species that belongs to the genus Schisandra of the family Schisandraceae. It is distributed and cultivated in northeastern China, far-eastern Russia, Japan, and Korea [8,9,10]. S. chinensis has attracted much attention due to its various pharmacologic effects on different body systems, including the nervous, endocrine, immune, circulatory, and gastrointestinal systems [11]. S. chinensis has various compounds, including lignans, nortriterpenes, sesquiterpenes, and phenolic acids [12]. S. nortriterpenoids are a structurally intriguing group of polycyclic, highly oxygenated, and fused heterocyclic natural products isolated from S. chinensis [12]. We isolated micrandilactone C (MC), a new schiartane nortriterpenoid, from the roots of S. chinensis in our previous study [13]. However, the pharmacological features of MC are not known yet. A previous study has shown that MC isolated from S. micrantha exhibits an EC50 value of 7.71 µg/mL (SI > 25.94) against human immunodeficiency virus (HIV)-1 replication with minimal cytotoxicity (>200 µg/mL) [14]. A nortriterpenoid kudsuphilactone B isolated from fruits of S. chinensis can induce caspase-dependent apoptosis in human cancer cells by regulating Bcl-2 family protein and mitogen-activated protein kinase signaling [15]. C21 nortriterpenoid (16,17-dehydroapplanone E), isolated from Ganoderma applanatum, has shown inhibitory effects on the release of nitric oxide (NO) by the lipopolysaccharide (LPS)-induced BV-2 microglial cell line derived from C57/BL6 murine [16]. Novel nortriterpenoid (compound 2) from fruits of *Evodia rutaecarpa* has shown potent neuroprotective activities against serum-deprivation-induced P12 cell damage [17]. These results suggest that MC might have beneficial activities for various pathological statuses including neurological disorders. Herein, we report that MC could ameliorate Huntington’s disease through its anti-inflammatory effects by inhibiting STAT3 pathways.
## 2.1. Animals and Ethical Approval
Male adult C57BL/6 mice (Narabiotec Co., Ltd., Seoul, Republic of Korea; weight: 23–25 g; $$n = 105$$; seed mice originated from Taconic Biosciences Inc., Rensselaer, NY, USA) were kept under constant temperature (23 ± 2 °C) and humidity (55 ± $5\%$) conditions with a 12 h light–dark cycle (light on 08:00 to 20:00), and fed food and water ad libitum. The mice were allowed to habituate in the housing facilities for 1 week before the experiments. All experimental procedures were reviewed and approved by the Institutional Animal Care and Use Committee of Kyung Hee University (KHUASP-19-018). In this process, the proper randomization of laboratory animals and handling of data were performed in a blinded manner in accordance with the recent recommendations from a NIH Workshop on preclinical models of neurological diseases [18].
## 2.2. Experimental Group, Model Induction, and Drug Treatment
The experimental group was divided into the following groups: the sham group (vehicle treatment, i.p. +saline, i.v.), 3-NPA group (70 mg/kg of 3-NPA, i.p. +saline, i.v.), 3-NPA + MC 1.25 group (70 mg/kg of 3-NPA, i.p. +1.25 mg/kg of MC, i.v.), 3-NPA + MC 2.5 group (70 mg/kg of 3-NPA, i.p. +2.5 mg/kg of MC, i.v.), and MC alone group (vehicle treatment, i.p. +2.5 mg/kg of MC, i.v.). The 3-NPA model induction was performed according to the method published in [8,19,20,21]. Briefly, 3-NPA (Sigma-Aldrich, St. Louis, MO, USA) was dissolved in saline (25 mg/mL) and passed through a 0.2 µm filter. The 3-NPA was intoxicated intraperitoneally daily for five days at a dose of 70 mg/kg. MC was isolated from roots of S. chinensis as previously described [13]. MC was administered daily for five days at one hour before every 3-NPA intoxication.
## 2.3. Behavioral Semi-Quantitative Assessment
The severity of the neurological impairment (motor disability) induced by 3-NPA was assessed by an experimenter who was unaware of the experimental conditions under constant temperature and humidity conditions in a quiet room using the behavioral scale as previously described [8,19,20,21,22,23]. The neurological impairment was evaluated at 24 h after the last (5th) 3-NPA intoxication.
## 2.4. Histopathological Analysis of Striatal Damage
To investigate the histopathological alterations of the striatum following 3-NPA intoxication, we used a previously described protocol [8,22]. Briefly, 24 h after the last (5th) 3-NPA intoxication, the mice ($$n = 5$$ per group) were anesthetized with isoflurane and then perfused intracardially with saline and iced $4\%$ paraformaldehyde in 0.1 M of phosphate buffer (PB, pH 7.4). Sequential coronal sections (30 μm in thickness) were acquired from the corpus callosum throughout the entire striatum (bregma 1.40~−1.30 mm) using the method published in [24]. Free-floating sections were collected in an antifreeze solution ($30\%$ sucrose in PBS) and stored at −20 °C.
## 2.5. Fluoro-Jade C (FJC) and Cresyl Violet Stains
To assess the striatal apoptosis in the striatum after 3-NPA-intoxication, FJC staining was performed using the method published in [25]. Briefly, 24 h after the last (5th) 3-NPA intoxication, free-floating brain sections (3 sections per brain) from all groups ($$n = 5$$ per group) were immersed in $70\%$ ethyl alcohol, washed with distilled water (DW), and incubated in $0.06\%$ potassium permanganate solution. The sections were washed with DW and then incubated in a solution of $0.001\%$ FJC (Millipore, Billerica, MA, USA). After washing with DW, these sections were air-dried, immersed in $100\%$ xylene, and coverslipped with DPX mountant (Sigma-Aldrich). The region of interest of each section was captured using a confocal laser scanning microscope (LSM 5 PASCAL, Carl Zeiss Microscopy GmbH, Münche, Germany). The number of FJC positive cells per section was manually and blindly counted. Additionally, 3 sections from the level of the mid-striatum were stained with $0.1\%$ cresyl violet dye. Stained sections were captured using a digital camera (DP-70, Olympus Co., Tokyo, Japan). The level of 3-NPA-induced striatal damage compared to the area of the whole striatum was measured using the NIH Image J program [http://rsbweb.nih.gov/ij/ (12 July 2022)].
## 2.6. Immunohistochemical and Immunofluorescence Evaluation
Immunohistochemistry was performed using the method published in [8,22]. Briefly, 24 h after the last (5th) 3-NPA intoxication, free floating brain sections (30 μm thickness; 3 sections per brain) from all groups ($$n = 5$$ per group) were incubated with rabbit anti-ionized calcium-binding adapter molecule (Iba)-1 (1:2000; WAKO, Chuo-Ku, Japan). The stained sections from the level of the mid-striatum were captured using a digital camera (DP-70, Olympus Co.) and the mean level of Iba-1-immunopositive area to whole striatal area was analyzed using the NIH Image J program [http://rsbweb.nih.gov/ij/ (12 July 2022)] *Immunofluorescence analysis* was performed as previously described [22,26,27]. Briefly, free floating brain sections (30 μm thickness) from each group ($$n = 5$$ per group) were blocked with either rabbit anti-phospho (p)-STAT3 (1:200; Cell Signaling Technology, Beverly, MA, USA) and rat anti-CD11b (1:500; Serotec, Oxford, UK). Additionally, the region of interest of each section was captured using a confocal laser scanning microscope (LSM 5 PASCAL, Carl Zeiss, Microscopy GmbH) and the number of p-STAT3+ per 500 μm2 and the ratio of CD11b (+) cells containing p-STAT3 (+) signal per striatum was manually and blindly measured.
## 2.7. Western Blot Analysis
Western blot analysis was performed using the method published in [8,22]. Briefly, 24 h after the last (5th) 3-NPA intoxication, the striatal proteins from all groups ($$n = 5$$ per group) were incubated with primary antibodies, including mouse anti-succinate dehydrogenase complex subunit A (SDHA) (1:1000; Abcam, Cambridge, UK), rabbit an-ti-pro-caspase-3 (1:1000; Cell Signaling Technology), rabbit anti-cleaved caspase-3 (1:500; Cell Signaling Technology), rabbit anti-pro-caspase-9 (1:1000; Cell Signaling Technology), rabbit anti-cleaved caspase-9 (1:1000; Cell Signaling Technology), rabbit anti-B-cell lymphoma 2 (Bcl-2) (1:1000; Santa cruz Technology), rabbit anti-Iba-1 (1:500; WAKO), rabbit anti-phospho (p)-STAT3, STAT3 (1:500; Cell Signaling Technology), mouse anti-inducible nitric oxide synthases (iNOS) (1:500; Santa Cruz Biotechnology, Santa Cruz, CA, USA), rabbit anti-interleukin(IL)-1ß, IL-6, tumor necrosis factor-α (TNF-α) (1:1000; Cell Signaling Technology), mouse anti-neuronal nuclear protein (NeuN) (1:2000; Millipore), mouse anti-HTT (clone mEM48; 1:500; Millipore), and mouse anti-HTT (clone 2Q75; 1:500; LifeSpan BioSciences, Seattle, WA, USA) antibodies. For the normalization of antibody signals, membranes were stripped and reprobed with antibodies against glyceraldehyde-3-phosphate dehydrogenase (GAPDH; 1:5000; Cell Signaling Technology) or STAT3. Data are expressed as the ratio of the corresponding protein signal against GAPDH or the STAT3s signal for each sample. Original images from Western blot assay in Supplementary Data S1.
## 2.8. Flow Cytometry
At 24 h following the last (5th) 3-NPA intoxication, mice ($$n = 3$$ per group) with representative behavioral scores in each experimental group were anesthetized by isoflurane (1–$2\%$) and perfused intracardially with saline. The striata were then carefully cropped. To test the microglia/macrophage population, single-cell suspensions refined from striata were prepared and fluorescently stained as previously described [26,28,29,30]. Microglia and macrophages were differentiated based on their relative CD45 expression levels [26,28,29,30]. Briefly, after acquiring unstained and single colored control samples to calculate the compensation matrix, 1 × 104 events were acquired within the combined gate based on physical parameters (forward scatter (FSC) and side scatter (SSC)).
## 2.9. Real-Time Polymerase Chain Reaction (PCR) Analyses
To measure the mRNA level of inflammatory factors, 24 h after the last (5th) 3-NPA intoxication, real-time PCR analysis using the striatal lysats from all groups ($$n = 5$$ per group) was performed using the SYBR Green PCR Master Mix as previously described [31,32]. Reactions were performed in duplicate in a total volume of 10 μL, each containing 10 pM of primer, 4 μL of cDNA, and 5 μL of SYBR Green PCR Master Mix. The mRNA levels of each target gene were normalized to that of GAPDH mRNA. Fold-induction was calculated using the 2−ΔΔCT method as previously described [33]. All real-time RT–PCR experiments were performed at least three times, and the mean ± SEM values are presented unless otherwise noted. The primer sequences are listed in Supplementary Materials. The expression levels of each gene were normalized to that of GAPDH.
## 2.10. STHdh Cell Culture
STHdh cell lines (STHdhQ111/Q111) (conditionally immortalized striatal neuron progenitor cell lines) were kindly provided by Prof. Hoon Ryu (Korea Institute of Science and Technology, Seoul, Republic of Korea) and were cultured according to the protocol from Coriell Institute for Medical Research (Camden, NJ, USA) as previously described [22].
## 2.11. Preparation of Conditioned Medium (CM) from BV2 Cells and Determination of Activity of STHdh Cells
To obtain CM, cultured BV2 cells were treated with MC (5 μM) at 1 h before stimulation with 3-NPA (1 mM) for 12 h. The culture medium was replaced with fresh medium and incubated for 24 h. CM-3-NPA (conditioned medium from 3-NPA-stimulated BV2 cells) and CM-3-NPA-MC (conditioned medium from 3-NPA-stimulated BV2 cells pretreated with MC) were collected and used to investigate the expression of inflammatory factors and p-STAT3 by Western blot analysis. CM-3-NPA and CM-3-NPA-MC were treated to STHdhQ111/Q111 cells for 24 h. CM-treated STHdhQ111/Q111 cells were collected to analyze the degree of neurodegeneration (NeuN) and huntingtin aggregation (EM48 and 2Q75) by Western blot analysis. In vitro assays were repeated at least three times, with each experiment performed in triplicate.
## 2.12. Statistical Analysis
Statistical analysis was performed using the IBM SPSS Statistics Version 26.0 (SPSS Inc., Chicago, IL, USA) for Windows. The data from experiments including the behavioral test, immunohistochemistry, Western blot, and PCR analysis were analyzed using Kruskal–Wallis test (a nonparametric test) for the comparison of three or more unmatched groups. The data are presented as mean ± SEM. p values of less than 0.05 were accepted as statistically significant.
## 3.1. Effects of MC on Neurological Score and Survival Rate after 3-NPA Intoxication
First, we determined whether MC could mitigate neurological signs and survival rate of mice following 3-NPA treatment. Figure 1A–C shows a representative neurological score, survival rate, and body weight (BW) of the sham, 3-NPA, 3-NPA + MC (1.25 and 2.5 mg/kg/day), and MC alone (2.5 mg/kg/day) groups. Twenty-four hours after the last (5th) intoxication of 3-NPA, the mice displayed symptoms of severe neurological deficits (score, 9.0 ± 0.4). However, the mice in 3-NPA + MC groups displayed significantly lower neurological scores (7.0 ± 0.4 and 4.8 ± 0.2 in MC 1.25 and 2.5 mg/kg/day groups, respectively) than the mice in the 3-NPA group (score, 9.0 ± 0.4) (Figure 1A). The survival rate at the end of the representative experimental set was increased to $57.1\%$ ($$n = 4$$/7) and $71.4\%$ ($$n = 5$$/7), respectively, in 3-NPA + MC 1.25 mg/kg/day and 3-NPA + MC 2.5 mg/kg/day groups, respectively, as compared to that in the 3-NPA group ($42.8\%$, $$n = 3$$/7) (Figure 1B). The mean loss of BW was significantly alleviated by 3-NPA. However, it was not significantly affected by MC treatment at 1.25 or 2.5 mg/kg/day (Figure 1C). Treatment with MC alone (2.5 mg/kg/day) did not significantly affect the neurological score, survival rate, or BW of normal mice.
## 3.2. Effects of MC on Striatal Cell Death and Apoptosis Induced Following 3-NPA-Treatment
It is known that 3-NPA-induced neurological dysfunction results from striatal cell death [19,20,21,23]. Thus, we explored whether MC could alleviate striatal cell death following 3-NPA-treatment. Twenty-four hours after the last (5th) 3-NPA treatment, coronal cryostat sections of brain including the striatum were subjected to cresyl violet dye (Figure 2A). Figure 2A shows representative striatal images from the sham, 3-NPA, 3-NPA + MC (1.25 and 2.5 mg/kg/day), and MC alone (2.5 mg/kg/day) groups. In the two representative experimental sets, it was found that $85.7\%$ ($$n = 6$$/7) of the surviving mice in the 3-NPA-treated group had visible bilateral striatal lesions (pale areas surrounded by dotted line), whereas this percentage was reduced to $71.4\%$ ($$n = 5$$/7) and $55.5\%$ ($$n = 5$$/9) in the groups treated with MC at 1.25 and 2.5 mg/kg/day, respectively (Figure 2B). Furthermore, in the 3-NPA group, the ratio of the mean lesion area to the entire striatum was $80.6\%$, whereas this ratio remarkably decreased to $56.1\%$ and $36.6\%$ in the group treated with MC at 1.25 and 2.5 mg/kg/day, respectively (Figure 2C). The results of the behavioral dysfunction (Figure 1A) and striatal cell death (Figure 2A–C) revealed that treatment with 2.5 mg/kg/day of MC was more effective in inhibiting 3-NPA toxicity than treatment with 1.25 mg/kg/day of MC. Thus, the dose of 2.5 mg/kg/day of MC was used in further studies. Since 3-NPA is an irreversible inhibitor of mitochondrial respiratory complex II and succinate dehydrogenase (SDH) [4,5,6], we explored whether MC could inhibit mitochondrial complex II activity using SDHA antibody in striatal lysate at 24 h after the last 3-NPA administration (Figure 2D). Protein expression level of SDHA was decreased in the 3-NPA group (0.43) compared to that in the sham group (0.77) but increased after treatment with MC at 2.5 mg/kg/day (0.61) (Figure 2D). Based on the results from the cresyl violet stain (Figure 2A–C), to further compare the levels of degenerating neuronal cells, we stained coronal cryostat sections with FJC anionic fluorescent dye (Figure 2E,F), a good marker of degenerating neurons [34,35]. The number of FJC (+) cells was increased to 43.6 ± 1.5 per section in the 3-NPA group, but decreased to 29.6 ± 0.9 in the 3-NPA + 2.5 mg/kg/day MC group (Figure 2E,F). To test whether the anti-neuronal cell death effect of MC might be related to apoptosis, we determined the protein levels of the representative apoptosis markers (cleaved caspase-9, cleaved caspase-3, and Bcl-2) in the striatum by Western blotting (Figure 2G–J). The protein expression levels of cleaved caspase-9 and cleaved caspase-3 were increased in the 3-NPA group (0.85 and 0.71, respectively) compared to those in the sham group (0.15 and 0.17, respectively), but decreased after treatment with MC at 2.5 mg/kg/day (0.55 and 0.44, respectively) (Figure 2G–J), similar to results of the FJC staining (Figure 2E,F). The protein expression level of Bcl-2 was also decreased in the 3-NPA group (0.51) compared to that in the sham group (0.75) but increased after treatment with MC at 2.5 mg/kg/day (0.80) (Figure 2G–J).
## 3.3. Effect of MC on Microglial Activation in the Striatum Following 3-NPA-Treatment
Microglia are migrated into degenerative site in the central nervous system (CNS) in cases of neurodegenerative diseases, including HD. They are then activated within/around the lesions in the CNS. These activated microglia can produce pro- and anti-inflammatory cytokines [36,37,38]. Thus, we explored whether MC could suppress microglial activation in the striatal lesions from all groups ($$n = 5$$ per group) following 3-NPA treatment (Figure 3A–C and Supplementary Data S2). In the striatal sections of the 3-NPA group, Iba-1 (a marker for microglia/macrophage lineage cells)-immunoreactive cells showed a morphology of the activated type with bigger cell bodies and extended (short and thick) processes than those in the sham group of CNS, which displayed typical forms of resting cells, including relatively small soma and long, thin processes [19,20,21,36] (Figure 3A–C). However, the mean level of Iba-1-immunopositive area to whole striatal area was clearly decreased in striatal sections of the 3-NPA + MC group than in the 3-NPA group (Figure 3A,B), in agreement with the alteration (0.53-fold in the 3-NPA group; 0.26-fold in the MC) in the protein expression of Iba-1 based on Western blot analysis (Figure 3C). The morphology of the Iba-1 immunoreactive cells based on immunohistochemistry and the Iba-1 protein expression based on Western blot analysis were not significantly affected by treatment with MC (2.5 mg/kg/day) alone (Figure 3A–C). Since Iba-1 can detect microglia and macrophage [19,20,21,36]; to discriminate both cells, flow cytometry was performed using striatum at 24 h after the last (5th) treatment of 3-NPA. Interestingly, the percentage of CD11b+/CD45+(low) cells representing microglial cells increased to 17.4 ± $1.1\%$ in the 3-NPA group compared to that of the sham group (4.2 ± $0.6\%$) but decreased to 10.2 ± $0.5\%$ in the 3-NPA + MC group compared to that of the 3-NPA group (Figure 3D,E). However, the percentage of CD11b+/CD45+(high) cells representing macrophages was not significantly different between the sham group and the other groups (Figure 3D,F). These findings suggest that MC might inhibit microglial migration and activation regardless of the macrophage and that MC might be closely associated with the reduction in striatal cell death and the mitigation of neurological impairment following 3-NPA treatment.
## 3.4. Effects of MC on Inflammatory Factors and STAT3 Pathways in the Striatum Following 3-NPA-Treatment
Migrated and activated microglia around (or within) CNS lesions can release inflammatory mediators (enzymes, cytokines, and chemokines) that are either beneficial or detrimental to neuronal survival [36,37,38]. Thus, we explored whether the inhibition of microglial activation by MC might induce changes in the mRNA expression of representative inflammatory enzymes (COX-2 and iNOS), cytokines (IL-1β, IL-6, and TNF-α), and chemokine (MCP-1) using real-time PCR analysis (Figure 4A–F). The mRNA expression levels of pro-inflammatory factors were increased in the 3-NPA group compared to the sham group, with the following results: COX-2: increase by 15.9-fold; iNOS: increased by 3.5-fold; IL-1β: increased by 28.7-fold; IL-6: increased by 51.4-fold; TNF-α: increased by 55.1-fold; and MCP-1: increased by 363.8-fold (Figure 4A–F). On the other hand, MC remarkably blocked these increases induced by 3-NPA with the following results: COX-2 by $8.0\%$, iNOS, by $3.5\%$, IL-1β, by $28.7\%$, IL-6, by $51.4\%$, TNF-α, by $55.1\%$, and MCP-1 by $89.0\%$, compared to those in the 3-NPA group (Figure 4A–F). Since STAT3 pathways are involved in neurodegeneration, including striatal toxicity [39,40], we examined these signaling pathways in the striatum after 3-NPA treatment (Figure 4G–J). The expression level of p-STAT3 protein was remarkably enhanced—by 5.3-fold—in the striatum at 24 h after the final 3-NPA treatment compared to that in the sham group. However, MC significantly inhibited the expression level of p-STAT3 protein by $49.3\%$ (Figure 4G). To determine whether STAT3 downregulation by MC was directly related to the reduction in neuronal cell death and microglial activation, we performed immunofluorescence staining for p-STAT3 in the striatum of the 3-NPA group. In agreement with the alteration in the expression level of p-STAT3 protein, the numbers of p-STAT3 immunoreactive cells and CD11b (+) cells were enhanced in striatal lesions after 3-NPA treatment, while these numbers were markedly reduced by MC treatment (Figure 4H–J). These findings suggest that MC could inhibit inflammatory response and striatal toxicity after 3-NPA treatment by inhibiting STAT3 pathways in the striatum and microglia.
## 3.5. Effects of MC on Pro-Inflammatory Factors and STAT3 Pathways in 3-NPA-Induced BV2 Cells
The STAT3 pathway plays an important role in microglial activation [41]. Microglial activation is pivotally involved in neuroinflammatory and neurodegenerative events processes such as 3-NPA-induced striatal toxic, adeno-associated viruses (AAV)/viral vector-induced, and transgenic mice models for HD [19,39,42]. Thus, we further investigated whether MC could control microglial activation in 3-NPA-induced BV-2 cell (Figure 5). MC significantly inhibited the enhancement in protein expression of a representative inflammatory enzyme (COX-2 and iNOS) and cytokines (IL-1β, IL-6, and TNF-α) as found using Western blot analysis: COX-2 by $48.7\%$, iNOS by $44.1\%$, IL-1β by $52.1\%$, IL-6 by $53.6\%$, and TNF-α by $43.2\%$, compared to those in the 3-NPA-treated group (Figure 5A–F). Next, we investigated whether these anti-inflammatory effects of MC were related to the reduced expression of p-STAT3. The expression of p-STAT3 was markedly enhanced in 3-NPA-stimulated BV2 cells (by $267.5\%$), compared to those in the sham group. However, MC impressively inhibited this enhancement (by $35.4\%$) (Figure 5A,G). MC itself did not significantly affect inflammatory enzyme/cytokines and STAT3 phosphorylation (Figure 5A,G). These results suggest that MC might inhibit STAT3 pathways and contribute to microglial downregulation as well as neuroprotection.
## 3.6. Effect of MC on STHdh Cell Death via Microglial Downregulation by Inhibiting STAT3 Pathway
Since the STAT3 pathway plays a critical role in neuron–microglia interactions [41], we further investigated whether these anti-inflammatory effects of MC could affect striatal cell death via the STAT3 pathway by controlling mHTT expression in HD (Figure 6). Impressively, CM-3-NPA significantly reduced the expression of NeuN protein (a marker of neuronal cells) in STHdhQ111/Q111 cells compared to the sham control. However, CM-3-NPA-MC significantly inhibited this reduction (Figure 6A,B). CM-3-NPA also enhanced the expression of EM48 and 2Q75 proteins (markers of mHTT) in the STHdhQ111/Q111 cell compared to the sham control, whereas CM-3-NPA-MC intriguingly diminished their expression levels (Figure 6A,C,D). These results indicate that MC might decrease the STHdhQ111/Q111 cell death related to the reduced expression of mHTT protein by down-regulating microglial activation.
## 4. Discussion
The results of the present study revealed that MC, a nortriterpenoid isolated from roots of S. chinensis, could ameliorate 3-NPA-induced HD-like symptoms by inhibiting STAT3 pathways. Pretreatment with MC ameliorated the neurobehavioral disorder (motor disability), improved the survival rate, and inhibited the neurodegeneration related to apoptosis in the striatum following 3-NPA intoxication. These results were consistent with the reduction in microglial activation and inflammatory response related to the reduction in p-STAT3 expression. Intriguingly, CM-3-NPA-MC reduced STHdhQ111/Q111 cell death by inhibiting mHTT expression. These beneficial activities of MC for HD-like symptoms were associated with the inhibition of microglial STAT3 pathways. In conclusion, MC might be a potential therapeutic agent for treating HD-like symptoms by inhibiting microglial STAT3 pathways. To the best of our knowledge, this effect of MC on neurological disorders has never been reported.
An inhibitor of SDH (mitochondrial complex II), 3-NPA is a source of reactive oxygen species [4,5,6]. It is known that 3-NPA can induce striatal degeneration by neurotoxic activity in rodents and result in gait abnormalities, which mimics the behavioral dysfunction and pathology caused by mutant Htt in animal models for HD and its patients. However, the 3-NPA-induced rodent model has nothing to do with mutant Htt expression [5,43]. Nevertheless, the model has been used to discover a therapeutic intervention for HD [5,43]. In the present study, the protein expression level of SDHA, a marker of mitochondrial complex II activity, was decreased in the striatum following 3-NPA treatment but enhanced by administration with MC (Figure 2). The enhancement of the mitochondrial complex II activity of MC was associated with decreased levels of behavioral impairment (Figure 1) and striatal cell death based on cresyl violet and FJC staining (Figure 2). Taken together, these results suggest that regulating mitochondrial complex II activity might be an attractive strategy to prevent striatal degeneration in 3-NPA-induced HD-like symptoms.
FJC staining is commonly used to label all degenerating mature neurons, including apoptotic, necrotic, and autophagic cells in brain tissue [34,35]. MC blocked the increase in the number of FJC (+) cells in the striatum induced by 3-NPA (Figure 2) associated with reduced levels of cleaved caspase-9/caspase-3 proteins (initiators of intrinsic apoptosis) and enhanced levels of Bcl-2 protein (regulator proteins of apoptosis) (Figure 2). These results suggest an anti-apoptotic activity of MC in striatal degeneration. Normally, 3-NPA can induce apoptosis by generating superoxide radicals [44] and activating the microglia surrounding apoptotic cells [45]. Cell death caused by the latter is called ‘secondary cell death’ or ‘delayed cell death’ [46]. STAT3-activation in microglia exacerbates neuronal apoptosis in the hippocampus of diabetic brains [47]. Thus, controlling microglial STAT3 is considered an attractive anti-apoptosis strategy to protect neurons in various pathological environments. In the present study, MC inhibited the expression of pro-inflammatory factors and STAT3 pathways in 3-NPA-induced BV2 cells (Figure 5). CM-3-NPA-MC significantly reduced STHdhQ111/Q111 cell death (NeuN) associated with mHTT expression (EM48 and 2Q75) (Figure 6). Taken together, these results suggest an anti-apoptotic activity of MC in striatal degeneration by inhibiting microglial STAT3 signaling.
Microglia, as brain-resident immune cells, are emerging as a central player in regulating the key pathways in CNS inflammation [48,49]. Microglia are recruited and activated around or within neurodegenerative lesions. Activated microglia can secrete inflammatory agents that are either beneficial or deleterious to neuronal survival [37]. Clinical studies using positron emission tomography have also demonstrated that the level of microglial activation is increased in proportion to the severity of HD symptoms [38,42]. Thus, handling microglial activation might be an attractive therapeutic strategy for neurological disorders including HD [37]. In the present study, MC inhibited microglial activation (Iba-1 immunoreactive cells) and decreased the mRNA or protein expression levels of pro-inflammatory enzymes (COX-2 and iNOS), cytokines (IL-1β, IL-6, and TNF-α), and chemokine (MCP-1) in the striata of 3-NPA-intoxicated mice and in 3-NPA-induced BV2 cells (Figure 3, Figure 4 and Figure 5). Thus, MC might inhibit microglial activation and inflammatory responses, leading to a reduction in striatal cell death.
STAT3 is a pivotal transcription factor for microglial activation and cytokine production [41,50], such as IL-1β [51], IL-6 [52], and TNF-α [53,54]. These cytokines have been identified as important mediators of microglia–neuron interaction during neurodegeneration [55]. The STAT3 signaling pathway is critically involved in behavioral dysfunction and the pathological events of HD and AD [40]. Thus, in this study, we hypothesized that STAT3 signaling in microglia might affect microglia–neuron interactions via secreted cytokines, resulting in striatal degeneration and behavioral dysfunction. As a result of testing this hypothesis, MC inhibited the mRNA or protein expression of representative inflammatory enzyme (COX-2 and iNOS), cytokines (IL-1β, IL-6, and TNF-α), and p-STAT3 in not only 3-NPA-intoxicated striatum, but also 3-NPA-stimulated BV2 cells (Figure 3, Figure 4 and Figure 5). MC also inhibited the level of co-staining of p-STAT3 in CD11b positive cells in striatum after 3-NPA-intoxication and protein expression of p-STAT3 in 3-NPA-stimulated BV2 cells (Figure 4 and Figure 5). These findings indicate that MC might reduce inflammatory responses by inhibiting STAT3 signaling in microglia. Furthermore, we investigated whether the downregulation of microglial p-STAT3 might affect the survival of STHdhQ111/Q111 cells expressing mHTT. Interestingly, CM-3-NPA-MC (conditioned medium from 3-NPA-stimulated BV2 cells pretreated with MC) significantly reduced STHdhQ111/Q111 cell death and mHTT expression compared to CM-3-NPA treatment (Figure 6). Taken together, MC might reduce striatal degeneration and mHTT expression through reduced inflammatory responses by inhibiting microglial STAT3 signaling.
Although the mechanisms involve in the anti-inflammatory effects of MC have not yet been reported, such effects might be indirectly explained by the positive effects of representative norteripenoids. For example, C21 nortriterpenoid (16,17-dehydroapplanone E), isolated from Ganoderma applanatum, can inhibit the secretion of NO in 3-NPA-induced BV-2 cells [16]. Ulmoidol, an unusual nortriterpenoid from Eucommia ulmoides Oliv. leaves, can suppress the production of proinflammatory mediators (TNF-α, IL-1β, IL-1, and PGE2) and reduce the expression of iNOS and COX-2 in 3-NPA-treated BV-2 cells [56]. Additionally, a nortriterpenoid (compound 2) from the fruits of *Evodia rutaecarpa* shows neuroprotective activities against serum-deprivation-induced P12 cell damage [17]. Taken together, these findings suggest that MC from S. chinensis might possess remarkable anti-inflammatory activity, which improves the neurological disorders associated with HD-like symptoms.
## 5. Conclusions
The exact mechanism underlying neuronal death and valuable therapeutics in HD-like symptoms has not yet been fully elucidated. Here, we found that MC could mitigate the striatal degeneration related to reduced inflammatory response and mHTT expression by inhibiting STAT3 signaling in microglia. Despite the relative lack of information on the efficacy and critical mechanisms of action of MC, our findings indicate that MC might be used as a potential therapeutic to improve HD-like symptoms by regulating the microglial STAT3 pathways. We also propose that it is necessary to determine the efficacy and mechanisms of action of MC in various pathological conditions, including neurological disease, in future works, in addition to identifying its chemical interactions in vivo.
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|
---
title: Blood Count-Derived Inflammatory Markers Correlate with Lengthier Hospital
Stay and Are Predictors of Pneumothorax Risk in Thoracic Trauma Patients
authors:
- Vlad Vunvulea
- Răzvan Marian Melinte
- Klara Brinzaniuc
- Bogdan Andrei Suciu
- Adrian Dumitru Ivănescu
- Ioana Hălmaciu
- Zsuzsanna Incze-Bartha
- Ylenia Pastorello
- Cristian Trâmbițaș
- Lucian Mărginean
- Réka Kaller
- Ahmad Kassas
- Timur Hogea
journal: Diagnostics
year: 2023
pmcid: PMC10000372
doi: 10.3390/diagnostics13050954
license: CC BY 4.0
---
# Blood Count-Derived Inflammatory Markers Correlate with Lengthier Hospital Stay and Are Predictors of Pneumothorax Risk in Thoracic Trauma Patients
## Abstract
[1] Background: *Trauma is* one of the leading causes of death worldwide, with the chest being the third most frequent body part injured after abdominal and head trauma. Identifying and predicting injuries related to the trauma mechanism is the initial step in managing significant thoracic trauma. The purpose of this study is to assess the predictive capabilities of blood count-derived inflammatory markers at admission. [ 2] Materials and Methods: The current study was designed as an observational, analytical, retrospective cohort study. It included all patients over the age of 18 diagnosed with thoracic trauma, confirmed with a CT scan, and admitted to the Clinical Emergency Hospital of Targu Mureş, Romania. [ 3] Results: The occurrence of posttraumatic pneumothorax is highly linked to age ($$p \leq 0.002$$), tobacco use ($$p \leq 0.01$$), and obesity ($$p \leq 0.01$$). Furthermore, high values of all hematological ratios, such as the NLR, MLR, PLR, SII, SIRI, and AISI, are directly associated with the occurrence of pneumothorax ($p \leq 0.001$). Furthermore, increased values of the NLR, SII, SIRI, and AISI at admission predict a lengthier hospitalization ($$p \leq 0.003$$). [ 4] Conclusions: Increased neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic inflammatory index (SII), aggregate inflammatory systemic index (AISI), and systemic inflammatory response index (SIRI) levels at admission highly predict the occurrence of pneumothorax, according to our data.
## 1. Introduction
Trauma is the world’s top cause of disability and death in the first four decades of life. In this age group, the number of young adults who die from trauma exceeds all deaths from cancer combined [1]. Thoracic injuries are highly significant in patients with severe trauma, occurring in up to $50\%$ of patients with polytrauma [2]. According to the current literature, the mortality rate following thoracic trauma varies between 25 and $50\%$, depending on the associated injuries [3,4].
The assessment of thoracic trauma severity determines the choice of first therapy and the subsequent clinical course when treating patients with polytrauma. Although thoracic trauma specifically has received little attention in the literature, there is a wealth of information on the mortality-associated risk factors following trauma in general [5,6].
Pathological inflammatory and anti-inflammatory responses that occur in the first hours following extensive trauma are one of the major contributing factors to mortality in post-traumatic patients and remain challenging to control and distinguish from a physiological immune reaction [7]. The balance between these two antagonistic inflammatory responses, as predictors of outcomes in trauma patients, has received a lot of attention recently. In response to severe injury, patients frequently experience a variety of anomalies in their host defense mechanisms [8]. Systemic inflammatory response syndrome (SIRS) is the result of an unbalanced inflammatory response that escalates and releases an excessive amount of inflammatory mediators, such as IL-1, IL-6, IL-8, and TNF [9]. The injury burden is increased by the progression of such an uncontrolled cytokine cascade and hyperinflammation. This can lead to detrimental and frequently fatal events such as SIRS and multiple organ dysfunction syndrome (MODS) [10]. Recently, there has been a growing interest in developing a trustworthy biomarker that can assess the prognosis of patients with thoracic trauma [11].
The neutrophil-to-lymphocyte ratio (NLR) is one of the most accessible markers. This ratio has been proven to significantly predict the outcomes of patients with COVID-19 infection [12,13,14,15], cardiovascular diseases [16,17,18,19,20], and kidney disease [12,21] and oncology [22,23,24]. Another well-studied biomarker is the platelet-to-lymphocyte ratio (PLR), which has been shown to have excellent predictive value for the prognosis of patients in the fields of orthopedics [19,25,26] and trauma care [27,28,29,30]. Based on routine blood tests at admission, several other ratios can be calculated, such as the monocyte-to-lymphocyte ratio (MLR), aggregate inflammatory systemic index (AISI), systemic inflammatory response index (SIRI), and systemic inflammatory index (SII).
The monocyte-to-lymphocyte ratio (MLR) has been proven to be a valid predictor of the occurrence of complication in strokes [31], and the outcomes and severity of hematological disorders [32] and oncological patients [33].
The NLR, PLR, and MLR, for example, have been the subject of a growing number of studies in recent years. However, their findings suggest that a combination of these ratios would increase their predictive value [34,35,36]. Thus, the aggregate inflammatory systemic index (AISI), systemic inflammatory response index (SIRI), and systemic inflammatory index (SII) were discovered and proven useful when evaluating the severity and prognosis of patients with various chronic and acute pathologies [37,38,39].
The prognosis ratios calculated from routine blood tests appear to be a helpful and cost-effective resource in trauma management. Although there are mentions in the literature of the correlation between the NLR and the outcomes of thoracic trauma patients [11], there are few to no papers published regarding the use of the PLR, MLR, SII, AISI, and SIRI as prognostic factors for the outcomes of patients with thoracic trauma.
The purpose of this study is to establish the prognostic value of inflammatory biomarkers and the underlying risk factors in patients with thoracic trauma.
## 2.1. Study Design
The present study was designed to be an observational, retrospective, analytical cohort study where we included all patients over the age of 18 who presented, were diagnosed with thoracic trauma, and admitted to the County Emergency Clinical Hospital of Targu Mureş, Romania, between January 2015 and December 2022. All patients included in our study underwent a radiological examination of either a conventional X-ray or a CT scan, and all were diagnosed with thoracic trauma as the main diagnosis. We excluded patients who passed away within the first 24 h, suffered severe bone fractures with need for specialized orthopaedical care, had a history of hematological or oncological disorders, presented thromboembolic events in the last two months, and patients with pneumonia. We also excluded patients suffering from mediastinal hematoma and aortic dissection as such patients are referred to the cardiovascular surgery department, not the thoracic surgery department. All patients included in our study suffered from peacetime injuries.
We initially split the patients in two categories: “Pneumothorax” and “No Pneumothorax” based on the findings at admission.
## 2.2. Data Collection
We collected the following data from our patients: age, sex, medical history (of diabetes mellitus—DM, arterial hypertension—AH, atrial fibrillation—AF, ischemic heart disease—IHD, myocardial infarction—MI, chronic obstructive pulmonary disease—COPD, peripheral arterial disease—PAD, chronic kidney disease—CKD, tobacco use, and obesity (BMI > 30)) and length of hospital stay (LOS). Moreover, we were interested in the routine blood tests at admittance. From these results, we extracted the following data: hemoglobin levels, hematocrit, neutrophil count, monocyte count, lymphocyte count, platelet count, sodium, and potassium. We were also interested in the number and location of rib fractures. All data were collected from the hospital’s integrated electronic database.
## 2.3. Inflammatory Biomarkers
From the results of the initial blood test at admittance, we managed to calculate the following ratios: MLR = monocytes/lymphocytesNLR = neutrophils/lymphocytesPLR = platelets/lymphocytesSII = (neutrophils × platelets)/lymphocytesSIRI = (monocytes × platelets)/lymphocytesAISI = (neutrophils × monocytes × platelets)/lymphocytes
## 2.4. Study Outcomes
The primary endpoint for our study was the risk of pneumothorax development. We also recorded the length of hospital stay as an outcome, making it our secondary endpoint.
## 2.5. Statistical Analysis
Software-wise, we used SPSS for Mac OS (28.0.1.0) (SPSS, Inc., Chicago, IL, USA). All systemic inflammatory marker associations with category factors were evaluated using chi-square tests, whilst differences in continuous variables were evaluated using Student t-tests or Mann–Whitney tests. The receiver operating characteristic (ROC) curve analysis was used to determine the cut-off values for inflammatory markers and evaluate their predictive potential. Based on the Youden index (Youden index = sensitivity + specificity 1, ranging from 0 to 1), the suitable NLR, MLR, PLR, SII, SIRI, and AISI cut-off values were determined using the ROC curve analysis.
## 3. Results
During our study period, we identified 611 patients suffering from thoracic trauma that met the inclusion criteria for our study. The mean age was 47.48 ± 18.66 (18–98) (Table 1). The majority of patients included were males (448, $73.32\%$), with 114 ($25.44\%$) of them suffering from pneumothorax at admission. At admission, 155 patients ($25.37\%$) presented with pneumothorax. The mean length of hospital stay was 6.73 ± 4.14 days.
After splitting the patients into two lots depending on the occurrence of pneumothorax, we noticed an increase in the mean age for the “Pneumothorax” group to 51.68 ± 19.39 ($$p \leq 0.002$$), as well as a higher incidence of tobacco use ($$p \leq 0.019$$) and obesity ($$p \leq 0.038$$). As for the etiology of trauma, we found the majority of patients suffered from blunt trauma ($\frac{539}{611}$ patients, $88.22\%$). In this category, we considered all patients who suffered from motor vehicle accidents, workplace accidents, accidental falls, sport-related injuries, and suicide attempts. In terms of patients who experienced penetrating trauma, we included all patients who experienced hetero-aggression and stabbings. They accounted for $11.78\%$ of all patients and $41.93\%$ of pneumothorax patients. Moreover, patients who suffered from posttraumatic pneumothorax showed higher sodium levels ($$p \leq 0.024$$), higher neutrophil ($p \leq 0.0001$), monocyte ($p \leq 0.0001$), and platelet ($$p \leq 0.009$$) counts, and lower lymphocyte ($p \leq 0.0001$) counts. All hematological ratios were higher in the “Pneumothorax” group ($p \leq 0.0001$). The length of hospital stay was also longer in the “Pneumothorax” group ($$p \leq 0.003$$).
The receiver operating characteristic curves of all hematological ratios were computed in order to assess if the initial values of these indicators were predictive for the occurrence of pneumothorax in patients with thoracic injuries (Figure 1). Table 2 displays the optimal cut-off value calculated using Youden’s index, the areas under the curve (AUC), and the prediction accuracy of the markers.
In terms of systemic inflammatory makers and the length of hospital stay, we computed the Spearman correlation, and we identified a positive correlation between the NLR, SII, SIRI, and AISI and length of hospital stay (all $p \leq 0.05$), as highlighted in Figure 2.
We proceeded with the multivariate analysis of age, risk factors, all inflammatory ratios, and the occurrence of pneumothorax within the patients in the second group, as shown in Table 3. Furthermore, older patients (OR:1.01, $$p \leq 0.02$$), the presence of COPD (OR:2.93, $$p \leq 0.02$$), as well as tobacco (OR:2.20, $$p \leq 0.01$$), act as predictive factors for pneumothorax risk. In contrast, obesity acts as protective factor against pneumothorax (OR:0.65, $$p \leq 0.03$$). We considered an increased value of the NLR as being a value higher than the identified cut-off (NLR > 6, $p \leq 0.001$). This is similar for a high MLR (MLR > 0.62, $p \leq 0.001$), PLR (PLR > 165.71, $p \leq 0.001$), SII (SII > 1632.86, $p \leq 0.001$), SIRI (SIRI > 6.17, $p \leq 0.001$), and AISI (AISI > 1479, $p \leq 0.001$).
## 4. Discussion
According to the recent literature, thoracic trauma is a frequently occurring presentation in injured patients [40]. Post-traumatic pneumothorax is a common complication of chest injuries, occurring in between 20 and $55\%$ of patients, associated with relatively high morbidity and mortality. The mean age reported in the literature varies between 39 and 61 years old [41,42,43,44,45].
However, it is a preventable cause of death. Early diagnosis of pneumothorax can aid in the management of such patients, prevent hemodynamic deterioration, or occurrence of other complications.
In the present study, the incidence of pneumothorax was $25.37\%$ ($$n = 155$$/611), with a mean age of 47.48 ± 18.66, are similar findings to those found in the literature.
Most studies found in the recent literature report a negative impact on the outcomes of trauma patients among smokers [46,47,48]. In spite of all these findings, a recent paper published by Grigorian et al., which included 282,986 patients with chest injuries, reports a significantly better outcome in smokers, with a lower number of ventilator days ($$p \leq 0.009$$) and a lower rate of in-hospital mortality ($p \leq 0.001$). However, smokers appear to develop a higher rate of pneumonia ($p \leq 0.001$) [49]. In our study, we identified a total of 34 chronic tobacco users ($5.56\%$) and identified smoking as a negative predictor of outcomes, with a higher incidence of pneumothorax occurrence (OR = 2.29, $$p \leq 0.01$$). A plausible reason for this discrepancy can be attributed to the high proportion of smokers included in the study of Grigorian et al. totaling 57,619 patients ($20.4\%$).
The role of obesity as a risk factor for the outcomes of trauma patients is a topic of debate in the current literature. There are plenty of papers, including complex meta-analyses, that advocate for poorer outcomes of obese patients following major trauma [50,51,52,53]. Some papers, however, found that obese patients suffering from trauma have a more favorable outcome with a faster recovery [54,55]. According to our findings, obesity is a protective factor for the development of pneumothorax in patients suffering from chest injuries (OR = 0.65, $$p \leq 0.003$$). One of the reasons for such paradoxical findings can be attributed to the protective role of the adipose tissue upon blunt chest injuries.
The type of trauma appears to also play an important role in the development of pneumothorax. We notice that the majority of patients included in our study suffered from blunt chest injuries, which is to be expected as we did not have any wartime injuries reported in the past few years. We also notice that the majority of patients with penetrating trauma develop pneumothorax ($\frac{65}{72}$), but as the number of patients suffering from penetrating trauma is low, we can consider these data as purely observational.
The predictive values of hematological ratios in trauma patients have reportedly been researched more and more, although with conflicting results. Additionally, there has been a significant rise in the need for prognostic tools in trauma patients with unfavorable evolution and decompensation.
Our study included 611 patients diagnosed with thoracic trauma. We identified the inflammatory biomarkers in patient blood samples at admission and determined the presence of pneumothorax using CT scans at admission. Our study’s most important outcome is that the high baseline values for the NLR, MLR, PLR, AISI, SII, and SIRI are strong predictors for the development of post-traumatic pneumothorax. To the best of our knowledge, this is the first study to demonstrate that patients with high hematological ratios were more likely to develop pneumothorax and that the ratios predict a longer hospital stay.
According to Soulaiman et al., there is a statistically proven association between the NLR at admission and the outcomes of trauma patients, where a higher NLR predicts an unfavorable outcome [8]. According to this study, the optimal cut-off value for the NLR at admission was 4, which is a close value to our findings, with an AUC = 0.63 ($70.3\%$ sensitivity and $56.4\%$ specificity), highlighting a satisfactory test quality. In comparison, we computed a cut-off value for the NLR of 6, with an AUC = 0.79, highlighting an increased test quality.
In contrast, other studies, such as the one conducted by Dilektasli et al., revealed no statistically significant association between the NLR calculated from the blood samples at admission and the outcomes of trauma patients [56].
These controverted findings inspired another study, conducted by Younan et al. [ 57], to investigate the association between the NLR and the outcomes of trauma patients. According to the aforementioned, an increasing trajectory of the NLR (calculated at admission, and 24 and 48 h later) is strongly associated with the outcomes of the patients ($$p \leq 0.002$$) and length of hospital stay ($p \leq 0.001$). The total number of patients included in their study appears to be more modest (207 patients); patients with all types of trauma were included, not just chest injuries. Despite all these limitations, the findings of their study appear to support ours.
According to Jo et al., the PLR has significant prediction power for the outcomes of trauma patients ($p \leq 0.0001$) [27]; however, they found a higher lymphocyte count in the non-survival group compared to the survival group (183.0 [141.0;230.0] vs. 227.0 [188.0;265.0]). The PLR was also lower in the non-survival group compared to the survival group (51.3 [32.3;77.9] vs. 124.2 [79.5;187.2]). These findings are contrary to ours, where the lower the lymphocyte count and the higher the PLR, the worse the outcome.
A recent study by Rau et al. [ 58], including 479 trauma patients, found that comorbidities and hematological ratios (NLRs, MLRs, and PLRs) do not possess any predicting capabilities in the outcomes of such patients. Although some of their findings appear to contradict ours, we must remember that their study included all types of trauma and survival was considered as the final outcome of patients. The fact that a majority of the patients included in their study had suffered from a head or neck injury can be an explanation for their findings. Another reason for the lack of association between the hematological ratios and the outcomes of trauma patients can be attributed to the selection criteria. Their study also included patients who underwent invasive procedures, such as surgery, or patients who required resuscitation or blood transfusion, which are factors that can alter hematological ratios. We have taken into account these possible limitations of such reputable studies; this is the reason why our study’s main focus was thoracic trauma, with specific exclusion criteria.
In the current study, according to the multivariate analysis, all the hematological ratios were able to predict the occurrence of pneumothorax ($p \leq 0.0001$ in all cases). Moreover, we proved that some increased hematological ratios can indirectly predict the occurrence of complications through an increased length of hospital stay (SII $$p \leq 0.022$$, $r = 0.093$; SIRI $$p \leq 0.008$$, $r = 0.108$; and AISI $$p \leq 0.009$$, $r = 0.106$). Lastly, the present paper also revealed a major risk factor for traumatic pneumothorax development in tobacco use (OR = 2.29, $$p \leq 0.019$$), whilst obesity is a protective factor (OR = 0.65, $$p \leq 0.038$$).
The findings of our previous studies on the role of hematological biomarkers as predictive factors in the outcomes of both specific splenic trauma [29] and abdominal trauma [30] support the findings of the current paper. In the first paper, we found a significant association between the NLR and the severity of splenic injury ($$p \leq 0.02$$). The findings of the second paper revealed that the NLR, PLR, MLR, AISI, SII, and SIRI are powerful predictors of the development of acute kidney injury, mortality, and a composite endpoint of these two outcomes in abdominally injured patients ($p \leq 0.001$ in all cases).
Nevertheless, the present study has a set of limitations. The first limitation relies on the design of the study as a retrospective monocentric study. Further improvement could be brought by extending the research to a multicentric prospective study. Secondly, due to the retrospective nature of our study, we were unable to gather enough data on chronic medications administered before admission (corticosteroids or anti-inflammatory drugs), which prevented us from assessing how various medications affect inflammatory biomarkers. Lastly, the study only analyzed the inflammatory biomarkers at admission. Repeated determination throughout the hospitalization period may better reflect the dynamics of the inflammatory process and may improve the quality of our findings.
In spite of all these limitations, we consider our findings to be a stepping stone toward the development of new risk scoring systems for the improvement of the overall management of thoracic trauma patients and the early identification of patients at risk. We consider these hematological ratios to be especially important, taking into account their ease of determination and the low cost of assessment.
## 5. Conclusions
Our data show that patients with thoracic injuries, who have elevated NLRs, PLRs, MLRs, SIIs, SIRIs, and AISIs at admission at values that are above our calculated cutoff, are likely to have sustained severe thoracic trauma, are likely to have developed pneumothorax, and will likely follow a long evolution with a long duration of hospitalization. Additionally, we proved that tobacco use is a strong predictor of the development of post-traumatic pneumothorax in such patients, whilst obesity is a protective factor.
Given the ease of use of such ratios and the low cost of these metrics, they can be used in clinical practice to categorize patient treatment groups, develop predictive patterns, and classify risk groups for admission.
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|
---
title: The Contribution of Scalded and Scalded-Fermented Rye Wholemeal Flour to Quality
Parameters and Acrylamide Formation in Semi-Wheat-Rye Bread
authors:
- Dovile Klupsaite
- Vytaute Starkute
- Egle Zokaityte
- Darius Cernauskas
- Ernestas Mockus
- Evaldas Kentra
- Rugilė Sliazaite
- Gabriele Abramaviciute
- Paulina Sakaite
- Vitalija Komarova
- Ieva Tatarunaite
- Sandra Radziune
- Paulina Gliaubiciute
- Monika Zimkaite
- Julius Kunce
- Sarune Avizienyte
- Milena Povilaityte
- Kotryna Sokolova
- João Miguel Rocha
- Fatih Özogul
- Elena Bartkiene
journal: Foods
year: 2023
pmcid: PMC10000374
doi: 10.3390/foods12050937
license: CC BY 4.0
---
# The Contribution of Scalded and Scalded-Fermented Rye Wholemeal Flour to Quality Parameters and Acrylamide Formation in Semi-Wheat-Rye Bread
## Abstract
The aim of this study was to evaluate the influence of scalded (Sc) and scalded-fermented (FSc) (with Lactiplantibacillus paracasei No. 244 strain) rye wholemeal flour on the quality parameters and acrylamide formation in semi-wheat-rye bread. To that purpose, 5, 10 and $15\%$ of Sc and FSc were used for bread production. Results showed that scalding increased fructose, glucose and maltose content in rye wholemeal. Lower concentrations of free amino acids were found in Sc when compared with rye wholemeal, but fermentation of Sc increased the concentrations of some amino acids (on average by 1.51 times), including gamma aminobutyric acid (GABA, by 1.47 times). Addition of Sc and FSc had a significant influence (p ≤ 0.05) on bread shape coefficient, mass loss after baking and most bread colour coordinates. Most of the breads with Sc or FSc showed lower hardness after 72 h of storage compared with the control (i.e., without Sc or FSc). FSc improved bread colour and flavour, as well as overall acceptability. Breads with 5 and $10\%$ of Sc had a similar level of acrylamide to the control, while its level in breads with FSc was higher (on average, 236.3 µg/kg). Finally, different types and amounts of scald had varying effects on the quality of the semi-wheat-rye bread. FSc delayed staling and improved sensory properties and acceptability, as well as the GABA level of wheat-rye bread, while the same level of acrylamide as was seen in control bread could be reached when using between 5 and $10\%$ of scalded rye wholemeal flour.
## 1. Introduction
Wheat bread is the most popular bread in many countries and accepted as a very convenient form of high energy food with good digestibility [1]. However, semi-wheat-rye bread takes a higher position in the East European market; it is also considered to be better balanced, because of the presence of various cereal varieties with differing compositions [2]. In the production of semi-wheat-rye bread, wheat flour has a very important technological function because of its gluten network, which gives elasticity to the wheat–rye dough [3]. This characteristic leads to better gas retention and improves the gas extension in the dough, leading to a higher porosity and specific volume of the bread [4].
Owing to consumer interest in healthy nutrition and taking into consideration that bread is a very popular product consumed on a daily basis, bakeries are always eager to produce healthier products [5]. One of the steps to improve bread properties is to eliminate added sugar (saccharose) from the main bread formula [6].
However, the sensory properties of bread are very important characteristics of bread choice, and most consumers prefer a sweeter tasting product [7]. The influence of saccharose consumption on public health continues to be a controversial topic but it is known that the consumption of added sugar is associated with the risk of developing a range of chronic diseases [8,9,10,11,12]. To avoid numerous health problems associated with added sugar consumption, the World Health Organization (WHO) published guidelines in 2015 that recommended reducing the intake of “free sugars” to <$10\%$ of calories per day [13].
The use of scalded flour (Sc) can be an attractive technology to increase the sweet taste of bread without addition of saccharose, and it has indeed been reported that scalded flour has a positive influence on glycaemic index [14]. This technology is very common in Eastern Europe because it can ensure the good characteristics and sweeter taste of cereal products without any additional sugar. In addition, scalded flour is a good substrate for the growth of lactic acid bacteria (LAB), which are usually used in bread production as a microbial starter culture for sourdough fermentation. These suitable characteristics for LAB growth and multiplication are associated with the resulting hydrolysis of the flour starch and production of fermentable sugars (usually monosaccharides) that are easily available for LAB consumption and their metabolic conversion of monosaccharides to organic acids and other important metabolites. Additionally, fermentation of scalded flour leads to the desirable sweet–sour taste of bread, which is generally preferred by consumers. Furthermore, the combination of these two steps—i.e., scalding and fermentation—can lead to the extension of bread shelf-life as well as acrylamide reduction, owing to the degradation and metabolism of acrylamide precursors in baking dough.
The European Food Safety Authority (EFSA) scientific opinion on acrylamide in foods concluded that dietary exposure to acrylamide potentially increases the risk of developing cancer for consumers in all age groups; thus, the food industry should reduce acrylamide concentration in foods [15]. Acrylamide is formed in food products during their thermal treatment at temperatures > 120 °C, especially during the Maillard reaction. Formation of acrylamide in bread depends on many factors, including temperature, processing, type of flour(s), recipe, etc. [ 16].
One of the possibilities for reducing acrylamide formation in bread is the use of sourdough technology because of the rapid pH drop and degradation of the main precursors of acrylamide in dough that can be achieved using selected LAB strains. However, studies on the influence of scalded flour fermented with Lactiplantibacillus paracasei on acrylamide formation in bread are limited. Moreover, it should be pointed out that the scalding technology increases acrylamide precursor (monosaccharides) formation in dough. For this reason, not only should consumer preference for a sweet-tasting product be considered, but also the safety parameters, i.e., acrylamide formation in bread prepared with scalded flour.
Our previous studies focused on acrylamide concentration reduction in cereal products (mostly in bread and biscuits) using selected LAB strains and their combinations, showed very promising results, albeit by selecting the most appropriate LAB strain for each product technology [17,18,19,20,21,22,23,24,25,26]. These previous findings were explained by the different composition of the fermentable substrate, which led to different metabolic activities of the employed LAB, as well as differing efficiencies in the reduction of the acrylamide precursors in dough and, subsequently, acrylamide concentration in the end product.
Despite significant progress being made in this field since our research was published, in general, studies about the influence of scalded flour and scalded flour fermented with Lactiplantibacillus paracasei (FSc) on acrylamide formation in semi-wheat-rye bread are still scarce.
The aim of this study was to evaluate the influence of scalded and scalded-fermented (with Lactiplantibacillus paracasei No. 244 strain) rye wholemeal flour on the quality parameters and acrylamide formation in semi-wheat-rye bread. For this purpose, different quantities of scalded (Sc) and scalded-fermented (FSc) rye wholemeal flour were tested for semi-wheat rye bread (W-R) preparation (5, 10 and $15\%$). In addition, Sc and FSc parameters (pH, total titratable acidity (TTA), colour characteristics, hardness, sugars (fructose, glucose, sucrose and maltose) concentration and amino acid profile), as well as W-R quality and safety characteristics (specific volume, shape coefficient, mass loss after baking, crust and crumb colour coordinates, sensory characteristics, overall acceptability, and acrylamide concentration) were analysed.
## 2.1. Materials Used for Bread Preparation
Wheat flour (type 550D, gluten $26\%$, carbohydrate content $68\%$, fibre content $3.9\%$, protein content $11.9\%$, fat content $1.7\%$ and ash 0.55–$0.62\%$) and rye wholemeal flour (fat content $1.1\%$, carbohydrate content $62.2\%$, fibre content $16\%$ and protein content $8.5\%$) obtained from ‘Malsena plius’ Ltd. mill (Panevezys, Lithuania) were used for the W-R preparation. The W-R samples were prepared without and with addition of Sc and FSc (5, 10 and $15\%$).
Lactiplantibacillus paracasei No. 244 strain showing versatile carbohydrate metabolism and tolerance to acidic conditions [27] was used for FSc preparation. Strain No. 244 was stored at −80 °C in a Microbank system (PRO-LAB DIAGNOSTICS) and propagated in a DeMan, Rogosa and Sharpe (MRS) broth (CM 0359; Oxoid Ltd., Hampshire, UK) at 30 °C for 48 h. This LAB strain was previously newly isolated and identified from a spontaneous rye sourdough, which is traditionally used in rye bread production [27]. The characteristics of the Lp. paracasei No. 244 strain are given in Table 1.
## 2.2. Scald Preparation and Fermentation
The Sc was prepared by using 1000 g of rye wholemeal flour mixed with 1000 mL of hot water (95 °C). The scalding process was carried out at 30 °C for 2 h. For Sc fermentation Lp. paracasei No. 244 strain was used. The Lp. paracasei No. 244 cell suspension (5 mL), containing about 8.9 log10 CFU/mL, was added into the Sc (cooled to 30 °C), followed by fermentation for 24 h at 30 °C. Prepared Sc and FSc samples were applied for W-R preparation by using 5, 10 and $15\%$ (% of the total flour content).
## 2.3. Breadmaking
The W-R formula consisted of 0.5 kg wheat flour, 0.5 kg rye flour, $1.5\%$ salt, $3\%$ instant yeast and 1000 mL water (control bread). Control W-R samples were prepared without the addition of Sc or FSc. The tested W-R groups were prepared by addition of 5, 10 and $15\%$ Sc or FSc to the main recipe. In total, seven groups of baking dough and respective W-R samples were prepared and tested. The dough was mixed for 3 min at a low speed, then for 7 min at a high-speed regime in a dough mixer (KitchenAid Artisan, OH, USA). Then, the dough was left at 22 ± 2 °C for 15 min relaxation. Subsequently, the dough was shaped into 425 g loaves, formed and proved at 30 ± 2 °C and $80\%$ relative humidity for 60 min. The bread was baked in a deck oven (EKA, Borgoricco, PD, Italy) at 220 °C for 25 min. The schematic representation of the experimental design is shown in Figure 1.
## 2.4. Evaluation of Non-Treated, Scalded (Sc) and Scalded-Fermented (FSc) Rye Wholemeal Flour Parameters
The Sc and FSc samples (after 0 (FSc-0h) and 24 (FSc-24h) h of fermentation) were analysed to evaluate their pH, TTA, colour characteristics, hardness, sugar (fructose, glucose, sucrose and maltose) concentration and amino acid profile.
The pH values of Sc and FSc were measured and recorded with a pH electrode (PP-15; Sartorius, Göttingen, Germany). For pH analysis, the electrode was immersed directly into the Sc or FSc sample.
The TTA was determined for a 10 g sample of Sc or FSc homogenized with 90 mL of distilled water and expressed as mL of 0.1 mol/L NaOH required to achieve a pH of 8.2.
Colour parameters were evaluated using a CIE L*a*b* system (CromaMeter CR-400, Konica Minolta, Japan) [28].
The hardness of Sc and FSc samples was measured as the energy required for sample deformation (CT3 Texture Analyzer, Brookfield, Middleboro, USA), viz.: 50 g of a Sc or FSc sample was placed in a Petri dish and compressed to $10\%$ of its original height at a crosshead speed of 0.5 mm/s; the resulting peak energy of compression was reported as Sc or FSc sample hardness.
To determine the sugar concentration, 1–2 g of sample was diluted in 60 mL of distilled/de-ionized water, heated to 60 °C in a water bath for 15 min, clarified with 2.5 mL Carrez I (85 mM K4[Fe(CN)6] × 3H2O) and 2.5 mL Carrez II (250 mM ZnSO4 × 7H2O) solutions, and made up to 100 mL with distilled/de-ionized water. After 15 min, the samples were filtered through a filter paper and a 0.22 μm nylon syringe filter before further analysis. A 2 mg/mL standard solution of a sugar mixture was prepared by dissolving 0.2 g of each of fructose, glucose, sucrose and maltose (Sigma-Aldrich, Hamburg, Germany) in 100 mL of distilled/de-ionized water. Chromatographic conditions were as follows: the eluent was a mixture of 75 parts by volume of acetonitrile and 25 parts by volume of water, the flow-rate was 1.2 mL/min, 20 μL was injected. A YMC-Pack Polyamine II 250 × 4.6 mm, 5 μm (YMC Co., Ltd., Tokyo, Japan) column was used. The column temperature was set at 28 °C. The detection was performed using an Evaporative Light Scattering Detector (ELSD) LTII (Shimadzu Corp., Kyoto, Japan).
For free amino acids and gamma aminobutyric acid (GABA) analysis, the homogenized sample (~100 mg) was weighed into a 1.5 mL tube and analytes were extracted with 1 mL of aqueous 0.1 M HCl solution by shaking for 1 h. The resultant mixture was centrifuged at 12,000 rpm for 5 min. For derivatization, 50 µL of the resultant supernatant was mixed with 100 µL of 100 mg/L diaminoheptane (as an internal standard) and diluted to 500 µL with 0.1 M HCl solution. The resultant mixture was alkalized by addition of 40 µL of 2 M NaOH and 70 µL of the saturated NaHCO3 solution. Derivatization was performed by adding 1 mL of 10 mg/mL dansyl chloride solution in acetonitrile and incubating the resulting mixture at 60 °C for 30 min. The reaction mixture was quenched using 50 µL of $25\%$ ammonia solution and filtered through a 0.22 µm membrane filter into the auto-sampler vial. The concentration of analytes was determined using a Varian ProStar HPLC system (two ProStar 210 pumps and a ProStar 410 autosampler; Varian Corp., Palo Alto, CA, USA) and a Thermo Scientific LCQ Fleet Ion trap mass detector (Thermo Fisher Scientific, San Jose, CA USA). For analyte detection, the mass spectrometer was operated in positive-ionization single-ion monitoring mode for specific ions corresponding to derivatized analytes. The analyte concentration was determined from a calibration curve, which was obtained by derivatizing the analytes at different concentrations. For the separation of derivatives, a Discovery® HS C18 column (150 × 4.6 mm, 5 µm; SupelcoTM Analytical, Bellefonte, PA, USA) was used. Mobile phase A was $0.1\%$ formic acid in $5\%$ aqueous acetonitrile, and phase B was $0.1\%$ in acetonitrile (% in volume). A flow-rate of 0.3 mL/min was used for the analysis. The injection volume was 10 µL. The analytical gradient was as follows: 0 to 10 min (linear gradient) 15 to $60\%$ B, 10 to 40 min (linear gradient) 60 to $95\%$ B, 40 to 48 min 95 B, followed by re-equilibration of the column for 10 min with $15\%$ B (increased to 0.6 mL/min flowrate). The limit of quantification (according to the lowest concentration used for calibration) was 0.02 µmol/g.
## 2.5. Evaluation of Bread Quality
After 12 h of cooling at 22 ± 2 °C, W-R samples were subjected to analysis of specific volume, crumb porosity, shape coefficient, mass loss after baking, crust and crumb colour coordinates, sensory characteristics, overall acceptability and acrylamide concentration.
Bread volume was established by the AACC method [29], and the specific volume was calculated as the ratio of volume to weight. The bread shape coefficient was calculated as the ratio of bread slice width to height (in mm). Mass loss after baking was calculated as a percentage by measuring loaf dough mass before baking and after baking. Crust and crumb colour parameters were evaluated using a CIE L*a*b* system (CromaMeter CR-400, Konica Minolta, Tokyo, Japan) [28]. Bread crumb hardness was determined as the energy required for sample deformation (CT3 Texture Analyzer, Brookfield, Middleboro, USA): bread slices of 2 cm thickness were compressed to $10\%$ of their original height at a crosshead speed of 0.5 mm/s; the resulting peak energy of compression was reported as crumb hardness. Three replicates from three different sets of baking were analysed and averaged.
Sensory characteristics and overall acceptability of breads was carried out by 10 trained judges according to the ISO method [30] using a 140 mm hedonic line scale ranging from 140 (like extremely) to 0 (dislike extremely).
## 2.6. Determination of Acrylamide in Bread
The acrylamide concentration was determined according to the method of Zhang et al. [ 31] with modification. The bread samples were homogenized in a blender (Ika A10, Staufen, Germany). Two grams of sample were weighed in a 50 mL centrifuge tube and diluted with 20 mL of distilled/de-ionized water. The sampling tube was briefly vortexed (ZX3 Advanced VELP, Usmate (MB), Italy) to mix the contents for 10 min. The sample tube was centrifuged at 4000 rpm for 10 min with a centrifuge (Hermle Z 306, HERMLE Labortechnik GmbH, Wehingen Germany). Next, 10 mL samples of the clarified aqueous layer solution were transferred to 15 mL centrifuge tubes and clarified with 100 µL of Carrez I (85 mM K4[Fe(CN)6] × 3H2O) and 100 µL of Carrez II (250 mM ZnSO4 × 7H2O) solutions. The sample tubes were then centrifuged at 4000 rpm for 10 min.
For the preparation of acrylamide standard solution (30.4 µg/L), 15.2 mg of acrylamide analytical standard ($99.8\%$ purity) was weighed and dissolved in a 1000 mL volumetric flask and diluted with de-ionized water. The obtained solution was diluted by pouring 2 mL of the obtained acrylamide solution into a 1000 mL measuring flask and diluted with de-ionized water.
Three millilitres of the sample supernatant (or standard solution) was derivatized in a glass sample tube by adding 1.5 g of potassium bromide (KBr), 1 mL of potassium bromate solution (0.1 M, KBrO3) and 0.3 mL of sulphuric acid solution ($50\%$, H2SO4). The mixture was mixed in a shaker and kept for 2 h in a refrigerator (~4 °C). The derivative was neutralized by adding 250 µL of sodium thiosulphate solution (1 M, Na2S2O3 × 5H2O) until the orange colour disappeared. About 1.5 g of sodium chloride (NaCl) was added to the derivatization mixture and the mixture was extracted with ethyl acetate (CH3COOC2H5) (2 × 5 mL). The collected ethyl acetate was concentrated with a concentration system (Christ CT 02-50, Frankfurt, Germany) at a temperature of 40 °C and under reduced pressure. The solvent was evaporated and dissolved in 0.5 mL of ethyl acetate (for the standard, in a volume of 3 mL). Next, 100 mg of anhydrous sodium sulphate (Na2SO4) and 20 µL of triethylamine ((C2H5)3N) (20 µL of triethylamine in 0.5 mL of a concentrated derivatization solution) were added to the solution in a 15 mL centrifuge tube, mixed and centrifuged for 10 min (4000 rpm). The supernatant was analysed with a gas chromatograph-electron capture detector (GC–ECD).
A gas chromatograph (Shimadzu GC-17A, Tokyo, Japan) was equipped with an electron capture detector (ECD), an integrator to measure peak areas, and a thermostated column. The capillary column was a Rxi-5Sil MS (Restek, Germany): length 30 m; inner diameter 0.25 mm; stationary phase film thickness 0.25 µm. Working conditions were: injection volume 1 μL; column temperature gradient 70 °C (hold 1 min), 3 °C/min to 140 (hold 0.5 min), and 15 °C/min to 280 (hold 4 min). The mobile phase was nitrogen at 18.0 cm/sec flow rate, with a split of 3.0. The injector temperature was 250 °C, the detector temperature was 260 °C and the detector current was 2 nA.
## 2.7. Statistical Analysis
The results were expressed as mean values (for baking dough and bread samples $$n = 3$$, and for bread sensory characteristics and overall acceptability $$n = 10$$ trained panellists) ± standard error (SE). In order to evaluate the effects of different quantities of scalded non-fermented and fermented rye wholemeal flour on semi-wheat-rye bread quality parameters, data were analysed using a one-way ANOVA and Tukey-HSD as post-hoc tests (statistical program R 3.2.1). Additionally, Pearson correlations were calculated between various parameters, as well as between the dough and scald characteristics with acrylamide content. The results were recognized as statistically significant at p ≤ 0.05.
## 3.1. Parameters of Scalded (Sc) and Scalded-Fermented (FSc) Rye Wholemeal Flour
Acidity characteristics (pH and TTA), colour coordinates and hardness of Sc and FSc (after 0 and 24 h of fermentation) are shown in Table 2. When comparing the acidity parameters (pH and TTA) of the samples, the addition of pure LAB strains decreased pH and increased the TTA by 4.34 and $18.3\%$, respectively. After 24 h of fermentation, the pH of the samples was reduced to 4.57 and the TTA was increased to 2.53 °N. However, significant correlation between the pH and TTA of the samples was not found. The decrease in pH and increase in TTA in FSc samples are mainly related to organic acid production by lactic acid bacteria and their ability to acidify the fermentable substrates [32].
Regarding the colour coordinates of the samples, significantly higher values of redness (a*) (by $13.8\%$), and similar values of lightness (L*) and yellowness (b*) in FSc samples after 24 h of fermentation were found, when compared with Sc samples. Fermentation with LAB may induce the release of such pigments as anthocyanins and phenolic compounds, which are present in the pericarp, testa and aleurone layer of the rye grains [33,34,35]. This could cause the observed increased values of the a* coordinate in scalded-fermented rye flour.
The hardness of FSc samples was around 3.5 times lower in comparison with non-fermented-scalded rye flour. This may be explained by the proteolytic activity of the LAB strain and the acidity-elicited activation of proteolytic enzymes in flour [36]. The activity of the proteolytic enzymes induces the weakening of the gluten network structure and decreases the hardness of the fermented product [37].
The sugar content is depicted in Table 3. Fructose and glucose were absent in non-scalded rye wholemeal flour. However, the scalding process led to fructose and glucose formation (in the Sc sample, the fructose and glucose content was, on average, 0.880 and 1.25 g/100 g, respectively). Moreover, the scalding process increased maltose concentration by 6.14 times, in comparison with non-scalded flour. In both the Sc and FSc samples, sucrose was not detected; in addition, fermentation of the scalded flour significantly increased the glucose concentration by $13.2\%$ in comparison with Sc. Fructose and maltose content in Sc and FSc remained similar, on average, 0.850 and 6.61 g/100, respectively. Due to the gelatinization of starch during scald production, amylases in flour tend to convert starch into maltodextrins and further into maltose and glucose [32]. This explains the results observed in our study, which are similar with those reported by Li et al. [ 38]. Despite the utilization of monosaccharides by LAB, most LAB strains, including Lp. paracasei, possess amylolytic activity and this could also cause the increase in mono- and disaccharides in fermented scald [39].
The content of free amino acids and GABA is given in Table 4. Concerning the free amino acid concentration, in the Sc and FSc samples the concentration was lower in most cases, in comparison with non-treated rye wholemeal flour: asparagine, on average, by 2.34 times; serine, on average, by 2.17 times; aspartic acid, on average, by 4.46 times; and proline, on average, by 2.31 times. In comparison, when considering arginine, glutamic acid, threonine, glycine and alanine concentrations, the highest content of these amino acids was found in non-treated flour in all cases. Moreover, when comparing Sc and FSc, the latter showed the higher content of these amino acids (on average, by 1.26, 1.14, 1.78, 1.66 and 1.71 times, respectively). An exception was observed with proline, the content of which in both the Sc and FSc samples was similar (on average, 0.968 µmol/g). The lower free amino acid content in Sc samples could be related to the protein denaturation or dilution effect [40]. Conversely, the higher content of some free amino acids in FSc may have occurred due to proteolytic activity in the LAB strain [41]. It has been reported that the content of such amino acids as tryptophan, glutamic acid, isoleucine, leucine and asparagine increased after fermentation of rye dough [42,43].
Vis-à-vis the gamma aminobutyric acid concentration in non-treated and scalded flour, significant differences were not observed, and GABA concentration was, on average, 0.469 µmol/g. However, in FSc samples GABA concentration was found to be 1.47 times higher, in comparison with non-treated flour and Sc samples. GABA possesses anticarcinogenic, antihypertensive, antidepressant and antidiabetic properties. It is a metabolic product of plants and microorganisms such as LAB; therefore, fermented foods are a potential source of this amino acid [44]. It has been reported that this health-promoting compound is found in rye malt sourdough after fermentation with Limosilactobacillus reuteri LTH5448 and LTH5795; wheat sourdough fermented with *Levilactobacillus brevis* CECT 8183; legume flour sourdough started with Lev. brevis AM7 and *Lactiplantibacillus plantarum* C48; and wheat, barley, chickpea, lentil and quinoa flour sourdoughs fermented with strains of Lp. plantarum, *Furfurilactobacillus rossiae* and Fructilactobacillus sanfranciscens [45,46]; however, no data on GABA concentration in Sc and FSc were found in the literature. The results obtained in our study show that the scalding process had no significant impact on the GABA content of rye flour, but fermentation of Sc with Lactiplantibacillus paracasei No. 244 strain increased GABA content by, on average, 1.47 times.
## 3.2. Bread Quality
The bread specific volume, porosity, shape coefficient, mass loss after baking, colour characteristics of the bread crust and crumb, as well as bread crumb images are given in Table 5. Observing the results of the specific volume of bread samples, no significant differences were detected and, on average, bread specific volume was 1.98 cm3/g. In comparison, for bread shape coefficient, samples prepared with $15\%$ of Sc and FSc showed a lower shape coefficient in comparison with other bread groups (on average, $30.2\%$ lower). The scald quantity used for bread preparation, and the scald fermentation and quantity interaction, were significant ($$p \leq 0.019$$ and p ≤ 0.0001, respectively) with respect to the bread shape coefficient (Table 6). A moderate positive correlation between the bread specific volume and shape coefficient was established ($r = 0.439$, $$p \leq 0.47$$). Significant differences between the control breads and breads prepared with $5\%$ of Sc and with $15\%$ of FSc mass loss after baking were not found. However, other bread sample groups showed $33.6\%$ higher mass loss after baking. The interaction between analysed factors (scald fermentation and quantity interaction) was significant for bread mass loss after baking ($$p \leq 0.025$$) (Table 6).
As previously verified, the high temperature used during scalding affects starch gelatinization and protein denaturation, which results in greater flour viscosity and dough elasticity, and bread specific volume [40]. The increased specific volume of bread or millet cake made with heated flours has been reported [47,48]. However, gluten proteins may become denaturized as a result of scalding, and an excessively high amount of rye scald in the bread formula can diminish bread volume [49]. Thus, it was reported that scalded wholemeal could improve bread volume, but the addition of $10\%$ of scalded flour in wheat bread resulted in a slightly lower bread volume [49]. Moreover, it was found that rye scald significantly affected the crumb cell diameter and area, as more cells per slice area could be obtained [40]. A lower pH in fermented rye scald may cause peptization and swelling of the proteins in the flour, increasing the consistency and subsequently the dough’s ability to retain carbon dioxide [50]. It has also been claimed that as the acidity rises, all of the moisture in the dough is bound by the undegraded starch, which may have an impact on mass loss after baking [51].
When comparing the colour characteristics of the bread crust, it was found that the addition of 10 and $15\%$ of Sc, as well as 5, 10 and $15\%$ of FSc, reduces bread crust lightness (L*), on average, by $18.2\%$. Analysed factors (scald fermentation and quantity) interaction were significant for bread crust L* ($$p \leq 0.0037$$) (Table 6). The lowest crust redness (a*) was obtained in breads prepared with $15\%$ of Sc (in comparison with control breads and the group of breads prepared with FSc, on average, it was lower by $25.0\%$). Both analysed factors were significant on bread crust a* coordinates (scald fermentation $$p \leq 0.022$$ and scald quantity $$p \leq 0.048$$). Addition of Sc and FSc (except $5\%$ of scalded flour) reduced bread crust yellowness (b*), in comparison with control samples, on average, by $14.0\%$. Scald quantity was a significant factor for bread crumb L* ($$p \leq 0.016$$) and the highest L* was recorded in samples prepared with 5 and $10\%$ of Sc and with $10\%$ of FSc (their L* coordinates were, on average, 57.2 NBS). The lowest crumb a* was found in the group of samples prepared with $15\%$ of Sc (4.15 NBS). All the analysed factors and their interactions were significant for bread crumb a* (Table 6). The highest crumb b* was attained in the control group samples (19.9 NBS) and, by increasing the Sc the quantity in the main bread formula, crumb b* coordinates decreased (in samples prepared with $5\%$ of Sc, on average, by $8.5\%$; in samples prepared with $10\%$ of Sc, on average, by $19.1\%$; in samples prepared with $15\%$ of Sc, on average, by $31.2\%$). Also, bread samples prepared with FSc showed lower b* coordinates in comparison with the control samples group (samples prepared with 5 and $15\%$ of FSc, on average, lower by $24.1\%$; samples prepared with $10\%$ of FSc, on average, lower by $20.6\%$). Scald quantity and scald fermentation x scald quantity interaction was significant for bread crumb b* coordinates (Table 6).
It has been reported that wheat bread with the addition of dietary fibre usually has a darker colour of crust and crumb [52]. The colour coordinates of the bread crust made with scalded rye in our study were similar with those obtained by Esteller et al. [ 53]. The higher percentage of seed coat and reducing sugars in rye wholemeal scald, as well as increased levels of some free amino acids in fermented scald, may contribute to the greater browning of baked bread [42,54]. This explains the lower values for the lightness of bread with Sc and FSc [53].
Bread texture hardness after 24, 48 and 72 h of storage are shown in Figure 2. When comparing bread hardness after 24 h of storage, control bread hardness was the lowest (0.1 mJ). The hardness of bread prepared with Sc (5, 10 and $15\%$), as well as $15\%$ of FSc was the highest, on average, at 0.3 mJ, and the hardness of bread prepared with 5 and $10\%$ of FSc was, on average, 0.2 mJ. After 48 h of storage, different trends were established. Bread samples prepared with $5\%$ of FSc showed lower hardness, in comparison with the control samples. After 72 h of storage, most of the bread groups prepared with Sc or FSc showed lower hardness in comparison with control breads (except for the bread group prepared with $15\%$ of scalded flour).
Rye flour contains arabinoxylans, whose extractability and swelling properties increase under acidic conditions [55]. These compounds are essential in the water binding process and the formation of a viscous dough. Due to the effect of arabinoxylans and the decreased activity of amylase, rye bread made with sourdough hardens more slowly when compared with wheat bread [56]. Moreover, the prolonged shelf-life of bread prepared with sourdough may be related to the higher content of exopolysaccharides synthesized by LAB [32]. These effects may explain the reduced hardness of breads with Sc or FSc after 72 h of storage.
## 3.3. Sensory Properties and Overall Acceptability of Bread Samples
Bread sensory properties are shown in Figure 3a,b—colour, taste, flavour and odour sensory characteristics; Figure 3c,d—texture sensory characteristics; and Figure 3e—overall acceptability. Comparing the colour sensory characteristics of bread, the bread sample groups prepared with FSc showed, on average, a $39.7\%$ higher colour acceptability, in comparison with control samples and samples prepared with Sc. In addition, samples prepared with FSc showed a higher odour intensity (on average, $30.6\%$ higher) in comparison with control samples and samples prepared with Sc. Addition of Sc to the main bread formula increased the bread odour intensity of the samples; in comparison with the Sc and FSc groups, on average a two times higher bread odour intensity was obtained in bread groups prepared with FSc. Bread sample groups prepared with FSc showed, on average, 0.6 times higher additive odour, in comparison with control samples and samples prepared with Sc. By increasing the percentage of Sc in the main bread formula, the flavour intensity of the samples was reduced; however, opposite tendencies were observed in bread prepared with FSc. The same tendencies were found in the bread flavour. The highest intensity of additive flavour was attained in bread group samples prepared with 10 and $15\%$ of FSc (on average, 58.7). The acidity of all the tested bread groups was not sensorily felt, and the highest bitterness was exhibited in samples prepared with $15\%$ of FSc. Analysis of between-subject effects showed that scald fermentation was a significant factor on all the analysed colour, taste, flavour and odour sensory characteristics (p ≤ 0.0001) (Table 7). However, scald quantity was a significant factor only for bread odour intensity and additive odour ($$p \leq 0.0003$$ and p ≤ 0.0001, respectively). Interaction of both analysed factors was significant for most of the analysed colour, taste, flavour and odour sensory characteristics (Table 7).
Regarding bread texture sensory characteristics (Figure 3b,d), the most acceptable porosity was evaluated in the control samples and breads prepared with $5\%$ of FSc. All the bread groups prepared with FSc showed, on average, 2.1 times higher brittleness, in comparison with control samples and breads prepared with Sc. Significant differences in the springiness and the texture moisture between the bread groups were not found. Nevertheless, the hardest samples were obtained in the bread groups prepared with $15\%$ Sc. Analysed factors and their interaction were significant on most of the analysed bread texture sensory characteristics except in scald fermentation. However, the interaction of factors was not significant for bread porosity, and scald quantity was not significant for bread springiness.
When comparing bread overall acceptability, significantly higher overall acceptability was exhibited in bread groups prepared with FSc, in comparison with control group breads and breads prepared with Sc (Figure 3e). Furthermore, scald fermentation and interaction of scald quantity and fermentation were significant for bread overall acceptability (Table 7).
Bread flavour is highly affected by the lactic and acetic acids, free amino acids, exopolysaccharides and volatile compounds (alcohols, aldehydes, ethers, etc.) formed during sourdough fermentation [57]. Different sourdoughs have shown to improve the sensory qualities of wheat breads [58]. Moreover, heat treatment of flour causes variations in flavour and browning during baking. Gao et al. [ 2018] observed higher sensory scores of rye-wheat breads made with heat-treated rye flour [40]. Djukic et al. [ 2013] reported that with the addition of rye scald to bread dough, the sensory qualities of the rye/wheat bread were enhanced [50]. Higher scores of bitterness for breads with Sc and FSc could be explained by the presence of wholemeal flour, which contains bitter taste-eliciting bran [59].
## 3.4. Acrylamide Concentration in Bread
Acrylamide concentrations (µg/kg) in the bread samples are given in Figure 4. The lowest concentration of acrylamide was found in the group of control samples and breads prepared with 5 and $10\%$ of Sc (on average, 149.0 µg/kg). Acrylamide content in samples prepared with FSc was, on average, 236.3 µg/kg. However, samples prepared with $15\%$ of Sc had, on average, a $61.9\%$ higher acrylamide concentration compared with control samples and samples prepared with 5 and $10\%$ of Sc, and, on average, a $39.6\%$ higher acrylamide concentration compared with the groups of samples prepared with FSc. Moderate negative correlations between acrylamide concentration in bread and bread shape coefficient, bread crumb L* and b* colour coordinates were obtained (r = −0.528, $$p \leq 0.014$$; r = −0.680, p ≤ 0.001; r = −0.443, $$p \leq 0.044$$, respectively). Scald quantity and scald fermentation and quantity interaction were significant for acrylamide concentration in bread samples (Table 8).
The potentially carcinogenic acrylamide is produced during the Maillard reaction and its main precursors are reducing sugars such as glucose and fructose, and the amino acid asparagine, which is a limiting precursor [60]. In addition to its high nutritional value, rye flour also contains a higher content of asparagine than wheat [16]. It has been reported that greater acrylamide formation in rye bread can also occur due to the high content of dietary fibre and ash in flour [61]. However, fermentation with LAB can lead to a reduction in acrylamide content in bread because the LAB use free asparagine in their metabolism [60]. Przygodzka et al. [ 2015] reported a weak effect of wheat flour extraction rates and their chemical components on acrylamide formation in breads baked at 240 °C, while this effect was not found in rye flour [16]. It was also claimed that the level of acrylamide was lower when bread was baked at a lower temperature with a longer baking time. In our study, the content of asparagine and fructose were similar in Sc and FSc; only glucose content was higher in FSc compared with Sc, and that probably led to the higher content of acrylamide in breads with FSc. This could also suggest that the starter culture did not successfully lower the free asparaginase content.
## 4. Conclusions
The addition of various cereal varieties to wheat bread could lead to healthier, but still tasty, products. The preparation of scalded rye flour is very common technology for rye bread production in Eastern Europe. It is valuable owing to its ability to increase the sweet taste of bread without addition of saccharose—a positive influence on the glycaemic index—while fermentation of scalded flour leads to a desirable sweet–sour bread taste and the extension of bread shelf-life. As studies about the rye scald fermentation with certain LAB and further uses for semi-wheat-rye bread production are still scarce, our study provides beneficial information in this field. We used the newly isolated Lactiplantibacillus paracasei No. 244 strain—which possesses versatile carbohydrate metabolism, tolerance to acidic conditions, and antimicrobial properties—for the rye scald fermentation. The outcome of this study showed that scalding caused higher levels of reducing sugars in rye wholemeal. Fermentation decreased the pH and hardness but increased the TTA and redness (a*) value of scald. In most cases, amino acid concentrations in Sc and FSc were lower than in rye wholemeal flour. However, fermentation of Sc increased the levels of certain amino acids, as well as GABA.
Scald type (unfermented and fermented) and quantity (5, 10 and $15\%$) affected the quality of semi-wheat-rye bread in certain parameters. Colour coordinates, shape coefficient and mass loss after baking of semi-wheat-rye breads were significantly influenced by the quantity and type of scald. Reduced hardness of breads with Sc (except with $15\%$ of Sc) or FSc was observed after 72 h of storage, compared with control samples. Scald fermentation was a significant factor on such sensory characteristics as colour, taste, flavour and odour of bread. Addition of FSc significantly improved overall acceptability of the tested bread. Compared with control bread, addition of 5 and $10\%$ of scald to the bread formula did not enhance the formation of acrylamide in wheat-rye bread. In summary, scald fermented with Lp. paracasei No. 244 enriches bread with GABA and improves bread sensory acceptability as well as staling.
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|
---
title: Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based
Convolutional Neural Networks
authors:
- Swaminathan Sundaram
- Meganathan Selvamani
- Sekar Kidambi Raju
- Seethalakshmi Ramaswamy
- Saiful Islam
- Jae-Hyuk Cha
- Nouf Abdullah Almujally
- Ahmed Elaraby
journal: Diagnostics
year: 2023
pmcid: PMC10000375
doi: 10.3390/diagnostics13051001
license: CC BY 4.0
---
# Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks
## Abstract
Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person’s growth of this condition. Among the essential elements at the top of the list are anxiety and long-term diabetes. If not detected early, this illness might result in permanent eyesight loss. The damage can be reduced or avoided if it is recognized ahead of time. Unfortunately, due to the time and arduous nature of the diagnosing process, it is harder to identify the prevalence of this condition. Skilled doctors manually review digital color images to look for damage produced by vascular anomalies, the most common complication of diabetic retinopathy. Even though this procedure is reasonably accurate, it is quite pricey. The delays highlight the necessity for diagnosis to be automated, which will have a considerable positive significant impact on the health sector. The use of AI in diagnosing the disease has yielded promising and dependable findings in recent years, which is the impetus for this publication. This article used ensemble convolutional neural network (ECNN) to diagnose DR and DME automatically, with accurate results of 99 percent. This result was achieved using preprocessing, blood vessel segmentation, feature extraction, and classification. For contrast enhancement, the Harris hawks optimization (HHO) technique is presented. Finally, the experiments were conducted for two kinds of datasets: IDRiR and Messidor for accuracy, precision, recall, F-score, computational time, and error rate.
## 1. Introduction
Computer-assisted health care, health care technology consulting, and health monitoring equipment are just a few of the current buzz words. Thanks to the connection and computing architecture that has drawn attention to the electronic era we live in, ordinary people now have the luxury of receiving diagnosis and treatment from the comforts of home with a single tap [1,2,3]. While routine illnesses and minor illnesses can usually be treated without visiting a doctor, some more severe illnesses still necessitate a great deal of effort from the medical establishment. Technology can help, but not replace human intervention. With the advancement in AI technology, technologies can now autonomously analyze a patient’s condition and identify a condition in a matter of seconds using the patient’s significant history and associated data [4,5,6]. By 2025, the amount of DR individuals suffering is predicted to rise from 382 million to 592 million. According to a study conducted in the Pakistani province of Khyber Pakhtunkhwa (KPK), 30 percent of diabetic individuals suffer from DR, with 5.6 percent going blind [7,8,9]. If mild NPDR is not treated in the beginning phases, it might progress to PDR. In another study, 130 people with DR symptoms were found in Sindh, Pakistan [10,11]. According to the findings, DR patients made up $23.85\%$ of the overall examined patients, with PDR patients accounting for $25.8\%$ [12,13]. Patients with DR are symptomatic in the beginning phases; however, as the disease progresses, it causes blobs, vision problems, distortions, and gradual visual acuity loss. Diabetic retinopathy is one of the issues previously mentioned in the article. Diabetic retinopathy is caused by diabetes destroying the blood flow on the retina’s inner, resulting in blood and other body fluids leaking into the tissues surrounding it. Soft, damaged tissue (also known as cotton wool patches) [14], hard exudates, microaneurysms, and hemorrhages form as little more than a result of the leaking [15]. It is the most common cause of visual loss in the working-age population [16]. Diabetic retinopathy (DR) is caused due to diabetes mellitus, which can damage the retina and even lead to the loss of vision. The DR has several stages of severity such as mild, moderate, and severe [17]. The severe stage of DR is termed as proliferative diabetic retinopathy (PDR), in which the formation of new vessels in the retina is observed [18]. However, the early detection of DR and proper diagnosis will reverse or reduce the growth of the effects caused by the disease. Diabetic macular edema (DME) is a condition in which the lesions caused by DR are observed in the middle portion of the retina called the macula. The DME is considered as a serious condition as the damage caused by it is irreversible. The identification of features such as micro-aneurysms, hard exudates, hemorrhages, etc., can be used to carry out the detection of these diseases. These micro-aneurysms refer to the red spots in the retina’s blood vessels with sharp margins formed in the early stages of the disease. The exudates are caused due to abnormality in the blood vessels, which are formed as yellowish-white spots in the outer layer of the retina. Hemorrhages also occur such as micro-aneurysms but have irregular margins caused due to the leakage of capillaries. The blockage of arteries also contributes to cotton wool spots, which occur as a white region in the retinal nerve. Several methods have been developed for the detection of DR and DME to provide diagnosis, but these traditional methods were inefficient in accurately detecting diseases. Deep learning techniques have been deployed for disease detection in which the retinal image (fundus image) is used as the input in which the features are extracted for detection. These approaches have been found to be more effective in identifying features than the traditional methods; however, these approaches also suffer from inaccuracy due to the presence of noises and artifacts in the input images.
Figure 1 describes the retina images for disease DR and DME. As a result, it is hard but critical to recognize DR to prevent the worst effects of later stages. Fundus imaging is utilized to diagnose DR, as mentioned in the preceding section. Manual analysis can only be performed by highly qualified subject matter experts and is thus cost and time intensive. As a result, it is critical to apply machine vision technologies to assess the retina image features and aid physicians and radiologists. Hands-on development and end-to-end learning are two types of computer vision-based methodologies. Traditional algorithms such as HoG, SIFT, LBP, Gaussian filters, and others are used to extract the features; however, they failed to preserve the scale, rotation, and brightness fluctuations [19].
Several existing approaches have integrated the preprocessing of input images and the deep learning-based detection of diseases in which the accuracy in the detection of diseases was observed to be improved. The common processes involved in these approaches are the preprocessing of input images, enhancement in contrast, and the extraction of features for the detection of diseases. The machine learning models such as support vector machine (SVM) and K-nearest neighbor (KNN) classifiers were found to be appropriate for detecting DR and DME. The severity of the disease was determined by the number of features identified by the model; however, the imbalance in the distribution of datasets resulted in the inefficient determination of severity. In particular, an effective mechanism in the detection of DR and DME, along with the determination of severity, is still in demand. The major aim of this research work was to provide the effective detection of DR and DME and to determine the disease’s severity to define the disease’s damage level on the patient. The accuracy of detection was achieved by performing the proper processing of the input retinal image. End-to-end learning understands the underlying rich traits dynamically, allowing for greater identification. Inside the retina imaging databases, many hand-on engineering and end-to-end learning-based algorithms have been used to identify the DR. Still, none of them can identify the mild stage. Accurate diagnosis of the weak stage is critical for controlling this devastating disease. Utilizing end-to-end deep ensembles models, this study attempted to discover all stages of DR (including the moderate stage). The findings revealed that the proposed strategy beats the current methods.
The major objective of this research is to provide precise classification between the DR and DME and to compute the severity of the diseases accurately. This objective can be achieved by fulfilling the sub-objectives, which are listed as follows, To minimize the noise level in the input image by performing effective preprocessing of the image;To maximize the precise identification of features from the preprocessed image by enhancing the contrast level;To maximize the accuracy of detection by incorporating the segmentation of lesions in the blood vessels;Effectively classify the images into three classes based on the extraction of significant features;To determine the severity of the disease based on the variation in the intensity of the features for diagnosis.
The major contributions of this paper are as follows: In our work, we performed preprocessing that included three processes such as noise removal using iterative expectation maximization, artifact removal using nonlinear filtering, and contrast enhancement using Harris hawks optimization; the preprocessed image was used to enhance the quality of the images, which led to high segmentation and detection accuracy. Preprocessing was performed to reduce noise and artifacts and improve the contrast, which increased the efficiency of feature extraction and reduced the false detection rate. Segmentation was performed before feature extraction and classification, which increased the detection accuracy. For segmentation, we proposed improved OPTICS clustering, which considers particular regions of interest and takes less time for segmentation, thus reducing latency and increasing the disease detection accuracy. Improved OPTICS clustering overcomes misalignment problems due to considering the particular region of interest, thus increasing the segmentation and detection accuracy. The extraction of features was carried out in the segmented images obtained from the previous process. Features such as micro-aneurysms, hemorrhages, and hard exudates, collectively termed as structural features, are considered the essential features; along with this, the shape features, orientation features, and color features are also considered for the classification of DR and DME. The ensemble CNN architecture was implemented for this purpose, which outperformed the ensemble CNN class prediction. From this, the classification of images was carried out in several classes, namely, normal, DR, and DME. Furthermore, the severity of the disease was computed by using conditional entropy in which the number of lesions is considered for the threshold generation. Based on the threshold, the severity level of the disease was classified into three classes: mid, moderate, and severe. The proposed research work is evaluated in terms of performance metrics such as accuracy, precision, recall, F-score, computation time, and error rate.
The rest of the paper is organized as follows: Section 2 illustrates the state-of-the-art in diabetic retinopathy and diabetic macular edema detection using specific approaches. Section 3 discusses the major problems that exist in this field. Section 4 describes the system model with the proposed algorithms and techniques in detail. Section 5 describes the experimental results of the proposed as well as previous methods. Section 6 concludes the paper by providing future enhancements.
## 2. Related Work
In the literature, the diagnosis of DR has received much interest. In [20], researchers offered a robust system that automatically recognized and classified retinal lesions (blood vessels, microaneurysms, and exudates) from retinal imaging. Blood vessels, microaneurysms, and exudates were first discovered using image processing methods. Following this, the retina properties of the vascular system, microaneurysm count, exudate area, contrast, and homogenization were evaluated from the images obtained. These characteristics were then fed into a fuzzy classifier that uses the information to classify healthy, mild NPDR, moderate NPDR, severe NPDR, and PDR stages. A sample of 40 color fundus images was obtained from the DIARETDB0, DIARETDB1, and STARE datasets using a fuzzy classifier, correctly classifying the images with an efficiency of up to 95.63 percent.
A reliable automated approach for detecting and classifying the various stages of DR has been suggested The optic disc and retina neurons are separated, and characteristics are retrieved using the gray level co-occurrence matrix (GLCM) approach. To identify various stages of DR, a fuzzy classifier and a convolutional neural network were used to classify them. DIARETDB0, STARE, and DIARETDB1 were the datasets used [21].
The unique clustering-based automatic region growth methodology was introduced in this study. Several types of features—waveform (W), co-occurrence matrix (COM), histogram (H), and run-length matrix (RLM)—were retrieved for the texture features, and several ML algorithms were used to achieve a classification performance of 77.67 percent, 80 percent, 89.87 percent, and 96.33 percent, respectively. The information fusion approach was utilized to create a fused hybrid-feature database to improve the accuracy of the classification. Two hundred and forty-five elements of the hybrids’ feature data (H, W, COM, and RLM) were extracted from each image, and 13 optimum characteristics were chosen using four methodologies: Fischer, mutual information feature selection, information gain, and the possibility of the dependent variable average correlation [22]. The number of DR patients outnumbered the number of practitioners by a large margin. As a result, manual clinical diagnosis or screening takes a long time. To avoid this problem, follow-up scanning is performed regularly, and automated DR identification and intensity classification are required. Several strategies for detecting retinopathy and classifying its severity and likelihood are presented here [23].
Exudates are the diagnostic indications of diabetic retinopathy, a retina condition caused by long-term diabetes that can lead to eyesight problems if not detected early. The procedure of recognizing and categorizing exudates from a retinal image has been made easier thanks to a medical screening program. The exudates are first segregated using the FCM technique and then transformed into discrete mother wavelets. The classifier is fed the texture textural properties retrieved by the grey-level co-occurrence matrix. The suggested program’s efficiency was evaluated by comparing it to the data from the publicly available dataset IDRID. MATLAB was used to formulate and construct a GUI [24].
This research has the proposed texture feature extraction characteristics of the GLDM method (contrast, angular second moments, density, median, and inverse difference moment) feature and feed-forward neural net classifier as a machine learning-based approach for DR detection and evaluation. According to the results of the trials and performance assessment, the suggested methodology had a detection performance of $95\%$ [25].
Diabetes is responsible for 50 deaths per 1000 live births amongst individuals over the age of 70. The identification of diabetes at a preliminary phase and the implementation of a suitable therapy may minimize the visual loss among the sufferers. Once symptoms of DR have been identified, the severity of the disease must be defined to recommend the appropriate treatment. Mild nonproliferative diabetic retinopathy (NPDR), moderate NPDR, severe NPDR, proliferative diabetic retinopathy (PDR), and no DR are the five phases of diabetic retinopathy severity. The techniques and issues associated with DR identification are summarized in this publication [26]. In [27], the authors proposed diabetic retinopathy classification using retinal images through an ensemble learning algorithm. The proposed work includes the following processes: retinal image collection, preprocessing, feature extraction, and feature selection and classification. In preprocessing, the noisy images, duplicate images, and black borders are removed from the images. Tone mapping is used to increase the contrast and luminance in the images. Two sets of features are extracted from images such as the histogram-based feature and GLCM feature extraction. Then, the features are concatenated to select the relevant features. Here, the GA algorithm is used for feature selection. Finally, classification was undertaken by the XGBoost algorithm using the selected features. Here, genetic algorithm (GA) was used for feature selection; it takes a lot of time to select the features, thus increasing feature selection and classification latency.
Early detection of diabetic retinopathy using retinal images for diabetes is presented in [28]. The proposed method includes four processes: preprocessing, segmentation, feature extraction, and classification. The preprocessing includes noise removal and contrast enhancement using histogram equalization (HE). The segmentation is performed by Gaussian derivative and Coye filter, which segments the EX, MA, and HM. The features are extracted from the segmented image and extract features such as EX, MA, and HM values. Finally, SVM is used to classify the images using the extracted features. Here, SVM was used for classification, which takes a lot of time for training when considering larger datasets, thus leading to classification latency.
A histogram equalization method for the early detection of diabetic retinopathy was presented in [29]. The proposed algorithm included three methods: histogram clipping, RIHE-RVE, and RIHE-RRVE, which addressed the issues of the illumination of the retinal images. To avoid enhancement, the histogram clipping algorithm was proposed. The simulation result showed that the proposed method achieved a high performance compared to the other state-of-the-art methods. Here, the histogram equalization method was proposed, however, it is an unselective process that may increase the background noise contrast while decreasing the functional input image.
The authors in [30] proposed CANet to detect diabetic retinopathy and macular edema for diabetes. The proposed work used ResNet50 to produce a feature map with various resolutions including a cross-disease attention network, disease-specific attention module, and disease-dependent attention module. The disease-specific attention module was used to learn the features of the two diseases. In this stage, the inter special relationship was evaluated to detect the diseases. A disease dependent attention module was used to evaluate the internal relationship between the DR and DME diseases. Here, raw images were considered for training and testing, thus increasing the high false positive rate due to the presence of noise and low contrast, also reducing the detection accuracy.
The authors in [31] proposed a deep learning algorithm to detect diabetic retinopathy disease in diabetic patients. The proposed method included two processes: diagnosing DR severity and the feature extraction of DR. The proposed system hierarchical multitask learning architecture aims to detect both the DR severity and DR feature extraction. Finally, the fully connected layer provides the output, and it considers the hybrid loss, cross-entropy loss, and kappa loss for reducing the errors in the levels of DR severity. The simulation results showed that the proposed model achieved a higher performance using traditional deep learning methods. Here, the traditional deep learning method was used to detect the DR severity levels and feature extraction of DR; however, it generated multiple convolutional layers, thus increasing the complexity and latency.
In [32], the authors proposed a modified contrast enhancement approach from the effective identification of features in detecting diabetic retinopathy and diabetic macular edema. The limitations of conventional contrast limited the adaptive histogram equalization (CLAHE) technique such as the fixed clip limit and region of context, resulting in the inefficient identification of minute features, but can be overcome by implementing modified particle swarm optimization (MPSO) to determine the optimal clip limit and region of context, thereby resulting in the precise identification of features that further help in the accurate detection of diseases. The global best solution of all the operating particles was computed by comparing the output provided by all the particles in the iteration, which resulted in enhanced image contrast. The optimization of the clip limit and region of context was performed by the MPSO algorithm for the purpose of enhancing the contrast of the input image, but the proposed algorithm possessed slow convergence and is stuck in the optimal local solution.
In [33], the authors proposed an approach for the detection of diabetic macular edema in an automatic manner. The macular edema was identified, and the severity of the disease was determined by implementing mathematical morphology. The retinal image was used as the input from the detection process that was carried out. Initially, the preprocessing of the input image was performed from the removal of noise and enhancement of the contrast. Furthermore, the localization of the macula was executed by removing the optic disc and locating the center of the fovea. Then, the exudates in the region of the macula were identified in order to determine the severity of the disease. The removal of artifacts such as reflection due to lighting was removed as a post-processing step to achieve an accurate determination of severity. The detection of the macula in the input retinal image was carried out by using mathematical morphology, but this approach resulted in less accuracy in the detection of the macula region.
In [34], the authors proposed a probability-based construction of the future retinal image in detecting diabetic retinopathy. The difficulty in identifying the future instances of lesions in the retinal image was addressed. Initially, the segmentation of lesions and vessels was carried out to identify the severity of the disease from the input retinal image. Then, the probability of future lesion location was computed by the construction of a probability map. Furthermore, the generated probability map, along with the structure of vessels, was considered for the systemization of future lesions in the retina. This method was found to be effective in predicting future lesions based on the progression of the severity of diseases. The future severity of diabetic retinopathy was determined by using the probability map and the features of the current vessels, but the lack of noise removal in the input image reduced the efficiency of this approach.
## 3. Problem Statement
An input fundus image is used to perform the identification of diabetic retinopathy and diabetic macular edema; however, the accuracy of the system is decreased by the increased false detection rate of the existing techniques. In addition, the following issues are encountered in the best detection of DR and DME, which are listed as:Difficulty in feature differentiation: The detection of DR and DME is based on various features such as hard exudates, hemorrhages, and micro-aneurysms, but the differentiation of these minute features from each other is a hard task, which degrades the computation of the accurate severity of diseases. Class Overlapping: Current techniques also consider illness severity; however, the sparse training data for each severity leads to class imbalance issues that degrade the classification accuracy. Inadequate preprocessing: Using the current methods for effective contrast enhancement with traditional preprocessing leads to difficulties distinguishing features from the background.
In [35], the authors proposed diabetic retinopathy detection using a deep convolution neural network (DCNN) for nonproliferative diabetic retinopathy. The proposed work includes three phases: preprocessing, candidate lesion detection, and candidate extraction. In preprocessing, the image contrast is enhanced using curve transformation. Then, the images are smoothened by a bandpass filter. In the lesion, the detection process includes four stages: optical disc removal, candidate lesion detection, vessel extraction, and preprocessing. In candidate extraction, the micro-aneurysms are detected to measure the coefficient between every pixel using Gaussian kernels. For this, a PCA algorithm was proposed to reduce the dimensionality. Finally, classification was undertaken by DCNN. In this way, the proposed work achieved high accuracy of nonproliferated diabetic retinopathy. The major issues determined in this paper are as follows:Here, preprocessing was performed to enhance the quality of the retinal images; however, the retina image still has noise due to the implementation of traditional contrast enhancement techniques, thus reducing the image quality, which leads to a high false detection and reduced detection accuracy. DCNN is used for feature extraction and the detection of nonproliferated diabetic retinopathy. Still, DCNN focuses on the whole image for the extraction of features without any particular region of interest, thus increasing the high latency for feature detection. The PCA algorithm was used to reduce the dimensionality, but the number of principal components must be selected otherwise it may cause information loss, thus reducing the detection accuracy.
The authors in [36] proposed a data augmentation method to improve the detection rate of proliferative diabetic retinopathy. The NVs were inserted onto pixels located on vessels. Vessel segmentation was performed by Otsu thresholding and the U-Net deep learning algorithm, and then optic disc segmentation was performed. The count of NVs was determined by selecting random values using a threshold. The next process is semi-random blood vessel generation, which is based on the tree structure. This process considers the shape and orientation of the NVs. For the vessel color assignment color, a matrix was proposed that calculates the weighted average of the RGB values of the images. Finally, DR grading and data augmentation was proposed to improve the NVs. Some of the significant problems in this research are as follows:Here, the Otsu thresholding method was used for vessel segmentation, which performed well; however, it did not provide an optimal result for noisy images. First, the noise is removed from the images, and then the thresholding is applied; otherwise, this method will fail, thus reducing the performance of vessel segmentation. The detection of diabetic retinopathy was carried out by performing the segmentation of neovessels in the retina. However, performing detection based on a single feature results in a high false detection rate. Here, the U-Net algorithm was also used for vessel segmentation, which takes a lot of time to learn the vessels from the retinal images at the middle layers, thus leading to high latency.
The authors in [37] proposed the analysis of retinal images to detect eye diseases for diabetes using the deep learning method. The proposed method considered two processes: detection and localization, and the segmentation of localized regions. For localization, the author proposed the FRCNN method, which extracts the features from the images that evaluate the affected portions. For the segmentation process, the author proposed the FKM clustering algorithm. The ground truth was generated for detecting the affected regions during training. Finally, the DME is classified into two classes such as DME and background. The serious issues in this paper are as follows:Here, raw images were considered for the localization and segmentation process, thus reducing segmentation and detection accuracy due to low contrast and the presence of noise in the retinal images. Faster RCNN was implemented for the extraction of features but the lack of pixel-to-pixel alignment in the region of interest caused misalignment, resulting in the degradation of the detection accuracy. The proposed approach was used for diabetic-based disease detection in the eye, but the detection of various diseases from the limited number of trained images resulted in class imbalances.
The authors in [38] presented an efficient framework for the detection of macular edema disease for diabetes. The proposed work used the combination of a deep convolution neural network (DCNN) and a meta-heuristic algorithm for feature extraction and feature selection, respectively. At the stage of feature extraction, the proposed work reduced the feature extraction complexity by reducing the prior knowledge. The SMOTE algorithm was used to perform class imbalance. *The* generic algorithm and binary particle swarm optimization algorithm were used to select the relevant features. The drawbacks in this paper are as follows:Here, the features were extracted from the noisy images, thus reducing the quality of the images and leading to poor feature extraction, thus increasing the macular edema’s false detection rate. The integration of the genetic algorithm and binary particle swarm optimization was used to determine the subset size. However, implementing these two algorithms increases the complexity and time consumption, thereby increasing the latency. DCNN was used for feature extraction and the detection of nonproliferated diabetic retinopathy, however, DCNN focuses on the whole image for the extraction of features without any particular region of interest, thus increasing the high latency for feature detection.
## 4. Proposed Model
In this research work, we concentrated on accurately detecting the DR and DME from the input fundus images. The severity of the disease is also determined based on the features extracted from the images. Figure 2 shows the architectural view of the proposed work. The description of the dataset is provided below: The properties of the blood vessels in the retinal image enable the ophthalmologist to assess retinal disease. The presence of lesions on the fundus image is the first sign of diabetic retinopathy. The preprocessing technique is mainly used to remove unwanted noise and enhance some image features.
The fundamental idea underlying OPTICS is to find the points associated by density to extract the cluster structure of a dataset. The approach generates a density-based representation of the data by constructing a reachability graph, an ordered collection of points. Each location in the list has a reachability distance associated with it, which measures how simple it is to get to that site from other points in the collection. Points with comparable accessibility distances are most likely in the same category.
Before sharing our preprocessed image with CNN, we converted the image to an array and mapped that array’s values in the range of 0 to 1 as the epoch was set at 235 to reach a deep network. The initial learning rate was kept at 1 × 10³, which is the default value for the Adam’s optimizer, and the die stack size was 32. We trained our model with more pictures, obtained only a few hundred of images for training, and generated more images from the existing dataset by passing parameters such as the rotating range, width changing range, height changing range, scissors range, zoom range, and pan on image data generator.
The classification of diabetic retinopathy is classified into two types: nonproliferative and proliferative. The term “proliferative” refers to whether the retina has neovascularization (abnormal blood vessel growth). Nonproliferative diabetic retinopathy refers to early illness without neovascularization (NPDR).
Dataset Collection: For accurate prediction of diabetic retinopathy and diabetic macular edema, we applied two kinds of retina fundus images: IDRiD and MESSIDOR. The description of these two datasets is as follows:IDRiD: Based on the presence of DR and DME disease, 516 images were loaded in the dataset. In addition, images were acquired through the field of view and stored in JPG format, and the size of each image was 800 KB. This dataset contained 81 color fundus images with the sign of DR. With this dataset, hard exudates (EX), microaneurysms (MA), soft exudates (SE), and hemorrhage (HE) based images are stored. MESSIDOR: This dataset was used, whose scope is to develop the DR and DME detection of images. In total, 1200 eye fundus images were used with the multiple pixel rates of 1440 × 960, 2240 × 1488, and 2304 × 1536.
The following steps implement a prediction of DR and DME.
## 4.1. Preprocessing
This is an initial step for DR and DME detection. To enhance the information for the disease diagnosis system, it is necessary to use some of the preprocessing steps as follows: (a) Noise Filtering: Fundus images are cropped by salt and pepper noises, which are removed from the input images using the iterative expectation maximization (IEM) approach. In this approach, uncertainty is overwhelmed by using IEM variables. Noise is removed in the zig-zag trajectory and edge, and the corner position of the image is denoised using IEM variables. A dynamic threshold was computed and adjusted accordingly for noise removal since the acquisition of each image was different with their resolution.
The proposed inverse dual tree initial ranging (IDTIR) procedure uses the iterative expectation maximization (IEM) algorithm. The IEM algorithm is an iterative method that effectively estimates the parameters of the statistical model. In the IEM algorithm, two major steps are executed to estimate the parameters accurately. These steps can be explained as follows:E-step—This step determines the current estimate of parameters by creating a function for the expectation of log-likelihood. The expectation step is the base of the proposed IEM algorithm. M-step—This step is the final step that computes the parameters in such a way that the expected log-likelihood function can be maximized (i.e., the likelihood function determined in the E-step is maximized to calculate the parameters.
The above two steps were iteratively executed to determine the final parameters. Let ωV,L be the parameter vector, and it can be represented as ωV,L=hV,L,TV,L∈μ for the Lth active channel path of the given ranging code. The set of parameters is represented as μ. The latest estimated parameter set is denoted as μ^ and can be formulated as follows, [1]ω^V,L=h^V,L,T^V,L∈μ^ E-Step Computation *In this* step, the expected value is calculated as [2]GωV,L|μ^≜lnPY|ωV,L,μ^α−‖Y−K^V,L−hV,LΓTV,L∁V‖2 Here, [3]K^V,≜∑ℵ=1N∑$s = 1$NBh^ℵ,sΓ(T^ℵ,s)∁ℵ−h^V,LΓT^V,L∁V
M-Step Computation *In this* step, the expected value is maximized as follows:[4]ω^V,L=argmaxGωV,L|μ^ After parameter estimation, the estimated parameters are updated in the parameter vector. These two steps are executed until the terminating condition is met. The channel coefficient is derived from the parameter vector by letting the derivative equal zero with the fixed timing offset.
(b) Artifact Removal: Blurriness, poor edges, and illumination are called artifacts, which are removed using the nonlinear diffusion filtering algorithm, which eliminates all kinds of artifacts and ensures the image quality in terms of illumination correction and edge preservation.
(c) Contrast Enhancement: Low contrast is one of the important issues of image classification. In this work, we considered contrast enhancement as an optimization problem with the intention of optimizing the pixel values based on the contrast level of the input image. To enhance the contrast level of the input image, we proposed the Harris hawks optimization algorithm, which improves the performance of the image brightness.
H2O is a recently developed meta-heuristic algorithm that performs better in solving optimization problems. The H2O algorithm mimics the cooperative strategy and chasing style of the Harris hawks in nature. Since it has an intelligent searching strategy and fast convergence rate, it works better than the conventional genetic algorithm, particle swarm optimization algorithm, etc. Due to the benefits of the H2O algorithm, it was adapted for contrast enhancement using the pixel intensity rate in the proposed system. The proposed H2ORSS algorithm detects the optimum threshold value for replacing the pixel intensity values with normal ones. The proposed H2ORSS algorithm involves three major processes: initialization, fitness value estimation, and update of hawks.
Initially, the image matrix is initialized as hawks with the population size of PS. For each hawk (Xi) in the population, the fitness function is estimated. The fitness function is determined in terms of the pixel intensity, neighbor intensity, and resolution. The fitness function of the ith hawk is expressed as follows, Once the fitness is computed for all hawks, then three sequential phases are executed to select the optimal solution.
Phase 1: Exploration Phase This phase relies on waiting, searching, and detecting prey. In every step, each Harris hawk is considered as the alternative solution. Based on the fittest solution, the position for each Harris hawk is updated as follows:[5]Xiter+1=Xranditer−𝓻1Xranditer−2𝓻2Xiter if 𝓸≥0.5Xpiter−Xaiter−𝓻3lb+𝓻4ub−lb if 𝓸<0.5 The location of the hawks in the next iteration is denoted as Xiter+1 and 𝓻1, 𝓻2, 𝓻3, 𝓻4 are the present location vectors of the hawks. Furthermore, 𝓸 is the random number selected in the range of 0 and 1, and ub,lb are the upper bound and lower bound, respectively. The average location of hawks (Xaiter can be estimated from the following expression:[6]Xaiter=1PS∑$i = 1$PSXiiter Phase 2: Transformation from Exploration to Exploitation
Next, the algorithm transforms the state from exploration to exploitation. In this transformation, the energy of the prey is dissipated due to evading behavior. The energy level of the prey is (Ep), which is expressed as follows:[7]Ep=2Eo1−iterTm Here, E0 is the initial state energy of the prey and tm is the maximum iteration. By varying the tendency of E0, the state of the prey can be judged.
Phase 3: Exploitation After judging the state of the prey, the Harris hawks attack the selected prey. In practice, the prey changes the evading behavior, frequently changing the attacking behavior. Four strategies are constructed in the H2ORSS algorithm for attacking prey based on evading behavior. Here, soft besiege and hard besiege are the basic strategies to attack the prey, which is decided as follows: If Ep≥0.5, then a soft besiege occurs, and if Ep<0.5, then a hard besiege occurs.
Soft Besiege This attacking strategy is selected when Ep≥0.5 and 𝓻≥0.5 by Harris hawks. This soft besiege attacking strategy is modeled as follows:[8]Xiter+1=ΔXiter−EpFXpiter−Xiter Here, F is the jump intensity of the prey during the evading process, and it is given as $F = 21$−𝓻5 and ΔXiter represents the difference in the location vector of prey in each iteration. This difference is estimated by using the following expression:[9]ΔXiter=Xpiter−Xiter Hard Besiege
If Ep<0.5 and 𝓻≥0.5, the hard besiege strategy is selected to attack the prey. *In* general, these probability values show that the prey’s energy is dissipated and has low evading energy. In this case, the position of Harris hawks is updated by the following equation:[10]Xiter+1=Xpiter−EpΔXiter Soft Besiege with Progressive Rapid Dives This strategy is selected when the prey has sufficient energy to evade form the attack. This situation is explained as Ep≥0.5 and 𝓻<0.5. Based on this behavior, the next position of the hawks is updated as follows:[11]Y=Xpiter−EpFXpiter−Xiter *As this* strategy involves progressive dives, the hawk’s dive is formulated as follows:[12]Z=Y+B∗lf𝒹 where B represents the random vector; lf𝒹 represents the levy flight with the dimension 𝒹. Thus, the next position is updated as follows: [13]iter+1=Y if fY<fXiter𝒵 if f𝒵<fXiter
Hard Besiege with Progressive Rapid Dives This situation is defined as the prey has not sufficient energy to escape. This situation is formulated as Ep<0.5 and 𝓻<0.5. The rule for this situation is formulated as follows:[14]Xiter+1=Y if fY<fXiter𝒵 if f𝒵<fXiter Here, Y is estimated using the upcoming Equation, [15]Y=Xpiter−Ep|FXpiter−Xaiter Based on the above rules, the position of each hawk is updated, and the optimal solution is derived over iteration. Finally, the optimum threshold value was computed for the prediction of contrast values throughout the images.
Algorithm 1 deals with Generalized Linear Model (GLM), which is used for regression and classification tasks, is one of the key algorithms in H2O. GLM is a versatile and effective modeling approach that can deal with different data kinds and distributions. Algorithm 1 Pseudocode for H2OInput: PS,MaxiteOutput: Optimal ThresholdBeginInitialize → hawks population Xi (C.U.i);While (Stopping Condition Not Met) do Compute → fitness functionFor (Xi∈XPS)doUpdate →Eo and F;Update →Ep using Equation [8];End ForIf (Ep≥1)ThenUpdate position using Equation [9];End IfIf (Ep<1)ThenIf (𝓻≥0.5&&|Ep|≥0.5) Update → position using Equation [10];Else If (𝓻≥0.5&&|Ep|<0.5)ThenUpdate → position using Equation [11];Else If (𝓻<0.5&&|Ep|≥0.5)ThenUpdate → position using Equation [12];Else If (𝓻<0.5&&|Ep|<0.5)ThenUpdate → position using Equation [13];End IfEnd IfEnd WhileEnd
## 4.2. Blood Vessel Segmentation
Blood vessels are important in computing the image intensity, edges, texture, and other analyses of image features. Analyzing the diagnosis over the segmented area increases the accuracy and precision rate of any disease. Hence, the optic disk is removed from the contrast-enhanced image, and then the blood vessels are extracted using improved mask RCNN, in which ROI alignment is the first step that predicts the region of interest from the input image. In this work, pixel-wise softmax was applied for accurate segmentation of blood vessels, which was better than the CNN, RNN, RCNN, and DCNN algorithms [39,40].
OPTICS clustering stands for ordering points to identify clustering structure. It is more similar to DBSCAN clustering. OPTICS algorithm includes two measurements, which are defined as follows, Core distance: This represents the minimum values of the radius essential to classify the given point as a core point. If the considered point is not a core point, then its core distance is indeterminate. Reachability distance: *This is* represented with respect to another cluster data point. The reachability distance between two points (x,y) is the highest of the core distance and then the Euclidean distance between the two points (x, y). The reachability distance is not defined if the y point is not a core point. Figure 3 represents the calculation of the reachability distance. *The* general procedure of M-OPTICS is defined as follows: Next, the proposed M-OPTICS explanation is defined as follows: M-OPTICS considers three important conditions: maximum radius, distance, and number of cluster points including the core distance, core points, and reachability distance. In the M-OPTICS algorithm, the point P is known as the core point when the point is on MinPts. The reachability distance and core distance calculations are given as follows:[16]CDo=∞, o,ε<MinPtsMinPts−Do, otherwise [17]R.D.p,o=maxCDo,Dp,o where p represents the object and o represents the center point. The core distance represents the lowest value, which is the radius. From the radius, the core point is different. RD represents the reachability distance, which is estimated as the highest core distance, and ε represents the radius of the data. The reachability distance data are clustered separately. The data similarities were measured by Jaccard similarity, which calculates the similarity between a finite set of samples. The calculation of the Jaccard similarity is defined as follows:[18]JD=1−JμA,B [19]JμA,B=μA∩BμA∪B where A and B represent the two points obtained from the blood vessels.
The CNN-based ensemble learning model was incorporated due to two major unique features: shared weights and local connections. The extraction of features from the input data using convolutional layers and determining the relationship between the obtained features using the pooling layers was implemented, which can be formulated as:[20]aql=∑$$p \leq 1$$Vapl−1∗Jpql+yql where Jpql, yql denote the trainable parameters, and V denotes the input features. The output provided by the nonlinear layer is computed as:[21]xd=fvd where function fvd denotes the output of the rectified linear unit. The performance of the model can be further improved by executing batch normalization. The dataset comprising of fused images of R dimensions comprised of a T number of training samples can be denoted as H=hi,cli|1≤i≤T, where the classes are cli∈Cl=1,2,…,M and the maximum count of classes is denoted as M. In the ensemble model, each model’s training is performed randomly. The input of each CNN will be H˜=hi˜,cli|1≤i≤T, which comprises r “R feature subspaces that are randomly selected.
For instance, i and j are two identical features with dimension d, and for that similarity function simpdi,j, which is computed by:[22]simpdi,j=vectori×vectorj‖vectori‖×‖vectorj‖ *For a* different number of CNN layers and the operations involved in this study, computational complexity was evaluated, which is described as follows:[23]Pvsi=1−μ×ON+μ×ON=ON where ON represents the sum of iterations for performing the feature extraction and classification μ ∈0,1 and then S.S.upd with respect to the fx as follows:[24]S.S.upd=argmaxipεidxifxPVsn=ON where ON represents the sum of iterations for S.S.upd, which provides the near optimum feature matches from the trained set. Once the features are extracted, they are then updated by the presented method.
The output obtained from each CNN is denoted as x=CNN H˜; the collective outputs obtained from the individual CNN are denoted as X=x1, x2,…, xL, where L denotes the ensemble’s size, and the global output of the ensemble model is obtained by using weighted averaging of the output of the individual CNN. The weighted average of the output of the individual CNN is formulated as: G=∑$j = 1$TwjsjTwith wj ≥ 0 [25]∑$j = 1$Twj=1 where sj denotes the score and wj denotes the weight of the j−thj=1, 2, 3 model.
The classifier diversity between any two CNN models is computed as:[26]CDi,j=TwNT where NT and Tw denote the total number of test samples and the difference of results caused by the samples. The diversity of the ensemble model is computed as the average of the classifier diversities, which can be formulated as:[27]ED=∑$i = 1$M∑$j = 1$MCDi,jL, i≠j where ED denotes the diversity of the ensemble model, and CD denotes the classifier diversity. The classification output achieved from the weighted averaging of the individual CNN models possessed increased accuracy than the individual CNN models. Figure 4 presented the SMDTR-CNN-based land cover classification for identifying normal, DR and DME.
Table 1 addresses the ensemble deep learning model below with their filters, filter size, stride, padding, and output image size. A CNN’s fundamental building block is a convolutional layer and includes a series of filters, the parameters of which must be learned throughout the training process. The filters are often smaller in size than the real image. The pooling layer’s function is to lower the spatial size of the representation in order to reduce the number of parameters and calculations in the network; it operates independently on each feature map (channels). Maximum pooling and average pooling are the two types of pooling layers. Max pooling is a procedure commonly used for the individual CNN convolution layers listed below when they are added to a model. Maxpooling minimizes the picture dimensionality by lowering the number of pixels in the preceding convolution layer’s output. The rectified linear activation unit (ReLU) is one of the few milestones in the deep learning revolution. It is basic, but it is superior to the activation features of its predecessors such as sigmoid or tanh.
## 5. Results and Discussion
The E-CNN performance was estimated with the accuracy, precision, recall, F-score, error rate, and computational time.
## 5.1. Accuracy
Accuracy is defined as the ratio of the received input image inventive classification scheme by the assessed classification scheme, which can be formulated as:[28]A=T1+T2T1+T2+F1+F2 From the above equation, F1,F2 denote the false positive and false negative values, respectively; and T1,T2 denote the true positive and true negative values, respectively. Accuracy is the significant metric for calculating the performance of the system.
## 5.2. Precision
Precision is computed by the ratio of excluding the significant classification result from the overall classification outcome. The meticulousness of the system can be measured using precision, which can be formulated as:[29]P=T2T1+F1
## 5.3. Recall
The recall is defined as the ratio of excluding the same classification result to the recovered results. The recall is used for measuring the comprehensiveness of the system, which can be formulated as:[30]R=T1T1+F2
## 5.4. F-Score
The F-score is computed by using the parameters of recall and precision by jointly assessing them. The results accuracy can be computed using F-score, which can be formulated as:[31]FS=2∗P∗RP+R
## 5.5. Computation Time
Computation time is the amount of time needed to complete a computational operation. Computation time is calculated by calculating the time elapsed between the classification completion and computation. The system’s efficacy is assessed in terms of computation time. It is appreciated whether the study obtained a greater accuracy with better precision of outcome in a shorter computing period.
## 5.6. Error Rate
In Table 2, the results analysis of all models is furnished in the numerical form for better understanding. The error rate is defined as the ratio of errors in the sample to the overall samples. The error rate is used to determine the system’s performance. A good system has a much lower error rate, which can be formulated as:[32]Error Rate=No of ErrorsNo of Samples As can be seen in Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10, we evaluated the proposed E-CNN to various state-of-the-art approaches such as SVM, KNN, enhanced CNN, and deep learning (DL). When analyzing performance, the optic disk (OD) is eliminated because it is a non-lesion area. The numerical findings suggest that our proposed E-CNN was superior. E-CNN had a mean accuracy of 99.84 percent, which was 4.38 percent greater than the benchmark. Although its effectiveness was equivalent to that of the Messidor database, it performed poorly in blood vessel segments.
Furthermore, as shown in Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10, the accuracy, precision, recall, f-score, computational time, and error rate produced by E-CNN were much better than those acquired by other approaches. The difficulty of misclassification was exacerbated in the lesions DR and DME due to fewer samples; however, our proposed technique could still meet this obstacle. The quantitative class labels are also shown in Figure 5 to further illustrate the suggested strategy’s efficiency. In the DR lesion segmentation challenge, one can see that the E-CNN was much more accurate and robust. We also performed an ablation experiment to prove the accuracy of the proposed E-CNN. The SVM is referred to as the baseline approach for convenience. The suggested strategy has been demonstrated to generate significant improvements over the baseline regarding four targets, as shown in Figure 5, Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10. The addition of preprocessing also improved the performance. The mean precision improved by 3.83 percent in comparison to the benchmark. Our proposed technology, in particular, can be simply integrated into other encoder–decoder networks, which we wish to conduct soon. Additionally, the proposed E-CNN achieved the greatest accuracy values in DR and DME diagnosis, demonstrating the efficacy of our proposed method.
In this work, ensemble convolutional neural networks (ECNNs) were used to classify images of diabetic retinopathy. A recently developed meta-heuristic method, the Harris hawks optimization (HHO) algorithm, was used to optimize the ECNN hyperparameters. Then, the Harris hawks optimization technique was used to improve the feature extraction and classification processes to obtain the most significant features. Compared to previous systems, the deep learning model provides extremely satisfactory results regarding the specificity, precision, accuracy, and recall.
## 6. Conclusions
All of the studies on the DR classification issue can be divided into two groups. The first is a binary DR diagnosis in which the individual possesses or does not. The problem with this technique is that after we realize a person has DR, we cannot tell how serious the disease is. Multi-class identification is the answer to this challenge. As previously mentioned, we classified DR into five classes or phases using multi-class classification. However, almost all of the associated studies, particularly in the early stages of DR, have been unable to appropriately define every one of the stages of DR. It is critical to identify the DR at a very early stage to treat the disease, as treating the disease at a much later date is challenging and can result in death. To our understanding, no other study has employed the IDRiR and Messidor databases to identify the milder phases of DR that we used in our study. Our approach outperformed the present advancements in detecting the mild stage. Furthermore, no one else has demonstrated the impact of a balanced dataset in previous research. The unbalanced dataset may have caused the classification accuracy to be skewed. The network can be trained on features correctly when samples in the classes are evenly distributed such as in a balanced dataset; however, in the case of asymmetrical distributions, the network performs for heavily tested classes. Furthermore, the present CNN architectures for DR identification do not consider the impact of varied hyperparameter tweaking (meta-learning) as well as its consequences. In the future, we plan to use some other deep-learning techniques for DR and DME disease classification.
Recently, CNN-based methodology has been considered to learn features for classification. However, tuning non-trainable hyperparameters for such networks is manual, intuitive, and non-trivial. In the future, a technique based on DR and DME will be proposed to adjust the CNN architecture parameters. The convolution and pooling layer number, the kernel number, and the kernel size of the convolution layer are determined by the upcoming proposed technique. Therefore, the number of untrainable hyperparameters can be reduced. There are some challenges in adapting DR and DME to a CNN. Based on the dimension of the input image, the maximum and minimum sizes of the kernel must be specified for clear classification.
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|
---
title: Thyroid Profile in the First Three Months after Starting Treatment in Children
with Newly Diagnosed Cancer
authors:
- Chantal A. Lebbink
- Cor van den Bos
- Miranda P. Dierselhuis
- Marta Fiocco
- Annemarie A. Verrijn Stuart
- Eef G. W. M. Lentjes
- Sabine L. A. Plasschaert
- Wim J. E. Tissing
- Hanneke M. van Santen
journal: Cancers
year: 2023
pmcid: PMC10000403
doi: 10.3390/cancers15051500
license: CC BY 4.0
---
# Thyroid Profile in the First Three Months after Starting Treatment in Children with Newly Diagnosed Cancer
## Abstract
### Simple Summary
Thyroid dysfunction during childhood may affect daily energy, growth, body mass index and bone development. Thyroid dysfunction may occur in children with cancer due to chemotherapy or other drugs, radiotherapy, the tumor itself or severe illness. The aim of this prospective study was to determine the percentage, severity and risk factors of changing thyroid hormone concentrations in the first three months of childhood cancer treatment. Subclinical hypothyroidism (normal thyroid hormones, with elevated thyroid stimulating hormone (TSH) according to age) was present in $8.2\%$ of children at diagnosis and $2.9\%$ of children three months after starting treatment. Subclinical hyperthyroidism (normal thyroid hormones, with lowered TSH values according to age) was present in $3.6\%$ of children at diagnosis and $0.7\%$ of children after three months. In $28\%$ of children, the concentration of free thyroid hormone (FT4) decreased by ≥$20\%$. We conclude that children with cancer are at low risk of developing hypo- or hyperthyroidism in the first three months after starting treatment but may develop a decline in FT4.
### Abstract
Background: Thyroid hormone anomalies during childhood might affect neurological development, school performance and quality of life, as well as daily energy, growth, body mass index and bone development. Thyroid dysfunction (hypo- or hyperthyroidism) may occur during childhood cancer treatment, although its prevalence is unknown. The thyroid profile may also change as a form of adaptation during illness, which is called euthyroid sick syndrome (ESS). In children with central hypothyroidism, a decline in FT4 of >$20\%$ has been shown to be clinically relevant. We aimed to quantify the percentage, severity and risk factors of a changing thyroid profile in the first three months of childhood cancer treatment. Methods: In 284 children with newly diagnosed cancer, a prospective evaluation of the thyroid profile was performed at diagnosis and three months after starting treatment. Results: Subclinical hypothyroidism was found in $8.2\%$ and $2.9\%$ of children and subclinical hyperthyroidism in $3.6\%$ and in $0.7\%$ of children at diagnosis and after three months, respectively. ESS was present in $1.5\%$ of children after three months. In $28\%$ of children, FT4 concentration decreased by ≥$20\%$. Conclusions: Children with cancer are at low risk of developing hypo- or hyperthyroidism in the first three months after starting treatment but may develop a significant decline in FT4 concentrations. Future studies are needed to investigate the clinical consequences thereof.
## 1. Introduction
Thyroid hormones are essential during childhood for adequate mental development, linear growth, bone development and metabolic regulation [1,2]. Signs and symptoms of thyroid dysfunction can be overweight, declining linear growth, mental retardation in the young, constipation (hypothyroidism), tachycardia and growth acceleration (hyperthyroidism), or fatigue and emotional imbalances (both). In children with cancer, thyroid dysfunction may present with symptoms that are regularly observed during childhood cancer treatment and thus may be overlooked.
The thyroid gland can be damaged in children with any type of cancer by the tumor itself, chemotherapy (e.g., busulphan), radiation exposure or immunotherapy, resulting in thyroidal hypo- or hyperthyroidism [3]. In several small studies, the prevalence of primary hypothyroidism during cancer treatment varied between 0 and $18\%$ [4,5,6,7,8,9]. Next to damage of the thyroid gland, thyroid hormone metabolism in children with cancer may also be distorted due to damage of the hypothalamic–pituitary region as a consequence of a brain tumor or cranial irradiation (central hypothyroidism). Moreover, specific drugs may influence the thyroid profile without actual thyroid or pituitary gland damage, as is seen, for example, after the administration of asparaginase with a decrease in thyroxine binding globulin (TBG) concentration [8] or after the administration of corticosteroids with lowered thyroid stimulating hormone (TSH), triiodothyronine (T3) and TBG concentrations and increased reverse T3 (rT3) concentrations [8,9].
Lastly, thyroid hormone metabolism may change during childhood cancer treatment as a consequence of an adaptive mechanism during illness called ”euthyroid sick syndrome” (ESS) [10]. In this case, concentrations of thyroxine (T4) and T3 decrease due to two mechanisms, [1] downregulation of hypothalamic thyrotropin-releasing hormone (TRH) secretion and [2] changed activity of the liver deiodinases, resulting in decreased conversion of T4 into T3 and increased conversion of T4 into rT3 [11]. In children, EES has been described during severe illness and anorexia and is thus not associated with the underlying disease per se, but with its severity [12]. For the presence of ESS, different definitions are used, and in the few small studies that have been conducted, the prevalence of ESS during childhood cancer treatment, depending on its definition, varied between 0 and $100\%$ [4,5,6,7,8,9]. When children with cancer have hypo- or hyperthyroidism due to pituitary or thyroidal damage, this is considered a pathophysiological state and needs treatment. However, in case of acute illness, changes in the thyroid profile (ESS) are considered “physiological” and may even be protective. Therefore, it is not recommended to treat children who develop low thyroid hormone concentration during acute illness with thyroid hormone [13].
In children who develop mild central hypothyroidism after treatment for a brain tumor, a decline in FT4 of >$20\%$, even within reference ranges, was shown to be clinically relevant [14]. Although mild central hypothyroidism may not be comparable with ESS, it may be hypothesized that a prolonged decline in the FT4 concentration of >$20\%$ in children who are not acutely but “chronically” ill (such as during a two-year treatment period for childhood leukemia) does impact bone, muscle and body mass index (BMI) development or daily energy [15]. This has not been studied thus far. Because there is lack of studies reporting on thyroid hormone metabolism in large cohorts of children treated with cancer, we aimed to evaluate the percentage, severity and risk factors of a changed thyroid profile in children during treatment for cancer.
## 2.1. Patients
We performed a prospective observational cohort during a two-year period (January 2020 to December 2021). The thyroid profile was measured at diagnosis and three months after starting chemotherapy or radiotherapy in newly diagnosed children (<21 years) with leukemia, lymphoma, sarcoma or a non-pituitary brain tumor at Princess Máxima Center for Pediatric Oncology. Children with known previous thyroid disease, Down syndrome, a thyroid cancer predisposition syndrome, a history of neck irradiation or meta-iodobenzylguanidine (MIBG) treatment, or a brain tumor in the hypothalamic–pituitary region were excluded.
## 2.2. Data Collection
The thyroid profile, using TSH, FT4 and rT3, was measured at the time of diagnosis (range of ±35 days from diagnosis) and three months later (range of 60–160 days after diagnosis). Anti-thyreoperoxidase (anti-TPO) concentrations were measured at diagnosis. Blood results were interpreted by the treating physician. In case of aberrant thyroid function tests (FT4 < or > reference range or TSH <0.30 or >10 mU/L) children were referred to the pediatric endocrinologist and treated if needed.
Clinical data on anthropometrics (height, weight and BMI), general well-being (body temperature, vomiting and nutritional status) and overall physical condition were extracted from patients’ electronic medical records on the day of blood sampling. Physical condition was scored as “good” (no complaints), “medium” (moderate complaints, “not feeling well” or “feeling tired”) or “poor” (severe complaints or “feeling ill”) as reported by the health care provider in the electronic patient chart.
## 2.3. Laboratory Assays
A description of the laboratory assays is shown in Supplementary File S1.
## 2.4. Definitions
Thyroidal hypothyroidism was defined as present if the plasma TSH concentration was above the reference range (5.0 mU/L), combined with a plasma FT4 concentration below the reference range. Thyroidal subclinical hypothyroidism was defined as present if the plasma TSH concentration was above the reference range (5.0 mU/L), combined with a plasma FT4 concentration within the reference range. Subclinical hyperthyroidism was defined as present if the plasma TSH concentration was below the reference range (5.0 mU/L), combined with a plasma FT4 concentration within the reference range. Central hypothyroidism was defined as present if the plasma FT4 concentration was below the reference range, combined with non-elevated TSH concentration in combination with non-elevated rT3 concentration. ESS was defined as present if the plasma FT4 concentration was below the reference range, combined with a non-elevated TSH concentration in combination with an elevated rT3 concentration.
## 2.5. Statistics
Data are presented as means ±SDs or medians (ranges) for continuous data variables, depending on the distribution. Data are presented as percentages for categorical variables. Differences between groups were examined using unpaired Student’s t-tests for normally distributed continuous data and Mann–Whitney U tests for continuous data with a skewed distribution. For categorical data, χ2 tests or Fisher’s exact tests (if the assumptions for chi-square were violated) were used. Between-time-point differences were evaluated using paired Student’s t-test for continuous data with a normal distribution and Wilcoxon matched-pair signed rank test for continuous data with a skewed distribution. To assess the violation of normality distribution, QQ plots of the residuals and the Shapiro–Wilk test were used. For statistical analysis of changes in thyroid hormone concentrations, only paired blood samples per patient were used. The Pearson correlation coefficient was estimated to study the strength of linear associations between two continuous variables.
Multivariable logistic regression analyses were used to estimate the association between covariates and two outcomes: elevated rT3 concentrations and ≥$20\%$ decline in FT4 concentrations. Independent variables included in the multivariable logistic regression were selected by estimating the univariate model and by considering the clinical relevance of each variable. Therefore, in the final regression model, not only variables that were significant in the univariate analysis were included, but also factors that were clinically relevant. Odds ratios (ORs) along with $95\%$ CIs are reported. Analyses were performed using SPSS, version 27.0. p-values of <0.05 were considered statistically significant.
## 2.6. Ethics
The research protocol was approved by the medical ethical committee of Princess Máxima Center (NedMec NL69960.041.19). For ethical reasons, blood samples for the study were only taken if sampling for clinical reasons was simultaneously performed. Informed consent was given by all children and/or their parents/legal representatives depending on age.
## 3.1. General Patient Characteristics
Of 519 children assessed for eligibility, 284 were included (Figure 1). Of the included children, 141 ($50\%$) were diagnosed with leukemia, 74 ($26\%$) with lymphoma, 38 ($13\%$) with sarcoma and 31 ($11\%$) with a brain tumor (Table 1). The median age at diagnosis was 9.4 years (range of 0.0–19 years), and $\frac{127}{284}$ ($45\%$) children were female.
## 3.2. Thyroid Profile
At diagnosis, TSH and FT4 were both measured in 220 children, in $81\%$ ($\frac{179}{220}$) of which, both were within reference ranges (Table 2). Three months after diagnosis, in $91\%$ ($\frac{252}{276}$) of children, both TSH and FT4 concentrations were found to be within reference ranges. In two children ($1.2\%$), elevated anti-TPO antibodies were detected, and both were euthyroid.
## 3.2.1. (Subclinical) Hypo- and Hyperthyroidism
At diagnosis, $8.2\%$ ($\frac{18}{220}$) of children had subclinical hypothyroidism with a median TSH concentration of 6.30 mIU/L (range of 5.00–11.00). In $3.6\%$ ($\frac{8}{220}$) of children, subclinical hyperthyroidism was found (median TSH of 0.21 mIU/L (range of 0.07–0.34)).
Three months after diagnosis, $2.9\%$ ($\frac{8}{276}$) of children had subclinical hypothyroidism (median TSH of 6.75 mIU/L (range of 5.30–11.00)). None of these children required treatment with thyroxine. In total, 2 of 276 children ($0.7\%$) had subclinical hyperthyroidism (TSH, 0.31–0.33 mIU/L) after three months.
## 3.2.2. ESS
At diagnosis, none of the children had ESS. After three months, $1.5\%$ ($\frac{4}{265}$) of children had developed ESS. In $33\%$ ($\frac{49}{148}$) of children, an isolated rT3 elevation was found at diagnosis (median rT3 concentration of 0.25 ng/mL (range of 0.22–0.58)) which increased to $50\%$ ($\frac{133}{265}$) after three months (median of 0.27 ng/mL (range of 0.22–2.36)). A significant, weak, positive correlation was found between the FT4 and rT3 concentrations three months after diagnosis ($r = 0.18$, $95\%$ CI 0.06–0.29).
Children with an isolated elevated rT3 concentration after three months were slightly younger (7.7 compared with 9.6 years), more frequently had a brain tumor ($74\%$ versus $48\%$; $$p \leq 0.009$$) and were less often treated with anthracyclines ($65\%$ versus $80\%$; $$p \leq 0.006$$) than those without. No associations were found between corticosteroid use <48 h earlier or physical condition and having elevated rT3. In multivariable analysis, brain tumor diagnosis was the only significant risk factor for developing an elevated rT3 concentration three months after diagnosis (OR 3.17, $95\%$ CI 1.19 to 8.41) (Table 3).
## 3.2.3. Central Hypothyroidism
After three months, $1.9\%$ ($\frac{5}{265}$) of children were suspected of having central hypothyroidism with lowered FT4 (median FT4 of 8 pmol/L (range of 8–9)), non-elevated TSH (median TSH of 2.80 mIU/L (range of 1.80–4.00)) and non-elevated rT3 concentrations (median of 0.17 ng/mL (range of 0.11–0.20)). All five had been diagnosed with leukemia at a median age of 5.4 years (range of 4.4–13.4). None was started on thyroxine treatment, but the thyroid profile was followed over time.
## 3.3. Decline in FT4 over Time
Overall, the median FT4 concentration declined significantly in three months’ time from a median of 16 to 14 pmol/l ($p \leq 0.001$), with no change in TSH ($$p \leq 0.334$$). Median rT3 concentrations significantly increased (0.18 versus 0.22 ng/ml; $p \leq 0.001$) (Table 2, Figure 2).
At time of diagnosis, $29\%$ ($\frac{82}{284}$) of children had received corticosteroids <48 h earlier or chemotherapy before the first measurement. In this group, at diagnosis, lower median TSH and a higher median FT4 concentration were found when compared with those who had not (TSH, 1.20 (range of 0.07–11.00) versus 2.30 mIU/L (range of 0.34–9.40); $p \leq 0.001$; FT4, 17 (range of 11–28) versus 16 pmol/L (range of 10–29); $$p \leq 0.017$$). In the 22 children who had received corticosteroids <48 h before the blood withdrawal after three months, no differences were found in either TSH or FT4 concentration. ( Supplementary File S2).
Due to the differences found in median plasma TSH and FT4 concentrations in the children who had already received corticosteroids <48 h earlier or chemotherapy before their first thyroid hormone measurement at diagnosis, these children were excluded from the analysis of the changes in thyroid function over time. TSH and FT4 concentrations were found to significantly decline in three months’ time (median TSH from 2.35 to 1.90 mIU/L; $p \leq 0.001$; median FT4 from 16 to 14 pmol/L; $p \leq 0.001$). The median rT3 concentrations increased significantly (0.16 to 0.22 ng/ml; $p \leq 0.001$) (Table 2).
The median overall change in FT4 concentration in children who had not received corticosteroids <48 h earlier or chemotherapy before the first measurement was −$11\%$ (range of −$47\%$ to +$100\%$). FT4 declines of ≥$10\%$, ≥$20\%$ and ≥$30\%$ were found in $41\%$ ($\frac{69}{136}$), $28\%$ ($\frac{38}{136}$) and $7.4\%$ ($\frac{10}{136}$) of children, respectively.
In children with a FT4 decline of ≥$20\%$, the median FT4 concentration declined from 17 (range of 10–29) to 12 pmol/L (range of 8–16), with no changes in median TSH and rT3 concentrations. Of these children, $36.1\%$ had an elevated rT3 concentration after three months. The univariate analysis showed that children with a ≥$20\%$ FT4 decline were of similar age (7.7 ± 5.1 years versus 10.0 ± 5.7; $$p \leq 0.200$$), more often received antimetabolites ($84\%$ versus $67\%$; $$p \leq 0.049$$)) and showed a trend towards more frequent treatment with vinca-alkaloids ($92\%$ versus $80\%$; $$p \leq 0.081$$) compared with those with no decline or a decline of <$20\%$. The multivariable analysis, however, did not show risk factors for a ≥$20\%$ FT4 decline (Table 3). No clinically significant effect of a ≥$20\%$ FT4 decline from baseline on BMI SDS or linear growth was found.
## 3.4. Radiotherapy
Radiotherapy was given to 21 ($7.4\%$) children in the three months, in seven children possibly including the thyroid gland, and in 20 children, possibly including the hypothalamic–pituitary region in the radiation field. In total, 18 of the 21 children were irradiated for a brain tumor, of which 7 were craniospinal tumors (medulloblastoma, $$n = 5$$ (total dose of 54.0 Gray), and ependymoma, $$n = 2$$ (total dose of 59.4 Gray)) and 11 were cranial tumors (high-grade glioma, $$n = 10$$ (total dose 13–60 Gy), and germ-cell tumor, $$n = 1$$ (total dose 40.0 Gray)). Three children were irradiated for a sarcoma ($\frac{2}{3}$ orbit, total dose of 45–50 Gray). Median FT4 in children with radiotherapy changed from 15 (range of 13–24) to 14 pmol/L (range of 8–23) ($$p \leq 0.034$$), while median TSH remained unchanged. Reverse T3 concentrations after three months were significantly higher in children who had received radiotherapy than those in children who had not (0.28 (range of 0.14–0.62) and 0.21 ng/mL (range of 0.10–2.36); $$p \leq 0.015$$).
## 4. Discussion
In this large prospective study investigating the percentage and severity of thyroid dysfunction in children treated for newly diagnosed cancer, we found a low percentage of (subclinical) hypo- and hyperthyroidism in the first three months after starting treatment, which may be considered reassuring. In addition, the percentage of children that developed ESS, in this study defined as having lowered FT4, normal TSH and increased rT3, was low. However, in a considerable percentage of children, the thyroid profile was found to have changed, with an individual decline in FT4 concentration of ≥$20\%$ in $28\%$ of children after three months. We did not detect clinical consequences of this change in FT4 in this relative short period of time, and future studies are needed with prolonged follow-ups.
Based on these results, we suggest that with the current treatment protocols, surveillance for hypo- and hyperthyroidism is unnecessary at this stage of treatment. However, our results do illustrate that the thyroid profile can severely change during cancer treatment in children, which may reflect adaptation to an altered metabolic state during illness or may be iatrogenic [16,17,18].
In ESS, the adaptive downregulation of TRH secretion may result in low-to-normal TSH concentrations with lowered thyroid hormone concentrations. Apart from this, in ESS, the alteration of liver deiodinases decreases the conversion of T4 into T3 and increases the conversion of T4 into rT3. In case of doubt between central hypothyroidism or ESS, the determination of rT3 may be used to differentiate them, as in true central hypothyroidism, rT3 is low, while in ESS, this is increased. The high percentage of isolated elevated rT3 concentration in our cohort may thus illustrate the presence of (mild) ESS, which may not be surprising, as these children undergo intensive treatment [19]. We could not correlate the rT3 increase to corticosteroid use, although $90\%$ of children had received different kinds of corticosteroids within the three months.
Brain tumor diagnosis was found to be a risk factor for elevated rT3. Although no associations were found among poor physical state, corticosteroids and elevated rT3, it must be considered that brain tumor patients may have been in worse physical state compared with others, amongst others caused by cranial radiotherapy. No central hypothyroidism was found, as expected, because radiotherapy is unlikely to cause pituitary dysfunction after such a short period of time [20].
Van Iersel et al. showed that an FT4 decline of >$20\%$ during prolonged follow-up, although within reference ranges, was associated with weight gain, reduced linear growth and less improvement of intelligence scores over time in childhood brain tumor survivors [14]. This FT4 decline was regarded as a reflection of mild central hypothyroidism. Even though the etiology of declining FT4 as result of mild central hypothyroidism and (mild) ESS may not be comparable, we hypothesize that prolonged lowered thyroid hormone concentrations in (non-acutely ill) children with cancer may contribute to adverse late effects, such as short stature, weight gain, dyslipidemia, fatigue or the pathogenesis of early frailty, on childhood cancer survivors [14,21,22,23]. Therefore, we aim to follow thyroid hormone parameters in relation to these possible adverse late effects until the end of cancer treatment in this large prospective cohort.
It is not recommended to treat children with thyroid hormone for ESS during acute illness [13]. When FT4 declines in time and remains lowered for a prolonged period in “chronically” ill children, this disease state may, however, be compared to adaptation of the hypothalamic–pituitary axes, which is also encountered in children with other chronic diseases. Examples of such diseases are cystic fibrosis or chronic kidney disease, whereby affected children develop low insulin-like growth factor-1 concentrations or delayed puberty due chronical illness [24,25]. In these situations, treatment with sex steroids or growth hormone to improve bone development and final height are considered [26,27]. With this in mind, thyroid hormone treatment might be beneficial in the situation of prolonged lowered thyroid hormones in children with chronic illness or prolonged disease. This question needs to be addressed in future studies.
Our study also has several limitations. Firstly, the results might not be applicable to all children with cancer, because for this study, we only included children treated for leukemia, lymphoma, sarcoma or a non-pituitary brain tumor. Future studies may be performed to investigate changes in the thyroid profile in children with other types of childhood cancer. Secondly, although we aimed to measure the thyroid profile before any drugs had been administered, $29\%$ of the children had already received corticosteroids <48 h earlier or chemotherapy before the first thyroid hormone measurement. For optimal analysis, we, therefore, excluded these children from analysis on changes in TSH and FT4 concentrations. Moreover, data on physical condition were scored by the researchers in three categories, based on the notes of the health care provider in the electronic patient chart, which may be considered a subjective way of physical condition scoring and thus a limitation.
## 5. Conclusions
Children with cancer, treated within current treatment protocols, do not seem to be at risk of hypo- and hyperthyroidism in the first three months of cancer treatment. In $28\%$ of children, however, the median FT4 concentration significantly decreased during cancer treatment. The long-term clinical consequences thereof have to be investigated in future studies.
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|
---
title: 'Zinc Supplementation Induced Transcriptional Changes in Primary Human Retinal
Pigment Epithelium: A Single-Cell RNA Sequencing Study to Understand Age-Related
Macular Degeneration'
authors:
- Eszter Emri
- Oisin Cappa
- Caoimhe Kelly
- Elod Kortvely
- John Paul SanGiovanni
- Brian S. McKay
- Arthur A. Bergen
- David A. Simpson
- Imre Lengyel
journal: Cells
year: 2023
pmcid: PMC10000409
doi: 10.3390/cells12050773
license: CC BY 4.0
---
# Zinc Supplementation Induced Transcriptional Changes in Primary Human Retinal Pigment Epithelium: A Single-Cell RNA Sequencing Study to Understand Age-Related Macular Degeneration
## Abstract
Zinc supplementation has been shown to be beneficial to slow the progression of age-related macular degeneration (AMD). However, the molecular mechanism underpinning this benefit is not well understood. This study used single-cell RNA sequencing to identify transcriptomic changes induced by zinc supplementation. Human primary retinal pigment epithelial (RPE) cells could mature for up to 19 weeks. After 1 or 18 weeks in culture, we supplemented the culture medium with 125 µM added zinc for one week. RPE cells developed high transepithelial electrical resistance, extensive, but variable pigmentation, and deposited sub-RPE material similar to the hallmark lesions of AMD. Unsupervised cluster analysis of the combined transcriptome of the cells isolated after 2, 9, and 19 weeks in culture showed considerable heterogeneity. Clustering based on 234 pre-selected RPE-specific genes divided the cells into two distinct clusters, we defined as more and less differentiated cells. The proportion of more differentiated cells increased with time in culture, but appreciable numbers of cells remained less differentiated even at 19 weeks. Pseudotemporal ordering identified 537 genes that could be implicated in the dynamics of RPE cell differentiation (FDR < 0.05). Zinc treatment resulted in the differential expression of 281 of these genes (FDR < 0.05). *These* genes were associated with several biological pathways with modulation of ID1/ID3 transcriptional regulation. Overall, zinc had a multitude of effects on the RPE transcriptome, including several genes involved in pigmentation, complement regulation, mineralization, and cholesterol metabolism processes associated with AMD.
## 1. Introduction
The retinal pigment epithelium (RPE) is a highly polarized monolayer of cells lining the back of the eye which provides critical support for the functioning of the adjacent photoreceptors. It is part of the outer blood–retina barrier that regulates the transport of metabolites between the bloodstream and the neural retina. The RPE undergoes structural and functional transitions during maturation, which are essential to fulfill its biological functions [1,2,3,4]. Because of its critical function, the RPE has been directly implicated in several retinal diseases, most notably age-related macular degeneration (AMD). A hallmark feature of AMD is the accumulation of protein-, lipid-, and mineral-rich deposits between the RPE and the choroidal microcapillary network [5,6,7]. The size and number of these sub-RPE deposits increase with disease progression [8]. Another hallmark is pigmentary changes associated with the RPE [9,10]. Both of these are linked to the progression to end-stage AMD [5] manifested as geographic atrophy (GA), characterized by progressive degeneration and loss of the RPE layer, or as neovascular (NV) AMD, which is characterized by abnormal leaky blood vessels that grow from the choroid into the sub-RPE space (Type 1), sub-retinal space (Type 2), or the retina (Type 3) [11], causing fluid accumulation and scarring [12]. Zinc is part of a nutritional supplement endorsed by the National Eye Institute (NEI) to slow the progression from mild/moderate to advanced AMD [13]. The biochemical pathways involved in these beneficial effects are not fully understood.
Recent studies showed that human primary RPE cells in long-term culture model the hallmark features of AMD. RPE cell-based models develop as monolayers with tight junctions and high transepithelial resistance (TEER), extensive pigmentation, specific gene expression profiles, and also sub-RPE deposits [14,15,16,17], many of which can be affected by zinc supplementation directly [15,17]. This in vitro model system can be manipulated experimentally and interrogated longitudinally under conditions resembling health and disease. In this study, we identified dynamic changes in gene expression and the effects of acute (1 week) zinc supplementation using single-cell RNA sequencing (scRNA-Seq). Our results elucidate several specific pathways involved in the maturation of RPE to a stage that develops hallmark changes of AMD (sub-RPE deposition and pigmentary changes) and how these are modified by zinc supplementation.
## 2.1. Retinal Pigment Epithelial (RPE) Cell Culture
Primary human fetal RPE cells (ScienCell, Carlsbad, CA, USA) from one donor were purchased and used at passage three (P3) for the complete study in duplicates/triplicates with unknown clinical or genetic background. Cells were seeded onto Corning 6-well transwell inserts (10 µm thick polyester inserts with 0.4 µm pore size, 4 × 106/cm2 pore density, Corning, Wiesbaden, Germany) in 125.000/cm2 of epithelial cell medium (EpiCM, ScienCell, Carlsbad, CA, USA). After one week in culture, cell culture media were replaced with *Miller medium* with $1\%$ FBS [18,19] and cells were cultured for two, nine, and nineteen weeks in duplicates. Two types of short-term zinc treatment were also conducted, where one–one extra replicates of untreated controls were taken for the two types of zinc treatment experimental setup. After one week or eighteen weeks in culture, cell culture media were replaced with *Miller medium* with $1\%$ FBS for an additional one week in the absence or presence of 125 µM externally added zinc (as zinc sulphate; Thermo Fisher Scientific, Waltham, MA, USA) both in the apical and basal chambers, resulting in ~10 nM bio-available or free zinc [15,20]. The resulting replicates were the following: duplicates of zinc-treated samples, triplicates of untreated controls at the two- and nineteen-week time point, and duplicates of untreated controls at the nine-week time point. Cellular differentiation was monitored through the development of cobblestone cell morphology and increase in pigmentation using light microscopy. The increase in transepithelial resistance (TEER) was measured using the EVOM2 Epithelial Voltohmmeter and STX2 electrodes (World Precision Instruments, Sarasota, FL, USA).
At the sample collection time, as detailed above, cells were washed with PBS (Thermo Fisher Scientific, Waltham, MA, USA) two times for one minute. Cells were detached by incubation with 0.15 % Trypsin-EDTA for thirty minutes at 37 °C. The trypsinization was stopped using $100\%$ FBS and trypsin neutralization solution (ScienCell, Carlsbad, CA, USA). The obtained single-cell suspensions were washed in PBS with $1\%$ BSA (Thermo Fisher Scientific, Waltham, MA, USA) 2 times for 5 min at 1000 rpm. After automatic cell counting (EVE, Thermo Fisher Scientific, Waltham, MA, USA), 7 × 105 cells/mL were prepared, and the cells were kept on ice for a maximum of ten minutes before proceeding with single-cell RNA sequencing. In parallel to single-cell sequencing, adjacent samples were fixed for fifteen minutes in $4\%$ PFA (Merck, Darmstadt, Germany) diluted in PBS (Thermo Fisher Scientific, Waltham, MA, USA) for immunofluorescence.
## 2.2. Experiment Overview
Our previous study showed individual differences in assaying primary hfRPE from different donors [17]. To overcome the variations introduced by variability in donor samples and to generate a reproducible zinc effect, in this manuscript, experiments were performed on primary hfRPE cells from a single donor. In the initial scRNA-Seq run, samples were obtained from RPE cells cultured for two weeks (2W), nine weeks (9W), and nineteen weeks (19W) (Supplementary Figure S1A) in duplicates. Cells were collected from two wells at these time points. A total of 7000 cells from each sample were loaded on 10× Genomics Chromium v1.3 with a target recovery of 4000. Libraries made from each sample were pooled and sequenced.
In the second run, samples originated from RPE cultures were treated with a zinc-supplemented medium for one week either after: [1] one week in culture or [2] eighteen weeks in culture in duplicates. We also included one–one sample from untreated RPE culture in this run and the transcriptomic profiles were generated in a pooled fashion as described above. The actual cell recovery of both runs ranged from 3000 to 4000 in each well, resulting in a total recovery of ~30,000 cells for the first run and ~15,000 for the second run. The raw scRNA-*Seq data* were processed using CellRanger v3.0.0. and then Seurat v3.1 to determine the heterogeneity of our specimens using unsupervised clustering, followed by annotation based on hierarchical clustering of a pre-defined set of canonical RPE marker genes [21,22,23,24] (Supplementary Table S3). For further analysis, we initially analyzed our samples of untreated control RPE cultures from the two runs (triplicates for 2W and 19W and duplicates for 9W cultures). We then separately analyzed the duplicate samples of our zinc-treated RPE cultures compared to the triplicate samples of untreated control RPE cultures of 2W and 19W.
## 2.3. scRNA-Seq
Approximately 7000 single cells per sample were processed with the Chromium system using the v3 single-cell reagent kit (10× Genomics, San Francisco, CA, USA). Barcoded libraries were pooled and sequenced on the NovaSeq platform (Illumina, San Diego, CA, USA), generating 150 bp paired-end reads as per 10× Genomics recommendations, with >30,000 reads per cell.
## 2.4. Bioinformatics
The raw scRNA-*Seq data* were processed using CellRanger version 3.0.0 (10× Genomics). The resulting filtered expression matrices were then imported into R for analyses using scRNA-Seq packages, Seurat (Version 3.1) (Stuart et al. 2019) and Monocle (Version 3.0) (Trapnell et al. 2014; Cao et al. 2019).
Cells were filtered to exclude those with <1000 or >8000 genes, or with >$20\%$ of counts aligned to mitochondrial genes, or >$40\%$ counts aligned to ribosomal genes. Cells passing QC were downsampled randomly to 1000 cells per sample to prevent over- or under-representation of any sample. Each sample was log-normalized using default Seurat parameters, with the top 3000 highly variable genes used for Seurat iterative pairwise integration. The integrated dataset was scaled to regress variance arising from read depth and mitochondrial and ribosomal expression. Principal Component Analysis was then performed on the integrated dataset, and Seurat’s JackStraw function was applied to determine the components used in UMAP and SNN clustering. Unsupervised clustering was run iteratively at resolutions ranging from 0.25 to 1, at increments of 0.25. At the highest resolution, a total of 13 clusters were detected. These clusters were observed in UMAP to form two overall, as-yet unannotated cell populations.
Using untreated cells only, the average expression for the clusters was determined for a set of 213 canonical RPE marker genes [21,22,23,24] (Supplementary Table S3) to which hierarchical clustering was applied. The clusters were segregated into two distinct branches, exhibiting characteristics of more and less differentiated RPE, which matched the distinction observed in UMAP. As such, the 13 unsupervised clusters were annotated to reflect these two overall cellular populations for downstream differential expression analysis.
Seurat’s Wilcoxon rank sum test was used for differential expression testing, using default FindMarkers parameters, with genes below 0.05 adjusted p-value considered significantly differentially expressed.
Monocle 3 [25] was used for pseudotime analysis, for which downsampled count data were imported from Seurat and independently processed and batch-corrected in Monocle using default parameters. For continuity, a pseudotime trajectory graph was calculated and projected on the UMAP coordinates preserved from Seurat analysis. The data were filtered to focus on the main less differentiated to more differentiated pseudotemporal trajectory, by excluding small branches not contributing to the main trajectory. This was followed by graph autocorrelation analysis to detect gene expression changes correlating with progress along the trajectory, filtered for significance at p-value and Q-value <0.05. Genes with expression significantly correlated with the trajectory were grouped into ‘modules’ of co-regulated genes and the average expression of each gene module calculated across pseudotime.
## 2.5. Functional Classification Pathway and Network Analysis
For pathway and network analysis, we used the GeneAnalytics (https://ga.genecards.org/#input; accessed on 14 March 2021) and STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) 11.0 (https://string-db.org/; accessed on 14 March 2021) [26] in combination with Cytoscape [27]. GeneAnalytics uses binomial distribution to test the null hypothesis that the queried genes are not over-represented within any superpath, GO term, or compound in the GeneAnalytics data sources. The presented score in each section is a transformation of the resulting p-value, corrected for multiple comparisons using the false discovery rate (FDR) method, with higher scores indicating a better match. The bar color, indicating the matching quality—high (dark green), medium (light green), low (beige)—is common for all sections. STRING in combination with Cytoscape implements classification systems such as Gene Ontology, KEGG, and systems based on high-throughput text mining and the used reference dataset was the human genome. The identified functional protein association network was validated via text mining, database information, co-expression, and experimental evidence.
## 2.6. Immunofluorescence
For immunofluorescence analysis, the cells on the transwell membrane were permeabilized in $0.5\%$ Triton-X (Merck, Darmstadt, Germany) in PBS for ten minutes at 4 °C and then washed in $0.1\%$ Tween20 in PBS (PBST) (Merck, Darmstadt, Germany) and blocked with $5\%$ goat sera (Merck, Darmstadt, Germany) in PBST for one hour at room temperature. Samples were then incubated with primary antibodies overnight: COL1A1 (Abcam plc, Cambridge, UK, dilution 1:200) and RPE65 (Merck Millipore, Darmstadt, Germany, 1:50), diluted in PBST containing $1\%$ goat sera. Following washing with PBST, the samples were incubated with secondary antibodies in 1:200 in PBST with $1\%$ goat sera for one hour in the dark at room temperature. Samples were washed with PBST for five minutes, then with PBS. Cell nuclei were then labelled with DAPI (Thermo Fisher Scientific, Waltham, MA, USA) diluted 1:1000 in PBS. Finally, samples were mounted onto Menzel–Glaser slides (Thermo Fisher Scientific, Waltham, MA, USA) in Vectashield (Vector Laboratories, Burlingame, CA, USA). For negative control, the primary antibody labelling was omitted. Cells were visualized using a Leica SP8 confocal microscope (Leica, Wetzlar, Germany). Images were obtained and analyzed with Leica Application Suite X Image software (Leica, Wetzlar, Germany).
## 3.1. Maturation of RPE Cells in Culture
Primary human fetal RPE cells from a single donor were cultured for 2 weeks (2W = short term), 9 weeks (9W = medium term), and 19 weeks (19W = long term) (Supplementary Figure S1A). We used culture conditions that, in our hands, reproducibly recapitulated key aspects of RPE cells as described in previous studies [15,16,17]. As time in culture increased, RPE cells were observed to develop pigmentation, hexagonal morphology (Supplementary Figure S1B), and a progressively increasing epithelial barrier function (112.9 ± 3.9 Ohm × cm2 at 2W, 195.2 ± 16.6 Ohm × cm2 at 9W, and 201.36 ±49 Ohm × cm2 in 19W in culture). The cell cultures also began accumulating sub-RPE deposits (Supplementary Figure S1C) containing lipids and hydroxyapatite that we have shown earlier [15,16,17]. To identify the transcriptomic profiles of RPE cells at the three time points, we collected cells from three wells at 2W and 19W and two wells at 9W in culture (see Section 2 for detail). Approximately 3000–4000 cells were captured from each well and processed on the 10× Genomics Chromium v1.3 platform, with transcriptomes generated for a total of 30,000 cells.
## 3.2.1. Unsupervised Clustering Analysis
To ensure equal representation from all conditions, all samples were downsampled to include an equal number [1000] of randomly selected cells in Seurat 3.1 [28]. Based on 3417 differentially expressed transcripts (Supplementary Table S1), the cells were automatically allocated into thirteen clusters and visualized on a Uniform Manifold Approximation and Projection (UMAP) plot (Figure 1A).
The lists of cluster-specific ‘marker’ genes were input into GeneAnalytics. *The* gene set analysis tool identified significant cluster-specific canonical pathways [29], labelled as superpathways for the functional analysis of the cell populations. Supplementary Table S2 contains information on the ‘marker’ genes, numbers of enriched pathways, and matched number of genes to the total number of genes in a pathway for each cluster. The software assigned the 312 genes in Cluster 0 to 18 superpathways, with respiratory electron transport and heat production of uncoupling proteins, metabolism, and visual cycle among the top five hits. The 205 genes in Cluster 1 were assigned to 44 superpathways, with degradation of extracellular matrix, ERK signalling, and phospholipase C pathway amongst the top five hits.
The 270 genes in Cluster 2 were associated with 19 superpathways with metabolism, respiratory electron transport, heat production of uncoupling proteins, and visual cycle amongst the top five hits. In Cluster 3, the 210 differentially expressed genes were associated with 76 superpathways with cytoskeletal signalling, ERK signalling, and focal adhesion among the top five hits. The 87 differentially expressed genes in Cluster 4 were associated with 29 superpathways with cytoskeletal signalling, ERK signalling, and integrin signalling among the top five hits. The 30 differentially expressed genes in Cluster 5 were associated with two superpathways: melanin biosynthesis and tyrosine metabolism. The 270 differentially expressed genes in Cluster 6 were associated with 50 superpathways, degradation of extracellular matrix, metabolism of proteins, and cell adhesion and ECM remodelling amongst the top five hits. In Cluster 7, 313 differentially expressed genes were associated with 110 superpathways with cytoskeletal signalling, ERK signalling, and degradation of extracellular matrix among the top five hits. The 303 differentially expressed genes in Cluster 8 were associated with 61 superpathways, degradation of extracellular matrix, ERK signalling, and phospholipase C pathway among the top five hits. In Cluster 9, we identified 302 differentially expressed genes associated with 21 superpathways with metabolism, visual cycle, and copper homeostasis among the top five hits. In Cluster 10, 198 differentially expressed genes were associated with 24 superpathways with metabolism, visual cycle, and oxidative stress among the top five hits. The 807 differentially expressed genes in Cluster 11 were associated with 86 superpathways, degradation of extracellular matrix, protein processing in the endoplasmic reticulum, and cytoskeletal signalling amongst the top five hits. Finally, in Cluster 12, 164 differentially expressed genes were associated with 11 superpathways with organelle biogenesis and maintenance, intraflagellar transport, and mitotic cell cycle among the top five hits. The identification of thirteen clusters shows that cells in culture are not homogenous.
## 3.2.2. Hierarchical Clustering Analysis Using Markers of Mature RPE Cells
We aimed to identify which of the unsupervised clusters most resemble mature RPE. We separated cells that were deemed to be more differentiated based on the expression of 213 RPE-specific genes we identified from several publications [21,22,23,24] (Supplementary Table S3). Hierarchical clustering based on the gene list divided the 13 clusters into two distinct groups (Figure 1B). We annotated clusters 0, 2, 5, 9, 10, and 12 as ‘more differentiated‘ RPE cells and the remaining clusters (1, 3, 4, 6, 7, 8, and 11) ‘less differentiated‘ cells (Figure 1B). We use the terms ‘more differentiated‘ and ‘less differentiated‘ from this point forward. The more and less differentiated cells are separated on the original UMAP (Figure 1(C1)). We calculated the proportion of more and less differentiated cells at the 2W, 9W, and 19W. Interestingly, nearly half of the cells were more differentiated even as early as 2W or 9W in culture (2W = $41\%$, 9W = $41\%$). By 19W, the proportion of the more differentiated cells increased to $73\%$ (Figure 1(C2)), with $27\%$ remaining less differentiated. Supplementary Table S4 lists the genes that define the more and less differentiated groups. The expression levels of three highly expressed representative genes from each group are presented as violin plots and UMAP plots in Supplementary Figure S2, highlighting the enrichment but not the exclusive presence of these genes in one or the other group.
Next, we tested whether the protein products of the genes that distinguish more and less differentiated cells show differential expression. One of the highly expressed mRNAs in the less differentiated cells was Collagen Type I alpha 1 chain (COL1A1), fibril-forming collagen. The RPE secretes the protein encoded by this gene and it is found in the sub-RPE space [30]. We found that the expression of COL1A1 gradually increased in the less differentiated group and decreased in the more differentiated group (Figure 2A). In contrast, Retinoid Isomerohydrolase (RPE65), a visual cycle component marker for differentiated RPE, was mainly expressed in the more differentiated group (Figure 2A). *Both* genes were expressed in the other group but at a low level in the opposing groups (Figure 2B).
Next, we determined the immunolocalization of the COL1A1 and RPE65 proteins in the 19W RPE monolayer. In line with the gene expression results, the cells with a strong RPE65 immunolabelling also had weak intracellular immunoreactivity for COL1A1 proteins, and cells with strong immunolabelling for COL1A1 showed weak labelling for RPE65 (Figure 2C; green: RPE65, red: COL1A1). Immunolabeling of COL1A1 is also present in the sub-RPE space. This extracellular immunoreactivity gradually increased with time in culture (Supplementary Figure S3), suggesting that the secreted COL1A1 accumulates as part of the developing extracellular sub-RPE material (Supplementary Figure S3).
As we identified more and less differentiated RPE cells in our hfRPE, we investigated whether more and less differentiated cells are also present in RPE cells directly isolated from human eyes. We used two independent previously published datasets: the scRNA-*Seq data* obtained from human embryos [31] or adult human eyes [32] (Supplementary Figure S4). We applied our cell grouping strategy based on 213 RPE-specific signature genes (Supplementary Table S2). Indeed, our analysis showed that both the embryonic RPE (Supplementary Figure S4(A1,A2)) and adult RPE (Supplementary Figure S4(B1,B2)) could be classified into more and less differentiated cell populations. Of note, the number of cells analyzed in the publication using adult RPE [32] was relatively low. Hence, clusters were less well separated.
## 3.3. Pseudotemporal Ordering of the Expressed RPE Genes
To identify the genes associated with transitioning from the less to the more differentiated cells, we performed a pseudotemporal ordering of our scRNA-Seq transcriptome profile using Monocle3 (Figure 3). This unsupervised analysis identified a main trajectory with 11 nodes (Figure 3(A1)). Based on the original cluster analysis depicted in Figure 1, node 1 corresponded to the less and node 10 to the more differentiated cells (Figure 3(A2)). The main trajectory was correlated with 537 variably expressed genes. Based on their pseudotemporal expression profile, these clustered into seven modules (Figure 3(B1); Supplementary Table S5). Modules 2 and 5 contained 175 genes with high expression at the early stages of the trajectory that gradually declined towards the end of the trajectory. GeneAnalytics identified 62 potential significant superpathways associated with these genes (Supplementary Table S6). Degradation of extracellular matrix, focal adhesion, and cell adhesion–endothelial cell contacts were amongst the top five ranked pathways. Modules 3, 4, and 6 contained 172 genes. These gradually increased towards the late stages of the trajectory. GeneAnalytics identified nine potential superpathways defined by these genes, including the transport of glucose, metabolism, and visual cycle among the top five hits (Supplementary Table S6). Modules 1 and 7 contained 190 genes. The expression of these genes transiently increased to a maximum at the middle of the trajectory followed by a decrease over pseudotime. These identified sixteen superpathways with degradation of extracellular matrix, ERK signalling, and cytoskeleton remodelling among the top five hits (Supplementary Table S6).
To identify potential transcriptional regulators of the pseudotemporal trajectory, GO term analysis was carried out in GeneAnalytics. This identified transcriptional regulator activity in two genes, ID1 and ID3, belonging to the combined Module 1 and 7, representing the transitional phase on the pseudotemporal trajectory.
Next, we examined the potential relationships between the most highly significantly correlated in the main trajectory using a cut-off value of Moran $I = 0.5$ (see Section 2). We identify 44 genes strongly influencing the main trajectory. Using GeneAnalytics in combination with STRING database and Cytoscape, we found that these genes do not appear to be randomly distributed. A total of 31 out of 44 genes showed a significant biological connection, validated via text mining, database information, co-expression, or experimental evidence ($p \leq 1.0$ × 10−16; Figure 3(B2)). The 44 genes were associated with six potential superpathways, including visual cycle, extracellular matrix degradation, and cell adhesion–extracellular matrix remodelling as the top hits (Supplementary Table S6).
## 3.4.1. Transcriptional Changes in Response to Acute Zinc Supplementation
Previous studies have shown that chronic zinc supplementation has clinical benefit associated with molecular and cellular changes [13,15,17,33,34], but the effects of acute or short-term zinc supplementation had not been studied in detail. To identify the effects of short-term zinc supplementation, we treated our RPE cultures for one week with a zinc-supplemented medium, using the same approach as we described earlier [15]. This acute zinc supplementation was carried out on less differentiated cells starting at the end of the first week in culture. Then, cells were harvested at the end of 2W or more differentiated cells at the end of the 18th week, and cells were harvested at the end of 19W. Gene expression changes with zinc supplementation were compared to cells in culture without zinc supplementation for either 2W or 19W. Cells with and without zinc supplementation were clustered using the process used in Figure 1(C1). While acute zinc supplementation did not noticeably change the proportion of the more and the less differentiated cells (Figure 4A), it significantly changed the expression of 472 genes in the more differentiated cells (Figure 4(B2)) and 149 genes in the less differentiated cells (Figure 4(B1)) at the two-week time point (Supplementary Table S7).
At 19W, zinc altered the gene expression of 487 genes in the more differentiated cells (Figure 4(B4)) and 417 genes in the less differentiated cells (Figure 4(B3)) (Supplementary Table S7) (logFC > 0.25, adjusted p-value < 0.05). We displayed the four datasets in a four-way Venn diagram to further analyze specific temporal zinc-induced gene expression changes (Figure 4C). We found 81 overlapping genes differentially expressed under all four conditions. Two-thirds of these 81 genes were identified as housekeeping genes by GeneAnalytics, confirming previous studies showing that zinc plays a role in regulating cellular homeostatic processes [35]. Relevant proteins include metallothioneins (MT1E, MT1F, and MT1X) that act as essential stress proteins to regulate immune homeostasis. In the more differentiated cells, 222 uniquely affected genes were at 2W and 163 at 19W (Figure 4C). In the less differentiated cells, only four genes were specifically affected by zinc supplementation at 2W and 94 genes at 19W (Figure 4C).
At 2W, we identified superpathways only in the more differentiated cells; these were cytoskeleton remodelling, focal adhesion, and degradation of extracellular matrix among the top five superpathways (Supplementary Table S8). At 19W, in the less differentiated cells, we identified presenilin signalling, SMAD signalling, and antigen-presenting cross-presentation amongst the top five superpathways (Supplementary Table S8). In contrast, in the more differentiated cells, we identified metabolism, ferroptosis, and protein processing in the endoplasmic reticulum amongst the top five superpathways (Supplementary Table S8). Information on the magnitude and direction of zinc-associated change in transcript abundance of these gene lists is provided in Supplementary Table S7. The analysis of these five gene lists by GeneAnalytics to identify superpathways is listed in Supplementary Table S8.
## 3.4.2. Influence of Zinc on Transcription Dynamics
We next determined the overlap between the 537 genes identified in the main trajectory in the pseudotemporal analysis (Figure 3(A2); Supplementary Table S5) and the list of the differentially expressed genes following the acute zinc supplementation (Figure 4(D1); Supplementary Table S7). This comparison identified 16 common genes (Supplementary Table S9). Using GeneAnalytics in combination with STRING database and Cytoscape, we found that these 16 genes show significantly (p-value < 1.0 × 10−16) more interactions than expected, validated by text mining, database information, co-expression, and experimental evidence (Figure 4(D2)) that relates to the respiratory electron transport and response to metal ions as biological function (Supplementary Table S9).
## 3.5. Sub-RPE Deposition-Related Gene Expression Pattern Depends on Maturation State and Zinc Supplementation
Our hfRPE culture developed sub-RPE deposits even without photoreceptors and the supporting choriocapillaris (Supplementary Figure S1C). This allowed us to analyze the expression of genes potentially involved in the sub-RPE deposit formation process. We compiled lists of genes associated with various aspects of sub-RPE deposit formation and analyzed the changes in expression throughout cell maturation and zinc supplementation (Supplementary Table S11). *Some* genes belong to more than one gene list (Figure 5).
## 3.5.1. Genelist 01
This contains 55 genes previously genetically associated with AMD (Figure 5A; Supplementary Table S11 [36]). We found that 52 out of the 55 genes in AMD-risk-associated risk loci were expressed in our RPE model (Supplementary Table S11). *Some* genes were expressed higher at 2W, like CFHR3, LIPC, SYN3, and VTN, while others were expressed higher at 19W, like ARHGAP21, RDH5, SKIV2L, SRPK2, TGFBR1, and TRPM3. Among the genes expressed higher in less differentiated cells were CFHR3, LIPC, TGFBR1, and VTN. In more differentiated cells, we found higher expression of PRLR, RDH5, RORB, SLC16A8, SPEF2, and VEGFA. From these 52 genes, CFH, COL8A1, CD63, TSPAN10, APOE, TIMP3, and SLC16A8 were significantly upregulated, while CFHR1, VEGFA, TRPM3, and RDH5 were significantly downregulated in response to acute zinc supplementation (Supplementary Table S7).
## 3.5.2. Genelist 02
This contains 66 complement-regulation-related genes. Several complement proteins have been implicated in AMD and are found in sub-RPE deposits (Figure 5C, Supplementary Table S11 [37,38,39]). A total of 41 out of the 66 identified complement genes were expressed in our hfRPE cultures, most showing low expression levels (Supplementary Table S11). *The* genes that were expressed higher in 2W cultures were C4B, C4BPB, C8B, C8G, CFHR3, CFP, CSMD1, CSMD3, TPSG1, and VTN, while the genes expressed higher in 19W cultures were C1S, CD55, CD59, and PTX3. Among the genes expressed higher in less differentiated cells were C4BPA, C4BPB, `C8G, CFHR3, CFP, CR2, CSMD1, FHL-1, ITGB2, PTX3, and VTN. The expressions of C2, C4A, C5, CR1, and SERPING1 were higher in the more differentiated cells. CFH and C1R were significantly upregulated, while CFHR1 and CLU were significantly downregulated in response to zinc supplementation (Supplementary Table S7).
## 3.5.3. Genelist 03
This contains cholesterol-metabolism-related genes (Figure 5B, Supplementary Table S11 [40,41]). A total of 42 out of the 51 identified genes were expressed in hfRPE (Supplementary Table S11). *The* genes that were expressed higher at 2W were ABCG5, ANGPTL8, APOA1, CD36, LIPC, and STAR, while the genes expressed higher at 19W were APOC1, LDLRAP1, and NPC1. Among the genes expressed higher in less differentiated cells were CD36, LIPC, PCSK9, and STAR. In the more differentiated cells, the expressions of CYP27A1, LIPG, LPL, and PLTP were higher. PLTP, ANGPTL4, APOE, LRPAP1, VDAC2, and TSPO were significantly upregulated, and NPC2 was significantly downregulated in response to zinc supplementation. Interestingly, CYP27A1 showed significant upregulation at 2W and significant downregulation at 19W in response to zinc supplementation (Supplementary Table S7).
## 3.5.4. Genelist 04
This contains mineralization-related genes (Figure 5D, Supplementary Table S11 [42,43,44,45]) that could be associated with the inorganic hydroxyapatite component of sub-RPE deposits. A total of 80 out of the identified 99 calcification-related genes were expressed in the RPE (Supplementary Table S11). *The* genes that were expressed higher at 2W were COL10A1, NKX3-2, PHEX, SPP1, TNFRSF11B, and WNT7B, while others were expressed higher at 19W, including AP1S2, BMP2, CLCN3, LAMP1, POSTN, SMAD1, and SOX9. Among the genes expressed higher in less differentiated cells were AP1S1, COL10A1, COL1A1, DLX5, IBSP, JAM2, LAMP1, MGP, MYORG, NKX3-2, PDGFB, PHEX, POSTN, and RUNX2. The expressions of ABCC6, BMP2, BMP7, CNMD, SOX6, and WNT6 were higher in the more differentiated cells. COL1A1, POSTN, CD63, LAMP1, and BMP4 were significantly upregulated and SLC20A1, SOX9, and BMP7 were significantly downregulated in response to zinc supplementation (Supplementary Table S7).
## 3.5.5. Genelist 05
This contains genes that are related to pigmentation (Figure 5E, Supplementary Table S11 [9,46,47]). Pigmentary abnormalities show strong correlation with sub-RPE deposit formation and the development of AMD, and we found that 19 out of the identified 21 genes were expressed in hpRPE (Supplementary Table S11). At 2W, we found no differentially expressed genes. At 19W, however, we found that AP3B1, AP3D1, BLOC1S6, HPS5, HPS6, and SLC24A5 were expressed higher. There were no highly expressed genes in less differentiated cells. In the more differentiated cells, the expression of OCA2 and SLC24A5 was higher. TYR, TYRP1, and DCT were significantly downregulated in response to zinc supplementation in our acute treatment (Supplementary Table S7).
## 4. Discussion
The RPE plays a pivotal role in maintaining the health of the retina, and changes in RPE function have been linked to the development and progression of AMD [48,49]. Optimal zinc balance is key for RPE function [50], and zinc deficiency contributes to AMD pathogenesis [51]. Based on these findings, it has been suggested that zinc supplementation can slow the progression of AMD [51,52], although the mechanism of this beneficial effect is not fully understood [53]. In this study, we used primary human fetal RPE cells and scRNA-*Seq analysis* to identify the transcriptomic changes and biologically plausible molecular pathways involved in the maturation of the RPE and the changes associated with zinc supplementation. The specific transcriptional changes and molecular pathways identified provide an improved understanding of RPE cell maturation and insight into how the function of RPE might be affected by acute zinc supplementation, which has relevance for the progression of AMD.
## 4.1. Study Rationale
Maturation of RPE cells is key to developing appropriate morphology, pigmentation [54], and production of key signature proteins that determine the function of these cells [55]. Different studies use a variety of sources to study RPE maturation and function, ranging from the immortalized ARPE-19 cells [56] to induced pluripotent stem-cell-derived RPE [57] and primary porcine [16] or human RPE [58]. As with all model systems, cellular models for RPE must replicate the in vivo situation as closely as possible. Recently we have shown that primary human fetal RPE cells develop the most critical features of native RPE, including the formation of pigmentation, tight junctions with high TEER values, and the expression of RPE signature genes and proteins [15,16]. Most importantly, the cells in culture can lay down sub-RPE deposits, a hallmark feature of AMD [15,16]. Despite demonstrating these in vivo-like features, the molecular signature for RPE maturation has not yet been fully explored. Previous studies have reported a variety of approaches to map molecular maturation. Earlier studies used microarrays [23,59] or bulk RNA sequencing. Most recently, a powerful tool capable of sequencing individual cells has been introduced. Single-cell RNA sequencing provides an unparalleled opportunity to identify cell heterogeneity [60]. Lidgerwood et al. [ 57] used pluripotent stem-cell-derived RPE to analyze transcriptomic changes after 1 month or 12 months in culture and analyzed these separately, then combined the data. In a subsequent study, the same group combined scRNA-Seq and proteomics in iPSC cells obtained from individuals with or without AMD to identify regulations in geographic atrophy [61]. Exciting opportunities are presented by scRNA-Seq studies using freshly isolated RPE from human eyes. RPE cells from both fetal and adult human eyes were analyzed in previous studies [31] and [32], respectively). Although both studies used a limited number of cells, they provide invaluable insight for cell-culture-based observations. In our study, we used primary fetal RPE cells that recapitulated features of RPE cells in vivo (Supplementary Figure S1). Despite their fetal origin, these cells developed sub-RPE deposits and varied pigmentation, suggesting that they recapitulate the hallmarks of AMD (Supplementary Figure S1) despite the relatively short time in culture (19W).
## 4.2. Heterogeneity of RPE Cells
*The* generation of scRNA-*Seq data* from a large number of cells allowed us to confidently determine that there is a significant degree of heterogeneity between the cells. A key observation was that some RPE cells could develop into more differentiated cells even after 2 weeks in culture, but even after 19 weeks, we still observed less differentiated cells (Supplementary Figure S1). Heterogeneity of RPE had been reported after multiple passages and over the years in culture [62] (Supplementary Figure S1), reflecting what had been reported for RPE in vivo [63,64,65] and in situ [62]. Despite the long-lasting heterogeneity, the melanosome precursor PMEL17 was expressed in both less and more differentiated cells. In fact, from the 19 pigmentation-related genes expressed in our cells, the only transcripts that showed elevated expression in the more differentiated RPE cells were OCA2 and SLC24A2 (Supplementary Figure S2(B1) and Supplementary Table S11, Figure 5E), suggesting that all cells could become pigmented [46,66].
COL1A1 was amongst the top transcripts in the less differentiated cells, and immunoreactivity of COL1A1 protein was able to distinguish the less differentiated cells from the more differentiated cells that express the RPE65 gene highly and are immunopositive for the RPE65 protein (Figure 2C). Immunoreactivity to the COL1A1 protein gradually increased in the sub-RPE space with time in culture (Supplementary Figure S3), suggesting that the half-life of this extracellular matrix protein is long in our culture system. This increase in sub-RPE COL1A1 may correspond to the role this protein plays in forming the extracellular matrix of Bruch’s membrane [67]. Other collagens were also expressed highly in the less differentiated cell population (Supplementary Table S4), reflecting their reported involvement in increased attachment and spread of RPE cells [68]. The only highly expressed transcript for collagen in the more differentiated cells was COL8A1 (Supplementary Table S4). The COL8A1 protein is a component of basement membranes in the eye and contributes to the formation of the basement membrane of RPE [21,69] and a genetic risk variant of AMD [70]. The findings on COL1A1 and RPE65 might be mechanistically important: the mature RPE cells (RPE65 expressing) could enable the performance of the visual cycle, while the less differentiated cells (COL1A1 expressing) can support the formation of ECM throughout life.
## 4.3. Transition from Less to More Differentiated RPE
As more and less differentiated cells are present at all three time points, we combined the scRNA-*Seq data* from the three time points and analyzed these datasets together, an approach different from a previous study [57]. This integrated approach helped us to identify a pseudotemporal trajectory of gene expression from less to more differentiated cells (Figure 3(A1)). This approach identified a well-defined main trajectory (Figure 3(A2)). The top genes with the highest score in the main trajectory were associated with regulating the visual cycle (RPE65, LRAT, TTR, RDH5) (Supplementary Table S6). Transcriptomic analysis of the bulk RNA isolated from RPE cells from aging human donor eyes recently reported a positive feedback mechanism between the upregulation of visual cycle genes and the accumulation of retinoid by-products [71]. As visual cycle-related bisretinoids are constituents of the accumulating lipofuscin in RPE [72], this upregulation could eventually lead to AMD-like pathogenesis [73] in this cell culture model. Indeed, there are ongoing clinical trials for visual cycle modulators as therapeutic options for AMD [74], and our cell culture model has the potential to serve as a preclinical tool for testing novel compounds.
## 4.4. Genes Involved in Transitioning RPE from Less to More Differentiated Cells
*The* genes associated with the main trajectory could be clustered into seven modules based on their transcriptional change along the pseudotemporal trajectory (Supplementary Table S5). The transcripts whose expression is transiently upregulated on the pseudotemporal trajectory likely represent the genes mediating the transition from the less to the more differentiated cells (Supplementary Table S5). *These* genes were associated with cellular and extracellular remodelling and metabolic pathways (Supplementary Table S6). Therefore, our data support the hypothesis that extracellular matrix remodelling of the Bruch’s membrane could become a therapeutic target to combat RPE loss [75] due to topographic changes in the RPE–Bruch’s membrane interface [68]. Alterations of the extracellular matrix may impact immune response as well as the secretion of pro-inflammatory cytokines, such as MCP-1 and IL-8 [68], and promote sub-RPE deposit formation [76,77,78,79,80]. Our data highlights potential molecular targets to achieve a regulation of this process.
Among the transiently expressed genes, we identified ID1 and ID3 (Supplementary Table S6). The corresponding helix–loop–helix (HLH) proteins form heterodimers with members of the basic HLH family of transcription factors, inhibiting DNA binding and preventing the formation of active transcriptional complexes [81]. ID proteins promote cell cycle progression and cell migration and restrict cellular senescence and the differentiation of a number of progenitor cell types [82,83]. Recent results indicate that the expression of ID family proteins may play an important role in regulating retinal progenitor cell proliferation and differentiation [84]. ID genes and proteins showed increased expression levels in the retina at embryonic and early postnatal stages and declined in the adult [84]. ID protein expression is silenced in many adult tissues but is re-activated in diverse disease processes [83,85,86]. ID proteins appear to play a crucial role in the angiogenic processes. It was proposed that inhibition of expression and/or function of ID1 and ID3 may be of therapeutic value for conditions associated with pathological angiogenesis [87]. In fact, the deletion of Id1/Id3 reduced ocular neovascularization in a mouse model of neovascular AMD [81]. In conclusion, drugs targeting ID1/ID3 could modulate RPE maturation and pathological changes in AMD.
## 4.5. Response to Acute Zinc Supplementation
Treatment with zinc has been reported to prevent progression to advanced AMD (for review, see [88]), at least partly due to a direct effect of zinc on the RPE [15,89,90]. In previous in vitro studies, we investigated long-term supplementation with zinc and found altered selective gene expression, protein secretion, and increased pigmentation and barrier function [15,17]. We identified several molecular pathways, such as cell adhesion/polarity, extracellular matrix organization, protein processing/transport, and oxidative stress response, involved in the beneficial effects of chronic zinc supplementation on the RPE. However, these studies could not address the complexity associated with cell heterogeneity and detailed temporal changes. We were particularly interested in exploring how zinc supplementation could affect the less and more differentiated cells in the short term to understand the potential to develop a more targeted intervention through supplementation.
To decipher the effects of acute zinc supplementation, RPE cells were treated with elevated zinc for 1 week following the protocols we used previously [17]. We found that acute zinc supplementation induced significant changes in gene expression in both short- and long-term cultures (Figure 4(B1–B4)) regardless of the temporal stage of the cells. We also identified 81 zinc-responsive transcripts (Figure 4C) that were common amongst all groups. These transcripts were enriched in housekeeping genes and contained transcripts for metallothioneins, ribosomal protein, and ATP synthases (Supplementary Table S8), indicating that zinc affects the cellular homeostasis of the RPE, similar to that of other systems [91].
Apart from the shared genes, specific changes were associated with the more or the less differentiated cell groups at both 2W and 19W in culture (Figure 4C). The four specific genes affected by short-term zinc supplementation in the less differentiated cell group (Supplementary Table S8) are genes linked to the integrity of Bruch’s membrane (COL8A1) [70], epithelial–mesenchymal transition (KRT17) [92], phagocytic activity and the rescue of the RPE (MFGE8) [93,94], and activity of heparan sulfate (SULF1) [22], suggesting that zinc might influence interaction with the local extracellular environment. In the more differentiated cell group in the 2W cultures, zinc affected biological processes including extracellular matrix organization, cellular polarity, and visual processes (Supplementary Table S8) that are critical for supporting the photoreceptors [95].
At 19W in culture, zinc affected the less differentiated cells via modulating proteolysis, DNA replication and RNA transcription, and amino acid metabolisms (Supplementary Table S8), probably to mitigate oxidative stress, one of the AMD-associated biological functions [96]. In the more differentiated cells at 19W in culture, zinc supplementation affected several metabolic pathways (Supplementary Table S8). Dysregulation of metabolic pathways is an important contributor to AMD pathophysiology [97]. This may directly explain the benefit of zinc supplementation in patients in the AREDS study [13,51,98]. Therefore, zinc supplementation has a multitude of effects on RPE, with some specific effects depending on cell differentiation and maturity. Identifying the specific molecular changes may help redefine treatment strategies based on zinc supplementation or nutritional interventions.
## 4.6. The Effects of Zinc on the Genes in the Pseudotemporal Trajectory
Earlier we identified 537 genes (Supplementary Table S5) in the main pseudotemporal trajectory (Figure 3(A2)). Zinc supplementation did not affect 240 genes (Figure 4(D1)). Of the remaining 297 genes, 16 were housekeeping genes (Supplementary Table S8; Figure 4C) associated with the mitochondrion, the activation of cytochrome-c oxidase and ubiquinone, and response to metal ions (Supplementary Table S9). This is in line with a previous observation that zinc supplementation can protect the RPE from oxidative-stress-induced cell death by improving mitochondrial function [89], and this could be behind the positive effect of zinc supplementation in the AREDS studies [13,51,98] or increased zinc intake through diet [34,99]. Metallothioneins (MT1F and MT1E) that belong to this group (Figure 4(D2), Supplementary Tables S7 and S9) are well-recognized mediators of zinc supplementation in the RPE [100] via mediating oxidative-stress-induced RPE damage [90] and differentiation of RPE [57].
The remaining 281 genes in the main trajectory (Figure 4(D1)) were associated with various biological processes including extracellular matrix organization, angiogenesis, collagen fibril organization, and visual perception (Supplementary Table S10). The composition of extracellular matrix has a profound effect on how the RPE attaches to the Bruch’s membrane [101]. Thus, gene expression modification by zinc could directly affect sub-RPE deposit formation [76].
We also found that acute zinc supplementation upregulated the expression of transcriptional regulators ID1 and ID3, a finding that had not been reported before. In addition, in a previous study, we identified TGFB1 as a potential upstream regulator effect of chronic zinc supplementation [17]. In our current study, we found that TGFB1 expression was also upregulated by acute zinc supplementation. Therefore, we carried out an Upstream Analysis in Ingenuity Pathway Analysis (QIAGEN, Redwood City) for the 190 transiently expressed genes in the combined pseudotime-correlated groups 1 and 7 (Supplementary Table S5). We identified a strong relationship for TGFB1 ($p \leq 6.98$ × 10−19) and also for ID1 ($p \leq 3.59$ × 10−5) and ID3 ($p \leq 1.23$ × 10−3) as potential upstream regulators for a group of genes among the transiently expressed group. In fact, TGFB1 was an upstream regulatory element for ID1 and ID3 (Supplementary Table S12). A direct molecular link between ID1 and TGFB1 had already been suggested [102]. Therefore, the positive effects of zinc supplementation could be directly through TGFB1 signalling, which involves ID1 and ID3. The receptor of TGFB1, TGFBR1, is an AMD genetic risk variant [36], suggesting that these findings are directly relevant to further studies on AMD.
## 4.7. AMD-Specific Gene Expression Changes
Based on literature searches, we generated gene lists that have been shown to contribute to the pathological changes associated with AMD and we examined the effects of cell maturation and zinc supplementation on these genes (Figure 5, Supplementary Table S11). Specific attention was paid to the activation complement system and lipid-metabolism-related genes, as these were the genetically most significantly associated pathways with AMD [36,103]. We also scrutinized genes associated with pigmentary changes and mineralization-associated genes due to their potential link with RPE function and/or sub-RPE deposit formation in AMD [5,45].
Not all genes involved in complement regulation were expressed in RPE cells (Figure 5B, Supplementary Table S11). This is perhaps not surprising, as the local activity of the complement cascade is influenced by a complicated mix of local and systemic regulatory factors, which is altered in AMD retina [4,104,105]. However, some complement genes that were expressed in the RPE were affected by acute zinc supplementation, including CFH, C1R, CFHR1, and CLU (Figure 5B, Supplementary Table S7). These transcriptomic changes are in line with our previous reports that zinc supplementation has a functional effect on CFH secretion [17] as well as oligomerization and activity [106], and zinc levels can regulate interferon gamma systematically, which, in turn, regulates expression of complement genes [107,108]. Apart from CFH, several complement proteins can bind zinc, and this binding alters their activity [109,110]. In addition, network analysis has highlighted elements of the complement regulation as potential targets for nutrient-affected pathways [111]. Finally, there is also clinical evidence that zinc supplementation can directly inhibit complement activation in AMD patients [104], suggesting that modulation of the complement system could be one of the ways that zinc supplementation affects the progression to AMD.
Of the 42 genes expressed in our RPE culture associated with cholesterol metabolism (Figure 5C, Supplementary Table S11), ANGPTL4, LRPAP1, VDAC2, APOE, PLTP, NPC2, TSPO, and CYP27A1 were altered in response to acute zinc supplementation (Figure 5C, Supplementary Table S7) [112,113,114]. These findings corroborate our previously reported effect of long-term zinc supplementation on lipid metabolism [17]. ANGPTL4 is a lipid-inducible feedback regulator of LPL-mediated lipid uptake. However it is also a multifunctional cytokine, regulating vascular permeability, angiogenesis, and inflammation [115]. The systemic level of ANGPTL4 is associated with NV AMD [116]. Reportedly, this protein indirectly induces RPE barrier breakdown [117]. LRPAP1 is a chaperon protein, generally controlling the folding and ligand–receptor interaction expression of the LRP receptors [118]. Its role in RPE and AMD remains elusive. VDAC2 is a ceramide sensor integrated into the mitochondrial membrane and its function relates to regulation of mitochondrial apoptosis [119,120]. Increased ceramide levels affect non-polarized RPE cells found in late stages of AMD [121]. APOE, a lipophilic glycoprotein with a major role in lipid transport, is one of the many constituents of the sub-RPE deposits and has been associated with increased AMD risk [122,123]. PLTP is a phospholipid transfer protein and is one of the main players in lipid homeostasis in ApoB-containing particles and high-density lipoprotein metabolism. PLTP plasma levels are associated with AMD [124], but their potential role in drusen formation remains elusive. NPC2 is a cholesterol transporter, effluxing cholesterol out of late endosomes in RPE. The lack of this protein is associated with age-related maculopathies [125]. TSPO is a translocator protein that transfers cholesterol from the mitochondrial outer membrane to the mitochondrial inner membrane and also plays role in oxidative stress and inflammation. It was recently implicated as a highly relevant drug target for immunomodulatory and antioxidant therapies of AMD [126,127]. CYP27A1 is involved in the elimination of 7-ketocholesterol from RPE, a toxic product of cholesterol auto-oxidation, which accumulates in drusen [128,129]. In summary, the aforementioned affected gene expressions in response to zinc suggest that zinc has an impact on sub-RPE cholesterol accumulation, oxidative stress, inflammation, and angiogenesis via the regulation of lipid–membrane interaction, lipid transport, and the elimination of toxic lipid byproducts.
In our cultures, we found 80 RPE-expressed genes associated with mineralization (Figure 5D, Supplementary Table S11). Out of these, we found that COL1A1, POSTN, CD63, LAMP1, BMP4, SLC20A1, SOX9, and BMP7 were altered in response to acute zinc supplementation (Figure 5D, Supplementary Table S7). The POSTN gene encodes a secreted extracellular matrix protein that functions in tissue development and regeneration and a potential anti-fibrotic therapeutic target for NV AMD [130]. CD63 is involved in the regulation of cell development, activation, growth, and motility [131], and together with LAMP1, it plays a role in autophagy, exosome secretion, and drusen formation [132,133]. BMP4 has been implicated in the disruption of RPE cell migration and barrier disruption in NV AMD [134]. The protein encoded by SLC20A1 is a sodium–phosphate symporter involved in vascular calcification but not reported in association with RPE function or AMD [135]. SOX9 plays a key role in regulating visual cycle gene expression in RPE [136] but also plays a role in the prevention of calcification [137]. BMP7 is hypothesized to be critical for the differentiation of the retinal pigmented epithelium during development [138]. It also has been implicated in prevention of vascular calcification [139]. Zinc supplementation is reported to inhibit phosphate-induced vascular calcification [140], but, as our results indicate, it may also have a (indirect) role in the prevention of drusen calcification.
In our cultures, most pigmentation-related genes were detected and their expression level either remained constant or increased throughout the culture time (Figure 5E, Supplementary Table S11). Only TYR, TYRP1, and DCT were altered in response to acute zinc supplementation (Figure 5D, Supplementary Table S7). TYR, TYRP1, and DCT are key to the production of melanin [46] and pigmentary abnormalities show a strong correlation with sub-RPE deposit formation and development of AMD [9]. TYR catalyzes the production of melanin from tyrosine, in which L-DOPA is produced as an intermediate [141,142]. The function of TYRP1 is in the biosynthesis of melanin from tyrosine, whilst TYRP1 catalyzes the oxidation of 5–6-dihydroxyindole-2-carboxylic acid to an indole, whilst DCT catalyzes the conversion of L-dopachrome into 5–6-dihydroxyindole-2-carboxylic acid [142]. These events lead to the activation of GPR143 signaling and may initiate several downstream effects, such PEDF, VEGF secretion, and/or exosome release [46]. Since we found an influence of zinc on the expression of these pigmentation-related genes, and given the data from literature above, zinc might also have an influence on GPR143 signaling. Surprisingly, acute zinc treatment resulted in downregulation of the aforementioned genes, despite long-term zinc supplementation enhancing RPE pigmentation [15]. At the transcriptional level, long-term zinc supplementation significantly altered the expression of 18 out of the 21 pigmentation-related genes (Supplementary Table S11, [17]), of which the majority were also downregulated, except for HPS5, HPS6, and LYST. These three upregulated genes are all related to intracellular trafficking, such as lysosomes and melanosomes [143,144,145].The negative effect of acute zinc supplementation on the gene expression of other pigmentation-related genes needs to be further investigated.
## 5. Conclusions
Primary hfRPE cultures that recapitulate the main phenotypes of aged RPE in vivo can help to dissect the molecular changes associated with RPE maturation and experimental manipulation, such as zinc supplementation. This cellular model provides an excellent platform for further preclinical studies to identify new treatment strategies for AMD. As reported in vivo, these cells retain a high degree of heterogeneity even after extended time in culture, which may help to understand the role of this heterogeneity in the human eyes. Identifying the transcriptional machinery, including transcriptional regulators ID1 and ID3, may help us to target pathways previously not considered for AMD. The data also show that the differentiation of RPE into cells that resemble those in vivo requires an extended time in culture, and experimental manipulation will need to consider this. The wide-ranging effects of zinc supplementation, from the regulation of housekeeping genes to very specific AMD-associated transcripts, build confidence that this intervention could indeed be a suitable intervention strategy to slow the progression to advanced-stage AMD, as suggested by the AREDS studies.
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---
title: Blackcurrant Alleviates Dextran Sulfate Sodium (DSS)-Induced Colitis in Mice
authors:
- Hye-Jung Moon
- Youn-Soo Cha
- Kyung-Ah Kim
journal: Foods
year: 2023
pmcid: PMC10000425
doi: 10.3390/foods12051073
license: CC BY 4.0
---
# Blackcurrant Alleviates Dextran Sulfate Sodium (DSS)-Induced Colitis in Mice
## Abstract
Previous studies have reported that anthocyanin (ACN)-rich materials have beneficial effects on ulcerative colitis (UC). Blackcurrant (BC) has been known as one of the foods rich in ACN, while studies demonstrating its effect on UC are rare. This study attempted to investigate the protective effects of whole BC in mice with colitis using dextran sulfate sodium (DSS). Mice were orally given whole BC powder at a dose of 150 mg daily for four weeks, and colitis was induced by drinking $3\%$ DSS for six days. Whole BC relieved symptoms of colitis and pathological changes in the colon. The overproduction of pro-inflammatory cytokines such as IL-1β, TNF-α, and IL-6 in serum and colon tissues was also reduced by whole BC. In addition, whole BC significantly lowered the levels of mRNA and protein of downstream targets in the NF-κB signaling pathway. Furthermore, BC administration increased the expression of genes related to barrier function: ZO-1, occludin, and mucin. Moreover, the whole BC modulated the relative abundance of gut microbiota altered with DSS. Therefore, the whole BC has demonstrated the potential to prevent colitis through attenuation of the inflammatory response and regulation of the gut microbial composition.
## 1. Introduction
Inflammatory bowel disease (IBD) refers to a chronic inflammatory condition of the intestinal tract that increases health and economic burdens due to an increase in global prevalence and lowers the quality of life [1]. Ulcerative colitis (UC), one of the typical IBDs, appears only in the colon and is marked by supercritical mucosal inflammation [2]. A cross-sectional study of 15 countries in Asia and the Middle East reported that UC is twice as prevalent as Crohn’s disease and occurs more frequently in men in their 30s [3]. In addition, it is essential to treat UC because it can develop into colorectal cancer if it persists for a long time [4]. UC is characterized by diarrhea, bloody stools, urgency, increased frequency of defecation, and, in severe cases, fever and weight loss [5]. It is estimated that UC is caused by the disruption of intestinal homeostasis due to genetic, microbiological, immunological, and environmental factors including diet, smoking, and stress [5,6]. Drugs such as 5-aminosalicylic acid (5-ASA), biological drugs (anti-tumor necrosis factor-α (anti-TNF-α) and anti-adhesion molecule inhibitors), immunosuppressants, and corticosteroids have been used to treat UC [5]. However, it has been reported that the remission rate of UC is only $15\%$ to $44.9\%$, and adverse events such as infection, UC flare, nasopharyngitis, myelosuppression, liver toxicity, and malignancy occur [5,6,7,8]. Therefore, to develop other safe and effective treatments, natural products using polyphenols such as apigenin and curcumin, and polysaccharides such as *Scutellaria baicalensis* Georgi, are being studied [9,10].
Anthocyanins (ACN), belonging to the flavonoid subgroup of polyphenols, are found in flowers, vegetables, and fruits and are water-soluble pigments in red, blue, and purple [11]. Various health benefits of ACNs have been discovered, in particular, ACN supplements have been shown to improve gut health by modifying the gut microflora and enhancing the intestinal barrier, thereby reducing the potential risk of inflammation [11,12,13]. ACN-rich foods include berries (blackcurrants, blueberries, and raspberries) and dark red vegetables (red cabbage, eggplant, and purple wheat), among which blackcurrants have been reported to have a higher total ACN content than blueberries [11,14].
Blackcurrant (BC) has been suggested to possess various health effects, including prevention of obesity, improvement of cognitive impairment due to aging, and reduction of diabetes-related cardiovascular dysfunction [15,16,17]. Recently, ACN dietary supplements consisting of BC and bilberry extracts have shown anti-inflammatory effects in intestinal epithelial cells [18]. Additionally, silver nanoparticles based on BC extracts were observed to restore inflammation of induced colitis in mice [19]. However, these studies are insufficient to confirm the effect of BC on improving intestinal inflammation. Furthermore, most of these studies have verified the physiological activity of BC extracts, and studies on BC in its whole form are rare. Therefore, the aim of this study was to investigate whether the intake of whole BC in mice alleviates dextran sulfate sodium (DSS)-induced colitis.
## 2.1. Materials and Reagents
The commercial freeze-dried powder of whole BC was obtained from Sujon Berries (Nelson, New Zealand). According to Willems et al. [ 2017], 1 g of Sujon’s BC powder contained 23.1 mg of anthocyanin, 0.9 g of carbohydrates, and 8.2 mg of vitamin C [20]. DSS was bought from MP Biochemical (MW: 36–50 kDa; Solon, OH, USA). A TNF-α enzyme-linked immunosorbent assay (ELISA) kit was bought from Invitrogen (Vienna, Austria), and interleukin (IL)-1β and IL-6 ELISA kits were purchased from R&D Systems (Minneapolis, MN, USA). The RNAiso Plus kit, PrimeScript RT Master Mix, and bicinchoninic acid (BCA) protein assay kits were purchased from Takara Bio, Inc. (Shiga, Japan). RIPA buffer was procured from Thermo Scientific Inc. (Rockford, IL, USA). Primary antibodies including phosphorylated-p65 (p-p65), p65, inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and β-actin were purchased from Cell Signaling Technology (Danvers, MA, USA).
## 2.2. Animals
The animal experiment was approved by the Animal Ethics Committee of Chungnam National University (IACUC approval number: 202112-CNU-214). Five-week-old male C57BL/6J mice were acquired from Central Lab Animal, Inc. (Seoul, Republic of Korea). The experimental design is illustrated in Figure S1. The mice were housed under the same conditions (temperature of 22 ± 2 °C, relative humidity of 50 ± $5\%$, and 12 h/12 h light/dark cycles) and acclimatized for six days. After the adaptation period, 24 mice were separated into three groups ($$n = 8$$ per group): Vehicle group, normal control group not treated with DSS; DSS group, DSS-treated control group; DSS + BC group, DSS and blackcurrant treatment group. In the DSS + BC group, BC powder diluted in phosphate-buffered saline (PBS) was orally administered at a dose of 150 mg/mice per day throughout the experimental period. The PBS dosage given to the Vehicle and DSS groups was the same as that given to the DSS + BC group. To induce colitis in the DSS group and the DSS + BC groups, $3\%$ DSS (w/v) in drinking water was given for six days from the 21st day of the experiment. One day before the experiment’s termination, DSS was replaced with normal water.
Symptoms of colitis were monitored daily using the DAI (disease activity index) while DSS was administered. The DAI, which was slightly modified from what Peng et al. [ 2019] described, was measured as scores for body weight loss (0, none; 1, 1–$5\%$; 2, 5–$10\%$; 3, >$10\%$), stool consistency (0, normal; 1, slightly loose feces; 2, loose feces; 3, watery diarrhea), and bloody stools (0, none; 1, slightly bloody; 2, bloody; 3, gross bleeding) [21]. Feces were collected the day before the sacrifice. Mice were euthanized after fasting for 12 h. The blood and colon tissues were obtained after the experiment was completed. Blood was centrifuged at 1100 g for 15 min to obtain the serum. After measuring the length and weight of colonic tissue samples, some were fixed in $4\%$ formalin for histological assessment. The remaining colon tissues were immediately frozen in liquid nitrogen and kept at −80 °C until the experiment.
## 2.3. Histologic Analysis
Hematoxylin and eosin (H&E) staining was accomplished on 4 μm thick sections of colon tissues fixed in $4\%$ formalin. Colon slides were examined using a light microscope (DM2500, Leica Microsystems, Wetzlar, Germany) installed at the Center for University-wide Research Facilities (CURF) at Jeonbuk National University (Jeonju, Republic of Korea). Histological damage to the colon tissue was evaluated by the scores of epithelium loss (0–3), crypt damage (0–3), depletion of goblet cells (0–3), and infiltration of inflammatory cells (0–3) [22].
## 2.4. Measurement of Inflammatory Cytokine Levels
Colon tissue was homogenized with lysis buffer, and the supernatant was separated. ELISA kits were used to quantify inflammatory cytokines (TNF-α, IL-1β, and IL-6) contained in the separated supernatant and serum, according to the manufacturer’s procedure.
## 2.5. Quantitative Real-Time PCR (qRT-PCR) Analysis
qRT-PCR analysis was performed with reference to Song et al. [ 2021] and the instructions of the manufacturer of the reagent [15]. Following the manufacturer’s directions for the RNAiso Plus kit (Takara Bio, Inc.), total RNA was extracted from the colon tissue. cDNA was synthesized from total RNA using PrimeScript RT Master Mix (Takara Bio, Inc.). The TOPrealTM SYBR green qPCR Premix (Enzynomics, Daejeon, Republic of Korea) and a 7500 real-time PCR system (Applied Biosystems, Foster City, CA, USA) were used to carry out the qRT-PCR. The relative expression of the target gene was determined using the 2 −ΔΔCt method and normalized to that of the internal reference GAPDH.
## 2.6. Western Blotting
Western blotting was carried out by referring to the experimental method of Jang et al. [ 2019] [22]. Total protein lysates were extracted by homogenizing the colon tissue in a radioimmunoprecipitation assay (RIPA) buffer containing protease and phosphatase inhibitors. The protein content of the supernatant obtained by centrifugation of the extract was quantified using a BCA assay kit. Loading buffer was added to the supernatant and inactivated at 95 °C for 10 min. Protein samples were electrophoresed on SDS–polyacrylamide gels and then transferred to polyvinylidene difluoride (PVDF) membranes. After blocking the membrane with $5\%$ skim milk, the antibody diluted to an appropriate concentration was applied for 24 h at 4 °C. After washing the membrane with tris-buffered saline with $0.1\%$ tween 20 (TBST), the secondary antibody was added, and the protein was identified using enhanced chemiluminescence (ECL) solution and the ChemiDoc system (ATTO LuminoGraph II, ATTO, Tokyo, Japan). The bands of the target proteins were quantified using Image J software (US National Institutes of Health, Bethesda, MD, USA) and normalized to β-actin.
## 2.7. Gut Microbial Community Analysis
Song et al. [ 2021] and Jang et al. [ 2019] were referred to for fecal collection and gut microbiota analysis [15,21]. The day after the DSS drinking was completed, feces were collected and stored at −80 °C in order to analyze the gut microbial community. The microbial community of the collected feces was analyzed by Macrogen Inc. (Seoul, Republic of Korea). In summary, a library for 16S metagenomic sequencing was prepared by amplifying the V3–V4 region of 16S rRNA using the Hercules kit on the Illumina platform to construct a library of DNA extracted from fecal samples. The sequencing results were analyzed using the QIIME2 program, and taxonomic information classification was confirmed using the BLAST program of the NCBI 16S database.
## 2.8. Statistical Analysis
Data were shown as the mean ± standard deviation (SD). Statistical analysis was performed using SPSS 18.0 software (SPSS Inc., Chicago, IL, USA). The significance of differences among groups was assessed using a one-way analysis of variance (ANOVA) by Duncan’s post hoc tests at $p \leq 0.05.$
## 3.1. Effects of Blackcurrant on Clinical Symptoms and Colon Damage in DSS-Induced Colitis
UC symptoms of colitis were identified as changes in body weight, disease activity index (DAI), colon length, and weight per length of the colon (Figure 1A–C). There was no significant difference in the change in body weight before DSS administration, but from the 6th day after DSS administration, both the DSS and DSS + BC groups were significantly reduced compared with the Vehicle control group (Figure 1A). Changes in DAI were checked daily during the DSS drinking period (Figure 1B). The DSS group showed a significantly higher DAI than the Vehicle group from the 22nd day. In contrast, the DSS + BC group showed significantly lower values than the DSS group until the 25th day. The DSS + BC group also showed an improved DAI on the final day of the experiment. The colon length was 4.73 ± 0.66 cm in the UC-induced DSS group, which was significantly shorter by about $29.9\%$ compared with 6.75 ± 0.33 cm in the Vehicle group (Figure 1C). In the DSS + BC group, colon length was 5.70 ± 0.42 cm, and a DSS-related decrease in colon length was significantly restored. In addition, the DSS + BC group showed a significantly reduced colon weight-to-length ratio.
## 3.2. Effects of Blackcurrant on Histological Changes in the Colon Tissue in DSS-Induced Colitis
Sections of the colonic tissue were stained with H&E and histopathological scores were given to confirm the extent of damage (Figure 2A,B). The Vehicle group had no damage or inflammatory response to the mucosa, submucosa, crypt structure, or goblet cells in the colon. However, severe epithelial erosion, deficiency of goblet cells, destruction of the crypt structure, and infiltration of many inflammatory cells into the mucosa and submucosa were observed in DSS-treated mice. Supplementation with blackcurrant alleviated damage to the mucosal layer of colonic tissue and infiltration of inflammatory cells caused by DSS, and significantly reduced the histological damage score.
## 3.3. Effects of Blackcurrant on the Levels of Pro-inflammatory Cytokines in the Serum and Colon Tissue in DSS-Induced Colitis
The levels of proinflammatory cytokines in the serum and colon are shown in Table 1. The DSS group showed significantly higher levels of serum TNF-α and interleukin (IL)-6 than the Vehicle group. The DSS + BC group showed significantly attenuated levels of serum TNF-α, which were elevated by DSS. In colon tissue, the levels of TNF-α and IL-1β in the DSS group were increased significantly compared with the Vehicle group. However, the levels of TNF-α and IL-1β increased by DSS treatment were significantly reduced in the DSS + BC.
## 3.4. Effects of Blackcurrant on the Nuclear Factor-Kappa-Light-Chain-Enhancer of Activated B cells (NF-κB) Signaling Pathway, Tight Junction (TJ) Proteins, and Mucin in DSS-Induced Colitis
We investigated whether BC affects the expression of genes and proteins related to the NF-κB signaling pathway, mucin, and TJ proteins (Figure 3A–D). The DSS group upregulated the genes of toll-like receptor-4 (TLR-4) and nuclear factor-kappa-light-chain-enhancer of activated B cells (NF-κB) related to the NF-kB signaling pathway compared with the Vehicle group (Figure 3A). Furthermore, an increase in the expression of iNOS, COX-2, pro-inflammatory cytokines (TNF-α, IL-1β, IL-6), and monocyte chemoattractant protein-1 (MCP-1), which are downstream genes of NF-κB, was observed in the DSS group. However, the expression levels of these excessive mRNAs were inhibited in the DSS + BC group, with a value similar to those of the Vehicle group.
Next, the effects of BC on the expression of genes encoding TJ proteins and mucin involved in barrier function were evaluated (Figure 3B). The DSS group significantly downregulated expression of all genes associated with TJ proteins and mucin compared with the Vehicle group. In contrast, the DSS + BC group showed higher expression of all such genes than the DSS group.
The expression of proteins related to the NF-κB signaling pathway, an inflammatory response pathway, was also examined (Figure 3C,D). As a result, it was found that the phosphorylation of NF-κB p65 (p-p65) and the protein expression of its downstream enzymes, iNOS and COX-2, were significantly increased in the DSS group compared with the Vehicle group. However, the DSS + BC group was revealed to inhibit the overexpression of p-p65, iNOS, and COX-2 increased by DSS. That is, it was shown that the administration of BC decreased the inflammatory response by inhibiting the NF-κB signaling pathway activated by DSS in the colon.
## 3.5. Effects of Blackcurrant on Modulation of the Gut Microbiome in DSS-Induced Colitis
The influence of BC on the diversity and relative abundance of the gut microbiome was analyzed (Figure 4). To confirm the α-diversity of the gut microbiota, the observed amplicon sequence variant (ASV), an index of evenness, and Chao1, an index of richness, were evaluated. There was no significant difference between all groups, but the α-diversity of the DSS + BC group tended to increase slightly compared with the DSS group (ASV; Vehicle, 116.00 ± 32.33; DSS, 106.80 ± 9.36; DSS + BC, 125.20 ± 36.53, Chao1; Vehicle, 117.61 ± 32.30; DSS, 108.66 ± 11.33; DSS + BC, 127.79 ± 37.95).
Regarding the composition of gut microbiota, the DSS group showed a distinct alteration from that of the Vehicle group (Figure 4A–D). In taxonomic community analysis at the phylum level, Firmicutes and Actinobacteria were reduced in the DSS group compared with the Vehicle group, whereas Bacteroidetes and Verrucomicrobia were increased (Figure 4A). Meanwhile, the DSS + BC group was found to modulate the changes in the phylum caused by DSS. The abundance of Ligilactobacillus, Enterococcus, and Bifidobacterium at the genus level was high in the Vehicle group (Figure 4B). However, DSS treatment diminished these genera and elevated the levels of Bacteroides, Escherichia, and Akkermansia. BC decreased Bacteroides levels and increased Ligilactobacillus compared with the DSS group. Moreover, at the species level, the administration of BC was shown to regulate the change in microbial composition due to DSS (Figure 4C). As a result of analyzing β-diversity with a principal coordinate analysis (PCoA) plot to confirm the relative similarity in the gut microflora between each group, it was distinguished by the first principal component (PC1) between the Vehicle and DSS-treated groups (Figure 4D). Moreover, the DSS and DSS + BC groups were distinguished by the second principal component (PC2), and supplementation with BC tended to modulate the gut microbial community.
## 4. Discussion
The cause of colitis is considered to be an imbalance in intestinal homeostasis due to the influence of genetic, microbiological, immunological, and environmental factors [5,6]. Natural products are being developed to treat UC, and ACNs are known to have positive effects on gut health [9,10,12,13,18,19]. Thus, the current study aimed to analyze how the beneficial effects of ACN-rich BC caused immunological and microbiological changes in the colon in mice with DSS-induced colitis. Indeed, a previous study reported that nonalcoholic steatohepatitis was prevented in mice fed a high-fat/high-sucrose diet containing $6\%$ whole BC powder, which was equivalent to consuming two cups of fresh BC per day in humans, for 24 weeks [23]. Based on a previous study, we explored the effect of oral administration of 150 mg/day (7.5 g/kg body weight (BW), total ACN content; 165 mg/kg BW) of whole BC powder to mice, which was less than the dose administered in the previous study. In addition, the anti-inflammatory effects in colitis mice induced by DSS when administered BC at this dose were confirmed as a result of this study.
Chemical induction of colitis using DSS in mice is the most widely used method because it reflects clinical symptoms and histological changes observed in humans [6,24,25]. DSS, which has a highly negative charge, acts directly on colonic epithelial cells as a chemical toxin and damages them, resulting in the depletion of mucin and goblet cells, epithelial erosion, and ulcers [24,25]. Destruction of the intestinal epithelial layer also increases colonic epithelial permeability, allowing commensal bacteria and related antigens to infiltrate the mucosa, followed by infiltration of immune cells such as neutrophils [22,24,25]. Immune cells infiltrating the lamina propria and submucosa reportedly secrete pro-inflammatory cytokines and disseminate inflammatory responses to underlying tissues [24,25]. The results of this work revealed that, when colitis was induced with DSS, clinical symptoms such as a decrease in body weight and colon length, as well as an increase in DAI and colon weight, were observed. Furthermore, histological changes were observed after inducing colitis with DSS, including epithelial loss, crypt damage, depletion of goblet cells, and infiltration of inflammatory cells. In contrast, the administration of BC had no effect on weight loss but showed beneficial effects on other clinical symptoms and histological changes following colitis induction.
In another study, the intake of 200 mg/kg BW of crude ACN isolated from the fruits of *Lycium ruthenicum* Murray had no effect on weight loss induced by DSS, similar to our results [21]. Previous studies also demonstrated that giving mice ACN-containing materials such as the water extract of maqui berry, ACN extracted from mulberry fruit and black rice relieved the pathological changes in the colon caused by DSS, like inflammatory cell infiltration and mucosal damage [26,27,28]. Additionally, when silver nanoparticles with a diameter of 213 nm based on blackcurrant extract were supplied to the DSS colitis mice model at a concentration of 2 mg/kg, only the macroscopic score and colon shortening were significantly improved [19]. Similar to the previous study, our study in which whole BC powder was administered also showed an improvement effect in these indicators, as well as a relieving effect in the colonic weight-to-length ratio. This difference is likely due to the difference in dose concentration.
Damage to intestinal epithelial cells caused by DSS was reported to worsen the inflammatory response by increasing the generation of pro-inflammatory cytokines [9,25,29]. It was also reported that the levels of TNF-α and IL-6 were altered in the serum of mice with early-stage colitis induced by one week of DSS administration [29]. Elevated levels of pro-inflammatory cytokines due to colitis can be reduced by various polyphenols, including ACNs [9,13,22]. In this study, except for IL-1β in the serum and IL-6 in the colon tissue, DSS treatment increased the levels of other pro-inflammatory cytokines, whereas BC administration decreased these levels. It was reported that treatment with petunidin 3-O-[rhamnopyranosyl]-(trans-p-coumaroyl)-5-O-[β-D-glucopyranoside] (P3G), isolated from the fruits of *Lycium ruthenicum* Murray, reduced all pro-inflammatory cytokines in the serum, but there was no difference in IL-1β levels in the crude ACN-administered group compared with the DSS-treated group, as in our study [21].
When mulberry ACN was administered, the inhibitory effect on pro-inflammatory cytokines in the colon decreased all indicators at a high concentration (200 mg/kg BW), but there was no change, except for IL-1β, at a low concentration (100 mg/kg BW) [26]. The major ACNs in BC are delphinidin-3-rutinoside, cyanidin-3-rutinoside, delphinidin-3-glucoside, and cyanidin-3-glucoside, and each food item contains different types of ACNs [11]. Therefore, the difference in effects on weight loss and pro-inflammatory cytokines was presumed to be due to differences in the types and intake of different ACNs in food, and differences in UC mouse models and disease stages. Moreover, previous studies have shown that BC extract decreases inflammation-related cytokines in bone-marrow-derived macrophages and vascular tissue in mice with type 2 diabetes mellitus [17,30]. Similarly, in the present study, BC was observed to reduce the production of pro-inflammatory cytokines, even when consumed in the form of whole BC powder.
Intestinal homeostasis is maintained by a barrier consisting of mucus, epithelial, and immune cells that prevent the penetration of bacteria and other antigens into the colon tissue [2,31]. DSS-induced loss of TJ proteins (ZO-1 and occludin) in mucus and mucin in the intestinal epithelial layers [21,26,31,32]. NF-κB is an inducible transcription factor that regulates the expression of genes encoding cytokines associated with immune and inflammatory responses and is involved in maintaining intestinal homeostasis [33]. When cells are stimulated externally through gut microbes, pro-inflammatory cytokines and toll-like receptors activate NF-κB (p-p65), which is known to be involved in the onset of inflammatory diseases by upregulating the expression of inflammation-related cytokines (TNF-α, IL-1β, and IL-6), chemokines (MCP-1), and inducible enzymes (COX-2, iNOS) [21,28,32,33].
In previous studies, the administration of ACN in mice with DSS-induced colitis and mice fed a high-fat diet increased the expression of factors related to mucin and TJ proteins in the colon, while downregulating the expression of target genes in the NF-κB signaling pathway [21,34]. In vitro, ACN-rich bilberry and BC extracts, as well as the 3-O-glucosides of cyanidin and delphinidin, have been shown to inhibit the activity of TNF-α-induced NF-κB in intestinal epithelial cells [18,35]. The results of this study demonstrated that BC intake enhanced the expression of genes related to mucin and TJ proteins in colitis-induced mice. Additionally, BC decreased the phosphorylation of the NF-κB subunit and downregulated the expression of NF-κB target genes and proteins, such as COX-2 and iNOS, which were shown to improve DSS-induced colitis.
Many studies have reported that changes in the community structure of gut microflora are associated with the development of colitis [10,21,22,24,25,26,28]. In the DSS-induced colitis model, maqui berry extract and ACNs of mulberry and *Lycium ruthenicum* Murray changed the α-diversity of gut microflora [21,28], but BC did not change it significantly.
However, it was confirmed that the treatment with BC had an effect on the β-diversity and gut microbial composition, which was distinct from that of the DSS group. Several studies using DSS-induced colitis mouse models revealed a reduction in Firmicutes and an increase in Bacteroidetes at the phylum level, and the intake of ACNs and flavonoids modulated their composition [36,37,38].
*The* genera Lactobacillus (some of the reclassified genera, Ligilactobacillus [39]) and Bifidobacterium in the colon, known to have beneficial effects on health in several studies, are reduced by DSS [22,28,40], and our results were similar. Similar to another chronic DSS animal study, this study observed that treatment with DSS increased the genus Akkermansia, and this increase was a positive correlation with IL-1β, a pro-inflammatory cytokine [40]. Although the genus *Akkermansia is* known to have anti-inflammatory effects, it is still controversial and more studies are required because its exact role in IBD is not known [41].
As a change in relative abundance at the species level, BC decreased Bacteroides acidifaciens, known colitis-associated bacteria, after DSS treatment, and increased Bacteroides caecimuris, which rose in the recovery phase after stopping DSS treatment [42]. In addition, BC administration tended to increase the abundance of Mucispirillum schaedleri, which has been reported to have a preventive effect against colitis caused by *Salmonella and* Alistipes putredinis, which decreases in IBD [43,44]. As such, BC modulated the composition of gut microbiota that was altered by DSS. However, further studies are required to investigate the precise mechanism for the role of gut microbiota in each in the alleviation of colitis by BC.
## 5. Conclusions
The intake of whole BC powder has been shown to prevent clinical symptoms and histological destruction caused by colitis. BC was observed to attenuate the levels of pro-inflammatory cytokines in serum and colon tissues and enhance the gene expression of mucin and tight junction proteins. Additionally, it downregulated the expression of target proteins and genes involved in the NF-κB signaling pathway. Furthermore, BC showed the potential to alleviate the intestinal inflammatory response by modulating the composition of gut microbiota altered by DSS. Therefore, in this study, whole BC powder showed a protective effect against DSS-induced colitis by regulating the inflammation-related NF-κB signaling pathway and gut microflora, confirming its potential as a natural dietary material to improve UC.
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|
---
title: The Effect and Mechanism of Corilagin from Euryale Ferox Salisb Shell on LPS-Induced
Inflammation in Raw264.7 Cells
authors:
- Minrui Wu
- Yuhan Jiang
- Junnan Wang
- Ting Luo
- Yang Yi
- Hongxun Wang
- Limei Wang
journal: Foods
year: 2023
pmcid: PMC10000429
doi: 10.3390/foods12050979
license: CC BY 4.0
---
# The Effect and Mechanism of Corilagin from Euryale Ferox Salisb Shell on LPS-Induced Inflammation in Raw264.7 Cells
## Abstract
[1] Background: Euryale ferox *Salisb is* a large aquatic plant of the water lily family and an edible economic crop with medicinal value. The annual output of Euryale ferox Salisb shell in *China is* higher than 1000 tons, often as waste or used as fuel, resulting in waste of resources and environmental pollution. We isolated and identified the corilagin monomer from Euryale ferox Salisb shell and discovered its potential anti-inflammatory effects. This study aimed to investigate the anti-inflammatory effect of corilagin isolated from Euryale ferox Salisb shell. [ 2] Methods: We predict the anti-inflammatory mechanism by pharmacology. LPS was added to 264.7 cell medium to induce an inflammatory state, and the safe action range of corilagin was screened using CCK-8. The Griess method was used to determine NO content. The presence of TNF-α, IL-6, IL-1β, and IL-10 was determined by ELISA to evaluate the effect of corilagin on the secretion of inflammatory factors, while that of reactive oxygen species was detected by flow cytometry. *The* gene expression levels of TNF-α, IL-6, COX-2, and iNOS were determined using qRT-PCR. qRT-PCR and Western blot were used to detect the mRNA and expression of target genes in the network pharmacologic prediction pathway. [ 3] Results: Network pharmacology analysis revealed that the anti-inflammatory effect of corilagin may be related to MAPK and TOLL-like receptor signaling pathways. The results demonstrated the presence of an anti-inflammatory effect, as indicated by the reduction in the level of NO, TNF-α, IL-6, IL-1β, IL-10, and ROS in Raw264.7 cells induced by LPS. The results suggest that corilagin reduced the expression of TNF-α, IL-6, COX-2, and iNOS genes in Raw264.7 cells induced by LPS. The downregulation of the phosphorylation of IκB-α protein related to the toll-like receptor signaling pathway and upregulation of the phosphorylation of key proteins in the MAPK signaling pathway, P65 and JNK, resulted in reduced tolerance toward lipopolysaccharide, allowing for the exertion of the immune response. [ 4] Conclusions: The results demonstrate the significant anti-inflammatory effect of corilagin from Euryale ferox Salisb shell. This compound regulates the tolerance state of macrophages toward lipopolysaccharide through the NF-κB signaling pathway and plays an immunoregulatory role. The compound also regulates the expression of iNOS through the MAPK signaling pathway, thereby alleviating the cell damage caused by excessive NO release.
## 1. Introduction
The immune stimulator, LPS, can activate multiple signaling pathways in macrophages, leading to a series of pathophysiological responses [1], and is often used in in vitro inflammation studies. Inhibition of excessive activation of macrophages and its mediated inflammation has been demonstrated to be beneficial in many disease models [2,3], suggesting that targeting macrophage activation is a promising strategy for preventing inflammatory diseases.
Euryale ferox *Salisb is* a large aquatic plant belonging to the water lily family, which has been frequently reported for its use in lowering blood sugar and blood lipid [4], in addition to its antioxidation properties [5]. Shuliang He et al. characterized the constituents of the volatile oil of Euryale ferox Salisb and identified its biological activity, particularly its antioxidant activity [6]. Additionally, Wen-Na Zhang et al. characterized the polysaccharide in Euryale ferox Salisb and investigated its hypoglycemic effect [7]. The biosynthesis mechanism of flavonoids in Euryale ferox Salisb was analyzed by Peng Wu et al. through metabolomic and transcriptomic analyses, which revealed the key factors involved in the biosynthesis of flavonoids in Euryale ferox Salisb, its main functional substances [8]. Transcriptomic analysis of Euryale ferox Salisb at different developmental stages was performed by Xian Liu et al. [ 9], knowledge of which is particularly useful for the development and utilization of Euryale ferox Salisb.
With an annual output of more than 1000 tons, the Euryale ferox Salisb shell accounts for around $40\%$ of the seed. It is often used as fuel or transported for disposal, resulting in waste of resources and environmental pollution [10]. Cheng Ying Wu et al. studied the antioxidant and anti-fatigue properties of phenolic extracts of the Euryale ferox Salisb shell, which led to the discovery of potential antioxidant agents [11].
Corilagin has been reported to exhibit various pharmacological activities, including inhibition of inflammatory development [12], antiviral [13], liver protection [14], and antitumor effects [15]. Li et al. [ 16] found that corilagin significantly reduced the levels of IL-6 and IL-1β in the serum of cells and mice and exhibited an anti-inflammatory role by downregulating the TLR4 signaling molecules, improving the extreme inflammatory state in patients with sepsis. Additionally, Tong et al. [ 17] found that corilagin may inhibit the activation of the nuclear factor-κB pathway in a STAT3-related manner and reduce the secretion of IL-1β and TNF-α, thereby reducing radiation-induced brain injury in mice. Previous studies have demonstrated that corilagin mainly improves cellular inflammation through the TLR signaling pathway, but there is no report of its activity in the LPS-induced RAW264. Previously, we isolated and identified the corilagin monomer from Euryale ferox Salisb shell; however, the anti-inflammatory effect was not evaluated.
We applied a network pharmacology approach to predicting the anti-inflammatory mechanism of corilagin, using the LPS-stimulated Raw264.7 cells as an in vitro inflammatory model to determine the effect of corilagin from Euryale ferox Salisb on the expression of inflammatory and anti-inflammatory factors in macrophages and the possible molecular mechanisms at three levels, i.e., biochemical factors, transcription, and protein levels. The theoretical groundwork is provided by developing and utilizing the Euryale ferox Salisb shell.
## 2.1. Materials and Chemicals
Raw264.7 cells were purchased from Shanghai Cell Bank, Chinese Academy of Sciences. The cell viability detection kit was purchased from Japan Tongren Reagent Company, and lipopolysaccharide for inducing inflammation was purchased from Sigma Company. The ELISA kit was purchased from Beijing Sizheng Bo Bio Co., Ltd. (Beijing, China), the ROS kit was purchased from Beijing Prilai Gene Technology Co., Ltd. (Beijing, China), and the reverse transcription kit was provided by Bao Bioengineering Co., Ltd. (Dalian, China). Primers were provided by Shanghai Sangon Biology Co., Ltd. (Shanghai, China). The primary and secondary antibodies used for Western blotting were provided by CST (Pi3K and p-Pi3K, Bioss; AKT and JNK, Wuhan Miting).
## 2.2. Separation and Identification of Corilagin from Euryale ferox Salisb Shell
Euryale ferox Salisb shells were dried and crushed, filtered with a 200-mesh sieve, ultrasonically extracted with $70\%$ ethanol, concentrated under reduced pressure, and freeze-dried to obtain Euryale ferox Salisb shell polyphenol alcohol extracts. The chitosan polyphenol extract was added with water and ultrasonicated before being fractionally extracted with petroleum ether, ethyl acetate, and n-butanol (v/$v = 1$:1). The extract phase of the Euryale ferox Salisb shell was collected and packed on a silica gel column (60 mesh) by the wet method for separation. Elution was carried out with a mixture of ethyl acetate and petroleum ether (2:1), and the elution fractions were collected. The fraction with the highest activity was concentrated and lyophilized. The eluent was petroleum ether:ethyl acetate (100:15), and the eluent was collected. The samples were separated on a Sephadex LH-20 (Hydroxypropyl Sephadex) chromatographic column with $50\%$ methanol and water, and the monomer compound was obtained by semi-preparative liquid phase, which was identified as corilagin (Figures S1 and S2).
## 2.3. Corilagin and Inflammation Target Prediction and Screening Application
The 2D structure of corilagin was obtained from the PubChem database, and the sdf file of the drug structure was imported into PharmMapper and Swiss Target Prediction, the TCMSP database, to obtain drug-related targets by merging and de-weighting. The disease-related targets were obtained from the GeneCards database by setting the search keyword “inflammation” as the genus “human origin”.
## 2.4. Construction of PPI Network and Acquisition of Crossover Genes
The species was set as the human species, and the minimum relationship score was 0.4. The key proteins with the cross-repetition of corilagin and inflammation were input into the protein interaction database (STRING), and the proteins without an interaction relationship were removed to obtain the protein interaction map.
## 2.5. HUB Genes’ Acquisition and KEGG and GO Enrichment Analysis
Cytoscape 3.9.1 was supplied with the protein–protein interaction diagram obtained from the STRING database to obtain the top 30 central target genes for interactions with other proteins in the network diagram. GO and KEGG analysis of central target genes were performed using the R-package clusterProfiler and enrichment plot. The data with p-value < 0.05 were screened, and the relevant legends were plotted using the R package ggplot2.
## 2.6. Docking Analysis
The top five degrees in the PPI network were used as the receptor proteins, the top nodes in the “active ingredient-target-disease” network were used as the ligands, and the structures of the receptor proteins were downloaded from the PDB database. The proteins and ligands were pre-processed using PyMOL-2.3.4. Subsequently, AutoDockTools software was used to pre-process the protein and ligands, and Vina was used for predicting the binding energy of the ligands of small size to the proteins, with the lowest binding energy indicating the optimal conformation. The receptor–ligand docking files were processed by PyMOL and uploaded to the online website called Plip to visualize the validation results.
## 2.7. Cell Culture and Model Establishment
Raw264.7 cells, the mouse monocytic leukemia cells, were cultured in a DMEM medium containing $10\%$ FBS and $1\%$ penicillin and streptomycin dual-antibodies at 37 °C in an incubator containing $5\%$ CO2. The culture was passaged when grown to more than $90\%$ in cell culture flasks, and selected experiments were performed on counted cells. The experiments were conducted by comparing different groups of experimental subjects: the control group (without LPS and corilagin intervention), the LPS stimulation group (with the addition of 1 μg/mL of LPS for intervention), the experimental group (different concentrations of corilagin were pretreated for 2 h and the final concentration of 1 μg/mL was added and LPS co-treated for 24 h), and the positive drug group (50 μmol/L of dexamethasone pretreatment for 2 h, LPS with a final concentration of 1 μg/mL added for 24 h).
## 2.8. Cell Morphology Observation and CCK-8 Assay to Detect the Proliferation Toxicity of Corilagin on Raw264.7 Cells
Cells were seeded into 6-well plates (at a density of 5 × 105 cells per well). The cells were divided into a normal control group, an LPS stimulation group, and a corilagin treatment group, and the experiments were conducted in replicates (2 wells for each group). The cellular morphology was observed by an inverted microscope.
To screen for the safe concentration of corilagin from the Gorgon husk source, the CCK-8 method was used to analyze the effect of corilagin on the survival rate of Raw264.7 cells.
Cells in the logarithmic growth phase were sampled for cell counting, and 100 μL of cell suspension was added to each well of a 96-well plate (density of 3000 cells per well). The surrounding wells were sealed with PBS and grown for 24 h.
The experiment was divided into the blank group (without cells and drugs), the control group (without drugs), and the experimental group. The drugs were prepared by diluting with complete medium to 2-fold gradient dilution, followed by filtration with a membrane of 0.22 μm pore size, and used immediately. The cultures were grown for 24 h before the analysis. When testing, the medium in the 96-well plate was aspirated, washed twice with PBS, and patted dry on thick paper. CCK-8 was prepared in the dark to avoid errors caused by residual CCK-8 in the pipette tip left when adding samples. A complete medium was used to dilute CCK-8, and the diluted solution was mixed well for later use. The cultures were incubated for 3 h in an incubator, and the OD value at a wavelength of 450 nm was determined.
## 2.9. Determination of NO Content by Griess Method
Raw264.7 cells were seeded into a 24-well plate (at a density of 2 × 105 cells per well) and placed in a cell incubator for 12 h. Different groups were cultured for 24 h according to the corresponding treatment, and the cell supernatant was collected. The NO content in the supernatant was detected by the Griess reagent method, and the amount of released NO of each group was calculated using the standard curve.
## 2.10. ELISA Method to Determine the Effect of Corilagin on the Secretion of Inflammatory Factors
Cell treatment was kept consistent with the pretreatment method used for cell morphology observation, and the cell supernatant was collected. The contents of TNF-α, IL-6, IL-1β, and IL-10 were determined according to the instructions of the ELISA kit.
## 2.11. Detection of Intracellular Reactive Oxygen Species by Flow Cytometry
The cells in the logarithmic growth phase were inoculated into 6-well plates and cultured for 12 h. Different groups were cultured for 24 h according to the corresponding treatment. The liquid in the 6-well plate was discarded and washed twice with PBS. The control group was added with 2 mL of complete medium. The base, lipopolysaccharide, and experimental groups were added with 2 mL of 20 μmol DCFH-DA diluted in a complete medium and incubated in an incubator for 2 h. After incubation, PBS was used for rinsing twice, i.e., 1 mL of PBS was added to each well and the cells were detached by pipetting, collected into a centrifuge tube, and centrifuged at 1000 r/min for 3 min, the supernatant was discarded, and 1 mL of PBS was added to each tube. The mixtures were mixed by pipetting, the cell suspension was transferred to a 1.5 mL flow centrifuge tube, and the intensity of the intracellular ROS fluorescence was measured by flow cytometry.
## 2.12. qRT-PCR Detection of TNF-α, IL-6, COX-2, and iNOS Gene Expression Levels
The six-well plate was taken out, and the medium discarded and rinsed twice with PBS. Then, 1 mL of RNA iso plus was added and left to stand for 1 min before pipetting. The cell suspension was collected by pipetting, transferred into a sterile tube, and added with 200 μL of chloroform, and the mixture was mixed well. Next, extraction was carried out on the ice for 15 min with inversion every 5 min. The EP tube with three layers of supernatant was carefully removed, and the supernatant was pipetted into another 1.5 mL EP tube using a 100 μL pipette. Then, 500 μL of isopropanol was added and mixed well, and the tube was placed on ice for 15 min. The mixture was then centrifuged at 12,000 rpm and at 4 °C for 15 min to recover a white precipitate. The supernatant was carefully removed to avoid disturbing the pellet. Next, 1 mL of $75\%$ ethanol was added, and the pellet was gently lifted. The wall of the tube was washed by inversion, at 7500 rpm. The supernatant was discarded, and the pellet was air-dried with the lid opened at room temperature for 5 min before the addition of an appropriate amount of DEPC water to dissolve the pellet. The RNA concentration was measured using a UV micropipette and adjusted to obtain the RNA concentration of 1000 ng/μL per tube. The RNA was reverse-transcribed into cDNA according to the protocols for reverse transcription, and the real-time fluorescence quantitative PCR reaction system using 10 μL of SYBR Premix Ex Taq TM, 8 μL of primers, and 2 μL of cDNA was conducted.
Primer sequences (Table 1): The cDNA sequences of each gene were retrieved from NCBI, and specific primer sequences were designed and synthesized by Shanghai Sangon Bioengineering Co., Ltd.
## 2.13. Western Blot Analysis of Key Proteins’ Expression in NF-κB, MAPK, and PI3K-AKT Signaling Pathways
After preconditioning cells, the excess medium in the well plate was aspirated, and 1 mL of PBS was used for washing twice. Cells were digested with 1 mL of trypsin, transferred to a 1.5 mL EP tube, and centrifuged at 3000 rpm for 1 min. The resulting supernatant was discarded. Next, 100 μL of lysis buffer (prepared by RIPA lysis buffer and protease inhibitor 1:100) was added to each tube and evenly pipetted. The cells were lysed on ice for 30 min to ensure complete cell lysis. The cells were then centrifuged at 12,000 rpm for 10 min at 4 °C, and the supernatant was collected to obtain the total protein. A 5× protein loading buffer 4:1 was added to the protein sample, mixed by vortexing, and incubated in a water bath at 95 °C for 10 min. The treated samples were stored in a −20 °C refrigerator for later use. The treated protein samples were transferred to $10\%$ SDS-polyacrylamide gel for electrophoretic separation. The PVDF membrane was placed on the glue and covered with wet filter paper and a sponge. The mounted membrane transfer system was secured with the membrane transfer clip, and the transfer was conducted at 200 mA for 1 h. The membrane was then blocked with $5\%$ skimmed milk at room temperature for 1 h with gentle shaking, followed by overnight incubation with a primary antibody at 4 °C. The blocked membrane was then washed thrice on a decolorizing shaker for 5 min at room temperature. Two hours later, the membrane was then washed thrice on a decolorizing shaker for 5 min. The membrane was then exposed to ECL, and the signal was analyzed using gel imaging software.
## 2.14. Data Statistics and Analysis
GraphPad Prism 8 was used for statistical analysis. All data are expressed as the mean ± standard deviation unless otherwise stated. The p-value of <0.05 was considered significant.
## 3.1. Acquisition of Corilagin and Inflammatory Targets and the “Corilagin-Target-Inflammation” Interaction Network
The 3D structure of corilagin from Euryale ferox Salisb shell was obtained from the PubChem database (Figure 1A). A total of 307 corilagin genes were obtained from the PharmMapper website and the Swiss Target Prediction database. Among human species, the GeneCard database showed that there were 111,109 genes related to inflammation. Then, 268 cross-repeating genes (Figure 1B) that appear in both drug targets and inflammatory targets were selected, suggesting that these genes may be the key genes in regulating inflammation of corilagin. To further investigate the relationship between corilagin and inflammation, a “Corilagin-Target-Inflammation” network has been constructed in Cytoscape 3.9.1 (Figure 2).
## 3.2. Construction of PPI Network and Acquisition of HUB Genes
The crossover genes were imported into the STRING website for protein interactions, and a protein interaction map containing 268 nodes and 3724 edges was obtained. Cytoscape software visualized the protein interaction map, and the top 30 target proteins of the protein interaction network were calculated using the MCC calculation method in the CytoHubba plugin (Figure 3). The results predicted that the above genes and related proteins play an important role in treating hepatocellular carcinoma.
## 3.3. GO and KEGG Pathway Analysis
The GO analysis (Figure 4A) revealed the expression of genes localized in the nucleus, while the KEGG (Figure 4B) analysis revealed the MAPK and TOLL-like receptor signaling pathways. Based on findings from the GO and KEGG analyses, it was concluded that corilagin may validate protein expression by regulating the MAPK signaling pathway, which is related to the secretion of inflammatory factors by macrophages, as well as in the NF-B and PI3K signaling pathways, which are closely related to toll-like receptors that attenuate the tolerance level of macrophages.
## 3.4. Validation of Molecular Docking
The binding energy (kcal/mol) between the target and corilagin was predicted based on molecular docking (Figure 5A), whereby the negative binding energy of the ligand and receptor usually indicates the binding affinity between them. The docking revealed that the binding energy was less than −5 kcal/mol between all hub gene targets and keratine, from which only three models with good binding energy were selected for visualization (Figure 5B–D).
## 3.5. The Effect of Corilagin on the Viability of Raw264.7 Cells and Changes in Cellular Morphology
The investigation of the effect of corilagin on the viability of Raw264.7 cells demonstrated that (Figure 6) cell viability was significantly decreased after incubation for 24 h at concentrations exceeding 100 µm/L, i.e., 25, 50, and 100 μmol/L were selected as the low, medium, and high doses of corilagin from the Gorgon shell source in the experiment.
Raw264.7 is a mononuclear macrophage derived from the leukemia virus in Balb/c mice [18]. The health of the cells can be impacted by the state of the cells, and since Raw264.7 cells are small and bright in appearance, they are not in a good shape. After the LPS stimulation, the cells formed a long shuttle with elongated false feet [19]. Additionally, pseudopodia were reduced in cells treated with corilagin, with most of the cells being round in shape (Figure 7). The results indicated that corilagin from Euryale ferox Salisb could mitigate inflammation by inhibiting the differentiation of Raw264.7 cells.
## 3.6. Effects of Corilagin on LPS-Induced NO Secretion by Raw264.7 Macrophages
NO is an endogenous synthetic gas signal molecule, synthesized in the cytoplasm, which quickly diffuses through the cell membrane. The molecule rapidly reacts with other free radicals, producing high levels of active peroxidase (oxidant) and other active nitrogen derivatives. These molecules can reflect inflammation and diseases such as atherosclerosis [20]. The effects of NO secretion on the volume of Raw264.7 cells induced by LPS via the Griess method were investigated. The results (Figure 8) depict that at the concentration of higher than 25, 50, and 100 μmol/L, the corilagin intervention resulted in a significant reduction in the volume of LPS-induced Raw264.7 cells’ supernatant, indicating the potential inflammation-relieving activity of corilagin, which concerns its ability to suppress the release of NO.
## 3.7. The Effect of Corilagin on the Expression of Inflammatory Factors in LPS-Induced Raw264.7 Macrophages
When macrophages are activated, inflammatory cytokines such as inflammatory enzymes and TNF-α, IL-6, IL-1β, and IL-10 are secreted. Following anti-inflammatory drug intervention, macrophages secrete anti-inflammatory factors, which are responsible for anti-inflammatory effects. The investigation of the effect of corilagin on the secretion of TNF-α, IL-6, IL-1β, and IL-10 in Raw264.7 cells (Figure 9) revealed that treatment with corilagin at 25, 50, and 100 μmol/ L significantly reduced the secretion of TNF-α and IL-6 in LPS-induced Raw264.7 cells. At 50 and 100 μmol/L, corilagin significantly reduced the secretion of IL-1β and IL-10 in LPS-induced Raw264.7 cells.
## 3.8. The Effect of Corilagin on the Content of Reactive Oxygen Species in Raw264.7 Cells
Activated oxygen is an active oxygen family, which includes superoxide and hydroxyl radicals that stimulate macrophages and neutrophils. This reactive oxygen species is produced by many biologically active media. Reactive oxygen species and other pro-inflammatory factors activate nuclear transcription factors (NF-κB) and cell nuclear binding, thereby promoting inflammatory factor transcription, sometimes resulting in inflammatory allergic reactions. These factors play a key role in the inflammation and wound healing processes, which lack specificity in bacteria. Excessive active oxygen can destroy the integrity of the mitochondrial membrane, resulting in changes in mitochondrial permeability; thus, eliminating tissue and damaging active oxygen is highly important. The effects of corilagin on the content of reactive oxygen species in Raw264.7 cells at 25, 50, 100, and 50 μmol/L of dexamethasone are presented in Figure 10. Following the intervention with corilagin at 25, 50, 100, and 50 μmol/L of dexamethasone, the intracellular ROS was reduced by $9.49\%$, $10.91\%$, $15.43\%$, and $8.73\%$, respectively. The results demonstrate that the corilagin from Euryale ferox Salisb shell can prevent the inflammatory reaction by reducing reactive oxygen species.
## 3.9. Effects of Corilagin on the Gene Expression of TNF-α, IL-6, iNOS, and COX-2 in LPS-Induced Raw264.7 Macrophages
The results (Figure 11) demonstrate that after the intervention of 25, 50, and 100 μmol/L derived from the Gorgon shell, the levels of gene expression of TNF-α, iNOS, and COX-2 were significantly reduced in a dose-dependent manner. Additionally, intervention by 100 μmol/L of corilagin significantly reduced the level of gene expression of IL-6. The nitric oxide synthase (NOS) can convert left-transverse arginine to left-transverse citrulline, thereby causing NO production, which can be classified into constitutive and inducible. Many studies have suggested that continuous and excessive NO production is primarily associated with the high expression level of inducible nitric oxide synthase (iNOS) [21]. The results indicate that the corilagin could reduce the secretion of NO by lowering the expression of iNOS, thereby exerting its anti-inflammatory effect. In addition, the level of COX-2 is increased in the inflammatory process, causing excessive production of PGE2, resulting in an excessive inflammatory response. The results also display that the corilagin could downregulate the expression of COX-2 to alleviate inflammation. TNF-α and IL-6 are the most common inflammatory cytokines, which play a vital role in inflammation. The results demonstrate that corilagin could reduce the gene expression of TNF-α and IL-6, thereby reducing the secretion of TNF-α and IL-6 and achieving an anti-inflammatory effect.
## 3.10. The Effect of Corilagin on the Expression of Key Proteins in the NF-κB Signaling Pathway in Raw264.7 Cells
The effect of corilagin from the Gorgon shell on the expression of NF-κB pathway-related proteins in Raw264.7 cells is presented in Figure 12. The protein assay of IκB-α revealed that LPS stimulation and intervention by Gorgon shell-derived corilagin produced no significant effect on phosphorylation of IκB-α. Additionally, the phosphorylation of P65 was significantly downregulated following LPS stimulation, whereas the treatment with 50 μmol/L of corilagin resulted in upregulation of the phosphorylation of P65.
## 3.11. The Effect of Corilagin on the Expression of Key Proteins in the MAPK Signaling Pathway in Raw264.7 Cells
The effect of corilagin from the Gorgon shell on the expression of MAPK pathway-related proteins in Raw264.7 cells is presented in Figure 13. The ERK protein assay revealed that LPS stimulation produced no significant effect on the phosphorylation of ERK, whereas the phosphorylation of ERK was significantly upregulated after the intervention of corilagin at 50 μmol/L. It was also found that the phosphorylation of JNK was significantly downregulated following LPS stimulation, while the intervention by 50 μmol/L of corilagin resulted in upregulation of phosphorylation of JNK. Additionally, both LPS stimulation and treatment with 50 μmol/L of corilagin produced no significant effect on the phosphorylation of P38.
## 3.12. The Effect of Corilagin on the Expression of Key Proteins in the PI3K-AKT Signaling Pathway in Raw264.7 Cells
The effect of corilagin from the Gorgon shell on the expression of PI3K-AKT pathway-related proteins in Raw264.7 cells is depicted in Figure 14. The PI3K protein assay revealed that both stimulations by LPS and intervention with 50 μmol/L of corilagin produced no significant effect on the phosphorylation of PI3K. Compared with the blank group, the phosphorylation of AKT protein was significantly downregulated following LPS stimulation, whereas the intervention with 50 μmol/L of corilagin produced no significant effect on the phosphorylation of AKT protein.
The investigation of the effect of the corilagin intervention revealed that no significant effect was produced on the expression of key proteins in the NF-κB signaling pathway in Raw264.7 cells. Similarly, stimulation by LPS produced no significant effect on the phosphorylation of IκB-α but significantly downregulated the phosphorylation of P65. In the MAPK signaling pathway, LPS stimulation had no significant effect on the phosphorylation of ERK and P38 but significantly inhibited the phosphorylation of JNK. In addition, in the PI3K-AKT signaling pathway, LPS stimulation had no significant effect on the phosphorylation of PI3K but significantly downregulated the phosphorylation of AKT. Previous studies have demonstrated that when macrophages develop lipopolysaccharide tolerance, LPS stimulation results in reduced P65 phosphorylation in the NF-κB signaling pathway, as well as that of ERK, JNK, and P38 in the MAPK signaling pathway [21]. The results from Western blot analysis demonstrate that the macrophages were lipopolysaccharide-tolerant and that LPS at 50 μmol/L was tolerated in this study. Additionally, the intervention of corilagin originated from the Gorgon husk significantly upregulated the phosphorylation of P65 and JNK proteins and relieved the lipopolysaccharide tolerance state of Raw264.7 cells, indicating its potential immunoregulatory role [21].
## 4. Discussion
Corilagin is a polyphenolic tannin compound [16], which is widely found in geranium, bead, white clover, longan, Phyllanthi fructus, and other plants [22]. Structurally, ellagic acid is a dilactone of hexahydroxy biphenyl acid (HHDP). Corilagin, a kind of ellagitannin which is a condensation of ellagic acid, has good antioxidant potential. After optimizing the extraction process of ellagitannin, Anindita Paul et al. obtained the result that ellagitannin could combine well with catalase through calculation and analysis [23]. Studies have demonstrated that corilagin exhibits antitumor, antiviral, and antibacterial activities in addition to anti-inflammatory and antioxidant effects, suggesting its potential use as an agent in the preventive treatments of cardiovascular diseases [13,22,24,25,26]. Corilagin sourced from Gorgon husk was extracted and identified during the early stages of a study on Gorilla husk by our research team, but its anti-inflammatory properties have not yet been revealed.
Cyber-pharmacology efficiently integrates research content, utilizing high-throughput computing methods and software. By setting different screening conditions, proteins interacting with small molecules can be accurately predicted, to predict protein-related metabolic pathways [27]. In this study, we identified 307 targets of corilagin using PharmMapper. The subsequent GO and KEGG enrichment analyses revealed that the anti-inflammatory effects of corilagin are primarily associated with MAPK and TOLL-like receptor signaling pathways. Additionally, LPS and other pro-inflammatory factors can induce the phosphorylation of MAPK signaling pathway-associated proteins and trigger the expression of iNOS genes in the nucleus.
Findings from this study demonstrate that corilagin, a Gorgon fruit source, could significantly inhibit the production of reactive oxygen species induced by LPS in macrophages, as well as the related oxidative stress responses in cells. These results further confirm the findings reported in previous studies regarding the positive correlation between the degree of oxidative stress and the degree of inflammation [28] and that inhibiting excessive production of reactive oxygen species can suppress the inflammatory response. Analysis of NO secretion revealed that the intervention of corilagin from Gorgon shell significantly reduced NO content in the supernatant of LPS-induced Raw264.7 cells.
Analysis of the gene expression of inflammatory factors demonstrated that the corilagin intervention significantly reduced the expression of IL-6 and TNF-α genes compared with LPS-induced macrophage inflammation. Corilagin also significantly downregulated the expression of iNOScox-2, and nitric oxide synthase (NOS) could convert L-arginine to L-citrulline, thereby causing the release of NO. Numerous studies have suggested that persistent and excessive NO production is mainly attributed to the overexpression of inducible nitric oxide synthase (iNOS) [29]. In addition, the expression of COX-2 is elevated during the inflammation process, causing the overproduction of PGE2, which results in an excessive response to inflammation [30]. This proves that corilagin, a source of a gorgonian shell, could reduce the secretion of NO by downregulating the expression of iNOS, thereby exerting its anti-inflammatory effect.
Many physiological and pathological responses in mammalian cells and tissues are mediated by MAPK signaling, including stress responses, inflammation, and apoptosis. Phosphorylation of ERK$\frac{1}{2}$ and p38 promotes the production of inflammatory factors, including TNFα, IL-6, and IL-8 [31]. In addition, TNF-α also stimulates the MAPK cascade and promotes IL-8 secretion [32]. The analysis conducted in this study revealed that phosphorylation levels of ERK and JNK proteins increased following treatment with corilagin.
Related studies have reported that p38 of MAPK and PI3K-Akt can regulate LPS-induced gene expression by controlling the hyperphosphorylation and nuclear translocation of p65 of NF-κB [33]. Corilagin could also significantly upregulate the phosphorylation of P65 protein. This study also examined the expression of related proteins in the PI3K-Akt signaling pathway, which revealed that LPS induction significantly reduced the phosphorylation level of AKT.
Previous studies have demonstrated that when macrophages develop lipopolysaccharide tolerance, LPS stimulation can reduce the phosphorylation of P65 protein in the NF-κB signaling pathway, as well as that of ERK, JNK, and P38 in the MAPK signaling pathway [34]. Western blot analysis demonstrated that the intervention of 50 μmol/L of gorgonian shell-derived corilagin could significantly upregulate the phosphorylation of P65 and JNK, relieving the lipopolysaccharide tolerance state of Raw264.7 cells, thereby exerting an immune-regulating effect. The results indicate that macrophages developed lipopolysaccharide tolerance and that corilagin interfered with the phosphorylation of P65 and JNK proteins and relieved the macrophages of their tolerance. Notably, lipopolysaccharide tolerance plays an immunomodulatory role [24].
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|
---
title: Prevalence of Malnutrition in Hospitalized Patients in Lebanon Using Nutrition
Risk Screening (NRS-2002) and Global Leadership Initiative on Malnutrition (GLIM)
Criteria and Its Association with Length of Stay
authors:
- Krystel Ouaijan
- Nahla Hwalla
- Ngianga-Bakwin Kandala
- Emmanuel Kabengele Mpinga
journal: Healthcare
year: 2023
pmcid: PMC10000444
doi: 10.3390/healthcare11050730
license: CC BY 4.0
---
# Prevalence of Malnutrition in Hospitalized Patients in Lebanon Using Nutrition Risk Screening (NRS-2002) and Global Leadership Initiative on Malnutrition (GLIM) Criteria and Its Association with Length of Stay
## Abstract
[1] Background: Prevalence studies on hospital malnutrition are still scarce in the Middle East region despite recent global recognition of clinical malnutrition as a healthcare priority. The aim of this study is to measure the prevalence of malnutrition in adult hospitalized patients in Lebanon using the newly developed Global Leadership Initiative on Malnutrition tool (GLIM), and explore the association between malnutrition and the length of hospital stay (LOS) as a clinical outcome. [ 2] Methods: A representative cross-sectional sample of hospitalized patients was selected from a random sample of hospitals in the five districts in Lebanon. Malnutrition was screened and assessed using the Nutrition Risk Screening tool (NRS-2002) and GLIM criteria. Mid-upper arm muscle circumference (MUAC) and handgrip strength were used to measure and assess muscle mass. Length of stay was recorded upon discharge. [ 3] Results: A total of 343 adult patients were enrolled in this study. The prevalence of malnutrition risk according to NRS-2002 was $31.2\%$, and the prevalence of malnutrition according to the GLIM criteria was $35.6\%$. The most frequent malnutrition-associated criteria were weight loss and low food intake. Malnourished patients had a significantly longer LOS compared to patients with adequate nutritional status (11 days versus 4 days). Handgrip strength and MUAC measurements were negatively correlated with the length of hospital stay. [ 4] Conclusion and recommendations: the study documented the valid and practical use of GLIM for assessing the prevalence and magnitude of malnutrition in hospitalized patients in Lebanon, and highlighted the need for evidence-based interventions to address the underlying causes of malnutrition in Lebanese hospitals.
## 1. Introduction
Nutritional risk and malnutrition are highly prevalent in hospitalized patients [1], and have been reported to range from 20 to $50\%$ in different European and South American countries with an average of $41.7\%$ worldwide [2]. There is abundant evidence that malnutrition is associated with increased morbidity, nosocomial infections and hospital readmission [3]. Recent studies have also demonstrated that malnutrition is associated with prolonged length of stay (LOS) in patients with acute illness or even chronic non-communicable diseases [4,5]. Consequently, malnutrition is identified as a major encumbrance for hospitalized patients and a driver of increased healthcare cost incurring a considerable economic burden, accounting for 2.1 and $10\%$ of the national health expenditures in Europe [6,7].
Nevertheless, malnutrition is still not addressed as a serious clinical problem due to the lack of clearly defined responsibilities and lack of unequivocally universally accepted diagnostic criteria [8,9]. Global efforts are being launched as well as a call to action to implement mandatory screening, establish a diagnostic code and develop national protocols to position nutrition as a healthcare priority [9,10]. Recently, the Global Leadership Initiative on Malnutrition (GLIM) has established a consensus for the diagnosis of malnutrition based on a combination of phenotypic and etiologic criteria and proposed it as a new tool to be validated in the disease-afflicted hospitalized population [11].
In the Middle East region, initiatives to study the prevalence of malnutrition in hospitals have been modest, with Turkey recently publishing a rate of $39\%$ [12]. An international multicenter study published in 2008 has reported a lower rate of $22\%$ of risk of malnutrition in two Lebanese hospitals [13]. Other prevalence studies in Lebanon have focused only on the rate of malnutrition in the community settings, with reported rates of $61.3\%$ malnutrition and a risk of malnutrition in older adults living in long-term care centers and lower rates of $48.3\%$ in older adults living in their homes [14,15].
## Context of the Study
Lebanon is a small country of the Middle East region covering an area of 10,452 km2 and having borders with both Syria and Israel, considered to be a conflict area. The country is divided into five main districts: north, Mount Lebanon, south, Bekaa Valley and the capital Beirut and its suburbs. In 2015, the population was estimated to be 6,847,712, including Lebanese people, foreign workers and refugees [16,17]. The highest population density is seen in Beirut and its suburbs. The south, north and Bekaa have the highest number of rural small villages.
Lebanon has one hundred and forty-four hospitals comprising 11742 beds, of which $78.3\%$ are private and $21.7\%$ are public. The number of beds is distributed as follows: 3806 ($32.4\%$) in Mount Lebanon, 2452 in Beirut ($20.9\%$), 1931 ($16.4\%$) in the south, 1852 ($15.8\%$) in the north and 1701 ($14.5\%$) in Bekaa. The annual hospital admission is declared to be 698,210 cases per year, with the highest percentages in Beirut and Mount Lebanon, $22.3\%$ and $29.6\%$, respectively [18].
According to the World Bank, the gross domestic product was estimated at USD 23.1 billion in 2021 compared to USD 52 billion in 2019. The drop in GDP per capita was a drastic $36.5\%$ in just two years and Lebanon was reclassified as a lower-middle-income country instead of an upper-middle-income country. These drastic changes have resulted in difficulties in the cost of medical treatments and health coverage, which relies both on National Social Security and private insurances [17].
The aim of this study was to determine the prevalence of malnutrition in Lebanese hospitals by using the newly proposed GLIM tool, and to explore its different criteria and their relationship with length of stay, an easily measurable outcome parameter that is directly related to hospital costs [19]. The findings of this study will be the first milestone to establish a national policy mandating nutritional screening and assessment in all hospitalized patients. They can also guide the authority in forming a surveillance system and evaluating strategies targeted at decreasing the rate of malnutrition in hospitals.
## 2.1. Design and Sample Size
The study is a cross-sectional, observational, multicenter study. The sample size was estimated as 330 hospitalized patients to achieve a $95\%$ confidence interval with a margin of error of 0.05 and $100\%$ expected response rate based on using the STEPS sample size calculator of WHO and on the number of yearly hospital admissions [18]. It was calculated considering a significance level of $5\%$ with $80\%$ power. The number of patients in a random sample of hospitals in the five districts of Lebanon was weighed against the number of admissions per district from the National Health Survey [18]. The distribution of samples according to districts to have a national representation is presented in Figure 1. Private hospitals were only included due to the restricted access to the public hospitals in the period of data collection. All adult patients, males and females aged 18 years and above, admitted to the different wards of the hospital during the period of data collection were recruited within 48 h of admission. Exclusion criteria included the following wards: gynecology (including all pregnant and lactating women), intensive care unit, psychiatry and short stay of less than 48 h.
## 2.2. Data Collection
Patient characteristics, i.e., age, gender, admission diagnosis, history of previous admissions, underlying diseases and number of home medications, were recorded. Patients were interviewed for history of weight loss, appetite and record of food intake. C-reactive protein levels (CRPs) were retrieved from the available blood tests from patients’ records. The length of hospital stay was calculated from the date of admission to the date of discharge.
Body weight and height were measured using the Detecto manual scale to the nearest 1 kg and 1 cm, respectively. BMI (weight kg/height m2) was calculated accordingly. Mid-upper arm muscle circumference (MUAC) was measured at the midpoint between the acromion and olecranon processes at the non-dominant arm using a non-stretchable tape measure to the nearest 0.1 cm. The MUAC was categorized into three groups: “normal”, “moderately depleted” for measurements <23 cm and “severely depleted” for those <20 cm [20]. Handgrip strength was measured with the non-dominant hand using the Saehan hydraulic hand dynamometer to the nearest 0.1 kg. The handgrip strength variable was categorized into two groups: “normal” and “low” accounting for the gender cut-off points being <27 kg and <16 kg for males and females, respectively [20].
## 2.3. Nutritional Status
The Nutrition Risk Screening (NRS-2002) tool was used for nutritional screening, followed by an evaluation of malnutrition using the GLIM criteria. NRS is a two-step tool consisting of evaluating BMI, assessing recent weight loss and changes in food intake and identifying a grading of severity of disease as a reflection of increased nutritional requirements. Patients with a total score of 3 or more in the final screening were nutritionally at risk [21].
GLIM diagnosis was performed as a two-step process by firstly identifying at least one phenotypic criterion and one etiologic criterion and secondly assessing the severity of malnutrition as being either “moderate” or “severe” based on the phenotypic criterion [22]. Weight loss and BMI were used to evaluate the phenotypic criteria. The third phenotypic criterion evaluated was muscle mass, using MUAC as the measurement and handgrip strength as the supportive measure. MUAC was used as a surrogate technique as endorsed in recent recommendations in usual situations where body composition techniques such as bioelectrical impedance analysis and dual-energy X-ray absorptiometry are not available in the hospitals [23]. GLIM criteria emphasize that handgrip strength should be used as an additional supportive measure when only anthropometric measurements are available [22]. Handgrip strength is commonly employed in practice to assess muscle function qualitatively [23].
Reduced food intake, chronic gastrointestinal condition affecting absorption and inflammatory condition assessed via CRP levels were the etiologic criteria. Cut-off points of the different etiologic and phenotypic criteria are described in Table 1.
## 2.4. Statistical Analysis
Statistical analysis was performed using STATA V17.1. Descriptive variables were described as n (%), mean ± standard deviation (SD) and median ± interquartile range (IQR). Cohen’s kappa (κ) was conducted to assess the agreement between NRS 2002 and GLIM. The length of hospital stay variable was then dichotomized into two groups with the median of 5 days used as the cut-off point: group one: ≤5 days and group two: >5 days. Mann–Whitney U and χ2 tests were performed to assess the differences in the length of hospital stay and history of hospital readmissions between the malnourished patients and those of normal nutritional status. Spearman’s rank correlations coefficient (rho) was used to measure the association between the non-parametric variables of length of hospital stay, handgrip strength and MUAC. Multiple logistic regression analysis was used to determine whether malnutrition with the GLIM criteria was independently associated with length of stay with adjustments for gender and admission diagnosis. All reported p-values were to a significance level of $5\%$.
## 2.5. Ethics
The study was completed in compliance with the guidelines of the Helsinki Declaration. The study protocol was reviewed and approved by the Institutional Review Board of the American University of Beirut (SBS-2020-0079). All participants reviewed and signed an informed consent form before participation.
## 3.1. Basic Characteristic
A total of 343 participants were enrolled in this study from May to October 2021. Baseline characteristics and distribution among districts are presented in Table 2. The mean age was 60 years (SD: 17 years) and the majority of the participants were less than 70 years old ($65.89\%$). Surgical procedures ($32.94\%$) and infectious diseases ($27.7\%$) were the main diagnostic criteria for hospital admissions.
## 3.2. Prevalence of Malnutrition
According to the NRS-2002 screening tool (Table 3), $31.20\%$ of the participants had scores that were greater than or equal to 3 and thus were identified as being “at risk of malnutrition”, of which $51\%$ were males and $49\%$ were females. Beirut ($38.27\%$) followed by the north ($38.00\%$) and Mount Lebanon ($33.00\%$) were the main districts identified by NRS-2002 as having participants at risk. The south had the lowest proportion ($18.97\%$) compared to Beirut and the result was statistically significant ($$p \leq 0.016$$).
As for GLIM, $21.28\%$ and $14.29\%$ were identified as being “moderately” and “severely” malnourished, respectively, accounting for a total of $35.57\%$ malnourished participants (Table 3). Half of the malnourished patients were male and the same proportion was female. Similarly to the NRS-2002 results identifying patients at risk of malnutrition, Beirut ($43.21\%$), the north ($42.00\%$) and Mount Lebanon ($34.00\%$) were the main districts with malnourished participants (Figure 2). Bekaa had the lowest proportion ($25.93\%$) compared to Beirut and the result was statistically significant ($$p \leq 0.043$$).
The strength of the agreement between NRS 2002 and GLIM in identifying at-risk-of-malnutrition and malnourished patients as per Cohen’s kappa κ was 0.7580 ($p \leq 0.001$), indicative of good agreement.
## 3.3. Frequency of the Different GLIM Criteria
The frequencies of the different GLIM criteria among malnourished patients are described in Figure 3. Among the 122 patients who were identified as “moderately” and “severely” malnourished according to GLIM, the most dominant phenotypic criterion was “weight loss”, accounting for $82\%$. The median weight loss percentage was 8.5 kg (IQR 6.25–10). As for the etiologic criterion, the most prominent was “reduced food intake” accounting for $88\%$ of patients, among which reduction in food intake for a period exceeding 2 weeks was the main measure ($41.8\%$). The number of patients with low handgrip strength was 92 ($75.4\%$). The mean handgrip strength of the males was 19.59 kg (SD = 4.28), whereas that of the females was 12.61 kg (SD= 2.44). As for the MUAC, 32 patients were identified as being moderately depleted ($26.2\%$) and 10 patients were identified as being severely depleted ($8.2\%$), a total of 42 patients ($34.4\%$). The mean MUAC was 21.56 cm (SD = 0.7) and 20.2 (SD = 2.8) for males and females, respectively. More than half of the moderately malnourished patients had normal BMIs ($54.9\%$).
## 3.4. Association of Malnutrition, Muscle Mass and Length of Hospital Stay
The patients’ median length of hospital stay was 5 days (IQR 3–10). There was a significant difference in the length of hospital stay between patients identified as malnourished according to GLIM criteria and those of normal nutritional status (11 days with IQR 9–15 versus 4 days with IQR 3–5, respectively, $p \leq 0.001$). When a median of 5 days was considered as the cut-off point, $90.9\%$ of malnourished patients had a length of hospital stay greater than 5 days compared to $9.1\%$ of patients of normal nutritional status, as shown in Table 4 ($p \leq 0.001$).
Handgrip strength and MUAC measurements were negatively correlated with the length of hospital stay (rho/ρ = −0.40, $p \leq 0.001$ and rho/ρ = −0.25, $p \leq 0.001$), regardless of the patient’s nutritional status. Patients with low handgrip strength measurements had a length of hospital stay greater than the median of 5 days ($74.4\%$ versus $25.6\%$, $p \leq 0.001$). As for patients with moderate and severe depletion in MUAC measurements, $84.4\%$ had a length of hospital stay greater than the median ($84.4\%$ versus $15.6\%$, $p \leq 0.001$) (Table 4).
## 3.5. Multiple Logistic Regression of Length of Hospital Stay
Having a malnutrition diagnosis was found to be an independent predictor of length of hospital stay, as shown in Table 5. Specifically, patients who were identified as malnourished according to GLIM criteria ($p \leq 0.001$) had higher odds of having a length of hospital stay that exceeded 5 days compared to those who were well-nourished. Age was excluded from the model because it was part of the malnutrition diagnosis. The Hosmer and Lemeshow goodness-of-fit test indicated that our model fit the data well with p-values of 0.2364.
## 3.6. Association of Malnutrition with Hospital Readmission
Patients who were identified as being malnourished according to GLIM criteria ($33.61\%$) were more likely to have been previously admitted to the hospital in the past 3 months compared to those identified as having a normal nutritional status ($3.17\%$) (χ2 = 60.51, $p \leq 0.001$).
## 4. Discussion
The prevalence rate of malnutrition risk among hospitalized patients was $31.2\%$ according to NRS-2002 and the prevalence of malnutrition according to the GLIM criteria was $35.6\%$. These figures is different from previous data collected in 2008 in two large Lebanese hospitals of the international multicenter study, where malnutrition risk was only screened and the rate was $22\%$ using the NRS-2002 tool [13]. In addition to the fact that our data are larger and more hospitals were included, this difference in rate reflects the increase in the risk of malnutrition in hospitalized patients in a country where economic crisis has drastically deteriorated. This crisis is affecting the access to and availability of nutrition care in hospitals [17].
The higher percentage of malnutrition according to GLIM was detected in the capital Beirut ($43.2\%$), where hospitals are larger and more complicated cases are admitted. A lower prevalence of $26\%$ was observed, on the other hand, in Bekaa where the population density is much lower [18]. The prevalence in the five districts is very similar to the rates reported in other countries, varying from $20\%$ to $50\%$ with higher ranges in developing countries [2,12]. One other recent study restricted to one hospital in Lebanon with a smaller sample size reported that $34.7\%$ of their sample population was at risk of malnutrition and $9.3\%$ were malnourished [24]. Although the percentage of at-risk patients is high, their lower rate of malnutrition is probably due to the use of a different tool, which was the Mini Nutritional Assessment MNA, specific to older adults [24].
The prevalence of risk of malnutrition when using NRS-2002 was slightly lower than the prevalence rate of the malnutrition diagnosis using GLIM criteria, reporting a rate of $31.2\%$. However, there was a good agreement statistically between the two tools. This concordance was also recently reported in a study on hospitalized patients in Turkey, where GLIM was correlated with NRS-2002 and not with other nutrition assessment tools [25]. Other studies have found a stronger correlation between GLIM and other screening tools such as the Malnutrition Universal Screening Tool (MUST), but the sample population was of older adults and those specifically having cancer [26,27,28]. Therefore, NRS-2002 is still considered to be a valid and more specific tool to be used for hospitalized patients during the screening process as recommended by clinical practice guidelines [29].
GLIM is considered to be a diagnostic tool to be used after screening to confirm nutritional assessment. It is different from other assessment tools as it has many different criteria and severity levels. In our study, we have studied the frequency of each phenotypic and etiologic criterion in patients diagnosed with moderate and severe malnutrition. The most frequent criteria were weight loss and low food intake, which are quick and easy to collect. This same combination of weight loss and low food intake was observed in a study on the validation of GLIM and was considered to be the most predictive with regard to worse clinical outcomes [30]. On the other hand, low BMI in our sample population was the least recorded criterion, with $16\%$ compared to $88\%$ for weight loss and $57\%$ for low muscle mass. More than half of malnourished patients had a normal BMI, reemphasizing the importance of not relying solely on BMI in nutrition assessment, an issue always challenged by clinicians [31].
Patients identified as malnourished by GLIM had a significantly longer length of stay (LOS) of 7 days and had significantly higher rates of previous hospital readmissions. Both LOS and the incidence of hospital readmissions are surrogate markers of a patient’s clinical outcomes and economical costs [32,33]. This strong correlation associates malnutrition with unexpected complications and a worsening clinical status of patients, highlighting the importance of identifying malnutrition early during hospitalization. The prediction model identifying malnutrition diagnosis as a predictor of length of stay independent of underlying diseases reinforced the association of malnutrition with worsening clinical outcomes. It demonstrates the validity of GLIM criteria to predict prolonged hospitalization as a health outcome [34].
Interestingly, a correlation with LOS was also found in our study with low MUAC and handgrip strength, independently of nutritional status. Handgrip strength has previously been linked to longer hospitalization but MUAC has never been studied from this perspective since it is commonly more used in the pediatric population [35,36]. Our findings may help in adding simple anthropometric measurements not requiring expensive tools such as MUAC in assessing muscle mass as part of GLIM criteria when body impedance analysis (BIA) or dual-energy X-ray absorptiometry DEXA are not available [37].
Our study findings of high prevalence rates support the need for increasing awareness towards malnutrition, which many global efforts are now targeting. Consequently, the newly developed European Nutrition for Health Alliance has started the Optimal Nutritional Care for All (ONCA) campaign, which launched a global call for action in 2013 to all countries to raise public awareness, establish a nutrition assessment pathway and develop national protocols to include effective nutrition care as a fundamental right to heath [16]. Other similar associations from different countries followed this path and launched an international call to action in a forum “Linking Nutrition Around the World” [9]. In addition, the United Nations Decade of Action on Nutrition emphasized that national policies should prioritize aligned health systems providing universal coverage of all essential nutrition actions [38]. Lebanon and other countries in the Middle East have not joined these global efforts yet. However, a national policy, supported by international instruments, is becoming a necessity to identify and target malnutrition, especially in the economic crisis that the country is going through.
It is important to mention that initiatives and policies targeting malnutrition should recognize the crucial role of dietitians in the nutrition care of the patient [39]. Clinical dietitians are integral members of the multidisciplinary team in the hospitals and they are uniquely qualified in the assessment and the management of malnutrition in the care pathway of the patients [40,41]. They are specialized in interpreting anthropometric measurements, recommending nutrition support plans and providing informational counseling to patients [39,42]. Their nutrition interventions will aim to improve the continuum of care of the hospitalized patients in enhancing clinical outcomes.
## Strength and Limitations
To our knowledge, this is the first study to report the prevalence of malnutrition in hospitalized patients in a national representative sample of hospitals in Lebanon and is one of the very few studies in the Middle East. Nutrition screening and assessment were conducted upon admission in a heterogeneous population of different medical and surgical diagnoses, making our study different from other prevalence studies conducted retrospectively and on a specific patient population. The GLIM tool that is newly developed was also used with simple anthropometric measurements that could be easily found in settings with minimal resources. Our study nevertheless has limitations. Data were collected from private hospitals only and public hospitals were excluded due to security reasons, meaning that patients admitted to these hospitals of usually lower socioeconomic status were not represented. The cut-off values we used for MUAC and handgrip strength to assess muscle mass were taken from consensus recommendations and were not validated in different patient populations. We therefore recommend that future studies clarify their cut-off values.
## 5. Conclusions
Our present study reports a considerable high prevalence of malnutrition in hospitalized patients upon admission that was directly associated with a longer length of stay, implicating worsening clinical outcomes. Since the identification of malnutrition remains an important first step to target its recognition and management in daily clinical practice, the use of GLIM criteria with simple, affordable and anthropometric measurements is considered to be both valid and a practical diagnosis step.
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|
---
title: A Triphenylphosphonium-Functionalized Delivery System for an ATM Kinase Inhibitor
That Ameliorates Doxorubicin Resistance in Breast Carcinoma Mammospheres
authors:
- Venturina Stagni
- Archontia Kaminari
- Claudia Contadini
- Daniela Barilà
- Rosario Luigi Sessa
- Zili Sideratou
- Spiros A. Vlahopoulos
- Dimitris Tsiourvas
journal: Cancers
year: 2023
pmcid: PMC10000448
doi: 10.3390/cancers15051474
license: CC BY 4.0
---
# A Triphenylphosphonium-Functionalized Delivery System for an ATM Kinase Inhibitor That Ameliorates Doxorubicin Resistance in Breast Carcinoma Mammospheres
## Abstract
### Simple Summary
Doxorubicin (DOX) is widely used in the treatment of breast cancer. However, resistance limits its effectiveness. In particular, breast cancer stem cells (BCSCs) are associated with DOX resistance. We have previously demonstrated the potential of a polymeric nanocarrier based on a suitably functionalized hyperbranched polyethylenimine that preferentially targets BCSCs. ATM kinase is a key mediator of DNA damage response, so its inhibition has become an attractive therapeutic concept in cancer therapy for the sensitization of cancer cells to chemotherapeutic drugs. Herein, we tested the potential of this drug delivery system that encapsulates an ATM inhibitor to target and sensitize mammospheres—considered as a model system of BCSCs—to an anticancer drug, while having a comparably lower cytotoxic effect against bulk tumor cells.
### Abstract
The enzyme ataxia-telangiectasia mutated (ATM) kinase is a pluripotent signaling mediator which activates cellular responses to genotoxic and metabolic stress. It has been shown that ATM enables the growth of mammalian adenocarcinoma stem cells, and therefore the potential benefits in cancer chemotherapy of a number of ATM inhibitors, such as KU-55933 (KU), are currently being investigated. We assayed the effects of utilizing a triphenylphosphonium-functionalized nanocarrier delivery system for KU on breast cancer cells grown either as a monolayer or in three-dimensional mammospheres. We observed that the encapsulated KU was effective against chemotherapy-resistant mammospheres of breast cancer cells, while having comparably lower cytotoxicity against adherent cells grown as monolayers. We also noted that the encapsulated KU sensitized the mammospheres to the anthracycline drug doxorubicin significantly, while having only a weak effect on adherent breast cancer cells. Our results suggest that triphenylphosphonium-functionalized drug delivery systems that contain encapsulated KU, or compounds with a similar impact, are a useful addition to chemotherapeutic treatment schemes that target proliferating cancers.
## 1. Introduction
Resistance to genotoxic therapies has been associated with increased DNA damage response (DDR) signaling, and many cancer defects in certain components of the DDR are highly dependent on the remaining DDR pathways for survival [1]. The enzyme ataxia-telangiectasia mutated (ATM) kinase is a key mediator of DDR, and its inhibition has become an attractive therapeutic concept in cancer therapy for the sensitization of cancer cells to chemotherapeutic drugs. In particular, cancer stem cells (CSCs) have been gathering increasing attention over the past decade as they play a crucial role in tumor progression, metastasis, and drug resistance [2].
It has been well documented that ATM kinase inhibition sensitizes cells to the cytotoxic effects of DNA double-strand break-inducing chemotherapeutic agents, including the topoisomerase II inhibitors etoposide, doxorubicin, and amsacrine, the topoisomerase I inhibitor camptothecin, and PARP inhibitors [3,4]. Furthermore, the ATM inhibitor KU-55933 (KU) was shown to sensitize p53-deficient cholangiocarcinoma cells to genotoxic agents, including gemcitabine, 5-fluorouracil, cisplatin, and doxorubicin. This sensitization was more potent when KU was combined with ATR (ataxia-telangiectasia mutated and Rad3-related kinase) inhibitor VE-821 [5]. KU has also been reported to block the phosphorylation of protein kinase B (Akt) and inhibit MDA-MB-453 and PC-3 cell proliferation [6], as well as to attenuate the phosphorylation and activation of AMP-activated protein kinase in a rat hepatoma cell line [7]. The inhibition of Akt was confirmed and extended when it was shown that glucose uptake, glycolysis, epithelial to mesenchymal transition, motility, and the proliferation of aggressive breast and prostate cancer cell lines with high Akt activity were blocked by KU [8].
Interestingly, ATM kinase is also an essential signaling mediator that enables the growth of cancer stem cells. Thus, the potential benefits of a number of ATM inhibitors, such as KU-55933 (KU), in overcoming the resistance of CSCs to chemotherapeutic agents are currently being investigated [9,10,11,12]. It has been found that the application of this ATM inhibitor effectively decreases the radiation resistance of the tumorspheres of cancer initiating cells, which are stem-like cells [13]. In fact, ATM is known to sustain the mammospheres of cancerous cells and are considered model CSC systems that are also known to exhibit high resistance to genotoxic agents, such as doxorubicin (DOX) [14,15].
ATM function is linked to mitochondria. It is well known that it regulates mitochondrial function and mitophagy [16,17,18,19]. In this connection, studies on the action of KU on cell lines have shown that its administration reduces the mitochondrial membrane potential and perturbs the tricarboxylic acid (TCA) cycle and oxidative phosphorylation [20,21,22,23]. KU has also been shown to suppress the proliferation of Hep G2 and SMMC-7721 cells by inducing mitochondrial dysfunction and by enhancing 5′-adenosine monophosphate-activated protein kinase (AMPK) phosphorylation [21].
Nutlin-3 (an MDM2 inhibitor that leads to non-genotoxic p53 activation) and KU synergize to induce apoptosis in a number of cancer cell types, including colorectal cancer cell lines, but do not kill non-transformed cells. The mechanism of cell death activation entails the blocking of autophagy and a consequent accumulation of both mitochondria and reactive oxygen species (ROS) [22]. It has also been reported that the inhibition of ATM with KU depleted mitochondrial DNA in wild-type fibroblasts [23]. Therefore, ATM suppression would be advantageous in eradicating cancer cells, in particular CSCs, but disadvantageous for normal cells because its function is indispensable in DNA repair, in preserving mitochondrial functionality, and in the selective removal of damaged mitochondria [17]. Consequently, there is a need of a delivery system for ATM inhibitors that, ideally, specifically targets cancer stem cells.
The essential role of mitochondria in cell function and the fact that mitochondrial dysfunctions are associated with a number of pathologies, including cancer, had led to the growth of so-called mitochondrial medicine and, in parallel with this, to the development of systems capable of the accurate and efficient delivery of therapeutic agents and/or imaging agents to mitochondria. A number of mitochondriotropic moieties, i.e., moieties that can target mitochondria, have already been identified, from mitochondria-penetrating peptides to delocalized lipophilic cations, such as the well-studied triphenylphosphonium cation (TPP). In the latter case, mitochondrial internalization is caused by the delocalized positive charge of the TPP and the large negative membrane potential of the mitochondria (ΔΨm = 150–180 mV) [24,25]. Mitochondrial targeting by drugs has been achieved by employing these mitochondriotropic agents, either by their direct conjugation with bioactive molecules or by their conjugation with a variety of drug-loaded delivery systems [26,27,28,29,30,31].
Previous studies by our group have indicated that TPP-functionalized hyperbranched polyethylenimine nanoparticles (PTPP) can encapsulate DOX and target mitochondria, causing severe cytotoxicity at low DOX concentrations [32]. Interestingly, this nanocarrier also showed selective cytotoxicity against mammospheres that depended on the expression of the gene encoding ATM kinase [33]. In this context, it has been reported that the enhanced tolerance of CSCs to chemotherapeutics or radiation correlates well with the changes in membrane potential, and that cells with higher membrane potential are more prone to continue dividing and form tumors compared with cells with lower membrane potentials [34]. Therefore, TPP-functionalized carriers may also have the ability to target cells with high membrane potential, such as CSCs [33,34]. Indeed, TPP-functionalized nanoparticles have shown that cell internalization is dependent on cell membrane potential, which enables them to be internalized preferably to cancerous but not to non-cancerous cells [35,36]. In this current study, we explore whether encapsulating an ATM inhibitor in a TPP-functionalized hyperbranched polyethylenimine nanocarrier can be effective against the mammospheres of drug resistant breast cancer cells—a close analogue to CSCs—without having considerable toxic effects against adherent cells. We further explore the resistance of mammospheres derived from breast cancer cell lines against DOX chemotherapy and the therapeutic benefit of ATM inhibition upon administration of the water insoluble ATM kinase inhibitor KU, encapsulated in the PTPP nanocarrier (PTPP–KU).
## 2.1. Chemicals and Reagents
RPMI-1640 medium, penicillin/streptomycin, L-glutamine, phosphate buffer saline (PBS), and trypsin/EDTA were all purchased from Biochrom (Berlin, Germany), while HyClone fetal bovine serum was obtained from Invitrogen (Carlsbad, CA, USA). Dimethyl sulfoxide (DMSO) and MTS solution were purchased from Merck KGaA (Calbiochem®, Darmstadt, Germany) and Promega Corp. (Madison, WI, USA), respectively, while 2-Morpholin-4-yl-6-thianthren-1-yl-pyran-4-one (KU-55933) was obtained from Sigma-Aldrich Ltd. (Poole, UK). Doxorubicin (D1515) was purchased from Sigma-Aldrich Corp. (St. Louis, MO, USA).
## 2.2. Preparation and Characterization of KU-Loaded PTPP Nanoparticles
The introduction of decyltriphenylphosphonium groups to hyperbranched polyethylenimine (PTPP) has been detailed in our previous publications [32,33]. Given that both PTPP and KU are practically water insoluble, a co-precipitation method was established for either the formation of PTPP nanoparticles or the encapsulation of KU in the PTPP nanoparticles, entailing the drop-wise addition of 100 μL DMSO solution of PTPP and KU (KU concentration 7 mΜ, PTPP concentration 7 mg/mL) in 10 mL RPMI medium under vigorous stirring. In a similar manner, empty PTPP nanoparticles were prepared by the drop-wise addition of 100 μL DMSO solution of PTPP (7 μg/mL) in 10 mL RPMI under vigorous stirring. In all cases, the obtained nanoparticles were centrifuged and redispersed in the appropriate amount of RPMI for obtaining, after bath sonication for ~20 s, typical nanoparticle dispersions of 100 μM KU and of 100 μg/mL PTPP, or empty PTPP nanoparticles of the same concentration. The PTPP concentrations in their dispersions were determined by dissolving them in ethanol and registering their absorbance at 275 nm and using the respective PTTP calibration curves in ethanol. For the determination of both the PTPP and KU concentrations in the respective nanoparticle dispersions, first order derivative spectroscopy was employed. Specifically, the spectra of both compounds separately, or of their mixtures in ethanol, were acquired and processed to obtain the first derivative spectra. The wavelengths 329 nm and 278 nm were selected for the KU and PTPP determinations, respectively, as at these wavelengths there is no interference from the other compound. Similarly, the derivative spectra of standard solutions were obtained, and the respective calibration curves for each compound at these wavelengths were also derived. The mean hydrodynamic radii of the dispersions of the PTPP and PTPP–KU nanoparticles in RPMI were determined using dynamic light scattering (DLS) (AXIOS-150/EX, Triton, Hellas, 50 mW laser source at 658 nm, Avalanche photodiode detector at an angle of 90°).
## 2.3. Cell Culture and Treatments
Human breast cancer cell lines MCF-7, MDA-MB-231, and SKBR3, obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA), were grown as described previously [37]. Briefly, cells were cultured in RPMI-1640 containing 2 mM L-glutamine and $1\%$ penicillin/streptomycin and supplemented with $10\%$ HyClone fetal bovine serum at 37 °C in a humidified CO2 incubator ($5\%$). The treatment concentrations in all the experiments always refer to the KU concentration in μΜ, while the concentration of PTPP always follows the same ratio with respect to KU (i.e., KU concentration 1 mM, PTPP concentration 1 mg/mL).
## 2.4. Mammosphere Cultures
Single cell suspensions of breast cell lines MCF-7 and MDA-MB-231 were grown in ultralow attachment 6-well plates (Corning) at a density of 4000 cell/mL in mammosphere medium (Dulbecco’s modified Eagle’s medium/F-12, containing 5 μg/mL insulin (Sigma), B27 (Invitrogen), 20 ng/mL epidermal growth factor (GIBCO), 10 ng/mL basic fibroblast growth factor (GIBCO), and $0.4\%$ bovine serum albumin (Sigma)), as described in [33]. After 10 days, the diameters of the mammospheres were measured in phase contrast pictures (ZOE) using the ImageJ software. The mammospheres (diameter > 50 μm) were counted and the efficiency of mammosphere formation was evaluated (%SFE = number of mammospheres/number of plated cells × 100). Mammosphere pellets were collected by gentle centrifugation (900 rpm, 5 min) to further analyze for protein extraction and DOX uptake.
## 2.5. MTS Assay
The cell viability of the MCF-7 and MDA-MB-231 and their derived mammospheres under normal and treatment conditions was measured using the MTS assay. In brief, cells were transferred into a single-cell suspension and plated into ultralow 96-well plates with a density of 400 cells/100 μL per well for the mammospheres, and, in adhesion conditions, in a TC-treated well at a density 1000 cells/100 μL medium. The cells were cultured in mammosphere medium [36] and treated with different doses of KU, PTPP, or PTPP–KU at 37 °C for 30 min before DOX (1 μM) was added. After 24 h, the wells were washed with RPMI, and MTS solution was added to each well and incubated at 37 °C for 3 h. Finally, the optical density (OD) was measured at a wavelength of 492 nm and the survival rates were calculated.
## 2.6. Quantification of DOX Uptake
The adherent cells and mammospheres in the 96 well plates were treated with KU (5, 10 μM), PTPP (5, 10 μg/mL), and PTPP–KU (PTPP concentrations: 5, 10 μg/mL, KU concentrations: 5, 10 μM, respectively) for 30 min, and then DOX (1 μM) was added. In addition to the control, we also had a number of wells with cells treated only with 1 μM DOX. After 3 h, the wells were washed with RPMI (without phenol red) and the DOX concentration was measured with an Infinite M200 plate reader (Tecan, Switzerland, λex = 510 nm, λem = 580 nm). Throughout the experiment, a medium without phenol red (colorless) was used to avoid any interference with the final DOX concentration measurement.
## 2.7. Western Blotting (WB)
The cells were lysed in RIPA buffer (50 mM Tris-HCl pH 8.0, 150 mM NaCl, $1\%$ NP40, 1 mM EGTA, 1 mM EDTA, $0.25\%$ sodium deoxycholate) supplemented with protease and phosphatase inhibitors (Roche Diagnostic, Mannheim, Germany). Proteins were resolved by $10\%$ SDS PAGE (about 30 μg of extract per lane was loaded) and transferred onto a nitrocellulose membrane (Protran BA83, GE Healthcare, Chicago, IL, USA) using a semi-dry system (Bio-Rad Laboratories S.r.l., Segrate, Italy). Blocking and antibody incubations were performed at room temperature in TBS containing $0.1\%$ Tween 20 and $5\%$ low fat milk for 1 h. The following antibodies were used: rabbit anti-PARP (Cell Signaling Technology, Danvers, MA, USA), mouse anti-ATM (Cell Signaling Technology, Danvers, MA, USA), anti-rabbit-pS15 p53 (Cell Signaling Technology, Danvers, MA, USA), and rabbit anti-Vinculin (Cell Signaling Technology, Danver, MA, USA). HRP-conjugated secondary antibodies (Bio-Rad Laboratories S.r.l., Segrate, MI, Italy) were revealed using the Clarity Western ECL Substrate (Bio-Rad Laboratories S.r.l., Segrate, Italy).
## 2.8. Statistical Analysis
Data are presented as mean ± standard deviation. All experiments were performed independently at least three times. Statistical significance for the various treatments was assessed using a Student’s t-test in GraphPad Prism (GraphPad Inc., La Jola, CA, USA). Significance was defined as * $p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001$, **** $p \leq 0.0001$, using a Student’s t-test.
## 3. Results
The functionalization of hyperbranched polyethylenimine with decyltriphenylphosphonium groups endows amphiphilicity to hydrophilic macromolecules, which is the basis of their self-assembly into nanoparticles in aqueous media. Due to its lipophilic character, the water-insoluble KU resides in PTTP nanoparticles whose chemical structure encompasses both hydrophilic (i.e., ethylene imine) and hydrophobic (i.e., decyltriphenylphosphonium) entities, the properties of which endow the solubilizing (or encapsulating) properties of nanoparticles. Thus, PTTP nanoparticles encapsulating KU, which is known to be insoluble in aqueous buffers, were formed in RPMI. They had a mean hydrodynamic radius of about 60 nm, as was revealed by DLS experiments (Supplementary Material, Figure S1). For comparison, empty PTPP nanoparticles formed under the same conditions were slightly smaller, i.e., 55 nm (Supplementary Material, Figure S1). The KU loading was found to be $28.3\%$ w/w corresponding to a KU concentration 1 mM when the PTPP concentration was 1 mg/mL.
To investigate the ability of these PTTP nanoparticles encapsulating KU to sensitize breast cancer cells to doxorubicin (DOX), we took advantage of the human breast adenocarcinoma cell line MCF-7 (luminal estrogen receptor-positive HER2-low), cultured either as adherent cells (“Adh”) or as tumor spheres (mammospheres, “MS”), which are a more suitable cellular model compared with adherent cells in that they more closely approximate the resistance to anticancer therapy of breast cancer cells [38]. Herein, we co-treated adherent MCF-7 cells and mammospheres with DOX at 1 µM and increasing concentrations of PTTP, PTTP–KU, and free KU (Figure 1A,B). Cell viability was measured as the percentage of the control treated with PTTP, PTTP–KU, or free KU, plus DOX, normalized to the same treatment without DOX (Figure 1A,B). As expected, the mammospheres were more resistant to DOX treatment compared with the adherent cells, which showed a reduction in cell viability of 30–$40\%$ compared with the non-treated (NT) cells (Figure 1A,B). We have previously reported that PTTP is effective in inhibiting the growth of cancer stem-like structures such as mammospheres [33]. It is of note that we confirmed these results, and that we also demonstrated that PTTP alone as well as PTTP–KU were slightly toxic to the adherent cells at all concentrations tested, and show considerable toxicity (ca $70\%$) to mammospheres (MS) only at a high concentration (10 μg/mL) (Supplementary Material, Figure S2). These results are consistent with those of previous studies which have indicated that the treatment of TPP-functionalized moieties increases cytotoxicity in cells grown in mammosphere conditions compared with cells grown in adherence conditions [33,39]. We attribute the observed toxicity to the fact that PTPP is preferentially internalized in the mitochondria of mammospheres, leading to an increase in mitochondrial stress [33]. Herein, we found that PTTP treatment can also effectively induce sensitization to DOX treatment in mammospheres, with a dose-dependent reduction in cell viability in DOX treated cells of about $40\%$ at lower doses (1 μM) and $60\%$ at higher doses (10 μM) (Figure 1B), compared with a reduction of only $10\%$ at higher doses (10 μM) in adherent cells treated with DOX (Figure 1A). Interestingly, the results demonstrated that PTTP–KU is more effective in sensitizing resistant mammosphere cells to DOX, compared with adherent cells or mammospheres treated with PTTP alone (Figure 1B). In mammospheres derived from MCF-7 cells, PTTP–KU co-treatment with DOX led to a significant reduction of $50\%$ of cell viability at 1 μM and to a reduction of $80\%$ at 10 μM. This reduction in cell viability was evident from our observations of the morphology of the mammospheres which showed a significant reduction in mammosphere size after five days of culture following treatment at low concentrations (PTPP–KU 1µM) (Figure 2) and at higher concentrations (PTPP–KU 5µM) (Supplementary Material, Figure S3). It is of note that free KU is not able to sensitize cells to DOX treatment (Figure 1A,B), which suggests that encapsulated ATM inhibitors play a specific and functional role in sensitizing resistant breast cancer cells to anticancer therapies.
It is well known that DOX uptake in cells is correlated with the ability of drugs to induce cell toxicity inside the cell [39]. Moreover, several papers have demonstrated that mammosphere resistance to anti-cancer drugs is also correlated with an impaired uptake of drugs in the cells [40,41]. We consistently asked whether the PTPP–KU-dependent increased sensitivity to DOX treatment in the mammospheres could correlate with an increased DOX uptake in the cells. In Figure 3A,B, we quantified the DOX uptake in the cells treated with PTTP, PTTP–KU, and free KU at 5 µM and 10 µM, co-treated with 1 µM DOX (the respective raw data are shown in Supplementary Material, Figure S4). After washing the cells, we measured the DOX-dependent fluorescence emission (Figure 3A,B).
Compared with the free DOX treatment, the treatment with PTTP and PTTP–KU at 10 µM led to a 30–$50\%$ increase in DOX uptake in the adherent condition (Figure 3A). Interestingly, co-treatment with PTTP–KU at 5 µM and 10 µM led to an increase DOX uptake of about 20–$50\%$ in the mammospheres (Figure 3B) in a dose-dependent manner, compared with mammospheres treated with free DOX. Conversely, treatment with PTTP or free KU did not increase DOX uptake. These results suggest that the encapsulation of the ATM inhibitor in this specific drug delivery system leads to an increased uptake of DOX that is correlated with an increase in the sensitivity of mammospheres to DOX treatment.
To verify whether encapsulated KU efficiently inhibits ATM kinase activity, we decided to look at a well-known marker of ATM kinase activation, the autophosphorylation on serine 1981, and also at the phosphorylation of p53 at Ser 15 (phospho-S15), which is activated by DNA damage induced by DOX treatment. We performed a Western blot analysis using phospho-S1981 ATM antibodies and phosho-S15 p53-specific antibodies to investigate ATM kinase signaling activation upon DOX stimulation with or without PTTP, PTTP–KU, or free KU treatment (Figure 4 and Figure S5). As expected, and as is shown in Figure 4, the ATM kinase phosphorylation on S1981 induced by the DOX treatment was inhibited in the mammospheres derived from the MCF-7 co-treated with free KU inhibitor (Figure 4). As expected, the PTTP–KU treatment, as well as free KU treatment, was able to reduce the ATM kinase phosphorylation on S1981 induced by the DOX treatment more efficiently than PTPP alone. It is of note that PTTP also reduced ATM phosphorylation on S1981, indicating a role of this nanoparticle in modulating ATM kinase activity (Figure 4) (see Discussion section). Moreover, DOX stimulation induced phosphorylation on S15 of p53, a well-known ATM substrate. Unexpectedly, p53 phosphorylation on S15 induced by DOX stimulation was slightly reduced by treatment with all the compounds (PTPP, PTTP–KU, and free KU), and total p53 was stabilized after DOX induction in all samples, suggesting that the effect on cell viability of different treatments is independent of p53 status (see Discussion section).
We consequently looked at the PARP protein under different conditions as a marker of the cell death process (Figure 4). We could not detect a reduction in PARP protein levels after DOX treatment because, as expected, mammospheres are resistant to DOX treatment (Figure 4). Interestingly, we observed a reduction in PARP levels in samples co-treated with DOX and PTTP–KU, suggesting that only PTTP–KU is able to sensitize mammospheres to DOX treatment, according to the results obtained in Figure 1.
Since p53 has a central role in regulating the DOX sensitivity of breast cancer resistant cells [42,43], and we found that phosphorylation on S15 of p53 is also inhibited by empty PTTP nanoparticles, we wanted to clarify the role of p53 in the sensitization of mammospheres to DOX treatment. To evaluate the necessity for intact p53 function, we also utilized cell lines with mutant p53. We therefore performed viability experiments in mammospheres derived from p53 WT cells (MCF-7) or mutant p53 cells (SKBR3 and MDA-MB-231 cells). We co-treated the cells with DOX (1 μM) and with increasing doses of PTTP, PTTP–KU, or free KU (Figure 5). Interestingly, PTPP–KU sensitized the mammospheres derived from all the above cell lines to DOX in a dose-dependent manner, suggesting that the role of PTPP–KU in sensitizing resistant mammospheres to DOX is independent of p53 status.
## 4. Discussion
Doxorubicin is a tetracycline antibiotic commonly used in the treatment of breast cancer, and it induces DNA damage by the inhibition of topoisomerase II and free radical generation as an anticancer mechanism [44,45]. Doxorubicin has severe side effects, including acute toxicity to normal tissue and cardiotoxicity, and its therapeutic effects can be minimized by the inherent multidrug resistance (MDR) of many tumor cells, in particular of breast cancer stem cells [45,46]. The MDR of breast cancer stem cells is a major challenge to successful chemotherapy, and mitochondria-targeting therapy represents a promising strategy that may enable us to overcome MDR [47]. There are various mechanisms associated with MDR, often involving acquired and intrinsic resistance. Unlike acquired MDR, which mechanism was originated from the overexpression of P-glycoprotein (P-gp), an ATP-dependent efflux pump, intrinsic MDR is often attributed to genetic or epigenetic changes which perturb the apoptosis signaling pathway [48,49,50]. Generally, the intrinsic pathway of apoptosis is often initiated at the mitochondria, making the mitochondria of MDR cancer cells an attractive intracellular target.
Herein, we have demonstrated via viability MTS assays and by PARP level, that encapsulating an ATM inhibitor (KU) in a previously developed TPP-functionalized drug delivery system (PTPP) is an effective means of sensitizing mammospheres to doxorubicin in a dose-dependent way (Figure 1 and Figure 4). Interestingly, we were also able to show that ATM inhibition using the PTPP–KU carrier increases DOX uptake in mammospheres (Figure 3). Drug resistance depends on mitochondria since, in general, mitochondrial function has been shown to control the susceptibility of MCF-7 and MDA-MB-231 cells to doxorubicin and paclitaxel [51]. The targeting of the mitochondrial metabolism has been shown to suppress doxorubicin resistance by controlling the drug efflux [52]. Therefore, the increased DOX uptake can tentatively be attributed to a reduced drug efflux. In line with this, it has been reported that an ATM inhibitor (AZ32) could influence multidrug resistance [53].
TPP-functionalized drug carriers are known to effectively target and be internalized by cells and organelles with large negative membrane potentials (a characteristic of rapidly proliferating malignant cells such as CSCs [33,34,35,36], as well as of mammospheres that are their close analogue). Our results are, therefore, consistent with an enhanced impact of the nanocarrier PTPP on mammosphere cells, which can be attributed to the large negative membrane potential of the mammosphere cells. Furthermore, this study demonstrates that (a) ATM has an essential role in the enhanced resistance of mammospheres to anthracycline treatment, and (b) that a mitochondriotropic nanocarrier is effective in abolishing the resistance of mammospheres to anthracycline treatment.
It has been demonstrated that ATM kinase is involved not only in DNA repair pathways, but also in the oxidative stress responses induced by several anti-cancer drugs [54], suggesting that ATM kinase plays a role in regulating mitochondrial functions. Enhanced DNA repair via efficient ATM kinase signaling is a well-known mechanism that contributes to doxorubicin resistance in cancer, but in this work, for the first time, we also described the role of mitochondria-targeting polymeric nanoparticles in sensitizing resistant cells to doxorubicin. Moreover, we hypothesize that ATM kinase activity could protect cells from DOX-induced mitochondrial damage and that directing this nanocarrier to mitochondria could reverse this function. Consequently, both increased DOX internalization and reduced DNA repair due to the inhibition of ATM kinase activity results in the observed toxicity.
This protective effect of ATM on cancer tumorsphere cells can be attributed to the induction of cell stress signaling networks through a mechanism mediated by ATM. Indeed, the nature of the main components of the mammalian stress response allows malignant tumor cells, especially upon exposure to drugs and cytotoxic conditions, to activate a phenotypic switch and pass into the tumorsphere state, whereby participating neoplastic cells take up properties of stem and progenitor cell clones, permitting at least a part of a malignant tumor to survive not only drug treatment [33], but also some “last generation” treatment schemes, including apoptosis inducers [55] and, notably, immunotherapy [56], especially therapy employing immune checkpoint inhibitors [57]. Thus, drug-induced cell stress allows malignant tumors to escape from antineoplastic treatment.
Although in normal cells doxorubicin and ATM converge on the activation of p53 [58], we here observed that, in the breast cancer cells used in this study, the impact of mitochondrial targeting, ATM inhibition, and the suppression of doxorubicin resistance were independent of the p53 status of the cells (Figure 5). This can be attributed to the multiplicity of ATM signaling networks related to the cell stress response. Even though ATM was previously shown to regulate mitophagy [14,18,33], which is a macromolecular degradation system that is involved in several major regulatory mechanisms, including the proteostatic stress response, and helps redistribute cellular materials to enable cells to adapt to adverse conditions, this is not the only role ATM plays in the cell stress response (although this may be a key role of ATM in the metabolic adaptation of mammospheres). There are other aspects of ATM’s function that also link apparently unrelated mechanisms to one another [17].
One example of this complexity can be illustrated by the activation of immune checkpoint function by the network of ATM interactions. On the one hand, it mediates the induction of transactivator nuclear factor-κB (NF-κB), leading to an increase in checkpoint inhibitor PD-L1 expression [56]. On the other hand, ATM activity is mutually dependent on PD-L1 expression, making ATM a central node between immune checkpoint function and DNA repair [59]. It must be noted that NF-κB itself can sustain breast cancer CSC, anthracycline resistance, and drug efflux, as well as the escape of cancer cells from host tissue restrictions and from several components of the immune response [60,61,62,63]. Importantly, it activates chromatin epigenetic changes that sustain these mechanisms over time, far beyond one single cell division [64]. It can therefore be expected that the inhibition of ATM impairs multiple mechanisms of survival in cancer cells, which are not limited to metabolic adaptation to cell stress, or to drug internalization, but which also include escape from the immune system and from host tissue biological surveillance.
## 5. Conclusions
In conclusion, our results have demonstrated that the encapsulation of an ATM inhibitor in a mitochondriotropic nanocarrier has the capacity to suppress doxorubicin resistance in breast cancer mammospheres independently of the cells’ p53 status. A notable effect of this combination is a substantial increase in the internalization of doxorubicin, suggesting that this strategy can potently reverse MDR in breast cancer stem cells. ATM now emerges as a pivotal regulator of breast cancer stem cell responses to stress signals, especially those signals that are induced by drugs used in antineoplastic treatment. We expect that our discovery will contribute to the development of important translational approaches to breast cancer, given the substantial therapeutic importance of breast cancer stem cells in mediating resistance to anti-cancer therapies [65]. Overall, we strongly believe that further investigation of the role of ATM kinase-dependent functions could be useful and lead to the design of new strategies for overcoming MDR in breast cancer stem cells.
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|
---
title: 'Formation of Nε-Carboxymethyl-Lysine and Nε-Carboxyethyl-Lysine in Heated
Fish Myofibrillar Proteins with Glucose: Relationship with Its Protein Structural
Characterization'
authors:
- Siqi Zhang
- Pengcheng Zhou
- Peng Han
- Hao Zhang
- Shiyuan Dong
- Mingyong Zeng
journal: Foods
year: 2023
pmcid: PMC10000450
doi: 10.3390/foods12051039
license: CC BY 4.0
---
# Formation of Nε-Carboxymethyl-Lysine and Nε-Carboxyethyl-Lysine in Heated Fish Myofibrillar Proteins with Glucose: Relationship with Its Protein Structural Characterization
## Abstract
The formation of advanced glycation end products (AGEs), including Nε-carboxymethyl-lysine (CML) and Nε-carboxyethyl-lysine (CEL), in a fish myofibrillar protein and glucose (MPG) model system at 80 °C and 98 °C for up to 45 min of heating were investigated. The characterization of protein structures, including their particle size, ζ-potential, total sulfhydryl (T-SH), surface hydrophobicity (H0), sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) and Fourier transform infrared spectroscopy (FTIR), were also analyzed. It was found that the covalent binding of glucose and myofibrillar protein at 98 °C promoted protein aggregation when compared with the fish myofibrillar protein (MP) heated alone, and this aggregation was associated with the formation of disulfide bonds between myofibrillar proteins. Furthermore, the rapid increase of CEL level with the initial heating at 98 °C was related to the unfolding of fish myofibrillar protein caused by thermal treatment. Finally, correlation analysis indicated that the formation of CEL and CML had a significantly negative correlation with T-SH content (r = −0.68 and r = −0.86, p ≤ 0.011) and particle size (r = −0.87 and r = −0.67, p ≤ 0.012), but was weakly correlated with α-Helix, β-Sheet and H0 (r2 ≤ 0.28, $p \leq 0.05$) during thermal treatment. Overall, these findings provide new insights into the formation of AGEs in fish products based on changes of protein structure.
## 1. Introduction
The Maillard reaction is one of the common reactions during food thermal processing, which plays a vital role in the formation of food flavor and color [1,2,3]. Advanced glycation end products (AGEs) are chemicals formed at the advanced stage of the Maillard reaction and regarded as harmful substances [4]. It is worth noting that long-term exposure to a high AGEs diet could cause accumulation in the body and promote inflammation, which may cause some chronic diseases such as diabetes and atherosclerosis [5,6]. Some AGEs, especially lysine-derived Nε-carboxymethyl-lysine (CML) and Nε-carboxyethyl-lysine (CEL), are most abundant and stable in protein and fat-rich foods, such as meat and meat products [1,7], and widely studied in foods [8,9,10].
The formation of CEL and CML in cooked fish products depends on the food composition and the type of thermal treatment, heating temperature and time. Niu et al. [ 2017] [11] reported that boiling at 100 °C for 30 min resulted in a significant increase in protein-bound CML (2.1–10.8-fold increase) and CEL (27–$242\%$ increase) from grass carp muscle. Liu et al. [ 2022] [12] showed that production of fluorescent AGEs in sturgeon fillets was greatly increased by frying time, and both frying temperature and time had an extremely significant effect on CML and CEL levels ($p \leq 0.001$). Our previous study showed that CML levels in fried hairtail fillets were higher than in boiled and baked ones, regardless of the cooking time [10].
Notably, since proteins are macromolecules with complex advanced structures, the glycation of protein during thermal treatments can also be regulated by changing the structure of the protein [13]. Recently, the interaction between the changes of protein structure and its glycation extent during thermal processing treatments has attracted extensive attention. By analyzing proteomics, Xu et al. [ 2020] [14] showed that ultrasonic pretreatment (UP) at 20 kHz induced in protein unfolding and aggregation behavior in a bovine serum albumin (BSA) or β-lactoglobulin (β-Lg) model system, which changed the glycation extent of the Lys and Arg. In addition, Huang et al. [ 2013] [15] monitored the number of glycation sites of ovalbumin by Fourier transform ion cyclotron mass spectrometry (FTICR-MS) before and after reducing the pair of the intrachain disulfide bond, and found that when the ovalbumin tertiary structure was disrupted after reducing the disulfide bond, the number of glycated sites of the protein increased. It is worth noting that mild oxidation led to the unfolding of pork myofibrillar protein and exposed more free amino acids, which facilitated the formation of CML by the Maillard reaction [16]. However, few studies have examined the relationship between the structure changes of fish protein and the formation of CML and CEL via glycation reaction.
In recent years, sturgeon aquaculture has developed rapidly in China, with an estimated annual harvest of 121,875 metric tons in 2021 (China Fishery Statistical Yearbook, 2022). Sturgeon is a precious food source with many active components that are beneficial for human’s health, and has high nutritional and economic value worldwide [17]. Thermal processing methods, such as steaming, boiling, baking or frying have been applied to treat sturgeon meat products [18,19]. Myofibrillar protein, accounting for 55–$65\%$ of muscle proteins, is the predominant component of proteins in fish muscle [20]. Our hypothesis is that the structural changes of myofibrillar protein (MP) during thermal treatments may affect the formation process of AGEs. Therefore, the structure properties of myofibrillar protein and glucose during the heating process were determined by total sulfhydryl, surface hydrophobicity, free amino content, particle size, ζ-potential, Fourier transform infrared spectroscopy, sodium dodecyl sulfate-polyacrylamide gel electrophoresis, and the formation of corresponding furosine, Nε-carboxymethyl-lysine (CML) and Nε-carboxyethyl-lysine (CEL) were analyzed. The correlations between these parameters and AGEs were also analyzed. These findings provide a theoretical basis for revealing the mechanism of AGEs formation in fish products during cooking or processing treatments.
## 2.1. Chemicals
Nε-carboxymethyl-lysine (CML), Nε-carboxyethyl-lysine (CEL), Nε-carboxymethyl-lysine-d4 (CML-d4), and Nε-carboxyethyl-lysine-d4 (CEL-d4) were bought from Toronto Research Chemicals Inc. (Toronto, Canada). Acetonitrile of HPLC grade was purchased from Merck (Darmstadt, Germany). All the other chemicals and reagents used in this study were of analytical grade.
## 2.2. Sample Preparation
Fresh hybrid sturgeons (*Acipenser baerii* × Acipenser schrenckii), weighing 1.8 ± 0.2 kg and 65 ± 5 cm in length, were purchased from a local sturgeon farm in Qingdao (Shandong province, China) and transported to the laboratory within an hour. The head, bones, and skin of the fish were manually removed and the fresh flesh was used for myofibrillar protein (MP) extraction.
The MP was extracted according to the method of Han et al., [ 2017] [21] with some modifications. The protein concentration was evaluated by the Biuret method [22].
For myofibrillar protein and glucose (MPG) heated samples, MP (10 mg mL−1) and glucose at the ratio of 10:1 (w/w) were dissolved in 50 mM phosphate buffer solution (pH 7.0). The solution was kept in 25 mL screw-cap tubes and heated in a water bath at 80 °C and 98 °C for 2.5, 5, 10, 15, 30 and 45 min, respectively. The choice of heating condition is according to light (80 °C) or strong (98 °C) cooking or processing of some fish foods [11,19]. Control experiments with MP suspensions (10 mg mL−1) heated without glucose were also conducted. The MP and MPG samples were stored at −60 °C for further analysis.
## 2.3. Analysis of CML and CEL
The levels of CML and CEL were assessed by the method of Sun et al. [ 2015] [23] with minor modifications. Briefly, two milliliters of sample suspension (5 mg mL−1) were incubated with 0.4 mL borate buffer (0.2 M, pH 9.2) and 0.08 mL sodium borohydride (2 M, dissolved with 0.1 M NaOH) at 4 °C for 8 h and then hydrolyzed by 1.6 mL 6 M HCl at 110 °C for 24 h. Next, the protein hydrolysate was dried in a vacuum oven (DZF-6050; Shanghai Jinghong Laboratory instrument Co., Ltd., Shanghai, China) at 60 °C and diluted with water to 4 mL, from which 1 mL was withdrawn and spiked with 20 μL internal standard (CML-d4, CEL-d4). After activating and balancing a Sep-Pak MCX column, the sample was purified by this column and eluted with 2 mL methanol containing $5\%$ ammonia water. Finally, the eluent was dried in nitrogen with a nitrogen evaporator (DC12H; Shanghai ANPEL Scientific Instrument Co., Ltd., Shanghai, China), reconstituted with 2 mL deionized water, and filtered through a 0.22 µm filter before LC-MS/MS analysis.
## 2.4. Analysis of Furosine
Furosine was determined according to the method described by Semedo Tavares et al. [ 2018] [10] with a slight modification. The analyses were performed using a HPLC system (Agilent 1100, San Leandro, CA, USA) equipped with an Alltima C8 column (250 mm × 4.6 mm, 5 μm, Grace Davison, Columbia, MD, USA). The column (250 × 4.6 mm Alltech, Nicholasville, KY, USA) temperature was set at 30 °C and the UV/VIS detector set at 280 nm. The mobile phase was performed at a flow rate of 1 mL/min with (A) water containing $0.4\%$ acetic acid, and (B) $0.3\%$ potassium chloride (KCl) in (A). The mobile phase consists of $98\%$ liquid A and $2\%$ liquid B.
## 2.5. Total Sulfhydryl (T-SH)
The T-SH content of samples was determined according to the method of Ellman [1959] [24]. The results were calculated by using a molar extinction coefficient of 1.36 × 104 M/cm and expressed in micromoles of T-SH per gram of protein.
## 2.6. Protein Surface Hydrophobicity (H0)
Bromophenol blue (BPB) can adhere the surface hydrophobic region of soluble proteins and insoluble proteins and is therefore used to estimate protein surface hydrophobicity [25,26]. The H0 content of samples was measured by the method of Zhang et al. [ 2020] [27] with some modifications. A total of 40 μL 1 mg mL−1 BPB (in deionized water) was added to 2 mL samples and the solution was stirred at 200 rpm for 10 min at room temperature. The mixtures were centrifuged for 10 min at 2000× g, and then the absorbance of the supernatant was measured at 595 nm. The results were expressed as the content of bound BPB.
## 2.7. Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis (SDS-PAGE)
The SDS–PAGE was performed using the method of Zhou et al. [ 2021] [28] with an $10\%$ separating gel and a $5\%$ stacking gel. Coomassie Brilliant Blue R was used for protein staining.
## 2.8. Fourier Transform Infrared Spectroscopy (FTIR)
The FTIR was determined according to the method described by Ren et al. [ 2022] [29] with some modifications. A Fourier transform spectrophotometer (Nicolet iS10, Thermo Scientific Corp., Madison, WI, USA) was used to obtain the infrared absorption value of samples. The dried powder samples were diluted with spectral grade potassium bromide (KBr) and the absorbance spectrum of KBr as background was used to eliminate interference for samples. The scanning range was 4000–400 cm−1 with a resolution of 4 cm−1 and 64 scans for each sample.
## 2.9. Particle Size and ζ-Potential
The average particle size and ζ-potential of samples were determined by using a laser particle size analyzer (Malvern Nano ZS, Malvern Instruments Ltd., Malvern, Worcestershire, UK) at 25 °C. Samples were diluted approximately 4-fold with the same buffer, mixed, and immediately transferred into plastic cuvettes for determination.
## 2.10. Free Amino Content
The free amino content of samples was quantified by the OPA (o-phthalaldehyde) spectrophotometric assay, as described by Church et al., [ 1983] [30]. The OPA reagent was prepared daily by combining the following reagents and diluting to a final volume of 50 mL with distilled water: 40 mg of OPA (dissolved in 1 mL of methanol), 25 mL of 100 mM sodium tetraborate, 2.5 mL of $20\%$ (w/w) SDS and 100 μL of β-mercaptoethanol. One hundred microliters of the solution of samples (5 mg mL−1) was added directly to 2 mL of OPA reagent and left at 35 °C in the dark for 2 min. The absorbance was measured at 340 nm. The standard curve was prepared with lysine at the range of 0–3 mmol L−1.
## 2.11. Statistical Analysis
All experiments were performed with three repeats ($$n = 3$$) and data expressed as mean ± standard deviation (SD). The statistical analysis was performed using SPSS 25 software. Significant differences between samples were identified at $p \leq 0.05$ by multi-way analysis of variance (ANOVA). Pearson’s correlation test evaluated the correlation between AGEs formation and structure changes of protein, and $p \leq 0.05$, $p \leq 0.01$ and $p \leq 0.001$, respectively, represented different levels of statistical significance.
## 3.1. CML and CEL Levels
The CEL and CML levels of MPG under different heating conditions are shown in Figure 1A,B, respectively. At both temperatures, the CML level of MPG significantly increased with heating time. Interestingly, the CEL level of MPG within the first 2.5 min of heating at 98 °C dramatically increased by $56.94\%$ when compared with the unheated samples, but then slightly increased by $10.88\%$ from 2.5 min to 30 min. At 80 °C, the CEL level in MPG sharply increased by approximately $33\%$ within the first 2.5 min of heating when compared with the unheated samples, but only increased by $3.43\%$ from 2.5 min to 10 min, and then significantly increased by $24.51\%$ from 10 min to 30 min. Moreover, within the first 10 min of heating, the CEL level of MPG at 98 °C was much higher than that at 80 °C, but there was no significant difference of CEL level between these two temperatures for 15 min to 45 min of heating. On the other hand, the CEL level of MPG during the whole heating process was much higher than that of CML, suggesting that myofibrillar protein and glucose were more likely to produce CEL under such thermal treatment conditions. Our previous study also found that the CEL level in fried sturgeon fillets was much higher than that of the CML [12].
To the best of our knowledge, the reason for the sharp increase of the CEL level during the initial heating stage is not clear. Yu et al. [ 2018] [16] found that, when the myofibrillar protein–glucose–linoleic acid system was mildly oxidized during heating, the myofibrillar protein began to unfold, and the exposed free amino groups could facilitate AGEs generation by the Maillard reaction pathway. Xu et al. [ 2020] [14] reported that five cycles of ultrasonic pretreatment (UP) up-regulated the glycation degree of BSA and β-Lg, possibly due to the unfolding behavior of protein induced by UP, which exposed additional glycation. Thus, we speculated that, during the initial heating, the rapid increase of CEL might be related to the unfolding of fish myofibrillar protein caused by thermal treatment. However, our results demonstrated that the formation of CEL was not obviously affected by further heating at higher temperatures, which might be attributed to the aggregation of fish myofibrillar protein caused by deeper thermal treatments to hide some glycation sites [13].
## 3.2. Furosine
Furosine is an important indirect indicator of Amadori products and often related to early stage Mailard reaction products [31]. Regardless of temperatures, the furosine content in MPG during the first 2.5 min of heating was markedly increased, but there were no significant changes during 5 min to 15 min of heating (Figure 2). A reasonable explanation might be that, in the later stage of heating, even though the Maillard reaction of fish myofibrillar protein and glucose produced a lot of furosine, some of these compounds could become degraded as Maillard progressed, generating intermediate and end products [32]. Furthermore, when MPG was heated at 80 °C and 98 °C for 45 min, the furosine increases were $30.09\%$ and $64.55\%$, respectively, more than those after 15 min. This was a similar trend to that observed by Mitra et al., [ 2018] [33], who reported that no significant changes in furosine content from pork samples heated at 58 °C for 72 min were found, but a longer heating time (17 h) significantly increased furosine content.
## 3.3. Total Sulfhydryl Content
The total sulfhydryl (T-SH) content of MP and MPG greatly varied with heating time and temperature (Figure 3). The T-SH content of MPG within the first 2.5 min of heating rapidly increased, and then significantly decreased with further heating. During the whole heating process, the T-SH content of MPG heated at 98 °C was much lower than that at 80 °C. A decrease in T-SH content was reported to be due to the fact that the sulfhydryl groups of protein intra-molecules formed disulfide bonds during heating [22]. Jiménez-Castaño et al. [ 2005] [34] documented that dry heating β-Lg with dextran enhanced polymerization of protein, and this polymerization occurred due to disulfide bonds. In this study, when heated at the same temperature for 10 min to 45 min, the T-SH content of MPG was much lower than that of MP. We surmised that this phenomenon was due to the fact that fish myofibrillar protein heated in the presence of glucose promoted protein aggregation to some degree, which caused the sulfhydryl groups of myofibrillar protein to form disulfide bonds.
## 3.4. Surface Hydrophobicity Content
Analysis of the surface hydrophobicity (H0) of molecules can be used to reflect the change of protein conformation [35]. The H0 of MPG and MP under different heating conditions is shown in Figure 4. Compared with the unheated samples, the H0 content of MPG and MP after heating greatly increased. Moreover, the H0 of MPG during the whole process was much higher than that of MP except after 2.5 min of heating. To the best of our knowledge, reasons for the changes of surface hydrophobicity in heated food protein and carbohydrates remain controversial. Jiang et al. [ 2021] [36] observed that the decrease of surface hydrophobicity of α-lactalbumin heated with xylose was possibly due to the protection of the surface hydrophobic groups by the attached xylose molecules when compared with α-lactalbumin heated alone. However, our results are similar to those of Bian et al. [ 2018] [37], who reported that the chicken myofibrillar protein heated with glucosamine resulted in higher surface hydrophobicity than myofibrillar protein heated alone, which might be related to protein unfolding. In our present study, the surface hydrophobicity of fish myofibrillar proteins in the presence of glucose was higher than that of fish myofibrillar proteins alone during heating, probably due to aggregation dissociation or protein unfolding [38].
## 3.5. SDS–PAGE
The electrophoretic pattern of MP (Figure 5A) and MPG (Figure 5B) under different heating conditions was monitored by SDS–PAGE. The myosin heavy chain (MHC), actin chain (AC) and tropomyosin chain (TM) bands intensity of MPG and MP heated at 80 °C did not decrease with heating time (channels 1 to 6). When MP was heated at 98 °C for 15 min to 45 min, the MHC, AC and TM bands intensity markedly decreased with heating time and. after 45 min of heating, the MHC band almost disappeared (channels 10 to 12), indicating self-aggregation of MP [22]. However, no obvious decrease in MHC, AC and TM bands intensity of MPG were observed at 98 °C for 10 min to 45 min, which could be attributed to the fact that steric hindrance due to glucose conjugated on the MP contributed to the decrease of MP self-aggregation propensity during heating [39]. In addition, the band above 180 KDa of MPG at 98 °C during the whole heating treatment was much denser than that at 80 °C. It is worth noting that, regardless of heating temperature, the band above 180 KDa of MPG was denser than that of MP during the whole heating process, suggesting that heating caused the covalent link of fish myofibrillar protein with glucose to form larger molecular mass polymers than myofibrillar proteins heated alone [40]. The protein polymers in this large molecular mass could be from myosin heavy chain, actin and tropomyosin, as the bands intensity of these proteins were reduced or missing during heating. This finding was consistent with that reported by Bian et al. [ 2018] [37], who revealed that myosin heavy chain, actin chain, tropomyosin chain or myosin light chain were the main protein reactants in the glycation of chicken myofibrillar protein heated with glucosamine. The present results further confirmed that fish myofibrillar protein and glucose heated at a higher temperature and for a longer heating time promoted protein aggregation, when compared with fish myofibrillar protein heated alone.
## 3.6. FTIR Analysis
The bands at 1600–1700 cm−1 and 1450–1550 cm−1 from amide I and II groups referred to C=O and C–N stretching, respectively [41]. As reported by Gu et al. [ 2010] [42], changes in the bands at 1180–953 cm−1 could correspond to the stretching of C–C and C–O and the bending mode of C–H bonds. Figure 6A,B show that, in our present study, the absorption intensities of MPG at wavenumbers of 1660 cm−1 (amide I), 1536 cm−1 (amide II), and 1158 cm−1 were much lower than those of MP at the same heating temperature. Qu et al. [ 2018] [35] indicated that functional groups such as -NH2 in proteins, especially lysine, were reduced and the absorption intensity at the wavenumbers of 1650–1600 cm−1, 1600–1500 cm−1, and 1200–1300 cm−1 decreased with the Maillard reaction of rapeseed protein isolate and dextran. Our previous study also found that decreased absorption intensity around the wavenumbers of 1680 cm−1, 1540 cm−1, and 1153 cm−1 were associated with covalent binding of silver carp myofibrillar protein to glucose during heating [29].
Further information about secondary structure contents of MP and MPG under different heating conditions was calculated by PeakFit 4.12 software and are shown in Figure 7. The α-helix content of MPG heated at 80 °C for 10 min to 45 min was markedly lower than that of MP, and the corresponding β-sheet content was significantly higher than that of MP; at 98 °C, the opposite trend was observed. Typically, α-helix structures are buried in the interior sites of polypeptide chains and related to stability of protein [43]. In this sense, evaluation of the secondary structure showed that the binding of glucose and fish myofibrillar protein during heating at 80 °C caused fish myofibrillar protein to become more flexible and disordered [44]. However, heating with a higher temperature (98 °C) might cause more glucose to bind to myofibrillar proteins, and the introduction of more hydroxyl groups from glucose causes an intermolecular interaction among the neighboring proteins, thereby increasing the α-helix content [39]. From the present results, the covalent binding of glucose and fish myofibrillar protein at a higher heating temperature could affect the secondary structure of myofibrillar protein.
## 3.7. Particle Size and ζ-Potential
The particle size of MP and MPG under different heating conditions is shown in Figure 8A. During the whole heating process, the particle size of MPG and MP heated at 98 °C was much smaller than that at 80 °C. The particle size of MPG heated at these two heating temperatures rapidly decreased with heating time. For MP, at these two temperatures, except for at 30 min, the particle size decreased significantly with heating time, and the decrease of particle size in MP might be attributed to the reduction of MP clusters and more uniform dispersion of MP in solution after heat treatment [27]. In addition, the increase of particle size in MP at 30 min might be related to protein aggregation [45]; the mechanisms behind this phenomenon still need to be further investigated. It is noteworthy that, when heated at 80 °C for 15 min to 45 min and 98 °C for 2.5 min to 30 min, the particle size of MPG was significantly smaller than MP. Generally, glycation modification of protein resulted in the increase of its particle size [46]. However, the conclusions of the present study were inconsistent with empirical results. This was most likely due to the fact that, during heating, the glucose unbound to myofibrillar protein underwent auto-oxidation and generated free radicals [47], that caused the aggregation formed by glucose and myofibrillar protein to shrink in size [45].
The ζ-potential can reflect the surface charge state of particles in a dispersion system which affects the surface electrical charge [48]. As shown in Figure 8B, the changes of negative charge in MPG did not have an obvious trend with heating time. However, it was noted that the negative charge of MPG during the whole heating process at 80 °C was much less than that of MP, except at 30 min of heating. At 98 °C, the negative charge of MPG within 10 min of heating was less than that of MP, but no significant differences between them were observed with further heating. Vate & Benjakul [2016] [49] reported that protein aggregation induced by oxidized tannic acid plausibly masked the charged amino acids present in natural actomyosin, resulting in a less negative charge on the protein surface. Thus, we hypothesize that fish myofibrillar protein heated in the presence of glucose promoted protein aggregation (Figure 5B) to some degree, masking the charged amino acids in myofibrillar proteins.
## 3.8. Free Amino Content
In a glycation reaction, the free amino content of protein and the reducing end of the sugar can form a Schiff base, and this causes the depletion of the available amino groups of proteins [50]. At the same heating temperature, the loss of free amino content from 5 min to 45 min of heating was greater in MPG when compared to MP (Figure 9). At both temperatures, the free amino content of MP and MPG rapidly increased within the first 2.5 min, and then decreased with further heating. Interestingly, the free amino content of MPG at 98 °C for 2.5 min of heating was $18.13\%$ higher than that at 80 °C. The higher free amino content of fish myofibrillar proteins and glucose with higher temperatures during the initial heating stage could be a consequence of the intensity of heating treatments, which enhanced the denaturation of protein and caused an increase in free amino content [51]. Moreover, regardless of heating temperature, the increase of free amino content of fish myofibrillar protein heated with glucose during the initial heating stage might be due to the expansion of protein caused by the thermal treatment, thus exposing more free amino acid [13].
## 3.9. Correlation Analysis
In order to further explore the effects of protein structural changes on formation of AGEs from fish myofibrillar proteins and heated with glucose, correlations between specific AGEs (CEL, CML), and the corresponding structural properties of myofibrillar proteins were analyzed by Pearson’s correlation analysis (Figure 10). The formation of CEL and CML were markedly negatively correlated with total sulfhydryl content (r = −0.68, $p \leq 0.05$ and r = −0.86, $p \leq 0.001$, respectively), but positively correlated with surface hydrophobicity content ($p \leq 0.05$); Furosine content was significantly correlated with CEL and CML levels ($r = 0.78$, $p \leq 0.01$ and $r = 0.92$, $p \leq 0.001$, respectively); however, there was a weak correlation between CEL and CML levels and α-Helix, β-Sheet, random coil and β-Turn. In addition, free amino content was significantly negatively correlated with CML level (r = −0.74, $p \leq 0.01$), but weakly negatively correlated with CEL level (r = −0.49, $p \leq 0.05$). Interestingly, particle size was significantly negatively correlated with CEL and CML levels (r = −0.87, $p \leq 0.001$ and r = −0.67, $p \leq 0.05$, respectively), suggesting that the formation of CML and CEL were greatly affected by the decrease of particle size during thermal treatments. The reason for this result is still not clear. Zhu et al. [ 2021] [45] found that, when myofibrillar proteins and glucosamine were exposed to protein peroxyl radicals (ROO⋅) derived from linoleic acid during heating, CML and CEL levels were significantly negatively correlated with particle size, which was related to the decomposition of myofibrillar proteins aggregation.
Currently, there are few studies about the obvious correlation between the total sulfhydryl content of heated food proteins and the formation of CEL and CML. However, our results showed that high CEL and CML levels were closely related to low total sulfhydryl content. Zhu et al. [ 2020] [1] reported that formation of a strong covalent bond between the disulfide bonds of myosin could promote the formation of CEL and CML. We hypothesized that the heating of fish myofibrillar proteins with glucose led to the aggregation of proteins via disulfide bonds, which could promote the formation of CEL and CML. Furthermore, the formation of CEL and CML levels was related to the increase of surface hydrophobicity content, but the mechanism behind this phenomenon is not clear. We speculate that the fish myofibrillar protein conformations disrupted and exposed more surface hydrophobicity groups during thermal treatments [13,29], which was related to the formation of AGEs.
The findings of the present study could mean that the formation of AGEs in fish myofibrillar protein might be affected by the protein structure changes during heat treatment, including the decrease of protein size and total sulfhydryl content and the increase of surface hydrophobicity content, which could be related to protein unfolding and aggregation. Some previous studies have also shown that the protein unfolding and aggregation induced by heat treatment might regulate the glycation process of food protein, such as bovine serum albumin, β-lactoglobulin [13] and pork myofibrillar protein [16]. Based on the results and correlation analysis presented above, a possible mechanism of CEL and CML formation in a heated fish myofibrillar protein and glucose model system was proposed (Figure 11). This formation mechanism includes the following two possible mechanisms: During the initial heating stage, fish myofibrils heated with glucose were unfolded and exposed free amino, sulfhydryl groups and surface hydrophobicity groups (Figure 11A), which were related to the up-regulation of glycation sites [13], resulting in the rapid increase of CEL and CML levels. As the extent of thermal treatments increased, fish myofibrillar proteins in the presence of glucose formed polymerization through disulfide bonds, which buried the free amino and sulfhydryl groups (Figure 11B). At this point, the aggregation of fish myofibrillar proteins caused some glycation sites to be hidden, which slowed down the formation of CEL and CML [13,14]. Based on the results presented above, it was summarized that with the increase of the extent of thermal treatments, the predominant influence factor of heating on the fish myofibrillar protein and glucose system gradually transferred from unfolding, which promoted the formation of CEL and CML, to aggregation, which slowed down the formation of CEL and CML.
## 4. Conclusions
In this study, the effects of changes in protein structure of a fish myofibrillar protein and glucose model system during thermal treatment on the formation of corresponding AGEs were investigated. It was found that the decrease of particle size, free amino content and total sulfhydryl content and the increase of hydrophobicity in myofibrillar protein and glucose during thermal treatment had a positive influence on the formation of CEL and CML. Furthermore, the correlation analysis showed that the CEL and CML levels were less affected by the protein secondary structure based on FTIR analysis. The underlying mechanism behind these results indicates that the formation of CEL and CML in fish myofibrillar protein heated with glucose was affected by its unfolding and aggregation, depending on the extent of thermal treatments. These findings help to enhance understanding of the relationship between the formation of AGEs and structure changes of fish protein during thermal processing methods, and provide some valuable references and guidelines for revealing the mechanism of AGEs formation in fish products.
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|
---
title: 'Cretan Aging Cohort-Phase III: Methodology and Descriptive Characteristics
of a Long-Term Longitudinal Study on Predictors of Cognitive Decline in Non-Demented
Elderly from Crete, Greece'
authors:
- Maria Basta
- Eleni Skourti
- Christina Alexopoulou
- Alexandros Zampetakis
- Andronikos Ganiaris
- Marina Aligizaki
- Panagiotis Simos
- Alexandros N. Vgontzas
journal: Healthcare
year: 2023
pmcid: PMC10000452
doi: 10.3390/healthcare11050703
license: CC BY 4.0
---
# Cretan Aging Cohort-Phase III: Methodology and Descriptive Characteristics of a Long-Term Longitudinal Study on Predictors of Cognitive Decline in Non-Demented Elderly from Crete, Greece
## Abstract
Identifying modifiable factors that may predict long-term cognitive decline in the elderly with adequate daily functionality is critical. Such factors may include poor sleep quality and quantity, sleep-related breathing disorders, inflammatory cytokines and stress hormones, as well as mental health problems. This work reports the methodology and descriptive characteristics of a long-term, multidisciplinary study on modifiable risk factors for cognitive status progression, focusing on the 7-year follow-up. Participants were recruited from a large community-dwelling cohort residing in Crete, Greece (CAC; Cretan Aging Cohort). Baseline assessments were conducted in 2013–2014 (Phase I and II, circa 6-month time interval) and follow-up in 2020–2022 (Phase III). In total, 151 individuals completed the Phase III evaluation. Of those, 71 were cognitively non-impaired (CNI group) in Phase II and 80 had been diagnosed with mild cognitive impairment (MCI). In addition to sociodemographic, lifestyle, medical, neuropsychological, and neuropsychiatric data, objective sleep was assessed based on actigraphy (Phase II and III) and home polysomnography (Phase III), while inflammation markers and stress hormones were measured in both phases. Despite the homogeneity of the sample in most sociodemographic indices, MCI persons were significantly older (mean age = 75.03 years, SD = 6.34) and genetically predisposed for cognitive deterioration (APOE ε4 allele carriership). Also, at follow-up, we detected a significant increase in self-reported anxiety symptoms along with a substantial rise in psychotropic medication use and incidence of major medical morbidities. The longitudinal design of the CAC study may provide significant data on possible modifiable factors in the course of cognitive progression in the community-dwelling elderly.
## 1. Introduction
As life expectancy increases, cognitive impairment becomes an inextricable facet of aging. Worldwide, it is estimated that over 55 million people live with dementia, a number that is about to rise to 139 million people by 2050, while a substantial percentage of dementia patients has yet to receive a formal diagnosis [1]. In contrast, normal cognitive aging comprises predictable age-related cognitive changes, as indicated by age and education-adjusted domain-specific scores that fall within 1.5 standard deviations from the population mean [2]. Persons who display domain-specific (i.e., not global) cognitive impairment, which is not considered serious mental disorder and does not interfere with daily functioning, are likely to be diagnosed with mild cognitive impairment (MCI) [3]. Individuals with MCI are considered at high risk of progression to dementia [4], with conversion rates ranging from 6 to $44.8\%$, according to a recent meta-analysis [5]. MCI incidence rates increase from $22.5\%$ for ages 75–79 to $60.1\%$ for individuals beyond 85 years old [6]. In Greece specifically, MCI prevalence ranges from $13.11\%$ [7] to $32.4\%$ (Cretan Aging Cohort) [8].
As a prodromal stage of dementia pathology, MCI constitutes a critical “window” for early intervention, and consequently, several studies have focused on identifying modifiable risk factors for cognitive deterioration. Sleep disturbances are a frequent, yet potentially modifiable, comorbid condition in the elderly, which appears to contribute significantly to cognitive impairment and disease prognosis [9]. According to a recent meta-analysis, sleep quality, measured by dysregulation in sleep architecture, was found to differentiate cognitively intact and MCI persons, with the latter group exhibiting increased sleep latency and less Cyclic Alternating Pattern expression compared to healthy individuals [10]. Findings regarding the association between sleep duration and cognitive impairment are rather controversial [11], with some studies indicating greater risk for cognitive decline among short (<6 h) and long sleepers (>8 h), or both [12,13], whilst other studies fail to report such an association. Cross-sectional analyses from the Cretan Aging Cohort (CAC) revealed significant associations between objective long sleep duration and executive deficits among persons diagnosed with MCI and cognitively non-impaired individuals [11], whereas long sleep duration in MCI and Alzheimer’s Disease (AD) patients may be driven by the presence of APOE (Apolipoprotein E) ε4 allele [14].
Other biomarkers (including genetic factors, pro-inflammatory cytokines and stress hormones) contribute to disease progression and differentiate between clinical categories (MCI, dementia). The APOE ε4 allele is an established risk factor for dementia, incident MCI, and rate of conversion from MCI to dementia [15]. Dysregulation of inflammatory response (a condition also known as “inflamm-aging”) seems to play a critical role in the pathogenesis of neurodegenerative diseases, although the underlying mechanisms are not clearly understood [16]. Elevated cerebrospinal fluid and plasma levels of Tumor Necrosis Factor-alpha (TNFa) and Interleukin-6 (IL-6) in AD patients [17] predict further cognitive decline [18] and have been linked to worse cognitive performance in both MCI and AD patients [19]. Impaired regulation of pro-inflammatory cytokine secretion has been found in sleep-related disorders and acute sleep deprivation [20,21]. Moreover, increased IL-6 plasma levels predict poor sleep quality [22] and relate to excessive daytime sleepiness in the cognitively intact elderly [23]. Elevated cerebrospinal fluid and plasma cortisol levels have been detected in both MCI and dementia patients, whereas increased cortisol may exert deleterious effects on memory recall via biphasic activation of specific receptors in the hippocampus, leading to downregulation of Long-Term Potentiation [24]. Additionally, overexpression of cortisol receptors in prefrontal areas may be associated with executive deficits emerging from irregular activity patterns in the prefrontal cortex [25]. The two processes may be interrelated, as impaired executive function mediates the relationship between basal cortisol levels and impaired memory recall [26].
Neuropsychiatric symptoms and mental morbidities are particularly common among elderly with and without neurocognitive disorders or MCI [27,28]. Depression prevalence among MCI patients may be as high as $32\%$ [29] and is considered a risk factor for dementia progression [30] and accelerated rate of cognitive deterioration (possibly moderated by APOE ε4 carriership) [31]. Patients with persistent depressive symptomatology are more likely to present hippocampal atrophy [32], whereas depression diagnosis is often accompanied by pronounced amyloid abnormalities [33]. Anxiety is another frequent comorbid condition (although not as extensively studied as depression), with prevalence rates reaching $21\%$ among MCI patients [34]. Significant anxiety symptoms can compromise daily functioning in MCI patients and increase the risk for dementia progression [35]. A trend towards reduced cognitive performance is present in patients with concurrent anxiety and depressive manifestations, although the contribution of anxiety symptoms on the observed cognitive deficits remains unclear [35]. Anxiety symptoms are also linked to elevated pro-inflammatory cytokines and hypercortisolemia, a condition that leads to dementia-associated brain atrophy due to long-term glucocorticoid exposure [36]. Last but not least, sleep dysregulation is a core depression symptom, and sleep-associated disturbances (insomnia symptoms, poor sleep quality) are overexpressed among MCI patients [37].
The CAC was established in 2013 to investigate sociodemographic, medical, lifestyle, inflammation and neuroendocrine, sleep-related, genetic, cognitive and neuropsychiatric characteristics of the elderly residing in mostly rural areas of the Heraklion prefecture in the island of Crete, Greece. The present report describes the protocol of a 7-year follow-up study on a subset of CAC participants, aimed to identify potentially modifiable predictors of cognitive deterioration among persons who were either cognitively non-impaired or were diagnosed with MCI. Similar large-scale prospective studies are being conducted in Greece and focus on sociodemographic information, medical and mental health indices, lifestyle factors and biomarkers (SHARE; Survey of Health, Ageing and Retirement in Europe [38,39]), as well as nutrition and neuropsychological markers of cognitive progression (HELIAD study; Hellenic Longitudinal Investigation of Aging & Diet [40]). However, to our knowledge, up to now, this is the first longitudinal cohort study conducted in Greece and among few worldwide with a relatively large, well-defined sample—including MCI patients—with a special focus on objective sleep, inflammation, stress and neuropsychiatric symptoms as possible modifiable factors for dementia.
## 2.1.1. Phase I–Phase II
During Phase I, 3140 community-dwelling participants (mean age 73.7 ± 7.8 years) [8] from rural areas of Heraklion, Crete (Cretan Aging Cohort) were examined. Eligible participants were those aged ≥60 years old who visited Primary Health Care Centers (staffed by physicians participating in the Primary Health Care research network of the CAC study) in both rural and urban areas of Heraklion and consented to participate in the study. Patients with acute symptomatology (terminal illnesses, severe movement impairment) were excluded from the study. Data from the 2011 national census were utilized in order to compare CAC participants to the whole Greek and Cretan population of similar age (for a more thorough analysis, see [8]). Demographic information and medical data were collected, and all participants were administered the Mini Mental State Examination (MMSE) test. Participants who had scored <24 points on MMSE ($$n = 636$$) were invited to a comprehensive neuropsychological and neuropsychiatric examination (Phase II), and a total of 344 consenting persons (comparable in terms of demographic and anthropometric measurements to the 636 participants) completed the evaluation. A control group ($$n = 181$$) of persons scoring ≥24 points on MMSE during Phase I was also formed using a proportional stratification process to match the low MMSE group on gender and place of residence. Of those, 161 persons consented and took part in Phase II examination [11]. During Phase II (2013–2014), all participants underwent full neuropsychological/neuropsychiatric/neurological evaluation, 3-day, 24-h actigraphy recording, and blood sampling (to measure baseline morning cortisol, pro-inflammatory cytokines and genetic biomarkers); medical history, sleep complaints and general functionality information were also recorded. Consensus clinical diagnoses for dementia and MCI were based upon the Diagnostic & Statistical Manual of Mental Disorders (DSM, 4th Edition) and the International Working Group (IWG) criteria, accordingly [8]. In total, 146 persons were found cognitively intact, whilst 231 participants were diagnosed with MCI of any type [8].
## 2.1.2. Phase III
The participant pool for the 7-year follow up study (Phase III) comprised all CNI persons ($$n = 146$$) and individuals who met the formal criteria for MCI ($$n = 231$$) during Phase II. Patients diagnosed with dementia were excluded from Phase III testing, which took place between October 2020 and August 2022 (see Figure 1). In total, 103 participants ($27.3\%$) had passed away in the intervening years, 56 persons ($14.9\%$) could not be located, and 63 persons ($16.7\%$) refused to participate, raising the total attrition rate (inability to participate for any reason) to $58.9\%$. In total, 149 MCI and 73 CNI individuals could not be retested. From the 274 survivors, 155 individuals completed the evaluation, although data from four participants were not included in the analyses due to severe medical comorbidities or sensory loss. Thus, the final response rate reached $55.1\%$. All participants were contacted by telephone and came from 11 different districts in the prefecture of Heraklion. Testing procedures were similar to those followed in Phase IΙ, permitting direct quantitative comparisons between the two time points on the majority of measures. Examination was conducted at participants’ homes and included medical history and physical examination, neuropsychological testing, a night of polysomnography recording and a 7-day, 24-h actigraphy, as well as a morning blood draw to assess stress and inflammatory biomarkers. The study was approved by the Ethics Committee of the University of Crete (number of approval: $\frac{61}{9}$-3-2020). A detailed description of the study protocol is provided below. Figure 1 presents a flow chart of the entire study.
## 2.2.1. Sleep Measurements
We collected data from 144 participants. Each participant underwent one night home sleep study ad libitum using a portable Type II7 16 channel polysomnography device (Alice, PDx, Philips, Respironics, Murrysville, PA, USA). The sleep study registered the following parameters: oral-nasal airflow via pressure cannula and thermistor, respiratory effort via the abdominal and chest belts, arterial oxygen saturation level via the pulse oximeter (oxygen saturation and pulse rate), body position detection (supine or non-supine), cardiac electrical activity, C3M2 and C4M1 electroencephalogram, electrooculogram and chin and leg electromyogram. Scoring was performed manually from a sleep expert physician according to the American Association Sleep Medicine scoring manual version 2.6.2020. Apnea/Hypopnea episodes followed the standard procedures (AASM, 2007) and Obstructive Sleep Apnea was defined as an Apnea/Hypopnea Index ≥ 15. Additional sleep variables, such as Sleep Latency, Total Sleep Time, Total Time in Bed, Sleep Efficiency and Wake Time after Sleep Onset were also scored according to the standard AASM 2007 criteria.
The majority of participants ($$n = 110$$) completed a 7-day, 24-h wrist actigraphy recording (Actigraph, GT3XP model, Pensacola, FL, USA) as a complementary means to estimate sleep duration and quality, using the same procedures followed in Phase II [11]. Sleep–wake cycle estimation was based on epochs of movement (peaks of activity) or movement absence (relatively quiet periods of activity) using the ActLife 6 software (ActLife v6.9.5 LLC, Pensacola, FL, USA) and complemented by sleep diaries. Data were collected and averaged for the 7-day and 3-day period separately, and specific variables of interest were calculated: night and 24-h total sleep time, night and 24-h total time in bed, sleep latency and efficiency, wake time after sleep onset, and number and mean duration of night awakenings. For 104 participants, actigraphy took place simultaneously or within 24 h from PSG recording. Six participants underwent actigraphy recording within 1–4 months from PSG recording due to technical issues.
## 2.2.2. Inflammatory Biomarkers
Single morning blood samples were collected (between 10:00 am and 12:00 pm) to assess inflammatory markers (IL-6, TNFa and C-Reactive Protein, $$n = 119$$) and plasma cortisol levels (available for116 participants). Blood samples were transferred to EDTA-containing tubes, refrigerated, centrifuged for plasma isolation and kept in deep freeze (−80 °C). Plasma TNFa and IL-6 were measured using the ELISA technique (Human TNF-alpha Quantikine HS ELISA and Human IL-6 Quantikine HS ELISA kits, R&D Systems Europe, Abington, UK). Plasma cortisol levels were measured using the ELISA technique (Cusabio Technology LLC, Texas, USA). The same procedure was followed at Phase II, rendering results comparable between the two phases [41].
## 2.2.3. Diagnosis of Neurocognitive Impairment
All participants underwent a thorough neuropsychological examination (mean duration ≤ 2.5 h). Domains evaluated included memory (episodic and verbal memory: Greek Memory Scale and Rey Auditory Verbal Learning Test, respectively; spatial memory: Taylor Complex Figure and working memory: Digits Reverse), language (naming ability: Boston Naming Test-short version and verbal fluency: the Semantic Verbal Fluency test) and attention/executive function (processing speed: Symbol Digits Modality test and visuomotor speed, task shifting and selective attention: Trails A & B). Raw scores were transformed into age and education-standardized values (based on normative values), and average z-scores on each cognitive domain were computed. Impaired performance on a given domain was considered if the average z-score was at least 1.5 SD below normative values. For the diagnosis of MCI, impaired performance in two or more tests within a given cognitive domain and intact functionality level (based on an Independent Activities of Daily Living (IADL) score > 0.78) were required. In cases of severe cognitive impairment, the MMSE test was administered instead. A Clinical Dementia Rating score was also calculated to aid cognitive status classification, especially in cases of severe cognitive impairment and significant sensory limitation.
Close relatives or caregivers were asked to complete scales measuring daily functioning (the 13-item Greek Independent Activities of Daily Living scale), current cognitive and neuropsychiatric symptoms (Cambridge Behavioral Inventory) and symptoms indicative of Lewy-body dementia (4-item Mayo Fluctuations Scale). An average IADL score < 0.78 points (range 0 to 1.00) was considered as indicative of significant functional impairment, a core criterion of severe cognitive impairment diagnosis (Dementia of any type). According to the IWG criteria, MCI diagnosis requires intact basic daily activities and relatively preserved instrumental daily functioning. Therefore, an IADL score > 0.78 points serves as a marker of adequate/preserved daily functionality in persons with mild cognitive impairment and CNI individuals.
## 2.2.4. Semi-Structured Interview
A comprehensive medical history was taken, including the following domains that were initially assessed at baseline:-Current and past medical conditions, with emphasis on illnesses and operations occurring during the follow-up period, including Traumatic Brain Injury (TBI), stroke and pharmacotherapy (any type of treatment with a special focus on psychotropic substances). We then calculated total number of major medical morbidities (hypertension, diabetes, heart/pulmonary/hematological/liver diseases, gastrointestinal conditions, hyper/hypothyroidism, cancer, arthritis).-Mental morbidities (i.e., depression and anxiety diagnosis) were assessed according to the DSM-5 criteria, based on a clinical interview, neuropsychological evaluation, and existing diagnosis following the same procedures described previously [28].-Anthropometric measurements: weight, height, and Body Mass Index were assessed as previously described [8].-A frailty composite index was calculated based on level of physical activity, self-reported symptoms of exhaustion and decreased appetite, and objectively assessed upper limb weakness (using a dynamometer measurement). Frailty level was then recorded into 3 classes (absence of frailty, pre-frailty, frailty).-Overall subjective memory difficulties were assessed via a single question (“Do you have any memory problems?”), requiring a yes/no response, whereas domain-specific memory complaints (difficulty recalling recent information, words and names) were assessed using single questions requiring a binary response (WHICAP medical package: Medical Conditions and WHICAP survey).-Sleep problems: we used a shortened version of the Penn State Sleep Questionnaire comprising 12 items (answered on a 4-point Likert scale ranging from 0 = absence of symptoms to 3 = serious symptomatology) in order to assess presence and severity of self-reported sleep complaints, sleep duration and napping throughout the day (apnea, snoring, excessive movements during sleep, difficulty falling/staying asleep, early awakening, overall quality of sleep and, lastly, average night sleep duration and time required for falling asleep, as well as napping frequency and duration, if applicable) [41].-Lifestyle habits: we recorded current smoking and drinking habits (number of cigarettes if a current smoker, smoking cessation and year of quitting, as well as frequency of alcohol consumption on a daily basis). We also estimated level of physical activity during the previous week (including frequency of participation in particular activities such as gardening, housework, handiwork, shopping), as well as based on participants’ responses to the question “How many days did you walk for more than 10 min in a row in a brisk manner during the last week?”, as previously described in detail [41].-Social support and frequency of social contacts: we calculated the total number of social contacts (close relatives and friends) reported by participants during the last month, the availability of emotional and practical support, using two questions adapted from the Social Support Questionnaire–Short Form [42]: “Is there anyone you can really count on when you need help? Is there anyone you can really count on to help you feel more relaxed when you are under pressure/stress?” and the quality of perceived support (“How satisfied are you with the level of support you receive?”), answered on a 5-point Likert scale ranging from 0 (not at all) to 4 (completely satisfied).
## 2.2.5. Neuropsychiatric Evaluation
Self-reported symptoms of anxiety and depression were assessed using the 7-item Hamilton Depression and Anxiety Scale-Anxiety subscale (HADS-A) and the 15-item Geriatric Depression Scale (GDS), respectively. Diagnosis of depression and anxiety during Phase III followed the same procedure as in Phase II, according to the DSM-5 criteria established through a clinical interview conducted by a specially trained physician and psychologist, scores on the aforementioned scales (using 7 and 4 points as cutoffs, respectively) and prescription of psychotropic medication(antidepressants/anxiolytics or antipsychotics) [28]. Furthermore, in Phase III, we recorded retrospectively major stressful events that occurred within the 7-year interval and calculated a new binary variable to indicate the presence of at least one major stressor in the period preceding the examination process. Major stressors included significant medical conditions (severe eyesight/hearing loss, cancer), death or illness of close relatives and finally, survival from natural disasters (there was consecutive severe and frequent earthquake activity in Crete in the time preceding Phase III assessment).
Following the same procedures as in Phase II, all relevant information (cognitive performance by domain, IADL score, neuropsychiatric symptoms) was evaluated by a certified psychiatrist (M.B), neurologist (C.C.) and neuropsychologist (P.S) to reach a consensus diagnosis according to theDSM-4 and DSM-5 criteria (for Phase II and III accordingly) for the diagnosis of Major Neurocognitive Disorder and the IWG criteria for the MCI diagnosis [43]. Dementia differential diagnosis was made on the basis of the following criteria: for the diagnosis of probable AD, vascular Dementia, Lewy Body Dementia, behavioral variant FTD and other types of Frontotemporal Dementia, the NINCDS-ADRDA, the NINDS-AIREN, the DLB Consortium, the International Consortium on behavioral variant Frontotemporal Dementia and the *Neary criteria* were utilized, accordingly [44,45,46,47,48]. Diagnosis of mixed dementia was made in cases of co-occurrence of signs suggestive of both probable AD and vascular dementia [49].
## 2.3. Statistical Analysis
SPSS 28.0 (IBM; 2022) was used for statistical analyses. In view of significant deviation from normality for a number of variables (as indicated by $p \leq 0.05$ on the Kolmogorov–Smirnov test), non-parametric tests (Wilcoxon signed-rank test and Mann–Whitney U test) were used to assess change over time and group differences at each Phase, respectively. The Chi square test was used to assess differences in proportions. The final sample size was sufficient to ensure $85\%$ power for detecting small-to-medium effect size group differences at $p \leq 0.05$ and also sufficient to ensure $95\%$ power for detecting small effect sizes of change over time at $p \leq 0.05.$
## 3. Results
Seventy-one CNI and 80 participants previously diagnosed with MCI in Phase II were re-evaluated in Phase III at an average interval of 7.12 years (SD = 0.92). Compared to the total participant pool (all persons in the CNI and MCI groups in Phase II, $$n = 377$$), those who were followed up were younger (72.8 vs. 77.2 years, $p \leq 0.001$), more likely to be women ($77.5\%$ vs. $63.3\%$, $$p \leq 0.004$$) and less likely to live alone ($$p \leq 0.03$$). There was a non significant tendency for followed-up persons to have achieved more years of education ($$p \leq 0.059$$). The total group and followed-up subgroup were comparable in terms of geographic origin ($$p \leq 0.4$$), major medical morbidities ($$p \leq 0.9$$) and previous occupation ($$p \leq 0.1$$). As evident in Table 1, the majority of participants in the current cohort were rural residents ($84.1\%$), previously occupied in domestic/agricultural work ($63.6\%$) and having attained 6 or fewer years of formal education ($92.1\%$).
In Phase II, the two diagnostic groups (i.e., CNI, MCI) were comparable in Body Mass Index, gender ratio, lifestyle habits, previous occupation, frequency of persons living alone, overall health (as indexed by the number of current major medical morbidities), and family history of dementia (see Table 1), with the exception of age (CNI < MCI, $p \leq 0.001$) and frequency of APOE ε4 carriers (CNI < MCI, $$p \leq 0.04$$). Moreover, the two diagnostic groups did not differ in psychiatric manifestations (severity of self-reported anxiety and depression symptoms, depression and anxiety diagnosis) or frequency of psychotropic medication use (see Table 2). In Phase III, the two groups were comparablein all variables. Occurrence of major stressors during the follow-up period was also very similar between the two groups, as was the frequency of persistent depression diagnosis (21.1 vs. $17.5\%$ for CNI and MCI, respectively, $$p \leq 0.6$$).
Over the follow-up period, participants in both groups reported increased anxiety symptoms ($p \leq 0.001$), although the frequency of anxiety diagnosis did not vary significantly ($$p \leq 0.6$$ and $$p \leq 0.2$$ within the CNI and MCI groups, respectively). This trend was paralleled by a concurrent increase in the use of at least one psychotropic medication, which reached significance in both groups ($p \leq 0.001$ and $$p \leq 0.005$$ in the CNI and MCI group, respectively). Whereas self-reported depression symptoms did not vary significantly across the two time points between CNI and MCI groups, the frequency of depression diagnosis changed significantly over time within diagnostic groups (increasing trend, statistically significant among CNI persons, $p \leq 0.001$). Alcohol use was reduced ($$p \leq 0.028$$ and $$p \leq 0.023$$ in CNI and MCI group, respectively). Finally, there was an increase in those living alone within the CNI group ($$p \leq 0.003$$) and in the average number of major medical morbidities in both groups ($$p \leq 0.001$$ and $p \leq 0.001$ in CNI and MCI groups, respectively), possibly as a result of aging.
## 4. Discussion
In this paper, we outline the study protocol and the sociodemographic, medical and mental health characteristics of the sample of a 7-year longitudinal study on aging, aiming to identify predictors of cognitive decline in community-dwelling elderly participants. The sample derived from the CAC included persons averaging 72.9 (range: 60–89) years old at baseline who either met criteria for MCI or were cognitively intact upon initial examination. Considering the age range of participants, we achieved satisfactory response rate ($55.1\%$) in this well-characterized, culturally homogeneous, mainly rural ($84.1\%$), low-literacy sample ($92.1\%$ had completed ≤6 years of formal education). This longitudinal study is rather unique as it involves multimodal measurements of a wide range of factors, which could act as either direct predictors of cognitive decline or as moderators of the impact of other variables on long-term cognitive status progression in this well-defined community-dwelling elderly sample.
Few studies have investigated the interplay between sleep abnormalities, mental and physical comorbid disorders, inflammatory biomarkers, stress-related hormones, behavioral/psychological symptoms and domain-specific cognitive performance among persons diagnosed with different levels of cognitive and functional impairment longitudinally. Until recently, the majority of actigraphy and polysomnography studies recruited small groups of cognitively intact and MCI participants [10]. To our knowledge, this is the first longitudinal study conducted in Greece and among few studies worldwide that uses several qualitative and quantitative measures, providing an objective, integrative assessment of sleep patterns, sleep-related disorders (Obstructive Sleep Apnea) and sleep macrostructure, as well as their interplay with cognitive performance and possible confounding factors (inflammatory and genetic biomarkers, mental and physical comorbidities, sociodemographic and lifestyle conditions)in a relatively large sample.
The two diagnostic groups (CNI and MCI) were relatively similar in sociodemographic, medical and emotional conditions at baseline, including family history of dementia, except that MCI persons were older and more likely to be APOE ε4 allele carriers. At follow-up, we noted a significant increase in the number of major medical morbidities, which is expected with advancing age. In terms of mental health, both groups reported increased severity of anxiety symptoms and use of psychotropic medications (anti-depressants and anxiolytics), possibly as a consequence of aging as well as the long-term and ongoing effects of two consecutive crises, namely the Greek financial crisis of 2009–2019, which resulted in further income reductions and increased unemployment, and the global pandemic crisis, which caused insecurity and exacerbated feelings of distress among Greeks [50,51]. Furthermore, depression diagnosis (based on the clinical interview and antidepressant prescription criteria) was notably increased at re-evaluation, especially among cognitively non-impaired persons. It should be stressed, though, that subjectively assessed depressive symptomatology remained relatively stable between the two measurement points (as opposed to increased frequency of depression diagnosis), assumingly due to increased anti-depressant use, which led to symptom alleviation at follow-up. Depression and anxiety are frequent comorbid conditions among the elderly, and their co-occurrence increases the chance of somatic symptoms and cognitive deterioration [52]. Development of depression and anxiety symptomatology is closely related to multimorbidity [35], presence of chronic illnesses, and stressful life events [52]. The number of medical morbidities increased in Phase III, and at the same time, one out of three participants reported at least one type of major stressful event. Major stressors that trigger feelings of threat or undermine functional independence (as in the case of severe sensory loss) predict both depressive and anxiety symptoms [53].
Given the demographic characteristics of the current population (low educational level and rural residence), lack of familiarity with the utilized techniques (actigraphy and polysomnography), the time-consuming nature of the study procedures and the lack of personal incentives (i.e., remuneration), the response rate can be considered satisfactory. Our project was delayed for 7 months due to COVID-19 pandemic restrictions, whereas excessive worrying about COVID infection during examination and/or inconsistent information about the effectiveness of protective measures against coronavirus expansion may have negatively affected the response rate. However, despite the adverse conditions and the insurmountable challenges posited by the pandemic, the Phase III response rate ($51.1\%$) was among the highest compared to similar studies conducted in Greece [40] and Southern Europe [54].
Lastly, some limitations of the current protocol should be discussed. Despite the fact that all testing procedures took place in participants’ homes to reduce the inconvenience of a hospital visit and to increase ecological validity, we could not control for the presence of environmental distractors during neuropsychological testing (although we opted for a distraction-free environment), fatigue or reduced compliance with the instructions pertaining to the polysomnographic process. In addition, although home PSG is a well-validated process for sleep assessment, it is associated with artifacts and data loss due to lack of continuous monitoring by overnight technical staff.
## 5. Conclusions
The current study aimed to identify modifiable risk factors for cognitive deterioration by embracing a comprehensive, multidisciplinary approach, utilizing user-friendly techniques. This is important given the high progression rates from MCI to dementia, the urgent need for timely interventions, as well as the complex interplay between risk factors for cognitive decline. Strengths of this study include the longitudinal design, the relatively large number of MCI patients recruited, the particular socio-economic and cultural characteristics of the current sample, the long follow-up interval and methodological advantages (presence of a control group), which we expect to result in scientifically valid and clinically useful findings in terms of modifiable factors predisposing to cognitive progression among the elderly.
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|
---
title: Low Gut Microbial Diversity Augments Estrogen-Driven Pulmonary Fibrosis in
Female-Predominant Interstitial Lung Disease
authors:
- Ozioma S. Chioma
- Elizabeth Mallott
- Binal Shah-Gandhi
- ZaDarreyal Wiggins
- Madison Langford
- Andrew William Lancaster
- Alexander Gelbard
- Hongmei Wu
- Joyce E. Johnson
- Lisa Lancaster
- Erin M. Wilfong
- Leslie J. Crofford
- Courtney G. Montgomery
- Luc Van Kaer
- Seth Bordenstein
- Dawn C. Newcomb
- Wonder Puryear Drake
journal: Cells
year: 2023
pmcid: PMC10000459
doi: 10.3390/cells12050766
license: CC BY 4.0
---
# Low Gut Microbial Diversity Augments Estrogen-Driven Pulmonary Fibrosis in Female-Predominant Interstitial Lung Disease
## Abstract
Although profibrotic cytokines, such as IL-17A and TGF-β1, have been implicated in the pathogenesis of interstitial lung disease (ILD), the interactions between gut dysbiosis, gonadotrophic hormones and molecular mediators of profibrotic cytokine expression, such as the phosphorylation of STAT3, have not been defined. Here, through chromatin immunoprecipitation sequencing (ChIP-seq) analysis of primary human CD4+ T cells, we show that regions within the STAT3 locus are significantly enriched for binding by the transcription factor estrogen receptor alpha (ERa). Using the murine model of bleomycin-induced pulmonary fibrosis, we found significantly increased regulatory T cells compared to Th17 cells in the female lung. *The* genetic absence of ESR1 or ovariectomy in mice significantly increased pSTAT3 and IL-17A expression in pulmonary CD4+ T cells, which was reduced after the repletion of female hormones. Remarkably, there was no significant reduction in lung fibrosis under either condition, suggesting that factors outside of ovarian hormones also contribute. An assessment of lung fibrosis among menstruating females in different rearing environments revealed that environments favoring gut dysbiosis augment fibrosis. Furthermore, hormone repletion following ovariectomy further augmented lung fibrosis, suggesting pathologic interactions between gonadal hormones and gut microbiota in relation to lung fibrosis severity. An analysis of female sarcoidosis patients revealed a significant reduction in pSTAT3 and IL-17A levels and a concomitant increase in TGF-β1 levels in CD4+ T cells compared to male sarcoidosis patients. These studies reveal that estrogen is profibrotic in females and that gut dysbiosis in menstruating females augments lung fibrosis severity, supporting a critical interaction between gonadal hormones and gut flora in lung fibrosis pathogenesis.
## 1. Introduction
An ever-growing synergy of human and animal investigations supports the important role of sex hormone regulation relating to immunity in the pathophysiology of chronic lung diseases [1,2]. IL-17 signaling has been implicated in numerous chronic lung diseases, such as idiopathic pulmonary fibrosis (IPF), lung cancer and pulmonary sarcoidosis [1,3,4,5]. Moreover, striking clinical disparities according to sex are observed in Th17 cell-mediated diseases. For example, although the incidence of IPF is higher in men, being of the female sex is predictive of better IPF clinical outcomes [6,7,8]. Among patients with pulmonary arterial hypertension, female patients have better survival than males [9,10,11]. These observations support the urgent need to identify relevant sex-specific mechanisms in chronic pulmonary inflammation.
Independent reports demonstrate that profibrotic signaling pathways converge on STAT3, an important molecular checkpoint for tissue fibrosis [12,13]. Immune cells, including CD4+ T cells, produce IL-6, which enhances collagen production through the induction of JAK/STAT3/IL-17A or JAK/ERK/TGF-β1 signaling in local and systemic environments [14,15,16,17]. Distinctions in clinical outcomes by sex support an investigation of the interplay of female gonadotrophic hormones with the STAT3-dependent induction of profibrotic cytokine expression. The interactions of the alpha subunit of the estrogen receptor (ERα) and STAT3 protein, both transcription factors, have been reported in breast cancers of epithelial origin, noting enhanced epithelial–mesenchymal transition (EMT) as well as augmented tumor metastasis [18]. However, the immunologic consequences of ERα binding to the STAT3 gene in CD4+ T cells of patients with lung fibrosis remain unexplored.
The observed disparate clinical outcomes in chronic lung diseases by sex support the investigation of the impact of gonadotrophic hormones on STAT3 signaling, specifically in the context of the profibrotic cytokines, IL-17A and TGF-β1. Here, we report that human females experiencing a loss of lung function due to progressive fibrosis, as well as female murine models of bleomycin-induced lung fibrosis, demonstrate increased T regulatory cells with TGF-β1 expression (immunosuppressive) in the fibrotic lung microenvironment. Lower estrogen states, such as those found in males and ovariectomized female mice, reveal increased IL-17A expression due to elevated percentages of pulmonary Th17 cells (pro-inflammatory). Moreover, the investigation of this estrogen–adaptive immunity interplay in distinct environments reveals that low gut microbial diversity further increases estrogen-induced lung fibrosis. These data demonstrate a distinct sex-specific role for STAT3 signaling in CD4+ T cells, thus paving the way for developing personalized (e.g., sex-based) immunotherapeutic strategies for chronic lung inflammation.
## 2.1. Human Study Approval
To participate in this study, all of the human subjects signed a written informed consent form, and the patients were enrolled at Vanderbilt University Medical Center. All of the human studies were approved by the appropriate institutional review board (VUMC 040187).
## 2.2. Study Population
For inclusion in this study, the clinical and radiographic criteria used to define sarcoidosis were applied [19]. IPF subjects were defined according to recent American Thoracic Society (ATS) guidelines [20], and systemic sclerosis patients were defined according to the 2013 American College of *Rheumatology criteria* [21]. Clinical lung progression was defined as previously described [22]. Pulmonary function testing was performed as clinically indicated. FVC decline was defined as a relative reduction of ≥$10\%$ in the percent of predicted FVC. There were four human cohorts in this study: 25 healthy controls (7 males and 18 females), 31 sarcoidosis patients (11 males and 20 females), idiopathic pulmonary fibrosis (IPF) patients (36 males and 9 females), and scleroderma patients (5 males and 6 females). Information related to the demographics of the study subjects is provided in Table 1.
## 2.3. Peripheral Blood Mononuclear Cells Isolation and Storage
The Ficoll–Hypaque density gradient centrifugation method was used to isolate peripheral blood mononuclear cells (PBMCs) from the whole blood of all four human cohorts in this study: healthy controls, sarcoidosis, IPF, and scleroderma patients, as previously described [23,24]. The PBMCs were then stored in fetal bovine serum containing $10\%$ dimethyl sulfoxide (DMSO) at a concentration of 10 × 106 cells/mL in a −80 °C freezer before being transferred to liquid nitrogen for prolonged storage or before use.
## 2.4. Chromatin Immunoprecipitation Sequencing (ChIP-Seq) Library Preparation
Primary CD4+ T cells were negatively selected using immunomagnetic bead separation (STEMCELL, EasySep #17951). Approximately 1 to 2.5 million total T cells were obtained from 5 to 10 million PBMCs. The T cells were first incubated with 2 mM disuccinimidyl glutarate for 35 min at room temperature; then, formaldehyde was added to a final concentration of $1\%$, and the cells were incubated for another 10 min at room temperature [25]. The nuclei were isolated using the Covaris truChIP Chromatin Shearing Kit and fragmented by sonication. Immunoprecipitation was performed using an anti-ERα antibody (Cell Signaling #8644) and protein A+G magnetic beads. The chromatins were de-crosslinked and purified using AMPure XP beads. ChIP-seq libraries were prepared according to Illumina protocols and were sequenced using 75 bp paired-end sequencing on an Illumina NextSeq, producing an average of 135,924,844 reads per library.
## 2.5. Sequencing Alignment and Peak Calling
The ChIP-seq reads were examined for technical artifacts using FastQC. No aberrant technical behavior was identified. The reads were trimmed for adapter sequences and decontaminated for sequencing artifacts by using bbduk. The trimming options were set to ktrim = right trimming, mink = 11, hdist = 1, qin = 33, tpe and tbo options enabled. BBDuk’s list of Illumina sequencing adapters was used to perform adapter trimming. Decontamination was performed against phiX adapters and bbduk’s database of sequencing artifacts. The decontaminated reads were aligned to version GRCh38 of the human reference genome using BWA-mem [26], with the following options: -L 100 -k 8 -O 5. Following the alignment, the peaks were called with respect to the input chromatin library using MACS2 [27], with the following options: -nomodel –shift -100 –extsize 200 g hs -q 0.05 -f BAMPE –keep-dup all –broad.
## 2.6. Murine Model of Pulmonary Fibrosis
All of the murine procedures were performed according to the protocol approved by the Institutional Animal Care and Use Committee at Vanderbilt University Medical Center (protocol #M1700043). For the murine model of bleomycin-induced pulmonary fibrosis, 5- to 8-week-old mice weighing approximately 17–22 g were used. The mice were anesthetized with an intraperitoneal injection of 80 μL of 20 mg/mL Ketamine/1.8 mg/mL Xylazine solution. Then, 75 μL containing 0.04 units of bleomycin (Novaplus Lake Forest IL) in saline or an equal volume of saline ($0.9\%$ sodium chloride) (Hospira Inc., Lake Forest IL), used as a control, was administrated intranasally to wild-type or ESR-1-/- mice, as previously described [28]. The lungs were harvested for histology, flow cytometry, or single-cell isolation, as previously described [16]. The mouse strains used are described in Table S1.
## 2.7. Ashcroft Scoring
The degree of fibrosis in the murine lung tissue was assessed using Ashcroft scoring, as previously described [29].
## 2.8. Sircol Assay
The collagen content was determined using a Sircol Collagen Assay kit (Biocolor, Newtown Abbey, UK), as previously described [30].
## 2.9. Flow Cytometry
Both murine and human flow cytometry experiments were conducted with an LSR-II flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA), and the information related to all the antibodies used in this study is listed in Table S2. Live cells were gated based on the forward and side scatter properties, and the surface staining of cells was performed as previously described [31]. Th17 cells were identified by flow cytometry using key transcriptional factors, such as STAT3, as previously described [32]. The cells were gated on singlets, live CD3+ and CD4+ cells. Data analysis was performed using FlowJo software (Tree Star, Ashland, OR, USA). A minimum of 50,000 events were acquired per sample.
## 2.10. In Vivo Implantation of Hormone Pellets to Ovariectomized Mice
Ovariectomy or sham surgeries were conducted at three weeks of age by the Jackson Laboratory, and the experiments were carried out when the ovariectomized or sham-operated mice were 6 weeks old. At 6 weeks of age, 60-day slow-release pellets (Innovate Research of America, Sarasota, FL, USA) containing 17β- estradiol 0.1 mg (E2), progesterone 25 mg (P4) or a combination of 17β-E2 (0.1 mg) and P4 (25 mg) were surgically placed subcutaneously into ovariectomized C57BL/6J mice, as previously described [33]. As a control, 25.1 mg of vehicle pellets (Innovative Research of America) was surgically placed into the sham-operated females or ovariectomized female mice. Three weeks (21 days) after the pellets were implanted, the mice were challenged with intranasal bleomycin (0.04 Units) and sacrificed 14 days later, as previously described [28]. Studies involving large and independent experimental cohorts of mice were performed at least twice.
## 2.11. Metagenomic Sequencing and Analysis of Gut Microbiota
Fecal pellets were collected from female mice at Day 14 in each housing cohort, and genomic DNA (gDNA) was extracted with the Qiagen DNAeasy extraction kit (Qiagen, Valencia, CA, USA), according to the manufacturer’s instructions. The gDNA concentration and quality were confirmed using the Bioanalyzer 2100 system (Agilent, Santa Clara, CA, USA). The metagenomic sequencing and analysis of fecal pellets was conducted as previously described [34]. The sequences of gut microbiota have been deposited into BioProject ID PRJNA899808. Wilcoxon Rank Sum tests in R were used to examine differences in Shannon diversity and evenness between the ABSL-1 and ABSL-2 environments. The code for all of the analyses can be found at http://github.com/emallott/PulmonaryFibrosisMicrobiota (accessed on 19 January 2023) [34].
## 2.12. Statistics
When comparing different experimental groups, we used an unpaired two-tailed Student’s t-test. Multiple-group comparisons were performed using a one-way analysis of variance (ANOVA) with Tukey’s post hoc test. Statistical analysis for all figures was carried out using Prism version 7.02 (GraphPad Software, San Diego, CA, USA). For a result to be considered statistically significant, a p-value of less than 0.05 was used.
## 3.1. The Nuclear Transcription Factor, Estrogen Receptor Alpha Subunit, Interacts with the STAT3 Gene Locus in CD4+ T Cells
The estrogen receptor alpha subunit (ERα) is not only a receptor but also serves as a transcription factor. To identify factors that may modulate STAT3 expression during lung fibrosis, we interrogated ChIP-seq datasets in the ENCODE 3 repository [35]. In five human cell lines, including cancers and EBV-transformed B lymphocytes, a significant enrichment of ERα binding was demonstrated within the STAT3 locus. Representative tracks among the technical replicates for each cell line were visualized in the WashU Epigenome Browser [36] (Figure 1A). The numbers of starting reads, decontaminated reads, alignment successes, and enriched peaks are given in Table S3. These findings in the Chip-seq datasets confirmed previous reports indicating that ERα, which is encoded by the ESR1 gene, and STAT3 are important in breast and ovarian cancer [18,37], supporting the hypothesis that the STAT3 gene locus is a frequent target of ERα activity in various cell types.
The targeting of ERα to the STAT3 gene locus in T cells has not been previously described. To determine whether ERα interacts with the STAT3 locus in CD4+ T cells through DNA binding activity, we performed genome-wide ChIP-seq for Erα-bound regions. Primary CD4+ T cells were derived from the PBMCs of six healthy individuals with varying demographics (Table S4). Of the six ChIP libraries (four females and two males), sample p1035928-8, which corresponds to a female, identified an over sixfold greater number of ERα-enriched regions relative to any other sample. We used the GREAT algorithm to perform ontology-based functional enrichment analyses on that sample. ERα-enriched sites were statistically significantly enriched in genes related to T-cell function and development (Table S5), suggesting that the peaks obtained from this ChIP capture are specific to CD4+ T-cell function and are not randomly organized across the genome.
Finally, we examined the STAT3 locus in detail. We found that sample p1035928-8 contains six ERα-binding regions within or proximal to the STAT3 genomic locus, including two in its promoter region (Figure 1B). Overlaying chromatin accessibility data from the ENCODE project [35], we noted that each of these regions exhibits DNase hypersensitivity in at least one ENCODE cell line. Three of these regions also displayed evidence of estrogen-related receptor alpha (ESRRA) binding in other cell lines (K562, GM12878). Taken together, these results demonstrate that ERα binds the STAT3 locus in CD4+ T cells, specifically at known regions of chromatin accessibility shared with various cell types.
## 3.2. Loss of the ESR-1 Subunit Represses IL-6 Expression but Augments pSTAT3 and IL-17A Expression in CD4+ T Cells
Because transcription factor ESR-1 (alpha subunit of ESR) was identified as binding to the STAT3 gene in CD4+ T cells, we investigated the role of the ERα subunit in profibrotic cytokine expression using a murine model of bleomycin-induced lung fibrosis in WT and ESR-1 knockout (ESR-1-/-) mice. Both murine cohorts were challenged intranasally with bleomycin and harvested on day 14. ESR-1-/- mice contain supernormal estrogen levels in their serum due to the loss of the ESR-1 signaling-mediated negative feedback loop [38]. We observed that female ESR-1-/- mice lost significantly less weight and had the same mortality compared to their WT counterparts (Figure 2A,B). Male ESR-1-/- mice also demonstrated reduced weight loss but had significantly increased survival compared to WT males (Supplemental Figure S1). We used flow cytometry to examine profibrotic cytokine expression in pulmonary CD4+ T cells of the murine cohorts. The levels of IL-6 and IL-23R, key mediators of Th17 cell differentiation, were significantly reduced in the lung CD4+ T cells of female ESR-1-/- mice compared to their WT counterparts (Figure 2C,D). Remarkably, the levels of pSTAT3 and IL-17A were increased in ESR-1-/- compared with WT mice (Figure 2E,F). These data demonstrate that ESR-1 has a key role in the induction of IL-6 and IL-23R expression in CD4+ T cells, as well as the repression of pSTAT3 and IL-17A expression in CD4+ T cells during the pulmonary fibrosis of females.
## 3.3. Loss of Gonadotrophic Hormones through Ovariectomy Reduces IL-6 Production and Augments pSTAT3 and IL-17A Expression from CD4+ T Cells
To further delineate the contribution of female gonadotrophic hormonal signaling to the progression of proinflammatory cytokine expression in the lung, we used female C57BL/6J mice that were ovariectomized or sham-operated at three weeks of age. Slow-release pellets containing either 17β-estradiol (17β-E2, 0.1 mg), progesterone (P4, 25 mg), the combination of 17β-E2 (0.1 mg) and P4 (25 mg) or a vehicle (25.1 mg) were subcutaneously implanted into adult ovariectomized female C57BL/6J mice at six weeks of age. At nine weeks of age, all groups were challenged with bleomycin, and the lungs were harvested 14 days later. There was no significant difference in weight loss or survival across the hormone treatment groups compared to the ovariectomized mice implanted with placebo pellets (Figure 3A,B).
We performed flow cytometric analysis of single-cell lung suspensions (SCLS) to assess alterations of CD4+ T cell populations. TGF-β and IL-17A are profibrotic cytokines that are expressed by regulatory T cells and Th17 cells, respectively. We began by comparing regulatory T and Th17 cell populations in sham-operated, menstruating female mice. We noted a significantly higher population of regulatory T cells compared to Th17 cells in the sham-operated mice (Figure 3C). We then assessed for IL-17A cytokine expression in response to the loss of female hormones. Ovariectomized mice displayed decreased CD4+IL-6+ T cells compared to the sham-operated mice; supplementation with both 17β-E2 and P4 in ovariectomized mice normalized IL-6 expression. Neither hormone individually restored IL-6 expression by CD4+T cells to the same levels as the sham-operated mice (Figure 3D). The same trends held for the IL-6 co-receptor GP130 (Figure 3E). Remarkably, and akin to our observation in ESR-1-/- mice, the levels of pSTAT3 were increased in the CD4+ T cells of ovariectomized mice compared to sham-operated animals, again returning to sham levels in ovariectomized mice by the addition of female hormones (Figure 3F). In accordance with an increase in pSTAT3, we also observed heightened CD4+IL-17A+ T cells in ovariectomized mice compared to sham-operated animals. The addition of 17β-E2, P4 or both to ovariectomized mice decreased IL-17A expression compared to the placebo (Figure 3G). A representative FACS plot is provided (Figure 3H). Overall, these findings reveal that female hormones repress inflammatory profibrotic cytokine expression by inhibiting pSTAT3 signaling and IL-17A expression in murine pulmonary CD4+ T cells following bleomycin administration.
## 3.4. Lung Quantification following the Loss of ESR-1 or Ovariectomy Reveals Reduced Collagen Content
To determine the physiologic significance of estrogen signaling for profibrotic cytokine expression, we performed histologic analysis and collagen quantification of the lung using the Sircol assay. Analysis of lung histology using trichrome staining noted significantly less fibrosis in ovariectomized mice without hormone replacement compared to the sham-operated mice or ovariectomized mice given dual estrogen (17β-E2)/progesterone (P4) hormone pellets (Figure 4A). Ashcroft scoring (Figure S2) and the quantification of collagen content (Figure 4B) revealed a nonsignificant decrease in collagen levels in ovariectomized mice compared to mice that underwent sham ovariectomy surgeries. The replacement of female hormones with a combination of estrogen and progesterone pellets increased fibrosis compared to the ovariectomized placebo group (Figure 4B). Similarly, a nonsignificant decrease in pulmonary collagen content was observed in ESR-1-/- mice compared to wild-type mice. The observation of a nonsignificant decline in the pulmonary lung content following the loss of estrogen signaling suggests that additional factors contribute to pathogenesis. We recently reported that the gut microbiota play an important role in lung fibrosis severity. ABSL-1 housing conditions favor gut microbiota diversity, whereas ABSL-2 conditions favor reduced gut microbiota diversity [34]. Using linear discriminant analysis (LDA) to examine species-level differences in the gut microbiota, 10 taxa were overrepresented in ABSL-1 mice, and five taxa were overrepresented in ABSL-2 mice. The overrepresented taxa in ABSL-2 mice included *Lachnospiraceae bacterium* A2, *Lachnospiraceae bacterium* 28–4, *Firmicutes bacterium* ASF500 and *Romboutsia ilealis* [34]. A higher relative abundance of Firmicutes in the lung microbiota of bleomycin-treated mice with fibrosis has been reported [39]. The species overrepresented in ABSL-1 mice included Staphylococcus nepalensis, Dubosiella newyorkensis, Acetatifactor muris, Lactobacillus animalis, *Lactobacillus murinus* and *Acutalibacter muris* [34].
No distinctions in the lung microbiota are present in these mice regarding the housing condition. Specifically, rearing environments that favor low gut microbiota diversity, such as ABSL-2 housing conditions, induce severe lung disease compared to ABSL-1 conditions. To confirm if gut microbiota impact female ILD severity, we began by assessing the lung collagen content in wild-type female mice who received intranasal bleomycin while housed in different environments: germ-free, ABSL-1 or ABSL-2 conditions. We noted significant distinctions in lung collagen content among wild-type females according to the rearing environment, with ABSL-2 female mice demonstrating the most severe disease compared to germ-free or ABSL-1 mice (Figure 4D). To determine the impact of estrogen signaling and gut microbiota on lung fibrosis severity, we assessed the lung collagen content among ovariectomized mice, as well as those ovariectomized with estrogen replacement, while housed under either ABSL-1 or ABSL-2 conditions. Remarkably, we noted that ovariectomized mice housed under ABSL-1 or ABSL-2 conditions did not demonstrate a change in the collagen content (Figure 4E). Equally noteworthy was the observation that a significant increase in lung fibrosis was noted among ovariectomized mice who received estrogen replacement and were housed in ABSL-2 conditions compared to those housed in ABSL-1 conditions. These findings reveal a synergistic relationship between estrogen signaling and gut dysbiosis regarding lung fibrosis severity (Figure 4E).
## 3.5. Female Gut Microbiota Demonstrate Significantly Less Diversity in ABSL-2 Housing Conditions
To investigate the hypothesis that the gut microbiota is an important contributor to the differences in fibrosis severity between female mice housed under ABSL-1 and ABSL-2 conditions, we performed metagenomic analysis on fecal pellets from female mice in each housing cohort. We did not detect microorganisms in the stool of female germ-free mice by sequencing and culture, as expected. Shannon alpha diversity, a measure of species richness and evenness, was considerably higher in female ABSL-1 mice compared with female ABSL-2 mice using a Wilcoxon rank sum test (Figure 5A). Additionally, Pielou’s evenness was higher in ABSL-1 compared with ABSL-2 female mice, but species richness did not differ significantly (Shannon diversity: Wilcoxon, $W = 108$, $$p \leq 0.015$$; Pielou’s evenness: Wilcoxon, $W = 106$, $$p \leq 0.021$$; Species richness: Wilcoxon, $W = 80.5$, $$p \leq 0.450$$). The female mice housed under ABSL-1 and ABSL-2 conditions differed significantly in their gut microbiome composition using Jaccard but not Bray–Curtis dissimilarities (PERMANOVA, Bray–Curtis: F1,22 = 2.392, R2 = 0.098, $$p \leq 0.079$$; Jaccard: F1,22 = 8.369, R2 = 0.276, $p \leq 0.001$). A similar investigation in male mice revealed that the ABSL-1 and ABSL-2 microbiomes were significantly different using both metrics (PERMANOVA, Bray–Curtis: F1,24 = 4.728, R2 = 0.165, $$p \leq 0.004$$; Jaccard: F1,24 = 6.519, R2 = 0.214, $p \leq 0.001$) (Figure 4). Alpha diversity did not differ significantly between floors for male individuals (all $p \leq 0.05$).
A comparison of female and male gut microbiota diversity according to the housing conditions reveals significantly greater gut diversity among females compared to males under ABSL-1 housing conditions (Figure 5B), whereas only greater species richness was noted among females under ABSL-2 housing conditions (Figure 5C). Beta diversity differences between ABSL-1 and ABSL-2 microbiota compositions also differed significantly when an analysis was conducted using both the Bray–Curtis dissimilarity metric index (Figure 5D) and the Jaccard index (Figure 5E), which account for the presence/absence of taxa and taxon abundance variation, respectively (PERMANOVA, ABSL-1 mice: Bray–Curtis: F1,17 = 4.424, R2 = 0.206, $$p \leq 0.014$$; Jaccard: F1,17 = 2.408, R2 = 0.124, $$p \leq 0.053$$; ABSL-2 mice: Bray–Curtis: F1,29 = 1.952, R2 = 0.063, $$p \leq 0.160$$; Jaccard: F1,29 = 7.944, R2 = 0.215, $p \leq 0.001$). These findings support the hypothesis that the female gut microbiome changes according to the rearing environment.
## 3.6. Patients with Progressive Fibrotic Lung Disease Display Sex-Specific Profibrotic Cytokine Profiles
Because of the role of female gonadotrophic hormones in reducing the CD4+ T cell-mediated proinflammatory and profibrotic environment in mouse models of lung fibrosis, we probed samples from human patients with fibrotic lung diseases for sex-associated differences. Consistent with the murine model of lung fibrosis, we observed higher levels of STAT3 mRNA and pSTAT3 protein in CD4+ T cells from the male compared to the female sarcoidosis patients (Figure 6A,B). We noted similarly increased mRNA and protein expression of the master transcription factor regulating IL-17A production, RORC, in CD4+ T cells from the male compared to the female sarcoidosis patients (Figure 6C,D). Additionally, among sarcoidosis patients experiencing disease progression, females expressed significantly higher IL-6 levels in their CD4+ T cells compared to males (Figure 6E).
We also assessed IL-17A and TGF-β1 production by sex, as CD4+ T cells promote pulmonary fibrosis through the STAT3-medicated production of IL-17A and TGF-β1 [16]. We observed higher IL-17A mRNA and protein expression in CD4+ T cells from male compared to female sarcoidosis patients (Figure 6F,G). CD4+ T cells from female sarcoidosis patients expressed significantly higher free TGF-b1 than males and the healthy female controls (Figure 6H). There were no distinctions in the TGF-b1 precursor protein, latency-associated peptide-TGF-β, among males compared to females (Figure 6I). These findings demonstrate the differential immune modulation of STAT3 signaling pathways in human CD4+ T cells of males (increased) and females (reduced) with fibrotic lung disease. Consequently, CD4+ T cells from males exhibit higher proinflammatory cytokine expression due to enhanced IL-17A production, whereas CD4+ T cells from females exhibit increased immunosuppressive cytokines due to greater TGF-β1 expression.
We assessed for a possible contribution of female hormones to other fibrotic diseases, including IPF and Systemic Sclerosis (SSc), by quantifying the serum 17β-E2 levels in age-matched patients and healthy controls. Serum 17β-E2 was greater in male SSc and IPF patients compared to age-matched male healthy controls (Figure 6J). These findings demonstrate the positive interplay of female gonadotrophic hormones in male- and female-predominant fibrotic lung diseases.
## 4. Discussion
This original report reveals the “ying–yang” effects of estrogen-induced lung fibrosis in female interstitial lung disease. Estrogen clearly augments the development of lung fibrosis (Figure 4); yet, the binding of ERα to the STAT3 promoter shifts profibrotic cytokine expression away from proinflammatory phenotypes mediated by IL-17A to immunosuppressive phenotypes mediated by TGF-β1 (Figure 2 and Figure 3). Human cytokine expression confirmed reduced pSTAT3 expression in females, leading to increased TGF-β1 production, whereas males display higher IL-17A levels.
The beneficial effects of estrogen were apparent. Although ESR-1-/- mice and surgical ovariectomy confirm estrogen’s profibrotic capacity in lung fibrosis, it is worth noting that Th17 cell differentiation is reduced due to the transcription factor ERα‘s ability in relation to the STAT3 promoter (Figure 1, Figure 2 and Figure 3). The loss of STAT3 signaling has been shown to shift the IL-6-JAK2-STAT3 induction of IL-17A to sustained IL-6-ERK-TGF-β1 expression in local and systemic CD4+ T cells [15,16]. This is the most likely explanation for the increased regulatory T cells noted in females and the increased STAT3 signaling and IL-17A production following ovariectomy (Figure 3 and Figure 6). Both ovariectomized and ESR-1-/- mice revealed significantly lower IL-6 and GP130 levels than sham-treated animals but increased pSTAT3 and IL-17A levels in CD4+ T cells (Figure 3). Higher estrogen states augment IL-6 production, but instead of inducing a proinflammatory state supported by increased CD4+ IL-17A levels, estrogen concomitantly inhibits STAT3 signaling. These immune alterations are likely relevant to other IL-17A-mediated diseases in the postmenopausal state, such as myocardial infarctions and osteoporosis [40,41]. Enhanced TGF-β1 expression protects against osteoporosis [42].
The pathologic effects of estrogen were also determined. A prior study noted increased ESR-1 expression in human IPF lung samples and that the chemical inhibition of ESR-1 results in reductions in bleomycin-induced pulmonary fibrosis in male mice [43]. *The* genetic and surgical ablation of estrogen-dependent signaling resulted in reductions in the pulmonary collagen content, which confirms the profibrotic nature of estrogen in female-predominant ILD (Figure 4). Remarkably, the observed reductions were not statistically significant, suggesting that other factors contribute to lung fibrosis severity in females. The induction of lung fibrosis in females under distinct housing conditions unveiled the role of the gut microbiome in lung fibrosis severity. Wild-type female mice treated with intranasal bleomycin demonstrate the greatest lung severity under ABSL-2 conditions and minimal fibrosis under germ-free conditions, thus confirming the important contribution of gut flora to female lung fibrosis (Figure 4D). Conditions that favor the loss of female gut microbial diversity, such as ABSL-2 housing conditions, lead to greater lung fibrosis compared to ABSL-1 conditions (Figure 4 and Figure 5). Equally noteworthy is the observation that fibrosis is synergistic between estrogen signaling and gut dysbiosis, suggesting that the profibrotic nature of estrogen is heavily influenced by gut microbiota and that the capacity of gut microbiota to induce fibrosis is influenced by the host hormone status. A growing body of literature supports crucial interactions between gut microbiota and estrogens [44,45]. The conjugation of glucuronic acid (GlcA) to a compound, such as estrogen, marks it for elimination via the GI or urinary tract. β-glucuronidase, an enzyme that deconjugates estrogen, mediates estrogen release into the serum in its active form [46,47]. Gut microbiota can inhibit or induce β-glucuronidase activity. In addition, it was previously noted that ABSL-2 stool contains reduced lactobacilli within the microbial community. Lactobacillus spp, which were elevated in ABSL-1 stool, can reduce fecal β-glucuronidase activity [45]; future studies that assess the capacity of lactobacilli to enhance urinary estrogen excretion and lower its serum levels are needed. Future studies defining the mechanisms by which ABSL-2 gut flora augment the estrogen induction of lung fibrosis are also warranted. Considering of the hormone status of the host, as well as defining the gut microbiome, is necessary to explain the clinical observations in females with ILD. TGF-β1 is the master regulator of fibrosis. Figure 6 demonstrates that TGF-β1 is most predominant in female sarcoidosis patients. In Figure 4, we see that gut dysbiosis augments lung fibrosis. When IL-6 induction occurs, downstream signaling can lead to either IL-17A or TGF-B1 expression. IL-17A expression leads to pulmonary inflammation. Estrogen signaling provides protection against proinflammatory fibrosis due to the capacity of the ERα to bind to the STAT3 promoter (Figure 1). This reduction in lung inflammation improves the prognosis. In menopausal females, the gut microbiome continues to drive lung fibrosis, but due to the reduced estrogen state, there is no inhibition of STAT3 expression and Th17 cell development. Lung fibrosis can now be mediated by IL-17A, which likely explains the increased symptoms after menopause.
There are some limitations that should be noted. This investigation focused on female ILD; investigations of the role of testosterone in lung fibrosis are needed. There are also reports indicating that estrogen drives Th17 cell differentiation in chronic lung diseases, such as asthma [48,49]. Concomitant immune-gut microbiome investigations of asthma models with ILD models are warranted, including an inquiry into the interplay of gonadal hormones. Additionally, asthma pathogenesis is very distinct from ILD, which may also impact T cell differentiation. Another consideration is that the gut microbiome is influenced by diet. The mice in the murine model had the same diet; future studies assessing the impact of food consumption on the gut microbial community, metabolomic syndromes and inflammation are warranted [50,51,52]. An investigation into the impact of gut dysbiosis on estrogen signaling or of estrogen signaling on gut microbial communities is warranted. Finally, we observed Th17 cell populations increasing following the gavage of ABSL-2 stool into germ-free mice compared to the gavaging of ABSL-1 stool. Future analysis definitively identifying the microorganism(s) responsible for Th17 cell differentiation is warranted, followed by an assessment of their presence in the stool of murine asthma models, as well as asthmatic patients and ILD patients.
## 5. Conclusions
Taken together, this investigation demonstrates that female gonadotrophic hormones are profibrotic yet, through the ERα binding of the STAT3 locus, reduce the inflammation induced by IL-17A expression in CD4+ T cells. The consequent reduction in inflammation is a likely contributor to the mortality benefit observed in premenopausal females with ILD. This study introduces another key contributor to lung fibrosis severity: gut dysbiosis. The synergistic impact of gut dysbiosis and estrogen on lung fibrosis supports a multi-pronged approach to the treatment of female-predominant lung fibrosis (Figure 7).
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|
---
title: PGRMC1 Ablation Protects from Energy-Starved Heart Failure by Promoting Fatty
Acid/Pyruvate Oxidation
authors:
- Sang R. Lee
- Moeka Mukae
- Kang Joo Jeong
- Se Hee Park
- Hi Jo Shin
- Sang Woon Kim
- Young Suk Won
- Hyo-Jung Kwun
- In-Jeoung Baek
- Eui-Ju Hong
journal: Cells
year: 2023
pmcid: PMC10000468
doi: 10.3390/cells12050752
license: CC BY 4.0
---
# PGRMC1 Ablation Protects from Energy-Starved Heart Failure by Promoting Fatty Acid/Pyruvate Oxidation
## Abstract
Heart failure (HF) is an emerging epidemic with a high mortality rate. Apart from conventional treatment methods, such as surgery or use of vasodilation drugs, metabolic therapy has been suggested as a new therapeutic strategy. The heart relies on fatty acid oxidation and glucose (pyruvate) oxidation for ATP-mediated contractility; the former meets most of the energy requirement, but the latter is more efficient. Inhibition of fatty acid oxidation leads to the induction of pyruvate oxidation and provides cardioprotection to failing energy-starved hearts. One of the non-canonical types of sex hormone receptors, progesterone receptor membrane component 1 (Pgrmc1), is a non-genomic progesterone receptor associated with reproduction and fertility. Recent studies revealed that Pgrmc1 regulates glucose and fatty acid synthesis. Notably, Pgrmc1 has also been associated with diabetic cardiomyopathy, as it reduces lipid-mediated toxicity and delays cardiac injury. However, the mechanism by which Pgrmc1 influences the energy-starved failing heart remains unknown. In this study, we found that loss of Pgrmc1 inhibited glycolysis and increased fatty acid/pyruvate oxidation, which is directly associated with ATP production, in starved hearts. Loss of Pgrmc1 during starvation activated the phosphorylation of AMP-activated protein kinase, which induced cardiac ATP production. Pgrmc1 loss increased the cellular respiration of cardiomyocytes under low-glucose conditions. In isoproterenol-induced cardiac injury, Pgrmc1 knockout resulted in less fibrosis and low heart failure marker expression. In summary, our results revealed that Pgrmc1 ablation in energy-deficit conditions increases fatty acid/pyruvate oxidation to protect against cardiac damage via energy starvation. Moreover, Pgrmc1 may be a regulator of cardiac metabolism that switches the dominance of glucose-fatty acid usage according to nutritional status and nutrient availability in the heart.
## 1. Introduction
Heart failure is an emerging epidemic, and patients with reduced ejection fraction rates have a mortality rate of >$70\%$ [1]. Despite extensive studies on the epidemiology and risk factors, the mortality rate of heart failure remains high [2]. Malnutrition is a known risk factor for myocardial damage [3]. Clinically, individuals are exposed to malnutrition-mediated cardiac risks during surgery, sepsis, and some serious diseases [4]. Currently used drugs for cardiomyopathy, such as angiotensin-converting enzyme inhibitors or beta blockers, reduce vasoconstriction and decrease the risk of death [5]. However, improving the function of the heart itself will provide a more fundamental breakthrough in the treatment of energy-starved heart failure. ATP production is mainly derived from fatty acid oxidation in the heart [6]. Heart failure with hypertension or ischemia is accompanied by decreased cardiac fatty acid oxidation [7]. Similarly, glucose oxidation, another pathway for ATP production, is also suppressed in heart failure [8]. As a failing heart lacks energy due to decreased glucose and fatty acid oxidation, targeting cardiac energy metabolism is the main research focus of many studies [9].
Although subtypes differ between sexes, the overall heart failure risk is comparable between men and women [10]. Some beneficial effects of androgen and estrogen on heart failure have been previously reported [11,12]. While synthetic progestin is considered to have deleterious effects, the influence of progesterone or canonical progesterone receptors in heart failure is neither beneficial nor deleterious [13]. One of the progesterone receptors, progesterone receptor membrane component 1 (Pgrmc1), has been reported to suppress obesity/diabetes-mediated cardiac lipotoxicity [14]. Pgrmc1 is a non-canonical progesterone receptor associated with reproductive functions, such as decidualization [15] and female fertility [16]. Recent studies have revealed the metabolic function of Pgrmc1, beyond the reproductive relationships, in liver [17] and adipose tissue [18], focusing on the anabolism of glucose and lipids. Regulation of insulin, a major anabolic hormone, by Pgrmc1 has also been reported in the pancreas [19]. Although Pgrmc1-related anabolisms have been extensively studied, the mechanism of Pgrmc1-related catabolism remains ambiguous. Furthermore, the regulation of cardiac health by Pgrmc1 has been investigated only in the energy-enriched state in diabetes. In this study, we investigated how Pgrmc1-related catabolism affects cardiac health during energy starvation. Based on previous reports on the apoptosis and necrosis of cardiomyocytes during glucose starvation in vivo and in vitro [20,21], we used glucose starvation mouse models (72 h fasting) to mimic cardiac ischemia under physiological conditions in this study. Additionally, an adrenergic stimulation model using isoproterenol injection was introduced to induce energy starvation in the heart based on previous studies indicating lowered ATP production from ADP in the isoproterenol model [22]. Unlike the overnutrition state, Pgrmc1 loss increased fatty acid and pyruvate oxidation in the heart during malnutrition. Our results indicated that maintenance of the major energy production pathway protected the Pgrmc1-ablated heart from energy starvation-induced injury.
## 2.1. Animals
Wild-type (WT) and Pgrmc1 global knockout (PKO) littermate mice [23] (8-week-old; C57BL/6 background) were grown in a pathogen-free facility at Chungnam National University under a standard 12:12 h light:dark cycle and fed standard chow diet with water provided ad libitum. The mice were fasted to starvation, and unexpected deaths during the experiment were recorded to assess the survival rate. Isoproterenol (230 mg/kg, subcutaneous) was injected for two weeks to induce adrenergic heart damage. To observe cardiac pumping in WT and PKO mice, fluorescent dye-labeled (DyLight 680 antibody labeling kit, Thermo Scientific, Waltham, MA, USA, 53056) bovine serum albumin (BSA) was intravenously injected into the mice. After 1 h, the mice were anesthetized and placed in an in vivo imaging system (IVIS; FOBI, Vancouver, BC, Canada). A video was recorded to observe cardiac pumping. Images of cardiac contraction/relaxation were also captured. All animal experiments were approved by the Chungnam Facility Animal Care Committee (CNU-00606) and adhered to their ethical guidelines.
## 2.2. Gene Expression Omnibus (GEO) Datasets
Public datasets (GEO) were used to determine PGRMC1 transcription levels in patients with cardiomyopathy. GSE29819 and GSE36961 datasets were selected, and all patients were included in the analysis.
## 2.3. Comprehensive Laboratory Animal Monitoring System (CLAMS)
CLAMS was used to assess the metabolic status of starved mice. Oxygen consumption (VO2) and carbon dioxide production (VCO2) rates were measured using an Oxymax system (Columbus Instruments, Columbus, OH, USA). Mice were placed at least 50 min before experiment for acclimation. The respiratory exchange ratio (RER) and respiratory quotient (RQ) were calculated as the ratio of VCO2 to VO2. The mice were fasted from midway through the light cycle to midway through the dark cycle.
## 2.4. RNA Isolation, Reverse Transcription, and Quantitative Reverse Transcription–Polymerase Chain Reaction (qRT-PCR)
RNA pellets were collected from the hearts of mice and H9c2 cells using TRIzol, chloroform, and isopropanol. RNA pellet was washed with ethanol and dissolved in diethyl pyrocarbonate-treated water. RNA concentration was measured, and the same RNA amounts for each sample were used for cDNA synthesis using an Excel RT Reverse transcriptase kit (SG-cDNAS100; Smartgene, Daejeon, Republic of Korea). Real-time PCR was carried out using specific primers (Table 1), Excel Taq Q-PCR Master Mix (SG-SYBR-500; Smartgene), and Stratagene Mx3000P (Agilent Technologies, Santa Clara, CA, USA) in a 96-well optical reaction plate. Negative controls containing water instead of the sample cDNA were used in each plate.
## 2.5. Western Blotting
Protein samples were resolved on 8–$12\%$ sodium dodecyl sulfate (SDS) polyacrylamide gels (running buffer: 25 mM Tris, 192 mM Glycine, $0.1\%$ SDS, and D.W.). After electrophoresis, the gels were blotted onto a polyvinylidene difluoride membrane (IPVH 00010; Millipore, Burlington, MA, USA) at 350 mA for 1–2 h with the transfer buffer (25 mM Tris, 192 mM Glycine, and $20\%$ (v/v) methanol). Membranes were blocked in $3\%$ BSA and incubated with primary antibodies overnight at 4 °C. Membranes were washed thrice with TBS-T to remove the excess antibodies and incubated overnight at 4 °C with the following secondary antibodies: goat anti-rabbit IgG horseradish peroxidase (HRP) (Catalog #31460) and goat anti-mouse IgG HRP (Catalog #31430; Thermo Fisher Scientific, Waltham, MA, USA) antibodies. After washing thrice with TBS-T, immunoreactive proteins were observed with ECL solution (Eta C Ultra 2.0; Cyanagen, Bologna, Italy) using a ChemiDoc system (Fusion Solo, Vilber Lourmat, Eberhardzell, Germany).
The following primary antibodies were used: PGRMC1 (13856; Cell Signaling Technology, Danvers, MA, USA), ribosomal protein lateral stalk subunit P0 (RPLP0; A13633; Abclonal, Woburn, MA, USA), poly(ADP ribose) polymerase (PARP; 9532; Cell Signaling Technology), C/EBP homologous protein (CHOP; #MA1-250; Invitrogen, Waltham, MA, USA), β-actin (sc-47778; Santa Cruz, Dallas, TX, USA), glycolysis antibody sampler kit (8337; Cell Signaling Technology), pAMPK, tAMPK (9957; Cell Signaling Technology), LC3B (L7543, Sigma-Aldrich, St. Louis, MO, USA), and α-tubulin (66031-1-Ig; Proteintech, Rosemont, IL, USA).
## 2.6. Blood and Plasma Measurements
For blood glucose measurement, the tail was snipped, and the blood glucose levels were measured using an Accu-Chek Active kit (Roche, Basel, Switzerland). During necropsy, blood was collected from the IVC. Plasma samples were analyzed to determine the levels of free fatty acids (FFAs; BM-FFA100, Biomax, Planegg, Germany), triglycerides (TGs; TG-1650, Fuji Film, Tokyo, Japan), and total cholesterol (TCHO; TCHO-1450).
## 2.7. Cell Culture
All the cell culture reagents were purchased from Welgene (Gyeongsan, Republic of Korea). H9c2 rat cardiomyocytes were maintained in Dulbecco’s modified Eagle’s medium (LM001-05; Welgene) supplemented with $5\%$ (v/v) fetal bovine serum (FBS, Punjab, Pakistan), penicillin (100 U/mol), and streptomycin (100 μg/mL). To reflect the plasma profile of mice, cells were incubated with a low-glucose/fatty acid medium (500 mg/L glucose, 110 µM palmitic acid, 220 μM oleic acid) for 24 h. For Pgrmc1 knockdown/overexpression experiments, cells were incubated with Opti-MEM (31985070; Gibco; without FBS) for 0.5 h and treated with the siRNA/plasmid and lipofectamine 2000 (11668027; Thermo Fisher Scientific). The siRNA sequence used was: 5′-CAGUUCACUUUCAAGUAUCA-U-3′. Medium containing FBS was later added after 6 h.
## 2.8. Cardiac Fibrosis Measurement
Tissues were fixed with neutral-buffered formalin, and trimmed tissues were washed with tap water. Tissues were subjected to serial dehydration and embedded in paraffin. The paraffin block was cut (5 μm) using a microtome, and the cut sections were attached to a silane-coated slide. Slides were immersed in xylene overnight and processed using a commercial kit (MST-100T; Biognost, Zagreb, Croatia), according to the manufacturer’s protocol, for Masson’s Trichrome staining. Regions of interest were observed under a light microscope.
## 2.9. Terminal Deoxynucleotidyl Transferase-Mediated dUTP Nick End-Labeling (TUNEL) Staining and Immunostaining
Frozen tissues were embedded in an optimal cutting temperature compound and cut (8 μm) using a cryostat. Slides were dried overnight and washed with TBS-T. TUNEL assay (11684795910; Roche, Basel, Switzerland) was performed according to the manufacturer’s protocol. After 4′,6-diamidino-2-phenylindole staining, the region of interest was observed under a fluorescence microscope. For immunostaining, frozen tissue slides were dried overnight and heated in oven (65 °C) for 10 min. Slides were immersed in distilled water and subsequently TBS-T. After blocking with $3\%$ BSA, slides were incubated with primary antibody (CD31, ab56299; Abcam, Cambridge, UK) overnight at 4 °C. The next day, slides were washed with TBS-T and incubated with secondary antibody (A21202, Life Technologies, Carlsbad, CA, USA) for 4 h at room temperature. The region of interest was observed under a fluorescence microscope.
## 2.10. Statistical Analysis
Data are reported as the mean ± standard deviation. Differences between means were analyzed via Student’s t-test and one-way analysis of variance followed by Tukey’s multiple comparison test using the Graph Pad Software (GraphPad Inc., San Diego, CA, USA). Statistical significance was set at $p \leq 0.05.$
## 3.1. PGRMC1 Expression Is Associated with Energy-Starved Cardiomyopathy
Using public clinical datasets, we collected data to investigate the relationship between PGRMC1 expression and cardiomyopathy. In GSE29819, both ventricles from patients with dilated cardiomyopathy showed lower PGRMC1 expression levels than those from non-failing donor hearts (Figure 1A). In GSE36961, the hearts of patients with dilated cardiomyopathy with left ventricular systolic dysfunction showed decreased PGRMC1 expression levels compared to those of normal individuals (Figure 1A). Interestingly, the expression levels of key enzymes involved in fatty acid oxidation and glycolysis were lower in the hearts of patients with dilated cardiomyopathy (Figure 1A).
Through several in vitro and in vivo experiments, we attempted to delineate the effects of energy starvation on cardiomyocyte health. We induced energy starvation in H9C2 cardiomyocytes and mice via glucose starvation (glucose 0 mg/L, FBS $1\%$) and fasting (72 h), respectively. As shown in Figure 1B, cells under glucose starvation were predisposed to apoptotic cell death. Furthermore, hearts from mice under starvation (72 h) showed increased protein levels of apoptotic markers (cleaved PARP) and endoplasmic reticulum stress markers (CHOP) compared to those under resting conditions (Con) (Figure 1C). PGRMC1 protein expression was markedly suppressed by fasting (Figure 1C). These results indicate that PGRMC1 levels are closely related to energy starvation-induced cardiomyocyte injury.
## 3.2. Loss of PGRMC1 Maintains the Whole-Body Metabolism during Starvation
Since there is no information on the physiological profile of PKO mice under starvation, we used CLAMS for comprehensive assessments. In CLAMS, VO2 levels were markedly reduced from 14 h fasting and reached baseline after 20 h fasting in WT mice. In contrast, VO2 levels were generally maintained at high levels in PKO mice during fasting. VCO2 levels showed a similar pattern as the VO2 levels. Levels of VCO2 markedly decreased after 14 h of fasting and reached baseline after 20 h of fasting in WT mice. In contrast, PKO mice maintained high VCO2 levels during fasting (Figure 2A). Additionally, the RER (VO2/VCO2) ratios were lower in PKO mice than in WT mice during prolonged fasting (Figure 2B). RQ calculation revealed that PKO mice are more likely to consume fat than glucose during prolonged fasting (Figure 2C). The heat production of PKO mice was highly maintained during fasting, notably from 14 h fasting, compared to that of WT mice (Figure 2D). The physical activity of PKO mice was also maintained during the prolonged fasting period, while that of WT mice was substantially diminished during the same period (Figure 2E). When mice were starved for a long period, some died unexpectedly due to an energy deficit. PKO mice were resistant to starvation-induced death compared to WT mice (Figure 2F). These results indicate that PKO mice are physiologically resistant to energy starvation.
## 3.3. Pgrmc1 Loss Increases Fatty Acid/Pyruvate Oxidation and Decreases Starvation-Induced Cardiac Injury
To investigate how Pgrmc1 will affect the heart under starvation, WT and PKO mice were starved for 72 h and exposed to cardiac malnutrition. Blood glucose levels were at baseline in both starved WT and PKO mice, showing no difference between the two groups (Figure 3A). Plasma lipid profiles increased in starved PKO mice. Notably, plasma FFA and TG levels were significantly higher in starved PKO mice than in starved WT mice (Figure 3A). Heart weight (HW) decreased in starved PKO mice, while the ratio of HW per body weight (BW) was similar (Figure 3B). Western blotting showed that starved PKO hearts had decreased cleaved PARP levels, which is an apoptotic marker, compared to starved WT hearts (Figure 3C). Concordantly, PKO hearts showed seemingly increased cardiac contractions in the IVIS using fluorescence (Figure S1).
Most hearts with hypertrophy or failure undergo metabolic alterations characterized by decreased fatty acid oxidation [24]. Fatty acid oxidation accounts for almost $70\%$ of cardiac energy production [25]. PKO hearts under starvation conditions showed significantly increased expression levels of mitochondrial fatty acid oxidation enzymes (carnitine palmitoyltransferase 2 (Cpt2) and very long-chain acyl-CoA dehydrogenase (Vlcad)) and peroxisomal fatty acid oxidation enzyme (acyl-CoA oxidase 1 (Acox1)) compared to WT hearts under starvation conditions (Figure 3D). Glycolysis is a rapidly induced cardiac metabolism process associated with heart failure [26]. PKO hearts under starvation had markedly decreased protein levels related to glycolysis (hexokinase (HK)-1, HK2, and pyruvate kinase M2 (PKM2)) (Figure 3E). Glucose oxidation accelerates cardiac function recovery following myocardial injury [27]. Likewise, dichloroacetate, a pyruvate dehydrogenase (PDH) activator, increases myocardial efficiency [28]. Cardiac PDH was higher in PKO than in WT plants under starvation conditions (Figure 3E). These results indicate that starved PKO hearts increase their main energy production and fatty acid/pyruvate oxidation and do not need to be exposed to metabolic alterations.
As plasma FFA levels were highly maintained in PKO mice, it should be tested whether these metabolic alterations are influenced by the levels of physiologically induced substrates. To limit the influential factors in vivo, we introduced H9c2 rat cardiomyocytes and knocked down Pgrmc1 by siRNA. The cells were exposed to low glucose (500 mg/L) and fatty acids (palmitic acid (110 µM)/oleic acid (220 µM)). PGRMC1 protein levels were lower in the PK (Pgrmc1 knockdown) group than in the CK (control knockdown) group (Figure 4A). Cleaved PARP levels were lowered in PK group (Figure 4A). Metabolic alterations followed in vivo results. The mRNA expression levels of Cpt2, Vlcad, and Acox1 were higher in the PK group than in the CK group (Figure 4B). The protein levels of HK1 and HK2 decreased in the PK group (Figure 4C). PDH levels increased in the PK group (Figure 4C). Collectively, in vitro Pgrmc1 knockdown in low-energy cardiomyocytes induced fatty acid/pyruvate oxidation and decreased cellular injury. To investigate whether metabolic alterations in the PK group increased energy production compared to that in the CK group under energy deficit, we introduced a seahorse flux analyzer system to measure cellular respiration. H9c2 cells were knocked down and starved in a medium containing low glucose (500 mg/L) and fatty acids (palmitic acid (110 µM)/oleic acid (220 µM)). In the mitochondrial stress test, the PK group had a higher maximal respiration rate than that of the CK group (Figure 4D). We also measured the mitochondrial fusion/fission gene expression levels to assess the mitochondrial balance [29]. PKO hearts had a mildly increased fission gene (dynamin-related protein 1; Drp1) expression level compared to WT hearts (Figure S2A). These results confirm that fatty acid/pyruvate oxidation by PK increases energy production even under reduced glycolysis.
## 3.4. AMPK Activation Is Associated with Pgrmc1-Induced Metabolic Alteration in the Heart
We investigated the possible mechanism of metabolic alterations induced by Pgrmc1. AMPK is a multi-functional protein kinase involved in the oxidation and uptake of metabolites [30]. Western blotting revealed that starved PKO hearts had increased phosphorylated AMPK (pAMPK) levels and decreased total AMPK (tAMPK) levels. Starved PKO hearts showed a higher p/t AMPK ratio than WT hearts (Figure 5A). In H9c2 cells, PK cells showed higher pAMPK and lower tAMPK levels than CK cells. Concordantly, PK cells showed an increased p/t AMPK ratio compared to that in CK cells (Figure 5A).
Metabolic effects of AMPK activation and inactivation in cardiomyocytes were assessed. PGRMC1 levels were not directly regulated by AMPK activation because treatments with 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR; AMPK activator) and compound C (Com C; AMPK inactivator) suppressed PGRMC1 expression. AMPK phosphorylation was increased by AICAR and decreased by Com C treatment (Figure 5B). HK1 levels were lowered by AICAR, whereas HK2 and PKM2 levels were increased by Com C. PDH levels were decreased by Com C (Figure 5B). In contrast, the expression levels of fatty acid oxidation enzymes were markedly increased by AICAR treatment (Figure 5C). Com C treatment decreased Cpt2 and Vlcad expression levels (Figure 5C). In summary, AMPK activation was related to the induction of fatty acid/pyruvate oxidation and decreased glycolysis. As Pgrmc1 loss increased AMPK activation and showed similar metabolic alterations to AMPK-activated cells, AMPK may be linked to metabolic modulation by PGRMC1 in starved hearts.
## 3.5. Pgrmc1 Ablation Protects the Heart from Isoproterenol-Induced Damage
We introduced isoproterenol cardiac injury model according to previous studies [31,32]. Mice were injected with isoproterenol (five times, total 230 mg/kg, 14 days) and sacrificed (Figure 6A). Masson’s trichrome staining revealed that isoproterenol-WT hearts showed large positive areas with fibrosis (Figure 6B). In contrast, isoproterenol-PKO hearts showed decreased fibrotic areas compared with WT hearts (Figure 6B). Transforming growth factor-beta mRNA expression levels decreased in isoproterenol-PKO hearts (Figure 6C). As heart failure markers, mRNA expression levels of actin alpha 1 and brain natriuretic peptide were decreased in isoproterenol-PKO hearts compared to those in WT hearts (Figure 6D). In metabolic assessments, isoproterenol-PKO hearts showed higher levels of fatty acid oxidation enzymes (Cpt2) than isoproterenol-WT hearts (Figure 6E). Furthermore, isoproterenol-PKO hearts had decreased glycolysis enzyme levels and increased PDH levels. Additionally, isoproterenol-PKO hearts showed an increased p/t ratio of AMPK (Figure 6F). Hence, isoproterenol-PKO hearts had altered cardiac metabolism, such as fasting-PKO cardiac metabolism, increased fatty acid/pyruvate oxidation and AMPK phosphorylation, and decreased glycolysis. Maintenance of the ATP-producing pathway, i.e., fatty acid/pyruvate oxidation, may provide cardioprotection under ischemic injury.
## 4. Discussion
Ischemic heart failure is prevalent worldwide [33]. Beyond traditional surgery, various methods using protein, cell, and gene therapeutics have been suggested for treatment [34]. Notably, several regulators of cardiac metabolism have been identified [35]. The heart relies heavily on long-chain fatty acids and utilizes glucose low-proportionally for energy production in the normal state [36]. Both fatty acid oxidation and glucose oxidation produce acetyl-CoA, which directly participates in the tricarboxylic acid cycle and electron transport chain and accounts for $95\%$ of myocardial ATP production [7]. In failing hearts, fatty acid availability substantially affects the myocardial function and efficiency [37]. Additionally, pyruvate oxidation, leading to the production of acetyl-CoA from glucose-derived pyruvate, is limited in heart failure, resulting in impaired ATP production [7]. Thus, failing hearts are etiologically or resultantly associated with impaired energy production via fatty acid/pyruvate oxidation.
During cellular stress, AMPK phosphorylation downregulates fatty acid synthesis but upregulates fatty acid oxidation [38]. Although fatty acid oxidation itself can suppress pyruvate oxidation, AMPK activation increases glycolysis and pyruvate oxidation. Due to its diverse effects, whether AMPK improves or deteriorates the cardiac health may differ according to the physiological state of the patient [39]. AMPK has been reported to increase overall ATP production to respond to the energy demand and provide tolerance against cardiac ischemia [40]. When the hearts were exposed to fasting or isoproterenol-induced energy starvation, PKO increased AMPK phosphorylation. Catabolic activation by PKO differed according to metabolic pathways; fatty acid and pyruvate oxidation increased, but glycolysis decreased. Fatty acid oxidation takes place predominantly in the mitochondria and peroxisomes in less magnitude [41]. Mitochondrial fatty acid oxidation enzymes [42], namely Cpt2 and Vlcad, and the peroxisomal fatty acid oxidation enzyme [43] Acox1 increased in PKO hearts. The high availability of plasma fatty acids in PKO may influence catabolic processes. However, exposure to the same amount of fatty acids in in vitro experiment also increased fatty acid oxidation in PK cells. Conversely, Pgrmc1-overexpressing (POE) cells exhibited decreased fatty acid oxidation (Figure S3). Hence, an increase in the fatty acid oxidation pathway affects cardiac energy metabolism in PKO. Paradoxically, PKO hearts have decreased levels of glycolytic enzymes, hexokinases, and pyruvate kinase but increased PDH [44]. When cells are exposed to the same amounts of glucose and fatty acids, PK cells still increase pyruvate oxidation but suppress glycolysis. Similarly, POE cells showed a mild increase in glycolysis (Figure S3). We speculated that the lactate source must be induced to increase pyruvate substrate and pyruvate dehydrogenase in limited sources from glycolytic products. Our results (data not shown) also showed the induction of lactate dehydrogenase in starved PKO hearts. Further studies on the regulation of lactate metabolism by Pgrmc1 should be performed. Glycolysis only accounts for <$10\%$ [45], while the oxidation of fatty acids (50–$70\%$) [46] and pyruvate (20–$40\%$) [7] comprises the majority of cardiac ATP production. Hence, starved PKO hearts may have increased overall ATP production. Mechanistically, PKO hearts showed increased AMPK phosphorylation, and AMPK inhibitor (Com C) treatment resulted in the opposite cardiac metabolism pattern compared to that of PKO. In line with this, AMPK activator (AICAR) treatment showed a cardiac metabolism pattern similar to that of PKO. Concordantly, PKO-altered cardiac energy metabolism may be linked to AMPK phosphorylation during cardiac injury. We also measured the cardiac autophagy, as AMPK is an autophagy promoter [47], but observed significantly down-regulated LC3B levels in PKO hearts. As Pgrmc1 is an autophagy promoter [48], cardiac autophagy was mainly affected by Pgrmc1 compared to AMPK. This is in accordance with our results, as autophagy is up-regulated in ATP-depleted and ischemic hearts [49].
We insist on the interpretation of conflicting metabolic alterations and functions of PKO hearts in light of a previous study. In our previous study, PKO hearts in diabetic conditions showed increased TG and fatty acyl-CoA accumulation [14], leading to lipotoxicity. However, TG deposits play an ATP-providing role [50], and fatty acyl CoA is directly related to oxidative phosphorylation in the heart [51,52]. In contrast to overnutrition hearts, the large pool of lipids in PKO can be the ATP pool for energy-deficient hearts. Additionally, in our previous study, cardiac glycolysis was induced only in overnutrition PKO and slightly decreased in normal PKO hearts [14]. In the energy-deficient state, glycolysis was significantly decreased in PKO hearts. In contrast, fatty acid oxidation was decreased in normal and overnutrition PKO hearts [14] but increased in malnutrition PKO hearts. We concluded that cardiac metabolic alteration by Pgrmc1 depends on glucose availability. In re-fed and diabetic mice, blood glucose levels were approximately 200 mg/dL [14], which were higher than those in starved mice (approximately 60 mg/dL). Pgrmc1 may be a physiological switch that regulates the preference of cardiac substrates for ATP production depending on the body’s nutrition. In energy-deficit conditions, Pgrmc1 reduces oxidation of fatty acids/pyruvates, thereby limiting ATP production in the heart.
The failing heart possesses a nearly $30\%$ ATP volume [53] and reduces the ATP-supplementing flux from the reserve (creatine kinase) by $50\%$ compared to the normal heart [54]. ATP depletion in the failing heart directly leads to contractile dysfunction because continuous ATP production/turnover is necessary for cardiac function [24]. Fatty acid oxidation is the major cardiac ATP-producing pathway, but it suppresses glucose oxidation, as per the Randle cycle [55]. Since glucose oxidation is a much more efficient ATP-production and less-oxygen-consuming pathway than fatty acid oxidation [28], its activation is therapeutically effective in a failing heart [56]. The fatty acid oxidation inhibitor etomoxir has been reported to exert cardioprotective effects by switching from energy metabolism to glucose oxidation [57,58]. However, adverse effects of fatty acid oxidation inhibition can also be observed in experimental/clinical reports [59,60]. Based on our results, Pgrmc1 inhibition increases both fatty acid and pyruvate oxidation and improves overall ATP production during energy starvation. Therefore, improvement in ATP-production via a Pgrmc1 inhibitor can be used as a novel therapeutic approach for energy-starved failing hearts. Additionally, PKO hearts reduced CD31 abundance in immunostaining (Figure S2C). This result is of clinical importance, as CD31 levels are markedly observed in the necrotic myocardium of deceased patients under ischemic heart disease [61]. Furthermore, CD31 blockade reduces damage in ischemia/reperfusion heart injury [62]. As Pgrmc1 promotes cellular processes of microvascular endothelial cells of the brain [63], further study is expected regarding Pgrmc1 and the cardiovascular system.
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|
---
title: Sulforaphane Potentiates Gemcitabine-Mediated Anti-Cancer Effects against Intrahepatic
Cholangiocarcinoma by Inhibiting HDAC Activity
authors:
- Fumimasa Tomooka
- Kosuke Kaji
- Norihisa Nishimura
- Takahiro Kubo
- Satoshi Iwai
- Akihiko Shibamoto
- Junya Suzuki
- Koh Kitagawa
- Tadashi Namisaki
- Takemi Akahane
- Akira Mitoro
- Hitoshi Yoshiji
journal: Cells
year: 2023
pmcid: PMC10000472
doi: 10.3390/cells12050687
license: CC BY 4.0
---
# Sulforaphane Potentiates Gemcitabine-Mediated Anti-Cancer Effects against Intrahepatic Cholangiocarcinoma by Inhibiting HDAC Activity
## Abstract
Intrahepatic cholangiocarcinoma (iCCA), the second most common primary liver cancer, has high mortality rates because of its limited treatment options and acquired resistance to chemotherapy. Sulforaphane (SFN), a naturally occurring organosulfur compound found in cruciferous vegetables, exhibits multiple therapeutic properties, such as histone deacetylase (HDAC) inhibition and anti-cancer effects. This study assessed the effects of the combination of SFN and gemcitabine (GEM) on human iCCA cell growth. HuCCT-1 and HuH28 cells, representing moderately differentiated and undifferentiated iCCA, respectively, were treated with SFN and/or GEM. SFN concentration dependently reduced total HDAC activity and promoted total histone H3 acetylation in both iCCA cell lines. SFN synergistically augmented the GEM-mediated attenuation of cell viability and proliferation by inducing G2/M cell cycle arrest and apoptosis in both cell lines, as indicated by the cleavage of caspase-3. SFN also inhibited cancer cell invasion and decreased the expression of pro-angiogenic markers (VEGFA, VEGFR2, HIF-1α, and eNOS) in both iCCA cell lines. Notably, SFN effectively inhibited the GEM-mediated induction of epithelial–mesenchymal transition (EMT). A xenograft assay demonstrated that SFN and GEM substantially attenuated human iCCA cell-derived tumor growth with decreased Ki67+ proliferative cells and increased TUNEL+ apoptotic cells. The anti-cancer effects of every single agent were markedly augmented by concomitant use. Consistent with the results of in vitro cell cycle analysis, G2/M arrest was indicated by increased p21 and p-Chk2 expression and decreased p-Cdc25C expression in the tumors of SFN- and GEM-treated mice. Moreover, treatment with SFN inhibited CD34-positive neovascularization with decreased VEGF expression and GEM-induced EMT in iCCA-derived xenografted tumors. In conclusion, these results suggest that combination therapy with SFN with GEM is a potential novel option for iCCA treatment.
## 1. Introduction
Intrahepatic cholangiocarcinoma (iCCA) is the second most common hepatic malignancy arising from intrahepatic bile duct epithelium [1]. The prognosis of iCCA is poor because of early local invasion; metastasis to the liver, lymph nodes, and other organs; and insufficient early diagnosis [1,2]. Currently, only a small number of patients with iCCA can undergo curative resection. Meanwhile, the treatment landscape of unresectable advanced iCCA has primarily been limited to chemotherapy. At present, the first-line chemotherapy for unresectable iCCA is gemcitabine (GEM) and cisplatin (CDDP) based on the ABC-02 study, and second-line chemotherapy includes 5-fluorouracil, folinic acid, and oxaliplatin (FOLFOX) based on the ABC-06 study [3,4]. However, median overall survival, even with these options, is limited to just one year [4]. Additionally, combination treatment with multiple anti-cancer drugs often results in severe adverse effects [3,4]. Currently, several approaches are employed to find novel combinatory treatments with standard chemotherapeutic drugs, including GEM for other types of cancer, such as the highly aggressive diffuse malignant peritoneal mesothelioma and pancreatic ductal adenocarcinoma [5,6]. Likewise, there is an urgent need to identify novel therapeutic targets for iCCA with less adverse event profiles by combining GEM.
Histone deacetylases (HDACs) play a key role in epigenetically regulating the expression and activity of various factors relevant to carcinogenesis and cancer development [7,8]. HDACs comprise a family of enzymes categorized into four classes in humans based on their homology to yeast HDAC analogs: classes I (HDAC1, 2, 3, and 8), II (HDAC4, 5, 6, 7, 9, and 10), III (sirtuins), and IV (HDAC11). Class I, II, and IV HDACs require zinc-dependent cofactors for their enzymatic activity, and class III HDACs require nicotinamide adenine dinucleotide-dependent cofactors [7,9]. Histone acetyltransferases (HATs) catalyze the transfer of an acetyl group from acetyl coenzyme A, while HDACs remove acetyl groups from histones and organize a non-permissive chromatin conformation, leading to interference with the transcription of cancer-related genes [10]. Aberrant HDAC activity leads to diverse transcriptional gene regulation relevant to cancer cell differentiation, angiogenesis, proliferation, apoptosis, migration, and metastasis [10,11]. HDAC activity represses p53 and BAX and induces BCL-2, which promotes cell cycle progression and regulates apoptosis in cancer cells [12,13]. Morine et al. have reported that intratumor HDAC expression is positively correlated with HIF-1α, a stimulus factor for local hypoxia and increased angiogenesis in resected iCCA tissues [14]. Thus, HDAC inhibitors have the potential to thwart cell growth, accelerate differentiation, and induce apoptosis, and they have been proposed as novel therapeutic options for a variety of malignancies, including iCCA [10,11,15].
Sulforaphane (SFN), an isothiocyanate cleavage product of glucoraphanin, can be obtained from damaged cruciferous vegetables such as broccoli, cauliflower, cabbage, and Brussels sprouts [16]. SFN possesses anti-oxidative properties with multiple pharmacological actions, including anti-diabetic and anti-microbial effects [17,18]. Remarkably, SFN has been suggested to display anti-cancer and chemopreventive properties by inhibiting HDAC activity and epigenetically modifying the expression of critical cytoprotective genes involved in the regulation of the cell cycle and apoptosis [19,20]. A recent report revealed that SFN could inhibit total HDAC activity in cancer cells [19]. Moreover, recent findings indicated that SFN augments the response to several carcinostatic agents by enhancing the sensitivity and suppressing the resistance of cancer cells to these agents [21,22].
Based on these findings, the present study investigated the combinatorial effect of SFN and GEM on human iCCA cell growth and malignant potential using iCCA-derived murine xenograft models.
## 2.1. Compounds and Cell Culture
d,l-sulforaphane (1-isothiocyanate-4-methylsulphinylbutane, purity ≥ $98\%$) was purchased from Toronto Research Chemicals Inc. (Toronto, ON, Canada), and gemcitabine (2′-deoxy-2′,2′-difluorocytidine, purity ≥ $98\%$) was purchased from Tokyo Chemical Industry Co., Ltd. (Tokyo, Japan). Two human iCCA cell lines, HuCCT-1 (cat: JCRB0425) and HuH28 (cat: JCRB0426) were obtained from the Japanese Collection of Research Bioresources Cell Bank (Osaka, Japan). These cells were cultured in RPMI-1640 (Nacalai Tesque, Inc., Kyoto, Japan) supplemented with $10\%$ fetal bovine serum (FBS) and $1\%$ ampicillin/streptomycin. The primary human biliary epithelial cell line (HIBEpiC, cat: #5100) was purchased from ScienCell Research Laboratories, Inc. (Carlsbad, CA, USA). HIBEpiC cells were cultured in Epithelial Cell Medium (ScienCell Research Laboratories) supplemented with $2\%$ FBS and $1\%$ epithelial cell growth supplement (ScienCell Research Laboratories), and $1\%$ ampicillin/streptomycin. The cells were grown at 37 °C in a $5\%$ CO2 atmosphere.
## 2.2. Human iCCA Xenograft Model
Six-week-old male athymic nude mice (BALB/cSlc-nu/nu) (Japan SLC, Inc., Shizuoka, Japan) were housed in stainless steel mesh cages (2/cage) under controlled conditions (temperature: 23 ± 3 °C, relative humidity: 50 ± $20\%$, 10–15 air changes/h, illumination: 12 h/d). The animals were allowed tap water access ad libitum throughout the study period. Eighty mice were used in total for the xenograft assay, and tumor inoculation was performed as described [23]. Briefly, a million cells were suspended in 200 μL of medium containing Matrigel (Corning, Tewksbury, MA, USA; 1:1), and the same type of million cells was inoculated subcutaneously into the bilateral flanks of each mouse. Tumors were measured with a caliper, and the tumor volume was calculated using the following formula:[1]12[(Width)2×Length] Five days after inoculation, mice were orally administered with SFN (50 mg/kg/day) or intraperitoneally injected with GEM (100 mg/kg) twice a week or concomitant administration [23,24] ($$n = 10$$). Saline solution was equivalently given to the vehicle group ($$n = 10$$). The condition and health of mice were monitored daily after the injection of tumor cells, and all mice were sacrificed 30 days after drug administration under anesthesia with barbiturate overdose (intravenous injection, 150 mg/kg pentobarbital sodium). All the animal procedures were performed as per the recommendations of the Guide for Care and Use of Laboratory Animals (National Research Council, Washington, DC, USA). The study was approved by the animal facility committee of Nara Medical University (Authorization number: #12853).
## 2.3. Detection of HDAC/HAT Activity and Total Histone H3 and H4 Acetylation
HuCCT-1 and HuH28 cells were treated with different concentrations of SFN (0–80 μM) or GEM (0–10 μM) for 3 h. To measure HDAC activity, nuclear extracts were obtained from cultured cells or 20 mg of subcutaneous tumor samples using an EpiQuik™ Nuclear Extraction Kit (Epigentek, Farmingdale, NY, USA) according to the manufacturer’s protocol. HDAC activity was measured in 10 μg of nuclear extract using an EpiQuik™ HDAC activity/inhibition assay kit (Epigentek) according to the manufacturer’s instructions. HAT activity was also measured in 10 μg of nuclear extract from cultured cells using an EpiQuik™ HAT activity/inhibition assay kit (Epigentek) according to the manufacturer’s instructions.
To detect total histone H3 and H4 acetylation, histone extracts were obtained from cultured cells using an EpiQuik™ Total Histone Extraction Kit (Epigentek). Histone H3 and H4 acetylation was detected in 100 ng of histone extract using an EpiQuik™ Total Histone H3 Acetylation Detection Fast Kit and an EpiQuik™ Total Histone H4 Acetylation Detection Fast Kit (Epigentek) according to the manufacturer’s instructions, respectively.
Dimethyl sulfoxide (DMSO, Nacalai Tesque, Inc.) was used as a vehicle, and HDAC activity and total histone H3 acetylation in cells treated with SFN and/or GEM were measured relative to that in the vehicle treatment group.
## 2.4. Histone H3 Peptide Array
HuCCT-1 and HuH28 cells were treated with a concentration of dimethyl sulfoxide (DMSO, Nacalai Tesque, Inc.) as a vehicle or SFN (20 μM) for 3 h. Nuclear extracts were obtained from cultured cells using an EpiQuik™ Nuclear Extraction Kit (Epigentek, Farmingdale, NY, USA) according to the manufacturer’s protocol. To profile the binding specificity of histone H3 acetylation, we used a Pre-Sure™ Histone H3 Peptide Array ELISA Kit (Epigentek) according to the manufacturer’s instructions and previous report [25]. Total nuclear extracts were diluted to 1 ug/mL, added to the array plate and incubated for 2 h at room temperature. Histone H3 Acetylation Antibody Panel Pack I and Pack II (Epigentek) were applied as primary antibodies to detect the binding of H3 lysines (K)9, K14, K18, K27, K36, K56, and K79 to histone peptides. Following the incubation with primary antibodies at 37 °C for 60 min, samples were incubated with secondary antibodies (0.4 μg/mL) at room temperature for 60 min and then developed at room temperature for 10 min away from light. Arbitrary units were measured at the absorbance (450 nm) to represent the relative levels of binding specificity and calculated the ratio to the values of Veh treatment.
## 2.5. Cell Viability Assay and Analysis of Cytotoxic Synergy
HuCCT-1 and HuH28 cells were seeded in 96-well plates with RPMI-1640, as previously described. Then, the cells were treated with different doses of SFN (0–80 μM) or GEM (0–10 μM) for 24 h. Cell viability was evaluated by The Premix Water-Soluble Tetrazolium salt (WST)-1 Cell Proliferation Assay system (Takara Bio, Kusatsu, Japan) according to the manufacturer’s protocol. Cell viability was assessed relative to that in the groups without each treatment, and half-maximal inhibitory concentration (IC50) was calculated via non-linear regression analysis using GraphPad Prism 9 ver 9.3.1 (GraphPad Software Inc., La Jolla, CA, USA) [26].
To assess the synergy of drug combinations, a combination index (CI) was calculated by the Chou-Talalay method using CompuSyn software version 1.0 (ComboSyn, Inc., New York, NY, USA) [27]. CI gives a quantitative definition of synergism (CI < 1), additive effect (CI = 1), and antagonism (CI > 1). For this purpose, the cells were also exposed to different concentrations of SFN and GEM for 24 h.
## 2.6. Statistical Analysis
All data were statistically analyzed using GraphPad Prism 9 software. Data were indicated as the mean ± standard deviation (SD). Means were compared between two groups by Student’s t-test. A one-way analysis of variance followed by Bonferroni’s post hoc test was performed for multiple comparisons. $p \leq 0.05$ denoted a statistically significant difference.
Additional methods can be found online in the Supplementary Material.
## 3.1. SFN Attenuates HDAC Activity and Promotes Histone H3 Acetylation in Human iCCA Cells
We initially examined the effects of SFN and GEM on HDAC activity, moderately differentiated HuCCT-1 cells and undifferentiated HuH28 cells. As presented in Figure 1A,B, SFN concentration-dependently reduced HDAC activity in both HuCCT-1 and HuH28 cells, and the suppressive effects were significant at concentrations exceeding 20 μM. On the contrary, GEM did not alter HDAC activity in these cells at any concentration (Figure S1A). Meanwhile, the activity of HAT, a key enzyme that acetylate conserved lysine amino acids on histone proteins, was significantly increased by treatment with SFN at concentrations exceeding 20 μM (Figure 1C,D). Reflecting the reduced HDAC activity, total histone H3 acetylation in both iCCA cell lines was increased by treatment with SFN in a concentration-dependent manner (Figure 1E,F). On the other hand, total histone H4 acetylation was not altered by treatment with SFN (Figure 1G,H). We further determined the acetylation patterns of specific lysine residues on the tails of histone H3 modified by treatment with SFN in iCCA cells. To this end, nuclear protein extracts of both HuCCT-1 and HuH28 cells treated with SFN (20 μM) were utilized for the identification of the acetylation profile of H3 on K9, K14, K18, K27, K36, K56 and K79. As shown in Figure 1I,J, treatment with SFN particularly increased acetylation as compared to vehicle treatment at H3K9 and H3K27 in both HuCCT-1 and HuH28 cells. Moreover, we confirmed that SFN did not affect HDAC activity in normal HIBEpiC cells (Figure S1B).
## 3.2. SFN Has a Synergistic Effect with GEM-Mediated Cell Growth Inhibition in Human iCCA Cells
Next, we investigated the impact of SFN and GEM at different concentrations on the viability of HuCCT-1 and HuH28 cells. As presented in Figure 2A,B, SFN efficiently ameliorated HuCCT-1 and HuH28 cell viability with IC50 values of 27.4 and 34.2 μM, respectively. Meanwhile, GEM attenuated the viability of both cell lines (IC50 of 0.57 μM for HuCCT-1 cells and 0.71 μM for HuH28 cells) as expected (Figure 2A,B). Both agents did not affect the cell viability of normal HIBEpiC at this range of concentrations (Figure S1C).
Based on the optimal concentrations, we calculated CI to evaluate whether the cytotoxic effect of combined SFN and GEM against iCCA cell growth is synergistic against iCCA cell growth. As shown in Figure 2C, the CI values calculated by CompuSyn software were $\frac{0.552}{0.624}$/0.497, $\frac{0.228}{0.406}$/0.183, and $\frac{0.227}{0.136}$/0.119, when SFN (6.8 μM) and GEM ($\frac{0.14}{0.28}$/0.57 μM), SFN (13.7 μM) and GEM ($\frac{0.14}{0.28}$/0.57 μM), and SFN (27.4 μM) and GEM ($\frac{0.14}{0.28}$/0.57 μM) were concurrently administered to HuCCT-1 cells, respectively. These CI values were less than 1.0, indicating that the combination of SFN with GEM has synergistic effects on suppressing the viability of HuCCT-1 cells. Combination treatment with SFN and GEM also exerted a synergistic effect against HuH28 cell viability. The CI values were $\frac{0.699}{0.738}$/0.628, $\frac{0.519}{0.714}$/0.487, and $\frac{0.323}{0.414}$/0.299 when SFN (8.5 μM) and GEM ($\frac{0.17}{0.35}$/0.71 μM), SFN (17.1 μM) and GEM ($\frac{0.17}{0.35}$/0.71 μM), and SFN (34.2 μM) and GEM ($\frac{0.17}{0.35}$/0.71 μM) were concurrently cultivated (Figure 2D).
We further confirmed that the combination of SFN and GEM at IC50 significantly suppressed the proliferative activity of HuCCT-1 and HuH28 cells in a time-dependent manner (Figure 2E,F).
## 3.3. SFN Induces G2/M Arrest and Promotes Apoptosis in Human iCCA Cells
SFN-mediated HDAC inhibition has been reported to enhance histone acetylation and derepress p21 and BAX gene expression, resulting in the induction of cell cycle arrest/apoptosis in several types of cancer cells [19,28,29]. Based on these findings, we examined the effects of SFN on the cell cycle/apoptosis and the expressions of associated genes, including these key targets in human iCCA cells. As presented in Figure 3A,B, SFN or GEM significantly blocked both HuCCT-1 and HuH28 cells in the G2 phase compared to the effects of the vehicle, and the drugs in combination had significantly stronger effects than either agent alone. The mRNA expression of CDKN1A and BAX were significantly increased by treatment with SFN as compared to vehicle treatment in both HuCCT-1 and HuH28 cells (Figure 3C). Treatment with SFN as well as GEM upregulated p21 and p-Chk2 and downregulated p-Cdc25C at the protein level, corresponding to the induction of G2/M arrest, in both cell lines (Figure 3D). SFN- or GEM-treated HuCCT-1 and HuH28 cells also increased pro-apoptotic BAX expression and decreased anti-apoptotic BCL-2 expression (Figure 3E). In both cell lines, combination treatment augmented the upregulation of BAX compared to the effect of every single agent (Figure 3E). SFN and GEM further enhanced the cleavage of caspase-3, reflecting the induction of cell apoptosis in both HuCCT-1 and HuH28 cells (Figure 3F).
## 3.4. SFN Inhibits Cancer Cell Invasion, Migration, Angiogenic Activity, and Epithelial-Mesenchymal Transition (EMT) in Human iCCA Cells
Next, we investigated the effects of SFN and GEM on malignant potential, including cell invasion, migration, angiogenic activity, and EMT in human iCCA cells. First, the effects of both agents on the invasiveness of HuCCT-1 and HuH28 cells were evaluated using a Matrigel invasion assay. Either drug alone significantly reduced the invasiveness of both HuCCT-1 and HuH28 cells (Figure 4A,B). It was noteworthy that concomitant treatment with SFN and GEM extensively reduced cell invasion to less than $20\%$ of the control, exceeding the effects of each drug (Figure 4A,B). Correspondingly, cell migration was also suppressed by treatment with SFN or GEM in both iCCA cells (Figure 4C). Moreover, combination treatment enhanced the suppressive effect of every single agent (Figure 4C). We next examined the effects of SFN on the angiogenic activity of iCCA cells. Treatment with SFN significantly reduced the mRNA expression of pro-angiogenic markers, including VEGFA, VEGFR2, HIF1A, and NOS3 in both HuCCT-1 and HuH28 cells (Figure 4D,E). Moreover, we assessed the effects of both agents on the EMT status. There were differences in EMT-related markers between HuCCT-1 and HuH28 cells, which have different levels of differentiation. Specifically, HuCCT-1 cells, which are moderately differentiated, exhibited higher expression of the epithelial markers CDH1 and KRT19 and lower expression of the mesenchymal markers CDH2, VIM, MMP2, and MMP9 than undifferentiated HuH28 cells, consistent with a previous report (Figure 4F) [30]. As presented in Figure 4G,H, treatment with GEM downregulated the epithelial markers and upregulated the mesenchymal markers in HuCCT-1 and HuH28 cells, indicating the EMT progression. Notably, SFN efficiently inhibited the GEM-induced progression of EMT in iCCA cells (Figure 4G,H).
## 3.5. SFN Potentiates the GEM-Mediated Reduction of the Human iCCA-Derived Xenograft Tumor Growth
Given the suppressive effects of SFN and GEM on human iCCA cell growth, the anti-cancer property of both agents was examined using iCCA-derived xenograft models (Figure 5A). Initially, we determined the experimental dose of SFN for in vivo study. As SFN is also known to exert anti-oxidative effects via Nrf2 activation, we measured the hepatic mRNA levels of anti-oxidative markers in nude mice treated with different doses of SFN to identify a dose that could exert bioactivity in mice [19]. As presented in Figure S2, oral administration of SFN for four weeks increased the hepatic mRNA expression of Hmox1, Nqo1, and Gstm3 in a dose-dependent manner even with concomitant GEM treatment (100 mg/kg twice a week), and we identified 50 mg/kg/day as the minimal dose that significantly induced these anti-oxidative genes.
Based on this result, we employed 50 mg/kg/day as the experimental dose for the xenograft assay. Serological assessments revealed that this dose of SFN did not cause hepatocellular, biliary, or renal damage in mice, even when used together and combined with GEM (Figure S3). In mice treated with either SFN (50 mg/kg/day) or GEM (100 mg/kg twice a week), the HuCCT-1 and HuH28-grafted subcutaneous tumor growth was markedly attenuated (Figure 5B). After treatment for 30 days, the subcutaneous tumor volumes and weights were significantly reduced in mice treated with either SFN or GEM relative to the findings in vehicle-treated mice (Figure 5B,C). Notably, concomitant treatment with both agents significantly potentiated their inhibitory effects on tumor growth relative to every single agent (Figure 5B,C). H&E staining illustrated that the viable cancer area in resected subcutaneous tumors was decreased by treatment with SFN and GEM (Figure 5D). We confirmed that the utilized dose of SFN effectively decreased HDAC activity in the resected subcutaneous tumor tissues to less than $60\%$ of that in the vehicle group (Figure 5E).
## 3.6. SFN Suppresses Cell Proliferation and Induces Apoptosis in Human iCCA-Derived Xenograft Tumors
We next quantitatively investigated cancer cell viability in xenograft tumors derived from HuCCT-1 and HuH28 cells (Figure 6 and Figure S4, respectively). In HuCCT-1–derived xenograft tumors, Ki67-positive cancer cell proliferation was attenuated by each drug alone, and the effect was enhanced by using the drugs in combination (Figure 6A). Quantitative analysis revealed the potent reduction of proliferative cells to less than $20\%$ of the control level by combination treatment (Figure 6B). Treatment with SFN and GEM significantly increased the nuclear expression of p21 and cytosolic expression of p-Chk2 and conversely decreased the expression of p-Cdc25C (Figure 6C–E). These findings aligned with the observation of G2/M arrest following treatment with SFN and GEM in iCCA cells. Meanwhile, we found that TUNEL-positive cell apoptosis was simultaneously increased by treatment with SFN and GEM in HuCCT-1–derived xenograft tumors (Figure 6F,G). Notably, the effects of SFN and GEM on intratumor cancer cell viability were also observed in the HuH28-derived xenograft tumors (Figure S4A–G)
## 3.7. SFN Attenuates Intratumor Angiogenesis and GEM-Mediated EMT in Human iCCA-Derived Xenograft Tumors
Moreover, the effects of SFN and GEM on malignant potential, including pathological angiogenesis and EMT in the xenograft tumors, were examined according to the findings of the in vitro study. As presented in Figure 7A, CD34-positive neovascularization in xenograft tumors derived from both HuCCT-1 and HuH28 was significantly reduced by treatment with SFN. However, these anti-angiogenic effects were not observed in GEM-treated mice (Figure 7A). The semi-quantitative analysis illustrated that the number of new CD34-positive intratumor vessels was decreased by $50\%$ in SFN-treated mice compared to that in vehicle-treated mice (Figure 7B). In parallel with reduced neovascularization, the intratumor expression of VEGFA and VEGFR2 was decreased in SFN-treated mice (Figure 7C). Regarding EMT-related markers, we found that the intratumor mRNA expression of epithelial markers (CDH1 and KRT19) was decreased in GEM-treated mice, and this effect was efficiently inhibited by SFN treatment (Figure 7D). In contrast, treatment with SFN considerably attenuated the GEM-mediated increases in mesenchymal markers (CDH2, VIM, MMP2, and MMP9, Figure 7D). Moreover, the effects of both agents on EMT-related markers were similarly observed at the protein levels (Figure S5A,B). These findings indicate that SFN ameliorated resistance to GEM by suppressing tumor angiogenesis and EMT in iCCA cells.
## 4. Discussion
This study first demonstrated that SFN, a phytochemical isothiocyanate agent, effectively augmented the inhibitory effect of GEM on iCCA growth. Our results demonstrated that SFN exerted multifunctional properties against the malignant potential of iCCA, including anti-proliferative, pro-apoptotic, anti-invasive/migratory, anti-EMT and anti-angiogenic effects. As the functional mechanism underlying these effects of SFN, we suggested the inhibitory action on HDAC activity as well as the inductive action on HAT activity leading to enhancement of histone H3, particularly H3K9 and H3K27 acetylation. Previous studies reported that the dysfunction of HDAC enzymes and altered acetylation status is relevant to the growth and malignant progression of CCA, including iCCA, and several HDAC inhibitors have displayed suppressive effects on iCCA [14,15,31,32,33]. For instance, chidamide, an HDAC inhibitor, has been reported to exert antitumor activities in iCCA by promoting HDAC3-mediated forkhead box O1 acetylation [15]. Another report has shown that peanut testa possessing HDAC inhibitory activity induces apoptosis in iCCA cells [34]. Moreover, a recent report has demonstrated that SFN increases HAT activity in human malignant melanoma cells [35]. These pieces of evidence support the possible involvement of epigenetic modification in the SFN-mediated anti-cancer property against human iCCA cells in our study. On the other hand, we found that SFN did not affect histone H4 acetylation. The present study did not identify a pharmacological mechanism to explain the differential effect of sulforaphane on H3 and H4 acetylation. Thus, a detailed analysis is required in the future.
The present study primarily elucidated that SFN effectively suppressed cell proliferation in both moderately differentiated and undifferentiated iCCA lines. Several reports have shown that SFN-mediated anti-cancer effects were involved in an increase of acetylated histone H3 specifically associated with the promoter region of the p21 and BAX genes in cancer cells [36,37]. Consistently with this evidence, our results showed that SFN increased p21 expression leading to the phosphorylation of Chk2 and dephosphorylation of Cdc25C, and consequently, it blocked cell cycle progression in the G2/M phase in human iCCA cells. SFN also upregulated BAX expression, downregulated BCL-2 expression, and suppressed the cleavage of caspase-3, indicating the activation of the mitochondrial apoptotic pathway. It is known that H3K9ac and H3K27ac are highly correlated with transcriptional activation [38]. Therefore, we hypothesized that SFN-mediated HDAC inhibition possibly promoted the transcriptional activity of p21 and BAX by binding to both genes, enhancing the binding of active modification of histones such as H3K9ac and H3K27ac to regulate the expressions of both genes, thereby suppressing cell proliferation and augmenting cell apoptosis. However, further investigation is necessary to clarify the histone acetylation in the promoters of p21 and BAX, as well as the possible targets downstream of decreased HDAC in SFN-treated human iCCA cells.
Of note, present results showed that a combination of SFN and GEM was likely to ameliorate xenograft iCCA tumor progression more potently than cultured cell growth. This discrepant finding is suggested to be attributable to the impact of SFN on other malignant phenotypes, including cell invasion, angiogenesis, and EMT. The combination treatment of SFN and GEM effectively suppressed cell invasion and migration at the doses with tolerable cytotoxicity, consistent with the results from Wang et al. that co-treatment of iCCA cells with several types of HDAC inhibitors (trichostatin A and valproic acid) and GEM inhibited cell invasion, migration [33]. Moreover, we found that treatment with GEM accelerated EMT, as indicated by the downregulation of epithelial markers and upregulation of mesenchymal markers in both cultured iCCA cells and xenografted tumors. It was noteworthy that SFN effectively inhibited GEM-induced EMT in both iCCA cell lines. Among the malignant phenotypes, EMT has recently gained attention as a potential mechanism of chemoresistance because of its ability to promote the acquisition of cancer stemness and confer resistance to chemotherapy [39]. Indeed, resistance to GEM in iCCA is also associated with EMT phenotype and cancer stem-like properties in the tumor [40]. EMT is regulated by epigenetic changes, including histone modifications, and HDAC inhibitors are considered to modify EMT-related factors’ expression depending on the cancertype [41]. Meanwhile, SFN is reported to inhibit EMT in several cancer cell types by molecular mechanisms independently of histone modification [42,43,44]. Recent studies illustrated that SFN could suppress the EMT in lung cancer cells by inhibiting the GSK3β/β-catenin pathway and ERK5 activation [42,45]. Li et al. also demonstrated that SFN-mediated inhibition of the sonic hedgehog–GLI pathway resulted in the suppression of EMT in pancreatic cancer [46]. These findings evoke a hypothesis that the SFN-mediated suppression of EMT in iCCA cells involves mechanisms beyond the inhibition of HDACs. Thus, additional analyses are needed to clarify the underlying mechanism.
Furthermore, tumor-associated angiogenesis and VEGF expression are known to be correlated with iCCA cancer progression, metastasis, and prognosis [47]. A previous observational study found that VEGF was expressed in $53.8\%$ of 106 patients with iCCA, and it was significantly associated with intrahepatic metastasis [48]. Notably, SFN exerts anti-angiogenic effects by inhibiting hypoxia-induced HIF-1α and VEGF expression in several cancers, including prostate, colon, and liver cancers [49,50,51]. Moreover, SFN has been demonstrated to directly suppress proliferation, tubular formation, and matrix metalloproteinase production in vascular endothelial cells [52]. We substantiated that SFN reduced the expression of pro-angiogenic genes such as VEGFA, VEGFR2, HIF-1α, and eNOS in iCCA cells and attenuated CD34-positive neovascularization in xenografted tumors. As the inhibition of angiogenesis has been reported to abolish chemoresistance to GEM, this anti-angiogenic property of SFN is potently associated with the augmentation of GEM-mediated anti-cancer effects on iCCA [53].
The empirical results reported in this study should be considered in light of some limitations. First, we demonstrated that the combination of SFN and GEM synergistically augmented the anti-cancer effect on human iCCA cells by calculating the CI. However, our study did not fully elucidate the pharmacological interaction between both agents to explain this synergistic effect. Although the inhibition of GEM-induced EMT by SFN is likely to be associated with this synergy, we will probably need further detailed research by comprehensive molecular profiling. Second, although we defined the dose of SFN (50 mg/kg/day) for the in vivo study, optimization is performed by evaluating the anti-oxidative property of SFN in the liver. We confirmed that this dose efficiently reduced HDAC activity in the xenografted tumors. Additionally, we observed that the doses of SFN and GEM did not cause hepatic, biliary, or renal toxicity in mice. Thus, these doses are assumed to be within the tolerance range for in vivo experiments. Third, the current first-line chemotherapy for iCCA is based on GEM and CDDP. Therefore, additional study is necessary to substantiate the enhanced efficacy of GEM and CDDP in combination with SFN in the latest clinical setting.
In summary, the present study demonstrated that combination with SFN synergistically augments the tumor suppressive effects of GEM on human iCCA cell growth. This effect of SFN is based on the inhibition of HDACs, leading to G2/M arrest; apoptosis; and suppressed invasion, migration, EMT, and angiogenesis. As a less-toxic phytochemical, SFN might eventually emerge as a viable modulator of GEM for patients with advanced iCCA.
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|
---
title: Fermented Soybean Paste Attenuates Biogenic Amine-Induced Liver Damage in Obese
Mice
authors:
- Ju-Hwan Yang
- Eun-Hye Byeon
- Dawon Kang
- Seong-Geun Hong
- Jinsung Yang
- Deok-Ryong Kim
- Seung-Pil Yun
- Sang-Won Park
- Hyun-Joon Kim
- Jae-Won Huh
- So-Yong Kim
- Young-Wan Kim
- Dong-Kun Lee
journal: Cells
year: 2023
pmcid: PMC10000487
doi: 10.3390/cells12050822
license: CC BY 4.0
---
# Fermented Soybean Paste Attenuates Biogenic Amine-Induced Liver Damage in Obese Mice
## Abstract
Biogenic amines are cellular components produced by the decarboxylation of amino acids; however, excessive biogenic amine production causes adverse health problems. The relationship between hepatic damage and biogenic amine levels in nonalcoholic fatty liver disease (NAFLD) remains unclear. In this study, mice were fed a high-fat diet (HFD) for 10 weeks to induce obesity, presenting early-stage of NAFLD. We administered histamine (20 mg/kg) + tyramine (100 mg/kg) via oral gavage for 6 days to mice with HFD-induced early-stage NAFLD. The results showed that combined histamine and tyramine administration increased cleaved PARP-1 and IL-1β in the liver, as well as MAO-A, total MAO, CRP, and AST/ALT levels. In contrast, the survival rate decreased in HFD-induced NAFLD mice. Treatment with manufactured or traditional fermented soybean paste decreased biogenically elevated hepatic cleaved PARP-1 and IL-1β expression and blood plasma MAO-A, CRP, and AST/ALT levels in HFD-induced NAFLD mice. Additionally, the biogenic amine-induced reduction in survival rate was alleviated by fermented soybean paste in HFD-induced NAFLD mice. These results show that biogenic amine-induced liver damage can be exacerbated by obesity and may adversely affect life conservation. However, fermented soybean paste can reduce biogenic amine-induced liver damage in NAFLD mice. These results suggest a beneficial effect of fermented soybean paste on biogenic amine-induced liver damage and provide a new research perspective on the relationship between biogenic amines and obesity.
## 1. Introduction
Biogenic amines are biologically activated low-molecular-weight nitrogenous organic compounds that are primarily produced by spoilage microorganisms mediating the enzymatic decarboxylation of amino acids. Representative biogenic amines include the aliphatic compounds putrescine, cadaverine, agmatine, spermine, and spermidine; the aromatic compounds tyramine and 2-phenylethylamine; and the heterocyclic compounds histamine and tryptamine. Although the toxicity of small amounts of biogenic amines is negligible, consuming large amounts of aromatic and heterocyclic compounds in food can be hazardous and cause serious health problems [1]. Unlike the fermentation process occurring under certain conditions and in certain environments, food decomposition results in the elevation of biogenic amine levels in foods. A high concentration of biogenic amines can adversely affect the nervous and vascular systems and may cause physiologically harmful reactions or intoxication [1,2]. Furthermore, biogenic amines ingested in large quantities from foods can enter the systemic circulation and consequently cause migraine, elevation of blood sugar levels, high blood pressure, Parkinson’s disease, schizophrenia, and depression [3]. In particular, histamine, a representative biogenic amine present in most foods, can cause histamine and scombrid poisoning if consumed in large amounts. Additionally, tyramine is as common as histamine and is abundant in various foods, including strong/aged cheeses, aged/smoked meats, wine, and avocados [2,4]. Similar to histamine, high levels of tyramine intake can have adverse health effects [1,2]. Additionally, the association effect of histamine and tyramine shows synergistic cytotoxicity in intestinal cells [5].
*In* general, small amounts of ingested biogenic amines from foods are physiologically metabolized and converted to less active forms via the detoxifying enzymes monoamine oxidase (MAO) and diamine oxidase (DAO) [6]. The mitochondrial enzyme MAO has two isoforms (MAO-A and -B) that are widely expressed in various organs, including the brain, heart, lungs, kidney, intestine, and liver [3]. MAO plays a physiologically important role in the metabolism of monoaminergic neurotransmitters in the central nervous system and biogenic amines in peripheral tissues [7]. Tyramine is a well-known substrate for MAO-A and causes hypertension and even death when combined with MAO inhibitors [8,9]. Furthermore, histamine is metabolized in the liver and eliminated from the blood after it becomes inactive [10]. Because DAO is rarely expressed in the livers of most species, histamine N-methyltransferase, instead of DAO, converts histamine to N-methylhistamine in the liver and is then metabolized by MAO-B [11,12]. In several studies, MAO activity has been used to estimate the levels of biogenic amines, especially histamine and tyramine [1,13]. Therefore, MAO activity can be expected to be essential in reducing biogenic amines in vivo. However, there is insufficient evidence to establish a correlation between biogenic amines and hepatic MAO activity.
Obesity, which has been increasing worldwide for decades, causes various health problems, such as metabolic disorders. Excessive fat accumulation induced by obesity causes metabolic diseases, such as type 2 diabetes mellitus (T2DM). Obesity contributes to the development of fatty liver, leading to nonalcoholic fatty liver disease (NAFLD) [14]. Recent studies suggest that T2DM is a critical risk factor for NAFLD development [14,15]. Several studies have also demonstrated that chronic and progressive fatty liver conditions caused by obesity can lead to advanced fibrosis, cirrhosis, hepatocellular carcinoma, and liver-related death [16,17,18,19]. Additionally, the interleukin 1 (IL-1) family of cytokines plays a pivotal role in NAFLD development. For instance, IL-1α and -1β promote fatty liver disease processes, including liver steatosis, hepatic damage, liver fibrosis, and the recruitment of immune cells induced by inflammation through IL-1 receptor signaling [20,21]. Although there is evidence for the deleterious effect of histamine and tyramine on the liver, the adverse risk of the relationship between NAFLD and biogenic amines has not been established.
Recent studies have documented that soybean-derived foods such as fermented soybean paste contain various beneficial components. The long-term ingestion of fermented soybean paste prevents high-fat diet (HFD)-induced metabolic disorders, including NAFLD and insulin resistance. Consequently, it lowers the incidence of T2DM [22,23,24]. Therefore, the aim of this study was to demonstrate increased hepatic damage caused by biogenic amines and the therapeutic role of fermented soybean paste after exposure to biogenic amines in HFD-induced NAFLD.
## 2.1. Animal Experiments
All experimental and animal care protocols were approved by the Gyeongsang National University Institutional Animal Care and Use Committee (GNU IACUC, GNU-200820-M0053) and performed following the National Institute of Health (NIH) guidelines and a scientifically reviewed protocol (GLA-100917-M0093). C57BL/6 mice were used in these experiments. The mice were fed a high-fat ($60\%$) diet (Research Diets, Inc., New Brunswick, NJ, USA) for 10 weeks after weaning to induce NAFLD.
## 2.2. Preparation of Fermented Soybean Paste Powder
The fermented soybean paste powder used in the animal experiments was selected based on the National Health and Nutrition Survey of the Ministry of Health and Welfare of Korea. It was collected from 15 types of traditionally fermented soybean paste and two types of factory-made products. After grinding, the fermented soybean paste samples were quantified using a sterilized container. Then, 1000 g of each of 15 types of traditional fermented soybean paste were mixed to produce a standard sample of 15 kg. Two factory-made fermented soybean pastes (7.5 kg each) were mixed to make a standard sample of 15 kg. Each sample was freeze-dried for 5 days, then pulverized. The prepared soybean paste powder was stored at −20 °C. Residual biogenic amines were not removed from the samples.
## 2.3. Measurement of Body Weight, Food Intake and Survival Rate
Mice were randomly assigned and fed either a normal chow diet (NCD) or an HFD containing $10\%$ or $60\%$ fat (Research Diets, Inc., New Brunswick, NJ, USA) for 10 weeks. Mouse body weights were measured daily during oral gavage administration. The food and water intake of the mice was measured at 12 h intervals during the last day of the experiment from 7 pm to 7 am and from 7 am to 7 pm. The survival/mortality of the mice was recorded after 6 days of oral gavage administration of drugs.
## 2.4. Drug Administration
Drugs for oral gavage administration, including histamine (histamine dihydrochloride, TCI, Tokyo, Japan) and tyramine (Cayman Chemical, Ann Arbor, MI, USA), were dissolved in $0.5\%$ carboxymethylcellulose (CMC, Sigma-Aldrich, St. Louis, MO, USA). The soybean paste powder was administered orally (75 or 750 mg/kg) with histamine and tyramine. A total volume of $0.5\%$ CMC was treated to avoid exceeding the recommended dose for mice (10 mL/kg).
## 2.5. ELISA Assay
Blood samples were collected from the hearts and stored in ethylene glycol tetra-acetic acid-coated tubes (Becton, Dickinson and Company, Franklin Lakes, NJ, USA). The blood samples were centrifuged at 3000 rpm for 10 min at 4 °C, and each sample’s supernatant (blood plasma) was collected. This process was performed twice to obtain clear blood plasma samples. The collected blood plasma was immediately used for the ELISA assay to avoid degradation effects on the results. The total MAO, MAO-A, -B, bile acids, and C-reactive protein (CRP) levels in blood plasma were determined using OxiSelected MONOAMINE OXIDASE ASSAY KIT (Cell Biolabs, Inc., San Diego, CA, USA), Mouse Total Bile Acids Kit, and Mouse C-Reactive Protein ELISA Kit (Crystal Chem, Elk Grove Village, IL, USA) according to the manufacturers’ instructions. The absorbance of the samples was measured using a Versamax microplate reader (Molecular Devices, LLC., San Jose, CA, USA). The blood concentration of each protein was calculated according to the manufacturer’s instructions.
## 2.6. Plasma Biochemical Assays
The blood samples were collected following the protocol described in the ELISA Assay section. Plasma aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels were measured using dedicated kits (IVD Lab, Uiwang, Republic of Korea) and a spectrophotometer (Shimadzu UV-1800 spectrophotometer, Tokyo, Japan).
## 2.7. Western Immunoblotting
Isolated liver samples were homogenized in RIPA buffer (Thermo Scientific, Rockford, IL, USA) on ice for 30 min and centrifuged twice at 13,000 rpm for 30 min at 4 °C. The concentrations of solubilized proteins in the supernatants were determined using a BCA protein assay (Thermo Scientific). Proteins in supernatants (10 μg) were separated using $10\%$ sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The separated proteins were transferred to a methanol-activated polyvinylidene difluoride membrane (Merck, Darmstadt, Germany). The membrane was blocked with a blocking buffer containing $5\%$ skim milk in a mixture of Tris-buffered saline and $0.1\%$ Tween-20 and washed three times for 10 min. Membranes were then probed with either a primary rabbit antiserum against IL-1β (1:1000, Abcam, Cambridge, MA, USA) or osteopontin (1:1000) (Abcam, Cambridge, MA, USA) or PARP-1 (1:1000, Cell Signaling Technology, Danvers, MA, USA) for 18 h at 4 °C, rewashed three times, and incubated with horseradish peroxidase-labeled goat anti-rabbit secondary antiserum (1:3000) (Thermo Fisher Scientific, Tewksbury, MA, USA) for 1 h at room temperature. Immunoreactive protein bands were detected using an iBright Western blot imaging system (Thermo Scientific, Tewksbury, MA, USA) with enhanced chemiluminescence reagents (Ab Frontier, Seoul, Republic of Korea; ratio of reagents A to $B = 1$:500). The same membrane was stripped and probed with mouse primary antiserum against β-actin (1:1000) (Sigma-Aldrich, St. Louis, MO, USA) to normalize the blots. Immunoreactive protein bands were semiquantified using a digital imaging camera and NIH Image 1.62 software.
## 2.8. IPGTT
After 16 h of fasting, glucose solution (2 mg/kg, i.p.) was administered to the mice. Blood glucose levels were measured at 0, 30, 60, 90, and 120 min using a glucose meter (MEDISENSOR, Daegu, Republic of Korea). A blood sample was collected from the tail vein of the mouse, and the first drop of blood was discarded. The area under the curve from the IPGTT was calculated using the trapezoidal rule.
## 2.9. Statistics
Statistical analyses were performed using one-way analysis of variance with Tukey’s multiple comparison test (GraphPad Prism 9.3.1, GraphPad Software, La Jolla, CA, USA). Data were considered significantly different when the p-value was <0.05. All statistical results are presented as mean ± SEM.
## 3.1. Changes in Survival Rate and Plasma CRP Levels after Repeated Exposure to Combined Biogenic Amines in Mice Fed an NCD
The mice were administered combined biogenic amines once a day for 6 days by oral gavage to determine the adverse effects of biogenic amines, histamine, and tyramine under NCD-fed conditions (Figure 1A). The combined biogenic amine administration was determined at three concentration levels—low (2 mg/kg histamine + 10 mg/kg tyramine, $$n = 12$$), medium (20 mg/kg histamine + 100 mg/kg tyramine, $$n = 12$$), and high concentration (200 mg/kg histamine + 1000 mg/kg tyramine, $$n = 12$$)—and $0.5\%$ CMC without biogenic amines was used as a control ($$n = 12$$). Three concentration levels of combined biogenic amines were used—low (2 mg/kg histamine + 10 mg/kg tyramine, $$n = 12$$), medium (20 mg/kg histamine + 100 mg/kg tyramine, $$n = 12$$), and high concentration (200 mg/kg histamine + 1000 mg/kg tyramine, $$n = 12$$)—and CMC ($0.5\%$) without biogenic amines was used as a control ($$n = 12$$). Body weight and food intake (but not water intake) were significantly reduced after treatment with high concentrations of biogenic amines (Figure 1B–D). However, medium and low concentrations of combined biogenic amines did not affect body weight and food intake. The survival rate of mice was $100\%$ ($$n = 26$$) in the CMC control group but decreased to $91.7\%$ (1 death out of a total of 12 mice) in the low-concentration group, $84.6\%$ (2 deaths out of a total of 13 mice) in the medium-concentration group, and $46.2\%$ (7 deaths out of a total of 13 mice) in the high-concentration administration group. Subsequently, to determine the adverse effects of biogenic amines on liver damage, we tested changes in CRP levels in blood plasma, a marker protein produced by hepatocytes and associated with NAFLD and inflammation [25]. Blood plasma CRP levels were significantly increased in the medium- (42.7 ± 7.9 ng/mL, $$n = 10$$) and high-concentration (52.3 ± 9.7 ng/mL, $$n = 13$$) groups but not in the group administered a low concentration (24.3 ± 0.9 ng/mL, $$n = 15$$) of biogenic amines compared with the control group (14.2 ± 2.1 ng/mL, $$n = 9$$) (Figure 1E,F). Therefore, the optimal dose for combined biogenic amine administration was determined to be a medium concentration, which increased CRP levels without affecting feeding behavior and survival. When histamine and tyramine were administered alone, there was no change in the survival rate of the experimental animals; however, the blood plasma CRP level increased significantly after tyramine was administered alone (Figure 1G,H).
## 3.2. Changes in Liver IL-1β Expression Levels after Repeated Exposure to Biogenic Amines in Mice Fed an NCD
Recent studies demonstrate that IL-1β cytokine is closely associated with inflammation, hepatic injury, and obesity [26,27]. Osteopontin is also a potential biomarker for numerous liver diseases [28,29]. Therefore, we investigated whether biogenic amines affect the expression levels of IL-1β and osteopontin in the mouse liver. Administration of histamine or tyramine alone did not change the liver expression levels of IL-1β, but the levels were increased by combined biogenic amine administration in the liver (NCD + CMC: $$n = 6$$; NCD + histamine 20 mg/kg: $$n = 6$$, NCD + tyramine 100 mg/kg: $$n = 6$$; NCD + histamine 20 mg/kg + tyramine 100 mg/kg: $$n = 6$$) (Figure 2A). Osteopontin expression levels in the mouse liver also showed an increasing tendency with biogenic amine administration, but the difference was not statistically significant (NCD + CMC: $$n = 6$$; NCD + histamine 20 mg/kg: $$n = 6$$, NCD + tyramine 100 mg/kg: $$n = 5$$; NCD + histamine 20 mg/kg + tyramine 100 mg/kg: $$n = 6$$) (Figure 2B). Therefore, in subsequent experiments, we used IL-1β as a marker to evaluate the effects of biogenic amines and hepatic damage in HFD-induced obesity. The full-length whole Western blot images for Figure 2 are shown in Figure A1.
## 3.3. Establishment of HFD-Induced NAFLD to Elucidate Biogenic Amine-Induced Liver Damage in Obesity
Leptin resistance is defined by a reduced sensitivity or a failure in brain response to leptin. Decreased tissue sensitivity to leptin leads to obesity and is closely linked to insulin insensitivity [15]. Furthermore, leptin indicates a predisposition to metabolic disorders, including fatty liver diseases [30,31]. Therefore, preliminary monitoring of leptin resistance is necessary to evaluate the effect of biogenic amines on obesity and NAFLD development. A previous study reported that C57BL/6 mice fed an HFD for 10 weeks showed symptoms of NAFLD [32]. Therefore, all mice used in our experiment were fed an HFD for 10 weeks to establish NAFLD. Mice fed an HFD ($$n = 12$$) showed decreased glucose tolerance compared to the NCD-fed group ($$n = 11$$) (Figure 3A,B). Additionally, fasting plasma glucose and plasma leptin levels were increased in HFD-fed mice for 10 weeks (Figure 3C,D). These data demonstrate that HFD-induced obese mice developed leptin resistance. These results demonstrate that this method establishes a model suitable for evaluating liver damage caused by biogenic amines in NAFLD.
## 3.4. Changes in Survival Rate and Liver Damage Markers after Single or Combined Biogenic Amine Administration in HFD-Induced Developmental NAFLD
To determine the effect of biogenic amines on the liver of HFD-induced obese mice, we tested the survival rate of experimental mice, IL-1β expression levels in the liver tissue, and blood CRP levels after oral gavage administration of biogenic amines. Survival rates were reduced after administration of both biogenic amine alone and a mixture of biogenic amines (CMC: 0 deaths out of 9, $100\%$; histamine 20 mg/kg: 1 death out of 13, $92\%$; tyramine 100 mg/kg: 2 deaths out of 12, $83\%$; histamine 20 mg/kg + tyramine 100 mg/kg: 7 deaths out of 33, $79\%$) (Figure 4A). Consistent with this result, liver IL-1β levels increased after biogenic amine treatment (HFD + CMC: $$n = 6$$; HFD + histamine 20 mg/kg: $$n = 7$$; HFD + tyramine 100 mg/kg: $$n = 7$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg: $$n = 6$$) (Figure 4B). Additionally, the survival rate was slightly lower in the HFD-fed group compared to the NCD-fed group (from $85\%$ to $79\%$) (Figure 4C). Blood CRP levels were significantly increased in both CMC ($$n = 8$$) and biogenic amine-administered groups ($$n = 11$$) after HFD was fed compared to the NCD-fed, CMC-treated group ($$n = 9$$) (Figure 4D). As biogenic amines are degraded by the biogenic amine-detoxifying enzyme MAO [6], we determined whether biogenic amines alter liver MAO levels after HFD-induced NAFLD. Liver MAO-A and total MAO levels were significantly increased by repeated combined biogenic amine administration compared to the NCD control group, but MAO-B levels did not change (NCD + CMC: $$n = 6$$; HFD + CMC: $$n = 6$$; HFD + histamine 20 mg/kg: $$n = 6$$; HFD + tyramine 100 mg/kg: $$n = 7$$; HFD + histamine 20 mg/kg + tyramine 100 mg/ kg: $$n = 6$$) (Figure 4E–G). Although bile acids are derived from hepatic cholesterol catabolism and are closely associated with NAFLD development [33], repeated administration of combined biogenic amines did not change the total bile acid levels in the blood plasma (NCD + CMC: $$n = 6$$; HFD + CMC: $$n = 10$$; HFD + histamine 20 mg/kg: $$n = 10$$; HFD + tyramine 100 mg/kg: $$n = 10$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg: $$n = 11$$) (Figure 4H). These results demonstrate that even in the early stages of NAFLD, ingested biogenic amines may be associated with fatty liver conditions and induce severe risks linked to death via excessive response to MAO. The full-length whole Western blot images corresponding Figure 4B are shown in Figure A2.
## 3.5. Fermented Soybean Paste Affects Changes in Survival after Combined Biogenic Amine Administration in HFD-Induced Developmental NAFLD
To determine the effect of fermented soybean pastes on biogenically induced liver damage in HFD-induced NAFLD, traditionally made fermented soybean paste (TSBP) and manufactured (factory-made) fermented soybean paste (MSBP) feeding were combined with biogenic amines. The decreased survival rate after combined biogenic amine administration was increased by both TSBP (HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 75 mg/kg: 1 death out of 9, $89\%$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 750 mg/kg: 0 deaths out of 10, $100\%$) and MSBP (HFD + histamine 20 mg/kg + tyramine 100 mg/kg + MSBP 75 mg/kg: 0 deaths out of 9, $100\%$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + MSBP 750 mg/kg: 0 deaths out of 9, $100\%$) in the early stage of NAFLD (Figure 5A,B). We also evaluated blood aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels to determine liver damage by biogenic amines in developmental NAFLD. As shown in Figure 5C,D, combined biogenic amines induced increased levels of ALT (HFD + CMC: $$n = 4$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg: $$n = 5$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 75 mg/kg: $$n = 5$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 750 mg/kg: $$n = 4$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + MSBP 75 mg/kg: $$n = 6$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + MSBP 750 mg/kg: $$n = 6$$), and AST levels (HFD + CMC: $$n = 4$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg: $$n = 6$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 75 mg/kg: $$n = 5$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 750 mg/kg: $$n = 5$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + MSBP 75 mg/kg: $$n = 5$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + MSBP 750 mg/kg: $$n = 5$$) were decreased by TBST and MSBP administration for 6 days. These data suggest that both TSBP and MSBP may be involved in reducing biogenic amine-induced toxic effects associated with developmental NAFLD.
## 3.6. Effects of Fermented Soybean Paste on Changes in Liver Damage Markers after Combined Biogenic Amine Administration in HFD-Induced Developmental NAFLD
We evaluated changes in liver damage markers to determine whether fermented soybean paste reduces biogenic amine-induced liver damage in HFD-induced NAFLD. Combined biogenic amine-elevated hepatic IL-1β expression levels in developmental NAFLD were significantly decreased by both TSBP and MSBP ($$n = 6$$ per group, Figure 6A). We also evaluated changes in cleaved PARP-1, known as a cellular stress sensor, in developmental NAFLD. As shown in Figure 6B, biogenic amine-induced cleaved PARP-1 expression levels were significantly decreased by both TSBP and MSBP ($$n = 6$$ per group). Additionally, blood CRP levels and biogenic amines upregulated by HFD were significantly downregulated by fermented soybean paste (NCD + CMC: $$n = 6$$; HFD + CMC: $$n = 6$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg: $$n = 11$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 75 mg/kg: $$n = 5$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 750 mg/kg: $$n = 6$$; HFD + histamine 20 mg/kg + tyramine 100 mg/ kg + MSBP 75 mg/kg: $$n = 6$$; HFD + histamine 20 mg/kg + tyramine 100 mg + MSBP 750 mg/kg: $$n = 6$$) (Figure 6C). In particular, HFD and biogenic amine-induced enhanced activity of MAO-A levels was reduced by the high dose of MSBP, while MAO-B and total MAO levels did not change in the liver (NCD + CMC: $$n = 6$$; HFD + CMC: $$n = 6$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg: $$n = 6$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 75 mg/kg: $$n = 6$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + TSBP 750 mg/kg: $$n = 6$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + MSBP 75 mg/kg: $$n = 5$$; HFD + histamine 20 mg/kg + tyramine 100 mg/kg + MSBP 750 mg/kg: $$n = 6$$) (Figure 6D–F). These results support the hypothesis that fermented soybean paste reduces biogenic amine-enhanced hepatic damage in developmental NAFLD. The full-length whole Western blot images corresponding to Figure 6A,B are shown in Figure A3 and Figure A4, respectively.
## 4. Discussion
Although we are constantly exposed to the risk of biogenic amines by ingesting food with high protein or free amino acid contents, the relationship between biogenic amines and obesity-induced metabolic diseases remains elusive. Biogenic amines generated from fermentation decomposition by microorganisms or biochemical activity, including that histamine, tyramine, agmatine, putrescine, cadaverine, spermine, and spermidine, are not only toxic but also act as a measure of the freshness of food and spoilage [2]. Aliphatic biogenic amines are commonly used as a decay indicator. Aromatic and heterocyclic compounds act as ‘vasoactive amines’, causing toxicity by stimulating the nervous and vascular systems when consumed in excess [1,2].
The results of the present study show that ingesting large amounts of concomitant histamine and tyramine can induce life-threatening health problems. For instance, repeated exposure to combined biogenic amines decreased the food intake, survival rate, and body weight of NCD-fed mice. Additionally, repeated exposure to combined biogenic amines increased blood CRP levels in a dose-dependent manner. Immunoreactivity of the hepatic damage marker IL-1β, a well-known indicator of liver damage [21,34,35], was increased by the combined administration of biogenic amines. In particular, blood CRP levels increased only in HFD-induced NAFLD. Combined biogenic amine administration increased hepatic IL-1β levels in NAFLD. In contrast, survival rates were decreased by combined biogenic amine administration under normal conditions and showed a tendency to further reduce the survival rate in NAFLD mice. As IL-1β is closely associated with obesity and inflammatory fatty liver disease [21,34,35], HFD-induced NAFLD may be a factor in enhancing the risk of biogenic amines. IL-1β expression levels were increased, but developmental liver damage and the fibrogenesis marker osteopontin did not change. Therefore, these data suggest that the interaction between obesity-related NAFLD and unexpected ingestion of a large amount of biogenic amines may exacerbate hepatic function directly related to the maintenance of life.
The main risk factors for developing NAFLD are obesity, T2DM, and other factors associated with metabolic syndrome [36]. *In* general, NAFLD is induced by long-term HFD exposure, although a recent study reported that NAFLD symptoms appeared when after 10 weeks on an HFD [32]. However, the relationship between obesity factors and biogenic amines in the early stages of fatty liver disease is unclear. We monitored glucose and leptin levels as indicators of obesity due to the provision of HFD for 10 weeks. Glucose and leptin levels in blood plasma were significantly increased by 10 weeks of HFD feeding compared to those in the NCD-fed group (Figure 3). Therefore, we used this as a model for developmental NAFLD because HFD-mediated metabolic processes involved in leptin resistance accelerate de novo lipogenesis, inflammation, and fibrogenesis in the liver and consequently cause NAFLD [30,37].
Several studies have shown that IL-1β and CRP are strong predictors of NAFLD [21,33,38]. Hepatic IL-1β expression and blood CRP concentration were increased in the HFD-induced NAFLD group after combined biogenic amine administration compared with HFD + CMC and NCD + CMC groups (Figure 4). Similarly, the survival rate of the combined biogenic amine-treated group was lower that of the control group. CRP is produced by hepatocytes and is involved in chronic liver disease. Recent studies have shown that high CRP levels have been observed in patients with liver dysfunction [39,40]. In particular, patients with liver cancer or cirrhosis with high CRP levels show poor prognoses [41,42,43,44]. Additionally, IL-1β plays a critical role in hepatic failure via NF-κB signaling and proinflammatory cytokine activation [27]. These findings suggest that obesity may interact with repeated conjugated biogenic amine administration to cause liver damage via IL-1β and/or CRP upregulation.
It has been documented that NAFLD is closely associated with the upregulated activity of MAO-A in the liver, which is associated with oxidative stress-mediated depressive symptoms [45,46,47]. In addition, MAO inhibitors can potentially decrease the gene and protein expression of the proinflammatory cytokines IL-1β, IL-6, TNF-α, and INF-γ [3,45]. Consistent with these findings, we found that hepatic MAO-A, total MAO levels, and IL-1β expression increased after combined biogenic amine administration in HFD-fed NAFLD mice, while blood bile acid levels did not change significantly. Although the changes in MAO-B levels were not significant, they did show an increasing trend. Therefore, these findings suggest that hepatic MAO-A and IL-1β may act as more helpful markers than total bile acid when evaluating the risk of biogenic amine-induced liver damage in obese mice. In addition, recent studies demonstrated that cleavage of PARP-1, as a necrotic cell death marker, is closely associated with NAFLD and oxidated stress-induced liver damage [48,49].
Interestingly, fermented soybean paste recovered the survival rate reduced by biogenic amines in NAFLD (Figure 5). Usually, hepatic steatosis due to NAFLD causes increased ALT and AST levels [50,51]. Since plasma ALT and AST levels are known indicators of hepatocyte damage, they are used as general clinical biomarkers to evaluate liver function [52]. As shown in Figure 5, it was confirmed that increased blood ALT and AST levels by combined biogenic amine treatment were decreased in the TSBP- and MSBP-treated groups of NAFLD mice. Biogenic amine-induced increased IL-1β, cleaved PARP-1 expression levels, and blood CRP were decreased by concomitant administration of fermented soybean paste in NAFLD (Figure 6A–C). Additionally, fermented soybean paste reduced biogenic amine-enhanced hepatic MAO-A activity, but MAO-B and total MAO activities did not change (Figure 6D–F). These findings suggest that fermented soybean paste may relieve hepatic damage by reducing MAO, IL-1β, and cleaved PARP-1 levels increased by biogenic amines in HFD-induced developmental NAFLD.
Several studies have reported that fermented soybeans provide various benefits, including antioxidative, anti-inflammatory, fibrinolytic, anticancer, and immune-enhancing effects [53,54]. These benefits are known to be caused by isoflavones or secondary metabolites produced by microorganisms involved in the fermentation process [23,53,54,55,56]. Although decarboxylase-containing microorganisms produce biogenic amines during soybean paste fermentation, certain microorganisms, such as lactic acid bacteria and amine oxidase gene-containing bacteria, reduce biogenic amines [55,57,58]. Among the microbes, Pediococcus acidilactici M28 and *Staphylococcus carnosus* M43, which are contained in Chinese soybean paste, can degrade biogenic amines, especially tyramine and histamine [59]. Additionally, *Staphylococcus is* one a dominant microbe in fermented Korean soybean paste [60]. Therefore, it is feasible that fermented soybean paste can reduce biogenic amine-mediated liver damage in NAFLD patients. However, the detoxification mechanism of biogenic amines by certain microorganisms remains unclear but may occur in a hepatic MAO-independent manner (Figure 7).
Controlling spoilage microorganisms during fermentation is challenging because the production method varies from household to household and region to region. In contrast, systematically produced MSBP may have a constant amount or concentration of beneficial bacteria containing amine oxidase. Therefore, MSBP may be more effective in improving the liver damage caused by NAFLD biogenic amines. In this study, we did not investigate the effects of biogenic amines produced in soybean paste or the adverse effect of other biogenic amines except for histamine and tyramine. Nevertheless, recent studies demonstrate that the microbial community of soybean paste is involved in the degradation of other biogenic amines in addition to histamine and tyramine [55,57,58]. Although the precise mechanism of the beneficial effect of fermented soybean pastes on biogenic amine-induced liver damage remains unclear, our data suggest that fermented soybean pastes may contribute to a reduction in biogenic amine-induced liver damage in NAFLD.
## 5. Conclusions
We identified an increased risk of hepatic dysfunction by biogenic amine ingestion in obesity and confirmed that both blood and liver biomarkers, including CRP, MAO, IL-1β, and PARP-1, are helpful markers for evaluating hepatic function in biogenic amine-induced liver damage in obesity. Although there is insufficient documentation of the complementary benefits of soybean paste and the increased risk of biogenic amines in obesity, we propose that fermented soybean paste is a promising candidate to alleviate liver damage caused by biogenic amines in NAFLD.
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|
---
title: 'Age Matters: The Moderating Effect of Age on Styles and Strategies of Coping
with Stress and Self-Esteem in Patients with Neoplastic Prostate Hyperplasia'
authors:
- Edyta Skwirczyńska
- Anita Chudecka-Głaz
- Oskar Wróblewski
- Karol Tejchman
- Karolina Skonieczna-Żydecka
- Michał Piotrowiak
- Kaja Michalczyk
- Beata Karakiewicz
journal: Cancers
year: 2023
pmcid: PMC10000508
doi: 10.3390/cancers15051450
license: CC BY 4.0
---
# Age Matters: The Moderating Effect of Age on Styles and Strategies of Coping with Stress and Self-Esteem in Patients with Neoplastic Prostate Hyperplasia
## Abstract
### Simple Summary
This survey-based study has assessed the types of coping mechanisms and QoL of patients diagnosed with and treated for prostate cancer. Patients using active forms of coping, seeking support and planning seem to have higher self-esteem, while maladaptive coping strategies in the form of self-blame can cause a significant decrease in patients’ self-esteem. Our results show that older patients, despite the use of adaptation strategies, have lower self-esteem. Early psychological assessment and mobilization of patients’ personal resources may allow patients to change stress coping methods towards more adaptive forms.
### Abstract
The aim of this study was to analyze coping mechanisms and their psychological aspects during the treatment of neoplastic prostate hyperplasia. We have analyzed strategies and styles of coping with stress and self-esteem of patients diagnosed with neoplastic prostate hyperplasia. A total of 126 patients were included in the study. Standardized psychological questionnaires were used to determine the type of coping strategy by using the Stress Coping Inventory MINI-COPE, while a coping style questionnaire was used to assess the type of coping style by using the Convergence Insufficiency Symptom Survey (CISS). The SES Self-Assessment Scale was used to measure the level of self-esteem. Patients using adaptive strategies of coping with stress in the form of active coping, seeking support and planning had higher self-esteem. However, the use of maladaptive coping strategies in the form of self-blame was found to cause a significant decrease in patients’ self-esteem. The study has also shown the choice of a task-based coping style to positively influence one’s self-esteem. An analysis related to patients’ age and coping methods revealed younger patients, up to 65 years of age, using adaptive strategies of coping with stress to have a higher level of self-esteem than older patients using similar strategies. The results of this study show that older patients, despite the use of adaptation strategies, have lower self-esteem. This group of patients should receive special care both from family and medical staff. The obtained results support the implementation of holistic care for patients, using psychological interventions to improve patients’ quality of life. Early psychological consultation and mobilization of patients’ personal resources may allow patients to change stress coping methods towards more adaptive forms.
## 1. Introduction
Among neoplastic diseases, prostate cancer is one of the most frequently diagnosed noncutaneous cancers in the recent years. Only in the United States, in 2017, 160,000 men were diagnosed with prostate cancer [1]. In Poland, prostate cancer is responsible for nearly $9\%$ of all cancer-related deaths. In comparison to Europe, where the 5-year survival rate is $83.4\%$, Poland holds a much lower percentage of $66.6\%$ [2]. Symptoms associated with its diagnosis include pain and worsening of physical condition, and these are present in more than $50\%$ of patients. The choice of prostate cancer treatment option is complex; however, the quality of sexual life is an important aspect that influences patients’ quality of life. Radical prostatectomy (RP) often negatively influences the sexual functioning, which contributes to an impaired sense of masculinity [3]. Radical prostatectomy and hormone therapy contribute to the loss of sexual function, causing the feeling of confusion and disorientation in patients. [ 4]. Previous studies have shown patients who experience emotional disorders and distress to be at a higher risk of poorer treatment outcomes, and have lower adherence to treatment plan, making the overall prognosis poorer than in emotionally stable patients [5]. It is estimated that $30\%$ of prostate cancer patients experience some form of emotional distress, defined as general suffering, and $10\%$ experience severe depression [6]. Studies have indicated that men diagnosed with prostate cancer experience emotional disturbances two to five times more often when compared to the general population [7]. Adequate social support is an important factor for reducing anxiety and depression. Its lack contributes to the deterioration of the quality of life. Patients coping with the disease on their own were found to more frequently experience depression and have worse mental well-being [8]. As for the body image issues present among cancer patients, Serbia et al. showed that psychological intervention conducted in women with breast cancer has influenced their adaptive approach to their bodies. Not only did the patients begin to view their bodies in a more positive way, but, also, their self-confidence and willingness to cooperate has increased. The results of the study indicate that patients’ approach can be dynamically changed under a psychological intervention, if properly conducted. The initial reluctance of patients to have contact with their bodies transformed into no difficulties upon physical contact with the body parts affected by surgery. Due to the mix of social and biological factors, symptoms of depression may be masked by unhealthy coping behaviors manifested in the form of psychoactive substance abuse, dangerous car driving or casual sexual contact, and, thus, are more difficult to diagnose [9]. From the time of diagnosis through the entire treatment process, patients experience strong emotional stimuli that may negatively affect their well-being and hospitalization. An optimalization of medical treatment and quality of life of patients with cancerous prostate hyperplasia is one of the greatest challenges the modern healthcare system has to face. Patients subjected to long-term stress exposure were proven to have a weakened immune response as well as more frequent metastasis formation and recurrences of the disease [10]. According to Dropkin’s definition, body image is the changing perception of one’s own appearance, functions and sensations. The experiences related to the changes in body image occur mostly on a subconscious level. Patients, after surgical prostatectomy, were found to experience pain and were surprised by the changes related to the outlook and function of the penis. Studies also indicate unfavorable changes caused by hormonal disorders, which contribute to increasing the marital distance and deterioration of the relationship [11].
A common belief that only older men are affected by prostate cancer poses another problem when it comes to patient treatment. In younger patients, the perspective of losing full sexual and physical activity may contribute to significant reduction of the quality of life. Studies show that older patients, despite the general health deterioration by prostate cancer, can maintain their subjective well-being and immunity at a relatively satisfactory level [12]. When discussing with their doctor, patients are reluctant to talk about the deterioration or loss of sexual function associated with the treatment process. Lack of a sensitive intimate issue discussion often led to social isolation, negatively impacting their family life [13]. Studies evaluating the differences between different coping strategies between patients of different ages have indicated worse functioning among younger patients. Due to the cancer diagnosis, they are often forced to revise their life plans. Moreover, they often experience loss of self-independence and economic difficulties. However, younger patients tend to have greater psychological resources that can be used to actively and confrontationally deal with cancer diagnosis and treatment [14,15].
There is a limited amount of research on the moderating effect of age in the context of strategies and styles for coping with stress and self-esteem in patients with prostate neoplastic hyperplasia. Its better understanding may contribute to changes in the recently used strategies. Demonstration of different coping strategies among patients of different ages will allow for a more efficient psychological intervention, integral for treatment.
The objectives of this study were to:Assess stress coping strategies in relation to patients’ self-esteem. Assess stress coping styles in relation to patients’ self-esteem. Identify the predictors of stress coping styles and strategies that determine patients’ self-esteem. Determine the influence of patients’ age as moderator of the relationship between self-esteem and ways of coping with stress.
## 2. Materials and Methods
We have conducted a cross-sectional single center study to analyze self-esteem and stress coping strategies among patients diagnosed with and treated for prostate cancer. The study included 140 patients who were qualified by a multidisciplinary board for radical prostatectomy from June to December 2021. The board consisted of oncologists, urologists, radiotherapists, cancer coordinators and a psychooncologist, who worked at the urology department of Pomeranian Medical University. The qualification was based on the results of biopsy and diagnostic imaging. Patients qualified for other treatment options including radiotherapy and/or hormone therapy were excluded from the study to maintain the homogeneity of the study group. As Polish language was the mother tongue used by all of the patients, Polish adaptations of the questionnaires were used for the study purpose. The questionnaires were provided by the psychologist at the time of hospital admission, as the patients were awaiting their surgery. All patients were provided with a proper explanation of the study and were given a possibility to withdraw at any timepoint of the study. Patients completed the questionnaires on their own in a hospital room. The questionnaires were handed in an envelope. Having filled the forms, patients were asked to seal them in an envelope and return them to the researcher. All patients have signed the informed consent form. Participants who refused to sign the informed consent form or did not fill the questionnaires completely were removed from the study. A total of 140 study participants were provided with the questionnaires, of whom 126 have returned fully completed forms.
Patients were asked to fill the following questionnaires: a demographic data questionnaire, the Coping Inventory for Stressful Situations (CISS), the Rosenberg Self-Esteem Scale and the Mini-COPE questionnaire. The demographic questionnaire consisted of 9 questions asking for patients’ age, place of residence, education, marital status, children, satisfaction with the relationship with wife/partner, satisfaction with relationships with children, financial situation and help from relatives and family. The scale’s reliability, depending on the age group, was calculated to equal 0.81 to 0.83.
An adaptation of the Mini-Cope questionnaire was provided to assess patient strategies of dispositional coping. A version by Oginska-Bulik and Hurczynski [2009] was used. The form included 28 statements assessing for 14 strategies of coping with stress. The half reliability of the questionnaire was 0.86. The internal consistency for most of the scales was assessed at a satisfactory level [16].
In order to examine styles of coping, an adaptation of the Coping Inventory for Stressful Situations (CISS) of Strelau et al. [ 17] was used. It consisted of 48 statements concerning stressful events and specific coping patterns used in specific situations. Three main coping styles were identified: task-focused, emotion-focused and avoidance-focused. The avoidance-focused style was divided into engaging in vicarious activities or seeking social contact. The survey has high accuracy and high internal consistency (0.78–0.90 in accordance with Cronbach’s alpha).
Finally, a Polish version of the Rosenberg self-esteem scale adapted by Łaguna, Lachowicz-Tabaczek and Dzwonkowska was used. The scope of the scale was to measure the general level of patients’ self-esteem. The questionnaire included 10 statements. The reliability of the scale was found to vary depending on the age of the patient, ranging from 0.81 to 0.83 [18,19].
Statistical analysis was performed using IBM SPSS Statistics 25. Basic descriptive statistics analyses were calculated using the Kolmogorov–Smirnov (K-S) test, Student’s t-tests for independent samples, correlation analyses with Pearson’s r coefficient and a stepwise linear regression analysis. α 0.05 was considered significant; however, test statistical results of α equal to 0.05 < $p \leq 0.1$ were interpreted as significant statistical trends.
## 3.1. Demographic Data
A total of 126 patients diagnosed with prostate cancer participated in the study. Due to missing/incomplete data, the number of responses to specific questions differed between the questionnaires, which is noted in the tables below. The youngest patients that participated in the study were 48, while the oldest were 82 years old. A total of 109 patients were married, and 30 were assessed to be in a good financial standing, choosing a 5 on a scale from 1 to 10, 1 being the lowest. Among the study population, 40 patients had a secondary education. Specific data are presented in the tables below (Table 1 and Table 2).
## 3.2. Analysis of Socio-Demographic Variables in the Inventory for Measuring Coping with Stress—Mini-COPE
In the analysis, we have checked whether the number of children was related to the type of coping strategy. Multiple correlations were tested using Pearson’s r coefficient. As demonstrated in Table 3, three were statistically significant. The number of children was positively correlated with the strategies of using sense of humor, self-denial and self-blame. However, the strength of the reported relationships was low.
As the next step, we have assessed whether relationship (marital) satisfaction was related to coping processes. A series of Spearman’s rho rank correlation analyses were performed. As shown in Table 3, one correlation was statistically significant, as relationship satisfaction correlated positively with emotional support strategy. The strength of this relationship was low. The other correlations were not statistically significant.
We have also tried to determine if paternal relationship satisfaction was related to the strategy of coping with stress. Pearson’s r coefficient correlations were performed, however, all of them were statistically insignificant. Similar investigations were performed to assess the impact of financial situation and the choice of stress coping strategy. No statistically significant results were found. The influence of help of the relatives was also determined. Active coping strategies were more frequent among patients who received help from family members. This group of patients also had a lower tendency for psychoactive substance use and self-blame.
## 3.3. Stress Coping Style and Strategies, as well as Self-Esteem, Depend on the Age of the Respondents
An analysis of the influence of patients’ age (under or over 65) on the type of stress coping style and self-esteem was performed using a series of moderation analyses with the Process macro. The association between task-focused style and self-esteem was significantly moderated by patients’ age. Based on the conditional effects, the association was found significant for patients aged <65 years ($B = 2.60$; SE = 0.74; $t = 3.51$; $$p \leq 0.001$$), but not significant for patients aged 65+ (B = −0.31; SE = 0.91; t = −0.34; $$p \leq 0.736$$).
Similarly, a moderated mediation model was used to analyze patients’ age as a moderator of coping strategies and self-esteem. We found a statistically significant effect of age moderation on active coping strategy and self-esteem. The correlation between these variables was statistically significant in the group of patients <65 ($B = 2.46$; SE = 0.65; $t = 3.77$; $p \leq 0.001$), while the studied relationship was insignificant among patients aged 65+ ($B = 0.27$; SE = 0.72; $t = 0.37$; $$p \leq 0.710$$). There was also a statistically significant effect of age moderation on the relationship between the strategy of positive re-evaluation and self-esteem, with a statistically significant association for patients up to 65 years of age ($B = 2.24$; SE = 0.62; $t = 3.59$; $p \leq 0.001$), and an insignificant association for patients aged 65+ (B = −0.99; SE = 0.75; t = −1.31; $$p \leq 0.191$$).
We have also found a significant effect of age moderation on seeking emotional support and self-esteem. Younger patients (<65 years old) with a lower tendency for choosing a strategy for seeking emotional support had lower self-esteem ($B = 2.02$; SE = 0.52; $t = 3.92$; $p \leq 0.001$). Among patients 65+, the relationship was not significant ($B = 0.14$; SE = 0.59; $t = 0.23$; $$p \leq 0.817$$).
The associations with the types of individual dimensions of coping with stress were evaluated. We have found a statistically significant effect of age moderation on the relationship between active coping and patients’ self-esteem. The correlation was statistically significant among patients <65 years of age ($B = 3.17$; SE = 0.75; $t = 4.23$; $p \leq 0.001$), while the effect was insignificant for patients aged 65+ ($B = 0.15$; SE = 0.91; $t = 0.16$; $$p \leq 0.873$$). A similar association was found for the dimension of seeking support and self-esteem. The correlation was statistically significant for patients aged <65 years old ($B = 2.40$; SE = 0.60; $t = 3.98$; $p \leq 0.001$), however, it was found to be insignificant for patients aged 65+ ($B = 0.27$; SE = 0.69; $t = 0.40$; $$p \leq 0.692$$). A graphical presentation of all statistically significant correlations is presented in Figure 1, while the remaining insignificant correlations are presented in Appendix A.
## 4. Discussion
In the case of cancer patients, an important aspect that should be taken into consideration is that the coping strategies do not change. Patients who tended to use specific methods of coping with difficult situations were likely to use identical strategies during cancer diagnosis and different stages of treatment [20,21]. In this study, we have assessed stress coping styles and strategies used by patients diagnosed with prostate cancer. We have also tried to evaluate how individual strategies impact patients’ self-esteem.
Trying to determine how to support prostate cancer patients, we have used our database to analyze the influence of sociodemographic variables on coping strategies. A result worth noticing was the fact that patients who received support from family and relatives tended to use an adaptive strategy in the form of active coping. Relatives’ support also correlated negatively with the use of psychoactive substances and self-blame, which are considered maladaptive strategies. Despite the weak correlations, these data provide the basis towards further investigation.
In our research, we have also examined the influence of adaptive stress coping strategy on patients’ self-esteem. A task-focused stress coping style was positively associated with patients’ self-esteem. Patients looking for information about their disease and actively cooperating with a doctor were characterized by higher self-esteem. On the other hand, the self-esteem was lower in patients using an emotion-based style. Similar findings were observed by Shakeri et al. [ 22], as cancer patients adopting an emotion-focused style of coping experienced reduced quality of life. This was due to the fact that, both at the time of diagnosis and in the later period, the accompanying emotions were usually negative. Emotions such as regret, anger and a sense of injustice negatively affect a patient’s mental sphere and may constitute a new source of stress. Social withdrawal and focus on subsequent stages of cancer treatment are a combination that may effectively increase patients’ positive self-esteem, allowing a view from a different, more positive perspective [22]. Studies have demonstrated that men use emotion-based strategies less frequently than women. This difference between male and female populations may be used at the beginning of cancer treatment, as, instead of concentrating on patients’ emotion suppression, the therapy can focus on subsequent treatment analysis and mobilization of patients’ personal resources [23]. Our data support the role of adaptive styles of coping with stress among prostate cancer patients. We have found patients using task-oriented coping strategy to have higher self-esteem. Our study has also demonstrated the non-adaptive style to influence patients’ self-esteem. Such patients tended to focus on their emotions as a coping method. Our results are consistent with previous studies assessing cancer patients. Among the studied styles, the strategy using avoidance was not significantly related to self-esteem.
Multiple coping strategies were found to influence patients’ self-esteem both positively and negatively. The first strategy that significantly related to self-esteem was active coping. Patients who were in contact with a stressor and have undertaken active steps to reduce it, initiated specific actions directly and increased their efforts to fight the disease were found to have higher self-esteem. Similar results were obtained in a meta-analysis conducted by Roesh et al., [ 2005], showing a positive relationship between self-esteem and active coping strategies [24]. Another correlation that positively related to self-evaluation was a planning-based strategy. Having identified the difficult situation, action plan formulation and analysis of different strategies were found to reduce the associated stress. Patients using a planning strategy tend to analyze their resources against the source of stress, in this case, prostate cancer, indicating the secondary nature of the assessment.
As a part of cancer treatment planning, patients can prepare for the upcoming treatment and its consequences, including the possible adverse effects of surgical prostate resection. The importance of patient preparation was noticed by Spendelow et al., [ 2017], who showed in their meta-analysis that the use of active coping strategies could reduce patients’ perception of both physical and psychological pain. The authors have also indicated that the timing of a patient’s recovery may be associated with specific strategies, and slower recovery was demonstrated among patients using non-adaptive strategies [25].
Our study has also shown a positive correlation between self-esteem and strategies for seeking instrumental and emotional support, which are based on seeking information and help. For a variety of reasons, patients may check for a second opinion to confirm the diagnosis and treatment options and/or to provide further guidance. Seeking emotional support is related to the need for help in the area of enduring the hardships of treatment and understanding of family and friends. Complications associated with prostate cancer treatment often include disturbance of physiological functions, not only related to urinary incontinence and nocturia, but also negatively affecting sex function, providing additional psychological burden [26]. Our results are consistent with the previous literature. Family support can significantly help for cancer patients and influence their treatment outcomes. A review conducted by Sukyati et al. has demonstrated that family support can even result in relapse prevention. Anxiety has a negative impact on health, lowering patients’ self-confidence, causing insomnia and lowering patients’ quality of life. Partners’ support can cause a greater control over the emotional sphere, which significantly reduces the level of anxiety. In addition, social support has been shown to contribute to a greater acceptance of the disease and a reduction of depressive symptoms.
As the last part of the study, we have evaluated the moderating effect of age on coping strategies and self-esteem. We have found some significant correlations between patients’ age, their stress response and self-confidence. In patients up to 65 years old, the use of active adaptive stress coping strategies was found to correlate with higher self-esteem. However, the correlation was insignificant for older patients. The differences between the two age groups may be caused by different stages of evolutionary psychology. Patients in later adulthood are in the culmination stage of life, and their developmental tasks concentrate mostly on contribution to the well-being of future generations, while younger patients concentrate more on self-actualization and integration. Our research did not reveal sociodemographic differences between the strategies used and the support of parents, families and children, therefore, from the beginning of the study, we did not assume any subdivision of patients. A previous study by Matzka et al. [ 15] found no influence of social support on patients’ resilience index. Cancer diagnosis provides additional psychological burden, and even the use of adaptive strategies does not increase patients’ self-esteem. Patients in their midlife (middle adulthood) using adaptive strategies of stress coping in the form of acceptance, active coping, positive reappraisal and seeking instrumental and emotional support were found to have higher self-esteem. These findings are particularly important due to the role of self-esteem in anxiety and cancer-associated stress reduction. Patients with higher self-esteem tend to have a more positive attitude, regardless of the difficulties encountered [27]. Self-esteem and self-determination can be used as resources during patients’ cancer treatment. If higher, patients can have a sense of control and power over situations in their lives, reducing negative psychological implications of cancer diagnosis and treatment [24]. The results of a meta-analysis by Roesch et al. have shown prostate cancer patients using active coping to have lower levels of anxiety and depression and were consistent with Lzararusa and Folkman’s transactional model. Patients who tend to approach the disease as a challenge more often use strategies based on active coping [23].
There is limited research on the influence of age on cancer patients’ stress coping styles and strategies. However, our findings highlight the need for further studies and provide an important direction towards working with cancer patients. In our study, among the younger group of patients, we have noticed that a task-oriented stress coping style and the use of adaptive strategies correlate with higher self-esteem. This allowed us to select a population of patients potentially able to withstand the negative emotions associated with cancer and requiring the least psychological intervention. In our study, we have also identified a group of patients that should receive special attention during psychological interventions. Among older respondents, social withdrawal and lower physical activity were more common. Our research revealed the importance of spouse/partner support, as it correlated with longer patient survival. Psychological support should be provided as a routine procedure for cancer patients and can include various adaptive stress coping strategies. Services should also consider inclusion of patients’ partners in the psychological support program. The results of our study may be helpful for future clinical trials. During patient consultation, standardized questionnaires to assess strategies and styles of coping with stress and self-assessment can be used for psychological evaluation. For patients using non-adaptive coping strategies, during psychological interventions, it is important to be able to reformulate their thinking and coping strategies and to work with the patients in order to make them use adaptive forms of coping. Patients undergoing prostatectomy usually are discharged home two days post-surgical treatment, and it is often the last time they have contact with a psychologist. An introduction of an interactive clinic organized, e.g., by Canadian organization Movember, would allow patients to have better psychological care. Patients can also be provided with educational materials and online psychological consultations to ease patient–psychologist contact. The importance of psychosocial support was previously demonstrated and is supported by the Stanford Chronic Disease Self-Management Program (CDSMP) present in the United States since 2010. The objective of the program is to enhance patients’ self-efficacy to have more confidence in their ability to fight against the disease. As a part of the program, during a 6-week workshop, individuals learn self-management through adaptive problem solving, activity planning, medication and symptom management, physical activity and communication with healthcare professionals. Its effectiveness was demonstrated by Salvatore et al., who showed positive effects on quality of life and health-related outcomes of cancer survivors [28,29].
## 5. Conclusions
For the majority of patients, cancer diagnosis is a difficult and complex process. Patients diagnosed with a malignant disease present with various coping mechanisms related to their coping resources, economic situation, education and previous experience. The incidence of prostate cancer is rising, and, each year, more and more patients will face its diagnosis. Regardless of cancer staging, even for patients presenting with advanced forms of the disease, cancer diagnosis is usually shocking and followed by a range of strong emotions experienced by both patients and their families. An important aspect that should be complementary with cancer treatment is psychological help. The aim of our research was to identify stress coping styles and strategies used by prostate cancer patients.
The results of this study are not only extremely important from the patients’ perspective, but also for the medical personnel involved in cancer treatment. Both adaptive and non-adaptive coping styles were found among patients diagnosed with prostate cancer. Given the relatively constant nature of coping styles, we can speak of specific models used by patients throughout the treatment. Our findings seem to be consistent with theoretical assumptions, as, in the case of personal coping resources and support of loved ones, patients tend to use a task-focused coping style, favoring higher self-esteem. On the other hand, a problem-based stress coping style was found to negatively influence patients’ self-esteem. We have also proved the use of adaptive stress coping strategies among prostate cancer patients to contribute to higher self-esteem. The study included two age group categories, differing in patients’ attitudes and approaches towards cancer diagnosis and treatment, showing that patients aged over 65 should receive special psychological care.
## 6. Limitations
There are several limitations to this study. The study has focused on one cancer type only. Further research is needed to assess for any differences between coping styles and strategies used by different cancer patient populations, genders and cancer diagnoses. Another limitation was that we did not compare changes of patient stress coping strategies over time. The strengths of the study included evaluation of the importance of family support, which was found to influence the use of adaptive or non-adaptive coping strategies. The study sample was also limited and included only 126 prostate cancer patients. Further studies on greater populations should be performed in order to confirm the study results and provide further knowledge on the psychological aspects of prostate cancer diagnosis and treatment.
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|
---
title: 'Importance of Asprosin for Changes of M. Rectus Femoris Area during the Acute
Phase of Medical Critical Illness: A Prospective Observational Study'
authors:
- Hilal Sipahioglu
- Hatice Kubra Zenger Ilik
- Nurhayat Tugra Ozer
- Sevda Onuk
- Sumeyra Koyuncu
- Sibel Kuzuguden
- Gulseren Elay
journal: Healthcare
year: 2023
pmcid: PMC10000515
doi: 10.3390/healthcare11050732
license: CC BY 4.0
---
# Importance of Asprosin for Changes of M. Rectus Femoris Area during the Acute Phase of Medical Critical Illness: A Prospective Observational Study
## Abstract
Asprosin, a new adipokine, is secreted by subcutaneous white adipose tissue and causes rapid glucose release. The skeletal muscle mass gradually diminishes with aging. The combination of decreased skeletal muscle mass and critical illness may cause poor clinical outcomes in critically ill older adults. To determine the relationship between the serum asprosin level, fat-free mass, and nutritional status of critically ill older adult patients, critically ill patients over the age of 65 receiving enteral nutrition via feeding tube were included in the study. The patients’ cross-sectional area of the rectus femoris (RF) of the lower extremity quadriceps muscle was evaluated by serial measurements. The mean age of the patients was 72 ± 6 years. The median (IQR) serum asprosin level was 31.8 (27.4–38.1) ng/mL on the first study day and 26.1 (23.4–32.3) ng/mL on the fourth study day. Serum asprosin level was high in $96\%$ of the patients on the first day, and it was high in $74\%$ on the fourth day after initiation of enteral feeding. The patients achieved 65.9 ± $34.1\%$ of the daily energy requirement for four study days. A significant moderate correlation between delta serum asprosin level and delta RF was found (Rho = −0.369, $$p \leq 0.013$$). In critically ill older adult patients, a significant negative correlation was determined between serum asprosin level with energy adequacy and lean muscle mass.
## 1. Introduction
Per the results of the world population estimates revised in 2017, Europe faces exceptional demographic changes. Accordingly, individuals aged 60 and above already constitute $25\%$ of the population, and by 2050, this ratio is expected to reach $35\%$. The number of those with an age of 80 and above will be tripled by 2050 [1]. In developed countries, the increased average life expectancy has also resulted in increased demand for hospitalization of the elderly population in hospitals and intensive care units (ICU). It has been determined that approximately more than $50\%$ of the patients hospitalized in the intensive care unit are above 65 years old [2,3,4]. A systematic review demonstrated a significantly high malnutrition prevalence (38–$78\%$) in ICU patients. This situation is correlated to an increase in morbidity, mortality, and hospital-related costs for patients [5].
The dependency on mechanical ventilation is correlated with malnutrition, length of hospital stay, ICU readmission, infection rates, and risk of hospital death, making this a critical dilemma in ICU patients’ care. There are significant challenges in accurately estimating energy requirements and hence the optimal dosing of nutrition. Critical illness results in hypermetabolism and hypercatabolism, putting patients in the ICU at high risk of malnutrition. The metabolic and hormonal changes in critical illness result in muscle wasting and associated ICU-acquired weakness, which can persist for years [6].
Muscle atrophy can occur relatively early in critically ill patients in intensive care units. Muscle atrophy occurs with increased destruction and decreased muscle protein synthesis [7,8]. Inflammation, immobilization, endocrine stress responses, rapidly developing nutritional deficit, impaired microcirculation, and denervation are conditions that accelerate muscle atrophy [9,10]. Additionally, muscle loss is common in humans due to aging. Accordingly, muscle loss caused by aging may deepen in the presence of critical illness. Reversing skeletal muscle catabolism can prevent muscle atrophy during critical illness and improve functional outcomes [11,12,13]. Proinflammatory mediators are used as an indicator of muscle atrophy during critical illness [14]. Ultrasound is widely used in clinical practice, greatly contributing to diagnosis and management of many conditions. While systematic ultrasound examinations have been conducted mainly by sonographers in an examination room, there is now considerable interest in having physicians perform ultrasounds at the bedside, as part of regular medical examinations. Studies using portable ultrasounds have been spreading not only in the emergency room and intensive care unit (ICU) settings, but also in out-of-hospital situations in, for example, primary care and long-term care facilities (e.g., nursing homes). Additionally, muscle ultrasound is a suitable method for evaluating patients with muscle atrophy. The ultrasonographic evaluation of quadriceps’ muscle thickness effectively determines the effect of nutritional interventions on muscle loss in critically ill patients [7,8].
Adipose tissue functions as an endocrine organ with central energy storage that creates a diversity of bioactive mediators and adipokines (adipose-derived secreted factors), possessing proinflammatory or anti-inflammatory impacts. Adipokines may easily move into the systemic circulation and perform their effects through an inter-cell communication network (autocrine, paracrine, endocrine). Furthermore, they preserve regulating several aspects of the normal metabolic processes in the human body, such as glucose and lipid homeostasis, insulin sensitivity, and inflammatory response [15]. Asprosin is a novel glucogenic adipokine discovered in 2016, mainly secreted from white adipose tissue, and has a critical role in the regulation of hepatic glucose release, insulin secretion, appetite, and inflammatory response [16]. Moreover, it activates the PKCδ/SERCA2-mediated endoplasmic reticulum stress/inflammation pathways in skeletal muscle and promotes insulin resistance [17]. Insulin resistance has been revealed to be relatively higher in critically ill patients compared to healthy patients [10]. Evidence suggests an association between asprosin secreted levels and weight loss extent as a result of bariatric surgery, including sleeve gastrectomy or cholecystectomy. Two studies indicated a significant decrease in serum asprosin levels after six months of weight loss surgical intervention [18,19]. During fasting, the circulating serum asprosin level rises according to the glucose requirement and decreases with the start of feeding. Providing adequate nutritional support to critically ill patients has a critical role in the clinical prognosis of the patient [20,21]. Considering the above-mentioned information, the relationship between asprosin, muscle mass, and nutritional support in critically ill elderly patients is unclear.
This study aimed to reveal the relationship between the serum level of asprosin, a new adipokine, the change in lean muscle mass in critically ill elderly patients, and the nutritional support given to the patients.
## 2.1. Study Design and Participants
This study presents a prospective observational design developed in a tertiary care hospital’s clinical-internal intensive care unit from March to September 2022. All patients over the age of 65 who were expected to stay in the intensive care unit for at least 4 days and were administered enteral nutrition support within the first 48 h after their admission to the ICU were included. Patients who could be fed orally, who had previously been treated with parenteral therapy, and who had contraindications for enteral nutrition were excluded from the study.
The study was approved by the local ethics committee (No: 586, date: 24 February 2022) and was conducted according to the Helsinki Declaration guidelines. Free and informed consent was obtained from the legal guardians of the study participants.
## 2.2. Data Collection
Demographic data, ICU admission diagnostics, comorbidities, APACHE II (Acute Physiology and Chronic Health Evaluation) scores, SOFA (Sequential Organ Failure Assessment) scores, and the Charlson comorbidity index were recorded at admission.
During the follow-up, the need for a mechanical ventilator, renal replacement requirement, and the number of days spent in the intensive care unit and hospital were recorded.
The energy target of the patients was calculated as 25–30 kcal/kg/day according to ESPEN Recommendations [20]. The daily energy intake of the patients by tube who actually received enteral nutrition for four days from the start of enteral nutritional support was recorded. The percentage of patients reaching the target energy was calculated. No adjustments were made for age or BMI when calculating energy targets. The risk of malnutrition in patients was determined by the NRS-2002 score. The NRS-2002 form was filled in by the nurses on the day that the patients were admitted to the intensive care unit, taking information from the patients and their relatives, and recorded in the patient file. The nutritional risk of the patients was determined, and a nutrition plan was made. Patients with NRS-2002 ≥ 3 were considered to be at risk for malnutrition.
## 2.3. Serum Asprosin Measurement
Here, 3 mL blood samples were collected in tubes, and the samples were centrifuged at 3000× g for 10 min at the 24th hour (first day) and fourth day after the start of enteral nutrition support. In our intensive care unit, feeding is interrupted at 11 am for all patients receiving enteral nutrition. Blood samples were drawn in the morning fasting before feeding was re-initiated. Then, 1 mL of serum supernatant was removed and collected in an Eppendorf tube. Serum samples were kept frozen at −80 °C. Serum asprosin protein concentrations were analyzed using the ELISA method (Cat. No. E4095HU). Delta (Δ) asprosin was calculated as the difference between the first and fourth day asprosin levels of the patients. The normal asprosin level was considered as <23.6 ng/mL (according to the reference range (kit used) determined by the Bioassay Technology Laboratory).
## 2.4. Ultrasonographic Assessment
Ultrasound measurements were performed at 24 h (day 1) and on day 4 after the start of enteral nutritional support. Philips ClearVue 550 system with a linear ultrasound probe (4–12 Mhz) was used for calculation while these measurements were in the supine position on the surface in B mode. The area of the rectus femoris (RF) muscle of the lower extremity quadriceps muscle was measured. The sensor was perpendicular to the thigh axis, and the point is located at $\frac{2}{3}$ of the distance from the anterior superior iliac spine to the upper border of the patella.
All ultrasonography (USG) measurements were performed by an intensive care specialist with five years of USG experience. Delta (Δ) RF was calculated as the difference between the RF area of the patients on the first and fourth days.
## 2.5. Statistics
Statistical analysis was performed using the IBM SPSS statistics program version 22 (IBM, New York, NY, USA). The normality distributions of continuous variables were examined using the Shapiro–Wilk test. According to the normal distribution, continuous variables were presented as mean ± SD or median (interquartile range). Categorical variables were shown as numbers (%, percentage). The correlation between the data was investigated using Spearman’s correlation test. The correlation coefficient was accepted as 0–0.29 (weak), 0.30–0.69 (moderate), and 0.70–1.0 (strong). A value of $p \leq 0.05$ was considered statistically significant for all analyses.
The study sample size was calculated as 42 patients, with a medium effect size according to the baseline asprosin level ($d = 0.5$), $80\%$ strength, and $5\%$ error probability using G-Power 3.1 software.
## 3. Results
Two hundred and fifty-one patients hospitalized in the intensive care unit were evaluated. Of these patients hospitalized in the intensive care unit, 94 were under the age of 65, and 94 did not receive enteral nutrition (26 who received oral nutrition, 68 who received parenteral nutrition) were excluded. First, 67 patients were included in the study. However, 12 patients died during the study period, and 7 patients were excluded since their serum blood was hemolyzed. Two patients were excluded from the study because they switched from enteral to oral feeding. As a result, a total of 46 patients were included in the study (Figure 1).
The mean age of the patients was 72 ± 6 years, and the median value of males (IQR) was 25 (54.3). The median (IQR) BMI of the patients was 22.0 (20.9–29.0), the mean APACHE II was 19.8 ± 6.98, and the median (IQR) Charlson comorbidity index was 6 (4–8). Metabolic disorders (n: 17, $37.0\%$) and sepsis/septic shock ($26.1\%$) were the most common reasons for hospitalization in the intensive care unit. Diabetes mellitus was present in 16 ($34.8\%$) of our patients, and at the same time, all the patients had received insulin therapy. The most common comorbidity in our patients was hypertension, in 29 patients ($63.0\%$). The malnutrition risk was found in 32 patients ($70.0\%$). The median percentage of reaching the daily energy requirement was 65.9 ± 34.1 during the 4-day follow-up. The daily energy requirements and the amount they can actually take are presented in Table 1. The mean daily protein intake was 0.4 ± 0.27 g/kg/day on the first day and 0.8 ± 0.47 g/kg/day on the fourth day.
Mechanical ventilation (21 ($45.7\%$)) and renal replacement requirement (21 ($45.7\%$)) of the patients were quite high. Demographic data and clinical characteristics of the patients are presented in Table 1.
Median (IQR) asprosin levels were 31.8 (27.4–38.1) ng/mL on the first day and 26.1 (23.4–32.3) ng/mL on the fourth day. The serum asprosin concentration of study participants significantly decreased ($p \leq 0.001$), and the delta asprosin value was −5.77 (−9.22 to 0.28) (Table 2).
The asprosin level was high in $95.7\%$ of the patients on the first day. This rate decreased on the fourth day of the study, and $73.9\%$ of patients had high asprosin levels (Figure 2).
Median RF was 1.68 (1.35–2.07) cm2 on the first day and 1.82 (1.38–2.01) cm2 on the fourth day ($$p \leq 0.196$$). The median delta RF was 0.15 (−0.43 to 0.46). The laboratory values of the patients on the first and fourth days are presented in detail in Table 3. The glucose level of the patients was 143 (110–194) mg/dL on the first day, 125 (103–170) mg/dL higher than on the fourth day ($p \leq 0.001$). The albumin value was statistically significantly lower on the fourth day than on the first day of the study (2.7 (2.4–3.1) g/L vs. 2.5 (2.2–2.9) g/L, $$p \leq 0.001$$).
A significant negative correlation was observed between the delta asprosin level and the delta RF value of the patients (Rho = −0.369, $$p \leq 0.013$$) (Figure 3).
There was a moderate correlation between the serum asprosin level of the patients and the received % of the daily energy target (Rho = 0.345, $$p \leq 0.027$$) (Figure 4).
The correlations between the serum asprosin value and the severity of illness and biochemical parameters of patients on both study time points are summarized in Table 4.
A negative correlation was determined between albumin and prealbumin levels and the first day and delta asprosin levels ($p \leq 0.05$).
## 4. Discussion
To the best of our knowledge, this prospective study is the first to investigate the serum asprosin value and its relationship between muscle mass and nutritional adequacy in critically ill older adult patients. Most of the participants had increased serum asprosin levels upon study admission. On the fourth day after enteral nutrition support initiation, the serum asprosin concentration of the study sample significantly decreased compared to the first day of the study. There was a significantly negative correlation between the delta asprosin value and the delta RF of patients. Besides, the delta asprosin value was significantly correlated with the received percentage of energy intake from daily energy requirements.
Almost all patients had elevated asprosin levels on the first day of the study. A significant decrease in asprosin levels was observed in our patients after four days of enteral nutrition. We think that the reason for the high first-day asprosin level in patients is malnutrition in adult patients and/or high insulin resistance developing in critical illness. The mean age of our patients was high, and $70.0\%$ were at risk of malnutrition in our study. The main concern in the elderly, especially the very elderly and those with multiple comorbidities, is reduced food intake and weight loss. Malnutrition in elderly patients delays recovery in both acute and chronic diseases and increases morbidity and mortality [22,23]. In response to starvation with a low-intake diet, asprosin is released from white adipose tissue and transported to the liver to mediate glucose release into the bloodstream. Additionally, asprosin is abundantly expressed in human skeletal muscle-derived mesoangioblasts, suggesting that the musculoskeletal system may play a role in regulating asprosin expression [24]. In a cross-sectional study by Hu et al., 46 patients with anorexia nervosa were included. It was found that these patients had a statistically significant increased plasma asprosin level compared to healthy controls [25]. Providing adequate nutritional support in critically elderly patients may be a key method in optimizing increased asprosin levels.
It was shown that insulin resistance in intensive care patients is considerably higher than in healthy patients [10]. In a cross-sectional study conducted by Goodarzi et al., it was reported that the serum asprosin level was statistically significantly positively correlated with Hba1c, HOMA-IR, and insulin levels in patients with a type 2 diabetes mellitus diagnosis and nephropathy [26]. Similarly, the first-day glucose values of our patients were higher than the glucose values after four days of feeding.
Factors including systemic inflammation, decreased peripheral blood flow, inactivity, insulin resistance, and decreased food intake might cause significant reductions in muscle mass in severely ill patients hospitalized in intensive care units. Malnutrition, depending on the negative nutritional balance between what is necessary for the patients and what they receive, is reliant on decreased muscle mass and functionality, which is considered common among ICU patients. Thus, the correct nutritional diagnosis of these patients is critical to support adequate dietary maintenance. Nevertheless, nutritional evaluation is challenging in intensive care units, particularly when monitoring nutritional status. Ultrasonography is a portable, non-invasive bedside method that may specify and measure skeletal muscle and has been used as a supportive examination tool to provide nutritional diagnostics. The ability to detect short-term changes by allowing serial measurements is one of the most advantageous aspects of ultrasonography compared to other anthropometric measurement instruments. The ultrasound examination of rectus femoris muscle thickness has been reported to be used in the monitoring of nutrition [27,28].
In the study of Duerrschmid et al., which experimented with mice, wild-type mice and mice with truncated mutations in the FBN1 gene were provided a high-calorie, high-fat diet. Mice with truncated mutations in the FBN1 gene had less fat and muscle content than wild-type mice. This study demonstrates that asprosin, encoded by FBN1, has an impact on nutrition and muscle mass [29]. One of our hypotheses in our study was that insulin resistance, being very common in intensive care units, can be improved as adequate nutrition is provided, and the increase in the asprosin level may be effective in this condition. Since we did not measure insulin resistance, we cannot clarify this.
This muscle wasting also adversely affects the clinical outcomes of the patients. Quadriceps’ muscle thickness is used to evaluate nutritional interventions in critically ill patients in the intensive care unit [30]. The present study demonstrated a negative correlation between the change in quadriceps’ muscle thickness and asprosin after four days of nutrition. In the current literature, it has been reported that the musculoskeletal system effectively regulates the level of asprosin [24,31]. Du et al. conducted a cross-sectional study of 120 cancer patients. A statistically significant positive correlation was found between the serum asprosin level and body fat mass in these patients [32]. Moreover, increased levels of asprosin may accelerate the reduction in muscle mass in critically ill elderly patients.
In intensive care units, giving sufficient nutritional substances and using them in the anabolic process positively contributes to the course of the disease. We determined a negative correlation between the serum asprosin level of our patients and the percentage of patients reaching the target energy.
The present study concluded a negative correlation between prealbumin and albumin with the asprosin value on the first day. Prealbumin and albumin values are frequently used as biomarkers of adequate nutrition. However, serum albumin and prealbumin levels are affected by many factors [33,34].
Biochemical measures are beneficial to obtain in the ICU setting. Nonetheless, improvements in such parameters are not consistently related to improvements in outcomes when controlled for illness severity. There may be several reasons for these limitations [35]. The significant fluid shifts in critical illness can impact the serum concentrations of the most commonly used biochemical indicators. Visceral or “hepatic” proteins, including albumin and prealbumin, are affected by the acute phase response, independent of nutrition status or nutrition input [36].
For example, the prealbumin level usually falls at the beginning of the ICU admission even when nutrition support has been entirely implemented, and the level may improve as the acute phase response decreases, even if the patient has not received adequate nutrition or has continued to lose weight. However, several studies have suggested that if the acute phase response is reasonably stable, the prealbumin levels then correlate with nutrition intake, but perhaps not the outcome. Prealbumin levels cannot be measured at all hospital laboratories [37,38]. Nonetheless, the serum albumin level is routinely measured in most hospitals and is a robust prognostic indicator even in critical illness, but it has a long half-life and does not correlate to significant alterations in nutrition input, making it less useful as a parameter for sequential monitoring of nutrition progress [39].
Malnutrition is a very common and vital issue in intensive care. There are no good markers to assess rapidly developing muscle wasting in patients with fractures. The prealbumin and albumin we used in our routine are affected by many parameters.
Our study suggests that asprosin, a new adipokine, can be used to monitor adequate nutrition and muscle loss. However, a larger number of patients and further studies are needed.
## 5. Limitations
The limitations of our study are that it is single-centered, and the number of patients is small. Our study findings included only elderly critically ill patients. This limits generalizability in critically ill patients. If we had also evaluated insulin resistance in our patients, we could better explain the pathophysiology.
Another limitation of our study was the inability to measure inflammatory and anti-inflammatory cytokines in patients due to cost. If we could measure the cytokine values, we could see the effect of inflammation on nutrition and asprosin more clearly.
Evaluation with ultrasonography for a longer time would have yielded more precise results to better evaluate the response of muscle mass in response to feeding in patients. The mitochondrial evaluation was not performed for the asprosin level in our study. More reliable results could have been obtained with this evaluation.
## 6. Conclusions
Nearly all the critically ill elderly patients had elevated serum asprosin levels. Serum asprosin levels decreased in those patients who received enteral nutritional support and ICU treatment. In this study, there was a negative correlation between the serum asprosin level and delta lean muscle mass. Additionally, the serum asprosin level correlated with nutritional adequacy. For future investigations, whether the serum asprosin level can be used as a biomarker in evaluating the adequacy of nutritional interventions in critically ill patients should be evaluated with larger sample sizes. The relationship between the asprosin level and ICU-acquired weakness should be clarified.
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|
---
title: Multiple Organic Contaminants Determination Including Multiclass of Pesticides,
Polychlorinated Biphenyls, and Brominated Flame Retardants in Portuguese Kiwano
Fruits by Gas Chromatography
authors:
- Virgínia Cruz Fernandes
- Martyna Podlasiak
- Elsa F. Vieira
- Francisca Rodrigues
- Clara Grosso
- Manuela M. Moreira
- Cristina Delerue-Matos
journal: Foods
year: 2023
pmcid: PMC10000518
doi: 10.3390/foods12050993
license: CC BY 4.0
---
# Multiple Organic Contaminants Determination Including Multiclass of Pesticides, Polychlorinated Biphenyls, and Brominated Flame Retardants in Portuguese Kiwano Fruits by Gas Chromatography
## Abstract
Global production of exotic fruits has been growing steadily over the past decade and expanded beyond the originating countries. The consumption of exotic and new fruits, such as kiwano, has increased due to their beneficial properties for human health. However, these fruits are scarcely studied in terms of chemical safety. As there are no studies on the presence of multiple contaminants in kiwano, an optimized analytical method based on the QuEChERS for the evaluation of 30 multiple contaminants (18 pesticides, 5 polychlorinated biphenyls (PCB), 7 brominated flame retardants) was developed and validated. Under the optimal conditions, satisfactory extraction efficiency was obtained with recoveries ranging from $90\%$ to $122\%$, excellent sensitivity, with a quantification limit in the range of 0.6 to 7.4 µg kg−1, and good linearity ranging from 0.991 to 0.999. The relative standard deviation for precision studies was less than $15\%$. The assessment of the matrix effects showed enhancement for all the target compounds. The developed method was validated by analyzing samples collected from Douro Region. PCB 101 was found in trace concentration (5.1 µg kg−1). The study highlights the relevance of including other organic contaminants in monitoring studies in food samples in addition to pesticides.
## 1. Introduction
The consumers’ interest in new and exotic fruits has intensified, mainly due to the growing knowledge regarding their bioactive composition and biological activities with pro-healthy effects. Kiwano (*Cucumis metuliferus* E. Mey), belonging to the Cucurbitaceae family, is a plant naturally occurring in South Africa, Nigeria, Namibia, Botswana, and Southern Sahara, being also sporadically found in Yemen [1]. In the last years, its exportation has grown in countries such as Kenya, New Zealand, France, and Portugal [1,2].
The ripe kiwano fruit is characterized by an orange skin with many blunt thorns on its surface and green, jelly flesh inside [1,2,3,4]. Kiwano fruit has low levels of carbohydrates and calories but high contents of water, minerals including magnesium, calcium, potassium, iron, phosphorus, zinc, copper, and complex B vitamins, vitamin C, and β-carotene [1,2]. Some pharmacological properties of this exotic fruit have been recently revised by Vieira et al. [ 3], including anticardiovascular, antidiabetic, antiulcer, antioxidant, anti-inflammatory, antimalarial, and antiviral activities.
Due to these beneficial properties, its production, exportation, and consumption have increased, leading to intensive cultivation. As such, these particular fruits contribute directly and importantly to food security and nutrition in most producing zones, however, some food safety issues are still little explored in these matrices. There are several ways of improving plant cultivation. One of them is the use of plant protection products, commonly known as pesticides, which may have a chemical source as well as a natural origin [5]. Pesticides are used to protect crops from the harmful activity of other plants, microorganisms, insects, or even animals [6]. Although higher yields of cultivation can be obtained by using pesticides [7], they represent a threat to animals and human health and lives. Other toxic chemical substances that are present in the environment due to man-made activity derived from different sources (e.g., plastics, industrial, etc.), are referred to as environmental pollutants (e.g., polychlorinated biphenyls (PCB), polybrominated diphenyl ethers (PBDE), polycyclic aromatic hydrocarbons (PAH), heavy metals). Many of these compounds can be resistant to environmental degradation and accumulate in soil and food [8].
Further, prolonged exposure to these agricultural chemicals, particularly by contaminated food consumption, may lead to chronic disorders, such as cancer, hormone disruption, diabetes, asthma, or infertility [9,10,11] and neurodegenerative disorders [12]. As an example of the pesticide family, organophosphorus pesticides (OPP) are highly toxic chemical compounds used as insecticides for crop protection [13]. These chemicals are neurotoxic, as they inhibit acetylcholinesterase (AChE), which causes malfunctions in muscular activity leading to seizures, paralysis, or even death [14]. Further, persistent organic pollutants (POP), including organochlorine pesticides (OCP), PCB, PBDE, and PAH, are organic lipophilic chemicals that bioaccumulate in fatty tissues, also causing adverse effects on human health and the environment [15,16]. Exposure to POP is associated with malfunctions in the reproductive and endocrine systems [17], being also responsible for the development of many cancer types. Apart from human health, the use of pesticides is deleterious to the environment. Because of this, many flora and fauna species are exposed to multiple contaminants. Water, soil, and air pollution caused by the use of chemicals leads to disturbances in the ecosystem and poses a threat to biodiversity [5,18]. Therefore, their use must be restricted [19]. Due to the toxicity of environmental pollutants, their content needs to be continuously monitored, and attention to them is crucial. Besides that, surveys of pesticide residues in fruit are important to validate conformity with strict regulations of newly open markets for the exportation of exotic fruit.
The European Commission establishes the maximum residue levels (MRLs) for pesticides to minimize the exposure of humans to harmful levels in food or feed [20]. Pesticides and several environmental pollutants have been reported in the literature on food [21,22,23,24,25,26,27,28]. However, there is a lack of studies regarding new fruits that are not yet legislated even though there is a high demand, and environmental contaminants are also not legislated [29,30,31]. Even more, one of the ambitious goals set by the European Green Deal and the Farm to Fork Strategy includes a $50\%$ reduction in the use of pesticides by 2030.
This strikes a challenge to analytical chemistry, namely in the development and validation of sensitive analytical methods. One of the best approaches for multiresidue analysis (simultaneously pesticide and other contaminants) in food samples is the extraction by Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) method [32]. It is a very convenient, time- and reagent-saving solid-phase extraction-based procedure consisting of two major steps [33]. In the first step, the fruit, vegetable, or other food sample is subjected to extraction with acetonitrile (MeCN) and salts (e.g., MgSO4, NaCl), followed by a second step in which a sample clean-up via dispersive solid-phase extraction (d-SPE) is performed [20]. Afterward, the extracted and purified compounds are commonly analyzed with the use of gas chromatography (GC)-based methods [34]. Particularly, GC coupled with a mass spectrometer (MS) is favored for such a complex multiple contaminants identification due to the low limits of detection (LOD) [35]. Tandem mass spectrometry, specifically GC-MS/MS and LC-MS/MS, and other selective detectors were reported to be more efficient in simultaneously detecting multiple contaminants [36].
Considering the beneficial properties associated with the kiwano and its increasing consumption, it becomes urgent to develop methodologies and evaluate this fruit’s safety [37]. To the best of our knowledge, there are no analytical methods developed or monitoring studies that report the chemical safety in terms of pesticides and other environmental contaminants, namely plastic-related chemicals and others associated with anthropogenic sources, in kiwano fruit samples. Therefore, the aim of this study was to optimize and validate an extraction methodology for the simultaneous analysis of 30 multiple contaminants (6 OPP, 12 OCP, 5 PCB, and 7 BFR) from kiwano fruit samples using QuEChERS method and d-SPE clean-up to detect trace levels of these contaminants using GC techniques.
## 2.1. Reagents and Standards
Analytical standards of high purity (≥$97\%$) for seven brominated flame retardant (BFR) compounds (2,4,4′-tribromodiphenyl ether (BDE28), 2,2′,4,4′-tetrabromodiphenyl ether (BDE47), 2,2′,4,4′,5-pentabromodiphenyl ether (BDE99), 2,2′,4,4′,6-pentabromodiphenyl ether (BDE100), 2,2′,4,4′,5,5′-hexabromodiphenyl ether (BDE153), 2,2′,4,4′,5,6′-hexabromobiphenyl ether (BDE154), and 2,2′,4,4′,5,5′-hexabromodiphenyl ether (BDE183)) were obtained from Isostandards Material, S.L. (Madrid, Spain). The five PCB standards (2,4,4′-trichlorobiphenyl (PCB28), 2,2′,4,5,5′-pentachlorobiphenyl (PCB101), 2,3′,4,4′,5-pentachlorobiphenyl (PCB118), 2,2′,4,4′,5,5′hexachlorobiphenyl (PCB153), and 2,2′,3,4,4′,5,5′-heptachlorobiphenyl (PCB180)) were acquired from Riedel-de Haën (Seelze, Germany). The eighteen pesticides with analytical grade (12 OCP (hexachlorobenzene (HCB), α-, β-, and ζ-hexachlorocyclohexane (HCH), [1,1,1-trichloro-2-(2-chlorophenyl)-2-(4-chlorophenyl)ethane] (o,p′-DDT), 2,2-bis(4chlorophenyl)-1,1-dichloroethylene (p,p′-DDE), 1-chloro-4-[2,2dichloro-1-(4-chlorophenyl)ethyl]benzene (p,p′-DDD), aldrin, dieldrin, α-endosulfan, methoxychlor, and lindane) and 6 OPP (chlorfenvinphos, chlorpyrifos, chlorpyrifos-methyl, dimethoate, parathion-methyl, and malathion) were obtained from Sigma-Aldrich (St. Louis, MO, USA). The internal standards (IS) 4,4′-dichlorobenzophone and triphenyl phosphate were from Sigma-Aldrich (St. Louis, MO, USA). QuEChERS extraction kits, clean-ups, and SampliQ GCB (Graphitized carbon black) SPE Bulk Sorbent were from Agilent Technologies (Santa Clara, CA, USA). Chromatography grade n-hexane and acetonitrile (MeCN) were purchased from Merck (Darmstadt, Germany) and Carlo Erba (Val de Reuli, France), respectively. Ultrapure water (UPW) with water sensitivity >18.2 MΩ⋅cm at 25 °C was produced with a Milli-Q water purification system (Millipore, MA, USA).
## 2.2. Samples
Ten kiwano fruits were supplied by a local farm located at Cinfães, Douro, Portugal. The mature fruits were collected in February 2019 from 10 different plants (random sampling) to obtain a representative set of fruits. The pulp of kiwano was separated from the orange skin, ground in a miller, homogenized, and finally, stored at −18 °C.
## 2.3. Extraction Procedure: Optimization and Validation
The 30 multiple contaminants were extracted from the kiwano samples based on the previously reported QuEChERS method with d-SPE clean-up [22]. The procedure, whose schematic illustration is shown in Figure 1, included five steps: [1] 5 g of kiwano pulp sample was weighed into a 50 mL polypropylene tube, [2] 8 mL of MeCN and 2 mL of UPW were added, and the tube was thoroughly vortexed for 1 min, EN QuEChERS (4 g MgSO4, 1 g NaCl, 1 g NaCitrate, 0.5 g disodium citrate sesquihydrate) were added, the tubes were shaken for 1 min with a vortex, and centrifuged for 5 min at 2490 rcf at room temperature, [3] 1 mL of the supernatant was transferred to the 2 mL d-SPE clean-up tube (150 mg of MgSO4, 50 mg of PSA, and 25 mg of GCB) and the tubes were vortexed for 1 min and centrifuged for 5 min at 2490 rcf at room temperature, [4] 900 µL of the final extract was transferred to a labelled vial, the extract was dried under nitrogen flow, and it was redissolved in 900 µL of n-hexane, and finally, [5] the sample was vortexed and 150 µL of the extract with the addition of 100 µg L−1 of the IS was added in the vial and was placed in the autosampler for the gas chromatography (GC) analysis. The IS was used to control the analytical quality of the GC analysis. Extractions were performed in triplicate.
For the optimization of the methodology, pre-spiking and post-spiking experiments were carried out to evaluate the extraction efficiency. The procedure for pre-spiking was the same as described above (Figure 1), with the difference that the sample in step 1 was contaminated with 7.5 µg kg−1 from the mixture of 30 multiple contaminants. The following steps remained the same, as shown in Figure 1. The procedure for the post-spiking had a change in step 4. Before injection in the GC, 7.5 µg kg−1 of the 30 multiple contaminants was added to the vial and redissolved in the kiwano fruit extract. The extraction efficiency was studied in terms of recoveries percentages comparing the results obtained between the pre-spiking and post-spiking studies.
The validation of the method developed was performed following the Eurachem guidelines and SANTE/$\frac{11312}{2021}$ document by studying several analytical parameters, such as the linearity, recovery at three spiking levels (7.5, 11.2, 14.9 µg kg−1) and 5 replicates matrix effects, and intra-day and inter-day precision (experiments with the 7.5 µg kg−1 spiking level by five repeated measurements in the same and intercalary days). Quantification was performed using matrix-matched calibration (linearity between 1.5–18.7 µg kg−1) and solvent calibration (linearity between 10–125 µg L−1). The analytical validation was performed in the GC coupled to an electron capture detector (GC-ECD) and GC coupled to a flame photometric detector (GC-FPD), and with the regression analysis, the linearity was evaluated, and the limits of detection and quantification (LOD and LOQ) were determined.
## 2.4. Equipment
The GC analysis was performed according to Dorosh et al. [ 22]. Briefly, the halogenated organic compounds (5 PCB, 7 BFR, and 12 OCP) were analysed using GC-ECD (GC-2010, Shimadzu, Quioto, Japan) and OPP using a GC -FPD (GC-2010, Shimadzu, Quioto, Japan). The presence of contaminants was confirmed by GC/MS. Confirmation was based on a comparison of sample GC retention time and product ion abundance ratios (mass to charge ratio, m/z) against those obtained for a reference standard. The system control and the data acquisition were performed in Shimadzu’s GC Solution software in GC-ECD and GC-FPD and Xcalibur software in GC/MS. The GC analysis was performed in triplicate.
## 2.4.1. GC-ECD
The analysis was performed using a capillary GC column Zebron-5MS (30 m × 0.25 mm × 0.25 μm) (Phenomenex, Madrid, Spain). The oven temperature was programmed at 40 °C for 1 min, increased to 120 °C at a rate of 15 °C/min where it was kept for 1 min. Then, the temperature was increased once more at a rate of 10 °C/min to 200 °C, where it was kept for 1 min, and lastly, the temperature was increased from 7 °C/ min to 290 °C and held for 10 min. The injection was performed in splitless mode. The temperatures of the injector and ECD were 250 °C and 300 °C, respectively. Helium was used as a carrier gas (1.3 mL/min), and nitrogen as a makeup gas (30 mL/min).
## 2.4.2. GC-FPD
The GC-FPD column was the same as the one described in Section 2.4.1. The carrier gas was helium at 1 mL/min with a linear velocity of 25.4 cm s−1. The detector was at 250 °C in injection was performed in splitless mode, and the analytes were detected at 290 °C. The column was programmed at 100 °C, which was kept for 1 min before increasing it to 150 °C at a rate of 20 °C/min, where it was held for 1 min. Following, the temperature was increased to 180 °C at 2 °C/min and kept for 2 min, and finally, increased at 20 °C/min to 270 °C, where it was kept for 1 min.
## 2.4.3. GC/MS Analysis
According to SANTE guidelines, confirmation of samples should be performed by MS detector. GC/MS analysis was performed with similar conditions of GC-ECD only in the positive samples observed in GC-ECD in order to have confirmation. GC/MS instrument, TRACE GC Ultra (Thermo Fisher Scientific, Austin, TX, USA) gas chromatograph coupled with a Polaris Q ion trap mass spectrometer was used. The transfer line and the ion source temperature were 260 and 270 °C, respectively. Data acquisition was performed first in full scanning mode from 50 to 500 m/z to confirm the retention times of the analytes. All standards and sample extracts were analyzed in selective ion monitoring (SIM) mode. PCB101 confirmation was performed with the identification of three m/z ions 326 > 324 > 286.
## 2.5. Statistical Analysis
Two-way ANOVA statistical analysis was applied to estimate significant differences among different analytical procedures using GraphPad software. Multiple comparisons were performed where each mean value was compared to each group of contaminants.
## 3. Results and Discussion
The extraction and clean-up steps for kiwano’ matrices were a challenging part of the method development due to its rich composition in carotenoids, steroids, alkaloids, saponins, glycosides, flavonoids, tannins, and phenolic compounds [1,3]. The optimization of analytical methods for the determination of 30 contaminants in kiwano samples included the two crucial steps of the QuEChERS procedure: [1] Sample extraction and [2] the d-SPE clean-up. Figure 2 shows the chromatogram obtained when the mixture of the 30 multiple contaminants was analyzed by GC-ECD and FPD in the method described previously in Section 2.4.1.1 and Section 2.4.2. The extraction recovery of the method was evaluated by spiking the kiwano sample with the multiple contaminant solutions at 7.5 µg kg−1. Four protocols were tested: [1] QuEChERS AOAC with additional d-SPE clean-up CL1 (150 mg of MgSO4, 50 mg of PSA, and 50 mg of GCB), [2] QuEChERS AOAC with additional d-SPE clean-up CL2 (150 mg of MgSO4, 50 mg of PSA, and 25 mg of GCB), [3] QuEChERS EN with additional d-SPE clean-up CL1, and [4] QuEChERS EN with additional d-SPE clean-up CL2.
The study of the evaluation of the method’s efficiency was carried out according to the guidelines of the SANTE document [38], being the range of recovery established 70 to $120\%$. In Figure 3, poor extraction recoveries were observed for some of the chemical families using QuEChERS AOAC. The OCP, PCB, and BFR compounds presented recoveries of less than $70\%$ using the QuEChERS AOAC and CL1, while for QuEChERS AOAC and CL2 only the PCB compounds. Since recovery percentages after the clean-up CL1 (150 mg of MgSO4, 50 mg of PSA, and 50 mg of GCB) for QuEChERS AOAC evaluation were not satisfactory, the approach testing test other QuEChERS contents (EN) and another d-SPE clean-up (CL2) was followed. After reducing GCB in the CL2 clean-up and using QuEChERS EN, an improvement in extraction recoveries for all targeted multiple compounds was stated. The most evident result on extraction efficiency is the negative influence of the amount of GCB used in the second step of the extraction. As previously reported, GCB adsorbs compounds such as pigments, anthocyanins, and carotenoids, as well as planar compounds [23,33]. Therefore, reducing its quantity in the cleaning step is one of the optimizations of this process. Although the lower amount of GCB did not absorb all the coloring compounds like the previous CL1 clean-up, the samples were still suitable for GC analysis. ANOVA statistical analysis was used to compare the mean recoveries of each cleaning test (CL1, CL2) between the target chemical groups (OCP, OPP, PCB, BFR). The two-way ANOVA statistical study showed that the recoveries are significantly different comparing the two different clean-up sets (CL1 and CL2) for OCP and BFR using QuEChERS AOAC while for QuEChERS EN all chemical groups were statistically different. Overall, the results showed that most of the compounds are in the 70–$120\%$ range when QuEChERS EN and CL2 are used. Figure 4 shows a summary of the results of the recovery studies. It was observed that in the satisfactory range 70–$120\%$, the highest number of contaminants was achieved with QuEChERS EN and CL2.
As previously reported, a detailed optimization is an extremely important step as it reveals which compounds show the best results. As reported by Fernandes et al. [ 22,23,24,35], this extraction method is suitable but needs to be optimized and studied for each group of compounds and matrices.
The results, displayed in Figure 3 and Figure 4, allowed us to assess that the best extraction and cleaning procedures for kiwano were QuEChERS EN with a clean-up CL2 (150 mg of MgSO4, 50 mg of PSA, and 25 mg of GCB), and this was selected for all further investigations.
## 3.1. Matrix Effects
In the present work, the matrix effect was evaluated by comparing the slope obtained with the calibration curves of each compound in the matrix phase and n-hexane. This evaluation was complemented by comparing the retention times of the chromatograms with the same concentration in the matrix phase and n-hexane, and no significant differences were observed. It is well described in the literature that some analytes in fruit extracts exhibit a matrix signal enhancement/suppression effect when analyzed by GC [23,39]. This effect occurs when interferences from fruit matrices (such as pigments, lipids, acids, etc.) compete with the target analytes in the GC injector [40]. Figure 5 shows that the different chemical families (OCP, OPP, PCB, and BFR) analyzed in kiwano fruits presented different matrix effects behaviors. The signal enhancement was observed with the use of both QuEChERS AOAC and EN with the CL2 cleaning step. Additionally, with QuEChERS AOAC and CL2 clean-up, the mean matrix factor value was higher than 1.2 in all the chemical families. The BFR are those with the highest signal increase. The QuEChERS EN showed a satisfactory matrix factor with CL1 clean-up. However, as shown in Section 3, the extraction efficiency was not acceptable with this extraction procedure. In any case, this study confirmed that the matrix effect was more evident when the lowest amount of GCB sorbent was used.
## 3.2. Method Validation
Method validation is an important requirement in the practice of an analytical method process. The reliability and robustness of the method to be used for real sample analysis should be studied considering several analytical parameters. Linearity, extraction recovery at three spiking levels (7.5, 11.2, 14.9 µg kg−1), precision, LODs and LOQs obtained by the regression analysis (based on the standard deviation of the response of the curve and the slope of the calibration curve), as well as matrix effects, were the parameters studied for the validation of analysis of multiple contaminants in kiwano samples. Table 1 summarizes the analytical parameters in order of retention time obtained by GC-ECD and GC-FPD.
Considering the matrix effects described in the previous section, the analytical validation process was carried out in kiwano extract. Matrix-matched calibration curves were obtained in kiwano extracts of the 30 target analytes with a coefficient of determinations greater than 0.991. LODs and LOQs ranged from 0.2 to 2.2 and 0.6 to 7.4 µg kg−1, respectively (Table 1). The mean recoveries at the three spiking levels of 7.5, 11.2, and 14.9 µg kg−1 ranged from $90\%$ and $122\%$ ($99\%$ on average) with relative standard deviation (RSD) values between $8\%$ and $15\%$. The method precision was determined through intra-day and inter-day repeatability experiments by five repeated measurements, and the results were less than $15\%$ of RSD, which is suggested as the acceptable precision (Table 1). When compared to other studies on exotic fruits [30], we can say that for organochlorine pesticides, for example, the analytical parameters, namely the LOD and LOQ, are much better in the present work. As for the BFR, a study in capsicum cultivars [23] already reported presents higher LOD and LOQ values than those obtained for Kiwano.
Although the European Union legislation for pesticides [41] does not include the kiwano fruit, the analytical parameters obtained for this method meet the requirements. As for the other studied compounds, most of them are not included in the food legislation, despite being frequently detected in food products. As an example, EFSA recommends BFR monitoring studies in food samples [42].
## 3.3. Kiwano Sample Analysis
After the method validation, the optimized method was applied to evaluate possible contamination in kiwano samples. Since the study was carried out on the kiwano pulp, as it is the edible part, the results are presented by pulp mass. The screening of the 30 multiple contaminants in a total of 10 kiwano samples led to the identification and quantification of PCB 101 (5.1 µg kg−1 in the kiwano pulp) in a single sample. GC/MS analysis confirmed the presence of PCB 101 (Figure 6). It was also confirmed that, except for one sample, the kiwano fruit samples are safe in terms of 12 OCP, 6 OPP, 7 BFR, and 5 PCB studied. The presence of pesticides is well reported in the literature on fruits [28,43,44,45], concerning other contaminants, the works are less represented. However, PCBs, mostly associated with anthropogenic sources, have been reported in grapes, and other several fruits [46,47] and BFR in red fruits [24], capsicum cultivars [23], among others [48]. This work was performed in a small number of samples, and *Portugal is* still in the beginning regarding this crop. However, it shows the great importance of including these fruits in monitoring studies and that it should be extended to a larger number of samples from different production sites. Furthermore, the results suggest the importance of including other organic contaminants in monitoring studies on food samples in addition to pesticides.
## 4. Conclusions
An analytical methodology based on an optimized QuEChERS technique was effectively applied for the simultaneous analysis of 30 multiple contaminants (12 OCP, 7 OPP, 5 PCB, and 7 BFR) in kiwano samples. The optimized QuEChERS procedure encompassed the study of two QuEChERS compositions (QuEChERS AOAC and EN) in addition to two d-SPE clean-up compositions (CL1 and CL2). Although matrix effects were observed, it was found that QuEChERS EN, in combination with CL2 clean-up, offered an improvement in overall extraction recovery of the multiple target contaminants. Based on these results, it can be concluded that analytical method optimization studies are crucial for the analysis of multiple compounds in complex matrices. The methodology meets the analytical requirements in terms of accuracy, sensitivity, and precision. The novelty of this study allows the evaluation of multiple contaminants in kiwano samples, ensuring their safe commercialization in terms of the presence of pesticides and other organic contaminants. The presence of PCB 101 in one kiwano fruit reinforces the need for monitoring studies of organic contaminants, such as PCBs and BFRs.
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title: Identification of a Link between Suspected Metabolic Syndrome and Cognitive
Impairment within Pharmaceutical Care in Adults over 75 Years of Age
authors:
- Zuzana Macekova
- Tomas Fazekas
- Michaela Krivosova
- Jozef Dragasek
- Viera Zufkova
- Jan Klimas
- Miroslava Snopkova
journal: Healthcare
year: 2023
pmcid: PMC10000537
doi: 10.3390/healthcare11050718
license: CC BY 4.0
---
# Identification of a Link between Suspected Metabolic Syndrome and Cognitive Impairment within Pharmaceutical Care in Adults over 75 Years of Age
## Abstract
The prevalence of metabolic syndrome (MetS) and cognitive impairment (CI) is increasing with age. MetS reduces overall cognition, and CI predicts an increased risk of drug-related problems. We investigated the impact of suspected MetS (sMetS) on cognition in an aging population receiving pharmaceutical care in a different state of old age (60–74 vs. 75+ years). Presence or absence of sMetS (sMetS+ or sMetS−) was assessed according to criteria modified for the European population. The Montreal Cognitive Assessment (MoCA) score, being ≤24 points, was used to identify CI. We found a lower MoCA score (18.4 ± 6.0) and a higher rate of CI ($85\%$) in the 75+ group when compared to younger old subjects (23.6 ± 4.3; $51\%$; $p \leq 0.001$). In the age group of 75+, a higher occurrence, of MoCA ≤ 24 points, was in sMetS+ ($97\%$) as compared to sMetS− ($80\%$ $p \leq 0.05$). In the age group of 60–74 years, a MoCA score of ≤24 points was identified in $63\%$ of sMetS+ when compared to $49\%$ of sMetS− (NS). Conclusively, we found a higher prevalence of sMetS, the number of sMetS components and lower cognitive performance in subjects aged 75+. This age, the occurrence of sMetS and lower education can predict CI.
## 1. Introduction
The prevalence of both metabolic syndrome (MetS) and cognitive impairment (CI) is increasing with age [1,2]. According to the international classification of MetS [3] the prevalence of MetS ranged from $37\%$ up to $60\%$ in the elderly population [4,5,6]. Although cognitive impairments and dementia are often age-related disorders and according to World Health Organisation affect approximately 20–$25\%$ older population, they are not part of normal ageing [7]. In 2019 already over 55 million people worldwide suffer from cognitive disorders, AD, or dementia, and this number will almost double every 20 years, expect reaching 78 million in 2030 and 139 million in 2050 [7]. *In* general, MetS impairs overall intellectual functioning [1,2], and CI is the most significant factor of therapy failure in chronic disorders [8], mainly in older adults [9]. The presence of MetS, according to the classification of the International Diabetic Federation 2006 for the European population [3], can also be routinely evaluated in pharmaceutical care in a community pharmacy. For assessment of CI, Montreal Cognitive Assessment (MoCA) can be used as a simple, easy-to-use, but reliable cognitive screening tool [10,11] with high sensitivity for mild cognitive impairment [12].
Community pharmacists are the most accessible and frequently contacted healthcare professionals worldwide [13,14] who may play a crucial role in the identification of individuals with chronic disorders [15,16,17], including those suffering from cognitive disorders [18,19] in case that pharmacist is trained in the diagnosis of this type of disorder. Nowadays, common pharmaceutical care such as preparation, storage and dispensation of medicines, the provision of expert advice on their correct and safe use, or advice on the possibilities of non-pharmacological regimen measures is being globally expanded by other professional pharmacists’ competences (for example the monitoring of biochemical parameters, blood pressure measurement, management of obesity, smoking cessation, etc.) which are gradually becoming a part of pharmaceutical care worldwide which is more patient-oriented, defined as the expanded pharmaceutical care. Pharmaceutical care provided in nursing homes or senior care centres brings additional benefits to older adults [20,21]. Identification of potentially preventable risk factors (such as MetS and/or its components) and/or early stages of serious illnesses (e.g., cognitive impairment and dementia) within pharmaceutical care might help in slowing the rate of their progress and further disability [8,14,22]. Assessment of cognitive functions in elderly patients with MetS components is critical, but due to lack of time, it is routinely performed by only $24\%$ of general practitioners, although $82\%$ believe screening is needed [23]. Thus, the extension of pharmaceutical care toward cognitive screening might provide significant benefits for patients and the healthcare system.
The association between MetS and CI appears to be age-dependent [24,25]. The presence and onset of cardiovascular risk factors for CI are crucial for vascular modifications that result in reduced cerebral blood flow and metabolism in the brain [26]. While younger old (60–74 years) may be more susceptible to the cardiovascular load imposed by MetS on central neural pathways regulating mental processes [25], on the other side, MetS might have a positive influence on health status in older old (75+) individuals [27,28].
In our pilot study, we focused on the risk of suspected MetS (sMetS) estimated when providing healthcare service by a pharmacist and its related CI in the elderly [10,11] and showed the feasibility of cognitive testing in pharmaceutical care and its potential in identifying sMetS subject affected by CI but we did not investigate the impact of MetS in different age groups of elderly patients. We concluded that a quick and simple cognitive assessment could be a helpful extension of pharmaceutical care [10,11]. As our previous findings showed: (i) $56\%$ of a random population over 60 years of age exhibited lower cognitive performance on the MoCA (ii) subnormal MoCA scores were significantly present with increasing age of the respondents, and (iii) the presence of MetS moderately but significantly correlated/associated negatively with the MoCA score [10]. Currently, in the same cohort as previously [10,11], we aimed to investigate whether sMetS has different effects on cognition in “younger old” (60–74 years) and “older old” (aged 75 years and over) individuals. Recent research reports diverse findings [1,22,24,29,30,31]. While MetS contributes to cognitive decline in “younger old” subjects [22,31], there is evidence that this effect may be weakened or vanished in 75+ individuals [24,29,30]. More detailed studies of the relationship between MetS and CI in the elderly population before the age of 75 and at the age of 75+ could have a global benefit [32], but further studies are needed.
In this study, we aimed to investigate the impact of sMetS on cognition in aging individuals, with respect to the age category of 75+ years. Subjects were provided with pharmaceutical counselling, which means the specific patient-oriented pharmaceutical care service in community pharmacy targeted at the identification of components of MetS and MetS itself (according to IDF classification), including screening of cognitive features of enrolled older patients. We hypothesized that sMetS estimated within pharmaceutical care has a different influence on cognitive performance in a younger elderly population aged 60–74 years and in the 75+ population. We expected that younger old sMetS+ individuals will achieve significantly worse cognitive performance compared to the same age group without sMetS. On the other side, we expected that the cognitive performance in sMetS+ and sMetS− older old individuals will be either without difference or in the sMetS+ group only slightly weaker than in sMetS− group.
## 2.1. Study Settings, Design and Sample Size
Here, we used data from a randomized pilot study in Slovakia [10,11], where 323 subjects were enrolled. Among them, 222 voluntary participants were interviewed in 16 community pharmacies, and 101 participants from 3 senior care centres aged 60 years and over were included, $63\%$ in the 60–74 years group and $37\%$ in the group 75+ (the age of the oldest participant was 95 years).
## 2.2. Study Participants and Selection
The participants ($68\%$ women, $32\%$ men), who visited a community pharmacy or lived in a senior care centre (between February 2018–February 2019) in Slovakia and who were willing to provide their general input data (socio-demographic information) and the list of all chronically used medications with the codes for their chronic diseases. Participants were randomly selected on the base of their voluntary consent and physical and mental ability to undergo screening. All respondents completed a simple data collection form in the Slovak language comprised of socio-demographic information (age, gender, education level), smoking and physical activity habits, and presence or absence of abdominal obesity, mediated by a pharmacist. The basic characteristics of the cohort sample are displayed in Table 1. Subsequently, a cognitive screening by the MoCA test was performed by trained pharmacists. Exclusion criteria were severe physical or mental health conditions that interfered with cognitive screening test realization and/or incompletely filled data collection form. We excluded 42 incompletely filled data collection forms. The forms were collected for one year (February 2018–February 2019), and the study was approved by the Ethics Committee of Faculty of the Pharmacy, Comenius University in Bratislava (EK FaF UK $\frac{01}{2018}$). All procedures followed the relevant guidelines and regulations under the Declaration of Helsinki.
## 2.3. Classification of MetS and Assessment of Cognitive Function
According to provided codes for patients’ chronic diseases and information about the present/absence of abdominal obesity, there were identified individual components of MetS. Suspected metabolic syndrome (sMetS) was assessed according to the International Diabetes Federation Worldwide Definition of MetS, 2005, modified for the European population [3]. Accordingly, patients were divided with respect to the presence (sMetS+) or absence of suspected MetS (sMetS−).
The Montreal Cognitive *Assessment is* one of the available cognitive screening instruments, which scans seven cognitive domains: executive functioning; visuospatial abilities; language; attention, concentration and working memory; abstract reasoning; memory and orientation. The Slovak version of the Montreal Cognitive Assessment (MoCA) [8] with a reduced cut-off of ≤24 points for cognitive impairment by Bartos and Fayette was used [33] by pharmacists who were trained in the MoCA screening tool. Administration time was approximately 15 min, participant achieved a score between 0–30 points.
## 2.4. Statistical Analysis
Data were analysed using the SAS Education Analytical Suite for Microsoft Windows, version 9.3 (Copyright © 2012 SAS Institute Inc., Cary, NC, USA). The continuous demographic and clinical variables of study groups (e.g., age, the MoCA score) were represented by simple arithmetic mean, standard deviation, or $95\%$ confidence interval. Categorical descriptive variables (e.g., sMetS status, MoCA status) were characterized by absolute frequencies and percentages. When comparing two groups with continuous data, a two-sample t-test was used. In addition, Pearson’s Chi-Square test and Fisher’s exact test of cross-tabulated data were performed to analyse the association between frequencies of categorical variables. The 0.05 significance level was used as a threshold for statistical significance for all tests, and 0.8 was taken as a minimally acceptable power of tests.
Exogenous variables are independent of the error term (e.g., metabolic symptoms and cognitive function) and they may have a significant impact on the validity of the measurement. We investigated these terms by standard procedures of regression diagnostics and control procedures were applied, like sample randomization and matching, and finally the ANOVA method was used as a statistical control to reduce the possible effect of extraneous variables.
We used random allocation which is a technique that minimizes confounders and eliminates systematic bias by allocating individuals for treatment and control groups solely by a chance. We chose this method for its simplicity and effectiveness in eliminating distortion.
Due to the pilot nature of the study, we did not perform an exact a priori calculation of the number of participants according to the case-control methodology. However, the power of the performed tests was controlled by appropriate post hoc calculations.
We also suggested simple predictive analytics to forecast the impact of patients’ age, sMetS status, and education level on cognitive performance in the MoCA test. As exclusive predictors in this model, the age (dichotomic groups 60–74 years vs. 75+), sMetS status (sMetS+/sMetS−) or MetS components (central obesity, high blood pressure, dyslipidaemias, diabetes mellitus 2) and education level (dichotomic groups “lower education” for 12 years and less, vs. “higher education” for 13 years and more, were used. The calculated output data were the MoCA status (MoCA normal/MoCA lower cognitive performance). The success score of the prediction model was expressed by the evaluation of the confusion matrix in percentage.
## 3.1. Prevalence of sMetS and Cognitive Impairment
The prevalence of sMetS in the study cohort was $18.5\%$ in 60–74 years participants and $27\%$ in 75+ (NS). On average, individuals 75+ achieved significantly lower MoCA score (18.4 ± 6.0) than patients aged 60–74 (23.6 ± 4.3). Lower cognitive performance (MoCA score ≤24) was more frequent in 75+ ($85\%$) vs. participants aged 60–74 years ($51\%$; $p \leq 0.001$). In both subcohorts (60–74 years vs. 75+), age had a significant influence on cognitive performance ($p \leq 0.05$; vs. $p \leq 0.001$, respectively).
## 3.2. Occurrence of sMetS and Patients’ Cognitive Performance
sMetS influenced MoCA score in 75+ seniors (see Figure 1) as we found a significantly higher occurrence of lower cognitive performance in MoCA in 75+ with sMetS ($97\%$), when compared to 75+ sMetS− group ($80\%$; $p \leq 0.05$; r2 = 0.063), the difference was −1.99 points in MoCA mean (NS). In contrast, the MoCA score in younger seniors was unaffected by the presence of sMetS. In participants aged 60–74 years, the prevalence of lower cognitive performance according to MoCA was $63\%$ in the sMetS+ group and $49\%$ in sMetS− (NS; the difference was −1.21 points in MoCA mean, NS).
## 3.3. Number of MetS Components and Patients’ Cognitive Performance
75+ individuals had a significantly higher number of MetS components (2.2 ± 0.9) than 60–74 participants (1.6 ± 1.1; $p \leq 0.001$) in both age groups, however, the number of MetS components was not associated with patients’ cognitive performance in MoCA.
## 3.4. Association between a MetS Status, Age, Education Level and Cognitive Performance
We proposed here a simple predictive model (see Figure 2) using three input categorical components, such as an occurrence or absence of sMetS and affiliation with a given age group (60–74 vs. over 75 years) and the observed output data expressed by the cognitive performance group (below or above the norm) with the success rate of classification of $73\%$ ($p \leq 0.001$). The odds ratio for the age group 75+ against the youngers was 5.54; Cl $95\%$ = 3.24–9.83 ($p \leq 0.001$), and this parameter for the occurrence of sMetS against the missing metabolic syndrome was as high as 2.04; CI $95\%$ = 1.11–3.87 ($p \leq 0.05$), respectively. The odds ratio for the lower education group against the higher was 3.88; CI $95\%$ = 1.87–8.46 ($p \leq 0.001$). We also performed an alternative predictive model based on the number of MetS components and patients’ cognitive performance expressed on the MoCA scale. The results of this model predicted a negative impact on the cognitive performance given by MoCA levels with the increasing number of MetS components ($r = 0.44$; $p \leq 0.05$) at the success rate of classification of $61\%$. The addition of other input parameters (gender, physical activity, smoking habits) that were available in the research did not improve the quality of the model.
## 4. Discussion
Previously, in a pilot study investigating the feasibility of cognitive screening within extended pharmaceutical care in elderly patients with sMetS [10,11], we reported that the population over 60 years of age exhibits lower cognitive performance in MoCA test and subnormal MoCA scores are significantly present with increasing age of study participants. In this investigation which widens previous findings, we hypothesized that sMetS has a different influence on cognitive performance in the younger elderly population aged 60–74 years and the 75+ population. The main results of the present study are as follows: (i) Presence of sMetS did not have a significant effect on achieved MoCA score in elderlies aged 60–74 years; (ii) sMetS has, thought moderate but significant, effect on achieved MoCA score in participants aged 75 years and more.
## 4.1. Prevalence of MetS and Cognitive Impairment in Elderly
Several recent studies reported that MetS increases the risk of developing CI or dementia for elderly patients aged 60–75 [22,31] but not in the 75+ elderly population [25,28,34]. These outcomes may have been related to a survival bias because participants with more severe MetS may have passed away earlier than reaching the older age [28]. Our findings did not show an association between sMetS and lower MoCA scores in participants aged 60–74 years compared to age-matched patients without sMetS. The potential explanations of controversy may lie in the possible influence of single MetS components as they strongly correlate with lower cognitive performance [2]. We can only speculate that there could be a more significant substantial influence of age than sMetS on CI in younger seniors.
## 4.2. Prevalence of MetS and Cognitive Impairment in Younger Elderly Patients
Recent studies conferred that MetS-related CI that has been observed in younger elderly participants aged 60–74 years [22,31] tends to diminish after reaching age 75+ [34] and can disappear or reverse in an oldest-old cohort [29,30]. Instead, our results showed the opposite, i.e., a minimal but significantly higher occurrence of MoCA ≤ 24 points in 75+ subcohort with sMetS when compared to the 75+ sMetS− group. Decelerated CI related to MetS was shown in the 75+ cohort [28], mainly in 85+ [30].
## 4.3. Prevalence of MetS and Cognitive Impairment in Older Elderly Patients
The presence of MetS in 75+ may be a protective evolutionary factor against the harmful aging process [28], and it may also have survival benefits in 75+ individuals with cardiovascular diseases [27,35]. Individuals with cardiovascular diseases who reached the age of 85+ may be relatively less susceptible to the adverse effects of MetS and its components [29,30]. Late-life MetS can also suppress the effects of other risk factors for the deterioration of cognitive features, such as malnutrition [36]. Weight loss may be a potential risk factor for CI or Alzheimer’s disease and a part of the process of dementia [37]. Our findings support the hypothesis that the effect of MetS on cognitive function with advancing age (after 75 years) is relatively weakened and that individuals with components of MetS aged 85+ years are probably more resistant to the effect of MetS on cognition.
## 4.4. Coexistence of the Three Risk Factors: Occurrence of MetS, Age 75+, Lower Education Predicts Lower Cognitive Performance
Our predictive model for estimation of CI status was able to discriminate between individuals with (MoCA score ≤ 24) and without impaired cognitive functions (MoCA score >24) using three simple variables— the age group (60–74 vs. over 75 years, presence or absence of MetS and lower and higher education level) and this was superior to the predictive model using the number of MetS components. It might represent a simple tool for pharmacists to identify risk patients for CI who could need an individual approach in pharmaceutical care, e.g., control and management of modifiable risk factors for CI, revision of the medical list, and management of medication with potential risk for CI. Risk patients for CI also may undergo cognitive screening in a pharmacy and then be advised to visit a specialist when needed. Although previously suggested predictive models [38,39] reached higher predictive performance than ours, they used various parameters such as subjective well-being, educational level, marital status, and the presence of other chronic diseases obtained within the medical examination. The advantage of our predictive model lies in applying a few easy predictors to collect within routine pharmaceutical counselling.
## 4.5. Possible Pathological Background Explaining the Link between sMetS and CI
Previously [10], we reported an influence of the individual sMetS components, type 2 diabetes mellitus, hypertension and obesity, but not dyslipidaemias, on lower cognitive performance. This is also relevant to current findings.
First, numerous epidemical studies supported that diabetes is closely related to a higher risk of cognitive decline [40], including mild cognitive impairment and dementia. At the same time, cognitive dysfunction is increasingly recognised as an important comorbidity and complication of diabetes that affects patients’ quality of life, diabetes self-monitoring, and is related to diabetes treatment-related complications [41]. Watts and colleagues [42] reported that insulin is an important predictor of cognitive performance and decline, in opposite directions. In healthy older patients with normal cognition, higher insulin predicted greater cognitive impairment on attention and verbal memory. In contrast, in the group with early Alzheimer’s disease, higher insulin was associated with better cognitive performance in attention and verbal memory. *In* general, hyperglycaemia is associated with lower cognitive abilities and with a prevalence of mild cognitive impairment in elderly subjects [2] and achieved a score in test Mini-Mental State *Examination is* negatively correlated with fasting hyperglycaemia in the elderly population [2]. Diabetes is in close association with a high risk for hyperglycaemia and hypoglycaemia events, mainly in the elderly, which may be caused by the disease itself or by the glucose-lowering medication and may lead to impairment of cognitive features. Cognitive dysfunction can also predict these complications. Early identification of individuals, particularly in older age, with mild cognitive decline and adequate intervention, can improve adherence and may help to avoid later complications [41].
Second, a number of studies unveiled a relationship between high blood pressure and cognition in the elderly population. Their results showed a significant association between elevated blood pressure and lower cognitive performance in older subjects [2,43]. Combination of hypertension in midlife and low diastolic blood pressure in late-life were in relationship with reduction of brain volume and lower cognitive performance in the aging population [44,45]. In addition, longitudinal study demonstrated that long duration hypertension predicted cognitive decline independent of age [46]. In line with this, women at the age of 75 years had faster declines in global cognition associated with higher systolic blood pressure and lower diastolic blood pressure [47].
Third, also a relationship between obesity and worsened cognitive performance was investigated by many studies though outcomes are controversial. While being overweight is related to a lower risk for cognitive decline in the elderly population, central obesity increases the risk for it [48]. While obesity, as a component of MetS, in young and middle age means a risk factor for cardiovascular and cerebrovascular events [49], likewise weight loss later in life can mean an early warning signal for both development of Alzheimer’s disease and mild cognitive impairment [37]. The possible explanation may lay in a possible key link between obesity, but also other components of MetS, and cognitive decline as a consequence of inflammation and oxidative stress in the brain tissues [50].
## 4.6. Limitations
Our study has certain limitations in addition to cohort size. First, we used only the medication list of patients and diagnoses on the prescription to identify sMetS components. Second, pharmacotherapy of other possible morbidities was not analysed.
Also, possible biases might occur. The main sources of probable data distortion in our research are selection, information, and confounding bias. We assume the most significant contribution of selection bias. It is well known that age, education and estimated premorbid intelligence correlate significantly with the total MoCA score. Since it was a pilot study, the extent of these individual contributions was not estimated.
## 5. Conclusions
We found a higher prevalence of sMetS, the number of sMetS components and lower cognitive performance in MoCA in patients aged 75+. We confirmed the hypothesis that advancing age has a significant influence on cognition in both age groups (60–74 years vs. 75+). We observed a moderate but significant link between sMetS and CI exclusively in individuals aged 75+ but not in younger old participants. This finding confirms that metabolic syndrome substantially contributes to loss of cognitive performance during senescence, and it should also be considered when providing pharmaceutical services, particularly in adults aged 75+. Considering that forgetfulness or impaired memory is a common reason for low adherence in the elderly, early identification of elderly patients with potential cognitive impairment can help control modifiable risk factors for CI, prevent irregular medication use or non-adherence to medication and thus delay further complications.
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|
---
title: 'Assessment of the Possibility of Using the Laryngoscopes Macintosh, McCoy,
Miller, Intubrite, VieScope and I-View for Intubation in Simulated Out-of-Hospital
Conditions by People without Clinical Experience: A Randomized Crossover Manikin
Study'
authors:
- Paweł Ratajczyk
- Przemysław Kluj
- Przemysław Dolder
- Bartosz Szmyd
- Tomasz Gaszyński
journal: Healthcare
year: 2023
pmcid: PMC10000538
doi: 10.3390/healthcare11050661
license: CC BY 4.0
---
# Assessment of the Possibility of Using the Laryngoscopes Macintosh, McCoy, Miller, Intubrite, VieScope and I-View for Intubation in Simulated Out-of-Hospital Conditions by People without Clinical Experience: A Randomized Crossover Manikin Study
## Abstract
The aim of the study was to evaluate the laryngoscopes Macintosh, Miller, McCoy, Intubrite, VieScope and I-View in simulated out-of-hospital conditions when used by people without clinical experience, and to choose the one that, in the case of failure of the first intubation (FI), gives the highest probability of successful second (SI) or third (TI). For FI, the highest success rate (HSR) was observed for I-View and the lowest (LSR) for Macintosh ($90\%$ vs. $60\%$; $p \leq 0.001$); for SI, HSR was observed for I-View and LSR for Miller ($95\%$ vs. 66,$7\%$; $p \leq 0001$); and for TI, HSR was observed for I-View and LSR for Miller, McCoy and VieScope ($98.33\%$ vs. $70\%$; $p \leq 0.001$). A significant shortening of intubation time between FI and TI was observed for Macintosh (38.95 (IQR: 30.1–47.025) vs. 32.4 (IQR: 29–39.175), $$p \leq 0.0132$$), McCoy (39.3 (IQR: 31.1–48.15) vs. 28.75 (IQR: 26.475–35.7), $p \leq 0.001$), Intubrite (26.4 (IQR: 21.4–32.3) vs. 20.7 (IQR: 18.3–24.45), $p \leq 0.001$), and I-View (21 (IQR: 17.375–25.1) vs. 18 (IQR: 15.95–20.5), $p \leq 0.001$). According to the respondents, the easiest laryngo- scopes to use were I-View and Intubrite, while the most difficult was Miller. The study shows that I-View and Intubrite are the most useful devices, combining high efficiency with a statistically significant reduction in time between successive attempts.
## 1. Introduction
Ensuring airway patency is the primary task of a paramedic in a patient with symptoms of respiratory failure [1]. It enables the delivery of oxygen to the lungs and the elimination of carbon dioxide from the body [2]. Various devices are used to obtain airway patency, e.g., oropharyngeal, nasopharyngeal, or supralaryngeal airway devices. However, the gold standard to ensure airway patency and at the same time to protect the lungs against the aspiration of food content is endotracheal intubation [2]. Correct intubation requires not only theoretical knowledge but also considerable manual skills, which deteriorate if not constantly improved [3]. This especially applies to people who do not perform it on a daily basis [1].
In out-of-hospital conditions, endotracheal intubation is most often performed at the ground level in conditions requiring the adoption of non-physiological and non-ergonomic body positions, often in unfavorable environmental conditions. This results in a significantly reduced level of comfort for the professional, which together with the stressful situation related to the patient’s life-threatening condition and responsibility for his or her health may translate into the effectiveness of intubation [4].
Difficult or failed tracheal intubation is a well-known cause of morbidity and mortality associated with anesthesia and emergency medicine [5]. It has been proven that repeated intubation attempts are associated with an increased incidence of adverse events [6], transport delay, prolonged hospitalization, poorer neurological outcomes [7] and increased mortality [8]. In the hospital setting, video laryngoscopy has been shown to reduce the number of failed intubations, improve the view of the glottis, and reduce airway trauma [1]. However, there are only a few heterogeneous studies comparing video laryngoscopy and direct laryngoscopy in the pre-hospital setting [9]. Moreover, in pre-hospital care, the success of intubation depends not only on the type of laryngoscope used, but also on the training and experience of the healthcare provider with the device.
All these factors result in prolonged intubation, when intubation in out-of-hospital conditions are performed by people with little experience [10].
Therefore, it seems reasonable to search for a device whose use by people with little or minimal clinical experience will result in the most effective and quickest endotracheal intubation, and at the same time will result in the shortest learning effect in the event of potential failures [4].
The aim of the study was to assess the possibility of using the following laryngoscopes, Macintosh, Miller, McCoy, Intubrite, VieScope and the I-View video laryngoscope, in simulated out-of-hospital conditions by providers without clinical experience, and to choose the laryngoscope among them that, in the case of a failed first intubation, offers the greatest possibility of successful second or third intubation as soon as possible. The secondary aim was to assess the learning and teaching aspect of laryngoscopy for paramedics regarding the third attempt of intubation using videodevices or other laryngoscopes.
In the available literature, there are little data comparing intubation times in consecutive intubation attempts. It seems to us that there is quite a significant dependency conditioning the potential usefulness of a given device in medical rescue, especially when it is used by people without clinical experience, as repeated, prolonged intubation attempts are associated with a later poor prognosis in patients [7].
## 2.1. Materials
In the study, we compared the majority of laryngoscopes available on the market that enable direct laryngoscopy, Macintosh (HEINE Optotechnik GmbH & Co. KG, Gilching, Germany), Miller (Scope Medical Devices Pvt. Ltd., Ambala City, India), McCoy (McCoy Truphatek, Jerusalem, Israel), Intubrite® (LLC; Vista, CA, USA), VieScope® (Adroit Surgical, Oklahoma City, OK, USA) with a dedicated 15 Fr Voir Bougie guidewire, and I-View™ VL video laryngoscope (Intersurgical Ltd., Wokingham, Berkshire, UK), in a simulated out-of-hospital setting when used by people with little clinical experience on a manikin model (Laerdal Airway Management Trainer Stavanger Norway manikin of universal difficulty) (Scheme 1.).
Endotracheal tubes No. 7 were used for intubation. In each case, the endotracheal tubes and guides were covered with a standard lubricant dedicated to simulators. Simulated out-of-hospital conditions were created by placing the manikin in a neutral position at floor level.
## 2.2. Study Design
The study was conducted from 21 February 2021 to 8 June 2021 at the Norbert Barlicki University Teaching Hospital No. 1 in Lodz. Sixty randomly selected students in the third year of Paramedic Science, full-time first-cycle studies at the Medical University of Lodz, qualified for the study. All students signed informed consent for voluntary participation in the study.
The exclusion criterion was prior clinical experience with the laryngoscopes used in the study.
All participants listened to a 45 min lecture on the construction of laryngoscopes and the principles of using them, as well as the anatomical structure and the method and technique of intubation. After the presentation, the instructor presented the correct intubation with each of the 6 tested laryngoscopes. Then, under the supervision of the teacher, the students participated in the workshop where they had the opportunity to intubate a manikin placed on the operating table at the optimal height for each participant with each of the tested laryngoscopes. After a month, 60 students took part in the actual study.
## 2.3. Study Protocol
After signing their informed voluntary consent to participate in the study, the following demographic and medical data of the test participants were recorded in pseudonymized form:SexAgeExperience level: the number of dummy intubations performed so far by the subject and which laryngoscopes were used for previous intubations.
Participants were asked to perform three endotracheal intubations on a certified airway training manikin (Laerdal Airway Management Trainer Stavanger Norway, universal difficulty) placed at floor level in a neutral position (out-of-hospital simulation), using each of the evaluated laryngoscopes.
Each participant used all devices in random order in a crossover arrangement. The order in which the laryngoscopes were used was randomized using sealed opaque envelopes. The locked randomization strategy was generated using the Randomizer Program (randomizer.org). Flow diagram is presented in Figure 1.
Timing began with taking the laryngoscope and ended with initial ventilation with a resuscitation bag after placement and sealing of the endotracheal tube. Intubation was considered successful after confirming the breathing movements of the manikin’s lungs. The attempt was defined as a failure in the absence of manikin breathing movements or for an intubation time of more than 60 s. The criterion of over 60 s defining the intubation attempt as unsuccessful was adopted due to the fact that the study was to assess the usefulness of the devices by people without clinical experience in intubation.
After each intubation attempt with a given laryngoscope, two subsequent intubation attempts with the same device were made. After the completion of three intubations with a given laryngoscope, there was a break of at least 2 h (in order to eliminate the impact of intubation with a given laryngoscope on the use of the next device). After the break, the subject proceeded to three intubations of the manikin with a randomly selected device.
The subject assessed intubation with a given laryngoscope on the basis of a subjective assessment of tracheal intubation difficulty (number rating scale 0–10, 0: no difficulty, 10: highest difficulty).
The following data were pseudonymously recorded for all simulations:Success of intubation, position of the tube: tracheal vs. esophageal (primary endpoint);Comparison of times to ventilation in the first, second, and third intubation attempts (secondary endpoint);Feelings of subjects (secondary endpoint).
## 2.4. Statistical Analysis
The distribution of continuous data was checked with the Shapiro–Wilk test. As the average time of intubation has a distribution other than normal for at least one laryngoscope ($p \leq 0.05$), continuous data were presented as median with IQR. Furthermore, the dependencies between them were assessed with the Kruskal–Wallis test with Dunn’s post hoc tests. Dependencies for dependent data (comparisons between approaches) were assessed with the usage of the t-student test for dependent data in the case of normal distribution and Wilcoxon’s test in other cases. In both cases, the Bonferroni correction was used. Nominal data were present as n (% of total) and assessed with a test chosen based on the size of the smallest subgroup. The statistical analysis was performed using Statistica 13.1PL (StatSoft, Poland, Krakow).
## 3.1. Demographic and Contextual Data
The study included 60 third-year students of Paramedic Science (18 women and 42 men). The average age of the respondents was 22 years. Among the surveyed, 21 students had intubated the manikin fewer than 10 times so far, 22 students had performed between 10 and 20 only manikin intubations so far, and 17 students had performed more than 20 only manikin intubations. Before, everyone had used only the Macintosh laryngoscope for only manikin intubation.
## 3.2. Primary Endpoint
For the first intubation, the highest success rate was observed for the I-View laryngoscope and the lowest for the Macintosh laryngoscope: 54 ($90\%$) vs. 36 ($60\%$; $p \leq 0.001$). In the case of the second intubation, the highest success rate was observed for the I-View laryngoscope and the lowest for the Miller laryngoscope: 57 ($95\%$) vs. 40 ($66.7\%$; $p \leq 0.001$). In the case of the third intubation, the highest success rate was again observed for the I-View laryngoscope, and the lowest this time for the Miller laryngoscope, McCoy laryngoscope and VieScope laryngoscope: 59 ($98.33\%$) vs. 42 ($70\%$; $p \leq 0.001$; see Table 1).
There were no significant dependencies in the success rate between first and second attempts, second and third attempts, and first and third attempts (see Figure 2). Comparing all laryngoscopes, the highest intubation efficiency was obtained for the I-View laryngoscope ($90\%$, $95\%$, $98.33\%$), followed by the Intubrite laryngoscope (83.33, $88.3\%$, $91.67\%$) and the VieScope laryngoscope ($65\%$, $80\%$, $70\%$). The effectiveness of the remaining laryngoscopes, Macintosh, McCoy and Miller, oscillated between $60\%$ and $73.33\%$ (see Table 1). An increasing learning curve in the use of the tested laryngoscopes was observed only for laryngoscopes I-View and Intubrite (see Figure 2).
## 3.3. Secondary Endpoints
There were significant differences between the mean time of intubation with the usage of the aforementioned laryngoscopes ($p \leq 0.001$). The statistically significant results of the performed post hoc Dunn’s test are shown in Figure 3.
A significant shortening of intubation time between the first and the third intubation was observed for the Macintosh laryngoscope (38.95 (IQR: 30.1–47.025) vs. 32.4 (IQR: 29–39.175), $$p \leq 0.0132$$), McCoy laryngoscope (39.3 (IQR: 31.1–48.15) vs. 28.75 (IQR: 26.475–35.7), $p \leq 0.001$), Intubrite laryngoscope (26.4 (IQR: 21.4–32.3) vs. 20.7 (IQR: 18.3–24.45), $p \leq 0.001$), and I-View laryngoscope (21 (IQR: 17.375–25.1) vs. 18 (IQR: 15.95–20.5), $p \leq 0.001$). Additionally, a significant shortening of intubation time between the first vs. second attempt and the second vs. third attempt was observed only for Intubrite and I-View laryngo scopes. In the case of the McCoy laryngoscope, a significant improvement was observed between the second and third approaches and the first and third approaches (see Figure 4).
According to the respondents, the easiest laryngoscope to use was the I-View laryngoscope, then the Intubrite, Macintosh, and McCoy, and finally the two laryngoscopes with straight blades: Miller and VieScope (see Figure 5).
## 4. Discussion
A significant reduction in intubation time between the first and third intubations was observed for the Macintosh laryngoscope, the McCoy, Intubrite laryngoscope and I-View laryngoscope. In addition, a significant reduction in intubation time between the first and second attempts and the second and third attempts was observed only with the Intubrite and I-View laryngoscopes. For the McCoy laryngoscope, there was a significant improvement in intubation times between the second and third attempts and the first and third attempts.
The I-View laryngoscope turned out to be the easiest device to use in relation to the feelings of the subjects. This is probably due to the fact that there is no need to keep a straight line between the eyes of the professional and the glottis. In simulation, where the manikin was intubated at the floor level, the lack of the need to maintain this line is important because it does not require the intubating person to assume a more forced, bent body position, which is uncomfortable and non-ergonomic [3]. In the case of the I-View laryngoscope, the possibility of evaluating the view of the glottis thanks to the device’s monitor makes the assumed body position less bent and more friendly to the examined person [3]. This is essential when a patient is intubated by people without experience in airway management. In this situation, if there is a choice between a Macintosh laryngoscope and video laryngoscopes, including I-View, some authors suggest choosing the latter [11].
In the case of intubation by anesthesiologists, Wakabayashi believes that despite the fact that video laryngoscopes give better visibility of the glottis and are easier to use, the effectiveness and times of intubation with a classic Macintosh laryngoscope are at an acceptable level. This is vital given the widespread availability of Macintosh laryngoscopes and the still limited availability of video laryngoscopes [12].
Among the video laryngoscopes, some authors suggest that the I-View laryngoscope is a suitable device for use in difficult conditions of pre-hospital care due to its ease and single use [13]. In their study, Maritz et al. showed that the use of video laryngoscopy provided better intubation conditions, enabled better visualization of the glottis, and thus facilitated intubation when used not only by anesthesiologists with extensive experience in conventional and video laryngoscopy, but also paramedics with little previous experience in conventional and non-conventional experience in video laryngoscopy [10,14]. Although the use of video laryngoscopes did not affect the success of intubation among anesthesiologists, in the hands of paramedics with little experience in intubation it reduced the failure rate from $14.8\%$ for the conventional Macintosh laryngoscope to $3.7\%$ for the video laryngoscope [10].
The high position of the Intubrite laryngoscope is probably related to the new, ergonomic handle of this laryngoscope [3]. The introduction of more ergonomic devices would reduce the professional’s workload, which is an important factor determining patient safety [5,15,16,17]. This applies in particular to people with little experience in intubation, in whom potential intubation difficulties may occur more often, especially in the group of obese patients. These patients, due to their physique and anatomy of the airways, may require greater strength to open the airways [18]. According to J. Tesler and J. Rucker, when the Intubrite laryngoscope is used in out-of-hospital conditions the percentage of the need for repeated intubation attempts and the percentage of tooth damage decreased compared to the Macintosh laryngoscope [4]. Similar results were obtained by T. Gaszyński, who stated that in the case of the Intubrite laryngoscope the patient’s body is less traumatized compared to Macintosh laryngoscope [19].
Macintosh and McCoy laryngoscopes in our study had similar first intubation success rates of $60\%$ and $65\%$, respectively, second intubation success rates of $73.3\%$, and third intubation success rates of $73.3\%$ and $70\%$, respectively.
Furthermore, both laryngoscopes showed a significant improvement in intubation time between the first and third attempts. Moreover, McCoy laryngoscope enabled improvement between the second and third attempts. Therefore, in the case of failure of the first intubation, they give a chance for the correct placement of the endotracheal tube by people without clinical experience in subsequent attempts. However, in terms of average intubation times, both laryngoscopes were inferior to the I-View and Intubrite laryngoscopes, yet the Macintosh laryngoscope turned out to be easier to use in our study.
There are different opinions in the literature regarding clinical situations in which one of these two laryngoscopes is more useful than the other.
In a similar research model in which inexperienced medical students intubated manikins with Macintosh and McCoy laryngoscopes, Higashizawa found that the time needed to correctly position the endotracheal tube was similar with both laryngoscopes but the McCoy laryngoscope was more difficult to operate. The author suggested that the Macintosh laryngoscope is more useful for teaching inexperienced medical students [18], whereas Yildirim showed that the use of the McCoy laryngoscope shortens and provides easier intubation than the use of the Macintosh laryngoscope [20]. However, Sethuraman came to different conclusions, stating that there is no advantage in using the McCoy laryngoscope over the Macintosh laryngoscope in the examination on manikins with difficult airways [21]. In turn, in patients with limited mobility of the cervical spine, Uchida showed that the McCoy laryngoscope facilitates intubation compared to the Macintosh laryngoscope [22] and it is also superior to some videolaryngoscopes [23]. Similar conclusions were drawn by Gabbott and Maharaj [24,25]. However, the latter author believes that, although the McCoy laryngoscope improves the visualization of the larynx more than the Macintosh laryngoscope in patients with both normal and difficult airways, reducing the number of intubation attempts and the number of optimization maneuvers required, it has proven to be more difficult and less reliable than the Macintosh laryngoscope [25,26,27,28,29,30,31]. In patients with morbid obesity, Nandakumar et al. found the McCoy laryngoscope to be as effective as the Macintosh laryngoscope, and concluded that due to its widespread availability and familiarity the latter laryngoscope should be used in this group of patients [26].
In our study, the successful first, second, and third intubation rates with the Miller laryngoscope were $73.3\%$, $66.7\%$, and $70\%$, respectively. There was no statistically significant reduction in intubation time between successive intubation attempts. It also turned out to be the most difficult laryngoscope to use among our subjects. Such a distant position of this laryngoscope in our list is probably due to the fact that the need to maintain a straight line between the subject’s eye and the entrance to the airway in the case of intubation of a manikin lying at the floor level requires adopting the least comfortable position of the body. The lack of or little possibility of lifting the epiglottis when using this laryngoscope also affects the effort of the professional. Vidhya came to different conclusions, believing that the Miller’s laryngoscope enables much better visualization of the larynx than the McCoy and Macintosh laryngoscope, even in patients with difficult airways [31]. Similarly, Achen claimed that Miller’s laryngoscope enabled better visualization of the airway entrance than the Macintosh laryngoscope, and therefore everyone should learn laryngoscopy using both laryngoscopes [32]. This is important because, according to other authors, although the view of the glottis was better with the Miller laryngoscope than with the Macintosh laryngoscope, intubation conditions turned out to be better with the Macintosh laryngoscope [33,34]. The Miller laryngoscope was superior to the Macintosh and McCoy laryngoscope for visualizing the glottis in children [35,36].
The VieScope laryngoscope, a variant of the Miller laryngoscope requiring two-stage intubation, was found to be similarly effective during the first intubation as the McCoy and Macintosh laryngoscopes: $65\%$, $65\%$, and $60\%$, respectively. For the second intubation, its effectiveness increased to $80\%$ and approached that of the Intubrite laryngoscope ($88.3\%$), while during the third intubation, its effectiveness decreased to $70\%$. There was no statistically significant difference between intubation times in consecutive trials. According to the respondents, this device was also as difficult to use as the Miller laryngoscope. Such a low rank of this laryngoscope, and likewise the Miller laryngoscope, may result from the need to maintain the line of the intubating eye to the entrance to the airway and the need to adopt a more strenuous body position compared to the I-View, Intubrite, McCoy, and Macintosh laryngoscopes. The VieScope laryngoscope was originally designed for battlefield medicine, to facilitate the intubation of patients with difficult airways by being always ready for use and by focusing light on target tissues. This was confirmed in Maślanka’s study, which showed that, taking difficult airways into consideration, the VieScope laryngoscope compared to the Macintosh laryngoscope had a shorter intubation time and a higher success rate on the first attempt [37]. Similar conclusions were drawn by Wieczorek et al., who compared the use of bébé VieScope and direct laryngoscopy during emergency intubation on a model of a pediatric manikin performed by paramedics with and without personal protective equipment [38]. In their prospective, multicenter, randomized study, Szarpak et al. proved that the VieScope laryngoscope enables more effective and faster intubation than the Macintosh laryngoscope in patients with suspected or confirmed diagnosis of COVID-19, who required pre-hospital cardiopulmonary resuscitation. In these studies, the study group consisted of paramedics with clinical experience and the ability to use various laryngoscopes. In our case, there was no scenario imitating difficult airways, which could result in the lack of advantage of this laryngoscope over other devices [39]. Additionally, the study group consisted of people without clinical experience. Another difficulty for the participants in the study was the fact that it requires two stages to intubate, which can make it difficult for inexperienced people to use. This translated into a result similar to that of the Miller laryngoscope in terms of reported subjective intubation difficulties.
Similar conclusions were reached by Ecker et al., who conducted their study on a manikin under simulated conditions of massive regurgitation. In the case of patients with lower esophageal sphincter insufficiency, intubation with the VieScope laryngoscope compared to the Macintosh laryngoscope turned out to be longer, similar to our study, and resulted in a greater amount of aspirated content into the airways. The study group consisted of experienced anesthesiologists, i.e., people who perform intubation on a daily basis and have experience in solving various situations that may occur during intubation [40].
The longer intubation time of the VieScope laryngoscope compared to other airway devices was again noted by Ecker when he compared it to the Glidescope video laryngoscope in both simulated normal and difficult airways [41]. The prolongation of intubation time using the VieScope laryngoscope was also found in the case of intubation of patients qualified for elective surgical procedures, with no advantage of this laryngoscope over the Macintosh laryngoscope in this group of patients [42].
The study showed that it is necessary to constantly practice methods of airway management, including endotracheal intubation [27,28,29]. It is particularly important to learn how to use multiple laryngoscopes, as it may be useful in unconventional situations requiring the modification of technique, equipment or body position [33]. Each exercise in this area reduces the risk of making a mistake, reduces the stress of people performing a given procedure and, most importantly, increases the chance of survival of the patient and their return to the state before the event [33]. A similar conclusion was drawn by Pieters et al. from their study comparing seven videolaryngoscopes in manikin settings [42]. They compared the Macintosh classic laryngoscope, Airtraq, Storz C-MAC, Coopdech VLP-100, Storz C-MAC D-Blade, GlideScope Cobalt, McGrath Series5, and Pentax AWS. They observed 65 anesthetists, 67 residents in anesthesia, 56 paramedics and 65 medical students, intubating the trachea of a standardized manikin model. The results underline the importance of variability in device performance across individuals and staff groups, which has important implications for which devices hospital providers should rationally use. It is proven that videolaryngoscopes offer a better view of the entrance to larynx [43], and therefore reduce the risk of possible injuries related to intubation efforts [44]; however, training is still needed to avoid possible problems with the use of videolaryngoscopy [45,46]. Using these tools for learning purposes for unexperienced providers, in addition, may provide greater applicability [43,47,48].
The study has several limitations. Firstly, it was conducted on a manikin model, where simulated out-of-hospital conditions were created by placing the manikin at floor level, without the influence of other external factors affecting the effectiveness of intubation. Secondly, difficult airway scenarios were not also studied. Finally, the study group consisted of Paramedic Science students who, nevertheless, had little previous experience in intubating a dummy with a Macintosh laryngoscope due to their limited years of study.
## 5. Conclusions
Taking into account the results of the study, the I-View and Intubrite laryngoscopes turned out to be the most useful devices for intubation in simulated out-of-hospital conditions by people with no clinical experience. They combined high efficiency of intubation with statistically significant shortening of intubation times between successive attempts. Due to the small study group and the manikin model, additional studies should be conducted on a larger group of subjects.
## Figures, Scheme and Table
**Scheme 1:** *From the left: Macintosh laryngoscope, McCoy laryngoscope, Miller laryngoscope, VieScope laryngoscope, Intubrite laryngoscope, I-View laryngoscope.* **Figure 1:** *Flow chart. Each participant performed intubation in all settings in a randomized controlled order. There were no drop-outs.* **Figure 2:** *Graph of the percentage success of intubation with a given laryngoscope in subsequent attempts.* **Figure 3:** *The mean time of intubation in different intubation approaches (Kruskal–Wallis test: $p \leq 0.001$, presented p are taken from the Dunn’s test).* **Figure 4:** *Graph of mean intubation times with a given laryngoscope in subsequent intubation attempts.* **Figure 5:** *The feelings of the respondents (0—no difficulties; 10—maximum difficulties).* TABLE_PLACEHOLDER:Table 1
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|
---
title: 11,12-EET Regulates PPAR-γ Expression to Modulate TGF-β-Mediated Macrophage
Polarization
authors:
- Xiaoming Li
- Sebastian Kempf
- Stefan Günther
- Jiong Hu
- Ingrid Fleming
journal: Cells
year: 2023
pmcid: PMC10000544
doi: 10.3390/cells12050700
license: CC BY 4.0
---
# 11,12-EET Regulates PPAR-γ Expression to Modulate TGF-β-Mediated Macrophage Polarization
## Abstract
Macrophages are highly plastic immune cells that can be reprogrammed to pro-inflammatory or pro-resolving phenotypes by different stimuli and cell microenvironments. This study set out to assess gene expression changes associated with the transforming growth factor (TGF)-β-induced polarization of classically activated macrophages into a pro-resolving phenotype. Genes upregulated by TGF-β included Pparg; which encodes the transcription factor peroxisome proliferator-activated receptor (PPAR)-γ, and several PPAR-γ target genes. TGF-β also increased PPAR-γ protein expression via activation of the Alk5 receptor to increase PPAR-γ activity. Preventing PPAR-γ activation markedly impaired macrophage phagocytosis. TGF-β repolarized macrophages from animals lacking the soluble epoxide hydrolase (sEH); however, it responded differently and expressed lower levels of PPAR-γ-regulated genes. The sEH substrate 11,12-epoxyeicosatrienoic acid (EET), which was previously reported to activate PPAR-γ, was elevated in cells from sEH−/− mice. However, 11,12-EET prevented the TGF-β-induced increase in PPAR-γ levels and activity, at least partly by promoting proteasomal degradation of the transcription factor. This mechanism is likely to underlie the impact of 11,12-EET on macrophage activation and the resolution of inflammation.
## 1. Introduction
The recruitment of neutrophils and monocytes to inflamed tissue and their differentiation into macrophages is a crucial step in the inflammatory process. However, once the neutrophil respiratory burst subsides, these and other cells, i.e., macrophages, eosinophils and lymphocytes, need to be removed to restore homeostasis [1]. To support the removal of apoptotic cells and tissue debris (efferocytosis), macrophage function is altered and the cells are reprogramed into a pro-resolving phenotype. Polarized macrophages are frequently broadly classified in two main groups, i.e., classically activated (M1) macrophages which are induced by T-helper 1 (Th-1) cytokines, i.e., the combination of bacterial lipopolysaccharide (LPS) and interferon γ (IFN-γ), and alternatively activated (M2) macrophages that have a pro-resolving and pro-angiogenic phenotype, and are induced by Th-2 cytokines [2,3]. The latter group can be further subdivided into more refined phenotypes: M2a, M2b, M2c, and M2d depending on the use of different stimuli such as interleukin (IL)-4 (M2a) or transforming growth factor β (TGF-β) (M2c). However, the phenotypic characterization of macrophages is highly complicated and there are many more distinct genetic fingerprints and metabolic states than are reflected in a basic M0/M1/M2 classification [4,5,6]. Indeed, additional subtypes have been identified such as macrophages stimulated by oxidized phospholipids, oxidized LDL, or hemoglobin [3].
TGF-β is a master immune regulator and checkpoint that has a major impact on immune suppression within the tumor microenvironment [7]. It has also been implicated in poor responsiveness to cancer immunotherapy [8]. In inflamed tissues, macrophage TGF-β synthesis is stimulated by the uptake of apoptotic cells, a step that is essential for the repolarization of pro-inflammatory macrophages into a pro-resolving phenotype (for reviews see [9,10]). Although endothelial TGF-β signaling drives endothelial-to-mesenchymal transition and vascular inflammation [11], there is some controversy about the exact impact of TGF-β on atherogenesis. Rather than promoting vascular inflammation, there is evidence suggesting that TGF-β signaling plays an important role in the protection against excessive plaque inflammation, loss of collagen content, and induction of regulatory immunity (reviewed by [12,13]). The current study set out to determine changes in macrophage gene expression associated with the repolarization of classically activated (M1) macrophages into a pro-resolving phenotype by TGF-β.
## 2.1. Animals
C57BL/6N mice (6–8 weeks old) were purchased from Charles River (Sulzfeld, Germany). Floxed sEH mice (Ephx2tm1.1Arte) were generated in the C57BL/6N background by TaconicArtemis GmbH (Cologne, Germany) and crossed with Gt(ROSA)26Sortm16(Cre)Arte mice (TaconicArtemis) expressing Cre under the control of the endogenous Gt(ROSA)26Sor promoter to generate mice globally lacking sEH (sEH−/−) as described [14]. Age-, gender- and strain-matched mice were used throughout, where possible littermates were used. In cases where studying littermates was not possible, cells were isolated from age-matched C57Bl/6N mice. Preliminary experiments revealed that responses were comparable in cells from C57Bl/6N and Cre-sEHflox/flox mice and different from those of the sEH−/− (Cre+ sEHflox/flox) mice. For the isolation of bone marrow, mice were sacrificed using $4\%$ isoflurane in air and cervical dislocation.
## 2.2. Monocyte Isolation and Macrophage Polarization
Murine monocytes were isolated from the bone marrow of 8–10-week-old mice and differentiated to naïve (M0) macrophages in RPMI 1640 medium (Invitrogen; Darmstadt, Germany), containing $8\%$ heat inactivated FCS supplemented with M-CSF (15 ng/mL, Peprotech, Hamburg, Germany) and GM-CSF (15 ng/mL, Peprotech, Hamburg, Germany) for 7 days. Cells were kept in a humidified incubator at 37 °C containing $5\%$ CO2. Thereafter M0 macrophages were polarized to classical activated M1 macrophages by treating with LPS (10 ng/mL; Sigma-Aldrich, Munich, Germany) and IFN-γ (1 ng/mL; Peprotech, Hamburg, Germany) for 12 h. Pro-resolving M2c macrophages were repolarized from M1 macrophages by the addition of TGF-β1 (10 ng/mL; Peprotech, Hamburg) for 48 h, as described [6].
## 2.3. RNA Isolation and Quantitative Real Time PCR (RT-qPCR)
Total RNA was extracted and purified from murine macrophages using Tri Reagent (ThermoFisher Scientific, Karlsruhe, Germany) based on the manufacturer’s instructions. Thereafter, RNA was eluted in nuclease-free water, and its concentration was determined (λ260 nm) using a NanoDrop ND-1000 (ThermoFischer Scientific, Karlsruhe, Germany). For the generation of complementary DNA (cDNA), total RNA (500 ng) was reverse transcribed using SuperScript IV (ThermoFischer Scientific, Karlsruhe, Germany) with random hexamer primers (Promega, Madison, WI, USA). Quantitative PCR was performed using SYBR green master mix (Biozym, Hessisch Oldendorf, Germany) and appropriate primers (Table 1) in a MIC-RUN quantitative PCR system (Bio Molecular Systems, Upper Coomera, Australia). Relative RNA levels were determined using a serial dilution of a positive control. The data are shown relative to the mean of the housekeeping gene 18S RNA.
## 2.4. RNA Sequencing
Total RNA was isolated from macrophages by using RNeasy Micro kit (Qiagen, Hilden, Germany) based on manufacturer’s instructions. The RNA concentrations were determined by using NanoDrop ND-1000 (ThermoFischer Scientific, Karlsruhe, Germany; λ 260 nm). Total RNA (1 µg) was used as input for SMARTer Stranded Total RNA Sample Prep Kit-HI Mammalian (Takara Bio, Kyoto, Japan). Trimmomatic version 0.39 was employed to trim reads after a quality drop below a mean of Q20 in a window of 20 nucleotides and keeping only filtered reads longer than 15 nucleotides [15]. Reads were aligned versus Ensembl mouse genome version mm10 (Ensembl release 101) with STAR 2.7.10a [16]. Aligned reads were filtered to remove: duplicates with Picard 2.25.5 (Picard: A set of tools (in Java) for working with next generation sequencing data in the BAM format), multi-mapping, ribosomal, or mitochondrial reads. Gene counts were established with featureCounts 2.0.2 by aggregating reads overlapping exons on the correct strand excluding those overlapping multiple genes [17]. The raw count matrix was normalized with DESeq2 version 1.30.1 [18]. Contrasts were created with DESeq2 based on the raw count matrix. Genes were classified as significantly differentially expressed at average count >5, multiple testing adjusted p-value < 0.05, and log2FC > 0.585 or <−0.585. The Ensemble annotation was enriched with UniProt data [19]. The PCA, volcano plots and pathway enrichment analysis were generated using http://www.bioinformatics.com.cn/srplot, an online platform for data analysis and visualization.
## 2.5. Phagocytosis Assays
M1 polarized macrophages were treated with either solvent or the PPAR-γ antagonist; GW9662 (10 µmol/L, Merck, Darmstadt, Germany), 2 h prior to repolarization to the M2c phenotype using TGF-β1. Thereafter, cells were incubated in RPMI medium supplement with $0.1\%$ BSA (37 °C, $5\%$ CO2) and containing pHrodo Red Zymosan bioparticles (10 μg/mL, Invitrogen). After 30 min the cells were washed to remove nonphagocytosed material and zymosan uptake was visualized and quantified using an automated live cell imaging system (IncuCyte, Sartorius, Göttingen, Germany).
## 2.6. PPAR-γ Activity
PPAR-γ activity was measured using a luciferase construct (PPRE-X3-Luc, Addgene No. 1015) which contains 3 response elements (AGGACAAAGGTCA) upstream of a luciferase reporter [20]. For transfection, M0 macrophages were incubated in RPMI medium containing $0.1\%$ BSA for 2 h prior to the addition of plasmid (100 ng/mL) and Lipofectamin 3000 Transfection Reagent (ThermoFischer Scientific, Karlsruhe, Germany) according to the manufacturer’s instructions. After 24 h, the cells were polarized to M1 and M2c macrophages and stimulated as described in the results section. Luciferase activity was measured 48 h after cell polarization or stimulation with 11,12-EET (1 µmol/L, Cayman Europe, Tallinn, Estonia) using a commercially available kit (ONE-Glo Luciferase Assay System, Promega, Walldorf, Germany).
## 2.7. Immunoblotting
Cells were lysed in RIPA lysis buffer (50 mmol/L Tris/HCL pH 7.5, 150 mmol/L NaCl, 10 mmol/L NaPPi, 20 mmol/L NaF, $1\%$ sodium deoxycholate, $1\%$ Triton and $0.1\%$ SDS) enriched with protease and phosphatase inhibitors and detergent-soluble proteins were resuspended in SDS-PAGE sample buffer. Samples were separated by SDS-PAGE and subjected to Western blotting as described [21]. Membranes were blocked in $3\%$ BSA, incubated with primary antibodies in the blocking solution and horseradish peroxidase-conjugated secondary antibodies. Protein bands were visualized using Lumi-*Light plus* Western blotting substrate (Roche, Mannheim, Germany) and captured by an image acquisition system (Fusion FX7; Vilber-Lourmat, Torcy, France). The antibody used to identify PPAR-γ was from Santa Cruz (Texas, USA; Cat. # sc-7196, 1:1000), anti-non muscle myosin was from abcam (Berlin, Germany; Cat. # ab75590, 1:1000), and the anti β-actin antibody was from Linaris (Eching, Germany; Cat. # MAK6019, 1:3000). The secondary antibodies were used were: goat anti-rabbit IgG H and L chain specific peroxidase conjugate, and a goat anti-mouse IgG, H and L chain specific peroxidase conjugate (both 1:20,000; Cat. # 401393 and Cat. # 401253, Merck).
## 2.8. Statistical Analyses
Data are expressed as mean ± SEM. Statistical analysis was performed using Student’s t test, or two-way ANOVA with a Tukey’s or Sidak’s post-test. Normalized data were compared using the Kruskal–Wallis rank sum test or Kruskal–Wallis test followed and Dunn’s multiple comparison test (using Prism 9.0.2, GraphPad Software Inc., San Diego, CA, USA) as indicated in the figure legends. Values of $p \leq 0.05$ were considered statistically significant.
## 2.9. Data and Material Availability
All data associated used this study are present in the paper or the Supplementary Materials.
## 3.1. Impact of TGF-β-Induced Macrophage Repolarization on Gene Expression
Bone marrow-derived monocytes were isolated from wild-type mice and differentiated to naïve (M0) macrophages in the presence of M-CSF and GM-CSF for 7 days. Thereafter, M0 macrophages were either polarized to classically activated (M1) macrophages by adding lipopolysaccharide (LPS) and interferon (IFN)-γ for 12 h or into pro-resolving M2c by treating M1 macrophages with TGF-β1 for 48 h. RNA-sequencing (RNA-seq) was then performed to identify changes in gene expression associated with macrophage polarization. Principal component analysis (PCA) confirmed that the three groups of macrophages clustered together with clear differences between the polarization types (Figure 1A, Table S1). As expected, the expression of the classical M1 marker genes Nos2, Ptgs2, Il1b, and Nlrp3 were significantly higher in M1 versus M2c polarized macrophages. On the other hand, the typical M2/M2c markers, i.e., Arg1, Vegfa were higher in M2c than in M1 polarized macrophages (Figure 1B). A closer analysis of the genes differentially expressed in M2c versus M1-polarized macrophages revealed additional marked differences, with TGF-β inducing the upregulation of 2952 genes and the downregulation of 2051 genes, including the pro-inflammatory genes Cxcr4, Ptgs2 and Angptl4. One of the genes whose expression was significantly increased in M2c macrophages was Pparg and gene set enrichment analysis identified changes in the expression of several targets of the peroxisome proliferator-activated receptor (PPAR) family of transcription factors (Figure 1C). PPAR-γ-regulated genes induced by TGF-β included Angptl4, Abcd2, Eepd1 and Tmem8.
## 3.2. TGF-β-induced M2c Macrophage Polarization Relies on PPAR-γ and Alk5 Activation
To determine the importance of PPAR-γ on the regulation of selected macrophage genes, we determined the impact of the PPAR-γ antagonist GW9662 on the expression of three selected genes in M2c macrophages, i.e., Cxcr4 (higher in M2c), as well as Ptgs2 and Ptx3 (both higher in M1). While there was no significant effect of PPAR-γ antagonism on Cxcr4 expression, cells treated with GW9662 expressed significantly higher levels of Ptgs2 and Ptx3 than cells treated with solvent (Figure 2A). One characteristic of the latter cells is their ability to phagocytose cell debris. While M2c polarized murine macrophages effectively phagocytosed zymosan, particle uptake was clearly reduced in cells treated with the PPAR-γ antagonist (Figure 2B). These observations imply that PPAR-γ activation is required for the down regulation of some pro-inflammatory genes as well as to support the induction of a pro-resolving phenotype by TGF-β.
Consistent with the latter observations, PPAR-γ expression was significantly elevated in M2c versus M1 or M0 macrophages (Figure 3A). Given that M2c polarization was induced by adding TGF-β to M1 polarized macrophages, we determined which TGF-β type I receptor, i.e., activin receptor-like kinase (Alk) 1 or Alk5, mediated the TGF-β-induced increase in PPAR-γ levels. While neither solvent, nor the Alk1 inhibitor; LDN193189 prevented the TGF-β-induced increase in PPAR-γ (Figure 3B), the response was abolished in macrophages pretreated with the Alk5 inhibitor; SD208.
## 3.3. PPARγ Activity in Differentially Polarized Macrophages from Wild-Type and sEH−/− Mice
Next, we set out to determine whether or not mediators known to regulate PPAR-γ were implicated in the TGF-β-induced changes in PPAR levels and gene expression. Given that arachidonic acid metabolism was one of the pathways altered by TGF-β (see Figure 1C), we focused on the role of the potential role of arachidonic acid epoxides. These fatty acid mediators; such as 11,12-epoxyeicosatrienoic acid (11,12-EET), are reported to activate PPAR-γ [22,23,24,25,26], and their cellular levels are largely determined by the activity of the soluble epoxide hydrolase (sEH). Therefore, a luciferase construct containing three PPAR-γ responsive elements was expressed in macrophages from wild-type mice that were then polarized to the M1 and M2c phenotypes. Consistent with the increase in PPAR-γ protein levels, luciferase activity was clearly increased in the M2c macrophages from wild-type mice (Figure 4A). Deletion of the sEH significantly blunted the latter response, which was reflected in the differential expression of PPAR-γ-regulated genes in M2c macrophages from the two genotypes (Figure 4B, Table S2). Indeed, the well-characterized PPAR-γ-regulated genes Gipr, Vldlr, and Rbp1 were all expressed at significantly lower levels in M2c macrophages from sEH−/− versus wild-type mice. A series of fatty acid epoxides are metabolized by the sEH and it was possible to demonstrate higher 11,12-EET and lower levels of its sEH-generated diol; 11,12-dihydroxyeicosatrienoic acid (11,12-DHET), in M2c polarized macrophages from sEH−/− versus wild-type mice (Figure 4C). Moreover, treating M1 polarized macrophages from wild-type mice with 11,12-EET prior to the repolarization with TGF-β, also decreased PPAR-γ activity (Figure 4D).
## 3.4. Regulation of PPAR-γ Levels by 11,12-EET
Comparison of the effects of 11,12-EET versus those of its diol; 11,12-DHET on PPAR-γ protein levels were assessed next. This revealed that the sEH substrate; 11,12-EET, effectively prevented the TGF-β-induced increase in PPAR-γ protein levels in murine macrophages (Figure 5A). 11,12-DHET had no effect. Somewhat unexpectedly, 11,12-EET altered PPAR-γ protein levels without altering Pparg expression (Figure 5B) indicating that 11,12-EET may affect the stability of the PPAR-γ protein. At least in adipocytes, ligand-dependent PPAR-γ activation is associated with its subsequent proteasomal degradation [27]. To determine whether or not 11,12-EET decreased PPAR-γ levels by stimulating its proteasomal degradation, experiments were performed in the absence and presence of the proteasome inhibitor MG132. As before, 11,12-EET, but not 11,12-DHET, decreased PPAR-γ protein levels in M2c polarized macrophages and proteasome inhibition prevented the effect (Figure 5C).
## 4. Discussion
The results of this investigation revealed that the TGF-β-dependent repolarization of classically activated (M1) macrophages into a pro-resolving, highly phagocytic phenotype (M2c), relies on the increased expression and activation of PPAR-γ. Deletion of the sEH, to increase cellular levels of fatty acid epoxides, largely prevented TGF-β-induced changes in macrophage gene expression as well as PPAR-γ activation. The effect seen in macrophages from sEH−/− was reproduced in cells from wild-type mice treated with the sEH substrate 11,12-EET and was attributed, at least in part, to the accelerated proteasomal degradation of PPAR-γ.
In our study, we set out to determine changes in macrophage gene expression associated with the repolarization of classically activated (M1) macrophages into a pro-resolving phenotype by TGF-β. It is not surprising that repolarization resulted in marked alterations in macrophage gene expression and a decrease in the expression of pro-inflammatory markers. However, the observation that many of the genes increased in TGF-β-treated macrophages were classical PPAR-γ targets, e.g., Abcd2, Eepd1, and Tmem8 was unexpected as TGF-β is a multifunctional cytokine that drives inflammation, fibrosis and cell differentiation, while PPAR-γ activation tends to promote the opposite effects [28]. The impact of TGF-β on gene expression was however consistent with its ability to increase PPAR-γ protein levels as well as transcription factor activity. The changes in gene expression were reflected in functional alterations as zymosan phagocytosis by TGF-β-repolarized macrophages was clearly attenuated in cells treated with a PPAR-γ inhibitor. Our results are consistent with recent reports from other groups that linked the actions of TGF-β with the activation of PPAR-γ signaling (reviewed by [29]). For example, TGF-β signaling and the upregulation of PPAR-γ was reported to be essential for the development and homeostasis of alveolar macrophages [30]. On the other hand, PPAR-γ was reported to interact with Stat3 and Smad3 to interfere with TGF-β signaling and account for the functional antagonism between BMP2 and TGF-β1 pathways in vascular smooth muscle cells [31]. Thus, it seems likely that a complex crosstalk exists between the two pathways. The results of our study also indicate that in macrophages, the TGF-β-induced increase in PPAR-γ expression relies on the activation of Alk5 and as such fits well with a previous report that TGF-β induces M2-like macrophage polarization via Snail-mediated suppression of a pro-inflammatory phenotype, as the induction of *Snail is* also mediated by Alk5 [20].
PPARs are ligand-inducible transcription factors and are considered important therapeutic targets as they exert anti-atherogenic and anti-inflammatory effects on the vascular wall and immune cells, as well as acting to reduce insulin resistance and dyslipidaemia [32]. However, unlike many receptors that possess a limited number of ligands, there are numerous natural PPAR-γ ligands, in particular mediators derived from polyunsaturated fatty acids [33]. The EETs are among the latter compounds and are generated by the sequential action of cytochrome P450 enzymes and the sEH [34]. These fatty acid mediators are particularly interesting given that their actions have been attributed to PPAR activation [22,23,24,25,26], and the inhibition or deletion of the sEH to increase EET levels has anti-atherosclerotic effects in mouse models [35,36]. In our study, we observed that the activity of PPAR-γ was lower in TGF-β-stimulated macrophages from sEH−/− (EET high) than from wild-type (EET low) mice. While these findings were consistent with the clearly decreased levels of PPAR-γ protein in sEH-deficient macrophages, they seemed to be a direct contradiction of previous reports. The timing of the experiments performed can go a long way to accounting for the observations made as PPAR-γ activity was generally assessed 48 h after TGF-β addition or stimulation with 11,12-EET. Thus, 11,12-EET probably initiates a transient increase in PPAR-γ activity that is terminated by an EET-stimulated pathway that results in PPAR-γ degradation. Given that PPAR-γ levels were not decreased by 11,12-EET in cells treated with MG 132 we propose that 11,12-EET can stimulate the proteasomal degradation of PPAR-γ. Certainly, PPAR-γ levels can be regulated by protein ubiquitination and degradation [27]. Which ubiquitin ligase was activated by 11,12-EET was not studied but there is circumstantial evidence to link 11,12-EET with increased ubiquitination as the cardiomyocyte-specific overexpression of CYP2J2, which generates 11,12-EET and has been reported to promote the ubiquitination of the pattern recognition receptor NLRX1 [37].
Taken together, our results indicate that macrophage levels of the sEH substrate; 11,12-EET, can modulate macrophage polarization by TGF-β, at least partly by promoting the ubiquitination and degradation of PPAR-γ. Given that sEH inhibition prevents the development of atherosclerosis in mice [35,36], and the conversion of inflammatory macrophages to the M2 phenotype drives atherosclerosis regression [38], it may be interesting to determine how much of the phenotype observed can be attributed to changes in PPAR-γ expression.
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|
---
title: 'Rational Food Design Targeting Micronutrient Deficiencies in Adolescents:
Nutritional, Acoustic-Mechanical and Sensory Properties of Chickpea-Rice Biscuits'
authors:
- Clara Talens
- Laura Garcia-Fontanals
- Paula Fabregat
- Mónica Ibargüen
journal: Foods
year: 2023
pmcid: PMC10000554
doi: 10.3390/foods12050952
license: CC BY 4.0
---
# Rational Food Design Targeting Micronutrient Deficiencies in Adolescents: Nutritional, Acoustic-Mechanical and Sensory Properties of Chickpea-Rice Biscuits
## Abstract
“Hidden hunger”, the deficiency of important mineral micronutrients, affects more than 2 billion people globally. Adolescence is unquestionably a period of nutritional risk, given the high nutritional requirements for growth and development, erratic or capricious diets and the increased consumption of snacks. This study applied the rational food design approach to obtain micronutrient-dense biscuits by combining chickpea and rice flours to achieve an optimal nutritional profile, crunchy texture and appealing flavour. The perception of 33 adolescents regarding the suitability of such biscuits as a mid-morning snack was examined. Four biscuits were formulated, with different ratios of chickpea and rice flours (CF:RF): G100:0, G75:25, G50:50 and G25:75. Nutritional content, baking loss, acoustic-texture and sensory analyses were carried out. On average, the mineral content of biscuits with the CF:RF ratio of 100:0 doubled compared with the 25:75 formula. The dietary reference values for iron, potassium and zinc reached $100\%$ in the biscuits with CF:RF ratios of 50:50, 75:25 and 100:0, respectively. The analysis of mechanical properties revealed that samples G100:0 and G75:25 were harder than the others. Sample G100:0 showed the highest sound pressure level (Smax). Sensory analysis showed that increasing the proportion of CF in the formulation augments the grittiness, hardness, chewiness and crunchiness. Most of the adolescents ($72.7\%$) were habitual snack consumers; $52\%$ awarded scores ≥ 6 (out of 9) to biscuit G50:50 for its overall quality, $24\%$ described its flavour as “biscuit” and $12\%$ as “nutty”. However, $55\%$ of the participants could not pinpoint any dominant flavour. In conclusion, it is possible to design nutrient-dense snacks that meet the micronutrient requirements and sensory expectations of adolescents by combining flours naturally rich in micronutrients.
## 1. Introduction
The new WHO European Regional Obesity Report 2022, published on 3 May 2022 by the WHO Regional Office for Europe, reveals that overweight and obesity rates have reached epidemic proportions across the region and are still escalating. None of the 53 member states is on track to meet the WHO Global Noncommunicable Disease (NCD) target of halting the rise of obesity by 2025 [1].
Early studies from several countries in the region indicate that the prevalence of overweight and obesity and high mean body mass index have increased in children and adolescents during the COVID-19 pandemic [2,3]. Moreover, adolescence is unquestionably a period of nutritional risk given the increased nutritional requirements for growth and development, erratic or capricious diets and the increased consumption of snacks, fast food and refreshing beverages. During this period, deficiencies of specific minerals such as Ca, Fe and Zn and vitamins such as A, D, B6, B12, riboflavin, niacin, thiamine and folic acid are common. Low intake of fibre and complex carbohydrates has also been noted [4].
The AVENA study (Alimentación y Valoración del Estado Nutricional en Adolescentes) conducted in 2012 with Spanish adolescents revealed that certain healthy dietary habits (i.e., mid-morning snack, afternoon snack, more than 4 meals per day, adequate eating speed) were associated with low body fat [5]). Moreover, public health recommendations in paediatric scientific journals suggest that most children should eat between four and six times a day [6,7].
Snacks can account for up to a third of the daily energy intake. Thus, it is of great interest to the food industry to provide snacks of high nutritional quality. This could be achieved by adding protective factors and avoiding risk factors (for infants and adolescents), so the products might be recommended as meeting nutritional requirements [8]. To improve the nutritional quality of snacks, one should reduce risk factors such as sugar, salt, refined grain flour and energy content. Likewise, the proportion of nutritionally valuable ingredients providing protein, fibre and micronutrients should be increased.
Legumes are a staple food in many Asian, African and Mediterranean countries. “ Hidden hunger”, the deficiency of important mineral micronutrients, affects more than 2 billion people globally [9]. Naturally rich in micronutrients (iron and zinc), legume flours could be used to formulate recipes for nutritionally rich and balanced biscuits. The cereal industry often uses rice as a substitute for wheat. Refined rice flour has nutritional disadvantages as it is high in carbohydrates but low in protein, fibre and micronutrients. However, rice flour could be combined with other ingredients, such as chickpea flour, to develop value-added snacks with a rich and well-balanced nutrient composition [10,11].
Chickpeas are highly nutritious due to their low lipid content (2.7–$6.48\%$), rich in polyunsaturated fatty acids ($66\%$), and high content of protein (17–$22\%$), starch ($52.5\%$), dietary fibre (18–$22\%$), bioactive compounds and essential vitamins and minerals. These legumes are a good source of calcium, potassium, magnesium, iron, zinc and important vitamins such as riboflavin (B2), niacin (B3), thiamine (B1), folic acid (B9), β-carotene (vitamin A precursor), vitamin E, vitamin C, pantothenic acid (B5) and pyridoxine (B6). Therefore, chickpea products can complement the vitamin pool supplied by other foods [12]. The demand for chickpeas as a functional ingredient in food production is on the increase. Chickpea flour has been incorporated into a variety of goods, such as breads, biscuits, pasta, snacks and dairy, to improve their nutritional value [11]. Goñi and Valentín-Gamazo [13] have shown that adding $25\%$ of chickpea flour to wheat pasta decreases the glycaemic index and increases the mineral, fat and resistant starch content. Moreover, the consumers of chickpeas and/or hummus have a higher nutrient intake of dietary fibre, polyunsaturated fatty acids (PUFAs), vitamins A, E and C, folic acid, magnesium, potassium and iron than non-consumers [14]. Importantly, chickpeas have high levels of the micronutrients usually lacking in diets consumed by adolescents.
The term Rational Food Design (RFD) has been used in previous research to refer to the design of food products with specific functionalities to satisfy “the needs and desires” of the consumer [15]. The official meaning of the word “rational” is “based on facts and reason”, therefore, RFD should rely on any type of scientific and technological knowledge to design food formulas and food structures. Previous studies have applied RFD principles to create high-quality healthy foods by including in the RFD approach imaging technologies, such as atomic force microscopy [16], or microtechnology for testing ingredient functionality [17]. However, to the best of our knowledge, the inclusion of the dietary reference values (DRV), provided by EFSA, as formula design variables, and acoustic-texture measurements, as response variables, during the RFD process has not been approached yet.
The aim of this study is twofold. Firstly, the study will include EFSA’s DRV for adolescents from 11 to 14 years, engaged in all types of physical activity, using the RFD approach to obtain micronutrient-dense biscuits by combining chickpea and rice flours, to achieve an optimal nutritional profile, crunchy texture and appealing flavour. Secondly, the perception of adolescents regarding the suitability of such biscuits as a mid-morning snack will be examined.
## 2.1. Rational Food Design
In this study, we suggest a widening of the use of RFD for targeted nutrition (aimed at the nutritional needs of specific groups). For this purpose, RFD could be applied in two steps of the food development process:1.during the formula design step, which is also part of the process of structure design, when two important decisions are made: [1] the target nutritional composition of the new or improved formula; [2] the ingredients and raw materials used based on their macro- and micronutrients composition.2.during the step preceding the consumer tasting, when a selection of the optimum samples has to be made based on acoustic-textural and sensory analysis.
The rationale applied during the formula design was based on EFSA’s DRV. For adequate energy distribution, the daily food intake should be supplied in 5 meals (Table 1). The proportions should be as follows: $20\%$ at breakfast, 10–$15\%$ at mid-morning meal, 25–$30\%$ at lunch, 10–$15\%$ at evening snack and $25\%$ at dinner. Eating between meals should be avoided [6,7,18].
Many scientific groups and societies recommend distributing energy and nutrients among four to five daily meals to improve health [19,20]. According to the American Heart Association [21], the mid-morning snack should be easily digestible and not excessive in calories to sustain the feeling of satiety and reach lunchtime with a sufficient but not unmanageable appetite. Table 2 shows the nutritional requirements for mid-morning snacks based on the European Food Safety Authority (EFSA) dietary reference values (DRV) for the EU [22]. The target population are healthy individuals of both genders, aged 11–14, engaged in all types of physical activity.
Therefore, although mid-morning snacks can reduce appetite during lunch, it is crucial to consider their nutritional quality.
## 2.2. Ingredients
Chickpea flour (Don Pedro) (CF) was obtained from Legumbres Pedro S.L. (Cadiz, Spain). The flour contained 15.0 g of moisture, 53.5 g of carbohydrates, 4.4 g of sugar, 20.5 g of protein and 6.6 g of fat (per 100 g).
Rice flour Remyflo R 200 T (RF) was purchased from BENEO-Remy N.V. (Leuven, Belgium). The composition per 100 g was 10.0 g of moisture, 81.0 g of carbohydrates, 8 g of proteins and 1 g of fat.
Sunflower oil, orange blossom water, sugar and salt were bought at the local supermarket (Makro, Derio, Spain).
## 2.3. Biscuit-Making Procedure
Four biscuits were formulated using different ratios of chickpea flour and rice flour (CF:RF). These ratios were: 100:0, 75:25, 50:50, 25:75. The biscuits were made following the formulations, in which only chickpea and rice flour percentages changed. The rest of the ingredients were kept constant (Table 3).
All ingredients were weighed using high-precision (±0.0001 g) scales AB304-S (Mettler Toledo, Greifensee, Switzerland). The powdered ingredients were mixed using a planetary mixer Sammic BM-5 (Sammic S.L., Azkoitia, Spain), for 30 s at 202 rpm. Then, water was added and the mixture was stirred for 10 s at 260 rpm. Finally, oil was mixed in for 30 s at 202 rpm. Batches of 500 g were produced in triplicate. The dough was removed from the mixer and allowed to rest for 10 min. It was processed further using a sheeting machine Sammic FMI-31 (Sammic S.L., Azkoitia, Spain). The dough sheets were flattened manually with a wooden rolling pin to an approximately 2.5-mm thickness and then cut into circles of 4.5 cm in diameter. The biscuits were baked in an electric oven (MIWE Condo type CO 2 0608, MIWE GmbH, Arnstein, Germany) at 150 °C for 25–35 min and then at 120 °C for 20–30 min. The biscuits were cooled for 30 min on a rack and stored in airtight containers until evaluation.
## 2.4. Physico-Chemical Analysis
Powdered samples of the four biscuit types were analysed according to the ISO standards for moisture (ISO 1442:1997), protein content (ISO 937:1978), crude fat (ISO 1443:1973), fatty acids profile (by chromatography), salt content estimated by analysis of chloride by potentiometric titration with silver nitrate solution, and total sugars (ISO 22184:2021). Total dietary fibre was determined by the AOAC enzymatic gravimetric method 991.43. The carbohydrate content was determined by difference. After proximate analysis, energy values were calculated using the *Atwater* general factors (4 kcal/g for protein and carbohydrates, 9 kcal/g for fat and 2 kcal/g for fibre). Samples were analysed in triplicate. The mineral composition (calcium, phosphorus, iron, magnesium, manganese, zinc, potassium and copper) was examined following the standard DIN EN 16943:2017-1. The selenium content was obtained following DIN EN 15763:2010-04. Sodium levels were established using gas chromatography–mass spectrometry (LC-MS/MS).
The Water activity was measured using a water activity meter (AquaLab PRE, Lab Ferrer, Cervera, Spain).
The baking loss (%) was determined on three independent samples using Equation [1], 24 h after baking, where *Wf is* the weight of the sample after baking and W0 is the weight before baking:% Baking loss = (Wf − W0)/W0 × 100 [1]
## 2.5. Instrumental Texture Analysis
The mechanical and acoustic properties of the biscuits were simultaneously measured employing a Texture Analyser (TA.HDplus, Stable Micro System Ltd., Surrey, UK) and a microphone in combination with a Deltatron preamplifier (Brüel Kjær, Nærum, Denmark). A 30-kg load cell was used and the key parameters were extracted employing the Exponent software (v.6.1.16.0, Stable Micro System Ltd.). Each biscuit was placed upside down on a perforated surface. The samples were penetrated using a cylinder probe of 4 mm in diameter at a test speed of 1 mm/s. The distance was 10 mm, and the trigger force was 5 g, based on the procedure of da Quinta, Alvarez-Sabatel [23]. Ten replicates were used for each biscuit type.
During the test, the acoustic emission was registered by a microphone calibrated using a calibrator type 4231 (94- and 114-dB sound pressure level [SPL], 1000 Hz) (Brüel Kjær). The distance of the microphone to the sample was 10 mm with an angle of 45° [24]. The filter function of the preamplifier screened out the background noise. No simultaneous activities were carried out in the laboratory to avoid noise interference.
The mechanical properties of the samples were determined using the following parameters: the maximum force required to break (N) as a direct measure of the hardness and the probe distance needed to break the sample (mm) as an indicator of the fracturability [25]. The crispness attribute was associated with several acoustic and mechanical parameters [23,26,27]. These were the maximum loudness perceived during the break evaluated as sound pressure level (SPL) in dB, number of sound peaks (peaks higher than 1 dB), number of force peaks (peaks higher than 0.2 N) and the linear distance (N*s). This last parameter, used as an indicator of jaggedness, was calculated as the length of an imaginary line joining all force peaks in a force–time graph [28].
## 2.6. Sensory Evaluation by Trained Assessors
A panel of seven assessors trained in quantitative descriptive analysis (QDA) of biscuits evaluated the samples. The panel was selected and trained following the ISO 8586:2012 procedure. Six 1-h sessions were performed for descriptor development, definition and training. Training sessions utilised reference standards. The attribute values were recorded using a 5-point scale. Finally, the four samples were evaluated in duplicate. The samples were labelled with a random three-digit numeric code and presented monadically in a randomised and balanced order. Still water was served for palate cleansing between samples.
The sensory evaluations were carried out at the Sensory Science Laboratory in AZTI (Derio, Spain) in individual booths designed in accordance with ISO 8589:2007.
## 2.7. Consumer Tasting
The sample consumer population was selected from visiting students from a local secondary school (11–12 years of age). An informed consent form was sent to the parents before the tasting session, indicating the objective of the study and listing the allergens. Thirty-three students participated; $33\%$ were female, and $67\%$ were male. The objective of the study was (i) to carry out a sensory evaluation of the newly developed biscuit and (ii) to establish whether they could identify chickpea flavour. The group assessed only one sample. The selection of the sample was based on the results of the texture measurements and trained panel sensory analysis.
The participants assessed the samples in individual cabins illuminated by fluorescent lamps. The sample was served in individual plates. Participants were asked to record their liking intensity scores for overall appearance, overall colour, overall aroma, overall texture and overall flavour (see Supplementary Material). A 9-point hedonic scale was used (9 = like extremely and 1 = dislike extremely). The overall liking was also assessed, together with the open question, “Are you able to identify a particular flavour?”. Finally, a questionnaire with two consumption habit questions was presented. The first question was, “Do you consume snacks?”. The second was a multiple-choice question, “How often do you eat biscuits?”. The options were: twice a week or more often, once a week, twice a month, once a month, once every 2–3 months or less frequently.
## 2.8. Statistical Analysis
The data were processed statistically using the software package XLSTAT 2019.1.2 (Addinsoft, Boston, MA, USA). Analysis of variance (ANOVA) and Tukey’s HSD test for comparison of sample means were used to identify nutritional properties and instrumental texture parameters that significantly differed between the samples. All data were expressed as means ± standard deviation (SD). The average sensory configuration obtained by the panel is displayed (as for Principal Component Analysis (PCA)) on a score plot representing the inter-product sensory distances.
Descriptive analysis was performed using the liking data recorded by consumers (the overall liking data and the individual attribute liking data reported on the hedonic 9-point scale).
## 3. Results
Results showed that, by using combinations of chickpea and rice flours, it was possible to apply a rational food design to create nutrient-dense and sensory-appealing biscuit-like structures with an improved nutritional profile (targeting EFSA’s DRV for adolescents 11 to 14 years, engaged in all types of physical activity), compared to the average profile of 49 plain biscuits found in the Mintel database.
## 3.1. Nutritional Composition
The mean values for the nutritional properties of the four different biscuits can be found in Table 4.
The protein content varied from $7.71\%$ to $13.92\%$, with the sample G100:0 showing the highest value and the G25:75 the lowest. Samples G75:50 and G50:50 did not significantly differ in their protein content ($10.54\%$ and $9.89\%$, respectively). These results indicate that the relative amount of protein in the biscuits increases with increasing chickpea flour (CF) content. This is not surprising, as rice flour (RF) contains less protein than CF.
A similar trend was observed for the fat content, with values ranging between $19.92\%$ and $21.15\%$; however, the differences between samples were smaller and not significant. These results reflect the lower fat content of RF compared to CF.
The carbohydrate content ranged from $49.63\%$ to $62.27\%$; the sample G25:75 had the highest level of carbohydrates, which was the lowest in sample G100:0 (the difference was significant). Samples G75:25 and G50:50 had intermediate profiles with $56.15\%$ and $56.56\%$ carbohydrate content, respectively. The carbohydrate content of the biscuits increases with rising RF content, which is consistent with the higher carbohydrate levels of RF compared to CF. However, a tendency for the sugar and fibre content to increase was observed for biscuits with larger amounts of CF. Sample G100:0 contained the most sugar and fibre ($18.84\%$ and $7.07\%$, respectively) and sample G25:75, the smallest amounts ($11.60\%$ and $2.59\%$). These differences were statistically significant. Thus, the sugar and fibre concentrations rise with increasing CF content. These observations are consistent with the differences in nutritional characteristics of these two flours [29].
No significant differences were seen between the salt contents of the different formulations, indicating that changes in the flour ratio did not affect this nutrient.
The data on the mineral content of the biscuits showed that the concentrations (in mg/100 g) of iron (1.26–3.39), potassium (233.74–576.70), zinc (1.00–1.99), phosphorus (111.67–209.73), magnesium (54.12–113.45), copper (0.25–0.62), manganese (0.93–1.10) and calcium (14.23–29.00) rise significantly with increasing CF content. On average, the mineral content increased by 2-fold when increasing the CF:RF ratio from 25:75 to 100:0.
For sodium, no significant differences were observed. For selenium, the concentration increases significantly with rising RF content, varying from 5.83 µg/100 g for the sample G100:0 to 9.08 µg/100 g for the G25:75. These results are consistent with the mineral content of chickpea and rice flours [29].
The energy content did not differ significantly between the samples. The energy values per 100 g ranged from 471.76 kcal for the sample G100:0 to 457.15 kcal for the G25:75. There is a tendency for the energy content to increase as the proportion of added CF increases.
## 3.2. Instrumental Texture
The results of the instrumental texture analysis, as well as the baking loss, moisture content and water activity of the four different biscuits can be found in Table 5.
Samples G100:0, G75:25 and G25:75 did not show significant differences between their percentages of baking loss ($29.26\%$, $29.21\%$ and $28.89\%$, respectively). The sample G50:50 showed greater baking loss ($30.07\%$) than G25:75. The moisture content of samples G100:0, G75:25 and G50:50 did not differ significantly ($3.96\%$, $3.59\%$ and $3.95\%$, respectively); the G25:75 showed increased moisture content ($6.50\%$). The highest water activity (aw) was observed for the sample G25:75 (0.47), followed by G100:0 (0.31), G50:50 (0.29) and G75:25 (0.27). Therefore, when the relative RF content was greater than $50\%$, the moisture content and aw of the biscuits increased. Changing the flour ratio did not affect the baking loss.
The analysis of mechanical properties revealed greater maximum force (Fmax) for the samples G100:0 and G75:25 (18.13 N and 19.68 N) than for G50:50 and G25:75 (11.68 N and 10.97 N, respectively). When the mixture contained more CF than RF, the hardness of the biscuit increased. However, all the samples showed similar distances at break (ranging from 0.83 mm to 0.70 mm) with no significant differences; they all had similar fracturability (or fragility) despite the differences in hardness. Sample G100:0 showed a significantly greater number of force peaks (NFP) (9.80) than samples G75:25, G50:50 and G25:75 (1.17, 2.60 and 2.20, respectively; differences not significant). Accordingly, sample G100:0 probably suffered more breaking events. Moreover, the sample G100:0 showed greater linear force peak distance (LDF) (94.76 N*s) than the other samples and sample G75:25 had significantly greater linear distance (81.35 N*s) than G50:50 (68.48 N*s). This could indicate that, as the CF content increased, the LDF also tended to increase. The sample G25:75 showed an LDF of 76.40 N*s, not significantly different from G75:25 and G50:50, like the NFP. This result might reflect the relationship between the NFP and LDF; the more fluctuations in force, the longer the line joining the force points.
The highest value of maximum sound pressure level (Smax) was obtained for sample G100:0 (75.74 dB), significantly higher than for samples G75:25 and G25:75 (65.36 dB and 67.99 dB). In contrast, no significant differences were detected between the numbers of sound peaks (NSP) for the four different biscuits.
Since the parameters LDF, NFP, NSP and Smax are the indicators of the crispness of a product [23,26,27], these results suggest that, when the biscuit is made with $100\%$ chickpea flour, its crispness will be significantly higher than when the mixture of the two flours is used.
Figure 1 shows force-time and sound-time curves obtained in the texture and acoustic event analysis for each biscuit. The graph for G100:0 (as an example) shows a force increase region, starting from the first contact between the probe and the biscuit until the first major drop in the force (at around 1 s). Within this region, the compression force increased almost linearly with the displacement, while acoustically it was very quiet. This suggests that the biscuit undergoes deformation but no major structural damage. The acoustic signals recorded in this linear domain were not considered, as they resulted from surface contact between the biscuit and the probe. Then, the compression force became jagged and many acoustic events were recorded within a very short period. This is when the structural breakdown occurs, at the first crack. There was no further increase in the compression force after that break, but rises and falls in the measured force could be observed. These force reductions reflect the ongoing minor structural fracture in the biscuit. At around 2 s, a sharp drop in the compression force occurred, which corresponded to the major structural breakdown. After this significant event, the biscuit remained on the test platform and it took another push for the fractured biscuit to finally fall to the texturometer base. At this point, the compression force reached zero. The acoustic signals recorded after this major breakdown were not considered [24].
We focused on acoustic signals in the jagged region of the force–time curve (between 1 and 2 s) (Figure 1, sample G100:0), where the biscuit breaking occurred. A group of acoustic events can be observed for each major force drop. Since these events did not gradually decrease in intensity and had no periodic pattern, they were probably not due to sound echoes or resonances. It is most likely that they reflected a series of structural element fracture events captured within a major force peak. It can be hypothesised that the energy dissipated from the biscuit break will spread out, probably in the form of sound. Therefore, the Smax should correspond to the energy released by the major structural breakdown [24].
Figure 1 shows that, for each drop in the compression force (force peak), an acoustic signal (sound peak) was detected, demonstrating the links between the acoustic events and the decrease in the force of a single break event (corresponding to the dissipated energy from the break). A correlation between the major structural breakdown (Fmax) and the Smax can also be observed.
## Sensory Profile of Biscuits
The panel generated seven descriptors to describe the biscuits; five referred to the texture (hardness, grittiness, fragility, prickliness and chewiness), and two to acoustic sensations (crispness and crunchiness) (Table 6).
Figure 2 presents the results of the PCA of the data generated by the sensory panel for the four biscuit formulations. Axis F1 explained $49.09\%$ of the sensory variation between the biscuits and Axis F2, $42.16\%$. The results indicated that the attributes “grittiness”, “fragility”, “hardness”, “chewiness” and “crunchiness” had discriminative power (ordered from the largest to the smallest, p-values < 0.01). However, the “crispiness” and “prickliness” attributes could not be used to distinguish the samples from each other because they did not have discriminatory power (p-values of 0.13, 0.43, respectively) and, consequently, did not appear in the PCA analysis (Figure 2). Figure 2 shows the vector (red line) corresponding to each attribute and the four biscuit samples (blue points).
In the PCA graph, the samples close to each other have similar sensory profiles, and larger distances indicate increased sensory differences. The evaluated sensory attributes are represented by vectors. The vector resultants help to characterise the samples: the higher the resultant on an axis, the higher the discriminating power of the attribute.
Table 7 presents the results of assessing the samples included in the PCA. The sample G100:0 differed from all the others; it showed the lowest fragility and the highest crunchiness and chewiness values. The G50:50 was the least crunchy and chewy and the most fragile. The G100:00 and G50:50 samples showed similar degrees of grittiness and hardness, greater than G75:25 and G25:75. In contrast, the sample G25:75 stood out from the others by showing the lowest grittiness and hardness. The sample G75:25 showed an intermediate sensory profile for grittiness, hardness, crunchiness, chewiness, and fragility, even though it had the crispiest texture. The fragility, crunchiness, and chewiness of G25:75 and G75:25 biscuits were very similar (no significant differences). Their fragility was lower than for G50:50 and greater than for G100:0 samples. The crunchiness and chewiness of G25:75 and G75:25 were lower than for the G100:00 sample and greater than for G50:50.
The samples G100:00 and G25:75 were very far from each other in the graph (Figure 2), indicating large sensory differences. Moreover, the separation between these samples occurs on Axis F2. This means that the differences between them were explained by the attributes whose resultant vector was located on this axis (grittiness, hardness and chewiness). Therefore, it can be concluded that changing the amount of CF in the formulation directly affects the texture of the biscuit, particularly its grittiness, hardness and chewiness. The grittiness, hardness and chewiness of the biscuit increase along with the growing amount of CF. Samples G75:25 and G50:50 were closer with respect to Axis F2, so their grittiness, hardness and chewiness were similar. These results were consistent with the fact that the G75:25 and G50:50 biscuits only differed by $25\%$ of the CF, while G100:0 had $75\%$ more CF than G25:75.
As the samples with larger amounts of CF (G100:0 and G75:25) tended to be less fragile than the others (G50:50 and G25:75), we can conclude that reducing the amount of CF increases the fragility of the biscuits. A greater crunchiness was observed as the ratio CF:RF increases from 50:50 to 100:0.
## 3.4. Consumer Tasting
Only one sample was assessed by the adolescents participating in the study, to avoid peer pressure when comparing samples or preferences. The selection of the sample was based on the results of the acoustic-texture measurements and the sensory analysis with the trained panel. The G50:50 had the best texture for children aged between 10 and 12. It was the easiest to chew (the least effort required to chew the biscuit before swallowing) and the most fragile (the least force needed to break it into pieces) (see Table 5 and Table 7). Considering the results of texture and sensory analysis, the biscuits with a higher proportion of CF (G100:0 and G75:25) could be too hard and difficult to chew and the biscuit G25:75 contained too much moisture.
Figure 3 shows the percentages for liking intensity scores (9-point hedonic scale) for overall appearance, colour, aroma, texture and flavour. The sensory attributes of overall appearance, overall colour, overall aroma and overall texture of the chickpea biscuit were evaluated positively (score ≥ 6) by $97\%$, $91\%$, $76\%$ and $52\%$ of the consumers, respectively. Only the overall flavour attribute was evaluated negatively (score ≤ 4) by $55\%$ of the consumers.
Regarding the overall liking results, $52\%$ of the adolescents gave a positive sensory evaluation of the overall quality of the chickpea biscuit (sum of choice percentages with scores ≥ 6). However, $33\%$ of participants gave this a negative assessment (sum of choice percentages with scores ≤ 4), stating that the texture seemed a bit too hard and that the biscuit did not have much flavour.
These results indicate that the overall liking of the biscuit was negatively affected by unappealing flavour and texture.
Adolescents participating in this study were not able to identify the main flavour of the biscuits. Some ($24\%$) described the chickpea biscuit flavour as “biscuit flavour”, and $12\%$ of the participants perceived a “nutty flavour”. However, $55\%$ were unable to pinpoint any dominant flavour, answering “no”, “I do not know” or “I cannot”, and $9\%$ described other flavours.
The answers to the two consumption habit questions (the frequency of snack consumption) revealed that $72.7\%$ of the participants consume snacks, most frequently in the form of biscuit and breadsticks. Biscuits were eaten by $14.3\%$ of the consumers several times a week, $9.5\%$ once a week and $14.3\%$ once a fortnight. The remaining $61.8\%$ indicated that they ate biscuits once a month or less frequently.
## 4. Discussion
Table 8 shows the average nutritional content of 49 different plain biscuits, obtained by searching the Mintel Database (October 2022).
Based on the average serving size from the Mintel search (Table 8), a standard 30 g serving size was assumed for the biscuits as mid-morning snack. It was possible to reach the percentages of the reference daily intake (indicated as AR or AI) of micronutrients recommended by EFSA, for adolescents from 11 to 14 years, engaged in all types of physical activity [22], indicated in Table 9.
For none of the four biscuits did the mineral content contained in one serving exceed the Tolerable Upper Intake Level (UL). For all the biscuits, a part of the reference daily intake of the analyzed minerals is covered with the consumption of one serving of biscuits (30 g). Taking into account that mid-morning meal should represent $15\%$ of the daily food intake, and assuming that mid-morning meal should cover approximately $15\%$ of the reference daily intake of minerals, the biscuits should be accompanied by another food that provides minerals to complete this $15\%$.
Among the top 20 ingredients found in the search (Figure 4), $93\%$ of the biscuits contained flavourings and salt and $86\%$, raising agents and emulsifiers. Wheat flour and white sugar were present in $71\%$ of the samples. Between $43\%$ and $57\%$ of the biscuits were supplemented with vitamin B6, riboflavin, vitamin B1, niacin, iron, vitamin A, vitamin D or other vitamins and minerals.
Compared to the commercial plain biscuits found in the Mintel search (Table 8, Figure 4), all the chickpea biscuit samples contained less carbohydrates, sugar and salt. However, they had a higher protein and fat (mainly unsaturated) content. The fibre content was augmented in all samples except for G25:75, which contained the lowest amount of CF (the main fibre source). Moreover, chickpea biscuits were rich in potassium, calcium, phosphorus, magnesium, iron, zinc, copper and manganese, and these minerals were naturally present in the ingredients (unlike the additives used in commercial biscuits). As can be seen in Figure 4, most of the vitamins and minerals in plain biscuits targeted at children and sold in Spain between 2017 and 2022 are added to the formulation rather than naturally present in the main ingredients.
The moisture content of the biscuits was similar to the moisture levels reported in other studies of protein-enriched biscuits with CF [30,31]. The only exception was the sample G25:75, in which the industry standard for moisture content in biscuits (1–$5\%$) was exceeded. This high moisture content could be due to the large proportion of starch (approximately $80\%$) in RF, which might have increased water retention.
Rababah and Al-Mahasneh [30] replaced some wheat flour in biscuits using CF at $3\%$, $6\%$, $9\%$ and $12\%$. For the biscuits enriched with $12\%$ CF, they reported an increase in protein content (from $16.82\%$ for the control to $19.64\%$) and fat content (from $14.13\%$ to $15.31\%$). This was to be expected, as the CF contains more protein and fat than the wheat flour. These data are consistent with the current study, where the protein and fat content of the chickpea biscuit increased proportionally to the amounts of CF added. As the chickpea-to-wheat flour ratios increased, the values for most of the liking attributes (overall impression, overall flavour and overall colour) decreased and the hardness of the biscuits increased. As a result, the fortification ratio of $3\%$ gave the best sensory results in descriptive analysis.
In contrast, Yadav and Yadav [31] reported a decrease in biscuit fat content with an increasing degree of wheat substitution with CF and plantain flour. However, this result seemed to be due to the low oil-holding capacity of these flours compared to wheat flour. Moreover, the authors reported an increase in protein content from $7.1\%$ for the $100\%$-wheat biscuit to $9.2\%$ for the $40\%$-chickpea biscuit (probably caused by the higher protein content of CF). The team also reported an increase in the amount of fibre in chickpea-enriched biscuits (again, most likely due to the high levels of fibre in CF). An increase in fracture strength, and therefore in hardness, of biscuits with the addition of plantain and CF was also observed (the highest at $40\%$ substitution). This is in agreement with the results of the present study.
Mancebo and Rodriguez [32] added pea protein (up to $20\%$) to a rice flour biscuit. They found that incorporating this protein decreases the biscuit hardness compared to $100\%$-rice flour biscuits. Dapčević et al. [ 33] replaced 10–$30\%$ of RF with buckwheat flour, which contains twice the protein of RF and less starch. They found that increasing the relative amounts of buckwheat decreased biscuit hardness and fracturability. Similarly, Gerzhova and Mondor [34] have demonstrated that adding canola protein to $80\%$ rice and $20\%$ buckwheat flour biscuits decreased hardness and increased thickness of the biscuit. Sarabhai and Prabhasankar [35] have reported that adding soy and whey protein to a rice flour biscuit reduced its breaking strength (and, therefore, hardness). The current report does not wholly concur with these studies. Our texture and sensorial results showed that the biscuit hardness, crunchiness and chewiness increased with the rising proportion of CF (and, therefore, protein) added to the formula. This inconsistency may be due to the much higher CF percentages used here in comparison with the studies mentioned above. In those studies, the maximum RF replacement was $30\%$ and the maximum protein concentrate addition was $20\%$.
The Fmax value was associated with the sensory attributes of hardness, grittiness, chewiness and fragility. Samples G100:0 and G75:25 had higher Fmax values and were considered harder, grittier and more chewy and less fragile than the sample G25:75. This is in agreement with the results of Segnini and Dejmek [36], in whose study the fracture force for a potato chip seemed to be a good predictor of the sensory texture attributes such as hardness, chewiness, crispness (evaluated as crunchiness) and tenderness.
The results of this study suggest that crunchiness was positively associated with the acoustic parameter Smax and the mechanical parameters Fmax, LDF and NFP; as the CF content of the biscuit increased, the values of Smax, Fmax, LDF, NFP and crunchiness tended to rise.
No association between texture and acoustic parameters and the sensory attribute of crispiness was detected. This is in disagreement with the results of da Quinta and Alvarez-Sabatel [23], Gouyo and Mestres [26] and Salvador and Varela [27]. Their studies have reported that the LDF, NFP, NSP and Smax are positively correlated with the crispness of a product. Considering these results, one might expect that the crispness of the $100\%$ chickpea biscuit would be significantly higher than for a product combining the CF with RF. However, the sensory panel did not detect this effect.
Fillion and Kilcast [37] have studied the perception of crispness and crunchiness in fruits and vegetables. They concluded that loudness was not considered when qualifying a product as crunchy or crispy, but it was used to assess the intensity of crunchiness or crispiness. The two attributes involve different frequencies of sound, a low frequency for crunchiness and a high frequency for crispiness. Furthermore, there was no correlation between hardness and crispiness when the hardness was very high. This suggested that a very hard texture could not be perceived as crispy and would be described as crunchy. According to that study, Smax and NSP could not be used to qualify a product as crispy or crunchy since these parameters do not reflect the frequency of the sound but its loudness. Therefore, these acoustic parameters could be related to both crispiness and crunchiness, depending on the product being evaluated.
This might be the reason why the very hard chickpea biscuits were not perceived as crispy (with a minimum score of 0 and a maximum of 2 on a scale from 1 to 5) but rather as crunchy (with a minimum score of 2 and a maximum of 4 on a scale from 1 to 5).
In this work, our approach has been to bring other types of knowledge into the RFD approach for targeted nutrition: the scientific knowledge, provided by EFSA’s DRV, and acoustic-texture measurements to apply RFD targeted to adolescents aged 11–14 years, engaged in all types of physical activity.
Increasing the addition of chickpea flour in a rice biscuit improves its nutritional pro-file, increasing by 2-fold the protein and the mineral content by increasing the CF:RF ratio from 25:75 to 100:0. However, these increase causes a rise in biscuit hardness, grittiness, chewiness and crunchiness, according to texture and sensorial analysis.
## 5. Conclusions
Efforts to develop targeted nutrition strategies for adolescents from 11–14 years should be scrutinized beyond the nutrient contents of food, and include EFSA’s DRV, as well as the acoustic-texture effect, in the case of biscuits. Increasing the addition of chickpea flour in a rice biscuit improves its nutritional profile, increasing by 2-fold the protein and the mineral content by increasing the CF:RF ratio from 25:75 to 100:0. However, these increase causes a rise in biscuit hardness, grittiness, chewiness and crunchiness, according to texture and sensorial analysis. A total of 33 adolescents participated in the study, of whom $52\%$ scored as ≥6 the overall quality of the chickpea biscuit, which was negatively affected by unappealing flavour and texture. This study shows that rational food design is a promising approach to design nutrient-dense and sensory-appealing microstructures aimed at adolescents by combining flours naturally rich in micronutrients. However, the authors acknowledge the limitations of this research in that future work should include the antinutritional factors of the plant-based ingredients used. In addition, including other disciplines, such as imaging technologies, digestion, nutrition and physiological responses, could bring multidimensional perspectives in the design of food matrices that are sensory-appealing and targeted to adolescents.
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|
---
title: 'Examining Factors Associated with Dynapenia/Sarcopenia in Patients with Schizophrenia:
A Pilot Case-Control Study'
authors:
- Ryuichi Tanioka
- Kyoko Osaka
- Hirokazu Ito
- Yueren Zhao
- Masahito Tomotake
- Kensaku Takase
- Tetsuya Tanioka
journal: Healthcare
year: 2023
pmcid: PMC10000555
doi: 10.3390/healthcare11050684
license: CC BY 4.0
---
# Examining Factors Associated with Dynapenia/Sarcopenia in Patients with Schizophrenia: A Pilot Case-Control Study
## Abstract
Sedentary behavior in patients with schizophrenia causes muscle weakness, is associated with a higher risk of metabolic syndrome, and contributes to mortality risk. This pilot case-control study aims to examine the associated factors for dynapenia/sarcopenia in patients with schizophrenia. The participants were 30 healthy individuals (healthy group) and 30 patients with schizophrenia (patient group), who were matched for age and sex. Descriptive statistics, Welch’s t-test, cross-tabulations, adjusted residuals, Fisher’s exact probability test (extended), and/or odds ratios (ORs) were calculated. In this study, dynapenia was significantly more prevalent in patients with schizophrenia than in healthy individuals. Regarding body water, Pearson’s chi-square value was 4.41 ($$p \leq 0.04$$), and significantly more patients with dynapenia were below the normal range. In particular, body water and dynapenia showed a significant association, with an OR = 3.42 and $95\%$ confidence interval [1.06, 11.09]. Notably, compared with participants of the healthy group, patients with schizophrenia were overweight, had less body water, and were at a higher risk for dynapenia. The impedance method and the digital grip dynamometer used in this study were simple and useful tools for evaluating muscle quality. To improve health conditions for patients with schizophrenia, additional attention should be paid to muscle weakness, nutritional status, and physical rehabilitation.
## 1. Introduction
The prevalence of schizophrenia in *Japan is* estimated at $0.7\%$ [1]. Patients with schizophrenia die on average 10 to 20 years earlier than healthy individuals [2,3]. The sedentary lifestyle common among patients with schizophrenia is associated with higher metabolic syndrome (cardiovascular changes due to diabetes mellitus, hypertension, and hypercholesterolemia) and contributes to mortality risk. Lifestyles that increase the risk of such metabolic syndrome have been identified as lacking regular physical activity, poor food intake, substance use, and high rates of smoking [4].
Strassnig et al. [ 5] developed a comprehensive model to conceptualize multimodal relationships that predict impaired activities of daily living in patients with schizophrenia. According to these authors, limitations in physical abilities interfere with activities of daily living and elicit a state of physical infirmity observed in other chronic illnesses. A high prevalence of sarcopenic obesity has also been reported in patients with schizophrenia [6]. However, little is known on the risk factors for dynapenia/sarcopenia in patients with schizophrenia.
Factors that contribute to severe limitations due to the pathophysiology of schizophrenia and the effects of medications are complex and include a sedentary lifestyle as well as factors due to the effects of medication therapy, which ultimately lead to a vicious cycle of obesity and cardiovascular metabolic risk [7]. A sedentary lifestyle and decreased functional motor skills in patients with schizophrenia reduce their quality of life [8]. Low physical activity levels are also associated with the use of antipsychotic drugs. This implies that increasing weight is related to limitations in physical functioning and restricts activities of daily living. Physical inactivity due to obesity also adds to the burden of schizophrenia in the form of reduced physical health-related quality of life. Rehabilitation programs focusing on these risk factors should be key for physical activity for both prevention and treatment of disease and disablement in patients with schizophrenia [9]. Moreover, antipsychotic medications have been associated with weight gain and obesity in schizophrenia. Patients with schizophrenia consume unhealthy food, and their dietary patterns identified a high consumption of saturated fat and low intake of fruit and dietary fiber [10]. In patients with schizophrenia, the ability to supply oxygen to muscles during exercise and the ability of muscles to consume oxygen (cardiopulmonary endurance) are poorer than in healthy individuals. This means the level of cardiorespiratory fitness may be extremely low in patients with schizophrenia, amounting to a state of deconditioning and a very low capacity for sustained physical activity that is high in intensity, activities promoting low to moderate activity levels may serve the population well and lead to highly relevant improvements in health prospects [11].
In a 12-year follow-up study of schizophrenia, the patient group had an excess of psychiatric and physical comorbidities (fractured neck of femur, parkinsonism, pneumonia, esophageal ulcer, respiratory failure, and bronchitis), including side effects of psychotropic drugs, compared to the age- and sex-matched controls. Specifically, their finding clearly demonstrates that parkinsonism-associated complications may play a dominant role in schizophrenia-related death in general hospitals. Reducing the risk of parkinsonism-associated complications due to accurate detection and management of side effects of psychotropic and somatic medication as well as of related drug–drug interactions, continuously monitoring physical status, and accurate detection of concomitant metabolic, cardiovascular, and respiratory diseases as well as creating awareness about preventive strategies for difficulty eating and aspiration pneumonia may help in reducing parkinsonism-associated fatal consequences in general hospitals in patients with schizophrenia [12].
In the context of socioeconomic challenges, schizophrenia leads to particularly unhealthy lifestyles that include poor diets, little exercise, marked sedentary behavior, and high rates of smoking with commensurately low physical activity levels [13]. As a result, compared to the general healthy population, patients with schizophrenia have severe symptomatic limitations in physical capacity. Negative symptoms reduce the likelihood of patients’ engagement in goal-directed behavior, including physical activity, which has been noted to increase obesity and cardiometabolic risk and induce poor physical conditions, resulting in sarcopenic obesity and muscle weakness [6].
According to the European working group on sarcopenia in older people 2 (EWGSOP 2), sarcopenia is suspected when [1] muscle weakness is confirmed, whereas sarcopenia is confirmed when [2] muscle mass or muscle quality decline is present in addition to muscle weakness [14]. As mentioned above, sarcopenia is defined as “loss of muscle mass or quality,” whereas dynapenia is defined as “loss of muscle strength” [15].
Sarcopenia is also associated with depressed mood, which in turn is associated with low muscle strength and physical performance [16]. Therefore, it is problematic that patients with schizophrenia frequently have negative symptoms such as depressed mood, which is associated with low physical function and low muscle strength. Regarding schizophrenia and nutritional status, Japanese inpatients with schizophrenia are more likely to be underweight and undernourished than outpatients [17]. Nutritional status is an issue for patients with schizophrenia in Japan. Therefore, it is important to consider the activities of daily living, dynapenia, sarcopenia/presarcopenia, and nutritional status when considering symptom management in hospitalized patients with chronic mental illness.
This pilot study aimed to examine the associated factors for dynapenia/sarcopenia in patients with schizophrenia.
## 2.1. Study Participants
This pilot case-control study enrolled 60 individuals in total comprising 30 healthy participants (healthy group) and 30 patients with schizophrenia (patient group), matched by age, ranging from 40 to 89 years, and sex.
## 2.2. Data Acquisition Period
The study’s data acquisition phase was from 17 August 2021 to 30 November 2021.
## 2.3. Target Selection Criteria
Healthy group: Employees working at Hospital A and its Geriatric Health Care Facility.
Patient group: Patients with schizophrenia admitted to Hospital A.
Both groups were matched by sex and age.
## 2.4. Exclusion Criteria
Healthy group: Individuals with a mental or physical disorder.
Patient group: Individuals unable to understand instructions owing to a medical condition or medication status or because of a physical disorder such as a history of cerebrovascular disease such as stroke or a neurological disease.
## 2.5.1. Body Mass
Tanita monitors use the latest bioelectrical impedance analysis technology, first developed by Tanita in 1992, to provide fast and accurate body composition results [18]. The RD-545 InnerScan Pro provides an in-depth analysis of 26 body composition measurements. The measurements included weight, body fat, muscle mass, muscle quality score, body mass rating, bone mass, visceral fat level, basal metabolic rate, metabolic age, total body water, and body mass index (BMI). The RD-545 InnerScan PRO can perform fat and muscle analysis individually for arm, leg, and trunk segments if hand electrodes are used [19]. The state of visceral fat accumulation is indicated as visceral fat level score measured by the RD-545 InnerScan Pro.
## 2.5.2. Age, Height, and Weight
Healthy group: Age and height were self-reported based on the hospital’s staff health examination form.
Patient group: Age and height were obtained from the medical records.
Weight was measured in both groups using a scale (RD-545 InnerScan Pro, TANITA Corporation. Tokyo, Japan).
## 2.5.3. Grip Strength of the Hands
A digital grip dynamometer (T.K.K.5401; Takei Scientific Instruments, Co., Ltd., Niigata, Japan) was used to individually measure the grip strength of each hand in a stable standing posture.
## 2.5.4. Skeletal Muscle Mass Index (SMI)
The total limb skeletal muscle mass (kg) was calculated from the information obtained from the body mass, and the data were divided by the square of the corresponding height (m2).
## 2.5.5. SARC-F Score
The SARC-F score was presented by Morley as a screening tool for sarcopenia at the EU/US committee on sarcopenia in the frail elderly at the Conference on Sarcopenia Research (ICSR) in Orlando in 2012 [20].
Data were self-reported by all participants using a questionnaire survey.
## 2.6. Sarcopenia/Dynapenia Assessment Method
This study adopted the diagnostic criteria proposed by the Asian Working Group for Sarcopenia (AWGS) [21]. The SARC-F score, grip strength, and skeletal muscle mass were used as indicators. The specific criteria were as follows: Grip strength can be used to assess muscle weakness [20,21]. Peripheral quantitative computed tomography, dual X-ray energy absorptiometry, and magnetic resonance imaging techniques can be used to assess skeletal muscle mass and quality [22]. Other than the aforementioned methods, bioelectrical impedance analysis can be used, which has the advantage of being inexpensive and portable. The cutoff values for sarcopenia in the Japanese population are 6.8 kg/m2 for men and 5.7 kg/m2 for women [23].
The SARC-F score was used to select participants with sarcopenia; those with a score of 4 or more points were selected. Based on the two sarcopenia criteria outlined in the Section 1, muscle weakness and loss of muscle mass or muscle quality were evaluated. [ 1] Grip strength was used as an indicator of muscle weakness, defined for men and women as having a grip strength of less than 26 kg and less than 18 kg, respectively. In addition, [2] skeletal muscle mass (kg/m2) was used as an indicator of muscle mass or muscle quality loss, and skeletal muscle mass loss was defined as a value less than 7.0 kg/m2 for men and less than 5.7 kg/m2 for women. Presarcopenia was defined as reduced skeletal muscle mass and normal grip strength. Dynapenia was defined as a normal skeletal muscle mass and decreased grip strength.
## 2.7. Statistical Analysis
Basic statistical parameters (mean ± standard deviation [SD], $95\%$ confidence interval [CI]) were calculated. Welch’s t-test was performed to compare the two study groups. For items that were significantly different between the two groups, cross-tabulations were performed, and adjusted residuals were calculated. Fisher’s exact probability test (Extended), Pearson’s chi-square test, and/or odds ratios (ORs) were calculated. All statistical analyses were performed using SPSS 21.0 (IBM Corporation). Statistical significance was set at $p \leq 0.05.$
## 3. Results
Among the study participants, $61.7\%$ ($\frac{37}{60}$) were women and $38.8\%$ ($\frac{23}{60}$) were men. The healthy group comprised $63.3\%$ ($\frac{19}{30}$) women and $36.7\%$ ($\frac{11}{30}$) men, whereas the patient group consisted of $60.0\%$ ($\frac{18}{30}$) women and $40.0\%$ ($\frac{12}{30}$) men.
In this study, dynapenia and sarcopenia/presarcopenia were assessed. Among the 30 participants in the patient group, $10.0\%$ ($\frac{3}{30}$ [$\frac{1}{18}$ women, $\frac{2}{12}$ men]) met the criteria for sarcopenia, $3.3\%$ ($\frac{1}{30}$ [$\frac{1}{12}$ men]) for presarcopenia, and $60.0\%$ ($\frac{18}{30}$ [$\frac{14}{18}$ women, $\frac{4}{12}$ men]) for dynapenia. The corresponding results of the healthy group showed that sarcopenia and presarcopenia were not present ($0\%$) and that $13.3\%$ ($\frac{4}{30}$ [$\frac{3}{19}$ women, $\frac{1}{11}$ men]) met the criteria for dynapenia.
Table 1 shows the results of Welch’s t-test. Body water content was significantly higher in the healthy group with 53.56 ± $3.94\%$ in the healthy group and 49.77 ± $6.58\%$ in the patient group ($t = 2.71$, $p \leq 0.001$).
The visceral fat level score was 6.60 ± 3.71 in the healthy group and 9.12 ± 5.35 in the patient group ($t = 2.11$, $$p \leq 0.04$$). Body fat content was 24.95 ± $6.05\%$ in the healthy group and 30.41 ± $9.00\%$ in the patient group ($t = 2.76$, $p \leq 0.01$). Likewise, BMI was 21.89 ± 2.30 kg/m2 for the healthy group and 23.88 ± 4.65 kg/m2 for the patient group ($t = 2.10$, $$p \leq 0.04$$).
Left grip strength was 29.16 ± 9.07 kg for the healthy group and 18.53 ± 8.38 kg for the patient group ($t = 4.71$, $p \leq 0.001$), whereas right grip strength was 30.05 ± 7.98 kg for the healthy group and 21.26 ± 10.92 kg for the patient group ($t = 3.56$, $p \leq 0.001$). These findings showed that for both sides, the grip strength of the patient group was significantly weaker than that in the healthy group.
As shown in Table 2, the patient group was significantly more likely to have dynapenia or sarcopenia/presarcopenia (Fisher’s exact test, $p \leq 0.0001$; OR, 17.88; $95\%$ CI [4.74, 67.43]).
The association of the study group with dynapenia, including sarcopenia and presarcopenia (hereafter referred to as dynapenia in the Section 3), was analyzed based on items with significant differences in Table 1. No significant association was found for the parameters of visceral fat level score, body fat, and BMI.
In contrast, for body water, the result of Pearson’s chi-square test was 4.41 ($$p \leq 0.04$$), and significantly more people with dynapenia were below the normal range. We also confirmed a significant association for body water (OR, 3.42, $95\%$ CI [1.06, 11.09]).
## 4. Discussion
As shown in Table 1, the patient group had significantly higher body fat, visceral fat level scores, and BMI. In addition, the average value of the patient group BMI is not at the obese level, and the high visceral fat level score was deemed a problem when considered overall from the cross-tabulation results in Table 2.
As shown in Table 2, the patient group had a high percentage of individuals diagnosed with dynapenia, with an OR of 17.88 times the risk of developing the disease compared with healthy individuals. Thus, it was suggested that being afflicted with schizophrenia is one factor associated with dynapenia. Moreover, Table 2 shows that no significant association by the study group was found for body fat, visceral fat level score, or BMI; however, body water content was significantly associated, with the OR indicating 3.42 times higher risk of dynapenia for the patient group than for the healthy group.
For these reasons, the patient group in this study may have increased fat, as well as decreased body water content and muscle mass, owing to a sedentary lifestyle [9,24]. Sex differences in body fat and water content in patients with schizophrenia have been reported [6]. The body water content was predominantly higher in the healthy group. Body water refers to water contained in various body compartments, including blood, lymphatic fluid, extracellular fluid, and intracellular fluid [25]. These fluids play important roles in the body, such as transporting nutrients and maintaining a constant body temperature, and they tend to decrease with age. In addition, people with high body fat tend to have a lower body water content [26]. This trend is also consistent with the previous study by Bulbul et al. [ 27] Therefore, it is necessary to focus on the trends of high body fat and low water content in the patient group.
Of the 307 participants in the study by Mori et al. [ 28], $60.9\%$ were assessed as normal, and $25.7\%$, $8.1\%$, and $5.2\%$ were found to have presarcopenia, sarcopenia, and dynapenia, respectively. Reduced grip strength is a critical indicator of dynapenia [29]. In this study, grip strength was significantly lower in patients with schizophrenia than in healthy individuals. Because many patients with schizophrenia have dynapenia, grip strength may be a convenient screening index for dynapenia in psychiatric hospitals.
The participants of the study by Kobayashi et al. were volunteers aged over 60 years who were in good general health [30]. Their study found that in Japan, the rates of sarcopenia, presarcopenia, and dynapenia were $10\%$, $22\%$, and $8\%$ in men, and $19\%$, $23\%$, and $13\%$ in women.
According to Neves et al., sarcopenia and dynapenia were identified in $15.3\%$ and $38.2\%$ of old persons [31]. In this study, $13.3\%$ of the healthy individuals had dynapenia, whereas $60.0\%$ of the patient group had dynapenia, $10.0\%$ had sarcopenia, and $3.3\%$ had presarcopenia. Thus, our data suggest that the prevalence of dynapenia is high among patients with schizophrenia.
Appetite regulation and physical activity affect energy balance and changes in body fat mass. In some patients, inflammation induces anorexia and fat loss along with sarcopenia. In others, appetite is maintained, despite the activation of systemic inflammation, leading to sarcopenia with normal or increased BMI. Inactivity contributes to sarcopenia and increased fat tissue in aging and disease [32].
In a previous study of the BMI status of hospitalized *Japanese schizophrenia* patients, underweight and obesity were characteristic in schizophrenia inpatients compared with the general population. In particular, regarding the characteristics of underweight, a previous study showed that the prevalence of hypotriglyceridemia was significantly higher in the underweight group than in the normal weight group and in overweight/obese schizophrenia inpatients [33].
Harvey and Strassnig [34] suggested that the cognitive limitations of people with schizophrenia not only correlate with disability directly, but contribute substantially to other skills deficits (functional capacity; social competence) that exacerbate disability outcomes. Impaired cognition and negative symptoms, particularly in the domains of reasoning and problem solving and reinforcement valuation, can lead to deficits in functional capacity that then lead to poor dietary and exercise choices, contributing to poor functional outcomes.
In another study, age, certification of long-term care, and malnutrition were identified as risk factors for sarcopenia [35]. Sarcopenia is thought to primarily explain the age-related loss of muscle strength, such as dynapenia, commonly seen in older people [36]. However, recent longitudinal data indicate that the loss of muscle strength occurs significantly faster than the accompanying loss of muscle mass [37]. On the other hand, gains in muscle mass and strength afforded by resistive training are associated with a small but significant improvement in physical performance. It is noteworthy that lower intensity mechanical loading such as aerobic exercise, despite being considerably less effective for inducing muscle hypertrophy, has been found to promote protein synthesis and expression of growth-related genes and inhibit the expression of muscle breakdown-related genes [37].
Muscle weakness is known to decrease physical function and increase the risk of mortality [38]. Regarding the changes in physical function associated with aging, muscle strength declines by $30\%$ and muscle area by $40\%$ between 20 and 70 years of age [39]. At the age of 75 years, muscle strength declines at a rate of 2.5–$3\%$ per year for women and 3–$4\%$ per year for men, and muscle mass is lost at a rate of 0.64–$0.70\%$ per year for women and 0.80–$0.98\%$ per year for men [37]. Kitamura, et al. [ 40] found sex-specific patterns of correlates with sarcopenia. Significant sarcopenia-related factors in addition to ageing were hypoalbuminaemia, cognitive impairment, low activity, and recent hospitalization among men and cognitive impairment and depressed mood among women. It is important to focus on these conditions.
Compared to young adults, older adults have a lower limb skeletal muscle index (ASMI, kg/m2) and a significantly higher body fat percentage [41]. It has been noted that diabetic patients with a high body fat percentage in addition to low BMI may develop sarcopenia [42]. Moreover, the prevalence of diabetes in patients with schizophrenia in Japan has been reported to be $8.6\%$ [43].
Protein intake is necessary for efficient muscle growth. A person with adequate muscle mass needs 1.0–1.2 g protein per kg of body weight per day for an older person to maintain muscle mass, i.e., about 60–72 g per day if the person weighs 60 kg [44]. However, this intake is not sufficient for those who must gain muscle mass due to sarcopenia, and they should have an intake of 1.2–1.5 g of protein per kg of body weight per day, i.e., 72–90 g per day if they weigh 60 kg [45]. Thus, it is important to control the balance of restricted caloric intake with guaranteed protein intake for patients with dynapenia. However, if a patient has kidney problems, it is critical to pay much more attention to an appropriate protein and calorie intake during the rehabilitation process [46].
Based on the BMI findings of our study, the patient group was not underweight. Our study subjects were inpatients; they have consumed a diet regulated by a psychiatrist and a dietitian. However, outpatients may not be eating an appropriate diet due to unbalanced diets, poverty, etc.
With this in mind, we should conduct the main case-control study following this pilot study. Furthermore, inpatients may have a lower average BMI than outpatients, who are free to eat whatever they want at home because their food intake is controlled to prevent excessive weight gain. It was considered important to keep these points in mind when managing their health.
## Limitations and Future Research
Since data on daily intakes, such as nutritional status, were not obtained in this study, it is necessary to obtain data on “official” caloric intake based on hospital diets, such as daily caloric intake, for better analyses in future studies. Additionally, it is necessary to consider “unofficial” caloric intake, such as snacks. Moreover, the patient’s amount of activity needs to be considered. This pilot study was a small-scale study conducted to inform, predict, and direct an intended future full-scale study. The association of low body water and dynapenia in patient participants suggests that low body water might be a risk factor for dynapenia in these patients.
Underweight is highly prevalent in Japanese inpatients with schizophrenia. Psychiatrists should be aware of underweight and their potential health risks. Treating psychiatrists should also be responsible for providing any necessary nutritional interventions [47]. Physical health appears to be achievable in people with schizophrenia being challenged by motivational difficulties with attending regular exercise and have beneficial implications for physical function during activities of daily living, lifestyle-related diseases, and early death. Specifically, physical training is an effective countermeasure to improve the low aerobic endurance and skeletal muscle strength in these patients [48].
Furthermore, the main study following this pilot study should include not only body composition (low body water, visceral fat level, and muscle mass), grip strength, and joint range of motion, but also medication content, heart rate variability, and motor velocity [49]. Other factors (physical function during activities of daily living, gait and psychiatric symptoms specific to schizophrenia, age, and length of hospitalization) also must be considered in dynapenia in patients with schizophrenia.
## 5. Conclusions
This pilot study examined the risk factors for dynapenia/sarcopenia in patients with schizophrenia. Patients with schizophrenia were overweight, had less body water than the healthy study participants, and were at a higher risk of dynapenia than participants in the healthy group. The impedance method used in this study is a simple and useful method for evaluating muscle quality in conditions such as dynapenia. To improve health conditions for patients with schizophrenia, additional attention should be paid to muscle weakness, nutritional status, and physical rehabilitation. Future research will include a larger study following on this pilot study.
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|
---
title: Validation of an LC-MS/MS Method for the Determination of Abscisic Acid Concentration
in a Real-World Setting
authors:
- Elisabetta Schiano
- Ilaria Neri
- Maria Maisto
- Ettore Novellino
- Fortuna Iannuzzo
- Vincenzo Piccolo
- Vincenzo Summa
- Lucia Grumetto
- Gian Carlo Tenore
journal: Foods
year: 2023
pmcid: PMC10000556
doi: 10.3390/foods12051077
license: CC BY 4.0
---
# Validation of an LC-MS/MS Method for the Determination of Abscisic Acid Concentration in a Real-World Setting
## Abstract
One of the most relevant aspects in evaluating the impact of natural bioactive compounds on human health is the assessment of their bioavailability. In this regard, abscisic acid (ABA) has attracted particular interest as a plant-derived molecule mainly involved in the regulation of plant physiology. Remarkably, ABA was also found in mammals as an endogenous hormone involved in the upstream control of glucose homeostasis, as evidenced by its increase after glucose load. The present work focused on the development and validation of a method for the determination of ABA in biological samples through liquid–liquid extraction (LLE), followed by liquid mass spectrometry (LC-MS) of the extract. To test method suitability, this optimized and validated method was applied to a pilot study on eight healthy volunteers’ serum levels to evaluate ABA concentration after consumption of a standardized test meal (STM) and the administration of an ABA-rich nutraceutical product. The results obtained could meet the demands of clinical laboratories to determine the response to a glucose-containing meal in terms of ABA concentration. Interestingly, the detection of this endogenous hormone in such a real-world setting could represent a useful tool to investigate the occurrence of impaired ABA release in dysglycemic individuals and to monitor its eventual improvement in response to chronic nutraceutical supplementation.
## 1. Introduction
2-cis, 4-trans-abscisic acid (ABA) is a sesquiterpenoid phytohormone synthesized via an indirect pathway from the cleavage products of carotenoids [1]. This molecule has been studied for several decades with regard to its pivotal role as a regulator of plant growth and response to abiotic and biotic stress [2,3]. Due to ABA involvement as a growth regulator, immature fruits have been found to contain the highest concentration of this phytohormone [4,5] in the context of vegetal matrices. In this regard, a screening of various immature fruits derived from fruit thinning has identified thinned nectarines (TN) as the richest source of this bioactive compound [6]. Nevertheless, ABA has sparked particular interest not only as a phytohormone commonly found in vegetables and fruits, but it has also been found in mammals as an endogenous hormone involved in the upstream control of glucose homeostasis [7,8,9] via interaction with its specific receptor lanthionine synthase C-like 2 (LANCL2) [10].
To date, the majority of evidence for the hypoglycemic effects of ABA in vivo has addressed a role in the stimulation of peripheral glucose uptake by increasing the expression and translocation of glucose transporter 4 (GLUT4) [11,12,13,14]. In addition, it is noteworthy to remark that in patients with type 2 diabetes mellitus (T2DM) or gestational diabetes, a decreased release of ABA have been found following a glucose load [15]. This evidence further strengthens the importance of monitoring serum concentrations of ABA in individuals with altered glucose metabolism and supplementing them with plant-based exogenous sources of ABA. In this context, several studies involving both animal and human models demonstrated the significant beneficial effects of ABA-containing nutraceuticals on the glycemic profile in prediabetic and diabetic subjects, in association with an insulin-sparing mechanism of action [6,14,16,17,18,19]. In virtue of its insulin-independent mechanism of action [13], ABA supplementation may be indicated as a useful approach to improve glucose tolerance in individuals with insulin deficiency in and/or insulin resistance. In this regard, there is a growing scientific consensus that sustained stimulation of insulin release from pancreatic β-cells under conditions of chronic hyperglycemia may ultimately contribute to their depletion [20]. In view of this evidence, hypoglycemic molecules able to decrease glycemia without increasing insulinemia are highly desirable as they could improve the survival and function of pancreatic β-cells. On the other hand, although a wide variety of bioactive compounds of natural origin have been tested for their beneficial potential in the control of diabetic conditions [21,22], the evaluation of their bioavailability still represents a crucial aspect [23,24]. Identification of ABA as a plant hormone is usually performed with various methods, mainly in plant matrices, such as gas chromatography/mass spectrometry (GC/MS) [25] and immunological assay, i.e., enzyme-linked immunosorbent assay (ELISA) [26]. Although these methods are able to assess ABA concentration levels, they are affected by some disadvantages. For instance, ELISA assay requires a long preparation time and has low specificity and reproducibility, while GC/MS requires derivatization of the sample [25]. Based on such considerations, the present work focused on the development and validation of a method for the determination of ABA by liquid mass spectrometry analysis (LC-MS), through liquid–liquid extraction (LLE) in a biological matrix, i.e., serum. Subsequently, the optimized and validated method was applied to test its suitability on serum samples from eight healthy volunteers that consumed a standardized test meal (STM) with the concomitant supplementation with a nutraceutical product based on TN rich in ABA, to test the method in a real-world setting. Finally, the glycemic and insulinemic response in the above-mentioned subjects was evaluated in association with ABA serum levels at different time points of analysis.
## 2.1.1. Participants and Standardized Test Meal Composition
Briefly, healthy subjects of both sexes were recruited in May 2019 by Samnium Medical Cooperative (Sant’Agata De’ Goti, Italy) as a subset of volunteers participating in a randomized clinical trial. The volunteers’ letter of intent, the protocol, and the synoptic documents of the study were submitted to the Scientific Ethics Committee of AO Rummo Hospital (Benevento, Italy). The study was approved by the committee (protocol no. 28, 15 May 2017) and was conducted in accordance with the Helsinki Declaration of 1964 (as revised in 2000). The study was listed on the ISRCTN registry (www.isrctn.com, accessed on 24 June 2022) with ID ISRCTN16732651. A total of 8 healthy subjects aged 18–83 years were invited to participate. Exclusion criteria were diabetes mellitus (DM) type 1 and type 2, liver, heart, or renal disease, drug therapy or intake of dietary supplements containing ABA, underweight (body mass index < 18.5 kg/m2), pregnancy or suspected pregnancy, birch pollen allergy. All participants received oral and written information about the study before giving written informed consent. Before inclusion in the study, volunteers were subjected to self-reporting questionnaires involving the following items: residence, occupation, smoking status, alcohol consumption, drug administration, and dietary habits. The volunteers meeting the inclusion criteria (body mass index 27–35 kg/m2; waist circumference, men ≥ 102 cm and women ≥ 88 cm) were assigned to consume a standardized test meal (STM), immediately after the administration of TN (1 g, lyophilized) containing 15 µg of ABA, as reported in our previous work [6]. TN treatment was self-administered as a tablet. The STM composition consisted of white bread (100 g) with 50 g of jam and 100 g of mozzarella and 200 mL of fruit juice. These amounts were chosen based on indications of a balanced meal, as they provided $50\%$ of calories from carbohydrates, $20\%$ from protein, and $30\%$ from fat [27].
## 2.1.2. Experimental Procedures
At the beginning of the study, the height, body weight, and waist circumference (WC) of all patients were measured and the Body Mass Index (BMI) was determined. Glucometabolic parameters were determined before the STM consumption as baseline, except for fasting plasma glucose (FPG) and fasting plasma insulin (FPI), which were evaluated before and after consuming the STM. After a 12-h fasting period, blood samples were collected to measure FPG, FPI, triglycerides (TG), total plasma cholesterol (TC), lipoprotein-cholesterol (HDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and glycated hemoglobin (HbA1c). The concentration of the above-mentioned parameters was assayed by enzymatic colorimetric methods (Diacron International, Grosseto, Italy). The *Friedewald formula* was used to calculate LDL cholesterol levels. Plasma insulin levels were measured by ELISA (DIA-source ImmunoAssay S.A., Nivelles, Belgium) on a Triturus analyzer (Diagnostic Grifols S.A., Barcelona, Spain). HbA1c was measured using a commercially available kit (InterMedical s.r.l, Grassobbio, Italy).
## 2.2.1. Chemicals and Reagents
The purity of ABA as primary standard was ≥$98\%$ HPLC and purchased from Sigma-Aldrich (Milan, Italy). Chromatographic-grade solvents, methanol, formic acid, and ethyl acetate were used (minimum purity $99.9\%$) and purchased by Sigma Aldrich, (Milan, Italy) as well as internal standard (IS), bis 4,4′- Sulfonyldiphenol, (BPS), (minimum purity $98\%$). Ultra-purified water Milli Q was produced in-house (conductivity 0.055 μS cm−1 at 25 °C, resistivity equals 18.2 MΩ·cm).
## 2.2.2. Real Sample Preparation and Extraction
Vacu-test® tubes were employed to collect blood samples, collected from the antecubital vein (5 mL); the samples were immediately centrifuged at 2200 rpm for 20 min and the supernatant was frozen and stored at −80 °C until processing. Both samples, synthetic and real samples, underwent liquid–liquid extraction (LLE). Briefly, the sample preparation was performed according to the following procedures: 75 µL of serum were transferred to a 2 mL vial, spiked into 40 µL of BPS 100 ppb solution, to achieve a final concentration of 40 ppb, with the addition of 340 µL of methanol and 2 µL of 12 N HCl solution. Each sample was successively vortexed and stored in ice for 2 min. Afterwards, the samples were added to 500 µL ethyl acetate, vortexed, and finally centrifuged at 10.000 rpm for 5 min at 4 °C. The supernatant (a fixed volume of 700 µL) was transferred to a 4 mL vial, dried in Savant™ SpeedVac™ (Thermo Scientific™, Hyannis, MA, USA) and stored until the analysis. Dried samples were dissolved in 50 μL of CH3OH:H2O $\frac{50}{50}$ v/v, vortexed, and after 45 min to facilitate the dissolution, another 50 μL of CH3OH:H2O $\frac{50}{50}$ v/v was added. The samples were again centrifuged at 3.500 rpm for 5 min and the supernatant was transferred to a 1.5 mL glass insert and injected into liquid mass spectrometry (LC-MS). BPS was chosen for its lipophilicity feature as an internal standard (IS) to assess the recovery of each extraction.
## 2.2.3. Equipment
Analytical determination was performed on an Ultimate 3000 LC system (Dionex/Thermo Scientific™, San Jose, CA, USA) coupled to a linear ion trap LTQ XL™ (Thermo Scientific™, San Jose, CA, USA), with an electrospray ionization source. The separation was performed on Luna® Omega 3 µm Polar C18 column (100 × 2.1 mm) (Phenomenex Torrance USA, Torrance, CA, USA). Tuning and data acquisition were carried out using Xcalibur and quantification using Qual Broswer software 4.4 version.
## 2.2.4. LC-MS/MS Conditions
The samples, 5 μL of each, were injected from the Autosampler (Ultimate 300) and analyzed under the following chromatographic conditions: eluent A aqueous added to $0.1\%$ v/v formic acid and eluent B acetonitrile, added to $0.1\%$ v/v formic acid, flow rate set to 0.4 mL min−1, at a room temperature of 35 ± 2 °C. Gradient elution was accomplished as follows: 0–2.0 min, $5\%$ B; 2.0–9.0 min, $95\%$ B; 9.0–12.0 min, $95\%$ B; 12.1–16.0 min, $5\%$ B. All mobile phases were vacuum-filtered through 0.45 μm nylon membranes (Millipore®®, Burlington, MA, USA). The electrospray ionization (ESI) mass spectrometer (MS) was operated in negative ion mode using selective reaction monitoring (SRM) with nitrogen as the nebulizer, auxiliary, collision, and curtain gas. The main working source/gas parameters of mass spectrometer were optimized and maintained as follows: curtain gas, 8; nebulizer gas, 8. The instrumental parameters employed were as follows: ESI spray voltage in the negative-ion mode, 4 kV; sheath gas flow-rate, 70 arb; auxiliary gas flow-rate, 20 arb; capillary voltage, −38 V; capillary temperature, 350 °C; and tube lens, 95 V. ABA was monitored as [M-H]− ion according to its m/z values.
## 2.2.5. Calibration Curve and Linearity
European validation guidelines were followed to validate the method [28]. Stock solutions of ABA were obtained dissolving the reference standard in $100\%$ methanol to obtain a final concentration of 2.000 ppm. Five solutions with different concentrations (40 ppb, 20 ppb, 10 ppb, 4 ppb, 2 ppb) were prepared by diluting this stock. The linearity ranges were tested using the average peak areas against the concentration (ppm) of ABA. Linear regression analysis and calibration curve parameters (Coefficient of Determination R2, slope, and intercept) were back-calculated from the peak areas using the regression line by the method of least squares, and mean accuracy values were determined [29,30].
## 2.2.6. Limits of Detection (LOD) and Quantification (LOQ)
LOD and LOQ were estimated as the concentrations providing signals equal to 3 and 10 times, respectively. They were calculated based on the following equations: LOD = SD∙3/S and LOQ = SD∙10/S [31], where SD is the standard deviation of the intercept response with the y-axis of the calibration curves, and S is the slope of a calibration curve. The spike level was 2 ppb in the appropriate range using a concentration and was assessed by running the measurement ten times.
## 2.2.7. Precision and Accuracy
The method’s precision was evaluated by running five replicates of the sample repeated in the same day and in two different days to cover both intra-day and inter-day precision, expressed as relative standard deviation (RSD%). Repeatability was assessed using the nominal concentration of ABA (2 ppb). The accuracy of this method was determined considering samples spiked with 2–40 ppb of ABA (quality control samples, QCs) and evaluated at each level in triplicate, and reported as a percentage of the nominal value.
## 2.2.8. Selectivity
Serum working calibration standards were prepared using sera already present in the archive of our laboratory and processed for other research, to assess the absence of ABA and that any signal interfered with the retention time of ABA. These sera, considered as blanks, were also employed to optimize the extraction process.
## 2.2.9. Carry-Over
Carry-over effect of the method was evaluated by injecting methanol solvent after running the highest concentrated samples of ABA spiked in the serum (three times) and observing the occurrence of signals within the retention windows of the target chemicals.
## 2.2.10. Matrix Effect
The matrix effect was investigated by calculating the ratio of the peak area in the presence of the matrix (matrix spiked with ABA post extraction) to the peak area in the absence of the matrix (ABA in methanol) [32]. The serum matrix blank was spiked with the analyte at each concentration of the linear range (2 ppb, 4 ppb, 10 ppb, 20 ppb, and 40 ppb). The ratio was calculated as follows:[1]Matrix effect %=peak area in presence of matrixarea in absence of matrix·100
## 2.2.11. Recovery
The recovery was assessed by evaluating the relative abundance of the BPS peak (I.S.) spiked before the extraction procedure and calculated as follows:[2]Recovery %=found concentrationstandard concentration·100 The results of the real samples were corrected for the recovery.
## 3.1. Anthropometric and Glucometabolic Parameters
The characteristics of the patient population at baseline are shown in Table 1. A total ofeight healthy adults (three men and five women) aged 18 to 45 years with a BMI between 18 and 25 kg/m2 met the inclusion criteria and were therefore eligible to participate in the study. The group was well balanced in terms of demographic and clinical factors.
## 3.2. Two-Hour Glycemic and Insulinemic Responses to Standardized Test Meal
Following the STM, which was preceded by administration of the nutraceutical supplement containing ABA, mean plasma glucose levels reached a peak at 30 min and gradually decreased to pre-prandial levels by 120 min. According to the plasma glucose response curve, the post-prandial insulinemic response curve peaked at 30 min and gradually declined to the pre-prandial level by 120 min (Figure 1). A similar trend can be observed for serum ABA concentrations after the consumption of STM and ABA-rich nutraceutical product in volunteers under our investigation, as shown in Figure 2.
## 3.3. Optimization of Chromatographic Method
Different “synthetic” samples with known ABA concentrations, i.e., methanolic solutions and serums spiked with ABA, were used for the method development. These samples were subjected to the above-mentioned method in order to evaluate the efficiency in isolating and detecting abscisic acid in the context of complex biological matrices. The proposed method of extraction and quantification of ABA was easy to handle and sensitive to the analysis in serum matrix, optimizing the method after several changes in operating. For the extraction procedures, there were distinct organic solvents in various percentages with water. Ethyl acetate as an extraction solvent was the most efficient solvent to extract ABA from the serum matrix (data not shown). The spike levels (40.0 ppb and 2.0 ppb) were in the recommended range, i.e., calculated LOD < spike level < 10 × calculated LOD. For LC-MS analysis, we optimized the method using different stationary reversed-phases (Luna®® Omega 3 µm Polar C18 column (100 × 2.1 mm) (Phemomenex Torrance USA) and an Inertsil ODS-3 column (2.1 mm × 100 mm, 5 μm) (Torrance, CA, USA), and by a varying gradient elution program, to achieve an adequate resolution for the two analytes from the interferents. Optimal transitions were obtained for ABA (C15H20O4, MW: 264.32 g/mol) at m/z 152.000, and for BPS (C12H10O4S, MW: 250.27 g/mol) at m/z 107.000. The linear R-squared values (r2 = 0.9981) show a good linearity in the range of the calibration curves performed in the serum matrix from 40 ppb to 2 ppb. The sensitivity of the developed method is appreciable from the listed LOD and LOQ parameters, with values of 1.59 ppb and 5.31 ppb, respectively. The RSD% of within-run precision was $2.30\%$, while the RSD% between-run precision was $12.01\%$. Repeatability was performed using the repeatedly frozen and thawed ABA samples, and we did not observe any differences in the raw data and degradation products. Recovery from the serum matrix, evaluated at high and low spiking concentrations (40 ppb and 2.0 ppb), resulted in $70.3\%$. Matrix effect was $39.97\%$ and variations in the experimental parameters did not result in any appreciable change in the method performance. Table 2 summarizes all method validation parameters.
These results demonstrate that the analytical method developed provides a reliable response relevant to the analysis of ABA in such a complex biological matrix. Selectivity is the ability of an analytical method to differentiate and quantify the analyte in the presence of other components in the sample. The selectivity of the method was evaluated by analyzing a blank sample, compared to a blank sample spiked with the lower limit of quantification LOQ (ABA equal to 2.00 ppb). As can be seen in Figure 3, the selectivity of this method was good.
## 4. Discussion
In the present work, a method for the determination of ABA in biological samples by liquid–liquid extraction (LLE), followed by liquid mass spectrometry (LC-MS) of the extract, was developed and validated. The above-reported method has significant advantages, as it does not require expensive operations, in terms of procedures and amounts of solvents used, and leads to results with a good level of accuracy, reproducibility, LOD and LOQ values. These results are better in terms of sensitivity than those achieved by Reverse-Phase HPLC-DAD analysis on food and beverage matrices [33]. Moreover, to the best of our knowledge, the scientific literature reports methods for determination of ABA by LC/MS, but in a matrix other than serum, such as in *Arabidopsis thaliana* [31], Rose Leaf Samples [32], and fresh *Oryza sativa* tissues [33]. The scientific works analyzing ABA in the serum matrix are not focused on the validation method, and therefore, do not report validation parameters for comparison [34,35]. Anyway, new strategies to detect ABA with high sensitivity are under development, as fluorescent probes, but performed on plant tissues [36].
Moreover, the application of the optimized method on serum samples of healthy volunteers who consumed a STM together with a nutraceutical product rich in ABA allowed us to evaluate its applicability in a suitable biological model. Accordingly, the STM composition of the present work provided $50\%$ of calories from carbohydrates, $20\%$ from protein, and $30\%$ from fat, in agreement with the guidelines for balanced nutrition [27]. In this manner, the glycemic and insulinemic response, together with the increase in plasmatic ABA, was evaluated in the closest to real-life setting.
The LC-MS analysis performed on the serums obtained from the eight volunteers showed different ABA levels at each time point. As observed in Figure 2, the found data confirmed the involvement of this endogenous hormone in the human response to glucose-containing foods. For all subjects, indeed, the serum ABA levels reached the highest concentration 30 min after the consumption of the STM and the nutraceutical product based on TN. In this regard, various studies carried out on human serums attempted to identify and quantify ABA levels, by performing different isolation and detection methods [11,15]. Specifically, plasma ABA levels have been shown to increase in normal glucose tolerant (NGT) subjects following an oral glucose load [14], but not in patients with T2D or in pregnant women with gestational diabetes mellitus (GDM). On the other hand, resolution of GDM one month after childbirth is associated with a restoration of the ABA response to glucose load [15]. Interestingly, a significant increase in ABA was observed in obese patients after biliopancreatic diversion (BPD), a bariatric surgery performed to reduce body weight and improve glucose tolerance, compared to pre-surgery levels [15].
Another observed difference between T2D and NGT individuals was related to fasting ABA values, which were significantly higher in T2D compared to NGT subjects (1.15 vs. 0.66 as median values, respectively). Nevertheless, the distribution of ABA values was found to be normal in NGT but not in T2D patients [15]. These alterations could be due to the heterogeneity of ABA-related dysfunction that occurs in T2D, such as the inability of ABA to increase in response to hyperglycemia or resistance to the activity of ABA. Overall, these observations suggest a role for ABA as a key hormone involved in the management of glucose homeostasis and highlight the importance of monitoring ABA levels in these groups of individuals. Notably, based on reports about daily consumption of fruits and vegetables containing ABA, epidemiological evidence indicates that the majority of the population assumes a very low intake of ABA from dietary sources [37]. Due to the multiple positive health effects attributed to the role of ABA [38], interest in supplementing this bioactive molecule through the administration of nutraceutical products rich in ABA is increasing over time, also in view of the nanomolar blood concentrations of this hormone required for its efficacy.
## 5. Conclusions
In conclusion, we herein developed and validated a method for the extraction and LC-MS/MS analysis of ABA in biological samples. Even if limited by the small sample size, requiring therefore confirmation through larger clinical evaluation, an added value is represented by the successful application of this method to real samples, which allowed the evaluation of ABA serum changes after the consumption of STM and an ABA-rich nutraceutical product. Overall, the results shown could provide a starting point for determining the response to a glucose-containing meal in clinical practice, in terms of ABA concentration. Indeed, serum detection of this endogenous hormone may be considered as a marker to assess the presence of an impaired ABA response in dysglycemic subjects. Undoubtedly, the use of this analysis would be of great interest for clinical trials involving the chronic administration of ABA-rich nutraceutical supplements with hypoglycemic potential.
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|
---
title: Blocking Store-Operated Ca2+ Entry to Protect HL-1 Cardiomyocytes from Epirubicin-Induced
Cardiotoxicity
authors:
- Xian Liu
- Yan Chang
- Sangyong Choi
- Chuanxi Cai
- Xiaoli Zhang
- Zui Pan
journal: Cells
year: 2023
pmcid: PMC10000558
doi: 10.3390/cells12050723
license: CC BY 4.0
---
# Blocking Store-Operated Ca2+ Entry to Protect HL-1 Cardiomyocytes from Epirubicin-Induced Cardiotoxicity
## Abstract
Epirubicin (EPI) is one of the most widely used anthracycline chemotherapy drugs, yet its cardiotoxicity severely limits its clinical application. Altered intracellular Ca2+ homeostasis has been shown to contribute to EPI-induced cell death and hypertrophy in the heart. While store-operated Ca2+ entry (SOCE) has recently been linked with cardiac hypertrophy and heart failure, its role in EPI-induced cardiotoxicity remains unknown. Using a publicly available RNA-seq dataset of human iPSC-derived cardiomyocytes, gene analysis showed that cells treated with 2 µM EPI for 48 h had significantly reduced expression of SOCE machinery genes, e.g., Orai1, Orai3, TRPC3, TRPC4, Stim1, and Stim2. Using HL-1, a cardiomyocyte cell line derived from adult mouse atria, and Fura-2, a ratiometric Ca2+ fluorescent dye, this study confirmed that SOCE was indeed significantly reduced in HL-1 cells treated with EPI for 6 h or longer. However, HL-1 cells presented increased SOCE as well as increased reactive oxygen species (ROS) production at 30 min after EPI treatment. EPI-induced apoptosis was evidenced by disruption of F-actin and increased cleavage of caspase-3 protein. The HL-1 cells that survived to 24 h after EPI treatment demonstrated enlarged cell sizes, up-regulated expression of brain natriuretic peptide (a hypertrophy marker), and increased NFAT4 nuclear translocation. Treatment by BTP2, a known SOCE blocker, decreased the initial EPI-enhanced SOCE, rescued HL-1 cells from EPI-induced apoptosis, and reduced NFAT4 nuclear translocation and hypertrophy. This study suggests that EPI may affect SOCE in two phases: the initial enhancement phase and the following cell compensatory reduction phase. Administration of a SOCE blocker at the initial enhancement phase may protect cardiomyocytes from EPI-induced toxicity and hypertrophy.
## 1. Introduction
Anthracyclines listed in the 22nd (the latest) version of the World Health Organization (WHO) model list of essential medicines are among the most efficacious and widely used chemotherapy drugs since the late 1960s [1]. Epirubicin (EPI) belongs to the anthracycline family; it is often used together with new generation targeted drugs and play a major role in the modern era of cancer treatment. EPI kills cancer cells likely via multiple mechanisms, including DNA adduct formation, reactive oxygen species (ROS) production, and lipid peroxidation. While EPI makes a great contribution to the improvement of treatment outcomes, dose-limiting cardiotoxicity hinders its clinical application and often leads to requirements for regimen modification or even discontinuation [2]. The anthracycline-induced cardiotoxicity can be manifested either acutely during the treatment period or chronically, from several weeks to even years after treatment has stopped [3]. The associated cardiac dysfunction has a broad range of symptoms including cardiac hypertrophy, cardiomyopathy, and ultimately congestive heart failure [4].
Cardiac hypertrophy is the enlargement of the heart, which can be divided into two categories: physiological and pathological, both of which develop as an adaptive response to cardiac stress, but their underlying molecular mechanisms, cardiac phenotype and prognosis are distinctly different. For example, Ca2+ signaling-related genes are only changed in pathological hypertrophy but not in physiological hypertrophy [5]. Studies have revealed that intracellular Ca2+ regulates the calcineurin–NFAT signaling pathway and thus initiating hypertrophy-related gene transcription [6,7,8,9]. An increase in intracellular Ca2+ leads to the activation of the phosphatase activity of calcineurin, the dephosphorylation of NFAT family members, and their translocation to the nucleus to initiate gene transcription [6].
Store-operated Ca2+ entry (SOCE) is a ubiquitous Ca2+ entry pathway activated in response to the depletion of sarcoplasmic or endoplasmic reticulum (SR/ER) Ca2+ stores. Although SOCE has been well-studied in non-excitable cells and skeletal muscles, the understanding of its important role in cardiomyocytes is emerging [10,11]. SOCE machinery components, including stromal interaction molecule 1 (Stim1) as an ER Ca2+ sensor and Orais and transient receptor potential channels (TRPCs) as plasma membrane Ca2+ channels, have been shown to be essential for heart development and to regulate heart remodeling after stress [12]. Accumulating evidence also shows enhanced SOCE in cardiac hypertrophy and heart failure [7,8,9]. While dysregulated Ca2+ signaling has been reported to contribute to EPI-induced cardiotoxicity [13,14,15], whether SOCE plays a role in this process and in the consequent cardiac remodeling remains unknown. Thus, the objective of the present study is to determine the specific role of SOCE in EPI-induced cell apoptosis and hypertrophy in cardiomyocytes.
## 2.1. Chemicals and Reagents
Claycomb cell culture medium was purchased from Sigma-Aldrich. FBS (fetal bovine serum), PBS (phosphate-buffered saline), HBSS (Hanks’ balanced salt solution), and penicillin/streptomycin antibiotic were purchased from Invitrogen/Thermofisher Scientific Pittsburgh, PA, USA. Other reagents used include BTP2 and ML204 (Millipore Sigma, St. Louis, MO, USA), EPI (Alfa Aesar, Haverhill, MA, USA), thapsigargin (TG, Adipogen, San Diego, CA, USA), fura-2 AM (Biotium 50033, Fremont, CA, USA), DAPI (Invitrogen D357, Carlsbad, CA, USA), and phalloidin (Enzo BML-T111, New York, NY, USA).
## 2.2. Cell Culture
HL-1 cardiomyocytes were maintained in *Claycomb medium* supplemented with $10\%$ FBS, 100 U/mL penicillin, 100 ug/mL streptomycin, 0.1 mM norepinephrine, and 2 mM L-glutamine [16,17]. HL-1 cells were cultured at 37 °C in a humidified $5\%$ CO2 incubator.
## 2.3. Measurement of Intracellular Ca2+ Concentration
Intracellular Ca2+ concentrations in the HL-1 cell line was measured following previously published procedures [17]. In brief, the intracellular Ca2+ was measured using a fluorescence microscope with a SuperFluo 40× objective (N.A. 1.3) connected to a dual-wavelength spectrofluorometer (Horiba Photon Technology International, Piscataway, NJ, USA). The excitation wavelengths were set at 350 nm and 385 nm and the emission wavelength was set at 510 nm. Cells were loaded with 5 μM fura-2 acetoxymethyl ester (Biotium, Fremont, CA, USA) for 30 min at 37 °C in the dark. Cellular endoplasmic reticulum (ER) Ca2+ stores were depleted by 10 μM TG in 0.5 mM EGTA dissolved in balanced salt solution (140 mM NaCl, 2.8 mM KCl, 2 mM MgCl2, 10 mM HEPES, pH 7.2). SOCE was observed upon the rapid exchange of extracellular solution to bath saline containing 2 mM CaCl2 at indicated time. The intracellular Ca2+ elevation was presented as ΔF350 nm/F385 nm.
## 2.4. Cytotoxicity Assay
HL-1 cells were seeded at 1.5 × 105 cells per well in a 29 mm glass-bottom dish. The cells were treated with vehicle, 20 µM BTP2, 1 µM EPI, or 20 µM BTP2 plus 1 µM EPI for 5 h. Then, the culture medium was removed, and cells were fixed with $4\%$ paraformaldehyde for 10 min at room temperature. The paraformaldehyde was removed and then cells were immersed in $0.1\%$ Triton X-100 in PBS for 10 min, washed with PBS twice, followed by incubation with PBS containing 6.6 µM phalloidin (Enzo, New York, NY, USA) for 15 min. The cells were washed with PBS three times, and then counter staining with PBS containing 1 µg/mL DAPI (1:500) for 5 min at room temperature in the dark. The cells were then washed with PBS twice and immersed in ProlongTM Gold antifade reagent (Life Technologies Corporate, Eugene, OR, USA). The fluorescence signals were observed using a DMi8 inverted microscope (Leica, Wetzlar, Germany) with a 40× objective (NA 1.3). The excitation/emission wavelengths set for DAPI and phalloidin were $\frac{405}{430}$ nm and $\frac{547}{572}$ nm, respectively. The imaging was performed at room temperature.
## 2.5. Western Blotting Analysis
HL-1 cardiomyocytes were lysed in modified RIPA buffer (150 mM NaCl, 50 mM Tris-Cl, 1 mM EGTA, $1\%$ Triton X-100, $0.1\%$ SDS, and $1\%$ sodium deoxycholate, pH 8.0) containing protease inhibitors cocktail (Sigma-Aldrich, US) as previously described [18,19]. Protein concentration was quantified using a BCA kit (ThermoFisher, Pittsburgh, PA, USA). Equal amounts of proteins were loaded onto SDS polyacrylamide gels, and the separated proteins were transferred to PVDF membranes (Bio-Rad, Hercules, CA, USA). The blot was incubated with $5\%$ non-fat dry milk blocking buffer (Bio-Rad, Hercules, CA, USA) for 1 h at room temperature and probed with specific primary antibodies in blocking buffer at 4 °C overnight. The primary antibodies used in this study included anti-caspase-3 (1:1000, catalog #9662, Cell Signaling Technology, Massachusetts, MA, USA) and anti-GAPDH (1:1000, GeneTex, Irvine, CA, USA). The next day, the blots were washed with PBST three times followed by incubation with secondary antibodies including the appropriate horse radish peroxidase (HRP)-conjugated goat anti-rabbit IgG (1:5000, Cell Signaling Technology, Massachusetts, USA) and anti-mouse IgG (1:5000, Cell Signaling Technology, USA). Signals were detected using the ECL detection method on a ChemiDoc instrument.
## 2.6. Cell Size Measurement
HL-1 cells seeded at 1 × 106 cells per well in a 6-well plate were treated with vehicle or 20 µM BTP2, 1 µM EPI, or 20 µM BTP2 plus 1 µM EPI for 5 h followed by switching to normal culture media for 24 h. The cells were then observed and phase contrast imaging was conducted using a DMi8 inverted microscope (Leica, Wetzlar, Germany). The cell surface area was quantified using ImageJ and Graphpad 6 software.
## 2.7. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR)
Total RNAs were extracted from HL-1 cells using Illustra RNAspin MiniRNA Isolation Kit and the quality and concentration of RNA were evaluated by photometrical measurement of $\frac{260}{280}$ nm. The primers were obtained from Sigma Aldrich. Four hundred nanograms of total RNA was applied for reverse transcription using the qScript microRNA Synthesis Kit (QuantaBio, Beverly, MA, USA) following the manufacturer’s protocol. cDNA was diluted 1:5 in DNase-, RNase-, and protease-free water and 2 μL template was used for PCR. The primer pairs for BNP and GAPDH were used. The sequences for the BNP primers are forward (5′–3′) GCCAGTCTCCAGAGCAATTC and reverse (5′–3′) TCTTTTGTGAGGCCTTGGTC. The sequences for the GAPDH primers are forward (5′–3′) AGGTCGGTGTGAACGGATTTG and reverse (5′–3′) TGTAGACCATGTAGTTGAGGTCA. For qRT-PCR, QuantaBio PerfecTa SYBR Green FastMix ROX was used according to the manufacturer’s procedure. The signals generated by integration of SYBR Green into the amplified DNA were detected in a real-time machine (StepOne Plus Real-Time PCR System, ThermoFisher Scientific, USA). Data were expressed as 2-ΔΔCT relative to GAPDH gene expression.
## 2.8. Immunofluorescence Staining
Cells were seeded into 29 mm glass-bottom dishes. The cells were fixed with $4\%$ paraformaldehyde for 10 min at room temperature. The paraformaldehyde was then removed and the cells were immersed in PBS containing $0.1\%$ Triton X-100 for 10 min. After washing with PBS three times, the cells were blocked in PBS containing $0.1\%$ Triton X-100 supplemented with $10\%$ horse serum for 30 min at room temperature. Then, the cells were incubated with rabbit anti-NFAT4 primary antibody (1:100, ProteinTech, Rosemont, IL, USA) in blocking solution at 4 °C overnight. The next day, the cells were taken out and washed with PBS three times, then incubated with Alexa Fluor 488-labelled secondary antibody (1:500, Abcam, Cambridge, MA, USA) at room temperature in the dark for 1 h to visualize the expression and localization of NFAT4. The cells were counter-stained with PBS containing 1 μg/mL DAPI (1:500) for 5 min at room temperature in the dark and then immersed in ProlongTM Gold antifade reagent (Life Technologies Corporate, Eugene, OR, USA). Images were taken using a Nikon A1R HD25 LSM confocal microscope with a 40× oil immersion objective (NA 1.3) using GFP and DAPI filters (Ex: $\frac{488}{405}$; Em: $\frac{509}{430}$ nm).
## 2.9. RNA-Seq Data Analysis
The RNA-Seq dataset GSE217421 was used [20]. Different human induced pluripotent stem cell (iPSC)-derived cardiomyocyte cell lines were treated with 2 µM EPI or DMSP for 48 h, followed by bulk RNA-seq analysis. Differentially expressed gene were identified between drug- and control treated cell lines. A total 17 EPI samples and 56 control samples covering five different cell types were used with each cell type having a different number of replicates as shown in Table S2. Table S3 shows all the control cell lines and replicate numbers. Two-way ANOVA of the effects of treatment (EPI vs. Con) and cell lines (five cell lines) was used for analysis.
## 2.10. Statistical Analysis
Data were analyzed using Graphpad Prism 6 software (Boston, MA, USA) unless indicated otherwise. The results were presented as mean ± standard deviation (SD) or as otherwise indicated. Comparisons between two groups were analyzed using a Student’s t-test. Comparisons among more than two groups were analyzed using one-way analysis of variance (ANOVA) followed by Bonferroni post hoc analysis. A p value of <0.05 was considered statistically significant in all experiments except the RNA-seq data analysis.
## 3.1. SOCE Machinery Genes Were Downregulated by EPI Treatment in Human iPSC-Derived Cardiomyocytes
RNA-seq data analysis of human iPSC-derived cardiomyocytes showed that cells treated with 2 µM EPI for 48 h had significantly reduced expression of SOCE machinery genes, i.e., Orai1, Orai3, TRPC3, TRPC4, Stim1, and Stim2, and increased expression of TRPC2 (Figure 1A, Table S1). The expression of Orai2, TRPC1, TRPC5, and TRPC6 were similar between the EPI and control groups.
To confirm the changes in SOCE in live cells, HL-1, a cardiomyocyte cell line derived from adult mouse atria was used for its easy culture and well-characterized cardiomyocyte properties. After being treated with 1 µM EPI or vehicle control ($0.1\%$ DMSO) for 6 h, HL-1 cells were loaded with 5 µM fluorescent Ca2+ indicator fura-2 AM at 37 °C in the dark for 30 min. The ER Ca2+ stores were depleted by 10 μM TG in BSS containing 0.5 mM EGTA. When re-introducing BSS containing 2 mM CaCl2, the intracellular Ca2+ level (presented as F350 nm/F385 nm) was monitored using live cell imaging and the SOCE was calculated as the difference (ΔF350/F385) between the peak and baseline before the addition of 2 mM Ca2+. Compared to vehicle control (black curve), SOCE was significantly reduced in the EPI-treated HL-1 cells (red curve) (Figure 1B,C).
## 3.2. Acute Treatment of EPI Increased SOCE in HL-1 Cardiomyocytes
In addition to transcriptional regulation, EPI can increase ROS production and lipid peroxidation. Oxidative stress has been shown to promote STIM1 oligomerization and alter channel activity [21]. We next examined whether acute treatment of EPI and its resulting oxidative stress can affect SOCE in HL-1 cells. Administration of BTP2, a SOCE inhibitor, significantly decreased ΔF350/F385 (0.142 ± 0.064) compared with that of the vehicle-treated control cells (0.188 ± 0.058, $$n = 35$$; ** $$p \leq 0.0051$$). This data confirmed the presence of BTP2-sensitive SOCE in HL-1 cardiomyocytes (Figure 2A,B,E). Contrary to prolonged treatment, acute treatment of EPI for only 30 min resulted in significantly enhanced SOCE in HL-1 cells (0.254 ± 0.069, $$n = 37$$) compared to those treated with the vehicle control (0.188 ± 0.058, $$n = 37$$; **** $p \leq 0.0001$). Addition of BTP2 could significantly decrease SOCE (0.045 ± 0.027, $$n = 36$$) in EPI-treated HL-1 cells compared to those treated with EPI alone (0.254 ± 0.069, $$n = 36$$; **** $p \leq 0.0001$), indicating that pharmacologically inhibiting SOCE with BTP2 can reduce the EPI-enhanced SOCE in HL-1 cells. Furthermore, ML204, a relative specific TRPC4 inhibitor could significantly reduce SOCE in HL-1 cells as well (Supplemental Figure S1).
## 3.3. BTP2 Diminished EPI-Induced ROS Production in HL-1 Cardiomyocytes
The reciprocal regulation between mitochondria and intracellular Ca2+ suggests that SOCE may regulate mitochondrial ROS production. Thus, ROS were measured by using DHE dye in HL-1 cells treated with EPI (Figure 3). In the HL-1 cells treated with 1 μM EPI for 30 min, ROS levels were significantly increased compared to that in vehicle control cells. Interestingly, BTP2 was able to significantly inhibit ROS production in HL-1 cells even in the presence of EPI (Figure 3).
## 3.4. BTP2 Inhibited EPI-Induced Apoptosis in HL-1 Cardiomyocytes
Disruption of F-actin is a hallmark for apoptosis [22]. We next examined the expression of F-actin in HL-1 cells using phalloidin staining. Reduced expression of F-actin was observed in cells treated with 1 µM EPI for 5 h compared to that of vehicle-treated control cells (Figure 4A), suggesting that EPI induced apoptosis in HL-1 cardiomyocytes. When co-treated with 20 µM BTP2, the EPI-induced degradation of F-actin was partially rescued (Figure 4A,B), indicating that BTP2 inhibited EPI-induced F-actin disruption.
Anthracyclines have been shown to induce apoptosis in HL-1 cardiomyocytes through caspase-3 [23]. We then examined the levels of cleaved caspase-3 in HL-1 cells. EPI induced abundant amounts of cleaved caspase-3, evidenced by the Western blot analysis (Figure 4C,D). The EPI-increased level of cleaved caspase-3 was significantly diminished by co-treatment with 20 µM BTP2. Consistent with the data from F-actin degradation, the cleaved caspase-3 analysis again indicated that EPI induced apoptosis in HL-1 cardiomyocytes, which could be alleviated by BTP2.
## 3.5. BTP2 Inhibited EPI-Induced Hypertrophy in HL-1 Cardiomyocytes
EPI-induced cardiac remodeling includes hypertrophy. SOCE plays a major role in pathophysiological hypertrophy. We thus examined whether BTP2 can inhibit EPI-induced hypertrophy in HL-1 cardiomyocytes. HL-1 cells were treated with vehicle control, 1 µM EPI, or co-treated with 1 µM EPI and 20 µM BTP2 for 5 h, followed by drug withdrawal and then growth in normal culture medium for 24 h. Phase contrast images were then taken of these cells and the surface area of the HL-1 cardiomyocytes was measured and quantified. EPI treatment increased the size of cardiomyocytes to almost twice that of vehicle-treated control cardiomyocytes (Figure 5A,B). In the BTP2 and EPI co-treatment group, the size of HL-1 cells was significantly reduced compared to that in the EPI group.
The expression of brain natriuretic peptide (BNP), a specific marker of cardiac hypertrophy [22], was also examined. As shown in Figure 5C, the mRNA level of BNP was significantly increased upon the treatment with 1 µM EPI (4.861 ± 0.697, $$n = 9$$) compared to that of vehicle-treated cells (control, 1.010 ± 0.155, $$n = 9$$). Consistent with the cell size analysis, BTP2 could significantly alleviate EPI-induced BNP expression (3.054 ± 0.260) in HL-1 cells.
These data indicate that blocking SOCE by BTP2 can reduce EPI-induced hypertrophy in HL-1 cardiomyocytes.
## 3.6. BTP2 Inhibited EPI-Induced NFAT4 Nuclear Translocation in HL-1 Cardiomyocytes
Nuclear factor of activated T cells (NFAT) was reported to be a critical nuclear transcriptional factor regulating cardiac hypertrophy [24]. We lastly examined whether SOCE contributes to EPI-induced hypertrophy through the NFAT pathway in HL-1 cells. Since NFAT4 is the most abundant one out of the five subtypes of NFAT expressed in cardiomyocytes [25], we focused on NFAT4 in this study. After being treated with vehicle, 1 µM EPI, 20 µM BTP2, or 1 µM EPI combined with 20 µM BTP2 for 5 h, HL-1 cells were cultured in growth media for another 24 h until fixation and immunostaining with anti-NFAT4 antibody. HL-1 cells treated with 10 µM ionomycin for 15 min were used as a positive control for NFAT4 immunostaining since ionomycin is a strong activator for NFAT signaling [26]. The nuclear translocation of NFAT4 was examined by confocal microscopy imaging. As shown in Figure 6, EPI treatment induced NFAT4 nuclear translocation (indicated by the white arrows), whereas co-treatment with BTP2 showed minimal NFAT4 nuclear translocation. This data suggested that the EPI-induced nuclear translocation of NFAT4 was inhibited by BTP2 in HL-1 cells.
## 4. Discussion
EPI is a widely used anthracycline chemotherapy drug, but it also causes cardiotoxicity and results in heart remodeling and even failure. This study confirmed that EPI can induce ROS production, cell apoptosis, and hypertrophy in cardiomyocytes. Furthermore, this study showed that acute treatment of EPI can increase SOCE in HL-1 cells and blocking SOCE by BTP-2 not only reduced EPI-enhanced SOCE (Figure 2), but also alleviated EPI-induced apoptosis (Figure 4) and hypertrophy (Figure 5). Although SOCE has been associated with hypertrophy in cardiomyocytes and heart failure, this study provides the first evidence, to our knowledge, that SOCE plays a key role in EPI-induced cardiotoxicity and hypertrophy. More importantly, this study may shed light on developing an approach to alleviate EPI-induced cardiotoxicity by targeting SOCE in the initial phase of EPI treatment (working model is shown in Figure 7).
It is well-known that SOCE has a complex nature and co-exists with other Ca2+ influx mechanisms, such as receptor-operated Ca2+ entry (ROCE). SOCE machinery may contain several molecules as channel complexes at the plasma membrane interacting with STIMs at the SR/ER. Previous reports showed that Orai1 is expressed in HL-1 cells and knockdown of Orai1 could abolish SOCE in HL-1 cells [27]. In addition, TRPC1, $\frac{3}{6}$, and 4 may also form SOCE channel complexes in hypertrophic cardiomyocytes [24] and STIM1 can bind and regulate TRPC1, TRPC4, and TRPC5 [28]. The current study showed evidence for a bona fide, BTP2-sensitive SOCE in HL-1 cells. Since BTP2 can block both Orai [29] and TRPC channels [30], our current data cannot exactly pinpoint whether Orais or TRPCs mediate SOCE in hypertrophic cardiomyocytes. RNA-seq analysis showed that treatment of EPI significantly reduced Orai1, Orai3, TRPC3, and TRPC4 expression in human iPSC-derived cardiomyocytes, which is consistent with reduced SOCE (Figure 1A, Supplementary Table S1). Interestingly, ML204, a relatively selective blocker of TRPC4 could significantly reduce SOCE in HL-1 cells (Supplementary Figure S1). These data suggest that these Orais and TRPCs may contribute to SOCE in cardiomyocytes. Future investigation is required to dissect the exact components in the SOCE channel complex in cardiomyocytes, which contribute to EPI-induced cardiotoxicity.
After cardiomyocytes survived the cardiotoxicity after EPI treatment, they may undergo cell remodeling which leads to hypertrophy, cardiac remodeling, and eventual heart failure. SOCE plays a major role in the pathogenesis of heart hypertrophy. Numerous studies suggest that pathological stimuli activate SOCE and further trigger the NFAT signaling cascade, which is critical for the regulation of growth gene expression and promotion of cardiomyocyte hypertrophy. Suppression of SOCE machinery genes, such as STIM1 and Orai1, attenuates the hypertrophic responses to pressure overload or agonists [31,32]. Our current findings are in line with these previous reports, indicating that EPI-induced cardiomyocyte hypertrophy could also be inhibited by SOCE blocker.
Interestingly, RNA-seq data analysis of human iPSC-derived cardiomyocytes showed that cells treated with 2 µM EPI for 48 h had significantly reduced expression of SOCE machinery genes, e.g., Orai1, Orai3, TRPC3, TRPC4, Stim1, and Stim2 (Figure 1). Intracellular Ca2+ measurement in live HL-1 cells confirmed that SOCE was indeed reduced in cardiomyocytes treated with EPI for 6 h (Figure 1B,C) or longer. The apparent discrepancy suggests that EPI may affect SOCE in two phases: the initial enhancement phase followed by cell compensatory reduction phase. The initial enhancement phase is likely due to immediately increased ROS production and lipid peroxidation right after administration of EPI. The rapid generation of ROS has been best studied in myocardial ischemia–reperfusion models [33]. In addition to the regulatory roles of ROS in many cellular events [34], oxidative stress is also able to promote STIM1 oligomerization, deplete ER Ca2+, and active SOCE [21]. Since there is a reciprocal regulation between mitochondria and SOCE, EPI-triggered initial mitochondrial ROS production could be further amplified by enhanced SOCE, which is supported by the evidence that blocking SOCE by BTP2 attenuated EPI-triggered ROS production (Figure 3). Additionally, ROS is also known to directly activate TRPCs channels [35,36]. During the initial enhancement phase, EPI triggers the apoptotic pathway in cardiomyocytes. The surviving cardiomyocytes from the initial phase may develop compensatory mechanisms at the transcription level. This may explain why prolonged treatment of EPI (at 48 h) resulted in a reduction in the expression of SOCE machinery genes.
Chemotherapeutic agents (anthracycline therapy in particular) have been reported to damage the F-actin of cells. In cardiac H9c2 cells, doxorubicin reduces number of F-actin filaments, especially at higher concentrations [37]. The reorganization of F-actin filaments and characteristic features of apoptosis have also been reported in Chinese hamster ovary cells, pancreatic β cells, breast cancer cells, and other cells upon doxorubicin treatment [38,39,40].
Others and our previous studies suggest that SOCE is an effective chemotherapy drug target [18,19,41,42,43]. The findings of the present study have shown that SOCE contributes to EPI-induced cardiotoxicity, indicating that SOCE blockers may be able to protect cardiomyocytes from the side effects of anthracycline chemotherapy drugs. Together, the results suggest that SOCE blockers may be dual-function drugs for both chemotherapy and cardio-protection.
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|
---
title: CRP/Albumin Ratio and Glasgow Prognostic Score Provide Prognostic Information
in Myelofibrosis Independently of MIPSS70—A Retrospective Study
authors:
- Nora-Medea Messerich
- Narasimha Rao Uda
- Thomas Volken
- Sergio Cogliatti
- Thomas Lehmann
- Andreas Holbro
- Rudolf Benz
- Lukas Graf
- Vikas Gupta
- Wolfram Jochum
- Izadora Demmer
- Tata Nageswara Rao
- Tobias Silzle
journal: Cancers
year: 2023
pmcid: PMC10000567
doi: 10.3390/cancers15051479
license: CC BY 4.0
---
# CRP/Albumin Ratio and Glasgow Prognostic Score Provide Prognostic Information in Myelofibrosis Independently of MIPSS70—A Retrospective Study
## Abstract
### Simple Summary
To assess prognosis in myelofibrosis (MF), age and degree of anemia and leukocytosis are taken into account together with the presence of blasts in the peripheral blood and constitutional symptoms (fever, night sweats, weight loss). The latter are signs of systemic inflammation, which plays a pivotal role in MF pathophysiology. Considering information about genetic changes can refine prognostication. The goal of our retrospective study was to assess the prognostic impact of two laboratory markers of inflammation that are readily available in clinical routine at low costs: C-reactive protein (CRP) and albumin. We found a significant prognostic impact of both parameters either alone or combined within the CRP/albumin ratio or the Glasgow Prognostic Score, which was independent of the Mutation-Enhanced International Prognostic Scoring System (MIPSS)-70. Therefore, assessing CRP and albumin helps to identify a vulnerable population of MF patients, which eludes current prognostic models, even if the presence of high-risk mutations is considered.
### Abstract
In myelofibrosis, the C-reactive protein (CRP)/albumin ratio (CAR) and the Glasgow Prognostic Score (GPS) add prognostic information independently of the Dynamic International Prognostic Scoring System (DIPSS). Their prognostic impact, if molecular aberrations are considered, is currently unknown. We performed a retrospective chart review of 108 MF patients (prefibrotic MF $$n = 30$$; primary MF $$n = 56$$; secondary MF $$n = 22$$; median follow-up 42 months). In MF, both a CAR > 0.347 and a GPS > 0 were associated with a shorter median overall survival (21 [$95\%$ CI 0–62] vs. 80 months [$95\%$ CI 57–103], $p \leq 0.001$ and 32 [$95\%$ CI 1–63] vs. 89 months [$95\%$ CI 65–113], $p \leq 0.001$). Both parameters retained their prognostic value after inclusion into a bivariate Cox regression model together with the dichotomized Mutation-Enhanced International Prognostic Scoring System (MIPSS)-70: CAR > 0.374 HR 3.53 [$95\%$ CI 1.36–9.17], $$p \leq 0.0095$$ and GPS > 0 HR 4.63 [$95\%$ CI 1.76–12.1], $$p \leq 0.0019.$$ *An analysis* of serum samples from an independent cohort revealed a correlation of CRP with levels of interleukin-1β and albumin with TNF-α, and demonstrated that CRP was correlated to the variant allele frequency of the driver mutation, but not albumin. Albumin and CRP as parameters readily available in clinical routine at low costs deserve further evaluation as prognostic markers in MF, ideally by analyzing data from prospective and multi-institutional registries. Since both albumin and CRP levels reflect different aspects of MF-associated inflammation and metabolic changes, our study further highlights that combining both parameters seems potentially useful to improve prognostication in MF.
## 1. Introduction
Both primary and secondary myelofibrosis (PMF/SMF) are caused by a complex interplay of (epi)genetic alterations in hematopoietic stem cells and inflammatory changes, which affect hematopoiesis and impact patient survival [1,2].
The Dynamic International Prognostic Scoring System (DIPSS) as a standard tool for prognostication considers age, anemia, leukocytosis, peripheral blast counts and constitutional symptoms [3]. It can be refined by incorporating information about cytogenetic aberrations, mutational profile or both [4]. In addition to these complex and expensive parameters, some routine laboratory markers add prognostic information, such as C-reactive protein (CRP) and albumin. Elevated CRP levels have been associated with several adverse disease features and a shorter leukemia-free survival [5,6], and albumin has been consistently shown to add additional prognostic information independently of DIPSS and several DIPSS-based prognostic scoring systems [7,8,9]. Furthermore, indices combining CRP and albumin such as the CRP/albumin ratio (CAR) [10] or the Glasgow Prognostic Score (GPS) [11] provide DIPSS-independent prognostic information in MF.
With regard to both CAR and GPS, it remains elusive as to whether they still add prognostic value if the molecular risk profile is considered. We therefore examined the prognostic impact of CAR and GPS in relation to the Mutation-Enhanced International Prognostic Scoring System (MIPSS)70, which includes the mutational profile without needing conventional metaphase cytogenetics [12].
## 2. Patient Population and Methods
We performed a retrospective chart review of patients diagnosed with MF at the Cantonal Hospital St. Gallen between 2000 and 2020 (Cohort A).
One hundred and eight patients were identified (47 female and 61 male, median age 72; pre-fibrotic MF: $\frac{30}{108}$ ($28\%$), PMF $\frac{56}{108}$ ($52\%$) and SMF $\frac{22}{108}$ ($20\%$)), and clinical and laboratory data were collected at the time of diagnosis and before commencement of treatment. All of the cases were reviewed individually, to ensure correct classification according to WHO2016 [13]. If the diagnostic work-up did not include next-generation sequencing (NGS), we performed mutational profiling using material from the diagnostic samples (see the Supplementary Materials “Supplementary Methods”). Detailed patient characteristics of the cases with MF in cohort A (PMF and SMF) are shown in Table 1.
The CAR was calculated by dividing the CRP concentration (mg/L) by the albumin concentration (g/L). The GPS was determined according to [14] (GPS 0: albumin ≥ 35 g/L and CRP ≤ 10 mg/L; GPS 1: either albumin < 35 g/L or CRP > 10 mg/L; GPS 2: both albumin < 35 g/L and CRP > 10 mg/L).
For CRP, we used the upper limit of normal from our local laboratory for dichotomization (≤/>8 mg/L), and for albumin, the median of our population was used (</≥40 g/L). For the CAR, we used a cut-off of </≥0.204, as proposed by [10] and a CAR of </≥0.374, representing the fourth quartile of our cohort. The methods applied for the statistical analysis are described in detail in the Supplementary Materials.
Plasma probes from an independent Canadian cohort (Cohort B) of 64 MPN patients (MF $$n = 28$$, PV $$n = 18$$, ET $$n = 18$$; Supplementary Table S1) and healthy controls ($$n = 16$$) were available to assess the correlation of high-sensitivity (hs)CRP and albumin levels with pro-inflammatory cytokines, which were measured as described in detail in the Supplementary Materials.
## 3.1. Levels of CRP and Albumin, the CAR in Different MF Subgroups and Their Association with Disease Characteristics
Within Cohort A, we found higher levels of conventional CRP in patients with MF (PMF: $$n = 56$$, median 5 mg/L, [IQR 2–18], SMF: $$n = 22$$, median 5 mg/L [IQR 3–9]) compared to pre-fibrotic MF ($$n = 30$$, median 1 mg/L, [IQR 1–8], $$p \leq 0.034$$). With regard to the albumin concentration, we found no difference (PMF median 40.5 g/L [IQR 37–42.6], SMF median 39 g/L [IQR 36.4–42.7], pre-fibrotic MF median 42 g/L [IQR 38–43.6], $$p \leq 0.253$$).
In MF, a CRP-elevation > 8 mg/L was associated with lower levels of hemoglobin and platelets, a higher percentage of peripheral blasts, higher LDH-levels, transfusion-dependency and the presence of constitutional symptoms, whereas levels of albumin < 40 g/L were associated only with the degree of anemia and with a lower body mass index (BMI), as shown in Table 1. An additional comparison of disease characteristics following the cut-offs used within the GPS (CRP ≤/> 10 mg/L and albumin </≥ 35 g/L) is provided in Supplementary Table S2.
There was no difference in CRP, albumin and the CAR between JAK2-mutated cases and CALR-mutated cases. With regard to JAK2-V617F variant allele frequency (VAF), we observed a significantly higher CAR in patients with a VAF > $50\%$ (median 0.243 vs. 0.095, $$p \leq 0.035$$) and a trend towards higher CRP values (median 7.5 vs. 4.5 mg/L, $$p \leq 0.071$$). No difference was noted for albumin (median 39 vs. 38 g/L, $$p \leq 0.158$$). Patients with high-risk mutations according to MIPSS70 showed a tendency towards a higher CAR (median 0.579 vs. 0.115, $$p \leq 0.051$$) but did not differ significantly with regard to the single parameters. Further details are shown in Supplementary Table S3.
MIPSS70 was available for $\frac{59}{78}$ patients ($76\%$): intermediate risk $\frac{43}{59}$ ($72.9\%$), high risk $\frac{14}{59}$ ($23.7\%$) and low-risk $\frac{2}{59}$ ($3.4\%$). Overall survival (OS) different significantly among these groups (Supplementary Figure S1).
Compared to the MIPSS70-intermediate patients, the MIPSS70-high-risk patients had significantly higher CRP levels (median 14 mg/L [IQR 5–30] vs. 5 mg/L [IQR 1–10], $$p \leq 0.012$$), but not lower albumin levels (median 38 vs. 39 g/L, $$p \leq 0.224$$). Accordingly, the CAR was higher in MIPSS70-intermediate patients (median 0.504 [$95\%$ CI 0.95–0.739] vs. 0.116 [$95\%$ CI 0.026–0.255,], $$p \leq 0.025$$). Given their low number, we did not include the MIPSS70-low risk group in this analysis.
## 3.2. Prognostic Impact of CRP, Albumin and Derived Indices (CAR and GPS) in MF
The probability of death rose continuously with lower albumin levels even in the range determined as normal (OR = 0.85, $95\%$ CI 0.73–0.99; $$p \leq 0.043$$, Supplementary Figure S2), and an albumin concentration below the population median was associated with a significantly shorter survival (albumin </≥ 40 g/L, median OS 50 [$95\%$ CI 38–62] vs. 101 [$95\%$ CI 51–151] months, $$p \leq 0.026$$). CRP > 8 mg/L ($$n = 24$$) was associated with shorter survival compared to CRP within the normal limits (≤8 mg/L, $$n = 47$$): median OS 44 [$95\%$ CI 0–89] vs. 89 [$95\%$ CI 56–122] months, $p \leq 0.001.$
Correspondingly, a higher CAR was associated with inferior survival (median OS CAR ≤/> 0.204: 89 [$95\%$ CI 67–111] vs. 44 [$95\%$ CI 3–85] months, $$p \leq 0.001$$; and CAR ≤/> 0.374: 80 [$95\%$ CI 57–103] vs. 21 [$95\%$ CI 0–62] months, $p \leq 0.001$). Similar results were obtained for patients with a GPS of 1 or 2 ($$n = 18$$) compared to patients with a GPS of 0 ($$n = 39$$): median OS 32 [$95\%$ CI 1–63] vs. 89 [$95\%$ CI 65–113] months, $p \leq 0.001.$ Kaplan–Meier curves for the patients for whom both CRP and albumin were available ($$n = 57$$) are shown in Figure 1A–D.
For all of the factors, a higher HR for mortality was observed in univariate Cox regression models (Table 2). Given the low number of MIPSS70-low-risk patients ($$n = 2$$), we dichotomized the cohort into a “MIPSS70dichlow/intermediate” risk group ($$n = 45$$) and a “MIPSSdichhigh” risk group ($$n = 14$$) for analyses in bivariate models. Here, CRP > 8 mg/L, albumin < 40 g/L, and both a CAR > 0.374 and a GPS > 0 retained their prognostic value together with MIPSS70dich, whereas a CAR > 0.204 did not (Table 2). In a separate analysis considering only the PMF patients ($$n = 35$$) and applying the same threshold for CAR (>0.374) and GPS (>0), the results remained significant, albeit with large $95\%$ confidence intervals (Table 3). Of note, for SMF, the very low number of cases ($$n = 12$$) for whom both CRP and albumin were available precluded a separate analysis.
For MIPSS70-intermediate patients with both CRP and albumin available ($$n = 35$$), OS was significantly shorter for albumin < 40 g/L, CAR > 0.374 and GPS > 0, whereas CRP ≤/> 8 mg/L was not associated with an adverse prognosis (Figure 2A–D).
## 3.3. Association of Levels of CRP and Albumin with Inflammatory Cytokines
Analysis of cohort B showed higher levels of hsCRP (median 10.07 vs. 7.02 mg/L; $p \leq 0.0004$) and lower levels of albumin (median 31.4 vs. 25.87 g/L; $$p \leq 0.0012$$) in MF versus MPN without fibrosis and/or the healthy controls. The VAF of the driver mutation was correlated only to levels of hsCRP ($$p \leq 0.008$$) (Supplementary Figures S3 and S4). The levels of interleukin-1β, interferon-γ, CCL17, I-TAC and ENA-78/CXCL-5 correlated positively with hsCRP, while no significant correlation was observed for IL-6, TNFα, IFNα, IL-8, IL-18, IL-10, IL-33, IL-17a, IL-23 and MCP-1 (Supplementary Figures S5 and S6). Albumin levels were inversely correlated to TNFα and MCP-1 (Supplementary Figures S7 and S8).
## 4. Discussion
CRP and albumin resemble surrogate markers for the extent of inflammation, a key element of MPN pathophysiology ([1,15]). Higher CRP levels are known to be associated with shortened leukemia-free and overall survival in univariate analyses [5,6], whereas for albumin, a prognostic value independent of several DIPPS-based scoring systems has been described previously [7,8,9]. As expected, we therefore found a significant impact of both parameters on survival in our cohort.
Levels of CRP were more closely related to the established adverse features of MF, which are in part or indirectly taken into account by current models, e.g., peripheral blasts, more severe anemia and/or transfusion-dependency or thrombocytopenia < 100 × 109/L, whereas only lower albumin levels were associated with a lower BMI as a measure of MF-induced cachexia. In addition, both factors were associated with levels of different cytokines, namely CRP with interleukin-1β, a driver of MF pathogenesis [16,17], and albumin with TNF-α, a key mediator of cachexia [18]. This implies that CRP and albumin probably reflect different aspects of MF pathophysiology. It is therefore of interest to combine them in the CAR or the GPS. For both parameters, a DIPSS-independent prognostic value has already been described in MF [10,11]. A recent report on acute myeloid leukemia patients not eligible for stem-cell transplantation illustrates that a combined assessment of CRP and albumin is of interest in myeloid malignancies in general [19].
We found a MIPSS70-independent prognostic value for both a CAR > 0.347 and GPS > 0. Hence, both parameters add prognostic information, even in the context of a molecular prognostic score. However, the relevant cut-off for the CAR used within our MIPSS70-based model was higher than that published for DIPSS-based prognostication [10]. This might be due to different composition of the patient populations, different access to potentially disease-modifying drugs such as ruxolitinib or the influence of age, which is part of the DIPSS but not the MIPSS70. Further studies are needed to define the optimal cut-off of the CAR to be used in the context of the single different scoring systems and/or to decide whether CAR or the GPS provides better prognostic information.
Malnutrition and/or activation of catabolic pathways leading to hypoalbuminemia are probably not sufficient to explain the prognostic impact of albumin, since levels still in the lower range of normal represent an adverse risk factor not only in our cohort, but also according to all of the reports currently available on the prognostic role of albumin in MF [7,8,9]. Several pleiotropic effects of albumin have been described [20]. Amongst others, it represents the main anti-oxidant in the extracellular space [21], and higher levels could be associated with an increased capability to counteract ROS-mediated inflammation, which is linked to disease progression [22] in MF. This would indicate a vicious cycle, if inflammation has reached a point where albumin synthesis is limited. However, this hypothesis warrants confirmation in further studies.
Considering albumin and CRP in clinical practice evidently helps to identify a more vulnerable population of MF patients who elude current prognostic models and could benefit from multimodal interventions. Both markers are associated with cardiovascular risk [23,24]; therefore, modifiable risk factors should be aggressively managed in MF patients with low albumin and elevated CRP levels and/or a higher CAR. The JAK2 inhibitor ruxolitinib controls not only constitutional symptoms and splenomegaly, but also lowers CRP levels and increases albumin concentration [25]. This may justify its use even in low-risk patients harboring one of the risk factors based on CRP and albumin, especially if splenomegaly is already present. Non-pharmacological interventions, such as physical exercise and nutritional interventions, can positively affect both parameters [26,27]. In this context, the Mediterranean *Diet is* currently under investigation in MF [28].
As this was a monocentric and retrospective study, the interpretation of our observations is subject to several limitations. Apart from a potential selection bias, the limited number of patients is most relevant, since it precludes defining the cut-off of the CAR that is best suited for prognostication or adjusting for possibly confounding factors such as age and treatment with disease-modifying drugs such as ruxolitinib. Due to the low number of patients, we had to combine cases of primary and secondary MF. Whether prognostic scores established for PMF are of value for patients with SMF is still a matter of debate [29,30], and the Myelofibrosis Secondary to PV and ET-Prognostic Model (MYSEC-PM) was developed especially for this population [31]. However, the MYSEC-PM does not consider the presence and type of additional non-driver mutations; hence, the MIPSS70 represents one of the currently suggested tools for prognostication in both PMF and SMF, if the mutational profile has to be considered [32]. A further limitation is the fact that conventional metaphase cytogenetics were available only for a minority of patients, precluding the assessment of the factors studied in the context of scoring systems, which consider chromosomal aberrations in addition to the mutational profile, such as the MIPSS70+ Version 2.0 [33].
## 5. Conclusions
Our data have shown for the first time that CAR and GPS add prognostic information independently of the MIPSS70-based molecular risk profile in MF. Albumin and CRP are easily available in clinical routine at low cost and represent potential biomarkers to faithfully identify a more vulnerable population of MF patients not identified by current prognostic model systems. Moreover, since CRP and albumin probably reflect different aspects of MF pathophysiology, including inflammation and metabolic aspects, combining both parameters seems particularly useful for MF prognostication. However, further studies involving multi-center registries with larger cohorts are necessary to validate the prognostic impact of albumin and CRP within the context of prognostic scoring systems considering both cytogenetics and the mutational status. In addition, it remains to be determined as to whether improving levels of CRP and albumin during therapy are associated with a better prognosis. Despite all limitations, our observations fit well into the emerging data and support the prognostic role of albumin and CRP in MF.
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|
---
title: HLA-B*57:01/Carbamazepine-10,11-Epoxide Association Triggers Upregulation of
the NFκB and JAK/STAT Pathways
authors:
- Funmilola Josephine Haukamp
- Zoe Maria Hartmann
- Andreas Pich
- Joachim Kuhn
- Rainer Blasczyk
- Florian Stieglitz
- Christina Bade-Döding
journal: Cells
year: 2023
pmcid: PMC10000580
doi: 10.3390/cells12050676
license: CC BY 4.0
---
# HLA-B*57:01/Carbamazepine-10,11-Epoxide Association Triggers Upregulation of the NFκB and JAK/STAT Pathways
## Abstract
Measure of drug-mediated immune reactions that are dependent on the patient’s genotype determine individual medication protocols. Despite extensive clinical trials prior to the license of a specific drug, certain patient-specific immune reactions cannot be reliably predicted. The need for acknowledgement of the actual proteomic state for selected individuals under drug administration becomes obvious. The well-established association between certain HLA molecules and drugs or their metabolites has been analyzed in recent years, yet the polymorphic nature of HLA makes a broad prediction unfeasible. Dependent on the patient’s genotype, carbamazepine (CBZ) hypersensitivities can cause diverse disease symptoms as maculopapular exanthema, drug reaction with eosinophilia and systemic symptoms or the more severe diseases Stevens-Johnson-Syndrome or toxic epidermal necrolysis. Not only the association between HLA-B*15:02 or HLA-A*31:01 but also between HLA-B*57:01 and CBZ administration could be demonstrated. This study aimed to illuminate the mechanism of HLA-B*57:01-mediated CBZ hypersensitivity by full proteome analysis. The main CBZ metabolite EPX introduced drastic proteomic alterations as the induction of inflammatory processes through the upstream kinase ERBB2 and the upregulation of NFκB and JAK/STAT pathway implying a pro-apoptotic, pro-necrotic shift in the cellular response. Anti-inflammatory pathways and associated effector proteins were downregulated. This disequilibrium of pro- and anti-inflammatory processes clearly explain fatal immune reactions following CBZ administration.
## 1. Introduction
The approval of a medical product requires extensive and distinct clinical trials. Yet, the preselected group of volunteers who attend those clinical trials is limited. Every single person has a unique genetic profile affecting the functionality of any cell type of the immune system. It becomes obvious that drug-hypersensitivity reactions in most cases disorganize the adaptive immune system, resulting in severe cellular autoimmune reactions. In the past, these reactions resulted in the mandatory determination of distinct genetic profiles and at worst in the exclusion of patients from the desired medication. It is clear that these scenarios of hypersensitivity reactions following drug treatment represent an unpredictable challenge for the health care system.
Adverse events occur when harmful symptoms arise after administration of a certain drug. If the harm is caused by application of the respective drug, the immunological reaction is termed an adverse drug event; if the drug was applied correctly at normal dosage, the reaction is termed an adverse drug reaction (ADR) [1,2,3,4,5,6]. ADRs usually occur in a dose-dependent and predictable manner and can be explained by the pharmacological toxicity of the drug [1,2,7]. Nevertheless, in $20\%$ of all ADRs, their occurrence seems idiosyncratic; those reactions are termed type B ADRs [1,2,8]. Yet, type B ADRs are often related to the immune system [1]. Since 2002, more and more type B ADRs have been described to be associated with the highly polymorphic human leukocyte antigen (HLA) molecules [9,10,11,12]. HLA molecules are cell surface proteins with a central function in immune surveillance. They present peptides to immune receptors of T and NK cells and, based on the origin of the presented peptide (i.e., self-peptide or pathogen-derived peptides), effector cell responses are prevented or induced [13,14]. The presentation of a diversity of peptides derived from different origins is unique in the ligand/receptor biology, since every peptide bound to an HLA molecule results in structural and biophysical alteration of the peptide-HLA complex. Therefore, it becomes clear that every subtle variation in the HLA molecule might facilitate binding and presentation of peptides that have not undergone selection by the thymus; the biological consequences are autoimmune-like reactions [15,16].
The anticonvulsant carbamazepine (CBZ) is widely used to treat various neurological diseases such as epilepsy, bipolar disorders or schizophrenia. However, CBZ administration can cause cutaneous type B ADRs in certain patients. These reactions have been described to be associated with the human leukocyte antigen (HLA) class I genotypes HLA-B*15:02 and HLA-A*31:01 [11,17].
Depending on the patient’s genotype, CBZ-induced ADRs are characterized by differential disease phenotypes. The symptoms range from mild skin rash such as maculopapular exanthema (MPE) and drug reaction with eosinophilia and systemic symptoms (DRESS) to more severe and potentially fatal Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) [18,19]. It has been shown that the more severe SJS and TEN occur mainly in HLA-B*15:02+ patients, whereas MPE and DRESS following CBZ-treatment more likely arise in HLA-A*31:01+ patients [11,20].
Positive and negative predictive values indicate that the clinical picture of HLA-associated ADRs cannot be explained exclusively by the presence of a certain HLA allele [21], hence other factors have to be taken into account [22]. We could demonstrate that CBZ treatment in soluble HLA-A*31:01-expressing cells and EPX treatment in soluble HLA-B*15:02-expressing cells result in different alterations in the cellular proteome that might contribute to the explanation of distinct clinical pictures of the diseases [23].
Recently, a further association of CBZ-induced ADRs has been described. The allele HLA-B*57:01 is unambiguously associated with SJS/TEN following treatment with CBZ in Europeans [24]: *The analysis* included 28 European patients with CBZ-induced SJS, SJS/TEN-overlap or TEN, 11 of them were carrying HLA-B*57:01 ($39.29\%$), whereas the frequency of this allele was $6.69\%$ in *European* general population controls. The onset of SJS/TEN following drug application should be close-meshed monitored, an algorithm of drug causality (ALDEN) has been adjusted to provide safe diagnosis [25]. The allele HLA-B*57:01 is originally known to be strongly associated with hypersensitivity to the antiretroviral drug abacavir (ABC) [26,27]. ABC-induced ADRs for HLA-B*57:01+patients vary from fever, fatigue, gastrointestinal symptoms to severe multiorgan failure. For this disease pattern, autologous cytotoxic T cells that attack in a manner like an autoimmune reaction, the body itself could be verified to be responsible [28]. Illing et al. [ 29] could impressively show that ABC occupies the peptide binding region (PBR) of HLB-B*57:01 resulting in a conformational change of the HLA molecule and therefore, in CD8+-mediated foreign recognition of the self-HLA-B*57:01 molecules bound to a foreign peptide. Since then, this finding provides the gold standard for understanding HLA-mediated ADRs [29]. Patients with susceptible HLA variants have not been permitted to take certain drugs. However, more and more clinical studies recently have demonstrated that drug-tolerant patients exist [21]; namely, patients with a certain HLA type who still could receive the questionable drug even though no immunological reactions occurred. This seems difficult to believe since drug binding and, subsequently, loading of a different peptide repertoire into the peptide binding region of the respective HLA molecule should still occur. However, in some cases a slight alteration in the amino acid sequence of bound peptides is not sufficient to trigger T cell responses. This would lead to maintained T cell tolerance in certain patients [30,31]. These drug-tolerant patients could receive the respective drug regardless of their HLA type. Appreciation of this phenomenon can certainly take place by a real-time view on the proteomic content of cell with the susceptible HLA type and the respective drug. Modern proteomics provide deep insight into the health status, biological and functional opportunities of a cell, and would therefore provide a stage to monitor pharmacovigilance. The observation of an association between HLA-B*57:01 and CBZ-mediated ADRs is in this respect remarkable, since it further emphasizes that CBZ hypersensitivity seems to be associated with several HLA alleles that differ structurally. CBZ hypersensitivity was formerly an immunological reaction that targeted patients with HLA-A*31:01 or B*15:02 following drug administration. We were recently able to demonstrate why CBZ hypersensitivity features completely different clinical pictures depending on the HLA type. Although HLA-A*31:01 would bind CBZ, B*15:02 would preferably bind EPX [32]. Both small-molecule (drug)/protein (HLA) complexes would alter the HLA-specific peptidome by the occupation of the PBR, yet the T cell response manifested by the HLA-specific clinical picture would differ significantly. Clarification for the relation between HLA molecule and drug could in this case be delivered by complete proteome analysis [23].
The aim of this work is to give a first insight into the complex molecular basics of HLA-B*57:01-associated CBZ-mediated ADRs. This knowledge will contribute to a comprehensive understanding of the mechanisms of CBZ hypersensitivities that seem to represent disparate diseases. To achieve sufficiency in genetically based CBZ immune effects, we performed full proteome analysis of HLA-B*57:01 expressing cells in response to CBZ or EPX treatment. Understanding the pharmacological and biological basis of distinct genetic profiles and drug interplay will guide towards personalized and safe medication.
## 2.1. Detection of CBZ and EPX Bound to sHLA-B*57:01 Molecules
The human B-lymphoblastoid cell line LCL721.221 (LGG promochem, Wesel, Germany) has been transduced with a lentiviral construct encoding for HLA-B*57:01 Exon 1–4, as previously described [33]. LCL721.221 cells expressing sHLA-B*57:01 molecules were cultured in RPMI 1640 (Lonza, Basel, Switzerland) supplemented with $10\%$ fetal calf serum (FCS, Lonza), 2 mM L-glutamine (c. c. pro, Oberdorla, Germany), 100 U/mL penicillin, and 100 µg/mL streptomycin (c. c. pro) at 37 °C and $5\%$ CO2 in the presence of 25 µg/mL CBZ or EPX and cell culture supernatants were collected twice a week. Affinity purification of sHLA-B*57:01 molecules post drug treatment was performed and protein concentration was calculated by Bicinchoninic Acid Assay (BCA) Protein Quantitation Kit (Interchim, San Diego, CA, USA). 150 ng purified drug-treated sHLA-B*57:01 molecules were applied to mass spectrometric drug quantification in solution as previously described [32].
## 2.2. Detection of CBZ- or EPX-Induced Modifications of the LCL721.221/HLA-B*57:01 Proteome
Proteome analysis was performed with 1 × 106 untreated, CBZ- or EPX-treated LCL721.221 and LCL721.221/sHLA-B*57:01 cells. Parental and sHLA-B*57:01-expressing LCL721.221 cells are not able to metabolize CBZ to EPX; this enables the analysis of CBZ and EPX treatment orthogonally. Cells were cultured in addition of 25 µg/mL CBZ or EPX for 48 h. After 24 h, drug addition was repeated. Cell harvest in RIPA lysis was performed as previously described [34] and calculation of protein concentration was performed by Bicinchoninic Acid Assay (BCA) Protein Quantitation Kit (Interchim, San Diego, CA, USA). Sample preparation, protein digestion and MS analysis was performed as previously described [23,35].
## 2.3. Data Analysis
The MaxQuant software (version 1.6.3.3; [36]) was used to search the obtained spectra against the Swiss-Prot reviewed UniProtKB database (version $\frac{01}{2021}$, 20,395 entries; [37]). Propionamid of cysteine was set as fixed modification and oxidation of methionine, N-terminal acetylation, deamidation of glutamine, and asparagine were set as variable modifications *The data* were processed with the Perseus software (version 1.6.2.3; [38]). In brief, proteins that resemble a possible contamination, only identified by sight or were reversed were excluded from further analysis as well as proteins that were not measured in all replicates. To exclude potential effects on protein abundance caused by transduction with sHLA-B*57:01, the proteome of untreated LCL721.221/sHLA-B*57:01 and parental LCL721.221 cells were subtracted from the corresponding CBZ- or EPX-treated cells. Visualization was performed with R [39]. In particular, the R packages complex heat map [40] and ggplot2 [41] were used. The heat map was generated by including the proteins that were positively tested in a Benjamini Hochberg FDR-based ANOVA. The Ingenuity Pathway Analysis software was used to perform an upstream analysis of significantly regulated proteins (IPA, QIAGEN Inc., https://www.qiagenbio-informatics.com/products/ingenuity-pathway-analysis (accessed on 24 November 2022)). Gene ontology analysis was performed with the GSEA software [42,43]. The mass spectrometry proteomics data were deposited to the ProteomeXchange *Consortium via* the PRIDE [44] partner repository with the dataset identifier PXD037502.
## 3.1. CBZ and EPX Bind to sHLA-B*57:01
To verify binding of CBZ or EPX to sHLA-B*57:01 molecules, sHLA-B*57:01 expressing cells were cultured in the presence of 25 µg/mL CBZ or EPX, and sHLA-B*57:01 containing cell culture supernatant was collected twice a week. Functional sHLA-B*57:01 molecules were affinity purified by an NHS column coupled to the mAb W$\frac{6}{32}$ and protein concentration was determined as previously described [45].
150 ng CBZ- or EPX-treated sHLA-B*57:01 molecules were applied to UPLC-MS/MS analysis for detection of CBZ or EPX in solution [32]. CBZ as well as EPX could be verified to bind to sHLA-B*57:01 molecules. In the solution with 150 ng CBZ/sHLA-B*57:01 molecules 0.033 ng/mL CBZ could be detected and in the EPX-containing sHLA-B*57:01 solution 0.020 ng/mL EPX could be detected (Figure 1).
## 3.2. Quantitative Proteomic Analysis after CBZ and EPX Treatment
The cellular proteomes of parental LCL721.221 cells and LCL721.221/sHLA-B*57:01 cells were analyzed in an LFQ-based approach. For comparison of CBZ or EPX treatment of HLA-B*57:01 expression and parental LCL721.221 cells, the proteomic content of untreated LCL721.221 and LCL721.221/sHLA-B*57:01 cells was subtracted from the drug-treated proteome abundances. In total, 4519 proteins could be identified. To exclude proteins that were induced through transduction with the HLA-B*57:01 allele, only proteins were included in the analysis that were measured in all replicates without imputation. After filtering, 2713 proteins were feasible for further research. By subtracting the untreated LCL721.221/sHLA-B*57:01 and parental LCL721.221 cells from the corresponding CBZ- or EPX-treated cells, possible effects on the proteome introduced by the transduction were excluded. The data were analyzed for their examinability through dimensionality reduction with t-SNE, and clustering of the different treatments confirmed that the data were feasible for further analysis. Distinct clustering also occurred in the heat map analysis (Figure 2).
## 3.3. EPX Treatment Induced a Strong Reaction in the Proteome of LCL721.221/sHLA-B*57:01 Cells
CBZ treatment induced a significant change of abundance ($p \leq 0.05$) of 335 proteins with only 35 changes more than 2-fold in LCL721.221/sHLA-B*57:01 cells when compared to parental cells (Figure 3A). However, EPX treatment led to 776 significantly changed proteins and 134 proteins with a difference greater than 2-fold (Figure 3B). Furthermore, we found ten proteins showing an overlapped regulation between CBZ- and EPX-treated regulation with one protein being co-upregulated and nine proteins being co-downregulated (Figure 3A,B and Figure S2). An upstream analysis with the IPA software was performed to find central regulators responsible for the change in abundance. For CBZ treatment, the serine/threonine kinase IKBKE was suggested as the only upstream kinase that is activated (p-value 0.0457; Z-Score 2.0). At the same time, treatment with EPX led to the regulation of 14 kinases, with the receptor tyrosine kinase ERBB2 as the most activated, and the insulin receptor INSR predicted to be the most inactivated kinase. According to IPA upstream analysis, ERBB2 is responsible for the upregulation of IKBKB, MCM5, POLD2 and MCM7. In contrast, INSR leads to the downregulation of SLC39A7, MTCH2, ECI1, TOMM40 and LSS. Other activated indicated upstream regulators were part of the MAPK protein family or involved in the MAPK signaling cascade. In comparison, downregulated upstream regulators were predicted to be G Protein alpha, Rb, PRKAA, ERN1 and CDKN1A (Figure 3C).
Further analysis of function classes of significantly regulated proteins via the IPA software showed that EPX treatment induced expression of proteins involved in “DNA Replication, Recombination, and Repair”. Furthermore, cell cycle-related proteins were found to be upregulated (Figure 4). Downregulated proteins were involved in “organismal death” and “glycogenesis”. CBZ treatment led to the downregulation of proteins involved in “organismal death”, “necroptosis”, and “cell death of epithelial cells” whereas proteins involved in “cell proliferation of Tumor cell lines” were upregulated.
Global GSEA enrichment analysis showed enrichment (Enrichment score: 0.58) in protein expression involved in an inflammatory pathway (“HP_CHRONIC_OTITIS_MEDIA”) in LCL721.221/sHLA-B*57:01 cells that were treated with EPX compared to parental LCL721.221 cells treated with EPX. ELF4H, NCE1, STAT3, DNAAF5, RAZIB, NFKB1 were upregulated following EPX treatment in LCL721.221/sHLA-B*57:01 cells and were downregulated in parental cells after EPX treatment (Figure 5).
EPX treatment induced the regulation of 14 pathways in the 25 most significant pathways predicted by the IPA software whereas CBZ treatment induced the regulation of 10 pathways. The most activated pathway after EPX treatment was predicted to be the “Necroptosis Signaling Pathway”, and “2-ketoglutarate Dehydrogenase Complex” was predicted to be most downregulated. CBZ treatment induced the most robust activation of the “G2/M DNA Damage Checkpoint Regulation” and inhibited “ELF2 Signaling” (Figure S1).
## 4. Discussion
Recent studies have demonstrated that besides HLA-A*31:01 and HLA-B*15:02, HLA-B*57:01 is also strongly linked to CBZ-induced ADRs. Although CBZ administration in HLA-A*31:01+ patients causes diseases such as MPE and DRESS, CBZ administration in HLA-B*57:01+ Europeans resulted in SJS/TEN disease phenotypes [24] as observed for HLA-B*15:02+ patients [11]. SJS and TEN manifest severe life-threatening cutaneous and mucosal necrosis and have to be treated by specialized burns units [46]. When SJS/TEN emerge as HLA-mediated ADRs that involve T cell recognition of foreign peptide/HLA-complexes, the withdrawal of the drug should assure recovery of the clinical state. SJS/TEN is such an intense impairment of the affected skin that recovery is rarely possible. Therefore, prevention of such an adverse condition is mandatory in pharmacovigilance management strategies. Although the prophylaxis of HLA-mediated ADRs is not feasible and individual patient cases are often underreported due to the polymorphic character of HLA molecules, the need for conscientious analysis of HLA-mediated ADRs immediately following their establishment should be obvious. HLA molecules exhibit unique properties in the immune system. Host HLA molecules bind foreign antigens. This exceptional co-recognition requires exquisite specificity and genetical restriction for the host T cells [13]. HLA diversity and corresponding T cell diversity restrict a comprehensive screening of patient cohorts in phase I, II and III studies [47,48] where pharmacokinetics and pharmacodynamics prior to admission of a drug are tested. In the era of fast and sophisticated methods to view into the cellular content, proteomics are the instrument for understanding and long-term prevention of HLA-mediated ADRs. HLA-restricted peptidomics and T cell analysis deliver indisputable answers to understand immune compatibility, but in some cases the host T cells fail to recognize the presented peptide/drug/HLA ligand of host origin. Understanding indistinct intracellular activities as metabolism, cytokine expression, and downregulation of certain proteins in drug-tolerant patients would certainly be beneficial for drug-sensitive patients with a susceptible HLA type. Utilizing proteomics as a mirror into cellular events should support this objective.
In the present study, we aimed to illuminate the underlying mechanism of HLA-B*57:01-mediated hypersensitivity to CBZ by full proteome analysis of CBZ- or EPX-treated LCL721.221/sHLA-B*57:01 cells. We chose the lymphoblastoid LCL721.221 cells, because these cells are not able to metabolize CBZ to EPX. The metabolization of CBZ to EPX occurs exclusively in hepatocytes and is catalyzed by cytochrome P450 enzymes [49]. Thus, the impact of CBZ and EPX treatment on the cellular proteome of LCL721.221 cells can be analyzed orthogonally.
Prior to proteome analysis, the specificity of drug-HLA interaction was determined via UPLC-MS/MS analysis. The selection of CBZ or the metabolite EPX by the respective HLA molecule is decisive for the fate of the HLA-expressing cell as previously described [32]. We could previously demonstrate that CBZ binds exclusively to HLA-A*31:01, leading to severe skin lesions and that the exclusive interaction between EPX-HLA-B*15:02 and not CBZ-HLA-B*15:02 [32] led to life-threatening SJS/TEN diseases. The present study showed that HLA-mediated ADRs have to be meticulously analyzed to comprehensively understand their clinical outcome. In this paper, we can show that both drug conditions CBZ and EPX are able to engage with HLA-B*57:01. The main question occurs if both or one drug condition would, in cooperation with HLA-B*57:01, impact the cellular content of the antigen-presenting cells and possibly their microenvironment. Therefore, LCL721.221 cells have been transduced with sHLA-B*57:01 and exposed to the respective drug. LCL721.221 cells are not able to metabolize CBZ to EPX and are thus a meaningful instrument to answer the question.
LCL721.221/sHLA-B*57:01 cells were treated with 25 µg/mL CBZ or EPX, respectively, and cell lysates were applied to full proteome analysis. By subtracting the proteomic changes that were introduced through transduction of the cells with the HLA-B*57:01 allele, we were able to observe the independent effects that occurred due to the interplay of CBZ or EPX with the HLA-B*57:01 molecule.
We found that EPX treatment induced significant changes in the proteome of LCL721.221/sHLA-B*57:01 cells. In contrast, CBZ treatment resulted in minimal changes in the proteome of LCL721.221/sHLA-B*57:01 cells. CBZ treatment of LCL721.221/sHLA-B*57:01 cells led to only 35 significantly regulated proteins whereas EPX treatment of the cells resulted in 134 significantly regulated proteins. Only a slight overlap of ten significantly regulated proteins could be detected in both CBZ- and EPX-treated cells (Figure 3A and Figure S2). Upstream regulator analysis via IPA revealed just one activated upstream regulator, the serine/threonine kinase IKBKE, responsible for the change in protein abundance of CBZ-treated cells. In contrast, 14 kinases were detected as regulated in EPX-treated cells (Figure 3C). Although UPLC-MS/MS analysis revealed equal binding of CBZ and EPX to sHLA-B*57:01 molecules (Figure 1), the CBZ-induced changes of the cellular proteome of LCL721.221/sHLA-B*57:01 seem to be marginal when compared to EPX-induced changes.
Following EPX treatment, the receptor tyrosine kinase ERBB2 could be described to be the most activated upstream regulator (Figure 3). ERBB2 is mainly involved in inflammatory and growth-associated processes [50]. Consequently, proteins that were predicted to be influenced and significantly two-fold upregulated were IKBKB, MCM5, MCM7, and POLD2. IKBKB is described to activate NFκB that is involved in inflammatory, pro-apoptotic and necrotic processes [51]. Additionally, NFκB has been found to be significantly enriched in the GSEA enrichment analysis in an overall inflammatory process (Figure 5). MCM5 and MCM7 are involved in DNA replication and are responsive to cytokine-induced gene transcription. MCM5 has been shown to be central for STAT1-mediated gene transcription [52]. In line with this, JAK1 has also been predicted to be activated (Figure 3). The JAK/STAT pathway plays a central role in reaction to external inflammatory stimuli [53]. Consistent with this finding, STAT5 upregulation has also been described for HLA-B*15:02 after EPX treatment [23]. The comparison of proteomic profiling of cells with intracellular small molecule/protein engagement [23] features clearly that EPX/HLA engagement triggers the upregulation of inflammatory pathways. The sudden upregulation of proteins that are described to be part of signal transduction pathways and thus triggers of autoimmune reactions through effector cell activation could not be predicted by conventional methods. We further describe the upregulation of POLD2, an enzyme that is involved in DNA repair processes and preserving DNA integrity [54]. POLD2 could recently be uncovered as a tumor suppressor [55] and prognostic biomarker in distinct cancers [56]. In coherence with POLD2 upregulation was the finding that more than 50 proteins involved in DNA repair, replication and recombination were regulated in response to EPX treatment and DNA regulatory processes were predicted to be activated (Figure 4). Moreover, FLT1 and EGFR were also indicated as activated. Both are known for their potential to induce apoptosis through either NFκB (FLT1) [57] or STAT3 (EGFR) activation [58]. FLT1 could be described as a therapeutic target in inflammatory events [59] whereas EGFR is known as a key regulator in cell division and cancer development [60]. In conclusion, the engagement of EPX/HLA-B*57:01 induces inflammatory, pro-apoptotic and necrotic processes in LCL721.221 cells when compared to parental LCL721.221 cells. These findings seem to be consistent with and might be a coherent explanation of the disease phenotype of SJS/TEN in HLA-B*57:01+ patients that is associated with keratinocyte death, cutaneous blistering and epidermal detachment [61].
In coherence with the upregulation of proinflammatory proteins, INSR could be observed to be inactivated after EPX treatment (Figure 3). INSR has been described as inhibiting inflammatory and cytokine-mediated processes when overexpressed [62]. These data illustrate the dignified intracellular cooperation between signal transduction proteins and the unpredictable interference of drug/HLA complexes. In addition, CDKN1A is predicted to be inhibited (Figure 3). Although CDKN1A is an inhibitor for cell proliferation in B cells, CDKN1A acts as an activator of proliferation and is closely regulated through Caspase-3 mediated degradation [63]. CDKN1A downregulation might suggest Caspase-3 activation and cleavage of CDKN1A.
The “Necroptosis Signaling Pathway” was detected to be the most activated pathway following EPX treatment of LCL721.221/sHLA-B*57:01 cells. Cell death through necroptosis is a form of programmed necrosis that is mediated by pattern recognition receptors (PRR) and diverse cytokines. Necroptosis of cells results in the secretion of damage-related pattern molecules (DAMPs) and, subsequently, an inflammatory immune response [64,65].
Our observations indicate an unbalance of pro- and anti-inflammatory processes through up- or rather downregulation of certain proteins that might lead to an excessive immune reaction in the affected patients with the susceptible HLA allele that is mainly caused by EPX.
Taken together, although EPX/HLA-B*57:01 cooperation introduced the described drastic changes in the proteome, alterations through CBZ/HLA-B*57:01 cooperation were only marginal. It seems obvious that, similar to CBZ-induced hypersensitivity in HLA-B*15:02+ patients, EPX is the main driver for the SJS/TEN phenotype in HLA-B*57:01 patients. The engagement of EPX and the HLA molecule seems to perturb the intracellular processing of healthy cells and produces a stress response resulting in DNA damage and consequently, extensive inflammation. The possibility to study the effect of EPX/HLA and CBZ/HLA orthogonally in the present study offers the potential to appreciate the different clinical outcomes of HLA-mediated CBZ hypersensitivity. The metabolization of CBZ to EPX occurs in the cytochrom P450 system in the liver. Although CBZ is metabolized to EPX, the balance between both drugs shifts towards an excess of EPX, the inflammatory life-threatening condition of concerned patients therefore becomes more severe and might shift from the initiation of SJS to TEN. TEN is a serious and fatal condition for which the outcome is in >$50\%$ of affected patients lethal or at least leads to incurable long-term harm.
To embed these findings into the biological context of systemic inflammation, the key processes that might drive the hypersensitivity reaction are pathways that were found to be upregulated, indicating a beginning of cell death in HLA-B*57:01 transduced cells, for example the strong activation of necroptosis pathway after EPX treatment (Figure S1). A recent study of the hypersensitivity reaction in HLA-B*15:02+ patients revealed that the presence of CD4+CD25+CD127loCD39+ Treg that can reduce the presence of extracellular ATP by degrading it via CD39 and CD73 to adenosine determines the conversion from a non-responder to a responder [66]. By taking this study into account, we hypothesize that releasing intracellular ATP into the extracellular matrix facilitated by inflammatory processes induced by EPX is the initial step towards a systemic inflammation when not enough CD4+CD25+CD127loCD39+ Treg are present to reduce the effect of extracellular ATP and subsequent IFNγ production.
## 5. Conclusions
The future of pharmacological appreciation of drug and medical safety relies on the comprehension of the functional consequences of individual immunogenetics.
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|
---
title: Pro-Apoptotic and Anti-Cancer Activity of the Vernonanthura Nudiflora Hydroethanolic
Extract
authors:
- Almog Nadir
- Anna Shteinfer-Kuzmine
- Swaroop Kumar Pandey
- Juan Ortas
- Daniel Kerekes
- Varda Shoshan-Barmatz
journal: Cancers
year: 2023
pmcid: PMC10000589
doi: 10.3390/cancers15051627
license: CC BY 4.0
---
# Pro-Apoptotic and Anti-Cancer Activity of the Vernonanthura Nudiflora Hydroethanolic Extract
## Abstract
### Simple Summary
Natural products derived from plants have numerous clinical applications, including anti-cancer activity. In the present study, we identified three different plant extracts as strong inducers of cell death that were not reported previously. We focused on the most potent of these plants, *Vernonanthura nudiflora* (Vern). We demonstrated that the plant extracts obtained by treatment with a water and ethanol mixture killed tumor cells via multiple routes. These include impairing cell energy and metabolism, generating reactive oxygen species, increasing intracellular Ca2+, and inducing mitochondria-mediated apoptosis. We connected these activities to increased levels of the mitochondrial gatekeeper protein, VDAC1, which is associated with metabolism and apoptosis regulation. In a glioblastoma mouse model, Vern extract strongly inhibited tumor growth and induced massive tumor cell death, including cancer stem cells, by inhibiting blood supply and modulating the tumor microenvironment. The multipronged effects of hydroethanolic Vern extract make it a promising candidate for treating cancer.
### Abstract
The mitochondrial voltage-dependent anion channel 1 (VDAC1) protein is involved in several essential cancer hallmarks, including energy and metabolism reprogramming and apoptotic cell death evasion. In this study, we demonstrated the ability of hydroethanolic extracts from three different plants, *Vernonanthura nudiflora* (Vern), *Baccharis trimera* (Bac), and Plantago major (Pla), to induce cell death. We focused on the most active Vern extract. We demonstrated that it activates multiple pathways that lead to impaired cell energy and metabolism homeostasis, elevated ROS production, increased intracellular Ca2+, and mitochondria-mediated apoptosis. The massive cell death generated by this plant extract’s active compounds involves the induction of VDAC1 overexpression and oligomerization and, thereby, apoptosis. Gas chromatography of the hydroethanolic plant extract identified dozens of compounds, including phytol and ethyl linoleate, with the former producing similar effects as the Vern hydroethanolic extract but at 10-fold higher concentrations than those found in the extract. In a xenograft glioblastoma mouse model, both the Vern extract and phytol strongly inhibited tumor growth and cell proliferation and induced massive tumor cell death, including of cancer stem cells, inhibiting angiogenesis and modulating the tumor microenvironment. Taken together, the multiple effects of Vern extract make it a promising potential cancer therapeutic.
## 1. Introduction
Numerous natural products with anti-cancer activity used clinically, such as paclitaxel docetaxel and taxol, are derived from plants [1]. Moreover, some compounds such as resveratrol that are produced in plant species considered to have health benefits are also shown to have pro-apoptotic effects, inducing cell death, and, as such, they act as anti-cancer agents [2,3,4,5]. Similarly, curcumin expresses a variety of therapeutic properties, including antioxidant, anti-inflammatory, and antiseptic activities, as well as anticancer effects in a variety of biological pathways involved in mutagenesis, apoptosis, tumorigenesis, cell cycle regulation, and metastasis [6]. Quercetin, a polyphenol derived from plants, has also been shown to have a wide range of biological actions, including anti-carcinogenic, anti-inflammatory, and antiviral activities, as well as attenuating lipid peroxidation, platelet aggregation, and capillary permeability [7]. In addition to the many plant species that are already used to treat or prevent the development of cancer, several species of plants have demonstrated anti-cancer properties and are used as herbal medicines in developing countries [8].
Here, we focus on the activity of extracts derived from three different plants. The *Vernonanthura nudiflora* species of perennial plant in the family Asteraceae includes more than 23,500 species spread over about 1600 genera [9], with distribution in Argentina, Brazil, and Uruguay [10]. The Vernonanthura (Vernonia) genera includes more than 1000 species [11]. The anti-proliferative and antioxidant activities of an organic extract of *Vernonanthura nudiflora* and some of its constituents have already been reported [12]. In addition, some metabolites isolated from the flowers of *Vernonanthura nudiflora* showed antimicrobial activities [9] The second plant tested for cytotoxicity is the *Baccharis trimera* used in folk medicine for the treatment of gastrointestinal disorders and hepatic diseases [13,14]; other biological activities reported for B. trimera include antihepatotoxic, antidiabetic, schistosomicidal, antioxidant, antinociceptive, and anti-inflammatory effects that are attributable to flavonoids, diterpenes, triterpenes, saponins, essential oils, and caffeoylquinic acids [15,16].
The third plant is Plantago major from the Plantaginaceae family, commonly known as great plantain, and used as a medicinal plant [17]. Plantago major contains several active compounds such as flavonoids, polysaccharides, terpenoids, lipids, iridoid glycosides, and caffeic acid derivatives [18]. It is used to treat various diseases such as constipation, cough, wounds, infection, fever, bleeding, and inflammation. In addition, water and ethanol extracts of Plantago major leaves show anti-inflammatory activity on oral epithelial cells.
Here, we present the pro-apoptotic activity of the hydroethanolic extracts from the indicated three plants while deciphering their mode of pro-apoptotic, anti-cancer activity involving mitochondria-mediated apoptosis and the mitochondrial gatekeeper protein, the voltage-dependent channel 1 (VDAC1).
Mitochondria are central to essential life functions for the generation of cellular energy and critical components of the biosynthetic pathways, and function as points for cellular decisions leading to apoptosis. One of the proteins in control of these cellular life and death decisions is the mitochondrial protein VDAC1. Proper cell activity requires an efficient exchange of molecules between the mitochondria and cytoplasm. Lying in the outer mitochondrial membrane (OMM), VDAC1 assumes a crucial position in the cell, forming the main interface between the mitochondrial and cellular metabolisms [19,20,21]. VDAC1 is a key protein in regulating metabolism, controlling the passage of adenine nucleotides, other metabolites, and Ca2+ in and out of mitochondria. VDAC1 is also an essential protein in regulating mitochondria-mediated apoptotic cell death and controls other biological and cellular functions [19,20,21]. VDAC1 is overexpressed in various cancer cell lines and different tumors, pointing to its importance for their survival [20,22,23,24,25]. The crucial role it plays in regulating the metabolic and energetic functions of mitochondria in cancer cells is demonstrated by findings that downregulating VDAC1 expression using siRNA decreases energy production and cell growth and inhibits tumor growth [22,26,27,28,29,30,31]. VDAC1 is overexpressed in many diseases other than cancer, and its overexpression is associated with cell death induction [32].
Mitochondria play a central role in apoptosis. During the transduction of an apoptotic signal into the cell, an alteration in the mitochondrial membrane permeability occurs [33,34]. This allows the release of apoptogenic proteins such as cytochrome c (Cyto c), apoptosis-inducing factor (AIF), and second mitochondria-derived activator of caspase/direct inhibitor of apoptosis-binding protein with low pI (Smac/DIABLO) [20,35]. When released from the mitochondria, all participate in the complex processes resulting in the activation of proteases and nucleases, leading to DNA and protein degradation and ultimately to apoptotic cell death [33]. Several mechanisms for releasing the pro-apoptotic proteins have been proposed [19,36,37]. These include a large channel formed by Bax and/or Bak oligomers [38,39] and a channel formed by hetero-oligomers of VDAC1 and Bax [40,41] or VDAC1 oligomers [36,37,42,43,44,45,46,47]. Defects in the regulation of apoptosis are often associated with drug resistance and diseases such as cancer [48], with apoptosis evasion being a cancer hallmark [49].
All the apoptotic proteins known to translocate to the cytoplasm following an apoptotic stimulus reside in the mitochondrial intermembrane space (IMS). Thus, only the permeability of the OMM needs to be modified for their release [50,51,52,53]. Hence, VDAC1 as an OMM channel could mediate Cyto c release. Recently, we demonstrated that VDAC1 oligomers form a large channel that mediates the release of Cyto c and other pro-apoptotic proteins [47,54]. Moreover, we found that cisplatin, selenite, H2O2, UV light, and more lead to apoptosis by inducing VDAC1 overexpression, thereby shifting the equilibrium towards oligomers, which leads to the release of pro-apoptotic proteins and, subsequently, apoptosis [43,44,45,47,54,55,56,57].
In the present study, we demonstrate that hydroethanolic extracts from three different plants, *Vernonanthura nudiflora* (Vern), *Baccharis trimera* (Bac), and Plantago major (Pla), can induce apoptotic cell death by increasing VDAC1 expression levels, its oligomerization, and subsequent apoptotic cell death. In addition, the plant extracts increased intracellular Ca2+ and ROS production and reduced cell survival. Vern extract and one of its compounds, phytol, inhibited tumor growth and reversed tumor oncogenic properties.
The plant extracts tested here showed pro-apoptotic anti-cancer activity and thus represent a promising therapeutic candidate for cancer treatment.
## 2.1. Materials
4′,6-diamidino-2-phenylindole (DAPI), dimethyl sulfoxide (DMSO), propidium iodide (PI), Tris, carbonyl cyanide p-trifluoro-methoxyphenyl hydrazone (FCCP), tetramethylrhodamine, methyl ester (TMRM), and trypan blue were purchased from Sigma (St. Louis, MO, USA). Dithiothretol (DTT) was purchased from Thermo Fisher Scientific (Waltham, MA, USA). Annexin-V (FITC) was obtained from Alexis Biochemicals (Lausen, Switzerland). Dulbecco’s modified Eagle’s medium (DMEM) and phosphate-buffered saline (PBS) were purchased from Gibco (Grand Island, NY, USA). Fluo-4-AM and MitoSOX-Red were acquired from Invitrogen (Waltham, MA, USA). TUNEL was obtained from Promega (Madison, WI, USA). Primary and secondary antibodies used in immunoblotting and immunofluorescence (IF), as well as their dilutions, are listed in Table S1, and XTT cell viability assay kits were obtained from Biological Industries (Beit Haemek, Israel). TLC silica gel 60 F254 plates were obtained from Merck (Darmstadt, Germany).
## 2.2. Cell Lines and Culture
U-87MG (human glioblastoma), SH-SY5Y (human neuroblastoma), HeLa (human cervix adenocarcinoma), MEFs (mouse embryonic fibroblasts), and PC-3 (human prostate cancer cells) were maintained at 37 °C and $5\%$ CO2 in DMEM medium supplemented with $10\%$ FBS, 1 mM L-glutamine, 100 U/mL penicillin, and 100 μg/mL streptomycin. Mycoplasma contamination was routinely evaluated on cell lines.
## 2.3. Plant Extracts
Vernonanthura nudiflora, Baccharis trimera, and the Plantago major plants were washed with distilled water, and the aerial parts were naturally dried up to a $50\%$ reduction in total mass. The material was introduced into reactors for maceration, with 0.20 g botanic material per 1 mL and $70\%$ of a hydroalcoholic solution of ethanol/water $\frac{70}{30}$, and agitated for a period of 21 days. The extract was later filtered through a filter with a pore size of 15 μm and kept at 4 °C. Before use, the hydroalcoholic was centrifuged for 5 min at 15,000× g.
The mixture was composed of a hydroethanolic filtered extract of *Vernonanthura nudiflora* ($40\%$), *Baccharis trimera* ($40\%$), and Plantago major ($20\%$).
## 2.4. Cell Treatment with Plant Extracts and Cell Death Analysis
Cells (6 × 105/mL at 70–$80\%$ confluence) were incubated with ethanol extract from Vern, Bac, or Pla plant extracts or their mixture at the indicated dilution in 2000 µL culture medium for 24 h or the indicated time at 37 °C and $5\%$ CO2. The cells were then trypsinized, centrifuged (1500× g, 5 min), washed with PBS, and analyzed for the desired activity. For cell death analysis, propidium iodide (PI) staining was performed by adding PI (6.25 µg/mL) to the cells, followed by immediate analysis by flow cytometry with the iCyt sEC800 -flow cytometry analyzer (Sony Biotechnology Inc., San Jose, CA, USA) and analysis with EC800 software.
For PI and annexin V-FITC staining, cells (2 × 105), untreated or treated with the plant extracts, were collected (1500× g for 5 min), washed, and resuspended in 200 μL binding buffer (10 mM HEPES-NaOH, pH 7.4, 140 mM NaCl, and 2.5 mM CaCl2). Annexin V–FITC staining was performed according to the recommended protocol. Cells were then washed once with binding buffer and resuspended in 200 μL binding buffer, to which PI was added immediately before flow cytometric analysis by flow cytometry with the iCyt sEC800 -flow cytometry analyzer (Sony Biotechnology Inc., San Jose, CA, USA) and analysis with EC800 software. At least 10,000 events were collected and recorded on a dot plot.
## 2.5. Cell Viability Assay
The effect of the plant extracts on SH-SY5Y cell survival was assayed using an XTT-based kit (Biological Industries, Beit Haemek, Israel) according to the manufacturer’s protocol. Cells were seeded in a 96-well plate and incubated at 37 °C with $5\%$ CO2, and 24 h later were treated with different concentrations of the extracts for the time indicated in the figure legends. XTT reagent was added to each well, and the absorbance was measured at 450 nm and 630 nm (Tecan, Infinite M1000, Mannedorf, Switzerland). The absorbance obtained at 630 nm was subtracted from the absorbance at 450 nm to obtain the specific reduced XTT reaction product.
## 2.6. Determination of Reactive Oxygen Species (ROS), Mitochondria Membrane Potential, and Intracellular Ca2+ Levels
Mitochondrial ROS Determination: For measuring mitochondrial accumulated ROS, SH-SY5Y cells were seeded in a 6-well plate (1 × 105/well). Cells were treated for 24 h with the indicated plant extract, and then were incubated with MitoSOX-Red, a mitochondrial superoxide indicator for live-cell imaging, for 10 min at 37 °C. Fluorescence was measured using flow cytometry (iCyt, Sony Biotechnology, San Jose, CA, USA). At least 10,000 events were recorded on the FL2 detector, represented as a histogram, and analyzed with EC800 software (Sony Biotechnology, San Jose, CA, USA). Positive cells showed a shift to an enhanced level of green fluorescence (FL2).
Mitochondrial Membrane Potential Determination: Mitochondrial membrane potential was determined using TMRM, a potentially sensitive dye, and a plate reader. SH-SY5Y cells were treated for 24 h with the indicated Vern plant extract and subsequently incubated with TMRM (400 nM, 20 min). The cells were then washed twice with PBS and examined withby flow cytometry with the iCyt sy3200 Benchtop Cell Sorter/Analyzer (Sony Biotechnology Inc., San Jose, CA, USA) and analysis with EC800 software. FCCP-mediated dissipation served as the control.
Cytosolic Ca2+ levels [Ca2+]i measurements: [Ca2+]i was analyzed using Fluo-4-AM. Cells were harvested after the appropriate treatment, collected (1500× g for 10 min), washed with HBSS buffer (5.33 mM KCl, 0.44 mM KH2PO4, 138 mM NaCl, 4 mM NaHCO3, 0.3 mM Na2HPO4, 5.6 mM glucose), supplemented with 1.8 mM CaCl2 (HBSS+), and incubated with 2 μM Fluo-4 in 200 μL of HBSS(+) buffer in the dark for 20 min at 37 °C. After washing the remaining dye, the cells were incubated with 200 μL HBSS(+) buffer, and changes in [Ca2+]i were measured immediately by FACS and analyzed by flow cytometry with the iCyt sy3200 Benchtop Cell Sorter/Analyzer (Sony Biotechnology Inc., San Jose, CA, USA) and analysis with EC800 software. Positive cells showed a shift to an enhanced level of green fluorescence (FL1).
Changes in cellular Ca2+ were monitored in live cells using the high content Operetta screening system (Perkin-Elmer, Hamburg, Germany). In each well, ten fields were imaged using a 20× wide field objective with an excitation filter of 520–550 nm and emission filter of 560–630 nm.
## 2.7. Cross-Linking Experiments
Cells were treated with the plant extract for the indicated time and concentration, harvested, washed with PBS, pH 8.3, and incubated for 15 min with the cross-linking reagent EGS at a ratio of 1 mg protein/mL/100 μM EGS. Aliquots (30 µg protein) were subjected to SDS-PAGE and immunoblotting using anti-VDAC1 antibodies. Quantitative analysis of VDAC1 dimers was performed using FUSION-FX (Vilber Lourmat, Marne-la-Vallée, France).
## 2.8. Gel Electrophoresis and Immunoblotting
Cells or tumor tissues were lysed using lysis buffer (50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1 mM EDTA, 1.5 mM MgCl2, $10\%$ glycerol, $1\%$ Triton X-100, supplemented with a protease inhibitor cocktail (Calbiochem, Welwyn Garden City, UK)). The lysates were then centrifuged at 12,000× g (10 min at 4 °C), and protein concentration was determined. Aliquots (10–20 μg of protein) were subjected to SDS-PAGE and immunoblotting using various primary antibodies (sources and dilutions are provided in Supplementary Information Table S1), followed by incubation with appropriate HRP-conjugated secondary antibodies (i.e., anti-mouse, anti-rabbit). Blots were developed using enhanced chemiluminescence (Biological Industries, Beit Haemek, Israel). Band intensities were analyzed by densitometry using FUSION-FX (Vilber Lourmat, Marne La Vallée, France) software, and values were normalized to the intensities of the appropriate β-actin signal that served as a loading control.
## 2.9. Gas Chromatography–Mass Spectroscopy (GC-MS) Analysis
CG-MS analysis was carried out using a 7890B Mass-Detector; 5977A, Agilent Technologies; Column 5MS UI. The compounds were identified using Library Name W 10N 14L (NIST MS Search 2.2). The various names representing each compound, quality of identification (maximum is $100\%$), and peak area (Ab*s) are given in Table S2.
## 2.10. TLC Separation
TLC silica gel 60 F254 plates (Merck, 20 × 20 cm) were used to separate the ethanol plant extracts and phytol and ethyl linoleate, using a mobile phase mixture of petroleum ether:diethyl ether:acetic acid (85:15:1 V/V/V). The plates were air dried and visualized by exposure to iodine vapor.
## 2.11. Xenograft Mouse Model
U-87MG cells (1.8 × 106 cells/mouse) were inoculated subcutaneously (s.c.) into the hind leg flanks of athymic eight-week-old male nude mice (Envigo). Tumor size was measured using a digital caliper, and volume was calculated. When it reached 50 mm3, mice were randomly divided into several groups (5 mice/group). One group was intratumorally injected with PBS containing $5\%$ ethanol (final concentration in the tumor was $0.14\%$), and other groups were treated with Vern plant extract to a final dilution of 1:100, 1:250, 1:300, or 1:500 or with phytol to a final concentration of 75 μM. The xenografts were injected three times a week. The mice were sacrificed 34 days post-cell inoculation, and tumors were excised. Tumors were fixed in $4\%$ buffered formaldehyde, paraffin-embedded, and processed for immunofluorescence (IF). These experimental protocols were approved by the Institutional Animal Care and Use Committee of Ben-Gurion University.
## 2.12. Immunofluorescence (IF) of Tumor Tissue Sections
Formalin-fixed, paraffin-embedded sections (5 μm thick) of U-87MG cell-derived tumors from control and Vern plant extract- or phytol-treated tumors were deparaffinized by placing the slides at 60 °C for 1 h and using xylene, followed by rehydration with a graded ethanol series (100–$50\%$). Antigen retrieval was performed in 0.01 M citrate buffer (pH 6.0) at 95 °C–98 °C for 20 min. After washing sections in PBS, pH 7.4, sections were incubated in $10\%$ normal goat serum, $1\%$ BSA in PBS containing $0.1\%$ Triton X100 for 2 h, followed by overnight incubation at 4 °C with primary antibodies (Table S1). Sections were washed thoroughly with PBS, pH 7.4, containing $0.1\%$ Triton-X100 (PBST), incubated with the fluorescently labeled secondary antibodies (Table S1) for 2 h, washed five times with PBST, and cover-slipped with fluoroshield mounting medium (Immunobioscience, Mukilteo, WA, USA). Fluorescent images were viewed with an Olympus IX81 confocal microscope. Quantitation of protein levels, as reflected in the staining intensity, was analyzed in the whole area of the sections using Image J software.
## 2.13. TUNEL Assay
Paraffin-embedded fixed tumor sections (5 μm thick) were processed for a Terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick-end labeling TUNEL assay using the Dead End Fluorometric TUNEL system according to the manufacturer’s instructions. Sections were deparaffinized, equilibrated in PBS, permeabilized with proteinase K (20 μg/mL in PBS), post-fixed in $4\%$ paraformaldehyde, and incubated in TdT reaction mix for 1 h at 37 °C in the dark. Slides were then washed in saline–sodium citrate buffer, counter-stained with PI (1 µg/mL), and cover slipped with fluoroshield mounting medium. Fluorescent images of apoptotic cells (green) and cell nuclei (red) were captured using a confocal microscope (Olympus 1 × 81). Quantification analysis of stained slides was performed using an Image J program.
## 2.14. Statistics and Data Analysis
Means ± SE of results obtained from three independent experiments are presented. Statistical significance is reported at $p \leq 0.05$ (*), $p \leq 0.01$ (**), $p \leq 0.001$ (***), or $p \leq 0.0001$ (****).
## 3.1. Apoptosis Induction by the Hydroethanolic Plant Extracts
The cell death activity of the three plant extracts (Vern, Bac, Pla) alone and combined was analyzed by propidium iodide (PI) staining and flow cytometry analysis (Figure 1A,B). The results show that Vern extract was the most potent, triggering massive cell death, followed by the Pla extract and then the Bac extract. Vern extract (1:500) induced more cell death than all three extracts combined at a 1:100 dilution and Bac extract alone (1:166), suggesting that it is five- and threefold more active, respectively (Figure 1B).
To determine whether the observed cell death induced by Vern was apoptosis, we analyzed apoptosis by Annexin V/PI and via FACS (Figure 1C and Figure S1). The results show that Vern extract induced apoptotic cell death.
Next, we analyzed the effects of the plant extract on cell survival using the XTT assay (Figure 1D–F). The Vern extract reduced cell survival following 24, 48, and 72 h incubation. In contrast, Bac extract showed some decreased survival following incubation for 48 h and 72 h, and Pla extract showed no decrease in cell survival. Considering that the XTT assay is based on reduced levels of NADH produced in the mitochondria, these results suggest that Vern plant extract, but not Bac and Pla, induced mitochondrial dysfunction. In addition, the results suggest that the cell death caused by the three plant extracts involves different active compounds and modes of action.
Vern plant extract similar to SH-SY5Y cells induced cell death in other cancer cell lines such as Hela and PC-3 (Figure S2A). In contrast, non-cancerous cell lines, such as MEFs, were less sensitive to the Vern plant extract (Figure S2B).
The following experiments were conducted to reveal the extract active compounds’ possible modes of action.
The ethanolic plant extracts induced VDAC1 overexpression and oligomerization.
We have shown that many apoptosis triggers, such as chemotherapy drugs, stress, and radiation, induce VDAC1 overexpression and oligomerization. We suggest that this is a general mechanism common to numerous apoptosis stimuli, although they act via different initiating cascades [42,43,44,46,56].
Thus, we tested the effects of the plant extracts on VDAC1 expression levels and its oligomerization (Figure 2). The most active Vern plant extract induced VDAC1 overexpression in both cell lines tested: the neuroblastoma-derived cell line SH-SY5Y and glioblastoma-derived U-87MG cell line (Figure 2A). In both cell lines, Vern extract highly increased the expressed VDAC1 level by three- to fourfold (Figure 2A) and induced similar pro-apoptotic activity (IC50 = 1:800) (Figure 2B).
The three extracts, in a concentration-dependent manner, increased VDAC1 oligomeric forms as stabilized by chemical cross-linking using EGS and monitored by immunoblotting [43] (Figure 2C). However, the highest level of VDAC1 oligomerization was induced by the Vern extract (Figure 2C,D), which aligns with the superior potency of its cell death-inducing activity.
Interestingly, we observed the presence of VDAC1 oligomers even without chemical cross-linking, even after exposing the cells to a high concentration ($1\%$) of the detergent SDS and heating at 70 °C for 5 min (Figure 2E,F). The level of oligomeric VDAC1 was highest with Vern extract, as found for VDAC1 overexpression, oligomerization, and apoptosis induction. This suggests that the VDAC1 oligomers induced by the plant extract are very stable.
The results support the suggestion that the active compounds in the Vern plant extract, via enhancing VDAC1 expression levels, lead to VDAC1 oligomerization and apoptosis.
The active compounds in Vern extract are resistant to high temperatures, as heating the extract for 10 min at 40, 60, or 80 °C had no effect on the extract cell death activity (Table S3).
## 3.2. Vern Extract Increased Intracellular Ca2+ and ROS Production
Reactive oxygen species (ROS) were shown to induce apoptosis [58]. Thus, we measured mitochondrial ROS and found that cell treatment with Vern extract induced their production (Figure 3A). This increase in mitochondrial ROS suggests that Vern extract at high concentrations induces dysfunction of the mitochondria, as also reflected in the decrease in XTT reduction.
Several studies have shown that an increase in [Ca2+] is involved in apoptosis induction and that Ca2+ is required for apoptosis-stimuli-induced VDAC1 overexpression and VDAC1 oligomerization [45,46,59]. Vern extract’s effect on cellular [Ca2+] levels was analyzed using Fluo-4 and FACS or by Operetta (Figure 3B–D). Both assays demonstrated that at high levels, this extract increased cellular [Ca2+] levels.
Cell treatment with Vern plant extract reduced the mitochondrial membrane potential (Δψ) only when high (over $80\%$) cell death was obtained (Figure S3).
The findings that the increase in ROS production, [Ca2+] levels, and dissipation of (Δψ) were not correlated with cell death and were observed only at high concentrations of the Vern plant extract and over $80\%$ cell death suggest that these effects are due to cell destruction, including of the mitochondria.
## 3.3. GS-MS Analysis of Extracts from Plants Vern, Bac, and Pla
To identify some of the chemical compounds present in the hydroethanolic extracts from the three different plants, Vern, Bac, and Pla, the extracts were subjected to gas chromatography–mass spectroscopy (GC-MS) analysis. Considering only those compounds with a score of over $90\%$ certainty, 12 and 13 compounds were identified in Vern and Pla extracts, respectively, and 20 in the Bac extract.
Some of these compounds are common to the three plant extracts, while others are only two or unique to just one (Table S2). As expected for ethanol extract, all identified compounds are hydrophobic, containing fatty acid derivatives such as palmitic acid ethyl ester (hexadecanoic acid ethyl ester, linolenic acid ethyl ester, (9,12,15-octadecatrienoic acid ethyl ester), and stearic acid ethyl ester (octadecanoic acid, ethyl ester). We tested the cell death induction activity of two compounds found in the extracts, namely phytol and ethyl linoleate (Table S2), both commercially available. The relative amounts of phytol and ethyl linoleate in the plant extracts were determined using known quantities of the two compounds and TLC (Figure 4A,B). The amounts of phytol were about 1800, 800, and 1200 nmol/mL (μM) in Vern, Bac, and Pla extracts, respectively.
Next, the activity of the phytol and ethyl linoleate in inducing cell death was analyzed by incubating SH-SY5Y cells with different concentrations (50–200 μM) for 24 or 48 h (Figure 4C). Phytol induced cell death with half-maximal cell death (IC50) of about $80\%$ at 70 μM. Ethyl linoleate showed weak cell death activity, increasing from $15\%$ in non-treated cells to about $40\%$ at 200 μM of ethyl linoleate (Figure 4C). Phytol also highly reduced cell survival, as analyzed using the XTT assay (Figure 4D), suggesting that its effect involves mitochondria dysfunction.
Next, we tested whether phytol- and ethyl linoleate-induced cell death was associated with increased VDAC1 expression levels and oligomerization. Both compounds, at the high concentrations used, induced VDAC1 overexpression (Figure 5A,B) and VDAC1 oligomerization (Figure 5C,D) in a concentration-dependent manner. In correlation with the higher activity of phytol in cell death induction, phytol increased both VDAC1 overexpression and oligomerization.
Since Vern extract induced cell death at a dilution of 500–1000, with phytol concentrations in these dilutions of 1.8 to 3.6 μM, cell death was observed at over 50 μM of phytol, suggesting that other compounds in the plant extracts are involved in cell death induction.
## 3.4. Anti-Tumor Activity of Vern Extract and Phytol
Next, we tested the effect of Vern extract at two dilutions and phytol on tumor growth (Figure 6). U-87MG cells were inoculated subcutaneously (s.c.) into the hind leg flanks of 7-week-old male athymic nude mice. When the tumor volume was around 50 mm3, the mice were divided into four groups with a similar average volume and treated with Vern extract or phytol (Figure 6A). Control tumors were injected with PBS containing $5\%$ ethanol (final concentration in the tumor $0.14\%$). Groups 2 and 3 were treated with Vern extract at a final dilution in the tumor of 1:250 or 1:500, and Group 4 was treated with phytol (75 μM). Treatment was given three times a week, and tumor growth was monitored (Figure 6B,C). All mice were sacrificed 34 days post-cell inoculation; tumors were excised, weighed (Figure 6D), and fixed; and sections were immunofluorescent-stained for selected proteins.
The results show that the tumors in the control grew exponentially with time and in a similar way when injected with Vern extract to a final dilution of 1:250. On the other hand, tumors treated with a higher dilution of Vern extract, 1:500, showed about a $70\%$ decrease in tumor volume and weight (Figure 6B–D). The results indicate that Vern extract at a higher concentration (1:250) is less effective than at the dilution of 1:500. Similar results with no effect on tumor growth were obtained using a 1:100 dilution of Vern extract (Figure S4). The decreased anti-cancer effect with increased Vern extract concentration may result from the protective activity of other compounds in the extract.
The results also show that phytol at the concentration used (75 μM) significantly inhibited tumor growth, yet less than Vern extract at 1:500 dilution (Figure 6B–D). Phytol has been shown to induce apoptosis in cells in culture [60,61,62], and, as also shown here, it also decreased cell survival (Figure 4C,D). For the first time, it induced VDAC1 overexpression and VDAC1 oligomerization (Figure 5) and inhibited tumor growth (Figure 6).
Tumor-fixed paraffin-embedded sections were stained for Ki-67, a proliferation marker, showing that both Vern extract and phytol inhibited cell proliferation by about $80\%$ (Figure 6E,F). Finally, we analyzed apoptosis by TUNEL staining (Figure 6G,H). While no TUNEL-positive cells were apparent in control tumors, most of the cells were TUNEL-positive in the Vern extract-treated tumors and to a lesser extent in phytol-treated tumors, with staining co-localizing with PI nuclear staining (Figure 6G, white arrows). Thus, Vern extract was more effective than phytol in cell death induction.
The results indicate that the treatments induced apoptotic cell death and suggest that the marked decrease in tumor size in the Vern extract- and phytol-treated xenografts can be attributed to both inhibition of cell proliferation (decreased Ki-67 staining) and cell death induction.
Next, we analyzed the effect of tumor treatment with Vern extract or phytol on the expression levels of proteins associated with metabolism, the microenvironment, and cancer stem cells (Figure 7, Figure 8 and Figure 9).
## 3.5. Vern Extract and Phytol Reduced the Expression of the Metabolic Enzyme in a Xenograft Mouse Model
The metabolic alterations that occur during malignant transformation involve a spectrum of functional aberrations and mutations that contribute to elevated glycolysis and increased expression levels of glucose transporters (Glut-1) and glycolytic enzymes as hexokinase (HK-I) [63]. IF of the control tumors derived from U-87MG cells showed high expression levels of Glut-1 and glyceraldehyde three-phosphate dehydrogenase (GAPDH) that were decreased in tumors treated with Vern extract or phytol (Figure 7A,B). Similarly, the expression of VDAC1 and HK-I was reduced in the Vern extract- and phytol-treated tumors (Figure 7C,D). The decreased expression levels of metabolism-related enzymes in the Vern extract- and phytol-treated tumors suggest decreased energy production in these treated tumors.
## 3.6. Vern Extract and Phytol Modulate the Tumor Microenvironment
A tumor contains cancer cells and non-cancerous cells, creating the tumor microenvironment (TME). Besides cancer cells, a tumor has fibroblasts [64], immune system cells [65], blood vessels [66], and extracellular matrix (ECM) components [67]. The TME plays a vital role in cancer growth and spread.
Angiogenesis is an underlying promoter of tumor growth, invasion, and metastases, with glioblastoma (GBM) being highly angiogenic [68]. Immunostaining of endothelial cell marker CD-31 showed that in the Vern extract- or phytol-treated tumors, there was a significant decrease in the number of blood vessels, with quantitation revealing decreases of about $70\%$ and $60\%$ in Vern extract and phytol-treated tumors, respectively, relative to control tumors (Figure 8A,B).
The effects of Vern extract and phytol on the TME were analyzed by IF staining for the fibroblast marker alpha-smooth muscle actin (α-SMA) (Figure 8C,D). Tumors treated with either Vern extract or phytol showed decreased α-SMA expression.
Accumulated recent evidence supports the cancer stem cell (CSC) hypothesis, which suggests that a sub-population of malignant cells exhibit the stem cell properties of self-renewal and differentiation. CSCs are resistant to conventional cytotoxic/anti-proliferative therapies. In GBM, the proteins Sox2, CD133, SSEA1, CD49f, Musashi-1, and Nestin are considered to be glioma stem cell CSC markers. The IF staining for Sox2 and Nestin of the tumor specimens demonstrated that in tumors treated with plant Vern extract or phytol, the expression levels of these CSC markers was highly reduced, by about $70\%$ (Figure 9). These results indicate that Vern plant extract and phytol treatment of U-87MG-derived tumors eliminated CSCs associated with tumor recurrence.
## 4. Discussion
Recently, significant attention has been placed on using nutraceuticals as therapeutic agents inducing cell death and suppressing cancer growth as an alternative treatment for cancer or in combination with chemotherapy [2,3,4,5,69,70,71]. Screening for plant-derived compounds with anti-neoplastic activity has contributed to identifying resveratrol, quercetin, curcumin, and others. In addition to their anti-cancer activity, these compounds also showed antioxidant, anti-inflammatory, anti-viral, and neuroprotective properties, lowered blood pressure, and improved cardio-metabolic markers and anti-aging effects [72,73,74,75,76].
Here, we present the activity of hydroethanolic extracts from three different plants—Vernonanthura nudiflora, Baccharis trimera, Plantago major, and their mixture—in cell death induction, VDAC1 overexpression, and oligomerization. The effects of Vern plant extract and one of its constituents, phytol, on tumor growth and oncological properties were tested.
## 4.1. Plant Extracts Inducing Cell Death Involve VDAC1 Overexpression and Oligomerization
Recently, VDAC1 has been recognized as a regulator of mitochondria-mediated apoptosis [36,37,42,43,44,45,46,47,54,55,56,57]. We demonstrated that many apoptosis inducers lead to VDAC1 overexpression and its oligomerization, resulting in the formation of a large channel that enables the release of pro-apoptotic protein from the IMS to the cytosol, thereby activating apoptosis [42,47,57].
This study showed that the three plant extracts induced massive cell death at relatively low doses (1:1000 of the original ethanolic extract). The plant extracts’ active compounds inducing apoptosis involved increased VDAC1 expression levels and oligomerization and, thereby, apoptosis. Thus, we propose that phytol and the plant extracts’ activation of apoptosis involve overexpression of VDAC1, shifting the equilibrium towards VDAC1 oligomers, allowing Cyto c release, and thereby, apoptosis [42,47,57] (Figure 10).
We have proposed a model for plant extract and phytol inducing VDAC1 overexpression leading to VDAC1 oligomerization, forming a large channel, mediating the release of apoptogenic proteins from the intermembrane space (IMS) to the cytosol, and activating the apoptosis cascade.
The active compound(s) in the plant extracts inducing VDAC1 overexpression may involve several possible mechanisms, such as the increase in ROS and intracellular Ca2+ levels, as both were shown to regulate gene expression. Ca2+-dependent gene transcription has been demonstrated in neurons [77,78,79] and other cells [80,81]. ROS was shown to upregulate gene expression for death receptors such as TRAIL (TNF-related apoptosis-inducing ligand) that appear to be mediated by transcription factors such as CHOP (C/EBP homologous protein) and p53 [82,83].
Here, we demonstrated that Vern extract highly elevated intracellular Ca2+ levels, as monitored using Fluo-4 and FACS analysis or by cell imaging using the Operetta imaging system (Figure 3). Similarly, Vern extract induced ROS production (Figure 3). Thus, these findings suggest the involvement of Ca2+ and ROS in triggering transcription factors controlling VDAC1 expression, with the increased VDAC1 level leading to its oligomerization, and apoptotic cell death [42,43,47,54]. The active molecule(s) in the extracts responsible for VDAC1 overexpression are yet to be identified.
## 4.2. GC-MS Analysis of the Ethanolic Plant Extracts
A preliminary GC-MS study showed that the ethanolic plant extracts contain many chemical entities at different levels (Table S2). The major compounds are fatty acids in their ethyl ester forms, such as palmitic acid ethyl ester (hexadecanoic acid ethyl ester), linolenic acid ethyl ester (9,12,15-octadecatrienoic acid ethyl ester), and stearic acid ethyl ester (octadecanoic acid ethyl ester). Interestingly, analysis of the constituents of ethanol root extract of the plant *Rauwolfia vomitoria* using GC-MS also showed the presence of fatty acids. Still, these were different from those found in the plant extracts tested here, such as ethyl oleate ($10.59\%$), 9,12-octadecadienoic acid ethyl ester ($8.26\%$), and palmitic acid, also known as hexadecanoic acid ethyl ester ($8.11\%$) [84].
Here, we showed that one of the identified compounds in the ethanolic plant extracts, phytol, was found to induce cell death and VDAC1 overexpression and oligomerization. Phytol has been reported to induce both apoptosis and protective autophagy [85].
Quantitative analysis of phytol amounts in the three plant extracts indicates that its concentration was highest in the Vern extract (5.9 mM) and about 7 mM and 2 mM in extracts B and C, respectively. Phytol-induced cell death was obtained at high concentrations (50 to 200 μM). Based on the estimated phytol concentration in the plant extracts and that Vern extract induced cell death at a 1000-fold dilution, the phytol concentration is about 13.5 μM below its effective concentrations in cell death induction (Figure 4). Thus, it is most likely that other compounds in the extracts contribute to its biological activity; however, their identification is the topic of another study.
Interestingly, our findings that phytol induced VDAC1 overexpression and oligomerization agree with the results that phytol was shown to modulate transcription in cells via a transcription factor—the peroxisome proliferator-activated receptor alpha (PPAR)—involved in regulating lipid metabolism in various tissues [86,87]. Phytol directly activates PPAR-alpha and regulates gene expression involved in lipid metabolism in PPAR-alpha-expressing HepG2 hepatocytes. It also modulates the retinoid X receptors (RXRs), which are nuclear receptors activated by various endogenous and natural ligands such as 9-cis retinoic acid, n-3 polyunsaturated fatty acids, and phytanic acid [88]. Thus, phytol may enhance VDAC1 expression by modulating transcription factor(s).
## 4.3. Vern Extract and Phytol Inhibit Tumor Growth and Alter Tumor Oncogenic Properties
The effects of Vern extract and phytol on tumor growth and tumor oncogenic properties were tested using a U-87MG-cell-derived tumor-based GBM mouse model. GBM is an aggressive brain cancer with high rates of relapse and mortality, mutational diversity, and poor treatment options.
The Vern extract decreased tumor size by about $70\%$ when used at the high dilution of 1:500 but was less effective at higher amounts, such as at 1:200 or 1:100 dilutions (Figure 6 and Figure S4). This finding can be explained when considering the composition of the extract compounds revealed by both GS-MS and LC-MS/MS analyses. In addition to the cell death-induced compounds, Vern plant extract contains compounds that support cell growth and are protective against cell death (Table S2). We suggest that the pro-survival compounds are active at high concentrations and overcome the activity of the pro-cell death compounds. At high dilution, the levels of pro-survival compounds are below their active concentrations; thereby, the effects of the pro-apoptotic compounds, which act at low concentrations, are evident. This observation suggests that it is possible to control the desired activity, supportive/anti- or pro-cell death, according to the extract concentration.
The results also show that phytol at the concentration used (75 μM) inhibited tumor growth. Phytol in cells in culture has been shown to induce apoptosis and protective autophagy [85]. Here, for the first time, it was demonstrated to inhibit tumor growth, and as discussed below, similar to the Vern extract, it induced multiple effects on the tumor. The impact of Vern extract on the tumors cannot be due to phytol, as at a dilution of 1:500, it contains about 30 μM phytol, which induced only $10\%$ cell death (Figure 4C).
The inhibition of tumor growth involves both inhibition of cell proliferation, reflected in an $80\%$ decrease in the levels of the cell proliferation marker, Ki-67 (Figure 6E,F), and cell death induction, showing that the Vern extract is about fivefold more active than phytol (Figure 6G,H).
Tumors require changes in the cellular metabolism and bioenergy of cancer cells [22], and their metabolic adaptation provides the tumor with the precursor needed for the biosynthesis of nucleic acids, fatty acids, cholesterol, and porphyrins [63,89]. Mitochondrial metabolism plays a vital role in the survival and development of cancer cells [90]. Here, we demonstrated that both Vern extract and phytol treatment of GBM in a mouse model significantly decreased the expression of metabolism-related enzymes involved in glycolysis and the TCA cycle (Figure 7), leading to reduced cell function and survival.
The decrease in the expression of metabolism-related enzymes in the tumors treated with Vern plant extract or phytol for 27 days may result from the massive cell death leading to cell distraction, including in the mitochondria and in the degradation of many cell proteins (Figure 7). Similar results were obtained with the VDAC1-based peptide [91].
Vern extract and phytol tumor treatment altered the tumor microenvironment, disrupting tumor–host interactions. The treated tumors showed a reduced expression of angiogenesis markers such as CD-31, decreasing blood supply.
Chronic inflammation caused by cancer cells stimulates surrounding cells, including fibroblasts and activated fibroblasts, with α-SMA expression producing an extracellular matrix including collagen. The fact that Vern extract and phytol alter the tumor microenvironment is reflected in the decreased α-SMA and Sirius red staining (Figure 8). α-SMA produced by cancer-associated fibroblasts (CAFs) contributes to remodeling and reconstitution to promote invasion and metastasis via the extracellular matrix, growth factors, and protease production [92], as well as to metastasis, and poorer prognosis [93] was highly decreased; thus, the treatment reduced these tumor properties.
As tumorigenesis is considered an interplay between tumor cells and the surrounding stroma host cells [49,94], alteration in the tumor microenvironment by the treatments suggests that this affects cancer progression, invasiveness, and treatment response.
Finally, CSCs, with their ability to self-regenerate, are considered to be responsible for initiating tumor growth and recurrence after therapeutic interventions and are associated with tumor resistance to anti-cancer therapies [95]. Here, we showed that tumor treatment with Vern extract or phytol resulted in the elimination of CSCs, as indicated by the decreased expression of the specific markers Nestin and Sox2 (Figure 9) [96].
## 5. Conclusions
We found that Vern extract at a high dilution and one of its compounds, phytol, have various effects on tumors, with their anti-cancer effects involving (i) apoptosis induction, (ii) inhibition of cell proliferation, (iii) re-modulation of the tumor microenvironment, (iv) impairment of cancer cell metabolism, and (v) eliminating CSCs, all leading to the observed inhibition of tumor growth. These findings, and considering that the side effects of these plant extracts are minor relative to those of conventional chemotherapy, suggest that the plant extract or a combination of its active compounds (yet to be identified) are a promising therapeutic approach for GBM and various other cancers.
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|
---
title: Heparan Sulfates Regulate Axonal Excitability and Context Generalization through
Ca2+/Calmodulin-Dependent Protein Kinase II
authors:
- Inseon Song
- Tatiana Kuznetsova
- David Baidoe-Ansah
- Hadi Mirzapourdelavar
- Oleg Senkov
- Hussam Hayani
- Andrey Mironov
- Rahul Kaushik
- Michael Druzin
- Staffan Johansson
- Alexander Dityatev
journal: Cells
year: 2023
pmcid: PMC10000602
doi: 10.3390/cells12050744
license: CC BY 4.0
---
# Heparan Sulfates Regulate Axonal Excitability and Context Generalization through Ca2+/Calmodulin-Dependent Protein Kinase II
## Abstract
Our previous studies demonstrated that enzymatic removal of highly sulfated heparan sulfates with heparinase 1 impaired axonal excitability and reduced expression of ankyrin G at the axon initial segments in the CA1 region of the hippocampus ex vivo, impaired context discrimination in vivo, and increased Ca2+/calmodulin-dependent protein kinase II (CaMKII) activity in vitro. Here, we show that in vivo delivery of heparinase 1 in the CA1 region of the hippocampus elevated autophosphorylation of CaMKII 24 h after injection in mice. Patch clamp recording in CA1 neurons revealed no significant heparinase effects on the amplitude or frequency of miniature excitatory and inhibitory postsynaptic currents, while the threshold for action potential generation was increased and fewer spikes were generated in response to current injection. Delivery of heparinase on the next day after contextual fear conditioning induced context overgeneralization 24 h after injection. Co-administration of heparinase with the CaMKII inhibitor (autocamtide-2-related inhibitory peptide) rescued neuronal excitability and expression of ankyrin G at the axon initial segment. It also restored context discrimination, suggesting the key role of CaMKII in neuronal signaling downstream of heparan sulfate proteoglycans and highlighting a link between impaired CA1 pyramidal cell excitability and context generalization during recall of contextual memories.
## 1. Introduction
Heparan sulfate proteoglycans (HSPGs) harbor long chains of variously sulfated polysaccharide residues. There are membrane-bound HSPGs, such as syndecans and glypicans, and secreted HSPGs, including agrin, perlecan, and collagen type XVIII. An increasing number of studies demonstrate that HSPGs have an important role in the nervous system during development and adulthood.
In the mouse brain, syndecan-1 and glypican-4 are highly expressed in the neural tube, where the precursor cells are proliferating [1]. These HSPGs are important for the proliferation of neural precursor cells and play a role as synaptic organizing molecules during synaptogenesis. Their heparan sulfate (HS) chains are essential for this role. Glypican 4 is bound to the presynaptic membrane via a GPI anchor and interacts with the postsynaptic protein, LRRTM4 (leucine-rich repeat transmembrane neuronal proteins), forming a trans-synaptic complex. This complex recruits other synaptic molecules to the synaptic cleft, contributing to the maturation of excitatory synapses. Mice deficient in glypican 4 exhibit a decreased number of synapses along with decreased expression of postsynaptic glutamate receptor subunit GluA1 and increased retention of presynaptic neuronal pentraxin 1 [2].
Syndecans are differentially expressed in various neural cell types and exhibit differential subcellular localization in neurons [3]. Contrary to glypicans lacking a cytoplasmic domain, transmembrane syndecans interact with specific cytoplasmic binding partners, such as Ca2+/calmodulin-dependent serine protein kinase (CASK), syntenin, synectin, and synbindin [4,5,6,7]. Syndecan 2 is highly expressed in synapses and influences activities of postsynaptic scaffolding proteins, thereby contributing to filopodia and dendritic spine formation [8]. Overexpression of full-length syndecan 2 in cultured immature hippocampal neurons accelerates dendritic spine formation, while a syndecan 2 deletion mutant that lacks the ability to bind to synthenin and CASK does not support spine maturation [4,9]. The association of cortactin and fyn with syndecan is increased rapidly after induction of long-term potentiation (LTP), while inclusion of soluble syndecan 3 into the rat hippocampal slices inhibits high-frequency stimulation-induced LTP [10]. Furthermore, syndecan 3 knockout mice exhibit strongly enhanced LTP and impaired hippocampus-dependent memory [11]. Secreted HSPG agrin is also involved in filopodia and dendritic spine formation. While downregulation of agrin in the cultured neurons in vitro and in vivo reduces the number of dendritic filopodia, overexpression of agrin in rodent hippocampal neurons stimulates filopodia formation in vitro [12].
An increasing number of structural, pharmacological, and genetic studies suggest a key role of the HS chains carried by HSPGs in mediating their activities. Interestingly, HSs bind to receptor protein tyrosine phosphatase sigma (RPTPσ) at the same site as chondroitin sulfates. Crystallographic analyses of this site reveal conformational plasticity that can accommodate diverse glycosaminoglycans with comparable affinities. HSs induced RPTPσ ectodomain oligomerization, stimulating neurite outgrowth. The oligomerization was inhibited by chondroitin sulfates, resulting in impaired neurite outgrowth [13]. In acute hippocampal slices, treatment with a mixture of heparinases 1 and 3, which removes highly and low sulfated HSs, respectively, impaired LTP of synaptic transmission [10,14]. This treatment also prevented the increase in the number of spines after induction of N-methyl-D-aspartate (NMDA) receptor-dependent LTP [14]. Conditional ablation of Ext1, a gene involved in HS synthesis, in a subpopulation of pyramidal neurons leads to an autistic phenotype [15], providing genetic evidence for the importance of HSs in shaping brain function on many levels, from cellular properties to complex behaviors. More targeted ablation of HSs on neurexin-1 also revealed structural and functional deficits at central synapses. HS directly binds postsynaptic partners neuroligins and LRRTMs [16].
Considering the high heterogeneity of HSs, we focused on a highly sulfated subset of HSs (HSHSs), which could be digested by heparinase 1. Such treatment of cultured hippocampal neurons resulted in a reduction in the mean firing rate of neurons [17,18], despite the upregulation of GluA1 protein expression [17]. Acute treatment of hippocampal slices with heparinase 1 reduced CA1 pyramidal cellular excitability and impaired hippocampal LTP [19]. Altered expression of ankyrin G (AnkG), as one of the major organizing proteins at the axon initial segment (AIS) in heparinase 1-treated hippocampal slices, led us to the hypothesis that HSHSs are involved in the modulation of neuronal activity through the changes in the AIS composition and function. Injection of heparinase 1 before fear conditioning impaired context discrimination [19], validating the importance of HSHSs at the systemic level.
Based on previous in vitro findings of increased autophosphorylation levels of CaMKII α and β isoforms after heparinase 1 treatment [17], we hypothesized that CaMKII is the key molecule involved in the modulation of axonal excitability due to a loss of HSHSs and provided in vitro and in vivo evidence verifying this hypothesis biochemically, immunocytochemically, and electrophysiologically. Our studies show that an increased level of autophosphorylated CaMKII in heparinase-treated neurons is responsible for reduced neuronal excitability, altered expression of AnkG in the AIS of CA1 pyramidal neurons, and impaired contextual discrimination.
## 2.1. Immunoblot Analysis
To access the level of endogenous CaMKII isoform expression and the level of its phosphorylation, murine hippocampal slices (treated with intact or heat-inactivated heparinase 1 in the same way as for electrophysiological recordings) were snap-frozen in isopropanol pre-cooled on dry ice. Later samples were homogenized in radioimmunoprecipitation assay (RIPA) buffer (ThermoFisher Scientific, Rockford, IL, USA) containing a protease inhibitor cocktail (Sigma-Aldrich P1860, St. Louis, MO, USA), a serine/threonine phosphatase inhibitor (Sigma-Aldrich P0044, St. Louis, MO, USA), and a tyrosine phosphatase inhibitor (Sigma-Aldrich P5726, St. Louis, MO, USA) using a glass tissue homogenizer. Non-soluble proteins were separated via centrifugation at 20,000 g for 15 min at 4 °C. The protein concentration of individual samples was measured using a DC Protein Assay (Bio-Rad, Hercules, CA, USA). A total of 10–30 μg of extract was resuspended in reducing ($5.0\%$ 2-mercaptoethanol) sample buffer (Bio-Rad, Hercules, CA, USA) and boiled at 100 °C for 5 min, separated via SDS-PAGE on $10\%$ acrylamide gels, and transferred to the polyvinylidene difluoride membranes. Membranes were blocked for 1 h at room temperature with $5\%$ Blotting-Grade Blocker (Bio-Rad, 1706404, Hercules, CA, USA) in Tris-buffered saline with Tween20 (TBS-T buffer), probed with appropriate primary antibody at 4 °C overnight and then for 1 h at room temperature with horseradish peroxidase (HRP)-conjugated secondary antibodies.
To estimate the total expression of α and β forms of CaMKII, mouse anti-CaMKII (G1, sc-5306; 1:200–1:1000) from Santa-Cruz (Paso Robles, CA, USA) was used. To induce activation of CaMKII, rabbit anti-phospho Thr $\frac{286}{287}$ CaMKII (p1005–286; 1:1000) from PhosphoSolutions (Aurora, CO, USA) was applied. To evaluate the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) level, mouse anti-GAPDH (MAB374; 1:15.000–20.000) from Millipore (Bedford, MA, USA) was used. HRP-conjugated secondary antibodies were donkey anti-rabbit (NA934V) from GE Healthcare (Buckinghamshire, UK), or goat anti-mouse [115-035-146] from Jackson ImmunoResearch (West Grove, PA, USA). Acquisition of chemiluminescent signal and densitometric analysis were performed using an Odyssey Fc Imaging System (LI-COR, NE, USA) and Image Studio Lite 5.2.5 software, respectively. The total levels of α or β forms of CaMKII and phospho Thr $\frac{286}{287}$ levels were standardized to the level of loading control (GAPDH) in each sample. Standardized values were further normalized to the randomly chosen control sample (loaded in each gel). To evaluate CaMKII phosphorylation on Thr $\frac{286}{287}$, the ratios of phospho-Thr $\frac{286}{287}$ signal to the total amount of CaMKII protein were calculated.
For statistical evaluation and the graphical representation of the data, the OriginPro 2022 9.9.0.225 software was used. The average ± SEM (standard error of mean) was calculated for control and experimental (heparinase-treated) groups, normalized to randomly chosen control samples. Statistical evaluation was carried out using a Mann–Whitney–Wilcoxon test.
## 2.2. Slice Preparation and In Vitro Electrophysiology
Acute hippocampal slices were prepared as described elsewhere [19] from 4- to 5-week-old male C57Bl/6 mice 1 day after injection with Heparinase 1, Ctrl (heat-inactivated Heparinase 1), or Heparinase 1 + autocamtide-2-related inhibitory peptide (AIP) into hippocampal CA1 area, which described below (Section 2.4). Transverse 350 µm thick hippocampal slices were obtained in ice-cold slice solution containing (in mM) 240 sucrose, 2 KCl, 2 MgSO4, 1.25 NaH2PO4, 26 NaHCO3, 1 CaCl2, 1 MgCl2, and 10 D-glucose. After slice recovery at room temperature, the slices were transferred to a submerged recording chamber and were perfused with ACSF (2–3 mL/min) containing (in mM) 124 NaCl, 2.5 KCl, 1.3 MgSO4, 1 NaH2PO4, 26.2 NaHCO3, 2.5 CaCl2, 1.6 MgCl2, and 11 D-glucose. The solution was saturated with $95\%$ O2/$5\%$ CO2 (Osmolarity, 290 ± 5 mOsm). Whole-cell patch clamp recordings were obtained from visually identified CA1 pyramidal neurons with a glass electrode (4–5 MΩ, Hilgenberg, Germany) containing (in mM) 120 K-gluconate, 10 KCl, 3 MgCl2, 0.5 EGTA, 40 HEPES, 2 MgATP, and 0.3 NaGTP (pH 7.2 with KOH, 295 mOsm) for measuring neuronal excitability. In the current clamp configuration, cells were held at −70 mV and injected from −75 mV to +400 pA with 25 pA increments. For measuring miniature excitatory postsynaptic currents (mEPSCs), 5 mM QX314 was added into the intracellular pipette solution while GABAA receptor antagonist picrotoxin (PTX, 50 µM, Tocris, Bristol, UK), GABAB receptor antagonist CGP55845 (3 µM, Tocris, Bristol, UK), and Na+ channel blocker tetrodotoxin (TTX, 1 µM, Tocris, Bristol, UK) were added to ACSF.
Miniature inhibitory postsynaptic currents (mIPSCs) were recorded with a glass electrode containing (in mM) 120 CsCl, 8 NaCl, 0.2 MgCl2, 10 HEPES, 2 EGTA, 0.3 Na3GTP, and 2 MgATP at pH 7.2 with CsOH, 290 mOsm. NBQX (25 µM, Tocris, Bristol, UK), D-APV (50 µM, Tocris, Bristol, UK), and TTX (1 µM) were added to ACSF to isolate action potential-independent mIPSCs. In vitro electrophysiological data were acquired using an EPC 10 amplifier (HEKA Elektronik, Germany) at a sampling rate of 10 kHz and low-pass-filtered at 2–3 kHz. The obtained data were analyzed offline using PatchMaster software v2X69 (HEKA Electronik, Germany), Clampfit 10 (Molecular Devices, U.S.A.), or MiniAnalysis (6.0.3 Synaptosoft, U.S.A.). The data were presented and analyzed using SigmaPlot 12 (Systat Software Inc, U.S.A.) and Prism 7 (GraphPad software, U.S.A.).
## 2.3. Immunocytochemistry in Hippocampal Cultured Neurons
Hippocampal neurons from embryonic C57BL6/J mice (E18) were extracted and cultured as described earlier [19]. The neuronal cells were plated on polyethyleneimine-coated (Sigma-Aldrich; 408727-100 mL) 18 mm coverslips in 12-well plates at a cell density of 150,000 per well. Neurons were maintained in 1 mL of neurobasal media (NB+ media) (Thermo Fisher Scientific, Waltham, MA, USA) containing $2\%$ B27 and $1\%$ L-glutamine and $1\%$ Pen-Strep (Gibco, Grand Island, NY, USA). Cultured neurons were fed with 250 µL of NB+ media on days in vitro (DIV) 14 and 17. On DIV 21–23, cultured hippocampal neurons were incubated with Heparinase-1 (0.5 U/mL, Sigma-Aldrich, H2519, MO, USA), Ctrl (heat-inactivated Heparinase-1), or Heparinase-1 + AIP (100 nM), as previously described [19], for 2 h at 37 °C. After the treatment, hippocampal neurons were washed with phosphate-buffered saline (PBS) and fixed with $4\%$ paraformaldehyde (PFA) for 10 min, and then permeabilized with $0.1\%$ Triton-X-100 in PBS for 10 min, washed 3 times, and blocked ($0.1\%$ Glycine + $0.1\%$ Tween-20 + $10\%$ Normal Goat Serum in PBS) for 60 min at room temperature. Then, the cells were stained with antibodies against AnkG (mouse monoclonal, 1:1000; Millipore, MABN466), pCaMKII (rabbit polyclonal, 1:1000; Phospho Solution, P-1005-286), MAP2 (chicken polyclonal, 1:500; Abcam, ab5543) and DAPI (Life Technologies, S36939), and finally mounted (Fluoromount; Sigma Aldrich, F4680-25ML, MO, USA) for imaging. Mounted coverslips were imaged using a Zeiss LSM 700 confocal microscope with a 63×/1.4 NA oil immersion objective. Image analysis was carried out as previously described [19]. Using the microtubule-associated protein 2 (MAP2) and AnkG signals, the AISs were analyzed from the soma edge over a 40 µm long distance with a line profile (width = 3.0) using Fiji (ImageJ version 1.53c) [19].
## 2.4. In Vivo Intrahippocampal Injection
Adult (2- to 4-month-old) male C57Bl/6j mice (Charles River) were used. At least 1 week before starting the experiments, mice were transferred to a small vivarium, where they were housed individually with food and water ad libitum on a reversed 12:12 light/dark cycle (light on at 9:00 p.m). All behavioral experiments were performed in the afternoons during the dark phase of the cycle when the mice were active, under constant temperature (22 ± 1 °C) and humidity (55 ± $5\%$). All treatments and behavioral procedures were conducted in accordance with ethical animal research standards defined by German law and approved by the Ethical Committee on Animal Health and Care of the State of Saxony-Anhalt, Germany, under the license numbers 42502-2-1159 and -1322 DZNE.
Injection guide cannulas and electrodes were implanted as previously described [19], but electrophysiological analysis is not included in the present study due to an insufficient quality of recordings. The coordinates for bilateral cannulas were AP = 2.0 mm and L = ±2.2 mm from bregma and midline, respectively. For intrahippocampal injection, we used a digitally controlled infusion system (UltraMicroPump, UMP3, and Micro4 Controller, WPI, U.S.A.) fed with a 10 μL Hamilton syringe and a NanoFil (35 GA) bevel-tip needle, as previously described [19]. The mouse was first anesthetized with 1–$3\%$ isoflurane and put into the stereotaxic frame. Heparinase 1 (Hep) from *Flavobacterium heparinum* (0.05 U/µL/site, Sigma-Aldrich, H2519), Heparinase 1 heat-inactivated at 100 ºC for 30 min (Ctrl), or Heparinase 1 + autocamtide-2-related inhibitory peptide (0.17 µg/µL/site, Sigma-Aldrich, SCP0001) (Hep + AIP) was injected into the hippocampal CA1 area at a rate of 3 nl/s. After waiting for another 5 min, the injection needle was removed.
## 2.5. Fear Conditioning
In this study, we used the previously described classical Pavlovian contextual fear conditioning paradigm in mice with a slight modification [19]. In this study, on day 0 (d0), mice were initially placed in a 20 × 20 × 30 cm chamber with a neutral context (CC-), gray walls, and gray plastic floor for 5 min. Next, mice were exposed to the conditioned context (CC+), which includes patterned walls and a metal grid floor, for 5 min after an interval of 1 h. During the CC+ phase, mice’s feet were shocked 3 times with mild intensity (0.5 mA, 1 s) with a 1 min inter-shock interval. Using a computerized fear conditioning system (Ugo Basile, Italy), the first memory retrieval session was carried out for 5 min for each mouse on day 1 (d1) with a 1 h interval following the sequence CC- and CC+. On day 2 (d2), mice were injected with the vehicle, Heparinase and Heparinase + AIP, into the hippocampus. Then, on day 3 (d3) the second memory retrieval test (test 2) was performed using a similar paradigm as that used on d1. A blinded trained observer used video recordings of each session for offline fear-conditioned behavioral analysis with the help of behavioral video acquisition and analysis software (ANY-maze, version 4.99, Stoelting Co., Wood Dale, IL). Finally, the overall context memory and discrimination performance for each mouse was estimated.
## 2.6. Statistics
Numerical data are reported as mean ± SEM, with n being the number of samples. Student’s t-test and multi-way ANOVA with suitable post hoc tests were used as indicated and performed in SigmaPlot or Prism. For non-Gaussian distributions, we used the Mann–Whitney–Wilcoxon test. Significance levels (p-values) are indicated in figures by asterisks.
## 3.1. Heparinase Treatment Elevates CaMKIIβ Autophosphorylation in the Mouse Hippocampus
We previously observed an increase in the GluA1 expression and CaMKII activity in cultured mouse hippocampal neurons after heparinase 1 treatment [17]. To investigate whether heparinase treatment also changes hippocampal CaMKII activity in vivo, Ctrl (heat-inactivated heparinase) or active heparinase 1 was injected into the dorsal hippocampus of 6-week-old mice. To investigate the level of endogenous CaMKII isoform protein expression and their activity, 24 h after injection of heparinase, hippocampal slices were acutely prepared and used for immunoblotting. CaMKIIα and CaMKIIβ are major isoforms in the hippocampus, and these molecules are activated during memory formation. Activation of CaMKIIα and CaMKIIβ was assessed via the analysis of phosphorylation at Thr286 and Thr287, respectively [20]. Consistent with our previous observation for the cultured hippocampal neurons [17], the activity of CaMKIIβ was strongly affected after heparinase injection in vivo. The ratio of phosphorylated to total CaMKIIβ was increased after heparinase treatment, while the effect on CaMKIIα was less prominent (Figure 1).
## 3.2. Enzymatic Digestion of HSHSs Does Not Change Synaptic Transmission to CA1 Pyramidal Neurons
Having found that heparinase treatment in vitro can up-scale mEPSCs, we measured glutamatergic transmission (mEPSC) and GABAergic transmission (mIPSC) to CA1 pyramidal neurons 1 day after heparinase injection in vivo. Unexpectedly, we did not observe changes in the mEPSCs’ amplitude or frequency (Figure 2A,C). Additionally, temporal parameters such as the rise and decay times were not affected (Figure 2A,C). The properties of mIPSCs were also unchanged by heparinase treatment (Figure 2B,D).
## 3.3. Impaired Neuronal Excitability after In Vivo Injection of Heparinase Is Rescued by CaMKII Inhibitor AIP
Next, we investigated the excitability of CA1 pyramidal neurons. We previously reported that acute heparinase treatment of hippocampal slices reduced action potential (AP) probability during theta-burst stimulation and hence decreased Ca2+ influx to dendritic spines during the induction of LTP [19]. Based on that study, we expected that one day of heparinase treatment may also result in reduced neuronal excitability in the CA1 pyramidal cells. Therefore, we performed patch clamp recordings in the current clamp configuration and measured the number of APs as a function of injected currents (Figure 3A), the threshold of action potential generation (Figure 3B), and other parameters characterizing the magnitude and shape of APs (Figure 3C–F). To verify the role of CaMKII in shaping the effects of heparinase, we employed AIP as a selective and potent inhibitor of CaMKII, which has been used in slices and in vivo [21,22,23]. We co-injected AIP with heparinase one day before recordings.
Compared with the control group, fewer APs were evoked in response to depolarizing currents after injection of heparinase (Figure 3A). Analysis of input–output curves showing the average number of APs for each intensity of stimulation revealed a significant reduction in cell excitability in the heparinase-treated neurons and restoration of excitability by AIP (Figure 3C). Another indicator of excitability is the spike threshold (Scott et al., 2014). After the heparinase treatment, neurons started to fire at more positive membrane potential in the heparinase group as compared to the control, and this effect was abrogated by AIP (Figure 3D). Analysis of two peaks in the second derivative of APs, which correspond to AP generation at the AIS and soma [24], revealed a tendency toward reduction in the magnitude of the first peak after heparinase treatment (but not the second peak or the interval between peaks), and a significant increase in the first peak after CaMKII inhibition, suggesting the modulation of AIS excitability (Figure 3E). An axonal site of heparinase action is also indirectly suggested by the absence of heparinase effects on the peak spike voltage (AP amplitude), which represents an indicator of somatic sodium channel availability (Figure 3F). Heparinase also reduced, in a CaMKII-dependent manner, the half-width and decay of the action potentials, suggesting some modulation of potassium channels (Figure 3G).
## 3.4. Increased Activity of CaMKII and Impaired Expression of AnkG at the Axon Initial Segment after Heparinase Treatment Are Abrogated by AIP
Our previous study revealed that the removal of HSHSs reduces AnkG expression at the AIS in vitro and in vivo [19]. As we in the present study observed the increased autophosphorylation of CaMKII after heparinase treatment, we investigated if the reduction in AnkG at the AIS correlates with changes in CaMKII phosphorylation at the same subcellular domain and whether the pharmacological inhibition of the CaMKII autophosphorylation with AIP could abrogate the effects of heparinase treatment on AnkG expression. To facilitate the quantitative analysis of protein expression in the AIS, it was performed in vitro as previously described [19]. We observed an increased level of pCaMKII at the AIS after digesting HSHSs, which was reduced by AIP to the control levels (Figure 4). Similar to our previous findings, the removal of HSHSs reduced the expression of AnkG along the 40 µm distance of the AIS relative to the control. In line with our electrophysiological recordings, co-incubating hippocampal neurons with heparinase and AIP restored the expression of AnkG to the control levels (Figure 4). Together with electrophysiological data, these results suggest that the reduction in AnkG expression at the AIS and reduced neuronal excitability after cleaving HSHSs are induced by the increased autophosphorylation of CaMKII.
## 3.5. Impaired Recall of Contextual Memories after Heparinase Treatment Is Rescued by Co-Administration of AIP
In our previous study, we found that heparinase injected before contextual fear conditioning did not affect the level of spontaneous freezing/immobility before conditioning but impaired context discrimination 24 h after conditioning [19]. This experiment, however, did not allow us to dissect whether HSHSs are essential for the acquisition, consolidation, or recall of contextual memories because re-expression of glycans is a slow process taking several weeks [25], and hence the removal of HSHSs before conditioning would result in impaired HSHS expression during acquisition, consolidation, and recall of memories for the next few days after conditioning. In the present study, we specifically tested if HSHSs are necessary for proper contextual memory recall by injecting heparinase on day 2 after contextual fear conditioning (Figure 5a), i.e., after the acquisition and consolidation of memories were successfully completed. This was confirmed by normal freezing time in the conditioned context and normal context discrimination on day 1 in mice pre-assigned to all experimental groups, i.e., control, heparinase, and heparinase plus AIP (Figure 5A). Additionally, on day 3 after conditioning, i.e., 24 h after heparinase injection, the freezing time in the conditioned context was normal in the control group, but heparinase-treated mice showed increased freezing in the neutral context CC- (Figure 5B) and impaired contextual discrimination (Figure 5C). Co-administration of AIP restored normal context discrimination after heparinase treatment, not affecting freezing time in the conditioned context.
## 4. Discussion
Our data show that enzymatic removal of HSHSs in the CA1 region of the hippocampus does not affect miniature postsynaptic currents but leads to reduced axonal excitability of pyramidal cells and impaired contextual discrimination, which correlate with increased activity of CaMKII in general, but particularly in the AIS. Inhibition of CaMKII with AIP normalizes excitability and expression of AnkG in the AIS after heparinase treatment, suggesting a causal link between HSPGs and regulation of axonal excitability via CaMKII autophosphorylation. Below, we discuss the functional importance and possible molecular mechanisms underlying these findings.
Highly expressed in excitatory synapses in the hippocampus, CaMKII has been studied in many aspects of synaptic function, such as synaptic strength and synaptic plasticity. Overexpression of α and β isoforms of CaMKII in cultured neurons has the opposite effects on mEPSCs’ strength frequency while CaMKIIβ-overexpressing cells exhibit an increase [26]. Thus, it is plausible to assume that in our experiments, the effects of increased CaMKIIβ activation were counterbalanced by increased activity of synaptic CaMKIIα, but we cannot exclude the saturation of CaMKIIβ effects under in vivo conditions of the present experiments.
The autophosphorylation of CaMKII, especially that of CaMKIIα, on the other hand, has been shown to reduce the excitability of CA1 neurons, which may impact learning [27,28], while inhibition of CaMKIIα autophosphorylation by a point mutation at T286A increased CA1 neuron excitability. These data are in line with our finding that autophosphorylation of CaMKII was increased after heparinase treatment in the AIS, while expression of AnkG was impaired but could be rescued by the AIP co-administration. Studies show that AIP specifically inhibits CaMKII relative to other kinases, such as protein kinase C (PKC), CAMKI, and CaMKIV, in rat brain extracts [29,30] and in mice [22,23]. The degree of specificity of AIP effects on CaMKIIα versus CaMKIIβ, however, has not been properly resolved.
The AIS, located between axonal and somatodendritic domains, is a key structure for the initiation of action potential firing. AnkG, neuronal cell adhesion molecule (NrCAM), βIV-spectrin, and voltage-gated sodium and potassium channels are major structural/functional components of the AIS, and their alteration affects AIS assembly and function [31]. βIV-spectrin, an AnkG interaction partner, serves as a bridge between AnkG and the actin-based cytoskeleton. Accordingly, animal models harboring AnkG gene deficiency exhibit abnormal animal behavior (such as ataxia) and neuronal excitability in the cerebellum, due to the mislocalization of sodium channels [32]. Progressive ataxia and tremors are also observed in different βIV-spectrin mutant mice (qv3J and βIV-null mice) [33]. Findings of the mislocalization of sodium channels in the AIS of cerebellar and hippocampal neurons in these mutant mice suggest that altered sodium channel expression is responsible for the neurological phenotypes of the mutants. In the cardiomyocyte, βIV-spectrin’s interaction with CaMKII leads to sodium channel phosphorylation via βIV-dependent targeting of CaMKII [34]. The abnormal kinetics of sodium channels and altered cellular excitability after a loss-of-function mutation in the βIV-spectrin gene in the qv3J mouse line suggest that the βIV-spectrin/CaMKII complex is an important component for Na+ channel regulation in cardiomyocytes. Interestingly, CaMKII is colocalized with βIV spectrin in the AIS of cerebellar Purkinje neurons as well, and qv3J mutant mice exhibit a relatively weak immunostaining signal of CaMKII in the AIS of Purkinje cells, implying that the βIV-spectrin/CaMKII complex would strongly affect cellular excitability in both the heart and the brain [34]. Thus, further studies are warranted to study the distribution of βIV-spectrin and ion channels in the AIS after the targeting of HSHSs.
Extracellularly, the secreted protein gliomedin is a key component at the nodes of Ranvier in the peripheral nerves. The deposition of gliomedin multimers at the nodal gaps facilitates the clustering of the axonodal cell adhesion molecules neurofascin and NrCAM and sodium channels by binding to HSPGs [35]. In cortical neurons, agrin binds to a tyrosine kinase receptor, which results in the elevation of intracellular Ca2+ and subsequent activation of the CaMKII signaling pathway [36]. Regarding potential protein carriers of HSHSs responsible for the regulation of CaMKII activity at the AIS, there are several candidates. Glypicans 1 and 2 are expressed axonally [37,38]. Glypican-4 is also enriched on hippocampal granule cell axons and can bind to its partner orphan receptor, GPR158 [39]. Additionally, syndecans are known to be localized at the nodes of Ranvier [40] and axons [3,41]. Syndecans 2 and 3 can directly bind to CASK protein via the PDZ domain [4] that regulates CaMKII activity in neurons [42]. Further studies on the AIS in mice deficient in these HSPGs could be instrumental to identify their role in AIS assembly and axonal excitability via regulation of CaMKII.
Our behavioral experiments for the first time suggest the role of HSHSs in the proper recall of contextual memories and show that in vivo inhibition of CaMKII by AIP could abrogate the hypergeneralization induced by heparinase. Previously, AIP has been shown to significantly protect neurons from NMDA-induced neurotoxicity [43], fully restore contractility in cardiac muscles of diabetic rats [44], inhibit doxorubicin-induced apoptosis of cardiac cells [45], and prevent the reinstatement of morphine-seeking behavior in rats [46]. As hypergeneralization is common for several conditions [47,48,49], targeting this mechanism might be of therapeutic value.
A similar loss of context discrimination is found when contextual memories are transferred from the hippocampus to the anterior cingulate cortex via the retrosplenial cortex. Moreover, high-frequency stimulation of memory engrams in the retrosplenial cortex one day after learning produces a recent memory with features normally observed in consolidated remote memories, including contextual generalization and decreased hippocampal dependence [50]. Thus, our data are consistent with the hypothesis that the recent contextual memory is distributed in several brain areas and, if the hippocampal engrams, in particular CA1, are not activated enough due to a loss of excitability induced by heparinase, another, presumably cortical, representation is used.
In summary, our data make a stronger link between HSHSs and regulation of neuronal excitability and implicate CaMKII in this regulation. Aberrant expression or activity of HSPGs is associated with some pathological conditions, such as glioblastoma, Fragile X syndrome, neuroinflammation, and Parkinson’s disease [51,52,53,54]. Additionally, HSPGs are known to bind and co-aggregate with amyloid beta (Aβ) [55,56]. In light of reported neuronal hyperexcitability in Alzheimer’s patients and models of Alzheimer’s disease [57,58], our work suggests that Aβ-HSPG interactions may affect the expression of HSPGs at the AIS, decreasing activation of CaMKII at the AIS and hence increasing neuronal excitability. At synapses, Aβ is known to inhibit autophosphorylation of CaMKII at Thr286 and impair synaptic plasticity [59]. Thus, our study suggests potential pathophysiological mechanisms and indicates an option to prevent these by targeting CaMKII signaling at the AIS.
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|
---
title: Decreasing the Crystallinity and Degree of Polymerization of Cellulose Increases
Its Susceptibility to Enzymatic Hydrolysis and Fermentation by Colon Microbiota
authors:
- Karel Thielemans
- Yamina De Bondt
- Luke Comer
- Jeroen Raes
- Nadia Everaert
- Bert F. Sels
- Christophe M. Courtin
journal: Foods
year: 2023
pmcid: PMC10000603
doi: 10.3390/foods12051100
license: CC BY 4.0
---
# Decreasing the Crystallinity and Degree of Polymerization of Cellulose Increases Its Susceptibility to Enzymatic Hydrolysis and Fermentation by Colon Microbiota
## Abstract
Cellulose can be isolated from various raw materials and agricultural side streams and might help to reduce the dietary fiber gap in our diets. However, the physiological benefits of cellulose upon ingestion are limited beyond providing fecal bulk. It is barely fermented by the microbiota in the human colon due to its crystalline character and high degree of polymerization. These properties make cellulose inaccessible to microbial cellulolytic enzymes in the colon. In this study, amorphized and depolymerized cellulose samples with an average degree of polymerization of less than 100 anhydroglucose units and a crystallinity index below $30\%$ were made from microcrystalline cellulose using mechanical treatment and acid hydrolysis. This amorphized and depolymerized cellulose showed enhanced digestibility by a cellulase enzyme blend. Furthermore, the samples were fermented more extensively in batch fermentations using pooled human fecal microbiota, with minimal fermentation degrees up to $45\%$ and a more than eight-fold increase in short-chain fatty acid production. While this enhanced fermentation turned out to be highly dependent on the microbial composition of the fecal pool, the potential of engineering cellulose properties to increased physiological benefit was demonstrated.
## 1. Introduction
Cellulose is the most abundant renewable material in nature, being the primary building block of the plant cell wall. It consists of long unbranched β-1,4-bound glucose polymers organized in long crystalline fibers with strong interactions between the different polymers [1]. The high degree of crystallinity, high degree of polymerization and low specific surface give cellulose a very recalcitrant character. This recalcitrance is important in the plant cell wall, since cellulose provides the plant with mechanical strength and resilience against breakdown, but is a major drawback in valorization strategies of (ligno)cellulose, e.g., in the context of biorefineries [1,2]. Furthermore, cellulose acts as an insoluble and recalcitrant dietary fiber in the human body when ingested.
The use of cellulose as dietary fiber in foods could be very relevant since the food industry increasingly searches for dietary fiber enrichment strategies. While a sufficient daily intake of dietary fiber is correlated with various health benefits, such as a decreased risk of colorectal cancer, obesity, cardiovascular diseases and diabetes mellitus type II [3,4,5,6], the average daily intake of dietary fiber is too low in Western diets [7]. Within dietary fiber fortification strategies, specific attention goes to (partially) fermentable dietary fiber. Fermentation of dietary fiber in the colon is correlated with different additional physiological benefits, linked to the production of short-chain fatty acids (SCFA), which are essential for colonic health h, glucose and cholesterol homeostasis and the regulation of the appetite [8,9,10]. The fermentability of cellulose in the human colon is very low, however [11]. Cellulolytic enzymes are produced by Ruminococcus, Enterococcus, Bacteroides or Prevotella species in the colon [12,13], but the highly ordered nature of cellulose limits the accessibility of the cellulosic fibers and the glucosidic β-1,4 bonds for enzymatic breakdown and results in very limited fermentability.
We can assume that the fermentability and the physiological benefits of cellulose with it could be improved by breaking this recalcitrance before ingestion. Such accessible cellulose would remain insoluble and indigestible but could be fermented to a greater extent in the colon. Previous research already stated that the fermentability of cellulose depends on its physical appearance [14], and some attempts to improve cellulose fermentability by reducing the particle size were already successful in human in vitro experiments [15,16]. However, the impact of the degree of polymerization and crystallinity has not been investigated in this context. At the same time, these structural parameters are known to affect cellulose accessibility greatly [17,18,19].
Plenty of (ligno)cellulosic biomass pretreatment protocols, such as milling, irradiation, ultrasonication, hydrothermal treatment or solubilization in ionic liquids, have been developed and optimized to alter these cellulose characteristics [17,20,21,22,23,24,25]. Moreover, several of them, such as ball milling, acid hydrolysis or solubilization in ionic liquid, are linked to an improved cellulose enzymatic accessibility as well [26,27,28]. In this study, two effective pretreatment methods, ball milling and acid hydrolysis, are combined to make cellulose with a lowered degree of polymerization, a lowered degree of crystallinity and the combination of both. These samples are used to gain insight into the effect of these parameters on the enzyme accessibility of cellulose and its fermentability by colon microbiota, using batch in vitro fermentations.
## 2.1. Materials
Microcrystalline cellulose (Avicel PH-101, $3.4\%$ moisture), citric acid (analytical grade), the Cellic CTec2 cellulase enzyme blend and all other analytical chemicals and solvents were purchased from Sigma-Aldrich (Deurne, Belgium).
## 2.2. Production of Dietary Fiber Samples Starting from Microcrystalline Cellulose
An overview of the production of the different dietary fiber samples is given in Figure 1 and Table A1. Microcrystalline cellulose (MC) was first depolymerized using a ball milling step and acid hydrolysis, similar to our previous work [29]. MC was pretreated in a planetary ball mill (PM100, Retsch GmbH, Haan, Germany) in batches of 20 g with 6 zirconium oxide balls (Ø 10 mm) to induce para-crystalline zones in the cellulose fiber. These ball-milled cellulose fibers are called amorphized cellulose (AC). Milling time (60–360 min) and speed (400–500 rpm) were varied. Afterwards, the paracrystalline zones in the AC fibers were hydrolyzed with a $10\%$ citric acid solution in water. Hydrolysis time (2–6 h) and temperature (90–130 °C) were varied (Table A1). The depolymerized insoluble cellulose samples were washed until neutral pH and dried for 45 h at 60 °C, yielding depolymerized cellulose (DC). After being dried, the DC sample was again treated in the planetary ball mill with 6 zirconium oxide balls (Ø 10 mm) at 500 rpm to produce amorphized depolymerized cellulose (ADC). Treatment times of 30, 60 and 360 min were used. All samples and respective production parameters are summarized in Table A1.
## 2.3. Characterization of Dietary Fiber Samples
The average degree of polymerization (avDP) of cellulose was determined viscometrically in triplicate, based on the method of the French Institute for Normalisation [30]. Fiber samples (0.075 g) were dissolved in a 0.5 M bis(ethylenediamine)copper(II)hydroxide solution (15 mL), and the viscosity of this solution at 25 °C was measured with a capillary viscometer, type nr. 509 04 (Schott Geräte, Jena, Germany). The avDP was calculated from the boundary viscosity of the solution (η), based on the empirical relation: Average DP^α = η/K, with α and K empirical constants, equal to 1 and 7.5 × 10−3, respectively. The boundary viscosity η was determined from ηa = η. C.10^((0,14.η. C)), with ηa the specific viscosity of the solution, and C the cellulose concentration (g/mL).
The crystallinity of the fiber samples was determined by X-ray powder diffraction (XRD) on a high-throughput STOE STADI P Combi diffractometer (STOE & Cie GmbH, Darmstadt, Germany) in transition mode with Ge[111] monochromatic X-ray inlet beams (λ = 1.5406 Å, Cu Kα source). Crystallinity indexes were determined by the peak-height method of Segal and coworkers [31].
## 2.4. Enzymatic Digestibility Analysis
The enzymatic digestibility of the dietary fiber samples was determined by calculating the enzymatic conversion (EC) after incubating samples with the Cellic CTec2 cellulase enzyme blend, as described by Chen and coworkers [32]. Cellulose was suspended ($1.0\%$ w/v) in a 50 mM sodium acetate buffer (pH 4.8) with 20 U Cellic CTec2 cellulase enzyme blend per gram cellulose and stirred at 900 rpm. After 1 h of incubation at 40 °C, the enzymes were denatured by heating the solution (5 min, 110 °C). The solid fraction was separated from the supernatant by centrifuging at 5000 g. The amount of glucose and cellobiose in the supernatant from cellulose hydrolysis was determined by high-performance-anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) on a Dionex ICS3000 chromatography system (Sunnyvale, CA, USA). Saccharides were separated on a Dionex CarboPac PA-100 column (4 × 250 mm), equilibrated with 90 mM NaOH. The enzymatic conversion was calculated from the amount of glucose (mg) and cellobiose (mcb) in the supernatant, and the amount of starting substrate (mc):[1]EC=mg+mcb1.1mc
## 2.5. In Vitro Fermentation of Dietary Fiber Samples Using Human Fecal Inoculum
In vitro fermentation experiments (trial 1, 2 and 3) were performed as described by De Preter et al. [ 33]. Fresh fecal samples of 8 healthy donors (consuming a mixed western diet, no history of antibiotic use in the last six months) were collected and pooled to make a 10 w/v% fecal slurry in phosphate-buffered saline. After intensive shaking, this fecal slurry was decanted, and the supernatant (referred to as the inoculum) was added to different fiber samples (25 mL to 100 mg cellulose) in triplicate. After being flushed with nitrogen gas, the tubes were incubated anaerobically for 48 h in a shaking water bath at 37 °C. At the end of incubation, the pH of the slurry was measured with a digital pH meter (Hanna Instruments HI 9025, Temse, Belgium). Aliquots were stored at −20 °C for the determination of short-chain fatty acid (SCFA) concentration and microbial analysis.
## 2.6. Short-Chain Fatty Acid Analysis
The amounts of acetate, propionate and butyrate in the fecal inoculum were determined according to the gas-chromatographic method described by Bautil et al. [ 34]. In this procedure, a $25\%$ (w/v) NaOH solution was added to the inoculum to create sodium salts of the SCFA, which were neutralized by adding a $50\%$ sulfuric acid solution afterwards. These salts were extracted to a diethyl ether phase, which was analyzed with an Agilent 6890 Series gas chromatograph with an EC-1000 Econo-Cap column (25 m × 0.53 mm, 130 °C, 1.2 μm film thickness) and helium (20 mL/min) as carrier gas. A flame ionization detector at 195 °C measured the different fatty acids. Within this analysis, 2-ethyl butyric acid was used as an internal standard.
## 2.7. Microbial Analysis
Microbial profiling was done as described by Falony et al. [ 35]. Nucleic acids were extracted from the aliquots using the RNeasy PowerMicrobiome kit (Qiagen, Venlo, The Netherlands). The manufacturer’s protocol was modified by adding a heating step at 90°C for 10 min and excluding DNA removal steps. Afterwards, the extracted DNA was amplified in triplicate using 16S primers 515F (59-GTGYCAGCMGCCGCGGTAA-39) and 806R (59-GGACTACNVGGGTWTCTAAT-39) targeting the V4 region. Deep sequencing was performed on a MiSeq platform (2-by-250 paired-end [PE] reads; Illumina, San Diego, CA, USA). Initial quality assessment, sequence filtering and trimming of the FASTQ files were carried out using the FASTQC software (version 0.11.9) and the ‘filterAndTrim’ function of the DADA2 algorithm pipeline package. Analysis thereafter was performed using the ‘mergePairs’ function of the DADA2 package, which merges the forward and reverse sequences. Any chimeric sequences which were produced during aberrant PCR annealing were identified and removed. Taxonomy was assigned to the sequences using a naïve Bayesian classifier method with the SILVA database (version 138.1) as a reference.
## 2.8. Statistics
Significant differences were detected by performing a one-way analysis of variance (ANOVA) using JMP Pro 16 (SAS institute), with a comparison of the mean values using the Tukey test (α < 0.05).
## 3.1. Production of Samples with Different DP and Crystallinity from Microcrystalline Cellulose
To investigate the impact of crystallinity and avDP on the enzymatic accessibility and fermentability by colon microbiota, a modification protocol using the combination of planetary ball mill treatments and acid hydrolysis was used (Figure 1). First, MC was treated in a ball mill to decrease the crystallinity of the cellulose by incorporation of paracrystalline zones. This decrease in crystallinity impacts the levelling-off degree of polymerization (LODP) of the cellulose, which represents the length of crystalline polymers that remain insoluble after a fast hydrolysis of the easily accessible paracrystalline zones [36]. Second, the ball-milled, or amorphized cellulose (AC) was hydrolyzed with citric acid at elevated temperature (90–130 °C) to hydrolyse the polymers in the paracrystalline zones. After this hydrolysis, depolymerized cellulose (DC) with a decreased avDP is obtained. At last, this DC was treated in the planetary ball mill another time for 30–360 min to produce amorphized depolymerized cellulose (ADC), which is expected to be highly accessible. All samples are listed in Table A1.
The influence of processing conditions (ball mill speed/time and acid hydrolysis time/temperature) on cellulose properties was extensively investigated in our previous study for the production of DC [29]. The avDP of the DC fibers can be finely tuned, and also the crystallinity of the DC fibers can be controlled by applying varying process parameters. In short, when the acid hydrolysis is not performed for long enough to hydrolyse all the paracrystalline zones of the AC fibers, the LODP will not be reached and the crystallinity of the resulting DC fibers will remain low [29]. Despite the extensive investigation of the impact of process parameters to produce DC, the impact of the second ball mill treatment on this DC was not yet investigated.
For a sample with relatively high crystallinity (DC with avDP of 32 AGU and crystallinity index of 0.62), the effect of this additional ball mill treatment on avDP and crystallinity is shown in Figure 2. Figure 2a shows that the peaks from crystalline planes in the refractogram of the DC fibers indeed disappeared due to the ball mill treatment. Milling a DC for only 15 min already disrupted most of the crystalline structure, but the crystalline reflection at 2θ of 22° was still more prominent in these refractograms than in those of ADC fibers with longer milling times. After 30 min of milling of the DC at 500 rpm, an amorphous refractogram was detected, of which the shape did not change anymore upon longer milling times.
Previously, it was shown that the crystallinity decrease during ball milling of unmodified MC was limited during the first 30 min of the milling process [29]. The breakdown of crystallites, therefore, occurred more slowly for unmodified MC than for this DC (Figure 2a). This faster decrease in crystallinity for the DC might be due to the different type of crystallites that need to be broken down. As visualized in Figure A1, $32\%$ of the crystallites in DC fibers were cellulose II polymorphs, while it is known that no cellulose II is present in unmodified MC [37]. We can hypothesize that cellulose II crystallites, formed during the first ball mill treatment and hydrolysis [38], are easier to decrystallize than cellulose I crystallites. The faster decrystallization of DC can also be caused by the lower avDP of the DC fibers.
Depolymerization of the DC fibers does not seem to occur during the ball mill treatment since no significant decrease in avDP was detected for the different ADC fibers (Figure 2b). Previous research stated that a ball mill treatment could not depolymerise cellulose shorter than 50 AGU [29]. This theory seems to be confirmed here since no depolymerization of the DC fibers (DP 32) occurred.
## 3.2. Influence of Cellulose Structural Properties on Enzymatic Accessibility
Figure 3 shows the enzymatic conversion of the modified cellulose into glucose or cellobiose after 1 h reaction with the commercial Cellic CTec2 enzyme blend under optimal conditions. Unmodified MC is compared with amorphized MC (AC124), DC and ADC with different avDP (Table A1). Only $30\%$ of the long crystalline MC was converted into glucose and cellobiose by the cellulase blend within one hour (conversion degree of 0.30 ± 0.04). Decreasing the crystallinity by ball milling (260 min) improved the conversion degree slightly to 0.35 ± 0.01, but decreasing the avDP had the opposite effect. Surprisingly, the DC was all less accessible for the enzyme blend than unmodified MC or AC, while Kumar and Wyman showed that a shorter DP results in higher accessibility [39]. We can hypothesize that a decrease in avDP from 168 to 28 AGU is not sufficient to compensate for the removal of para-crystalline zones and the presence of cellulose polymorph II in the DC fibers, two structural properties that lower enzymatic accessibility. This hypothesis can be confirmed by the positive association between avDP and enzymatic digestibility of the different DC samples. These various DC samples also slightly differed in crystallinity: the crystallinity of DC104 was lower than the crystallinity of DC28, since the LODP was not reached for the longer DC fibers (Table A1). This is because the mildest hydrolysis conditions were used for making DC104, resulting in the remaining of some easily accessible paracrystalline zones after drying. It seems that these small differences in crystallinity have a more significant impact on enzymatic conversion than the differences in avDP.
Since the DC samples showed lower enzymatic digestibility for the cellulase blend than MC or AC, it can be concluded that a DP decrease to values lower than 100 AGU is not of interest to increase the enzymatic accessibility of cellulose. However, this DP decrease pays off once the short cellulose is made amorphous again in the ball mill. ADC with an avDP of 28 AGU had a conversion degree after 1 h of 0.52 ± 0.07, higher than the AC sample. Furthermore, there seems to be a negative correlation between the avDP and enzymatic digestibility for these amorphous samples. Even within the small DP range of 20 to 110 AGU, shortening the cellulose avDP enhances its enzymatic digestibility once a low crystallinity is assured.
## 3.3. Effect of Enhanced Accessibility of Cellulose on Fermentation in the Human Colon
A correlation between the enzymatic accessibility of cellulose samples for the Cellic CTec2 enzyme blend and the fermentability by colon microbiota can be expected since the fermentation of complex carbohydrates starts with hydrolysis by excreted microbial hydrolytic enzymes as well [40]. The behaviour of the fiber samples in the human large intestine was evaluated in three independent batch fermentation experiments using fecal inocula. In Figure 4, the production of linear SCFA and the pH evolution during each fermentation experiment are shown. In these experiments, MC, AC, DC and ADC with different avDP were added to the fecal inocula (Table A1).
In trial one (Figure 4a,b), only a limited amount of linear SCFA was produced in the fecal inoculum without cellulose addition (blank) during the incubation time of 24 h. Adding dietary fiber samples to the inoculum, however, resulted in enhanced production of SCFA during incubation. The majority of SCFA was only produced after the first 8 h of incubation had passed. As described by Mikkelsen et al., cellulose fermentation in a batch in vitro system is slow compared to other readily fermentable carbohydrates, such as arabinoxylans and glucans [14]. In this secondary fermentation phase, it became clear that only a limited amount of MC was fermented within 24 h. During the incubation of MC, the linear SCFA concentration only increased from 10.83 ± 2.63 mmol/L to 19.99 ± 0.71 mmol/L. Breaking cellulose crystallinity by ball milling increased the fermentability already slightly. The average SCFA production after 24 h from the AC sample was 0.57 times higher than the SCFA production from MC. Decreasing the avDP of cellulose was a more effective way to improve the accessibility of cellulose for the gut microbiota: the linear SCFA concentration produced by fermentation of DC with DP 59 AGU and 32 AGU was 2.6 and 1.8 times higher compared to unmodified MC. Contrary to the breakdown by the CTec2 enzyme blend, the microbiota in this pooled inoculum could access the DC better than the AC. Furthermore, the slightly lower crystallinity of DC59 resulted in a slightly higher fermentation degree for DC59 than for DC32. The highest SCFA production, however, was obtained upon the addition of ADC to the fecal pool, with a linear SCFA concentration of 41.5 ± 6.4 mmol/L at the end of incubation. By reducing both the degree of polymerization and crystallinity of MC, the formation of linear SCFA by fermentation could be multiplied by a factor of 4.2. Based on the difference in mass of linear SCFA between the blank and ADC-enriched inoculum at 48 h, a minimal degree of fermentation (MDOF) of 45.8 ± $10.9\%$ could be derived for the ADC25 sample, while this was only 7.6 ± $0.9\%$ for MC. This MDOF is an underestimation of the actual fermentability since it only takes into account the mass of linear SCFA as a fermentation product. Furthermore, adding ADC to the fecal pool resulted in the largest pH drop, from 6.57 ± 0.01 to 5.67 ± 0.08 (Figure 4b). In vivo, such a pH drop could be associated with different physiological benefits, such as the repression of pathogen growth and proteolytic fermentation [9].
In a second in vitro fermentation experiment, two different chain lengths of ADC were investigated (Figure 4c,d). Additionally, the ball mill posttreatment time for the ADC was reduced to 1 h, instead of 6 h. Although a different pool of human feces was used, the same trends could be observed for the fermentability of these modified celluloses: unmodified MC was only fermented to a minimal extent, while decreasing the DP of the cellulose resulted in higher production of linear SCFA from the cellulose, up to a factor of 5.4 for DC32 after 48 h. The highest linear SCFA production was found for ADC samples ADC27 and ADC37 (8.2 and 8.4 times higher than for MC, respectively). The small difference in avDP, 37 versus 27, did not induce a significant difference in the fermentability of the ADC sample. The ADC samples were fermented to at least 42.6 ± $3.6\%$, while the MDOF of MC was only 5.7 ± $0.2\%$. The enhanced fermentation resulted in a larger pH drop of the ADC37-enriched inoculum (pH 5.58) than the MC-enriched inoculum (pH 6.02) (Figure 4d). Furthermore, it was demonstrated in this trial that this decreased pH resulted in a lowered production of branched SCFA as well (Figure A2). MC addition reduced the relative amount of branched SCFA from $8.5\%$ to $7.9\%$, but the addition of ADC caused a further decrease to $6.0\%$. This is the first indication of a lowered protein fermentation in the inocula.
Detailed analysis of the acetate, butyrate and propionate concentrations demonstrate that the relative amounts of butyrate and propionate also increased after 48 h upon the addition of ADC. The relative amount of butyrate in total linear SCFA was $13.1\%$ for the blank fecal slurry, while this was $17.6\%$ for the fecal slurry with ADC37 addition (Figure A2). This enhanced butyrate production suggests an additional physiological benefit since enhanced butyrate production is linked to a lower risk of colon inflammation and cancer [41].
In this second trial, DC and ADC samples were mainly fermented between 24 and 48 h, while the main cellulose fermentation happened between 8 and 24 h in the first trial. The presence or absence of easily accessible fibers in the starting inoculum of the trials partly explains this difference. This was hypothesized since a fast production of linear SCFA after 4 h was observed in the second trial, while it was absent in the first trial. Therefore, the microbial community in the inoculum needed more time in this trial to switch to cellulolytic fermentation metabolism than in the absence of other (more easily fermentable) carbohydrate fibers in the first trial.
A third trial (Figure 4e,f) showed a different fermentability behaviour for the ADC. No significant differences compared to the fermentability of unmodified MC were observed, and the pH decrease during the experiment was very limited for both the MC- and ADC-enriched inocula. Next to this trial, two other repetitions showed similar behaviour with no cellulose fermentation occurring for MC or ADC (data not shown). The starting microbial composition of the inoculum within every trial is different, of course, since other donors were used for the experiments. In Figure 5, the composition of the microbiome of the three in vitro trials is given at the genus level at the starting point of the experiment and after incubation with ADC.
For in vitro fermentation trials 1 and 2, the microbiome composition was dominated by Bifidobacterium and Blautia species. The proportion of Bifidobacterium species was lower for the first trial than for the second. Surprisingly, these Bifidobacteria seemed to dominate the cellulose fermentation in this first trial. After 24 h, when the ADC fermentation had already taken place, the DNA proportion from Bifidobacterium species increased from $10.7\%$ to $37.0\%$, while other genera seemed to be suppressed. The fermentation of ADC might be driven by Bifidobacteria, but this enrichment can also be the result of the presence of glucose, which is released from cellulose by others. In the second trial, a different evolution of the microbial community was observed. While the microbial community was also enriched in Bifidobacteria at 24 h (when the ADC was not fermented yet), Ruminococcus species took the upper hand between 24 h and 48 h, which is the period that the ADC fermentation took place.
The microbiota in the third trial did not switch to a cellulolytic metabolism within the given time frame of 48 h. The microbial composition of the starting pool of this experiment was clearly less dominated by the Bifidobacterium and Blautia species than the ones from Trial 1 and Trial 2. Furthermore, during the experiment, Bacteroides dominated the medium instead of Bifidobacterium or Ruminococcus. Consequently, we can hypothesize that the switch to cellulose fermentation occurs only if specific specialized microorganisms are present, and the composition in the pool allows them to take the upper hand. Based on Trial 1 and 2, the authors hypothesize that this switch depends on the presence and abundance of specific Ruminococcus or Bifidobacterium species, but further research is needed to confirm this statement. Despite the comparable starting microbial community of those two trials, the fermentation of both ADC samples caused an enrichment of different microorganisms, demonstrating the complexity of this fermentation process.
## 4. Conclusions
The combination of ball milling with acid hydrolysis was demonstrated to be a valuable strategy for increasing the enzymatic accessibility of microcrystalline cellulose since it can selectively decrease the avDP and crystallinity of cellulose simultaneously. These modifications effectively resulted in an enhanced digestibility by a commercially available cellulase blend. Within an avDP range of 20–110 AGU, the avDP impacted the hydrolysability by this enzyme blend, once a low crystallinity was ensured. Furthermore, the enhanced accessibility of such amorphized depolymerized cellulose resulted in a higher fermentation degree compared to unmodified cellulose upon incubation with a pooled fecal inoculum from human subjects. With this modification, the minimal degree of fermentability of cellulose (based on the mass of SCFA produced from cellulose) within 48 h could be enhanced from $5\%$ to $45\%$. This could be observed in two independent studies. However, other efforts did not show this enhanced cellulose fermentation. Microbial analyses of the fecal inocula revealed the complexity of cellulose fermentation in batch systems. Performing a detailed analysis of the cellulose fermentation metabolism in the human colon is, therefore, key to fully revealing the effect of DP and crystallinity of cellulose on fermentation in batch conditions. Until this is investigated, the authors would like to stress that the interpretation of in vitro fermentation results always has to be performed with caution, and total characterization of the microbial pool is always encouraged. However, we can conclude that engineering the properties of cellulose to high accessibility can improve the fermentation in the colon as well, be it under specific circumstances. With this work, the first step is taken towards a highly functional cellulose-type dietary fiber additive.
## 5. Patents
The use of amorphized depolymerized cellulose as partially fermentable dietary fiber is patented in EP$\frac{2022}{0784403}$ (not published).
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|
---
title: Antioxidant Capacity, Nitrite and Nitrate Content in Beetroot-Based Dietary
Supplements
authors:
- Joanna Brzezińska-Rojek
- Svitlana Sagatovych
- Paulina Malinowska
- Kamila Gadaj
- Magdalena Prokopowicz
- Małgorzata Grembecka
journal: Foods
year: 2023
pmcid: PMC10000616
doi: 10.3390/foods12051017
license: CC BY 4.0
---
# Antioxidant Capacity, Nitrite and Nitrate Content in Beetroot-Based Dietary Supplements
## Abstract
Due to the high content of bioactive substances, beetroot and its preserves might be a valuable constituent of a diet. Research into the antioxidant capacity and content of nitrate (III) and (V) in beetroot-based dietary supplements (DSs) worldwide is limited. The Folin–Ciocalteu method, CUPRAC, DPPH, and Griess methods were used to determine total antioxidant capacity, total phenolic content, nitrites, and nitrates content in fifty DSs and twenty beetroot samples. Moreover, the safety of products was evaluated because of the concentration of nitrites, nitrates, and the correctness of labelling. The research showed that a serving of fresh beetroot provides significantly more antioxidants, nitrites, and nitrates than most daily portions of DSs. Product P9 provided the highest dose of nitrates (169 mg/daily dose). However, in most cases, the consumption of DSs would be associated with a low health value. The acceptable daily intake was not exceeded in the cases of nitrites (0.0015–$0.55\%$) and nitrates (0.056–$48\%$), assuming that the supplementation followed the manufacturer’s recommendation. According to European and Polish regulations, $64\%$ of the products tested did not meet all the requirements for labelling food packaging. The findings point to the need for tighter regulation of DSs, as their consumption might be dangerous.
## 1. Introduction
Beetroot (*Beta vulgaris* L.) is a rich source of nutrients and bioactive substances such as fibre, carbohydrates, and phenolic compounds. In addition, this vegetable contains macro- and microelements such as potassium, iron, calcium, copper, sodium, and zinc, as well as vitamins B1, B2, B3, B6, biotin, and B12. Red beetroot owes its characteristic intense colour to betalain pigments-betacyanins: betanin (the dominant pigment), isobetanin, betanidin, isobetanidin, vulgaxanthin I and II, and indixanthin [1,2]. Beetroot peel has the highest betanin content. A correlation between antioxidant activity and the content of betacyanins has been found [3]. Betacyanins, along with phenolic acids, flavonoids, and ascorbic acid, are responsible for the antioxidant properties of beetroot. Furthermore, this vegetable is rich in nitrites and nitrates [4]. The oral bioavailability of nitrates from plants is $100\%$ [5].
Beetroots might lead to several health-promoting effects, such as a stimulating effect on the circulatory and immune systems; improving the functioning of the endothelium; regulating the level of blood pressure; protecting the liver, the intestines, and the kidneys against toxic compounds; protecting against radiation consequences; and strengthening the gastric mucosa [4,6,7,8]. Due to these effects, the consumption of beetroot products may be beneficial in the cases of diabetes [9,10,11], post-menopausal women [11], diseases of the cardiovascular system [12,13], and athletes’ support [14,15,16]. Moreover, beetroot products with an appropriate concentration of inorganic nitrites can be an effective ergogenic agent, acting faster than a product containing only nitrate salts [17].
An excessive supply of nitrates may pose a health risk; therefore, in the interest of the health of consumers, a maximum acceptable daily intake (ADI) has been established that does not harm health when consumed throughout life. For nitrates, it is 0–5 mg/kg b.w. NO3¯ ions (corresponding to 0–3.7 mg NaNO3), while for nitrites, it is 0–0.2 mg/kg b.w. NO2¯ ions (corresponding to 0–0.07 mg of NaNO2) [18,19]. The main source of nitrates in the diet is vegetables. It is estimated that they provide 80–$85\%$ of the nitrates consumed. The supply of these compounds in drinking water, meat, or processed foods is much less important [5]. The total amount of nitrates consumed from all sources should be monitored, as there is a risk of exceeding the ADI, especially in children who have a lower body weight. Poisoning with nitrites may lead to methemoglobinemia or the development of neoplasms due to the formation of N-nitrosamines, which are carcinogenic. N-nitrosamines can be formed in the acidic environment of the stomach from nitrites in their reaction with secondary and tertiary amines [5].
Beetroot is consumed in various forms, such as fresh vegetables, juice, pickles, chips, and gel [20]. Dietary supplements containing *Beta vulgaris* L., manufactured in the form of tablets, lozenges, capsules, juices, powder, and many others, are also popular. However, producers often do not standardise products, which casts doubt on their effectiveness. Consequently, several potential risks for consumers appear, such as exposure to an excessive supply of nitrates or nitrites and loading the body with a product without health-promoting properties due to the lack of data on effectiveness compared to a fresh vegetable or the content of bioactive substances. A potential risk is also associated with the mislabelling of finished products.
Total antioxidant capacity (TAC) describes the antioxidant properties of a complex material (such as beetroot and beetroot preserves) consisting of numerous compounds. It is not just the sum of the antioxidant capacities of individual bioactive compounds. The TAC is the result of the synergistic effects of the different bioactive substances, trace elements, metals, vitamins, and other food constituents [21,22]. It was decided to determine the TAC instead of the concentrations of individual antioxidant substances because both the DSs and the vegetables are complex matrices, and their biological effect will be the result of the interaction of various components.
The research aimed to assess the quality and safety of beetroot-based DSs in comparison with beetroot samples. The TAC, total phenolic content (TPC), nitrites, and nitrate contents of fifty beetroot-based DSs (in the form of tablets, capsules, and powders) and twenty samples of beetroots available on the Polish market were determined for this purpose. Vegetables were divided into three subgroups: peeled, unpeeled, and skins. Reference was made to the average values for conventional and organic beetroots to compare DSs with vegetables. Manufacturers usually do not provide information on how the beetroot has been processed before manufacturing beetroot-based DSs. On several products, there was information that whole beetroot was used, which is why we also included vegetables with skins in our analysis. DSs are concentrated forms, so they can potentially provide a significant amount of bioactive substances, especially antioxidants and nitrogen compounds. As a result, the DS results were compared to vegetables to determine which are better sources of antioxidants, nitrites, and nitrates. The health risk was assessed because of the realisation of ADI for nitrites and nitrates. Furthermore, the correctness of the labelling of finished products was assessed based on Polish and European regulations because misinformation might also be dangerous for consumers. Statistical analyses were applied to verify the potential correlation between different methods of antioxidant capacity assessment and between TAC and nitrate and nitrite content. Moreover, it was assessed whether the content of antioxidants, nitrites, and nitrates differed statistically significantly in individual subgroups of beetroots. Despite the growing popularity of beetroot supplements, there is a lack of research on the quality and safety of their use.
## 2.1.1. Sample Preparation
The process of collection and sample preparation has been shown in Figure 1. Tables S1 and S2 provided in-depth details about beetroots and dietary supplements (DSs), respectively. Beetroot samples were lyophilized in an Alpha 1–4 LD plus freeze dryer (Christ, Osterode am Harz, Germany). Every DS was signed according to the alphanumeric code, including the formulation and the sequence number. Moreover, the letters (A, B, and C) were used to mark the same DSs with other serial numbers. To be more specific, ten of the examined products did not meet the requirements for labelling the category of dietary supplements, but they were tentatively included in the group of DSs in the following section of the work. At the purchase stage, they were described by sellers as “dietary supplements”. Only the verification of the labelling showed that they do not legally belong to this group, they are just traditional food products. They were marked in orange in Table S2. The exclusion criteria for beetroot-based DSs were: other forms of products (such as juice, shot, gel and bar); beetroot was not a main ingredient but an auxiliary substance; and unavailability for Polish consumers in the mentioned time period.
Only ceramic tools were used for sample preparation. In total, seventy samples of DSs and vegetables were analysed in triplicate.
## 2.1.2. Reagents and Standards
Reagents for the Folin–Ciocalteu assay were as follows: anhydrous sodium carbonate (purity > $99.5\%$, Chempur®, Piekary Slaskie, Poland), Folin–Ciocalteu reagent (analytical grade, Chempur®, Piekary Slaskie, Poland), gallic acid (Sigma-Aldrich®, Darmstadt, Germany). Reagents for the CUPRAC assay were as follows: ethanol $96\%$ (LiChrosolv®, Darmstadt, Germany), 6-Hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Sigma-Aldrich®, Buchs, Switzerland), copper (II) chloride (Sigma-Aldrich®, St. Louis, MO, USA), ammonium acetate (Sigma-Aldrich®, Darmstadt, Germany), neocuproine (Sigma-Aldrich®, Darmstadt, Germany). Reagents for the DPPH assay were as follows: methanol (LiChrosolv®, Darmstadt, Germany), 2,2-diphenyl-1-picrylhydrazyl (Sigma-Aldrich®, Darmstadt, Germany).
Reagents for the nitrites and nitrates determination were as follows: hydrochloric acid 35–$38\%$ (Chempur®, Piekary Slaskie, Poland), acetic acid min. $99.5\%$ (Chempur®, Piekary Slaskie, Poland), sodium nitrite (Chempur®, Piekary Slaskie, Poland), sodium tetraborate (POCH, Gliwice, Poland), sulfanilamide (Sigma-Aldrich®, Darmstadt, Germany), N-naphthylethylenediamine dihydrochloride (Sigma-Aldrich®, St. Louis, MO, USA), zinc acetate dihydrate (POCH, Gliwice, Poland), Carrez solution I—Potassium hexacyanoferrate (II) 0.25 mol/L (aqueous solution) (VWR, Leuven, Belgium), ammonium buffer pH 9.6 (obtained from ammonia (POCH, Gliwice, Poland) and hydrochloric acid $37\%$ (VWR, Leuven, Belgium), cadmium sulphate (VI) (Chempur®, Piekary Slaskie, Poland), zinc sticks ≥$99.99\%$ (Merck Millipore, Darmstadt, Germany).
Ultrapure water (18.2 MΩ·cm, Millipore Simplicity System, Billerica, MA, USA) was used for all aqueous solutions.
## 2.2. The Total Antioxidant Capacity (TAC) and Total Phenolic Content (TPC)
The assessment of the TAC was carried out using the CUPRAC and DPPH assays. Moreover, the Folin–Ciocalteu (FC) method was applied to determine TPC.
## 2.2.1. Extract Preparation for TAC and TPC Determination
Based on the conditions described by Capanoglu et al. [ 23] and other literature reports on the extraction of beetroot products [24,25,26,27,28], optimisation of the extraction was performed using seven combinations of solvents in three variants of extraction (Figure 2). Each variant was examined in triplicate with a threefold measurement. Variant C was chosen as the most optimal (based on results of ANOVA test): two-stage ultrasound-assisted extraction with $50\%$ MeOH + $0.1\%$ FA (marked in yellow in Figure 2; $p \leq 0.05$). Table S3 summarises the obtained results. The extraction was carried out on the selected lyophilizate; therefore, the results are expressed in mg GAE/g of lyophilizate.
Figure 3 depicts the extraction procedure. Each sample was examined in triplicate with a threefold measurement. Lyophilizates and DSs were homogenized in a mortar straight before analyses. The samples were kept in the freezer all of the time and were thawed at room temperature before starting the individual analyses.
## 2.2.2. TPC Determination
The total phenolic content (TPC) in the extracted samples was determined using the Folin-Ciocalteau reagent (FCR), according to the optimised and validated method developed by the authors based on literature research [20,29,30,31,32,33,34]. The mutual ratio of the reagents used (FCR and Na2CO3) and the incubation time before the measurement were optimised according to the scheme shown in Figure 2. Each variant was examined in triplicate with a threefold measurement. The obtained results are summarised in Tables S4 and S5. Model 1 was found to be the most efficient (ANOVA; $p \leq 0.05$), thus, 5 mL of FCR was mixed with 10 mL of Na2CO3, and 30 min of incubation was applied. It was assumed that the composition of supplements in the form of tablets may differ the most from pure lyophilizate due to the presence of auxiliary substances enabling the tabletting process; thus, the time of incubation was also optimised for the product in a tablet.
Sample extracts (x mL) and Mili-Q water ((1.0 − x) mL) were placed in a centrifuge tube to have a volume of 1 mL. Next, 5 mL of FCR was added, the sample was mixed, and it was left for 3 min. Then, 10 mL of saturated sodium carbonate solution (150 g/L) was added. The test tubes were carefully blended after the addition of each reagent using a vortex (Lab dancer, VWR®, Gdansk, Poland). The absorbance was measured threefold at 760 nm (Genesys 10S, Thermo Fisher Scientific, Waltham, MA, USA) after incubation (30 min at room temperature without light). Results were expressed in gallic acid equivalents (mg GAE/g of product and mg GAE/daily dose of product for DSs or mg GAE/g dry weight (d.w.) and mg GAE/100 g fresh weight (f.w.) of beetroot).
Analogously, a calibration curve was prepared to range from 0.1 to 10 µg/mL. The calibration curve was made in three independent replications with threefold measurements.
## 2.2.3. CUPRAC
The CUPRAC assay was carried out as described by Apak et al. [ 35]. Analogously, a calibration curve was prepared in the range of 0.0005 to 0.07 uM/mL of Trolox. Three independent replications with threefold measurements were used to create the calibration curve. Results were expressed in Trolox acid equivalents (TE) (µmol TE/g of product and µmol TE/daily dose of the product, or µmol TE/g d.w. and µmol TE/100 g f.w. of beetroot). The incubation time of the samples was previously optimised. Results after 30 min and 60 min of incubation did not differ significantly, so the first one was applied.
## 2.2.4. DPPH
The DPPH assay was carried out as described by Ravichandran et al. [ 36]. The absorbance of the sample was measured threefold at 515 nm (Genesys 10S, Thermo Fisher Scientific, Waltham, MA, USA) after incubation (30 min at room temperature). Results were expressed as a percentage of the antioxidant activity, which was calculated as follows:[1]Activity (%)=Ac − AsAc×$100\%$ Ac—absorbance of control; As—absorbance of a sample.
## 2.3. The Nitrite and Nitrate Determination
Quantification of nitrites and nitrates in beetroot (*Beta vulgaris* L.) and beetroot products was carried out by spectrophotometry using Griess reagents I, II and III according to ISO 6635-1984 (E) [37].
## 2.3.1. Extraction for Nitrites and Nitrates Determination
A total of 1.0 to 10 g of the test sample were weighed, according to the expected nitrite content. Then, 3.0 g of activated carbon, 5 mL of disodium tetraborate solution, and 100 mL of hot, purified water were added to each sample. The flasks were shaken for 15 min at 80 °C. Next, 2 mL of potassium hexacyanoferrate (II) and 2 mL of zinc acetate solution were added to the samples. The solutions, after cooling to room temperature, were transferred to 200 mL volumetric flasks, made up to the mark, and shaken. Finally, solutions were filtered into conical flasks through paper filters.
## 2.3.2. Nitrites Determination
At least 10 mL of solution was transferred to the 50 mL volumetric flask and diluted into 30 mL with purified water. Then, 5 mL of solution I (sulfanilamide dissolved in water with hydrochloric acid) and 3 mL of solution III (hydrochloric acid) were added. The content of the flask was thoroughly mixed and left for 1 min at ambient temperature, protected from light. Next, 1 mL of solution II ($0.1\%$ solution of N-(1-naphthyl)ethylenediamine dihydrochloride) was added, mixed carefully, and left for 3 min at ambient temperature, protected from light. After making up to the mark with water, the solution was mixed. The absorbance at a wavelength of 538 nm was measured within 15 min using the spectrometer. Results were expressed as µg/g of NO2¯ and µg/daily dose of NO2¯ or µg/g d.w. of NO2¯and µg/100 g f.w. of NO2¯ of beetroot, which is calculated as follows:[2](NO2−)=m1×200V1×m0 m0—the mass, in grams, of the test portion; m1—the mass, in micrograms, of nitrite ion (NO2¯) contained in the aliquot portion (V1) of filtrate taken, read from the calibration graph; V1—the volume, in millilitres, of the aliquot portion of filtrate taken.
Analogously, a calibration curve was prepared to range from 0.0 to 0.06 µg/mL of nitrites. The calibration curve was made in three independent replications with threefold measurements.
## 2.3.3. Nitrates Determination
About 2 g of the cadmium and 5 mL of the buffer solution, and an aliquot portion of the filtrate (10 mL or less) were placed in a 25 mL conical flask. The flask was agitated for 5 min. Next, the solution was filtered into a 50 mL one-mark volumetric flask and made up to the mark. The determination proceeded analogously to total nitrites (Section 2.3.2) using 10 mL of the test solution. Results of nitrate determination were expressed as µg/g of NO3¯ and µg/daily dose of NO3¯ or µg/g d.w. of NO3¯and µg/100 g f.w. of NO3¯ of beetroot, which was calculated as follows:[3](NO3−)=1.348(m2×10 000V3×V2×m0−m1×200V1×m0) m2—the total mass of nitrite, in micrograms of nitrite ion (NO2¯), contained in the volume (V2) of test solution taken, read from the calibration graph; V2—the volume, in millilitres, of the test solution taken for the spectrometric measurement; V3—the volume, in millilitres, of the aliquot portion of the filtrate taken for the preparation of the test solution;
m0, m1, V1—have the same meanings as in Equation [2].
The ratio between the relative molecular masses of the nitrate ion (NO3¯) and nitrite ion (NO2¯) is 1.348.
## 2.4. Validation
The following validation parameters were determined for all methods: linearity range, precision, accuracy, the limit of determination (LOD), and the limit of quantification (LOQ). The LOD and LOQ were computed as described by Huber [38]: [4]LOD=3.3SDab SDa—standard deviation of the intercept for the calibration curve; b—slope for the calibration curve. [ 5]LOD=3×LOD Table 1 shows the results of the validation. Due to the lack of reference material corresponding to the analysed material, accuracy was determined using the method of standard addition (GA in the FC assay and Trolox in the CUPRAC assay) to the chosen DS and lyophilizate and was expressed as recovery. DPPH assay was validated based on gallic acid standard solutions. The average recovery for the selected methods was in the range of 80–$120\%$ which was an acceptable level for such an analysis. The precision was computed as the coefficient of variation for all the results obtained in all the analysed samples during validation. The signal obtained for standards (Sexpected) and the signal calculated from the calibration equation (Scalculated) were applied for the calculation of recovery for calibration curves (Rcc): [6]Rcc=⌊Sexpected− Scalculated⌋Sexpected
## 2.5. Labelling Assessment
Thirty-four packages of DSs and eight traditional food products were assessed. Before evaluating packaging and labelling, each product’s registration in the register of products subject to notification of first market placement was checked [39]. As a result, eight products could not be included in the “dietary supplement” category due to the lack of appropriate labelling on the packaging and registration with the Chief Sanitary Inspectorate (GIS). Requirements on food and DS labelling are specified in Regulation (EU) No $\frac{1169}{2011}$ [40] and the Act on Food and Nutrition Safety of 25 August 2006 [41]. As food, DSs have been assessed because of the requirements specified in the Regulation of the Minister of Health of 9 October 2007 [42]. The correctness of the labelling was assessed according to the following criteria [39,40,41,42]: Labelling in Polish;The name of the food;The list of ingredients;The net amount of food;The date of minimum durability or best-before date;The presence of the term “dietary supplement”;Indication of the recommended daily portion of the product;The presence of a warning regarding not exceeding the recommended daily portion;A statement that dietary supplements cannot be used as a substitute for a varied diet;A statement that they should be kept out of the reach of small children.
In addition, the manufacturer should provide information on the content of active ingredients per recommended daily portion and information on the content of vitamins and minerals in percentages concerning the reference daily intake (RDI). Particular attention was paid to the health claims on the packaging of the tested products, which were compared with the list of permitted health claims defined in Regulation No. $\frac{1924}{2006}$ [43,44] and with the statements contained in the register of the European Food Safety Authority [45]. The difference in the number of analysed packages versus the total number of analysed DSs is due to the fact that some products were purchased in multiple repetitions (different lot numbers), resulting in the same package design.
## 2.6. Statistical Analyses
The data were reported as the mean ± standard deviation of three independent samples, each measured three times. Statistical analyses such as the ANOVA Kruskal–Wallis test, the U Mann–Whitney test, or Spearman’s rank correlation coefficient preceded by an analysis of the normality (the Shapiro–Wilk test) of the distribution were used to compare the treatments. They were performed by the Statistica for Windows (version 13, Statsoft, Cracow, Poland) software package. Differences at $p \leq 0.05$ were deemed significant. The validation parameters for spectrophotometric assays, the overall mean, and the standard error values were calculated using the Microsoft Office Excel software (version 2007 12.0.6787.5000 SP3 MSO, Microsoft Corporation, Redmond, WA, USA).
## 3.1. Total Phenolic Content and the TAC
The averaged values of TAC (CUPRAC, DPPH) and TPC (FC) in the analysed beetroot and DSs, divided into groups (tablets, capsules, powders), are shown in Table 2 and Table 3, respectively. For the FC, results are expressed in gallic acid equivalents (GAE), for CUPRAC in Trolox equivalents (TE), and for DPPH as a percentage reduction in DPPH. Tables S6 and S7 show the full characteristics of the beetroot and beetroot-based DSs studied due to their TAC, TPC, nitrate, and nitrite contents. Powders were characterised by significantly higher TPC (mg GAE/d. d.; Table S8) than tablets (U Mann–Whitney test, $$p \leq 9.9$$ × 10−5) and capsules (U Mann–Whitney test, $$p \leq 4.4$$ × 10−5). However, lyophilizates showed the highest TPC compared to any group of DSs. There was no statistically significant difference between tablets and capsules (U Mann–Whitney test, $$p \leq 0.99$$). In the case of TPC per gram of a product, a statistically significant difference was found only between tablets and lyophilizates (U Mann–Whitney test, $$p \leq 0.0049$$), which could be caused by the presence of excipients in tablets used in tabletting processes. In the FC assay, the product marked as T7 (41 mg GAE/d. d.) was characterised by the highest TPC among the tablets, C8A (42 mg GAE/d. d.) and C8B (41 mg GAE/d. d.) among the capsules, and product P9 (251 mg GAE/d. d.) among the powders.
Powders showed higher TAC than tablets (U Mann–Whitney test, $$p \leq 1.6$$ × 10−4) and capsules (U Mann–Whitney test, $$p \leq 2.3$$ × 10−5). Lyophilizates provided a higher TAC than DSs. Considering TAC expressed as TE/g, lyophilizates showed significantly more antioxidants than all DS formulations. The highest TAC among the tablets was found in the T7 product (350 μmol TE/d. d.), the C13 (363 μmol TE/d. d.) among capsules, and the P9 product (3520 μmol TE/d. d.) among the powders. It is worth noting that product P9 exhibited a higher TPC (251 mg GAE/d. d.) and TAC (3520 μmol TE/d. d.) than average beetroots (Table 2). Different trends between the FC and CUPRAC methods may result from the variability of the conditions under which the tests were conducted and the reaction mechanisms. In the CUPRAC method, for example, the antioxidant potential is tested at pH = 7, which is close to the pH of human blood, as opposed to the FC method, which tests at pH 8–9. These changes in pH can influence the development of various antioxidant capacities of products, especially considering the complex matrix of DSs. In addition, the reaction with the DPPH radical is specific to individual antioxidants; they can react at different rates. Despite the concentrated DS formula as dried material, the daily portion of fresh beetroot (100 g f.w.) was richer in antioxidants.
In the DPPH method, the highest TAC was shown by the product T7 ($90\%$) among the tablets and the products C6 and C8B among the capsules—the activities of which were both $90\%$—while the activity of the product C8A was $74\%$. Among the powders, the product P11 ($90\%$) showed the highest TAC. The content of antioxidants in the dietary supplements T2A, T2B, and T2C was greatly varied, despite the use of the standard material standardisation procedure. Moreover, all three supplements were sold as the same commercial product. Three other products were characterised by a high RSD ($30\%$ for T6; $41\%$ for T10; and $33\%$ for C2). In all cases, the analysis was repeated, but the analogous results were obtained, and the Q-Dixon test did not show a significant error. A high RSD could be caused by the heterogeneity of the supplement, as the method has been validated and the analysis conditions have not changed.
In a study conducted by Guldiken et al. [ 20], in which the content of antioxidants was measured using colorimetric methods in fresh beetroot, the following results were obtained: 255 mg GAE/100 g of fresh weight in the FC method and 15,538 μmol TE/100 g (3889 mg TE/100 g) in the CUPRAC method. The values obtained by the FC method are comparable to those obtained in this work for the majority of whole and peeled beetroot lyophilizates (Table S7). However, the values obtained by the CUPRAC method in this work are lower for most samples. Only the samples of skins 6Sk and 7Sk, which were from organic farming, can be considered comparable (9779 and 8932 μmol TE/100 g, respectively). This may be due to the differences in the profile of compounds and antioxidant capacity between different varieties and the freshness of the material analysed. In this study, the vegetables were processed and freeze-dried immediately after purchase. However, there is no way to trace the storage conditions of fresh material before purchase. There is a lack of reports in the literature regarding the TPC and TAC of DSs made from beetroot. Comparing the results obtained for supplements per gram in capsules (0.68 to 33 mg GAE/g), tablets (2.0 to 41 mg GAE/g) or powders (4–61 mg GAE/g) with the results obtained by Guldiken et al. [ 20] for dried beetroot 3.3 mg GAE/g (347 mg GAE/100 g), there can be observed a lower TPC in dried beetroot compared to our supplements calculated per g of d.w.
Moreover, Spearman’s rank correlation coefficient was performed to test for a potential correlation between the antioxidant potential results obtained by different methods. A fairly strong relationship (0.7–0.9) or a very strong relationship (>0.9) was observed between the results obtained by the FC, CUPRAC, and DPPH methods in all the groups analysed (beetroots, capsules, tablets, and powders) (Table S9). In the study by Apak et al. [ 46], the correlation between the CUPRAC and FC methods was comparable and amounted to $r = 0.966.$ Another study by Güçlü et al. showed a high correlation ($r = 0.93$) between the FC and CUPRAC methods [47].
## 3.2. Nitrate and Nitrite Content
*In* general, significantly lower levels of nitrite ions (0.21–78 µg/d. d.) than nitrate ions (0.197–169 mg/d. d.) were found in DS samples. Due to the lack of a normal distribution (the Shapiro–Wilk test, $p \leq 0.05$), the U Mann–Whitney test was applied to check for statistically significant differences. It was found that supplements in tablets ($$p \leq 0.000295$$) and capsules ($$p \leq 0.014038$$) contained significantly fewer nitrite ions, as well as supplements in tablets ($$p \leq 0.262612$$) had statistically significantly fewer nitrate ions than lyophilized vegetables, considering their content in 1 g of product (dry weight for beetroot). The nitrite ion content of powders and beetroots did not differ significantly, nor did the nitrate ion content of beetroots, capsules, and powders. Moreover, individual parts of beetroot (peeled beetroot, skins) did not differ significantly in the content of nitrites ($$p \leq 0.133615$$) and nitrates ($$p \leq 0.830324$$) (the U Mann–Whitney test, $p \leq 0.05$). All results of the U Mann–Whitney test were shown in Table S10. An average portion of conventional beetroots provided more nitrites (49 µg/100 g f.w.) and nitrates (90 mg/100 g f.w., Table 3) than most of the other products analysed. Only products P9 (169 mg/d.d.), P10 (99 mg/d.d.), and P13 (131 mg/d.d.) contained more nitrates than beetroots. The highest content of nitrite ions was found in supplement number C4 (8.4 µg/g) in the case of capsules, T3A (2.48 µg/g) for tablets, and P13 (6.4 µg/g) for powders. The highest level of nitrate ions was found in T11 (3746 mg/kg), C16 (15,186 mg/kg), C7B (11,924 mg/kg), P13 (13,110 mg/kg), and P9 (10,224 mg/kg) DSs.
In all tested vegetable samples, a significantly lower content of nitrite ions (0.702 µg/g–15 µg/g) than nitrate ions (423 mg/kg-8801 mg/kg) was determined. The highest content of nitrite ions among vegetables was found in skin samples, except for group 7, where the highest level of these ions was determined in a sample of peeled beetroot. In the case of nitrate ions, the situation was the opposite: the skin samples were characterised by the lowest content of these ions, except for sample no. 1, where their level was the highest in the batch. Moreover, individual subgroups of beetroot (peeled, unpeeled, skins) differed in terms of TAC and TPC, regardless of the method used to assess the potential (ANOVA Kruskal–Wallis test: FC $$p \leq 0.0022$$, CUPRAC $$p \leq 0.0016$$, DPPH $$p \leq 0.006$$). The skins were the richest in antioxidants (Dunn’s test, $p \leq 0.05$). There were no statistically significant differences in the content of nitrites and nitrates between the individual subgroups (ANOVA, $p \leq 0.05$). For the comparison of vegetable supplements, reference was made to the average values for conventional and organic beetroots (Table 2). Manufacturers usually do not provide information on how the beetroot has been processed before preparing supplements from it. Several products contained information that whole beetroot was used, which is why we also included vegetables with skin in our analysis. The content of these compounds in beetroot depends primarily on the amount of nitrogen fertilization, agrotechnical treatments, and the plant growth phase [48]. Gościnna and Czapski [48] observed higher contents of nitrates in the middle parts of the root compared to its outer parts. Although they used a different division of the tuber (into 4 parts), it can be considered that the conclusions from our study and their research are consistent-beetroot skin is characterised by a lower content of nitrates.
Only four DS had a nitrate content declaration. Supplements C10 and C11 did not contain nitrates (<LOQ) despite their presence being declared by the manufacturer. Both products were produced by the same manufacturer but were available under different trade names and with different graphic designs. Product C1 contained a negligible amount of nitrates compared to the declaration ($4.2\%$). Product P9 contained nitrates in amounts close to the declared one ($85\%$). Simultaneously, it is the product that contains the most nitrates per daily portion of all the tested foods, as well as more than the average portion of fresh beetroot.
In the years 2003–2004, research on the content of nitrites and nitrates was carried out on certain vegetables purchased in random shops in Olsztyn [49]. Among these vegetables, beetroot was included, which was classified as a plant with a high content of nitrate—an average of 1408.17 mg/kg. A high level of nitrates (III) was determined in the analysed beetroots (on average 11.4 mg/kg), which differed from the average values for this vegetable. The content of nitrite and nitrate ions in the beetroot samples in this study was 0.120–2.935 mg/kg for nitrites and 102.30–1619.80 mg/kg for nitrates, respectively.
## Health Risk Assessment
In terms of nitrite content ($2.1\%$ ADI for NO2¯), none of the products tested posed a risk (Table 4). Fresh beetroot (100 g) provided more NO3¯ (15–$20.1\%$ ADI for NO3¯, conventional and organic, respectively) than any of the analysed DSs in the form of tablets ($3.2\%$ ADI for NO3¯) or capsules ($5.1\%$ ADI for NO3¯). DSs in powders provided a similar amount of the substance as a serving of beetroot (based on the realisation of the ADI). Product P9, marketed by the manufacturer as having an “increased dose of nitrates”, had the highest nitrate dose ($48\%$ of the ADI for NO3¯) and the lowest nitrite levels ($0.21\%$ of the ADI for NO2¯). That is why the manufacturer advertised it as a product for athletes to be consumed before and after training “to increase the body’s efficiency, accelerate regeneration after training, and reduce accumulated lactic acid” (information on the packaging). In comparison, the recommended daily dose of products in capsules provided a maximum of $3.2\%$ ADI for NO3¯ and $5.1\%$ ADI for NO3¯ in tablets.
Keller et al. [ 50] analysed eighteen DSs in terms of the content of nitrites and nitrate and determined the percentage of ADI to evaluate the exposure to these compounds through the intake of the recommended portion. The ADI for nitrate amounted to $22\%$ in the case of the Neo40 supplement, described by the manufacturer as “improving the functioning of the cardiovascular system”, and $97\%$ in BeetElite—advertised as “improving exercise endurance and increasing oxygen supply in the body. The ADI for nitrites amounted to $450\%$ and $225\%$, respectively. The rest of the DSs were characterised by values within the range of 0.01–$47.26\%$ for nitrates and 0.00–$21.11\%$ for nitrites [50]. The reason for such a high content of nitrites and nitrates in some supplements was most likely their composition—rich in nitrates dehydrated or concentrated forms of vegetables or concentrated vegetable juices. The supplements analysed for this work did not pose a risk to the consumer because of the ADI.
Studies have shown [50] that consumption of beetroot products is more beneficial than supplementation with nitrate salts because the flavonoids and vitamin C present in beetroot reduce the risk of nitrosamine formation. Haem iron may increase the risk of the formation of these compounds [50]. However, beetroot supplements, if fortified with iron, are in the non-haem form (usually iron gluconate or fumarate; see T2, T5, and T6). Because of ADI limits, all sources of nitrates in the diet should be considered when estimating daily nitrate and nitrite consumption and performing a safety assessment. Green leafy vegetables and root vegetables such as beetroot constitute rich sources of these substances [51,52]. Moreover, nitrates are added to meat and meat products to prevent Clostridium botulinum, Listeria monocytogenes, Bacillus cereus, Clostridium perfringens, and *Staphylococcus aureus* growth, improve their colour, and develop their characteristic flavour [19,53,54]. According to some studies, their high concentrations in water (>50 mg/L) can cause methemoglobinemia and gastrointestinal carcinogenesis [55]. It should be mentioned that hypotensive effects were observed after the nitrate dose corresponding with the upper limit of the WHO ADI. Ashor et al. [ 56] described the hypotensive effect after using beetroot juice rich in NO3¯ (70 mL containing 400 mg). Similarly, Mills et al. [ 57] discovered a hypotensive effect after 6 months of drinking beetroot juice rich in NO3¯ (70 mL containing 694 mg of NO3¯). Furthermore, Kapil et al. [ 58] reported that 4 weeks of supplementation with beetroot juice containing 450 mg of NO3¯ had beneficial therapeutic effects on endothelium and arterial stiffness. However, no therapeutic effects were observed with additional daily administration of 300 mg of NO3¯ [59,60]. Considering these values, the tested supplements are probably not able to exert a hypotensive effect even with long-term use, as the highest content of nitrates found in product P9 amounted to $48\%$ of ADI.
## 3.3. The Correlation between the Antioxidant Potential and the Content of Nitrites and Nitrates in Beetroot and Beetroot–Based Products
A statistically significant negative correlation (Spearman’s rank correlation analysis) was found only in beetroot samples, both between the results of the antioxidant potential obtained by the FC and CUPRAC methods and the content of nitrates (Table S11). The group of beetroot samples was more homogeneous in terms of composition than DSs. Some of the DSs were enriched in various substances such as nitrates, iron compounds, and vitamin C, which could have disturbed the existence of a potential correlation. The Spearman’s rank correlation coefficient equals −0.54 for the FC method and −0.62 for CUPRAC, which means that dependence is moderate.
## 3.4. Labelling Assessment
An assessment of thirty-four packages of dietary supplements and eight traditional food products was carried out because of Polish and European food labelling regulations. It was found that $64\%$ of packaging did not meet the legal requirements for food labelling, $12\%$ were not reported to the Chief Sanitary Inspectorate, and $6\%$ did not have the term “dietary supplement” on the packaging, despite having registration in the GIS in this category. Furthermore, $26\%$ of products were not fully labelled in Polish, as a result of which the consumer is not able to get acquainted with the information presented on the packaging in detail and its content is not formulated understandably. It is worth emphasising that $21\%$ of the tested products contained prohibited health or non-registered claims, which means that $15\%$ of the products suggested that they had the properties of preventing or treating diseases. It is also worth noting that the product P9, which contained the highest amount of nitrates and was sold as a supplement, contained significant labelling deficiencies, including the lack of the wording “dietary supplement”. Moreover, part of the labelling was only in English, and the dosage was not precisely defined, which poses a risk of nitrate overdose.
The detailed results of the analysis are summarised in Table 5.
## 4. Conclusions
The importance of this study was to determine and compare TAC, TPC, nitrite, and nitrate content in beetroot-based DSs and beetroot. Moreover, the safety of consumption of DSs because of nitrites, nitrates, and the correctness of labelling were assessed.
The research revealed that TAC, TPC, nitrate, and nitrite concentrations expressed per unit of product weight (g or kg), DSs in capsules, and DSs in powders were comparable to the average beetroot. Tablets contained notably fewer of these substances, which might result from the presence of auxiliary substances used for tabletting. However, the average portion (100 g) of conventional or organic beetroot provided significantly more nitrates, nitrites, and substances with antioxidant properties than most of the DSs in capsules, tablets, and powders dosed according to the manufacturer’s recommendations. Only P9 ($48\%$ ADI for NO3¯), P10 ($28\%$ ADI for NO3¯), and P13 ($37\%$ ADI for NO3¯) delivered higher doses than beetroots. Most of the products did not have the declared content of nitrates. The antioxidant content in a serving of tablets or capsules was negligible, so their use has a low health value. In many of the samples studied, the nitrate content was not correlated to the antioxidant potential. A statistically significant negative correlation was found only in beetroot samples between the results of the FC and CUPRAC methods and the content of nitrates.
The labelling assessment has shown that $64\%$ of packaging did not meet the legal requirements for food labelling. Some DSs contained illegal health claims that suggested healing properties or were misleading. This situation might result in reduced effectiveness or withdrawal from conventional therapies by consumers who would choose adulterated DSs. There were significant deficiencies in labelling, including a lack of full labelling in Polish, unclear dosage and others. Such deficiencies, combined with unknown nitrite and nitrate content, may result in consumers overdosing on these substances as a result of incorrect product intake. In addition, the unknown content of nitrates and nitrites may pose a threat to the consumer because the content of these compounds in vegetables varies depending on the place of origin and growing conditions. The conducted research indicated a strong need for more rigorous control when launching DSs, both in terms of composition and labelling. Although production and labelling guidelines are published, there is a lack of decisive action by national and European authorities related to the control and withdrawal of defective products.
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|
---
title: 'Simplifying carb counting: A randomized controlled study – Feasibility and
efficacy of an individualized, simple, patient‐centred carb counting tool'
authors:
- Shulamit Witkow
- Idit F. Liberty
- Irina Goloub
- Malka Kaminsky
- Olga Otto
- YonesAbu Rabia
- Ilana Harman Boehm
- Rachel Golan
journal: Endocrinology, Diabetes & Metabolism
year: 2023
pmcid: PMC10000617
doi: 10.1002/edm2.411
license: CC BY 4.0
---
# Simplifying carb counting: A randomized controlled study – Feasibility and efficacy of an individualized, simple, patient‐centred carb counting tool
## Abstract
The Scc tool is a simple tool enabling accurate insulin dosing to all diabetes patients treated with basal and bolus insulin. The SCC tool has the potential to apply to all diabetes patients, particular to those who are uncomfortable with the use of advanced technology, or who do not have access to such technology due to age, education or language barriers.
### Introduction
The purpose of this study was to introduce and test a simple, individualized carbohydrate counting tool designed for persons with Type 1 Diabetes Mellitus (T1DM) in order to determine whether the tool improved A1C levels for participants with age, education or language barriers.
### Methods
In a randomized controlled trial, 85 participants were offered six diabetes instructional sessions free of charge over a six‐month period. Forty‐one received guidance using the regular carbohydrate counting (RCC) method. Forty‐four received guidance using an individualized ‘Simple Carb Counting’ (SCC), involving two customized tables prepared for participants.
### Results
The simple, individualized SCC tool for carbohydrate counting was non‐inferior to the standard method of RCC. The SCC tool was more effective among participants aged 40 and older, while no differences were found when comparing participants by education level. Irrespective of intervention group, all participants improved their A1C level ($9.9\%$ = 13.2 mmol/L vs $8.6\%$ = 11.1 mmol/L, $$p \leq .001$$). A greater improvement in A1C level was seen in newly diagnosed participants (−6.1 vs −0.7, $$p \leq .005$$, −3.4 vs 0.9, $$p \leq .032$$) in both the RCC and SCC groups. All participants expressed improved emotional level per their PAID5 questionnaires (Problem Areas in Diabetes Scale‐PAID), (10.6 (±5.7) vs 9.5 (±5.7), $$p \leq .023$$), with women reporting greater improvement than men.
### Conclusions
SCC is a simple, individualized, feasible, low‐tech tool for carbohydrate counting, which promotes and enables accurate insulin dosing in people with T1DM. It was found more effective among participants aged 40 and older. Additional studies are needed to corroborate these findings.
## INTRODUCTION
Achieving glycaemic control in Type 1 diabetes mellitus (T1DM) is vital in order to reduce diabetes‐related complications. 1, 2 Treatment recommendations include multiple daily injections of prandial and basal insulin, or continuous insulin infusion (CSII). As carbohydrates are the major nutrient affecting the post‐prandial response, it is important to educate individuals on matching prandial insulin doses to carbohydrate intake by means of carbohydrate counting (CC). The checking of pre‐meal blood glucose levels and co‐ordination of anticipated physical activity 3 are also recommended for optimizing glucose control.
CC takes into account the carbohydrate intake per meal, and enables adjustment of the prandial insulin dosage necessary to achieve individual post‐prandial glucose targets. 4 To manage CC, the patient has many variables to consider: (i) the personal insulin/carb ratio (I:C) – the amount of insulin the patient needs to overcome a 15‐gram portion of carbohydrates; (ii) the accurate assessment of the amount of carbohydrates consumed; (iii) the insulin sensitivity response (IS), that is, the decrease in blood glucose after injecting one unit of insulin to counter an elevated glucose level; (iv) the individual target glucose level pre‐ and post‐meal; and (v) the level of glucose prior to the meal. This means that in order to normalize post‐prandial glucose levels, individuals need to be highly motivated to maintain wellbeing through on‐going compliant conduct, and to be possessed of high mathematical and literacy skills. Studies 5 have shown the difficulties of successfully carrying out CC, especially concerning the over‐ and underestimation of carbohydrate content in various foods that can lead to post prandial hypo or hyperglycaemia respectively. In a systematic review 6 of CC efficacy in managing T1DM, Bell et al 6 found only partial benefit in achieving glycaemic control through CC, notwithstanding the difficulty of complying with CC instructions. These difficulties are added to other barriers patients with T1DM cope with following dietary recommendations such as, frequent glucose measurements and importantly their feelings about having diabetes.
In recent years, new technologies have been developed to simplify CC. 7 *While various* commercial applications and bolus calculators may lead to better glucose control, such options are not available to all populations including lower socioeconomic status, older people and those with lower technological skills. In an attempt to overcome disparities in access and other barriers, the Diabetes Clinic of Soroka University Medical Center (SUMC) developed a simple, easy‐to‐use tool – Simple Carb Counting ‘SCC.’ This tool includes adjustments for those of differing educational backgrounds, cultures and cognitive abilities. It affords persons with diabetes continued enjoyment of their personal eating habits and food preferences through the successful adoption of the SCC method into their daily routines with ease and accuracy.
The aim of this randomized controlled study was to test the feasibility and efficacy of SCC, compared to the regular CC method. Our *Hypothesis is* that this simple tool will enable all people with T1DM improve diabetes control.
## METHODS
We performed an open‐label randomized controlled trial at the Diabetes Clinic of SUMC, a tertiary 1200‐bed hospital that treats a diverse population, including Bedouin and other Arabs, and Jews from the general, ultra‐Orthodox and Ethiopian sectors. Patients with T1DM were eligible for this study if they were (i) over 18 years old, (ii) treated either with an insulin pump or with multiple daily injections of insulin and (iii) had a hemoglobinA1c (A1C) level equal to, or >8.5. The study excluded pregnant or lactating women, patients with severe renal failure, heart failure or under active treatment for cancer. All participants signed an informed consent statement. The study was approved by the SUMC Helsinki Committee on 8 October 2015, approval number 0320‐15, and is registered at ClinicalTrials.gov, ID NCT04132128, and conducted from November 2015 to July 2017.
Using simple randomization, we assigned participants to one of the two groups, (RCC) Regular carbohydrate counting, or (SCC) simplified individualized tool for carbohydrate counting. The selection process was purely random for every assignment made by Diabetes Clinic staff.
All participants were allowed up to six instructional sessions with a registered dietitian who is also a diabetes care and education specialist during a period of about 6 months. All sessions were free of charge and lasted at least 60 min. During the first session, participants were introduced to the carbohydrate counting method to which they were assigned at randomization. Subsequent sessions were dedicated to reinforcing and practicing their method and changes in insulin dosage parameters were made if needed. All patients treated with insulin pumps were encouraged to use the bolus calculator. The primary end‐point was level of A1C 6 months after the intervention. Additional data parameters were collected including those of demographics, education and duration of diabetes. Weight was measured and blood studies were conducted before and after the intervention to determine baseline A1C and lipid levels. In order to identify depression and diabetes‐related distress, the participants were asked to complete the PAID5 questionnaire (Problem Areas in Diabetes Scale‐PAID) 8 at baseline and post‐intervention.
## Regular carbohydrate counting (CC)
During the instructional sessions, the rationale for carbohydrate counting was explained. Commercial booklets containing a list of the carbohydrate content of foods were provided, and participants were introduced to websites or cellular phone applications designed to assist the public with determining the carbohydrate content of various foods. The participants were taught to calculate the amount of insulin needed using their personal I:C ratio, IS, correction factor, and the glucose target goals prescribed by the Diabetes Clinic team. Participants were encouraged to keep a food diary to assist them with carbohydrate counting.
## Simplified individualized carbohydrate counting (SCC)
The SCC tool consisted of two tables written in the participant's native language and adjusted to the participant's specific requirements. Insulin doses were calculated by professional staff using personalized I:C ratios and IS.
First Table: The first table, derived from patients' personal IS, listed the number of units that participants needed to administer in order to correct every pre‐meal blood glucose level so as to reach their target glucose.
Second Table: The second table contained a list of food items derived from participants' personal eating habits, as recorded in their food diaries. The list consisted mainly of the most common foods they regularly consumed, the carbohydrate content of those foods and the number of insulin units needed, as calculated by the personal I:C ratio per usual portion of each food item. High carbohydrate content foods that participants included in their diet were listed, not for healthy nutrition education, but for purposes of facilitating carb counting. Foods that did not contain carbohydrates, such as protein or fat items, were also listed to ensure that the patient realized that these foods contained no carbs. Patients treated with insulin pumps received a personalized table containing the carbohydrates in their food list as grams to accurately calculate the amount of carbs entered to the calculator (Figures 1 and 2).
**FIGURE 1:** *SCC carb counting chart for MDI users.* **FIGURE 2:** *SCC carb counting chart for insulin pump users.*
At each instructional session, participants' tables were reviewed, personal dosing was tested, food items were added to, or deleted from participants' lists as warranted, and the logic of the method was reiterated for the purpose of reinforcing participants' understanding and compliance.
## Statistical analysis
On the basis of the expected A1C difference of $0.8\%$ between groups with $90\%$ power providing an SD of $0.4\%$ and a significance level of $0.05\%$ we estimated that the sample size should consist at least 37 participants in each group. We used descriptive statistics to summarize the data, reporting results as means and standard deviations. Categorical variables were summarized as counts and percentages. Paired t‐test was used to examine within changes in A1C between baseline and follow‐up, and Student's t‐test was used to examine between differences as to the two intervention groups. We further analysed the data after stratifying the study population by sex, education level (above and below 12 years of school), age (above and below age 40) and duration of diabetes (more or <5 years since diagnosis). p values are shown. All analyses were performed with IBM SPSS.
## RESULTS
In total, 107 men and women were recruited for the study, of whom 22 were excluded, as shown in Figure 3. Of the 85 people who were deemed eligible, $48.2\%$, $$n = 41$$, of participants (23 women, 18 men) were assigned to RCC method group, and $51.8\%$, $$n = 44$$, of participants, (17 women, 27 men) to the SCC method group. The mean age of participants was 43.1 (18–74). About $43\%$ of the RCC method group were treated with an insulin pump versus $36\%$ of the SCC group ($$p \leq .48$$). All patients monitored their glucose by self‐monitoring of blood glucose (SMBG) (Figure 1 and Table 1).
**FIGURE 3:** *Flow diagram – Trial of standard carb counting versus simple individualized carb counting.* TABLE_PLACEHOLDER:TABLE 1 The participants were followed for a mean period of 6 months, during which all participants in the study improved their A1C level between baseline and follow‐up ($9.9\%$ = 13.2 mmol/L vs $8.6\%$ = 11.1 mmol/L, $$p \leq .001$$). Other biomarkers did not change from baseline in either of the groups.
We stratified the study population by participant age (older and younger than age 40). Among older participants (mean age 55.2 (±9.4)), only those who used the SCC method exhibited significant improvement in A1C level, from baseline (9.6 (±1.3) = 12.7 mmol/L to 8.6 (±1.1) = 11.1 mmol/L, $$p \leq .002$$). While those using the RCC method showed some improvement, it did not reach statistical significance (9.7 (±1.3) = 12.9 mmol/L to 9.2 (±1.5) = 12.1 mmol/L, $$p \leq .09$$). ( Figure 4).
**FIGURE 4:** *HbA1c results SCC vs RCC.*
Among younger participants (mean age 29.6 (±6.3)), a significant improvement was found in both the RCC group (10.2 (±2.3) = 13.7 mmol/L to 8.3 (±1.4) = 10.6 mmol/L, $$p \leq .001$$) and SCC group (10.3 (±1.9) = 13.8 mmol/L to 8.3 (±1.1) = 10.6 mmol/L, $$p \leq .002$$).
We stratified the study population by level of education (above and below 12 school years). Both higher and lower educated participants in the SCC group demonstrated a significant improvement in A1C level. The results for those with above and below 12 school years were −$1.4\%$ (±2.0) vs −$1.3\%$ (±1.9) respectively, $$p \leq .7.$$
We found a greater improvement in A1C levels when we compared participants with more recently diagnosed diabetes (<5 years from diagnosis, $$n = 13$$) to those whose diabetes was of longer duration ($$n = 72$$). ( −6.1 vs −0.7, span >−3.4 vs 0.9, $$p \leq .032$$) in both the RCC and SCC groups.
We measured the degree of patient compliance by the number of instructional sessions attended during the study period. Compliance was fairly good for all participants, with a mean of 4.8 visits (out of the six allowed). No differences were found in compliance between participants in the RCC group and those in the SCC group, but women were more compliant than men, with a mean of 5.3 (1.3) visits, compared to 4.4 (1.9) for the men ($$p \leq .01$$). Compliance tended to be higher among participants with more than 12 years of education, compared to those with fewer years of education (4.5 visits (±1.9) vs 5.3 (±1.3), $$p \leq .08$$). The degree of compliance correlated with decreased A1C level post‐intervention.
Sixty‐three participants (30 in RCC and 33 in SCC) completed the PAID5 questionnaire at baseline and post‐intervention. All participants reported increased satisfaction, as exhibited by a decreased PAID5 score (10.6 (±5.7) vs 9.5 (±5.7), $$p \leq .023$$). Based on the questionnaire responses, there were no differences in diabetes‐related emotional distress between participants in the RCC group and those in the SCC group. Yet, when the investigators stratified the data by sex, they found a significant improvement among women, who reported a decrease in diabetes‐related emotional distress from 11.5 to 9.9, $$p \leq .04$$, compared to men, 9.9 to 9.18, $$p \leq .27$$ (Table 2).
**TABLE 2**
| Unnamed: 0 | Baseline PAID Score (±SD) | Post‐intervention PAID Score (±SD) | p‐value | p‐value (SCC vs RCC) |
| --- | --- | --- | --- | --- |
| All participants (N = 63) | 10.6 (5.7) | 9.5 (5.7) | 0.02 | |
| All SCC (N = 33) | 12.5 (5.4) | 11.1 (5.5) | 0.03 | |
| All RCC (N = 30) | 9.3 (5.4) | 8.42 (5.6) | 0.09 | 0.69 |
| All women (N = 30) | 11.5 (5.8) | 9.9 (5.6) | 0.02 | |
| SCC women (N = 18) | 13.11 (5.5) | 11.28 (5.6) | 0.04 | |
| RCC women (N = 12) | 9 (5.6) | 7.75 (5.1) | 0.15 | 0.7 |
| All men (N = 33) | 9.9 (5.7) | 9.18 (5.9) | 0.13 | |
| SCC men (N = 12) | 10.67 (6.2) | 9.83 (6.1) | 0.23 | |
| RCC men (N = 21) | 9.48 (5.5) | 8.81 (5.9) | 0.21 | 0.9 |
| Education >12 y (N = 31) | 8.65 (4.4) | 7.87 (4.7) | 0.12 | |
| SCC education >12 y (N = 15) | 9.5 (4.9) | 8.3 (5.2) | 0.11 | |
| RCC education >12 y (N = 16) | 7.9 (3.9) | 7.4 (4.4) | 0.32 | 0.6 |
| Education <12 y (N = 32) | 12.6 (6.3) | 11.1 (6.2) | 0.02 | |
| SCC education <12 y (N = 15) | 14.8 (5.5) | 13.1 (5.5) | 0.08 | |
| RCC education <12 y (N = 17) | 10.6 (6.4) | 9.3 (6.5) | 0.08 | 0.77 |
| Age > 40 (N = 33) | 9.1 (6.0) | 8.1 (5.3) | 0.07 | |
| SCC age > 40 (N = 13) | 11.3 (6.6) | 9.6 (5.4) | 0.08 | |
| RCC age > 40 (N = 20) | 7.75 (5.2) | 7.2 (5.2) | 0.2 | 0.4 |
| Age < 40 (N = 30) | 12.3 (5.1) | 11 (5.9) | 0.04 | |
| SCC age < 40 (N = 17) | 12.8 (5.2) | 12 (6.1) | 0.11 | |
| RCC age < 40 (N = 13) | 11.7 (5.6) | 10.3 (5.8) | 0.11 | 0.9 |
During the 6 month follow‐up, two patients from the RCC group were hospitalized with diabetic ketoacidosis (DKA). There were no hospitalizations or ER visits due to hypoglycaemia in both groups.
## DISCUSSION
The findings showed that the SCC simple, individualized tool for carbohydrate counting was non‐inferior to the standard method of RCC. The SCC tool was more effective among participants aged 40 and older, while no differences were found when comparing participants above and below 12 school years. However, significant improvement in A1C level was observed in all participants. Participants in both RCC and SCC groups who were diagnosed with diabetes within the previous 5 years exhibited significantly greater improvement in A1C level, compared to participants with diabetes of longer duration.
The SCC method presented in our study was developed in order to overcome difficulties and barriers that the diabetes clinic patients encountered in implementing CC, as described in several studies. Kawamura et al 5 tested the errors in carbohydrate content estimation among 37 paediatric patients, their parents, and their health care professionals, including physicians and dietitians. In all groups studied, they found overestimation of the carb content in foods with small amounts of carbs, and underestimation in foods with high carb content.
While past experience in CC was important, some foods, such as rice, were hard to estimate even by experienced participants. In the qualitative study of Gürkan et al 9 investigators interviewed adolescents with diabetes, finding multiple barriers to effective treatment. Among the barriers were patients' negative feelings about having diabetes, as well as personal and environmental barriers. Personal barriers included lack of knowledge about the disease, trouble with glucose measurement, and difficulty following dietary recommendations. These findings were corroborated by Ahola et al 10 who found that many patients experienced difficulty managing their post‐prandial glucose, and were subject to a high percentage of time in a hyperglycaemic state.
In this study, we present an option to overcome some of the barriers described in the above studies. Through personalization, flexibility, and a departure from a restrictive diet paradigm, SCC affords persons with diabetes an opportunity to continue eating their usual diet, including the customary dishes of their culture, and to go on with their life‐long social dining habits.
The study showed superiority in reaching glycaemic control in participants older than age 40 who used the SCC method, compared to those in the RCC group. Treating older patients with T1DM is complicated by the combined challenges of insulin‐dependent diabetes, age‐related complications, and possible comorbidities, all of which negatively affect the older population's ability to self‐manage diabetes. 11 The SCC tool presented in the study simplifies the tasks needed for carbohydrate control, and consequently leads to better glycaemic control especially for older age group.
Contrary to these findings, the study demonstrates that SCC was non‐inferior in people with various levels of education. In a cross‐sectional multicentre study of 768 subjects under age 18 with T1DM, Gesuita et al 12 found that only $28.1\%$ of participants reached target A1C values (<$7.5\%$). A strong correlation was found between higher socio‐economic status (SES), higher level of education and higher ability to follow ordinary CC. Significantly, Gesuita et al. highlighted the need for an accessible tool for non‐privileged populations.
Recently diagnosed participants in both RCC and SCC groups showed the greatest benefit in improving glycaemic control. This may be explained in two possible ways, one psychological and one physiological. When first diagnosed, many patients are highly motivated to do well. In addition, in the early period after diagnosis, sometimes called the ‘honeymoon period,’ the pancreas seems to do better and secretes more insulin, although this phenomenon decreases with time and differs with each patient.
All participants exhibited significant decreases in their PAID5 scores. Studies have shown 13, 14 that people with diabetes suffer from higher levels of psychological distress than does the healthy population. People with T1DM are three times more likely to develop depression than those without diabetes. 15 Moreover, psychological distress has been shown to be associated with hyperglycaemia, complications and higher mortality rate. 16, 17 Thus, there is a consensus that treating psychological stress and achieving psychological wellbeing ought to be one of the treatment goals of diabetes care. 18 A study by Zagarins et al 19 revealed a correlation between improvement of glycaemic control and alleviation of overall psychological stress, but not in depression. Our study corroborates these findings, and underscores the need for on‐going diabetes education, better understanding and treatment of diabetes and promoting a greater sense of self‐efficacy among patients in controlling the disease, as means of improving not only metabolic control, but also mental health.
## Limitations
The intervention tool was introduced at a single diabetes clinic in a tertiary teaching hospital with one registered dietitian/diabetes care and education specialist. The method was not tested on paediatric patients, a population that has more difficulties with glycaemic control than others. A larger population of people from the lower socio‐economic and more culturally diverse backgrounds should be studied in order to corroborate the results and establish generalizability across populations.
## CONCLUSIONS AND IMPLICATIONS
In large measure, the research into T1DM treatment is focused on advanced technologies including insulin pumps, continuous glucose monitoring, the artificial pancreas and various applications to support CC and diabetes management. Studies have shown 20 that although advanced applications are accessible and improve glycaemic control, only a small percentage of the population with T1DM chooses to use them. This may be explained by the human factor, that is, personal expectations, perceptions of the burden of new technologies, user‐friendliness and long‐term cost. The SCC tool tested in this study has the potential to apply to all diabetes patients, and in particular to those who are uncomfortable with the use of advanced technology, or who do not have access to such technology.
In conclusion this study presents a simple, feasible, low‐tech tool that simplifies carbohydrate counting and which promotes and enables accurate insulin dosing in people with T1DM. Additional studies are needed to corroborate these findings.
## AUTHOR CONTRIBUTIONS
Shulamit Witkow: Conceptualization (lead); data curation (lead); investigation (equal); methodology (equal); resources (lead). Idit F Liberty: Data curation (equal); formal analysis (supporting); investigation (equal); methodology (equal); project administration (equal); supervision (lead); validation (equal); writing – original draft (lead); writing – review and editing (lead). Irina Goloub: Data curation (supporting); resources (supporting). Malka Kaminsky: Conceptualization (supporting); data curation (equal); investigation (supporting); resources (supporting). Olga Otto: Data curation (equal); investigation (supporting). Yones Abu Rabia: Data curation (supporting); investigation (supporting); resources (equal). Ilana Harman Boehm: Conceptualization (equal); data curation (equal); investigation (equal); methodology (equal); resources (equal). Rachel Golan: *Formal analysis* (lead); writing – review and editing (supporting).
## CONFLICT OF INTEREST STATEMENT
The Authors declare that there are no conflicts of interest.
## DATA AVAILABILITY STATEMENT
Raw data were generated at the diabetes centre of SUMC. Derived data supporting the findings of this study are available from the corresponding author [IFL] on request
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---
title: Blocking EREG/GPX4 Sensitizes Head and Neck Cancer to Cetuximab through Ferroptosis
Induction
authors:
- Aude Jehl
- Ombline Conrad
- Mickaël Burgy
- Sophie Foppolo
- Romain Vauchelles
- Carole Ronzani
- Nelly Etienne-Selloum
- Marie-Pierre Chenard
- Aurélien Danic
- Thomas Dourlhes
- Claire Thibault
- Philippe Schultz
- Monique Dontenwill
- Sophie Martin
journal: Cells
year: 2023
pmcid: PMC10000618
doi: 10.3390/cells12050733
license: CC BY 4.0
---
# Blocking EREG/GPX4 Sensitizes Head and Neck Cancer to Cetuximab through Ferroptosis Induction
## Abstract
[1] Background: Epiregulin (EREG) is a ligand of EGFR and ErB4 involved in the development and the progression of various cancers including head and neck squamous cell carcinoma (HNSCC). Its overexpression in HNSCC is correlated with short overall survival and progression-free survival but predictive of tumors responding to anti-EGFR therapies. Besides tumor cells, macrophages and cancer-associated fibroblasts shed EREG in the tumor microenvironment to support tumor progression and to promote therapy resistance. Although EREG seems to be an interesting therapeutic target, no study has been conducted so far on the consequences of EREG invalidation regarding the behavior and response of HNSCC to anti-EGFR therapies and, more specifically, to cetuximab (CTX); [2] Methods: EREG was silenced in various HNSCC cell lines. The resulting phenotype (growth, clonogenic survival, apoptosis, metabolism, ferroptosis) was assessed in the absence or presence of CTX. The data were confirmed in patient-derived tumoroids; [3] Results: Here, we show that EREG invalidation sensitizes cells to CTX. This is illustrated by the reduction in cell survival, the alteration of cell metabolism associated with mitochondrial dysfunction and the initiation of ferroptosis characterized by lipid peroxidation, iron accumulation and the loss of GPX4. Combining ferroptosis inducers (RSL3 and metformin) with CTX drastically reduces the survival of HNSCC cells but also HNSCC patient-derived tumoroids; [4] Conclusions: The loss of EREG might be considered in clinical settings as a predictive biomarker for patients that might undergo ferroptosis in response to CTX and that might benefit the most from the combination of ferroptosis inducers and CTX.
## 1. Introduction
With nearly 932,000 new cases and 450,000 deaths in 2018 worldwide, head and neck cancers, and, more particularly, head and neck squamous cell carcinoma (HNSCC), rank sixth among the most frequently observed cancers in the world [1]. Locally advanced HNSCC (LA-HNSCC, stage III/IV) represents $70\%$ of patients at diagnosis. They require primary surgery followed by adjuvant (chemo)-radiotherapy or definitive chemoradiotherapy including cetuximab (CTX) [2,3]. The chimeric antibody CTX targets the epidermal growth factor receptor (EGFR), which happens to be overexpressed in more than $90\%$ of HNSCC. CTX prevents ligand binding and dimerization with other HER family members [4,5]. Once bound, CTX blocks EGFR phosphorylation and signal transduction and promotes EGFR internalization, thus turning down the oncogenic EGFR signaling [6,7]. Unfortunately, some patients do not benefit from CTX treatment, and others show recurrences soon after the end of the treatment. Intrinsic or therapeutically acquired resistance to CTX is extensively studied to understand the mechanisms involved. We have recently shown the involvement of the caveolin-1/epiregulin/YAP axis in the resistance to CTX and irradiation therapy [8].
Epiregulin (EREG), encoded by the EREG gene located on chromosome 4q13.3, belongs to the ErbB family of ligands. The 162 amino acids transmembrane proform of EREG is proteolytically cleaved by ADAM17 [9] to release a soluble form of 46 amino acids [10]. EREG shares 24–$50\%$ of its sequence with those of other members of the EGF family [10]. EREG binds to EGFR (extracellular domain I and III that partially overlaps the EGF binding site [11]) and ErbB4. It stimulates homodimers of EGFR and ErbB4 in addition to heterodimers of ErbB2 and ErbB3, leading to the activation and the transduction of downstream signaling pathways [12]. In contrast to EGF, EREG leads to complete EGFR recycling and not to lysosomal degradation [13]. EREG induces less stable EGFR dimers than EGF, but unexpectedly, this weakened dimerization elicits more sustained EGFR signaling than EGF [14]. Low or non-existent in most human tissues, the elevation of EREG expression is observed in the early stages of cancer development, in which EREG induces epithelial-mesenchymal transition [15]. EREG is a transcriptional target of the oncogenic KRAS and is also overexpressed in cells with an oncogenic mutation of EGFR and BRAF [16]. EREG also promotes tumorigenicity, metastasis, drug resistance and cell plasticity and modulates the tumor microenvironment and metabolism [17]. The increased expression of EREG is associated with short overall survival in patients with HNSCC/OSCC [11,15,18,19,20]. Elevated levels of EREG appear to be a potential predictive biomarker of anti-EGFR therapies in several cancer types including HNSCC [21]. We recently reported that the overexpression of caveolin-1 in HNSCC is associated with the total loss of EREG as well as the oncogenic addiction to EGFR. Silencing EREG activates the YAP/TAZ pathway, which enables cells to resist CTX/radiotherapy. The resistance of HNSCC cells to therapy is linked to the protection of the mitochondria and drives the recurrence of caveolin-1-expressing HNSCC tumors [8].
How the loss of EREG affects the cellular metabolism and how it might influence the response to anti-EGFR therapies is only sparsely documented in HNSCC. Here, we aimed to clarify this point and highlighted that the loss of EREG sensitizes cells to CTX through the induction of ferroptosis. Our study reveals the glutathione metabolism as a targetable vulnerability of HNSCC that should be exploited in CTX-based therapies.
## 2.1. Cell Culture, Transfection and Drugs
The CAL27, CAL33 and SCC9 cell lines were purchased from the ATCC® and DSMZ (authenticated by STR profiling). All cell lines tested negative for mycoplasma contamination. CAL27 and CAL33 were grown in DMEM (PAN Biotech, Aidenbach, Germany) supplemented with 2 mM ultra-glutamine, 0.5 mM sodium pyruvate and $10\%$ heat-inactivated FBS (Gibco, Dutscher SAS, Brumath, France). SCC9 were grown in DMEM-F12 (PAN Biotech) supplemented with 2.5 mM ultra-glutamine, 15 mM HEPES, 400 ng/mL hydrocortisone (Sigma-Aldrich, St Quentin Fallavier, France) and $10\%$ FBS (Gibco). EREG expression was downregulated by transfecting the cell lines with 25 nM siRNAEREG (Human EREG and the respective control siRNACtrl, SMARTPool from Dharmacon) using Lipofectamine 2000TM (Invitrogen, Thermo Fischer Scientific, Illkirch, France). Efficient EREG silencing was determined by Western blot. When indicated, cells were treated with solvent, 30 nM of CTX (Erbitux™, 5 mg/mL, Merck, ICANS, Strasbourg, France), 5 µM RSL3 (MedChemExpress, Clinisciences, Nanterre, France) or 1 mM metformin (MedChemExpress) alone or in combination with CTX.
## 2.2. Sphere Evasion Assay
After the treatments, 500,000 cells were resuspended in 1 mL of regular culture medium supplemented with $20\%$ methylcellulose. Spheroids were formed using the hanging drop culture method. Drops of 20 µL cell suspension were placed onto the lids of 60 mm dishes, which were inverted over the dishes. The dishes were cultured in humidified chambers for 48 h to allow the formation of round aggregates. The spheroids were harvested and seeded in plastic 24-well plates (6 spheres/well) for an additional 24 h to allow for the evasion of cells from the attached spheres. Pictures were taken using the Evos XI Core microscope (AMG, Thermo Fischer Scientific, Illkirch, France), with 10× magnification. The results were expressed, in pixels, as the evasion area of the cells relative to the area of the attached sphere (total area − area of the sphere), determined using ImageJ (https://imagej.nih.gov, 1.53t).
## 2.3. IncuCyte® Assay
After transfection, the cells were seeded (4000 for CAL33 and 8000 for CAL27 and SCC9 cells/200 µL/well) in 96-well plates. The plates were placed at 37 °C, and the confluence, growth, cell health and morphology were monitored for 164 h/7 days. The percentage of confluence was determined using the IncuCyte® analysis software after normalization to day 0 (Essen BioScience, Sartorius, Goettingen, Germany).
## 2.4. Clonogenic Survival Assay
A total of 72 h after the transfection and/or an additional 48 h of treatment with the solvent, 30 nM CTX, 5 µM RSL3, 1 mM metformin or co-treatments, the cells were seeded (500 for CAL27 and SCC9 and 1000 for CAL33 cells/2 mL/well) in 6-well plates and allowed to grow for 10 days. The cells were colored with crystal violet at $0.1\%$ (Sigma-Aldrich, St Quentin Fallavier, France). The colonies were counted to determine the plating efficiency (PE) and the surviving fraction (SF). PE = number of surviving cells/number of cells plated. SF = PE of the experimental group/PE of the control group.
## 2.5. Metabolic Assays
After the treatment, 20,000 cells were plated in a Seahorse XF Cell Culture microplate in XF growth medium (non-buffered DMEM containing 10 mM glucose, 4 mM L-glutamine and 2 mM sodium pyruvate). The OCR (oxygen consumption rate), ECAR (extracellular acidification rate) and ATP consumption were measured using the ATP rate assay procedure under basal conditions and in response to 1.5 μM oligomycin and 0.5 µM rotenone/antimycin A with the XFp Extracellular Flux Analyzer (Seahorse Bioscience, Agilent, Les Ulis, France). The metabolic profiles were analyzed using the online Seahorse analytics platform.
## 2.6. Western Blot
A total of 72 h after the transfection and/or an additional 48 h of treatment with the solvent, 30 nM CTX, 5 µM RSL3, 1 mM metformin or co-treatments, the cells were lysed with the lysis buffer ($1\%$ Triton, 100 nM NaF, 10 mM Na4O7P2, 1 mM Na3VO4, protease inhibitor cocktail (Roche, Meylan, France) in PBS) for 30 min at 4 °C and then sonicated. The supernatant was recovered by centrifugation at 20,000× g for 10 min at 4 °C. In total, 5 to 20 μg of proteins were separated on a 4–$20\%$ TGX-denaturing polyacrylamide gel (SDS-PAGE Bio-Rad Marnes-La-Coquette, France) and transferred to polyvinylidene difluoride (PVDF) membrane (Amersham, Sigma-Aldrich, St. Quentin Fallavier, France). After 1 h of blocking at room temperature, the membranes were probed with appropriate primary antibodies (see Supplementary Table S1) overnight at 4 °C. The membranes were subsequently incubated with anti-rabbit or anti-mouse antibodies conjugated to horseradish peroxidase (Promega, Charbonnieres les-Bains, France), developed using chemoluminescence (ECL, Bio-Rad, Marnes-La-Coquette, France) and visualized with an Las4000 image analyzer (GE Healthcare, Tremblay-en-France France). The quantification of non-saturated images was carried out using ImageJ software (National Institutes of Health, Bethesda, MD, USA). GAPDH was used as the loading control. The results were expressed as histograms representing the mean ± SEM of the ratios protein/GAPDH normalized against the controls.
## 2.7. Quantification of Intracellular Fe2+ Accumulation
A total of 72 h after the transfection and/or an additional 48 h of treatment with 30 nM CTX, the cells were seeded at 20,000 cells for CAL27 and CAL33 and at 15,000 cells for SCC9 for 24 h in 96-well plates with opaque walls. The intracellular accumulation of Fe2+ was determined using the intracellular probe FerroOrange at 1 µM for 30 min (Dojindo, TebuBio, LePerray en Yvelines, France). The fluorescence intensity was measured with a Varioskan LUX (Thermo Scientific, Illkirch, France) plate reader. In parallel, the cells were seeded at 30,000 cells for CAL27 and at 20,000 cells for CAL33 and SCC9 in an eight-well LabTek for imaging with an LEICA TCS SPE II confocal microscope (Leica Microsystems SA, Nanterre Cedex, France), with a ×20 magnification objective, and analyzed with ImageJ software.
## 2.8. Detection of the Accumulation of Lipid Peroxides
A total of 72 h after the transfection and/or an additional 48 h of treatment with 30 nM CTX, the cells were seeded for 24 h at 30,000 for CAL27 and at 20,000 for CAL33 and SCC9 in an eight-well LabTek. The accumulation of lipid peroxides was determined using the Liperfluo kit at 1 µM for 30 min (Dojindo, China). The cells were also seeded into LabTek wells for imaging with an LEICA TCS SPE II confocal microscope (Leica Microsystems SA, Nanterre Cedex, France), with a ×20 magnification objective, and analyzed with ImageJ software (https://imagej.nih.gov, 1.53t).
## 2.9. Tumoroids Culture
The study was approved by the Scientific Committee of the tumor bank and the Department of Pathology of the CHU Strasbourg-Hautepierre (France). The patients have signed an informed consent form. Tumor extractions were carried out in the Department of Cervico-Facial Surgery of the CHU Strasbourg-Hautepierre (France). The resected pieces were histologically diagnosed. The tumoroids were extracted from head and neck cancer surgical resection following the protocol developed by Driehus et al. [ 22] and cultured in advanced DMEM/F12 supplemented with GlutaMax, Penicilin/streptomycin, 10 mM HEPES, 10 µM Y-27632 (Euromedex, Souffelweyersheim, France), 0.5 µg/mL Capsofungin (Sigma), 1× B27 supplement (Thermo Fisher Scientific), 1.25 mM N-acetyl-L-cysteine (Sigma-Aldrich), 10 mM Nicotinamide (Sigma-Aldrich), 500 nM A83-01 (Sigma-Aldrich), 0.3 µM CHIR99021 (Sigma-Aldrich), 50 ng/mL human EGF (PeproTech, Thermo Scientific, Illkirch, France), 10 ng/mL human FGF10 (PeproTech), 5 ng/mL human FGF2 (PeproTech), 1 µM Prostaglandin E2 (Bio-techne, R&D Systems, Noyal Châtillon sur Seiche, France) and 1 µM Forskolin (Bio-techne), $4\%$ (col/col) RSPO3-Fc fusion protein conditioned medium (ImmunoPrecise, IPATherapeutics, Utrecht, Netherlands) and $4\%$ (vol/vol) Noggin-Fc fusion protein conditioned medium (ImmunoPrecise). Quality control of the tumoroids was performed. The tumoroids were plated at 2500 cells/10 µL of $70\%$ Cultrex UltiMatrix reduced growth factor basement membrane Extract (R&D Systems, Noyal Châtillon sur Seiche, France) in 24-well plates. The tumoroids were treated with the solvent, 30 nM CTX, 5 µM RSL3, 1 mM metformin or co-treatments 7 days after plating for an additional 10 days. The cell viability was assessed after the exposure of the cells to trypan blue (Bio-Rad) and reading via a TC20 Automated Cell Counter (Bio-Rad). Moreover, this culture was monitored by imaging at ×4 and ×20 magnification via an Evos XI Core microscope (AMG).
## 2.10. Immunohistochemistry on Tumoroids
Following the recovery, the tumoroids were fixed in PFA $4\%$ for 20 min and washed in PBS. After a 15 min permeabilization step in PBS/$0.1\%$ Tween-20 and a 60 min blocking step in PBS/$0.1\%$ Triton X-100/$2\%$ BSA/$5\%$ NGS, the tumoroids were incubated overnight at 4 °C with primary antibodies (Rabbit anti-EREG, #CSB-PA007779NA01HU, Cusabio Technology, dilution $\frac{1}{300}$; Mouse anti-Caveolin-1, #66067-1-lg, Proteintech, dilution $\frac{1}{1000}$). After washing in PBS/$0.1\%$ Triton X-100/$0.2\%$ BSA, the cells were incubated for 3 h at room temperature with appropriate secondary antibodies (Life Technologies; dilution $\frac{1}{500}$) and DAPI (#D9542; Sigma-Aldrich, St Quentin Fallavier, France; 1 µg/mL). After washing twice in PBS/$0.1\%$ Triton X-100/$0.2\%$ BSA and twice in PBS, the slides were mounted using FUnGI medium ($50\%$ (v/v) glycerol, $9.4\%$ (v/v) dH2O, 10.6 mM Tris base, 1.1 mM EDTA, 2.5 M fructose and 2.5 M urea). Images were acquired using an LEICA TCS SPE II confocal microscope (Leica Microsystems SA, Nanterre Cedex, France), with a 20× magnification objective, and analyzed with ImageJ software (https://imagej.nih.gov, version 1.53r, access on 3 May 2021) or Imaris software (Imaris x64 9.3.1—22 May 2019).
## 2.11. Statistical Analysis
Quantitative variables are presented as their mean and standard deviations and were compared to univariate analyses with a Student’s t-test if they followed a Gaussian distribution (Shapiro–Wilk tests were used to assess the Gaussian distribution) or with a Wilcoxon’s rank test if they followed a non-Gaussian distribution.
## 3.1. Silencing EREG Prevents Survival and Growth and Sensitizes to CTX
We recently reported that decreased EREG expression conferred Cav1-overexpressing cells resistance to CTX/radiotherapy [8]. We postulated that it was the result of a decrease in EREG-driven oncogenic addiction to EGFR. To go further, EREG was silenced in a panel of three basal-like HNSCC cell lines using siRNA. The basal expression of Cav1, EREG and EGFR was not altered by the transfection of siRNActrl. Silencing EREG does not alter Cav1 expression and exhibits a significant reduction in EGFR, which is in contrast with the molecular alterations observed previously (Figure 1A). EREG-silenced cells (siRNAEREG) show reduced clonogenic survival (Figure 1B). Although CTX significantly reduces the survival of control (siRNActrl-transfected) cells, its effect is even more pronounced in EREG-silenced cells (Figure 1B; with 33 ± $10\%$ and 51 ± $5\%$ inhibition by CTX for siRNActrl-CAL27 and siRNAEREG-CAL27, 23 ± $6\%$ and 49 ± $7\%$ inhibition by CTX for siRNActrl-CAL33 and siRNAEREG-CAL33 and 27 ± $5\%$ and 45 ± 5 inhibition by CTX for siRNActrl-SCC9 and siRNA EREG-SCC9, respectively).
Reduced clonogenic survival is associated with an altered growth capacity, reflected here by the inability of cells to reach confluency. Again, CTX is more prone to blocking the growth of cells silenced for EREG (Figure 1C; with 19 ± $1\%$ and 28 ± $1\%$ inhibition by CTX for siRNActrl-CAL27 and siRNAEREG-CAL27, 39 ± $1\%$ and 46 ± $1\%$ inhibition by CTX for siRNActrl-CAL33 and siRNAEREG-CAL33 and 17 ± $3\%$ and 5 ± $2\%$ inhibition by CTX for siRNActrl-SCC9 and siRNAEREG-SCC9, respectively).
The cleavage of PARP, reflecting apoptosis induction, could only barely be detected and only in cells exposed to CTX (Figure 1D). No additional cleavage could be observed in siRNAEREG-transfected cells when compared to the controls. Thus, apoptosis induction could not account for the reduction in survival and growth observed in siRNAEREG-cells remaining untreated or treated with CTX. Taken together, these data show that the concomitant silencing of EREG and EGFR targeting would be more effective in inhibiting tumor growth and survival.
## 3.2. Silencing EREG Promotes Mitochondrial Dysfunction and Inhibits Autophagy in Reponse to CTX
The metabolic reprogramming of cancer cells has a beneficial effect not only on tumor growth and survival but also on metastasis and chemoresistance. We therefore investigated how EREG might affect the mitochondrial metabolism. The ATP Rate assays reveal that all three cell lines exhibit different basal oxygen consumption (OCR), extracellular acidification (ECAR) and ATP production. CAL33 cells are the most energetic (Figure 2A, left). EREG-silencing significantly reduces OCR and ECAR (Figure 2A, right) and ATP production in all three cell lines, with the highest efficiency in the most energetic cell line, CAL33 (Figure 2A, right, with a 22 ± $3\%$, 41 ± $3\%$ and 26 ± $1\%$ inhibition in siRNAEREG-CAL27, siRNAEREG-CAL33 and siRNAEREG-SCC9, respectively). Although CTX significantly reduces the production of ATP in both siRNActrl- and siRNAEREG-transfected cells, SCC9 seem less sensitive to it (Figure 2A, right). EREG-silenced cells treated with CTX appear less metabolically active. Thus, silencing EREG and blocking EGFR with CTX cause mitochondria dysfunction, which is more important in highly metabolic cells.
Autophagy is a critical protective mechanism against mitochondrial dysfunction. It maintains cellular homeostasis by removing damaged macromolecules and organelles, including mitochondria. The expression of ULK-1, a kinase regulating the early stages of the autophagosome formation, is only induced in cells exposed to CTX, and no differences are observed between siRNActrl- and siRNAEREG-transfected cells. In contrast, the silencing EREG reduces the expression of Beclin1 and LC3B in CAL33 and the expression of LC3B in SCC9 without affecting CAL27 cells (Figure 2B). Although CTX does not affect Beclin1 and LC3B by itself in any cell line tested, it reduces their expression even further in siRNAEREG-transfected CAL33 and SCC9 cells without affecting CAL27 cells (Figure 2B). The data show that silencing both EREG and EGFR signaling inhibits autophagy.
## 3.3. Silencing EREG Promotes Ferroptosis in Response to CTX
We next focused on ferroptosis, a different class of cell death, characterized by the accumulation of ferrous ions (Fe2+) and the increase in the production of lipid reactive oxygen species (ROS). The accumulation of Fe2+ was monitored using the FerroOrange probe. Silencing EREG significantly reduces Fe2+ staining in CAL27 and CAL33 cells (Figure 3A,B, with a 33 ± $5\%$, 43 ± $7\%$ and 0 ± $5\%$ inhibition in siRNAEREG-CAL27, siRNAEREG-CAL33 and siRNAEREG-SCC9, respectively). Although CTX does not affect Fe2+ accumulation in siRNActrl-cells, it significantly induces Fe2+ staining in siRNAEREG-transfected CAL27 and CAL33 without affecting SCC9 (Figure 3A,B). Lipid peroxides were monitored using the LiperFluo probe and revealed similar staining profiles to the ones observed in Figure 3B. Thus, silencing EREG reduces the accumulation of lipid peroxides in CAL27 and CAL33 cells (Figure 3C). Turning to CTX, it significantly induces lipid peroxides staining in siRNAEREG-transfected CAL27 and CAL33 without affecting SCC9 (Figure 3C). Altogether, EREG-silencing reprograms cells to induce ferroptosis in the presence of CTX.
## 3.4. EREG-Silencing Uncovers the Vulnerability of Cells to GPX4 Inhibition
It has been shown that ferroptosis is initiated either by the loss of glutathione peroxidase 4 (GPX4, an enzyme involved in lipid repair) or the depletion of cystine. GPX4, together with its co-factor glutathione (GSH), catalyzes the inhibition of lipid peroxides. Its loss is concomitant with the accumulation of lipid peroxides in membranes, which leads to ferroptosis. Silencing EREG does not affect GPX4 expression in any of the cell lines tested. The exposure of siRNActrl-transfected cells to CTX does not affect it either (Figure 4A). However, the treatment of siRNAEREG cells with CTX results in a significant inhibition of GPX4 expression in all three cell lines (Figure 4A; with a 24 ± $9\%$, 42 ± $6\%$ and 26 ± $5\%$ inhibition in siRNAEREG-CAL27, siRNAEREG-CAL33 and siRNAEREG-SCC9, respectively).
The cystine/glutamate antiporter system (also called x-c or xCT (coded by the genes SLC7A11 and SLC3A2)) imports extracellular cystine that will be further reduced into cysteine. Cysteine acts as a precursor for the synthesis of GSH, the cofactor of GPX4. GPX4 is also a direct transcriptional target of NRF2. SLC7A11 (as well as SLC1A5 and SLC7A5) and NRF2 are under the control of the oncogene c-Myc [23]; we investigated how EREG and/or CTX might affect c-Myc expression in our system. Silencing EREG or exposing siRNActrl-transfected cells to CTX does not affect c-Myc expression in any of the cell lines tested (Figure 4B). However, the treatment of siRNAEREG cells with CTX results in a significant inhibition of c-Myc expression in all three cell lines (Figure 4B; with a 33 ± $14\%$, 23 ± $9\%$ and 19 ± $6\%$ inhibition in siRNAEREG-CAL27, siRNAEREG-CAL33 and siRNAEREG-SCC9, respectively).
Pharmacological inhibitors such as RSL3 have been reported to either degrade GPX4 or inhibit its function. RSL3 reduces the surviving fraction of siRNActrl- and siRNAEREG-transfected cells to similar levels as CTX (Figure 4C compared to 1B for untreated cells). The surviving fraction of both siRNActrl- and siRNAEREG-transfected cells is even further inhibited when RSL3 is combined with CTX (Figure 4C). Metformin, already used in the treatment of diabetes, was recently described as promoting ferroptosis in different ways, including by the inhibition of SLC7A11 [24,25]. We therefore studied the effects of this non-specific inducer of ferroptosis in our model. Metformin reduces the surviving fraction of siRNActrl- and siRNAEREG-transfected cells to similar levels as CTX in CAL33, but it was far more potent in CAL27 and SCC9 cells (Figure 4D, compared to 1B for untreated cells). The surviving fraction of both siRNActrl- and siRNAEREG-transfected cells is even further inhibited when metformin is combined with CTX (Figure 4D). The exposure of the cells to RSL3 or metformin alone or in combination with CTX results in a significant inhibition of GPX4 expression, which is even more pronounced in EREG-silenced cells (Figure 4E). Altogether, the data show that GPX4 is crucial for cell survival and that its disappearance sensitizes to CTX.
## 3.5. GPX4 Inhibition Sensitizes the Patient-Derived Tumoroid to CTX
In order to validate our hypothesis, we exposed patient-derived tumoroids to CTX, RSL3, metformin and a combination of drugs for 7 days. Tumoroids are treated 7 days after plating in 3D drops of basement membrane extract (BME dotted circle, Figure 5A, left panel before treatment) to allow for formation. After 7 days of treatment, the tumoroids were photographed (pictures only shown for T1) at a low magnification (×4, Figure 5A middle panel) to follow the growth in the 3D BME drops (dotted circle) characterized by an increase in the size and at a high magnification (×10, Figure 5A right panel) to observe the variations in the morphology related to the different treatments. CTX and RSL3 alone do not affect the growth or the viability (Figure 5A,B) of tumoroids when compared to the untreated tumoroids. In contrast, the combination of CTX and RSL3 clearly reduces the size (Figure 5A) and the viability to 60 ± $4\%$ and 59 ± $5\%$ in tumoroids 1 and 2, respectively (Figure 5B). The non-specific inducer of ferroptosis, metformin by itself, reduces the size (Figure 5A) and the viability of tumoroids 1 and 2 to 48±$6\%$ and 31 ± $5\%$, respectively (Figure 5B). The combination of CTX and metformin reduces the viability even further in tumoroid 1 but not in tumoroid 2 (12 ± $2\%$ and 25 ± $8\%$ in tumoroids 1 and 2, respectively; Figure 5B) and is more efficient than CTX and RSL3. Reduced viability is associated with the appearance of debris in 3D BME drops (arrows in Figure 5A).
Finally, the exposure of tumoroids to CTX does not affect GPX4 expression (Figure 5C). GPX4 is significantly reduced by RSL3 alone (30 ± $10\%$) and even further when RSL3 is combined with CTX (80 ± $9\%$). Similar results were obtained in T2 and T3 (data not shown). It is also significantly reduced by metformin alone (76 ± $14\%$) and totally lost when metformin is combined with CTX (Figure 5C). Altogether, the data confirm the efficacy of targeting xCT and GPX4 in CTX-resistant tumors.
## 4. Discussion
The dysregulation of EREG may contribute to the progression of various cancers including HNSCC and may be a putative mechanism of resistance to EGFR-targeted therapies. EREG is usually overexpressed in HNSCC and correlates with short overall survival and progression-free survival [11,15,18,19,20]. EREG conducts an even more potent mitogenic signal than EGF in HNSCC mimicking EGFR oncogenic mutations [11,20]. Job et al. recently described a subgroup of HPV-negative HNSCC named “basal” sharing molecular similarities such as the upregulation of genes involved in the EGFR signaling pathway including EREG and AREG [21]. Cells sharing these characteristics appear to be more sensitive to EGFR-targeted therapy, with CTX being the least efficient. Because the suppression of EREG expression reduces cell survival, the authors suggested that cells may be addicted to an EREG feedback loop and that EREG should be considered as a functional biomarker for HNSCC sensitivity to EGFR blockade [21]. In line with this study, we observed that HNSCC tumor cells expressing caveolin-1 could use EREG silencing, but not AREG silencing, to overcome oncogenic dependence on EGFR and develop resistance to CTX/irradiation combination therapy [8]. The resistance was due, at least in part, to the silencing of the HIPPO pathway, leading to the activation of YAP/TAZ [8]. The cross-suppression of both AREG and EREG has also been reported to lead to the emergence of CTX resistance, which is related to the loss of cell addiction to EGFR, compensated by the hyperactivation and addiction to FGFR3 in melanoma [26]. We show here that the direct suppression of EREG expression reduces both EGFR expression and HNSCC basal cell survival. Rather than driving resistance to CTX, the loss of EREG reduces survival even further. While it cannot be excluded that long-term EREG silencing may lead to the emergence of CTX resistance, the acute targeting of EREG combined with CTX is effective in reducing cell survival and could be a feasible antitumor strategy for HNSCC. Fepixnebart (LY3016859, developed by Eli Lilly and Co.) is a monoclonal antibody that binds epiregulin and TGF-α and is well tolerated and efficient in neutralizing both targets [27]. It is currently in phase II for back pain and neuropathic pain and in phase III for diabetic neuropathies. It would be interesting to determine its anti-tumor effect and adjuvant effect for EGFR-targeting therapies. Besides its autocrine feedback loop, EREG is also secreted by the component of the tumor microenvironment such as macrophages [15] and cancer-associated fibroblasts (CAF) [28]. Macrophages-derived EREG induces EGFR-TKI resistance in NSCLC, and CAF-derived EREG promotes OSCC invasion and metastasis through the induction of EMT. Thus, targeting EREG might not only prevent therapy resistance but also HNSCC progression.
Aberrant metabolism and metabolism reprogramming represent malignant tumor hallmarks that are required for cancer cells to proliferate and progress. The metabolism of cancer cells is mainly based on nonoxidative glycolysis, followed by the fermentation of lactic acid to produce ATP, a phenomenon known as the Warburg effect. EREG/EGFR signaling enhances glycolysis by increasing glucose consumption, lactate production, extracellular acidification (ECAR) and the intracellular levels of ATP as well as by activating several glycolytic genes [29,30]. However, HNSCC also depend on glutamine for producing energy [31], which is imported in cells by the Na+-glutamine/Na+-cysteine exchanger ASCT2. Besides serving as a source of carbon and nitrogen for macromolecule synthesis, glutamine provides α-ketoglutarate for the tricarboxylic acid (TCA) cycle and contributes to the production of the most powerful antioxidant, glutathione (GSH) (for a review, see [23]). The production of GSH also requires cysteine, which is imported into cells through the x-c or xCT cystine/glutamate antiporter. GSH serves as a cofactor of the glutathione peroxidase 4 (GPX4) to suppress destructive lipid reactive oxygen species (ROS). This pathway plays a key role in the regulation of ferroptosis, which is a regulated cell death triggered by an iron-dependent lipid peroxidation [32]. Both GPX4 and x-c antiporter are crucial regulators of ferroptosis. Here, we show that silencing EREG as well as blocking EGFR lead to mitochondrial defects characterized by a reduction in ATP production, oxygen consumption (OCR) and ECAR. Combining EREG silencing with an EGFR blockade shifts cells from an energetic state toward a less metabolic phenotype. If the dysfunction of the mitochondria could affect cell survival through energetic stress, death could neither be attributed to apoptosis, which was undetectable, or to autophagy, which was inhibited. Mitochondria play a major role in regulating oxidative metabolism, are the main source of reactive oxygen species (ROS) and are the primary site of Fe2+ iron storage and utilization. Therefore, the dysregulation of mitochondria induced by the silencing of EREG and the inhibition of EGFR might alter the iron metabolism and generate a redox imbalance strong enough to trigger ferroptosis. Here, we show that the silencing of EREG combined with the blockade of EGFR lead to the accumulation of Fe2+ and lipid peroxides associated with the downregulation of GPX4. No ferroptosis could be achieved by the loss of EREG alone or by the treatment with CTX. Accordingly, although CTX downregulates ASCT2 via a CTX-dependent EGFR endocytosis, it does not alter survival by itself. However, it decreases the intracellular uptake of glutamine and the levels of GSH, which sensitizes HNSCC to ROS-induced death [33,34]. In colorectal cancer cells, CTX neither affected proliferation or survival by itself, even though it inhibited NRF2 signaling (known to promote GPX4 and HO-1 transcription). By targeting NRF2/HO-1, CTX enhances RSL-3-induced ferroptosis [35]. Further studies will be needed to determine if those molecular targets are also altered by the loss of EREG. However, we did show the inhibition of c-Myc and GPX4 expression in EREG-silenced cells treated by CTX. We previously reported that the oncogene c-Myc, a known target of EREG/EGFR, is downregulated by CTX in EREG-/caveolin-1-expressing cells [8,11]. As c-Myc regulates ASCT2, LAT1, x-c antiporter as well as NRF2 [23,36], it deserves further investigations.
Inducing ferroptosis seems to be an attractive potential anti-cancer strategy with broad clinical implications. Several preclinical studies show that ferroptosis inducers can synergize with traditional chemotherapeutics [35,37,38]. They either target the depletion of the cellular antioxidant GSH through the x-c antiporter (Erastin) or directly target GPX4 (RSL3). Here, the loss of GPX4 is associated with the induction of ferroptosis in EREG-silenced cells exposed to CTX. Inhibiting GPX4 by using RSL3 reduces the survival of control cells exposed to CTX to levels equivalent to those observed in EREG-silenced cells exposed to CTX. However, RSL3 also sensitizes EREG-silenced cells to CTX to an even greater extent. The maximum decrease in cell viability corresponds to a steep decrease in GPX4. The data uncover the value of targeting GPX4 to effectively sensitize tumor cells to CTX. Accordingly, the overexpression of GPX4 was described in EGFR-TKI-resistant lung adenocarcinoma and colorectal cancers. RSL3 restores their sensitivity to EGFR-TKI [35,38]. Similar results could be obtained using metformin, which is widely used for the treatment of type 2 diabetes mellitus (T2DM). Its use in T2DM has been associated with cancer incidence and mortality decreases, including in HNSCC (for a review, see [39,40,41,42]). This effect seems to be due to the reduction in circulating insulin, since both the insulin–IGF system and hyperglycemia have been associated with cancer risk. However, metformin also has a direct anti-tumor effect via the induction of energetic stress. It inhibits the mitochondrial respiratory chain complex I, leading to mitochondrial dysfunctions, changes in the levels of ROS and the iron homeostasis (for a review, see [43]). Acting independently of GPX4, metformin also downregulates SLC7A11 (the catalytic unit of the x-c cytine/glutamate antiporter), protein stability and expression by inhibiting UFM1 expression and the subsequent UFMylation of SLC7A11 [25]. Metformin increases intracellular total ROS and lipid ROS levels and reduces intracellular GSH, which ultimately leads to ferroptosis [25]. Metformin was also recently reported to induce ferroptosis in breast cancer by inhibiting autophagy [44]. In our hands, metformin was more effective in reducing cell survival than RSL3, which is probably due to its multiple targets. Its co-administration with CTX in control cells demonstrated an adjuvant effect of metformin that is even more pronounced in EREG-silenced cells. Again, the decrease in cell viability is associated with the loss of GPX4. Although the targeting of the x-c antiporter or GPX4 sensitizes control cells to CTX treatment, the most striking effects are seen in cells where EREG is lost.
Tumoroids are 3D tumor-resembling cellular clusters generated from primary patient material. They closely recapitulate the 3D tissue architecture, cellular composition and characteristics (including genetic and cellular intratumor heterogeneity as well as resistance to therapy) of the tumor from which they were derived, offering useful benefits over conventional 2D cell culture and 3D multicellular spheroids. They can be grown long-term without genetic or functional changes [45,46]. The results obtained to date indicate that tumoroids respond in a largely consistent manner to the patients they were derived from [47], show heterogeneous sensitivities to standard treatments [48,49] and might predict a patient’s clinical outcome. Tumors hold promise for biomarker identification, drug discovery and aiding personalized therapy. For all these reasons, we have chosen to generate HNSCC tumoroids to validate our therapeutic approaches combining ferroptosis inducers with CTX. As a proof of concept, patient-derived HNSCC tumoroids showing resistance to CTX were co-treated with RSL3 or metformin. Tumoroids survival was drastically decreased with RSL3-CTX co-treatment and was almost completely abrogated in response to metformin-CTX. Metformin combined with CTX was therefore more effective in reducing viability than RSL3/CTX. As stated above, it might be related to the fact that RSL3 only targets GPX4, whereas metformin has a multitude of targets, some of which, such as SCL7A11, act further upstream in antioxidant signaling. The data also underline a heterogeneity in the response of tumoroids. Indeed, if metformin sensitizes tumoroid 1 to CTX, this is not the case for the second. An inhibition of EGFR expression by metformin could lead to this desensitization to CTX, as previously observed [50]. Further studies will be necessary to understand this heterogeneity. The maximum diminution of cell viability is associated with the strongest reduction in GPX4 levels.
## 5. Conclusions
To our knowledge, this is the first study reporting that a loss of EREG might sensitize HNSCC to CTX through the induction of ferroptosis. To date, only a high expression of EREG was considered to predict the response of a patient to anti-EGFR therapies. However, care should be taken, since emerging studies report that secreted EREG in the microenvironment might support therapy resistance and tumor progression [15,28]. Thus, using EREG expression levels to identify patients likely to benefit from EGFR-TKI therapies could lead to the exclusion of some who would be better responders. Here, we propose combining ferroptosis inducers with CTX. Our data clearly show that the combination of both reduces the survival of tumors expressing EREG and that the effect is even more pronounced in tumors where EREG is lost. This is also the first study validating the efficacy of using ferroptosis inducers in combination with CTX to inhibit survival in a patient-derived tumoroid model resistant to CTX. In conclusion, a loss of EREG might be considered in clinical settings as a predictive biomarker for patients that might benefit the most from the combination of ferroptosis inducers and CTX.
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|
---
title: A clinical trial about effects of prebiotic and probiotic supplementation on
weight loss, psychological profile and metabolic parameters in obese subjects
authors:
- Rym Ben Othman
- Nadia Ben Amor
- Faten Mahjoub
- Olfa Berriche
- Chaima El Ghali
- Amel Gamoudi
- Henda Jamoussi
journal: Endocrinology, Diabetes & Metabolism
year: 2023
pmcid: PMC10000630
doi: 10.1002/edm2.402
license: CC BY 4.0
---
# A clinical trial about effects of prebiotic and probiotic supplementation on weight loss, psychological profile and metabolic parameters in obese subjects
## Abstract
The supplementation with prebiotics and probiotics showed an improvement in lean mass, glycaemic profile, insulin resistance and uric acid more than diet alone.
### Introduction
The management of obesity is difficult with many failures of lifestyle measures, hence the need to broaden the range of treatments prescribed. The aim of our work was to study the influence of pre and probiotics on weight loss psychological profile and metabolic parameters in obese patients.
### Methods
It is a clinical trial involving 45 obese patients, recruited from the Obesity Unit of the National Institute of Nutrition between March and August 2022 divided into three groups: diet only (low‐carbohydrate and reduced energy diet), prebiotics (30 g of carob/day) and probiotics (one tablet containing Bifidobacterium longum, Lactobacillus helveticus, Lactococcus lactis, Streptococcus thermophilus/day). The three groups were matched for age, sex and BMI. Patients were seen after 1 month from the intervention. Anthropometric measures, biological parameters, dietary survey and psychological scores were performed.
### Results
The average age of our population was 48.73 ± 7.7 years, with a female predominance. All three groups showed a significant decrease in weight, BMI and waist circumference with $p \leq .05.$ Only the prebiotic and probiotic group showed a significant decrease in fat mass ($$p \leq .001$$) and a significant increase in muscle strength with $$p \leq .008$$ and.004, but the differences were not significant between the three groups. Our results showed also a significant decrease in insulinemia and HOMA‐IR in the prebiotic group compared to the diet‐alone group ($$p \leq .03$$; $$p \leq .012$$) and the probiotic group showed a significant decrease in fasting blood glucose compared to the diet alone group ($$p \leq .02$$). A significant improvement in sleep quality was noted in the prebiotic group ($$p \leq .02$$), with a significant decrease in depression, anxiety and stress in all three groups.
### Conclusions
The prescription of prebiotics and probiotics with the lifestyle measures seems interesting for the management of obesity especially if it is sarcopenic, in addition to the improvement of metabolic parameters and obesity‐related psychiatric disorders.
## INTRODUCTION
Today, obesity is a global epidemy according to the World Health Organization, given the increase in its frequency in the world, and its responsibility in the appearance of several chronic pathologies, such as type 2 diabetes, hypertension, cardiovascular diseases, respiratory diseases, osteoarticular diseases, cancer and other pathologies.
In 2021, the WHO announced that more than $40\%$ of men and women, or 2.2 billion people, are overweight and that an unbalanced diet was responsible for at least 8 million deaths per year. It is estimated that by 2025, 167 million people would be at risk of impaired health due to obesity. 1 In Tunisia, the prevalence of obesity was $26.2\%$ in 2016 according to the results of the “Tunisian Health Examination Survey‐2016”. 2 This disease is multifactorial, among the contributing factors of obesity are: a high‐fat diet, a sedentary lifestyle, but also the imbalance of the intestinal flora, “the gut microbiota” 3 which today represents the focus of several publications.
Gut microbiota is defined by all the beneficial microorganisms that live and grow in the intestine. It is set up from birth and evolves according to different factors such as antibiotic treatments or diet (presence of fibres, richness of foods in pre and probiotics). Probiotics are living microorganisms, they are bacteria such as Lactobacilli, Bifidobacteria, Streptococci and many others or yeasts. They can be present naturally in our diet, especially in fermented foods such as certain yoghurts or fermented milks, whereas prebiotics represent substrates for these bacteria which allow them to ensure their growth and thus exercise their beneficial roles, they are also provided by our diet, from the dietary fibres present in vegetables and fruits, such as carob, chicory and others.
Today, the microbiota is considered a therapeutic revolution, where researchers use its enrichment to prevent or treat certain diseases including obesity, 4 such as faecal transplantation, 5 but also the enrichment of the microbiota by prebiotics and probiotics to treat obesity. 6, 7 Hence, our interest in transposing these theoretical results to clinical practice.
Aim: The objective of this interventional clinical trial was to evaluate the effects of a probiotic supplement containing Bifidobacteruim, Lactobacillus strains and a prebiotic supplement by carob on the changes in body composition and metabolic biomarkers in subjects with obesity (main purpose), we also checked the psychological profile of the population (quality of sleep, stress, anxiety and depression) as secondary purpose.
## MATERIALS AND METHODS
We conducted a prospective interventional study at the obesity unit at the Zouhair El Kallel National Institute of Nutrition and Food Technology of Tunis, from March 2022 to August 2022.
We included in our study obese patients (BMI ≥30 kg/m2) aged over 18 years. Patients with: renal failure, hypothyroidism, cancer, diabetic patients on insulin, on long‐term corticosteroid therapy, former patients of the obesity unit were not included. No participants dropped out of the study during the intervention period.
Forty‐five patients were recruited on their first visit to the obesity unit (T0) and were randomly assigned to three groups matched for age, sex and BMI. All participants were enrolled in the weight loss program at the beginning of the study and followed a low‐carbohydrate, reduced‐energy intake eating plan provided by the same dietician. First group called “diet only”: on low‐calorie diet alone without any intervention (15 patients).Second group: 15 patients on the same diet plan but additionally received prebiotic supplementation (2 carob beans/day about 30 g) called “prebiotic group”. Third group: same diet with probiotic supplementation ($$n = 15$$). The probiotic component used in the study was one tablet containing an association of four microbiological strains which are: Bifidobacteruim longum, Lactobacillus helveticus, Lactococcus lactis, *Streptococcus thermophilus* (1 tablet (10.109 UFC/capsule)/day) called “probiotic group”. The probiotic supplement was produced by Pileje Labs.
Patients were reassessed after 1 month (T1) and we track adherence by regular phone calls.
All subjects gave their informed consent for participating in the study. The study was approved by the ethical committee of the national institute of nutrition of Tunis and the clinical trial was registered under number PACTR202210705998795 in the Pan African Clinical Trial Registry.
Body mass index (BMI) was calculated using body weight and height measured with bare feet and in minimal clothing according to the World Health Organization definition and classification. 8 Body composition parameters (body fat mass and percentage and body lean mass) were acquired before and after 1 month of intervention by impedance meter TANITA BC418MA.
We took the waist circumference of the patients. Muscle strength was measured by the handgrip. Sarcopenia was defined by muscle strength lower than 27 kg for men and 16 for women.
A biological assessment was carried out at T0 and T1 including: fasting glycaemia, HbA1C, Cholesterol, triglycerides, HDL, calculated LDL Friedwald formula, 9 insulinemia, calculated HOMA‐IR (HOMA‐IR = (insulin (mU/l) x glycaemia (mmol/l))/22.5), AST, ALT, GGT, creatinine and calculated eGFR.
Blood glucose results were interpreted according to American diabetes association guidelines. 10 We looked at the physical examination for blood pressure and other complications of obesity such as hernia, sleep apnoea syndrome, osteoarthritis and NASH and if necessary we completed with the necessary radiological examinations.
All patients benefited from an interview including food survey, stress questionnaire (Cunji), sleep questionnaire (Epworth), symptoms of depression and anxiety (HADS).
For the evaluation of stress, we used the brief stress evaluation scale, this is the scale of Cungi 1997. 11 This scale is made up of 11 items, and for each the response is from 1 to 6.
The evaluation of the quality of sleep was carried out using the Epworth Sleepiness Scale, 12 this questionnaire assesses the level of daytime sleepiness of the patient. It is composed of eight items, and for each situation, the patient must select an answer from (0 to 3). The interpretation is as follows: A total of less than 10 suggests that there is no excessive daytime sleepiness. A total of 10 and above suggests excessive daytime sleepiness.
To assess the depressive state of the patients, we used the “HAD” scale (Hospital Anxiety and Depression Scale). 13 *This is* a structured questionnaire of 14 items. This questionnaire consists of two subscales, each having 7 items, one for anxiety, the other for depression. Each item is rated on a 4‐point scale, that is from 0 to 3, evaluating the intensity of symptoms over the past week. The scores therefore range from 0 to 21 and the highest scores correspond to the presence of more severe symptoms. The addition of the scores obtained for each item allows the following interpretation: Less than 7 points: no symptoms of depression. Eight to 10 points: doubtful symptomatology. Eleven and over: certain symptomatology.
## Statistical analysis
The three‐variable ANOVA with Student's t test for paired series were used for group comparison of the body composition and metabolic parameters at T1 and T0 (SPSS Statistics, v. 25). The results were expressed as mean ± SD, and mean differences were considered significant at $p \leq .05.$
## RESULTS
The average age of our population was 48.73 ± 7.7 years with extremes ranging from 33 to 63 years. Half of the population ($51\%$) was over 50 years old. The majority of participants were female $93.3\%$ ($$n = 42$$) against $6.7\%$ ($$n = 3$$) of men. Past medical history, complications and lab test results are present in Table 1.
**TABLE 1**
| Unnamed: 0 | Diet only (%) | Prebiotic (%) | Probiotic (%) | p |
| --- | --- | --- | --- | --- |
| Past medical history | Past medical history | Past medical history | Past medical history | Past medical history |
| Diabetes | 6.7 | 26.7 | 13.3 | .3 |
| Hypertension | 6.7 | 20 | 33.3 | .2 |
| Dyslipidaemia | 6.7 | 26.7 | 13.3 | .3 |
| Active smokers (%) | 6.7 | 13.3 | 6.7 | .7 |
| Osteoarthritis (%) | 33.3 | 20 | 26.7 | .6 |
| Sleep apnoea syndrome (%) | 26.7 | 66.7 | 33.3 | .07 |
| Hernia (%) | 13.3 | 6.7 | 13.3 | .6 |
| NASH (%) | 24 | 30 | 24 | .42 |
| Diabetes (%) | 13.3 | 53.3 | 20 | .06 |
| Prediabetes (%) | 13.3 | 6.7 | 33.3 | .06 |
| Insulin resistance (%) | 6.7 | 0 | 7.1 | .65 |
| High TG levels (%) | 46.7 | 66.7 | 26.7 | .18 |
| Low HDL levels(%) | 46.7 | 46.7 | 33.3 | .27 |
| High LDL levels(%) | 26.7 | 33.3 | 33.3 | .4 |
Blood pressure values are comparable in the three groups.
Our three groups were matched for BMI. There was no statistically significant difference for anthropometric measurements (weight, height, IMC, fat mass, muscle mass and waist circumference) between the three groups. In addition, the majority of patients in all three groups had normal muscle strength. Sarcopenia at T0 was noted in $20\%$ in the diet‐only group, $6.7\%$ in the prebiotic group and $13.3\%$ in the probiotic group.
In each group, $93.3\%$ of patients were sedentary.
At recruitment, we performed a frequency questionnaire consumption of foods rich in prebiotics and probiotics such as coffee, tea, garlic, onion, fermented foods, cacao, yoghurts and fruits. There were no differences between groups.
No patient reported alcohol consumption and none had a regular consumption of carob.
Most of the patients of the three groups had a high level of anxiety, depression and stress but without statistically significant difference.
The result of the intervention after 1 month are in Table 2.
**TABLE 2**
| Unnamed: 0 | Diet only | Diet only.1 | Diet only.2 | Prebiotic | Prebiotic.1 | Prebiotic.2 | Probiotic | Probiotic.1 | Probiotic.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | T0 | T1 | p | T0 | T1 | p | T0 | T1 | p |
| Weight (kg) | 103.7 | 101.2 | .001 | 103.5 | 101.6 | .003 | 106.09 | 104.4 | .02 |
| Fat mass (kg) | 46.7 | 44.8 | .07 | 47.3 | 44.3 | .001 | 47.5 | 45.01 | .001 |
| Lean mass (kg) | 54.06 | 53.5 | .3 | 55.1 | 55.6 | .2 | 55.5 | 56.4 | .08 |
| Waist circumference (cm) | 119 | 117.3 | .01 | 124 | 120 | .03 | 122 | 119 | .001 |
| Muscle strength (kg) | 24.4 | 24.3 | .8 | 27.4 | 28.8 | .008 | 24.8 | 26.5 | .004 |
| Systolic blood pressure (mmHg) | 13 | 12.8 | .6 | 13 | 12.2 | .03 | 13.3 | 12.6 | .01 |
| Fasting glucose (mmol/l) | 5.3 | 5.5 | .27 | 7.5 | 5.1 | .2 | 5.66 | 5.6 | .6 |
| HbA1c (%) | 5.6 | 5.5 | .03 | 6.6 | 6.3 | .3 | 5.8 | 5.6 | .003 |
| Insulin (μUI/l) | 18.4 | 15.2 | .07 | 23.8 | 14.5 | .002 | 17.5 | 13.7 | .005 |
| HOMA‐IR | 4.3 | 3.8 | .2 | 9.1 | 3.8 | .009 | 4.5 | 3.4 | .009 |
| Cholesterol (mmol/l) | 5.2 | 4.8 | .03 | 5.3 | 4.9 | .005 | 5.2 | 4.6 | .08 |
| HDL (mmol/l) | 1.6 | 1.05 | .9 | 1.07 | 1.08 | .8 | 1.2 | 1.22 | .7 |
| LDL (mmol/l) | 3.2 | 2.9 | .05 | 3.3 | 2.9 | .003 | 3.2 | 2.8 | .004 |
| Triglycerides (mmol) | 1.7 | 1.6 | .4 | 1.9 | 1.4 | .001 | 1.6 | 1.4 | .03 |
| ALAT (UI/l) | 21.2 | 18.4 | .03 | 21.3 | 20.8 | .7 | 21.1 | 17.8 | .01 |
| Uric acid | 277.2 | 289.4 | .4 | 346.3 | 365.3 | .3 | 295.7 | 284.2 | .1 |
| Epworth | 9.8 | 8.6 | .06 | 8.7 | 7 | .02 | 10.2 | 7.9 | .03 |
| Anxiety | 13.3 | 11.2 | .02 | 11.4 | 9.4 | .01 | 13.3 | 11.6 | .06 |
| depression | 12.4 | 9.9 | .001 | 11.2 | 8.06 | .01 | 11.5 | 8.9 | .001 |
| Stress | 40.1 | 33.4 | .01 | 36.2 | 31.3 | .001 | 35.6 | 29.3 | .002 |
The results of anthropometric measurements after the intervention in the three groups showed a statistically significant decrease in weight, BMI and WC, but muscle strength has increase only with pre and probiotics.
The population has significantly decreased energy and macronutrient (protein, carbohydrate and lipid) intake, with a significant decrease in sugar and sodium intake.
A significant increase in fibre intake was noted in the diet and prebiotic group but not in the probiotic group. The quality of sleep was not improved by the diet only and probiotics did not enhance anxiety.
Taking probiotics was associated with the occurrence of diarrhoea in $20\%$ of cases ($p \leq .001$).
Then we compared the diet alone versus prebiotics group for all the parameters listed in Table 3. The difference was not significant. Then it was the diet alone group versus probiotics and finally prebiotics versus probiotics.
**TABLE 3**
| Unnamed: 0 | Mean difference (T0–T1) | Mean difference (T0–T1).1 | p | Mean difference (T0–T1).2 | Mean difference (T0–T1).3 | p.1 | Mean difference (T0–T1).4 | Mean difference (T0–T1).5 | p.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Diet only | Prebiotics | p | Diet only | Probiotics | p | Prebiotics | Probiotics | p |
| Fasting glycaemia (mmol/l) | −0.18 | 1.6 | .016 | −0.18 | 0.06 | .02 | 1.6 | 0.06 | .3 |
| HbA1c (%) | 0.12 a | 0.2 | .17 | 0.12 a | 0.18 a | .3 | 0.2 | 0.18 a | .4 |
| Insulin (μUI/l) | 2.9 | 9.3 a | .03 | 2.9 | 3.8 a | .3 | 9.3 a | 3.8 a | .2 |
| HOMA‐IR | 0.5 | 5.3 a | .012 | 0.5 | 1.04 a | .1 | 5.3 | 1.04 a | .2 |
| Uric acid | −12.2 | −19.07 | .75 | −12.2 | 11.6 | .001 | −19.07 | 11.6 | .02 |
Our conclusion is that the different therapeutic means are equal for the dietary survey, the different scores (stress, sleep, anxiety and depression).
The influence of the three means on weight loss is equivalent even if it is the diet alone group which reduced the weight more except for the lean mass which was clearly increased by probiotics compared to diet ($$p \leq .05$$). On the other hand, significant differences between the three means were found in the results of the blood tests represented in Table 3.
Prebiotics and probiotics were better than diet for the reduction of fasting glycemia and insulin resistance but probiotics did not lower uric acid as much as others.
## DISCUSSION
This study was an interventional clinical trial designed to examine the effects of a combination of probiotic bacteria B. longum, L. helveticus, L. lactis, S. thermophilus and a prebiotic supplement by 30 g/day of carob on changes in body composition, metabolic biomarkers and psychological profile in obese human subjects enrolled on a weight loss program. The weight loss program was a low‐carbohydrate, energy‐restricted eating plan. The study has confirmed that a low‐carbohydrate, restricted‐energy diet can be effectively used for weight loss in obese individuals.
Our work has some strength—to our knowledge in Tunisia no one studied the association between prebiotics or probiotics and obesity, the only Tunisian study that has worked on the microbiota has studied the imbalance of the microbiota in diabetic patients. 14 The use of carob as a prebiotic for weight loss is an innovation that fits into abandoned Tunisian habits. Carob is available at a nominal cost less than some fruits and vegetables. Our study focused on several parameters apart from anthropometry, such as biology and other assessment tests such as the Epworth score, the HAD and the Cungi stress score but it has some limitations like the small number of patients for each group and microbiological analysis for the gut microbia was not performed. In addition, the study was conducted over a month; perhaps a longer duration of intervention would show other results.
Many studies have shown the effect of pre or probiotic on the weight loss. Sergeev et al., 15 compared the effect of symbiotic supplementation (prebiotic and probiotic) on the body composition of obese patients against a placebo group which received only a low‐calorie diet, they found a significant decrease in weight in both groups. However, the study of Hiel et al., 16 using inulin as prebiotic compared to placebo, found a significant reduction in weight in the prebiotic group. This difference may be due to the difference in the prescribed diet and also to the difference in the number of patients. In addition, the study by Stenman et al., 17 which is a study that compared the effect of prebiotic alone, probiotic alone and prebiotic+probiotic to a placebo group, found that only the probiotic alone group presented weight loss compared to the other groups. Some other studies did not found a difference between groups. 18, 19 This difference may be due to the difference in the diet given and also the type of prebiotic and probiotic used. Similarly, Rodriguez in their studies showed that there were responders and non‐responders in obese patients treated with prebiotics depending on the initial species of intestinal flora present in the host during the intervention. 20 Indeed, the microbiota intervenes in the regulation of energy expenditure by acting on specific hormones, thanks to a bidirectional signalling between the brain and the intestine, the gut microbiota regulates appetite and energy expenditure then follows a weight regulation. 21 Prebiotics act on the microbiota by increasing the production of short‐chain fatty acids, which in turn causes a cascade of modifications leading to weight reduction and improved metabolic parameters. 22 Our study showed a significant increase in muscle strength in both the prebiotic group and the probiotic group. As well as Zahao and Kang in their studies. 23, 24 Alteration of the gut microbiota has been shown to directly affect muscle strength. Probiotics, prebiotics and short‐chain fatty acids are potential new therapies to improve lean mass and physical performance. Strains of Lactobacillus and Bifidobacterium (present in Lactibiane*) can restore age‐related muscle loss. The pathways by which microbiota influence muscle are diverse and complex. 25 Our results showed a beneficial effect of prebiotics and probiotics on carbohydrate metabolism. These results were in agreement with the study conducted by Miller et al., 26 which found that the symbiotic yoghurt protected mice against diabetes by significantly improving fasting blood glucose levels versus unenriched control yoghurt. In addition, a preparation rich in fibre and lactulose as prebiotics used in an old clinical study, 27 showed a decrease in blood sugar in 10 obese patients.
Oral supplementation with prebiotics and probiotics acts on the regulation of glycaemia, the mechanism of action consists in reducing the secretion of inflammatory markers such as IFN‐γ and IL‐1β by increasing the production of IL‐10 anti‐inflammatory. In addition, probiotics stimulate the secretion of the neurotransmitter GABA which decreases the production of glucagon and stimulates the production of insulin. 28, 29 Our study showed a decrease in uric acid in the probiotic group with a significant difference compared to the diet‐alone group and the prebiotic group. To study the effect of probiotics on uric acid, there was first the pilot study of Garcia‐Arroyo carried out in 2018 on six rats which affirmed this hypothesis. 30 Then other studies followed with the same results. 31, 32 The decrease in energy intake found after prebiotic and probiotic supplementation is explained by the stimulation of leptin secretion and the decrease in ghrelin secretion, which increase satiety and consequently decrease in intake. In addition, the reduction of microbiome lipopolysaccharides by pre and probiotics promotes reduced appetite by increasing satiety. 33 A decrease in Epworth score was found in all three groups. Our study was consistent with others. 34, 35 However, the study by Buigues et al. 36 did not show conclusive results of prebiotics on sleep quality. Following the fermentation of fibres from prebiotics by microbiota, there will be production of butyrate which improves sleep quality 37 but the mechanisms involved are more complex than that. 38
The three means were comparable in their influence on depression and anxiety. Other studies proved a good improvement of these symptoms when patients took probiotic. 39, 40 It has been shown that probiotics stimulate the production of inhibitory neurotransmitters such as the neurotransmitter GABA, which causes a reduction in anxiety and depression. 41 On the other hand, the imbalance of the gut microbiota is responsible for the occurrence of depression by the decrease in the production of some lipid metabolites (endogenous cannabinoids). 42 As for the stress, prebiotics and probiotics increase the production of serotonin, which is a molecule involved in mood regulation, by stimulating the synthesis of tryptophan 43 which improves the symptoms of stress.
## CONCLUSION
The imbalance in the functioning of the body is due on the one hand to the imbalance of the gut microbiota because of obesity which alters the beneficial microorganisms and on the other hand this alteration which further promotes obesity by several mechanisms and signalling pathways. 44 The intestinal microbiota, as it is called the second brain, intervenes in the regulation of the functioning of the organism, which has been demonstrated by several studies. Hence the importance of modulating the gut microbiota with prebiotics and probiotics to treat obesity and improve related metabolic parameters.
In the light of this study and other studies, it is advisable to take certain measures to treat obesity: *Follow a* diet balanced in energy intake to prevent the alteration of the gut microbiota. Enrich the diet with foods rich in prebiotics and probiotics, either to prevent the onset of obesity or to treat it. Treatment with pre and probiotics should be considered in case of sarcopenic obesity. Adopt treatment with prebiotics and probiotics, especially if obesity is linked to a glycaemic disorder. Prescription of prebiotics and probiotics can Improve the quality of sleep, anxiety and stress in some cases.
## AUTHOR CONTRIBUTIONS
Nadia Ben Amor: Visualization (equal). Faten Mahjoub: Visualization (equal). Olfa Berriche: Visualization (equal). Chaima El Ghali: Investigation (equal). Amel Gamoudi: Project administration (equal). Henda Jamoussi: Writing – review and editing (equal).
## FUNDING INFORMATION
This research received no funding.
## CONFLICT OF INTEREST
The authors declare no conflict of interest.
## DATA AVAILABILITY STATEMENT
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
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|
---
title: 'Examining dyslipidaemia, metabolic syndrome and liver enzyme levels in patients
with prediabetes and type 2 diabetes in population from Hoveyzeh cohort study: A
case–control study in Iran'
authors:
- Negar Dinarvand
- Bahman Cheraghian
- Zahra Rahimi
- Samaneh Salehipour Bavarsad
- Amirhooshang Bavarsad
- Narges Mohammadtaghvaei
journal: Endocrinology, Diabetes & Metabolism
year: 2023
pmcid: PMC10000631
doi: 10.1002/edm2.401
license: CC BY 4.0
---
# Examining dyslipidaemia, metabolic syndrome and liver enzyme levels in patients with prediabetes and type 2 diabetes in population from Hoveyzeh cohort study: A case–control study in Iran
## Abstract
Our results indicated a significant increase in liver enzymes, lipid profile and MetS status in both pre‐diabetic and T2MD subjects, with the differences being more pronounced in diabetic individuals.
### Introduction
Type 2 diabetes mellitus (T2DM) is among the world's top 10 leading causes of death. Additionally, prediabetes is a major risk factor for diabetes. Identifying diabetes co‐occurring disorders can aid in reducing adverse effects and facilitating early detection. In this study, we evaluated dyslipidaemia, metabolic syndrome (MetS), and liver enzyme levels in pre‐diabetic and T2DM patients in the Persian cohort compared to a control group.
### Materials and Methods
In this cross‐sectional study, 2259 pre‐diabetes, 1664 T2DM and 5840 controls (35–70 years) who were selected from the Hoveyzeh cohort centre were examined. Body mass index, blood pressure, fasting blood glucose (FBG), total cholesterol (TC), high‐density lipoprotein cholesterol (HDL‐C), triglyceride (TG) and liver enzymes: γ‐glutamyltransferase (GGT), alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined using the standard protocols. MetS subjects were also identified based on the National Cholesterol Education Program guidelines.
### Results
Prediabetes and T2MD were closely correlated with the lipid profile, MetS, and liver enzymes (ALT, GGT, ALT/AST). MetS increases the risk of T2DM by 12.45 [$95\%$ CI: 10.88–14.24] fold, while an increase in ALT/AST ratio increases the risk of T2DM by 3.68 [$95\%$ CI: 3.159–4.154] fold. ROC curve analysis also revealed the diagnostic roles of GGT, ALT, AST and the ALT/AST ratio among pre‐diabetics, diabetics and the control group. The GGT level corresponds to the highest AUCs (0.685) with the highest sensitivity ($70.25\%$).
### Conclusions
Our results indicated a significant increase in liver enzymes, lipid profile and MetS status in both pre‐diabetic and T2MD subjects, with the differences being more pronounced in diabetic individuals. Consequently, on the one hand, these variables may be considered predictive risk factors for diabetes, and on the other hand, they may be used as diagnostic factors. In order to confirm the clinical applications of these variables, additional research is required.
## INTRODUCTION
Diabetes mellitus, as a metabolic disorder, 1 is one of the most prevalent global public health issues 2 and contributes to a rise in morbidity and mortality. 3 According to estimates from the International Diabetes Federation (IDF), 1 in 11 individuals between the ages of 20 and 79 had type 2 diabetes mellitus (T2DM) in 2015, 4 which could reach 629 million by 2045. 2 *Diabetes is* hyperglycaemia resulting from insulin deficiency, insulin resistance or both. 1, 3 *Prediabetes is* a major diabetes risk factor. 2 *It is* a hyperglycaemic condition marked by impaired fasting glucose (IFG), impaired glucose tolerance (IGT) or glycated haemoglobin (A1C) of $6.0\%$–$6.4\%$, or a combination of these. 1, 2 *Both dyslipidaemia* and hypertension are significant risk factors for T2DM. According to the American Diabetes Association, patients with T2DM who have dysregulated levels of lipids such as total cholesterol, triglycerides, very‐low‐density lipoprotein (VLDL), low‐density lipoprotein (LDL) and high‐density lipoprotein (HDL) are diagnosed with diabetic dyslipidaemia. Alternatively, lipid markers may be a useful predictor of risk in diabetic patients. 5 In addition, prediabetes and T2DM are common metabolic syndrome (MetS) manifestations. 1 Some studies indicate that individuals with metabolic syndrome are four times more likely to develop T2DM. 6 MetS are characterized by hypertriglyceridemia, low HDL cholesterol, abdominal obesity or a high BMI ratio, glucose intolerance or insulin resistance, hypertension and microalbuminuria. 7
Insulin resistance syndrome may result in hepatic dysfunction, resulting in T2DM. 6 Therefore, patients with advanced liver disease have a higher incidence of diabetes than the general population. 8 Conversely, releasing free fatty acids (FFAs) due to T2DM decreases hepatic mitochondrial function. In turn, this causes further triglyceride storage in the hepatocyte and, ultimately, liver damage. 8 Serum levels of liver enzymes, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), and to a lesser extent γ‐glutamyltransferase (GGT), are frequently used as indicators of liver damage. 9 In the past decade, several studies have linked serum concentrations of these enzymes to multiple metabolic syndrome symptoms, including hepatic insulin resistance, T2DM and dyslipidaemia. 9, 10, 11 Since then, little research has been conducted on the relationship between dyslipidaemia, metabolic syndrome and liver enzyme levels in pre‐diabetic and T2DM patients. In order to determine the relationship between these risk factors and the development of prediabetes and diabetes in the adult population of Hoveyzeh cohort centre, this study was conducted on three groups: healthy, pre‐diabetic and T2DM.
## MATERIALS AND METHODS
We conducted a cross‐sectional study in men and women aged 35–70 who underwent a comprehensive health screening exam at the Hoveyzeh cohort centre for Prospective Epidemiological Research Studies in Iran (PERSIAN), a region in Iran's Southwest Khuzestan province, between 1 May 2016 and 31 August 2018. Therefore, 10,009 people were recruited in Hoveyzeh cohort centre. Patients with any of the following conditions at baseline were excluded from the study: a history of cancer, renal failure, known liver disease, ALT>3 times normal, alcohol consumption, recent (1 year) MI, acute coronary syndrome, stroke and weight loss of more than 5 kg in a month as well as microvascular complications. Finally, 9763 of 10,009 cases had the criteria for this study. All participants then completed questionnaires, including demographic information, cigarette smoking, opium use, consumable drugs, disease history and physical activity.
First, blood samples for analysis were obtained from the antecubital vein of patients and subjects who had fasted for 10 to 12 h. In the central laboratory of the Hoveyzeh cohort centre, all biochemical parameters were measured using standardized protocols on automated equipment.
Fasting serum glucose was assayed using the hexokinase/glucose‐6‐phosphate dehydrogenase method. Diabetes was defined as FBS levels ≥126 mg/dl or receiving anti‐diabetic drugs or self‐reported diagnosis of diabetes.
Standard enzymatic colorimetric techniques were used to measure serum total cholesterol (TC), triacylglycerol (TG) and high‐density lipoprotein cholesterol (HDL‐C) levels. The level of low‐density lipoprotein cholesterol (LDL‐C) was determined using the Friedewald et al. formula (LDL‐C = TC ‐ HDL ‐ VLDL cholesterol). 9 The levels of AST, ALT and GGT were determined using the International Federation of Clinical Chemistry's method. All these analyses were done using commercial kits (Pars Azmon Inc.).
MetS is defined by three or more of the following National Cholesterol Education Program criteria: high TG (≥150 mg/dl); low HDL‐C (≤40 mg/dl) for men and <50 for women; high fasting blood sugar (≥100 mg/dl) or known type 2 diabetes; hypertension (at least $\frac{135}{85}$ mmHg or receiving antihypertensive medication); and a waist circumference greater than 102 cm for men and 88 for women. 6, 12, 13
## Statistical analysis
The statistical analyses were conducted using SPSS (v. 15.0). For quantitative variables, data were presented as mean ± standard deviation; for qualitative variables, data are expressed as frequency (number (%)), The normality of data was determined using the Kolmogorov–Smirnov test, and the chi‐square test was used to determine the association between qualitative variables.
Differences between the two groups were calculated by Mann–Whitney tests for skewed data. In addition, the Kruskal–Wallis test was used to compare variables in three groups.
Moreover, logistic regression analysis was employed to calculate studied risk factors for prediabetes and diabetes vs. control group. Then, multivariable model was performed for adjusting of age, gender and BMI. Receiver operating characteristic (ROC) curve analysis was used to determine the prognostic relationship of liver enzymes and lipid profile in prediabetes and diabetes. All p‐values were two‐tailed, and $p \leq .05$ were considered statistically significant.
## Characteristics of the study participants according to FBS tertiles
The final database contained 9763 subjects (3809 males and 5954 females); subjects were divided into three groups based on FBS levels. Table 1 illustrates the characteristics of three distinct groups. T2DM prevalence was $17.0\%$ ($18.1\%$ in males and $16.4\%$ in females), prediabetes prevalence was $23.1\%$ ($21.0\%$ in males and $24.5\%$ in females), and control prevalence was $59.8\%$ ($60.9\%$ in males, $59.1\%$ in females). Participants with prediabetes and T2DM were older and had a higher BMI, waist circumference, diastolic blood pressure (DBP) and systolic blood pressure (SBP) than control subjects.
**TABLE 1**
| Variables | Fasting glucose (mg/dl) | Fasting glucose (mg/dl).1 | Fasting glucose (mg/dl).2 | Fasting glucose (mg/dl).3 |
| --- | --- | --- | --- | --- |
| Variables | FBS ≤100 5840 (59.82%) | FBS: 100–125 2259 (23.14%) | FBS≥126 1664 (17.04%) | p‐Value |
| Anthropometrics | Anthropometrics | Anthropometrics | Anthropometrics | Anthropometrics |
| Gender | Gender | Gender | Gender | Gender |
| Male | 2321 (60.9%) A | 799 (21.0%) B | 689 (18.1%) C | <.0001** |
| Female | 3519 (59.1%) A | 1460 (24.5%) B | 975 (16.4%) C | <.0001** |
| Age (year) | 47.03 ± 8.79 A | 50.53 ± 9.27 B | 52.81 ± 8.89 C | <.0001* |
| Waist Circumference (cm) | 97.62 ± 11.83 A | 102.45 ± 12.09 B | 103.15 ± 11.46 B | <.0001* |
| BMI (kg/m2) | 28.14 ± 5.15 A | 30.03 ± 5.52 B | 29.63 ± 5.23 C | <.0001* |
| Diastolic blood pressure (mmHg) | 70.32 ± 10.97 A | 72.60 ± 11.38 B | 73.16 ± 11.51 B | <.0001* |
| Systolic blood pressure (mmHg) | 110.46 ± 16.95A | 115.49 ± 18.90 B | 118.27 ± 20.19 C | <.0001* |
| Metabolic syndrome | Metabolic syndrome | Metabolic syndrome | Metabolic syndrome | Metabolic syndrome |
| No | 4409 (75.5%) A | 630 (27.9%) B | 330 (19.8%) C | <.0001** |
| Yes | 1413 (24.5%) A | 1629 (72.1) B | 1334 (80.2%) C | <.0001** |
| Biochemicals | Biochemicals | Biochemicals | Biochemicals | Biochemicals |
| FBS (mg/dl) | 88.97 ± 6.54 A | 108.25 ± 6.96 B | 201.48 ± 67.98 C | <.0001* |
| LDL (mg/dl) | 105.62 ± 31.17A | 109.51 ± 33.83A | 106.86 ± 37.33B | <.0001* |
| TG (mg/dl) | 147.6 ± 84.2 A | 170.06 ± 107.3 B | 202.02 ± 135.4 C | <.0001* |
| Total Cholesterol (mg/dl) | 185.76 ± 37.1 A | 193.97 ± 40.8 B | 196.34 ± 47.9 B | <.0001* |
| HDL (mg/dl) | 50.68 ± 12.24 A | 50.34 ± 11.75 A | 49.21 ± 11.61 B | <.0001* |
| Hepatic enzymes | Hepatic enzymes | Hepatic enzymes | Hepatic enzymes | Hepatic enzymes |
| AST (units/L) | 18.06 ± 7.62 A | 19.30 ± 9.19 A | 17.36 ± 9.05 B | <.0001* |
| ALT (units/L) | 20.52 ± 13.72 A | 22.03 ± 14.88 B | 22.25 ± 13.35 C | <.0001* |
| GGT (units/L) | 24.14 ± 16.61 A | 27.05 ± 17.68 B | 34.73 ± 34.01 C | <.0001* |
| ALT/AST | 1.06 ± 0.384 A | 1.10 ± 0.380 B | 1.28 ± 0.514 C | <.0001* |
In prediabetes and T2DM, biochemical variables, including TG, were significantly higher than in the control group. Compared to the control group, prediabetes and diabetes had significantly higher mean total cholesterol levels, whereas there was no significant difference between prediabetes and diabetes. In addition, the mean LDL in diabetes and normal groups was significantly higher than in the prediabetes group, but there was no significant difference between diabetes and normal groups. In contrast, the HDL level was significantly lower in T2DM compared to prediabetes and the control group, whereas there was no significant difference between prediabetes and the control group.
Those who developed prediabetes and T2DM had significantly higher levels of hepatic enzymes, including GTT and ALT, compared to the control group. In contrast, the mean AST was significantly lower in T2DM than in prediabetes and the control group, and there was no significant difference between prediabetes and the control group (Table 1).
## ROC curve analysis
Receiver operating characteristic curve analysis revealed the significance of GGT, ALT, AST and the ALT/AST ratio in identifying prediabetes or diabetes (Table 2, Figure 1). The ROC curve analysis is presented in Table 2.
Roc curve analysis of GTT, ALT, AST and ALT/AST in diabetes vs. control group was as follows: AUC = 0.685; ($95\%$ CI: 0.673–0.694; $p \leq .0001$; Cut‐off value: >21.36; Sensitivity: $70.25\%$; Specificity: $57.76\%$), AUC = 0.564; ($95\%$ CI: 0.553–0.575; $p \leq .0001$; Cut‐off value: >14; Sensitivity: $70.85\%$; Specificity: $39.88\%$), AUC = 0.588; ($95\%$ CI: 0.577–0.600; $p \leq .0001$; Cut‐off value: <14; Sensitivity: $43.33\%$; Specificity: $71.64\%$), AUC = 0.669; ($95\%$ CI: 0.658–0.679; $p \leq .0001$; Cut‐off value: >1.06; Sensitivity: $68.87\%$; Specificity: $57.29\%$), respectively. Similar results were also observed in the case of prediabetes vs. control group, including: AUC = 0.573; ($95\%$ CI: 0.562–0.583; $p \leq .0001$; Cut‐off value: >20.33; Sensitivity: $57.10\%$; Specificity: $53.89\%$), AUC = 0.537; ($95\%$ CI: 0.526–0.548; $p \leq .0001$; Cut‐off value:>15; Sensitivity: $60.69\%$; Specificity: $45.26\%$), AUC = 0.513; ($95\%$ CI: 0.526–0.548; $$p \leq .082$$; Cut‐off value: >25; Sensitivity: $14.52\%$; Specificity: $88.48\%$), AUC = 0.542; ($95\%$ CI: 0.531–0.553; $p \leq .0001$; Cut‐off value: >0.95; Sensitivity: $61.66\%$; Specificity: $46.25\%$).
## Logistic regression analysis
According to logistic regression analysis, some liver enzymes, lipid profiles and metabolic syndrome were associated with an increased odds of developing prediabetes or diabetes (Table 3). The estimated ORs for metabolic syndrome in the prediabetes and diabetes groups were 7.966 ($95\%$ CI: 7.139–8.889; $p \leq .0001$) and 12.45 ($95\%$ CI: 10.88–14.24; $p \leq .0001$), respectively. In the case of AST, however, the odds ratio (0.976) indicated a reduction in diabetes odds ($95\%$ CI: 0.968–0.984; $p \leq .0001$). On the contrary, the ALT/AST ratio increases the odds of prediabetes and diabetes development by 1.347 ($95\%$ CI: 0.968–0.984; $p \leq .0001$) and 3.623 ($95\%$ CI: 3.159–4.154; $p \leq .0001$, respectively). After the adjustment for age, sex and BMI, there was almost no difference with the results obtained from univariate analysis. However, both analyses display significantly positive relationships between ALT/AST ratio and metabolic syndrome with prediabetes and diabetes.
**TABLE 3**
| Prediabetes | Prediabetes.1 | Prediabetes.2 | Prediabetes.3 | Diabetes | Diabetes.1 | Diabetes.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Variable | Odds Ratios | 95% CI | p‐Value | Odds Ratios | 95% CI | p‐Value |
| model 1 a | model 1 a | model 1 a | model 1 a | model 1 a | model 1 a | model 1 a |
| GGT | 1.009 | 1.006–1.012 | <.0001 | 1.024 | 1.021–1.027 | <.0001 |
| AST | 1.010 | 1.004–1.016 | <.0001 | 0.976 | 0.968–0.984 | <.0001 |
| ALT | 1.007 | 1.004–1.010 | <.0001 | 1.008 | 1.004–1.012 | <.0001 |
| ALT/AST | 1.347 | 1.190–1.525 | <.0001 | 3.623 | 3.159–4.154 | <.0001 |
| LDL | 1.003 | 1.002–1.005 | <.0001 | 1.001 | 0.999–1.002 | <.0001 |
| HDL | 0.997 | 0.993–1.001 | <.0001 | 0.989 | 0.984–0.993 | <.0001 |
| TG | 1.002 | 1.001–1.002 | <.0001 | 1.005 | 1.004–1.005 | <.0001 |
| TC | 1.005 | 1.004–1.006 | <.0001 | 1.006 | 1.005–1.007 | <.0001 |
| MetS | 7.966 | 7.139–8.889 | <.0001 | 12.45 | 10.88–14.24 | <.0001 |
| model 2 b | model 2 b | model 2 b | model 2 b | model 2 b | model 2 b | model 2 b |
| GGT | 1.010 | 1.007–1.013 | <.0001 | 1.024 | 1.021–1.027 | <.0001 |
| AST | 1.015 | 1.009–1.021 | <.0001 | 0.979 | 0.970–0.988 | <.0001 |
| ALT | 1.012 | 1.009–1.016 | <.0001 | 1.014 | 1.010–1.018 | <.0001 |
| ALT/AST | 1.595 | 1.382–1.842 | <.0001 | 5.632 | 4.776–6.640 | <.0001 |
| LDL | 1.002 | 1.000–1.003 | .040 | 0.999 | 0.997–1.000 | .151 |
| HDL | 0.996 | 0.992–1.001 | .087 | 0.988 | 0.983–0.993 | <.0001 |
| TG | 1.002 | 1.002–1.003 | <.0001 | 1.005 | 1.004–1.006 | <.0001 |
| TC | 1.004 | 1.002–1.005 | <.0001 | 1.005 | 1.003–1.006 | <.0001 |
| MetS | 6.833 | 6.100–7.654 | <.0001 | 10.67 | 9.268–12.28 | <.0001 |
## DISCUSSION
In the current study, we observed a significant increase in all metabolic risk factors and liver enzymes, except for HDL‐C and AST, in both prediabetic and T2MD subjects, with the differences being more pronounced in diabetic individuals.
In subjects with prediabetes and T2DM, the mean LDL, TG and TC levels were higher. Consistent with these findings, Dhoj et al. 14 demonstrated that diabetes is associated with a high prevalence of dyslipidaemia characterized by elevated levels of cholesterol, TG and LDL. Additionally, Jasim et al. 5 identified TG as one of the promising biomarkers for predicting prediabetes and T2DM. These findings support that diabetes patients are more susceptible to co‐occurring diseases such as hyperglycaemia, chronic renal failure, hypothyroidism and polypharmacy, with drugs known to have adverse effects on lipid profiles. Patients with diabetes must therefore be treated to prevent coronary artery disease. 15 Individual metabolic syndrome characteristics (such as higher BMI, waist circumference, DBP and SBP levels, among others) were associated with the prevalence of prediabetes and T2DM, according to the findings of this study. Thus, $80\%$ of subjects with T2DM and $72\%$ in the prediabetes group had MetS, whereas only $24\%$ of the control group exhibited metabolic syndrome symptoms. In addition, Ogedengbe et al. 16 found that the prevalence of MetS among T2DM patients is extremely high.
This study revealed that liver enzymes, including ALT and GGT but not AST, and the ALT/AST ratio were significantly elevated in prediabetes and T2MD cases. However, some studies have found no correlation between elevated ALT and diabetes, possibly due to the ethnic diversity of the study populations. 6 Forlani et al. 17 reported a high prevalence of elevated ALT, AST and GGT levels in T2DM, which is consistent with our findings. Although there are no clear biological explanations for the relationships between liver indicators and glucose metabolism, one possible mechanism is that MetS and T2DM increase the risk of liver damage, increasing liver enzyme levels. 9 To reduce the risk of liver damage, prediabetics and diabetic patients may require a comprehensive clinical, laboratory and histological examination.
In addition, GGT, ALT and the ALT/AST ratio, but not AST, can be used to identify prediabetes and diabetes based on ROC results. Among prediabetic and diabetic subjects, the GGT level has the highest areas under the curve (AUC) and the highest sensitivity compared to the control group. In contrast, logistic regression analysis revealed that higher levels of ALT, GGT and ALT/AST were independent risk factors for prediabetics and diabetics and that an increase in the ALT/AST ratio increased the risk of T2MD by 3.68‐fold, whereas lower AST levels were associated with the risk of diabetes. Sun‐Hye et al. 18 observed that higher levels of GGT and ALT and a lower AST/ALT ratio were independent risk factors for diabetes and impaired fasting glucose (IFG). Additionally, Zhao et al. 19 evidenced that the ALT/AST ratio may be a useful indicator of insulin resistance (IR) in the Chinese population.
According to several studies, elevated GGT and ALT levels are also beneficial for identifying early markers of dysregulated glucose metabolism, which strongly correlate with prediabetes and diabetes. 20 A second proposed mechanism for the relationship between hepatic indices and glucose metabolism is that elevated serum ALT and GGT levels indicate hepatic steatosis, resulting in hepatic insulin resistance (IR). 18 IR is a risk factor for T2DM. 19 Therefore, it is unknown whether T2DM increases liver enzyme levels or whether elevated liver enzyme levels increase the risk of developing T2DM. Therefore, additional research is required to clarify these theories.
In contrast to our findings, some studies have found that elevated GGT levels, but not ALT or AST, can be used to predict the onset of T2DM. 9 Sattar et al. 21 also demonstrated that elevated ALT levels within the ‘normal’ range predict diabetes independently of elevated AST levels. Although we did not examine the role of gender in transaminase levels in this study, a possible explanation for these contradictory findings may be that transaminase levels are gender‐specific, according to the findings of some studies. 22 Consequently, it appears that using the ratio of variables, such as ALT/AST, rather than each variable individually may be more effective in evaluating diabetes patients.
## CONCLUSION
Our results indicated a significant increase in liver enzymes except AST, lipid profile except HDL‐C, and MetS status in both prediabetic and T2MD subjects, with the differences being more pronounced in diabetic individuals. On the one hand, these variables or their ratio may be considered predictive risk factors for diabetes, and on the other hand, they may be utilized as diagnostic factors. However, it is unknown whether T2DM increases liver enzyme levels or whether elevated liver enzyme levels increase the incidence of T2DM, and the pathophysiologic pathways underlying this association are unclear. Therefore, additional research is required to clarify these theories and validate their clinical applications.
## AUTHOR CONTRIBUTIONS
N. M. designed and supervised the study. N. D. wrote the paper. S. SP., Z. R. and B. C. analysed data. All authors read and approved the final manuscript.
## CONFLICT OF INTEREST
The authors declare no conflict of interest.
## ETHICAL APPROVAL
This study was approved by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences (Ethical code: IR. AJUMS. REC.1398.455), and the informed consent was taken from all patients who participated in Hoveyzeh Cohort.
## DATA AVAILABILITY STATEMENT
Data will be made available on request.
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|
---
title: Additional Active Movements Are Not Required for Strength Gains in the Untrained
during Short-Term Whole-Body Electromyostimulation Training
authors:
- Holger Stephan
- Udo Frank Wehmeier
- Tim Förster
- Fabian Tomschi
- Thomas Hilberg
journal: Healthcare
year: 2023
pmcid: PMC10000632
doi: 10.3390/healthcare11050741
license: CC BY 4.0
---
# Additional Active Movements Are Not Required for Strength Gains in the Untrained during Short-Term Whole-Body Electromyostimulation Training
## Abstract
Recommendations for conventional strength training are well described, and the volume of research on whole-body electromyostimulation training (WB-EMS) is growing. The aim of the present study was to investigate whether active exercise movements during stimulation have a positive effect on strength gains. A total of 30 inactive subjects (28 completed the study) were randomly allocated into two training groups, the upper body group (UBG) and the lower body group (LBG). In the UBG ($$n = 15$$; age: 32 (25–36); body mass: 78.3 kg (53.1–114.3 kg)), WB-EMS was accompanied by exercise movements of the upper body and in the LBG ($$n = 13$$; age: 26 (20–35); body mass: 67.2 kg (47.4–100.3 kg)) by exercise movements of the lower body. Therefore, UBG served as a control when lower body strength was considered, and LBG served as a control when upper body strength was considered. Trunk exercises were performed under the same conditions in both groups. During the 20-min sessions, 12 repetitions were performed per exercise. In both groups, stimulation was performed with 350 μs wide square pulses at 85 Hz in biphasic mode, and stimulation intensity was 6–8 (scale 1–10). Isometric maximum strength was measured before and after the training (6 weeks set; one session/week) on 6 exercises for the upper body and 4 for the lower body. Isometric maximum strength was significantly higher after the EMS training in both groups in most test positions (UBG $p \leq 0.001$–0.031, $r = 0.88$–0.56; LBG $$p \leq 0.001$$–0.039, $r = 0.88$–0.57). Only for the left leg extension in the UBG ($$p \leq 0.100$$, $r = 0.43$) and for the biceps curl in the LBG ($$p \leq 0.221$$, $r = 0.34$) no changes were observed. Both groups showed similar absolute strength changes after EMS training. Body mass adjusted strength for the left arm pull increased more in the LBG group ($$p \leq 0.040$$, $r = 0.39$). Based on our results we conclude that concurring exercise movements during a short-term WB-EMS training period have no substantial influence on strength gains. People with health restrictions, beginners with no experience in strength training and people returning to training might be particularly suitable target groups, due to the low training effort. Supposedly, exercise movements become more relevant when initial adaptations to training are exhausted.
## 1. Introduction
Whole-body electromyostimulation (WB-EMS) is a training method that can complement or to some extent replace traditional resistance training, as it can be used alone, superimposed, or combined (different training time points). Since several electrodes are used [1], different muscles can be stimulated at the same time [2]. Strength improvements can be achieved with both high-intensity resistance training and WB-EMS [3]. Previous studies have shown that WB-EMS is applicable in healthy people [4] and also in patients, e.g., in people suffering from Parkinson [5] or sarcopenic obesity [6]. In conventional resistance training, the one repetition maximum (1-RM) is used to describe the training intensity [7]. Since it represents the maximal voluntary contraction, a comparison between electromyostimulation (EMS) and normal contraction is possible [8]. A low-cost way to determine the intensity of strength training is to capture the perceived exertion using the Borg scale [9], which is also used in WB-EMS training [10,11]. In contrast to the 1-RM, where voluntary force production under external load is recorded, the perceived exertion reflects the internal load. For beginners in conventional strength training, at least 2 training sessions per week are recommended. Both multi-joint and single-joint exercises can be performed, using a variety of equipment and the own body weight. Per set, 8 to 12 repetitions should be completed at 60–$70\%$ of the repetition maximum [12]. To provide a safe and effective application of WB-EMS, guidelines recommend restricting the duration of one session to a maximum of 20 min. Moreover, the frequency should be limited to one session a week for at least the first eight weeks or a minimum interval of four days should be maintained thereafter [13]. Perceived exertion should be rated approximately as “hard” to “hard+” (lower during initial training) [13], corresponding to 5 to 6 on the Borg CR 10 scale [14]. Nevertheless, in some trials, the training frequencies were higher [2,15], and sometimes lower with one session a week [16,17] compared to the aforementioned recommendation after familiarization. The aggregated training stimulus consists of the number of sessions a week and the length of the training period. Usually, eight sessions or more have been conducted in strength related WB-EMS studies with healthy subjects [10,18]. Early strength improvements due to strength training can be attributed mainly to neural factors. From the third to fifth week on, strength development is mainly caused by hypertrophy [19]. Increases after very few sessions (as seen after three training sessions) are supposedly attributable to lower antagonist activity or motoric improvements of synergists [20]. Elgueta-Cancino and colleagues [21] elicited less inhibitory activity in the cortex, higher corticospinal excitability, and altered motor unit activation as assumed mechanisms of initial strength gain. Muscle growth and strength gain can also be achieved by compact training (eight weeks with three sessions a week) with neuromuscular electrical stimulation [22]. Similar to conventional strength training early strength gains owing to EMS-training are achieved without muscle growth [23].
The body of research on WB-EMS training is growing [24]. EMS can be superimposed on maximum or sub-maximum voluntary dynamic or isometric contractions or applied without any concomitant voluntary contraction. Nevertheless, little is known about the importance of active exercise movements during stimulation. Strength gains due to EMS with exercise movements were previously shown [25,26] and some authors addressed the impact of EMS superimposed on intense strength training [27,28]. To our knowledge, only Kemmler and colleagues [29] investigated the effects of smaller, WB-EMS accompanying movements. In this randomized controlled trial (RCT), participants trained once a week for 12 weeks. However, only older females with little muscle mass were included for the comparison between dynamic use (movements during stimulation) and passive use (only isometric contractions during stimulation) limiting the generalizability of the results obtained. Therefore, the present study aims to investigate whether active exercise movements during stimulation have a positive effect on strength gains of selected upper and lower body muscles in young healthy subjects of both sexes in training sessions using mobile, easily accessible fitness equipment, or the own body mass. We hypothesized that WB-EMS combined with concurrent exercise movements will result in higher strength gains than WB-EMS alone. Hence, this study was designed to clarify whether movement sequences are necessary for strength gains during WB-EMS or, whether the electrostimulation alone induces strength gains. The results might help fitness professionals and EMS-users to optimize recommendations for WB-EMS training depending on individual goals and requirements.
## 2.1. Subjects
The number of subjects to be included in the study was determined using an a priori sample size calculation for statistical comparison of the means of two unpaired groups (using the program GPower 3.1) based on the mean of the effect sizes (Δ strength leg extension: $d = 1.67$; Δ strength leg flexion: $d = 0.79$) reported by Kemmler and colleagues [29]. This study is similar to the present study. A predefined lower limit of statistical power of $80\%$ and anα error probability of 0.05 were assumed. A dropout rate of $20\%$ was further added. Based on the results of this calculation, a total of 30 subjects were initially recruited for participation. Subjects were included when being aged between 20 and 40 years and having abstained from physical activity for at least six months prior to the start of the study. Access was possible for both sexes. Subjects were excluded when acute injuries or physical complaints were reported or when contraindications as listed by Kemmler and colleagues [30] or Stöllberger and Finsterer [31] were present (e.g., epilepsy, bleeding disorders). No other exclusion criteria were defined (e.g., BMI, VO2max). The study was conducted in accordance with the principles of the Declaration of Helsinki [32] and approved by the ethics committee of the University of Wuppertal (MS/BBL 200114 Wehmeier). All subjects signed a written consent to participate in the study.
## 2.2. Experimental Design
The procedure was based on a randomized controlled trial design (Figure 1). Subjects were randomly assigned to two training groups (with the program RandList 1.2), with the number of subjects in both groups being equal. In the upper body group (UBG), WB-EMS was accompanied by exercise movements of the upper body only and in the lower body group (LBG) by exercise movements of the lower body only. Therefore, the UBG served as a control when lower body strength is considered, and the LBG served as a control when upper body strength is considered. With this design, WB-EMS without exercises and WB-EMS with exercises could be compared. Intervention duration was set to six weeks, training frequency to one session/week, and the duration of the training session to 20 min. Before and after the training period, maximum force was determined during various exercises. Blinding of subjects was not possible because the intervention is identifiable. Blinding of the investigator was not applicable because the training instructions and the test instructions were given by the same person, a professional EMS trainer with a bachelor’s degree in sports science. Subjects were asked to maintain their dietary habits and to keep their physical activity levels constant, which also meant avoiding additional physical activity. All interventions and measurements were conducted in an EMS studio (go!Orange—Studio für EMS, Remscheid, Germany).
## 2.3. WB-EMS Procedure
Both the UBG and the LBG received the same WB-EMS application (miha bodytec II; miha bodytec GmbH, Gersthofen, Germany) once a week. Subjects wore thin tight-fitting underwear. The vest with wetted electrodes was placed on the upper body and the wetted electrode bands on the arms, buttocks, and legs (miha bodytec). During the 20-min training, the upper and lower back, abdominal muscles, buttocks, muscles around the thigh, chest, and muscles around the upper arm were stimulated with 85 Hz of 350 μs wide rectangular pulses in biphasic mode. Both the duration of the pulse interval (stimulation on) and the pulse pause (stimulation off) were set to 4 s. The pulses were ramped up to the targeted intensity without delay (full intensity directly available) and similarly ramped down to zero (direct interruption of the stimulation) at the end of the stimulation phase. To maintain the same conditions, the stimulation intensity was adjusted to 6–8 on a scale of 1 (hardly noticeable) to 10 (painful) [33]. Regardless of group affiliation, muscles were voluntarily tensed during the stimulation episode.
## 2.4. Exercise Procedure
Both groups received WB-EMS and performed exercises meanwhile. ( Supplementary Figures S1–S3). The UBG used upper body exercise movements (chest and upper back including shoulders and arms) and the LBG used lower body exercise movements (buttocks and thigh muscles including abductors and adductors). The UBG training consisted of rowing, butterfly reverse, latissimus pulls, pushups, butterfly, biceps curls, and triceps pulldowns. The LBG training consisted of squats, lunges, adductions, abductions, hip lifts, and leg raises. Both groups exercised the trunk (abdomen and lower back) with back extensions, crunches, and oblique crunches. Selected exercises were performed with additional fitness equipment (fitness tubes and elastic bands, each with varying resistance, and a Swiss Ball). During the first 1 to 2 sessions (depending on the training level), subjects maintained the position over the period of stimulation that they had taken at the onset of the stimulus. One set of 12 repetitions was performed per exercise, with each repetition beginning with the onset of the pulse. To maintain the same physical load level, i.e.,16 to 17 on the Borg RPE scale [33], the number of movements during an impulse interval could be increased up to three. If the training stimulus was not sufficient after the aforementioned customization, the originally targeted static exercise position should be maintained during the interval break. However, overexertion led to a backward correction. Another way to increase the intensity to the desired level was to increase the resistance either by giving an additional fitness tube or rubber band, or by using a version that offered more resistance.
## 2.5. Isometric Strength Testing Procedure
Isometric maximum strength (N) was determined during 10 different exercises (arm adduction, arm pull, leg extension, and leg curl, each unilateral left and unilateral right, as well as during biceps curl and triceps pulldown, each bilateral) in standardized positions (Supplementary Figure S4) pre (initial measurement) and post (final measurement) intervention using a mobile device (KD 9363 including DMS measuring amplifier GVS-2; ME-measuring systems GmbH, Hennigsdorf, Germany), which was more practicable than the determination of the 1-RM. Reliability of the isometric maximum strength measurement method was verified by Runkel and colleagues for several test positions (triceps pulldown, biceps curl, arm pull, sit-up, leg curl, leg extension) in healthy subjects with a comparable body mass index [34] by a high interclass correlation coefficient ($r = 0.764$ to 0.934). At both time points, the tests were performed three times in each position. The pause was set to 10 seconds between individual tests. In each case, the maximum value was used for analysis. The whole testing procedure lasted approximately 20 min.
## 2.6. Statistical Analysis
Due to the presence of some discordant values (see box plots), skewed distribution in some cases (Shapiro–Wilk test), partial heterogeneity of error variances (Levene’s test), and partial heterogeneity of covariances (Box test), nonparametric statistical tests were employed. The differences between the initial and the final maximum isometric strength were determined separately for each group using the Wilcoxon test. The initial and the final values were compared between the groups using the Mann–Whitney U test. Absolute differences were calculated by subtracting the initial values from the final values, and relative differences were calculated by dividing the final values by the initial values (the initial value was set to $100\%$). Group comparisons were performed using the Mann–Whitney U test for absolute and relative differences. The significance level was set to < 0.05. Two-tailed analyses were used. The results of the non-parametric tests were used to calculate the effect sizes [35]. A distinction was made between large effects (r ≥ 0.5), medium effects (< 0.5 to 0.3), and small effects (< 0.3 to 0.1) [36]. Statistics were calculated using SPSS (IBM SPSS Statistics for Windows, Version 28.0., IBM Corp., Armonk, NY, USA) and Excel (Microsoft Excel for Windows, 16.0., Microsoft Corp., Redmond, WA, USA). An intention-to-treat analysis was not possible due to dropouts occurring at baseline.
## 3. Results
Of the included subjects, 28 completed the study. The dropouts occurred due to personal reasons. The characteristics of the groups did not differ significantly from each other (Table 1) and the total training volume was similar in both groups. Most subjects ($$n = 9$$ in each group) completed five sessions and no adverse effects occurred. The body mass remained unchanged in both the UBG and the LBG (Table 1). Neither the initial nor the final values differed significantly between the two groups. Isometric maximum strength was significantly higher after EMS training in both groups, both in absolute terms (Table 2 UBG; Table 3 LBG) and body mass adjusted (N/kg), except for left leg extension in the UBG and biceps curl in the LBG. The changes in absolute strength were similar in both groups (Table 4). Body mass adjusted strength during left arm pull showed a higher increase in the LBG (Figure 2). In the other test positions, group affiliation made no difference (Figure 2, Figure 3 and Figure 4). Furthermore, the LBG achieved a higher percentage strength gain in left arm pull, both absolute (Table 4) and body mass adjusted (UBG median $114.25\%$ vs. LBG median $137.05\%$; $$p \leq 0.020$$; $r = 0.44$).
## 4.1. Overview
Significant strength changes were observed in both groups after about five weeks training (one session per week). The percentage differences between the initial and final tests were higher than those found in the reliability analysis of the test device by Runkel and colleagues [34]. Therefore, the changes could be attributed to training. LBG training improved left arm pull strength more than UBG training. However, there were no group differences in the other exercises. Initial values between the two groups were not significantly different, but possibly at clinically relevant levels. If the higher initial values had been due to differences in training history, a lower ability to further increase strength would have be needed to be considered [37]. However, subjects should have abstained from intense physical activity for at least six months before starting the study.
## 4.2. Accompanying Voluntary Activity
Little is known about the effects of movements for strength gain during EMS. During local application, movements are usually avoided and isometric contractions are performed. Maffiuletti [38] summarized that there are no differences in strength increase between EMS and EMS superimposed on voluntary contractions. However, the conclusion is based on the results of isometric interventions. Although movements are thought to promote the activity of stimulated muscles [26], our results failed to show a consistent influence of active exercise movements on strength gains. Furthermore, strength gains from conventional resistance training depend, among others, on the range of motion used [39,40]. However, isometric contractions at multiple joint angles might cover at least in part the physiological range of motion. For EMS training, Maffiuletti [38] recommends changing the joint position and furthermore, changing the electrode positioning to increase recruitment. Admittedly, Kemmler and colleagues [29] demonstrated the benefit of movement during WB-EMS use, with participants exercising in supine position. In contrast, our participants performed exercises in different positions. Therefore, any movements of body parts that were not primarily intended for the exercises and possible differences in resistance to gravity might have influenced the results. Furthermore, it needs to be considered that additional fitness equipment (fitness tubes and elastic bands with different resistance as well as a Swiss Ball) was used for selected exercises. However, exercise movements using additional fitness equipment did not affect the results. In addition, both the UBG and LBG performed exercises for the trunk. Therefore, both groups received partially similar dynamic training stimuli (three exercises). Movements inevitably lead to changes in muscle length and shape (e.g., biceps muscle during curl). Hence, changes in the electrode contact were very likely to occur. Furthermore, training that aims to enhance endurance and strength at the same time, such as EMS superimposed on cycling [41,42], requires movements. However, stimulation intensity must be considered to ensure the range of motion [43].
## 4.3. Training Models and Adaptations
Supraspinal mechanisms appear to be responsible for the initial strength development through EMS training [23]. Bezerra and colleagues [44] showed increased strength after EMS superimposed onto maximum isometric quadriceps contractions, not only of the exercised leg but also of the unexercised leg, confirming neural contribution. The potential to use EMS for rapid strength gains was demonstrated by Deley and colleagues [45], who reported that maximum dynamic leg extension torque in prepubertal girls could be increased by up to $50.6\%$ with three weekly isometric applications over a three-week period. According to Adams [46], atrophic patients as well as casualties are target groups for the use of EMS. After 5 to 6 weeks, a 10 to $15\%$ enhancement of muscle function can be achieved, but three sessions a week are recommended. Several studies confirmed the impact of WB-EMS on strength [10,26]. However, to our knowledge, only Kemmler and colleagues [29] have studied the effects of exercise during WB-EMS to date. In most cases, the lower body was investigated. Von Stengel and Kemmler [25] showed that leg/hip strength can be improved with 1.5 WB-EMS training sessions (with unloaded, low effort exercises) per week over a 14 to 16 week period, regardless of age. Furthermore, strength gains due to unloaded WB-EMS were similar compared to a HIT training after 16 weeks with three sessions in two weeks [3]. An increase in strength was also observed after shorter training periods. For example, WB-EMS superimposed on jumps twice a week over seven weeks significantly improved leg strength in contrast to normal jump training [10,47,48]. In the study by Wirtz and colleagues [28], leg flexors strength increased only after combining stimulation of multiple body parts with loaded squats ($100\%$ 10 RM) twice per week and it was higher three weeks after the six-week training compared to the same training without stimulation. Dörmann and colleagues [18] showed significant improvements in leg strength after a four-week, eight-session WB-EMS training program that were similar to those seen in the control group, which performed the same training that included strength exercises, without additional stimulation, and in which intensification was accomplished using other training tools. However, not only leg muscles but also upper body muscles could benefit from dynamic WB-EMS. Reljic and colleagues [26] observed improvements throughout the entire body after a 12-week WB-EMS program with slight motions, consisting of two sessions per week. Our results suggest that even fewer training sessions are beneficial than previously described, whether or not exercise movements are performed during stimulation, which appears to be due to neural factors. Therefore, not only locally applied EMS training regimens have the potential to increase strength, but also WB-EMS training regimens without additional exercise movements.
## 4.4. Transferability
Benefits from WB-EMS can also be expected, for example, for patients suffering from sarcopenia, sarcopenic obesity, and low back pain [14]. It might be useful especially for beginners to start WB-EMS training with a five-week training period without additional exercise movements to improve basic strength before starting a more challenging exercise program. WB-EMS without additional exercise movements can be a first access to training when health conditions do not allow conventional exercises or when a lack of compliance exists. Relative to WB-EMS, local application appears to be superior in gaining strength [49]. However, the lack of focus on selected zones owing to stimulation of the entire body is a suggested explanation for the difference [14]. Therefore, only target muscles could be stimulated and not all available electrodes could be used, even if an electrode suit is worn, or zones could be stimulated in an individual order.
## 4.5. Limitations
We have shown that the effect of WB-EMS on strength gains is independent of the concomitant exercise movements. Nevertheless, some limitations need to be acknowledged. A test of core strength would have been useful, as both groups performed core strength exercises under the same conditions and a higher strength can be expected as observed in the study by Berger and colleagues [1], although they used a more extensive training program. Owing to two dropouts, the group sizes were slightly different, which affected the comparison. Furthermore, the strength gains of the dynamically trained muscles might have been underestimated, since only isometric strength was tested. It must also be mentioned that the increase in strength might have been influenced by deviations from the predefined number of training sessions. To evaluate the intensity of the movement sequences, an unstimulated group could have been used. Furthermore, an inactive group could have been used as a reference for the interventions. However, the study focused on the comparison between the EMS application without and the application with concurring exercise movements. When using WB-EMS training technology, the load parameters must be set with care to avoid unintended side effects, particularly during the first sessions of novices when adaptation to the load has not yet occurred in the form of the “repeated bout effect” [50].
## 5. Conclusions
WB-EMS training without accompanying movement exercises leads to substantial strength gains even during a short WB-EMS training period. At the beginning of WB-EMS training, electromyostimulation is more important for strength gains than active exercise movements. Therefore, future studies should examine the effects of exercise movements during long-term training periods, or consider individuals already adapted to WB-EMS training or strength training. The transferability of the results to a collective experienced with WB-EMS or strength training should be questioned, as movements (and maybe other approaches, e.g., additional mass or complicating tasks) may become more relevant when initial adaptations to training are exhausted. Since the training effort with WB-EMS is low, people with health restrictions, beginners without experience in strength training, and those returning to training might benefit from these results. These groups could refrain from exercise movements during the first WB-EMS training sessions and integrate them during the course of the subsequent training.
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|
---
title: 'Two‐year trends from the LANDMARC study: A 3‐year, pan‐India, prospective,
longitudinal study on the management and real‐world outcome in patients with type
2 diabetes mellitus'
authors:
- Ashok K. Das
- Sanjay Kalra
- Shashank Joshi
- Ambrish Mithal
- Prasanna Kumar K. M.
- Ambika G. Unnikrishnan
- Hemant Thacker
- Bipin Sethi
- Subhankar Chowdhury
- Amarnath Sugumaran
- Senthilnathan Mohanasundaram
- Shalini K. Menon
- Vaibhav Salvi
- Deepa Chodankar
- Saket Thaker
- Chirag Trivedi
- Subhash K. Wangnoo
- Abdul H. Zargar
- Nadeem Rais
journal: Endocrinology, Diabetes & Metabolism
year: 2023
pmcid: PMC10000633
doi: 10.1002/edm2.404
license: CC BY 4.0
---
# Two‐year trends from the LANDMARC study: A 3‐year, pan‐India, prospective, longitudinal study on the management and real‐world outcome in patients with type 2 diabetes mellitus
## Abstract
This 2‐year interim analysis of the 3‐year long LANDMARC study highlights the T2DM burden, management practices and related complications, across metropolitan and non‐metropolitan cities of India. It indicates that the burden of uncontrolled T2DM in *India is* high with $20.8\%$ of participants achieving glycaemic control in 2 years (HbA1c < $7\%$; 53 mmol/mol); and $17.6\%$ having microvascular and $3.1\%$ having macrovascular complications. There is need for effective diabetes management to meet glycaemic targets and prevent CV risk and vascular complications in a developing country like India with high prevalence of T2DM.
### Introduction
There are limited data on the real‐world management of diabetes in the Indian population. In this 2‐year analysis of the LANDMARC study, the management of type 2 diabetes mellitus (T2DM) and related complications were assessed.
### Method
This multicenter, observational, prospective study included adults aged ≥25 to ≤60 years diagnosed with T2DM (duration ≥2 years at enrollment) and controlled/uncontrolled on ≥2 anti‐diabetic agents. This interim analysis at 2 years reports the status of glycaemic control, diabetic complications, cardiovascular (CV) risks and therapy, pan‐India including metropolitan and non‐metropolitan cities.
### Results
Of the 6234 evaluable patients, 5318 patients completed 2 years in the study. Microvascular complications were observed in $17.6\%$ of patients ($\frac{1096}{6234}$); macrovascular complications were observed in $3.1\%$ of patients ($\frac{195}{6234}$). Higher number of microvascular complications were noted in patients from non‐metropolitan than in metropolitan cities ($p \leq .0001$). In 2 years, an improvement of $0.6\%$ from baseline ($8.1\%$) in mean glycated haemoglobin (HbA1c) was noted; $20.8\%$ of patients met optimum glycaemic control (HbA1c < $7\%$). Hypertension ($\frac{2679}{3438}$, $77.9\%$) and dyslipidaemia ($\frac{1776}{3438}$, $51.7\%$) were the predominant CV risk factors in 2 years. The number of patients taking oral anti‐diabetic drugs in combination with insulin increased in 2 years (baseline: $\frac{1498}{6234}$ [$24.0\%$] vs. 2 years: $\frac{1917}{5763}$ [$33.3\%$]). While biguanides and sulfonylureas were the most commonly prescribed, there was an evident increase in the use of dipeptidyl peptidase‐IV inhibitors (baseline: $\frac{3049}{6234}$, $48.9\%$ vs. 2 years: $\frac{3526}{5763}$, $61.2\%$).
### Conclusion
This longitudinal study represents the control of T2DM, its management and development of complications in Indian population.
### Clinical Trial Registration Number
CTRI/$\frac{2017}{05}$/008452.
## INTRODUCTION
India has been severely affected by the global diabetes epidemic. As per the 10th edition of International Diabetes Federation's (IDF) diabetes atlas [2021], India has 74.2 million people living with diabetes currently, with an age‐adjusted prevalence of $9.6\%$ among adults. India is expected to have 124.9 million people in the age range of 20–79 years living with diabetes by 2045. 1 The American Diabetes Association (ADA) recommends a combination of modified lifestyle and pharmacological treatment to achieve good metabolic control in diabetes and long‐term maintenance. 2, 3 Earlier studies have demonstrated that baseline glycated haemoglobin (HbA1c) and body mass index (BMI) can serve as important biomarkers to understand the disease aetiology and to identify suitable treatment options. 4, 5 Long‐term uncontrolled diabetes can cause cardiovascular (CV) diseases and damage kidneys, nerves and other vital organs. If optimum diabetes control is achieved, these serious complications can be delayed or prevented altogether. 1 This has also been substantiated by a study demonstrating that the occurrence of variations in glycaemic levels was associated with microvascular and macrovascular complications. 6 The INSPIRED study, which was conducted in India, included 19,084 individuals (aged 10–97 years) with type 2 diabetes mellitus (T2DM) and varying phenotypic characteristics. The findings of this study emphasized the association between increased hazards of retinopathy and nephropathy with a rise in blood glucose levels. 7 The Million death study also conducted in India showed that diabetes was associated with a significantly increased odds of stroke mortality (odds ratio, $95\%$ confidence intervals [CI]: 1.6, 1.4–1.7, $p \leq .0001$). 8 The American College of Cardiology (ACC)/American Heart Association (AHA)/Heart Failure Society of America (HFSA) guideline for the management of heart failure states that diabetes and heart failure often occur concomitantly, and each disease independently increases the risk of the other. 9 Additionally, a review of clinical evidence‐based research on the association between T2DM and myocardial infarction (MI) showed that not only T2DM is strongly associated with MI, but it also increases the risk of developing MI and related complications. 10 The review further discusses that in people with T2DM, MI is the primary cause of death and that T2DM leads to an increase in the risk of coronary events in individuals both with or without previous history of coronary events. 10 Hence, tight glycaemic control is essential in the early stages of diabetes.
The LANDMARC study (LongitudinAl Nationwide stuDy on Management And Real‐world outComes of diabetes in India) was a 3‐year comprehensive, robust, longitudinal and prospective study. It aimed to collect data on glycaemic therapy and diabetes complications in people with diabetes living in different regions of India (including metropolitan and non‐metropolitan cities). The study protocol, 11 baseline data 12 and 1‐year results 13 have been published earlier. The 1‐year results of the LANDMARC study indicate the progression of vascular complications and accumulation of CV risk among Indian patients with T2DM. Hypertension and dyslipidaemia, the most common CV risk factors reported, were pronounced in those who were overweight/had obesity. About one‐fifth of the patients had optimal glycaemic control (HbA1c < $7\%$). Patients from both non‐metropolitan and metropolitan cities were comparable in terms of improvement in glycaemic status and having optimum control. 13 The aim of this 2‐year interim analysis was to further evaluate and understand the diabetic complications and T2DM management pattern in adult patients with T2DM across India (including a sub‐analysis of metropolitan and non‐metropolitan cities).
## Study design
This was a multicenter, observational, prospective study conducted over 3 years (conducted between March 2017 and July 2021). The study was divided into seven visits with an interval of 6 months each. The present manuscript includes results from the second year (within a window period of ±90 days) of the 3‐year evaluation period.
## Study patients
Adults aged between ≥25 years and ≤60 years with T2DM for ≥2 years and who were controlled/uncontrolled on ≥2 anti‐diabetic agents at the time of enrollment were included in the study. The details of the study design, methodology, inclusion/exclusion criteria and statistical analysis have been published previously. 11, 12, 13
## Study assessments
At the end of the second year (visit 5), data related to glycaemic control status (fasting plasma glucose [FPG], post‐prandial glucose [PPG] and HbA1c) were collected. The proportion of patients with macrovascular complications (non‐fatal MI, non‐fatal stroke, CV death and peripheral vascular disease [PVD]), microvascular complications (retinopathy, nephropathy and neuropathy) and CV risk factors (hypertension, dyslipidaemia and albuminuria) was assessed. The glycaemic parameters and complications in patients from metropolitan and non‐metropolitan cities were assessed. The proportion of patients taking oral anti‐diabetic drugs (OADs) and injectable glucose‐lowering drugs was also assessed. Data related to anthropometry (weight) and frequency and severity of hypoglycaemia episodes were collected.
## Data collection
The data related to the study end‐points were collected prospectively every 6 months up to the end of the study at 36 months. The 450 sites that were selected for this study represent the four geographical regions (East, West, North and South), urban/rural practice across India. The study design was planned to mirror real‐life management of patients with T2DM; hence, none of the assessments were mandated. The available data were recorded in electronic‐Case Report Forms (e‐CRFs). Data quality control was performed by qualified designated personnel. An adverse drug reaction related to any Sanofi product (clinical signs, laboratory values or other) was reported and followed up until the clinical recovery was complete and laboratory results (if clinically significant) had returned to normal or until progression had been stabilized. This was a planned interim analysis to assess the changes in the disease characteristics from baseline and may require modification in the assessment parameters for subsequent interim and final analyses.
## Statistics
A minimum sample size of 4387 was decided assuming that the percentage of patients with composite incidence of non‐fatal MI, stroke and CV death after 3 years would have been $3\%$. The study planned to evaluate 6300 patients to estimate the composite incidence percentage with a precision of at least $1\%$, considering a $30\%$ rate of patients dropping out from the study before the end of the 3 years.
## Ethics
The protocol complies with the Declaration of Helsinki and this study was conducted in accordance with the principles laid by the 18th World Medical Assembly (Helsinki, 1964) and all subsequent amendments. The study was also in accordance with the guidelines for Good Epidemiology Practice (US & European) 14, 15 and aligned to the local regulations, ethics committee(s) (institutional review board/independent ethics committee) and competent authorities. The study was approved by the ethics committees of all participating sites (or a central ethics committee, where applicable). All the patients provided written informed consent before data collection/documentation.
## Demographics and baseline characteristics
Among the 450 sites, data from 382 sites were analysed for the 2‐year visit. Of the 6279 patients recruited, 6234 patients were evaluated; of these, 5318 patients completed 2 years in the study (Figure 1). At baseline, the mean ± standard deviation (SD) age of the patients was 52.1 ± 9.2 years with $57.0\%$ ($\frac{3552}{6234}$) of the study population in the age range of 50 to 65 years; more than half of the patients ($56.6\%$, $\frac{3526}{6234}$) were men. The mean ± SD baseline BMI was 27.2 ± 4.6 kg/m2, and the majority of the patients were obese ($66.8\%$, $\frac{4149}{6215}$) (Table 1). Most of the patients ($74.4\%$, $\frac{4640}{6234}$) were taking only OADs. ( Table S1).
**FIGURE 1:** *Patient disposition. *Reasons for CV death were sudden death ($$n = 19$$), myocardial infarction ($$n = 9$$), stroke ($$n = 1$$) and coronary artery procedure ($$n = 1$$). CV, cardiovascular; n, number of patients.* TABLE_PLACEHOLDER:TABLE 1
## Glycaemic status
In 2 years, all the glycaemic parameters improved (decreased) significantly from baseline (mean change ± SD: HbA1c: −0.6 ± $1.7\%$; FPG: −14.6 ± 54.5 mg/dl; and PPG: −22.0 ± 79.0 mg/dl; $p \leq .0001$) (Figure 2). When patients were stratified by HbA1c levels, a significant ($p \leq .0001$) reduction in the number of patients in the HbA1c $8\%$–$8.9\%$ and HbA1c ≥ $9\%$ subgroups were noted at the 2‐year visit compared with baseline; while a significant ($p \leq .0001$) increase in patient numbers in the HbA1c $7\%$–$7.9\%$ subgroup was noted. Overall, $20.8\%$ ($\frac{1297}{6234}$) of the patients met optimum glycaemic control (HbA1c < $7\%$) in 2 years (Figure 3).
**FIGURE 2:** *Change in glycaemic parameters at the end of 2 years. Values are presented as mean ± standard deviation. For change from baseline, HbA1c: n = 3020, FPG: n = 3668, PPG: n = 3454. FPG, fasting plasma glucose; HbA1c, glycated haemoglobin; n, number of patients analysed; PPG, postprandial glucose.* **FIGURE 3:** *Proportion of patients across HbA1c categories (N = 6234). Data presented as n (%) from baseline (N = 6234). HbA1c was not measured for all patients and hence the percentage may not add up to 100%.p‐values are reported using McNemar's test with the null hypothesis that the proportion of paired samples is equal. Patients who met each criterion and those who did not meet the criteria are considered as binary outcomes for the test. The p‐values reported are not adjusted for inflation in type I error. *p < .0001. HbA1c, glycated haemoglobin; N, number of patients analysed; n, number of patients with non‐missing results at the visit.*
## Microvascular and macrovascular complications in 2 years
Microvascular complications were noted in $17.6\%$ of patients ($\frac{1096}{6234}$) (Table 2); while new macrovascular complications were noted in $3.1\%$ of patients ($\frac{195}{6234}$) (Table 3). The most frequently noted microvascular complication was neuropathy, which was reported in $0.6\%$ of patients ($\frac{32}{6234}$), followed by nephropathy in $0.3\%$ of patients ($\frac{19}{6234}$) and retinopathy in $0.1\%$ of patients ($\frac{6}{6234}$) (Table 2). Overall, 57 new events of microvascular complications were reported in 55 patients at the 2‐year visit. Nephropathy, neuropathy and retinopathy were significantly ($p \leq .0001$) higher in patients with CV risk factors, while retinopathy was found to be significantly ($$p \leq .0351$$) higher in the HbA1c ≥ $7\%$ subgroup. ( Table S2).
In 2 years, the most reported new macrovascular complications included CV death ($0.1\%$, $\frac{9}{6234}$) and PVD ($0.1\%$, $\frac{4}{6234}$) (Table 3). A total of 41 deaths were reported; of which, 30 deaths were attributed to CV causes (sudden death [$$n = 19$$], MI [$$n = 9$$], stroke [$$n = 1$$] and coronary artery procedure [$$n = 1$$]) and remaining 11 deaths were due to other causes (Figure 1). Three patients were hospitalized between the 1‐year to 18‐month period due to MI (one patient) and acute coronary syndrome (ACS; two patients). One patient was hospitalized due to ACS, heart failure and unstable angina between the 18‐month to 2‐year period (Table S3).
## Cardiovascular risk factors
An increasing trend in the CV risk profile of patients was observed (baseline: $52.6\%$, $\frac{3281}{6234}$ vs. 2‐year: $55.1\%$, $\frac{3438}{6234}$). Among the 63 new cases (69 events) of CV risk factors, dyslipidaemia and hypertension (33 cases, each) were the most commonly reported (Table 4). Hypertension was noted more in men than women ($$p \leq .0019$$). Patients with hypertension and dyslipidaemia were greater in the subgroup having BMI ≥ 23 kg/m2 versus BMI < 23 kg/m2 ($p \leq .0001$ and $$p \leq .0525$$, respectively). Patients having hypertension and dyslipidaemia were higher in the subgroup having uncontrolled HbA1c levels (≥$7\%$) versus controlled HbA1c levels (<$7\%$) (Table 4).
**TABLE 4**
| CV risk factors | Total N = 6234 | Total N = 6234.1 | Total N = 6234.2 | Total N = 6234.3 |
| --- | --- | --- | --- | --- |
| CV risk factors | Baseline | 2 years | Patients at risk with new CV risk factors until 2 years, n | Patients with new CV risk factors at 2 years |
| Total number of CV risk factors, Ne | 4419 | 4698 | | 69 |
| Patients with CV risk factors | 3281 (52.6) | 3438 (55.1) | 6234 | 63 (1.0) |
| Hypertension b | 2566 (78.2) | 2679 (77.9) | 3588 | 33 (0.9) |
| Dyslipidaemia b | 1635 (49.8) | 1776 (51.7) | 4491 | 33 (0.7) |
| Albuminuria b | 153 (4.7) | 169 (4.9) | 6234 | 3 (0.0) |
| Family history of PCD b | 65 (2.0) | 65 (1.9) | ‐ | ‐ |
| No complications | 2562 | ‐ | ‐ | ‐ |
| Unknown c | 391 | ‐ | ‐ | ‐ |
## Glycaemic trends and vascular complications in metropolitan and non‐metropolitan cities
The baseline age, disease duration and HbA1c parameters were comparable across patients of non‐metropolitan and metropolitan cities (Table S4A). In 2 years, an improvement (decrease) was noted in all the glycaemic parameters (HbA1c, FPG and PPG) in patients from both metropolitan and non‐metropolitan cities (Table S4A).
The microvascular complications (neuropathy, nephropathy and retinopathy) were significantly ($p \leq .0001$) higher in patients from non‐metropolitan than in metropolitan cities (Table S4B). The number of CV deaths was higher in patients from non‐metropolitan than in metropolitan cities ($19.7\%$, $\frac{25}{135}$ vs. $7.4\%$, $\frac{5}{70}$). Of the newly reported cases of macrovascular complications in the second year, in non‐metropolitan cities, PVD was reported in four patients, MI in one patient, CV death in eight patients and stroke in one patient; while in metropolitan cities, one new case was reported (CV death) (Table S4C). Overall, the number of diabetes‐related complications in metropolitan and non‐metropolitan cities increased from baseline over 2 years (Table S4C).
## Anti‐diabetic treatment therapies
In 2 years, the total proportion of patients taking OAD + insulin increased (baseline: $24.0\%$, $\frac{1498}{6234}$ vs. 2 years: $33.3\%$, $\frac{1917}{5763}$), while the proportion of those taking only OADs, decreased (baseline: $74.4\%$, $\frac{4640}{6234}$ vs. 2 years: $64.8\%$, $\frac{3735}{5763}$) (Table S1).
Biguanides and sulfonylureas were the most prescribed OADs at baseline and 2 years (biguanides, baseline: $93.0\%$, $\frac{5798}{6234}$ and in 2 years: $92.7\%$, $\frac{5340}{5763}$; sulfonylureas, baseline: $76.3\%$, $\frac{4759}{6234}$ and in 2 years: $77.7\%$, $\frac{4480}{5763}$). The highest increase in OAD addition was seen for dipeptidyl peptidase 4 (DPP4) inhibitors (baseline: $48.9\%$, $\frac{3049}{6234}$ vs. 2‐years: $61.2\%$, $\frac{3526}{5763}$) followed by sodium‐glucose cotransporter‐2 inhibitors (baseline: $10.5\%$, $\frac{654}{6234}$ vs. 2 years: $21.3\%$, $\frac{1227}{5763}$) (Table S5). The mean ($95\%$ CI) change in HbA1c from baseline was −0.5 (−0.5, −0.4) in ≤3 OAD subgroup and −0.4 (−0.6, −0.2) in >3 OAD subgroup. Improvement in the glycaemic parameters in 2 years was more in the ≤3 OAD versus >3 OAD subgroup (p values were 0.8193, 0.1139 and 0.5541 for HbA1c, FPG and PPG, respectively) (Table 5).
**TABLE 5**
| Unnamed: 0 | Change in HbA1c (%) a | Change in FPG (mg/dl) a | Change in PPG (mg/dl) a |
| --- | --- | --- | --- |
| | n; mean (95% CI) | n; mean (95% CI) | n; mean (95% CI) |
| Insulin‐naïve | 1454; −0.4 (−0.5, −0.4) | 1821; −9.9 (−12.1, −7.8) | 1705; −17.2 (−20.6, −13.8) |
| Insulin | 505; −1.0 (−1.2, −0.9) | 721; −25.8 (−30.5, −21.0) | 697; −38.7 (−45.2, −32.2) |
| p‐value b | <.0001 | <.0001 | <.0001 |
| Basal (with/without prandial) insulin | 234; −1.1 (−1.4, −0.9) | 323; −26.0 (−32.9, −19.2) | 323; −38.3 (−47.5, −29.0) |
| Premix (with/without prandial) | 198; −1.0 (−1.3, −0.8) | 298; −27.9 (−35.5, −20.4) | 282; −37.3 (−47.9, −26.8) |
| p‐value b | .5758 | .7125 | .8915 |
| Basal long‐acting (without prandial) insulin | 174; −1.3 (−1.6, −1.0) | 238; −23.8 (−31.5, −16.0) | 230; −40.0 (−51.4, −28.7) |
| Premix (without prandial) insulin | 205; −1.1 (−1.3, −0.8) | 304; −27.7 (−35.3, −20.2) | 289; −39.7 (−50.2, −29.3) |
| p‐value b | .2520 | .4747 | .9696 |
| ≤3 OAD | 843; −0.5 (−0.5, −0.4) | 1110; −10.9 (−13.5, −8.3) | 1024; −17.4 (−21.6, −13.3) |
| >3 OAD | 218; −0.4 (−0.6, −0.2) | 262; −6.0 (−11.8, −0.1) | 247; −14.6 (−23.1, −6.0) |
| p‐value b | .8193 | .1139 | .5541 |
In 2 years, the commonly prescribed injectables were basal and premix insulins (basal insulin, baseline: $13.5\%$, $\frac{839}{6234}$ and 2 years: $20.6\%$, $\frac{1188}{5763}$; premix insulin, baseline: $11.0\%$, $\frac{683}{6234}$ and 2 years: $14.7\%$, $\frac{849}{5763}$) (Table S5). The change from baseline in all three glycaemic parameters in 2 years was significantly more in the insulin‐receiving subgroup than in the insulin‐naïve subgroup ($p \leq .0001$). The mean ($95\%$ CI) change in HbA1c from baseline was −1.0 (−1.2, −0.9) in insulin subgroup and −0.4 (−0.5, −0.4) in insulin‐naïve subgroup ($p \leq .0001$, both) (Table 5).
## Adverse drug reactions
A total of 19 events (18 patients) of hypoglycaemia were recorded between the 1‐year to 18‐month period. In the latter 6 months of the 2‐year visit, $0.3\%$ of patients ($\frac{17}{6234}$) reported hypoglycaemic events (documented symptomatic, $$n = 11$$; asymptomatic, $$n = 2$$; nocturnal, $$n = 4$$). ( Table S3). One adverse drug reaction (hypoglycaemia) by one patient was noted until the end of 2 years.
## DISCUSSION
This pan‐India, real‐world, large‐scale, longitudinal study is designed to assess glycaemic control, treatment trends, CV risks and development of macro‐ and microvascular complications over 3 years in Indian adults with T2DM. Herewith, we report an interim analysis done at the 2‐year time point. Over 2 years, while there was an overall improvement in glycaemic status, only 1 in 5 patients achieved HbA1c < $7\%$. Approximately one‐third of the patients in metropolitan ($30.6\%$) and non‐metropolitan ($35.4\%$) cities had HbA1c < $7\%$ at the end of 2 years. While also highest at baseline among microvascular complications, the proportion of patients with neuropathy showed an increase at the end of 2 years. Hypertension and dyslipidaemia were the most reported CV risks. The 2‐year results show that the majority of the patients with T2DM are treated with OADs. Biguanides and sulfonylureas are the most commonly used OADs in Indian routine clinical practice.
In this study, most of the patients (~$90\%$) were aged between 31 and 65 years and had obesity ($67\%$). With age it is difficult to reduce weight as the deposition of central fat becomes more pronounced and obesity sets in. Obesity paves the way for lifestyle disorders, one of them being T2DM. 16 Previous reports demonstrate that obesity is a well‐established risk factor for chronic illnesses like T2DM. 17, 18 Worsening of T2DM leads to CV risks and vascular complications. 6, 7, 8, 9, 10 A possible mechanism linking T2DM and obesity with subsequent CV complications is inflammation and lipid accumulation due to overexpression of cytokines (tumour necrosis factor‐α, interleukin (IL)‐1, IL‐6, leptin, resist in monocyte chemoattractant protein (MCP)‐1, plasminogen activator inhibitor (PAI)‐1, fibrinogen and angiotensin) by adipose tissue, which have a deleterious effect on blood vessels and can lead to the development of MI and cardiomyopathy. 19 Current treatment recommendations instate close monitoring and control of glycaemic levels to improve cardiac outcomes. 2 However, a considerable gap exists between diabetes care followed in real practice versus that recommended by evidence‐based guidelines. 20 The results of this study shed light on the real‐world burden of uncontrolled diabetes in India. In this 2‐year analysis, $20.8\%$ of the study population had optimal glycaemic control (HbA1c < $7\%$). In the GOAL study, $29.7\%$ of patients had glycaemic control after 12 months, and in the wave −7 of IDMPS study, $25.2\%$ of patients had optimal glycaemic control. 21, 22 The proportion of patients having optimum glycaemic control worsened over 2 years despite an increase in the use of OADs, reiterating the need for early control.
Despite improvement in the HbA1c levels in $0.6\%$ of patients in 2 years, there was an overall increase in the number of patients with microvascular and macrovascular complications. Those patients who were overweight or had suboptimal glycaemic control or CV risk factors had more complications versus those without these comorbidities, thus, substantiating an established fact that high BMI and poor glycaemic control lead to vascular complications. 18, 19, 23 The UKPDS 38 study examined the effect of tight control of blood pressure on macrovascular and microvascular complications in patients with T2DM. After 9 years of follow‐up, the results showed a $34\%$ reduction in macrovascular complications (MI, sudden death, stroke and PVD) and a $37\%$ reduction in the risk of microvascular complications (retinopathy requiring photocoagulation, vitreous haemorrhage and fatal or non‐fatal renal failure) in tightly controlled blood pressure group compared with the less tightly controlled group. 24 *In this* study, neuropathy was the most reported complication in 2 years. These results are consistent with the observation of the 1‐year LANDMARC study 13 and A1chieve study. 25 *It is* a well‐known fact that in people with T2DM, uncontrolled high blood sugar for a long duration degenerates the neurons, leading to a loss of sensory function or diabetic neuropathy. 26 Previous reports have established the observation that hypertension and dyslipidaemia are generally prevalent in people with diabetes. 27, 28 The 2‐year data in the present study also showed that all three microvascular complications (neuropathy, nephropathy and retinopathy) and heart failure were reported in more patients with CV risks than without CV risks.
As anticipated with the progressive nature of the disease after 2 years in the study, half of the patients who had diabetes for >10 years at baseline were taking OAD + insulin and those who had diabetes for ≤10 years at baseline were predominantly on OADs. Biguanides and sulfonylureas continued to remain the most used OAD classes in 2 years. Similar to the 1‐year results, the highest addition was seen in patients on DPP4 inhibitors. 13 There was a shift from the use of OADs towards the introduction of insulin as the need for injectables is common in people with longer duration of diabetes. 2, 30 In 2 years, improvement in glycaemic parameters was significantly higher in the insulin receiving subgroup than in the insulin‐naïve subgroup ($p \leq .0001$), which is in alignment to the 1‐year results of the LANDMARC study. 13 The ORIGIN study showed that the progression of diabetes was substantially reduced with timely insulin treatment in comparison with standard of care. 29 *There is* also evidence that in patients who are diagnosed with severe hyperglycaemia (HbA1c > $9\%$–$10\%$), insulin can control gluco‐ and lipo‐toxicity within a few days of therapy. 30 Therefore, timely initiation and intensification of insulin treatment can help achieve glycaemic control and improve the treatment outcomes.
There was an improvement in the HbA1c levels in patients from both metropolitan and non‐metropolitan cities. The microvascular complications were significantly more in patients from non‐metropolitan cities than in metropolitan cities ($p \leq .0001$). The number of CV deaths and newly reported macrovascular complications were also higher in patients from non‐metropolitan cities than in metropolitan cities. Difference in the disease management between metropolitan and non‐metropolitan cities could result in variable outcomes. Further studies can help understand the diabetes management patterns in metropolitan and non‐metropolitan cities.
LANDMARC is one of the first‐of‐its‐kind large‐scale longitudinal studies from India involving 6234 patients from 382 centers to investigate microvascular and macrovascular complications, glycaemic control and therapy pattern in patients with T2DM over 3 years across India. This real‐world data analysis provides a longitudinal course of the T2DM burden, management practices and related complications across the nation including a sub‐analysis across metropolitan and non‐metropolitan cities of India. This study is inherent to the limitations associated with a real‐world study. Moreover, being observational in nature, any study‐specific procedures or screening for complications or CV risks were not possible. Furthermore, this study does not capture data on factors such as financial status, educational qualification of the patients and access to treatment facilities that warranted a better understanding, if investigated.
## CONCLUSIONS
The results of the second year of the LANDMARC study showed high burden of uncontrolled diabetes in patients with T2DM from India. In 2 years, $17.6\%$ of the study population had microvascular complications, predominantly neuropathy. A higher number of complications were observed in patients from non‐metropolitan versus metropolitan cities. Hypertension was the most reported CV risk. In 2 years, an increase in number of injectables was also observed. These 2‐year trends are similar to those observed in the 1‐year results of the LANDMARC study. This pan‐India, real‐world study highlights the need for effective diabetes management including enhanced awareness among patients and providers to meet glycaemic targets and prevent CV risk and vascular complications in a developing country like India with high prevalence of T2DM.
## AUTHOR CONTRIBUTIONS
Ashok Kumar Das: Conceptualization (equal); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (lead); methodology (equal); project administration (supporting); resources (supporting); software (supporting); supervision (lead); validation (lead); visualization (lead); writing – original draft (equal); writing – review and editing (equal). Sanjay Kalra: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Shashank R Joshi: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Ambrish Mithal: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Prasanna Kumar KM: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Ambika G Unnikrishnan: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Hemant Thacker: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Bipin Sethi: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Subhankar Chowdhury: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Amarnath Sugumaran: Conceptualization (equal); data curation (equal); formal analysis (supporting); funding acquisition (equal); investigation (equal); methodology (equal); project administration (equal); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Senthilnathan Mohanasundaram: Conceptualization (equal); data curation (equal); formal analysis (supporting); funding acquisition (equal); investigation (equal); methodology (equal); project administration (equal); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Shalini Kesav Menon: Conceptualization (equal); data curation (equal); formal analysis (supporting); funding acquisition (equal); investigation (equal); methodology (equal); project administration (equal); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Vaibhav Salvi: Conceptualization (supporting); data curation (equal); formal analysis (equal); funding acquisition (supporting); investigation (equal); methodology (equal); project administration (equal); resources (supporting); software (equal); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Deepa Chodankar: Conceptualization (equal); data curation (equal); formal analysis (lead); funding acquisition (supporting); investigation (equal); methodology (lead); project administration (equal); resources (lead); software (lead); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Saket Thaker: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Chirag Trivedi: Conceptualization (equal); data curation (equal); formal analysis (equal); funding acquisition (equal); investigation (equal); methodology (equal); project administration (equal); resources (equal); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Subhash Kumar Wangnoo: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Abdul Zargar: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal). Nadeem Rais: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (equal); methodology (supporting); project administration (supporting); resources (supporting); software (supporting); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal).
## FUNDING INFORMATION
This study was funded by Sanofi.
## CONFLICT OF INTEREST
AKD, AM, AGU and NR received honoraria from Sanofi and other pharmaceutical companies. KMPK is on the advisory board of Sanofi and received an honorarium for his talks. SJ received speaker/advisory/research grants from Abbott, AstraZeneca, Biocon, Boehringer Ingelheim (BI), Eli Lilly, Franco Indian, Glenmark, Lupin, Marico, MSD, Novartis, Novo Nordisk, Roche, Sanofi, Serdia, Twinhealth and Zydus. SK received honoraria/speaker fees from Eli Lilly, Novo Nordisk and Sanofi. HT received honoraria from MSD, Novartis, Sanofi and other companies for advice and lectures. BS received an honorarium from Aventis, Novo Nordisk, Eli Lilly, BI and MSD. SC received honoraria/grants from Biocon, BI, Intas, Novartis, Sanofi and Serdia. SKW has nothing to declare. AHZ received honoraria from Novo Nordisk, Eli Lilly, Johnson & Johnson, AstraZeneca, BI and Sanofi. AS, SM, SKM, DC, VS, ST and CT are employees of Sanofi and may hold stock options.
## PREVIOUS PRESENTATIONS AND PUBLICATIONS
Part of the data from this paper was presented at the 82nd Scientific Sessions of American Diabetes Association (ADA) 2022, New Orleans, LA, and the 58th Annual Meeting of the European association for the study of Diabetes, Stockholm, 19–23 September 2022. The protocol of this study is published in Diabetic Medicine; DOI: 10.1111/dme.14171. The baseline data and 1‐year data of this study have been published in Endocrinology, Diabetes & Metabolism; their DOIs are 10.1002/edm2.231 and 10.1002/edm2.316, respectively.
## DATA AVAILABILITY STATEMENT
Qualified researchers may request access to person‐level data and related study documents including the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan and data set specifications. Person‐level data will be anonymized, and study documents will be redacted to protect the privacy of study patients. Further details on Sanofi's data sharing criteria, eligible studies and the process for requesting access can be found at https://vivli.org/.
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|
---
title: 'Investigating the relationship between haematological parameters and metabolic
syndrome: A population‐based study'
authors:
- Mohammad Javad Najafzadeh
- Amir Baniasad
- Reza Shahabinejad
- Mahdieh Mashrooteh
- Hamid Najafipour
- Mohammad Hossein Gozashti
journal: Endocrinology, Diabetes & Metabolism
year: 2023
pmcid: PMC10000634
doi: 10.1002/edm2.407
license: CC BY 4.0
---
# Investigating the relationship between haematological parameters and metabolic syndrome: A population‐based study
## Abstract
Chronic inflammation plays a role in Metabolic syndrome (MetS); hematologic inflammatory parameters can be used as MetS predicting factors. Adults with high WBC count, RDW, MHR, and NHR without any associated underlying chronic disease must be screened because they are at high risk of developing MetS.
### Background
Metabolic syndrome (MetS) is a global public health concern. Chronic inflammation plays a role in MetS; haematological inflammatory parameters can be used as MetS predicting factors.
### Objective
Hereditary and environmental factors play an important role in the development of MetS. This study aimed to determine the relationship between haematological parameters and MetS in the adult population of southeastern Iran, Kerman.
### Methods
This cross‐sectional study was a sub‐analysis of 1033 subjects who participated in the second phase of the Kerman Coronary Artery Disease Risk Factor Study (KERCADRS). Metabolic syndrome was diagnosed according to Adult Treatment Panel III (ATP III) definition. Pearson correlation coefficient was used to investigate the relationship between haematological parameters with age and components of metabolic syndrome. The role of WBC, neutrophil, lymphocyte and monocyte in predicting metabolic syndrome was evaluated using the receiver operating characteristic (ROC) curve.
### Results
White blood cell (WBC) and its subcomponent cells count, red cell distribution width (RDW), monocyte to HDL ratio (MHR) and Neutrophil to HDL ratio (NHR) had a significant positive correlation with the severity of MetS. The cut‐off value of WBC was 6.1 (×103/μL), the sensitivity was $70\%$, the specificity was $52.9\%$ for females, the cut‐off value of WBC was 6.3 (×103/μL), the sensitivity was $68.2\%$ and the specificity was $46.7\%$, for males.
### Conclusion
WBC and its subcomponent count, RDW, MHR and NHR parameters are valuable biomarkers for further risk appraisal of MetS in adults. These markers are helpful in early diagnoses of individuals with MetS.
## INTRODUCTION
Metabolic syndrome (MetS) has had different definitions since 1988, which was first introduced by Reaven. 1 Based on the latest definition of this syndrome, MetS includes at least three factors from the following disorders: central obesity, hypertension, elevated fasting glucose and dyslipidemia (reduced high‐density lipoprotein (HDL) or elevated triglycerides (TG)), 2 which increases the risk of insulin resistance, diabetes mellitus, cerebrovascular disease, cardiovascular disease, common cancers, osteoporosis and total mortality. 3, 4, 5 Prevalence and incidence of MetS have increased significantly following the increase in urbanization, improper nutrition and lack of physical activity, and it has become a global health concern. 6, 7 Although the underlying mechanism of MetS has not been known yet, oxidative stress, chronic inflammation and insulin resistance seem to be the most likely mechanism. 8 A growing number of studies emphasize the association of MetS components and haematological parameters, including white blood cell (WBC), red blood cell (RBC), platelet (PLT) count and haematocrit (HCT) level as potential indicators markers of thrombotic and inflammatory states. 9, 10, 11, 12 Meng et al. demonstrated that leukocyte was a good marker for assessing the risk of MetS and cardiovascular disease. 13 Some studies reported that WBC and PLT counts were significantly correlated with the numbers of MetS components. 14, 15 Ahmadzadeh et al. pointed out that high haemoglobin (HB) levels and HCT can also indicate MetS development. 16
Since inflammation plays a role in MetS, these haematological inflammatory parameters can be used as MetS predicting factors. Performing cost‐effective CBC tests can easily measure haematological parameters from peripheral blood. Hereditary and environmental factors play an important role in the development of MetS. Currently, no study has investigated the characteristics of MetS and its relationship with blood parameters in the population of southeastern Iran. Therefore, this study aimed to determine the relationship between haematological parameters and MetS in southeastern Iran, Kerman.
## Study design and participants
This cross‐sectional study was a sub‐analysis of 1033 subjects who participated in the second phase of the Kerman Coronary Artery Disease Risk Factor Study (KERCADRS). 17 The sampling method was a cluster from the entire population of Kerman residents. In the first phase of the KERCADRS, according to the post‐office list of city residents, 250 postal codes were selected randomly. We invited people over 15 to participate in the study. In the first phase, 24 people were collected in each cluster. In the second phase (420 clusters including 24 participants), people were contacted again, and 1033 who met the inclusion criteria from February 2017 to October 2018 were included in our study (Figure 1). None of the included participants had a history of chronic infectious or inflammatory diseases or the use of any drugs known to affect haematological parameters or lipoprotein metabolism. More details about the data collection method have been published in the study of Najafipour et al. 17
**FIGURE 1:** *The flowchart of included participants.*
## Data collection
After obtaining informed consent forms from the subjects, demographic data (age, gender) and anthropometric information were collected. A trained interviewer asked participants about cigarette smoking and opium use. People who routinely smoked cigarettes or consumed opium at the time of data collection were considered cigarette smokers and opium addicts, respectively. Height in the standing position without shoes, from heel to head, with an error of 0.5 cm error, weight without shoes and extra clothing with an error of 100 g on a digital scale, body mass index (BMI) which the weight (kg) of the patients was divided by the square of their height (m2), waist circumference (WC) in the standing position with 20–30 cm distance between the feet were measured. WC (cm) was measured at the umbilical level. Hip circumference (HC) (cm) was measured based on the largest circumference around the buttocks. Waist‐to‐hip ratio (WHR) was calculated by dividing WC by HC. After 10 min of rest, blood pressure (BP) was measured with a standard manometer from the right arm in the sitting position according to the World Health Organization (WHO) standards, and the blood samples were taken after 12–14 hours of fasting and kept at room temperature. CBC, fasting plasma glucose (FPG) and serum lipids (HDL cholesterol and TG) were tested by routine laboratory methods.
According to Adult Treatment Panel III (ATP III) definition, the presence of at least two of the following five factors is required for the diagnosis of metabolic syndrome: blood pressure over $\frac{130}{80}$ mm Hg or consumption of antihypertensive drugs, TG level over 150 mg/dl, FPG over 100 mg/dl or consumption of anti‐diabetic medication like insulin, HDL cholesterol level less than 40 mg/dL (men) or 50 mg/dl (women) and WC over 102 cm (men) or 88 cm (women).
## Sample size estimation
In the study of Oda and Kawai, the mean WBC in women with three components of metabolic syndrome was 5416 ± 1163 and in women with only two components of metabolic syndrome was 5077 ± 1358. 18 *The minimum* sample size required based on the mentioned numbers and considering the power of 0.8 and alpha of 0.05 for each group was considered to be at least 275 people.
## Statistical analysis
Statistical analysis was performed using SPSS version 16 software (SPSS Inc.). Quantitative variables were reported as mean ± standard deviation, and qualitative variables were reported as numbers and percentages. Qualitative variables were compared between the two groups using the Pearson chi‐square or Fisher's exact test. Quantitative variables were compared separately between individuals with and without metabolic syndrome in male and female groups using the independent samples t test. Pearson correlation coefficient was used to investigate the relationship between haematological parameters with age and components of metabolic syndrome. The relationship between haematological parameters and the variable number of components of metabolic syndrome was investigated using Spearman's correlation coefficient. The role of WBC, neutrophil, lymphocyte and monocyte in predicting metabolic syndrome was evaluated using the receiver operating characteristic (ROC) curve and with MedCalc® Statistical Software version 20.013 (MedCalc Software Ltd). The optimal cut‐off point was determined using the Youden index.
## RESULTS
A total of 1033 individuals (660 women, 373 men) were included in this study, and the sociodemographic, laboratory parameters and clinical characteristics of the participants are summarized in Table 1.
**TABLE 1**
| Unnamed: 0 | Male | Male.1 | p value a | Female | Female.1 | p value a.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | Normal | Syndrome Metabolic | p value a | Normal | Syndrome Metabolic | p value a |
| | N = 291 | N = 82 | p value a | N = 463 | N = 197 | p value a |
| Age (years) | 45.48 ± 16.21 | 51.90 ± 13.84 | <.001 | 40.29 ± 14.07 | 53.33 ± 11.72 | <.001 |
| Smoking, n (%) | 69 (23.7) | 18 (22.0) | .739 | 3 (0.6) | 1 (0.5) | .655 |
| Opium addiction, n (%) | 64 (22.0) | 17 (20.7) | .807 | 21 (4.5%) | 15 (7.6%) | .111 |
| BMI (kg/m2) | 24.99 ± 4.22 | 29.18 ± 3.86 | <.001 | 26.38 ± 4.86 | 30.81 ± 5.17 | <.001 |
| WC (cm) | 88.26 ± 11.94 | 101.29 ± 9.37 | <.001 | 83.30 ± 11.83 | 98.16 ± 10.47 | <.001 |
| WHR | 0.89 ± 0.07 | 0.96 ± 0.05 | <.001 | 0.82 ± 0.08 | 0.93 ± 0.08 | <.001 |
| SBP (mmHg) | 114.98 ± 16.01 | 127.99 ± 16.92 | <.001 | 108.98 ± 15.56 | 122.60 ± 17.43 | <.001 |
| DBP (mmHg) | 74.98 ± 9.39 | 82.38 ± 11.28 | <.001 | 71.79 ± 10.46 | 78.11 ± 9.46 | <.001 |
| FPG | 90.86 ± 24.05 | 116.20 ± 41.39 | <.001 | 86.67 ± 18.34 | 120.40 ± 47.38 | <.001 |
| TG (mg/dl) | 127.03 ± 66.25 | 221.15 ± 163.26 | <.001 | 100.11 ± 43.90 | 184.71 ± 78.75 | <.001 |
| HDL (mg/dl) | 46.43 ± 10.55 | 37.87 ± 7.27 | <.001 | 52.97 ± 12.41 | 44.31 ± 9.57 | <.001 |
| LDL (mg/dl) | 109.31 ± 31.35 | 101.07 ± 37.57 | .077 | 109.29 ± 35.07 | 113.65 ± 40.42 | .188 |
| Cholestrol (mg/dl) | 181.08 ± 37.04 | 181.66 ± 43.56 | .912 | 182.07 ± 43.53 | 194.74 ± 45.56 | .001 |
| WBC (×103/μL) | 6.76 ± 1.68 | 7.08 ± 1.68 | .131 | 6.30 ± 1.66 | 6.92 ± 1.52 | <.001 |
| Neutrophil (×103/μL) | 3.46 ± 1.25 | 3.72 ± 1.38 | .119 | 3.35 ± 1.21 | 3.70 ± 1.12 | .001 |
| Lymphocyte (×103/μL) | 2.48 ± 0.73 | 2.51 ± 0.73 | .793 | 2.24 ± 0.66 | 2.46 ± 0.68 | <.001 |
| Monocyte (×103/μL) | 0.58 ± 0.17 | 0.61 ± 0.16 | .174 | 0.51 ± 0.14 | 0.54 ± 0.15 | .005 |
| RBC (×106/μL) | 5.33 ± 0.54 | 5.35 ± 0.60 | .745 | 4.78 ± 0.47 | 4.78 ± 0.49 | .951 |
| HB (gr/dl) | 15.19 ± 1.26 | 15.33 ± 1.28 | .378 | 13.24 ± 1.18 | 13.40 ± 1.38 | .177 |
| HCT (%) | 46.04 ± 3.51 | 45.87 ± 4.22 | .741 | 41.08 ± 3.57 | 41.31 ± 4.23 | .516 |
| PLT(×103/μL) | 220.67 ± 49.95 | 215.24 ± 45.32 | .376 | 254.18 ± 62.34 | 255.26 ± 56.46 | .834 |
| MPV (fL) | 10.27 ± 0.87 | 10.22 ± 0.77 | .640 | 10.54 ± 0.94 | 10.47 ± 0.90 | .355 |
| RDW‐SD | 42.97 ± 3.14 | 42.76 ± 3.24 | .602 | 42.94 ± 2.98 | 43.56 ± 3.10 | .017 |
| RDW‐CV | 13.89 ± 1.35 | 13.94 ± 1.26 | .787 | 14.02 ± 1.34 | 14.16 ± 1.39 | .242 |
| NLR | 1.49 ± 0.68 | 1.59 ± 0.76 | .233 | 1.57 ± 0.62 | 1.61 ± 0.67 | .539 |
| PLR | 94.85 ± 31.90 | 91.59 ± 28.99 | .405 | 120.76 ± 40.62 | 110.52 ± 37.39 | .003 |
| PMR | 403.18 ± 128.29 | 367.38 ± 96.79 | .007 | 529.20 ± 169.01 | 499.46 ± 162.80 | .037 |
| MHR | 0.013 ± 0.005 | 0.0176 ± 0.01 | <.001 | 0.010 ± 0.004 | 0.013 ± 0.004 | <.001 |
| NHR | 0.08 ± 0.04 | 0.10 ± 0.04 | <.001 | 0.07 ± 0.03 | 0.09 ± 0.03 | <.001 |
In both males and females, in the participants with MetS, age, BMI, WC, WHR, systolic blood pressure (SBP), diastolic blood pressure (DBP), FPG, TG, monocyte to HDL ratio (MHR) and Neutrophil to HDL ratio (NHR) were significantly higher compared with the participants without MetS. In females with MetS, WBC, Red Cell distribution width‐standard deviation (RDW‐SD), Neutrophil, Lymphocyte and Monocyte were significantly higher than the females without MetS (Table 1).
HDL and platelet to Monocyte ratio (PMR) were significantly lower in participants with MetS compared with those without MetS in males and females. In females with MetS, the platelet to Lymphocyte ratio (PLR) was significantly lower than those without MetS (Table 1).
In males with MetS, smoking, opium addiction, WC, WHR, SBP, DBP, monocyte, RBC, HB, HCT, MHR and NHR were significantly higher than these parameters in females with MetS. In females with MetS, BMI, HDL, LDL, cholesterol, PLT, MPV, PLR and PMR were significantly higher than these parameters in males with MetS (Table 2).
**TABLE 2**
| Unnamed: 0 | Syndrome Metabolic | Syndrome Metabolic.1 | p Value a |
| --- | --- | --- | --- |
| | Male | Female | |
| | N = 82 | N = 197 | |
| Age (years) | 51.90 ± 13.84 | 53.33 ± 11.72 | 0.381 |
| Smoking, n (%) | 18 (22.0) | 1 (0.5) | <0.001 |
| Opium addiction, n (%) | 17 (20.7) | 15 (7.6%) | 0.002 |
| BMI (kg/m2) | 29.18 ± 3.86 | 30.81 ± 5.17 | 0.011 |
| WC (cm) | 101.29 ± 9.37 | 98.16 ± 10.47 | 0.020 |
| WHR | 0.96 ± 0.05 | 0.93 ± 0.08 | 0.001 |
| SBP (mmHg) | 127.99 ± 16.92 | 122.60 ± 17.43 | 0.019 |
| DBP (mmHg) | 82.38 ± 11.28 | 78.11 ± 9.46 | 0.001 |
| FPG | 116.20 ± 41.39 | 120.40 ± 47.38 | 0.485 |
| TG (mg/dl) | 221.15 ± 163.26 | 184.71 ± 78.75 | 0.057 |
| HDL (mg/dl) | 37.87 ± 7.27 | 44.31 ± 9.57 | <0.001 |
| LDL (mg/dl) | 101.07 ± 37.57 | 113.65 ± 40.42 | 0.018 |
| Cholestrol (mg/dl) | 181.66 ± 43.56 | 194.74 ± 45.56 | 0.028 |
| WBC (×103/μL) | 7.08 ± 1.68 | 6.92 ± 1.52 | 0.434 |
| Neutrophil (×103/μL) | 3.72 ± 1.38 | 3.70 ± 1.12 | 0.935 |
| Lymphocyte (×103/μL) | 2.51 ± 0.73 | 2.46 ± 0.68 | 0.640 |
| Monocyte (×103/μL) | 0.61 ± 0.16 | 0.54 ± 0.15 | 0.001 |
| RBC (×106/μL) | 5.35 ± 0.60 | 4.78 ± 0.49 | <0.001 |
| HB (gr/dl) | 15.33 ± 1.28 | 13.40 ± 1.38 | <0.001 |
| HCT (%) | 45.87 ± 4.22 | 41.31 ± 4.23 | <0.001 |
| PLT(×103/μL) | 215.24 ± 45.32 | 255.26 ± 56.46 | <0.001 |
| MPV (fL) | 10.22 ± 0.77 | 10.47 ± 0.90 | 0.030 |
| RDW‐SD | 42.76 ± 3.24 | 43.56 ± 3.10 | 0.056 |
| RDW‐CV | 13.94 ± 1.26 | 14.16 ± 1.39 | 0.212 |
| NLR | 1.59 ± 0.76 | 1.61 ± 0.67 | 0.883 |
| PLR | 91.59 ± 28.99 | 110.52 ± 37.39 | <0.001 |
| PMR | 367.38 ± 96.79 | 499.46 ± 162.80 | <0.001 |
| MHR | 0.0176 ± 0.01 | 0.013 ± 0.004 | <0.001 |
| NHR | 0.10 ± 0.04 | 0.09 ± 0.03 | 0.002 |
As shown in Table 3, we have considered the number of metabolic components as a measure to determine the severity of MetS, WBC count, Neutrophil, Lymphocyte, Monocyte, RDW‐SD, Red cell distribution width‐coefficient of variation (RDW‐CV), PLR, MHR and NHR parameters that were significantly correlated with the severity of MetS. The correlation was positive in mentioned parameters except for PLR. WBC was significantly correlated with all metabolic components except age (Table 3).
**TABLE 3**
| Variables | Age (years) b | WC (cm) b | FPG (mg/dl) b | TG (mg/dl) b | HDL (mg/dl) b | SBP (mmHg) b | DBP (mmHg) b | Number of components a |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| WBC (×103/μL) | −0.001 | 0.139** | 0.095** | 0.165** | −0.161** | 0.080* | 0.069* | 0.191 |
| Neutrophil (×103/μL) | −0.029 | 0.122** | 0.081** | 0.095** | −0.112** | 0.049 | 0.054 | 0.164** |
| Lymphocyte (×103/μL) | 0.022 | 0.087** | 0.069* | 0.188** | −0.138** | 0.074* | 0.047 | 0.150** |
| Monocyte (×103/μL) | 0.059 | 0.113** | 0.045 | 0.089** | −0.139** | 0.086** | 0.063* | 0.089** |
| RBC (×106/μL) | −0.027 | 0.097** | −0.021 | 0.132** | −0.128** | 0.154** | 0.176** | −0.029 |
| HB (gr/dl) | 0.074* | 0.130** | −0.001 | 0.178** | −0.142** | 0.150** | 0.161** | −0.026 |
| HCT (%) | 0.123** | 0.136** | −0.044 | 0.142** | −0.053 | 0.173** | 0.188** | −0.038 |
| PLT (×103/μL) | −0.099** | −0.035 | −0.026 | −0.005 | 0.117** | −0.015 | 0.025 | 0.034 |
| MPV (fL) | −0.037 | −0.031 | 0.059 | −0.085** | −0.038 | −0.075* | −0.092** | 0.016 |
| RDW‐SD* | 0.280** | 0.152** | −0.046 | −0.026 | 0.088** | 0.080** | 0.057 | 0.067* |
| RDW‐CV | 0.037 | 0.074* | −0.017 | −0.025 | −0.033 | 0.060 | 0.051 | 0.106** |
| NLR | −0.025 | 0.047 | 0.030 | −0.037 | 0.002 | −0.007 | 0.020 | 0.025 |
| PLR | −0.077* | −0.106** | −0.073* | −0.158** | 0.195** | −0.074* | −0.022 | −0.107** |
| PMR | −0.101** | −0.126** | −0.059 | −0.070* | 0.204** | −0.087** | −0.034 | −0.054 |
| MHR | 0.022 | 0.231** | 0.077* | 0.300** | −0.649** | 0.080* | 0.053 | 0.335** |
| NHR | −0.038 | 0.224** | 0.101** | 0.268** | −0.579** | 0.058 | 0.054 | 0.371** |
Genders had different accuracy of WBC, Neutrophil, Lymphocyte and Monocyte in predicting MetS. The accuracy of WBC was higher for females (AUC = 0.632; $p \leq .001$; $95\%$ confidence interval [CI]: 0.594–0.669) than for males (AUC = 0.564; $$p \leq .074$$; $95\%$ confidence interval [CI]: 0.512–0.615) (Table 4).
**TABLE 4**
| Unnamed: 0 | WBC (×103/μL) | Neutrophil (×103/μL) | Lymphocyte (×103/μL) | Monocyte (×103/μL) |
| --- | --- | --- | --- | --- |
| Male | Male | Male | Male | Male |
| AUC (95% CI) | 0.564 (0.512–0.615) | 0.566 (0.514–0.617) | 0.503 (0.451–0.555) | 0.549 (0.497–0.600) |
| Optimal cut‐off point | 6.34 | 3.41 | 2.37 | 0.43 |
| Sensitivity (%) | 68.29 | 57.32 | 56.10 | 93.9 |
| Specificity (%) | 46.74 | 56.70 | 51.89 | 17.87 |
| Youden index | 0.150 | 0.140 | 0.080 | 0.118 |
| p Value | .074 | .065 | .943 | .154 |
| Female | Female | Female | Female | Female |
| AUC (95% CI) | 0.632 (0.594–0.669) | 0.608 (0.569–645) | 0.609 (0.566–0.642) | 0.570 (0.532–0.609) |
| Optimal cut‐off point | 6.15 | 3.67 | 2.36 | 0.44 |
| Sensitivity (%) | 70.05 | 50.25 | 52.28 | 77.16 |
| Specificity (%) | 52.92 | 71.0 | 63.93 | 36.93 |
| Youden index | 0.230 | 0.212 | 0.162 | 0.141 |
| p Value | <.001 | <.001 | <.001 | .003 |
The cut‐off value of WBC was 6.1 (×103/μL), the sensitivity was $70\%$, the specificity was $52.9\%$ for females, the cut‐off value of WBC was 6.3 (×103/μL), the sensitivity was $68.2\%$, the specificity was $46.7\%$, for males. Neutrophil for males (AUC = 0.566) and WBC for females (AUC = 0.632) had better accuracy in predicting MetS compared to other parameters (Table 4) (Figures 2 and 3).
**FIGURE 2:** *Areas Under the ROC curve (AUC) for WBC, Neutrophil, Lymphocyte and Monocyte in predicting metabolic syndrome for males.* **FIGURE 3:** *Areas Under the ROC curve (AUC) for WBC, Neutrophil, Lymphocyte and Monocyte in predicting metabolic syndrome for females.*
## DISCUSSION
We found that MetS affected the haematological parameters of the patients, including WBC and its subcomponent cell count, RDW, PLR, MHR and NHR.
In our study, WBC and its subcomponent cells count had a significant positive correlation with the severity of MetS, especially in females. Our results were in parallel with previous studies, which had reported a significant difference in the WBC and its subcomponent cells, between participants with or without MetS. 11, 16, 19 Yang et al. reported that the number of total leukocyte‐related parameters were elevated in individuals aged 60 years or above. 20 Ahmadzadeh et al. demonstrated that MetS components were significantly correlated with WBC and its subcomponent cells count. 16 In Hedayati et al. study in western Iran, the means of WBC count in the MetS group were significantly higher than the control group. 21 Consistent with our data in a study by Chen et al., contrary to the platelet‐related parameters, the WBC‐related parameters had significant changes in patients with MetS. 22 *In a* study on a total of 100 healthy subjects and 200 patients with MetS, total leukocyte and neutrophil counts were significantly increased in all groups of MetS patients compared to the healthy group. 23 Insulin resistance and chronic inflammation are associated with metabolic syndrome by synthesizing some cytokines leading to an increase in WBC and its subcomponent cells count. 24 Lorenzo et al. observed an association between the increased risk of diabetes and elevated WBC, neutrophil and lymphocyte counts due to insulin resistance/sensitivity mechanism. 25 In addition, the relationship between higher levels of WBC count and higher BMI values has been observed in both sexes. 26 *In this* study, it was found that in the group of patients with MetS, women had greater BMI, higher cholesterol, PLT and platelet‐related ratios, and men had a history of more smoking and opium consumption, higher BP, HB, HCT, RBC, monocytes and monocytes‐related ratios. Consistent with our results in another study, it has been determined that the predominant feature of MetS in women was abdominal obesity and impaired lipid profile, and in men, it was high BP and impaired lipid profile. 27 In our study, RDW had a significant positive correlation with the severity of MetS, especially in females; however, this correlation was not observed in mean platelet volume (MPV). So far, minimal studies have been done in this field. Consistent with our data, Farah et al. indicated that both RDW and MPV markers increased as the severity of MetS increased. 28 Abdel‐Moneim et al. found higher levels of MPV in MetS patients. 23 Zhao et al. demonstrated that MPV was inversely related to MS in women. 29 In another study, no significant difference in the MPV between those with and without MetS was observed. 16
In our study, MHR and NHR had a significant positive correlation with the severity of MetS. A recent study demonstrated that NHR and Lymphocyte to HDL ratio (LHR) were significantly correlated with the prevalence of MetS; also, the correlation was more profound in females. 30 A recent study showed that both MHR and NHR were significantly increased in patients with nascent MetS. 31 Considering that monocyte is an indicating factor for inflammatory conditions and atherosclerosis, 32, 33 some studies revealed that the ratio of MHR is a suitable predictor to determine the development and severity of MetS and cardiovascular events. 34, 35 According to our result, Neutrophil to Lymphocyte ratio (NLR) was not recognized as a MetS predictive factor. Ryder et al. observed no association between NLR and obesity or insulin resistance. 36 Contrary to our results, it was found in two studies that patients with MetS had a higher NLR. 23, 37 Liu et al. relieved that the risk of MetS increased with increasing NLR, and NLR was mentioned as a factor for predicting the development of MetS. 38 In addition, this ratio has been mentioned as a predictive factor for diabetes in obese individuals. 39 PLR had a significant negative correlation with all metabolic components except DBP in this study. In another study, it was reported that the amount of PLR in patients with MetS was higher than in patients without MetS, and the amount of PLR had a significant positive correlation with C‐reactive protein (CRP) levels. 40 In Abdel‐Moneim et al. study, the PLR was significantly higher in all patients with MetS than in healthy subjects. 23 The cut‐off points for WBC and its subcomponent cell counts are used to determine the potential risk of developing MetS. The cut‐off value of WBC was 6.1 (×103/μL) and 6.3 (×103/μL) for females and males, respectively, in our study. Our results are confirmatory of previous study findings. Pei et al. reported a cut‐off point of 5.6 (×103/μL) for men and 5.8 (×103/μL) for women, 41 and De Oliveira et al. reported a cut‐off point of 7.5 (×103/μL) for men and 5.6 (×103/μL) for women. 42
## Limitations
Our study had multiple limitations. Firstly, this is a cross‐sectional study, and the analysis of the causative effects was not performed. Secondly, the study sample size was small, and the number of males was smaller than females. Further, our study population included people from southeastern Iran, Kerman; we cannot generalize our results to the whole Iran population.
## Future directions
The measurement of haematological parameters is easily available in most parts of the world. However, unfortunately, in public health policies, these parameters do not have a place in the diagnosis and follow‐up of patients with MetS, which will cause these patients to be missed and impose a lot of financial and social costs on the global health system. The results of our study can b an incentive to conduct prospective studies that will lead to the inclusion of haematological parameters in the diagnostic criteria of MetS. Measuring these haematological parameters is cost‐effective and convenient and facilitates screening patients suspected of MetS and their follow‐up. Prospective studies are required to explain the causality effects between MetS and haematological parameters, confirm our data and evaluate the need to change the risk assessment criteria for MetS.
## CONCLUSION
The higher levels of WBC and its subcomponent cell count, RDW, MHR and NHR could redict an increased chance of developing MetS, regardless of gender differences. WBC also correlated with MetS components, such as WC, FPG, TG, HDL, SBP and DBP; these parameters are easy to access in patients. Considering that no study has been done on this topic in the population of Southeast Iran, our findings provide additional evidence for using these markers for the early detection of MetS components, which ultimately improves the existing clinical practice in identifying and following MetS patients.
## AUTHOR CONTRIBUTIONS
Mohammad Javad Najafzadeh: Conceptualization (equal); investigation (equal); writing – original draft (equal); writing – review and editing (equal). Amir Baniasad: Conceptualization (equal); formal analysis (equal); methodology (equal); writing – review and editing (equal). Reza Shahabinejad: Data curation (equal); investigation (equal); software (equal); writing – original draft (equal). Mahdieh Mashrooteh: *Formal analysis* (equal); investigation (equal); methodology (equal); software (equal). Dr Hamid Najafipour: Conceptualization (equal); investigation (equal); methodology (equal); project administration (equal); writing – review and editing (equal). Mohammad Hossein Gozashti: Conceptualization (equal); investigation (equal); methodology (equal); project administration (equal); writing – original draft (equal); writing – review and editing (equal).
## FUNDING INFORMATION
The Kerman University of Medical Sciences funded this research project (Reg. No. 95000008).
## CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
## ETHICAL APPROVAL
The study protocol was reviewed and approved by the ethics committee of the Kerman University of Medical Sciences (ethic code: IR.KMU.REC.1395.775). Informed consent was obtained from all participants in the study.
## DATA AVAILABILITY STATEMENT
The data supporting this study's findings are available from the corresponding author upon reasonable request.
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|
---
title: 'A population‐based cohort of adult patients with diabetes mellitus in a Western
District of Austria: The Diabetes Landeck cohort'
authors:
- Veronika Haslwanter
- Ursula Rochau
- Uwe Siebert
- Hans‐Robert Schönherr
- Willi Oberaigner
journal: Endocrinology, Diabetes & Metabolism
year: 2022
pmcid: PMC10000636
doi: 10.1002/edm2.395
license: CC BY 4.0
---
# A population‐based cohort of adult patients with diabetes mellitus in a Western District of Austria: The Diabetes Landeck cohort
## Abstract
The aims of this project and study are to establish a cohort including all adult DM patients in a district in western Austria, describe the demographic and clinical characteristics of these patients, and provide an estimation of diabetes prevalence, both at an overall level and stratified by gender and age groups.
### Introduction
Diabetes mellitus (DM) has become an important and exacerbating health epidemic, with severe consequences for both patients and health systems. The National Diabetes Strategy of Austria addresses the lack of high‐quality data on DM in Austria and the need for developing a national data network. The aims of our study are to establish a cohort including all adult diabetes patients in a district in western Austria, describe the demographic and clinical characteristics of this cohort, and provide an estimation of diabetes prevalence.
### Methods
We recruited a population‐based cohort of adult patients with a diagnosis of DM in cooperation with a network of all caregivers. Data collection was based on a case report form, including patient characteristics, clinical parameters and long‐term complications.
### Results
In total, 1845 patients with DM were recruited and analysed. We observed an overall prevalence of $5.3\%$ [$95\%$ CI: $5.0\%$–$5.5\%$]. For the subsequent main analysis, we included 1755 patients with DM after excluding 90 patients with gestational DM. There were significant differences between genders in the distribution of specific clinical parameters, patient characteristics, and the long‐term complications diabetic foot, amputation and cardiovascular disease.
### Conclusion
To the best of our knowledge, we established the first diabetes cohort study in Austria. Prevalence and the proportion of specific long‐term complications were lower when compared to the international context. We assume that the Diabetes Landeck Cohort has reached a high degree of completeness; however, we were not able to identify independent data sources for a valid check of completeness.
## INTRODUCTION
Diabetes mellitus (DM) has become a considerable and exacerbating health epidemic that has severe consequences for both the patients and health systems. 1 According to the tenth edition of the International Diabetes Federation (IDF) Atlas, approximately 537 million people aged 20 to 79 years suffered from DM worldwide in 2021, representing a prevalence of approximately $10.5\%$. 2 The global prevalence is expected to increase in the future, with 783 million people predicted to suffer from DM by 2045, corresponding to a prevalence of around $10.9\%$. 2 In Europe, one in eleven adults has DM, which means that 61 million people suffer from DM, and the prevalence is estimated to increase from $9.2\%$ in 2021 to $10.4\%$ in 2045. Furthermore, the economic burden associated with DM is substantial: The IDF reports that the share of the global health expenditure for DM ranges from $3.4\%$ in the Middle East and North African region to $19.6\%$ in Europe and to $43\%$ in North America and the Caribbean. 2 Patients with DM suffer from a lifelong burden caused by the treatment and disease complications. One of the main challenges is an increased risk of developing long‐term complications such as neuropathy, nephropathy and cardiovascular disease. 3, 4 In addition, patients with DM have an increased risk of early mortality. Worldwide, about 6.7 million people aged between 20 and 79 years are estimated to die from DM or its complications in 2021. 2 For Austria, the number of patients with DM aged 20–79 years in 2021 was estimated at about 450,000, corresponding to a prevalence of $6.6\%$. In addition, the number of undiagnosed DM cases was estimated at approximately 150,000. 2 Due to the increasing number of patients with DM and the long‐term complications, considerable DM‐associated healthcare expenditures are also expected in Austria. The annual costs of DM and its comorbidities in Austria amount to an estimated 3 billion euros. 5 Therefore, the main targets of the diabetes strategy of the Austrian Federal Ministry of Labor, Social Affairs, Health and Consumer Protection from 2017 are to reduce the incidence of DM and to prevent long‐term complications. 6 This Austrian strategy document states that there is a lack and necessity of valid data on DM in Austria. Moreover, a report of the Austrian Court of Audition on DM prevention and DM care from 2019 points to the lack of high‐quality data. 7 To provide valid data on DM in Austria, a project called the Diabetes Landeck Cohort was initiated, with the aim to set up a population‐based cohort of patients with DM in a district in western Austria. This cohort should serve as a basis to collect and analyse valid and comprehensive data on DM.
The aims of this project and study are to establish a cohort including all adult DM patients in a district in western Austria, describe the demographic and clinical characteristics of these patients, and provide an estimation of diabetes prevalence, both at an overall level and stratified by gender and age groups.
## Settings of the study and data collection
The recruitment of the cohort titled ‘Diabetes Landeck Cohort’ started in 2018 and ended in June 2021. Eligible participants were adults (aged ≥20 years) with type 1 DM (T1DM) or type 2 DM (T2DM) or other types of diabetes (e.g. gestational, latent autoimmune diabetes in adults) and with the main residence in the district of Landeck in the western part of Austria. The district of *Landeck is* a well‐defined region with clear geographical borders, either high mountains within Austria or national borders towards Italy and Switzerland in the south. The district of Landeck with a population of 35,148 persons aged 20 and older consists of the city of Landeck with a population of 6120 persons aged 20 and older and 30 municipalities. This rural area is rather typical for rural areas in Austria. The study was conducted by the Research Unit of Diabetes Epidemiology of the Department of Public Health, Health Services Research and Health Technology Assessment at UMIT TIROL. Before beginning data collection, a case report form (CRF) was designed by diabetes and public health experts. The CRF includes questions on patient characteristics, and questions on time‐varying data based on visits at care units. A network connecting all care units has been established including 22 general practitioners, four diabetes specialists in private practice, the hospital in Zams, the Medical University Innsbruck and retirement homes in the district of Landeck. Data collection was mainly performed by two qualified study nurses who visited the care units. Direct documentation by the treating physician was also technically possible but conducted only in individual cases due to the heavy workload of the physicians caused by the COVID‐19 pandemic. All cohort data are stored in the pseudonymized web‐based database software ASKIMED. 8 ASKIMED provides the possibility to store visits for each patient at different care units and allows the documentation by different persons and the mapping of the necessary access rights. A pseudonym was generated based on each patient's social security number (with a SHA‐2 procedure), which makes it impossible, due to the current computing performance, to identify the specific patient based on the pseudonym. For research purposes, data were transferred to the statistical software Stata (Version 17). 9 In order to address aspects of data privacy, all patients had to sign a written consent form. For data protection reasons, the data of the very few patients who did not agree to sign the consent have not been included into the database for analysis. In addition, every care unit signed a contract stating the rights and obligations between the care units and the research group. The ethics committee at the Medical University of Innsbruck approved the complete project and the project was carried out in accordance with the Helsinki Declaration of 1975, as revised in 2008. A scientific advisory board supervised the study and decided in favour of a minimal data set to build up a population‐based cohort, given restrictions by the limited budget and the COVID‐19 pandemic.
## Patient characteristics
We collected the following patient characteristics: diagnosis, age, migration background, diagnosis site, year of diagnosis, diabetes duration, family history of coronary heart disease and family history of DM, participation in the Austrian disease management program, smoking status, participation in a diabetes education program and sufficient knowledge on diabetes according to the physician.
All diagnoses were clinically confirmed according to the criteria of the Austrian Diabetes Society (ADS) based on a fasting glucose or oral glucose tolerance test or haemoglobin A1c (HbA1c). 10 The study protocol did not include patients with prediabetes in the study population. Gestational diabetes (GDM) was documented separately. Diagnosis of GDM was based on the HAPO criteria. 11 Age at the last visit was used for the analysis and was cut into categories, namely 20–49, 50–64, 65–74 and ≥75 years, taking into account the definition of geriatrics of the elderly/old person. The migration background was classified based on Schenk's approach and adapted to the Austrian situation. 12 During documentation, participants were asked whether their DM diagnosis had been made in the hospital or at a physician's office (diagnosis site). For most patients, the year of diagnosis was recalled from patients' memory, and we decided to collect the exact year only for patients diagnosed during the past 10 years. The diabetes duration was defined as the difference between the year of DM diagnosis and the year of last contact with a care unit and was analysed in the following groups: 0–4 years, 5–9 years, and 10 years or longer. We documented family history of coronary heart disease and family history of DM. Family included children, parents and/or siblings. Participation in the Austrian Disease Management Program during the study period 13 was also documented. Additionally, the smoking status at the time of diagnosis was recorded retrospectively in the categories ‘active smoker’ or ‘ex‐smoker’ and ‘never smoker’. We assessed whether each patient had participated at least once in a diabetes education program and whether the patient had sufficient knowledge on DM according to the physician.
Each patient's life status was verified by a study nurse who inspected hospital/private care records and local newspapers in case there were no up‐to‐date visit records. Pseudonymization prevented linking records with official mortality data. It was therefore impossible to systematically check the patients' life status.
## Clinical parameters
Data collection included the following clinical parameters: body mass index (BMI), HbA1c, low‐density lipoprotein (LDL), systolic and diastolic blood pressure, microalbumin, physical activity, eye and foot inspection, hypoglycaemia (requiring external help for recovery), diabetes‐specific therapy and lipid therapy.
Mean values were computed for BMI, HbA1c, LDL, and systolic and diastolic blood pressure, which were collected at each visit. BMI was calculated according to the formula BMI = weight/height2 (kg/m2), and the classification was based on WHO recommendations. In addition, obesity was defined as a BMI ≥30 kg/m2. 14 The mean values of HbA1c were categorized into four groups (<$6.5\%$, $6.5\%$–$7.49\%$, $7.5\%$–$8.99\%$ and >$9\%$) based on the ADS guidelines. 10 Increased blood pressure was defined as systolic pressure ≥140 mmHg or diastolic pressure ≥90 mmHg according to WHO guidelines. 15 It should be noted that increased blood pressure is based on blood pressure measurements only. We did not collect information on the diagnosis of hypertension and/or medication.
LDL is the primary therapeutic target for lipid control in patients with DM. The LDL classification was based on current recommendations of the ESC/EAS. 16 We did not collect information on the diagnosis of hyperlipidaemia, but we surveyed the proportion of patients with well‐controlled LDL levels. We recorded whether microalbumin was determined at least at one visit. Patients were asked if they were physically active (defined as at least moderate activity for less than two and a half hours per week). We also documented if an ophthalmologist had performed at least one eye inspection during visits. The inspections followed the recommendations of the ADS. 10 Furthermore, we recorded foot inspections during the study period. Foot inspection was defined as at least the removal of shoes and socks and the examination of the feet by the diabetes assistant or physician. Furthermore, we noted severe hypoglycaemia (requiring external help for recovery) during the study period and calculated a cumulative number of events. Finally, we recorded information on the diabetes‐specific therapy. DM treatment categories were lifestyle adaptation only, metformin only, oral antidiabetic drugs (OADs) without metformin, insulin only, insulin and oral antidiabetic drugs, or another form of therapy. Each form of therapy was documented if it was observed during at least one visit over the study period.
## Long‐term complications
We documented the following long‐term complications: neuropathy, nephropathy, retinopathy, cardiovascular and cerebrovascular disease, diabetic foot ulcers and amputations based on the recommendations of the ADS. 10 *Neuropathy is* defined as nerve injuries due to DM and is confirmed with a positive monofilament test. Nephropathy requires positive albumin results at two subsequent visits. Retinopathy is diagnosed according to the guidelines provided by the Austrian Ophthalmologist Society. 17 Cardiovascular late complication was defined as myocardial infarction, bypass or percutaneous coronary intervention. Cerebrovascular late complication was defined as minor and major strokes, including transient ischaemic attacks. We used the strict definition of the long‐term complication diabetic foot according to the guidelines of the ADS 10 and therefore only collected information on the presence of diabetic foot ulcers. For amputations, we documented any non‐traumatic amputation due to diabetic foot ulcers. For all late complications, the year of the first occurrence was recorded if the diagnosis was made within the past 10 years. In all other cases, we only documented the diagnosis of the respective late complication without the year of occurrence. For the analysis we counted every long‐term complication, not only long‐term complications diagnosed in the study period.
The most frequent long‐term complication was cardiovascular disease ($$n = 332$$, $19.2\%$), followed by nephropathy ($$n = 313$$, $18.1\%$) and neuropathy ($$n = 223$$, $12.9\%$). We observed significant differences between women and men for diabetic foot, amputation and cardiovascular disease. More men than women suffered from these three long‐term complications (Figure 1). Further details for the total population and genders are presented in Table 3.
**FIGURE 1:** *Long‐term complications stratified by gender ($$n = 1728$$, ‘GDM only’ and missing values excluded)* TABLE_PLACEHOLDER:TABLE 3 In our study, we observed 90 cases with GDM that did not develop T2DM. A mean value of HbA1c < 6 was documented for $94\%$ of patients with GDM. Only three patients with GDM showed a HbA1c ≥ 6.5. Lifestyle adaptation was sufficient in $82.5\%$ of patients with GDM, and insulin therapy was necessary for $13.4\%$.
See Tables S1–S2 for patient characteristics and clinical parameters according to the age groups 20–49, 50–64, 65–74 and ≥75 years. Briefly, we observed differences in the following patient characteristics: diabetes duration, migration background and smoking status. We also found a difference in the participation in education programmes and sufficient diabetes knowledge, but the percentage was still very high across all age groups. For the clinical parameters stratified by age group, we observed differences in the BMI, LDL, microalbumin, diabetes therapy, physical activity, foot inspection, and for most of the long‐term complications. Further details on patient characteristics and clinical parameters are described in Tables S1–S2.
## Statistical analysis
Patient characteristics are described as counts and percentages for categorical data. We present all results stratified by gender and age groups (Tables S1–S2 only). Fisher's exact test or the chi‐squared test were used to test differences across gender and age groups. Statistical significance was established as $p \leq .05.$ Cases with GDM that did not result in a life‐long type of DM (called ‘GDM only’) were described in a separate part because they differ in many clinical aspects. The main analysis of demographic and clinical parameters does not include cases with ‘GDM only’. In case of missing data, we report data and percentages for non‐missing values in a first step and present the number of cases with missing values in a second step; this procedure was adopted for every variable reported in the result tables.
For the computation of prevalence figures, we included all patients with DM detected in our study in the region of Landeck. Population data for the region of Landeck per age group were obtained from Statistics Austria. 18 According to this population data, 35,148 individuals aged 20 and older were living in the district of Landeck at the time of the study, with slightly more women (17,839, $50.8\%$) than men (17,309, $49.2\%$). The population remained rather stable during the study period. Prevalence was computed for the entire district and for subregions, which are defined by the geographic structure of the following districts: Kaunertal, Stanzertal, Sonnenterasse, Oberes Gericht and Paznauntal. Prevalence and $95\%$ confidence intervals ($95\%$ CI) are provided for the overall cohort and also stratified by gender, subregions and age groups. We applied the concept of period prevalence, that is, we included patients with at least one visit at a care unit stay during the study period as a prevalent case.
All statistical analyses were performed using Stata Version 17. 9
## Study population/diabetes Landeck cohort
The recruitment started in 2018 and ended in June 2021. In total, we recruited and analysed 1845 cases with DM including 90 cases with ‘GDM only’. For the main analysis, we excluded ‘GDM only’ cases resulting in a total 1755 cases with DM, $5.7\%$ with T1DM, $92.4\%$ with T2DM and $1.9\%$ with another DM diagnosis. Of the 1755 patients, 25 ($1.4\%$) died during the study and two subjects were lost in the follow‐up. At the last visit, $9.6\%$ were aged 20–49 years, $27.2\%$ were 50–64 years old, $26.6\%$ were aged 65–74 years, and $36.6\%$ were 75 years or older. We observed 812 ($46.3\%$) women and 943 ($53.7\%$) men. Patient characteristics of the cohort according to gender are presented in Table 1. We only describe results with significant differences between female and male patients. Age distributions differed between genders. For example, in the age group ≥75, the proportion of women with DM was higher than for men. Among patients with DM, women had a longer diabetes duration ($55.9\%$ ≥10 years vs. $49.2\%$ in men) and more often reported a family history of diabetes ($38\%$ in women vs. $31.7\%$ in men). In men, we observed a substantially higher percentage of active smokers ($14.1\%$ in men vs. $7.2\%$ in women) and ex‐smokers compared to women ($53.4\%$ in men vs. $19.4\%$ in women).
**TABLE 1**
| Unnamed: 0 | Female | Female.1 | Male | Male.1 | Total | Total.1 | p‐value a |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | N | % b | N | % b | N | % b | p‐value a |
| Age group | Age group | Age group | Age group | Age group | Age group | Age group | Age group |
| 20–49 | 67 | 8.3 | 101 | 10.7 | 168 | 9.6 | <.001* |
| 50–64 | 170 | 20.9 | 308 | 32.7 | 478 | 27.2 | |
| 65–74 | 215 | 26.5 | 251 | 26.6 | 466 | 26.6 | |
| ≥75 | 360 | 44.3 | 283 | 30.0 | 643 | 36.6 | |
| Total | 812 | 100.0 | 943 | 100.0 | 1755 | 100.0 | |
| Missing values c | 0 | 0 | 0 | 0 | 0 | 0 | |
| Diagnosis | Diagnosis | Diagnosis | Diagnosis | Diagnosis | Diagnosis | Diagnosis | Diagnosis |
| T1DM | 40 | 4.9 | 59 | 6.3 | 99 | 5.7 | .112 |
| T2DM | 760 | 93.7 | 856 | 91.3 | 1616 | 92.4 | |
| Other DM | 11 | 1.4 | 23 | 2.5 | 34 | 1.9 | |
| Total | 811 | 100.0 | 938 | 100.0 | 1749 | 100.0 | |
| Missing values | 1 | 0.1 | 5 | 0.5 | 6 | 0.3 | |
| Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site | Diagnosis site |
| Hospital | 243 | 32.1 | 325 | 36.8 | 568 | 34.6 | .048* |
| Private practice | 514 | 67.9 | 559 | 63.2 | 1073 | 65.4 | |
| Total | 757 | 100.0 | 884 | 100.0 | 1641 | 100.0 | |
| Missing values | 55 | 6.8 | 59 | 6.3 | 114 | 6.5 | |
| Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration | Diabetes duration |
| 0–4 years | 185 | 22.8 | 258 | 27.4 | 443 | 25.2 | .016* |
| 5–9 years | 173 | 21.3 | 221 | 23.4 | 394 | 22.5 | |
| ≥10 years | 454 | 55.9 | 464 | 49.2 | 918 | 52.3 | |
| Total | 812 | 100.0 | 943 | 100.0 | 1755 | 100.0 | |
| Missing values | 0 | 0 | 0 | 0 | 0 | 0 | |
| Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes | Family history of diabetes |
| No | 482 | 62.0 | 611 | 68.3 | 1093 | 65.4 | .007* |
| Yes | 295 | 38.0 | 283 | 31.7 | 578 | 34.6 | |
| Total | 777 | 100.0 | 894 | 100.0 | 1671 | 100.0 | |
| Missing values | 35 | 4.3 | 49 | 5.2 | 84 | 4.8 | |
| Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease | Family history of coronary heart disease |
| No | 621 | 79.7 | 727 | 81.4 | 1348 | 80.6 | .386 |
| Yes | 158 | 20.3 | 166 | 18.6 | 324 | 19.4 | |
| Total | 779 | 100.0 | 893 | 100.0 | 1672 | 100.0 | |
| Missing values | 33 | 4.1 | 50 | 5.3 | 83 | 4.7 | |
| Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program | Participation in disease management program |
| No | 726 | 89.4 | 843 | 89.4 | 1569 | 89.4 | 1.000 |
| Yes | 86 | 10.6 | 100 | 10.6 | 186 | 10.6 | |
| Total | 812 | 100.0 | 943 | 100.0 | 1755 | 100.0 | |
| Missing values | 0 | 0 | 0 | 0 | 0 | 0 | |
| Life status | Life status | Life status | Life status | Life status | Life status | Life status | Life status |
| Alive | 800 | 98.5 | 928 | 98.4 | 1728 | 98.5 | .416 |
| Deceased | 12 | 1.5 | 13 | 1.4 | 25 | 1.4 | |
| Lost/moved | 0 | 0.0 | 2 | 0.2 | 2 | 0.1 | |
| Total | 812 | 100.0 | 943 | 100.0 | 1755 | 100.0 | |
| Missing values | 0 | 0 | 0 | 0 | 0 | 0 | |
| Migration background | Migration background | Migration background | Migration background | Migration background | Migration background | Migration background | Migration background |
| No | 705 | 87.3 | 793 | 84.3 | 1498 | 85.6 | .087 |
| Yes | 103 | 12.7 | 148 | 15.7 | 251 | 14.4 | |
| Total | 808 | 100.0 | 941 | 100.0 | 1749 | 100.0 | |
| Missing values | 4 | 0.5 | 2 | 0.2 | 6 | 0.3 | |
| Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status | Smoking status |
| Active smoker | 55 | 7.2 | 125 | 14.1 | 180 | 10.9 | .000* |
| Ex‐smoker | 147 | 19.4 | 474 | 53.4 | 621 | 37.7 | |
| Never smoker | 557 | 73.4 | 288 | 32.5 | 845 | 51.3 | |
| Total | 759 | 100.0 | 887 | 100.0 | 1646 | 100.0 | |
| Missing values | 53 | 6.5 | 56 | 5.9 | 109 | 6.2 | |
| Participation in education program | Participation in education program | Participation in education program | Participation in education program | Participation in education program | Participation in education program | Participation in education program | Participation in education program |
| No | 168 | 21.0 | 209 | 22.5 | 377 | 21.8 | .483 |
| Yes | 632 | 79.0 | 720 | 77.5 | 1352 | 78.2 | |
| Total | 800 | 100.0 | 929 | 100.0 | 1729 | 100.0 | |
| Missing values | 12 | 1.5 | 14 | 1.5 | 26 | 1.5 | |
| Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) | Sufficient diabetes knowledge (according to physician) |
| No | 115 | 15.1 | 109 | 12.2 | 224 | 13.5 | .097 |
| Yes | 648 | 84.9 | 785 | 87.8 | 1433 | 86.5 | |
| Total | 763 | 100.0 | 894 | 100.0 | 1657 | 100.0 | |
| Missing values | 49 | 6.0 | 49 | 5.2 | 98 | 5.6 | |
Gender differences were found for specific clinical parameters, such as BMI, HbA1c, LDL, determination of microalbumin, DM therapy and physical activity. Table 2 shows the comparison of these characteristics between genders. We observed more overweight subjects (i.e. BMI = 25.0–29.99) in men than women. The proportion of obesity (i.e. BMI≥30) was similar for both genders. Women had a significantly greater proportion of well‐controlled HbA1c levels (HbA1c < 6.5) than men. The distribution of LDL showed a shift to higher values in men compared to women. Microalbumin was identified more frequently in men than in women, and more men were physically active ($28.5\%$ vs. $21.9\%$). We also observed differences in DM therapy. More women than men only adapted their lifestyle ($22.3\%$ vs. $16.2\%$). In men, metformin was more frequently used than in women, for example, more men ($22.9\%$) received oral antidiabetic drugs therapy AND insulin than women ($18.5\%$). Further details on clinical parameters are shown in Table 2.
**TABLE 2**
| Unnamed: 0 | Female | Female.1 | Male | Male.1 | Total | Total.1 | p‐value a |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | N | % b | N | % b | N | % b | p‐value a |
| BMI | BMI | BMI | BMI | BMI | BMI | BMI | BMI |
| <18.5 | 9 | 1.1 | 5 | 0.5 | 14 | 0.8 | .015* |
| 18.5–24.99 | 174 | 22.0 | 165 | 18.0 | 339 | 19.8 | |
| 25.0–29.99 | 275 | 34.7 | 379 | 41.4 | 654 | 38.3 | |
| ≥30 | 334 | 42.2 | 367 | 40.1 | 701 | 41.0 | |
| Total | 792 | 100.0 | 916 | 100.0 | 1708 | 100.0 | |
| Missing values c | 20 | 2.5 | 27 | 2.9 | 47 | 2.7 | |
| HbA1c | HbA1c | HbA1c | HbA1c | HbA1c | HbA1c | HbA1c | HbA1c |
| 0–6.49 | 320 | 41.9 | 325 | 36.2 | 645 | 38.8 | .033* |
| 6.5–7.49 | 254 | 33.3 | 303 | 33.7 | 557 | 33.5 | |
| 7.5–8.99 | 158 | 20.7 | 235 | 26.2 | 393 | 23.7 | |
| 9–99 | 31 | 4.1 | 35 | 3.9 | 66 | 4.0 | |
| Total | 763 | 100.0 | 898 | 100.0 | 1661 | 100.0 | |
| Missing values | 49 | 6.0 | 45 | 4.8 | 94 | 5.4 | |
| LDL | LDL | LDL | LDL | LDL | LDL | LDL | LDL |
| <55 | 77 | 10.2 | 110 | 12.6 | 187 | 11.5 | .017* |
| 55–69 | 92 | 12.2 | 111 | 12.7 | 203 | 12.5 | |
| 70–99 | 202 | 26.7 | 273 | 31.3 | 475 | 29.2 | |
| ≥100 | 386 | 51.0 | 378 | 43.3 | 764 | 46.9 | |
| Total | 757 | 100.0 | 872 | 100.0 | 1629 | 100.0 | |
| Missing values | 55 | 6.8 | 71 | 7.5 | 126 | 7.2 | |
| Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) | Blood pressure (measured) |
| Within normal range | 585 | 74.0 | 670 | 73.0 | 1255 | 73.4 | .661 |
| Increased (≥140/90) | 206 | 26.0 | 248 | 27.0 | 454 | 26.6 | |
| Total | 791 | 100.0 | 918 | 100.0 | 1709 | 100.0 | |
| Missing values | 21 | 2.6 | 25 | 2.7 | 46 | 2.6 | |
| Microalbumin | Microalbumin | Microalbumin | Microalbumin | Microalbumin | Microalbumin | Microalbumin | Microalbumin |
| No | 361 | 47.8 | 357 | 40.7 | 718 | 43.9 | .004* |
| Yes | 395 | 52.2 | 521 | 59.3 | 916 | 56.1 | |
| Total | 756 | 100.0 | 878 | 100.0 | 1634 | 100.0 | |
| Missing values | 56 | 6.9 | 65 | 6.9 | 121 | 6.9 | |
| Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy | Diabetes therapy |
| Only metformin | 313 | 38.9 | 403 | 43.1 | 716 | 41.1 | .008* |
| OAD without metformin | 51 | 6.3 | 50 | 5.3 | 101 | 5.8 | |
| Only insulin | 106 | 13.2 | 112 | 12.0 | 218 | 12.5 | |
| OAD + insulin | 149 | 18.5 | 214 | 22.9 | 363 | 20.9 | |
| Another form of therapy | 6 | 0.7 | 5 | 0.5 | 11 | 0.6 | |
| Only lifestyle adaptation | 179 | 22.3 | 152 | 16.2 | 331 | 19.0 | |
| Total | 804 | 100.0 | 936 | 100.0 | 1740 | 100.0 | |
| Missing values | 8 | 1.0 | 7 | 0.7 | 15 | 0.9 | |
| Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia | Number of occurrences of hypoglycaemia |
| 0 | 790 | 97.5 | 914 | 97.0 | 1704 | 97.3 | .812 |
| 1 | 12 | 1.5 | 17 | 1.8 | 29 | 1.7 | |
| ≥2 | 8 | 1.0 | 11 | 1.2 | 19 | 1.1 | |
| Total | 810 | 100.0 | 942 | 100.0 | 1752 | 100.0 | |
| Missing values | 2 | 0.2 | 1 | 0.1 | 3 | 0.2 | |
| Physical activity | Physical activity | Physical activity | Physical activity | Physical activity | Physical activity | Physical activity | Physical activity |
| Inactive | 568 | 78.1 | 609 | 71.5 | 1177 | 74.5 | .003* |
| Active | 159 | 21.9 | 243 | 28.5 | 402 | 25.5 | |
| Total | 727 | 100.0 | 852 | 100.0 | 1579 | 100.0 | |
| Missing values | 85 | 10.5 | 91 | 9.7 | 176 | 10.0 | |
| Eye inspection | Eye inspection | Eye inspection | Eye inspection | Eye inspection | Eye inspection | Eye inspection | Eye inspection |
| No | 303 | 37.4 | 355 | 37.7 | 658 | 37.6 | .921 |
| Yes | 507 | 62.6 | 587 | 62.3 | 1094 | 62.4 | |
| Total | 810 | 100.0 | 942 | 100.0 | 1752 | 100.0 | |
| Missing values | 2 | 0.2 | 1 | 0.1 | 3 | 0.2 | |
| Foot inspection | Foot inspection | Foot inspection | Foot inspection | Foot inspection | Foot inspection | Foot inspection | Foot inspection |
| No | 170 | 21.0 | 185 | 19.6 | 355 | 20.3 | .512 |
| Yes | 640 | 79.0 | 757 | 80.4 | 1397 | 79.7 | |
| Total | 810 | 100.0 | 942 | 100.0 | 1752 | 100.0 | |
| Missing values | 2 | 0.2 | 1 | 0.1 | 3 | 0.2 | |
## Prevalence
In the age group ≥20 with a total population of 35,148, we observed an overall diabetes prevalence of $5.3\%$ ($95\%$ CI: 5.0–5.5). Prevalence was slightly higher in men ($5.5\%$, $95\%$ CI: 5.1–5.8) than in women ($5.1\%$, $95\%$ CI: 4.7–5.4, difference not statistically significant). Prevalence differed between subregions: The lowest prevalence was observed in the subregion Sonnenterasse with $3.1\%$ ($95\%$ CI: 2.4–3.9) and the highest in the central area Landeck and surroundings with $6.1\%$ ($95\%$ CI: 5.7–6.5). The prevalence was $3.4\%$ ($95\%$ CI: 2.9–3.9) in the region Paznauntal, $4.1\%$ ($95\%$ CI: 3.1–5.5) in Kaunertal, $4.6\%$ ($95\%$ CI: 4.1–5.3) in Stanzertal and $6.0\%$ ($95\%$ CI: 5.4–6.6) in the region Oberes Gericht. Regarding gender, there were no statistically significant differences in prevalence for the subregions. The results for the subregions, genders and age groups are shown in Table 4.
**TABLE 4**
| Unnamed: 0 | Females | Males | Total |
| --- | --- | --- | --- |
| | Prevalence in % (95% CI) | Prevalence in % (95% CI) | Prevalence in % (95% CI) |
| Total | 5.1 (4.7–5.4) | 5.5 (5.1–5.8) | 5.3 (5.0–5.5) |
| Regions | Regions | Regions | Regions |
| Landeck/surroundings | 5.8 (5.3–6.4) | 6.5 (5.9–7.1) | 6.1 (5.7–6.5) |
| Oberes Gericht | 5.9 (5.2–6.8) | 6.0 (5.2–6.9) | 6.0 (5.4–6.6) |
| Sonnenterasse | 3.6 (2.4–4.9) | 2.6 (1.7–3.7) | 3.1 (2.4–3.9) |
| Kaunertal | 4.6 (3.0–6.7) | 3.7 (2.3–5.5) | 4.1 (3.1–5.5) |
| Paznauntal | 2.8 (2.2–3.6) | 3.9 (3.2–4.8) | 3.4 (2.9–3.9) |
| Stanzer Tal | 4.4 (3.6–5.3) | 4.9 (4.1–5.8) | 4.6 (4.1–5.3) |
| Age groups | Age groups | Age groups | Age groups |
| 20–49 | 1.8 (1.5–2.1) | 1.1 (0.9–1.4) | 1.5 (1.3–1.6) |
| 50–64 | 3.6 (3.0–4.1) | 6.3 (5.6–7.0) | 4.9 (4.5–5.4) |
| 65–74 | 11.0 (9.6–12.6) | 14.3 (12.6–16.2) | 12.5 (11.4–13.7) |
| ≥75 | 15.4 (13.8–17.0) | 18.1 (16.1–20.3) | 16.5 (15.2–17.8) |
## DISCUSSION
We established, to the best of our knowledge, the first population‐based cohort of patients with DM in Austria following the recommendations of the National Diabetes Strategy. Our cohort should support data‐driven healthcare decision‐making and should contribute improving outcomes of patients with DM.
In total, we analysed 1845 adult cases with DM (including ‘GDM only’) and observed an overall prevalence of $5.3\%$, with no statistically significant difference between genders but substantial differences between subregions. In addition, we identified differences between women and men in the following patient characteristics: diabetes duration, diagnosis site, family history of diabetes, smoking status, distribution of age groups, and for specific clinical parameters such as BMI, HbA1c, LDL, determination of microalbumin, DM therapy and physical activity. We also observed significant differences between female and male patients for the long‐term complications diabetic foot, amputation and cardiovascular disease.
In order to derive unmet needs and healthcare services quality gaps, it is important to set our results into the context of other countries. The prevalence of DM varies strongly across the globe and between European countries. The IDF Diabetes Atlas reports a prevalence ranging from $4.0\%$ in Ireland to $15.9\%$ in Turkey, and reports a prevalence for Austria of $6.6\%$, which is clearly below the European average of $9.2\%$. 2 The overall prevalence of $5.3\%$ in the district of *Landeck is* even lower than the prevalence estimate of the IDF for Austria, which lies above the $95\%$ CI of the district Landeck ($95\%$ CI: $5.0\%$–$5.5\%$). One reason for this difference within Austria could be the so‐called east–west gap in Austria. A report by the Federal Ministry of Health showed that mortality due to DM in western *Austria is* lower than in eastern Austria. 19 *There is* also an evident east–west variation in cardiovascular mortality among persons over 64. Overweight/obesity, which is a major risk factor for DM, 20 is an even more significant problem in the population over the age of 64 in the east of Austria than in the west. 19 However, these estimates are not standardized, for example, for age, sociodemographic characteristics or proportion living in urban areas.
Our prevalence estimation for the district of *Landeck is* in line with the reported prevalence of $4\%$ in Switzerland, 21 which borders Landeck. Germany also shows a southwest‐to‐northeast gradient. The regional standardized prevalence was highest in the east, with $12.0\%$ ($95\%$ CI: $10.3\%$–$13.7\%$), and lowest in the south, with $5.8\%$ ($95\%$ CI: $4.9\%$–$6.7\%$). 22 However, we cannot exclude an underestimation of the prevalence in our study because we were unable to validate the completeness of the population‐based cohort with independent data sources.
When comparing the frequencies with the literature, it should be noted that we included all DM patients treated in a region, not only those treated in a study using only a sample of the target population. Therefore, we predominantly compare our figures with results from population‐based diabetes registries. In addition, it is worth noting that our proportion of patients with high blood pressure should not be compared with hypertensive patients of study results from the published literature. The same is true for patients with higher LDL levels versus hyperlipidaemia.
In the following, we compare the parameters obesity, glycaemic control, smoking status, foot inspections and participation in a diabetes education program with data from the Scottish diabetes registry, 23 the Swedish registry, 24 and with results from the DAWN2 study. 25 We also compare diabetes therapies with *Austrian data* 26 and with data from a diabetes surveillance system for Germany at the Robert Koch Institute. 22 In our study, $42.2\%$ of women and $40.1\%$ of men were obese. In Scotland, the proportion of obese patients was at $55\%$. 23 In Sweden, obesity was also more frequent, with a prevalence of $61\%$ for women and $54\%$ for men, although data from Sweden are only available for patients in the age group 30–60. 24 Overall, the proportion of obese patients in our study is lower compared to global numbers and the Austrian average, 27 which fits the east–west gradient in Austria mentioned above.
Concerning glycaemic control, the proportion of patients with HbA1c < $7.5\%$ was $55.4\%$ in Scotland 23 and $72.3\%$ in our study ($75.2\%$ in women and $69.9\%$ in men). One reason could be that the population in this western part of *Austria is* less obese and more physically active. Even overweight patients perform substantial physical activity. In addition, diabetic care is rather well structured.
The participation in a diabetes education program was $78.7\%$ in the DAWN2 study, 25 which is nearly identical to our results of $78.2\%$. The DAWN2 study reported $62.5\%$ foot inspections (vs. $79.7\%$ in the Diabetes Landeck Cohort); however, the DAWN2 data were based on self‐assessments. In the Diabetes Landeck Cohort, the proportion of active smokers was $10.9\%$ (two times as many men than women), which is lower compared to $14.3\%$ of the study of Panisch et al. 28 and to $17.7\%$ in Scotland. 23 Our findings on diabetes therapy data do not describe the patients' current therapy (at the last contact) but rather summarize all therapy modalities that were documented during the study period. The proportion of patients with DM who did not need diabetes‐specific therapy (lifestyle adaptation only) is relatively high ($19\%$) but is similar to the Swedish registry. 24 The percentage of DM patients treated with OADs corresponds to $72\%$ in the publication of Engler et al., 26 which describes patients in the diabetes register in Tyrol. Among 45‐ to 79‐year‐old people with T2DM in Germany, $29.6\%$ of women and $37.2\%$ of men received metformin monotherapy in 2010. 22 This percentage is higher in our study ($38.9\%$ in women and $42.1\%$ in men). Also, the percentage for OAD+ insulin is higher in our cohort, which included all patients with DM compared to the study across Germany that only assessed patients with T2DM. 22 Comparisons of diabetes‐specific therapies with the literature should be carefully interpreted as many studies exclude elderly patients. In contrast, in our cohort, all patients with DM were registered, and the proportion of patients aged ≥75 was $36\%$.
When comparing our results with published data, it is essential to consider that the frequency of the respective long‐term complications depends on several factors, such as diabetes duration, age, gender and distribution of other risk factors. However, it also depends on the definition (and it is worthwhile to mention that definitions of each late complication differ in some respect), the extent of appropriate screening measures and the diagnostic methods used. Nonetheless, in the following paragraph, we attempt to compare our data on long‐term complications to the literature.
In Germany, the proportion of patients with DM with documented chronic kidney disease (as an indicator of nephropathy) was $15.1\%$ (women: $14.9\%$; men: $15.3\%$) in 2013. 22 For the Diabetes Landeck Cohort, the percentage is higher ($18.1\%$) compared to Germany, but differences in the documentation and diagnosis standards complicate a comparison. Desphande et al. 29 have provided an overview of the prevalence of the most common diabetes complications among individuals with T2M. 29 They reported a frequency of nephropathy of $28\%$. As the disease DM progresses, the frequency of nephropathy is low in the first 10–15 years after the diagnosis and then increases significantly. In the Diabetes Landeck Cohort, the diabetes duration is less than 10 years in $50\%$ of patients; this may explain the lower frequency of nephropathy, with $19.4\%$ in women and $17.0\%$ in men compared to the data of Desphande et al. 29 Another explanation may be the relatively high proportion of patients with an HbA1c below $7.5\%$. The frequency of nephropathy correlates strongly with blood glucose control. 30 In 2013, $13.5\%$ of adults with DM had documented diabetic polyneuropathy in Germany (women: $12.7\%$; men: $14.4\%$). 22 The proportion of patients with neuropathy is similar to that in our study (women: $12.6\%$; men: $13.1\%$). In Germany, $37.1\%$ of adults with T2DM have cardiovascular disease, with a significantly lower prevalence in women ($30.6\%$) than in men ($42.8\%$), but this includes long‐term complications and comorbidities. 22 In our study, we observed lower frequencies but also a significant gender difference (women: $12.3\%$; men: $25.2\%$) for cardiovascular disease as a long‐term complication. Our study and the meta‐analysis of Einarson 31 show nearly identical results for cerebrovascular long‐term complications. The percentage of retinopathy in our cohort is very small ($2.2\%$), but the prevalence of retinopathy increases progressively in patients with DM with increasing duration of the disease, 32 and we could be confronted with problems in communication diagnoses from ophthalmologists to care givers. In Germany, $5.7\%$ of women and $6.6\%$ of men with DM had documented diabetic foot syndrome in 2013. 22 For the Diabetes Landeck Cohort, the percentage is lower for women ($2.5\%$) and similar for men ($5.3\%$).
All patient data registered in this study were pseudonymized according to EU data protection laws. This means that the patient can no longer be identified in the research database, and implausible data can no longer be verified. The process of pseudonymization is based on the Austrian social security number, which means that patients cannot be registered without a social security number; however, the proportion of individuals without a social security number was fairly small in our study (about $0.3\%$ of all patients). 33 One strength of our study is that it covers a well‐defined population and region, all care providers are clearly identified, and the area is served almost exclusively by one hospital. We established a network of general practitioners, diabetes specialists in private practices, retirement homes and the hospital in a defined district. Cooperation with all care units was excellent, with very few exceptions. Another strength was the use of a workable documentation system. Furthermore, by limiting the data to a minimum basic dataset, we achieved an acceptable data quality and thus gained a valid representation of the quality of care. Our study design could be a prototype for other countries and can contribute to assess a good overview of the quality of care of patients with DM, given a limited budget. We were also able to identify the problems in the data collection, which is important for future updates of the cohort or planning of diabetes registries in Austria.
This study has several limitations. First, most of the study period was dominated by the COVID‐19 pandemic. The documentation took place during one of the most difficult periods in the Austrian healthcare system in recent decades, and this could have affected our data quality. The care units could not be visited at the predefined time intervals for a more extended period due to the COVID‐19 lockdown restrictions, and caregivers had less time to maintain and update records. This may lead to an underestimation of the true prevalence. Second, some general practitioners retired during the study, which limited cooperation. A third limitation that became evident during the study was the lack of coding DM diagnoses in most practice systems. This means that patients with DM can currently only be identified by a free text search, which is usually very time‐consuming and can be associated with measurement bias. 34 This applies particularly to patients with DM who are not detected by the search criteria, which can lead to an underestimation of the true prevalence. It should also be considered that not all essential information is stored in the medical records and therefore was not accessible to the study nurse. Most physicians did not have the time to document or complete data themselves. Therefore, the majority of data documentation was completed by the study nurse who did not have direct contact with the patients. A fifth limitation of our study is that only a minimal data set was registered due to budget restrictions and the current conditions. For example, we were not able to collect data on triglycerides, ischaemic cardiomyopathy or other types of cardiomyopathy, or specific event types of cerebrovascular diseases or vascular events. As our results represent the population structure of the district Landeck and were not standardized to the Austrian population, crude comparisons with Austria should be interpreted with caution, see the discussion of the so‐called east–west gap in Austria. Another significant limitation, the need for a uniform definition of long‐term complications, applies to all published studies and was also a challenge we faced in our study. Finally, we were not able to verify the completeness of the prevalence estimate with independent data resources and could not systematically survey the patients' life statuses.
## CONCLUSIONS
As explicitly stated in Austria's National Strategy Report on diabetes, there is a lack of high‐quality data on DM in the country. 6 There are, to the best of our knowledge, no systematic population‐based data on patients with DM in Austria. The Diabetes Landeck Cohort closes this gap for one region in Austria and provides, for the first time in Austria, a nearly complete set of patients with DM living in a well‐defined region. Some results, such as the diabetes prevalence or the frequency of some long‐term complications, are lower compared to international data. We succeeded in establishing a population‐based cohort and related database; however, we were not able to identify independent sources to verify our results. Therefore, for the future, we strongly suggest evaluating both completeness and comparability of data with well‐accepted methods. *In* general, documentation by study nurses who should ideally be located in the care units is recommended to obtain valid data, because many important data are not stored in the practice systems or cannot be accessed in a systematic way. To access patients with specific diagnoses, support for the coding of diagnoses by physicians in private practices should be developed and applied in the practice systems. To make the best use of already existing data in the Austrian healthcare system, we recommend developing and/or optimizing systems to link different databases (e.g. civil registration and death data). The Diabetes Landeck Cohort should allow to evaluate and improve the quality of care of patients with DM in the future. *In* general, the cohort should be optimized and updated because high‐quality data provide an essential basis to optimize the care of patients with DM. The data could also be used to supplement a biobank, for long‐term monitoring of diabetes patients, for questions in health services research and healthcare economics, and for the investigation of new electronic communication methods between physicians and patients. Further research is needed, and in a subsequent step, we will extend this study by carefully taking the limitations into account.
## AUTHOR CONTRIBUTIONS
Veronika Haslwanter: Conceptualization (lead); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); project administration (equal); validation (equal); visualization (lead); writing – original draft (lead). Ursula Rochau: Supervision (supporting); writing – original draft (supporting); writing – review and editing (supporting). Uwe Siebert: Supervision (supporting); writing – original draft (supporting); writing – review and editing (supporting). Hans Schoenherr: Conceptualization (supporting); data curation (equal); methodology (equal). Wilhelm Oberaigner: Conceptualization (supporting); formal analysis (equal); methodology (equal); supervision (lead); writing – review and editing (equal).
## FUNDING INFORMATION
We thank the Tyrolean Health Fund (Tiroler Gesundheitsfond, TGF) for funding this project.
## CONFLICT OF INTEREST
The authors have no competing interests.
## ETHICS APPROVAL
The present study was approved by the Ethics Committee of the Medical University of Innsbruck and was carried out in accordance with the Helsinki Declaration of 1975, as revised in 2008.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from Tiroler Gesundheitsfond. Restrictions apply to the availability of these data, which were used under licence for this study.
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|
---
title: Levothyroxine therapy, calculated deiodinases activity and basal metabolic
rate in obese or nonobese patients after total thyroidectomy for differentiated
thyroid cancer, results of a retrospective observational study
authors:
- Rosario Le Moli
- Pasqualino Malandrino
- Marco Russo
- Dario Tumino
- Tommaso Piticchio
- Adriano Naselli
- Valentina Rapicavoli
- Antonino Belfiore
- Francesco Frasca
journal: Endocrinology, Diabetes & Metabolism
year: 2023
pmcid: PMC10000637
doi: 10.1002/edm2.406
license: CC BY 4.0
---
# Levothyroxine therapy, calculated deiodinases activity and basal metabolic rate in obese or nonobese patients after total thyroidectomy for differentiated thyroid cancer, results of a retrospective observational study
## Abstract
Because of controversial issues about the optimal T4 replacement dose in obese hypothyroid subjects and the great importance of thyroid hormones in energy homeostasis, glucose and lipid metabolism, body composition and resting energy expenditure (REE), we compared the correlation between L‐T4 administered dose, thyroid hormone levels and TSH secretion with estimated basal metabolic rate (BMR) and total deiodinase activity (GD) in obese and nonobese athyreotic subjects. We aimed to define individualized set points that might provide appropriate therapeutic and biochemical targets to be clinically tested in obese and nonobese patients.
### Introduction
Therapy for hypothyroid obese patients is still under definition since the thyrotropin‐stimulating hormone (TSH) level is a less reliable marker of euthyroidism than nonobese patients. Indeed, TSH levels positively correlate with body mass index (BMI), and this increase may be a compensatory mechanism aimed at increasing energy expenditure in obese people. In contrast, the correlation of BMI with thyroid hormone levels is not completely clear, and conflicting results have been obtained by several studies. The L‐T4 replacement dose is more variable in obese hypothyroid patients than in nonobese patients, and a recent study indicated that the L‐T4 replacement dose is related to lean body mass in obese thyroidectomized patients. We aimed to study the correlations of L‐T4‐administered dose, thyroid hormone levels and TSH secretion with basal metabolic rate (BMR) and total calculated deiodinase activity (GD) in obese and nonobese athyreotic patients. We also looked for individualized L‐T4 replacement dose set points to be used in clinical practice.
### Methods
We studied retrospectively 160 athyreotic patients, 120 nonobese and 40 obese. GD was calculated by SPINA Thyr 4.2, the responsiveness of the hypothalamic/pituitary thyrotrope by Jostel's thyrotropin (TSH) index and BMR by the Mifflin‐St. Jeor formula, the interplay of GD and BMR with L‐T4, thyroid hormones and TSH index (TSHI) was also evaluated.
### Results
In our study, the L‐T4 dose was an independent predictor of GD, and approximately $30\%$ of athyreotic patients under L‐T4 therapy had a reduced GD; FT4 levels were higher and negatively modulated by BMR in obese athyreotic patients respect to nonobese, in these patients a T4 to T3 shunt, in terms of TSHI suppression is observed suggesting a defective hypothalamic pituitary T4 to T3 conversion and a resistance to L‐T4 replacement therapy.
### Conclusions
L‐t4 dose is the most important predictor of GD, BMR modulates T4 levels in obese athyreotic patients that are resistant to L‐T4 replacement therapy.
## INTRODUCTION
Levothyroxine (L‐T4) therapy has a long record in clinical use, with a defined pharmacological profile and safety in hypothyroidism management. Obesity and thyroid disorders are common among the general population and may be associated with both clinical and molecular aspects. This relationship has become epidemiologically relevant in the context of the significantly increased prevalence of obesity worldwide. However, treatment for obese patients with subclinical or overt hypothyroidism is still under definition regarding both the threshold and modality (liquid L‐T4 vs. pills; L‐T4 monotherapy vs. liothyronine [L‐T3]/L‐T4 combinations). The prerequisite for treatment with L‐T4 is the presence of hypothyroidism, and the goal is restoration of euthyroidism. Achievement of a thyrotropin‐stimulating hormone (TSH) value within the age‐adjusted euthyroid range is the accepted therapeutic target, as several studies indicate improvement in symptoms, quality of life and cardiovascular risk. 1, 2, 3, 4 However, among euthyroid subjects, TSH levels usually correlate with body mass index (BMI), being higher in obese than in normal subjects. 5 TSH elevation in obese euthyroid people may be a compensatory mechanism in the pituitary‐thyroid axis aimed at increasing energy expenditure. 6, 7 At variance with TSH, the correlation between BMI and thyroid hormones (T4 and T3) is not clear, as several studies obtained conflicting results. Some studies indicate that BMI is negatively related to FT4 and positively related to FT3. Another study, by contrast, indicated hyperactivation of the pituitary‐thyroid axis with increased FT4 levels in obese patients. 8, 9 Other studies describe a decreased FT4/FT3 ratio in obese patients. 5, 10, 11, 12 This adaptation of thyroid hormone homeostasis in obese subjects has been attributed to leptin and insulin actions. 3 The observation of higher TSH and lower FT4 in obese euthyroid people is in accordance with increased L‐thyroxine replacement dose in hypothyroid obese patients. L‐T4 replacement therapy is approximately 1.6 μg/kg in hypothyroid patients with any functional thyroid tissue, while in obese patients, the correct T4 replacement dose is more variable. Recently, the American Thyroid Association (ATA) task force identified obesity as a morbid condition implying an increase in the L‐T4 replacement dose because of reduced thyroid hormone absorption. 2 This observation is reinforced by the evidence that in obese subjects, acute overload of L‐T4 administration takes longer to achieve a plasmatic concentration peak in comparison with nonobese people. 1 However, a recent study indicated that in obese thyroidectomized patients, the L‐T4 replacement dose is positively related to lean body mass. Indeed, the ideal body weight (IBW) should be preferred to real body weight (RBW) for L‐T4 dose titration because lean body mass results in a better predictor of T4 requirement than fat mass. 6, 7 Because of these controversial issues about the optimal T4 replacement dose in obese hypothyroid subjects and the great importance of thyroid hormones in energy homeostasis, glucose and lipid metabolism, body composition and resting energy expenditure (REE), 10, 12, 13, 14 we compared the correlation between L‐T4‐administered dose, thyroid hormone levels and TSH secretion with estimated basal metabolic rate (BMR) and total deiodinase activity (GD) in obese and nonobese athyreotic subjects. Moreover, we aimed to define individualized set points that might provide appropriate therapeutic and biochemical targets to be clinically tested in obese and nonobese patients.
## Patients
We retrospectively evaluated 1150 thyroidectomized patients referred to our outpatient thyroid clinic between 2010 and 2015 who were also subjected to 131I ablation because of differentiated thyroid cancer (DTC). In all patients, thyroglobulin levels were between 0.01 and 0.5 ng/ml, and antithyroglobulin antibody (TgAb) was negative. In this cohort, devoid of functional thyroid tissue, all circulating T4 levels originated from levothyroxine replacement therapy. These patients obtain circulating T3 from the conversion of exogenous T4 and represent an ideal model to study peripheral tissue ability to generate biologically active hormones. We excluded from the analysis patients with hypothalamic/pituitary, gastric, intestinal or neurological diseases and pregnant women ($$n = 72$$) and those who were taking combined T3/T4 thyroid replacement therapy and/or other drugs interfering with thyroid hormone homeostasis ($$n = 198$$). Patients with variations in L‐T4 daily dose, body weight and thyroid hormone level fluctuations within 3 months before the start of the study were also excluded ($$n = 720$$). Finally, 160 athyreotic patients under L‐T4 therapy were included in the analysis (Figure 1). All patients were euthyroid on the basis of their TSH, FT4 and FT3 levels within the normal range.
**FIGURE 1:** *Flow chart of study patients selection*
## Phenotypic evaluation of the study patients
Clinical records included a detailed history, physical examination, standardized questionnaire documenting sex, age, height, weight and BMI. BMI was calculated as weight in kilograms divided by the square of height in metres (kg/m2) and considered a categorical variable according to the World Health Organization (WHO). Obesity was defined as BMI ≥ 30, which is an adequate indicator of obesity and is associated with increased body fat mass. In our study, 120 patients had a BMI ≥ 30, while 40 had a BMI < 30.
## Basal metabolic rate (BMR) evaluation
We evaluated BMR by the Mifflin‐St. Jeor formula (MSTF). The MSTF equation is commonly used in the assessment of basal metabolism and is more particularly used in obese patients. The MSTF was also applied differently to female and male sex as follows: Females = 9.99 × weight (kg) + 6.25 × height (cm) − 4.92 × age (years) − 161.Males = 9.99 × weight (kg) + 6.25 × height (cm) − 4.92 × age (years) + 5.
We studied the effect of the L‐T4 replacement dose on thyroid hormone homeostasis, estimated BMR and total deiodinase activity (GD) in obese and nonobese patients. Data were collected from patients after thyroidectomy, 131I administration and a persistent euthyroid state under replacement therapy for approximately 3 months with any significant change in L‐T4 dose administration, daily caloric intake and body weight. A subgroup of 45 patients maintaining the same replacement dose over the last 6 months was also studied to better evaluate the interplay between the L‐T4 administered dose and total GD in the long term.
## Evaluation of stimulated deiodination (GD)
GD, which reflects the maximum stimulated activity of deiodination, was calculated by SPINA Thyr 4.2 (Structure Parameter Inference Approach by Johannes W. Dietrich, Lab XU44, Bergmannsheil University Hospitals, Ruhr University of Bochum, D‐44789 Bochum, NRW, Germany), which is a mathematical tool for the integrated interpretation of laboratory results. SPINA allows calculation of GD from TSH, FT4 and FT3 serum levels obtained from routine laboratory assays. The method is based upon mathematical/cybernetic modelling of processing structures. 15 In particular, the SPINA algorithm is based on equilibrium analysis of a compartmental nonlinear model: GD = β31Km1+FT41+K30TBGFT3α31FT4, where β31 is the clearance exponent for T3, Km1 is the dissociation constant of type 1 deiodinase, K30 is the dissociation constant of T3 at thyroxine‐binding globulin, and α31 is the dilution factor for triiodothyronine. On the basis of several studies, normal values of calculated GD vary between 21 and 26 nmol/s. 15, 16 Hence, a GD < 21 nmol/s is considered low.
## Responsiveness of the hypothalamic/pituitary thyrotrope
We also assessed the responsiveness of the hypothalamic/pituitary thyrotrope by Jostel's thyrotropin (TSH) index: (JTSHI) = ln([TSH]) + β[FT4] and obtained a standardized TSH index (TSHI) = JTSHI − $\frac{2.7}{0.676}$ for statistical comparison.
## Laboratory measurements
Serum TSH was assessed by an ultrasensitive enhanced chemiluminescence immunoassay (ECLIA) assay. Serum hormones were measured by microparticle enzyme immunoassay (Abbot AxSYM‐MEIA) with interassay coefficients of variation of less than $10\%$ over the analytical ranges of 1.7–46.0 pmol/L for FT3, 5.15–77.0 pmol/L for FT4 and 0.03–10.0 mU/L for TSH. The within‐run and between‐run precisions for the FT3, FT4 and TSH assays showed coefficients of variation <$5\%$. Measurement of antithyroglobulin antibodies (TgAbs) by an automated chemiluminescence assay system (AntiTg, Ready Pack). Thyroglobulin levels were measured with a second‐generation chemiluminescent Tg immunoassay (Tg Access; Beckman Coulter) with a functional sensitivity of 0.1 ng/ml.
## Statistical analysis
Statistical analysis was performed using the SPSS package (IBM SPSS Statistics for Windows, Version 26.0. IBM Corp). For the descriptive analysis, continuous variables were expressed as the mean ± standard deviation (SD) or median (with its 25th–75th percentile); categorical variables were expressed as numbers and percentages. Univariate analysis of variance (ANOVA) was performed to identify predictive variables significantly associated with the clinical outcome. The shapes of the distribution of each variable were evaluated by visual inspection of the population pyramid charts; for distributions of similar shapes, we reported the medians, and for distributions of different shapes, we reported average ranks. The Mann–Whitney U test was used to analyse the continuous variables without a normal distribution. Categorical variables were analysed by the Chi‐square test, if cells with fewer than five expected cell numbers were found, by Fisher's exact test. Complete and partial bivariate analysis was used to evaluate no categorical variables, and Pearson's coefficient was computed. Binary logistic regression analysis was performed for the outcome variables. Covariates were selected on the basis of the results of univariate analysis, and the final model was built using forced entry and a hierarchical method. Linearity of the continuous variables with respect to the logit of the dependent variable was assessed by the Box‐Tidwell procedure, and a Bonferroni correction was applied using all terms in the model to assess its statistical significance. Multicollinearity was excluded after checking tolerance and variance inflation factor statistics and the proportion of the variance of each predictor's b value attributed to each eigenvalue. The ability of the model to discriminate between outcome categories was investigated in more detail by elaborating the ROC curve. This analysis was performed for LT4 × week/BMR ratio vs deiodinase activity on the basis of the regression outputs. Youden's best cut‐off was also calculated, and the greater values were chosen to balance the better sensitivity and specificity for the studied variable.
## Univariate analysis
Structure parameter influence assay (SPINA) revealed that GD was reduced in $\frac{50}{160}$ ($31.2\%$) of the thyroidectomized patients (Table 1).
**TABLE 1**
| Age (years) | 44.6 (13.9) |
| --- | --- |
| Sex (F/M) | 117/43 |
| Weight (kg) | 73.1 (18.3) |
| Height (cm) | 163.6 (12.2) |
| BMI (kg/Height2) | 27.1 (6.1) |
| TSH (mU/L) | 1.6 (0.4–2.9) |
| FT4 (pmol/L) | 14.1 (11.6–21.9) |
| FT3 (pmol/L) | 4.1 (2.1–5.4) |
| FT3/FT4ratio | 0.25 (0.05) |
| BMR (Kcal/24 h) | 1419.1 (265.5) |
| LT‐4 × week/BMRr (μg) | 0.6 (0.4–1.4) |
| LT‐4 × week (μg) | 835.7 (238.7) |
| GD (nmol/s) | 24.1 (12.0–40.0) |
| GD < 21 nmol/s (n/%) | 50/31.2 |
| TSHI | 2.0 (0.0–3.9) |
Patients were divided into two groups according to normal (≥21 nmol/s) or low (<21 nmol/s) GD. Sex, age, BMI and BMR were not different between the two groups (Table 2). Univariate analysis revealed that FT3 and the FT3/FT4 ratio were significantly reduced in patients with low GD compared to patients with normal GD ($p \leq .004$–.0001). However, in low GD TSHI, FT4, LT‐4 weekly cumulative dose (LT‐4 × week) and the ratio between LT‐4 weekly cumulative dose and basal metabolic rate (LT‐4 × week/BMR) were significantly increased ($p \leq .0001$) (Table 2).
**TABLE 2**
| Unnamed: 0 | GD activity <21 nmol/s (n = 50) | GD activity ≥21 nmol/s (n = 110) | p |
| --- | --- | --- | --- |
| Age (years) | 42.9 (17.3) | 45.2 (12.1) | 0.6 |
| Sex (F/M) | 34/16 | 83/27 | 0.2 |
| Weight (kg) | 73.3 (20.2) | 73.1 (17.5) | 0.9 |
| Height (cm) | 162.9 (16.9) | 163.9 (9.5) | 0.7 |
| BMI (kg/Height2) | 26.8 (6.7) | 27.1 (5.6) | 0.6 |
| TSH (mU/L) a | 0.8 (0.1) | 1.0 (0.1) | 0.4 |
| FT4 (pmol/L) a | 18.1 (2.0) | 14.2 (2.6) | 0.0001 |
| FT3 (pmol/L) a | 3.7 (0.1) | 4.3 (0.0) | 0.0001 |
| FT3/FT4 ratio a | 0.20 (0.05) | 0.30 (0.05) | 0.0001 |
| BMR (Kcal/24 h) a | 1435.1 (40.3) | 1409.8 (24.7) | 0.4 |
| LT‐4 × week/BMRr (μg/BMR) | 0.6 (0.2) | 0.5 (0.1) | 0.01 |
| LT‐4 × week (μg) | 910.2 (259.2) | 802.5 (222.3) | 0.006 |
| GD (nmol/s) | 18.3 (0.3) | 26.6 (0.3) | 0.0001 |
| TSHI a | 2.2 (0.1) | 1.7 (0.1) | 0.004 |
## Binary logistic regression analysis and ROC curve
Variables reaching statistical significance by univariate analysis were then analysed by binary logistic regression analysis models. LT‐4 × week/BMR was independently and inversely related to GD [B = −3.88, wald = 7.6, $R = 0.021$ (0.001–0.329; $95\%$ confidence interval (CI)), $$p \leq .006$$], FT3 levels were directly and independently related to GD [$B = 2.81$, wald = 25.1, $R = 17.4$ (5.6–53.4, $95\%$ CI) $$p \leq .0001$$]. In contrast, BMR, BMI, body weight, TSH and FT4 were not independently related to GD. To evaluate the effect of LT4 × week/BMR on GD, we used a classic receiver operating characteristic (ROC) model that was very well validated by the study of area under the curve (AUC) = 0.81 ± 0.073 (0.66–0.95, $95\%$ CI, $$p \leq .001$$). To better define the cut‐off of LT‐4 dose beyond which GD was reduced, we researched the best cut‐off of Youden's statistic (YS). YS = 60 indicates that LT‐4 × week/BMR > 0.56 mcg × week/kcal is a good predictor of suppressed GD with sensitivity = $83\%$ and specificity = $77\%$ (e.g. a total of 144 mcg of LT‐4 daily dose reduces GD in patients with 1800 kcal/die estimated BMR).
## Linear regression, complete or partial bivariate analysis with calculation of Pearson coefficient
FT3 and FT4 were increased in obese patients compared with nonobese patients ($$p \leq .07$$ and $$p \leq .01$$, respectively), while GD and LT‐4 × week/BMR were similar in the two groups (Table 3).
**TABLE 3**
| Unnamed: 0 | Non‐obese (n = 120) | BMI ≥30 < 35 (n = 20) | BMI ≥35 (n = 20) | p |
| --- | --- | --- | --- | --- |
| Sex m/f | 28/92 | 8/12 | 7/13 | |
| Age (years) | 43.4 (14.5) | 46.7 (11.5) | 48.3 (11.1) | 0.1 |
| Weight (kg) | 65.5 (12.2) | 89.3 (10.8) | 101.6 (16.4) | 0.0 |
| Height (cm) | 163.1 (12.7) | 167.9 (10.5) | 160.9 (8.8) | 0.4 |
| BMI (weight/[height]2) | 24.3 (3.2) | 31.6 (1.3) | 39.7 (4.9) | 0.0 |
| TSH (mU/L) | 0.8 (0.05) | 1.0 (0.2) | 1.3 (0.2) | 0.6 |
| TSHI | 1.7 (0.8) | 2.1 (0.7) | 2.2 (0.2) | 0.1 |
| FT4 (pmol/L) | 15.6 (2.4) | 16.7 (3.2) | 17.7 (4.2) | 0.01 |
| FT3 (pmol/L) | 3.9 (0.6) | 4.1 (0.6) | 4.3 (0.5) | 0.07 |
| FT3/FT4 ratio | 0.25 (0.04) | 0.24 (0.05) | 0.24 (0.05) | 0.1 |
| BMR | 1336.8 (216.6) | 1633.4 (254.8) | 1688.1 (258.4) | 0.0 |
| GD (nmol/s) | 24.1 (4.8) | 23.7 (5.5) | 24.4 (6.1) | 0.2 |
| GD < 21 (nmol/s) n/% | 36/30 | 7/35 | 7/35 | 0.3 |
| Lt4 × week/BMR | 0.6 (0.1) | 0.6 (0.1) | 0.6 (0.1) | 0.3 |
| Lt4 × week (μg) | 779.2 (214.1) | 1006.9 (270.3) | 1002.0 (173.1) | 0.0 |
Partial bivariate analysis revealed that FT4 levels were positively related to BMI and negatively related to BMR after subtraction of the BMI effect: $$p \leq .01$$ and $$p \leq .02.$$ Pituitary thyreotropic activity, evaluated by TSHI, was positively related to BMI and LT‐4 × week/BMR: $R = 0.13$, $$p \leq .05$$, $R = 0.14$, $$p \leq .03$$ and inversely related to GD: $$p \leq .0004$$ (Figure 2). FT4 levels were positively related to TSHI in both obese and nonobese patients. In obese patients, the FT4 to TSHI increment was 3.3 times greater than the increment in nonobese patients: $0.1\%$ versus $0.03\%$, R 2 =.24, $$p \leq .001$$ versus R 2 =.04, $$p \leq .02$$ (BMI ≥ 30 vs. BMI < 30) (Figure 3).
**FIGURE 2:** *Linear correlation of SPINA GD (nmol/s) with TSHI* **FIGURE 3:** *(A and B) Correlation between TSHI and FT4 in obese and non‐obese athyreotic patients*
In obese patients ($$n = 40$$), FT3 levels were inversely related to TSHI; a TSHI increment of 1 unit was related to an FT3 decrement of $0.095\%$: R 2 =.1; $$p \leq .045.$$ In contrast, FT3 levels were not related to TSHI variations in nonobese patients: R 2 =.002, $$p \leq .58$$ (Figure 4).
**FIGURE 4:** *(A and B) Correlation between TSHI and FT3 in obese and non‐obese athyreotic patients*
These data confirm that the feedback sensitivity of thyroid hormones with the pituitary is significantly different in obese and nonobese patients.
## DISCUSSION
Several lines of evidence indicate that hypothyroid patients under levothyroxine replacement therapy may present impaired T3 production and a reduced T3/T4 ratio. 13 The T3 pool derived from intrathyroidal conversion is absent and fails to maintain normal FT3 levels. As a consequence, their peripheral tissues may be underexposed to circulating T3. Our previous data indicate that $29.6\%$ of levothyroxine‐treated athyreotic patients have a reduced FT3/FT4 ratio, and this percentage may progressively increase with increasing replacement levothyroxine dose. 17 These changes may be due to an imbalance between central and peripheral deiodinase activity that may disrupt thyroid hormone homeostasis in this subset of hypothyroid patients. 12, 13, 14 In our study, we evaluated the total deiodinase activity (GD) by the SPINA cybernetic model. 15, 16 We found that our athyreotic patients with impaired GD received a larger dose of LT‐4 and had increased FT4 and TSHI levels, while the FT3/FT4 ratio and FT3 levels were reduced (all <0.0001). GD was reduced in $31.2\%$ of study patients, confirming our previous report since GD is well correlated with the FT3/FT4 ratio 17 (Table 2). To better evaluate the interplay between GD, BMR and LT‐4 weekly cumulative dose (LT‐4 × week), we evaluated the ratio between LT‐4 × week and basal metabolic rate (LT‐4 × week/BMR) calculated by the formula of Mifflin St.‐Jeor. By this tool largely used to evaluate BMR in obese patients, 18, 19 we demonstrated that total GD activity was independently and inversely related to LT‐4 × week/BMR. According to this view, we analysed a subgroup of 45 patients with a stable LT‐4 dose, caloric intake and level of thyroid hormones for almost six months, and we found that a LT‐4 × week/BMR value of 0.56 mcg × week/Kcal can predict the impairment of GD (<21 nmol/s) with good sensitivity and specificity ($$p \leq .01$$). To our knowledge, this is a new finding with a possible clinical implication in athyreotic patients receiving LT‐4 substitutive therapy. Interestingly, estimated BMR, BMI, age and sex were similar between the patients with normal or reduced GD, suggesting that LT‐4 dose and FT3 production are the two independent stronger predictors of GD. Cross‐sectional and longitudinal studies comparing post‐ and presurgical levels of L‐T4 prove that higher L‐T4 doses are associated with the suppression of deiodinase activity. 16 FT4 and FT3 were higher in our obese (BMI ≥ 30) than in nonobese patients (BMI < 30) ($$p \leq .01$$, $$p \leq .07$$), and TSHI was positively related to BMI and LT‐4 × week/BMR and inversely related to GD. However, GD and LT‐4 × week/BRM were not different between obese and nonobese patients, suggesting that BMI is not an independent determinant of GD. The pituitary thyrotropic activity, expressed by the relationship between TSHI and thyroid hormone levels, was different between nonobese and obese patients. TSHI suppression was constantly exerted by increasing levels of FT4 in nonobese patients, while this suppression was significantly attenuated at higher levels of FT4 in obese patients, suggesting increased hypothalamic–pituitary resistance in response to increased T4 levels. The increment of FT4 for each unit of TSHI increment was significantly higher in obese patients than in nonobese patients ($$p \leq .04$$) (Figure 3). However, in accordance with the FT4 results, increasing levels of FT3 constantly suppressed TSHI in nonobese patients, while this suppression was increased at increasing levels of FT3 in obese patients (Figure 4). This T4 to T3 shunt, in terms of TSHI suppression observed in obese patients, suggests a defective hypothalamic pituitary T4 to T3 conversion. Moreover, FT4 levels were positively related to BMI as well as to T4 dose but only partially and inversely related to BMR when BMI effect was subtracted (Pearson, $$p \leq .01$$). Considering that FT4 levels in athyreotic patients are entirely dependent on LT‐4 adsorbed dose and on the extent of T4 degradation, 17 this finding unravels a role of BMR on the modulation of FT4 bioavailability both in nonobese and in obese patients, those with greater lean body mass that leads to increased BMR. 6, 7, 8 Differently than some recent studies, 20 we did not evidence a statistically significant correlation of GD with BMR, however differently than the others studies we evaluated patients athyreotic by total thyroidectomy and 131I ablation, this might contribute to increase the severity of suppression of the feedback loop and the ability to relay type 1 and type 2 allostatic load to T3 production. Moreover, we did not evaluate separately free fat mass and lean body mass. Under normal conditions, thyroid hormones and TSH are inversely correlated, while in patients with resistance to thyroid hormone, higher thyroid hormone levels correspond to high TSH levels due to a possible condition of resistance to FT4, such as in obese patients. 9, 10, 21, 22 One study demonstrated that deiodinase ubiquitination was an important factor in restoring euthyroidism. Indeed, the ubiquitin proteasome system in the hypothalamus of obese mice fails to maintain adequate function. Hence, a defective function of the ubiquitin proteasome system, resulting in deiodinase imbalance, might play a major role in the regulation of the response to thyroid hormones in obese subjects. 9, 21, 22, 23 Thyroid hormone action is modulated by the hypothalamic pituitary thyroid axis, 6 and cell membrane transport, tissue deiodination and degradation and thyroid hormone metabolism in the liver may play an important role. 9, 22, 23, 24 Metabolism of exogenous substrates in the liver occurs by enzymes that either modify and/or conjugate the functional groups to endogenous substrates to increase their solubility to be readily eliminated. Approximately half of obese subjects display several abnormalities in liver enzymatic activity due to steatosis. 25, 26, 27 In particular, increasing BMI and thyroid hormone receptor β are inversely correlated with different stages of nonalcoholic fatty liver disease (NAFLD), 28, 29 which, in turn, is related to decreased multidrug resistance protein (MRP2) activity in the liver. This condition is associated with alterations in the expression and function of enzymes and transporters resulting in an altered glucuronoconjugation of thyroid hormones. 23 However, our study is descriptive and does not allow any direct evaluation of mechanistic insights related to T4 activation, degradation and stability.
## CONCLUSIONS
Approximately one‐third of athyreotic patients under LT‐4 replacement therapy have reduced GD. GD activity is inversely and independently related to LT‐4 dose and FT3 levels. We found that an LT‐4 weekly cumulative dose of 0.56 mcg/kcal was an independent predictor of reduced GD, while sex, age, BMI or BMR were not. FT4 levels are higher in athyreotic obese patients, who therefore appear more resistant to LT‐4 replacement therapy. Indeed, FT4 is positively related to BMI and inversely related to BMR, which, in turn, negatively modulates the FT4 increment, especially in patients with elevated body lean mass. Other metabolic pathways both centrally and perimetrically might be involved in FT4 and FT3 degradation.
## AUTHOR CONTRIBUTIONS
Pasqualino Malandrino: Data curation (equal); validation (equal). Marco Russo: Data curation (equal); validation (equal). Dario Tumino: Data curation (equal); validation (equal); visualization (equal). Tommaso Piticchio: Data curation (equal); formal analysis (equal); visualization (equal). Adriano Naselli: Data curation (equal); formal analysis (equal); software (lead); supervision (equal); writing – review and editing (equal). Valentina Rapicavoli: Data curation (equal); resources (equal). Antonino Belfiore: Conceptualization (equal); funding acquisition (equal); methodology (equal); project administration (equal); resources (equal); supervision (equal); validation (equal); visualization (equal); writing – review and editing (equal). Francesco Frasca: Conceptualization (equal); funding acquisition (equal); investigation (lead); methodology (equal); project administration (equal); resources (equal); supervision (equal); validation (equal); visualization (equal); writing – review and editing (equal). Rosario Le Moli: Conceptualization (equal); data curation (equal); formal analysis (equal); investigation (equal); methodology (equal); project administration (equal); software (equal); supervision (equal); validation (equal); visualization (equal); writing – original draft (equal); writing – review and editing (equal).
## CONFLICT OF INTEREST
The authors declare no conflict of interest.
## INSTITUTIONAL REVIEW BOARD STATEMENT
The studies involving human participants were reviewed and approved by Ethics Committee Garibaldi Nesima Hospital ‐ Catania.
## INFORMED CONSENT
Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## DATA AVAILABILITY STATEMENT
The data presented in this study are available on request from the corresponding author.
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|
---
title: 'Association between Pro‐oxidant‐Antioxidant balance and high‐sensitivity C‐reactive
protein in type 2 diabetes mellitus: A Study on Postmenopausal Women'
authors:
- Hassan Ehteram
- Sara Raji
- Mina Rahmati
- Hanieh Teymoori
- Samaneh Safarpour
- Nahid Poursharifi
- Mona Hashem Zadeh
- Reza Pakzad
- Hossein Habibi
- Naser Mobarra
journal: Endocrinology, Diabetes & Metabolism
year: 2022
pmcid: PMC10000638
doi: 10.1002/edm2.400
license: CC BY 4.0
---
# Association between Pro‐oxidant‐Antioxidant balance and high‐sensitivity C‐reactive protein in type 2 diabetes mellitus: A Study on Postmenopausal Women
## Abstract
Serum PAB, hs‐CRP concentration, and lipid profile were significantly different between postmenopausal women with and without diabetes mellitus. These differences may contribute to the development of coronary complications.
### Introduction
Oxidative stress known as a predictive marker for cardiovascular and metabolic diseases could be measured through pro‐oxidant antioxidant balance (PAB). The present study aimed to evaluate PAB and its association with high‐sensitivity C‐reactive protein (hs‐CRP) in the serum of postmenopausal women with diabetes mellitus.
### Methods
In this case–control study, 99 diabetic and 100 healthy postmenopausal women without diabetes mellitus were recruited. Serum PAB values, hs‐CRP, lipid profile, insulin, and vitamin D levels were measured. Moreover, insulin resistance (HOMA‐IR, HOMA‐β and QUICKI), waist circumference (WC), waist‐to‐hip ratio (WHR), waist‐to‐height ratio (WHtR), and body mass index (BMI) were calculated.
### Results
Serum PAB, hs‐CRP, insulin resistance, HOMA‐β, QUICKI, low‐density lipoprotein cholesterol (LDL‐C), high‐density lipoprotein cholesterol (HDL‐C), and triglycerides (TG) levels were significantly higher in the postmenopausal women with diabetes mellitus, while there was no significant difference in the total cholesterol (TC), serum insulin, WC, WHR, WHtR and vitamin D levels between the groups. Pearson correlation coefficient showed that HDL‐C and insulin levels were directly correlated with serum PAB. Also, there was a significant direct relationship between LDL‐C and insulin levels and hs‐CRP. There was no meaningful relationship between serum insulin and vitamin D levels and other assessed parameters. Backward logistic regression showed a positive relationship between diabetes mellitus and serum PAB and an inverse relationship with serum HDL levels.
### Conclusions
Serum PAB, hs‐CRP concentration, and lipid profile were significantly different between postmenopausal women with and without diabetes mellitus. These differences may contribute to the development of coronary complications.
## INTRODUCTION
An imbalance between the production of oxidants and their scavengers leads to oxidative stress (OS). OS may also stimulate the production of inflammatory factors, such as high‐sensitivity C‐reactive protein (hs‐CRP). Hs‐CRP is an inflammatory marker induced via cytokines, especially interleukin‐6 (IL‐6). OS and hs‐CRP are predictive markers of cardiovascular disease (CVD) and metabolic diseases, including type II diabetes. 1, 2, 3 Type II diabetes mellitus (T2DM) is a severe, multifactorial and metabolic disease, which affects women more than men in many countries. T2DM increases CVD risk by 2–3 folds, which leads to a higher mortality rate than in non‐diabetic people. 4 Moreover, OS increases in menopausal women, which is associated with loss of ovarian follicular function and oestrogen (E2) production because E2 has antioxidant activity. 5 After menopause, the production of antioxidants is reduced, and OS increases. 6, 7 Thus, menopause may be a risk factor for OS, CVD, osteoporosis, and diabetes. Although OS and inflammation are well established in postmenopausal women, there are limited studies about pro‐oxidant‐antioxidant balance (PAB), CRP levels and their association with insulin resistance in diabetic postmenopausal women. 1, 8, 9 We sought to assess the serum PAB values using a modified PAB assay to measure the pro‐oxidant burden and antioxidant capacity. This study also evaluated hs‐CRP and whether serum PAB values are associated with hs‐CRP in diabetic postmenopausal women.
## Study groups
This case–control study was carried out on 99 postmenopausal women who had recently been diagnosed with only diabetes type II and attended the Women's Health Research Center in Gorgan, Iran. The control group included 100 healthy participants age‐matched to the patient group recruited between January 2017 and June 2018 for routine check‐ups. This group consisted of postmenopausal women with no diabetes. Clinical history and other relevant data were collected from all participants. They were excluded if they had taken vitamin supplements, hormones, anti‐inflammatory drugs, and fish oil capsules. Moreover, smokers and pregnant subjects were excluded from the study. Those suffering a myocardial infarction (MI), acute infection or any acute illnesses were excluded. One hundred and 99 subjects met the inclusion/exclusion criteria. They were informed about the study protocol, written consent was obtained from each participant and the research was approved by the Mashhad University of Medical Sciences Ethics Committee (NO: IR.MUMS.REC.1399.533).
## Anthropometric parameters and blood collection
After overnight fasting, 5 ml of venous blood was drawn into EDTA and plain tubes, centrifuged at 2500 rpm for 15 min at room temperature, and serum was allocated to several microtubes and stored at −70°C until analysis. Furthermore, body weight, height, waist circumference (WC), and hip circumference (HC) were measured to calculate the waist‐to‐hip ratio (WHR), waist‐to‐height (WHtR), and body mass index (BMI) (kg/m2).
## Biochemical analysis processing
Fasting glucose and lipid profile indices, including total cholesterol (TC), triglyceride (TG), and HDL‐C, were measured by enzymatic methods and commercial kits using the BT‐3000 Auto‐analyser (Biotechnica). Moreover, LDL‐C was indirectly evaluated in participants with the Friedewald formula.
The levels of insulin were assessed using commercial kits using a radioimmunoassay from the Immuno Nuclear Corporation (Stillwater). Insulin resistance was calculated using the HOMA equation: HOMA‐IR = [Fasting insulin (μIU/ml) fasting glucose (mM/L)]/22.5.
Also, homeostasis model assessment of β‐cell function (HOMA‐β) and quantitative insulin sensitivity check index (QUICKI) were used to assess β‐cell function and insulin sensitivity, respectively, as follows: HOMA‐β: (fasting plasma insulin [μU/ml] * 20)/(fasting blood glucose [mmol/l] – 3.5) and QUICKI: 1/(log fasting blood glucose [mmol/l] + log fasting plasma insulin [μU/ml]).
Furthermore, serum 25‐hydroxyvitamin D [25(OH) D] levels were assessed using a commercial ELISA kit (25‐Hydroxyvitamin D ELISA kit; Immuno Diagnostic Systems).
## Measurements of hs‐CRP
The PEG (polyethylene glycol)‐enhanced immuno‐turbidometry method and commercially available kits on an Alcyon® analyser (Abbott) were used to measure hs‐CRP levels.
## Assessment of PAB
Serum PAB values were measured in all subjects as previously described by Alamdari et al. 10 In the first step, we added horseradish peroxidase enzyme and chloramine‐T as oxidizing agents to TMB. Redox index resulted in the combined activity of a colour cation (by oxidants) or reduced to a colourless compound (by antioxidants). In standard solutions, various proportions ($0\%$–$100\%$) of 250 μM hydrogen peroxide (as an oxidizing substance) were mixed with 3 mM uric acid (in 10 mM NaOH) (as an antioxidant). The absorption of 10 μl samples was measured with an enzyme‐linked immunosorbent assay (ELISA) reader at 450 nm for the reference, 630 nm, and the values of PAB were expressed in arbitrary (Hamidi. Koliakos [H.K]) units).
## Statistical methods
The normality of the data was assessed by the Kolmogorov–Smirnov test. The mean and SD (for normal distribution) and median and interquartile range (IQR) (for non‐normal distribution) were used to describe the study variables. The independent student t‐test (for variable normality distribution) was used to compare the mean of study variables between case and control groups. A logistic regression method was used to determine the variables related to diabetes, including age, BMI, PAB, systolic blood pressure (SYSp), diastolic blood pressure (DIAp), GLUCOSE (Glc), insulin, InsulinR, TC, LDL, HDL, TG, hs‐CRP and vitamin D. Based on the Hosmer–Lemeshow method, simple logistic regression was utilized to determine the relationship between study variables and diabetes. Then, the variables with $p \leq .2$ were added to the final model and analysed using multiple logistic regressions. We used SPSS for Windows software (version 18 software package SPSS Inc). A p‐value less than.05 was considered statistically significant.
## Participants' characteristics and demographic findings
All data showed a normal distribution. Demographic data, including age, BMI, SYSp and DIAp, were not significantly different between the two groups. Except for serum TC, insulin, vitamin D, WC, WHR and WHtR, other laboratory findings in diabetic subjects were significantly different from the non‐diabetic subjects ($p \leq .05$). Table 1 shows the features of the two groups.
**TABLE 1**
| Variables | Diabetic (n = 99) | Control (n = 100) | p‐value |
| --- | --- | --- | --- |
| Age (y) | 65.33 ± 5.34 | 61.20 ± 5.990 | .283 |
| BMI (kg/m2) | 26.6 ± 2.0 | 26.2 ± 2.4 | .253 |
| SYSp (mmHg) | 13.03 ± 1.16 | 13.22 ± 1.20 | .951 |
| DIAp (mmHg) | 7.49 ± 0.66 | 7.53 ± 0.75 | .729 |
| Glc (mmol/L) | 198.72 ± 69.78 | 94.87 ± 5.68 | <.001* |
| TC (mg/dl) | 152.56 ± 10.77 | 150.19 ± 10.68 | .122 |
| LDL (mg/dl) | 144.72 ± 33.27 | 131.70 ± 31.43 | .005* |
| HDL (mg/dl) | 46.03 ± 9.03 | 49.07 ± 10.10 | .026* |
| TG (mg/dl) | 166.19 ± 37.01 | 155.59 ± 26.76 | .022* |
| Vit D (ng/ml) | 19.29 ± 10.58 | 19.63 ± 8.12 | .798 |
| PAB (H.K) | 0.40 ± 0.29 | 0.22 ± 0.13 | <.001* |
| hs‐CRP (mg/dL) | 5.11 ± 6.03 | 2.96 ± 3.07 | .002* |
| Insulin R** (μU/mL × mmol/L) | 4.10 ± 4.22 | 2.63 ± 2.52 | .003* |
| HOMA‐ β (%) | 1.09 ± 0.89 | 1.85 ± 1.38 | <.001* |
| QUICKI | 0.32 ± 0.03 | 0.36 ± 0.04 | <.001* |
| Insulin (μU/ml) | 9.33 ± 6.31 | 8.45 ± 6.48 | .335 |
| WC (cm) | 95.49 ± 7.19 | 94.31 ± 10.3 | .352 |
| HC (cm) | 102.54 ± 7.18 | 101.64 ± 5.88 | .337 |
| WHR | 0.93 ± 0.07 | 0.93 ± 0.11 | .767 |
| WHtR | 0.56 ± 0.04 | 0.55 ± 0.06 | .717 |
## PAB values, hs‐CRP concentration and insulin resistance among postmenopausal women
Serum PAB levels in the diabetic subjects were significantly higher than in the control group ($p \leq .001$) (Table 1). Also, serum hs‐CRP concentrations were statistically different in the two groups ($$p \leq .002$$) (Table 1). Unsurprisingly, in diabetic women, there was a statistically significant difference in insulin resistance, HOMA‐β and QUICKI compared to non‐diabetic women (all $p \leq .05$), whereas no considerable difference was demonstrated between diabetic patients and healthy participants in serum insulin concentrations ($$p \leq .335$$).
## The relationship between serum PAB values, BMI, and hs‐CRP concentrations and other laboratory parameters
As shown in Table 2, the Pearson correlation coefficient analysis was performed to evaluate the correlation between serum PAB values, BMI, hs‐CRP concentrations and other laboratory parameters. Scatter plots graphically showed a strong and positive uncorrected association between serum PAB values and hs‐CRP levels ($r = .258$ and $$p \leq .010$$) (Figure 1). We did not find any significant correlation between PAB values and insulin resistance ($r = .095$ and $$p \leq .347$$) (Figure 2). Moreover, serum PAB and hs‐CRP levels were positively correlated with serum insulin ($r = .212$, $$p \leq .035$$; $r = .211$, $$p \leq .037$$), respectively. Among the other study factors, a significant association was observed between serum PAB values and LDL‐C levels ($r = .209$, $$p \leq .038$$) and a negative correlation with HDL‐C levels (r = −0.224 and $$p \leq .026$$). Moreover, a comparison of the relationship between BMI and other values showed a significant correlation between BMI and TG levels ($r = .207$ and $$p \leq .042$$). In addition, we did not find any association between vitamin D levels and other laboratory parameters listed in this study.
## Multiple logistic regressions
Logistic regression in the backward approach explained that InsulinR (OR: 1.16, p: 0.012), cholesterol (OR: 1.033; p: 0.047) and LDL‐C (OR: 1.017; p: 0.002) levels, and PAB values (OR: 174.89; $p \leq .001$) had a positive association with diabetes mellitus in patients compared to non‐diabetic women (Table 3). Moreover, these results showed that diabetes had an inverse association with HDL‐C (OR: −0.932; $p \leq .001$).
**TABLE 3**
| Unnamed: 0 | Variable | OR | 95% CI | p‐value |
| --- | --- | --- | --- | --- |
| Multiple logistic regression (entered approach, pseudo R 2 = .396) | InsulinR | 1.151 | 1.019–1.300 | .024 |
| Multiple logistic regression (entered approach, pseudo R 2 = .396) | Cholesterol | 1.032 | 0.998–1.066 | .063 |
| Multiple logistic regression (entered approach, pseudo R 2 = .396) | LDL‐C | 1.015 | 1.004–1.026 | .007 |
| Multiple logistic regression (entered approach, pseudo R 2 = .396) | HDL‐C | 0.935 | 0.900–0.973 | .001 |
| Multiple logistic regression (entered approach, pseudo R 2 = .396) | TG | 1.010 | 0.999–1.022 | .073 |
| Multiple logistic regression (entered approach, pseudo R 2 = .396) | Vit D | 0.997 | 0.962–1.033 | .877 |
| Multiple logistic regression (entered approach, pseudo R 2 = .396) | PAB | 140.451 | 16.426–1200.97 | <.001 |
| Multiple logistic regression (entered approach, pseudo R 2 = .396) | hs‐CRP | 1.064 | 0.971–1.166 | .186 |
| Multiple logistic regression (backward approach, pseudo R 2 = .373) | Insulin R | 1.165 | 1.034–1.313 | .012 |
| Multiple logistic regression (backward approach, pseudo R 2 = .373) | Cholesterol | 1.033 | 1.01–1.067 | .047 |
| Multiple logistic regression (backward approach, pseudo R 2 = .373) | LDL‐C | 1.017 | 1.006–1.028 | .002 |
| Multiple logistic regression (backward approach, pseudo R 2 = .373) | HDL‐C | −0.932 | 0.897–0.968 | <.001 |
| Multiple logistic regression (backward approach, pseudo R 2 = .373) | PAB | 174.893 | 21.563–1418.518 | <.001 |
## DISCUSSION
To our knowledge, this is the first case–control study to report PAB values and investigate the relationship between hs‐CRP levels and PAB values in postmenopausal women with and without diabetes mellitus. The main finding of the present study was the serum PAB and hs‐CRP elevation in diabetic postmenopausal women compared to non‐diabetic cases.
This finding is in accordance with earlier studies demonstrating the presence of systemic inflammation in diabetes. The increased level of OS is significantly associated with metabolic parameters in diabetic patients. 11, 12 OS can be induced by inflammation 4, 13; for example, higher concentrations of interleukin‐6 are an important stimulant for the production of hs‐CRP 14 and inflammation can induce the production of free radicals. 15 The present study showed that serum hs‐CRP levels were positively associated with serum PAB values in diabetic women. Moreover, earlier reports support the presence of high OS and hs‐CRP levels in stroke, cardiovascular and beta‐thalassemia patients. 16, 17 *There is* strong evidence of the correlation between inflammation and OS because both factors contribute to the pathogenesis of diabetes. 18 Moreover, diabetic postmenopausal women also had higher levels of blood glucose and HOMA‐IR index. In correlation with previous studies, dysregulated lipid metabolism in diabetics has been reported, which could be attributed to increased lipolysis due to impaired insulin function in adipose tissue. In addition, the accumulation of free fatty acids in the liver leads to the high hepatic synthesis of TGs and results in hypertriglyceridemia. 11, 19 *In this* study, as shown by Barrett‐Connor et al., 20 no relationship was observed in total cholesterol between diabetic and non‐diabetic subjects. We did not find any significant difference between serum hs‐CRP, glucose, TG, LDL‐C levels, and BMI. These results were inconsistent with those of Yang et al. 21 The reason may be due to the menopause subjects and the changes in the oestrogen hormone and its function in the liver. Moreover, parallel to our report, earlier reports have suggested that OS plays a major role in developing insulin resistance. 22, 23 Consistent with many studies, 23, 24 we can suggest that diabetic women have significantly altered lipid profiles than healthy postmenopausal subjects. Contrary to our work, many studies have reported that increased BMI values were strongly associated with hs‐CRP and OS levels. 25 We suggest that independent of BMI, OS may also be an essential determinant of hs‐CRP levels in diabetic people. Therefore, the link between OS and hs‐CRP levels may involve pathways unrelated to BMI. In line with the study by Goodarzi et al., 7 there was no significant difference in BMI between the two groups. Moreover, consistent with Zaman et al., the patient and control groups were overweight but not obese. 26 Overweight women are not necessarily diabetic, and diabetes mellitus is not the only reason for the BMI increase in overweight type 2 diabetics; other factors may be involved. In addition, in line with our study, many studies have shown that people with diabetes also have a low BMI, and some have a very low BMI. 26, 27 On the contrary, unlike some studies, 28 our study found that diabetes mellitus in our diabetic patients was not necessarily dependent on insulin. Therefore, it can be concluded that in people with type 2 diabetes, other factors may have a role in the incidence of diabetes. Hence, it can alter insulin levels in people who have diabetes without a statistically considerable difference from healthy subjects.
In contrast to previous literature, 29, 30 our findings demonstrated a positive relationship between serum hs‐CRP and insulin levels because inflammatory markers decrease insulin secretion and signalling in peripheral tissues. Moreover, interleukin‐6 decreases insulin signalling in the liver. 31 In the present study, we found an irreversible correlation between PAB values and HDL‐C levels in line with A. Cagnacci et al. 32 because oxidants can be reduced by the antioxidant enzyme paraoxonase carried by HDL‐C lipoproteins. 33, 34 Moreover, we found a significant relationship between TG levels and BMI. This finding demonstrated that high TG can cause obesity and ultimately increase BMI in diabetic postmenopausal women. Besides, in contrast to the Cardiovascular Health Study and research by Mendall et al., surprisingly, no relationship was found between hs‐CRP levels and BMI in women. Due to this controversy with the prior investigation, we think that diabetes in postmenopausal women can cause these outcomes. Our finding was in agreement with that of Kahn et al., 35, 36 indicating that diabetic postmenopausal women were characterized by insulin resistance. Moreover, it has been noted that insulin has a significantly negative relationship with higher hs‐CRP levels and PAB values. However, in Table 3, PAB values showed a positive correlation with LDL‐C levels and an irreversible association with HDL‐C levels. Therefore, the evidence supporting these results is that HDL cholesterol is the major lipoprotein carrier of antioxidant enzymes, and LDL is the main factor correlated with oxidative markers.
Our study had a few limitations. The present work focused only on PAB values. However, several other factors can affect these biochemical parameters in OS, including sex hormones. Another limitation was the small sample size.
## CONCLUSIONS
We found significantly higher PAB values in diabetic postmenopausal women. Moreover, we demonstrated that increased hs‐CRP concentrations are strongly associated with PAB values, a reliable OS marker. This finding was independent of BMI and insulin resistance in diabetic postmenopausal women. Measurement of PAB hs‐CRP levels and other biochemical parameters may be a valuable marker for OS and inflammation and a helpful diagnostic factor to prevent injury and develop coronary artery disease. Future studies with larger sample sizes on PAB values and hs‐CRP may lead to the more practical use of these two markers in clinical diagnosis and follow‐up of diseases and better the quality of life for patients.
## AUTHOR CONTRIBUTIONS
Hassan Ehteram: Conceptualization (supporting); writing – review and editing (equal). Sara Raji: Data curation (equal); writing – original draft (equal); writing – review and editing (equal). Mina Rahmati: Data curation (equal); writing – review and editing (equal). Hanieh Teymoori: Data curation (equal); writing – review and editing (equal). Samaneh Safarpour: Data curation (equal); writing – review and editing (equal). Nahid Poursharifi: Data curation (equal); writing – review and editing (equal). Mona Hashem Zadeh: Data curation (equal); writing – original draft (equal); writing – review and editing (equal). Reza Pakzad: *Formal analysis* (lead); writing – review and editing (equal). Hossein Habibi: Writing – review and editing (equal). Naser Mobarra: Conceptualization (lead); supervision (lead); writing – review and editing (equal).
## FUNDING INFORMATION
This study is funded by Mashhad University of Medical Sciences (Grant No: 981826)
## CONFLICT OF INTEREST
The authors declared no conflicts of interest.
## ETHICAL APPROVAL
The Ethics Committee of Mashhad University of Medical Sciences approved the study (IR.MUMS.REC.1399.533).
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
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---
title: Impact of Longkong Pericarp Extract on the Physicochemical Properties of Alginate-Based
Edible Nanoparticle Coatings and Quality Maintenance of Shrimp (Penaeus monodon)
during Refrigerated Storage
authors:
- Narin Charoenphun
- Bharathipriya Rajasekaran
- Suguna Palanisamy
- Karthikeyan Venkatachalam
journal: Foods
year: 2023
pmcid: PMC10000639
doi: 10.3390/foods12051103
license: CC BY 4.0
---
# Impact of Longkong Pericarp Extract on the Physicochemical Properties of Alginate-Based Edible Nanoparticle Coatings and Quality Maintenance of Shrimp (Penaeus monodon) during Refrigerated Storage
## Abstract
The objective of this study was to evaluate the impact of varying concentrations of longkong pericarp extract (LPE) on the physicochemical properties of alginate-based edible nanoparticle coatings (NP-ALG) on shrimp. For developing the nanoparticles, the alginate coating emulsion with different LPE concentrations (0.5, 1.0, and $1.5\%$) was ultrasonicated at 210 W with a frequency of 20 kHz for 10 min and a pulse duration of 1s on and 4 off. After that, the coating emulsion was separated into four treatments (T): T1: Coating solution containing basic ALG composition and without the addition of LPE or ultrasonication treatment; T2: ALG coating solution converted into nano-sized particles with ultrasonication and containing $0.5\%$ LPE; T3: ALG coating solution converted into nano-sized particles with ultrasonication and containing $1.0\%$ LPE; T4: ALG coating solution converted into nano-sized particles with ultrasonication and containing $1.5\%$ LPE. A control (C) was also used, where distilled water was used instead of ALG coating. Before coating the shrimp, all the coating materials were tested for pH, viscosity, turbidity, whiteness index, particle size, and polydispersity index. The control samples had the highest pH and whiteness index and was followed by the lowest viscosity and turbidity ($p \leq 0.05$). Among the T1–T4 coating materials, T4 coating had higher turbidity, particle size, polydispersity index, but lower pH, viscosity, and whiteness index ($p \leq 0.05$). To study the quality and shelf-life of the shrimp, all coated shrimp samples were refrigerated at 4 °C for a period of 14 days. At 2-day intervals, physiochemical and microbial analyses were performed. The coated shrimp also had a lower increase in pH and weight loss over the storage period ($p \leq 0.05$). Coatings containing $1.5\%$ LPE significantly reduced the polyphenol oxidase activity in the shrimp ($p \leq 0.05$). The addition of LPE to NP-ALG coatings demonstrated dose-dependent antioxidant activity against protein and lipid oxidation. The highest LPE concentration ($1.5\%$) led to increased total and reactive sulfhydryl content, along with a significant decrease in carbonyl content, peroxide value, thiobarbituric acid reactive substances, p-anisidine, and totox values at the end of the storage period ($p \leq 0.05$). Additionally, NP-ALG-LPE coated shrimp samples exhibited an excellent antimicrobial property and significantly inhibited the growth of total viable count, lactic acid bacteria, Enterobacteriaceae, and psychotropic bacteria during storage. These results suggested that NP-ALG-LPE $1.5\%$ coatings effectively maintained the quality as well as extended the shelf-life of shrimp during 14 days of refrigerated storage. Therefore, the use of nanoparticle-based LPE edible coating could be a new and effective way to maintain the quality of shrimp during prolonged storage.
## 1. Introduction
Shrimp has become increasingly popular among consumers for its distinctive taste, but it is not just a delicacy. It is also a rich source of protein, healthy PUFA, vitamins, and minerals [1]. The high moisture content and various nutrients in shrimp makes it more vulnerable to changes in physical, biochemical, and microbiological characteristics and such alterations can negatively impact the quality and shelf-life of shrimp, ultimately resulting in reduced market value [2]. Several chemicals including ethylenediaminetetraacetic acid, benzoic acid, polyphosphates, ascorbic acid, and sodium chloride have been tried as preservatives to maintain the quality and extend the shelf-life of shrimp during storage. However, sulfite-based formulation produces allergic reactions, thus synthetic preservatives exert an adverse effect on human health. Moreover, to preserve the quality of shrimp during prolonged storage, various advanced techniques have been studied, such as modified atmospheric packaging, vacuum packaging, high-pressure treatment, the use of plant extract, and applying edible coatings [3]. The edible coating is a promising and reliable method for extending the shelf-life of shrimp among the various techniques studied [4]. It creates a barrier against oxygen, slows down oxidation, prevents microbial contamination, reduces moisture loss, and preserves flavor [5]. Edible coatings also serve as a means of delivering food additives, such as antioxidants and antimicrobial agents. Using natural plant extracts in these coatings can extend the shelf-life of perishable foods, such as fruits, vegetables, and seafood [6]. Alginate (ALG), a polysaccharide derived from brown algae, is widely used as a coating material. It is considered a GRAS (generally recognized as safe) substance and is composed of D-mannuronic acid and L-guluronic acid. Alginate is popular as a coating material because it can form a strong gel and maintain its insolubility when reacting with multivalent cations [7]. Many studies have shown that incorporating preservatives into coatings can help prevent quality changes in perishable foods during prolonged storage [1,8]. However, consumers often prefer preservatives that come from natural sources to ensure the safety of their food. Phenolic compounds, which are widely found in plants, are one such example of these natural preservatives. Phenolic compounds are well known for their antimicrobial and antioxidant properties, making them a potent alternative to synthetic agents [9]. Longkong (Aglaia dookkoo Griff.) is an economically valuable, non-climacteric tropical fruit belonging to the Meliaceae family, primarily found in southern Thailand [4]. Longkong fruit is composed of three main parts—pericarp, flesh, and seeds—and the pericarp contains a high level of polyphenols. Studies indicate that longkong pericarp extract (LPE) exhibits multiple biological and pharmacological effects, including radical scavenging, germicidal, cytostatic, antimalarial, and depigmentation [10]. In addition, LPE contains a rich source of lansic acid, lansiosides, lansiolic acid, and iso-onoceratriene. These chemical compounds in LPE could control the hormonal imbalance in humans and promote anti-baldness, antipyretic and anti-feeding activities. Nevertheless, ellagic acid and corilagin in the LPE could promote anti-fibrosis and anti-glaucoma effects. Incorporating nanotechnology and natural plant extracts into the coating medium is an effective approach to preserve the stability of bioactive compounds and as well as food products against deterioration from oxidation and microorganism [11]. Additionally, the use of nanotechnology to produce particles of nano-dimension increases the surface area per unit weight, which results in better dispersion of the active substances in the coating medium, thereby enhancing its functionality and bioactivity [12]. Furthermore, the use of nano-sized particles allows for the controlled and gradual release of bioactive substances during prolonged storage [13]. Additionally, nanotechnology can incorporate active substances without altering the sensory characteristics of foods and increase their shelf-life [14]. Although, the antibacterial activity of LPE and alginate coating is well known, their combination, especially in the nanoparticles system, has not been studied. Therefore, the present study utilized this opportunity to examine the properties of LPE-added ALG-based edible nanoparticles coating and to investigate their preventive effect on the quality maintenance of shrimp during 14 days of storage at refrigerated conditions.
## 2.1. Raw Materials, Chemicals, and Reagents
Longkong fruits fully ripe were harvested from a local garden in Surat Thani province, Thailand. Black tiger shrimp (Penaeus monodon) measuring 6–8 cm in length were purchased from a nearby farm. The shrimp were placed on ice at a ratio of 1:2 (shrimp:ice) and transported to the lab within an hour. Upon arrival, the shrimp were rinsed with cold water and kept on ice until use, not exceeding 5 h. Food-grade sodium alginate (Keltone LV, ISP, San Diego, CA, USA) was used as a biopolymer for coating formulation. The analytical-grade solvents and chemical agents utilized in this study encompassed chloroform, ethanol, methanol, ethyl acetate, sodium hydroxide, Triton X-100, sodium chloride, 1-3,4-dihydroxyphenylalanine (DOPA), Tris, glycine, sodium ammonium sulfate, 5,5′-dithio-bis-(2-nitrobenzoic acid) (DNTB), ethylenediamine tetra acetic acid (EDTA), guanidine chlorate, dipotassium hydrogen phosphate, sulfosalicylic acid, potassium dihydrogen phosphate, hydrochloric acid, potassium iodide, sodium thiosulfate, urea, anhydrous sodium sulfate, trichloroacetic acid (TCA), ascorbic acid, thiobarbituric acid (TBA) (Merck, Darmstadt, Germany), Tween 80 (Labchem, Zelienople, PA, USA), 1,1,3,3-tetramethoxypropane (MDA) (Sigma-Aldrich, St. Louis, MO, USA), and acetic acid (Lab-Scan, Pathum Wan, Bangkok, Thailand). The media used for the microbiological analyses, namely plate count agar, peptone, deMan, Rogosa, Sharpe (MRS) agar, and violet red bile glucose agar (VRBG), were all analytical grade and purchased from Merck, Darmstadt, Germany.
## 2.2. Preparation of Longkong Pericarp Extract (LPE)
Upon the arrival of the longkong fruits, their pericarps were isolated from the flesh and washed using cold water with $2\%$ ascorbic acid. The longkong pericarp extract (LPE) was obtained as guided by Nagarajan et al. [ 4] with some modifications. First, the pericarps were dried in a hot air oven at 40 °C until a consistent weight was reached, and the dried sample was grounded into fine powder. To prepare the LPE, 5 g of pericarp powder was mixed with 100 mL of absolute ethanol. The mixture was then heated with agitation in a water bath at 40 °C for 4 h. The resulting solution was then placed in the solvent evaporator to remove the solvent thoroughly at 40 °C. The final LPE was freeze-dried, stored in a sealed amber bottle, and kept at −20 °C until required for further experimentation.
## 2.3. Preparation of Coating Solution
The ALG coating solution was prepared by following the method of Sharifimehr et al. [ 5] with some modifications, dissolving $1\%$ ALG (w/v) and $4\%$ Tween 80 (v/v) in distilled water, and then adding LPE at different concentrations while constantly stirring. The mixture was thoroughly stirred using a magnetic stirrer to obtain a homogenous solution. The particle size of the coating was reduced to the nanoscale using ultrasonication (Hielscher UP200Ht, Hielscher Ultrasonics GmbH, Teltow, Germany) at 210 W, a frequency of 20 kHz for 10 min with a pulse duration of 1 s on and 4 s off. The temperature was maintained at 25 °C during the ultrasonication process. Four treatments (T) were used: T1: Coating solution containing basic ALG composition and without the addition of LPE or ultrasonication treatment; T2: ALG coating solution converted into nano-sized particles with ultrasonication and containing $0.5\%$ LPE; T3: ALG coating solution converted into nano-sized particles with ultrasonication and containing $1.0\%$ LPE; T4: ALG coating solution converted into nano-sized particles with ultrasonication and containing $1.5\%$ LPE. A control (C) was also used, where distilled water was used instead of ALG coating. All the tested coating solutions were measured for physicochemical properties as shown in Section 2.5.
## 2.4.1. pH
The pH of the coating solution was measured using a digital pH meter (Mettler-Toledo GmbH, Giessen, Germany).
## 2.4.2. Viscosity
To determine the viscosity of the coating solution, a digital tabletop Brookfield viscometer (Brookfield DVE viscometer, Middleborough, MA, USA) was utilized. First, 150 mL of the coating solution was placed in a beaker measuring 70 mm in diameter and 125 mm in height. A Viscometer equipped with a number 2 spindle, set to run at a speed of 12 rpm, was used to measure viscosity. The outcomes were expressed in centipoise (cP).
## 2.4.3. Turbidity
The turbidity of the coating solution was measured using a turbidimeter (Hanna Instruments, model HI 93703, Woonsocket, RI, USA), and the results were expressed in percentages.
## 2.4.4. Whiteness Index
The HunterLab colorimeter (MiniScan EZ 4000, Hunter Associates, Inc., Reston, VA, USA) was utilized to determine the color of the coating solution following the methodology outlined by Josewin et al. [ 15]. After calibrating the instrument using a white standard plate ($L = 91.83$, a = −0.73, $b = 1.52$), the lightness (L*), redness/greenness (a*), and yellowness/blueness (b*) were measured. The whiteness index (WI) was subsequently calculated using the following formula:[1]WI=100−(100−L*)2+(a*)2+(b*)2
## 2.4.5. Particle Size and Polydispersity Index (PDI)
The particle size and polydispersity index (PDI) were assessed using a method adapted from Venkatachalam [16]. Backscatter detection at a 170° scattering angle was utilized in the process. The sample was stabilized within the device for 60 s before data collection at 25 °C. The particle size outcomes were reported in nanometers, while the PDI values were expressed as the polydispersity index.
## 2.5. Coating and Storage of Shrimps
The schematic illustration of the ALG coating preparation and coating of the shrimp is shown in Figure 1. The shrimp at a refrigerated temperature (4–8 °C) were coated by immersing them fully in a respective coating solution, as described in Section 2.4, and subsequently, the coated shrimps were placed on a wire rack for 1 min to allow excess coating material to drip off. The drying process of the coating material on the surface of the shrimp was facilitated by exposing coated shrimps to air blown by an electric fan. Next, the shrimps were arranged in polystyrene trays with 12 shrimps per tray, then wrapped with polyolefin film, and kept at 4 °C for 14 days. At every two-day interval during the storage period, samples were chosen randomly for shelf-life analysis as per the protocol described in Section 2.6.
## 2.6.1. Weight Loss
Prior to weighing, the stored shrimps were first dried off from any surface moisture by using a clean paper towel. Then, they weighed on an electronic weighing balance (SECURA124-1CIT, Sartorius, Goettingen, Germany). The following formula was used to determine the weight loss (WL) in the samples [17]:[2]WL (%)=W1−W2W1×100 where W1 and W2 denote the weight of shrimp on the initial and final day of storage, respectively.
## 2.6.2. pH
The pH of the shrimp samples was determined using a digital pH meter (Mettler-Toledo GmbH, Giessen, Germany). To prepare the samples, 5 g of shrimp meat was homogenized in 25 mL of distilled water for 1 min. The homogenate was then filtered through a muslin cloth and the filtrate was collected and measured according to Ebadi et al. [ 3].
## 2.6.3. Extraction of Polyphenol Oxidase
The crude polyphenol oxidase (PPO) was extracted in accordance with the method of Basiri et al. [ 18]. PPO from the cephalothorax was pulverized in the presence of liquid nitrogen and mixed using a pestle and mortar with 0.05 mol/L sodium phosphate buffer (pH 7.2) containing 1.0 mol/L NaCl and 0.2 g/100 mL Triton X-100 (1:3 w/v ratio). The mixture was mixed continuously for 30 min, followed by refrigerated centrifugation for 30 min at 8000× g. The supernatant was subjected to $40\%$ saturation with sodium ammonium sulfate and left to stand at 4 °C for 30 min and centrifuged (12,000× g for 30 min at 4 °C) to collect the pellet. After centrifugation and precipitation with sodium ammonium sulfate, the resulting pellet was dialyzed using buffer overnight with buffer changes (15 volumes with three changes), and the insoluble material was collected by centrifugation (3000× g at 4 °C for 30 min). The resulting supernatant served as the “crude PPO extract.” To determine PPO activity, the method described by Simpson et al. [ 19] was followed using DOPA as the substrate. Specifically, 100 µL of the crude PPO extract was mixed in 1100 µL of the buffer solution containing 400 µL of 0.05 mol/L phosphate buffer (pH 6.0), 600 µL of 15 mmol/L DOPA, and 100 µL of deionized water. The mixture was incubated at 45 °C and the increase in absorbance at 475 nm was monitored using a UV-160 spectrophotometer (Shimadzu, Kyoto, Japan) for 3 min at 30-s intervals to measure the formation of dopachrome. PPO activity was defined as an increase in absorbance of 0.001 at 475 nm and expressed as one unit of PPO activity. The control was run in the same manner, except that deionized water was used instead of the PPO extract. The relative PPO activity was determined as the residual activity compared to the control by using the following formula:[3]Relative activity (%)=BA×100 where A: PPO activity of control; B: PPO activity of the sample.
## 2.6.4. Total Sulfhydryl Content
The shrimp protein isolate was prepared based on the procedure by Liu et al. [ 20]. The procedure involved dissolving the sample in a buffer (0.086 M Tris, 0.09 M glycine, 4 mM EDTA) at pH 8 and centrifuging at 10,000× g for 15 min to eliminate any insoluble protein. The total sulfhydryl content was then quantified via Ellman’s technique [21] by mixing 4.5 mL of the supernatant with 0.5 mL of Ellman’s reagent (10 mM DTNB) and the absorbance was read at 412 nm with a spectrophotometer (RF-15001, Shimadzu, Kyoto, Japan). The sulfhydryl content was quantified in µmol sulfhydryl/g protein using a molar extinction coefficient of 13,600 M−1 cm−1. In addition, the protein content of the isolate was determined using the Biuret method [22].
## Reactive Sulfhydryl Content
The samples were placed in a solubilizing buffer (0.086 M Tris, 0.09 M glycine, 4 mM EDTA, 8 M urea) at pH 8 and then centrifuged at 10,000× g for 15 min to remove any insoluble protein. The reactive sulfhydryl content was determined using the DTNB assay [23]. Specifically, the supernatant (4.5 mL) was mixed with 10 mM DTNB (0.5 mL), then the absorbance was recorded at both 412 nm and 540 nm using a spectrofluorometer (RF-15001, Shimadzu, Kyoto, Japan). The reactive sulfhydryl value was calculated using the following equations:Reactive sulfhydryl content (µmol/g) = 73.53 × (A412 − 1.6934 × A532 + 0.009932)[4] where A412 and A532 were the absorbance 412 nm and 532 nm of the assay solution, respectively.
## 2.6.5. Carbonyl Content
Carbonyl groups were detected by reactivity with 2,4-dinitrophenylhydrazine (DNPH). The method of Parrilla-Taylor et al. [ 24] was used. The samples were initially homogenized with 1 mL of $5\%$ sulfosalicylic acid and centrifuged at 15,000× g, then the supernatant was discarded and the pellet was mixed with 10 mM DNPH in 2 M HCl and allowed to incubate at 25 °C for 1 h. The protein was precipitated by adding 0.5 mL of $20\%$ TCA and centrifuged for 5 min at 15,000× g. The HCl was used as blanks. The resulting pellets were washed three times with a solution of ethanol and ethyl acetate (1:1, v/v) and resuspended in 6 M guanidine chlorate, followed by incubation at 37 °C for 15 min. The samples were then centrifuged for 5 min at 15,000× g, and the supernatant was collected for spectrophotometric measurement of protein carbonyl content at the maximum absorbance within the range of 360–401 nm. The results were reported as nmol of carbonyl proteins per gram of sample.
## 2.6.6. Peroxide Value (PV)
The method used for lipid extraction in the shrimp was based on the approach described in the study of Bligh and Dyer [25]. A 25 g sample was homogenized with a mixture of chloroform, methanol, and distilled water (50:100:50) at 9500 rpm for 2 min at 4 °C using an IKA labortechnik homogenizer (Model T18, Bangkok, Thailand). Then, 50 mL of chloroform was added and homogenized at 9500 rpm for 1 min and 25 mL of distilled water was added and homogenized again for 30 s. The homogenate was then centrifuged at 3000 rpm at 4 °C for 15 min using a refrigerated centrifuge (Beckman Coulter, Avanti J-E Centrifuge, Fullerton, CA, USA). Finally, the chloroform phase was collected. It was then transferred into a 125 mL Erlenmeyer flask that contained about 2–5 g of anhydrous sodium sulfate, shaken well, and decanted into a round-bottom flask through a Whatman No.4 filter paper. Subsequently, the solvent was evaporated at 25 °C using an EYELA N-100 rotary evaporator (Tokyo, Japan), and the remaining solvent was removed by nitrogen flush. To determine the peroxide value (PV), the Kim et al. [ 6] method was adopted. The lipid sample (1.0 g) was combined with 25 mL of a solvent mixture (chloroform: acetic acid) at a ratio of 2:3 (v/v) and vigorously shaken, and 1 mL of saturated potassium iodide was introduced. After incubating the mixture in the dark for 5 min, 75 mL of distilled water was added and shaken followed by the addition of 0.5 mL of starch solution ($1\%$, w/v) as an indicator. The mixture was then titrated against 0.01 N sodium thiosulfate solution, and the peroxide value was expressed as milliequivalents per kilogram of lipid.
## 2.6.7. Thiobarbituric Acid Reactive Substance (TBARS)
The TBARS analysis was performed in accordance with the method of Benjakul and Bauer [26]. To begin, 1 g of minced shrimp meat was combined with 9 mL of a $15\%$ TCA solution that contained $0.375\%$ TBA. The resulting mixture was then subjected to heating in boiling water for 10 min, followed by cooling with running water. After this, the mixture was centrifuged for 20 min at 4000× g, the supernatant was collected, and its absorbance was measured at 532 nm using a UV-160 spectrophotometer (RF-15001, Shimadzu, Kyoto, Japan). The TBARS value was then determined by calculating the standard curve of MDA (ranging from 0–2 ppm) and expressed as mg MDA per kilogram of shrimp meat.
## 2.6.8. Anisidine Value (AnV)
The Anisidine value (AnV) was determined using the method developed by Okpala et al. [ 27]. First, 100 mg of the lipid sample was dissolved in 25 mL of isooctane. Then, 2.5 mL of the resulting solution was mixed with 0.5 mL of $0.5\%$ AnV in acetic acid (w/v) and kept in dark for 10 min. After that, the reaction mixture was read using a UV-Vis spectrophotometer (RF-15001, Shimadzu, Kyoto, Japan) at 350 nm and the following formula was applied to calculate the AnV value in the samples:[5]AnV=25 × ((1.2×A2)−A1W) where A1 and A2 represent the absorbances measured at 350 nm before and after the addition of AnV, respectively. Furthermore, W stands for the weight of the sample (g).
## 2.6.9. Total Oxidation Value
The totox oxidation (TOTOX) value (TV) was determined using the protocol outlined by de Abreu et al. [ 28]. The TV is determined by adding the peroxide value and Anisidine value (AnV), as follows:[6]TV=2PV+AnV
## 2.7. Total Volatile Basic Nitrogen (TVB-N)
The Conway microdiffusion technique [13] was used to evaluate the TVB-N level in shrimp using 0.1 M KOH. The results were expressed in milligrams of nitrogen per 100 g of shrimp.
## 2.8. Microbiological Analyses
To conduct the microbiological tests, randomly selected shrimp samples were taken from the same tray, and each test was performed in triplicate. For these experiments, 10-g samples were transferred into sterile zipper bags containing 90 mL of peptone water and were homogenized using a stomacher at 250 rpm for 2 min. Subsequently, serial dilutions were made in test tubes containing $0.1\%$ peptone water, and the diluted samples (0.1 mL) were then spread on the surface of dry media for microbial enumeration. Total viable plate counts (TVC) and psychrotrophic bacteria counts were determined by the pour plate method using plate count agar, which was incubated at 37 °C for 48 h and 7 °C for 10 days, respectively [29]. Enterobacteriaceae was evaluated by the spread plate method using violet red bile glucose (VRBG, Merck, Darmstadt, Germany) agar incubated at 37 °C for 24 h. Lactic acid bacteria were enumerated by the spread plate method using MRS agar incubated at 30 °C for 72 h [11]. The results were expressed in log CFU/g.
## 2.9. Statistical Analysis
The statistical analysis in this study was conducted using a completely randomized design, with all experiments performed in triplicate. Mean values were compared using analysis of variance (ANOVA) and Duncan’s multiple range test. The data were analyzed using the Statistical Package for Social Sciences (SPSS) version 6 for Windows (SPSS Inc., Chicago, IL, USA).
## 3.1.1. pH, Viscosity, and Turbidity
The pH values of the tested coating samples are illustrated in Figure 2A. On average, the pH of the coatings varied between 6.1 and 7.1. The pH of the control and T1 coating was found to be in the neutral range ($p \leq 0.05$), while the pH of the NP-ALG-LPE coating (T2–T4) was observed to be impacted by the LPE concentration, showing a decrease in pH as the LPE concentration increased from $0.5\%$ to $1.5\%$ ($p \leq 0.05$). Of all the samples, T4 had the lowest pH (at 6.2) value, while the control had the highest pH (at 7.1) value. Generally, the pH of longkong pericarp is slightly acidic (at 4.71) in nature [16], which may have affected the pH of the NP-ALG-LPE coatings. A study by Chen et al. [ 30] found that the presence of organic acids in longkong pericarp, such as glycolic acid, malic acid, and citric acid, can contribute to the low pH. Fresh longkong pericarp typically has a relatively stable pH due to its high moisture content [31]. However, the extraction process may have concentrated the naturally occurring organic acids in the pericarp, leading to a decrease in the pH of the coatings [32]. In addition, the use of ascorbic acid during extraction could have also lowered the pH of the LPE. A lower pH in the NP-ALG-LPE coatings can be beneficial as it can inhibit the growth of microorganisms. Gokoglu [33] also noted that organic acids can reduce the pH and inhibit the growth of spoilage and pathogenic bacteria. Similarly, Baek et al. [ 13] found that the pH of the ALG coating decreased with the addition of grapefruit seed extract due to the presence of organic compounds. The viscosity of the different coatings is shown in Figure 2B. *In* general, T1 had the highest viscosity, while the control had the lowest viscosity ($p \leq 0.05$). Among the NP-ALG-LPE coatings (T2–T4), no significant differences were observed, regardless of the LPE concentrations ($p \leq 0.05$). This could be due to the reduction of molecular weight of ALG caused by the ultrasonication process, which decreased the thickening properties of the ALG [34]. *In* general, the viscosity of the coatings is primarily determined by the material used [8]. The turbulence and cavitation effect of ultrasound causes an irreversible ordered–disordered conformation transition of ALG molecules, resulting in reduced particle size [5]. However, a lower viscosity of the coating could provide a thinner film over the food samples. Pilon et al. [ 14] stated that a thin layer over the product has better barrier properties during prolonged storage. As noted by Rodriguez-Turienzo et al. [ 35], the viscosity of a whey protein isolate-based coating was found to be decreased when ultrasonication treatment was applied in the coating emulsion and thus attributed to the formation of nano-sized particles. The turbidity levels of the different coatings are shown in Figure 2C. The NP-ALG-LPE coatings (T2–T4) that were subjected to ultrasonication had lower turbidity compared to T1 ($p \leq 0.05$). This can be attributed to the reduction of starch granules in the ALG caused by the ultrasonication process, which leads to a decrease in opacity [36]. The overall transparency of NP-ALG-LPE coatings was improved as a result of ultrasonication. The solubility of ALG increased in samples T2, T3, and T4 due to ultrasonication, while T1 without ultrasonication had more undissolved particles [37]. Jambrak et al. [ 38] postulated that ultrasonication destroyed the crystalline region of starch granules, resulting in higher solubility of corn starch. Among the NP-ALG-LPE coatings (T2–T4), turbidity increased with augmenting concentrations of LPE from $0.5\%$ to $1.5\%$, respectively ($p \leq 0.05$). Generally, the higher turbidity of the coating negatively impacts the product’s appearance, thus reducing marketability [5]. Increasing the coating ingredients promoted more polymers to be present in the coating, giving rise to greater light scattering [39]. Moreover, the higher concentration of LPE ($1.5\%$) in T4 had a larger particle size. Therefore, more light scattering increased the turbidity of the coating.
## 3.1.2. Whiteness Index, Particle Size, and Polydispersity Index
Figure 3A displays the whiteness index (WI) of the tested coating samples. Generally, coatings with higher WI have a more desirable commercial appeal. The T1 coating had a higher WI than the NP-ALG-LPE coatings (T2–T4) with a significant difference ($p \leq 0.05$). The decrease in WI of NP-ALG-LPE coatings (T2–T4) was attributed to the yellowish-brownish color of LPE [4]. As the concentration of LPE increased from 0.5–$1.5\%$, the WI significantly decreased ($p \leq 0.05$). According to Lichanporn et al. [ 31], brown pigments are mainly present in the pericarp area of longkong fruits. Therefore, the higher amount of LPE ($1.5\%$) used in the coating resulted in a reduction of whiteness. Figure 3B illustrates the particle size of various coatings. *In* general, the NP-ALG-LPE coatings (T2–T4) had smaller particle sizes than T1 with a significant difference ($p \leq 0.05$). The shear force created by ultrasonic cavitation caused a reduction in the particle size of polymers in the coatings [40]. Among the NP-ALG-LPE coatings, the particle size increased as the concentration of LPE increased from $0.5\%$ to $1.5\%$ with a significant difference ($p \leq 0.05$). T2 had the smallest particle size, followed by T3 and T4 ($p \leq 0.05$). The particle size of NP-ALG-LPE coatings ranged from 218 to 260 nm. This is in line with the findings of Baek et al. [ 13] and Lin et al. [ 41], who reported particle sizes of 206 nm and 247 nm for ultrasonicated ALG and chitosan-based coatings, respectively. Figure 3C shows the polydispersity index (PDI) of the tested coating samples. The PDI measures the heterogeneity of a coating based on its particle size [42]. Typically, a lower PDI indicates a more uniform distribution and smaller size of polymers in the coating [43,44]. *In* general, NP-ALG-LPE coatings (T2–T4) had a lower PDI compared to T1 with a significant difference ($p \leq 0.05$). This difference was likely due to the disruption of structural integrity, induction of dissociation, and degradation of ALG molecules caused by the cavitation effect of ultrasonication [5], resulting in a reduction in the size of ALG molecules. This implies that ultrasonicated NP-ALG-LPE coatings have a homogenous dispersion of particles in the medium. Among the NP-ALG-LPE coatings, T2 had the lowest PDI. The increase in LPE concentration from $0.5\%$ to $1.5\%$ resulted in larger particle size and uneven distribution, leading to a higher PDI in T4 coating, followed by T3, as compared to T2 ($p \leq 0.05$). This is consistent with the particle size results shown in Figure 3B. Thus, the application of ultrasound decreases the particle size and PDI of the coating.
## 3.2.1. Physicochemical Properties
Figure 4A shows the weight loss of control and coated shrimps during storage at 4 °C. Generally, weight loss represents moisture loss of fresh food, impacting both economic benefits and product quality, making it a crucial factor for marketability [8,12]. Weight loss in all samples rose as storage days progressed ($p \leq 0.05$). The probable cause of weight loss in the shrimp sample was due to the denaturation and degradation of protein through autolysis and microbial enzymes, resulting in structural breakdown during storage [32]. Consequently, the water-holding capacity of shrimp muscle significantly decreased with storage time. At the end of storage, the T4-coated sample had the lowest weight loss as compared to the other samples. No significant difference was noted between the control and T1-coated sample until day 6 of storage ($p \leq 0.05$). Afterward, the T1-coated sample displayed a noticeable decrease in moisture loss compared to the control ($p \leq 0.05$) due to the barrier properties of ALG, which reduced moisture loss during storage [13]. The semi-permeable coating layer acted as a barrier to the flow of O2, CO2, and H2O, thus reducing moisture loss [6]. Moreover, the protein–polysaccharide complex formed between ALG, and the muscle protein of shrimp could enhance the overall water-holding capacity of shrimp during storage [45]. In addition to this, dehydration needs to be considered because it is one of the important quality parameters during refrigerated storage. According to Song et al. [ 8], the primary reason for the decrease in dehydration of products coated with ALG is that the gel coating serves as a sacrificial agent. Thus, moisture in the gel evaporates before any substantial desiccation occurs in coated food. Additionally, no significant difference was observed among NP-ALG-LPE coated shrimp samples until the 10th day of storage. However, at the end of storage, the T4-coated sample had the lowest weight loss, followed by T3, when compared to T2 ($p \leq 0.05$). This was due to the formation of an additional layer on the shrimps, providing further resistance to mass transfer and slowing the increase in water loss after a certain storage period [39]. As a result, the addition of LPE to the ALG coating on shrimp effectively reduced moisture loss during prolonged storage. pH is recognized to be one of the important indicators for identifying the changes in pH of control and coated shrimps during storage microbial spoilage in seafood or aquatic products [32]. On day 0, the samples’ pH was 6.6 to 6.9, consistent with previous reports [1,6]. The slightly lowered pH of the NP-ALG-LPE-coated samples (T2–T4) was due to the reduced pH of the coatings with the addition of LPE (0.5–$1.5\%$). This was in agreement with the pH of the NP-ALG-LPE coatings (Figure 4B). Overall, pH values were gradually increased in all samples over the storage period. At the end of storage, the lowest pH was observed in the T4-coated shrimp, while the control sample had the highest pH ($p \leq 0.05$). The rise in pH was associated with the accumulation of alkaline compounds, primarily produced due to microbial activity. Ebadi et al. [ 3] noted that endogenous enzymes and microorganisms breaking down protein in shrimp produce volatile bases like ammonia and trimethylamine, causing an increase in shrimp muscle pH during storage [46]. T4-coated shrimp had a smaller increase in pH, which matched the controlled growth of the microorganisms. *In* general, shrimp is considered unacceptable if the pH exceeds 7.6 [47]. The control sample reached this limit on day 6, while coated shrimp remained within acceptable limits throughout storage. Among NP-ALG-LPE-coated shrimp samples (T2–T4), the pH value of the samples was strongly decreased with increasing LPE concentration ($p \leq 0.05$). This suggests that LPE may help to slow microbial growth, thus reducing spoilage and decomposition. Shrimp coated with T4, which contains $1.5\%$ LPE, showed lower pH due to the inhibitory effect of LPE against microbial action. This is because LPE contains high levels of polyphenols [31], and their activity against microbes is dependent on concentration. Polyphenols interact with microbial membranes, altering their permeability and functions and leading to cell death [48,49].
## 3.2.2. Brown Pigment Enzyme Activities
Figure 5A displays the levels of PPO activities in both control and coated shrimp samples during storage at 4 °C. PPO is a major contributor to browning in the shrimp samples during prolonged storage. Enzymatic browning, also known as melanosis, affects the quality and acceptance of shrimps. PPO is the main cause of browning, converting colorless quinones into dark pigments [50]. PPO triggers enzymatic browning by catalyzing the reaction between substrates, oxygen, phenolic compounds, and reaction by-products, causing black spot formation [6]. Among the samples, the control exhibited the highest PPO activity. This was attributed to the basic mechanism of melanosis, in which PPO converts colorless monophenols into diphenols, which then react with oxygen to form highly colored quinones, leading to the formation of brown polymers through reaction with amino acids [51]. Shrimp coated with T4 had the lowest PPO activity during storage due to the higher concentration of LPE ($1.5\%$) compared to T3 ($1.0\%$) and T2 ($0.5\%$) ($p \leq 0.05$). The inhibition of PPO activity by phenolic compounds can occur through various mechanisms, including direct inhibition of PPO, scavenging of oxygen, and reduction of quinones back to diphenols to prevent melanin formation [46]. Balti et al. [ 52] found that shrimps coated with microalgal exopolysaccharides enriched with $1.5\%$ red seaweed extract (contains rich sources of polyphenols) reduced PPO activity more effectively than those coated with $0.5\%$ and $1.0\%$ extract during cold storage. Hence, the results suggest that coating shrimp with $1.5\%$ LPE effectively inhibits PPO activity during storage, making it a promising natural inhibitor.
## 3.2.3. Protein Oxidation
Figure 6A,B depicts the total and reactive sulfhydryl groups of the control and coated shrimps during storage at 4 °C. Sulfhydryl is a highly active group found in myofibrillar protein, which possesses weak secondary bonds [53]. Overall, sulfhydryl content decreased in all samples with increasing storage time ($p \leq 0.05$). This indicated that prolonged storage of shrimp had a significant effect on protein oxidation. However, the decrease of sulfhydryl groups was found lower in coated samples (T1–T4) when compared to the control ($p \leq 0.05$). Morachis-Valdez et al. also reported that carp fillets coated with chitosan showed less reduction in sulfhydryl content than uncoated ones during 5 months of storage at −18 °C [54]. *In* general, protein oxidation leads to a decrease in sulfhydryl groups, which become disulfides [55]. Additionally, muscle protein denaturation and aggregation are related to disulfide bonds [4]. Among the NP-ALG-LPE coatings (T2–T4), T2 showed the greatest reduction while T4 showed the least loss in the sulfhydryl groups. The loss of sulfhydryl groups affects the structural, functional, and nutritional properties of shrimp muscle protein [56]. This negative effect can be reduced with the use of LPE which showed a superior protective effect and showed greater stability during storage. The carbonyl content in control and coated shrimps during storage at 4 °C is shown in Figure 6C. Carbonyl is a marker of protein oxidation, measured using DNPH (2,4-Dinitrophenylhydrazine) [57]. Protein oxidation decreases shrimp quality and nutrition due to the loss of essential amino acids and reduced digestibility [58]. Carbonyl content increased in all samples during storage ($p \leq 0.05$), indicating oxidative damage to amino acid side chains, such as lysine, proline, arginine, and histidine [59]. On day 4, the control sample had higher carbonyl content than all coated samples (T1–T4), which increased over time. Shrimp coated with NP-ALG-LPE (T2–T4) significantly prevented the level of protein oxidation as compared with the ALG coating alone (T1) and control ($p \leq 0.05$). However, the addition of LPE in the NP-ALG coating (T2–T4) did not significantly affect the carbonyl content in the samples until day 8. At the end of storage, samples coated with T3 and T4 had lower carbonyl content than T2 ($p \leq 0.05$). Secondary products of lipid oxidation, such as aldehydes (e.g., malondialdehyde and 4-hydroxy-2-nonenal) or ketones, can react with amino acid residues through covalent bonds, known as Michael addition reactions, leading to indirect oxidation of protein [60]. Hence, LPE could scavenge this oxidation process in the stored shrimps as it contains an abundant level of polyphenols which act as a primary scavenger for oxidation and oxidation-induced byproducts.
## 3.2.4. Lipid Oxidation
The peroxide value (PV) is a measure of major lipid oxidation products (hydroperoxides) in a sample [61]. The abstraction of hydrogen from fatty acid double bonds produces fatty acid-free radicals, which further react with oxygen to form hydroperoxides [50]. All samples showed a gradual increase in PV (Figure 7A) level over storage ($p \leq 0.05$), indicating oxidation of fatty acids in shrimp muscle, producing hydroperoxides or peroxides [52]. On day 6 of storage, a significant difference was observed between control and coated samples ($p \leq 0.05$), with control samples having higher PV, indicating greater lipid oxidation. Coated samples, however, showed lower PV during storage ($p \leq 0.05$), attributed to the oxygen barrier capacity of ALG reducing oxygen diffusion and preventing lipid oxidation [6]. Shrimp coated with T4 had the lowest PV compared to control and T1, T2, and T3 throughout storage ($p \leq 0.05$). In addition, PV did not exceed the acceptable limit of 18–20 meq/kg [62]. This showed that the antioxidant activity of LPE in preventing lipid oxidation was concentration dependent. Lipid oxidation is generally accelerated during storage due to high levels of polyunsaturated fatty acids in crustacean cell membranes [11], but phenolic compounds in LPE might scavenge free radicals, reducing lipid oxidation by lowering lipid radicals [35]. Thus, shrimp coated with T4, with the lowest PV, showed the best protection against lipid oxidation. The increase in TBARS (a measure of lipid oxidation) was due to partial oxidation and dehydration of unsaturated fatty acids [32]. All samples showed a rise in TBARS over 14 days of storage ($p \leq 0.05$) (Figure 7B). Shrimps are susceptible to lipid oxidation, which can occur through autoxidation, photosensitized oxidation, or enzymatic reactions, such as lipoxygenase, peroxidase, and microbial enzymes [17]. In the present study, shrimp coated with $1.5\%$ LPE (T4) showed the lowest TBARS level compared to T1 and control samples at all storage times ($p \leq 0.05$), followed by T3 and T2. The decrease in TBARS values was consistent with the decrease in peroxide values (PV), indicating the ability of LPE to scavenge free radicals and prevent the formation of secondary oxidation products. The NP-ALG coating acted as a barrier to oxygen permeation and a carrier for the antioxidants in LPE, thus reducing the production of secondary oxidation products in shrimp [13]. Notably, T4 had the highest amount of LPE ($1.5\%$) in its coating, thus resulting in the lowest TBARS value. Polyphenols, which are abundant in longkong fruit, have a strong reducing capacity and are known to retard and inhibit lipid oxidation [63]. Souza et al. [ 64] demonstrated that a polyphenol-rich leaf extract from an Amazonian plant act as a powerful antioxidant in human LDL protein by reducing TBARS levels. Furthermore, the TBARS value of all groups was below the acceptable limit (1–2 mg MDA/kg) [11], consistent with the study by Dehghani et al. [ 65]. Thus, shrimp coated with T4 showed higher stability against lipid oxidation. The secondary oxidation products of shrimp were measured using AnV, which detects non-volatile oxidation products, such as aldehydes and ketones [27]. AnV reacts with oxidation products to produce a yellow product [66]. The trend of AnV was similar to other lipid oxidation product assays in this study. The control had higher AnV compared to coated samples, with T4 having the lowest AnV (Figure 7C). This suggests that LPE in ALG coating reduces secondary oxidation product formation, especially in non-volatile compounds. The results from AnV are consistent with PV and TBARS, confirming that LPE in ALG coating is an effective way to maintain shrimp quality. The totox value measures the total lipid degradation products and indicates the oxidative stage of a product [67,68]. The totox value combines primary and secondary oxidation products and is commonly used in the food industry. The totox value increased over time for all samples, with the highest value found in the control (Figure 7D). A lower totox value indicates better shrimp quality. Among the NP-ALG-LPE-coated shrimps, T4 showed the lowest totox value compared to T2 and T3. Generally, the coating application on the food surface can cause the migration of compounds from the coating into the food [69]. As a result, coating with a higher LPE content ($1.5\%$) resulted in higher levels of migrated substances and a stronger antioxidant effect in the shrimp, providing greater stability against lipid oxidation.
## 3.2.5. TVB-N
The results of TVB-N levels in control and coated shrimp during storage at 4 °C are shown in Figure 8. TVB-N indicates decomposition of protein by microorganisms or endogenous enzymes in foods, particularly seafoods [70]. An analytical technique called TVB-N (Total Volatile Basic Nitrogen) measures the levels of nitrogenous chemicals in shrimp or seafood products, which reveals the level of freshness. TVB-N provides the total base volatile nitrogen content that begins to accumulate in the tissues with degradation during shrimp storage [65]. TVB-N, the combination of ammonia (from amino acid degradation), di-methylamine (generated by self-degrading enzymes), and trimethylamine (result of spoilage bacteria), generally rises with bacterial growth, enzyme degradation, or a combination of both during storage [71]. At day 0, TVB-N levels of all samples were below 10 mg N/100 g, showing freshness of the raw material. The control’s TVB-N content rapidly rose with storage time ($p \leq 0.05$), reaching 58.4 mg N/100 g on day 14. Shrimp coated with the NP-ALG-LPE coatings (T2–T4) showed a slower increase in TVB content compared to the control and T1-coated samples during storage ($p \leq 0.05$). Additionally, samples coated with T4 had lower TVB content compared to those coated with T2 and T3 ($p \leq 0.05$). The European Commission considers TVB-N levels of 30–35 mg N/100 g as the upper acceptable limit [72]. The amount of TVB-N increased as a sum of ammonia, dimethylamine, trimethylamine, and volatile amine compounds. A small amount of ammonia is generally found during the first weeks of storage, and the total volatile alkalinity is slowly increased during storage. This may be caused by the amine removal process (deamination) of amino acids [73]. This study found that the NP-ALG-LPE-coated samples (T2–T4) had remained within acceptable limits at the end of storage. The reduced TVB-N content in the NP-ALG-LPE-coated samples was due to either reduced degradation of non-protein nitrogen compounds or slowed bacterial growth, or both. This shows LPE’s antibacterial properties. Olatunde et al. [ 74] also found that coconut husk extract reduced the increase in TVB in Asian sea-bass slices during 12 days of storage at 4 °C. Moreover, the quality and shelf-life of shrimp coated in active edible coatings made of gelatin and orange peel essential oil were determined. The shelf-life of shrimp was evaluated over a 14-day storage period by TVB-N analysis. Compared to the control group, the addition of orange peel essential oil in the edible gelatin coating improved the quality and prolonged the shelf-life of the shrimp. The incorporation of orange peel essential oil helped in preserving the chemical and microbial quality of the shrimp [75].
## 3.2.6. Microbial Analysis
The initial total viable count (TVC) of the shrimp was around 2.4–2.9 log CFU/g, similar to the result of 2.5–3 log CFU/g reported by Mohebi et al. [ 76]. During 14 days of storage, a general increase in TVC was observed in all samples ($p \leq 0.05$), with the highest increase seen in the control (Figure 9A), reaching 23.15 log CFU/g on the 14th day ($p \leq 0.05$). Dipping the shrimp in ALG reduced the increasing trend of TVC but adding LPE significantly enhanced the microbial inhibitory effect ($p \leq 0.05$). There were no significant differences among NP-ALG-LPE-coated samples (T2–T4) until day 4 of storage ($p \leq 0.05$), but the T4-coated sample had the lowest TVC among all tested samples at the end of storage ($p \leq 0.05$). This indicates that the inhibitory effect increased with increasing LPE concentration (0.5–$1.5\%$) ($p \leq 0.05$). Phytochemical compounds can damage bacterial cells by disrupting cell membranes and precipitating cell protein, causing death [52]. Kim et al. [ 6] found that shrimp coated with chitosan-alginate containing grape seed extract reduced TVC during 15 days of storage at 4 °C. Liu et al. [ 77] reported that an alginate-calcium coating with methanol extract from citrus fruit effectively reduced the increasing trend of TVC compared to those coated with $1\%$ chitosan. Lactic acid bacteria (LAB) are facultative anaerobes and form a significant part of the natural microbiota in seafood stored in anaerobic conditions [78]. On the initial day, the count of lactic acid bacteria (LAB) in all samples was 1.0 log CFU/g (Figure 9B). During storage, there was an observed increase in LAB count in all samples ($p \leq 0.05$). However, samples coated with T1–T4 exhibited a reduced increment compared to the control ($p \leq 0.05$). The NP-ALG coating had an inhibitory effect on LAB growth, which was intensified by the addition of LPE at a different concentration. T1, T2, T3, and T4 reduced the LAB count by 2, 2.16, 2.67, and 3.15 log CFU/g compared to the control, respectively. Khaledian et al. [ 11] found that shrimp coated with a tragacanth gum-based coating containing lime peel extract inhibited LAB growth during 10-day storage at 4 °C. Among the NP-ALG-LPE-coated shrimp samples, T4 had a lower LAB count due to higher LPE concentration ($1.5\%$) and greater antimicrobial effect. This can be attributed to the presence of terpenoids and lansiosides, which are the two major antibacterial substances found in longkong fruit [79,80]. A smoked eel fillet coated with carboxy methylcellulose containing rosemary extract (200–800 ppm) also showed a decrease in LAB count [81]. Enterobacteriaceae are used as a measure of hygiene [11]. Their presence in seafood, especially in cases of contaminated water or delayed chilling after capture, increases the likelihood of spoilage [82]. The initial count of Enterobacteriaceae in all samples was 0.95 log CFU/g (Figure 9C). During storage, the count of this bacteria increased continuously in all samples ($p \leq 0.05$). Among the NP-ALG-LPE-coated shrimp samples (T2–T4), T4 had the lowest count of Enterobacteriaceae as compared to the control ($p \leq 0.05$). T4 was effective in slowing the growth of Enterobacteriaceae, followed by T3 and T2 ($p \leq 0.05$). This suggests that the antimicrobial effect of LPE was dose dependent. Alsaggaf et al. [ 83] found that shrimp coated with chitosan enriched with pomegranate peel extract reduced the microbial count during 30-day storage at 4 °C, with higher PPE concentration (0.5–$2.0\%$) improving the antimicrobial activity. At the beginning of shrimp storage, the psychrotrophic bacteria count (PBC) of all samples was recorded at 1.85 log CFU/g (Figure 9D). Over time, the PBC of the control sample continued to rise ($p \leq 0.05$), but the count decreased in shrimp coated with ALG (T1) ($p \leq 0.05$). Gram-negative psychrotrophic bacteria are a key cause of spoilage in iced and/or refrigerated seafood [84]. Shrimp coated with NP-ALG-LPE coatings had an even lower level of PBC as compared to T1 and the control ($p \leq 0.05$), which confirms the antibacterial activity of LPE. Other studies have also found similar results in shrimp samples treated with green tea extract [46] and fish fillets treated with microalgal exopolysaccharides and red seaweed extract [52]. The use of NP-ALG enriched with LPE as an active edible coating is a promising solution for maintaining the quality of shrimp during refrigerated storage by limiting the growth of multiple bacteria.
## 4. Conclusions
The study investigated the impact of adding LPE (0.5–$1.5\%$) to the ALG coating, using the ultrasonication process to convert the ALG-LPE coating to become nano-sized, and tested various physiochemical properties in the coating emulsion and as well as on the shrimp samples to control the quality loss and prolong the shelf-life for up to 14 days at 4 °C. The results showed that LPE addition to a coating emulsion significantly increased the viscosity, turbidity, particle size, and polydispersity index of the coating material while lowering the pH and whiteness index. The best results were observed when $1.5\%$ LPE was added to the NP-ALG-based coating and tested shrimp samples, which resulted in lower pH, weight loss, and polyphenol oxidase activity, as well as stronger antioxidant effects against protein and lipid oxidation in the shrimp samples. The study also found that the microbial count, including total viable count, lactic acid bacteria, Enterobacteriaceae, and psychrotrophic bacterial count, was lower in the shrimps coated with the ALG-LPE coating. Thus, the results suggest that the addition of $1.5\%$ LPE to the ALG coating can effectively maintain the quality of shrimp for up to 14 days of storage and is a good alternative to synthetic preservatives. In summary, synergistic effect of an alginate-based nanoparticle coating and LPE can serve as promising approach for the quality maintenance of seafood during storage. This finding could be potentially beneficial for food industries to extend the shelf-life of food products without the addition of synthetic preservatives. Further research is anticipated to rationally designed edible coatings with precisely adjusted coating properties for the effective enhancement of food products’ quality and storability.
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|
---
title: 'Results of DUET: A Web-Based Weight Loss Randomized Controlled Feasibility
Trial among Cancer Survivors and Their Chosen Partners'
authors:
- Wendy Demark-Wahnefried
- Robert A. Oster
- Tracy E. Crane
- Laura Q. Rogers
- W. Walker Cole
- Harleen Kaur
- David Farrell
- Kelsey B. Parrish
- Hoda J. Badr
- Kathleen Y. Wolin
- Dori W. Pekmezi
journal: Cancers
year: 2023
pmcid: PMC10000640
doi: 10.3390/cancers15051577
license: CC BY 4.0
---
# Results of DUET: A Web-Based Weight Loss Randomized Controlled Feasibility Trial among Cancer Survivors and Their Chosen Partners
## Abstract
### Simple Summary
Effective and scalable diet, exercise, and weight management interventions are needed for primary cancer prevention in the general public, as well as for cancer control and tertiary prevention among the growing population of cancer survivors. A 6-month online intervention, entitled “Daughters, dUdes, mothErs, and others Together” (DUET), was designed to promote weight loss, a healthful diet, and increased physical activity among cancer survivors and their chosen partners. Fifty-six cancer survivor-partner dyads ($$n = 112$$ participants in total) were recruited into a randomized controlled trial that compared DUET to a waitlist control. The trial surpassed all feasibility endpoints with regard to uptake, retention, and safety; the DUET intervention also resulted in significant weight loss and reductions in caloric intake, as well as having a promising impact on physical activity and performance, blood glucose, and indicators of inflammation.
### Abstract
[1] Background: A healthful diet, regular physical activity, and weight management are cornerstones for cancer prevention and control. Yet, adherence is low in cancer survivors and others, calling for innovative solutions. Daughters, dUdes, mothers, and othErs fighting cancer Together (DUET) is a 6-month, online, diet-and-exercise, weight-loss intervention to improve health behaviors and outcomes among cancer survivor-partner dyads. [ 2] Methods: DUET was tested in 56 dyads (survivors of obesity-related cancers and chosen partners) ($$n = 112$$), both with overweight/obesity, sedentary behavior, and suboptimal diets. After baseline assessment, dyads were randomized to DUET intervention or waitlist control arms; data were collected at 3- and 6-months and analyzed using chi-square, t-tests, and mixed linear models (α < 0.05). [ 3] Results: Retention was $89\%$ and $100\%$ in waitlisted and intervention arms, respectively. Dyad weight loss (primary outcome) averaged −1.1 (waitlist) vs. −2.8 kg (intervention) ($$p \leq 0.044$$/time-by-arm interaction $$p \leq 0.033$$). Caloric intake decreased significantly in DUET survivors versus controls ($$p \leq 0.027$$). Evidence of benefit was observed for physical activity and function, blood glucose, and c-reactive protein. Dyadic terms were significant across outcomes, suggesting that the partner-based approach contributed to intervention-associated improvements. [ 4] Conclusions: DUET represents a pioneering effort in scalable, multi-behavior weight management interventions to promote cancer prevention and control, calling for studies that are larger in size, scope, and duration.
## 1. Introduction
Over the past four decades, the prevalence of obesity has tripled [1], as has the number of cancer survivors [2]. While these trends are not directly related, obesity is an acknowledged risk factor for 13 different cancers [3]. Obesity and weight gain after a cancer diagnosis also are linked to a poorer prognosis [4]. Thus, weight management has been suggested as a means of primary cancer prevention [5], as well as tertiary prevention of second cancers and other prevalent forms of comorbidity among cancer survivors [6].
Given that social support plays an integral role in achieving the lifestyle changes that underlie weight management [7], buddy systems have been implemented in weight loss programs to enhance efficacy [8]. With respect to cancer, buddy pairings that unite cancer survivors with individuals in their social networks and which emphasize weight management to prevent or control cancer may be one way to expand reach and fortify intervention uptake, especially those that are minimal touch.
To date, there have been two dyadic interventions that have promoted diet, physical activity, and weight loss as a means of cancer prevention and control among both cancer survivors and their family members. The Daughters and Mothers (DAMES) study included 68 breast cancer survivors (mothers) who had overweight or obesity and were insufficiently active, paired with their adult biological daughters who had similar lifestyle behaviors and increased adiposity. Randomized dyads received 12 months of bimonthly print materials that were either team-tailored, individually tailored, or standardized (not tailored at all) [9]. Although baseline-to-follow-up improvements in weight, waist circumference, and accelerometry-measured physical activity were observed across all arms, as hypothesized, the tailored interventions resulted in significantly greater improvements in waist circumference and physical activity than the standardized intervention. Surprisingly, only the individually or personally tailored intervention and not the team-tailored intervention (which provided tailored feedback for both the survivor and daughter simultaneously) resulted in significant reductions in body mass index ([BMI] among cancer survivors but not daughters. While DAMES was deemed feasible in terms of safety, retention ($90\%$), and achievement of the accrual target, recruitment of mother-daughter dyads was difficult ($3\%$ enrollment rate [#consented/#contacted]).
Subsequently, “Healthy Moves” focused on survivor-spouse dyads ($$n = 22$$), which were randomized to intervention arms that targeted the survivor alone (using a modified version of the DAMES individually tailored intervention) or both the survivor and spouse (using a modified version of the DAMES team-tailored intervention, plus nine videoconferencing sessions with a marriage counselor to enhance spousal communication) [10]. Compared to DAMES, Healthy Moves had a higher enrollment rate ($12.7\%$); however, despite intensive efforts to enhance communication among couples assigned to the spouse-survivor arm, improvements in weight and fruit and vegetable consumption (and related physical function outcomes) were similar across arms. Spouses receiving the intervention versus spouses of dyads randomized to the survivor-alone condition (i.e., who did not receive the intervention) experienced significant improvements in lifestyle behaviors and physical function outcomes.
Building on this research, the current study, Daughters, dUdes, mothers, and othErs fighting cancer Together (DUET) trial, sought to expand eligibility by encouraging survivors to select any partner they felt could participate with them in the weight loss intervention [11]. Furthermore, instead of relying on a tailored, mailed print intervention that was informed by mailed surveys, DUET incorporated newer technology, i.e., Fitbits® and Aria® scales, for monitoring and used a more contemporary and scalable web-based platform to deliver the intervention. Herein, we report the main outcomes of the DUET trial that was aimed at promoting weight loss among cancer survivors and their chosen supportive partners. Hypotheses were that dyads assigned to the DUET intervention would lose significantly more weight (primary outcome) at 6-month follow-up than dyads assigned to the waitlisted control; moreover, the intervention also would result in more favorable changes in secondary outcomes, such as other measures of adiposity (e.g., waist circumference), diet quality, physical activity, quality-of-life, and physical performance, as well as related biomarkers (e.g., insulin, glucose, total and high-density lipoprotein (HDL) cholesterol, triglycerides, leptin, adiponectin, and c-reactive protein (CRP).
## 2.1. Overview
DUET was a single-blinded, 2-arm randomized controlled trial (RCT) that tested a 6-month web-based lifestyle intervention against a waitlist control among 56 dyads. Each dyad comprised a survivor of an obesity-related cancer, and their chosen partner, both of whom had obesity or overweight, were insufficiently active, and consumed suboptimal diets. This trial was approved by the University of Alabama at Birmingham (UAB) Institutional Review Board [300003882] and registered within ClinicalTrials.gov (NCT04132219). Detailed methods for DUET were published upon attainment of the accrual target of 56 dyads which ensued over a 9-month period ranging from October 2020 to July 2021 [11]; these methods are briefly summarized below.
## 2.2. Participants: Recruitment, Screening, Consent, and Randomization
Study invitations were distributed to adult survivors of localized renal cancer and loco-regional ovarian, colorectal, prostatic, endometrial, and female breast cancers (obesity-related cancers with 5-year survival rates >$70\%$) identified from the UAB cancer registry, and listings of individuals expressing previous interest in lifestyle RCTs. Additionally, the Love Research Army (https://drsusanloveresearch.org/love-research-army) (accessed on 2 March 2023 initiated a series of email “blasts” to its members, and a recruitment website was established. Study staff provided telephone follow-up on mailings and contacts by placing up to six calls at various days and times. The study was explained, and interested survivors were screened for eligibility. Inclusion criteria were: [1] BMI ≥25 kg/m2; [2] moderate-to-vigorous physical activity (MVPA) < 150 min/week; [3] English speaking and writing; [4] educational attainment ≥5th grade; and [5] daily internet use and mobile phone ownership. Exclusion criteria were: [1] adhering to modified diets or enrolled in structured diet or exercise programs; [2] recent physician’s advice to limit PA and/or health issues precluding unsupervised PA or weight loss; and [3] residence in an assisted-nursing facility. Once eligibility was established and cancer case status (type and date of diagnosis) was verified by treating physicians of self-referrals, the survivor was asked to identify a partner with whom they interacted in person on at least a biweekly basis. Partners had identical inclusion/exclusion criteria (cancer survivorship was optional).
Telephone or Zoom calls were scheduled to review the study and acquire signed consent electronically (Adobe Sign®, San Jose, CA, USA). Participants completed baseline assessments, and their addresses were used to derive rural-urban commuting area codes (RUCA) as well as to estimate the distance between dyad members using Google Maps (https://www.google.com/maps) (accessed on 2 March 2023) since rural-urban status and proximity of dyad pairs could potentially affect access to healthy food procurement and exercise opportunities and support [9,12,13]. Dyads were randomly and evenly assigned to the DUET intervention or waitlist control using a permuted block design (block size = 4).
## 2.3. DUET Intervention
The DUET web-based intervention was adapted from two previously established programs: [1] the tailored mail-based, dyadic DAMES intervention, which was expanded to meet the needs of cancer survivors beyond those with just post-menopausal breast cancer and for partners beyond just biological daughters [9]; and [2] SurvivorSHINE, a web-based diet and exercise program for cancer survivors [14,15]. Like both of these interventions, DUET was theoretically grounded on Social Cognitive Theory (SCT) and emphasized skills training, modeling, incremental goal setting (with reinforcement), overcoming barriers, and self-monitoring (through the incorporation of new technologies, i.e., Fitbits and Aria Scales) [7]. Concepts from Interdependence Theory [16] and the Theory of Communal Coping [17] also guided the dyads’ commitment to relationship quality and the development of mutual goals to promote the adoption and maintenance of health behaviors and the provision and request for social support.
Upon randomization, one dyad member was mailed a box of supplies that included two sets of Portion Doctor ® tableware, two Fitbits (Inspire®), two Aria 2® digital scales, and two sets of instructions to connect to MyFitnessPal® to automate weight and exercise tracking and provide additional reinforcement and support. Fitbit accounts also were linked to the password-protected, interactive DUET website, which formed the central core of the DUET intervention. Here each dyad member received tailored guidance over 24 weeks based on World Cancer Research Fund—American Institute of Cancer Research (WCRF-AICR) guidelines [18]. Thus, each dyad member was encouraged to set incremental goals that would eventually lead over the course of the 6-month intervention to exercising (including aerobic, resistance, flexibility, and balance) at least 150 min a week and adhering to a plant-based diet that included ample amounts of whole grains, vegetables and fruit (V and F), and limited amounts of red and processed meats, sugar, and refined (fast) food, while promoting a loss of roughly 0.5 kg per week. The website was designed with the following key features: [1] My Profile; [2] Topical Content; [3] Tip of the Day; [4] Sessions; [5] Tools; [6] News You Can Use; and [7] Support. Participants initially logged in to “My Profile” to enter age, height, gender, and current data on night-time snacking and intakes of V and F, whole and processed grains, red and processed meats, added sugars, supplement use, and alcohol. Survivors were prompted for data on cancer type, treatment, and coping style (Fighting Spirit or Fatalist) [19], which were used to provide tailored feedback, e.g., graphical displays with motivational messaging on overcoming treatment-related barriers (such as intolerance of high fiber V and Fs among survivors of colorectal cancer treated with a colostomy or urinary incontinence among survivors of prostate cancer), and calorie budgets to promote a loss of 0.5 kg w−1 [20]. Additionally, discrete tabs were provided to facilely reference topical information on healthy weight, healthy eating, and exercise. Daily tips for weight management, diet, and exercise were continually refreshed over the 6-month intervention as a means to enhance engagement. Furthermore, 24 weekly interactive sessions averaging 15 min in length were created using Articulate Storyline software (Articulate Global, LLC, New York, NY, USA) to guide participants through topics such as portion control, grocery shopping and food preparation, and various forms of exercise (aerobic, resistance, balance, and flexibility). A variety of tools also were provided on goal setting, customized meal plans, recipes, grocery lists, exercise guides, etc., in formats that could be downloaded and printed off. A tab entitled “News You Can Use” provided “take-away” summaries of recently released news stories and research pertaining to diet and exercise for cancer control. Finally, the webpage offered tips, such as active listening, to enhance dyad-based support. To enhance engagement with the website, Short Message System (SMS) text messages were issued thrice weekly. On each Monday of the 24-week intervention, dyads received a “push” message with a direct website link to the newly-released weekly session. On Wednesdays, dyads received a support message to reinforce the weekly content, and on Fridays, a “call-to-action” inquired about progress towards incremental goals.
## 2.4. Waitlist Control
Waitlisted dyads received all DUET resources and programming once 6-month follow-up data were collected.
## 2.5. Measures
Because DUET was implemented during COVID-19, several measures were adapted for remote assessment and were captured via Zoom® (San Jose, CA, USA); validation study results were published previously [21].
## 2.5.2. Dietary Intake (Captured at Baseline and 6 Months)
Two 24-h dietary recalls of a non-consecutive weekday and weekend day were conducted via telephone by a registered dietitian using the Automated Self-Administered (ASA-24) dietary assessment tool (https://epi.grants.cancer.gov/asa24) (accessed on 2 March 2023) at baseline and 6-months. Averaged intakes were obtained for calories, and diet quality was assessed using the Healthy Eating Index (HEI)-2015 [23].
## 2.5.3. Physical Activity and Sleep (Captured at Baseline and 6 Months)
Objective PA data were captured using Actigraphs® (Fort Walton, FL, USA) with instructions to wear the device at waist level on a provided belt during waking hours and to move the device to a provided wristband upon retiring to sleep. This procedure was followed for 7 days and accompanied by a written log. Minutes of MVPA were then downloaded and processed with similar methods used previously [24]. The Godin Leisure Time Exercise Questionnaire (GLTEQ) was administered online, giving excellent reliability and validity among cancer survivors [25].
## 2.5.4. Physical Performance (Captured at Baseline and 6 Months)
Several physical performance measures were adapted for remote delivery and validated [21]. Dyads were mailed soccer cones, measuring tapes, 8′ lengths of cord, and stickers before assessments to perform measures. Trained assessors recorded images of testing via Zoom and then replayed them to capture accurate times and observations. Once data were entered into databases, videos were erased. Details of the remote assessment of the 30-s chair stand, 8′ get-up-and-go, sit-and-reach, back scratch, 2-min step test, and balance testing are reported by Pekmezi et al. [ 11] and Hoenemeyer et al. [ 21]
## 2.5.5. Circulating Biomarkers (Captured at Baseline and 6 Months)
Participants received print and video instruction (https://youtu.be/lBPLS4PoHv4) (accessed on 2 March 2023) to self-collect 5 dried blood spots (DBS) on a designated card. These were dried for >4 h at room temperature, then inserted into a foil pouch with desiccant and frozen (0 F° or below) until analyzed. DBS eluents were batch-tested against known standards for insulin, glucose, leptin, adiponectin, high-density lipoprotein (HDL), and total cholesterol, triglycerides, and c-reactive protein (CRP) at the University of Washington as described previously [26]. To assure the validity of DBS assays in the current sample and data presented in this report, assays were performed using traditional multiplex methods on sera collected via phlebotomy from 36 participants at baseline and compared to a matched analysis of assays performed on DBS samples collected at the same time. Coefficients (R2) generated by ordinal logistic regression indicated strong correlations and were as follows: glucose = 0.981; CRP = 0.979; triglycerides = 0.979; total cholesterol = 0.963; leptin = 0.919; HDL = 0.899; adiponectin = 0.799; and insulin = 0.700. Values are expressed in plasma equivalent terms.
## 2.5.6. Online Surveys
Online surveys were administered via REDCap® (https://projectredcap.org) (accessed on 2 March 2023) at baseline, 3- and 6-months, though demographic information was only collected at baseline.
## 2.5.7. Safety
All participants were encouraged to call a toll-free study number to report any adverse events. In addition, changes in health status were systematically ascertained in both study arms at 3 and 6 months. Events considered permanently disabling, life-threatening, or resulting in overnight hospitalization were deemed “serious,” with attribution to the intervention explored further.
## 2.6. Statistical Considerations
While accrual, retention, and safety form the basis of this feasibility trial, between-arm differences (i.e., differences between the intervention group and the waitlist control group) in weight loss (primary outcome) from baseline to 6 months were formally tested. Power calculations were performed using nQuery (version 8.5; GraphPad Software DBA Statistical Solutions, San Diego, CA, USA). These calculations assumed a standard deviation of 4.6 kg for the mean weight loss, as presented in our DUET protocol paper [11]. Assuming a sample size of 25 dyads/arm, a common standard deviation of 4.6 kg, a two-sided two-group t-test, and a significance level of $5\%$, there was >$80\%$ power to detect between-arm differences in weight loss of −3.72 kg or greater.
To determine whether important demographic and clinical characteristics of the sample were evenly distributed between the two study arms, the chi-square test (or Fisher’s exact test if the assumptions for the chi-square were not valid) for categorical study variables and the two-group t-test for continuous study variables were used. Distributions of continuous study variables were examined using stem-and-leaf, box, and normal probability plots and the Kolmogorov–Smirnov test; variables deviating from normal distribution were log10 transformed prior to analysis. All analyses were performed using SAS software (version 9.4; SAS Institute, Inc., Cary, NC, USA).
Arm differences in weight loss were assessed using an intent-to-treat approach. General linear mixed models, in particular, mixed model repeated measures analyses, were used to test for between-arm differences (two study arms), within-arm differences (three time points), and the arm-by-time point interaction simultaneously. These analyses were performed using PROC MIXED of SAS. This method accounts for the repeated measurements as well as the covariance between survivors, partners, or dyad members. A compound symmetry covariance matrix was assumed. This method provides tests of statistical significance (Type 3 tests which produce an F value and a p-value) for the between-arm effect, within-arm effect, and the interaction effect. When any of these effects were statistically significant, the Tukey–Kramer multiple comparisons test (performed using PROC MIXED of SAS) was used to determine which specific pairs of means for that effect were significantly different and also identified the time points at which those differences occurred. Such testing was helpful in comparing the multiple groups of survivors, partners, and dyads over two time points (when most outcomes were assessed) as well as three points (for survey data). Analyses were performed separately for survivors, partners, and combined dyads (thus, three sets of analyses were performed). Post-randomization exclusions (i.e., the three participants who either received gastrointestinal surgery or developed a cancer recurrence within 2 weeks of randomization) were omitted from 3- and 6-month analyses. Otherwise, all available data were used, though if a dyad member dropped out, data from that dyad were excluded from the dyadic analysis.
Analyses of secondary continuous outcomes were performed using general linear mixed models, as described in the previous paragraph for the primary outcome. These analyses were again followed by the use of the Tukey–Kramer multiple comparisons tests (for post hoc testing).
## 3.1. Study Sample, Retention, and Safety
Sample characteristics are shown in Table 1. Overall, participants were diverse in terms of race, age, and geography (Alabama, Illinois, Mississippi, North Carolina, and Tennessee). Most were female, urban dwellers, and non-smokers, and roughly half reported being college graduates and currently employed with annual incomes >USD 50,000. Mean levels of V and F intake and PA were low as compared to the guidelines [5,6,18], while the average BMI was in the obese range, and participants reported an average of three other health conditions in addition to their cancer diagnosis. Survivors tended to be “long-term” (i.e., having diagnoses more than 5 years out), with most reporting early-stage cancers, of which a high proportion were breast cancer. A small number of previous cancer diagnoses (four breast, two gynecologic, and one testicular) were reported among partners. Given the high proportion of breast cancer survivors with spousal partners, survivors were significantly more likely to gender identify as female, while partners were more likely to report as male (p-values < 0.05). There were no statistically significant differences detected between the intervention vs. the waitlist control arms for any of the characteristics collected.
The CONSORT diagram (Figure 1) shows an enrollment rate of ~$5.5\%$ ($$n = 61$$/$$n = 1114$$). Of the 112 participants enrolled, three exclusions occurred within two weeks after randomization within the waitlisted arm (one survivor developed a cancer recurrence, another received emergency gastrointestinal surgery, and one partner received bariatric surgery), all of which were discontinued from the study and analysis, since all of these conditions affect the primary outcome (weight status). Additionally, three waitlisted partners were lost to follow-up (two of three dropped out when their survivor did so). Thus, the retention rate was $89\%$ in the waitlisted arm and $100\%$ in the intervention arm; the difference was statistically significant ($$p \leq 0.027$$).
Adverse events totaled 14 and 16 in waitlist and intervention arms, respectively. All events were non-attributable, and all except four were non-serious (two cancer recurrences, one myocardial infarction, and one acute cholecystitis), with no differences in events noted between the waitlist control and intervention and the intervention arms.
## 3.2. Changes in Adiposity
Significant weight loss occurred in both study arms (Table 2), though the magnitude of weight loss was significantly larger in survivors, partners, and dyads randomized within the DUET intervention arm. Dyads assigned to the DUET intervention lost significantly more weight (an average of 2.8 kg or $3.2\%$ of their body weight) as compared to dyads that were waitlisted (who lost an average of 1.1 kg or $1.2\%$ of their body weight). Findings related to waist circumference paralleled the results for weight loss, but differences between the two study arms did not reach statistical significance.
## 3.3. Changes in Dietary Intake and Physical Activity
Both study arms also significantly reduced their caloric intakes, with reductions being particularly notable among partners and dyads within the DUET intervention arm. However, calorie intake was significantly less among survivors assigned to the intervention arm than those randomized to the waitlist control (Table 2). While values for diet quality increased among intervention participants as compared to decreasing values among controls over the study period, these differences did not achieve statistical significance. Both study arms also showed significant increases in MVPA assessed either via self-report or accelerometry over the study period, though increases among survivors within the DUET study arm were of greater magnitude. That being said, differences between study arms did not reach statistical significance.
## 3.4. Changes in Physical Performance
As shown in Table 3, both study arms experienced significant improvements in several indices of physical performance (i.e., 30-s chair stand, 8′ get-up-and-go, sit-and-reach, and 2-min step test) over the study period, with DUET intervention arm Survivors showed improvements of greater magnitude for all four tests and DUET dyads in 3-out-of-4 tests (i.e., all except the 2-min step test). DUET partners also showed notable improvements in the 30-s chair stand. However, in comparing improvements in the two study arms over time, significant differences were only detected for the flexibility measure, i.e., the sit-and-reach among survivors and dyads.
## 3.5. Changes in Circulating Biomarkers
As shown in Table 4, both study arms experienced significant decreases in circulating glucose, and while decreases were particularly noteworthy among DUET-assigned partners and dyads and among waitlisted survivors, these beneficial effects did not differ in statistical significance between study arms. Significant decreases over time also were observed among partners and dyads in both study arms for total cholesterol, as well as HDL cholesterol among all three subgroups (i.e., survivors, partners, and dyads). The effects on HDL cholesterol were particularly notable among dyad members of both study arms, with DUET dyads experiencing significantly greater decreases in HDL cholesterol than waitlisted dyads. Similarly, levels of CRP also decreased significantly over time among survivors and dyads in both study arms, and while these differences were particularly notable among survivors within the DUET intervention arm, no statistically significant differences were noted when the waitlist vs. the intervention arm were compared. Data on the adipokines, leptin, and adiponectin were less consistent, though significantly higher increases in leptin were observed among partners in the DUET intervention than among the waitlist control. No differences were detected for circulating levels of insulin or triglycerides.
## 3.6. Changes in Patient-Reported Outcomes
Significant improvements in physical QOL were observed over time among survivors of both study arms, though differences were not observed among other subgroups and also not for mental QOL. Further, no differences between the DUET intervention arm vs. the waitlist control were detected (Table S1). While social support and self-efficacy for both diet and exercise increased over the 6-month period for intervention participants compared to decreasing levels among controls, these differences did not achieve statistical significance. In contrast, barriers decreased significantly over time in both study groups, with survivors and dyads reporting significantly fewer barriers to pursuing a low-calorie diet and partners and dyads reporting significantly fewer barriers toward exercise. While no statistically significant differences were identified between arms, p-values for time-by-arm interactions approached significance (e.g., $$p \leq 0.051$$).
## 3.7. Model Dyadic Terms
Of note, the models generated for DUET uncovered several significant dyadic terms, suggesting that the relationship established between the survivor and their partner appeared important for influencing effects on body weight ($$p \leq 0.009$$), waist circumference ($$p \leq 0.023$$), diet quality ($$p \leq 0.009$$), subjectively- and objectively-assessed PA (p’s < 0.001), sleep efficiency ($p \leq 0.001$), most physical performance tests (except the sit-and-reach) (p’s < 0.007), HDL cholesterol ($$p \leq 0.038$$), CRP ($$p \leq 0.002$$) and mental health (PROMIS; $p \leq 0.001$). Trends also were noted for caloric intake ($$p \leq 0.083$$), diet self-efficacy ($$p \leq 0.089$$), sleep fragmentation index ($$p \leq 0.0504$$), adiponectin ($$p \leq 0.095$$), and total cholesterol ($$p \leq 0.076$$).
## 4. Discussion
The DUET diet and exercise intervention was found to be feasible and resulted in significant weight loss among cancer survivors and their chosen partners. The $3.2\%$ loss in body weight was not only statistically significant as compared to the $1.2\%$ weight loss among controls but also is considered clinically significant and of the magnitude shown to exert favorable effects on glucose control and blood lipids by the American Heart Association, American College of Cardiology and The Obesity Society guidelines panel for the management of obesity and overweight [36]. This intervention is one of a handful of dyadic-based lifestyle interventions among cancer survivors [9,10,38,39,40] and among the few that promote change in multiple behaviors. Additionally, it is the only one that has employed a web-based platform. Moreover, while evidence is less consistent across both dyad members and as compared to the waitlist control, the DUET intervention also was associated with favorable effects on waist circumference, caloric restriction, self-reported and objective PA, as well as physical performance and blood glucose and CRP. While the relatively modest sample size may have limited power to detect differences in self-efficacy and social support, the intervention appeared to decrease the number of barriers affecting adherence to a calorically-restricted diet or increased PA. Thus, the theoretical concepts of SCT on which the DUET intervention was framed appear supported by these data and should be preserved in future trials. [ 7,11] DUET achieved these favorable effects with minimal touch and excellent retention and safety; hence, results support future web-based interventions. Heretofore, variable success has been reported for multi-behavior, web-based interventions among cancer survivors. Bantum et al. [ 41] evaluated a comprehensive, 6-week, web-based symptom management program (including PA, weight management, and a healthful, plant-based diet) among 352 adult survivors of various cancers in Hawaii. The “Surviving and Thriving” intervention resulted in significant increases in moderate PA as compared to waitlisted controls but did not show concomitant increases in V and F intake (weight status was unreported). Kanera et al. [ 42] reported similar findings with a 6-month, web-based program (“Cancer Aftercare Guide”) among 462 Dutch survivors of mixed cancers and again found significant increases in self-reported moderate PA (+74.7 min/week) in the intervention arm but failed to detect significant increases in V and F intake in controlled analyses. By achieving improvements in calorie control and evidence of improvement in both self-reported and objectively-measured PA, DUET contributes to the unfolding science related to multi-behavior, web-based interventions and also demonstrates an impact on weight status. DUET is the first web-based intervention that reduced obesity and decreased caloric intake among cancer survivors, though, like the other studies also failed to detect significant differences in diet quality (albeit the sample size was six-to-nine-fold smaller and likely precluded our ability to detect between-arm differences).
DUET also promoted improvements in levels of CRP and glucose that have been observed in other more intensive weight loss interventions among cancer survivors, [43,44] though few differences were detected in other biomarkers typically associated with weight loss, i.e., leptin, adiponectin, and insulin [45,46]. Curiously, some participant groups (e.g., partners assigned to the intervention) experienced increases in leptin rather than decreases [46] and decreases in HDL cholesterol despite increased PA. Seasonal variation in circulating lipids may explain this latter finding since most participants were accrued during the summer when HDL cholesterol peaks and then descends towards its winter time nadir (corresponding to a 6-month follow-up) [47].
Unlike our previous Reach-out to ENhancE Wellness among older cancer survivors (RENEW) RCT that found significant improvements in both physical and mental QOL with a home-based diet and exercise weight management intervention among 641 breast, prostate, and colorectal cancer survivors [48], the current RCT only detected significant improvements in physical QOL, which it observed in both study arms. A probable explanation for this discrepancy was the smaller sample size, as the DAMES study [9], which also has a more modest sample size, likewise was unable to detect changes in this outcome.
An innovation of DUET was the expansion of dyadic composition beyond the family unit. This expansion increased the intervention reach and also likely enhanced DUET’s accrual (56 dyads in 9 months) versus Healthy Moves (22 dyads over 15 months) and DAMES (68 dyads over 2 years). While spouses and children still comprised roughly two-thirds of chosen partners, other family members, friends, and neighbors comprised the remainder. Furthermore, the significance of the dyadic term for most outcomes suggested that the synergy of the dyads was still strong, despite eligibility not being contingent on family relatedness. Yet, the inability to ascertain an eligible and willing partner served as an enrollment barrier. With social isolation and a lack of companionship reported at levels topping $20\%$ in Western countries [49], and rates of living alone nearing $30\%$ [50], there is an obvious need to explore effective “match-making” where cancer survivors could be paired with others based on factors that have the potential to create the synergy observed with naturally-occurring partnerships.
The DUET trial had several strengths. It formally tested a theoretically-grounded intervention using a randomized controlled design and validated, rigorous measures that were captured by trained staff blinded to study condition. Moreover, the study sample was diverse in geographic range and age, and the proportion of non-Hispanic Whites was representative of the U.S. population ($62.5\%$ vs. $59.3\%$) [50]. In addition, retention was excellent in both the intervention and waitlisted arms over the 6-month study period—exceeding the $70\%$ benchmark that characterizes a tier-1 trial [51]; however, drop-out was significantly higher in the waitlisted arm. While differential drop-out can result in bias, this threat is considered minimal given the relatively small numbers ($$n = 6$$). Like many feasibility studies, DUET had a relatively modest sample size which may have undermined the statistical power to detect differences. Moreover, because of the focus on feasibility, statistical comparisons, of which there were many, were uncontrolled for multiple testing and may have uncovered spurious findings. The trial also did not assess whether weight loss and behavior changes were maintained long-term. Another weakness was the relatively low enrollment rate, i.e., uptake, especially among survivors of cancers other than breast, which may limit the generalizability of findings. However, the preponderance of breast cancer survivor participation in the current study is a phenomenon that has been reported commonly in traditionally delivered clinical interventions [52], as well as those that are digital [53].
## 5. Conclusions
DUET represents a pioneering effort in scalable, multi-behavior weight management interventions to promote cancer prevention and control. DUET not only demonstrated feasibility, safety, and excellent retention, it also promoted significant weight loss via caloric restriction and increased PA. Moreover, it exerted favorable effects on physical performance and markers of glucose metabolism and inflammation. Future studies are needed which are larger in size, scope, and duration and which assess longer-term effects of buddy-system interventions—interventions that include family members but that also extend cancer control to friends and neighbors.
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|
---
title: Assessment of Blood Microcirculation Changes after COVID-19 Using Wearable
Laser Doppler Flowmetry
authors:
- Elena V. Zharkikh
- Yulia I. Loktionova
- Andrey A. Fedorovich
- Alexander Y. Gorshkov
- Andrey V. Dunaev
journal: Diagnostics
year: 2023
pmcid: PMC10000665
doi: 10.3390/diagnostics13050920
license: CC BY 4.0
---
# Assessment of Blood Microcirculation Changes after COVID-19 Using Wearable Laser Doppler Flowmetry
## Abstract
The present work is focused on the study of changes in microcirculation parameters in patients who have undergone COVID-19 by means of wearable laser Doppler flowmetry (LDF) devices. The microcirculatory system is known to play a key role in the pathogenesis of COVID-19, and its disorders manifest themselves long after the patient has recovered. In the present work, microcirculatory changes were studied in dynamics on one patient for 10 days before his disease and 26 days after his recovery, and data from the group of patients undergoing rehabilitation after COVID-19 were compared with the data from a control group. A system consisting of several wearable laser Doppler flowmetry analysers was used for the studies. The patients were found to have reduced cutaneous perfusion and changes in the amplitude–frequency pattern of the LDF signal. The obtained data confirm that microcirculatory bed dysfunction is present in patients for a long period after the recovery from COVID-19.
## 1. Introduction
The propagation of coronavirus infection, also known as COVID-19, has caused a huge number of illnesses and deaths. To date, there have been more than 650 million confirmed cases of SARS-CoV-2 infection and more than 6 million deaths worldwide (according to the Johns Hopkins University Coronavirus Resource Center). Three years after the first reported cases of SARS-CoV-2 infection, the pandemic is still far from being over. Despite the development and widespread implementation of vaccines and containment measures, COVID-19 still has a significant impact on the lives of millions of people worldwide. Emerging evidence suggests a close link between severe clinical COVID-19 and an increased risk of its vascular complications, such as thromboembolism [1]. Approximately 40–$45\%$ of cases are asymptomatic with SARS-CoV-2, but clinical observations suggest that complications may occur even in the asymptomatic course of the disease [2].
Although COVID-19 was originally considered a respiratory disease, it has now been established that it affects multiple organs and systems, including the cardiovascular system, gastrointestinal system, brain, kidney, liver, skeletal muscle, and skin of infected patients [3,4]. Recently, there is increasing evidence of the negative impact of this disease on the microcirculatory system of the blood [5,6,7]. It is known that SARS-CoV-2 affects the microcirculatory bed, causing edema and damage to endothelial cells, affects the development of microthrombosis, and capillary blockage, and causes a variety of other negative effects [8]. The development of these disorders, in addition to the direct threat to the patient’s life and health, can also be a key factor in the development of long-term consequences of coronavirus infection, significantly reducing the quality of life of patients. Serious concerns are caused by the fact that proinflammatory status and procoagulation activity can remain in patients for a long time after the recovery [9].
Recent observations show that a fairly large proportion of patients who have recovered from a coronavirus infection subsequently suffer long-term effects of the disease [10]. These include symptoms such as weakness, breathlessness, chest, and joint pain, confusion, memory and concentration problems (so-called “brain fog”), mood changes, etc. These and other symptoms can persist for months after the disease itself and significantly reduce patients’ quality of life [11]. These disorders are referred to as “long COVID” or post-COVID syndrome. Current research is largely focused on the acute stage of SARS-CoV-2, but ongoing monitoring of the long-term effects of the disease is also necessary. In this context, the need for research into the rehabilitation of patients after coronavirus infection is clear.
There is a significant body of evidence suggesting that cardiovascular complications of coronavirus can also occur in an asymptomatic course [2], making it even more difficult to detect such complications at an early stage. This means that there will be an urgent need for both diagnostic and rehabilitative measures in the next few years for patients who have suffered from this disease. In addition, there are risks of a similar clinical outcome not only with COVID-19 but also with possible future epidemics of respiratory infections. Existing diagnostic methods routinely used in clinical practice do not allow adequate assessment of blood flow at the microcirculatory level. Currently, there is a need to develop new approaches to the diagnosis of microcirculatory disorders occurring in coronavirus infection, as well as to develop strategies for individual therapy and rehabilitation of patients after COVID-19.
Despite the widespread prevalence of the disease and the incidence of cardiovascular complications, as well as the proven extensive involvement of microvasculature in pathological processes, only very few papers have been published to date on the noninvasive assessment of blood microcirculation after COVID-19 [12,13,14].
One of the most common and applicable methods for diagnosing the state of the blood microcirculation system is laser Doppler flowmetry (LDF) [15,16]. This method is widely used in the diagnosis of complications of diabetes mellitus [17,18], rheumatic diseases [19], hypertension [20] and a number of other socially important diseases. Over the years, different modifications of the conventional laser Doppler technique had been introduced, including several attempts at developing wearable devices [21,22,23].
In the COVID-19 clinic, the main focus of research using LDF was on studying the dynamic characteristics of blood flow, including the application of functional tests. It has been shown that, during the acute phase of COVID-19, patients demonstrate a reduced vasodilatory response to local heating and reduced microvascular reactivity [24]. The correlations between microcirculatory parameters measured by LDF and laboratory test results of patients during the acute period of the disease were also analysed [25]. Another study using laser speckle contrast imaging technology demonstrated reduced vasodilation in patients with COVID-19 in response to acetylcholine and sodium nitroprusside, which persists for at least 3 months after the disease [26]. We did not find any studies in the English-language literature devoted to spectral analysis of LDF recordings in patients who underwent COVID-19. Since it is known that such analysis provides valuable diagnostic information about the state of systems regulating blood flow, including the nervous system and endothelial function, the present work aimed to fill the gaps in this area.
In this context, this work aimed to comprehensively examine the changes in blood microcirculation that occur both in the acute period of the COVID-19 disease and in the long term during rehabilitation procedures.
## 2.1. Experimental Equipment
A distributed system consisting of 4 wireless wearable microcirculatory blood flow analysers implementing LDF method “LAZMA PF” (LAZMA Ltd, Russia; in EU/UK this device made by Aston Medical Technology Ltd., UK as “FED-1b”) was used for data recording in this study [27,28,29]. These analysers use VCSEL die chips (850 nm, 1.4 mW/3.5 mA, Philips, The Netherlands) as a single-mode radiation source. The analysers are implemented without optical fibres with direct skin irradiation from a window at the back of the instrument. This allows for avoiding fibre coupling losses as well as decreasing the movement artefacts which are common in fibre-based LDF monitors. The devices operate autonomously on internal battery power and transfer the measured signal via Bluetooth and/or Wi-Fi. The devices also have built-in motion and temperature sensors to eliminate the possible influence of motion artefacts and temperature changes on the recorded signal. When processing motion sensor data, recordings simultaneous with the subject’s movements are identified as potential sources of distortion of the LDF gram and filtered using special software. The appearance of the analysers (left) as well as the options for mounting them on the volunteer’s hands (right) are shown in Figure 1.
## 2.2. Experimental Protocol
The present study comprised 2 phases. The first stage involved a dynamic assessment of the processes occurring in the blood microcirculatory system during the acute period of coronavirus infection. During routine daily LDF measurements, an 18-year-old male patient was found to be accidentally infected with SARS-CoV-2 (confirmed by PCR analysis of nasopharyngeal swabs). The patient had not been vaccinated against COVID-19 prior to the study nor did he have previous experience with COVID-19. The measurements were carried out in the supine position, each lasting for 10 min. To record signals, analysers were attached to the pads of the third fingers and big toes, as well as on the dorsal surfaces of the wrists and the inner parts of the upper third of the shins. The positioning and attachment of wearable devices on the patient’s body during the study are shown in Figure 2. The measurements were taken 10 days before the onset of the disease and during 26 days after the recovery. No measurements were taken during the acute phase of the disease (7 days) because of the patient’s poor well-being. A total of more than 170 LDF signals were measured and processed over the entire study period for this patient.
The second phase of the study involved the comparison of blood microcirculation parameters measured by LDF in a group of patients undergoing rehabilitation procedures after COVID-19 and a group of conditionally healthy volunteers with no previous history of coronavirus infection. The main group consisted of 23 subjects who had long COVID symptoms for a prolonged period of time after the recovery from an acute coronavirus infection and were undergoing rehabilitation in a private healthcare facility. Three of them had had a severe COVID-19 infection; all the other patients experienced moderate symptoms of COVID-19. Patients in the main group were measured between 1 and 6 months after the recovery. The mean age of the main group was 58±9 years. The control group included 13 conventionally healthy volunteers of a matching age who were measured in 2019 before the pandemic spread, suggesting that the volunteers in the control group had never encountered COVID-19. Volunteers with any history of cardiovascular or other serious chronic diseases affecting the circulatory system were excluded from the study. The study was conducted with the subject in the supine position in a relaxed state and consisted of a 10-min measurement of microcirculation using a wearable LDF device (“LAZMA-PF”). The analysers were attached to the dorsal surface of the forearms at a point 2 cm above the styloid process and on the inside of the upper third of the shins (see Figure 2C,D) as these points proved to be the most informative from the previous stage of the study. Figure 3 shows a diagram of the experimental design of the study.
## 2.3. Data Analysis
In the present study, the analysed parameters were the value of the index of blood microcirculation—Im and amplitudes of blood flow oscillations in the different frequency bands corresponding to different mechanisms of microcirculatory blood flow regulation, measured in relative perfusion units (p.u.) [ 30]. The endothelial (Ae) band (0.005–0.021 Hz) reflects the vascular tone regulation due to the endothelium activity, both NO-dependent and independent; the neurogenic (An) band (0.021–0.052 Hz) represents the influence of neural innervation on blood flow; the myogenic (Am) band (0.052–0.145 Hz) corresponds to vascular smooth muscle activity; and respiratory (Ar) and cardiac (Ac) bands (0.145–0.6 Hz and 0.6–2 Hz, respectively) carry information about the influence of heart rate and movement of the thorax on the peripheral blood flow [31,32]. To calculate the amplitude–frequency spectra of the LDF signal, we used a mathematical apparatus of wavelet transform implemented in the software of wireless wearable analysers “LAZMA-PF”. This software performs a continuous wavelet transform using the complex-valued Morlet wavelet as the analysing wavelet.
In addition, the parameter of nutritive blood flow (Imn), estimated by a well-known algorithm [33], was calculated. The use of this parameter makes it possible to estimate the distribution of blood flow along capillary and shunt vessels.
The statistical analysis of the data was performed in Origin Pro 2021 software. Due to the limited sample size, a non-parametric Mann–Whitney U test was used to check the statistical significance of differences. Values of $p \leq 0.05$ were considered significant. The results are presented as the mean ± SD unless otherwise indicated.
## 3. Results
The first phase of the study demonstrated that COVID-19 results in changes in microcirculatory blood flow regulation mechanisms, which can be measured by assessing the spectral characteristics of the LDF signal. The results of the measurements are shown in Table 1.
No significant changes were observed in fingers and toes in this measurement. However, there was a general trend towards a decrease in microcirculation after the disease, and also in the magnitude of the nutritive blood flow in the upper extremities. Figure 4 shows box plots of the amplitude of blood flow oscillations for the stages before and after the disease, measured in wrists and shins.
A statistically significant decrease in the amplitude of myogenic oscillations was found in the arms after the disease. In the legs, a significant decrease in the amplitudes of respiratory and cardiac oscillations was observed. Similar changes can be traced in the upper extremities, but they do not reach statistically significant levels there. Figure 5 shows the dynamic changes in blood flow oscillations measured in wrists (a) and shins (b).
The figures show that COVID-19 causes high-amplitude changes in the magnitude of endothelial and neurogenic blood flow oscillations immediately after the recovery, which probably caused a high variability of these values at the “After” stage and failure to achieve a statistically significant difference in them when there is a trend for their increase after the disease. These changes are especially pronounced in the upper extremities. In the legs, there is a significant drop in the amplitude of the cardiac oscillations immediately after the disease and of the respiratory oscillations one week after the recovery, which also correlates with the results obtained in the upper extremities.
The results of the second stage of the experimental study were subsequently analysed. Table 2 presents the data obtained from the second stage of the study.
Both upper and lower extremities show significantly lower values of microcirculation and nutritive blood flow. Whisker boxes for these parameters are shown in Figure 6.
An increase in overall oscillatory blood flow activity was also noted in both upper and lower extremities, with statistically significant differences in the neurogenic, respiratory and cardiac ranges in wrists. Whisker boxes for the respiratory and cardiac oscillations measured in wrists are shown in Figure 7.
## 4. Discussion
In the present work, we obtained experimental data, which confirm the presence of microcirculatory bed dysfunction for a long period after the recovery from COVID-19. The first part of the study, which included daily measurements of one volunteer for 10 days before his disease and almost a month after the recovery, showed that after a month the parameters did not recover to their original values.
This stage of the studies revealed a decrease in the myogenic activity of microcirculation in the upper extremities. It is worth noting that the changes in the patterns of peripheral blood flow oscillations in the post-COVID phase have not yet been studied in detail. Myogenic oscillations play an important role in the process of oxygen delivery to biological tissues [34]. A decrease in myogenic oscillations leads to an increase in the dynamic resistance of microvessels and, as a consequence, to a decrease in the nutritive blood flow. Combined with the observed decrease in neurogenic regulatory activity, this change may indicate the activation of blood flow shunt pathways. In addition, some studies show that high temperature can inhibit vasomotion [35,36], so the decrease in myogenic activity revealed in our study may be a consequence of the high body temperature of the patient during the period of the disease.
The period immediately after the recovery from COVID-19 in this study was also characterized by decreased values of respiratory and cardiac microcirculatory oscillations in both upper and lower extremities (with significant differences in legs). In this case, dynamic observations show that cardiac fluctuations are reduced immediately after the disease, and respiratory fluctuations change during the week after the recovery.
Another interesting observation of this study was the increased amplitude of endothelial oscillations in the post-COVID phase and the dynamics of these changes. Numerous studies demonstrate endothelial dysfunction as one of the main pathogenic mechanisms of COVID-19 [37,38], which can persist for more than 12 months after the recovery. Studies also show that long COVID-19 symptoms, especially nonrespiratory symptoms, are due to persistent endothelial dysfunction [39]. In our work, we observed increased amplitudes of these fluctuations both in the early stages of recovery from the disease and in the later stages (in the second phase of the study), although these differences did not reach a statistically significant level.
In a group of patients undergoing rehabilitation after COVID-19, the most interesting observation in the amplitude–frequency spectrum of the LDF signal, in our opinion, was an increase in the amplitude of neurogenic oscillations. A decrease in neurogenic tone leads to the dilation of the arterioles [40,41] and, consequently, the amplitude of cardiac oscillations significantly increases (which we can observe in our study).
The lumen size of skin arterio-venous anastomoses (AVA) is regulated exclusively by neurogenic mechanisms, so we can assume that they also expand amidst the decrease of neurogenic tone. The dilation of AVA leads to arterio-venous shunting of the blood bypassing the capillary channel, which explains the significant decrease of Imn, a decrease of the number of functioning capillaries [13,14], reduction of perfusion (Im) and venular overflow due to arterial blood discharge that in its turn leads to the dilation of venules [40,41] and a significant increase of the amplitude of respiratory-driven blood flow oscillations amplitude.
## Study Limitations
The present study was conducted on a small group of patients, some of whom had comorbidities, so there is no certainty that the results will be true for the broader study population. The data obtained, however, should be taken into account for the development of new diagnostic criteria in assessing the degree of microcirculatory disturbances and rehabilitation processes in recently recovered patients. There is a need for additional studies with a larger group of patients, including patients with different courses of COVID-19 (mild, moderate, and severe disease).
Despite the already three-year history of coronavirus infection and the undoubted advantages of the LDF method for diagnosing microcirculatory disorders, there are almost no studies devoted to spectral analysis of LDF signal in COVID-19 pathology. In this pilot study, we demonstrated the possibilities of laser Doppler flowmetry coupled with the wavelet analysis of the obtained signals to detect microcirculatory disorders in patients who have undergone COVID-19 that makes it a promising tool for future research and assessment of the dynamical changes in microcirculation during the recovery process.
## 5. Conclusions
The present work demonstrates the use of laser Doppler flowmetry and peripheral blood flow oscillations analysis to diagnose vascular disorders in patients who have undergone COVID-19 in their early and advanced stages of recovery. Our work demonstrated a significant increase in the amplitude of neurogenic oscillations in the upper extremities of patients undergoing COVID-19, which, as we suggest, may be a factor preceding dilation of arterioles and venules and redirection of microcirculatory blood flow from the nutritive to the shunt pathways.
The obtained data show that optical noninvasive technologies have the potential for further application, but more research is needed to fully understand the changes in the mechanisms of blood flow regulation that occur after an infection.
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|
---
title: RG-I Domain Matters to the In Vitro Fermentation Characteristics of Pectic
Polysaccharides Recycled from Citrus Canning Processing Water
authors:
- Jiaxiong Wu
- Sihuan Shen
- Qiang Gao
- Chengxiao Yu
- Huan Cheng
- Haibo Pan
- Shiguo Chen
- Xingqian Ye
- Jianle Chen
journal: Foods
year: 2023
pmcid: PMC10000670
doi: 10.3390/foods12050943
license: CC BY 4.0
---
# RG-I Domain Matters to the In Vitro Fermentation Characteristics of Pectic Polysaccharides Recycled from Citrus Canning Processing Water
## Abstract
Canned citrus is a major citrus product that is popular around the world. However, the canning process discharges large amounts of high-chemical oxygen demand wastewater, which contains many functional polysaccharides. Herein, we recovered three different pectic polysaccharides from citrus canning processing water and evaluated their prebiotic potential as well as the relationship between the RG-I domain and fermentation characteristics using an in vitro human fecal batch fermentation model. Structural analysis showed a large difference among the three pectic polysaccharides in the proportion of the rhamnogalacturonan-I (RG-I) domain. Additionally, the fermentation results showed that the RG-I domain was significantly related to pectic polysaccharides’ fermentation characteristics, especially in terms of short-chain fatty acid generation and modulation of gut microbiota. The pectins with a high proportion of the RG-I domain performed better in acetate, propionate, and butyrate production. It was also found that Bacteroides, Phascolarctobacterium, and Bifidobacterium are the main bacteria participating in their degradation. Furthermore, the relative abundance of Eubacterium_eligens_group and Monoglobus was positively correlated with the proportion of the RG-I domain. This study emphasizes the beneficial effects of pectic polysaccharides recovered from citrus processing and the roles of the RG-I domain in their fermentation characteristics. This study also provides a strategy for food factories to realize green production and value addition.
## 1. Introduction
Citrus fruits, including orange (Citrus sinensis), tangerine or mandarin (Citrus reticulata), grapefruit (Citrus vitis), lemon (Citrus limonum), and lime (Citrus aurantifulia), are among the most extensively cultivated and consumed fruits around the globe [1,2,3]. Some citrus fruits are consumed directly by people, and the rest are processed into products in different forms. Citrus canning has a dominant position in the citrus processing industry, and the total export of canned citrus in *China is* 265,800 tons, accounting for over $50\%$ of the total global amount [4,5]. The most important step of citrus canning processing is eliminating segment membranes, and the traditional process is chemical hydrolysis with hydrochloric acid and sodium hydroxide. This process is more practical and economical for industrial manufacture. However, the chemical treatment method generates substantial amounts of processing water, which is rich in pectic polysaccharides and polyphenols [5,6]. The waste-processing effluents are disposed of via different methods, such as by releasing them into rivers directly and discharging them into the city sewage system, thereby leading to many environmental problems [7]. Therefore, processing industries need to efficiently extract or recycle these value-added compounds or phytochemicals.
Pectin, an acidic heteropolysaccharide, is abundant in the cell wall of higher plants and is extensively used in the food industry as a gelatinizer, stabilizer, emulsifier, and fat substitute [8,9,10]. Structurally, pectin comprises the homogalacturonan (HG), rhamnogalacturonan-I (RG-I), and rhamnogalacturonan-II (RG-II) domains [11]. As the “smooth region” of pectin, HG is a linear chain comprising α-1,4-D-galacturonic acid (GalA) with different methoxycarbonyl groups. RG-I is the “hairy region” of pectin and is formed by the backbone of repeating disaccharides of GalA and α-1,2-L-rhamnose (Rha) and the side chains of neutral sugars (such as α-L-arabinose and galactose). RG-II, which is also the “hairy region” of pectin, comprises the GalA chain and various sugars such as xylose, fucose, and apiose.
Pectin is gradually being considered as a prebiotic candidate because of its indigestibility and microbiota accessibility [12,13]. Pectin has been confirmed to enable improvements in inflammatory bowel disease, obesity, and cancer, probably due to its fermented metabolites [8,14]. Moreover, several studies have pointed out that slowly fermentable and highly polymerized carbohydrates may be more important than quickly fermentable carbohydrates. The reason may be that the lack of fermentable carbohydrates in the distal colon caused by carbohydrates’ fast fermentation will induce gut microbiota to degrade protein and produce harmful metabolites, but slowly fermentable carbohydrates can reach the distal colon and be fermented there [15,16,17]. Many studies have also reported the biological activities of RG-I-type pectic polysaccharides, which may be attributed to the function of neutral sugar chains. These studies have found that arabino-oligosaccharides and galactosan can regulate gut microbiota and be considered as potential prebiotics [18,19]. Therefore, studying the fermentation characteristic of pectins (especially RG-I-type pectic polysaccharides) is important and meaningful.
The relationship between the structure and function of pectin is attracting research attention, and many studies have focused on pectin obtained using different extraction methods (e.g., ultra high pressure technology and microwave irradiation-associated extraction technology) or different sources (such as citrus, Ganoderma atrum, and seaweed) to explore the influence of its structure on fermentation characteristics [20,21,22,23]. Our previous study found that the structure of pectic polysaccharides from three citrus canning processes was different, which might lead to differences in functions [4]. However, most studies did not consider the diversity of factors and the individual effects of different pectins’ molecular weight (Mw), degree of esterification (DE), or monosaccharide composition on their fermentation characteristics, which still remain unclear. Herein, we extracted three different RG-I-type pectic polysaccharides from citrus canning wastewater. Structurally, the Mw and DE of these three pectins were similar, and the most significant difference among them was found in the proportion of the RG-I domain. Then, we evaluated their fermentation characteristics using an in vitro human fecal batch fermentation model and found that the RG-I domain was crucial for pectin in short-chain fatty acid (SCFA) generation and modulation of gut microbiota. This study aims to explore the beneficial effects of reclaimed RG-I pectic polysaccharides and further clarify the structure–activity relationship.
## 2.1. Materials and Chemicals
Satsuma mandarin (Citrus unshiu Marc. Owari satsuma) and sweet orange (*Citrus sinensis* (L.) Osbeck) were obtained from the Citrus Research Institute of Zhejiang Province, China. Fucose (Fuc), rhamnose (Rha), arabinose (Ara), galactose (Gal), glucose (Glc), mannose (Man), glucuronic acid (GlcA), galacturonic acid (GalA), xylose (Xyl), SCFA standards, 1-phenyl-3-methyl-5-pyrazolone (PMP), and fructooligosaccharide (FOS) were all purchased from Sigma-Aldrich (Shanghai, China). All other chemicals used in this study were analytically pure or chromatographically pure.
## 2.2. Extraction of RG-I Pectic Polysaccharides from Citrus Segment Membranes
From our previous study published by Shen et al. [ 4], three different pectic polysaccharides rich in the RG-I domain from segment membranes of satsuma mandarin and sweet orange were extracted using a novel citrus canning processing technology (recycling wastewater for electrolyte beverage production). The monosaccharide composition of the pectic polysaccharides is presented in Table 1. The content of the RG-I domain was calculated using the formula RG-I (%) ≈ 2Rha(mol%) + Ara(mol%) + Gal(mol%). On the basis of the proportion of the RG-I domain, these pectic polysaccharides were named RG-46, RG-56, and RG-67.
RG-46 was extracted from satsuma mandarin segment membranes using the acid–alkali sequential extraction method. In detail, mandarin segment membranes were first treated with $0.4\%$ citric acid and $0.1\%$ HCl for 40 min at 30 °C. After filtration, the membranes were treated with $0.2\%$ NaOH and $0.1\%$ KOH for 10 min. Then, the alkaline extracting solution was precipitated overnight with $95\%$ ethanol at a volume ratio of 1:1. The sediments were redissolved in ultrapure water and then dialyzed with 500-mesh dialysis bags for 48 h. After vacuum freeze drying, RG-46 was obtained. RG-67 was extracted from sweet orange segment membranes under similar conditions (the only difference was that the sweet orange segment membranes were treated with $0.4\%$ citric acid and $0.1\%$ HCl for 50 min rather than 40 min). RG-56 was extracted by repeating the extraction method of RG-67 three times according to the circulating water system used in factory production.
## 2.3. Monosaccharide Composition Analysis
According to the method proposed by Yan and colleagues [6], monosaccharide composition analysis was carried out. About 1 mL of pectic polysaccharide solution (2–3 mg/mL) or fermented supernatant was mixed with 4 mol/L trifluoroacetic acid (TFA, 1 mL) into an ampoule and then sealed using an alcohol blast burner. The system was hydrolyzed at 110 °C for 8 h. After cooling, samples were dried with methanol and blown with nitrogen to remove redundant TFA. Then, 1mL of distilled water was added to redissolve monosaccharides and mixed with 1 mL of PMP solution (0.5 mol/L) at 70 °C for 30 min. After derivation, the system was leached with chloroform three times to remove excrescent PMP. The supernatant was filtered using 0.22 μm membranes and analyzed via HPLC (Waters, Milford, MA, USA). The HPLC system was equipped with a Zorbax Eclipse XDB-C18 column for monosaccharide analysis. Solvent A was a 0.05 mol/L KH2PO4 buffer (adjusted pH of 6.80) with $15\%$ (V:V) acetonitrile, and solvent B was the same buffer with $40\%$ (V:V) acetonitrile. The flow rate was 1 mL/min, and the column temperature was 25 ± 5 °C. The elution program was 0–$15\%$ B (0–10 min), 15–$25\%$ B (10–30 min), and 25–$0\%$ B (30–45 min). All detections were carried out at 250 nm.
## 2.4. Molecular Weight Analysis
High-performance size-exclusion chromatography (HPSEC) was used to measure the molecular weight. Pectic polysaccharide samples or supernatants of fermented medium were filtered using 0.22 μm membranes and then detected with a multi-angle laser light scattering and refractive index detector (MALLS-RI) at 40 °C. A Shodex OHpak SB-G column, SB-804 HQ (10 μm, 8.0 × 300 mm, exclusion limit 1 × 106) column, and SB-806 HQ (13 μm, 8.0 × 300 mm, exclusion limit 2 × 107) column were used together for analysis. All samples were eluted with 0.15 mol/L NaCl solution containing $0.02\%$ proclin at the flow rate of 0.5 mL/min for 60 min. The dn/dc value used here was 0.138. All data analyses were conducted using ASTRA software (Version7.1.8, Wyatt Technologies Co., Santa Barbara, CA, USA).
## 2.5. Degree of Esterification Analysis
The degrees of methyl esterification (DM) and acetylation (DA) were determined together via HPLC (Waters, Milford, MA, USA) [24]. In brief, 5 mg of pectic polysaccharide was mixed with 0.5 mL of an aqueous solution containing 10 mmol/L CuSO4 and 10 mmol/L isopropanol. After mixing thoroughly, 0.5 mL of NaOH (1 mol/L) was added to the system. Then, the system was incubated at 4 °C for 30 min to initiate saponification and then centrifuged at 8000× g/min for 10 min. The supernatant was obtained, and the pH was adjusted to 3. After filtering with 0.22 μm filter membranes, the samples were used for analysis. The internal standard method was used for determination, and thus the response factors of methanol and acetic acid needed to be measured first. A chromatographic mixture of methanol, acetic acid, and isopropanol with a mass ratio of 3:1:1 was prepared and injected into the HPLC system. The HPLC system was equipped with a C18 column (SinoChrom ODS-BP, 5 μm, 250 × 4.6 mm) and refractive index detector (RID). The mobile phase used was 4 mmol H2SO4, and the flow rate was 0.8 mL/min. The column temperature was 30 °C, and elution time was 30 min. The calculation formulas were as follows: FR = (MMeOH or MHAc × AIPA)/(MIPA × AMeOH or AHAc) DM = (FR × AMeOH × MIPA × 1,760,000)/(MSample × AIPA × GalA% × 32) DA = (FR × AHAC × MIPA × 1,760,000)/(MSample × AIPA × GalA% × 60) where FR is the response factor, MeOH represents methanol, HAc represents acetic acid, IPA represents isopropanol, Sample represents different pectic polysaccharides, A represents the peak area, M represents mass, and GalA% represents the mass fraction of galacturonic acid in the samples.
## 2.6. In Vitro Human Fecal Microbiota Fermentation
The fecal microbiota fermentation experiment was carried out according to the method described by Yu et al., with some amendments [25]. An amount of 35 mg of FOS (as a positive control group) and RG-46, RG-56, and RG-67 were added to 6.3 mL of carbon-free medium (10.0 g/L casein peptone, 2.5 g/L yeast extract, 1.5 g/L NaHCO3, 1.0 g/L cysteine-HCl, 0.9 g/L NaCl, 0.45 g/L KH2PO4, 0.45 g/L K2PO4, 0.09 g/L MgSO4·7H2O, 0.09 g/L CaCl2, 10.0 mg/L hemin, 10.0 mg/L vitamin B6, 5.0 mg/L vitamin B2, 5.0 mg/L p-aminobenzoic acid, 2.0 mg/L vitamin B7, 2.0 mg/L folic acid, 0.8 mg/L resazurin solution, and 0.1 mg/L vitamin B12) and then mixed thoroughly. Fresh human excrement was collected from six healthy donors (3 men and 3 women, 18 < BMI < 24) who did not have any gastrointestinal diseases and had not taken antibiotics in the last three months. The excrement was dissolved in PBS ($10\%$, w/v) and then filtered with four layers of gauze after homogenization. An amount of 0.7 mL of the fecal slurry was immediately added to 6.3 mL of medium to make sure that the final concentration of polysaccharides was 5 mg/mL. All samples were incubated at 37 °C in an anaerobic chamber and sampled at 0 h, 4 h, 8 h, 12 h, and 24 h during the fermentation. After centrifugation (10,000× g, 5 min, 4 °C) and pH measurement, the supernatant and pellet were kept at −80 °C for further analysis. All samples were analyzed in triplicate in the anaerobic chamber.
## 2.7. Measurement of pH, SCFAs, and Total Sugar
The fermentation supernatant of different fermentation periods was taken to determine the pH degree. The supernatant was diluted fourfold and then filtered using hyperfiltration membranes for the determination of SCFAs. The short-chain fatty acid concentrations of all groups were analyzed using an Agilent 6890 N GC-FID, which was equipped with an HP-INNOWAX column. Nitrogen was the carrier gas, and the flow rate was 20 mL/min. The inlet temperature and detector temperature were both 240 °C. The initial oven temperature was 100 °C, which was then heated to 180 °C at a rate of 4 °C/min. The injection volume of each sample was 1 μL, and the running time was 20.5 min. The SCFA concentrations were calculated according to the standard curve formed by the concentration peak area.
The total sugar content was determined using the phenol-sulfuric acid method. In brief, 1 mL of $6\%$ phenol solution and 5 mL of concentrated sulfuric acid were added to 1 mL of fermented supernatant. The system was mixed evenly and heated in a boiling water bath for 10 min, and then the absorbance value was measured at 490 nm.
## 2.8. Gut Microbiota Analysis
The gut microbiota composition was analyzed in samples collected from the 24 h fermentation. The samples were centrifuged at 10,000× g/min for 5 min to obtain the fecal pellets. Total bacterial DNA was extracted using a TIANamp Stool DNA Kit. All extracted DNA samples were sent to the analysis company for quality identification and gut microbiota analysis. The V4 region of 16S rDNA was selected for amplification, and the Illumina Miseq platform was used for high-throughput sequencing and bioinformatics analysis. All analyses were based on sequencing reads and operational classification units (OTUs).
## 2.9. Statistical Analysis
All experiments were repeated three times in parallel. The data are expressed as the mean ± standard deviation. Statistical analysis was accomplished using SPSS Statistics 26 software (IBM Corp, Armonk, NY, USA), and the significance of the differences was evaluated using one-way ANOVA ($p \leq 0.05$). Origin 8.0 software (OriginLab Corp., Northampton, MA, USA) and GraphPad Prism 8 software (Graphpad Corp., Boston, MA, USA) were used for mapping.
## 3.1. Analysis of Monosaccharide Composition, Molecular Weight, and Degree of Esterification
The monosaccharide composition of the three pectic polysaccharides with different proportions of the RG-I domain (RG-46, 56, and 67) is shown in Table 1. Large differences existed in the monosaccharide composition among RG-46, RG-56, and RG-67. GalA, Ara, and Gal were the main monosaccharides, accounting for over $30\%$, $20\%$, and $10\%$ of the total monosaccharides, respectively. Although the Rha amount was significantly lower than that of the above monosaccharides, it was indispensable to the pectic polysaccharides’ primary structure. The chain backbone of the RG-I domain was composed of repeating GalA and Rha [26]. Apparently, a higher content of Rha meant a longer chain in the RG-I domain. The side chains were composed of neutral sugars (Gal and Ara) intertwined with each other and absorbed into the backbone, forming the RG-I domain [27]. Clearly, the content of Ara in RG-67 was significantly higher than that in RG-46 and RG-56. According to the formulas, the molar percentages of the HG and RG-I domains were calculated. The proportion of the HG domain in RG-46 was significantly higher than that in the other samples, and the proportion of the RG-I domain in RG-67 was the highest.
The molecular weight of the three pectic polysaccharides is listed in Table 1. The molecular weight of three RG-I-type polysaccharides was very close, and the order was RG-67, RG-56, and RG-46 from high to low. This finding may be related to the primary structure and multistage structure of polysaccharides; for example, the manifold and long neutral sugar chains in RG-67 resulted in its relatively large Mw. It was found that the Mw and Mn were positively correlated with the proportion of the RG-I domain. Although the Mw of the three RG-I polysaccharides were close, the difference in the Mn among them was significant, leading to varying polydispersity (Mw/Mn). A larger polydispersity corresponds to a wider sample distribution [28]. Therefore, RG-46 was evenly distributed, while RG-56 and RG-67 were more widely distributed.
The degrees of methyl esterification (DM) and acetylation (DA) were also measured. It was obvious that the DM of these three pectic polysaccharides was small, accounting for only about $10\%$. The DM was related to the proportion of the RG-I domain, and RG-67 had the highest degree. Meanwhile, the value of DA was extremely low and could not even be detected in RG-56 and RG-67. This indicates that a low esterification degree and low acetylation degree are distinguishing features of pectic polysaccharides extracted via the alkaline process, on account of the saponification reaction [29].
## 3.2. pH Change and SCFA Generation during Fermentation
The initial pH of each group was within 6.8 to 7.2 (as shown in Figure 1a). The pH of FOS and all pectic polysaccharides decreased during fermentation, especially within 0 to 4 h. The results suggested that the pH of FOS gradually decreased during the whole in vitro fermentation. However, the pH changes of RG-67, RG56, and RG-46 did not reflect those of FOS; they tended to be constant or slightly increase within 12 to 24 h. The final pH decline degree of RG-67 was relatively greater than that of the other samples.
During fermentation, the substrates were degraded by gut microbiota and transformed into a series of complex metabolites. SCFAs, as the main metabolites in carbohydrate metabolism, benefit human gut health by maintaining intestinal homeostasis, improving metabolic syndrome, and exerting anti-inflammation and antitumor activities [30,31,32]. The concentrations of SCFA accumulation during fermentation are shown in Figure 1b–e. Obviously, all substrates could significantly promote the production of SCFAs, and the concentration increased with fermentation. The total SCFA concentration in all samples at 24 h was significantly higher than that in the blank group. Among the four samples, the order of the total SCFA concentrations at 24 h was RG-67, RG-56, RG-46, and FOS from high to low. These results suggested that RG-I-type pectic polysaccharides could produce more SCFAs than the traditional prebiotic FOS, and that the proportion of the RG-I domain is positively correlated with SCFA production.
Acetate was the dominant SCFA (as shown in Figure 1c), followed by propionate and butyrate (as shown in Figure 1d,e), whereas the contents of isobutyrate, valerate, and isovalerate were very low [33]. Compared to FOS and the other two RG-I pectins, RG-67 performed the best in SCFA production, regardless of whether it was acetate, propionate, or butyrate. The growth trends of acetate and butyrate among the groups were similar to the results of the total SCFAs, revealing that a high RG-I domain proportion could lead to more acetate and butyrate production. However, this correlation between the RG-I domain and SCFAs was not shown in propionate production. These results might be related to the different bacteria involved in the degradation of different RG-I pectic polysaccharides.
## 3.3. Degradation of Pectic Polysaccharides during Fermentation
The results of the HPSEC in RG-46, RG-56, and RG-67 showed a backward retention time and decreased corresponding peak area with fermentation (Figure 2a–c). This indicates that the molecular weight and molar mass of the pectic polysaccharides gradually decreased during fermentation. During the whole fermentation, 0–4 h was the main period for the degradation of pectic polysaccharides, and nearly half of the substrates were utilized by microbiota. A low Mw fraction at 40–43 min also indicated remarkable degradation. Notably, RG-67 showed a slower degradation speed, although it was almost exhausted before the end of fermentation. Considering its SCFA accumulation, this could be explained by the slow fermentability of RG-67, which was associated with solubility.
According to the analysis of the monosaccharide composition at different time points, the relative percentage of monosaccharides remaining from 4 h to 24 h was analyzed and compared with the amounts at 0 h. Microbiota manifested a similar preference to monosaccharides among RG-46, RG-56, and RG-67. Specifically, arabinose, xylose, galactose, and mannose in the pectic polysaccharides were more easily utilized, but galacturonic acid, rhamnose, fucose, glucose, and glucuronic acid were relatively more difficult to degrade. We noted that gut microbiota possessed distinct sugar preferences, and neutral sugars were more easily selected than acidic sugars, which is consistent with previous findings [34,35]. The total carbohydrate contents are also shown in the diagram, indicating that the sugar utilization rates differed among groups. It was noted that pectins with a high RG-I domain proportion showed a relatively slow degradation speed. These results were in accordance with the Mw degradation and utilization of monosaccharides, meaning that a high proportion of the RG-I domain prolonged the fermentation and metabolized into more SCFAs by microbiota after fermentation. The concentration and sites of SCFA generation were found to be vital for human gut health, and the lack of carbon sources could promote bacteria to degrade protein and nitrogen metabolism and generate numerous noxious gases to harm gut health [15]. Based on the above results, RG-I pectic polysaccharides (especially RG-67) might benefit humans by prolonging fermentation and promoting the level of fermentable carbohydrates in the distal colon.
## 3.4. Diversity and Composition Analysis of Gut Microbiota
In response to the effect of gut microbiota, all four substrates were degraded to varying degrees and transformed into a series of metabolites. However, these substrates and metabolites modulated the composition of the gut microbiota in turn. The alpha diversity of gut microbiota among the five groups is shown in Table 2. Sobs, Chao, and Ace are the indices of community richness, reflecting the richness of the microbiota composition in the groups. Clearly, RG-46, RG-56, and RG-67 were better than FOS in facilitating microbiota richness. Shannon, Simpson, and Heip are the indices of community diversity. A higher value of the Shannon and Heip indices or a lower value of the Simpson index indicates the samples’ superiority in microbiota diversity. In summary, RG-46, RG-56, and RG-67 all had better performances than FOS in increasing bacteria diversity.
The composition analysis of gut microbiota among the blank, FOS, RG-46, RG-56, and RG-67 groups is shown in Figure 3. The Venn diagram (Figure 3a) among different groups was used to count the number of common and unique OTUs in multiple samples, showing the similarities and overlaps of OTUs more intuitively. A total of 258 OTUs were shared by the five groups, and each group had its own unique OTUs. Principal component analysis at the genus level (Figure 3b) reflected a large difference in the microbiota composition of the blank, FOS, and pectic polysaccharide groups. However, RG-46, RG-56, and RG-67 performed similarly in the PCA analysis, which may be due to a minor disparity in their structures.
The relative proportions of community abundance at the phylum, genus, and species levels are shown in Figure 3c–e. Bacteroidota, Firmicutes, Proteobacteria, and Actinobacteria were the main phyla in microbiota, accounting for over $98\%$ [36]. After 24 h of fermentation, the abundances of Bacteroidota and Actinobacteriota increased in the FOS and RG-I pectic polysaccharide groups. As a strong competitor in the gut ecosystem, *Bacteroidota is* involved in many important metabolic activities in the human colon, including carbohydrate fermentation, nitrogen utilization, and bioconversion of bile acids and other steroids [37,38]. Apparently, RG-I pectic polysaccharides are comparable to traditional prebiotics such as FOS in promoting Bacteroidota growth. However, the four samples inhibited the development of Proteobacteria, including the pathogenic bacteria Salmonella, Escherichia coli, and Shigella.
According to the result of community abundance at the genus level (Figure 3d), the composition of gut microbiota changed significantly compared with the blank group. FOS and the RG-I pectic polysaccharides markedly enhanced the relative abundances of Prevotella, Bifidobacterium, and Lactobacillus and substantially decreased the relative abundances of Escherichia-Shigella, Klebsiella, and Megamonas. As traditional probiotics, Bifidobacterium and Lactobacillus proliferated in the three RG-I pectic polysaccharides, and the high-RG-I domain pectic polysaccharides performed better. The dominant bacteria in RG-46, RG-56, and RG-67 were Bacteroides, Prevotella, Phascolarctobacterium, and Bifidobacterium, showing a similar microbiota composition to that of the FOS group. All RG-I pectic polysaccharides showed stronger effects in promoting Bacteroides and Phascolarctobacterium, indicating that these two bacteria may play a crucial role in the degradation and utilization of pectic polysaccharides. Meanwhile, they led to a reduction in the abundances of Escherichia-Shigella and Klebsiella, indicating that RG-I pectic polysaccharides possessed conspicuous abilities in hindering the growth and breeding of conditional pathogens.
The community abundance at the species level (Figure 3e) could provide more detailed information about the composition of gut microbiota. A huge discrepancy existed among the blank, FOS, and RG-I pectic polysaccharide groups, although the microbial community structures of RG-46, RG-56, and RG-67 were homogeneous. All RG-I pectic polysaccharides could significantly facilitate the growth of specific bacteria, such as Bacteroides_vulgatus, metagenome_g_Prevotella, Phascolarctobacterium_faecium, Bifidobacterium_pseudocatenulatum, and Bacteroides_thetaiotaomicron. These bacteria were the main microflora of the degraded pectic polysaccharides, and the high content of the RG-I domain affected their growth and propagation. The most obvious evidence for this is that the abundance of Bacteroides_vulgatus and Bifidobacterium_pseudocatenulatum in RG-67 and RG-56 was evidently higher than that in RG-46.
## 3.5. Group Variation Analysis of Gut Microbiota
Figure 4a–d show the results of the one-way ANOVA with Welch’s post hoc test based on the abundance of the top 20 genera among the five groups. Figure 4a shows the five genera with the highest abundance in the gut microenvironment, indicating that RG-I-type pectic polysaccharides had different abilities in modulating gut microbiota.
The traditional prebiotic FOS showed outstanding capacities in promoting Bifidobacterium and Prevotella. As a traditional probiotic, Bifidobacterium plays an important role in the biological barrier, antitumor effect, immune function, and gut health [39]. Prevotella is a key bacterium in host–microbiome interactions, particularly in relation to nutrition and complex carbohydrate metabolism [40].
RG-46, RG-56, and RG-67 also showed the ability to stimulate these bacteria’s growth. Interestingly, Bacteroides and Phascolarctobacterium showed distinct preferences for RG-I-type pectic polysaccharides. In addition to the above bacteria, all RG-I pectic polysaccharides could selectively promote Blautia, Sutterella, Dorea, Collinsella, Monoglobus, and Eubacterium_eligens_group. Most of the dominant bacteria in RG-I polysaccharides are involved in the degradation of carbohydrates and the generation of SCFAs, benefiting gut health through multiple mechanisms. For example, Blautia has potential probiotic properties, such as preventing inflammation and promoting the production of SCFAs to maintain intestinal homeostasis [41].
Sutterella, Dorea, and Monoglobus are related to the fermentation of pectin, consistent with literature reports [42]. Among them, *Monoglobus pectinilyticus* was identified as a pectin-degrading specialist bacterium that participates in degrading various pectins, RG-I, and galactans to produce degradation products that are presumably shared with other bacteria [43]. Another bacterium with specificity for RG-I pectic polysaccharides is Eubacterium_eligens_group, which was significantly abundant in RG-46, RG-56, and RG-67 but hardly existed in the FOS and blank groups. We came to the same conclusion as other studies that the growth of Eubacterium_eligens_group and Blautia occurs only with pectic polysaccharides [44]. Notably, Eubacterium_eligens_group is related to butyrate production, but the effects and specific mechanisms in maintaining human health and pectin degradation remain unclear [45,46].
Linear discriminant analysis effect size (LEfSe) multilevel discriminant analysis (Figure 4e) is a useful tool for discovering biomarkers among different groups. Each group had its specific biomarkers, and linear discriminant analysis (LDA) scores of those biomarkers were calculated. Only biomarkers with LDA scores greater than 3.5 are shown in Figure 4. Phascolarctobacterium, Sutterella, and Lachnospira were the main biomarkers in RG-46 at the genus level. RG-56 and RG-67 also possessed their typical genera as biomarkers. In RG-56, they were Bacteroides, Collinsella (butyrate producer), and Eubacterium_eligens_group [47]. However, in RG-67, the biomarkers were TM7x, Monoglobus, Granulicatella, and Lachnospiraceae_NK4A136_group. This finding indicates that the proportion of the RG-I domain and high neutral sugar contents affected the modulation of intestinal microbial communities, as shown by the changes in the dominant bacteria and biomarkers.
## 3.6. Function Prediction and Pearson Correlation Analysis
PICRUSt was used to standardize the OTU abundance table, and then the corresponding green gene ID of each OTU was used to annotate its clusters of orthologous groups (COG) functions. This method can offer annotation information on the function levels and their abundance in different groups. FOS served as the control group, and the functional abundances of RG-46, RG-56, and RG-67 are compared in Figure 5a. The abundance of the COG function in RG-67 was higher than in FOS and the other RG-I pectic polysaccharides, especially in carbohydrate transport and metabolism, cell wall/membrane biogenesis, and energy production and conversion. Combined with its abilities in SCFA production and microbiota modulation, RG-67 may have the best performance in promoting energy metabolism and maintaining intestinal health.
The correlation analysis (Figure 5b) of the intestinal microflora composition, monosaccharides, and SCFAs showed that monosaccharides played a crucial role in regulating bacteria and generating SCFAs. For the high-RG-I domain pectic polysaccharides, Ara, GalA, Gal, and Rha were the core components, conferring polysaccharides special functions and promoting the growth of probiotics. GalA, as the monosaccharide with the highest proportion among the polysaccharides, maintained a positive relationship with Bifidobacterium ($p \leq 0.05$) and a negative relationship with Megamonas ($p \leq 0.01$). The presence of Ara was highly positively correlated with the development of Bacteroides, Bifidobacterium, and Monoglobus, and was also connected with the accumulation of acetic acid, propionic acid, and butyric acid. Another neutral sugar, Gal, also had unique effects in affecting gut microbiota, such as restraining the reproduction of Escherichia-Shigella ($p \leq 0.01$), Subdoligranulum, Megamonas, Lachnoclostridium, Klebsiella, and Lachnospira. In the results based on RG-46, RG-56, and RG-67, it was found that Rha, Ara, GalA, and Gal had better capacities than the other monosaccharides in this aspect. Ara and Rha were fundamental to the RG-I domain, and their contents determined the polysaccharides’ functional characteristics and the regulation of gut microbiota to some extent. The above might explain why RG-67 performed better in SCFA generation and modulation of gut microbiota. Furthermore, Bacteroides, Phascolarctobacterium, Prevotella, Bifidobacterium, and Monoglobus were positively correlated with the formation of SCFAs.
## 4. Conclusions
Three different pectic polysaccharides (RG-46, RG-56, and RG-67) with high RG-I domain proportions recovered from citrus canning processing wastewater were used in this study, and the relationship between the RG-I domain and in vitro fermentation characteristics was investigated. Structurally, the main difference among RG-46, RG-56, and RG-67 was the proportion of the RG-I domain. The fermentation results showed that the RG-I domain was significantly related to pectic polysaccharides’ fermentation characteristics, especially in SCFA generation and modulation of gut microbiota. RG-67 performed better than RG-56 and RG-46 in producing acetate, propionate, and butyrate. The main bacteria that participated in degrading the RG-I pectic polysaccharides were Bacteroides, Phascolarctobacterium, Bifidobacterium, and Blautia. Furthermore, Eubacterium_eligens_group and Monoglobus showed particular preferences for RG-I-type pectic polysaccharides, and their abundances were closely related to the proportion of the RG-I domain. Our study had limitations. For example, the structure–function relationship between the RG-I domain of pectic polysaccharides and its effect on health needs to be further determined in vivo. Nevertheless, the present study highlighted the prebiotic potential of RG-I pectic polysaccharides and revealed their salutary influence on human intestinal ecology in vitro. Our finding provide more information about the structure–function relationship and offer feasible ideas for citrus or other food processing enterprises to recover functional ingredients from processing byproducts, which is helpful for the realization of green production and value addition.
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|
---
title: Nucleolin Regulates Pulmonary Artery Smooth Muscle Cell Proliferation under
Hypoxia by Modulating miRNA Expression
authors:
- Jihui Lee
- Hara Kang
journal: Cells
year: 2023
pmcid: PMC10000680
doi: 10.3390/cells12050817
license: CC BY 4.0
---
# Nucleolin Regulates Pulmonary Artery Smooth Muscle Cell Proliferation under Hypoxia by Modulating miRNA Expression
## Abstract
Hypoxia induces the abnormal proliferation of vascular smooth muscle cells (VSMCs), resulting in the pathogenesis of various vascular diseases. RNA-binding proteins (RBPs) are involved in a wide range of biological processes, including cell proliferation and responses to hypoxia. In this study, we observed that the RBP nucleolin (NCL) was downregulated by histone deacetylation in response to hypoxia. We evaluated its regulatory effects on miRNA expression under hypoxic conditions in pulmonary artery smooth muscle cells (PASMCs). miRNAs associated with NCL were assessed using RNA immunoprecipitation in PASMCs and small RNA sequencing. The expression of a set of miRNAs was increased by NCL but reduced by hypoxia-induced downregulation of NCL. The downregulation of miR-24-3p and miR-409-3p promoted PASMC proliferation under hypoxic conditions. These results clearly demonstrate the significance of NCL–miRNA interactions in the regulation of hypoxia-induced PASMC proliferation and provide insight into the therapeutic value of RBPs for vascular diseases.
## 1. Introduction
Hypoxia induces changes in gene expression which can trigger adaptive processes, such as cell proliferation or motility [1]. The alterations in numerous RNA-binding proteins (RBPs) and microRNAs (miRNAs) under hypoxic conditions have been investigated to better understand the mechanisms underlying adaptive cellular processes [2,3,4,5]. Hypoxia-regulated RBPs or miRNAs bind to specific target mRNAs for the selective regulation of expression in response to hypoxia [6,7].
Hypoxia induces structural changes in the medial compartment of the pulmonary arterial wall, including pulmonary artery smooth muscle cell (PASMC) proliferation, hypertrophy, matrix protein production, and recruitment of adventitial or circulating cells. These changes contribute to pulmonary vascular remodeling and hypertension [8,9]. Several miRNAs have been shown to modulate gene expression and PASMC function during the pathogenesis of vascular disorders under hypoxia [10,11]. However, the functions of RBPs in vascular cells under hypoxic conditions are not fully understood. Recent evidence suggests that RBPs play a role in vascular smooth muscle cells (VSMCs). For example, Hu Antigen R (HuR) contributes to the proliferation of human aortic smooth muscle cells in response to platelet-derived growth factor (PDGF) signaling [12]. In addition, the downregulation of heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1) protects against atherosclerosis by suppressing VSMC proliferation [13]. These findings suggest that RBPs contribute to the regulation of PASMC function under hypoxic conditions. Elucidating the molecular mechanism by which RBPs mediate PASMC functions under hypoxia is expected to provide a basis for the development of therapeutic strategies for pulmonary vascular diseases.
Nucleolin (NCL) is an RBP implicated in the response to hypoxia. Under hypoxia, NCL regulates the expression of matrix-metalloproteinase-9 (MMP-9) and collagen prolyl 4-hydroxylase-alpha(I) (C-P4H-alpha(I)), which are involved in ECM remodeling in human fibrosarcoma cells [14,15]. In addition to the hypoxic response, NCL is involved in a variety of biological processes, such as DNA transcription, ribosomal biogenesis, and the regulation of RNA stability [16,17,18,19,20,21]. Moreover, NCL regulates the expression of several miRNAs, including miR-15a/16, miR-21, miR-221, and miR-103. The abrogation of NCL expression affects the biogenesis of specific miRNAs, including miR-21, miR-221, and miR-103; however, the specific underlying mechanisms are unknown [22]. NCL is involved in the primary miRNA processing of miR-15a/16 through direct interactions with the microprocessor complex, DGCR8, and Drosha [23].
Interestingly, functional interactions between RBPs and miRNAs have been reported in various cancer cells [24]. Some miRNAs regulate RBP expression, and, conversely, some RBPs can modulate miRNA expression in cancer [25,26]. However, little is known about the functional relationships between RBPs and miRNAs in vascular diseases. We have hypothesized that NCL regulates the expression of hypoxia-responsive miRNAs in PASMCs and is associated with hypoxic vascular disorders. In this study, we observed that NCL levels in PASMCs are altered in response to hypoxia. To investigate the role of NCL in PASMCs in response to hypoxia, we examined its interactions with miRNAs and the functional relevance of NCL–miRNA interactions in the responses of PASMCs to hypoxia, such as their proliferation.
## 2.1. Hypoxia Downregulates NCL Expression by Histone Deacetylation in PASMCs
To identify RBPs involved in the regulation of the PASMC phenotypes under hypoxia, we searched for RBPs with mRNA expression changes in the PASMCs in response to hypoxia in the next-generation RNA sequencing results from our previous studies [27]. Among the thirteen RBPs with established roles in the hypoxia-induced responses of various cells (CIRBP, CPEB1, CPEB2, HNRNPA2B1, HNRNPL, IREB2, NCL, PTBP1, PTBP3, RBM3, TIA1, ZFP36, and ZFP36L1), the NCL mRNA levels showed the greatest difference between the hypoxia-exposed and control PASMCs (Figure 1A) [14,15,28,29,30,31,32,33,34,35,36,37,38,39,40]. These results were confirmed using qRT-PCR analysis of the transcript levels in hypoxia-exposed PASMCs after 24 h (Figure 1B). Hypoxia significantly reduced the NCL mRNA levels to $49\%$ of those in the control, which is consistent with the RNA sequencing data. None of the other 12 genes investigated showed significant changes in PASMCs under hypoxic conditions. A reduction in NCL protein levels following hypoxia was validated by immunoblotting (Figure 1C). The level of hypoxia-inducible factor 1-alpha (HIF1α) protein was examined by immunoblotting to confirm the hypoxic conditions in the PASMCs (Figure 1D). As expected, significant induction of HIF1α upon hypoxia was observed.
To investigate whether the hypoxia-induced decrease in NCL is specific to PASMCs, a variety of cells, including pulmonary arterial endothelial cells (PAEC), HEK293, and HeLa, were exposed to hypoxia for 24 h, and the NCL mRNA and protein levels were examined using qRT-PCR and immunoblotting. Neither the mRNA nor protein levels of the NCL were changed by hypoxia (Figure 1E,F). The induction of HIF1α with hypoxia exposure was confirmed in the PAEC, HEK293, and HeLa cells (Figure 1F). These results indicate that NCL expression is downregulated by hypoxia in the PASMCs specifically, suggesting that it plays an important role in the regulation of PASMC functions in response to hypoxia. The unique responsiveness of PASMCs to hypoxia has been reported [41]. Hypoxia increases the proliferation of PASMCs, whereas it inhibits proliferation in many other cells [41]. PASMC-specific reduction of NCL expression may contribute to inducing the unique responsiveness of PASMCs to hypoxia.
Repression of NCL can be mediated by histone deacetylation via histone deacetylase 1 (HDAC1) and HDAC2 [42]. We examined whether histone deacetylation is involved in the hypoxia-induced repression of NCL in PASMCs. PASMCs were treated with a histone deacetylase (HDAC) inhibitor, sodium butyrate (NaBu), and exposure to hypoxia. As determined by qRT-PCR (Figure 1G), the downregulation of NCL by hypoxia was abolished upon treatment with NaBu, suggesting that histone deacetylation is responsible for the repression of NCL under hypoxic conditions. To examine the role of HDAC1 or HDAC2 in the repression of NCL under hypoxia, endogenous HDAC1 and HDAC2 were reduced in PASMCs using small interfering RNAs (si-HDAC1 and si-HDAC2). The repression of NCL in response to hypoxia exposure was prevented in the PASMCs transfected with si-HDAC1 or si-HDAC2, suggesting that HDAC1 and HDAC2 are involved in the repression of NCL (Figure 1H). The knockdown of HDAC1 and HDAC2 was confirmed by qRT-PCR and immunoblotting (Figure 1I,J). According to previous studies, the HDAC1 expression levels were elevated in the lungs of patients with idiopathic pulmonary arterial hypertension and rats exposed to hypoxia, and HDAC inhibitors prevented hypoxia-induced pulmonary hypertension [43,44,45]. Therefore, hypoxia is likely to downregulate NCL expression specifically in PASMCs by histone deacetylation.
## 2.2. NCL Inhibits PASMC Proliferation
As hypoxia stimulates the proliferation of PASMCs, the role of NCL in this process was investigated. First, NCL expression in PASMCs was downregulated using siRNAs. PASMCs transfected with siRNA against NCL (si-NCL) for 24 h were stained with a Ki-67 antibody to quantify the proliferating cells (Figure 2A). Hoechst dye was then used for nuclear staining. The percentage of Ki-67-positive cells among the si-NCL-transfected cells was approximately 1.85-fold higher than that in negative control siRNA-transfected cells, suggesting that the downregulation of NCL is sufficient to promote the proliferation of PASMCs. We then overexpressed exogenous NCL mRNAs in PASMCs using Nucleofector (Lonza) for 48 h and examined the changes in the number of Ki-67-positive proliferating cells by immunofluorescence staining (Figure 2B). The empty pEGFP-N1 vector (Addgene) was used as a control. The percentage of proliferating cells decreased significantly to $48\%$ when exogenous NCL mRNAs were overexpressed, indicating that NCL inhibited the proliferation of PASMCs. These results suggest that NCL is involved in the regulation of PASMC proliferation.
To investigate the significance of the hypoxia-induced downregulation of NCL on PASMC proliferation, we examined whether the hypoxia-induced increase in proliferation was affected by NCL overexpression (Figure 2C). PASMCs transfected with exogenous NCL mRNA for 48 h were exposed to hypoxia for 24 h and then stained with a Ki-67 antibody. Approximately $5.1\%$ of the control cells were Ki-67-positive under normoxia. The percentage of proliferating cells increased to $12\%$ under hypoxia, and this increase in cell proliferation was not detected in NCL-overexpressing cells. The results suggest that the hypoxia-induced downregulation of NCL is essential for the promotion of PASMC proliferation under hypoxic conditions.
Cell proliferation was also examined by the cell counting assay. The number of viable cells increased 24 h after the transfection of PASMCs with si-NCL (Figure 2D), whereas the number of cells decreased 24 h after the transfection of PASMCs with an NCL-overexpressing vector (Figure 2E). The number of cells increased by hypoxia decreased when NCL was overexpressed (Figure 2F). These results corroborate the observation of Ki-67 immunofluorescence staining. The efficiency of the NCL knockdown or overexpression was confirmed by qRT-PCR and immunoblotting analyses (Figure 2G,H).
## 2.3. NCL Binds to a Subset of miRNAs
In PASMCs, miRNAs play important roles in the cellular responses to hypoxia [10,11]. Interestingly, recent reports have suggested that NCL is involved in the biogenesis of several miRNAs [22,23]. Therefore, we hypothesized that hypoxia-induced changes in NCL expression result in the modulation of specific miRNAs, thereby promoting the proliferation of PASMCs. As NCL has RNA-binding properties, we searched for miRNAs associated with NCL in the PASMCs. RNAs were immunoprecipitated with an antibody against NCL or rabbit IgG as a negative control, followed by NGS-based small RNA sequencing (GSE184972). The small RNA sequencing library was made from the total RNAs from 1.5 × 106 PASMCs and two samples per condition were sequenced. We identified 39 miRNAs that were specifically pulled down by NCL antibodies (≥2-fold change in pull-down samples using NCL antibodies in comparison to those using IgG, $p \leq 0.05$), supporting the potential role of miRNAs in NCL-induced changes in PASMCs (Table 1).
The binding of NCL to these miRNAs was validated by qRT-PCR after immunoprecipitation with an NCL antibody or rabbit IgG. The four miRNAs most highly enriched in NCL pull-down samples from the small RNA sequencing data (i.e., miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p) showed approximately 5.5- to 33.5-fold higher levels in the pull-down samples using NCL antibodies than in those using IgG (Figure 3A). The level of miR-497-5p, which does not bind to NCL based on the results of sequencing data after immunoprecipitation, was also confirmed in the NCL pull-down sample. As expected, miR-497-5p was not enriched in either the NCL or IgG pull-down samples.
To determine whether a conserved motif exists within the sequences of the 39 potential target miRNAs of NCL, we analyzed the miRNA sequences using the motif-based sequence analysis tool MEME Suite 5.1.1 (Figure 3B). The most frequently observed motif was 5′-G/UGCUC-3′, and its position along the miRNA sequences was not identical. Since NCL is known to regulate mRNA stability by binding to a GC-rich element, it is likely that NCL binds to miRNAs with high GC contents [46].
To further confirm the interactions between the NCL and miRNAs, PASMCs were transfected with biotinylated miRNAs (bio-miR), such as bio-miR-24-3p or bio-miR-409-3p with known roles in the regulation of PASMC function, followed by affinity purification using streptavidin beads and immunoblotting with an NCL antibody (Figure 3C) [47,48,49,50]. Biotinylated Caenorhabditis elegans miR-67 (bio-cel-miR-67) was used as a negative control. An immunoblot analysis indicated that NCL binds to bio-miR-24-3p and bio-miR-409-3p but not to bio-cel-miR-67 (Figure 3C). The expression of exogenous biotinylated miRNAs in PASMCs transfected with bio-miR-24-3p or bio-miR-409-3p was confirmed by qRT-PCR (Figure 3D). Taken together, these results further support the hypothesis that NCL binds to specific miRNAs.
We subsequently examined whether the 5′-G/UGCUC-3′ motif is critical for the binding of NCL to miRNAs. Mutations were introduced in the motifs of bio-miR-24-3p and bio-miR-409-3p (bio-miR-24-3p mutant and bio-miR-409-3p mutant) (Figure 3B). PASMCs were transfected with these mutants or bio-cel-miR-67, followed by a pull-down assay (Figure 3C). Mutations in the motif abrogate the binding of NCL to miRNAs, suggesting that the 5′-G/UGCUC-3′ motif serves as an NCL-binding site. These results suggest that NCL binds to specific miRNAs via this binding site and selectively regulates miRNA expression.
## 2.4. NCL Modulates miRNA Expression
We examined whether NCL affects the expression levels of miRNAs that bind to NCL. PASMCs were transfected with negative control siRNA (control) or si-NCL for 24 h and miRNA levels were measured by qRT-PCR. When NCL was downregulated by siRNAs, the expression levels of the miRNAs, including miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p, were reduced by 51–$70\%$ compared with levels in the control (Figure 4A). We subsequently overexpressed exogenous NCL mRNAs in PASMCs using Nucleofector (Lonza) for 24 h and examined the levels of these miRNAs (Figure 4B). The expression levels of four miRNAs were 1.3–2.8-fold higher in the NCL-overexpressed cells than in the control cells transfected with the empty pEGFP-N1 vector (control). The level of miR-497-5p, which does not bind to NCL, was not affected by the knockdown or overexpression of NCL. These results suggest that NCL binds to certain miRNAs and regulates their expression.
Previous studies have shown that NCL can affect the biogenesis of miRNAs or promote targeted mRNA degradation of miRNAs via interactions with miRNA-associated proteins, such as DGCR8, Drosha, or Ago2 [23,51,52]. Thus, we investigated their interactions in PASMCs. The cellular NCL from the PASMCs was immunoprecipitated with an NCL antibody or IgG control and analyzed by Western blotting with antibodies against DGCR8, Ago2, or NCL (Figure 4C,D). Conversely, lysates of PASMCs were immunoprecipitated with antibodies against DGCR8, Ago2, or IgG, and Western blotting was used to determine whether NCL was present in the pull-down (Figure 4E,F). We found that NCL binds to DGCR8 and Ago2 in PASMCs. These results further support the role of NCL in the regulation of miRNA expression and activity.
## 2.5. NCL Mediates Hypoxia-Induced Regulation of miRNA Expression
Given that hypoxia downregulates NCL expression, miRNAs regulated by NCL are expected to show lower expression under hypoxic conditions. We examined the changes in the expression levels of miRNAs, including miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p, under hypoxia using qRT-PCR. The expression levels of miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p were all reduced by exposure to hypoxia for 24 h (Figure 5A). These results suggest that the hypoxia-induced downregulation of NCL is responsible for the reduced expression of certain miRNAs.
To determine whether the modulation of NCL expression influences the hypoxia-induced regulation of miRNA expression, we overexpressed exogenous NCL mRNAs in PASMCs using Nucleofector for 24 h before hypoxia exposure and examined the levels of miR-423-3p, miR-744-5p, miR-24-3p, and miR-409-3p using qRT-PCR. When NCL was overexpressed, the hypoxia-induced reduction in the miRNA levels was restored (Figure 5B). These results indicate that the hypoxia-induced modulation of NCL expression controls the expression of certain miRNAs.
## 2.6. Downregulation of miR-24-3p and miR-409-3p Promotes PASMC Proliferation under Hypoxia
We determined the biological consequences of NCL-mediated miRNA regulation in hypoxic PASMCs. As NCL was observed to regulate PASMC proliferation (Figure 2), we further examined whether miRNAs regulated by NCL, such as miR-24-3p and miR-409-3p, would affect PASMC proliferation. PASMCs were transfected with control, miR-24-3p mimic, miR-409-3p mimic, miR-24-3p antisense inhibitor RNA (anti-miR-24-3p), or anti-miR-409-3p and stained with a Ki-67 antibody. miR-24-3p and miR-409-3p mimics significantly reduced the number of Ki-67-positive proliferating cells by $61\%$ and $70\%$, respectively, compared to cell counts in the control (Figure 6A). Conversely, cells transfected with anti-miR-24-3p or anti-miR-409-3p showed increased numbers of proliferating cells (i.e., 1.83-fold or 1.62-fold higher than counts in the control) (Figure 6B). These results demonstrate that the downregulation of miR-24-3p and miR-409-3p is required to promote PASMC proliferation.
To examine whether the modulation of miR-24-3p or miR-409-3p affects the hypoxia-induced proliferative response of PASMCs, PASMCs were transfected with control, miR-24-3p mimic, or miR-409-3p mimic prior to exposure to hypoxia and stained with a Ki-67 antibody. The hypoxia-induced increase in PASMC proliferation was inhibited in cells transfected with miR-24-3p mimic or miR-409-3p mimic (Figure 6C). Therefore, it is likely that the hypoxia-induced downregulation of NCL promotes PASMC proliferation by downregulating a subset of miRNAs, including miR-24-3p and miR-409-3p.
We also carried out a cell counting assay to determine cell proliferation. Consistent with the results of Ki-67 immunostaining, the rate of proliferation decreased in PASMCs transfected with miR-24-3p, or miR-409-3p mimics (Figure 6D). In contrast, the proliferation of PASMCs was promoted by anti-miR-24-3p, or anti-miR-409-3p (Figure 6E). The hypoxia-induced increase in cell proliferation was inhibited by miR-24-3p or miR-409-3p (Figure 6F). Therefore, the downregulation of miR-24-3p and miR-409-3p is essential for promoting PASMC proliferation under hypoxia. To confirm the overexpression or downregulation of miR-24-3p and miR-409-3p, their levels were measured in the PASMCs at 24 h after transfection with the control, miR-24-3p mimic, miR-409-3p mimic, anti-miR-24-3p, or anti-miR-409-3p (Figure 6G,H).
## 3. Discussion
RBPs are important regulators of gene expression via post-transcriptional regulation. Under hypoxia, RBPs regulate the expression of hypoxia-inducible genes. However, the role of RBPs in the functions of PASMCs under hypoxic conditions and the molecular mechanisms underlying their effects are not yet fully understood. In this study, we identified NCL as an essential regulator of PASMC proliferation under hypoxia and characterized its molecular function. miRNAs act as critical mediators of the response to hypoxia in PASMCs. As recent studies have revealed that NCL is involved in the biogenesis of specific miRNAs, we evaluated NCL–miRNA interactions in PASMCs by immunoprecipitation and small RNA sequencing. Thirty-nine miRNAs enriched in NCL pull-down were identified. We further found that the hypoxia-induced downregulation of NCL affects the expression of these miRNAs and demonstrated that NCL-mediated miRNA regulation induces the proliferation of PASMCs under hypoxic conditions.
Given that an RBP deficiency is associated with cardiovascular developmental defects, RBPs may play a critical role in maintaining cardiovascular health. Recently, RBPs have been implicated in systematic cardiovascular disease via the post-transcriptional regulation of target genes. For example, quaking (QKI) in VSMCs binds to myocardin and derives alternative splicing in response to vessel injury [53]. The identification of the modulation of NCL in PASMCs under hypoxia extends our understanding of the functions of RBPs in vascular conditions and provides new targets for the treatment of vascular diseases.
NCL expression has been shown to be regulated by HuR and several miRNAs. HuR interacts with the 3′UTR of NCL and promotes its translation, whereas miR-494, miR-194, and miR-206 suppress NCL expression [54,55]. In this study, we observed that both the mRNA and protein levels of NCL were significantly reduced by hypoxia. We examined whether the levels of miR-494, miR-194, or miR-206 increased in hypoxia-exposed PASMCs to suppress NCL expression. Our previously generated small RNA sequencing data showed that miR-494 and miR-194 levels did not change in response to hypoxia, and the expression of miR-206 was not determined [27]. It is therefore unlikely that these three miRNAs are responsible for the decrease in NCL expression in PASMCs under hypoxia. Rather, we elucidated the role of HDAC in the transcriptional repression of NCL under hypoxia. NCL has been linked to a variety of pathologies, including carcinogenesis, and thus, elucidating the regulatory mechanisms underlying its expression should provide a basis for the development of new therapeutic strategies for a variety of diseases, including hypoxia-induced vascular diseases.
There is emerging evidence of the involvement of RBPs in the regulation of miRNA biogenesis. For example, HNRNPA1 promotes Drosha cleavage by restructuring pri-miR-18a [56]. NCL has also been reported to enhance the maturation of specific miRNAs, including miR-21, miR-221, and miR-222, and is consequently involved in the pathogenesis of cancer [22]. We have demonstrated that NCL controls the fate of miRNAs in response to hypoxia in PASMCs. NCL binds to and regulates certain miRNAs, particularly those that contain the 5′-G/UGCUC-3′ sequence. We have provided the first evidence to elucidate the biochemical interactions between NCL and miRNAs in PASMCs and their role in the proliferation of PASMCs. The regulation of miRNA expression by NCL is essential for PASMC responses to hypoxic conditions.
The proliferation of VSMCs is a hallmark of several vascular pathologies as well as hypoxia-induced remodeling [41,57]. Multiple miRNAs involved in the proliferation of VSMCs have also been explored [58,59,60]. For example, miR-24 inhibits high glucose-stimulated VSMC proliferation by targeting high mobility group box-1 (HMGB1) [49]. Overexpression of miR-24 reduced neointimal hyperplasia and VSMC proliferation by inhibiting the Wnt4 signaling pathway [47]. In addition, miR-24 suppressed the platelet-derived growth factor-BB (PDGF-BB) signaling pathway by decreasing the expression levels of activator protein 1 (AP-1) and the PDGF-receptor (PDGF-R), resulting in the inhibition of VSMC proliferation and vascular remodeling [50]. The results imply that miR-24 may also regulate VSMC proliferation under hypoxia. While few previous studies have explored the function of miR-409 in VSMCs, decreased miR-409 expression levels were observed during high phosphate-induced vascular calcification, triggering VSMC de-differentiation [48]. This finding suggests that miR-409 may be involved in the regulation of VSMC proliferation. We have demonstrated that the target miRNAs of NCL influence the proliferation of PASMCs. For example, miR-24-3p and miR-409-3p inhibit PASMC proliferation and their overexpression further prevents hypoxia-induced proliferation. These results add a layer of valuable information about a specific set of miRNAs that regulate the proliferation of PASMCs. In addition, as the target miRNA level is regulated by the level of NCL expression, it is clear that NCL–miRNA interactions are essential for the regulation of PASMC proliferation. As miRNAs are potent regulators of cellular function in pathophysiological conditions, our illustration of NCL–miRNA interactions and the role of NCL in PASMC functions via the regulation of miRNAs improves our general understanding of the mechanisms underlying the pathogenesis of vascular conditions related to hypoxia. To explore the potential therapeutic benefits of NCL or interacting miRNAs on pulmonary hypertension, it is necessary to investigate whether modulation of NCL or interacting miRNAs is effective in attenuating pulmonary vascular remodeling in animal models, such as a chronic hypoxia-induced rat model.
## 4. Conclusions
In this study, we provide clear evidence for the role of the RBP nucleolin (NCL) in hypoxia-induced PASMC proliferation. NCL is downregulated by histone deacetylation under hypoxic conditions in PASMCs, which consequently promotes PASMC proliferation. Furthermore, we demonstrated that these effects of NCL are mediated by interactions with a subset of miRNAs using immunoprecipitation and NGS-based small RNA sequencing. Thirty-nine miRNAs were found to be enriched in NCL pull-down, and NCL regulates particular miRNA expressions via the 5′-G/UGCUC-3′ binding sites. Hypoxia-mediated regulation of NCL affects miRNA expression, and these miRNAs, such as miR-24-3p and miR-409-3p, are involved in the proliferation of PASMCs under hypoxia. Collectively, the identification of NCL-miRNA interactions in hypoxia-induced PASMC proliferation provides a basis for further studies of the molecular mechanisms underlying vascular diseases.
## 5.1. Cell Culture and Hypoxia
Human primary pulmonary artery smooth muscle cells (PASMCs) were purchased from Lonza (CC-2581) and were maintained in Sm-GM2 medium (Lonza, Basel, Switzerland) containing $5\%$ fetal bovine serum (FBS). For hypoxia, the cells were placed in fresh medium and incubated in a sealed modular incubator chamber (Billups-rothenberg Inc., San Diego, CA, USA) for 24 h at 37 °C after flushing with a mixture of $5\%$ CO2, $1\%$ O2 and $94\%$ N2 for 4 min.
## 5.2. Sodium Butyrate (NaBu) Treatment
NaBu was purchased from Sigma-Aldrich (St. Louis, MO, USA, #B5887). The cells were treated with 10 mM NaBu for 24 h.
## 5.3. Quantitative Reverse Transcriptase-PCR (qRT-PCR)
Quantitative analysis of the change in expression levels was performed using real-time PCR. The mRNA levels were normalized to 18S rRNA. The primers used were as follows: 18S rRNA, 5′-GTAACCCGTTGAACCCCATT-3′ and 5′-CCATCCAATCGGTAGTAGCG-3; CIRBP, 5′-CTTTTTGTTGGAGGGCTGAG-3′ and 5′-CTTGCCTGCCTGGTCTACTC-3′; CPEB1, 5′-TCTGCCCTTCCTGTCTCTGT-3′ and 5′-TATGCTGAAGGGGTCTTTGG-3′; CPEB2, 5′-GCGAGTTGCTTTCTCCAATC-3′ and 5′-CCTGGCATTCATCACACATC-3′; HNRNPA2B1, 5′-GGCTACGGAGGTGGTTATGA-3′ and 5′- ATAACCCCCACTTCCTCCAC -3′; HNRNPL, 5′-AGATCACCCCGCAGAATATG -3′ and 5′-CAAGCCATAGACCATGAGCA -3′; IREB2, 5′-GCACCGGATTCAGTTTTGTT-3′ and 5′-CTTAGCGGCAGCACTATTCC-3′; NCL, 5′-GAAGGAAATGGCCAAACAGA-3′ and 5′-ACGCTTTCTCCAGGTCTTCCA-3′; PTBP1, 5′-ACGGACCGTTTATCATGAGC-3′ and 5′-GTTTTTCCCCTTCAGCATCA-3′; PTBP3, 5′-CATTCCTGGGGCTAGTGGTA-3′ and 5′-CCATCTGAACCAAGGCATTT-3′; RBM3, 5′-CAGGCACTGGAAGACCACTT-3′ and 5′-CTCTCATGGCAACTGAAGCA-3′; TIA1, 5′-TGCTATTGGGGCAAAGAAAC-3′ and 5′-GCGGTTGCACTCCATAATTT-3′; ZFP36, 5′-TCCACAACCCTAGCGAAGAC-3′ and 5′-GAGAAGGCAGAGGGTGACAG-3′; and ZFP36L1, 5′-GAGGAAAACGGTGCCTGTAA-3′ and 5′-CTCTTCAGCGTTGTGGATGA-3′. For the quantification of mature miRNAs, such as miR-423-3p (MS00004179), miR-744-5p (MS00010549), miR-24-3p (MS00006552), miR-409-3p (MS00006895), and miR-497-5p (MS00004361), the miScript PCR assay kit (#218073, Qiagen, Hilden, Germany) was used according to the manufacturer’s instructions. Data analysis was performed using a comparative CT method in the Bio-Rad software 3.1. The levels of the miRNAs were normalized to U6 small nuclear RNA or U61 small nucleolar RNA (SNORD61). Three experiments were performed in triplicate, and the mean results with standard errors are presented.
## 5.4. miRNA Mimics and Anti-miRNA Oligonucleotides
Chemically modified double-stranded RNAs designed to mimic the endogenous mature miR-24-3p and miR-409-3p were purchased from Genolution Pharmaceuticals (Seoul, Republic of Korea). Antisense inhibitor RNAs (anti-miR-24-3p and anti-miR-409-3p) and negative control miRNA were purchased from Bioneer (Daejeon, Republic of Korea). The miRNA mimics and anti-miRNA oligonucleotides were transfected at 5 nM and 25 nM, respectively, using RNAi Max (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol.
## 5.5. RNA Interference
Small interfering RNA (siRNA) duplexes were synthesized by Genolution Pharmaceuticals (Seoul, Republic of Korea) and Integrated DNA Technologies (Coralville, IA, USA). NCL siRNA (si-NCL): 5′-GGAUAGUUACUGACCGGGA-3′, HDAC1 siRNA (si-HDAC1): 5′-AGUUUCCUUUUUGAGAUACUAUUTT-3′, and HDAC2 siRNA (si-HDAC2): 5′-GAAUUUCUAUUCGAGCAUCAGACAA-3′. Negative control siRNA (Genolution) was used as a control. The cells were transfected with 100 nM si-NCL, 25 nM si-HDAC1, or si-HDAC2 using RNAi Max (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol.
## 5.6. Immunoprecipitation
PASMC lysates were prepared in lysis buffer (20 mM Tris–HCl [pH 7.5], 100 mM KCl, 5 mM MgCl2, and $0.5\%$ NP-40) containing a protease inhibitor cocktail (#11697498001, Roche, Basel, Switzerland). The lysates were precleared with DynabeadsTM Protein G (#10007D, Invitrogen, Carlsbad, CA, USA) at 4 °C with gentle rotation for 1 h. The precleared lysates were incubated with DynabeadsTM Protein G coated with 2 μg each of rabbit anti-nucleolin antibody (#ab22758, Abcam, Cambridge, UK) or rabbit IgG (#2729, Cell Signaling Technology, Danvers, MA, USA) at 4°C for overnight. For DGCR8 IP and Ago2 IP, 5 μg of mouse anti-DGCR8 antibody (#60084-1-Ig, Proteintech, Rosemont, IL, USA) and mouse anti-Ago2 antibody (#ab57113, Abcam) were used, respectively. A reaction containing mouse IgG (#sc-2025, Santa Cruz Biotechnology, Dallas, TX, USA) served as a negative control. Unbound materials were washed off using NT2 buffer (50 mM Tris–HCl [pH 7.5], 150 mM NaCl, 1 mM MgCl2, and $0.05\%$ NP-40). All collected protein complexes were eluted with 2X Laemmli sample buffer supplemented with β-mercaptoethanol and boiled. The boiled supernatants and input ($2\%$) samples were resolved by SDS-PAGE and analyzed by immunoblotting with the anti-NCL antibody (#ab22758), anti-DGCR8 antibody (#60084-1-Ig), or anti-Ago2 antibody (#ab57113).
## 5.7. RNA Immunoprecipitation
PASMC lysates were prepared in lysis buffer (20 mM Tris–HCl (pH 7.5), 100 mM KCl, 5 mM MgCl2, and $0.5\%$ NP-40) containing 40 U/μL RiboLock RNase Inhibitor (#EO0381, Thermo Fisher Scientific, Waltham, MA, USA) and a protease inhibitor cocktail (#11697498001, Roche, Basel, Switzerland). The lysates were incubated with DynabeadsTM Protein G (#10007D, Invitrogen, Carlsbad, CA, USA) coated with 2 μg each of rabbit anti-NCL antibody (#ab22758, Abcam, Cambridge, UK) or rabbit IgG (#2729, Cell Signaling Technology, Danvers, MA, USA) at 4 °C for 2 h. Unbound materials were washed off using NT2 buffer (50 mM Tris–HCl (pH 7.5), 150mM NaCl, 1 mM MgCl2, and $0.05\%$ NP-40). The pellet was subsequently incubated with NT2 buffer containing RNase-free Dnase I (1 U/μL) (#EN0521, Thermo Fisher Scientific, Waltham, MA, USA) at 30 °C for 15 min and NT2 buffer containing $0.1\%$ SDS and 0.1 mg/mL Proteinase K (#25530049, Thermo Fisher Scientific, Waltham, MA, USA) at 55 °C for 15 min. The RNA was extracted using Trizol in the presence of glycoblue (#AM9515, Thermo Fisher Scientific, Waltham, MA, USA) and analyzed by NGS-based small RNA sequencing or qRT-PCR.
## 5.8. NGS-Based Small RNA Sequencing
The extracted RNA was qualified using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). cDNA libraries were constructed with the NEBNext Multiplex small RNA library prep kit (NEB, Ipswich, MA, USA) using the total RNA from RNA immunoprecipitation, according to the manufacturer’s instructions. Briefly, adapter ligation, reverse transcription, PCR amplification, and purification using a QIAquick PCR Purification Kit (Qiagen) and AMPure XP beads (Beckman Coulter, Brea, CA, USA) were conducted to generate a library product. The yield and size distribution of the small RNA libraries were assessed by high-sensitivity DNA analysis on an Agilent 2100 Bioanalyzer. High-throughput sequences were produced by single-end 75 sequencing using the NextSeq 500 system (Illumina, San Diego, CA, USA). Sequence reads were mapped using the Bowtie 2 software tool to obtain the BAM file (alignment file). A mature miRNA sequence was used as a reference for mapping. Read counts mapped onto the mature miRNA sequence were extracted from the alignment file using bedtools (v2.25.0) and Bioconductor, which uses R (version 3.2.2) statistical programming language (R development Core Team, 2011). The read counts were then used to determine the expression levels of miRNAs. The quantile normalization method was used to compare samples.
## 5.9. Biotinylated miRNA Pull-Down Assay
The 3′-biotinylated miR-24-3p mimic (bio-miR-24-3p), 3′-biotinylated miR-409-3p mimic (bio-miR-409-3p), 3′-biotinylated miR-24-3p mutant (bio-miR-24-3p mutant), 3′-biotinylated miR-409-3p mutant (bio-miR-409-3p mutant), and 3′-biotinylated control Caenorhabditis elegans miR-67 mimic (bio-cel-miR-67) were synthesized by Integrated DNA Technologies (Coralville, IA, USA). PASMCs were transfected with 150 nM bio-miR-24-3p, bio-miR-409-3p, bio-miR-24-3p mutant, bio-miR-409-3p mutant, or bio-cel-miR-67 mimic using RNAi MAX (Invitrogen, Carlsbad, CA, USA). Twenty-four hours later, the cells were trypsinized and lysed in lysis buffer (20 mM Tris–HCl (pH 7.5), 100 mM KCl, 5 mM MgCl2, and $0.5\%$ NP-40) containing 40 U/μL RiboLock RNase Inhibitor (#EO0381, Thermo Fisher Scientific, Waltham, MA, USA) and a protease inhibitor cocktail (#11697498001, Roche, Basel, Switzerland) on ice for 20 min and centrifuged at 12,000 rpm for 10 min at 4 °C. The lysates were incubated with Streptavidin Mag Sepharose (#GE28-9857-38, Sigma-Aldrich, St. Louis, MO, USA) at 4 °C for 4 h, and unbound materials were washed off using NT2 buffer. The pull-down sample was boiled in 2X Laemmli sample buffer supplemented with β-mercaptoethanol. The boiled pull-down and input ($1\%$) samples were resolved by SDS-PAGE and analyzed by immunoblotting with the anti-NCL antibody (#ab22758, Abcam).
## 5.10. NCL Expression Plasmid
The plasmid GFP-NCL was a gift from Michael Kastan (Addgene plasmid #28176; http://n2t.net/addgene:28176; RRID: Addgene_28176) [61]. PASMCs were transfected with 1 μg of the GFP-NCL or the empty pEGFP-N1 vector (Addgene, Watertown, MA, USA) using the P1 Primary Cell 4D-NucleofectorTM X kit (Lonza, Basel, Switzerland) according to the manufacturer’s protocol.
## 5.11. Immunoblotting
Cells were lysed in TNE buffer (50 mM Tris–HCl (pH 7.4), 100 mM NaCl, and 0.1 mM EDTA) and total cell lysates were separated by SDS-PAGE, transferred to PVDF membranes, immunoblotted with antibodies, and visualized using an enhanced chemiluminescence detection system (Bio-Rad Laboratories, Hercules, CA, USA). Antibodies against NCL (#ab22758), HIF1α (#ab2185), and Ago2 (#ab57113) were purchased from Abcam (Cambridge, UK). An anti-β-actin antibody (#sc47778), anti-HDAC1 antibody (#sc81598), anti-HDAC2 antibody (#sc-81599), and anti-DGCR8 antibody (#60084-1-Ig) were purchased from Santa Cruz Biotechnology (Dallas, TX, USA) and Proteintech (Rosemont, IL, USA).
## 5.12. Immunofluorescence Staining
Equal amounts of PASMCs were seeded in chamber well slides and transfected with control mimic, miR-24-3p, miR-409-3p, anti-miR-24-3p, or anti-miR-409-3p. Cells were exposed to normoxia or hypoxia and then fixed in $2\%$ paraformaldehyde, blocked in $3\%$ BSA in PBS, and permeabilized in $0.1\%$ Triton X-100 (Sigma-Aldrich, St. Louis, MO, USA) in PBS. The slides were sequentially probed with rabbit anti-human Ki-67 antibody (#ab16667, Abcam) and goat anti-rabbit IgG (H + L) cross-adsorbed secondary antibody Alexa flour 488 (#A-11008, Thermo Fisher Scientific). Nuclei were stained with Hoechst 33342 (#62249, Thermo Fisher Scientific). The slides were imaged by a Zeiss Axio Imager Z1 microscope (Oberkochen, Germany). At least 2000 cells were counted per condition, and the percentages of Ki-67-positive cells were presented. The results are the mean ± S.E. for triplicate assays.
## 5.13. Cell Counting Assay
Equal amounts of PASMCs were seeded in plates and transfected with negative control siRNA, si-NCL, pEGFP-N1 vector, GFP-NCL, control mimic, miR-24-3p mimic, miR-409-3p mimic, anti-miR-24-3p, or anti-miR-409-3p. The cells were trypsinized and manually counted using a hemocytometer. The total cell numbers were compared and presented as a fold change.
## 5.14. Statistical Analysis
All experiments were performed with at least three independent repetitions. The results were presented as the mean with standard error. Statistical analyses were performed by an analysis of variance followed by Student’s t-test, multiple t-test, one-way ANOVA, or two-way ANOVA using Prism 8 software (GraphPad Software Inc., San Diego, CA, USA). p-values of <0.05 were considered significant and are indicated with asterisks. *, **, ***, and **** represent p-values less than 0.05, 0.005, 0.0005, and 0.0001, respectively.
## 5.15. Accession Numbers
The RNA sequencing dataset generated during the current study is available from the corresponding author on reasonable request. The accession numbers for the data reported in this paper are GEO: GSE184972.
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|
---
title: 'Combination of Muscle Quantity and Quality Is Useful to Assess the Necessity
of Surveillance after a 5-Year Cancer-Free Period in Patients Who Undergo Radical
Cystectomy: A Multi-Institutional Retrospective Study'
authors:
- Naoki Fujita
- Masaki Momota
- Hirotaka Horiguchi
- Itsuto Hamano
- Jotaro Mikami
- Shingo Hatakeyama
- Hiroyuki Ito
- Takahiro Yoneyama
- Yasuhiro Hashimoto
- Shoji Nishimura
- Kazuaki Yoshikawa
- Chikara Ohyama
journal: Cancers
year: 2023
pmcid: PMC10000682
doi: 10.3390/cancers15051489
license: CC BY 4.0
---
# Combination of Muscle Quantity and Quality Is Useful to Assess the Necessity of Surveillance after a 5-Year Cancer-Free Period in Patients Who Undergo Radical Cystectomy: A Multi-Institutional Retrospective Study
## Abstract
### Simple Summary
Although continuous surveillance after a 5-year cancer-free period in patients with bladder cancer who undergo curative surgery is recommended, optimal candidates for continuous surveillance remain unclear. Sarcopenia is associated with an unfavorable prognosis in bladder cancer. We aimed to investigate the impact of low muscle quantity and quality (defined as severe sarcopenia) on prognosis after a 5-year cancer-free period in patients who underwent radical cystectomy. Our results showed that the 10-year recurrence rate after a 5-year cancer-free period was low (approximately $5\%$), and severe sarcopenia was not associated with increased recurrence risk. Moreover, severe sarcopenia was selected as a significant risk factor for mortality unrelated to bladder cancer. Taken together, patients with severe sarcopenia might not need continuous surveillance after a 5-year cancer-free period, considering high mortality unrelated to bladder cancer.
### Abstract
Background: *Although continuous* surveillance after a 5-year cancer-free period in patients with bladder cancer (BC) who undergo radical cystectomy (RC) is recommended, optimal candidates for continuous surveillance remain unclear. Sarcopenia is associated with unfavorable prognosis in various malignancies. We aimed to investigate the impact of low muscle quantity and quality (defined as severe sarcopenia) on prognosis after a 5-year cancer-free period in patients who underwent RC. Methods: We conducted a multi-institutional retrospective study assessing 166 patients who underwent RC and had five years or more of follow-up periods after a 5-year cancer-free period. Muscle quantity and quality were evaluated using the psoas muscle index (PMI) and intramuscular adipose tissue content (IMAC) using computed tomography images five years after RC. Patients with lower PMI and higher IMAC values than the cut-off values were diagnosed with severe sarcopenia. Univariable analyses were performed to assess the impact of severe sarcopenia on recurrence, adjusting for the competing risk of death using the Fine-Gray competing risk regression model. Moreover, the impact of severe sarcopenia on non-cancer-specific survival was evaluated using univariable and multivariable analyses. Results: The median age and follow-up period after the 5-year cancer-free period were 73 years and 94 months, respectively. Of 166 patients, 32 were diagnosed with severe sarcopenia. The 10-year RFS rate was $94.4\%$. In the Fine-Gray competing risk regression model, severe sarcopenia did not show a significant higher probability of recurrence, with an adjusted subdistribution hazard ratio of 0.525 ($$p \leq 0.540$$), whereas severe sarcopenia was significantly associated with non-cancer-specific survival (hazard ratio 1.909, $$p \leq 0.047$$). These results indicate that patients with severe sarcopenia might not need continuous surveillance after a 5-year cancer-free period, considering the high non-cancer-specific mortality.
## 1. Introduction
Bladder cancer (BC) is the tenth most common cancer worldwide [1]. Radical cystectomy (RC) with pelvic lymph node dissection and urinary diversion remains the gold standard treatment for muscle-invasive and high-risk non-muscle-invasive BC [2].
Continuous surveillance after a 5-year cancer-free period in patients who undergo RC is recommended by the European Association of Urology guidelines [2]. However, late recurrence after RC has been reported to be infrequent [3,4]. Moreover, optimal candidates for continuous surveillance remain unclear. Identifying these candidates may be helpful in the development of individualized surveillance protocols.
Sarcopenia is represented by two dysregulation patterns of body composition: loss of skeletal muscle quantity (myopenia) and quality (myosteatosis) [5]. Although sarcopenia has been reported to be associated with unfavorable prognosis in patients who underwent surgical treatment for various malignancies [6,7], the prognostic value of sarcopenia in patients who undergo RC remains controversial [8,9,10,11]. Moreover, no study has investigated its impact on oncological outcomes and non-cancer-specific mortality after a 5-year cancer-free period. Considering the close relationship between sarcopenia and high mortality caused by non-malignant diseases [12,13,14], we hypothesized that patients with low muscle quantity and quality (defined here as severe sarcopenia) might have a high non-cancer-specific mortality; therefore, they might not need continuous surveillance after a 5-year cancer-free period.
The aim of the present study was to evaluate the impact of low muscle quantity and quality on recurrence-free survival (RFS) and non-cancer-specific survival after a 5-year cancer-free period in patients with BC who underwent RC.
## 2.1. Ethics Statement
This study followed the principles of the Declaration of Helsinki and was approved by the ethics committees of the Hirosaki University Graduate School of Medicine (authorization number: 2019-099-1) and all hospitals included in this study. Written consent was not obtained due to the public disclosure of the study information (opt-out approach).
## 2.2. Patient Selection
To include patients who had sufficient follow-up periods (five years or more) after a 5-year cancer-free period, we retrospectively evaluated 431 patients with BC who underwent RC between October 1995 and December 2012 at one academic center and five general hospitals. We excluded 193 patients who experienced local recurrence and/or distant metastasis, died from any cause, or were lost to follow-up within five years after RC and 72 patients who had no information on their heights or digital computed tomography (CT) scans available for body composition analysis. Ultimately, 166 patients were included in this study (Figure 1).
## 2.3. Evaluation of Variables
The following variables were analyzed: age, sex, Eastern Cooperative Oncology Group performance status (ECOG PS), hypertension (HTN), diabetes mellitus, history of cardiovascular disease (CVD), chronic kidney disease (CKD), clinical stage, neoadjuvant chemotherapy (NAC), urinary diversion, pathological outcomes, and adjuvant chemotherapy. Age and comorbidities at five years after RC were used in the analyses. Renal function was evaluated by estimated glomerular filtration rate (eGFR) using a modified version of the abbreviated Modification of Diet in Renal Disease *Study formula* for Japanese patients [15] and CKD was defined as eGFR < 60 mL/min/1.73 m2. Tumor stage was assigned according to the 2009 TNM classification system recorded by the Union of International Cancer Control. Tumor grade was classified according to the 1973 World Health Organization classification system.
## 2.4. NAC and Adjuvant Chemotherapy
Since September 2004, patients have received two–four courses of NAC, composed of a platinum-based combination regimen of gemcitabine plus cisplatin, gemcitabine plus carboplatin, or methotrexate, vinblastine, adriamycin, and cisplatin. Regimens were selected based on guidelines regarding eligibility for the proper use of cisplatin, overall patient status, and the clinician’s discretion. The cycles were repeated every 21 days.
Adjuvant chemotherapy was not routinely administered. Indications for adjuvant chemotherapy included pT4, pathological lymph node involvement, grade 3, lymphovascular invasion, or positive surgical margins in patients who were not treated with NAC. Patients were selected for adjuvant chemotherapy at the clinician’s discretion. We administered one–three courses of adjuvant chemotherapy to patients with a feasible postoperative status for toxic chemotherapy. Adjuvant chemotherapy comprises a platinum-based combination regimen of gemcitabine plus cisplatin, gemcitabine plus carboplatin, or methotrexate, vinblastine, doxorubicin, and cisplatin.
## 2.5. Surgical Procedures
RC was performed using a previously described basic technique [16]. Briefly, the patients underwent RC, standard pelvic lymph node dissection, and urinary diversion (orthotopic ileal neobladder construction, ileal conduit diversion, or cutaneous ureterostomy).
## 2.6. Follow-Up Schedule
The follow-up schedule after the 5-year cancer-free period comprised annual urine analysis, urine cytology, blood chemistry, and lung, abdominal, and pelvic CT scans.
## 2.7. Evaluation of Muscle Quantity and Quality
Muscle quantity was evaluated using the psoas muscle index (PMI). We measured the cross-sectional areas of the right and left psoas muscles on plain CT images at the level of the third lumbar vertebra (L3) five years after RC. The muscles were identified based on their anatomical features, and the bilateral psoas muscle areas were evaluated using manual tracing. The PMI was calculated by normalizing these cross-sectional areas to their height (cm2/m2) [17].
Muscle quality was evaluated based on intramuscular adipose tissue content (IMAC) using L3 level plain CT images five years after RC. We precisely traced the multifidus muscle and subcutaneous fat to measure their CT values (Hounsfield units). IMAC was calculated by dividing the CT value of the multifidus muscles by that of the subcutaneous fat. A higher IMAC indicates a greater amount of adipose tissue in the skeletal muscles and, therefore, lower skeletal muscle quality [18].
Since the ranges of PMI and IMAC in men and women are quite different [19,20], and their optimal cut-off values for mortality in patients with BC have been to be established, their optimal cut-off values for non-cancer-specific mortality were calculated separately for men and women using receiver operating characteristic (ROC) curves. In the present study, we defined patients with both low muscle quantity and quality as patients with severe sarcopenia. Patients were divided into two groups: those with lower PMI and higher IMAC values than their cut-off values (severe sarcopenia group) and those with higher PMI and/or lower IMAC values (control group) (Figure 1).
## 2.8. Statistical Analysis
SPSS version 24.0 (SPSS Corp., Armonk, NY, USA), R 4.0.2 (The R Foundation for Statistical Computing, Vienna, Austria), and GraphPad Prism 5.03 (GraphPad Software, San Diego, CA, USA) were used for statistical analyses. Quantitative variables are expressed as medians with interquartile ranges. Differences in quantitative variables between the two groups were analyzed using the Mann–Whitney U test. Categorical variables were compared using Fisher’s exact test or the chi-squared test. RFS, overall survival (OS), and non-cancer-specific survival were evaluated using the Kaplan–Meier method and compared using the log-rank test. Moreover, the cumulative incidences of recurrence was estimated and death before recurrence was defined as a competing risk. The Gray test was performed to compare cumulative incidences between the control and severe sarcopenia groups. Subsequent univariable analyses were performed to assess the impact of severe sarcopenia on recurrence, adjusting for the competing risk of death using the Fine-Gray subdistribution hazards model. Univariable Cox proportional hazards regression analyses were performed to identify the significant factors associated with RFS. Univariable and multivariable Cox proportional hazards regression analyses were performed to evaluate the impact of severe sarcopenia on non-cancer-specific survival. These outcomes were calculated from five years after RC to the date of the first event or last follow-up. Recurrence was defined as local pelvic recurrence, remnant urothelial recurrence, or distant metastasis. Non-cancer-specific mortality was defined as death unrelated to BC. Statistical significance was set at $p \leq 0.05.$
## 3.1. Patients’ Backgrounds
The median age and follow-up period after the 5-year cancer-free period were 73 years and 94 months, respectively. Of the 166 patients, 85 ($51\%$) and 19 ($11\%$) received NAC and adjuvant chemotherapy, respectively. The patients’ backgrounds are summarized in Table 1.
## 3.2. Evaluation of Muscle Quantity and Quality
The median PMI values in men and women were 6.18 cm2/m2 and 4.65 cm2/m2, respectively. The optimal cut-off values of PMI for non-cancer-specific mortality in men and women was 5.28 cm2/m2 and 6.35 cm2/m2, respectively. Of the 166 patients, 94 ($57\%$) and 72 ($43\%$) had PMI values higher and lower than the cut-off values, respectively.
The median IMAC values in men and women were −0.46 and −0.33, respectively. The optimal cut-off values of IMAC for non-cancer-specific mortality in men and women was −0.49 and −0.04, respectively. Of the 166 patients, 84 ($51\%$) and 82 ($49\%$) had IMAC values lower and higher than the cut-off values, respectively.
Patients were divided into two groups: those with lower PMI and higher IMAC values (severe sarcopenia group, $$n = 32$$) and those with higher PMI and/or lower IMAC values (control group, $$n = 134$$) (Figure 1). No significant differences in patients’ background were observed between the two groups, except for age and ECOG PS (Table 1).
## 3.3. BC Recurrence
By the end of the follow-up period after the 5-year cancer-free period, nine ($5.4\%$) patients experienced BC recurrence, including recurrence in the upper urinary tract ($$n = 3$$), lymph nodes ($$n = 2$$), urethra ($$n = 1$$), local pelvis ($$n = 1$$), neobladder ($$n = 1$$), and distant metastasis ($$n = 1$$). Of the nine patients who experienced BC recurrence, eight ($6.0\%$) and one ($3.1\%$) patients were in the control and severe sarcopenia groups, respectively. The 5-year and 10-year RFS rates were $95.2\%$ and $94.4\%$, respectively (Figure 2A). Almost all recurrence detection rates ([number of patients with recurrence/number of patients with surveillance during a certain period] × 100) were under $1\%$ throughout the entire follow-up period (Figure 2B).
In the Gray test, the cumulative incidence rate of recurrence was not significantly different between the control and severe sarcopenia groups (Figure 2C, $$p \leq 0.528$$). In the univariable analyses, none of the patient factors, clinical stage, perioperative chemotherapy, or pathological outcomes were significantly associated with shorter RFS (Table S1). Similarly, none of lower PMI, higher IMAC, and severe sarcopenia showed significant higher probabilities of recurrence (Figure 2D; subdistribution hazard ratio [SHR] 0.664, $95\%$ confidence interval [CI] 0.168–2.630, $$p \leq 0.560$$; SHR 0.814, $95\%$ CI 0.220–3.010, $$p \leq 0.760$$; SHR 0.525, $95\%$ CI 0.067–4.140, $$p \leq 0.540$$; respectively).
## 3.4. OS and Non-Cancer-Specific Survival
By the 5-year, 10-year, and end of the follow-up period after the 5-year cancer-free period, 29 ($18\%$), 51 ($31\%$), and 64 ($39\%$) patients died from any cause, respectively. The main causes of death during the entire follow-up period were other malignancies ($25\%$) and CVD ($22\%$), followed by infectious diseases ($14\%$) (Figure 3). Of the 64 patients who died from any cause, six ($9.4\%$) died of BC (Figure 3).
The OS in patients with lower PMI values was significantly shorter than that in patients with higher PMI values (Figure 4A, $$p \leq 0.003$$). The OS in patients with higher IMAC values was significantly shorter than that in patients with lower IMAC values (Figure 4B, $$p \leq 0.025$$). The OS in the severe sarcopenia group was significantly shorter than that in the control group (Figure 4C, $p \leq 0.001$).
By the end of the follow-up period after the 5-year cancer-free period, 37 ($28\%$) and 21 ($66\%$) patients in the control and severe sarcopenia group died from non-cancer-specific cause, respectively. The 5-year and 10-year non-cancer-specific mortality rates in the severe sarcopenia group were significantly higher than those in the control group (Figure 5A,B; $38\%$ vs. $10\%$, $p \leq 0.001$; $56\%$ vs. $21\%$, $p \leq 0.001$; respectively). The non-cancer-specific survival in patients with lower PMI values was significantly shorter than that in patients with higher PMI values (Figure 5C, $$p \leq 0.001$$). The non-cancer-specific survival in patients with higher IMAC values was significantly shorter than that in patients with lower IMAC values (Figure 5D, $$p \leq 0.010$$). The non-cancer-specific survival in the severe sarcopenia group was significantly shorter than that in the control group (Figure 5E, $p \leq 0.001$). In univariable analyses, age, EGOS PS, HTN, CKD, lower PMI, higher IMAC, and severe sarcopenia were significantly associated with shorter non-cancer-specific survival (Table 2). In multivariable analyses, lower PMI and higher IMAC were not significantly associated with shorter non-cancer-specific survival (HR 1.267, $95\%$ CI 0.696–2.308, $$p \leq 0.439$$; HR 1.377, $95\%$ CI 0.688–2.757, $$p \leq 0.367$$; respectively), whereas severe sarcopenia was significantly associated with shorter non-cancer-specific survival (HR 1.909, $95\%$ CI 1.007–3.619, $$p \leq 0.047$$) (Table 3). Age and CKD were also associated with shorter non-cancer-specific survival (Table 3).
## 4. Discussion
To the best of our knowledge, this is the first study to evaluate the impact of low muscle quantity and quality (defined here as severe sarcopenia) on oncological outcomes and non-cancer-specific mortality after a 5-year cancer-free period in patients with BC who underwent RC. The results of the present study showed that the 10-year recurrence rate after the 5-year cancer-free period was low (approximately $5\%$), and severe sarcopenia was not associated with increased recurrence risk. In contrast, severe sarcopenia was identified as a significant risk factor for non-cancer-specific mortality. These results suggest that patients with severe sarcopenia may not need continuous surveillance after a 5-year cancer-free period. Although a prospective validation study with a larger sample size is warranted, these results might be helpful for clinicians to optimize individualized surveillance protocols after a 5-year cancer-free period.
In the present study, neither severe sarcopenia nor low muscle quantity or quality was associated with BC recurrence after a 5-year cancer-free period. Although several studies have investigated the impact of preoperative sarcopenia on oncological outcomes in patients who underwent RC [8,9,10,11], to our knowledge, there is no available evidence about its impact on oncological outcomes after a 5-year cancer-free period in both BC and other malignancies. However, our results are consistent with those of previous studies that have focused on preoperative sarcopenia. Smith et al. revealed that sarcopenia evaluated by total psoas area was not associated with worse 2-year survival in 200 patients with BC who underwent RC [10]. Likewise, Wang et al. demonstrated no significant association between sarcopenia and shorter disease-free survival in 112 patients with BC who underwent RC [21]. In contrast, Ornaghi et al. reported opposite results. They conducted a systematic literature review to investigate the impact of sarcopenia on long-term mortality rates in patients with BC treated with RC and revealed that sarcopenia was significantly associated with unfavorable 5-year cancer-specific survival (CSS) (HR 1.73, $p \leq 0.05$) [22]. Similarly, a systematic review and meta-analysis conducted by Hu et al. demonstrated that sarcopenia was associated with poor CSS in patients with BC who underwent RC (HR 1.73, $p \leq 0.001$) [23]. Although we do not have a clear answer about our negative results, these conflicting results might be caused by the varied definitions of sarcopenia between studies due to the lack of international consensus. Because the lack of available evidence and several limitations in the present study, especially the small number of recurrence events, prevent us from making definitive conclusions, further prospective studies with an appropriate sample size and recurrence events are warranted.
In the present study, severe sarcopenia (low muscle quantity and quality) was associated with increased non-cancer-specific mortality after a 5-year cancer-free period, whereas low muscle quantity or quality alone had marginal effects. Although many studies have investigated the impact of both low muscle quantity and quality on prognosis in a single study of several malignant and non-malignant diseases [5,24,25,26], the combined effects of these parameters have rarely been reported. Hopkins et al. assessed 968 patients with colorectal cancer who underwent curative resection and demonstrated that both low muscle quantity and low muscle radiodensity were independently predictive of worse OS (HR 1.45 and HR 1.53, respectively), but the presence of both increased the HR for OS (HR 2.23) [27]. Similarly, Caan et al. assessed 1628 female patients with colorectal cancer who underwent surgical resection and revealed that patients with both low muscle quantity and high total adipose tissue area had a higher risk of overall mortality (HR 1.64) than patients with low muscle quantity or high total adipose tissue area alone (HR 1.38 and HR 1.30, respectively) [28]. Although the included patient populations and evaluated outcomes in these studies were different from those in the present study, these results indicate the potential additive effects of low muscle quantity and quality on prognosis in patients with malignancies.
It is unclear how other diseases contribute to the mortality of BC survivors after RC. In the present study, only six ($3.6\%$) patients died from BC after the 5-year cancer-free period, whereas 58 ($34.9\%$) died from other causes (Figure 3). Kong et al. reported similar results. They assessed 81,843 patients with BC who survived 5–10 years after treatment ($93.9\%$ were treated with surgery) and demonstrated that only $6.9\%$ of them died from BC while $47.9\%$ died from other causes, including CVD ($11.0\%$), pulmonary disease ($7.7\%$), and other cancers ($3.0\%$) [29]. Moreover, late recurrence after RC has been reported to be infrequent [3,4]. These results indicate that the contribution of other diseases to mortality after a 5-year cancer-free period is much greater than that of BC. The association between sarcopenia and increased mortality caused by CVD and infectious diseases has been reported [12,13,14,30]. Moreover, our results showed a relationship between sarcopenia and increased non-cancer-specific mortality after a 5-year cancer-free period. Taken together, patients with sarcopenia might not need continuous surveillance after a 5-year cancer-free period in patients who undergo RC.
The present study had several limitations. First, we were unable to control for selection bias and other unquantifiable confounders in retrospective studies. Moreover, patients without available CT scans for muscle quantity and quality measurements were excluded, which might have caused a selection bias. In addition, skeletal muscle loss is associated with aging and patients in the severe sarcopenia group were significantly older than patients in the control group in the present study (Table 1). Thus, it might have caused an association bias regardless of the adjustment for age in the multivariable analyses. Second, a relatively small number of patients were enrolled, and the number of recurrence events was also small. Moreover, the small number of cancer-specific deaths prevented us from evaluating cancer-specific survival. Third, sarcopenia was assessed by manual tracing, which may have been subject to human error. Finally, given its retrospective nature, we had no information on other frailty metrics, such as walking speed, grip strength, and nutritional status.
## 5. Conclusions
Recurrence after a 5-year cancer-free period in patients with BC who underwent RC was infrequent. Severe sarcopenia was not associated with an increased recurrence risk but was associated with non-cancer-specific mortality. Thus, patients with severe sarcopenia may not need continuous surveillance after a 5-year cancer-free period.
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|
---
title: Clustering of Health and Oral Health-Compromising Behaviours in Army Personnel
in Central Peninsular Malaysia
authors:
- Ahmad Asyraf Azil
- Zamros Yuzadi Mohd Yusof
- Jamaludin Marhazlinda
journal: Healthcare
year: 2023
pmcid: PMC10000684
doi: 10.3390/healthcare11050640
license: CC BY 4.0
---
# Clustering of Health and Oral Health-Compromising Behaviours in Army Personnel in Central Peninsular Malaysia
## Abstract
Health- and oral health-compromising behaviours (HOHCBs) impact the health readiness of military personnel, resulting in decreased fitness performance, thus affecting combat readiness. This study aimed to identify the clustering patterns and number of HOHCBs in army personnel in Central Peninsular Malaysia. Thus, a cross-sectional study using a multistage sampling technique and a validated 42-item online questionnaire was conducted to assess ten health (medical screening, physical activity, sedentary lifestyle, smoking status, alcohol consumption, substance abuse, aggressive behaviours, sleep, and road safety habits) and five oral health behaviour domains (tooth brushing, fluoridated toothpaste use, flossing, dental visits, and bruxism). Each HOHCB was dichotomised into healthy and health-compromising behaviour and analysed using hierarchical agglomerative cluster analysis (HACA). With the majority being males ($92.5\%$), of other ranks ($96.8\%$), and healthy ($83.9\%$), 2435 army members of a mean age of 30.3 years (SD = 5.9) participated, with a response rate of $100\%$. HACA identified two clustering patterns: (i) ‘high-risk behaviours’ (30 HOHCBs) and (ii) ‘most common risk behaviours’ (12 HOHCBs) with a mean clustering number of 14.1 (SD = 4.1). In conclusion, army personnel in Central Peninsular Malaysia displayed 2 broad HOHCB clustering patterns, ‘high-risk’ and ‘most common risk’, with an average of 14 HOHCB clusters per person.
## 1. Introduction
The health readiness of military personnel is one of the core sub-factors that directly contribute to ‘combat readiness’, which refers to the military’s preparedness and ability to perform during military operations [1,2]. One of the primary concerns and critical factors that may significantly impact the health readiness of military personnel is health-compromising behaviours [3,4].
Health-compromising behaviours, also referred to as health risk behaviours, are defined as detrimental actions that increase the likelihood of illness or impede recovery [5]. For example, tobacco use, excessive alcohol consumption, physical inactivity, and an unhealthy diet are health-compromising behaviours known to be significant risk factors for cardiovascular diseases, chronic respiratory diseases, cancer, and diabetes. These non-communicable diseases (NCDs), also known as chronic diseases, have become global health problems, as they account for increased health morbidity and mortality worldwide [6]. When coupled with poor oral hygiene practices, these health-compromising behaviours are also known as oral health-compromising behaviours. Evidence has shown that both health- and oral health-compromising behaviours (HOHCBs) negatively affect oral health and overall health, increasing the risk of NCDs [7,8]. More importantly, NCDs such as diabetes mellitus, cardiovascular diseases, chronic respiratory diseases, obesity, and oral diseases (e.g., dental caries, periodontal disease, and oral cancer) directly or indirectly share several behavioural risk factors and intermediary determinants [9,10].
These risk factors may not occur in isolation; instead, they commonly co-occur or cluster together [11,12]. For example, a systematic review only focusing on United Kingdom studies reported strong evidence of behavioural clustering of alcohol consumption, smoking, and unhealthy diet in general adult populations [11]. In Spain, three cluster groups, namely, ‘cluster 1: unhealthy lifestyle with moderate risk’, ‘cluster 2: unhealthy lifestyles with high-risk’, and ‘cluster 3: healthy lifestyles with low risk’, comprising seven health-related lifestyles (i.e., smoking, alcohol consumption, drug abuse, physical activity, sedentary habits, dietary habits, and diet quality) were identified in adults [13]. Meanwhile, a study conducted in military personnel assessing the clustering of lifestyle factors in the Hungarian Defence Forces discovered sixteen distinct profiles, including eating habits, smoking status, daily physical activity, sports habits, mental toughness, psychosomatic symptoms, and sleep apnoea symptoms [14]. Other studies related to the clustering of HOHCBs were mainly conducted in the adolescent population [12,15,16,17,18]. For example, a study performed in Saudi Arabian adolescents found two clusters of HOHCBs, namely, non-adherence to preventive behaviours and the undertaking of risk behaviours, clustering among nine health behaviours and three oral health behaviours (i.e., dietary intake, physical activity, sedentary behaviour, smoking status, alcohol consumption, drug use, physical fighting, bullying, use of electronic media communication, frequency of tooth brushing, use of fluoridated toothpaste, and flossing behaviour) [15,16]. Similarly, a study performed in Brazilian adolescents found that the first cluster reflected a combination of the lack of adherence to preventive behaviours and the undertaking of risky conduct, and the second cluster reflected an unhealthy lifestyle [17].
Most importantly, HOHCBs may ultimately impact combat readiness, particularly the health readiness of military personnel. This may result in decreased performance and fitness, affecting military personnel’s physical and mental health, which may later contribute to non-combat injuries (e.g., NCDs) and combat casualties [19,20]. Therefore, determining whether HOHCBs cluster together in military personnel could help to further understand and facilitate prevention, which could benefit combat readiness. In addition, it helps identify the military populations at the greatest risk to plan targeted health promotion and intervention programmes. These initiatives should focus on multiple behaviours, which promises a more significant impact on public health than conventional interventions focusing on a single behaviour, for example, through the common risk factor approach [10]. As such, further evidence, insights, and understanding regarding the occurrence of HOHCBs and how these may cluster and influence the health readiness of army personnel is of paramount importance and very valuable.
To our knowledge, no local study has been conducted on the clustering of HOHCBs in Malaysian adults, and limited studies have been performed in military personnel. The only research study on the clustering of HOHCBs in Malaysia focused on Malaysian adolescents [21]. In addition, previous studies in this area focused on a limited number of behaviours, such as dietary habits, smoking, alcohol consumption, and physical activity. This research gap can limit the understanding of how the diverse health and oral health behaviours are connected and may influence the military population’s health readiness. Therefore, the objective of this study was to identify the clustering patterns and to assess the clustering number of HOHCBs in army personnel in Central Peninsular Malaysia.
## 2.1. Study Design and Sample
A cross-sectional study was conducted in army personnel in an army division of Central Peninsular Malaysia. Using the G*Power 3.0.10 programme, with the power of the study set to $80\%$, the significance level of 0.050, the dropout rate of $10\%$, and the design effect of 1.2 [16,21], the sample size required for this study was found to be 1768. Thus, the proposed minimum sample size for the study was 1800.
A multistage sampling method involving stratified, proportionate, and simple random sampling was employed in different stages. First, the army division, which was divided into four main army troop/brigade groups, was further stratified into four subgroups: Headquarters, Combat Elements, Combat Support Elements, and Services Support Elements. Subsequently, the required sample was divided proportionately according to the ratio of army personnel between each group and subgroup. Each subgroup’s randomly chosen army units were further stratified into officer and other rank groups. After each unit gathered its army personnel, those who fulfilled the inclusion and exclusion criteria within each rank group were invited as the research respondents. The inclusion criteria were all regular (professional) army personnel in service. Those on the resettlement list and outside the unit, training/studying in military/non-military institutions, on active duty, on long vacation, and on sick leave during the data collection period, along with Regular Reserves and Volunteer Force army personnel, were excluded. Based on the ratio, the number of respondents who participate should achieve the minimum number required by the researcher. In this study, all randomly selected army units satisfied the number of respondents needed; thus, the selection of other units was unnecessary.
## 2.2. Study Instrument
The validated online self-administered questionnaire (Google Forms) used in this study was developed and customised based on several existing validated questionnaires used in eleven studies (345 items) measuring health and oral health behaviours [21,22,23,24,25,26,27,28,29,30,31]. Only relevant questions were customised to address the objectives of the present study. The content was validated by the expert committee on the HOHCB questionnaire for Malaysian military personnel. Following a thorough review of the literature and several discussions among members of the expert committee, ten health behaviour domains (‘medical visit’, ‘physical activity’, ‘sedentary lifestyle’, ‘dietary intake’, ‘smoking’, ‘alcohol consumption’, ‘drug and substance abuse’, ‘sleep’, ‘aggressive behaviour’, and ‘road safety behaviour’) and five oral health behaviours (‘toothbrushing’, ‘fluoridated toothpaste’, ‘flossing’, ‘dental visit’, and ‘bruxism’) were identified to be of great importance to military personnel and Malaysian Armed Forces (MAF). Therefore, the HOHCB items were either adopted fully, developed as new items, or modified to fit the military circumstances by rewording, combining, and excluding irrelevant items.
The questionnaire was pre-tested on 20 army members to assess its face validity, including whether they understood the purpose of the questionnaire, its instructions, the meaning of the items, and the adequacy of the response options, as well as whether they had other related comments. A pilot study involving 248 military members assessing the validity and reliability of the questionnaire revealed a Cronbach’s alpha of acceptable reliability (α = 0.800), with the α-value of all items ranging from 0.300 to 0.980. The factor analysis revealed 16 factors with acceptable Kaiser–Meyer–Olkin (KMO) sampling adequacy values (KMO = 0.740) and factor loadings ranging from 0.303 to 0.968. The questionnaire redistributed to a group of respondents ($$n = 20$$) after ten days for test–retest reliability assessment reported a good weighted kappa value for health and oral health behaviours ($K = 0.72$, $p \leq 0.010$) and excellent intraclass correlation coefficient (ICC = 0.900, $95\%$ CI (0.880–0.910), $p \leq 0.010$).
After considering the pre-test and pilot study findings and items essential from military and public health viewpoints, the expert committee agreed on a total of 52 health behaviour items and 9 oral health behaviour items in the final questionnaire. Regarding the sociodemographic background, data included military profile (rank, type of service, and years of service) and general profile (date of birth, gender, ethnicity, marital status, number of children, education level, and medical history), and the respondents’ socioeconomic status was assessed based on their family monthly income [32].
Finally, the self-administered questionnaire comprised ten health-compromising behaviours and five oral health-compromising behaviours. Several items were grouped under the same behaviour; thus, for determining the clustering of HOHCBs, the 42 most relevant items were selected to represent the domains of HOHCBs. Details of each domain are explained in the subsequent sub-sections.
### 33 Selected Items)
The respondents were asked about the frequency of visiting a medical doctor or medical staff for health check-ups or screenings. The response options included ‘12 months or less’, ‘13 months to two years’, and ‘more than two years. Those with more than twelve-month intervals between medical visits were considered to engage in health-compromising behaviour [33].
Six questions elicited the respondents’ frequency of performing three forms of physical activity in the past seven days: (i) vigorous physical activity, (ii) moderate physical activity, and (iii) walking activity. The response was assessed by day (from zero to seven days) and duration (minute).
The metabolic equivalent task (MET) minutes per week (MET-min week−1) were calculated as minutes of [activity/day] × [days per week] × [MET level] [34]. Respondents who had either (i) 75 min of vigorous physical activity, (ii) 150 min of moderate physical activity, or (iii) 600 min of MET of a combination of vigorous, moderate, and/or walking activities in the past seven days were considered to engage in healthy behaviour [28,35].
The respondents were prompted to recall the amount of time (hours) that they spent sitting or lying at work (e.g., using a computer), at home (e.g., watching television), during free time (e.g., chatting with friends), and while travelling (e.g., commuting to and from work), and the amount of screen time (hours) spent in both work and daily personal situations. The response options for both questions were ‘one hour or less’, ‘more than one hour to two hours’, ‘more than two hours to four hours’, ‘more than four hours to six hours’, ‘more than six hours to eight hours’, and ‘more than eight hours’.
A sedentary lifestyle refers to individuals who spend more than eight hours per day sitting and lying [36,37,38], as well as more than two hours of screen time per day using electronic devices. Those with a sedentary lifestyle are considered to engage in health-compromising behaviour [39].
Respondents were asked if they had smoked (i) a cigarette and (ii) other forms of tobacco products (i.e., electronic cigarette, shisha/hookah, snuff/chew tobacco, cigar, or other) in the past 30 days and the (iii) frequency of being exposed to tobacco product smoke in the previous week. The respondent who smoked any tobacco products on one or more days in the past 30 days was considered a ‘current smoker’ and thus to engage in health-compromising behaviour, while those who had been exposed to any tobacco product smoke for at least one day were considered to engage in health-compromising behaviour.
The question regarded the frequency of drinking at least one alcoholic beverage in the past 30 days. The respondent was considered a ‘current drinker’ if they had consumed alcoholic drinks on one or more days in the past 30 days, and they were categorised as displaying health-compromising behaviour [28].
Any illegal use of substances such as opioids (e.g., marijuana, heroin, and morphine), amphetamine or methamphetamine (e.g., ecstasy, syabu, ice, and yaba pills), kratom (ketum), and inhalants (e.g., glue and petrol) was defined as drug and substance abuse [28]. It also included the abuse and misuse of prescription medicines, such as painkillers, cough syrup, or sleeping pills [27].
The respondents were asked whether they had been using any forms of drugs or engaged in substance abuse. The response options ranged between ‘Never’, ‘Yes, for the past 30 days’, and ‘Yes, in the entire life but not the past 30 days’. Respondents who had abused drugs or substances at least once in the past 30 days were categorised as current drug users practising health-compromising behaviour [28].
The respondents were asked about their sleeping hours (i) during the working week and (ii) on weekends or holidays, as well as the amount of time that they required to feel refreshed and to function normally (ranging from ‘four hours or less’ to ‘more than eight hours’), and (iii) the perceived quality of sleep in the past seven days (‘excellent’, ‘good’, ‘moderate’, and ‘poor’). Respondents whose daily sleeping hours were equal to or greater than their perceived hours to feel refreshed and function well engaged in healthy behaviour, while others were deemed to engage in health-compromising behaviour [27].
Aggressive behaviours included yelling or shouting, kicking or smashing objects, threatening with physical violence, and physically fighting or hitting an individual in the past 30 days. The choices of response ranged from ‘none’ to ‘five times or more’. Respondents who had been involved in any form of aggressive behaviour for one or more days in the past 30 days were considered to engage in health-compromising behaviour [27].
Respondents were asked about their frequency of wearing (i) seat belt and (ii) helmet, as well as (iii) texting and (iv) calling others while driving or riding a vehicle in the past seven days. The questions on the seat belt and helmet concerned the driver/rider and passenger. The response options were ‘always’, ‘most of the time’, ‘seldom’, ‘never’, and ‘I did not drive or ride a vehicle in the past seven days’. Respondents who claimed that they ‘always’ wore a seatbelt and helmet and ‘never’ texted or called while driving or riding a vehicle in the past seven days were determined as practising healthy behaviour, and vice versa [27].
### 9 Items)
Respondents were asked about the frequency of brushing their teeth and the time spent tooth brushing daily. Answer options ranged from ‘rarely’, ‘less than three times a week’, ‘three to four times a week’, ‘once a day’, and ‘twice a day or more’, and ‘after waking up from sleep’, ‘before breakfast’, ‘after main meals’, ‘after eating sugary food or drink’, and ‘before sleep’. Those who brushed their teeth less than twice a day and did not brush before sleep were considered as displaying oral health-compromising behaviours.
Respondents had to indicate whether their toothpaste was ‘fluoridated’, ‘not fluoridated’, ‘not sure’, or ‘I did not even know what fluoride was’ and subsequently state the brand of their toothpaste. Those who used non-fluoridated toothpaste were considered as practising oral health-compromising behaviours.
Respondents were asked about the frequency of flossing in the past 30 days. The response options were ‘once daily’, ‘once every two days’, ‘once to twice a week’, ‘once to twice a month’, ‘rarely or never’, and ‘I do not know floss’. Those who flossed less than once every two days were considered as practising oral health-compromising behaviours.
Respondents had to indicate the time interval since their (i) last dental visit, whether it was ‘6 months or less’, ‘more than six months to one year’, ‘more than one year to 2 years’, or ‘more than two years’. In addition, respondents had to state the main reason for their most recent dental visit, both on (ii) their initiative or (iii) as a part of service requirements. The response options were ‘dental check-up’, ‘preventive treatment’, ‘treatment of related oral health problems’, and ‘I did not visit the dentist in the past 12 months’.
Those who had had their dental check-up more than 12 months before and whose last dental visit was for oral health problem treatment, or those who had not visited the dentist in the past 12 months were considered as practising oral health-compromising behaviours.
This question elicited the respondents’ awareness of clenching and grinding their teeth while asleep as told by their spouse or family members. The response options were either ‘Yes ‘or ‘No’, and those with bruxism had oral health-compromising behaviours.
## 2.3. Conduct of the Study
The authors obtained ethical permission from relevant authorities, including Universiti Malaya and MAF, before conducting the study. Once approval was obtained, an online briefing was held for the person in charge (PIC) of the army units via Google Meet. In addition, face-to-face briefings were conducted with units that could not attend the online session.
The questionnaire was administered to army personnel of Central Peninsular Malaysia units. The briefing session (physical, online, or video briefing) and questionnaire distribution were conducted according to the availability of each unit. Those who agreed consented to participate in the study. The respondents then scanned a quick response (QR) code or visited a website link to a Google form via their smartphone or personal computer. While answering the survey questions, a researcher was present (or in a WhatsApp group/personal message/call) to help the respondents if they faced any questions or problems. After the respondents completed the questionnaire, the researchers checked each questionnaire to ensure all sections were completed and submitted. Any incomplete questionnaires were rectified by informing the respondents (or through PICs) and asking them to complete their attempts.
## 2.4. Data Management
In order to protect the confidentiality of the online data, the Google documents were password-protected and deleted from the ’cloud‘ after they were downloaded. Only the researchers had access to the password-protected documents stored on the external hard drive. Each respondent was given an individual identifier, and their names and Army Identification Numbers were not utilised in any data analysis or presentation.
There were $6.4\%$ of respondents who had at least one missing value, where the proportion of items with a missing value ranged from $1.4\%$ to $5.4\%$. No respondents were excluded from the study because the missing value was far less than the $20\%$ cut-off point. Subsequently, in the data analysis, we used mean values (unit imputation) to replace the missing values. Therefore, the completion rate was $100\%$ ($$n = 2435$$).
All data were entered into Statistical Package for Social Sciences (SPSS) software, version 25. They were initially checked, cleaned, and explored using descriptive statistics and graphs for each variable. Next, means and standard deviations (SDs) were used to describe the continuous variables, while frequencies and percentages were used for categorical data. The data on each HOHCB were then dichotomised into binary codes (0 = healthy behaviour; 1 = health-compromising behaviour) for the 42 HOHCB items (Table S1).
## 2.5. Statistical Analysis
Hierarchical agglomerative cluster analysis (HACA) with the between-groups linkage clustering method and squared Euclidean distance measure without pre-determined cluster membership was used in the analysis. HACA was chosen because it allows researchers (i) to compare the clustering result with an increasing number of clusters, (ii) because there is no need to make an a priori decision about the final number of clusters, and (iii) because it is more stable than the non-hierarchical method [40].
Two-step HACA with the between-group linkage clustering method and squared Euclidean distance measure without pre-determined cluster membership were performed in this study. The first step was to generate a squared Euclidean proximity matrix of all 42 HOHCB items, while the second step involved the use of a dendrogram to visualise the distance and cluster combination. The number of clusters was counted and reported as frequency and percentage, including the mean number of clustering.
In addition, repeated HACA on different sub-samples and K-means cluster analysis were used to validate the findings compared to the two-step HACA findings.
## 3.1. Sociodemographic Characteristics
As shown in Table 1, the mean age of all the 2435 army members (response rate = $100\%$) was 30.3 years (SD = 5.9), with the majority being male ($92.5\%$). In addition, most respondents were of Malay ethnicity ($77.0\%$), married ($67.5\%$), had received education up to the secondary school level ($87.4\%$), and had no medical conditions ($83.9\%$).
Military-wise, almost all respondents were army personnel of other ranks ($96.8\%$), with two-thirds of them being junior non-commissioned officers (NCOs) ($66.2\%$). Nearly $60\%$ of them had served for 12 years or less. According to the respondent’s Corps and Regiments, less than half of them were from the Combat Element ($45.3\%$), and approximately one-fourth were from the Combat Support Element ($28.9\%$), while the remaining quarter was from the Services Support Element ($25.7\%$). Socioeconomically, the vast majority of respondents were in the bottom $40\%$ (B40; MYR 4849 and below) family income group ($92.6\%$).
## 3.2.1. Hierarchical Agglomerative Cluster Analysis
Euclidean distances were calculated between all pairs of cases, where a low coefficient indicated high similarity between the two variables, thus classified as one group. Conversely, a high coefficient indicated low similarity between the variables, and they became more heterogeneous than the previous combinations (Table S2).
As shown in Figure 1, the cluster began with the smallest squared Euclidean distance of high consumption of carbonated drinks and acidic foods (9.000). Subsequently, the cluster was combined with high sweet-food consumption, high sweet-drink consumption, and sitting and lying behaviours to form a small group, which merged with other behaviours and groups, forming Sub-cluster 1a (1001.348). This sub-cluster consisted of 25 behaviours (high consumption of carbonated, acidic, and sweet foods and drinks; extended sitting and lying; drug use; physical inactivity; non-fluoridated toothpaste use; not following tooth-brushing recommendations; alcohol consumption; not wearing a helmet and seat belt; aggressive behaviour; bruxism; symptomatic dental visits; high fatty-food consumption; and extended screen time). Meanwhile, another smaller group comprising two behaviours, namely, medical screenings and dental check-ups, formed Sub-cluster 1b (845.000). Next, inadequate sleep during work and holidays formed a cluster (584.000) combined with substandard sleep quality to form Sub-cluster 1c (886.000). Subsequently, Sub-cluster 1a was merged with Sub-cluster 1b (1043.580) and Sub-cluster 1c (1065.741). Finally, these three sub-clusters (Sub-clusters 1a + 1b + 1c) were combined to form a broad cluster, Cluster 1.
At the bottom of the dendrogram are Sub-clusters 2a, 2b, and 2c. Both Sub-cluster 2a (low plain-water consumption, and texting and calling while driving) (1072.000) and Sub-cluster 2b (exposure to smoke, and smoking cigarettes and other tobacco products) (744.500) had three behaviours, respectively. Sub-cluster 2c had six behaviours (unrecommended vegetable, fruit, cereal/cereal-product, milk/dairy-product, and poultry/fish/meat/legume consumption; and infrequent flossing) (1001.500). Sub-cluster 2a was subsequently merged with combined Sub-cluster 2b and Sub-cluster 2c (1111.944) to form a broad cluster, Cluster 2 (Sub-clusters 2a + 2b + 2c).
Cluster 1 was named ‘unhealthy lifestyles with high-risk behaviours’ and comprised 30 HOHCBs. Cluster 2 was named ‘most common risk behaviours’ and contained 12 HOHCBs. At the end of the analysis, Cluster 1 and Cluster 2 were merged into one large cluster (1399.217). The validity and stability of the cluster analysis were confirmed by performing repeated HACA on different sub-samples randomly drawn from the study sample (SPSS random sample of cases; approximately $25\%$ of respondents for each sample) [12,16].
## 3.2.2. K-Means Cluster Analysis
K-means cluster analysis requires an a priori number of clusters to be extracted from the data before performing the analysis. Therefore, using information obtained with the HACA, K-means cluster analysis with two cluster memberships was used. As a result, Cluster 1 comprised $50.3\%$ ($$n = 1224$$) of respondents. Regarding the average score (mean) for each HOHCB, the cluster consisted of 17 HOHCBs. These included infrequent medical screenings; extended screen time (work); extended screen time (personal); low fruit consumption; high consumption of fried food; inadequate sleep during working days; flossing infrequently/never flossing; infrequent dental check-ups; symptomatic dental visits upon self-initiative; low vegetable consumption; unrecommended rice/noodle/cereal/tuber, milk/dairy-product, and poultry/fish/meat/egg/legume consumption; cigarette smoking; exposure to cigarettes/tobacco products smoke; and texting and calling while driving. In contrast, Cluster 2 comprised $49.7\%$ of respondents, with nine HOHCBs. Eight were the same as in Cluster 1, with one distinct behaviour, low plain-water consumption. In addition, there were 24 HOHCBs with a mean score equal to zero for both cluster memberships (Table S3).
## 3.3. Clustering Number of HOHCB
The clustering HOHCB number for each respondent was calculated, and the total score was interpreted as the clustering HOHCB number for all respondents. The clustering number for the 42 behaviours ranged from 0 (no HOHCBs) to 29 HOHCBs, with the average number of clustering behaviours in army personnel being 14.1 (SD = 4.1). The most common clustering number among the respondents was 15 ($9.2\%$), followed by 13 ($9.0\%$), 16 ($8.7\%$), and 14 ($8.7\%$) HOHCBs (Table 2).
## 4. Discussion
As this is the first study investigating HOHCB clustering in this population in Malaysia, this study investigated multiple important and relevant HOHCBs in the adult and MAF populations, consisting of 15 HOHCB domains (health behaviours: 10 domains, with 33 items; oral health behaviours: 5 domains, with 9 items), for a total of 42 items. In comparison with other studies with a lesser number of items, ranging from 2 [41] to 17 items [17], conducted in adolescents and adults in the United Kingdom, the United States, Brazil, Saudi Arabia, Malaysia, and Korea, the present study considered a large number of HOHCB items, including most HOHCB domains from previous studies. The reason was to allow us to conduct a complete and all-inclusive assessment of all relevant HOHCBs in the MAF population to reflect the actual situation and use the information to advocate for comprehensive health and oral health programmes.
HACA and K-means cluster analysis revealed two broad clusters in respondents with a relatively similar pattern. Nevertheless, one cluster membership contained more HOHCBs than the other, reflecting a high-risk behaviour clustering group. The first cluster, ‘high-risk behaviours’, consisted of 30 HOHCBs. The cumulative and synergistic negative effects of health-compromising behaviours increase the risk of adverse effects on health [5,42]. Hence, the respondents in this group may have a higher chance of developing diseases, with those diseases having a higher chance of having greater severity. Physical inactivity and sedentary lifestyles, as well as fast food, fatty-food, and high sugar intake, contribute to increased risk of obesity and overweight, cardiovascular diseases, musculoskeletal disorders, and cancer [43,44]. Furthermore, both drug abuse and alcohol harm health and functional ability. They may also have medical (e.g., addiction, immunodepression, infectious diseases, and mortality), physical (e.g., physical abuse and injuries, agitation, and restlessness), social (e.g., crime, interpersonal and relationship problems, and loss of employment), and psychological (e.g., mental illness, violent and aggressive behaviour, and suicidal behaviour) implications. Meanwhile, sleep deprivation increases the risk of depression, suicide, post-traumatic stress disorder, accidents and injuries, cardiometabolic disorders, and even mortality [45]. The risk of major road traffic injuries, including thoracic, head, and neck injuries, and even mortality is increased when seatbelts and helmets are not used [46,47]. Moreover, unrecommended tooth-brushing frequency, non-fluoridated toothpaste use, and symptomatic dental visits are associated with oral health diseases such as dental caries, periodontal disease, and edentulousness, all of which negatively impact the quality of life [48,49].
The second cluster comprised a total of 12 HOHCBs, involving respondents who displayed (i) ‘unhealthy nutrition intake’ (unrecommended cereal and cereal-product consumption; unrecommended fish, poultry, meat, egg, and legume consumption; unrecommended milk and dairy-product consumption; low fruit intake; and low plain water intake), (ii) ‘tobacco product use’ (smoking cigarettes, use of other tobacco products, and exposure to tobacco product smoke), (iii) ‘risky driving’ (texting and calling while driving), and (iv) flossing infrequently or never flossing. Most of these HOHCBs were among the eleven most prevalent health-compromising behaviours in the army personnel in this study. At least two-fifths of respondents engaged in these HOHCBs ($44.2\%$ (other tobacco product use)–$82.0\%$ (unrecommended cereal and cereal-product consumption)). Thus, the second cluster may represent the most common HOHCBs in army personnel. The possibility of adverse health impacts also exists in the second cluster group. For instance, malnutrition, micronutrient deficiencies, anaemia, gastrointestinal diseases, and osteoporosis have all been linked to unhealthy dietary consumption [50,51]. Tobacco products (e.g., cigarettes and electronic cigarettes) and second-hand smoke are associated with several adverse health effects, such as respiratory diseases, cardiovascular diseases, and cancer, all of which contribute to premature mortality [52,53]. Texting and calling without a hands-free device while driving endanger drivers, passengers, and other road users, as these behaviours increase the likelihood of accidents due to the driver’s inability to focus on the road, respond to major traffic events, and maintain vehicle control within the lane [54,55]. Flossing infrequently or never flossing is associated with an increased risk of interproximal caries and periodontal disease [56].
However, no direct comparison can be made between the present study and previous studies of clustering HOHCBs, given that the present study comprised a different number of clustering patterns with varying HOHCB composition. In previous studies, the number of clustering patterns ranged from two [12,16,17,21,57] to six clusters [58]. A study in the Hungarian Defence Forces personnel reported 16 cluster profiles, but the clusters were also classified according to military characteristics [14].
Nevertheless, the present study’s findings are comparable in terms of ‘risk categorisation’. In this study, the clustering patterns in army personnel were categorised into ‘high-risk behaviours’ and ‘most common risk behaviours’, a type of categorisation similar to that in studies in Europe, China, Australia, the United Kingdom, and the United States [13,38,57,59,60,61,62]. For instance, Hobbs et al. [ 38] identified three clusters, (i) ‘lower risk’, (ii) ‘moderate risk’, and (iii) ‘elevated risk’ in Australian adults. A United Kingdom study conducted in adults by Mawditt et al. [ 60] also performed similar categorisations: (i) ‘risky’, (ii) ‘moderate’, and (iii) ‘mainstream’ clustering pattern groups. These studies classified the clustering patterns as ‘high-risk’ and ‘low/moderate/mainstream’, where mainstream behaviour is similar to the most common risk behaviour. Additionally, a study performed in Hong Kong adults by Chan and Leung [57] identified two clusters: (i) ‘healthy’ and (ii) ‘less healthy’. This risk categorisation was based on the type and number of risk behaviours in the cluster. It determined the level of the clustering group in relation to the likelihood of developing diseases and the severity of diseases. A greater number of HOHCBs, for example, increase the risk of disease. Furthermore, categorisation may aid in determining which clusters to focus on, such as the targeted population group and high-risk behaviour approach in health promotion activities [10,12].
At the same time, some compromising behaviours clustered together within the same cluster group. Similar to Cluster 1 (‘high-risk behaviours’) in the present study, Ali [21] reported that alcohol consumption, aggressive behaviours (bullying and physical fighting) and drug users clustered in the same group in Malaysian adolescents. In addition, Jordao, Malta, and Freire [17], and Skalamera and Hummer [62] reported that alcohol consumption clustered with drug use in young adults in the United States and Brazil. In terms of sedentary lifestyles and unrecommended dietary intake, Hobbs et al. [ 38] revealed that physical inactivity, extended sitting time, and fast-food consumption clustered within the ‘elevated risk’ cluster. A similar pattern was reported by Skalamera and Hummer [62], where physical inactivity and fast-food consumption clustered together, but with the addition of no doctor or dentist visits. Regarding Cluster 2 (most common risk behaviours), the results align with the findings by Ali [21], in which inadequate consumption of vegetables and fruits, and low consumption of milk and dairy products clustered in a group.
Identifying the two HOHCB cluster patterns in army personnel, as conducted in the present study, is essential for oral health promotion activities. The findings assist in better understanding risk factor clustering and could facilitate prevention, which could benefit the combat readiness of military populations. Thus, health promotion and intervention programmes could be prioritized and target high-risk personnel. These initiatives should focus on multiple behaviours, which promises a more significant impact on public health than conventional interventions focusing on a single behaviour, for example, through the common risk factor approach. For instance, in the population-based approach, health promotion activities should focus on behaviours in the ‘Cluster 2: most common risk behaviours’ clustering pattern, since they involve several of the most common HOHCBs in army personnel. The goal is to empower army personnel regarding proper oral hygiene self-care (particularly, flossing), healthy nutrient consumption, smoking control programmes, and road safety from a health standpoint. Therefore, tackling this behaviour cluster may help reduce the prevalence of the most common risk behaviours in army personnel. Nevertheless, in a situation where resources are available, targeting the first cluster (high-risk behaviours) has its advantages. As the cluster consists of 30 health-compromising behaviours, addressing this behaviour cluster might eventually tackle many HOHCBs in the army.
Equally important is the number of clustering HOHCBs that can occur in one army member. This study revealed that on average, army personnel could have 14.1 (SD = 4.1) clustering behaviours, with the most common clustering number in the respondents being 15 ($9.2\%$), followed by 13 ($9.0\%$), 16 ($8.7\%$), and 14 ($8.7\%$) HOHCBs. However, the results cannot be compared with those of other studies, because studies on clustering numbers are minimal, with different numbers of HOHCBs being investigated. Nonetheless, this could be one of the novelties of the present study in terms of clustering behaviour research, as the findings prove that an individual can simultaneously engage in multiple HOHCBs. For example, on average, one army member in this study engaged in 14 HOHCBs, which is alarming. Knowing the clustering number could also be the focus of prevention and promotion activities, prioritising those with a higher number of clustering risk behaviours. Additionally, identifying the clustering number could complement the use of information on clustering patterns. For example, during the planning and execution of an intervention, we could concentrate on one clustering pattern and target those with high clustering numbers within the cluster. Hence, it is highly recommended that future studies investigate the clustering patterns and clustering number of HOHCBs at the individual level.
This study has a few limitations. First, the results heavily depend on the respondents’ truthfulness in the use of the self-administered questionnaire. Some HOHCB items might have been under-reported or over-reported, e.g., substance abuse and alcohol consumption. Second, the present study only involved army personnel in Central Peninsular Malaysia. Similar data on the navy and air force personnel who are also part of the three main MAF branches were not included. They may have different HOHCB clustering patterns. This also includes other army formations such as other infantry divisions and army troops. However, the main strength of the study is that limited studies have looked into clustering patterns and clustering numbers of HOHCBs, especially in military personnel, and this study is one of the few that have done so. Although the findings are here mostly compared with studies in the general population, the results are still beneficial to providing evidence and assisting in reorienting the health promotion approaches in the army population. Thus, more studies on the clustering of HOHCBs in military personnel should be conducted in the future, since the HOHCBs are linked to the health readiness and, by extension, the combat readiness of the MAF.
## 5. Conclusions
This study identified two broad HOHCB clustering patterns in army personnel in Central Peninsular Malaysia, ‘high-risk’ behaviours and ‘most common risk behaviours’, with an average of 14 HOHCB clusters for each army member. As HOHCBs significantly impact the health readiness and, eventually, the combat readiness of the army population, taking cognisance of these findings could assist policy makers or health managers in formulating strategies for effective health promotion and disease prevention programmes targeting the army personnel engaged in these two HOHCB clusters. The common risk factor approach becomes very relevant and could be applied. With limited resources, future health prevention and promotion policies and activities should focus on army personnel engaged in Cluster 2 ‘most common risk behaviours’, as the findings show that this cluster comprises the most prevalent health-compromising behaviours in army personnel, followed by those engaged in Cluster 1 behaviours. Furthermore, policymakers or health managers could further prioritise those with a high number of clustered risk behaviours within this targeted group. These strategies can hopefully tackle the factors that contribute the most to the army population’s health readiness, thus further maintaining the army’s combat readiness.
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|
---
title: Preparation and Characterization of Intracellular and Exopolysaccharides during
Cycle Cultivation of Spirulina platensis
authors:
- Yuhuan Liu
- Yu Wang
- Leipeng Cao
- Zhenghua Huang
- Yue Zhou
- Ruijuan Fan
- Congmiao Li
journal: Foods
year: 2023
pmcid: PMC10000700
doi: 10.3390/foods12051067
license: CC BY 4.0
---
# Preparation and Characterization of Intracellular and Exopolysaccharides during Cycle Cultivation of Spirulina platensis
## Abstract
The dried cell weight (DCW) of *Spirulina platensis* gradually decreased from 1.52 g/L to 1.18 g/L after five cultivation cycles. Intracellular polysaccharide (IPS) and exopolysaccharide (EPS) content both increased with increased cycle number and duration. IPS content was higher than EPS content. Maximum IPS yield (60.61 mg/g) using thermal high-pressure homogenization was achieved after three homogenization cycles at 60 MPa and an S/I ratio of 1:30. IPS showed a more fibrous, porous, and looser structure, and had a higher glucose content and Mw (272.85 kDa) compared with EPS, which may be indicative of IPS’s higher viscosity and water holding capacity. Although both carbohydrates were acidic, EPS had stronger acidity and thermal stability than IPS; this was accompanied by differences in monosaccharide. IPS exhibited the highest DPPH (EC50 = 1.77 mg/mL) and ABTS (EC50 = 0.12 mg/mL) radical scavenging capacity, in line with IPS’s higher total phenol content, while simultaneously showing the lowest HO• scavenging and ferrous ion chelating capacities; thus characterizing IPS as a superior antioxidant and EPS as a stronger metal ion chelator.
## 1. Introduction
Spirulina platensis is a multicellular filamentous cyanobacterium and has been nicknamed the “Edible Queen” by the FAO and the FDA for its nutritional value [1,2]. S. platensis and its derivates have been widely used in dietary supplements and other food products targeted at the health-aware consumer, and are increasingly gaining recognition as functional ingredients [3,4]. Among its most promising derivatives, the polysaccharides of S. platensis (PSP) have attracted significant attention due to their antioxidant, antiaging, antiviral, anti-inflammatory, and immunomodulatory potential, as well as their physicochemical attributes [5,6]. Previous studies have shown that PSP is bioactive and comprises polymeric carbohydrates composed of long chains of monosaccharide units bound together by glycosidic linkages, and that its multiple biological activities are closely related to monosaccharide composition, diverse glycosidic linkages, molecular weight, and spatial configuration [7,8]. Moreover, PSP is interesting for its potential to improve intestinal function and health, and to prevent cancer cell proliferation. Therefore, with the increasing demand for PSP in trade markets, the cost-efficient preparation of PSP is currently a pressing research need.
Broadly, PSP can be isolated from the cell bodies of S. platensis and from culture media to obtain intracellular polysaccharides (IPSs) and exopolysaccharides (EPSs), respectively. IPSs consist of complex acid sulphate polysaccharides, and account for 15–$20\%$ of the cell mass of S. platensis cell mass [2,8]. Variations in IPS content are related to culture conditions, including carbon source, light, pH, salinity, and cultivation time. Large-scale extraction of IPS is usually performed by cell disruption, chemical maceration, and enzymatic treatment [9]. On the other hand, EPS is a water-soluble heteropolysaccharide that is secreted during growth and binds tightly to S. platensis cell walls, forming a protective capsule against dehydration and toxic agents [10,11,12]. EPS content in S. platensis cultures is potentiated by high salinity and low nutrient availability [13]. EPS is obtained by ultrafiltration with membranes intended for the appropriate molecular weight. Both IPS and EPS harbor a variety of functional groups, including -OH, -COOH, -SO3H, and -CH3, which, together with the structural diversity of these polysaccharides, are thought to give them bioactive properties such as antifungal and antioxidant activity, free-radical scavenging, and inhibition of lipid peroxidation [14,15,16]. However, little is known about how IPS and EPS content may be affected during cycle cultivation, and distinct structural-functional profiles of IPS vs. EPS are yet to be characterized.
Therefore, the aims of the present work are to investigate the influence of S. platensis cycle cultivation on IPS and EPS content, and to characterize the structural-functional properties of IPS and EPS; in particular, their physicochemical properties, monosaccharide composition, molecular weight, functional groups, and antioxidant activity. Additionally, an optimized method for the preparation of IPS and EPS based on thermal water coupled with high-pressure homogenization (HPH) is offered. The data herein described thus provides theoretical and technical resources for the cost-efficient production and adequate application of IPS and EPS from S. platensis.
## 2.1. Cultivation of S. platensis
S. platensis strains (FACHB: GY-D18) were purchased from the Institute of Hydrobiology, Chinese Academy of Science, PR China. Batches of S. platensis were cultured in 500 mL Erlenmeyer flasks with 300 mL of modified Zarrouk’s medium for eight days at 25 °C under cycle illumination (5500 lx). After harvesting cells, culture medium was reused to culture a second batch of S. platensis, for a total of five cultivation cycles. Deionized water and analytical grade chemicals and solvents were used in all cases. Erlenmeyer flasks and culture medium were sterilized at 121 °C for 20 min before use.
## 2.2. Evaluation of S. platensis Growth
A 20 mL aliquot of cell suspension was filtered through a Whatman filter paper and dried at 105 °C for 24 h. Biomass was calculated according to the method of Zhou et al. [ 17] and expressed as dry cell weight (DCW; g), following the formula:DCW (g/L) = (Wn − W0)/0.02 Growth rate (g/L/d) = (DCWn+1 − DCWn)/1 where Wn corresponds to total dry weight (g) of the filter paper with algae, W0 represents the dry weight (g) of the filter paper alone, 0.02 is the aliquoted volume (L), and n is time (days).
## 2.3. Experimental Design of RSM for Extraction of IPS from S. platensis
IPSs were extracted from S. platensis cell bodies using hot water coupled with high-pressure homogenization (HPH; GJJ-$\frac{0.06}{100}$, Shanghai Taichi Light Industry Equipment Co, LTD), according to the experimental design of RSM (Table 1). Extracts were centrifuged at 7104× g for 10 min, and supernatants were concentrated at 80 °C. Concentrated supernatants were treated five times with savage reagent (Chloroform: n-butanol = 4:1) to remove protein, then mixed (1:4) with $95\%$ ethanol and allowed to precipitate for 12 h at 4 °C. Precipitates were suspended in a small aliquot of deionized water before dialysis, and then dialyzed in molecular weight cut-off bags (8–10 kDa) for 48 h to eliminate residual salts. Finally, samples were lyophilized, weighed, and kept for further experiments. The yield of IPS (mg/g) was calculated as follows:Yield (mg/g) = c × n × v / m where c is the concentration (mg/mL), n is the dilution factor, v is the sample volume (mL), and m is the dried sample weight (g).
## 2.4. Extraction of Extracellular Polysaccharide (EPS)
EPSs were obtained from S. platensis culture medium according to the method of Li et al. [ 13]. The medium was filtered through a 0.45 μm membrane, and the filtrate was concentrated at 50 °C and then precipitated with ice-cold ethanol (1:4 v/v) at 4 °C for 12 h. Precipitates were dialyzed, lyophilized, weighed, and stored in the same manner as IPS extracts.
## 2.5. Chemical Composition Analysis of IPS and EPS
Total sugar and protein content were determined by the phenol-sulfuric acid and the Coomassie brilliant blue methods, respectively [18]. Total phenol content was estimated using the Folin–Ciocalteu reagent and measuring absorbance at 750 nm; gallic acid (20–100 µg/mL) served as standard, based on Chaiklahan et al. [ 8]. Phenol content was expressed in gallic acid equivalents. Ash content was determined based on weight loss after 4 h at 550 °C.
The monosaccharide composition of IPS and EPS was determined with a precolumn derivation HPLC method using 1–phenyl–3-methyl-5–pyrazalone (PMP) (Ma et al., 2019). Samples were thoroughly hydrolyzed to monosaccharides by treatment with 4 M trifluoroacetic acid for 8 h at 110 °C, and then mixed with the PMP solution and chloroform. Samples were then analyzed by HPLC (Agilent, Santa Clara, CA, USA) and UV detection at 245 nm. A 4:21 (v/v) mixture of acetonitrile and 0.125 mol/L KH2PO4 was used as the mobile phase at a flow rate of 0.8 mL/min, at 30 °C.
The molecular weight (Mw) of IPS and EPS was determined by gel permeation chromatography (GPC, ELEOS System, Wyatt Technology Co., Goleta, CA, USA), based on the method of Zhang et al. [ 10] with slightly modified chromatographic conditions: 0.2 mol/L NaNO3 served as mobile phase at a flow rate of 0.5 mL/min and a column temperature of 25 °C; injection volume was 20 μL.
## 2.6. Fourier-Transform Infrared (FTIR) Spectroscopy and Scanning Electron Microscopy (SEM)
Infrared spectra of IPS and EPS were obtained using a FTIR spectrometer (Thermo Scientific Nicolet IS50, MA, USA) according to the method of Sasaki et al. [ 19]. A dried 1.0 mg sample was ground and pressed into tablets mixed with 100 mg of KBr. Tablets were scanned at a wavelength range of 4000-400 cm−1.
The surface morphology of IPS and EPS was observed by SEM. The powdered sample was sprinkled on the surface of a piece of double-sided tape which was adhered to the microscope’s aluminum column, and then sputter-coated with platinum powder using an ion sputter coater for observation.
## 2.7. Zeta Potentials and Thermal-Gravimetric (TG) Analysis
Sample solutions (1.0 mg/mL) were prepared in ultrapure water. Zeta potentials were determined at 25 °C in the pH range of 2.0–9.0 using a Zeta sizer Nano-ZS particle diameter and potentiometric analyzer (Malvern Instruments, MC, UK). All samples were measured in triplicate.
The thermodynamic characteristics of IPS and EPS samples were analyzed by differential scanning calorimetry (DSC) (Netzsch, DSC 214 Polyma, Selb, Germany). A 5.0 mg sample was weighed in an aluminum pan, using an empty pan as reference. Measurements were performed under nitrogen flow (40 mL/min), at a heating rate of 10 °C/min in a range of 30 °C to 800 °C.
## 2.8.1. DPPH Radical Scavenging Assay
The scavenging activity of different concentrations of IPS and EPS on 2,2-diphenyl-1-picrylhydrazyl (DPPH) radicals was evaluated according to the method of Su et al. [ 20], with slight modifications. Briefly, 1.0 mL of polysaccharide extract (0–2.5 mg/mL) was thoroughly mixed with 1.0 mL of DPPH solution (0.2 mmol/L in $95\%$ ethanol). The mixture was allowed to react for 30 min, protected from light, and absorbance was then measured at 517 nm with a UV/Vis spectrophotometer. In this step, $95\%$ Ethanol and 0–2.5 mg/mL ascorbic acid were used as blank and positive control, respectively. DPPH radical scavenging activity was calculated with the formula:DPPH radical scavenging ability (%) = (A0 − A1 + A2) × 100/A0 where A0 represents the absorbance of the DPPH solution alone, A1 is the absorbance of the DPPH solution containing the sample, and A2 is the absorbance of the ethanol solution with the sample.
## 2.8.2. ABTS Radical Scavenging Assay
Scavenging activity on 2,2′-Azino-bis (3-ethylbenzthiazoline-6-sulfonate) (ABTS) radicals was analyzed as described by Tian et al. [ 6], with some modifications. Equal volumes of an aqueous solution of 7.0 mmol/L ABTS and 2.45 mmol/L K2S2O8 were mixed and allowed to incubate at RT for 12 h, while protected from light, to acquire the ABTS+• solution. This ABTS radical solution was then diluted with phosphate buffer solution (pH 7.4) until reaching an absorbance of 0.70 ± 0.02 at 734 nm. A 100 μL aliquot of the diluted ABTS+• solution was mixed with 100 μL of sample (0.1–2.5 mg/mL), and absorbance was measured at 734 nm after 30 s oscillation. Deionized water and ascorbic acid (0–2.5 mg/mL) served as blank and positive control, respectively. ABTS radical scavenging activity was calculated as follows:ABTS radical scavenging ability (%) = (A0 − A1 + A2) × 100/A0 where A0 is the absorbance of the diluted ABTS+• solution alone, A1 is the absorbance of the diluted ABTS+• solution mixed with the sample, and A2 stands for the absorbance of the sample in deionized water.
## 2.8.3. HO• Radical Scavenging Assay
HO• scavenging activity was assayed according to the method described by Ji et al. [ 21] with some modifications as follows: A 1.0 mL aliquot of a sample solution (0–2.5 mg/mL) in $95\%$ ethanol was thoroughly mixed with 1.0 mL of each of the following: 9 mmol/L H2O2, 9 mmol/L FeSO4, and 9 mmol/L salicylic acid. The solution was then incubated at 37 °C for 60 min with cycle-shaking, and absorbance was measured at 510 nm. Ascorbic acid (0–2.5 mg/mL) was used as a positive control. Hydroxyl radical scavenging activity was calculated as follows:HO• scavenging ability (%) = (A0 − A1 + A2) × 100/A0 where A0 is the absorbance of deionized water, A1 is the absorbance of the sample, and A2 is the absorbance of the solution without sample.
## 2.8.4. Fe2+ Chelating Ability
Fe2+ chelating ability was determined as described by Chang et al. [ 22], with minor modifications. Briefly, 1.0 mL of sample (0–2.5 mg/mL) was mixed with 3.7 mL deionized water and 0.1 mL of 2.0 mmol/L FeCl2·6H2O solution, vigorously stirred for 30 s, and then 0.2 mL of 5 mmol/L ferrozine solution was added. The mixture was incubated for 10 min at 25 °C and absorbance was measured at 562 nm. Deionized water and sodium ethylenediamine tetra acetic acid (EDTA-Na2) (0–2.5 mg/mL) were used as blank and positive control, respectively. Chelating capacity (%) was calculated with the formula:Chelating ability (%) = (A0 − A1) × 100/A0 where A0 is the absorbance of the reaction solution without sample and A1 is the absorbance of the reaction solution with the sample.
## 2.8.5. EC50 Calculation
EC50 represents the mass concentration of the sample when clearance is $50\%$. To calculate EC50 values for DPPH, ABTS, and HO• radical scavenging activity, and for Fe2+ chelating capacity, the clearance ratios of different sample concentrations were plotted and fitted linearly.
## 2.9. Statistical Analysis
All the experiments were conducted in triplicate. Data plotting was performed with Design Expert 13, Origin 2021, and IBM SPSS Statistics 26. Analysis of variance (ANOVA) was carried out wherever applicable and $p \leq 0.01$ was regarded as a significant difference. For all figures and tables, data were presented as mean ± std ($$n = 3$$) of the three independent replicates.
## 3.1. Change of S. platensis and Polysaccharide Content during Cycle Cultivation
Figure 1 shows the growth curve of S. platensis and the growth in polysaccharide content (IPS and EPS) during cycle cultivation. DCW of S. platensis and polysaccharide content showed a linear increase with prolonged cultivation time. The growth of S. platensis behaved as a parabola, reaching its maximum rate (0.24 g/L/day) on the fourth day of cultivation. After eight days in culture, the DCW of S. platensis reached 1.52 g/L, representing a $660\%$ increase, and appeared as a regular spiral filament under the microscope (Figure 1a). Total polysaccharide content significantly increased with extended cultivation time, with IPS (80.08 mg/L) reaching a concentration three times higher than that of EPS (27.94 mg/L) by the end of cultivation (Figure 1b).
As shown in Figure 1c, the DCW of S. platensis gradually decreased from 1.52 g/L to 1.18 g/L with each cultivation cycle. This is likely explained by a decrease in the microalgae photosystem II, as a consequence of the accumulation of dissolved organic matter (DOM) and increased viscosity of the culture medium [13]. IPS content increased together with the number of cycles, which may also be due to DOM accumulation and reduced DCWP, reaching 203.34 mg/L (or a $136\%$ increase) after five cultivation cycles. Meanwhile, EPS content increased to 52.62 mg/L by the second cycle and remained stable in subsequent cycles. Reusing culture media several times is likely to curb the availability of nitrogen and other nutrients, which can in turn contribute to increasing the C/N ratio and thus promote the incorporation of carbon into the EPS fraction [11].
## 3.2. Single-Factor Test of IPS Extraction
With the gradual increase in the solid–liquid ratio, the extraction rate was the first to increase and then decrease (Figure 2a), and when the material–liquid ratio was 1:30 g/mL, the extraction rate could reach 54.30 ± 0.75 mg/g. With the increase in high-pressure homogenization pressure, the extraction rate of intracellular polysaccharide reached the maximum at 60 MPa (Figure 2b), and the extraction rate was 48.13 ± 0.90 mg/g. The highest extraction rate was achieved when the number of extractions was three (Figure 2c), and it was 48.40 ± 0.29 mg/g.
## 3.3. Optimization of IPS Extraction
To optimize the extraction procedure of IPS from S. platensis, a total of 17 experiments with three independent variables (A = S/I ratio; B = pressure; C = number of homogenizations) were performed following a Box–Behnken design (BBD) (Table 1). IPS yield ranged from 39.87 to 60.33 mg/g (dry weight) across all 17 experiments. Based on multiple regression analysis on the experimental data, a second-order polynomial equation expressing the relationship between each variable was generated:Yield (%) = −161.98 + 4.97A + 3.17B + 33.46C − 0.11AB + 0.06AC − 0.08BC − 0.07A2 − 0.02B2 − 5.19C2 The results and RSM analysis are presented in Table 2. The F value for the model was 363.16 ($p \leq 0.0001$), indicating that the model was statistically significant. The p value of the linear (A; B; C), interaction (AB; BC), and quadratic term coefficients (A2; B2; C2) were all lower than 0.01, which implied that these variables had significant effects on the extraction yield. The correlation coefficient (R2) was 0.9979, indicating that the predicted and observed values were similar and that the model was a good fit. In addition, the determination coefficient (R2adj) was 0.9951, which indicated that only $0.49\%$ of the total variation could not be captured by the regression model. The p value for lack of fit was 0.1532, which means that lack of fit and pure error were not significantly different. These results thus indicated that the regression model could adequately predict IPS extraction yield.
The relationship between independent and response variables and response is visually represented as a 3D surface response (Figure 3a). For S/I ratio and pressure, the projection of 3D response surface at the bottom was elliptical, indicating that the mutual interaction between S/I ratio and pressure was significant. A similar trend was observed for S/I ratio and number of homogenizations, and for pressure and number of homogenizations (Figure 3b,c). The peak point at their response surfaces also simultaneously existed in their minimum elliptical, indicating that there was an extremum value in the chosen range.
Based on multiple regression and 3D surface response analyses, the optimal conditions for IPS extraction were predicted as follows: S/I ratio = 1:30.79; pressure = 61.08 MPa; and three homogenizations, for an extraction yield of 60.20 mg/g. A verification experiment was performed under the optimal conditions predicted by the model (S/I ratio = 1:30; pressure = 60 MPa; and three homogenizations). The observed IPS extraction yield was 60.61 mg/g, which was not statistically different from the predicated value. Therefore, the regression model was suitable for the prediction of IPS extraction from S. platensis.
## 3.4. IPS and EPS Composition
Table 3 shows the chemical compositions of IPS and EPS. Both IPS and EPS contained more than $65\%$ total sugars and less than $5\%$ protein. The phenolic content in IPS ($7.3\%$) was higher than in EPS, indicating a stronger antioxidant capacity. The carbohydrates present in both IPS and EPS but in different ratios (Figure 4) included mannitol, ribose, rhamnose, glucuronic acid, galacturonic acid, glucose, galactose, xylose, and fucose. However, the proportion of each monosaccharide content was statistically significantly different in IPS vs. EPS (Table 3). IPS’s main monosaccharides were glucose ($83.62\%$), rhamnose ($4.42\%$), fucose ($3.25\%$), and glucuronic acid ($2.39\%$). In comparison, EPS contained mainly fucose ($19.99\%$), rhamnose ($15.61\%$), glucose ($14.75\%$), galacturonic acid ($11.13\%$), and galactose ($10.78\%$) content, and had a lower molecular weight (185.13 kDa). This indicated that IPS may have a higher viscosity than EPS [19]. These differences in monosaccharide content and Mw between PSP fractions point to remarkably distinct functional properties and potential when used as food additives e.g., as thickening stabilizers.
## 3.5. FTIR Spectrum Analysis and SEM Imaging
The FTIR spectra of IPS and EPS indicated large similarities in the functional groups contained in both polysaccharide fractions (Figure 5a). The absorption peaks observed at around 3413 and 2925 cm−1 are typical of the O−H and C−H stretching vibrations in rhamnose and fucose, respectively [23]. The amide I band at 1650 cm−1 can be taken to represent the symmetrical and asymmetrical stretching vibration of C=O in COO− and −NHCOCH3, together with the bending vibration in the N-H bond [24]. Similarly, the amide II band with peak absorption at 1542 cm−1 can be mainly attributed to the symmetrical stretching vibration in the C−O bond. Next, absorption peaks at 1400–1200 cm−1 represent variation angle vibrations. The absorption peak at 1240 cm−1 can be attributed to the asymmetrical stretching vibration in −S=O, indicating that both IPS and EPS contained a small number of -SO3H groups [6]. The presence of the pyran ring and the carbohydrate skeleton (C−O−C) is indicated by their characteristic peaks at 1153 and 1064 cm−1, respectively [10]. Finally, the absorption peaks at 898 and 819 cm−1 correspond to the deformation mode of the β−D−pyranoside bond (C−H) and α−Mannitose, respectively [25].
To better understand their physical properties, the surface and microstructure of IPS and EPS was visualized by SEM. IPS and EPS were remarkably different in shape and size (Figure 5b). IPS presented a smooth surface with irregular thin stripes at 2000× magnifications. At 5000× magnifications, IPS exhibited a loose, finely lamellar, and porous web-like structure; these characteristics could imply an enhanced solubility exposure of active groups in IPS. In contrast, EPS had a relatively smoother and flatter surface, and a more coarsely lamellar, less porous structure. Because of its fibrous and porous structure, IPS has likely more versatile application in various foods, and may be especially superior for its water holding capacity compared with EPS [10].
## 3.6. Zeta Potential and TG Analysis
The changes in zeta potential of IPS and EPS solutions in response to pH are shown in Figure 6a. As pH increased from 2.0 to 9.0, the zeta potentials of IPS and EPS decreased from −25.73 to −29.77, and from −26.43 to −37.5, respectively. The smaller differential between IPS and EPS in this pH range may be explained by the high glucose content in IPS. Although both extracts had negative zeta potentials, meaning both of them are acidic polysaccharides, EPS showed a more negative potential than IPS overall. This points to EPS’s stronger acidity which, in agreement with previous reports, is probably due to a higher abundance of −SO3H in EPS.
Thermal stability is a crucial physicochemical property for the commercial application of polysaccharides. The TG and derivative TG curves were experimentally determined for IPS and EPS (Figure 6b). Analysis of weight loss revealed three major stages: [1] 50–200 °C; [2] 200–500 °C; and [3] 500–800 °C. Weight loss during the first stage was $4.78\%$ for IPS and $10.11\%$ for EPS, and could be attributed to the evaporation and dehydration of adsorbed and surface water from the polysaccharide’s surface. During the second stage, weight loss was approximately $58.47\%$ (IPS) and $53.03\%$ (EPS), and was possibly due to the degradation of long carbohydrate chains and the depolymerization of fragments. By the third stage, weight loss slowed down, only decreasing by $29.94\%$ (IPS) and $13.71\%$ (EPS), which could also be due to the fact that the remaining compounds were further carbonized and some carbonates were converted into CO2. Maximal weight loss reached $93.84\%$ (IPS) and $77.36\%$ (EPS) at 800 °C. These results showed that IPS and EPS are thermally stable below 220 °C, and that EPS’s thermal stability is higher, possibly as a consequence of its higher fucose and rhamnose content.
## 3.7. Antioxidant Capacity Analysis
DPPH is a stable nitrogen−centered radical and is widely used for the in vitro evaluation of the antioxidant capacity of natural products [10]. IPS and EPS showed an overall strong scavenging activity on DPPH radicals (Figure 7a), and this was dependent on the concentration of polysaccharide, reaching its maximum value at 2.5 mg/mL ($65.9\%$ and $44.7\%$ for IPS and EPS, respectively). The EC50 value of IPS (1.77 mg/mL) was lower than that of EPS (4.67 mg/mL). The greater ability to scavenge DPPH radicals of IPS is consistent with its higher phenolic content. For comparison, the DPPH scavenging activities of S. platensis−derived polysaccharides are superior to those derived from other bacteria and microalgae, specifically *Pseudomonas fluorescens* (approximately $30\%$ at 1.0 mg/mL EPS) [26] and *Sargassum carpophyllum* ($66.6\%$ at 12 mg/mL IPS) [6].
Scavenging of ABTS radicals is another common indicator of the antioxidant potential of natural compounds. Both IPS and EPS were shown to be strongly capable of scavenging ABTS radicals in a concentration-dependent manner (Figure 7b). Scavenging activity reached $95.26\%$ at 1.0 mg/mL and $94.47\%$ at 2.5 mg/mL for IPS and EPS, respectively, and these values were not statistically different from the positive control at the same concentration. The EC50 values for ABTS radical scavenging were 0.12 mg/mL (IPS) and 0.60 mg/mL (EPS); this heightened scavenging activity for IPS may be explained by a lower sulphate/sugar content ($p \leq 0.05$). Likewise, PSP extracts showed better ABTS radical scavenging performance when compared with polysaccharides sourced from *Oudemansiella radicata* mushroom (EC50 = at 0.2 mg/mL ORP) [25] and *Botryococcus braunii* (EC50 = 5.13 mg/mL EPS) [27].
HO• is a highly reactive radical known for its deleterious biological effects, including red blood cell death, DNA damage, and cell membrane degradation, and is prominently implicated in ageing [28]. For this reason, scavenging HO• radicals constitutes an important antioxidant defense mechanism. Both IPS and EPS presented scavenging activity on HO• radicals and this also increased with concentration (Figure 7c). The scavenging capacity of PSPs is directly related to the function of electrons and hydrogen, as supported by previous reports [6]. The EC50 of IPS (1.72 mg/mL) was higher than that of EPS (0.75 mg/mL), $p \leq 0.05$; this superior ability of EPS to scavenge HO• radicals may stem from its rich alcohol hydroxyl groups in the structure of fucose.
The chelating ability of IPS and EPS on ferrous ions also increased at higher concentrations (Figure 7d). EPS had the strongest chelating capacity ($85.91\%$) at 1.0 mg/mL. Remarkably, this was higher than the positive control’s ($73.90\%$) and IPS’s ($40.84\%$) chelating abilities at same concentration. The EC50 of IPS and EPS were 1.54 mg/mL and 0.38 mg/mL, respectively. Thus, EPS showed a stronger chelating power on ferrous ions, which is probably due to the abundance of COO− and SO42- in EPS.
## 4. Conclusions
In these results, the content and functional properties of IPS and EPS were investigated during cycle cultivation of S. platensis. The results showed that the DCW of S. platensis gradually decreased with the increase in number of cycles during cycle cultivation, and the IPS and EPS content gradually increased with the increase in number of cycles and extension of time during cycle cultivation, and IPS content was far higher than EPS. The maximum yield of IPS (60.61 mg/g) could be obtained under the condition of 1:30 S/I ratio and 60 MPa, three times, using thermal-HPH technology. The same carbohydrates were present in both IPS and EPS but in different ratios. IPS has more loose fibrous porous structures, higher glucose, and larger Mw than EPS, indicating higher water holding capacity and viscosity. Both IPS and EPS were shown to be acidic carbohydrates, but the acidity and thermal stability of EPS were stronger than those of IPS, which might be closely related to the monosaccharide content. IPS exhibited a better scavenging capacity on DPPH and ABTS radicals than EPS, possibly due to higher total phenol content, and far lower scavenging ability on OH• radicals and lower ferrous ion chelating ability than EPS, which indicated that IPS showed high antioxidant capacity, but EPS had strong chelating ability on metal ions. These results could provide theoretical direction for the cost-efficient production and adequate application of IPS and EPS from S. platensis as food additives or medicinal ingredients. In future studies, the extraction efficiency of IPS should be improved for large-scale production. Moreover, the rheology, water holding capacity and number of major functional groups of IPS and EPS should also be further analyzed to better understand their functional properties.
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---
title: Exploring the Impact of α-Amylase Enzyme Activity and pH on Flavor Perception
of Alcoholic Drinks
authors:
- Maria João Santos
- Elisete Correia
- Alice Vilela
journal: Foods
year: 2023
pmcid: PMC10000705
doi: 10.3390/foods12051018
license: CC BY 4.0
---
# Exploring the Impact of α-Amylase Enzyme Activity and pH on Flavor Perception of Alcoholic Drinks
## Abstract
The introduction of a drink in the mouth and the action of saliva and enzymes cause the perception of basic tastes and some aromas perceived in a retro-nasal way. Thus, this study aimed to evaluate the influence of the type of alcoholic beverage (beer, wine, and brandy) on lingual lipase and α-amylase activity and in-mouth pH. It was possible to see that the pH values (drink and saliva) differed significantly from the pH values of the initial drinks. Moreover, the α-amylase activity was significantly higher when the panel members tasted a colorless brandy, namely Grappa. Red wine and wood-aged brandy also induced greater α-amylase activity than white wine and blonde beer. Additionally, tawny port wine induced greater α-amylase activity than red wine. The flavor characteristics of red wines due to skin maceration and the contact of the brandy with the wood can cause a synergistic effect between beverages considered “tastier” and the activity of human α-amylase. We can conclude that saliva-beverage chemical interactions may depend on the saliva composition but also on the chemical composition of the beverage, namely its constitution in acids, alcohol concentration, and tannin content. This work is an important contribution to the e-flavor project, the development of a sensor system capable of mimicking the human perception of flavor. Furthermore, a better understanding of saliva–drink interactions allow us to comprehend which and how salivary parameters can contribute to taste and flavor perception.
## 1. Introduction
From an early age, taste and aroma have been known for walking together in a single direction [1]. It is the combination of that leads to the recognition of the most varied sensory experiences to which the human being’s palate is constantly subjected.
Trigeminal sensations—or “mouthfeel”—are defined as “a group of sensations that is characterized by the tactile response in the mouth” [2] and described as “tactile properties (sensation) perceived from the moment that food or drink—solid, semi-solid or liquid—is placed in the mouth until swallowed” [3]. These sensations, together with the taste and olfactory sensations (ortho- and retronasal perception), contribute to the flavor of the food/drinks that are essential in the acceptance of these products by consumers [4]. Understanding the factors affecting flavor perception may provide clues to drive food consumption toward keeping a proper nutritional status [5].
Apart from food characteristics, flavor perception is strongly influenced by oral physiology, namely saliva [5]. Saliva’s constitution is approximately $99\%$ water, and the rest is inorganic and organic compounds [6,7]. The pH of saliva is between 6.2 and 7.4 [8]. Being slightly acidic or basic, saliva is a highly viscoelastic fluid and has unique properties that facilitate chewing, digestion, homeostasis, and flavor perception, among many other things [9,10]. Moreover, in addition to dilution, the buffering capacity of saliva has also been reported to decrease the response to acid stimulation, although the concentration of organic acids in food and drinks and, therefore, titratable acidity also contributes to sourness [11].
The different constituents of saliva are responsible for the interaction and formation of new compounds within the oral cavity [6], and salivary proteins are of the utmost importance since they fulfill various roles, such as oral digestion (amylases and lipases), neutralization of toxic molecules (proline-rich proteins and histatins), defense against microorganisms (immunoglobulins and peroxidases), lubrication of the oral cavity (mucins), and the transport of flavor molecules (lipocalins) [12]. These enzymes are secreted by the salivary glands or originate from the lysis of desquamated epithelial cells [12]. Given the presence of microbiota in the mouth, some enzymes can be of bacterial origin [13]. The total protein concentration in saliva is around 0.2–2.0 mg/mL, and more than 4000 different proteins are present [14]. Among others, two enzymes may contribute to flavor-taste perception: lipase and α-amylase. Several researchers state that orally expressed lipases might hydrolyze triacylglycerols and, consequently, release non-esterified fatty acids (NEFA). However, fatty acids are poorly soluble in aqueous solvents, which could prevent their access to their sensory receptors. It has also been hypothesized that salivary proteins could play a role in the transport of NEFA in the mouth [5]. The α-amylase enzyme is the main protein in human saliva and is responsible for catalyzing the hydrolysis of 1,4-glycosidic bonds in starch and other polysaccharides, such as glucose and maltose, which are smaller sugars that can be detected by the sweet receptors in the mouth [15,16]. Moreover, the secretion of salivary proteins, such as α-amylase, has been reported to be dependent on the diet [17], and the salivary proteome and glucose levels have been related to sweet taste sensitivity in young adults [18] and α-amylase concentrations in healthy children [19].
Regarding aroma perception, namely the retro-nasal pathway, volatile molecules, before being detected by olfactory receptors, are influenced by factors such as body temperature and saliva pH, allowing the identification of different and equally important aromas [20,21]. When volatiles are released from food into the saliva phase, chemical and biochemical reactions occur, which could affect the volatile concentration and retro-nasal aroma perception [22]. Several authors note that salivary proteins, such as mucins and α-amylase, can ‘trap’ aroma compounds depending on their structure [23]. Some aroma compounds can be individually metabolized in the oral cavity, leading to the formation of new metabolites with different odor thresholds [24,25]. Moreover, the persistence of an aroma compound also depends on its metabolism in the oral cavity, as referred to by Muñoz-Gonzalez et al. [ 26], who found that the perceived aroma intensity of the compounds that are metabolized in the oral cavity decreases faster than that of the nonmetabolized compounds. Furthermore, aroma compounds may adsorb onto the mucosal pellicle, while the aggregation of the mucosal pellicle by tannins may disturb these interactions [27].
The main objective of this work was to study the differences in pH and the variations that occur in saliva enzyme activity, lipase, and α-amylase, depending on the type of alcoholic beverage that is consumed. This work is an important contribution to the e-flavor project, the development of a sensor system capable of mimicking the human perception of flavor in alcoholic drinks, namely wine. Furthermore, a better understanding of the saliva–drink interactions allow us to comprehend which and how salivary parameters can contribute to taste and flavor perception. To this end, we analyzed the enzymatic activity of lipase and α-amylase and pH changes before and after the contact of alcoholic drinks with the tasters’ saliva.
## 2.1. Ascending Method of Limits for Primary Taste Detection Thresholds in Aqueous and Hydroalcoholic Solutions
Aqueous and hydroalcoholic ($12\%$, v/v) solutions associated with each basic taste (sweet, salty, acid, and bitter) were prepared to determine the sensory and perception threshold of the tasters. For each basic taste attribute, seven aqueous solutions with different concentrations were prepared (Table S1). For the acid taste attribute, acids were tested in separated solutions (Table S2). In each aqueous solution, 0.5 L of water (bottled water—Caldas of Penacova) was used. The procedure was similar to the preparation of the $12\%$ hydroalcoholic solutions (Tables S3 and S4). Before preparing the solutions, a mother solution was prepared with water and ethanol, with a concentration of $12\%$ (v/v), simulating the alcohol level of table wines.
The final pH of each of the solutions at different concentrations was also measured (Table S2).
The solutions were presented to the tasters in ascending order of concentration [28,29] at a temperature of 20 °C. After tasting, the tasters had to indicate on the tasting sheet if the solution caused any sensation—sensation threshold—and identify the taste in question—perception threshold.
## 2.2. Tasting Panel Characterization and Laboratory Conditions
For this study, two different panels were used. The first panel (P1) of tasters was composed of 11 individuals, of which 10 were female and only 1 was male. The ages of the panel were between 39 and 59 years, and they had previously participated in assessments of food and beverages in several published works [30,31,32]. The second panel (P2) of tasters was composed of 19 individuals, of which 4 were female and 15 were male. The ages of the panel were between 20 and 50 years, and they were the untrained panel. In order not to impair the results of the sensory evaluation, the tasters were non-smokers and were told not to use any type of perfume or other cosmetics with a strong aroma and not to eat or drink anything, except water, for one hour before the start of the tests.
All evaluations were conducted from 4:00 to 6:00 p.m. and took place in individual tasting booths in a sensory laboratory [33] using the 21.5 cl transparent ISO wine-tasting glasses [34]. Sessions were carried out under controlled conditions at 20 °C (±2 °C) and relative humidity of $60\%$ (±$20\%$).
A volume of 20 mL was used in all the tasting sessions. This volume was chosen to make it possible for the tasters to put all the liquid in their mouths for 10 s and then spit it back into the glass to be collected and stored.
## 2.3.1. Solutions and Alcoholic Beverages
Each basic taste was associated with an aqueous solution with a certain concentration of the taste in question. For bitter taste and astringency in the lipase and α-amylase tests, we choose to use a tannin solution and not quinine, as tannin is an alcoholic drink component important for bitter taste and astringency.
The selection of concentration was based on the concentration where most tasters perceived and identified the taste (Table 1). The procedure for the preparation of the hydroalcoholic solutions (ethanol $12\%$, v/v) was identical to what was described above.
Regarding alcoholic beverages, each taster from panel one (P1) was presented with one beer (blonde beer, with an alcohol content of $4.9\%$ (v/v) and pH 4.34), two wines (white wine with an alcohol content of $13.0\%$ (v/v) and pH 3.37, and red wine with an alcohol content of $13.9\%$ (v/v) and pH 3.87) and two brandies (a colorless brandy and a wood-aged brandy with alcohol contents of $41.0\%$ (v/v) and $40.0\%$ (v/v), respectively, and pH 4.30 and 3.93, correspondingly). To P2 (panel two) were presented two wines, one red wine and one port wine, with alcohol contents of $13.5\%$ and $19\%$ (v/v), respectively.
The wines, beer, and brandies were stored in a climate-controlled dark cellar maintained at 11 °C ± 1 °C. The day before the sensory sessions, the white wine and beer were stored at 4 °C ± 2 °C in a fridge.
## 2.3.2. Tasting Procedure and Saliva Collection
In the first part of the work, for P1, saliva was collected from the members of the panel during two tasting sessions, one for the aqueous and hydroalcoholic solutions and another for the alcoholic drinks (beer, wine, and brandy). In the second part of the work, for P2, saliva was collected after tasting two alcoholic beverages, namely red wine, and port wine. The procedure was similar in both sessions.
The solutions described in Table 1 and the beverages in both procedures were presented to the tasting panel in codded 21.5 cl transparent ISO wine-tasting glasses [34] with a quantity sufficient for the taster to place in the mouth (20 mL). The tasting procedure was explained, and it was requested that the tasters place the sample in their mouths, swirl the liquid through the mouth cavity, and wait for 10 s. After 10 s, they were asked to spit the solution into the respective glass and wait 5 min before proceeding to the next solution/beverage. Next, in the first part, the temperature and pH of each expectorated solution/beverage were measured using a glass thermometer and a potentiometer (Hanna Instruments Inc., Woodstock, GA, USA), respectively. Then, for both tests, the samples were pipetted into 5 mL glass vials in the case of aqueous and hydroalcoholic solutions and into plastic 1.5 mL Eppendorf microtubes in the case of alcoholic beverages. All samples were coded and stored at −18 °C until enzymatic activity determination.
## 2.3.3. Lipase and α-Amylase Activity Determination
Lipase enzyme activity was only measured for the first part of the study for the alcoholic beverages beer, wine, and brandy. The lipase enzyme assay kit (Abnova, Taipé, Taiwan) is based on a method in which -SH groups (thiol groups), formed from tributyrate-dimercaptopropanol (BALB) lipase cleavage, interact with 5,5 dithiobis (2-nitrobenzoic acid)—DTNB—to form a yellow-colored product. Color intensity was measured with the aid of a microplate reader (SPECTROstarNano, BMG LABTECH, Offenburg, Germany) at 412 nm. The measured absorbance is proportional to the enzyme activity in the sample [35]. The enzyme kit contained the reagents described in Supplementary Table S5. The detection threshold of the enzyme kit is in the range of 40 to 1600 U/L, with minimum and maximum values.
To prepare the working reagent, the colored reagent—DTNB—was mixed with assay buffer, and the flask was shaken to obtain a better homogenization. Then, 0.8 mL of BALB reagent was added.
During the experimental procedure, 150 µL of water (H2O) was transferred to one well, and 150 µL of the calibrator (a substance not defined in the enzymatic kit—Table S1) was transferred to another well of the microplate. In the remaining wells, 10 µL of sample and 140 µL of working reagent prepared as mentioned above were added. The plates were covered, and the reaction mixture was homogenized.
The OD412nm was read in the microplate reader at 10 min (OD10min) and 20 min (OD20min) after the start of the reaction. The activity of the lipase enzyme was calculated according to Equation [1]. [ 1]Lipase enzyme activity=OD (20min.)−OD (10min.)OD (calibrator)−OD (H2O)×735 U/L where OD20min/OD10min represents the OD412nm values of the sample at 20 min and 10 min, respectively, and ODcalibrator and ODH2O represent the OD412nm values of the calibrator and water at 20 min.
The number “735” is the equivalent activity (U/L) of the calibrator under the assay conditions.
For the determination of α-amylase, enzymatic kits from Biovision (Milpitas, CA, USA) were used. For each kit, a standard curve and respective equation of the straight line were drawn.
The assay uses ethylidene–pNP–G7 as a substrate. Ethylidene–pNP–G7 is specifically cleaved by α-amylase. The final chromophore release is measured with the help of a microplate reader (SPECTROstarNano, BMG LABTECH, Offenburg, Germany) at a wavelength of 405 nm. The enzyme kit contained the reagents described in Table S5.
The α-amylase enzyme kit was previously stored at a temperature of −18 °C.
For the use of the kit, the buffer was warmed to room temperature (±20 °C), and the samples and positive control were kept on ice during the assay. The nitrophenol standard curve (standard curve) was prepared with 0, 2, 4, 6, 8, and 10 µL of nitrophenol (2 nM) in duplicate, making a final volume of 50 µL with distilled water on the microplate.
An absorbance/concentration curve was plotted for 0, 4, 12, 16, and 20 nmol/well of nitrophenol. In one of the microplate wells, 5 µL of positive amylase control and 45 µL of distilled water were added.
Regarding the samples, 50 µL of each sample plus 50 µL of assay buffer and 50 µL of substrate mixture were added to each well, and two repetitions were performed. In the end, each microplate well contained a total volume of 150 µL. The content of the microplate was homogenized before reading.
Optical density (OD) was measured at a wavelength of 405 nm to obtain the OD T0. After eight minutes (T1), the OD was measured again.
The standard nitrophenol curves were drawn from the results, and the equations were defined. The variation of OD [ΔOD = OD (T1) − OD (T0)] was calculated for the standard curve of nitrophenol, namely for the “y” of the first-degree equation, and the “x” of nitrophenol generated by the amylase between T0 and T1.
Finally, the α-amylase activity was calculated according to Equation [2]. [ 2]Amylase enzyme activity (mUmL)=BT × V Where B is the nitrophenol amount from the standard curve (in nmol), T is the time between T0 and T1 (in min), and V is the pretreated sample volume added to the reaction well (in mL).
## 2.4. Beverage Sensory Evaluation through a Descriptive Analysis (DA) Sensory Test
Regarding the alcoholic beverages, in addition to performing the procedure described under Section 2.3.2, the tasters carried out a descriptive analysis [36]. For P1 (panel one), a tasting sheet for each class of beverage (beer, wine, and brandy) was constructed based on a list of attributes previously defined. The same process was carried out for P2 (panel two) for red wine and port wine, with new tasting sheets with descriptors specifically chosen for the work (Supplementary Table S6). In both tasting sessions, with P1 and P2, the tasters were asked to check the attributes they considered adequate to describe the different drinks.
## 2.5. Data Analysis
Data were presented as mean (M) and standard deviation (SD) when appropriate. The assumption of normality of distributions in each of the solutions/beverages studied was evaluated using the Shapiro-Wilk test. The assumption of homogeneity of variances was evaluated using the Levene test. To determine whether saliva influences the pH values of different alcoholic beverages, a one-sample t-test was performed.
To assess whether there were significant differences in α-amylase and lipase activity between aqueous and hydroalcoholic solutions and to verify whether alcohol influenced the substances representing the basic tastes, a Kruskal–Wallis test and the Wilcoxon–Mann–Whitney test were used, respectively, since the conditions of normality and homogeneity of variances were not verified. To identify which basic tastes were significantly different, non-parametric pairwise comparisons were performed.
To verify whether there were significant differences in alcoholic beverages (beer, wines, and brandies), in panel one (P1), in the enzymatic activity of lipase and α-amylase, a univariate analysis of variance (ANOVA) was executed, followed by a post hoc HSD Tukey test whenever possible.
To find out whether there were significant differences between red wine and port wine for panel two (P2) in the enzymatic activity of α-amylase, the Student’s t-test for independent samples was performed.
The statistical analyses were performed using SPSS Statistics (version 27.0), and in all analyses, a probability value of ≤0.05 was considered significant.
The sensory data obtained after the descriptive analysis (DA) test was graphically represented using the percentage of a citation for each descriptor.
## 3.1. Primary Taste Detection Thresholds in Aqueous and Hydroalcoholic Solutions
In the determination of the primary taste detection threshold in aqueous and hydroalcoholic solutions, there were some divergences in the answers given by the tasters. Nevertheless, in aqueous solutions, for $78\%$ of the tasters, the sensation threshold of malic acid occurred at the concentration of 0.2 g/L and for about $22\%$ at the concentration of 0.0 g/L. The threshold of perception occurred for $44.5\%$ of the tasters at concentrations of 0.2 g/L or 0.5 g/L, while the remaining $11\%$ achieved it at the concentration of 1.0 g/L.
Regarding the threshold of sensation of citric acid and lactic acid, $90\%$ of the individuals reported the existence of some substance at a concentration of 0.2 g/L and $10\%$ at a concentration of 0.0 g/L. As for the perception threshold, for citric acid, $70\%$ of the tasters detected the basic acid taste at the concentration of 0.2 g/L and $30\%$ at the concentration of 0.5 g/L, while for lactic acid, the tasters were divided into the following percentages: $50\%$ at the concentration of 0.2 g/L; $40\%$ at a concentration of 0.5 g/L, and $10\%$ at a concentration of 0.8 g/L. For lactic acid, two tasters mentioned that they detected a bitter basic taste and a salty basic taste instead of the acidic/sour taste.
For succinic acid, it was possible to verify that the sensation threshold of $90\%$ of the tasters was at a concentration of 0.2 g/L, and the other $10\%$ detected it at a concentration of 0.0 g/L. For the threshold of perception, the responses varied between a concentration of 0.2 g/L for $30\%$ of the tasters and a concentration of 0.5 g/L for $70\%$ of the individuals.
The salty taste was recognized by the panel, with a visible divergence in concentrations. Regarding the threshold of sensation of salty taste identification, $11\%$ of the tasters reported a change in the taste of the solution at concentrations of 0.0 g/L and 0.5 g/L, while $33\%$ reported a change at 1.0 g/L and the remaining $45\%$ at 0.25 g/L. As for the threshold of perception, this was divided into different concentrations, with $11\%$ of the taster’s detecting saltiness, represented by NaCl, at concentrations of 0.25 g/L and 4.0 g/L, $22\%$ at 0.5 g/L and 2.5 g/L, and finally, $34\%$ at of 1.0 g/L.
Concerning the basic sweet taste, $11\%$ of the tasters noticed the existence of something in the solutions at 0.0 g/L, 7.5 g/L, and 10 g/L, and $33.5\%$ in the solutions with 2.5 g/L and 5.0 g/L of sugar. As for the bitter taste, it was detected by $11\%$ of the tasters at concentrations of 2.5 g/L, 7.5 g/L, and 13 g/L, and $33.5\%$ of the tasters detected it at concentrations of 5.0 g/L and 10 g/L.
Concerning the primary taste detection threshold in hydroalcoholic solutions, for $75\%$ of the tasters, the sensation threshold of succinic acid occurred at the concentration of 0.2 g/L and for about $25\%$ at the concentration of 0.5 g/L. Regarding the threshold of perception, $17\%$ of the tasters reported this at the concentration of 0.2 g/L, $33\%$ at the concentration of 0.5 g/L, $42\%$ at a concentration of 0.8 g/L, and the remaining $8\%$ at a concentration of 1.0 g/L.
For the threshold of sensation of lactic acid, $92\%$ of the individuals reported the existence of some substance at the concentration of 0.2 g/L, while $8\%$ perceived it at the concentration of 0.5 g/L. As for the perception threshold, for lactic acid, $25\%$ of the tasters detected it at the concentrations of 0.2 g/L or 1.0 g/L, while for $42\%$ of the tasters, it was detected at the concentration of 0.5 g/L, and $8\%$ detected it at a concentration of 1.2 g/L.
As for the previous acids, for citric acid, the sensation threshold concentrations of 0.2 g/L and 0.5 g/L were perceived by $83\%$ and $17\%$ of the tasters, respectively. The perception threshold occurred for $42\%$ of the tasters at 0.2 g/L, $33\%$ at a concentration of 0.5 g/L, and $25\%$ at a concentration of 0.8 g/L.
For the determination of the threshold of the sensation of bitter taste, $92\%$ of the tasters perceived the taste at a concentration of 0.5 g/L and $8\%$ at a concentration of 2.5 g/L. The perception threshold, for $75\%$ of the tasters, occurred at a concentration of 0.5 g/L, while for $17\%$, it occurred at 2.5 g/L, and for the remaining $8\%$ at a concentration of 5.0 g/L. All tasters identified the basic bitter taste corresponding to tannins, and the sensation of astringency was also mentioned throughout the tasting.
For the sweet taste, $67\%$ of the tasters had the sensation of sweetness at a concentration of 2.5 g/L, while $17\%$ perceived it at a concentration of 5.0 g/L and $8\%$ at concentrations of 7.5 g/L or 10 g/L. Regarding the sweet taste perception threshold, $33\%$ of the panel detected it at concentrations of 2.5 g/L or 5.0 g/L, $17\%$ at a concentration of 7.5 g/L, and $8\%$ at concentrations of 10 g/L or 13 g/L.
Considering all these results, it was possible to choose average values of concentrations of compounds to prepare solutions for the next task: to determine the pH changes and the lipase and α-amylase activity after basic taste solutions, including the aqueous and hydroalcoholic solutions, came into contact with human saliva.
## 3.2.1. Temperature and pH
The temperature of aqueous/hydroalcoholic solutions and alcoholic beverages was measured before and after contact with the tasters’ oral cavity. It was found that the temperature of the solutions/drinks during the 10 s they were in the mouth increased by approximately 3.5 ± 1.0 °C.
To determine whether saliva influences the pH values of different alcoholic beverages, a one-sample t-test was performed after verifying the assumptions of data normality ($p \leq 0.05$). The pH values after contact with saliva are significantly higher than the initial values of the solutions ($p \leq 0.05$), except for the hydroalcoholic lactic acid solution (Table 2).
In both the aqueous solutions and hydroalcoholic solutions, the acidic solutions showed high resistance to pH changes. For example, in the citric acid solution, the pH changed from 3.19 to 3.39 and from 3.36 to 3.53 in aqueous and hydroalcoholic solutions, respectively. On the contrary, the aqueous solutions that stood out for having a lower resistance to changes in pH were the tannin and sodium chloride solutions, where an increase in pH values from 3.72 to 4.34 and from 4.96 to 5.92, respectively, was observed. In hydroalcoholic solutions, a significant change occurred in the tannin solution, in which the pH varied from 3.88 to 4.36, and in the sucrose solution, in which the pH varied from 6.19 to 6.70 (Table 2).
In alcoholic beverages, there were also significant differences in pH variation (Table 2). In colorless brandy and colored brandy, pH values were significantly higher after contact with human saliva, ranging from 4.30 to 4.84 ($p \leq 0.001$) in the case of colorless brandy and from 3.93 to 4.45 ($p \leq 0.001$) in the colored brandy. In contrast, for red and white wines, pH values were lower. In red wine, the pH dropped from 3.87 to 3.74 ($p \leq 0.05$); for white wine, the pH dropped from 3.37 to 3.22 ($p \leq 0.05$). However, the pH of the blonde beer did not significantly change ($$p \leq 0.941$$).
## 3.2.2. Lipase and α-Amylase Enzymes Activity
A Kruskal–Wallis test was performed to verify if there are differences in the enzymatic activity (lipase and α-amylase enzymes) in aqueous or hydroalcoholic solutions once the conditions of normality ($p \leq 0.05$) and homogeneity of variances ($p \leq 0.05$) were not verified.
The higher value of amylase activity was obtained in the sucrose and sodium chloride solutions (93.28 and 100.91 mU/mL, respectively), and the lower value was found in the tannin solution (0.49 mU/mL). The results obtained show significant differences in α-amylase activity in the presence of aqueous solutions (Table 3). According to the multiple pairwise comparisons of mean ranks, the differences per column are between tannin and sucrose solutions ($$p \leq 0.008$$), tannin and NaCl ($$p \leq 0.004$$), lactic acid and NaCl ($$p \leq 0.025$$), lactic acid and sucrose ($$p \leq 0.048$$), and citric acid and NaCl ($$p \leq 0.048$$).
In hydroalcoholic solutions, the results also indicated statistically significant differences in α-amylase activity. According to the multiple pairwise comparisons of mean ranks, the differences are, per column, between the solutions of tannin and lactic acid ($$p \leq 0.002$$), tannin and sucrose ($$p \leq 0.001$$), succinic acid and sucrose ($$p \leq 0.016$$), and tannin and citric acid ($$p \leq 0.014$$). The lowest value of enzymatic activity was found in the tannin solution (0.82 mU/mL), and the highest value was found in the sucrose solution (85.21 mU/mL).
To verify whether alcohol influenced α-amylase activity when tasters tasted solutions representing basic tastes, a Wilcoxon–Mann–Whitney test was performed (Table 4) once the conditions of normality ($p \leq 0.05$) and homogeneity of variances ($p \leq 0.05$) were not verified. It was possible to confirm that the differences observed between aqueous and hydroalcoholic solutions concerning lactic acid were significant ($$p \leq 0.025$$, Table 4). For this acid, aqueous solutions presented lower values of amylase activity than the hydroalcoholic solution.
To verify if there are differences in the enzymatic activity (lipase and α-amylase enzymes) in alcoholic beverages tasted by P1 (beer, wines, and brandies), an ANOVA was performed once the conditions of normality ($p \leq 0.05$) and homogeneity of variances ($p \leq 0.05$) were verified. The analysis of the results for alcoholic beverages for the α-amylase enzyme revealed that in at least one of the beverages, the mean value is significantly different from the others (F[3, 5] = 56.59; $p \leq 0.001$) (Table 5). According to the post hoc test, the colorless brandy induced a significantly higher α-amylase enzyme activity (148.11 mU/mL) compared to the other beverages ($p \leq 0.001$). Similarly, red wine elicited greater enzyme activity (13.84 mU/mL) than white wine (4.15 mU/mL, $$p \leq 0.006$$) and blonde beer (2.03 mU/mL, $p \leq 0.001$) (Table 5).
Regarding the lipase enzyme activity, results from the aqueous and hydroalcoholic solutions were not considered for analysis because they were below the detection threshold of the lipase enzymatic kit (40 U/L). Only in the citric acid hydroalcoholic solution (Table 3) was it possible to observe a measurable lipase activity (57.95 U/L). However, results for lipase were obtained after saliva contact with alcoholic beverages, except for red wine. The higher enzymatic activity was observed for color brandy and white wine (400.80 and 270.65 U/L, respectively), and the lower enzymatic activity was observed for colorless brandy (41.49 U/L). Due to the considerable variations in the standard deviation, the differences were not significant (Table 5).
To verify if there are differences in the enzymatic activity (α-amylase) in alcoholic beverages (red wine and port wine, tasted by P2), a Student’s t-test for independent samples was performed. The results indicate that the differences found were significant ($$p \leq 0.005$$), as shown in Table 6. Furthermore, the amylase values of port wine are significantly higher than those found in red wine ($$p \leq 0.003$$).
## 3.3. Sensory Profile of the Alcoholic Drinks Determined by Descriptive Analysis (DA) Sensory Test
Figure 1 shows the percentage of citations of each descriptor obtained after data analysis of the DA test tasting report for beer, wines, and brandies obtained with the help of the first panel (P1) composed of 11 individuals. Descriptors with percentages inferior to or equal to $10\%$ were discarded. Despite the reduced number of tasters, the DA test was chosen because it was only intended to verify which attributes, from the list of attributes presented for each type of beverage, were identified by the tasters. For the blonde beer (Figure 1a), the descriptors that present the higher percentage of citations (higher than $60\%$) are “foaming” and “foaming color”, “malt aroma”, “acidity”, “bitterness”, “sparkling”, the mouthfeel sensation of bubbles, and “malt taste”.
As can be seen in Figure 1b, the tasters perceived the red wine as more “fruity” (red fruit, black fruit, dried fruit sensations) and “sweeter” than the white wine. In red wine, the descriptors “tannin/astringent”, “body”, and “spices” also presented a percentage of citation higher than $60\%$, and the “spicy” sensation was only mentioned in the red wine. However, white wine was felt as having more “citrus” and “tropical fruits” and more “mineral”, “acidic”, and “floral” flavors.
Regarding the brandies (Figure 1c), although the alcoholic degree is similar for both drinks (41–$40\%$, v/v—Table 5), the colorless brandy (Grappa) was perceived as having more “alcohol” and with a “vegetable/herbaceous” aroma. The varnish aroma of the descriptor “ethyl acetate” was also mentioned by twice as many tasters in the colorless brandy. The colorful brandy (Aguardente Velha) was characterized by the aromas “spices/wood”, “roasted/burnt”, “smoke/ash”, and was also perceived to be more “sweet”, “persistent”, “fruity, and “spiced”.
To better understand what the tasters’ reaction was regarding the sensory analysis of alcoholic beverages, a more specific descriptive analysis was carried out for red wine (with more descriptors to be evaluated), as shown in Figure 2a, and another typical Portuguese beverage was taste-tested, port wine, as shown in Figure 2b. For this second sensory analysis, a second panel (P2) of 22 tasters was used.
Concerning red wine, it can be seen, once again, that the most quoted characteristics were “astringent”, “tannin”, and “wood/spices”. “ Red berries” (>$60\%$) and “acidity” (>$35\%$) also stand out. Port wine is a fortified wine with very distinctive characteristics. The most mentioned attributes were “dried fruits”, “sweet taste”, and “wood”, with more than $55\%$, and “caramel” and “alcohol”, with approximately $65\%$.
## 4.1. Sensation and Perception Threshold Determination
In some cases, the panelists felt the presence of a taste even in the absence of the substance in an aqueous solution. These results can be explained by considering several aspects: (i) it was possible that the concentration of the taste was too low for the assessor to correctly identify what taste it was; (ii) the latest meal that tasters ate (one to two hours before the tasting) may have influenced their taste perception. During the tasting session, the members of the panel described the last meal they made to find possible explanations for future results. It was possible to verify that tasters who felt the presence of a taste even in the absence of the substance ate foods with strong and persistent flavors, such as spicy sausages, hamburgers, and fries. The bibliography indicates that the taste of “meat” also evokes other mouth sensations such as “astringency” and “succulence” [37,38]. Regarding fried potatoes, more specifically French fries, they can bring quite complex flavors that come from frying [39]. A high lipolytic activity caused by the ingestion of these foods leads to an increase in the perception of fat and its aromatic compounds [11]. Studies also have shown that xenobiotic-metabolizing enzymes of saliva can be overexpressed after the consumption of certain foods rich in bioactive molecules [40], namely with salivary glutathione transferases, aldehyde dehydrogenase, and NADPH quinone oxidoreductases, which are overexpressed after the consumption of coffee or broccoli [41]. Thus, it is possible to infer that the food eaten two hours before the tasting can have a considerable impact on the results obtained, as all the taste sensations can last for more than an hour after the meal in the mouth.
Another explanation for these strange results may be related to the analytical composition of the water—Caldas de Penacova—used in the preparation of the solutions. This water has a pH of 5.2 ± 0.4 and can be felt as slightly acidic, eliciting the sensation of “acid taste” even if no acid is present in the water.
Moreover, some confusion of perceptions was acknowledged. For instance, one taster, when detecting malic acid, classified it as having a “bitter taste”. The reason for the wrong classification may be related to the fact that the taster confuses the “sour taste” with the “bitter taste”, which reveals the importance of training the panelists and allowing them to familiarize themselves with the basic tastes. Furthermore, past a certain level of sourness, perception may be altered, and it has been known for many years that some people are extremely sensitive to the taste of bitter substances, while others perceive little or no bitter taste [42]. Another reason that can explain the confusion regarding the perception of the several tastes may be the fact that the acids studied, in addition to acidity, may confer characteristics such as bitterness and astringency to the solutions [43]. Furthermore, according to some authors, in addition to activating H+ ion receptors, acids also can stimulate nociceptive receptors that are connected to the nerve endings of the trigeminal nerve, which is responsible for sensations such as astringency [44].
In addition, tannin solutions were always accompanied by a feeling of astringency, a strong sensation characterized by roughness and dryness in the oral cavity. The feeling of astringency is produced by the binding and precipitation of salivary proteins and phenolic compounds, such as tannins [45]. Furthermore, we must not forget that the state of mind the tasters faced at the moment of the test, and the emotions they felt at that moment, could also be the cause of a change in sensation and perception [46].
In hydroalcoholic solutions, besides all the reasons mentioned above, ethanol becomes the most important factor for the difference in the responses obtained. Ethanol is an oral chemosensory stimulus and has complex sensorial attributes that are detected by multiple sensorial receptors and afferent fibers [2,47]. Neurophysiological studies have demonstrated a positive association between responses to alcohol and sweet stimuli in nerve fibers [48]. Several authors have described the sweet taste of ethanol in an aqueous solution containing low levels of ethanol (0–$4\%$ alc. vol.) [ 49,50], while others show that increasing the ethanol content of red and white wines to cover a range generally observed in dry wines (from 12 to $14\%$ v/v) revealed no modification of the perception of wine sweetness, suggesting that ethanol has no direct effect on the sweet taste of wine [51]. In our results, the percentage of panelists that detected sweetness in aqueous and hydroalcoholic solutions was similar for the same sugar concentration, so, indeed, ethanol did not influence the results.
According to the literature, ethanol also has a strong effect on bitter taste sensitivity. A bitter taste and a burning sensation have been associated with higher levels of ethanol (10–$22\%$ v/v) [49,50], while more recent studies [51] showed that ethanol was not directly responsible for the perceived bitterness in white wine. In our work, the bitter taste elicited by the tannin solutions was perceived at lower concentrations in hydroalcoholic solutions than in aqueous solutions. Furthermore, as was mentioned, all tasters identified the bitter taste corresponding to tannin and perceived the solution astringency, contrary to what was mentioned by McRae et al. [ 52], who referred to the interference of ethanol with hydrophobic interactions between proteins and tannins, which may lead to a reduction of tannin precipitation and a decreased astringent sensation.
## 4.2. Influence of Saliva on the pH Variations
Saliva, like other body fluids, has a buffering capacity that allows it to absorb or release hydrogen ions (H+) to minimize changes in their concentration, that is, in the pH value [11,53]. The differences obtained in the pH values before and after contact with the human saliva allowed us to conclude that saliva influences the change in pH in alcoholic beverages in aqueous and hydroalcoholic solutions. An exception was seen for the solution with lactic acid in a hydroalcoholic medium. This acidic solution was the only one that did not show a significant difference in terms of pH change after the expectoration, that is, after contact with human saliva. Possible explanations for what happened may be related to lactic acid being a milder acid [54], being naturally present in the oral microflora [55] and having a pKa of about 3.85 (in the range of 19–23 °C), which can lead to a lower saliva–hydroalcoholic solution complex pH value [56].
When analyzing what occurred in the alcoholic beverages, we verified that there were divergences in the pH values that varied according to the beverage analyzed. In the case of brandies, the contact time of the drink with saliva and its buffering capacity was, once again, a limitation for the greater action of the pH of the saliva in these drinks. On the other hand, the slight decrease that occurred in the pH of red and white wine can be explained by considering a drink made up of different acids and in different concentrations. These acids, in particular, tartaric, malic, and citric acids, are responsible for limiting the pH of the wine as well as giving it a buffering capacity, which can be effective depending on the acidic components that are present [53], capable of causing an inhibition of the expected effect on the impact of saliva pH.
Regarding the blonde beer, the pH remained unchanged. Interestingly, in the blonde beer, the descriptor “foaming” presented a high frequency of citation, showing that beer has a high foam formation. According to Dysvik et al. [ 57], pH has a very complex effect on the foaming properties of beer, and higher pH values may be associated with higher foam production. Indeed, the pH of beer changes during the brewing process. First, we can consider that water will have a pH of over 7; when combined with crushed malt, the pH of the grain and water mixture drops considerably compared to the initial pH of the water alone. This observed pH decrease is the result of the precipitation of phosphates and amino acids derived from the malt. Phosphates, such as phosphoric acid, will disassociate. The presence of other minerals within the brewing water can interfere with the pH decrease during the brewing process. Specifically, the carbonate and bicarbonate ions associated with temporary water hardness can act as buffers to pH decrease. Usually, the pH of an infusion mash is around 5.2–5.6 [58]. During fermentation, the pH continues to drop. Yeast cells take in ammonium ions (which are strongly basic) and excrete organic acids (including lactic acid). Therefore, the biggest drop in pH is caused by fermentation and the acids, namely lactic acid, that is formed [58]. The beer presented to the tasters showed a high and persistent foam, following its higher pH (4.34), when compared with white and red wine’s pH (3.37 and 3.87, respectively). As the pH is closer to the pH of saliva, it is natural that the pH change becomes more difficult within 10 s of contact with saliva. On the other hand, lactic acid is one of the acids more present in beer [57] and is also one of the most important components responsible for the perceptible acidity of this drink (Figure 1a—descriptor “acidity”). Additionally, lactic acid is a much milder acid than tartaric and malic acid. Furthermore, the pKa of lactic acid can lower the pH of the saliva-beer complex to values very close to the beer pH.
## 4.3. Enzymatic Activity Elicited by Aqueous and Hydroalcoholic Solutions and Alcoholic Beverages
The salivary α-amylase is an endoglycohydrolase encoded by the gene Amy1. It hydrolyzes internal α-1,4-glucoside bonds of starch to the disaccharide maltose and moderate-length oligosaccharides called limit dextrins. These products adhere to chewed food and hold the bolus together for swallowing [59]. Human salivary amylase is inactivated in the acid pH of the gastric lumen [60] but more stable in the presence of the mouth saliva pH or in solutions where the pH is closer to the natural saliva pH, such as in the sucrose solutions tasted (pH between 5.67 and 6.19 in aqueous and hydroalcoholic solutions, respectively, Table 2). The high α-amylase activity in the sucrose solutions, both aqueous and hydroalcoholic (Table 3), reinforced that α-amylase is protected when in contact with these solutions and able to aid in the digestion of carbohydrates and starch [61]. Moreover, the acidic solutions (succinic, citric, lactic, and malic acids) in the two stock solutions (aqueous and hydroalcoholic) also elicited a relevant activity of the α-amylase enzyme (Table 3). The human body tends to increase saliva flow to dilute the acidity and thus protect itself [62], and the increase in saliva flow implicates an increase in the concentration of the α-amylase enzyme, although some acid inactivation may occur due to the lower pH of the acidic solutions, which explains why the activity of α-amylase is higher in sucrose solution than in acid solutions, despite the increase in saliva flow.
Regarding the low enzyme activity observed in tannin solutions (0.49 and 0.82 mU/mL in aqueous and hydroalcoholic solutions, respectively), the attraction between tannin and the α-amylase enzyme is reflected in the precipitation of the tannin—α-amylase complex, which may be responsible for the inactivation of the enzyme [63,64,65,66].
Regarding the salty taste, in aqueous solutions, the sodium chloride solution showed the greatest activity of the α-amylase enzyme (100.91 mU/mL, Table 2). These results are probably due to the properties of the salt, which, in addition to reducing the adsorption of proteins by the glass flasks where the saliva-solutions samples were stored, also has chloride ions, which are an essential cofactor for the enzyme’s activity [64].
Overall, aqueous solutions elicited lower enzyme activity compared to the hydroalcoholic solutions (Table 3 and Table 4). These differences may be due to the ethanol present in the hydroalcoholic solutions. This compound causes stress to the taster due to the heat and burning felt when tasting, triggering an increase in the production of α-amylase [16]. In lactic acid, this variance is considerably high as, in the hydroalcoholic solution, a many-fold increase in α-amylase activity occurred when compared with the activity observed in the aqueous solution.
Thus far, ‘food–saliva interactions’ has been used as a general term to refer to all possible interactions between food ingredients and saliva compounds. An example is the work of Zhao et al. [ 67], which studied the interaction between human salivary α-amylase and sorghum procyanidin tetramer. Recently, food–saliva interactions have also been investigated in beer [68], wine [69], and other food systems [70].
Regarding beer, Ramsey et al. [ 68] hypothesized that ethanol and saliva interactions, namely, ethanol and α-amylase interactions, directly impact the sensory and flavor properties of beer. These authors found that ethanol has a subtle inhibitory effect on the binding of hydrophobic compounds to α-amylase, thereby increasing their headspace concentration in the $5\%$ (v/v) alcoholic beers as compared to the $0\%$ beers. The sensory data did not show significant differences in orthonasal perception between these two kinds of beer, yet retronasal results showed that $0\%$ lager was perceived as maltier with reduced fruitiness, sweetness, fullness/body, and alcohol-warming sensation ($p \leq 0.05$). In our work, the blonde beer with $4.9\%$ (v/v) alcohol content was characterized by the sensory descriptors of foaming, malt aroma, acidity, bitterness, and sparkling, the mouthfeel sensation of bubbles, and malt taste. This beer induced lower α-amylase enzyme activity when compared to red wine, probably due to the lower alcoholic concentration content when compared with the other beverages.
Red wine is perceived to be more fruity and sweeter, and brandy aged in oak wood is considered sweet, persistent, fruity, and spiced; these drinks induced a greater activity of the α-amylase enzyme. This phenomenon probably occurred because the vinification process of red wine—which allows for an increase in reducing sugars—and of colored spirits—which makes it less aggressive and with more roasted and caramelized aromas—caused a positive synergistic effect between these drinks and the activity of the enzyme.
Small molecules, such as ethanol, influence the compound retention effect and the hydrolysis function by reacting with salivary amylase [71]. Interestingly, despite its alcoholic concentration ($41\%$ (v/v)), the colorless brandy (bottle-aged) showed significantly higher α-amylase activity compared to the other analyzed beverages (Table 5), showing that something must have triggered a higher production of amylase once some of it reacted with ethanol. According to several authors [64,65,66], α-amylase activity increases in response to stress, whether physical or psychological. Thus, the high alcohol content (41.0 %, v/v) of the brandy and the fact that it caused an alcohol sensation in the mouth and sensations of varnish and vegetable/herbaceous aroma (DA test, Figure 1c) when it was tasted may indicate an increase in stress and anxiety on the part of the taster as a physiological response to tasting. The roasted and caramelized aromas and flavors presented in the colored brandy came from the contact it had with the wood during the manufacturing process. These changes produced by the wood make the burning sensation less intense, and consequently, the tasters preferred this brandy compared to the colorless one, meaning that it probably did not cause so much stress when tasting, leading to lower α-amylase activity when compared to the colorless brandy (Table 5).
To confirm the hypotheses and obtain more reliable data, a second panel (P2) with a larger number of tasters was used, and new tests and saliva samples were taken to assess amylase enzyme activity. The beverages in question were again a red wine and, this time, a port wine. It was possible to verify that the enzyme activity was higher in port wine (with $19\%$ (v/v) alcohol content) compared to red wine ($13.5\%$ (v/v)), as seen in Table 6.
The port wine used was a tawny wine. This style of wine presents a sugar content of 40g—65 g/L, so it is a very sweet wine. Its aroma ranges from jam to dried fruits, such as hazelnuts and walnuts [72]. The aging in wood gives the fortified wines some touches of caramel and wood resulting from the Maillard reactions [72,73]. All these descriptors were also mentioned by the tasters. These characteristics, together with the alcohol and acidity also detected by the tasters, as seen in Figure 2a, tend to reinforce the idea of increased α-amylase activity in this type of beverage.
The spectrophotometric methods for the determination of lipase activity make use of synthetic lipase substrates transformed upon enzyme-catalyzed hydrolysis into products able to be detected spectrophotometrically. However, this method was not efficient in determining the lipase activity after tasting aqueous and hydroalcoholic solutions, and the results were not considered for analysis because they were below the threshold for detection of the lipase enzyme kit (40 U/L). Moreover, DTNB (5,5 dithiobis (2-nitrobenzoic acid) can also react with free thiol from salivary proteins, leading to background variation from one saliva sample to another depending on the individual salivary protein concentration [74]. Only in the citric acid hydroalcoholic solution was it possible to observe a measurable activity, that is, a value within the detection threshold of the lipase enzyme kit. This situation may be related to the fact that the lipase enzyme is present in low concentrations in human saliva [62], and, therefore, the lipase enzyme activity kit used in this study was not the most suitable for measuring its activity.
Regarding alcoholic beverages, positive results regarding the activity of this enzyme were obtained after saliva contact with these beverages, except for red wine. One possible explanation for the lipase activity being measurable in alcoholic beverages is the fact that the beverages are complex and fatty acids are part of their composition. These fatty acids come from raw material, the fermentative activity of yeasts, and from the yeast themselves once they release fatty acids into the medium (wine or beer) after finishing the fermentation process. Nonetheless, and as referred to by Stoytcheva et al. [ 75], a common disadvantage of the spectrophotometric methods is the low substrate specificity of the enzyme towards the synthetic substrate analogs, which can cause some discrepancies in the results.
## 5. Conclusions
The composition and buffering capacity of saliva influence the perception of flavor. The pH of aqueous hydroalcoholic solutions and alcoholic beverages remained close to their initial values and not to the pH values of human saliva, that is, between 6.2 and 7.4. Thus, it is possible to conclude that the buffering capacity of saliva is not sufficient to maintain a constant pH after contact with the solutions/beverages.
The α-amylase activity significantly increased when solutions contained acids and/or ethanol and decreased in the presence of tannin, probably due to its precipitation caused by tannin–protein interactions.
When comparing red wine with port wine, we also verified a higher α-amylase activity in port wine, probably due to its sweet taste and higher alcoholic degree.
Regarding the activity of the lipase enzyme, it was observed that the existing concentration in human saliva may be below the detection thresholds of the enzyme kit used. However, in the case of alcoholic beverages, lipase activity was visible.
More sensitive kits, or other analytical methods, are needed to improve saliva enzyme activity determinations.
Looking ahead, a wider study not only of other enzymes/proteins that are relevant to the interaction of human saliva with beverages but also with various types of wine and alcoholic and non-alcoholic beverages will allow a better understanding of saliva and beverage interactions.
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|
---
title: Prediction of Gastrointestinal Tract Cancers Using Longitudinal Electronic
Health Record Data
authors:
- Andrew J. Read
- Wenjing Zhou
- Sameer D. Saini
- Ji Zhu
- Akbar K. Waljee
journal: Cancers
year: 2023
pmcid: PMC10000707
doi: 10.3390/cancers15051399
license: CC BY 4.0
---
# Prediction of Gastrointestinal Tract Cancers Using Longitudinal Electronic Health Record Data
## Abstract
### Simple Summary
Cancers of the gastrointestinal tract—including the esophagus, stomach, and intestines—are often diagnosed at an advanced stage, when curative treatments are rare. These cancers can all cause gastrointestinal bleeding, but this often occurs gradually and may be unnoticed by patients. Changes in routine laboratory parameters such as the complete blood count may be able to show these subtle changes prior to clinical presentation or the development of iron deficiency anemia. The aim of our study was to develop models for the prediction of luminal gastrointestinal tract cancers (esophageal, gastric, small bowel, colorectal, anal) using data routinely available within an electronic health record, in a retrospective cohort from an academic medical center. The cohort included 148,158 individuals, with 1025 gastrointestinal tract cancers. We found that longitudinal prediction models using the complete blood count outperformed a single timepoint logistic model for 3-year cancer prediction.
### Abstract
Background: Luminal gastrointestinal (GI) tract cancers, including esophageal, gastric, small bowel, colorectal, and anal cancers, are often diagnosed at late stages. These tumors can cause gradual GI bleeding, which may be unrecognized but detectable by subtle laboratory changes. Our aim was to develop models to predict luminal GI tract cancers using laboratory studies and patient characteristics using logistic regression and random forest machine learning methods. Methods: The study was a single-center, retrospective cohort at an academic medical center, with enrollment between 2004–2013 and with follow-up until 2018, who had at least two complete blood counts (CBCs). The primary outcome was the diagnosis of GI tract cancer. Prediction models were developed using multivariable single timepoint logistic regression, longitudinal logistic regression, and random forest machine learning. Results: The cohort included 148,158 individuals, with 1025 GI tract cancers. For 3-year prediction of GI tract cancers, the longitudinal random forest model performed the best, with an area under the receiver operator curve (AuROC) of 0.750 ($95\%$ CI 0.729–0.771) and Brier score of 0.116, compared to the longitudinal logistic regression model, with an AuROC of 0.735 ($95\%$ CI 0.713–0.757) and Brier score of 0.205. Conclusions: Prediction models incorporating longitudinal features of the CBC outperformed the single timepoint logistic regression models at 3-years, with a trend toward improved accuracy of prediction using a random forest machine learning model compared to a longitudinal logistic regression model.
## 1. Introduction
Malignancies of the gastrointestinal (GI) tract—including esophageal, gastric, small bowel, colorectal, and anal cancers—are a leading cause of morbidity and mortality, with over 200,000 new diagnoses and approximately 80,000 deaths per year in the United States [1]. While routine screening is recommended for colorectal cancer (CRC), many patients go unscreened, particularly in vulnerable and underserved populations [2]. Recent studies have also noted a rising incidence of CRC in younger patients for whom screening may be impractical or ineffective [3,4,5,6]. As a result, even with lowering the age for initiation of CRC screening to 45 [7,8], existing screening programs for GI tract cancers remain inadequate, and there is no routine screening recommended for GI tract cancers in average-risk adults other than for CRC (e.g., stool testing or colonoscopy).
As GI tract cancers often do not present clinically until they are at an advanced stage, early diagnosis is critical for improving outcomes [9,10,11,12,13]. Improved diagnosis could be achieved by leveraging a physiological common link in luminal GI tract cancers: gradual occult blood loss, ultimately resulting in iron deficiency anemia (IDA) [14]. Healthcare providers often obtain complete blood counts (CBCs) as part of routine clinical care [15,16], but clinicians do not always diagnose IDA accurately and may not obtain the recommended diagnostic evaluation of bidirectional endoscopy (esophagogastroduodenoscopy [EGD] and colonoscopy) for patients with new-onset IDA [15,17,18]. One approach that has been described is the use of electronic trigger tools based on concerning patterns in laboratory data such as new-onset IDA [19]; however, they have not been widely adopted in clinical practice. In addition, site-specific prediction models have examined the association between longitudinal changes in CBCs and the diagnosis of CRC [20,21,22], but new models for prediction of occult malignancy within the entire GI tract are needed. Such models could utilize existing longitudinal laboratory data combined with other patient characteristics stored within the electronic health record (EHR) and serve as automated tools to help improve diagnosis.
In this paper, we describe the development of models for the prediction of luminal GI tract cancers (esophageal, gastric, small bowel, colorectal, anal) using a single-center retrospective cohort. We developed and compared models using single timepoint logistic regression, longitudinal logistic regression, and longitudinal random forest machine learning.
## 2. Materials and Methods
The study was conducted as a single-center, retrospective cohort study of patients receiving care at an academic medical center (Michigan Medicine, Ann Arbor, MI, USA) between 2004–2018. This study was approved with a waiver of informed consent by the University of Michigan Institutional Review Board (HUM00156237), due to the large retrospective nature of the study. Data analysis and model development were performed using SAS 9.4 (SAS Institute, Cary, NC, USA) and Python 3.8 (Python Software Foundation, Wilmington, DE, USA). Elements of the TRIPOD guidelines for transparent reporting of multivariable prediction models were used [23].
Subjects were identified as individuals from the Michigan Medicine Clinical Data Warehouse who had at least 2 CBCs within a rolling 2-year time frame between 1 January 2004 and 31 December 2013. Michigan *Medicine is* a large referral center as well as a primary care system. We used the presence of 2 CBCs to identify patients seeking regular care at Michigan Medicine and to provide at least two data points for a longitudinal prediction model. Subjects were excluded if age < 18, given the low incidence of GI tract cancers and paucity of routine blood draws in a pediatric population. Data were collected from the date of subject’s inclusion until 31 December 2018 (or diagnosis of GI tract cancer), including laboratory values from complete blood counts (CBCs), basic metabolic panels (BMPs), age, sex, self-reported race (as documented in the EHR demographics field), and Body Mass Index (BMI, in kg/m²). Data pre-processing was performed in SAS 9.4 (SAS Institute, Cary, NC, USA), with merging of the demographic, laboratory, biometric, and cancer registry data into a unified file. Biologically implausible laboratory or BMI values were excluded.
## 2.1. Predictor Variables
Each model included patients’ demographic variables (age, sex, race), BMI, the individual component variables of the CBC, and the most recent BMP components. We included all the variables from the CBC since subtle changes within laboratory parameters other than hemoglobin or hematocrit may also reflect an iron-deficient state, e.g., elevated red cell distribution width (RDW), low mean cellular hemoglobin (MCH), and low mean cellular volume (MCV) [24,25]. We also included the BMP in these models, which may reflect comorbidities with associated potential links to GI tract cancers, e.g., reported associations between CRC and chronic kidney disease [26] (suggested by elevated blood urea nitrogen and creatinine) and gastric cancer and diabetes [27,28,29] (as might be suggested by hyperglycemia). As machine learning methods can identify patterns that are imperceptible to clinicians, we included all variables from the CBC and BMP, as these methods tend to perform better with additional data rather than making fixed assumptions about the importance of individual predictors.
## 2.2. Primary Outcome
The primary outcome was the diagnosis of a GI tract cancer, as determined by linkage to the University of Michigan Cancer Center Registry, which contains confirmed pathologic diagnoses of all cancers diagnosed at Michigan Medicine. We chose this method due to the lack of specificity of International Classification of Diseases (ICD)-$\frac{9}{10}$ codes at differentiating between the time of a diagnosis and the time of documentation in a chart (e.g., potential that a newly charted diagnostic code may reflect documentation of an existing GI tract cancer that occurred many years prior rather than a new diagnosis of cancer). In addition, during the study period, Michigan Medicine updated its EHR system (beginning in 2012), which resulted in overlapping usage of ICD9 and ICD10 codes beginning in 2012. As a result, we selected the cancer registry as it provided a consistent source of confirmed cancers.
We limited the outcomes to the diagnosis of luminal GI tract cancers, as defined for this study as cancers of the esophagus, stomach, small intestine, colon, rectum, or anus. Non-luminal GI tract cancers such as pancreaticobiliary cancers or liver cancers were excluded from this analysis as we were primarily interested in potential effects of occult GI tract bleeding, as might be reflected by changes in the CBC. For individuals with GI tract cancers within the cancer registry, we used the date of the diagnosis as the individual’s final observation. For individuals with no GI tract cancer within the registry, we used the date of the last recorded CBC to define the end of the observation period.
## 2.3. Model Development
We used three techniques of prediction model development: [1] single timepoint multivariate logistic regression; [2] multivariate logistic regression incorporating summarized longitudinal features; and [3] random forest machine learning incorporating longitudinal features. For each prediction technique, we developed a prediction model for diagnosis of a GI tract cancer at 6-months, 1-year, 3-years, and 5-years. The eligible sub-population for each time interval was determined in SAS and exported to Python for model building. For each model prediction interval, subjects were included who had at least 2 CBCs prior to the prediction interval, and at least one CBC within the year prior to the beginning of the prediction interval. For example, for the 1-year prediction interval, subjects were included who had at least one CBC between 1 and 2 years prior to the final observation (diagnosis of GI tract cancer/no cancer). For the 6-month prediction, we included those subjects who had at least one CBC between 6 and 12 months prior to the final observation.
For the single timepoint multivariate logistic regression prediction models, we selected observations on the date of the CBC laboratory draw that was closest to the prediction window. Predictor variables included: age, sex, race, most recent BMI, values from the individual components of the CBC on that date (Hemoglobin [Hgb], platelets [Plt], White Blood Cell [WBC] count, etc.), and the values from the most recently available BMP (Sodium [SOD], Glucose [Gluc], blood urea nitrogen [UN], creatinine [Creat], etc.).
To incorporate longitudinal elements into the logistic regression and random forest machine learning models, we calculated summary statistics for each subject, summarizing the trends of the individual components of the CBC in the 3 years prior to the prediction window. For example, for each individual component of the CBC (Hgb, Plt, WBC, etc.), we summarized the values over the prior 3 years by the maximum and minimum; the maximum and minimum slope of each predictor variable (i.e., where the slope is the ratio of the change in value/difference in time between two consecutive observations); and the total variation (mean of the absolute value of the slopes). Because the laboratory data were obtained through routine clinical care (irregular intervals that varied between individuals), the use of slopes between observations helped to better describe changes in laboratory values over time. These summary statistics were then added to the predictor variables in the base single timepoint logistic regression models for the longitudinal logistic regression and longitudinal random forest machine learning models. Missing values for individual summary statistics or individual laboratory parameters were determined through imputation using the median value observed across the cohort.
## 2.4. Statistical Analysis
We calculated descriptive statistics of the cohort at baseline inclusion. For each prediction interval (6-months, 1-year, 3-years, 5-years), we performed a random $\frac{70}{30}$ split, with $70\%$ of the individuals in a training set and $30\%$ in a testing set. Within each prediction interval, we used a training set to fit single timepoint logistic regression, longitudinal logistic regression, and longitudinal random forest machine learning models and evaluated prediction performances using the same testing set. We repeated this procedure 10 times and reported the mean performance characteristics on the testing set over 10 random splits. We implemented logistic regression models with L2 regularization to minimize the potential effects of overfitting. To tune the optimal penalty coefficient for regularized logistic regression, we conducted 5-fold cross-validation, and then the model was fitted with the selected coefficient using the training set.
For the longitudinal machine learning model, we used the random forest technique. Random forest machine learning is an ensemble, tree-based machine learning algorithm used to classify individuals [30,31], which has been used in multiple prior models and described in detail [32,33,34]. Briefly, each tree classifies the individuals independently. Next, the random forest combines the decisions from each tree to generate a final classification for an individual, which can be understood as the majority vote from trees. We also used 5-fold cross-validation to tune the hyperparameters related to the number, size, and feature of trees in the random forest. For both logistic regression and random forest models, we adjusted the class weight using a built-in argument in the Python scikit-learn package to solve the problem of imbalanced classification (rare events of cancers relative to the population).
Finally, for each model, we determined the area under the receiver operator curve (AuROC), Brier score (measurement of overall performance), and the optimal (maximal) sensitivity/specificity using the test dataset. To balance the sensitivity and specificity, we determined the optimal cut-point, defined here as the point closest to the perfect classification point [0, 1] on the receiver operator curve. We also determined the relative variable importance rankings for the predictor variables in these models. In addition, we performed additional analysis of the performance of the models at predicting cancers by age categories and by GI tract tumor.
## 3.1. Baseline Cohort
We identified 148,158 individuals who met the inclusion criteria (Table 1). The mean age was 49.4 (SD = 17.3) and the majority were women ($62.1\%$, $$n = 91$$,$\frac{995}{148}$,158). Most of the subjects were Caucasian ($81.3\%$, $$n = 120$$,$\frac{385}{148}$,158), with $10.5\%$ being African American ($$n = 15$$,$\frac{510}{148}$,158), and $4.6\%$ Asian ($$n = 6795$$/148,158). Within the cohort, we identified 1025 GI tract cancers during the study period: the majority were CRCs ($53.5\%$, $$n = 548$$/1025), followed by gastric cancer ($16.6\%$, $$n = 170$$/1025), esophageal cancers ($12.5\%$, $$n = 128$$/1025), anal cancers ($8.6\%$, $$n = 88$$/1025), small bowel cancers ($7.7\%$, $$n = 79$$/1025), and not otherwise specified within the GI tract ($1.2\%$, $$n = 12$$/1025).
## 3.2. Single Timepoint Prediction Using Logistic Regression
We developed prediction models for the diagnosis of GI tract cancer at 6-months, 1-year, 3-years, and 5-years using multivariate logistic regression at a single timepoint (the last CBC prior to the prediction interval). We included patients’ age, sex, race, BMI, individual components of the CBC, and the most recent BMP (on or prior to the date of the CBC used for prediction). The results of the models’ performance are shown in Table 2. For 6-month prediction of GI tract cancer, the area under the receiver operator curve (AuROC) was 0.697 ($95\%$ CI 0.679–0.715), corresponding to a sensitivity of 0.603 and specificity of 0.690 in this population, with a Brier score of 0.007. At increasing time periods of prediction, the AuROC increased; however, the Brier score also increased to above 0.2, indicating lower performing models (Table 3).
## 3.3. Longitudinal Logistic Regression Model
We developed longitudinal logistic prediction models for the diagnosis of GI tract cancer (at 6-months, 1-year, 3-years, 5-years) using the predictor variables from the single timepoint logistic regression model with the addition of summary variables of the longitudinal CBCs (maximum/minimum, total variation, maximum/minimum slopes). Addition of these longitudinal features led to higher AuROCs for prediction at 6-months, 1-year, and 3-years as compared to the corresponding single timepoint logistic regression models (Table 2). For example, the 3-year AuROC was 0.735 ($95\%$ CI 0.713–0.757) compared to 0.683 ($95\%$ CI 0.665–0.701) for the single timepoint logistic regression prediction model (Figure 1). The 1-year longitudinal logistic regression AuROC was 0.705 ($95\%$ CI 0.689–0.722) with a Brier score of 0.008, compared to the 1-year single timepoint logistic regression model of 0.683 ($95\%$ CI 0.665–0.701) with a Brier score of 0.224 (indicating poor performance).
## 3.4. Longitudinal Random Forest Machine Learning Model
We developed longitudinal random forest machine learning prediction models for diagnosis of GI tract cancer (at 6-months, 1-year, 3-years, 5-years) using the predictor variables from the single timepoint logistic regression model with the addition of summary variables of the longitudinal CBCs. The random forest model AuROCs were greater than both logistic regression models for 6-months, 1-year, and 3-year predictions (Figure 1), with an AuROC of 0.750 at 3 years ($95\%$ CI 0.729–0.771) and a Brier score of 0.116. However, the confidence intervals of the AuROCs overlapped with the longitudinal logistic regression model for all three time periods (Table 2). The variable importance factors for the random forest machine learning models at 1-year and 3-years demonstrated that the most recent (last) mean platelet volume (MPV), minimum MPV, and age were the three most heavily influential variables in these models (Figure 2).
## 3.5. Subanalysis by Age and Tumor Type
We analyzed the longitudinal logistic regression and longitudinal random forest machine learning prediction models for their prediction success at 1- and 3-years by age group and category of GI tract cancer. We selected three age categories: age less than 50, age 50 years or older and less than 75, and age greater than or equal to 75. These ages were selected as they corresponded with screening age groups for colorectal cancer screening during this study period: CRC screening was recommended starting at age 50, not recommended for those less than 50, and individualized screening decision was recommended between ages 75 and 85. There was a trend toward lower AuROCs for those older than 75, suggesting that the models performed less well in this age group, although some of the confidence intervals overlapped, suggesting this was not a statistically significant difference (Table A1).
To describe the imbalanced nature of these groups (overall cohort population was younger, with a median age of 49.4), we determined the imbalance ratio as the ratio of the number of negative samples (individuals without cancer) to the number of positive samples (individuals with cancer) in each category. These findings are consistent with established epidemiological trends of increasing GI tract cancers with age. Although the study was not powered to predict individual GI tract cancers, we calculated the performance of the models on the prediction of individual cancers (Table A2). In this setting, the imbalance ratios were more pronounced, e.g., there were only 30 small bowel cancers with sufficient longitudinal data to make 3-year predictions. The models performed less well in the setting of fewer events for this category.
## 4. Discussion
The results of this retrospective single-center cohort study demonstrate that data within the electronic health record (including CBCs, BMPs, age, sex, race, BMI) can be used to help predict the diagnosis of luminal GI tract cancers (esophageal, gastric, small intestine, colorectal, and anal), with an AuROC of up to 0.750 for prediction of GI tract cancer at 3 years ($95\%$ CI 0.729–0.771; Brier score = 0.116) with a random forest machine learning model, compared to the longitudinal logistic regression model with an AuROC of 0.735 ($95\%$ CI 0.713–0.757) with a Brier score of 0.205. While there was a trend toward improvement with machine learning compared to longitudinal logistic regression, the overlapping confidence intervals mean the model is not definitively better. One possible explanation is the relative rarity of GI cancers compared to the cohort as a whole and the general need for more events to outperform logistic regression in machine learning techniques [35]. In addition, this lack of superiority of ML has been found in other clinical prediction domains as well, highlighting the strengths of multivariate logistic regression and the difficulties in outperforming these models with newer techniques [34,36]. At 5-years’ prediction, when longitudinal changes would be less likely to have immediate predictive power, the single timepoint logistic regression model had a higher point estimate AuROC than the longitudinal models at 0.703 ($95\%$ CI 0.686–0.720), but with a Brier score of 0.213 (indicating overall lower performance). Nonetheless, this study demonstrates important signals that prediction models of luminal GI tract cancers may be useful adjunctive tools to existing clinical intuition and practice guidelines (e.g., that patients with overt GI bleeding or IDA should undergo endoscopic evaluation) [17].
One important aspect of the random forest machine learning method is that predictor variable associations can be identified that may not be otherwise intuitive. For example, mean platelet volume (MPV) was one of the most important variables in the longitudinal random forest machine learning model. There has been growing interest in the potential usefulness of MPV as a marker of systemic inflammation in GI tract cancers, with possible diagnostic implications for gastric and colorectal cancers [37,38,39,40,41] and possible prognostic implications for esophageal cancers [42]. Clinicians rarely use this feature in routine practice, with a prior clinician survey reporting that clinicians consider MPV to be the least useful component of the CBC [24]. Other predictor variables, such as age, are likely more intuitive to clinicians, consistent with established epidemiologic data of increasing incidence of GI tract cancers with increasing age [2,3].
While these models would be inadequate to replace existing CRC [7,8] screening programs [43], they might still have adjunctive utility. For example, guidelines have already implicitly established a tolerance for the “number needed to scope,” or the number of colonoscopies needed to detect one cancer. For example, guidelines recommend bidirectional endoscopy (EGD and colonoscopy) for new-onset IDA [17], with a number needed to scope for a diagnosis of cancer of between to 10 and 100 (incidence ranging between 1 and $10\%$) [44,45]. Similarly, the threshold for fecal immunochemical testing (FIT) for CRC screening has a PPV of 2.9–$7.8\%$ for diagnosis of GI tract cancer [46], corresponding to a number needed to scope of approximately 13 to 35 to diagnose one cancer. Thus, using these reference points, a prediction model utilizing EHR data could be calibrated to achieve a higher specificity, while tolerating a lower sensitivity (as this would not be replacing routine screening), until the positive predictive value reached an acceptable threshold for recommending diagnostic endoscopy. This type of model would be complementary to existing screening programs.
There are several limitations to this study. First, this study was performed retrospectively at an academic medical center, using data collected through routine clinical care, and thus may not apply to other clinical practice settings. The eligible population included all patients receiving care at Michigan Medicine, which includes patients seen by Michigan Medicine primary care providers and specialists. We further narrowed our cohort to individuals with longitudinal follow-up within the Michigan Medicine health system by requiring at least two CBCs over two years. To maximize our eligible cohort, we did not exclude individuals based on the type of provider(s) seen at Michigan Medicine or other exclusions. For this exploratory study, we did not have an external validation cohort, so the accuracy of the models may decline in other settings, as there may be other unmeasured differences between populations. Second, because our inclusion criteria required the presence of at least two CBCs per patient (to determine longitudinal trends), there may be unknown, systematic differences between these patients and those with fewer CBCs (who were excluded from the cohort). An alternate model incorporating a single CBC could have advantages in a setting where primary care follow-up is limited or where CBCs are less commonly obtained. It is also reasonable to consider whether the increased performance of a longitudinal model is “worth” the added computational complexity that would be required for its deployment. Third, for purposes of this analysis, we considered the diagnosis of any GI tract cancer as a binary outcome, given the relatively rare incidence of GI tract cancers relative to the cohort size. However, the tumor biology of GI tract cancers is heterogenous. As a result, there may be different patterns specific for individual subtypes of GI tract cancer that this study was not powered to detect. Models focused solely on a single organ have the potential to have higher specificity but would not be designed to detect other GI tract tumors. Fourth, we were limited by predictor variable inclusion in our models due to a high degree of missingness of some suspected useful variables (CBC with differentials, ferritin), potentially limiting the predictive power relative to prior models for CRC that incorporated CBCs with differentials [20]. An additional limitation is that these models do not incorporate additional clinical history such as the findings of prior EGD or colonoscopy procedures, but we intentionally chose to focus on readily ascertainable parameters from existing EHR data for easier potential use in the future.
## 5. Conclusions
Nonetheless, despite these limitations, this work offers some important contributions to the diagnostic evaluation of GI tract cancers, demonstrating that logistic regression or random forest machine learning models using EHR data can help predict the presence of GI tract cancers. Improved diagnosis in this domain is critical. First, given epidemiologic trends with an increasing incidence of CRC at younger ages, additional detection strategies are needed to identify diseases earlier in this younger cohort, who would not yet have undergone routine CRC screening. Second, given limited endoscopic access in some settings, methods to identify patients at greatest risk of GI tract cancer are increasingly important, as they could help prioritize GI diagnostic evaluations on those individuals at greatest risk. Third, prior work has demonstrated that IDA is not always diagnosed or evaluated fully, meaning that additional automated methods of helping clinicians identify patients at increased risk of GI tract cancers are needed. In summary, these models could help fill an important need and assist clinicians in the diagnosis of GI tract cancers. Further refinement and evaluation of these models in a larger cohort, with external validation, is needed prior to any potential prospective clinical evaluation.
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|
---
title: Leveraging Tumor Microenvironment Infiltration in Pancreatic Cancer to Identify
Gene Signatures Related to Prognosis and Immunotherapy Response
authors:
- Jiabin Yang
- Liangtang Zeng
- Ruiwan Chen
- Leyi Huang
- Zhuo Wu
- Min Yu
- Yu Zhou
- Rufu Chen
journal: Cancers
year: 2023
pmcid: PMC10000708
doi: 10.3390/cancers15051442
license: CC BY 4.0
---
# Leveraging Tumor Microenvironment Infiltration in Pancreatic Cancer to Identify Gene Signatures Related to Prognosis and Immunotherapy Response
## Abstract
### Simple Summary
Pancreatic ductal adenocarcinoma (PDAC) has an insidious onset and rapid progression, and its morbidity and mortality are increasing year by year. Currently, there are limited therapeutic methods and no effective therapeutic guidance. Tumor microenvironments (TME) of PDAC are highly specific and associated with the failure of chemotherapy, radiotherapy, and immunotherapy. Different TMEs have different sensitivities to treatment modalities. Therefore, constructing a prediction model based on TME classification and giving corresponding treatment measures according to the classification results will provide a new idea for clinical precision diagnosis and treatment. Further verification of gene function related to TME will greatly provide effective potential clinical treatment targets for personalized therapy.
### Abstract
The hallmark of pancreatic ductal adenocarcinoma (PDAC) is an exuberant tumor microenvironment (TME) comprised of diverse cell types that play key roles in carcinogenesis, chemo-resistance, and immune evasion. Here, we propose a gene signature score through the characterization of cell components in TME for promoting personalized treatments and further identifying effective therapeutic targets. We identified three TME subtypes based on cell components quantified by single sample gene set enrichment analysis. A prognostic risk score model (TMEscore) was established based on TME-associated genes using a random forest algorithm and unsupervised clustering, followed by validation in immunotherapy cohorts from the GEO dataset for its performance in predicting prognosis. Importantly, TMEscore positively correlated with the expression of immunosuppressive checkpoints and negatively with the gene signature of T cells’ responses to IL2, IL15, and IL21. Subsequently, we further screened and verified F2R-like Trypsin Receptor1 (F2RL1) among the core genes related to TME, which promoted the malignant progression of PDAC and has been confirmed as a good biomarker with therapeutic potential in vitro and in vivo experiments. Taken together, we proposed a novel TMEscore for risk stratification and selection of PDAC patients in immunotherapy trials and validated effective pharmacological targets.
## 1. Introduction
Pancreatic ductal adenocarcinoma (PDAC) is one of the most challenging cancers in alimentary malignancies [1,2]. For most patients with PDAC, cytotoxic chemotherapy remains the mainstay of treatment. However, despite recent improvements in chemotherapeutic regimens and treatment modalities, their survival benefits remain limited [3]. In addition, many efforts have been made to develop targeted therapies for PDAC, but there has been no substantial improvement [4,5]. Progress in strategies targeting homologous recombination defects, while substantial, currently shows applicability and efficacy in only a small proportion of patients [6]. Furthermore, PDAC is known to lack an effective immune response with low immunogenicity, which results in rapid cancer progression and a limited response to cancer immunotherapy [7,8]. Despite the aggressive molecular behavior driven by intrinsic oncogenic genetic alterations, the tumor microenvironment (TME) of pancreatic cancer has been deemed to be responsible for the above dilemma [9,10,11,12].
PDAC is characterized by extensive deposition of desmoplastic stroma, which may comprise more than $80\%$ of the whole tumor mass [13,14]. The extracellular matrix, vessels, and stromal cells comprise the TME of pancreatic cancer [15]. The cell component surrounding PDAC cells consists predominantly of cancer-associated fibroblasts, various immune cells, and endothelial cells. The complex interactions between TME cells and cancer cells contribute to tumor progression in a multifaceted way [16]. For example, cancer-associated fibroblasts, infiltrated inflammatory cells, and desmoplastic stroma enhance cancer growth, invasion, metastasis, and treatment resistance in direct or indirect ways [15,17]. The immune-suppressor cells in TME establish an immunosuppressive tumor microenvironment, which results in rapid cancer progression and a low immune response to immunotherapy [18]. Accumulating studies have revealed that the TME context correlates with clinical outcomes, therapy benefits, and immune response [19,20,21,22,23,24,25].
By now, although clinical decision-making based on molecular subtypes has been well established in some cancer types, subtypes of PDAC do not currently provide effective support for clinical decisions [26]. However, accumulating molecular subtypes have been defined in PDAC with the development of the genome project, which defined various PDAC subtypes with distinct tumor biological behaviors and clinical characteristics [27]. Although the several mechanisms associated with the role of TME have been highlighted in some previous subtypes [27,28,29,30,31], the comprehensive landscape of cells infiltrating the TME of PDAC has not yet been elucidated, as well as that there is a lack of a molecular subtype based on TME signatures to inform treatment decisions, including the applicability of immunotherapy. Therefore, in the present study, we evaluated the cell components of the TME in PDAC using computational algorithms and then established subtypes based on TME infiltration signatures. Finally, a robust TMEscore and an effective biomarker capable of risk stratification and informing treatment decisions were developed.
## 2.1. Sample Data Collection
The expression data (RPKM) of 182 PDAC patients were downloaded from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/ (accessed on 1 January 2020)) by using TCGAbiolinks. The fragments per kilobase of exon model per million (FPKM) data from TCGA were transformed into transcripts per kilobase per million (TPM) values. Additionally, other public PDAC datasets were obtained through the retrieval of the GEO database (https://www.ncbi.nlm.nih.gov/geo/ (accessed on 1 January 2020)) with the following retrieval strategy: (“pancreatic neoplasms” [MeSH Terms] OR pancreatic cancer [All Fields]) AND “Homo sapiens” [porgn] AND (“Expression profiling by array” [Filter]). Samples with survival data were retained for further analysis. The detailed information on the retrieved datasets was summarized in Supplementary Table S1.
## 2.2. Estimation of Cell Components in TME
The ESTIMATE algorithm was used to calculate the level of stromal cell and immune cell infiltration in each sample and further infer tumor purity [32]. Single-sample gene set enrichment analysis (ssGSEA) [33,34], a deconvolution algorithm based on gene set enrichment analysis (GSEA), was used to qualify the relative abundance of 29 cell types within the TME. The ssGSEA was run using the GSVA R package. *Signature* genes of each cell type were obtained from previous publications [35,36]. The ssGSEA score was normalized to a unity distribution, in which zero is the minimum score and one is the maximal score for each cell type. In some analyses, the immune infiltrations were also quantified by the GSVA algorithm [37], for which the normalized GSVA scores were obtained from a recent publication [38].
## 2.3. Consensus Clustering for Infiltrating Cells of TME
Unsupervised consensus clustering was performed on normalized ssGSEA scores of TME cell components by using the ConsensusClusterPlus R package (parameters: reps = 1000, pItem = 0.8, pFeature = 1). The complete method and Manhattan distance were used as the clustering algorithm and distance metric, respectively.
## 2.4. Identification of TME Signature Genes
To identify the signature genes of TME, we first estimated the differentially expressed genes (DEGs) associated with TME subtypes determined by consensus clustering of TME infiltration. The DEGs among TME subtypes were obtained using the limma R package with the selection criteria of Log2FoldChange > 1 and an adjusted p-value < 0.05 (Benjamini–Hochberg correction). Next, the random forest method was used to evaluate the contribution of these DEGs to the cluster grouping of the TME cell population, and the genes that had less influence on the grouping were filtered out. Finally, 74 DEGs that influenced the prognosis were obtained. An unsupervised clustering method (K-means) with Ward. D and Euclidean distance was used to classify patients into subgroups based on the 74 DEGs. The ConsensusClusterPlus R package was adopted and used to annotate gene patterns and define gene clusters.
## 2.5. Development of TMEscore
After obtaining the two gene clusters in the above part, the genes in each cluster were extracted to serve as the TME gene signature sets, respectively: TME signature gene set A from cluster 1, and TME signature gene set B from cluster 2. The ssGSEA algorithm was used to calculate the enrichment score of each TME signature gene set for each sample. Thereafter, the TMEscore for each sample was obtained by using the following formula: TMEscore = TMEscoreB − TMEscoreA, where TMEscoreB stands for the ssGSEA score of TME signature gene set B, TMEscoreA stands for the ssGSEA score of TME signature gene set A. A schematic diagram illustrating the process of generation of the TMEscore was described in Supplementary Figure S1.
## 2.6. Functional Annotation and Enrichment Analysis
A functional annotation analysis was conducted based on the GO database (http://geneontology.org/page/go-database (accessed on 1 January 2020)) and the KEGG database (http://www.kegg.jp/kegg/ko.html (accessed on 1 January 2020)). KEGG and GO term gene set enrichment analysis (GSEA) was conducted using the clusterProfiler R package. We also estimated the enrichment of pathways among TME gene clusters or samples with different TMEscores by running gene set enrichment analysis (GSEA) [33,39]. For GSEA analysis, gene sets for certain pathways were collected via searching the MSigDB database (https://www.gsea-msigdb.org/gsea/msigdb/ (accessed on 1 January 2020)). GSEA was performed using the GSEA software and visualized by the ggplot R package.
## 2.7. Mutation Analysis
TCGA-PAAD mutation data were downloaded in January 2020 from the GDC data portal. The copy number events were filtered for those with at least 10 supporting probes and a segment mean >0.2 (amplifications) or <−0.2 (deletions), as recommended by a previous study [40,41]. The waterfall plots of mutational landscapes were drawn using the maftools Bioconductor package [42]. Mutation types were ordered by their potential impact, from most deleterious to least.
## 2.8. Cell Lines and Cell Culture
Human cell lines BxPC-3, PANC-1, AsPC-1, Capan-2, MIA-PaCa2, SW1990, and hTERT-HPNE were purchased from the ATCC (American Type Culture Collection, Rockville, MD, USA). Cells were cultured in DMEM (Gibco, Billings, MT, USA) or RPMI 1640 medium (Gibco, USA). All media were supplemented with $10\%$ fetal bovine serum (FBS, BI, Israel) and $1\%$ penicillin/streptomycin. All cells were cultured in a humid environment containing $5\%$ CO2 at 37 °C.
## 2.9. Cell Transfection and Lentiviral Infection
For cell transfection, the siRNAs for knocking down F2R-like Trypsin Receptor1 (F2RL1) and the F2RL1 overexpression plasmid were purchased from IGE (Guangzhou, China). Transfection was performed using the Lipofectamine 3000 kit (Invitrogen, Cat# L3000015, Waltham, MA, USA) according to the manufacturer’s instructions.
For lentivirus infection, in order to construct stable knockdown cell lines, the shRNA sequence of F2RL1 was cloned into a pLKO.1-Puro vector by IGE, and then the lentivirus packaging plasmids containing psPAX2 and pMD2G were cotransfected into HEK-293T cells (ATCC, RRID: CVCL_0063). After transfection for 72 h, lentivirus was collected and concentrated. Subsequently, the cell lines were infected with lentivirus and selected by treatment with puromycin (Solarbio, Beijing, China) for 2 weeks. All the sequences of oligonucleotides are shown in Supplementary Table S9.
## 2.10. RNA Extraction and qRT-PCR Analysis
The total RNA of PDAC cell lines was extracted using Trizol reagent (Takara Bio, Shiga, Japan) according to the instructions. Subsequently, the total RNA was reverse transcribed into cDNA using the Hiscript III Reverse Transcriptase Kit (Vazyme, Nanjing, China). Finally, qRT-PCR was used to detect the expression of RNA using the ChamQ Universal SYBR qPCR Master Mix kit (Vazyme, Nanjing, China). GAPDH was used as an internal control. The sequences of primers are listed in Supplementary Table S9.
## 2.11. Colony Formation Assay
A total of 500 cells transfected with siRNA or stably overexpressing F2RL1 were cultured in 6-well plates in a humidified atmosphere containing $5\%$ CO2 at 37 °C for 2 weeks. After that, the colonies were fixed in $4\%$ paraformaldehyde for 20 min, stained with $0.1\%$ crystal violet for 15 min, and washed twice with phosphate-buffered saline (PBS). Count colonies manually. Each group repeated the experiment at least three different times.
## 2.12. EdU Assay
The cells transfected with siRNA or stably overexpressed F2RL1 were pre-seeded in 24-well plates and cultured at 37 °C containing $5\%$ CO2 for 24 h. Then, using BeyoClickTM EdU-555 detection kits (Beyotime, Shanghai, China) and according to the manufacturer’s instructions, the cells were stained with EdU for 2 h, fixed with $4\%$ paraformaldehyde for 15 min, incubated with click reaction solution for 30 min and stained with Hoechst 33,342 for 10 min. The images were obtained by fluorescence microscope Nikon TI-S (Nikon, Tokyo, Japan). Each group repeated the experiment at least three different times.
## 2.13. Wound Healing Assay
The cells transfected with siRNA or stably overexpressed F2RL1 were seeded in a 12-well plate according to 2 × 105 cells/well and cultured in a moist environment containing $5\%$ CO2 at 37 °C until the cell density was about $90\%$. Then adherent cells were scraped with the 10 μL sterile pipette tips in a straight line, and the images were obtained by an inverted microscope, the Nikon TI-S (Nikon, Tokyo, Japan), at 0 h and 24 h, respectively. The cell migration distance was measured and calculated. Each group repeated the experiment at least three different times.
## 2.14. Transwell Assay
The cells transfected with siRNA or stably overexpressed F2RL1 were added to 200 μL serum-free medium with or without Matrigel (BD Biosciences, Franklin Lakes, NJ, USA), respectively, and seeded in a Transwell chamber, then placed in a 24-well plate. 700 μL complete medium was added to each well in the lower layer of the 24-well plate in advance. PANC1 was incubated for about 8 h, and BxPC-3 was incubated for about 48 h. Then the Transwell chamber was removed, fixed with $4\%$ paraformaldehyde for 15 min, and stained with $0.1\%$ crystal purple for 15 min. Images were obtained by an inverted microscope, the Nikon TI-S (Nikon, Tokyo, Japan), and the number of cells that migrated or invaded was counted. Each group repeated the experiment at least three different times.
## 2.15. Animal Experiment
To construct a subcutaneous tumorigenicity animal model, 5 × 106 BxPC-3 cells in suspension, stably overexpressing F2RL1 and Vector, were injected subcutaneously into the left dorsal side of BALB/c nude mice ($$n = 5$$) aged 4 to 5 weeks, purchased from the Guangdong Medical Laboratory Animal Center. Tumor growth was measured every 4 days, and tumor volume was recorded. Volume = 0.5 × length × width2. Four weeks later, all the mice were sacrificed, and the tumor tissue was dissected, weighed, and fixed with $37\%$ formalin and embedded in paraffin.
## 2.16. Immunohistochemistry (IHC)
Immunohistochemical staining was performed on paraffin sections, which were first treated at 60 °C for 2 h, then dewaxed with xylene, rehydrated with different grades of ethanol, repaired antigen with EDTA, and blocked with normal goat serum. The sections were incubated with primary antibodies at 4℃ overnight and secondary antibodies at room temperature for 2 h. Finally, DAB chromogenic reagent was used to label the antigen, followed by counterstaining with hematoxylin. The staining was judged by two independent observers. Images were obtained by a microscope, the Nikon 80i (Nikon, Tokyo, Japan). The antibodies used in this study are listed in Supplementary Table S10.
## 2.17. Statistical Analysis
The distribution of two sets of continuous variables was compared using a t-test. If continuous variables did not follow a normal distribution, the Mann–Whitney U test was applied. Unless explicitly stated, the association between categorical variables was evaluated using Pearson’s chi-square test. To divide the samples assessed into groups according to high versus low TMEscore, the MaxStat R package was used. Survival curves were compared using the Kaplan–Meier method with a log-rank t-test. The influence of the TMEscore on survival was additionally evaluated through the Cox proportional hazard model. The independence of association was verified by a multivariate Cox regression model of survival. The resulting p-values of differently expressed genes between two groups were corrected for multiple testing by the Benjamini–Hochberg method. All reported p-values are two-sided. R (version 3.6.3) and SPSS (version 17.0; SPSS Inc., Chicago, IL, USA) were used to perform statistical analyses. Figures were generated with the ggplot R package and GraphPad Prism 8 (GraphPad Prism Software, San Diego, CA, USA). Two-sided $p \leq 0.05$ was considered significant.
## 3.1. Identification of Tumor Microenvironment Subtypes in PDAC Cases
The flow chart of the study is shown in Figure 1A. First, we defined the tumor microenvironment (TME) infiltration pattern of each tumor as the relative abundance of an array of twenty-eight cell populations of immune cells and fibroblasts. TME cell profiles were estimated via the ssGSEA algorithm (Supplementary Table S2). To select the optimal cluster number, we grouped the ssGSEA scores of the resectable PDAC tumors from the TCGA dataset using hierarchical clustering. As a result, we obtained three robust subtypes of PDAC (named TMEgroups1–3) (Figure 1B) (Supplementary Table S3). The TMEgroup3 is defined as the smallest group of cases ($\frac{23}{177}$, $13.0\%$), followed by TMEgroup1 ($\frac{71}{177}$, $40.1\%$), and TMEgroup2 ($\frac{83}{177}$, $46.9\%$). TMEgroup3 was associated with better overall survival in comparison with the other two TMEgroups (Log-rank test, $$p \leq 0.017$$, Figure 1C). In many solid tumors, the degree of immune infiltration of the TME is highly correlated with immunotherapy efficacy [43]. Due to the high tumor heterogeneity and TME heterogeneity in PDAC, the specific biological characteristics are different from other solid tumors [44]. As previous studies have shown, the abundance of immune cells tends to predict a poor prognosis [45,46]. The overall survival rate of TMEgroup3, which had the fewest immune cells, was higher than that of the other two groups.
## 3.2. Identification of the TME Signature Genes and Functional Annotation
To identify the signature genes associated with TMEgroups, differentially expressed genes (DEGs) between each TMEgroup and others were obtained using the limma R package, and the results are shown in Supplementary Figure S2 and Supplementary Table S4. As shown in the Venn diagram of Figure S2, there was no overlap between the DEGs from each TMEgroup, suggesting the high specificity of DEGs for each TMEgroup. Next, a random forest method was then used to estimate the contribution of these DEGs to the clustering of the TME cell population, and 74 genes with influence on the clustering were finally retained. By performing unsupervised hierarchical cluster analysis based on the 74 TME-related DEGs, we identified 2 robust groups for TCGA-PAAD samples: TMEgeneGroup1 and TMEgeneGroup2. *The* gene symbols for signature genes for each group were summarized in Supplementary Table S5. The patient-level annotation of the DEGs is visualized in Figure 2A. A significant decreased overall survival was found in TMEgeneGroup1 (Log-rank test, $$p \leq 0.0034$$, Figure 2B). The differences in class assignments between the two clustering methods were visualized with an alluvial diagram (Figure 2C). Most deaths occurred in TMEgroup3 and all deaths that occurred in TMEgroup2 were assigned to TMEgeneGroup1, suggesting signature genes had better prognostic discrimination values, such as the identification of patients at a high risk of death from subgroups with better prognosis.
In addition, we analyzed the tumor purity of the TCGA dataset, and the results showed that there was a difference between TMEgeneGroup1 and TMEgeneGroup2 in the tumor purity, suggesting that the tumor may be related to immune infiltration of TME (Supplementary Figure S2G, Supplementary Table S7). Further, we found that tumor purity was negatively correlated with the immune ssGSEA (Supplementary Figure S2H). Moreover, we further analyzed the laser microdissected dataset from Maurer C et al. [ 47]. Through the comparison of stromal and epithelial cells, we obtained 6308 DEGs representing stromal regions, which were further intersected with our 74 DEGs. Interestingly, the results showed that there were 41 duplicate DEGs (Supplementary Figure S2E), suggesting that those 41 DEGs we analyzed had the characteristics of representing stromal cells in non-tumor regions. Subsequently, we also verified the effect of ssGSEA used in this project in reflecting TME characteristics (Supplementary Figure S2F). The above results show that the difference in TME can be better reflected by screening effective DEGs to distinguish cell components.
To further explore the biological function and mechanism behind the signature genes, DEGs between TMEgeneGroup1 and TMEgeneGroup2 were determined (Supplementary Table S6). Functional annotation analysis of these DEGs was conducted. Significantly enriched KEGG pathways and GO biological processes were summarized in Supplementary Figure S3. We found TMEgeneGroup1 and TMEgeneGroup2 had distinct differences in the enriched pathways and biological processes. Genes overexpressed in TMEgeneGroup1 were involved in several well-known carcinogenesis mechanisms, such as the PI3K/Akt signaling pathway and the p53 signaling pathway. In addition, mechanisms involved in extracellular matrix composition, cell-extracellular matrix interaction, and cell adhesion were enriched in TMEgeneGroup1. In contrast, genes overexpressed in TMEgeneGroup2 were mainly involved in signal molecule transduction-related mechanisms, such as the cAMP signaling pathway, ligand-receptor interaction, single release, chemical synaptic transmission, and regulation of membrane potential. Furthermore, using disease network enrichment analysis with all DEGs between TMEgeneGroup1 and TMEgeneGroup2 (Figure 2D), we found enriched disease gene sets related to chronic pancreatitis, cholangiocarcinoma, and breast carcinoma stage IV. In addition, we identified seven core DEGs: GSTP1, ERBB2, MUC1, F2RL1, PTGS2, CCND1, and CXCL8, which were involved in all three gene sets.
## 3.3. Establishment of TMEscore Model Based on TME Signature Gene Sets
As mentioned above, the unsupervised hierarchical cluster analysis was based on the 74 most representative DEGs and separated the PDAC cohort into 2 distant patient clusters (Figure 2A). Subsequently, the 74 DEGs were divided into 2 distinct clusters, termed TME signature gene set A (enriched in TMEgeneGroup1) and TME signature gene set B (enriched in TMEgeneGroup2). Furthermore, based on the two TME signature gene sets, we estimated two TME-related scores using the ssGSEA algorithm as described in the “Methods” part: TMEscoreA from TME signature gene set A and TMEscoreB from TME signature gene set B, thus obtaining the final TMEscore through the following formula: TMEscore = TMEscoreB − TMEscoreA.
After having identified the TMEscore for each patient in the TCGA-PAAD cohort, we sought to determine whether the TMEscore could effectively predict prognosis. As shown in Figure 3A–C, low TMEscoreA was correlated with improved survival (Log-rank test, $$p \leq 0.0005$$) in TCGA-PAAD patients, and a low TMEscoreB was associated with poor survival (Log-rank test, $$p \leq 0.00152$$). At last, survival analysis revealed that patients with a low TMEscore had a less favorable outcome (Log-rank test, $$p \leq 0.00065$$). Multivariate Cox models revealed that the TMEscore was an independent prognostic variable for overall survival (HR = 1.72. $95\%$CI 1.07–2.80, $$p \leq 0.025$$) (Figure 3D). Next, we tried to validate the prognostic value of TMEscore with seven external data sets obtained from the GEO database. As shown in Supplementary Figure S4, upon stratification of the samples according to TMEscore, significant differences in overall survival were found between the TMEscore low and high groups for all datasets except GSE28735 ($$p \leq 0.14$$, sample size = 43, the dataset with the lowest sample size), which confirmed the robust prognosis stratification ability of the TMEscore.
## 3.4. Exploring the Biological Characteristics of Patients with Different TMEscore
To explore the underlying molecular mechanisms associated with the TMEscore, we first compared the profile of oncogenic mutations between patients with a low and high TMEscore (Figure 4A). We found significantly increased mutation rates in KRAS, TP53, and CDKN2A in the low TMEscore group. Afterward, the GSEA was performed to evaluate the pathways associated with the TMEscore (Figure 4B). The significantly enriched gene sets in samples with a low TMEscore were correlated with KRAS, NF-κβ, P53, MEK, AKT, and cell cycle signaling pathways, all directly associated with tumor development. On the other hand, genes up regulated in neurons and in response to overexpressing Src were enriched in samples with a high TMEscore. The GSEA results were consistent with the differences in mutation spectras between patients with a low and high TMEscore. Additionally, these results also indicated that the TME-based score could reflect tumor-intrinsic mechanisms at the level of driver gene profiles.
Next, we analyzed the infiltration of cell populations between two groups. Figure 4C displays the differences in TME cell infiltration in the two groups with high and low TMEscores. Overall, most cell populations were increased in samples with a high TMEscore, especially since the infiltration of activated CD4/CD8 T cells was significantly increased. In light of the well-recognized close relationship between TME and immune status, we further compared the well-known biological mechanisms/pathways associated with TME and cancer-immune phenotypes between the two groups. The feature genes for each mechanism/pathway were summarized in Supplementary Table S8. As shown in Figure 4D, the ssGSEA analysis confirmed a significant enrichment of genes representing EMT, the TGF-β pathway, the Wnt pathway, homologous recombination, mismatch repair, and DNA damage repair in low TMEscore samples. This suggests that the TMEscore may reflect tumor environment infiltrations, tumor-intrinsic mechanisms, and immune status.
## 3.5. Characterization of Immune-Phenotypes across Samples with Different TMEscore
To further delineate the link between TMEscore and immune status, we evaluated the relationship between a well-established immune phenotype and TMEscore. The immune phenotypes of 156 TCGA-PAAD samples were obtained from a previous publication, which classified tumors into three immune phenotypes: poor cytotoxicity, intermediate cytotoxicity, and high cytotoxicity on the basis of cytotoxic infiltration [38]. The abundance of cytotoxic cells was estimated by either ssGSEA (Figure 5A) or GSVA (Figure 5B). As shown in the heatmaps (Figure 5A,B) and the result of the chi-square test (Figure 5C), we observed that the cytotoxic level of the immune phenotype tended to increase with the elevated level of TMEscore. A previous study reported that tumors with a highly cytotoxic immune phenotype tend to show an increased abundance of cytotoxic infiltration with ectopic expression of negative immune checkpoints [38]. So, we next estimated the infraction of activated CD8 T cells, effector memory T cells, and γδ T cells by ssGSEA (Figure 5D) and GSVA (Figure 5E). Both algorithms showed increased infiltration of CD8 T cells and effector memory T cells in samples with high TMEscores. Additionally, we observed the same increase in the cytotoxic score (Figure 5F). These results collectively suggested that a high TMEscore indicates a TME with high cytotoxic activity. Therefore, we next estimated the enrichment of gene sets associated with the T cell–inflamed phenotype, which correlates with improved responsiveness to therapies dependent on T cell killing, such as checkpoint blockade and adoptive cell therapy [48]. We used three independent gene sets for each comparison, including gene sets that have been confirmed to be predictive of response to immunotherapy across different cancer types. Notably, all gene sets displayed significant enrichment in the high TMEscore group (Figure 5G). Meanwhile, we also observed the enrichment of immune checkpoints.
At last, we assessed the enrichment of gene programs defining PDAC subtypes and their association with the TMEscore (Figure 5H). The expression of genes defining the ADEX (aberrantly differentiated endocrine exocrine) subtype [30] was increased in TMEscore-high tumors, while the genes defining the pancreatic progenitor subtype tended to increase in TMEscore-low tumors. TMEscore-high tumors were statistically enriched for the immune gene programs (GP6 and GP8) from Bailey and colleagues [30]. These two gene programs contain signature genes for CD8+ T cells and B cells, supporting the finding that TMEscore-high tumors had high cytotoxic infiltration. Furthermore, TMEscore-high tumors were enriched for the normal stroma gene program. The normal stroma gene signature contains markers of pancreatic stellate cells, which have been linked to an immunosuppressive tumor microenvironment through blocking antigen presentation [29,49,50]. In contrast, TMEscore-low tumors were enriched for the activated stroma gene program. The activated stroma was characterized by a more diverse set of genes associated with activated fibroblasts and activated inflammatory stromal responses, both of which were responsible for a low antitumor immune response [29].
## 3.6. The Predictive Value of TMEscore for Response to Immune Therapy
The above data suggested that the TMEscore can stratify PDAC patients into distinct clusters or subtypes not only with different tumor-intrinsic characteristics but also with different stromal statuses and immune environments. To further validate this finding, we performed GSEA and found enrichment of immune sensing pathways involved in T cell priming (STING and NLRP3 inflammasome signaling) in TMEscore-low tumors (Figure 6A). We found that both STING and NLRP3 inflammasome signaling were enriched in samples with low TMEscore. Consistent with this, the antigen presentation activity, which was measured by the ssGSEA score of two independent gene sets reflecting the antigen presentation mechanism (APM), was elevated in samples with a low TMEscore (Figure 6B,C). Interestingly, the expression of signature genes of Batf3-dendritic cells, a key antigen-presenting cell population for driving T cell immunity and response to immunotherapy in PDAC [51,52], was increased in TMEscore-high tumors (Figure 6D). These results support the above hypothesis that a low TMEscore identified tumors with normal stroma status, which was characterized by blocked antigen presentation. In addition, we found both the CD8/CD4 ratio and the CD8/Treg ratio were significantly increased in TMEscore-high tumors (Figure 6E,F). Both of them were biomarkers of elevated cytotoxic activity. We also found that downregulated genes after IL15, IL2, or IL21 stimulation were enriched in TMEscore-high tumors (Figure 6A). IL15, IL2, and IL21 were responsible for the expansion of cytotoxic T cells; therefore, the negative correlation between TMEscore and the expression of these gene sets supported a cytotoxic environment with exhaustion in TMEscore-high tumors. Collectively, the stratification of patients with TMEscore based on transcriptional profiling may differentiate between tumors with different immune evasion mechanisms and different immunotherapy responses.
Immune checkpoint blockade therapy, such as inhibitors targeting the PD1–PDL1 axis, shows promising prospects for cancer treatment. We subsequently explored the prognostic value of the TMEscore in patients who received immune-checkpoint therapy. As there is currently no available cohort with both transcriptome and survival information for immune therapy in pancreatic cancer, we used two well-known solid tumor cohorts receiving immune checkpoint blockade therapy. As shown in Figure 6G–J, patients with high TMEscores had significantly longer overall survival than those with low TMEscores in both the GSE78220 [53] cohort (anti-PD1) and IMvigor210 [54] cohort (anti-PDL1). In line with survival analysis, patients with a high TMEscore also showed an increased response rate to both anti-PD-1 (GSE78220) and anti-PD-L1 (IMvigor210) antibody treatment. Taken together, our data suggest that TMEscore could predict the response to checkpoint inhibitor immunotherapy.
## 3.7. F2RL1 Was Significantly Associated with the Malignant Progression of PDAC
To further explore potential drug therapeutic targets for PDAC, we screened the aforementioned core DEGs: GSTP1, ERBB2, MUC1, F2RL1, PTGS2, CCND1, and CXCL8, and verified the expression between BxPC-3 and PANC-1 cell lines. The results showed that the expression of F2RL1 was significantly high (Figure 7A,B). Currently, the mechanism of F2RL1 in the malignant progression of pancreatic cancer remains unclear, and more scientific evidence is needed. TCGA and Genotype-Tissue Expression (GTEx) database analysis showed that F2RL1 was highly expressed in PDAC tumor tissues compared with non-tumor tissues (NAT) (Figure 7C). Then, univariate and multivariate Cox analyses revealed that F2RL1 was an independent influence factor on the overall survival (OS) and disease-free survival (DFS) of PDAC patients (Figure 7D, Supplementary Tables S11 and S12). Importantly, Kaplan–*Meier analysis* showed that PDAC patients with high F2RL1 expression had shorter OS and DFS, suggesting that F2RL1 is associated with the malignant progression of PDAC (Figure 7E,F). Notably, further ssGSEA correlation analysis showed that the expression of F2RL1 was related to some immune cells in TME (Figure 7G). Further, we used the TISCH platform (http://tisch.comp-genomics.org (accessed on 2 January 2023)) to analyze the expression of F2RL1 at the cellular level. The results showed that F2RL1 was mainly distributed in malignant cell subsets (Figure 7H–J). Moreover, through functional annotation analysis of malignant cells, the enrichment results of the KEGG pathway and GO biological process showed that F2RL1 was mainly related to extracellular matrix composition, signaling pathways, cell adhesion, and other mechanisms (Figure 7K,L). The above results suggested that the upregulation of F2RL1 could promote the malignant progression of PDAC.
## 3.8. As an Effective Therapeutic Target, F2RL1 Promotes the Proliferation and Invasion of PDAC In Vitro and Vivo
Given that F2RL1 was associated with a poor prognosis for PDAC, we further explored its biological function. We analyzed the expression of F2RL1 in PDAC cell lines, and the results showed that the expression of F2RL1 was significantly high in PANC-1 and BxPC-3 (Figure 8A). Further, we verified the transfection efficiency of F2RL1 in BxPC-3 and PANC-1 cell lines by knockdown and overexpression of F2RL1 (Figure 8B–E). The colony formation assay (Figure 8F–H, Supplementary Figure S5A–C) and EdU assay (Figure 8I–K, Supplementary Figure S5D–F) showed that the proliferation ability of cells, compared with the control group, was decreased after F2RL1 expression was down-regulated. The overexpression of F2RL1 showed the opposite effect. These results suggested that the upregulation of F2RL1 can promote the proliferation of PDAC cells. The transwell assay (Figure 8O–Q, Supplementary Figure S5J–L) and the wound healing assay (Figure 8L–N, Supplementary Figure S5G–I) showed that, compared with the control group, the cell invasion ability was weakened after F2RL1 was down-expressed, while the effect was opposite after F2RL1 was over-expressed. Therefore, these results indicated that the overexpression of F2RL1 could promote the proliferation and invasion of PDAC cells in vitro.
Further, we verified the carcinogenic function of F2RL1 in a subcutaneous tumorigenicity mouse model (Figure 9A,B). Animal experiments showed that, compared with the sh-NC group ($$n = 5$$), the tumor volume and weight in the sh-F2RL1#1 group were lower (Figure 9C,D). Remarkably, IHC staining showed lower levels of Ki-67 expression in stable knockdown F2RL1 tissues (Figure 9E,F). Therefore, the downregulation of F2RL1 can strikingly inhibit the proliferation of PDAC in vivo.
Collectively, F2RL1 may be a potential biomarker for predicting survival outcomes in patients with PDAC.
## 4. Discussion
It is well acknowledged that the TME is of vital importance in cancer progression and therapeutic responses [55]. In this context, we evaluated the infiltration pattern of TME cells through the computational integration of their signature genes and developed a TMEscore that robustly predicts the prognosis for PDAC patients. Through comparison with the established PDAC molecular subtypes, we found the TMEscore differed across multiple established PDAC subtypes. Overall, tumors with a high TMEscore tend to share transcriptional commonalities with ADEX/Exocrine-like subtypes, which were defined by the transcriptional expression of multiple genes associated with terminally differentiated pancreatic tissues [30]. Instead, tumors with a low TMEscore were enriched with squamous/classical subtypes which reflect the molecular characteristics of squamous tumors across multiple tissue types, such as hypoxia response, metabolic reprogramming, and TGF-β signaling [30]. The close relationship between the TMEscore and tumor essential character-based subtypes suggested a correlation between TME and tumor cell-intrinsic properties. On the other hand, as expected, TMEscore-low tumors showed a subtype of “activated stroma” [29], while tumors with a high TMEscore displayed a “normal stroma” subtype [29]. Beyond that, TME high-score tumors were enriched for the immune gene programs (GP6 and GP8) from Bailey and colleagues [30]. These two gene programs were associated with B cells and CD8+ T cell infiltration signatures and T cell co-inhibitory phenotypes, respectively [30]. Therefore, the TMEscore is a comprehensive index reflecting tumor intrinsic features, stromal states, and immunophenotype.
PDAC is strikingly resistant to traditional treatment [56,57,58]. Immune checkpoint inhibitors, represented by PD-1/PD-L1 blockers, are widely believed to be a promising modality in pancreatic cancer, but the high prevalence of immunotherapy resistance of PDAC remains a main obstacle [59]. Effective identification in PDAC patients with potential benefits from immunotherapy could facilitate the translation of immunotherapy agents from preclinical research to clinical trials or applications. However, the biomarker for the efficiency of immunotherapy is thus far lacking [60]. A strong relationship between the tumor mutational burden (TMB) and the activity of immune checkpoint inhibitor (ICI) therapies has been identified across multiple cancers, but not pancreatic cancer [61]. MMR-D pancreatic cancer has been reported to respond to checkpoint inhibitor therapy, but it occurs in less than $1\%$ of PDAC patients [62,63]. The MSI-H/dMMR phenotype is also very rare in PDAC [62]. The essential role of TME in immunosuppressants has been well known, and the impact of TME on the immune behavior of tumors has become a research focus in the immunotherapy of PDAC [9,64,65,66]. Therefore, personalized immunotherapy for solid tumors can be realized based on the characteristics of TME cell components. With the help of several computational algorithms, the TMEscore established in this study could represent the landscape of the whole infiltration. Notably, in the context of the lack of immunotherapy in the PDAC cohort, we also determined that a high TMEscore is associated with increased response to anti-PD-L1/anti-PD-1 agents and improved survival time in two cohorts of patients with other solid tumors. Therefore, our present TMEscore might help predict the response to immune checkpoint inhibitors and thus promote the precision immunotherapy of PDAC.
The predictive value of our TMEscore was supported by some facts. The finding that cytolytic activity in PDA did not correlate with TMB or neoantigen load reveals the distinct difference between pancreatic and others in antitumor immunity [50]. It seems that the immune privilege of pancreatic cancer depends much more on intrinsic oncogenic processes than that of other cancers [50]. Several intrinsic oncogenic mechanisms responsible for the immunosuppression in pancreatic cancer have been identified, including the Kras mutation [67,68], the CDKN2A mutation [69], the TGF-beta within the TME [70], the activation of WNT/beta-catenin signaling [71], PTEN loss, and PI3K–AKT pathway activation [72,73,74,75,76], which were all highly enriched in TMEscore-low tumors. The essential role of TME as an immunosuppressant has been well known, and the heterogeneous TME shaped by infiltrated cells might illustrate the underlying mechanisms of altered response to immunotherapy. Importantly, a low TMEscore may indicate the presence of stubborn intrinsic immunosuppression. Taken together, different TME cell components are reflected according to different immune infiltration subtypes, which has significant clinical value for further guiding the refinement of immunotherapy.
Despite the tumor-intrinsic acquired resistance, tumor-extrinsic acquired resistance also contributes to immunosuppression and resistance to immunotherapy [77,78]. The tumor-extrinsic immune microenvironment can be mostly reflected by the evaluation of the degree of T cell-inflamed phenotype. A baseline T cell-inflamed TME phenotype has been demonstrated to correlate with responsiveness to checkpoint blockade therapy and adoptive cell therapy [48]. Multiple T cell-inflamed gene signatures have been established and proven to be correlated with clinical response to PD-1PD-/L1 blockade across a variety of tumor types. Through assessing the T cell-inflamed status of PDAC tumors via the well-established signatures, including T cell-inflamed signatures [79], IFN-related genes [79], effector T cell signatures [80], and cytotoxic genes [81], we found significant enrichment of all signatures in TMEscore-high tumors. In addition, ratios of CD8+ to CD4 and Treg were also elevated in TMEscore-high tumors. All these results support the TMEscore-high tumors have T cell-inflamed phenotypes and vice versa for the TMEscore-low tumors. Despite the T cell-inflamed phenotype, there is other evidence providing support for a negative correlation between resistance to immunotherapy and TMEscore. It is known that NLRP3 signaling drives resistance to anti-PD-1 immunotherapy and is responsible for adaptive immune suppression through promoting the production of IL-1β in pancreatic carcinoma [82]. Our GSEA results indicated an underlying enrichment of NLRP3-related genes in TMEscore-low tumors. Furthermore, the gene sets associated with down-regulated genes in response to the stimulation of IL-2, IL-15, or IL-21 in T cells were negatively enriched. The impaired response to the proliferation stimulus suggested a dysfunctional state of infiltrated cytotoxic cells. The enrichment of multiple inhibitory checkpoint molecules in the group with a high TMEscore was also observed. Collectively, the TMEscore-high tumor has a series of characteristics suitable for immunotherapy.
There are some interesting findings that should be noted in our present study. F2R-like Trypsin Receptor 1 (F2RL1), as a G-protein-coupled receptor, can be activated by serine proteases and plays a key role in tumor progression [83]. We have confirmed that the upregulation of F2RL1 in PDAC tumors can significantly promote tumor proliferation, invasion, and migration. Moreover, it has been reported that the enrichment of F2RL1 in the tumor matrix region is increased [84,85], which is also similar to our results, suggesting that it mediates TME matrix remodeling to further drive tumor malignant progression. Therefore, whether there are subsets of cells with F2RL1 as a marker in the TME of PDAC as a bridge or not, the interaction between the tumor and TME remains to be further explored. The exact site structure of receptors and ligands also needs to be clarified. This provides new insights into individualized treatment schemes based on risk stratification based on the TMEscore and targeted therapy for F2RL1. In addition, the STING pathway, a cytosolic DNA sensing pathway, is a proximal event required for optimal type I interferon production, dendritic cell activation, and priming of CD8+ T cells against tumor-associated antigens [86]. STING pathway activation with antigen-presenting cells in the tumor microenvironment leads to the spontaneous generation of antitumor CD8+ T-cell responses [86]. Interestingly, we found the STING pathway was enriched in low TMEscore samples, which was somewhat at odds with that. Low TMEscore was associated with low cytotoxic infiltration and low T cell-inflamed activity, but the high APM score of TMEscore-low samples was in coherence with the enrichment of the STING pathway. The different STING activity and antigen presentation scores between TMEscore high and low samples could potentially be explained by the difference within TME. For example, the normal stromal phenotype was considered associated with the immunosuppressive pancreatic stellate cells, which inhibit the function of dendritic cells [29]. A previous study has reported that combining STING-based agonists with checkpoint modulators could enhance antitumor immunity in murine pancreatic cancer [87]. Therefore, considering the character of the low STING activity, high TME group, the combination of STING pathway agonists with immune checkpoint inhibitors seems to be a promising strategy in these patients.
Taken together, the present TMEscore was associated with a variety of molecular hallmarks of immunosuppression and antitumor immunity. The prognostic value of the TMEscore was validated in multiple PDAC cohorts and two cohorts of patients treated with immune checkpoint inhibitors, suggesting the TMEscore has the potential to improve precision immunotherapy. The distinct tumor cell-intrinsic and tumor-extrinsic characteristics among tumors with different TMEscores indicate different treatment strategies according to the TMEscore category. Due to the strong intrinsic immunosuppression mechanisms and low cytotoxic infiltration, patients with a low TMEscore are unlikely to benefit from treatment with checkpoint inhibitors alone, but their response to immunotherapy might be rescued through a combined blockade of other intrinsic suppressive molecules, such as TGF signaling or others. Patients with a high TMEscore might benefit more from the immune checkpoint blockage, and their sensitivity to immunotherapy might be enhanced through the association of chemotherapy and/or a STING agonist in order to promote antigen presentation.
## 5. Conclusions
In summary, our study systematically analyzed TME-related genes and proposed a novel TMEscore for risk stratification and selection of PDAC patients in clinical practice. In terms of the future perspectives and implications of our study, it could be useful to further validate the predictive value of the TMEscore in prospective cohorts of patients receiving treatments based on immunotherapy. Furthermore, a comprehensive evaluation of TMEscores with other molecular and genetic markers may provide direction for developing new comprehensive treatment strategies.
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|
---
title: Caregiving Self-Efficacy of the Caregivers of Family Members with Oral Cancer—A
Descriptive Study
authors:
- Ching-Hui Cheng
- Shu-Yuan Liang
- Ling Lin
- Tzu-Ting Chang
- Tsae-Jyy Wang
- Ying Lin
journal: Healthcare
year: 2023
pmcid: PMC10000709
doi: 10.3390/healthcare11050762
license: CC BY 4.0
---
# Caregiving Self-Efficacy of the Caregivers of Family Members with Oral Cancer—A Descriptive Study
## Abstract
In Taiwan, oral cancer is the fourth most common cause of cancer death in men. The complications and side effects of oral cancer treatment pose a considerable challenge to family caregivers. The purpose of this study was to analyze the self-efficacy of the primary family caregivers of patients with oral cancer at home. A cross-sectional descriptive research design and convenience recruiting were adopted to facilitate sampling, and 107 patients with oral cancer and their primary family caregivers were recruited. The Caregiver Caregiving Self-Efficacy Scale-Oral Cancer was selected as the main instrument to be used. The primary family caregivers’ mean overall self-efficacy score was 6.87 (SD = 1.65). Among all the dimensions, managing patient-related nutritional issues demonstrated the highest mean score (mean = 7.56, SD = 1.83), followed by exploring and making decisions about patient care (mean = 7.05, SD = 1.92), acquiring resources (mean = 6.89, SD = 1.80), and managing sudden and uncertain patient conditions (mean = 6.17, SD = 2.09). Our results may assist professional medical personnel to focus their educational strategies and caregiver self-efficacy enhancement strategies on the dimensions that scored relatively low.
## 1. Introduction
In 2018, oral cancer incidence and death were the highest among men in Taiwan, and oral cancer was the fourth most common cause of cancer-induced death in men [1]. When patients with oral cancer undergo treatment and experience its side effects [2,3,4], patients themselves, their families, and medical caregivers encounter great challenges in caregiving.
Stage classification of oral cancer includes four stages according to the size of the primary tumor (T), involvement of locoregional lymph nodes (N), and distant metastases (M) [5,6,7,8]. Stage I is determined by T1–2 and N0–1, stage II by T1–2 and N2 or T3 and N0–2, and stage III by T4 or N3. Stage IV is for patients with metastatic disease [7]. This classification can aid in treatment planning, the estimation of recurrence risk, and the assessment of patient survival [5]. The overall 5-year survival rate for patients in a cohort study at Memorial Sloan Kettering Cancer Center was $63\%$ [9]. In a multicenter retrospective analysis, an advanced T stage was significantly correlated with poor overall survival and disease-specific survival of patients [10]. Lymph node involvement is the most important prognostic factor in oral cancer. The survival rate is reduced by $50\%$ when compared with those with similar primary tumors without neck lymph node involvement [11,12]. The impact of oral cancer at different stages on patients’ physical symptoms and impairments was supported, especially the impact of advanced oral cancer [13].
Oral cancer treatment may involve the combined use of surgery, chemotherapy, and radiotherapy, among which surgery is the most essential [14]. However, surgical treatment may change patients’ facial appearance and cause oral disabilities, such as impaired communication and eating functions [2]. In addition, patients with oral cancer encounter the side effects of chemotherapy or radiotherapy. Therefore, care for oral cancer is more challenging than that for other cancers [15].
In Taiwan, family members play a crucial role in the home care of patients with oral cancer, as exemplified by the trends during outpatient treatment. For instance, these family members handle patients’ nutritional problems, make care decisions, manage disease-related emergencies, and seek relevant resources [16]. However, the difficulties they encounter during home care [16] may discourage these family members from putting effort into patient care, particularly when they lack belief in their own capability, worsening the subsequent care results.
Self-efficacy refers to an individual’s capability belief or perceived capability to perform specific health care behavior [17]. During health care processes, self-efficacy is an essential ability that helps individuals overcome difficulties and strive for better health [18]. Self-efficacy is a key factor that affects health care behavior [19] because self-efficacy positively affects individuals’ behavioral motivation and persistence when they encounter care difficulties [18].
In the research literature, investigations that examined the difference in gender regarding self-efficacy produced inconsistent findings. Several researchers described self-efficacy as one factor that accounts for gender differences [20,21]. While some researchers suggested that men reported greater self-efficacy than women [20], others suggested that females reported greater efficacy than men [21]. In contrast, no gender differences regarding self-efficacy ratings were noted in some studies [22,23,24].
Bandura [25] also suggested that age may be a factor that contributes to personal efficacy due to the biological processes of aging resulting in declining ability. Research on the effects of age on self-efficacy has produced mixed results [20,22,23,24]. Several studies indicated no relationship between self-efficacy ratings and age [20,22,24].
Educational and socio-economic levels may also be personal factors that are associated with self-efficacy since they lead to better access to resources. A researcher has suggested self-efficacy expectations as one factor that accounts for educational differences in responses to outcome measures [22]. However, several studies showed no relationship between self-efficacy and educational levels [23,24]. Most studies on the effects of economic levels on self-efficacy showed no significant difference [23,24].
Understanding the self-efficacy of family caregivers can assist medical teams to understand their capability belief in taking care of patients with oral cancer at home, identify relevant influential factors, and provide countermeasures to enhance their capability belief in patient care. This may improve the home care quality for patients with oral cancer. Therefore, the purpose of this study was to assess the self-efficacy of the primary family caregivers of patients with oral cancer at home.
## 2.1. Study Design
The current study adopted a cross-sectional descriptive research design and convenience recruiting to facilitate the sampling and discussion on the self-efficacy of the primary family caregivers of patients with oral cancer at home.
## 2.2. Sample and Procedure
In total, 107 primary family caregivers of outpatients were recruited for a structured questionnaire survey. The participants were enrolled from the radiology outpatient department of a teaching hospital in northern Taiwan from May 2016 to May 2018. Only patients who [1] were aged ≥20 years; [2] were diagnosed as having oral cancer; and [3] received oral cancer-related surgery, chemotherapy, or radiotherapy were included. Moreover, the family caregivers of these patients were required to be [1] aged ≥20 years, [2] recognized as the primary family caregivers by the patients, and [3] living with the patients.
After this study passed the ethical review and the family caregivers signed the informed consent form, a research assistant distributed our questionnaires to the family caregivers. The assistant checked whether the retrieved questionnaires were completely filled out immediately after the caregivers submitted them. The participants who missed items were asked to fill them out. Regarding patient medical characteristics, they were all collected from medical records by the research assistant.
## 2.3. Ethical Considerations
This study was approved by the institutional review board of a teaching hospital in northern Taiwan (VGHIRB No.: 2014-04-001AC). The research assistant verbally explained the research objective, data protection principles, and research procedures to obtain the participants’ consent and asked them to sign the informed consent form. Codes were used in the questionnaire in place of personal information to protect participant privacy. For participants who were unwilling to proceed with the survey or were not physically suitable for further investigations, the research assistant acknowledged their withdrawal intention and stopped collecting their data.
## 3.1. Sociodemographic Variables
The current study collected the sociodemographic variables of the family caregivers and patients’ medical characteristics. The collected sociodemographic variables were sex, age, marital status, education level, religious affiliation, employment status, and household income. The collected medical characteristics were the time of sickness, stage of cancer, current treatment status, and treatment side effects. Information related to the family caregivers, such as the family caregivers’ relationships with the patients, manner of care, and care time, were also collected.
## 3.2. Caregiver Caregiving Self-Efficacy Scale-Oral Cancer
The current study applied the Caregiver Caregiving Self-Efficacy Scale-Oral Cancer (CSES-OC) [26] to estimate the self-efficacy of the family caregivers. The scale consisted of 18 items. According to factor analysis, the scale could be divided into four subscales: acquiring resources (AR; six items), managing sudden and uncertain patient conditions (MS; five items), managing patient-related nutritional issues (MN; four items), and exploring and making decisions on patient care (MD; three items). Some examples of the items for AR are “I am confident that I am able to acquire financial support”, “I am confident that I am able to seek consultation on the provision of sick family member care”, and “I am confident that I am able to acquire respite from caregiving”. Examples for MS are “I am confident that I am able to manage the sudden onset of conditions in the sick family member”, “I am confident that I am able to handle uncertainty about cancer progression”, and “I am confident that I am able to handle the sick family member’s uncertainty about death”. Examples for MN are “I am confident that I am able to prepare a suitable diet” and ”I am confident that I am able to improve the sick family member’s willingness to eat”. Examples of the items for MD are “I am confident that I am able to explore the most suitable care for the sick family member” and ” I am confident that I am able to make decisions on sick family member care”. The Cronbach’s alpha of each subscale ranged between 0.78 and 0.91, and that of the overall scale was 0.95. The test–retest reliability with a 2-week interval was $r = 0.83$ ($p \leq 0.001$), and its criterion-related validity with the General Self-Efficacy Scale was $r = 0.59$ ($p \leq 0.001$). Regarding the scale used, an 11-point Likert-type scale ranging from 0 (not at all confident) to 10 (completely confident) points was adopted, where the higher the total score, the higher the self-efficacy [26].
## 3.3. Statistical Analysis
The current study used SPSS for Windows (version 22.0; SPSS, Chicago, IL, USA) for the data processing. Descriptive statistics, such as means, SDs, frequencies, and percentages, were obtained to examine the family caregivers’ sociodemographic variables, patients’ medical characteristics, caregiver–patient relationships, manner of care, care times, and caregiving self-efficacies. The differences in the variables in caregiving self-efficacy (e.g., family caregivers’ sociodemographic variables, patients’ medical characteristics, caregiver–patient relationships, and manner of care) were estimated using the independent sample t-test and analysis of variance (ANOVA). In addition, a Pearson product–moment correlation test was performed to verify the correlation between caregiver age, care time, patient time of sickness, and caregiving self-efficacy.
## 4.1. Sociodemographic Variables of the Primary Family Caregivers and the Manner of Care
The current study recruited 107 primary family caregivers as participants, with a mean age of 51 years (SD = 10.8 years, range = 20–70 years). Among the participants, $91.6\%$ were female, $72.9\%$ were the patients’ spouses, $56.1\%$ had an education level of senior high school and above, $87.9\%$ were married, $26.2\%$ were continuing their job, $47.7\%$ had an annual household income of <TWD 500,000, $86.9\%$ had a religious affiliation, and $26.2\%$ had a chronic disease (Table 1). Moreover, $41.1\%$ provided care with the assistance of other caregivers, $40.2\%$ provided care without rest, $83.20\%$ had no experience in patient care, and the mean care time was 36.4 months (SD = 40.3 months, range = 1–171 months; Table 1).
## 4.2. Patients’ Medical Characteristics
Among the 107 patients with oral cancer, the mean time of sickness was 42.5 months (SD = 44.4 months, range = 1–171 months). Of all the patients, $36.4\%$ had stage IV oral cancer, $78.5\%$ had completed their treatment, and $36.4\%$ were still experiencing the side effects of the treatment (Table 2).
## 4.3. Caregiving Self-Efficacy of the Primary Family Caregivers
The CSES-OC was used to measure the self-efficacy of the primary family caregivers. The overall and subscale (i.e., AR, MS, MN, and MD) scores were considered. The mean overall self-efficacy score was 6.87 (SD = 1.65). Moreover, of all the subscales, MN demonstrated the highest mean score of 7.56 (SD = 1.83), followed by MD (7.05, SD = 1.92), AR (6.89, SD = 1.80), and MS (6.17, SD = 2.09) (Table 3).
## 4.4. Differences in the Sociodemographic Variables of the Primary Family Caregivers and Manner of Care in Caregiving Self-Efficacy
No significant correlations were discovered between the overall self-efficacy score and age ($r = 0.06$, $p \leq 0.05$) and between the overall self-efficacy score and care time ($r = 0.08$, $p \leq 0.05$). Moreover, no significant differences were noted for the other sociodemographic variables and manner of care in caregiving self-efficacy (Table 1).
## 4.5. Differences in Medical Characteristics in Caregiving Self-Efficacy
No significant correlations were discovered between the time of sickness and the overall self-efficacy score ($r = 0.11$, $p \leq 0.05$). Moreover, the differences among patients’ other medical characteristics in the overall self-efficacy were nonsignificant (Table 2).
## 5. Discussion
In this study, the researchers analyzed the caregiving self-efficacy of the primary family caregivers of patients with oral cancer. Results of the current study may aid professional caregivers in understanding the capability belief of primary family caregivers in facing challenges during the care process and the most challenging tasks they are likely to encounter.
According to the self-efficacy classification proposed by Kobau and DiIorio [27], a self-efficacy score of 4–7 (range: 0–10) denotes a moderate level of self-efficacy. Here, the mean caregiving self-efficacy score was 6.87, indicating that the caregivers in this study had moderate self-efficacy. However, because the scoring methods used for measuring self-efficacy have varied between previous relevant studies [28,29,30], the researchers could not compare the results of the current study with those of other studies directly. The mean self-efficacy score of the current study was close to that of Liang, Yates, Edwards, and Tsay [22], where the opioid-taking self-efficacy of patients with cancer was estimated, and it was slightly lower than that of Kobau and DiIorio [27], where the self-efficacy of patients with epilepsy was assessed. The possible reason for this was that the care difficulty differed between diseases, which may have affected the participants’ perceived level of capability.
Here, the caregiving self-efficacy in the MN dimension scored the highest, with a mean score of 7.56. Handling the nutritional issues of patients might not be the most challenging task for caregivers. Increasing their willingness to eat and preparing suitable food for them [26] were found to be essential behavior tasks to promote their physiological recovery.
The self-efficacy in the MD dimension scored the second highest, with a mean score of 7.05. In this dimension, the behavior tasks relevant to caregiving self-efficacy included managing the side effects due to cancer treatment and making treatment-related decisions [26]. These types of behavior tasks aim at providing home-based medical assistance.
Moreover, the AR dimension scored the third highest, with a mean score of 6.89. Here, the caregiving self-efficacy-related behavior tasks encompass managing emotional issues, receiving care counseling, and being able to rest during the care process [26]. Emotional management was related to tasks such as dealing with the emotions of patients who were facing oral cancer treatment and prognosis, as well as the emotions of caregivers themselves [16,26]. According to the current results, this was the second most challenging set of behavioral tasks. It was a self-assistance behavior task related to the maintenance of the physical and mental health of the caregivers themselves.
Finally, the MS dimension scored the lowest, with a mean score of 6.17. For caregivers, handling the safety and death issues of patients was the most challenging task. The caregiving self-efficacy-related behavior tasks include handling sudden situations, managing the uncertainty in the disease process, and managing poor prognosis [26]. These most difficult care tasks indicate the care priorities for patients and their family caregivers for health care professionals.
Family caregivers’ capability belief (i.e., self-efficacy) is a key factor that affects subsequent care behavior and care results [31,32]. Professional medical personnel can increase family caregivers’ capability belief according to the four sources of efficacy beliefs in the self-efficacy theory: family caregivers’ performance accomplishment, vicarious experience, professional caregivers’ verbal persuasion, and consideration of family caregivers’ physical and emotional arousal [17,32]. Furthermore, professional medical personnel could integrate relevant educational strategies, including diary logs [33], videos, and brochures [32], to improve family caregivers’ capability beliefs in taking care of patients with oral cancer.
In this study, the researchers adopted a cross-sectional descriptive research design. Therefore, the current study could not obtain the changes in family caregiving self-efficacy with respect to the patient’s condition or required care time. The present study involved all patients in the disease period. The timing of patient enrollment was not controlled. Some patients were still undergoing their course of treatment, some patients had finished their treatment. Different times or stages of treatment may affect the challenge of the care of family and, therefore, may affect their ability cognition. In addition, the sample size was small for all sociodemographic and medical variable groups. It is unlikely that statistical differences could be detected in this population. On the other hand, the current research used convenience sampling, which may have caused sampling deviation. Families with large care loads may have been eliminated naturally. The samples were collected from a teaching hospital in northern Taiwan alone, which might affect the inference of the current results.
## 6. Conclusions
Our current results indicated that family caregiving self-efficacy scores in the CSES-OC MS and AR items were the lowest and the second lowest, respectively. The current study recommends that professional medical teams focus their educational strategies and caregiver self-efficacy enhancement strategies on the dimensions that scored relatively low (i.e., handling patients’ safety and death issues and managing physical and mental health problems through self-assistance). For example, issues in these dimensions include managing the emotional distress of a sick family member and the caregiver themself, handling uncertainty about the sick family member’s cancer progression and death, and managing the sudden onset of conditions in the sick family member. Through family caregivers’ performance accomplishment, vicarious experience, professional caregivers’ verbal persuasion, consideration of caregivers’ physical and emotional arousal, and using educational media, the self-efficacy of family caregivers regarding taking care of a patient with cancer may be increased. The current results are from an exploratory study. The cut-off point of the self-efficacy score in this study refers to the research results of other patient groups. The current study suggests that more studies are needed.
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|
---
title: Decoy Receptors Regulation by Resveratrol in Lipopolysaccharide-Activated Microglia
authors:
- Rosa Calvello
- Chiara Porro
- Dario Domenico Lofrumento
- Melania Ruggiero
- Maria Antonietta Panaro
- Antonia Cianciulli
journal: Cells
year: 2023
pmcid: PMC10000713
doi: 10.3390/cells12050681
license: CC BY 4.0
---
# Decoy Receptors Regulation by Resveratrol in Lipopolysaccharide-Activated Microglia
## Abstract
Resveratrol is a polyphenol that acts as antioxidants do, protecting the body against diseases, such as diabetes, cancer, heart disease, and neurodegenerative disorders, such as Alzheimer’s (AD) and Parkinson’s diseases (PD). In the present study, we report that the treatment of activated microglia with resveratrol after prolonged exposure to lipopolysaccharide is not only able to modulate pro-inflammatory responses, but it also up-regulates the expression of decoy receptors, IL-1R2 and ACKR2 (atypical chemokine receptors), also known as negative regulatory receptors, which are able to reduce the functional responses promoting the resolution of inflammation. This result might constitute a hitherto unknown anti-inflammatory mechanism exerted by resveratrol on activated microglia.
## 1. Introduction
Resveratrol (3,5,4′-trihydroxy-trans-stilbene), also called polyphenol, is a stilbenoid belonging to the phytoalexin superfamily, mostly found in red grapes, blueberries, raspberries, mulberries, and peanuts [1]. Resveratrol has two isomers with trans and cis configurations. In this regard, the trans-resveratrol is the non-toxic stereoisomer that has been widely described to have beneficial effects on health [2].
Resveratrol is part of a group of compounds that act as antioxidants do, protecting the body against diseases, such as diabetes, cancer, heart disease, ileitis, obesity, and neurodegenerative disorders, such as Alzheimer’s (AD) and Parkinson’s diseases (PD) [3,4,5,6]. In this respect, in experimental models of both AD and PD, it has been showed that resveratrol exerts neuroprotective actions; however, its application in therapeutic protocols is limited by its poor bioavailability due to quick metabolization in the intestine and liver [3,4,5,6,7].
Resveratrol is able to cross the blood–brain barrier (BBB) via tight junctions, thus carrying out a protective action in the brain tissue that could reduce the loss of neurons, which arises due to neurodegenerative diseases [4,5,6,7,8].
Many studies carried out in recent years have focused on researching the therapeutic potential for the additional treatment of neurodegenerative diseases of many natural compounds, in particular those extracted from plants [5,6,7,8,9,10]. Among the vast diversity of natural compounds that have been studied for their neuroprotective effects, there are polyphenolic compounds, such as curcumin, capsaicin, epigallocatechin gallate, and resveratrol too [7,8,9,11,12,13]. Apart from having antioxidant and anti-inflammatory actions, resveratrol modulates the intracellular signals involved in neurons survival and inhibits beta-amyloid (Aβ) protein aggregation [10,11,12,13,14].
Consistent with these data, it was reported in a mouse model of Parkinson’s-like disease that a resveratrol treatment protects the dopaminergic (DA) neurons of the Substantia Nigra pars compacta (SNpc) against neurotoxic insult by modulating inflammatory reactions through SOCS-1 activation [11,12,13,14,15].
Decoy receptors are involved in mechanisms of immune evasion adopted by pathogens, including IL-1R2 and atypical chemokine receptors (ACKRs). In IL-1R2, the lack of the intracellular TIR domain makes this receptor unable to initiate signal transduction following binding with IL-1 [12,16]. The main types of ACKRs are ACKR1, ACKR2, ACKR3, and ACKR4 [13,17]. These molecules are able to recognize and bind specific growth factors or inflammatory cytokines efficiently; however, they are structurally incapable of initiating and transducing signals, acting as a molecular trap for the agonist and for signaling receptor components. All of these members, also referred as chemokine-binding proteins, scavengers, receptor antagonists, negative regulatory receptors, anti-inflammatory ligands, and decoys, act as brakes in the functional responses [14,18].
IL-1R2 functions as a negative regulator of several IL-1 family members, as well as of TLRs, thus it is involved in several pathophysiological contexts in which inflammation and innate and adaptive immune responses play a significative role [19]. ACKR2, previously known as D6, through inhibiting inflammation, mediates the resolution of inflammation in various conditions such as infections, autoimmune diseases, cancer, and neurodegenerative conditions [14,15,16,17,18,19,20].
We reported in a previous work that in LPS-activated cells, the pre-treatment of microglia with resveratrol up-regulated the phosphorylation of JAK1 and STAT3, as well as the expression of the suppressor of cytokine signaling (SOCS)3, demonstrating that the JAK-STAT signaling pathway is involved in the anti-inflammatory effect exerted by resveratrol [15,16,17,18,19,20,21].
The aim of the present research was designed to determine the potential anti-inflammatory effects of resveratrol through the regulation of decoy receptor expression, IL-1R2 and ACKR2, on the activated microglia after prolonged exposure to LPS. The results obtained from this study provide, for the first time, evidence of a new anti-inflammatory mechanism exerted by resveratrol on the activated microglia.
## 2.1. Cell Culture and Treatments
The murine microglial cell line N13 was grown in RPMI 1640 basal medium enriched with $10\%$ heat-inactivated fetal bovine serum (FBS), $1\%$ L-glutamine (2 mM), and $1\%$ penicillin-streptomycin solution (100 U/mL penicillin; 100 μg/mL streptomycin) (Life Technologies-Invitrogen, Milan, Italy) in a CO2 incubator set to $5\%$ CO2 at 37 °C in a humidified atmosphere until $70\%$ confluence. For the treatments, we used 10 μM resveratrol (trans-3,40, 5-trihydroxystilbene; purity > $99\%$ GC; Sigma Aldrich, St. Louis, MO, USA) and the cell wall component LPS of *Salmonella typhimurium* at a concentration of 100 ng/mL. N13 cells were submitted to a single treatment with LPS or resveratrol and to a combined treatment with resveratrol, followed up an hour later by LPS (Sigma Aldrich) for 72 h.
## 2.2. Cytotoxicity Assay
Cell viability of N13 cells was evaluated by the MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay. The cells were seeded in 96-well multi-well plates at a density of 8 × 103/well to be treated with LPS alone or in presence of resveratrol. The MTT was solubilized in PBS 1X to be added to the wells at a working concentration of 0.5 mg/mL starting from a stock solution of 5 mg/mL. After 4 h of incubation in a CO2 incubator at 37 °C in a humidified atmosphere, the formazan crystals were solubilized in Dimethyl sulfoxide (DMSO) keeping the plates in agitation for 20 min. Since the amount of formazan is directly proportional to the number of viable cells, it is quantified by measuring the optical density at 560 nm and subtracting the background at 670 nm by using a Victor Multiplate Reader (Wallac, Perkin Elmer, Milan, Italy).
## 2.3. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) and End-Point PCR
Cells were harvested, and the total RNA was extracted by using the TRIzol isolation reagent (Invitrogen, Milan, Italy) according to the manufacturer’s instructions. Once isolated, the RNA was reverse transcribed back into cDNA, causing a reaction between 3 μg of total RNA, 40 U of RNase Out (Invitrogen), 40 mU of oligo dT, 0.5 mM dNTP (PCR Nucleotide Mix, Roche Diagnostics, Milan, Italy), and 40 U of Moloney Murine Leukemia Virus Reverse Transcriptase (Roche Diagnostics). The cDNA synthesis was initiated at 37 °C for 59 min and terminated at 95 °C for 5 min to remain at 4 °C. The cDNA was amplified by performing a polymerase chain reaction (PCR) for 30 cycles using a thermal cycler (Eppendorf, Milan, Italy) together with the cDNA of GAPDH, which was used as a reference gene. At the completion of the PCR, TriTrack Loading Dye 6X (Thermo Fisher, Waltham, MA, USA) was added to the amplified samples prior to be loaded onto the agarose gel. The DNA bands were quantified by densitometry with the ImageJ software, and the results were normalized with GAPDH. Primer sequences for the tested genes are reported in Table 1.
## 2.4. Electrophoresis
After 72 h from the treatments, the cells were harvested and lysed with a lysis buffer ($1\%$ Triton X-100, 20 mM Tris-HCl, 137 mM NaCl, $10\%$ glycerol, 2 mM EDTA, 1 mM phenylmethylsulfonyl fluoride (PMSF), 20 μM leupeptin hemisulfate salt, and 0.2 U/mL aprotinin) and subjected to many cycles of freezing and thawing to facilitate the lysis. The lysates were obtained by centrifugation at 12,800× g for 20 min at 4 °C, and the proteins were quantified by the Bradford’s protein assay [22,23,24,25,26,27,28,29,30,31,32,33,34,35]. A quantity of 25 μg of proteins from each sample were diluted with a sample buffer (0.5 M Tris-HCl pH 6.8, $10\%$ glycerol, $10\%$ (w/v) SDS, $5\%$ β2-mercaptoethanol, and $0.05\%$ (w/v) bromophenol blue), and then boiled for 3 min. At the end, the samples were loaded onto $4\%$–$12\%$ SDS precast polyacrylamide gels (BioRad Laboratories, Tokyo, Japan) and fractionated in relation to the size by applying a voltage of 200 V.
## 2.5. Western Blotting
Upon completion of electrophoresis, the resolved proteins were transferred onto ni-trocellulose membranes and blocked with $5\%$ fat-free milk diluted in a solution containing $0.1\%$ (v/v) Tween $20\%$ and PBS to avoid nonspecific binding. After three ten-minute washes with $0.1\%$ Tween 20-PBS (T-PBS), the membranes were incubated overnight at 4 °C with mouse monoclonal antibody (mAb) anti-CD11b (1:200), anti-iNOS (1:200) mAb, anti-COX-1 (1:200) mAb, anti-COX-2 (1:200) mAb, anti-phospho-cPLA2 (1:200), anti-phospho-IkBα (1:200) mAb, anti-IL-1R2 (1:100) mAb, and anti-ACKR2 receptor (1:100) mAb, and mouse polyclonal Ab anti β-actin (all from Santa Cruz Biotechnology, Inc., Milan, Italy) according to the manufacturer’s protocol. Then, the membranes were washed with $0.1\%$ Tween 20-PBS (for 20 min, 3 times) and incubated with specific horseradish peroxidase (HRP)-conjugated secondary antibody anti-mouse (Santa Cruz Biotechnology, Milan, Italy) diluted to 1:10,000 for 1 h in agitation and in the dark. At the end, the protein bands were highlighted by chemiluminescence, and images were acquired using a ChemiDoc Imaging System. The bands were normalized against β-actin, and the results, expressed as means ± SD, were provide as the relative optical density.
## 2.6. Enzyme-Linked Immunosorbent Assay (ELISA)
The sandwich ELISA was performed following the kit manufacturer’s instructions to measure the levels of TNF-α (Cat. # BMS607-3 Thermo Fisher—Invitrogen Technology, Milan, Italy) and IL-1β (Cat. # BMS6002 Thermo Fisher—Invitrogen Technology, Milan, Italy) cytokines in the cell culture supernatants withdrawn after 72 h from the treatments. Since the intensity of the signal is directly proportional to the concentration of the antigen, the concentration was quantified and expressed in pg/mL. The determinations were performed in triplicate.
## 2.7. NO Production
NO, quantified as the NO2− concentration in the cell culture supernatants, was determined by the Griess assay. The supernatants were collected after 72 h from the treatments and centrifuged to remove possible cellular residues. After adding the Griess Reactive ($0.1\%$ N-(1-naphthyl) ethylenediamine dihydrochloride and $1\%$ sulfanilamide in $2.5\%$ H3PO4) (1:1 v/v), the samples were incubated in the dark at room temperature for 10 min. At the end, the absorbance was spectrophotometrically measured at 540 nm by using the conditioned medium as a blank to clear the interference of nitrites. The NO2− concentration was calculated by interpolation on a standard curve of sodium nitrite (NaNO2) and is expressed as μmol/mL.
## 2.8. PGE2 Assay
To measure the PGE2 in the cell culture supernatants, we performed the PGE2 assay. N13 cells (3 × 106/well) were seeded in 6-well plates, pre-treated with resveratrol for 1 h, and then stimulated with LPS at a concentration of 100 ng/mL. The cultures were maintained at 37 °C for 72 h in a humidified air containing $5\%$ CO2. The PGE2 levels were determined in the supernatant using the competitive binding immunoassay (Cayman Chemical, Ann Arbor, MI, USA) according to the manufacturer’s instructions. Unstimulated cells were included as a control. The optical density was measured at λ = 405–420 nm using a precision microplate reader, and the PGE2 concentration, expressed in ng/mL, was determined by using a PGE2 standard curve.
## 2.9. Statistical Analysis
The statistical analysis was carried out with the software package MINITAB Release 14.1 (Minitab Ltd., Coventry, UK). The results were analyzed by the ANOVA one-way followed by the Tukey test, assuming that the p-values ≤ 0.05 were significant.
## 3.1. Effects of Resveratrol and Prolonged LPS Treatment on Cell Viability of N13 Microglial Cells
The effect of the pre-treatment with resveratrol on the N13 cells treated with LPS was verified by MTT cell viability test. We used an optimal concentration of LPS (100 ng/mL) and an optimal non-toxic resveratrol concentration (10 μM) selected on the basis of the experiments reported in our previous works [15,16,21,23]. Furthermore, in the experiments of this work, we used prolonged exposure to LPS by treating the N13 cells with LPS for 72 h. The viability of the cells exposed for 72 h to 100 ng/mL LPS was significantly reduced in comparison to that of the untreated cells; however, the pre-treatment with resveratrol was able to significantly increase the cell viability in the cells treated with LPS with respect to that of the cells treated with LPS alone (Figure 1).
## 3.2. Resveratrol Modulates CD11b Expression Levels in LPS-Treated N13 Microglial Cells
The pre-treatment with resveratrol of the N13 cells treated with LPS determined the modulation of the expression of the microglial activation marker CD11b both at transcriptional and post-transcriptional levels (Figure 2). In particular, resveratrol is able to determine a significant decrease in the mRNA expression levels of CD11b in the N13 cells treated with LPS in comparison to those observed in cells treated with LPS alone (Figure 2A). The same results were observed for CD11b protein expression. In this context, the treatment with LPS induced a significantly higher increase in the CD11b protein expression levels in the cells treated with LPS in comparison to that of the control cells. In addition, resveratrol showed the ability to significantly decrease the CD11b protein expression levels in the N13 cells treated with LPS compared to that observed in the cells treated with LPS alone (Figure 2B). These results confirm the role of resveratrol as a modulator of microglial activation even in case of prolonged exposure to LPS.
## 3.3. Effects of Resveratrol on Nitric Oxide Production and Inducible Nitric Oxide Synthase Protein Expression Levels in LPS-Treated N13 Microglial Cells
To evaluate the effect of resveratrol on NO production in the N13 cells subjected to prolonged exposure to LPS, the levels of NO produced by the microglia treated for 72 h with LPS in the absence and in the presence of resveratrol were tested. The levels of NO released by the untreated cells and those treated with resveratrol alone were low. The treatment of microglia with LPS for 72 h, on the other hand, resulted in a significant increase in NO release compared to that which was observed in the control cells. Conversely, the cells treated with LPS pre-treated with resveratrol showed a significant reduction of NO production in comparison to that of the cells treated with LPS alone (Figure 3A). In addition, to evaluate whether the inhibitory effect of resveratrol on NO production could derive from an action of resveratrol on the inducible isoform of NO synthase (iNOS), the protein expression of iNOS after the different treatments was determined by the Western blot. Again, significantly higher levels of iNOS protein expression were found in the cells treated with LPS alone in comparison to that which was shown by the untreated cells. Similarly, as observed for NO release, the pre-treatment with resveratrol was able to significantly inhibit the expression of iNOS in the microglia submitted to prolonged exposure to LPS (Figure 3B).
## 3.4. Effects of Resveratrol on Pro-Inflammatory Cytokine Production in LPS-Treated N13 Microglial Cells
Pro-inflammatory cytokines production levels were assessed in culture supernatants by ELISA both in the presence and absence of resveratrol. As shown in Figure 4, there was a marked increase in TNF-a and IL-1β production in the microglial cells after 72 h of LPS stimulation. No effect by the treatment with resveratrol alone on the pro-inflammatory cytokine production was observed in the microglial cells. Moreover, we observed that the treatment with 10 μg/mL of resveratrol in the LPS-treated cells significantly down-regulated the production levels of pro-inflammatory cytokines in comparison to those of the N13 cells stimulated with LPS alone, suggesting that resveratrol was able to negatively modulate the production levels of pro-inflammatory cytokines in LPS-activated microglial cells.
## 3.5. Effects of Resveratrol on Arachidonic Acid (AA) Pathway in LPS-Treated Microglia
Cyclooxygenase-2 (COX-2) and phospholipase A2 (cPLA2) participate in eicosanoid production, such as prostaglandin E2 (PGE2), which is implicated in the Arachidonic Acid (AA) pathway and is a key factor in neuroinflammatory and neurodegenerative diseases. Moreover, it is well known that COX-1 could be an important player in neuroinflammation by being predominantly localized in the microglia, and thus, being implicated in the secretion of prostaglandins (PGs) in response to microglia activation [17,24]. For this reason, in microglial cells exposed for a prolonged time to LPS, we have verified the anti-inflammatory ability of resveratrol in terms of the modulation of the of COX-1, COX-2, and p-cPLA2 protein expression. In addition, the evaluation of COX activity by the quantification of the PGE2 production by the enzymatic conversion of AA has been widely used and is well accepted as a method to evaluate potential COX inhibitors [18,25]. Therefore, we also verified the inhibitory action of resveratrol on the release of PGE2 in N13 cells treated with LPS for 72 h. From our results, it appears to be evident that resveratrol is able to determine a significant decrease in the expression levels of COX-1, COX-2, and p-cPLA2 in the cells treated for 72 h with LPS that had undergone a pre-treatment of 1 h with resveratrol in comparison to those of the cells treated with LPS alone (Figure 5A–C). We observed similar results in the PGE2 release assay. Resveratrol, in fact, determined a significant decrease in the release of this inflammatory mediator in the microglia exposed to the prolonged treatment with LPS and pre-treated with resveratrol compared to those subjected to the treatment with LPS alone (Figure 5D). From these results, it is, therefore, possible to highlight that resveratrol, in cases of prolonged inflammation, is able to show an anti-inflammatory effect by inhibiting the AA pathway.
## 3.6. Effects of Resveratrol on NF-kB Pathway in LPS-Treated N13 Microglial Cells
In order to evaluate NF-kB activation, we measured the levels of the phosphorylated form of IkBα (p-IkBα), the inhibitory complex of NF-kB, since its phosphorylation is an essential step for NF-kB activation. In this regard, we determined the expression of p-IkB in cell lysates obtained from LPS-stimulated N13 microglial cells. In this context, we observed that the LPS treatment for 72 h significantly increased the expression level of phosphorylated IkB-α protein compared to that of the control cells, and the resveratrol pre-treatment significantly prevented this increase, as revealed by the densitometric analysis (Figure 6). These data indicate that resveratrol inhibited NF-kB activity in the LPS-treated N13 cells by suppressing the degradation of IkB-α, and consequentially, relieving the pro-inflammatory mediator’s expression.
## 3.7. Effects of Resveratrol on IL1-R2 and ACKR2 Decoy Receptor Expression in LPS-Treated Microglia
IL-1R2 is a decoy receptor that causes a block of signal transduction after IL-1 binding. By regulating IL-1R2 expression, cells can modulate inflammation in response to exogenous stimuli. It has been showed that the up-regulation of IL-1R2 in microglial cells and brain endothelial cells attenuates CNS inflammation [12,16].
ACKR2, also known as the D6 decoy receptor, scavenges various inflammatory chemokines, thus affecting the inflammatory microenvironment. In this regard it is thought that the D6 decoy receptor could be a resolving agent in the neuroinflammatory processes because of its capacity to scavenge chemokines, leading to the alleviation of inflammation in different situations, including neuroinflammatory-based neurological disorders [20]. Therefore, in our study, we verified the ability of resveratrol to modulate the expression of decoy receptor IL-1R2 and decoy receptor ACKR2 both in terms of mRNA and protein expression. The analysis of mRNA expression for both the decoy IL1-R2 receptor and the decoy ACKR2 receptor showed a significantly reduced expression of both these receptors in the microglial cells subjected to prolonged exposure to LPS in comparison to that of those cells treated with Resveratrol alone. Interestingly, in the cells exposed to LPS but pre-treated with resveratrol, there was a drastic and highly significant increase in mRNA expression for both of the decoy receptors studied in comparison to that of the cells treated with LPS alone (Figure 7A,B).
These results were confirmed by the Western blotting analysis on IL1-R2 and ACKR2 protein expression. Additionally, in this case, resveratrol was able to cause a significant increase in the protein expression of both IL1-R2 and ACKR2 decoy receptors in the cells treated for 72 h with LPS that received a pre-treatment of 1 h with resveratrol in comparison to that of those cells treated with LPS alone (Figure 7C,D).
All together, these results certainly confirm the already known anti-inflammatory effect that resveratrol elicits on microglial cells in case of neuroinflammation. At the same time, however, these experiments demonstrate, for the first time, the ability of resveratrol to modulate the expression of IL1-R2 and ACKR2 decoy receptors, which could represent a new potential therapeutic target especially in cases of the prolonged inflammation of the CNS.
Based on the previous results evidencing that the resveratrol treatment on the LPS activated microglia responses exerts both an inhibition of pro-inflammatory mechanisms and an induction of anti-inflammatory responses [15,16,21,23], we aimed, in this study, to expand our knowledge regarding the other possible effects of this polyphenolic compound on the inflammatory responses of microglia submitted to a prolonged LPS treatment. Here, we demonstrated that resveratrol, without affecting the viability of these cells, is able to specifically interfere with the pro-inflammatory responses induced by LPS in terms of both the decreased production of IL-1β and the increased production of the IL-1β decoy receptor. IL-1β, a member of the IL-1 family, is a potent pro-inflammatory cytokine in the acute and chronic phases of inflammation, therefore, the reduced production of IL-1β after 72 h of incubation in resveratrol-treated cells demonstrates that this polyphenol could limit the amplification phase of inflammation.
To analyze whether the resveratrol-treated microglia display a reduced ability to react to pro-inflammatory stimuli, we also investigated the response of the cells to LPS in terms of NO and of TNF-a release. In this regard, we detected that after 72 h of treatment, resveratrol was able to significantly reduce the production of both of these mediators. Moreover, we also demonstrated that after a prolonged incubation of microglia cells to LPS, the resveratrol treatment was able to counteract the pro-inflammatory processes down-regulating the IkB degradation, which resulted significantly reduced in comparison to that of the cells treated with LPS alone.
NF-kB is considered to be the most important transcription factor involved in the inflammatory responses, thereby in the regulation of NO, TNF-α, and IL-1β [19,20,26,27].
Previously published papers have reported in other cell types [16,23,28,29] that resveratrol significantly inhibited the degradation of IκBα in microglia stimulated with LPS, as well as the subsequent iNOS expression and production of TNF-α, suggesting that resveratrol can modulate the signaling pathways triggered by pro-inflammatory stimuli, such as LPS. However, in the present study, we observed that this action of resveratrol on the production of TNF-α and the degradation of IκB-α is also evident after a more prolonged incubation time, evidencing how this compound is effective at modulating the inflammatory responses protracted over time and not only in the acute ones.
In addition, we also demonstrated that the resveratrol treatment determined a significant reduction of COX-1, COX-2, and p-cPLA2, which are all mediators of pro-inflammatory responses. Cyclooxygenase exists as COX-1 and COX-2 distinct isoforms [23,24,30,31] and converts arachidonic acid (AA) released by PLA2 acting at the sn-2 position of membrane phospholipids into prostaglandins and other lipid mediators. Both isoforms are important pro-inflammatory enzyme, whose abnormal expression is a significant marker of neuroinflammation, as previously reported [24,31]. Moreover, AA plays also a key role in inflammation and neurodegenerative disorders [25,32]. In mammalians, there are the three major classes of PLA2s, secretory, calcium-independent, and calcium-dependent ones: among them, the calcium-dependent cytosolic PLA2α (cPLA2α) has received the most attention because the cPLA2-AA-COX-2 pathway is an important signaling pathway in different inflammatory paradigms and neurodegeneration [26,33]. In this regard, it has been demonstrated that the oxidative responses observed in many types of brain damage are associated with increased COX activity [27,34]. Moreover, it was reported that a treatment with COX inhibitors may significantly reduce in neuronal and microglial cell LPS- and IL-1β-induced oxidative damage [28,35]. The results of our study are in accordance with ones showing that in mouse microglial cells, the reduction of COX-2 expression observed after a resveratrol treatment could be determined by the inhibition of NF-κB activation [29,36].
Therefore, our data evidence that NF-κB pathway inhibition through the targeting of IκB phosphorylation by resveratrol ultimately may reduce a pro-inflammatory phenotype, thereby down-regulating different mediators, including COX-1, COX-2, and p-cPLA2.
One aspect that is particularly important emerging from our study was the ability of resveratrol to modulate the expression of the so-called decoy receptors, such as IL-1R2 and ACKR2.
IL-1R2, first identified on monocytes, neutrophils, dendritic and B cells, in both human and mice, has been reported to be largely involved in driving myeloid cells polarization, and consequently, orientating the immune response. In fact, anti-inflammatory M2 stimuli, such as IL-4, IL-13, IL-10, IL-27, and aspirin, lead to the up-regulation of IL-1R2 expression, whereas the M1 phenotype activated by pro-inflammatory molecules (such as LPS, IFNγ, and TNF-α) exhibits a down-regulation of IL-1R2 [12,16].
The modulation of IL-1R2 expression has been reported in many cell types as a way to counterbalance and limit sustained inflammation in response to exogenous stimuli. In this regard, IL-1R2 up-regulation in the microglia and brain endothelial cells reduced the brain inflammation in experimental models of IL-1β-induced neurotoxicity, as previously reported [30,31,32,37,38,39].
ACKRs are a group (four in humans) of proteins with a high degree of homology with chemokine receptors. ACKRs are chemotactic receptors; however, since they are devoid of the structural domains required to activate canonical G protein-dependent receptor signaling and chemotactic functions, they do not transduce signals through G proteins and lack chemotactic activity [33,40]. Consequently, ACKRs fail to initiate classical signaling pathways after ligand binding, playing a crucial role as regulatory components of chemokine networks in many physiological and pathological processes.
Interestingly, the resveratrol treatment enhanced the expression of the anti-inflammatory IL-1β decoy receptor IL-1R2 and increased the expression of the other decoy receptor, ACKR2. IL-1R2 is the decoy receptor for IL-1; when IL-1R2 binds to IL-1β, signal transduction cannot be triggered, and consequently, the pro-inflammatory action of this cytokine is neutralized [34,41]. Therefore, the increased expression of IL-1R2 on the microglia surface indicates a reduced responsiveness of these cells to IL-1β stimulation, significantly dampening the pro-inflammatory profile. Moreover, IL-1R2 also exists in soluble form that can be rapidly shed, so the increased release of the soluble form by IL-1R2-overexpressing cells could neutralize the action of IL-1β on other cells, thus reducing the extent of the pro-inflammatory responses.
The results of our pioneering work describe, for the first time, that the resveratrol treatment of the microglia exposed to a prolonged pro-inflammatory stimulus is able to counterbalance inflammatory responses through the regulation of decoy receptors. These findings suggest that the naturally occurring polyphenol resveratrol ability to drive microglial activation, thus regulating the inflammatory response, may help to explain its neuroprotective effects in several in vivo models of neuroinflammation.
## 4. Conclusions
The results of the present in vitro study suggest that polyphenolic compounds, such as resveratrol, may be useful in the treatment of inflammation associated with neurodegeneration and that clinical studies may evaluate the possibility of their use as a therapeutic support strategy. The results of this study highlight the direct effects of resveratrol in the regulation of functional in vitro responses by microglial cells, therefore it would be of considerable importance to investigate the effect of this polyphenol in vivo for future clinical use also in nano-formulations or intranasal spray applications, for example, in order to overcome the bioavailability problems linked to the BBB or metabolism of endothelial cells. In light of these results, we plan to carry out further studies to clarify the modulation mechanisms of the decoy receptors underlying the neuroprotective effects of polyphenols.
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|
---
title: Quantitative Analysis of Oat (Avena sativa L.) and Pea (Pisum sativum L.) Saponins
in Plant-Based Food Products by Hydrophilic Interaction Liquid Chromatography Coupled
with Mass Spectrometry
authors:
- Anastassia Bljahhina
- Dmitri Pismennõi
- Tiina Kriščiunaite
- Maria Kuhtinskaja
- Eeva-Gerda Kobrin
journal: Foods
year: 2023
pmcid: PMC10000715
doi: 10.3390/foods12050991
license: CC BY 4.0
---
# Quantitative Analysis of Oat (Avena sativa L.) and Pea (Pisum sativum L.) Saponins in Plant-Based Food Products by Hydrophilic Interaction Liquid Chromatography Coupled with Mass Spectrometry
## Abstract
This work presents the sample extraction methods for solid and liquid sample matrices for simultaneous quantification of oat (*Avena sativa* L.) and pea (*Pisum sativum* L.) saponins: avenacoside A, avenacoside B, 26-desglucoavenacoside A, and saponin B and 2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP) saponin, respectively. The targeted saponins were identified and quantified using a hydrophilic interaction liquid chromatography with mass spectrometric detection (HILIC-MS) method. The simple and high-throughput extraction procedure was developed for solid oat- and pea-based food samples. In addition, a very simple extraction procedure for liquid samples, without the need to use lyophilisation, was also implemented. Oat seed flour (U-13C-labelled) and soyasaponin Ba were used as internal standards for avenacoside A and saponin B, respectively. Other saponins were relatively quantified based on avenacoside A and saponin B standard responses. The developed method was tested and successfully validated using oat and pea flours, protein concentrates and isolates, as well as their mixtures, and plant-based drinks. With this method, the saponins from oat- and pea-based products were separated and quantified simultaneously within 6 min. The use of respective internal standards derived from U-13C-labelled oat and soyasaponin Ba ensured high accuracy and precision of the proposed method.
## 1. Introduction
The demand for sustainable protein sources in food production is continuously growing [1,2]. Oat (*Avena sativa* L.) and pea (*Pisum sativum* L.) proteins in the form of concentrates or isolates can act as an alternative to animal proteins due to their potential ability to provide desirable technological properties in plant-based meat and dairy substitutes [3,4]. Pea protein is an insufficient source of methionine but, on the other hand, has a high content of the essential amino acid lysine and branched-chain amino acids-leucine, isoleucine, and valine [4]. In contrast to pea, oat contains enough methionine but a scarce amount of lysine [3]. Blending oat and pea proteins in products is one way to achieve a complete essential amino acid profile [5], and such products are already available on the market. However, one of the main obstacles in the application of plant-based proteins in food production is their bitter and astringent off-taste [6,7,8]. It has been suggested that saponins might be the main cause of this sensation [9,10,11,12,13,14] influencing consumer acceptance.
Saponins are a diverse group of secondary defence metabolites widely spread in plant species [15]. Saponins investigated in this study are amphiphilic molecules, with polar water-soluble sugar moieties attached to a nonpolar, water-insoluble steroid or triterpene core [16]. Oats, as the only cereals capable of accumulating saponins, contain bisdesmosidic steroidal saponins avenacoside A and B, and monodesmosidic 26-desglucoavenacoside A in their leaves and grains (Figure 1) [9,10,12,13,17]. Saponin B and 2,3-dihydro-2,5-dihydroxy-6-methyl-4H-pyran-4-one (DDMP) saponin are monodesmosidic triterpenoid saponins found in peas [18,19]. Besides being taste-active bitter compounds, saponins have also been reported as antinutrients. As such, they may affect nutrient absorption by inhibiting metabolic and digestive enzymes [20] and by binding to minerals such as zinc and iron [21]. High concentrations of saponins in the diet may lead to hypocholesterolemic effect [22], hypoglycemia [23], inefficient protein digestion, vitamin and mineral uptake in the gut, and the development of a leaky gut [24]. Despite the reported negative nutritional impact, some studies have also shown positive cholesterol-lowering [25] and anticancerogenic [26] effects of saponins.
The analysis of saponins could be performed using a wide range of classical methods such as gravimetry [15,27], hemolysis [28], bioassays [29], and spectrophotometry [30]. In addition, different saponins could be separated and analysed using chromatographic methods, e.g., thin-layer chromatography [31,32], gas chromatography [33], and high-performance liquid chromatography (HPLC) [19]. The detection of the saponin class compounds could be carried out using the simplest optical detection methods [34,35], but these methods usually lack the selectivity and sensitivity of more advanced analytical techniques such as mass spectrometry [9,17,36,37,38]. The saponin extraction and the pre- and post-extraction sample clean-up before the LC analysis [9,17,19,39] are required to obtain a clean extract which would minimise matrix effect in mass spectrometric measurements. However, these sample preparation procedures are time-consuming and unsuitable for routine analysis of large amounts of samples. This creates the need for an improved, efficient, sensitive, more selective, and reproducible extraction method of saponins prior to the analysis. The use of liquid chromatography coupled with mass spectrometry (LC-MS) allows more precise and selective determination of the contents of different types of saponins in various plant species: oat [9,13,17], pea [19,32], and soya [39,40]. Although, the amounts of saponins have been quantified mainly from the seeds or husks of numerous oat and pea varieties [9,19,32], there is a lack of data concerning the concentrations of saponins in processed food ingredients, and the half- and end-products produced therefrom. To the best of our knowledge, there is no versatile method for the determination of saponins derived from different plant species in various food matrices.
The objective of this study was to develop simple sample extraction methods for solid and liquid plant-based food sample matrices for the selective and quantitative determination of five oat and pea saponins: avenacoside A, avenacoside B, 26-desglucoavenacoside A, saponin B, and DDMP saponin, using hydrophilic interaction liquid chromatography with mass spectrometric detection (HILIC-MS). To our knowledge, there are no reports on simultaneous HILIC analysis of the above-mentioned saponins in solid and liquid samples containing concurrent oat and pea ingredients.
## 2.1. Chemicals and Materials
HPLC-grade acetonitrile (MeCN), methanol (MeOH), ethanol (EtOH), hexane, propan-2-ol (IPA), and formic acid (FA) (for MS, $98\%$) were purchased from Honeywell (Charlotte, NC, USA). The standard compounds avenacoside A, saponin B (soyasaponin I), and soyasaponin Ba phyproof® were purchased from Sigma-Aldrich (Darmstadt, Germany). Uniformly isotopically labelled oat seed flour (U-13C oat seeds, *Avena sativa* 97 atom%) was obtained from IsoLife BV (Wageningen, The Netherlands). Ultrapure water (18.2 MΩ·cm) was prepared with MilliQ® HX 7040SD equipped with MilliQ LC-Pak (Merck KGaA, Darmstadt, Germany). Biotage Isolute® PLD+ and C18 columns (100 mg/1 mL) were purchased from Biotage Sweden AB (Uppsala, Sweden). Amicon Ultra-0.5 centrifugal filter units (3, 10, 30, 50 kDa) and Millex-LCR filters (pore size 0.2 µm, filter dimension 13 mm) were obtained from Merck KgaA (Darmstadt, Germany).
## 2.2. Food Samples
Yellow pea flour, whole-grain oat flour, and oat and pea drinks were purchased from a local supermarket. Pea protein isolate (Bang & Bonsomer Estonia OÜ, Tallinn, Estonia), pea protein concentrate (Aloja-Starkelsen Ltd., Limbažu novads, Latvia), and oat protein concentrate (Lantmännen, Stockholm, Sweden) were obtained from producers. The composition and nutritional information available on the product label of these products is available in Supplementary Table S1. Untreated and extruded blends of pea protein isolate, oat protein concentrate, and pea protein concentrate (52:28:20, w/w) were produced in-house by following a previously published protocol [41].
## 2.3. Extraction Method for Solid Samples and for Liquid Samples
Sample extraction methods 1A, 1B, 2A, 2B, and 2C, which were tested during solid sample extraction method development, are described in the supplementary information.
Solid sample extraction (method 2D) was performed according to Heng et al. [ 19] with some modifications. Powdered non-defatted solid sample (100 mg) was weighed into a 10 mL volumetric flask ($$n = 3$$), filled with aqueous EtOH ($70\%$, v/v), mixed thoroughly, and ultrasonicated for 30 min (without additional heating). After ultrasonication, samples were centrifuged (14,000× g for 10 min at 10 °C) to remove insoluble matter. The supernatant (500 µL) was passed through PLD+ columns by applying positive pressure to remove proteins and phospholipids. The obtained filtrate was diluted to receive an aqueous MeCN ($50\%$, v/v) solution. The diluted filtrate (100 µL) was transferred to the LC-MS vials, mixed with 50 µL soyasaponin Ba working solution and 50 µL U-13C-oat extract working solution, and injected into the LC-MS.
A homogeneous liquid sample was weighed (0.25 g) into a 5 mL volumetric flask ($$n = 3$$), filled with ultrapure water, and mixed thoroughly. Diluted sample solutions were centrifuged (14,000× g for 15 min at 10 °C) to remove insoluble matter. Sample supernatant (200 μL) and 800 µL MeCN were transferred into the next tube, mixed thoroughly, and centrifuged (14,000× g for 15 min at 10 °C) to remove precipitated proteins. The supernatant (500 µL) was passed through PLD+ columns. The obtained filtrate (300 μL) was transferred into a clear tube and diluted with 180 µL ultrapure water to obtain an aqueous MeCN solution ($50\%$, v/v). The diluted sample filtrate was combined with internal standard solutions as described for solid samples and injected into the LC-MS.
## 2.4. Preparation of Standard Solutions
The stock solution of avenacoside A (500 mg/L) was prepared in ultrapure water and the aliquots were stored at −80 °C. The stock solution of saponin B (500 mg/L) was prepared in aqueous EtOH ($60\%$, v/v) and aliquots were stored at −80 °C. The internal standard stock solution of soyasaponin Ba (100 mg/L) was prepared in MeOH.
The stock solution of U-13C-oat seed flour extract containing 13C51-avenacoside A was prepared using the previously described solid sample extraction method 2D with some modifications. U-13C-oat seed flour (150 mg) was weighed into a 50 mL volumetric flask, filled with EtOH ($70\%$, v/v), and mixed thoroughly. The flask was ultrasonicated for 30 min (without additional heating) and the obtained solution was centrifuged (17,000× g for 10 min at 10 °C) to remove insoluble matter. The supernatant was passed through PLD+ columns using a vacuum manifold. The cleaned extract was aliquoted and stored at −80 °C.
The internal standard working solutions were prepared freshly before the analysis. The working solution of internal standard soyasaponin Ba was prepared by diluting stock solution in the aqueous MeCN ($50\%$, v/v). The U-13C-oat extract working solution was prepared by diluting the stock solution two-fold with neat MeCN.
## 2.5. Liquid Chromatography Mass Spectrometry
Samples were analysed using a Waters UPLC® system (Waters Corporation, Milford, MA, USA) coupled with a Waters Quattro Premier XE Mass Spectrometer equipped with ZSpray™ Source and controlled by Waters MassLynx™ 4.1 (V4.1 SCN805, Waters Corporation, Milford, MA, USA). Mobile phases were as follows: (A) $0.1\%$ FA in ultrapure water, (B) $0.1\%$ FA in MeCN. Weak needle wash was composed of MeCN in ultrapure water ($90\%$, v/v), and strong needle wash consisted of IPA in MeCN ($50\%$, v/v). The seal wash solution was aqueous MeCN ($50\%$, v/v). Samples were stored in an autosampler which was set at 8 °C. The injection volume was 2 µL. Saponins were separated using BEH Amide column (1.0 × 50 mm, 1.7 μm) coupled with BEH Amide VanGuard Pre-column (2.1 × 5 mm) from Waters Corporation (Milford, MA, USA). The final gradient was as follows: 0–0.17 min at $10\%$ A, 0.17–3.5 min linear gradient 10–$70\%$ A, 3.5–4.0 min at $70\%$ A, 4.0–4.5 min linear gradient 70–$10\%$ A, 4.5–6.0 min at $10\%$ A. The column temperature was held at 50 °C during all experiments. The flow rate was set at 200 µL/min.
The analytes were ionised under negative electrospray ionisation (ESI-) and optimised source conditions. The source temperature was set to 120 °C, and high-purity nitrogen was fed into the source at 25 L/h (cone) and 600 L/h (desolvation) and desolvation gas was heated to 350 °C. The capillary voltage was set to −1.5 kV, cone voltage to 80 V, and extractor voltage to 3 V. For measurement of analytes, a set of m/z values for single-ion-recording (SIR) experiments was recorded simultaneously during one chromatographic run. For saponin quantification, deprotonated molecules [M-H]- were chosen based on a scan-type experiment. Mass-to-charge ratios (m/z ± 0.5 Da) for SIR channels were set as follows: avenacoside A—m/z 1061.5; avenacoside B—m/z 1223; 26-desglucoavenacoside A—m/z 899.5; 13C51-avenacoside A—m/z 1112.5 (internal standard); saponin B—m/z 941.5; DDMP saponin—m/z 1067; soyasaponin Ba—m/z 957.5 (internal standard). Data acquisition was performed in Waters MassLynx™ V4.1 (SCN805, Waters Corporation, Milford, MA, USA). Data analysis was performed in Waters QuanLynx™ V4.1 (SCN805, Waters Corporation, Milford, MA, USA) and Microsoft Excel® (Microsoft 365 Apps for enterprise).
## 2.6. Calibration and Quantification
The working solution was prepared by diluting standard stock solutions 100 times with MeCN:H2O:EtOH solution (50:36:14, v/v). Internal standards, soyasaponin Ba and U-13C-oat extract, were added before injection to the autosampler vial, and their concentration in the vial was set at 0.75 mg/L and 0.3 mg/L, respectively. Calibration curve standard solutions (100 µL) were mixed with internal standards working solutions (50 µL U-13C-oat extract working solution and 50 µL soyasaponin Ba working solution). Calibration curves were built for avenacoside A (0.01–2.44 mg/L) and saponin B (0.01–2.48 mg/L) using eight-point measurements of serially diluted standards, which were run in triplicate. The regression was found by fitting points to the linear equation. The external standard calibration curves were built by correlating the concentrations of external standards to the response factors, which were calculated according to Equation [1]. response factor (RF) = (area of analyte)/(area of internal standard)[1] As only the avenacoside A standard was commercially available, other analytes of interest (avenacoside B and 26-desglucoavenacoside A) were quantified relatively using the avenacoside A calibration curve. Avenacoside B and 26-desglucoavenacoside A results are presented in avenacoside A equivalents. Avenacosides were quantified using isotopically labelled 13C-avenacoside A as an internal standard. As DDMP saponin could not be sourced commercially, its quantification was based on the saponin B standard curve, and the results are given in saponin B equivalents. Both were quantified using soyasaponin Ba as an internal standard.
## 2.7. Validation of the Method
The following parameters were assessed during method validation: linearity, limit of detection (LOD), limit of quantification (LOQ), precision, specificity, sample extraction recoveries, and matrix effect (ME). Developed extraction methods for solid and liquid samples were validated separately. Oat protein concentrate and pea protein isolate were used to validate the solid sample extraction method. Saponin determination in liquid samples was validated using oat and pea drinks.
The linear range and linearity were evaluated via repeated measurements of standard solutions of avenacoside A and saponin B consisting of 8 individual points obtained from serial dilution of stock solutions. For the calculation of LOD and LOQ values for avenacoside A and saponin B compounds, the standard deviation (SD), obtained by analysing the peak areas of the lowest standard concentration point, was multiplied by three or ten, respectively [42].
To determine the intra-day precision of the instrumental method, oat protein concentrate and pea protein isolate extracts containing all analytes and internal standards were injected six times, and for inter-day precision, sample extracts were studied across three independent days to confirm the stability of the retention times and peak areas. The precision of the extraction methods was determined by repeatability (intra-day) and intermediate precision (inter-day). Repeatability was carried out by performing six repeated analyses of the samples on the same day, while the intermediate precision of the method was assessed using samples that were analysed on three different days over two months under the same experimental conditions.
The total recoveries for avenacoside A and saponin B were evaluated by spiking the solid and liquid samples with a known amount of avenacoside A and saponin B at three different concentration levels. For estimation of solid sample extraction method recovery, oat protein concentrate and pea protein isolate (100 mg) were weighed into a 10 mL volumetric flask ($$n = 3$$). Aliquots of avenacoside A and saponin B standard solutions (10 mL) at three different concentrations were prepared in aqueous EtOH ($70\%$, v/v) separately. These solutions were added to oat protein concentrate and pea protein isolate, mixed thoroughly and subjected to the solid sample extraction method as described above. The recoveries of avenacoside A and saponin B in oat and pea liquid samples were determined by cross-matrix spiking both sample matrices. For estimation of liquid sample extraction method recovery, separate standard stock solutions of avenacoside A and saponin B were prepared (200 mg/L). These solutions were added in different volumes to 0.25 g of liquid sample (oat and pea drink) ($$n = 3$$) weighed into a 5 mL volumetric flask, mixed thoroughly, and subjected to the liquid sample extraction method as described above. The total recovery was calculated using Equation [2] [43], total recovery (%) = (Cspiked/(Cunspiked + Cspike)) × $100\%$[2] where *Cspiked is* the amount of saponin determined in the spiked sample, *Cunspiked is* the amount of saponin in the unspiked sample, and *Cspike is* the amount of saponins at three different concentration levels.
ME as one of the most problematic issues in LC-MS was evaluated for all four sample matrices (oat protein concentrate and pea protein isolate and plant-based drinks) by post-extraction sample spiking with calibration curve standard solutions, then constructing a calibration curve based on response factors and spiked standard concentrations, and comparing the matrix-matched calibration curve slope with the calibration curve slope in solvent (Equation [3]) [42] ME (%) = slopematrix-matched/slopesolvent × $100\%$.[3] *Statistical analysis* was carried out using Excel® (Microsoft® 365 for enterprise). The results are presented as mean ± SD or relative standard deviation (RSD).
## 3.1. Development of Liquid Chromatography Method
The HPLC method was developed and assessed by analysing external standards and compounds available in oat and pea sample matrices. During development of the liquid chromatography method, two types of stationary phase chemistry were tested (C18 and HILIC) as well as different column dimensions. The best separation performance in terms of time of analysis, selectivity, and efficiency was achieved by the BEH Amide column (1.0 × 50 mm, 1.7 μm). Based on the literature [9,19] and scan-type experiments of oat flour and pea flour sample extracts, m/z values for SIR channels were chosen for the detection and relative quantification of targeted compounds without existing standard compounds in these sample matrices. Avenacoside B and 26-desglucoavenacoside A were found to be present in the oat sample matrix in addition to avenacoside A. DDMP saponin also occurred in the pea sample matrix besides saponin B. MRM experiments were conducted during development of a methodology but we have found that the MRM approach did not bring any more selectivity but significantly reduced sensitivity by not producing consistent fragments. The example of a chromatogram obtained by injecting the oat and pea flour extracts is shown in Supplementary Figure S1.
## 3.2. Development of Sample Extraction Methods
Two previously published extraction methods (avenacosides in grain and husks of oats [9] and saponins in peas [19]) were the starting points for the development of a method for simultaneous saponin extraction from oat and pea matrices. As both extraction methods were time-consuming, a more efficient sample preparation was proposed for saponin quantification. All samples were analysed using LC-MS method described in the Materials and Methods section.
Table 1 shows the main steps of extraction methods and saponin extraction yields obtained by reference methods (1A and 2A) and modified methods (1B, 2B, 2C, and 2D). To demonstrate the efficiency of the optimized methods, oat protein concentrate and pea protein isolate were analysed in duplicate.
Since both reference methods [9,19] started by fat elimination, defatted oat protein concentrate (fat $18.9\%$) and pea protein isolate (fat $4.7\%$) were extracted using methods 1A, 1B, 2A, and 2B. The oat protein concentrate extracted using method 1B gave $37\%$ higher avenacoside A concentration compared to method 1A, and method 2B resulted in $50\%$ higher yield than method 2A. Overall, the highest avenacoside A content in oat protein concentrate was achieved using extraction method 2B. Using method 1B, the pea protein isolate gave two times higher saponin B yield than using extraction method 1A, and method 2B gave a $76\%$ higher yield than method 2A. Thus, the highest saponin B amount from pea protein isolate was extracted using method 2B. Although both improved methods 1B and 2B gave similar saponin yields in analysed matrices, it was decided to proceed with more process-efficient method 2B, as method 1B utilizing two-step methanol reflux extraction is very time-consuming.
The necessity for fat removal before saponin extraction from the matrix was determined. For this, saponins from four samples (oat flour and protein concentrate and pea flour and protein isolate) were extracted using extraction methods 2B and 2C, and lastly, the extracts were filtered through different filtering devices (the molecular weight cut-off filters with different membrane pore sizes (3, 10, 30, and 50 kDa), 0.2 μm syringe filter, and ISOLUTE® PLD+ Protein and Phospholipid Removal columns) before the LC-MS analysis. The results of this experiment are shown in Supplementary Figure S2.
No significant differences in avenacoside A, avenacoside B, saponin B, and DDMP saponin content were determined in Soxhlet-defatted and non-defatted oat and pea matrices. On the other hand, different molecular cut-off sizes had a significant impact on the recovery of saponins. The 3 kDa and 10 kDa cut-off filters showed inferior performance irrespective of the sample matrix and saponin type determined. The maximum recovery of analytes in the samples was achieved using 50 kDa and in some cases 30 kDa cut-off devices. In all sample matrices except oat protein concentrate, the application of PLD+ columns and syringe filters gave even better results than 30 kDa or 50 kDa cut-off filters. Although the PLD+ and 0.2 μm syringe filters gave quite similar analyte recovery, the application of PLD+ columns resulted in clearer MS chromatograms with a minimum number of interfering peaks in the chromatogram baseline. Moreover, filtering through the PLD+ column enables an easy transition of the procedure to a high-throughput workflow in the case of using 96-well PLD+ plates. The ISOLUTE® PLD+ proprietary multifunctional sorbent phase is optimised to selectively retain proteins and phospholipids [44]. The results indicated that pre-extraction fat removal is not necessary before saponin extraction and could be omitted and the application of PLD+ columns is the best solution for post-extraction clean-up of sample extracts. This resulted in a modified method 2C (described in Table 1).
The influence of ultrasonic power on the saponin extraction yields was also investigated. Saponins from oat protein concentrate, pea protein isolate, and oat and pea flours were extracted using methods 2C and 2D (results are shown in Supplementary Table S2). The results showed that ultrasonication did not have a statistically significant effect on saponin yield but considering the extraction time the application of ultrasonication is preferable. It should be noted that heating taking place during sonication had no effect on the analytes. During this experiment, the ultrasonic bath heated itself from ambient temperature (23 °C) to 40 °C in 30 min. Previous research has shown that the exposure of DDMP saponin to a temperature higher than 40 °C has a profound effect on its degradation into saponin B [18]. However, in another study, it was reported that the pure DDMP saponin in methanolic solution started to decrease in concentration when heated at 65 °C [45].
Based on the obtained results and considering the extraction time and yield, method 2D was utilized for analysis and validation of all solid samples.
Liquid food samples were analysed without the need to use lyophilisation before the sample extraction. The sample preparation method was based only on the application of ISOLUTE® PLD+ cartridges for sample extract purification before LC-MS analysis, previously chosen as the most efficient for cleaning the extracts of the solid samples.
## 3.3. Validation of the Method
When the chromatographic methods and sample extraction methods were developed, validation was performed to evaluate the linear range, LODs and LOQs, precision, recoveries, and matrix effect of the proposed method. The linearity of response and other calibration parameters for avenacoside A and saponin B are presented in Table 2. Linearity for these two saponin standards was obtained in the concentration range of 0.01–2.5 mg/L. The LOQs were estimated from the lowest point of the calibration curve ranging from 0.015 mg/L for avenacoside A and 0.014 mg/L for saponin B. The obtained LOQ results were lower than or in accordance with previous research [9,13,17,39].
After linearity was found to be acceptable for avenacoside A and saponin B, the repeatability of the method was appraised. Repeatability of retention times and peak areas were studied first with six replicate injections of oat protein concentrate and pea protein isolate extract. Table 3 shows the repeatability of retention times, peak areas, and the precision of solid and liquid sample extraction methods. RSDs of peak areas for all saponins did not exceed $6\%$. Intra- and inter-day RSDs were at a similar level, indicating that the methods are reproducible to an acceptable extent for the routine analysis of oat and pea products. Intra-day and inter-day RSDs were determined by extracting oat protein concentrate, pea protein isolate, and plant-based drinks on different days. The RSD of the intra-day precision ranged from 6 to $13\%$ and inter-day precision from 7 to $11\%$ in powdered oat and pea samples. For oat and pea plant-based drinks, the intra-day precision ranged from 3 to $12\%$ and inter-day precision from 7 to $16\%$. The precisions for the DDMP saponin pea drink were not evaluable despite multiple measurements (DDMP saponin content in this sample was <LOQ).
The recoveries were determined in oat protein concentrate and pea protein isolate powder by spiking the oat matrix with avenacoside A and the pea matrix with saponin B. The recovery of analytes in the case of the liquid sample extraction method was investigated separately. Table 4 shows the recovery results of powdered and liquid samples. The recoveries of avenacoside A and saponin B ranged from 90 to $115\%$ and from 82 to $100\%$ in oat protein concentrate and pea protein isolate, respectively. In the oat drink, the recovery of avenacoside A ranged from 96 to $113\%$ and saponin B from 98 to $113\%$. In the pea drink matrix, the recoveries of avenacoside A and saponin B were from 94 to $106\%$ and from 89 to $98\%$, respectively. According to validation guidelines, the acceptable recovery range for this method should be in the range of 80 to $110\%$ [46]. Thus, the mean values of obtained recoveries were acceptable for both matrices. The recovery results obtained with the current procedure were similar to ones reported for previously proposed methods [9,13].
Oat protein concentrate ME on avenacoside A was $100\%$, and pea protein isolate ME on saponin B was $110\%$. Avenacoside A and saponin B ME were $107\%$ and $105\%$ in the oat drink and $105\%$ and $102\%$ in the pea drink, respectively. All measured ME were in the optimal range between 90 and $110\%$ [47].
The stock solution of U-13C-oat seed flour extract was analysed for purity. The unlabelled avenacosides were not detected; thus, isotopically labelled avenacoside A was regarded as fully labelled. The working solution of 13C-oat flour was added into the LC-MS vial before the analysis to assess the quantity of analytes and take into account ME. Moreover, recovery experiments confirmed that the method could be used even with internal standards added post-extraction.
Overall, the method has demonstrated acceptable validation performance in terms of recovery, sensitivity, specificity, and precision, and could be characterised as robust and effective and could potentially be applied in a high-throughput environment. Thus, the developed sample extraction method and the LC-MS method are suitable tools for the analysis of oat and pea saponins in different matrices, e.g., flours, protein concentrates and isolates, mixed matrices, and liquid plant-based drinks.
## 3.4. Determined Concentrations of Saponins in Food Ingredients, Half- and End-Products
High sensitivity and reproducibility as well as very short analysis time make the developed method suitable for routine quality analysis of oat- and pea-based food ingredients and foods, as well as products containing oat and pea components. The results of saponin contents in various samples are shown in Table 5.
In whole-grain oat flour, the contents of avenacoside A, avenacoside B, and 26-desglucoavenacoside A were 23.4 ± 2.9 mg/100 g, 14.0 ± 1.5 mg/100 g, and below LOQ, respectively. According to previous research, the concentrations of avenacosides and their ratios are different and depend largely on the variety of oats [9]. According to the latter study, the average avenacoside A content in oat grain in 16 analysed varieties was 36 ± 8 mg/100 g, avenacoside B content was in the range of 30 ± 4 mg/100 g, and 26-desglucoavenacoside A was 2.4 ± 0.8 mg/100 g [9]. Indeed, the contents of avenacoside A differed up to two-fold depending on the variety, and the ratios of avenacoside A to avenacoside B varied from 0.9 to 1.7 [9]. According to Günther-Jordanland et al. [ 2020], avenacoside A and avenacoside B content in oat flour has been reported to be 24.6 mg/100 g and 21.9 mg/100 g, respectively [13]. Thus, the concentration of avenacosides in the whole-grain oat flour determined in the present study is in a good correspondence with the results reported before [9,13]. In oat protein concentrate ($53\%$ protein; Table S1), avenacoside A content was 42.3 ± 3.0 mg/100 g, avenacoside B was 33.8 ± 0.7 mg/100 g, and 26-desglucoavenacoside A was 5.1 ± 0.2 mg/100 g. According to specification (Table S1), this product was manufactured from oat bran. Previous research has shown that the average content of avenacoside A and avenacoside B in three analysed oat bran products was 26 ± 7 mg/100 g and 8 ± 2 mg/100 g, respectively [17], which is similar to concentrations determined in the whole-grain flour in the current study. Thus, the increased content of avenacosides in oat protein concentrate should be ascribed to the partial concentration of the oat saponins together with the protein fraction during the production process of oat protein concentrate. In an oat drink, avenacoside A content was 4.6 ± 0.1 mg/100 g, avenacoside B was 2.7 ± 0.2 mg/100 g, and 26-desglucoavenacoside A was below LOQ. As it was a commercial liquid product with low dry matter content, it resulted in an apparently lower content of measured saponins. Nevertheless, according to specification (Table S1), the product contains only $1\%$ of protein and the oat base is the only protein source in the oat drink. In this respect, considering the oat drink and, e.g., the whole-grain oat flour ($12.5\%$ of protein), the ratio of avenacosides to protein is much higher in the oat drink. One can suppose the considerable migration of saponins into the liquid phase when soaking the oats during the initial step of oat drink manufacture.
In pea flour ($17.9\%$ protein; Table S1), saponin B content was 6.2 ± 0.4 mg/100 g and relatively quantified DDMP saponin content was 61.1 ± 2.0 mg/100 g. In fact, our findings are inconsistent with the results of Reim and Rohn [2015], who analysed saponin B and DDMP saponin contents in hulls and peas in six different pea varieties using the HPTLC method [32]. They reported that saponin content in peeled peas was 10 to 40 mg/100 g of saponin B and 0 to 20 mg/100 g of DDMP saponin depending on pea variety [32]. Nonetheless, the present findings of high DDMP content in pea flour are comparable with the results of Heng et al. [ 2006]: the DDMP saponin content varied from 70 to 150 mg/100 g DM, whereas saponin B varied from 0 to 40 mg/100 g DM [19]. Our results confirm that the DDMP saponin is the predominant naturally occurring saponin present in pea. The high level of DDMP saponin in pea flour was observed in the current study most likely because it has not been thermally treated and DDMP saponin has not been converted into saponin B. In pea protein concentrate ($46.9\%$ protein; Table S1), the saponin B content was 80.3 ± 1.6 mg/100 g and DDMP content was 107.6 ± 4.1 mg/100 g. Saponins are found in the cotyledons and are often associated with the protein bodies of legumes [4]. Therefore, saponin accumulation in pea concentrate produced by dry milling and air classification is evident [4], which is in accordance with at least twice higher levels of saponins in pea protein concentrate compared to pea flour determined in our study. In pea protein isolate ($75\%$ protein; Table S1), saponin B content was 243.8 ± 6.2 mg/100 g and DDMP content was 10.8 ± 0.7 mg/100 g. These results show that protein wet extraction and isoelectric precipitation, likely performed to achieve protein isolate, degrade unstable DDMP saponin naturally occurring in peas into saponin B. In the pea drink, saponin B content was 3.5 ± 0.2 mg/100 g and DDMP saponin was below LOQ. According to the product specification (Table S1), it contains $2\%$ of protein, and the only protein source is pea. Although the exact production process of the pea drink is unknown, taking into account the content of saponin B per 1 g of pea drink protein (1.75 mg), the probable pea protein source should contain at least 175 mg of saponins (sum of saponin B and DDMP saponin, as DDMP saponin is converted into saponin B during drink pasteurization) per 100 g of pure pea protein.
To test the applicability of the developed method for simultaneous determination of oat and pea saponins from one matrix, the blend of pea isolate, oat protein concentrate, and pea protein concentrate was used. In addition, the part of the mixture was extruded according to the previously published article [41]. Results show that avenacoside A, avenacoside B, 26-desglucoavenacoside A, saponin B, and DDMP saponin content in the blend were 13.5 ± 1.0 mg/100 g, 10.9 ± 0.3 mg/100 g, 1.3 ± 0.3 mg/100 g, 123.9 ± 6.2 mg/100 g, and 27.1 ± 3.5 mg/100 g, respectively. Considering that this blend was composed of $52\%$ pea protein isolate, $28\%$ oat protein concentrate, and $20\%$ pea protein concentrate, which were also analysed separately, the recoveries of avenacoside A, avenacoside B, 26-desglucoavenacoside A, saponin B, and DDMP saponin were $114\%$, $115\%$, $90\%$, $95\%$, and $100\%$, respectively. In the extruded blend, avenacoside B and 26-desglucoavenacoside A content did not change significantly, avenacoside A content decreased by $21\%$, and saponin B content increased from 123.9 to 132.9 mg/100 g, which could potentially happen due to DDMP saponin conversion into saponin B during extrusion cooking.
## 4. Conclusions
In conclusion, the HILIC-MS-based method for oat and pea matrices, with a relatively simple extraction procedure for solid and liquid samples, allowing the simultaneous quantification of avenacoside A and saponin B, and the relative quantification of avenacoside B, 26-desglucoavenacoside A, and DDMP saponin, was employed for analysis of saponins in various food ingredients and products. Oat protein concentrate, pea protein isolate, and oat- and pea-based drinks were chosen for development and validation of the sample extraction methods. The optimised HILIC-MS method was able to absolutely quantify avenacoside A and saponin B in the matrices; other compounds were quantified based on existing standard compounds. The validation of the improved methods for both sample types (solid and liquid) showed the acceptable linear range, LODs and LOQs, precisions, recoveries, and MEs. Generally, an inter-day precision was below $20\%$. The accuracy and the precision of quantification were achieved by using the labelled internal standard (13C-avenacoside A) obtained from U-13C-labelled oat flour and with soyasaponin Ba as internal standards. The content of saponins was measured in different plant-based oat and pea products (ingredients, half- and end-products). This method could be potentially extended for other plant-based sample matrices, and the absolute quantification of all analytes could be achieved if the missing saponin standards were to arrive on the market.
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|
---
title: 'Monitoring Moroccan Honeys: Physicochemical Properties and Contamination Pattern'
authors:
- Abir Massous
- Tarik Ouchbani
- Vincenzo Lo Turco
- Federica Litrenta
- Vincenzo Nava
- Ambrogina Albergamo
- Angela Giorgia Potortì
- Giuseppa Di Bella
journal: Foods
year: 2023
pmcid: PMC10000722
doi: 10.3390/foods12050969
license: CC BY 4.0
---
# Monitoring Moroccan Honeys: Physicochemical Properties and Contamination Pattern
## Abstract
The physicochemical traits and an array of organic and inorganic contaminants were monitored in monofloral honeys (i.e., jujube [Ziziphus lotus], sweet orange [Citrus sinensis], PGI Euphorbia [Euphorbia resinifera] and Globularia alyphum) from the Moroccan Béni Mellal-Khénifra region (i.e., Khénifra, Beni Méllal, Azlal and Fquih Ben Salah provinces). Moroccan honeys were in line with the physicochemical standards set by the European Union. However, a critical contamination pattern has been outlined. In fact, jujube, sweet orange, and PGI Euphorbia honeys contained pesticides, such as acephate, dimethoate, diazinon, alachlor, carbofuran and fenthion sulfoxide, higher than the relative EU Maximum Residue Levels. The banned 2,3′,4,4′,5-pentachlorobiphenyl (PCB118) and 2,2′,3,4,4′,5,5′-heptachlorobiphenyl (PCB180) were detected in all samples and quantified in jujube, sweet orange and PGI Euphorbia honeys; while polycyclic aromatic hydrocarbons (PAHs), such as chrysene and fluorene, stood out for their higher contents in jujube and sweet orange honeys. Considering plasticizers, all honeys showed an excessive amount of dibutyl phthalate (DBP), when (improperly) considering the relative EU Specific Migration Limit. Furthermore, sweet orange, PGI Euphorbia and G. alypum honeys were characterized by Pb exceeding the EU Maximum Level. Overall, data from this study may encourage Moroccan governmental bodies to strengthen their monitoring activity in beekeeping and to find suitable solutions for implementing more sustainable agricultural practices.
## 1. Introduction
Honey is a sweet product of the bee *Apis mellifera* endowed with very specific physicochemical properties, which make it unique from other viscous solutions, as well as precious healthy and therapeutic properties, mainly related to the presence of enzymes, vitamins, phenolics and minerals, which make it a functional food [1]. However, despite its biological activities, honey is not free of contaminants, and the monitoring of its chemical safety is still crucial today not only for assuring product quality and consumer health protection, but also for preserving the environment with its landscapes and biodiversity.
Bees are exposed to numerous pollutants during their foraging activities: their hairy bodies can easily retain a variety of environmental contaminants, and they can be contaminated through food resources when collecting pollen and nectar from flowers or water [2,3,4]. As a result, xenobiotic residues are transferred into and accumulate in bee products, including honey [5,6], which can be regarded not only as food products but also as reliable indicators of environmental contamination [7,8].
Among organic pollutants, organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) represent substances with a high environmental persistence and detrimental effects on human health, whose production is severely banned or restricted all over the world according to the International Stockholm Convention [9]. Nevertheless, their residues are still found in the environment, in food—honey included—and in biological matrices [10,11,12,13]. While not listed under the Stockholm Convention, polycyclic aromatic hydrocarbons (PAHs) were, in June 1998, one of 16 groups of chemicals listed under the United Nations Economic Commission for Europe (UN-ECE) Convention on Long Range Transboundary Air Pollution (LRTAP), that was signed by 33 countries and the European Commission.
The surrounding environment, with its urban, industrial, and agricultural activities, and even beekeeping [14], is not the only source of contamination. Plastic additives, such as phthalates non-phthalate plasticizers (PAEs and NPPs) and bisphenols (BPs) may also occur in honey, and their presence may be explained not only by the chemical load of the same raw material, due to the ubiquitous presence of such contaminants, but also by the processing involving the direct contact of honey with plastic materials [15,16,17].
To protect the authenticity of honey from adulteration events, precise standards on its physicochemical quality have been provided by the Codex Alimentarius [18], and then, further adjusted by the EU Community [19]. However, the international legislative framework on the chemical safety of such bee products is still fragmentary. Indeed, the International Codex Standard for honey refers only to contaminants such as pesticides and heavy metals and suggests that the respective maximum residue limits (MRLs) and maximum levels (MLs) should comply with the standards set by the Codex Alimentarius Commission for contaminants in food [20]. In this scenario, the EU took the lead in regulating apicultural products in September 2018 by approving the “Technical guidelines for determining the magnitude of pesticide residues in honey and setting Maximum Residue Levels in honey” [21]. On the other hand, for heavy metals that may exhibit potential toxicity to consumers, and other organic contaminants, such as PAHs, the MLs set by Regulation (EC) No. $\frac{1881}{2006}$ for a variety of foodstuffs may be (improperly) considered [22].
Similar issues are also experienced when considering process contaminants. Specific migration limits (SMLs) have been applied by the EU to those contact materials containing plastic additives with the potential of leaching into food. SMLs have been set up by the Commission Regulations No. $\frac{10}{2011}$ for some PAEs and No. $\frac{213}{2018}$ and No. $\frac{1907}{2006}$ for bisphenol A and S [23,24,25], and, in the absence of legal limits of process contaminants in food, they are often used, albeit improperly, to obtain an idea of the degree of food contamination.
Since the EU market is a major honey importer and greatly affects the agricultural practices of exporter countries, beekeepers all over the world tend to adopt the mentioned EU legislations as a reference for quality control of honey.
Due to its important floristic, faunal and landscape diversity, *Morocco is* endowed with an important and unique beekeeping potential, resulting one of the most valuable territories for honey production in the *Mediterranean area* [26]. Here, beekeeping is a well-rooted tradition and one of the most profitable businesses, thanks to the conspicuous production not only of honey, but also of pollen, propolis, beeswax, and royal jelly. A market overview showed that the Moroccan honey production increased from 4.7 tons to almost 8 tons between 2010 and 2020, with a turnover of around 101 million [27]. Moreover, a vibrant modernization of the sector has started in the last decade, thanks to the leverage effect of the Green Morocco Plan (GMP), the National Initiative for Human Development (NIHD) and, not least, the Moroccan Ministry of Agriculture, setting a referential catalog for high-quality terroir products, including honey, with the final aim to label them under Geographical Indications, Designations of Origin or Agricultural Labels, thus, promoting their consumption [28]. The Béni Mellal-Khénifra region, placed between the High and Middle Atlas mountain ranges and the Tadla Plain, is well known for its rich and varied botanical diversity allowing for significant honey production [29]. Particularly, the region boasts the monofloral Euphorbia honey of Tadla-Azilal, labeled with Protected Geographical Origin (PGI) and produced from Euphorbia resinifera, an endemic Moroccan species mainly distributed in Azilal and Béni Mellal areas [30].
A literature overview suggests that, over the past decade, major efforts have been devoted to the characterization of physicochemical, melissopalynological, antioxidant and microbiological traits of Moroccan honey [28,31,32,33,34,35,36,37,38,39,40,41], while very few and fragmentary data are available on contaminants [42,43]. However, the chemical safety of such a product showing a considerable ability to accumulate xenobiotics from the surrounding environment should not be overlooked, also in view of the patchy regulatory framework, as well as the ongoing modernization facing Moroccan beekeeping and its labelled products.
Within this background, the aim of the study was to explore the physicochemical traits and the contamination pattern of monofloral honeys from diverse areas of the Béni Mellal-Khénifra region, including the PGI Euphorbia honey of Tadla-Azilal. Data from the main quality indicators (i.e., moisture, sugars, pH, electrical conductivity, and acidity), as well as from an array of organic chemicals -including regulated (i.e., pesticides, PAEs, NPPs and BPs) and banned/restricted substances (i.e., OCPs, PCBs and PAHs)- and inorganic pollutants (i.e., potentially toxic elements) were employed to statistically evaluate the relation between the quality and safety of honey and the actual production scenarios of the Moroccan region. In addition, the dietary exposure to contaminants derived from the consumption of such honeys was evaluated.
## 2.1. Study Area
The Béni Mellal-Khénifra region is a new Moroccan region created according to the new administrative subdivision of 2015; it represents $4\%$ of Morocco’s national territory and covers 28 088 km2, of which more than $65\%$ is mountainous. The region is located in central Morocco, between the High and Middle Atlas and the Tadla Plain, and comprises five provinces: Azilal, Béni Mellal, Fquih Ben Salah, Khénifra and Khouribga (Figure 1).
Thanks to diversified climates and landscapes, this region has a rich natural heritage and high biodiversity, with a significant potential for agricultural development. Indeed, the Béni Mellal-Khénifra region has an urbanization rate ($49\%$) lower than the national average ($60.36\%$), and more than half of the population lives in rural areas ($51\%$), being strongly committed to the agricultural sector, which not only constitutes the major economic activity of the region, but it is also in the process of modernization as required by the GMP [44,45]. However, intense agricultural practices have been seen as a relevant factor for environmental pollution. In this respect, recent studies have reported in diverse areas cases of agricultural soils and groundwater/wastewater intended for irrigation being contaminated, especially in terms of heavy metals and pesticides [46,47,48,49].
## 2.2. Honey Samples
The study was conducted on 12 honey samples produced in 2021 by beekeepers located in diverse provinces from the Béni Mellal-Khénifra region of Morocco. They included $$n = 3$$ honey samples from *Ziziphus lotus* (i.e., jujube honey) produced in Khénifra, $$n = 3$$ honeys from *Citrus sinensis* (i.e., sweet orange honey) collected in the Béni-Mellal province, $$n = 3$$ PGI honeys from *Euphorbia resinifera* (i.e., Euphorbia honey) produced in the Tadla-Azilal area, and $$n = 3$$ honeys from *Globularia alypum* obtained from the Fquih Ben Salah province. Honeys from this study derived from pooling a given type of honey from different hives of a given area and they were collected in glass jars of ~125 g and stored at room temperature in a dark place until analysis.
## 2.3. Chemicals and Reagents
Analytical standards of $$n = 108$$ pesticides, $$n = 18$$ PCBs, and $$n = 13$$ PAHs were purchased from Sigma-Aldrich (Chicago, Il, USA), Fluka Analytical (Milan, Italy) and Dr. Ehrenstorfer (Augsburg, Germany). For pesticides, deuterated analogues intended as internal standards (ISs) were carbofuran-d3, dimethoate-d6, atrazine-d5, cyprodinil-d5, imazalil-d5, malathion-d6, methiocarb-d3, and trifloxystrobin-d6. They were all provided by Toronto Research Chemicals (Toronto, CA, USA). For the analysis of PCBs and PAHs, the deuterated analogues were naphtalene-d8, acenaphtene-d10 and phenanthrene-d10, from Cambridge Isotope Laboratories Inc. (Andover, MA, USA). Analytical standards of $$n = 10$$ PAEs and $$n = 8$$ NPPs (certified purity ≥$96\%$) were provided by Supelco (Bellefonte, PA, USA). DBP-d4 and DEHP-d4 were the ISs purchased from Cambridge Isotope Laboratories Inc. Analytical standards of $$n = 9$$ BPs (certified purity ≥$97\%$) were purchased from Sigma-Aldrich (Steinheim, Germany), while the IS 13C12-BPA was obtained from Cambridge Isotope Laboratories.
Solvents (i.e., acetonitrile, water, and n-hexane, LiChrosolv and Parasol grade) were purchased from Merck (Darmstadt, Germany). The Q-sep QuEChERS extraction kit (4 g MgSO4 and 1 g NaCl) and QuEChERS d-SPE (750 mg MgSO4, 250 mg of primary and secondary amines PSA and 125 mg C18) were purchased from Agilent Technologies Italia S.p. A. (Milan, Italy).
Overall, the contact of laboratory equipment and solvents with samples, the sample preparation time, and the solvent volumes were mandatorily minimized to significantly reduce the background contamination caused by solvents and laboratory materials. Glassware and stainless-steel instruments were washed with acetone, rinsed with hexane, dried at 400 °C for at least 4 h, and finally wrapped with aluminum foil until analysis. All solvents were tested before use, and due to the negligible levels of background contamination, they were employed throughout the analytical procedures with no further purification.
For the screening of inorganic elements, HNO3 ($65\%$ v/v) and H2O2 ($30\%$ v/v) were of Suprapur grade (Mallinckrodt Baker, Milan, Italy). Ultrapure water (<5 mg/L TOC) was obtained from a Barnstead Smart2Pure 12 water purification system (Thermo Scientific, Milan, Italy). A standard solution of Re (1000 mg/L in $2\%$ HNO3) was provided by Fluka (Milan, Italy) and employed as IS. Single-element standard solutions of inorganic elements such as K, Na, K, Mg, Ca, Fe, Mn, Cr, Co, Ni, Cu, Zn, Al, Pb, Cd and As, at a concentration of 1000 mg/L in $2\%$ HNO3 (Fluka, Milan, Italy) were used to prepare multielement stock standard solutions. To avoid undesirable cross-contamination, laboratory glassware and plastic instruments necessary for sample collection, handling, and storage, as well as polytetrafluoroethylene (PTFE) digestion vessels, were washed with $5\%$ HNO3 before use.
## 2.4. Physicochemical Parameters
Moisture (%) and total soluble solids (TSS) represented by soluble sugars content and expressed as °Brix, were obtained from the tables of correspondence between a given water content/°Brix and the refractive index calculated for each sample at 20 °C. If the index was not determined at a temperature of 20 °C, the correction temperature was considered, and the result was reduced to a temperature of 20 °C [50].
Free, combined, and total acidity were determined by the titrimetric method proposed by Bogdanov and colleagues [50]. Briefly, the titration of the honey sample (10 g diluted with 75 mL of distilled water) was carried out with 0.05 N NaOH to pH 8.5 (free acidity). Then, a 10 mL volume of NaOH was added and titrated again with 0.05 N HCl to pH 8.3 (combined acidity). Total acidity was calculated obtained by the sum of free and combined acidities.
The pH and electrical conductivity for every honey sample were determined by a pH/conductivity meter. Approximately 10 g honey was dissolved in 75 mL distilled water and the pH and electrical conductivity were measured. For electrical conductivity, the quantity of honey to be weighed was calculated using the following Equation [1]:[1]$M = 20$×100100−AM: mass of honey (g); 20: is the theoretical nominal mass of honey; A: water content in %.
The ashes were obtained by drying 5 g of every honey sample at 600 °C until constant weight, according to the AOAC protocol [51]. For the determination of minerals (K, Mg, Na, and Ca) and essential trace elements (Mn, Fe, Cu, Zn, Se, Cr, Co and Ni), see Section 2.7.
## 2.5. Pesticide, PCB, and PAH Residues
For extraction of pesticides, PCBs, and PAHs from honey samples, the procedure of Saitta et al. [ 52], with some modifications, was used as described below. Briefly, 10 g of honey was weighed into a tube with 10 mL of water and 10 mL of acetonitrile, and vortexed for 5 min. Then, Q-sep QuEChERS kit and d-SPE (described in Section 2.2) was added and centrifuged for 5 min (5000 rpm). At the end, 5 mL of the organic phase was collected, reduced to 1 mL in a rotary evaporator at 30 °C and reduced to a volume of 0.5 mL volume under a stream of nitrogen. Before instrumental analysis, a known amount of every IS was added to every sample. The multiresidue screening was performed by a Thermo Scientific Trace GC Ultra coupled with a TSQ Quantum XLS triple quadrupole mass spectrometer equipped with a TrisPlus RSH automatic sampler. Separation conditions, and mass spectrometry (MS) details are available in our previous work [6]. Compound identification occurred by comparison of their retention times and mass spectra with those of corresponding commercial standards. The quantitative procedure was carried out in multiple reaction monitoring (MRM) mode, exploiting the IS normalization. The MRM transitions, as well as the main figures of merit of analytical validation are reported in Table S1. Every honey sample was monitored in triplicate, along with analytical blanks.
## 2.6. PAEs and NPPs Residues
The extraction of plasticizers from the various honey samples was performed according to a method already reported in Liotta et. al. [ 6], with some modifications. Briefly, 5 g of honey was weighed into a tube and extracted with 10 mL of acetonitrile. Then Q-sep QuEChERS was added and centrifuged for 5 min (5000 rpm). Approximately 2 mL of the organic phase were collected, evaporated to 1 mL in a rotary evaporator at 30 °C and finally reduced to a volume of 0.5 mL volume under nitrogen stream. Before instrumental analysis, a known amount of DBP-d4 and DEHP-d4 was added to every sample. The multiresidue screening was carried out by a gas chromatography system (GC-2010, Shimadzu, Japan) equipped with an autosampler (HT300A, HTA, Italy) and coupled to a single quadrupole mass spectrometer (QP-2010 Plus, Shimadzu, Japan) according to the operating conditions already described in our previous work [53]. Identification of PAEs and NPPs occurred by comparison of their retention times and mass spectra with those of corresponding commercial standards, while the quantitative assay was performed in SIM mode, considering the base peak ion out of three characteristic mass fragments for each target analyte (Table S2) and using the IS normalization. The parameters of acquisition, as well as the main figures of merit of analytical validation are reported in Table S2. Measurements were conducted in triplicate for every sample, alternated with analytical blanks.
## 2.7. BP Residues
For the extraction of the nine bisphenols, the method already proposed by Liotta et al. [ 6] with some modification, was applied. Briefly, 5 g of honey was placed in centrifuge tubes with 10 mL of ultrapure water and 10 mL of acetonitrile, and vortexed for 5 min. Then, 4 g of MgSO4 and 1.5 g of NaCl were added. The obtained mixture was vortexed for 5 min, and centrifuged at 4000 rpm for 10 min. Then, 5 mL of supernatant was added to the QuEChERS d-SPE cleaning tube, vortexed and placed in centrifuge at 4000 rpm for 10 min. Hence, 1 mL of supernatant was recovered and filtered by 0.22 µm nylon filter and analyzed by HPLC-MS/MS. Analysis was performed on an LC apparatus (Prominence UFLC XR system, Shimadzu, Kyoto, Japan) consisting of a controller (CBM-20 A), binary pumps (LC-20AD-XR), degasser (DGU-20A3R), column oven (CTO-20AC), and autosampler (SIL-20 A XR). An electrospray ionization (ESI) source interfaced the LC system to a triple quadrupole mass spectrometer (MS) (LCMS-8040, Shimadzu, Kyoto, Japan). Data were acquired in MRM mode and the resulting ion transitions were used for the identification and quantification (internal standard method) of BPs. MRM transitions and the main figures of merit of analytical validation for every target analyte are reported in Table S3. Every honey sample was monitored in triplicate along with analytical blanks.
## 2.8. Inorganic Elements
Mineralization of honey samples was carried out following the method proposed by Di Bella and coworkers [54]. About 0.5 g of each honey sample was weighed, and 1 mL of IS at 0.5 mg/L was added. The samples were digested with 7 mL of HNO3 ($65\%$, v/v) and 1 mL of H2O2 ($30\%$, v/v) in a microwave ETHOS 1 digestion system (Milestone, Bergamo, Italy) using the following instrumental parameters: 15 min at 1000 W up to 200 °C, 15 min at 1000 W at 200 °C. The digested samples were conveniently diluted with ultrapure water and their analysis was carried out by means of a single quadrupole inductively coupled plasma-mass spectrometer (ICP-MS, iCAP-Q, Thermo Scientific, Waltham, MA, USA) according to the operating conditions already reported in our previous studies [5,55,56]. All samples were processed in triplicate along with analytical blanks. The analytical validation of the ICP-MS method is reported in Table S4.
## 2.9. Statistical Analysis
Statistical analysis was carried out using the SPSS 13.0 software package for Windows (SPSS Inc., Chicago, IL, USA). Initially the non-parametric Kruskal–Wallis test was applied on log-transformed data to assess differences between honey samples, with a statistical significance at $p \leq 0.05.$ Subsequently, a Principal Component Analysis (PCA) was conducted on a starting data matrix where the cases [12] were the analyzed honey samples and the variables [54] were the values of physicochemical parameters, as well as pesticides, PCBs, PAHs, plasticizers, BPs residues and element concentrations that were higher than their respective LOQs. When concentrations were below the limit of quantification (LOQ), these were replaced with half the limit of detection (LOD/2). Then, the data set was normalized to achieve independence of the different variables scale factors and a PCA was performed to evaluate the differentiation of honey samples in relation to the different production context and/or floral origin according to the investigated variables.
## 2.10. Assessment of the Dietary Exposure to Contaminants
To evaluate the health risks of organic and inorganic contaminants derived from the intake of Moroccan honey, the relative estimated daily intakes (EDI) were calculated by multiplying the mean contaminant concentration found in every sample (mg/Kg or µg/Kg) by the amount of honey consumed daily (g/day) and dividing the obtained result by the consumer’s body weight (Kgbw). Hazard quotient (HQ), which is the ratio between a given EDI and the corresponding oral reference dose (RfD) proposed by the U.S. Environmental Protection Agency (US EPA,) was also employed to assess the plausibility of risk. An HQ (dimensionless) >1 entails a high non-carcinogenic risk.
## 3.1.1. Moisture, TSS, Acidity and pH
The values of moisture, TSS, free, combined, and total acidity, and pH of Moroccan honeys are shown in Table 1.
The moisture of honey is strictly related with the harvest time and practices performed by beekeepers, and, not least, the level of honey maturity reached in the hive [57]. This parameter influences honey flavor, color, density, and viscosity, and determines its stability and granulation during storage [58]. In the honeys investigated, moisture values were always below the maximum limit ($20\%$) set by the Codex Alimentarius and EU standards [18,19]. This may be due not only to the correct time of extraction by Moroccan beekeepers, but also to the current use of modern hives with better moisture control. Specifically, moisture values ranged between 14.93–$16.57\%$, thus indicating a good degree of maturity of all products. Despite the small variability, the upper and lower moisture values, represented respectively by the jujube honey from Khénifra and the sweet orange honey produced in Béni Mellal province, were significantly different ($p \leq 0.05$), which may suggest a variation in such parameters in relation to the climatic conditions [59].
For TSS, minimum/maximum values of 82.67 °Brix/85.83 °Brix were found respectively in sweet orange and G. alypum honeys, fall within the acceptability range (78.8 and 85 °Brix). Indeed, TSS values are known to decrease with the increasing concentration of starch, molasses, glucose, and distilled water. As a result, this parameter is inversely related to the moisture content and useful in the detection of adulteration events [60].
Honey acidity and pH are correlated with each other, being dependent on the level of organic acids and enzymatic activity in honey. As a result, their variation in honey samples could be attributed to the floral origin rather than the environmental context [57].
These physicochemical parameters are generally intended as a marker of honey freshness since the higher the acidity and the lower the pH, the better the environment that inhibits microorganism growth [61]. In the present study, all products showed acidity values below the EU standards 50 meq/kg [14], thus suggesting the absence of undesirable fermentation and/or bacterial spoilage. Specifically, free, and total acidity values ranged from 15.41 meq/kg and 16.39 meq/kg in sweet orange honeys (Khénifra) to 39.28 meq/kg and 39.88 meq/kg in G. alypum honeys (Fquih Ben Salah), thus yielding significantly different results in these types of honey ($p \leq 0.05$).
All samples analyzed showed an acidic character. Although non significantly different ($p \leq 0.05$), pH values were in accordance with the acidity values and varied probably due to the different floral origin. In fact, the lowest pH was observed in G. alypum honey (3.98), while the highest values were found in sweet orange and jujube honey (4.24).
Overall, the physicochemical traits discussed in this study were in line with those reported for the recent production of Moroccan honey from Middle Atlas, also with the same floral origin [28,32,33,38,40,41,58].
## 3.1.2. Electrical Conductivity, Ash, and Mineral Content
The electrical conductivity, ash, and mineral profile (K, Ca, Na, and Mg) and essential trace elements (Mn, Fe, Zn, Cu, Se, Cr, Co, and Ni) of investigated Moroccan honey are reported in Table 2.
The electrical conductivity of the honey is closely related to the concentration of minerals and organic acids and its assessment is useful in the discrimination between blossom and honeydew honeys. In fact, such a parameter tends to be higher in honeydew honeys and it varies in relation to the same honeydew content. The Codex Alimentarius and EU legislation require blossom honeys to have conductivity values not higher than 800 µS/cm [18,19]. As a result, honeydew honeys generally show higher values than 800 µS/cm. Additionally, in monofloral honeys, this parameter shows great variability according to the floral origin [62,63].
In Moroccan honeys, conductivity varied from 157.00 µS/cm to 633.67 µS/cm, respectively in sweet orange and G. alypum honey, which consequently gave significantly different results ($p \leq 0.05$). On the other hand, jujube and Euphorbia honeys showed intermediate and non-significantly different conductivities (respectively, 381.33 µS/cm and 362.67 µS/cm, $p \leq 0.05$). All values were below the maximum limit (800 µS/cm) set by the Codex Alimentarius and EU standards for such parameter [18,19].
Differently from conductivity, there is no specific legislation on maximum level of ash, minerals, and trace elements content in honey, which, consequently, are not yet considered as a quality parameter by either the Codex Alimentarius or the EU. However, they are very important quality markers of honey, reflecting both the floral source of honey as well as its environmental context of production [64].
Ashes followed the same trend of electrical conductivity in investigated honeys, ranging from 0.34 g/Kg in sweet orange honey from Béni Mellal to 1.17 g/Kg in jujube honey from Khénifra ($p \leq 0.05$). In terms of concentrations, similar considerations could also be made for the element profile.
Considering minerals, K was the most abundant mineral in all honeys analyzed, followed by Ca, Na and Mg. The predominance of K over the other minerals was already highlighted in other honeys—Moroccan and not—being a peculiar characteristic of such a bee product [33,40,41,58,63,64].
G. alypum honey from the Fquih Ben Salah province had the highest concentration of K (849.73 mg/Kg), while the lowest value of K was found in the sweet orange honey from Béni Mellal (102.80 mg/Kg). The highest Ca content was found in the PGI Euphorbia honey from Azilal (125.62 mg/Kg), and the lowest in sweet orange honey from Béni Mellal (81.70 mg/Kg). In jujube and G. alypum honeys, Na was the third mineral element with a concentration of 76.84 mg/Kg and 89.99 mg/Kg, respectively; while in sweet orange and PGI Euphorbia honeys, Mg was the third most abundant mineral, with a concentration of 65.46 mg/kg and 69.54 mg/Kg, respectively.
The mineral content of honeys under study agreed with the range of values reported for jujube and sweet orange honeys from the Béni Mellal-Khénifra region [40], as well as for the PGI Euphorbia honey [37,58].
For essential trace elements, the most significant contributions to the element profile came from Fe, Zn and Mn. Specifically, the highest concentration of Fe was found in G. alypum honey with a concentration of 16.51 mg/Kg, while the sweet orange honey had the lowest amount (6.89 mg/Kg). On the other hand, the PGI Euphorbia honey showed the most abundant concentrations of Mn and Zn (4.00 mg/Kg and 6.98 mg/Kg, respectively). Other essential trace elements (i.e., Cu, Se, Cr, Co, and Ni) were revealed at concentrations ≤1 mg/Kg.
Differently from major elements, no efforts have been devoted to the screening of trace elements in honey from the Béni Mellal-Kenifra region. However, Bettar et al. and Moujanni et al. recently revealed lower Fe and Mn contents for the PGI Euphorbia honey (respectively, 4.37–5.5 mg/Kg and <1 mg/Kg) [37,58].
Overall, it could be argued that the elemental profiles of different honeys are greatly affected by the floral source. Indeed, elements are primarily introduced from the soil into the nectar via the root system of the plant. Additionally, bees are in contact with the surrounding environment during foraging and further amounts of inorganic elements can be accidentally transferred from soil, water and soil to the hive. As a result, the elemental profile of honey is a bio-accumulative picture of the geographical context as well as of the activity near the apiary site [65,66].
## 3.2. Pesticide, PCB, and PAH Residues
No honey samples were shown to be free of pesticides. However, among the $$n = 108$$ pesticides investigated, no OCPs were revealed, and only $$n = 11$$ pesticides were found at levels higher than the respective LODs, mostly belonging to the organophosphate class (OPs) and its metabolites (Table 3). The jujube honey from the Khénifra province was among the samples with the highest number of quantifiable pesticides ($$n = 8$$), detected moreover at the highest levels. Such honey stood out for the highest level of carbaryl (1060.90 µg/Kg, $p \leq 0.05$), acephate (1251.19 µg/Kg, $p \leq 0.05$) and cyromazine (2060.99 µg/Kg, $p \leq 0.05$). Additionally, it was the only honey to show quantifiable residues of quinalphos (5.92 µg/Kg) and fenthion sulfoxide (16.53 µg/Kg). Intermediate and similar levels of contamination were found in the sweet orange honey produced in Béni Mellal and the PGI Euphorbia honey collected in Azilal, in which the most abundant residues were carbaryl (146.30 µg/Kg and 277.41 µg/Kg, $p \leq 0.05$) and cyromazine (223.72 µg/Kg and 113.60 µg/Kg, $p \leq 0.05$). Finally, the G. alyphum honey from the Fquih Ben Salah province had the lowest number of quantifiable pesticides ($$n = 5$$). In such honey, the most abundant residues were confirmed to be carbaryl, acephate and cyromazine. However, they were found at very low levels when compared with the other honey samples ($p \leq 0.05$). According to the Regulation (EC) No. $\frac{396}{2005}$ and subsequent amendments [67], $75\%$ of investigated samples (all samples from jujube, sweet orange, and the PGI Euphorbia honeys) widely exceeded the MRL of 0.05 mg/kg for carbaryl and cyromazine, as well as the MRL of 0.02 mg/Kg for acephate, while $50\%$ of the samples (all samples from jujube and sweet orange honey) exceeded the MRLs of 0.01 mg/kg for dimethoate and diazinon. All samples of jujube honey greatly exceeded the MRLs of 0.01 mg/kg, 0.05 mg/kg, and 0.01 mg/kg respectively for alachlor, carbofuran and fenthion sulfoxide. As mentioned in the introduction section, very few efforts have been devoted to the assessment of the chemical safety of Moroccan honeys. A recent study conducted on the Euphorbia honey reported the identification and quantification of the 202 pesticides, including the ones detected in this study. However, contrasting results were obtained, since the detected residues were always within the set MRLs, thus indicating a good quality of the PGI product [42].
The pesticide fingerprint of different honeys clearly reflects the different agronomic practices of the different provinces of such Moroccan region and, more specifically, a more pronounced and prolonged use of OPs in the Khénifra province. The persistence of these pesticides on plants and soil can create shifts in the entire food chain. In fact, regardless of the type of honey, worker bees may transfer such contaminants from the pollen and nectar of plants to the hive, thus being inevitably incorporated into the different hive products [52].
Of the $$n = 18$$ PCBs under analysis, $$n = 2$$ compounds were found. Specifically, PCB118 was detected in all honey samples and quantified only in jujube honey from Khénifra province (0.71 µg/Kg, $p \leq 0.05$) and PCB180 was revealed in all types of honey and quantified in $75\%$ of samples (0.42–0.73 µg/Kg, $p \leq 0.05$), apart from G. alypum honey from Fquih Ben Salah province (Table 3). Of the $$n = 13$$ PAHs investigated, $$n = 6$$ congeners were present in all samples, but quantified in $50\%$ of them (i.e., jujube and sweet orange honeys). In particular, the jujube honey from Khénifra was the most contaminated product, with $$n = 5$$ PAHs detected at a level >LOQ. Between these, chrysene (2.10 µg/Kg), anthracene (1.54 µg/Kg) and fluorene (1.14 µg/Kg) were the most abundant toxicants. To follow, $$n = 4$$ PAHs were quantified in the sweet orange honey from Béni Mellal, with the most abundant compounds represented by benzo[a]anthracene (1.71 µg/Kg) and chrysene (1.62 µg/Kg) (Table 3). As previously mentioned, there are still no regulatory limits for PCBs and PAHs in honey and no toxicological consideration can be made in reference to the Reg. ( EC) No. $\frac{1881}{2006}$ [22], since it fixes the ML of just one PAH, namely the benzo[a]pyrene, and establishes a ML for the sum of PCBs, taking into account the share of fat in food.
However, the monitoring of PCBs and PAHs in Moroccan honey is very scarce. To the best knowledge of the authors, only Chakir et al. investigated diverse honey samples from different South, Center–South and East Moroccan regions and from many floral origins, including C. sinensis and E. resiniphera [68]. The study reported that a small share of samples was contaminated with PCBs, with concentration levels between 0.06 and 5.1 μg/kg. Furthermore, PAHs were present in all investigated samples with levels in the same range or slightly higher than those observed in this study (0.26–7.58 μg/kg). However, congeners such as dibenzo(a,h)anthracene and acenaphthylene were revealed at the highest levels.
A literature review pointed out that honey from the *Mediterranean area* produced during the last decade was poorly monitored with respect to pesticides, PCBs, and PAHs. In this respect, few recent works on Italian honey generally showed higher standards of chemical safety. In fact, organophosphorus pesticides were detected in the order of ng/g and not exceeding the relative MRLs; in addition, PAHs, such as acenaphthylene, fluorene, phenanthrene and pyrene, were found in the range >LOD-7.70 ng/g. PCBs were absent in all honeys investigated [52,67,69].
## 3.3. Plasticizers and BPs
Plasticizer and bisphenol residues revealed in the several honeys from the Béni Mellal-Khénifra region are shown in Table 4. Five PAEs (i.e., DEHP, DEP, DPrP, DiBP, and DBP) and five NPPs (i.e., DEA, DiBA, DBA, DEHA and DEHT) were determined at a concentration >LOQ in $100\%$ samples. Among PAEs, DEP was the most abundant congener (0.94–3.17 mg/Kg $p \leq 0.05$) in the various honey, except for the G. alypum honey (0.94 mg/Kg, $p \leq 0.05$), followed by DBP (0.49–1.05 mg/Kg, $p \leq 0.05$) and DiBP (0.45–0.79 mg/Kg, $p \leq 0.05$) in all honey samples.
Among the NPPs, DBA was the most abundant compound (8.62–12.42 mg/Kg, $p \leq 0.05$) in the Moroccan honeys, except for the G. alypum honey (0.50 mg/Kg, $p \leq 0.05$), followed by DEA, with a concentration ranging between 1.30–5.65 mg/kg ($p \leq 0.05$). Furthermore, three BPs were also determined, namely BPA, BPB and BPAF. BPA was detected in $100\%$ samples but quantified only in sweet orange and PGI Euphorbia honey samples (respectively, 7.74 µg/kg and 8.07 µg/kg, $p \leq 0.05$); BPAF was determined in all samples except the G. Alypum honey (1.48–158 µg/kg, $p \leq 0.05$); and BPB was the most abundant BP in all Moroccan honeys (4.16–8.75, $p \leq 0.05$). Among the determined plasticizers, Reg. ( EU) No. $\frac{10}{2011}$ has established SMLs from food contact material for DBP (0.3 mg/Kg), DEHP (1.5 mg/Kg), as well as DEP, DiBP and DEHT (60 mg/Kg) [23]. On this basis, all Moroccan honeys had an excessive amount of DBP, this PAE being at a concentration level 1.5–3 times higher than the relative SML. On the other hand, the regulatory SML for BPA migration from food contact material is 600 ng/g [24]. Table 4 shows that none of the honey samples contained BPA at concentrations higher than the SML.
Similarly to pesticides, PAHs and PCBs in the jujube honey from Khénifra and the sweet orange honey from Béni-Mellal demonstrated the highest levels of plasticizers and BPs, while the G. alypum honey from the Fquih Ben Salah province was generally the least contaminated product. However, in such honey, one PAE, i.e., DEHP, and one NPP, i.e., DEHT, were found at the highest levels (1.06 mg/kg and 1.14 mg/Kg, $p \leq 0.05$).
To the best of our knowledge, there is no literature regarding plasticizers and BPs in Moroccan honey. However, recent efforts can be observed in the determination of such chemicals in honey from the Mediterranean basin. In this respect, Lo Turco and coworkers [16] determined much lower levels of PAEs (e.g., DEP, 0.006 mg/Kg; DiBP, 0.042 mg/Kg; DBP, 0.039 mg/Kg; and DEHP, 0.191 mg/Kg) in Sicilian and Calabrian honeys than those determined in Moroccan honeys, and BPA was lower than analytical LOQ in all samples. More recently, Notardonato and colleagues [15] confirmed the higher purity of Italian honey, pointing out lower frequencies of PAE determination in honey samples, with only the DEHP found at concentrations (0.005–0.960) similar to those of the Moroccan honeys under study. Indeed, other PAEs, such as DEP (0.020–0.371 mg/Kg), DiBP (0.028–0.299 mg/Kg), DBP (0.019–0.550 mg/Kg) yielded much lower levels. However, Italian honeys revealed higher levels of BPA (18.8–996.8 µg/kg).
Since plasticizers and BPs could be easily released from the plastic components of honey production equipment (e.g., honey extractor and uncorkers), it may be assumed that the Moroccan honey could be contaminated by such compounds during production steps, as already observed not only in honey [15,16] but also in a variety of processed food [70,71]. Additionally, prolonged periods of storage with given conditions in terms of temperature, humidity, and light, may affect not only the peculiar physicochemical properties of honey, but also cause gradual polymer degradation, and, consequently, the migration of plastic additives from the plastic packaging into the honey [72]. However, due to the ubiquitous presence of plasticizers and BPs in the environment, the contamination of honey from the nectar source should always be considered [17].
## 3.4. Potentially Toxic Elements
The profile of potentially toxic trace elements of Moroccan honeys is reported in Table 5. Among investigated elements, Al was the most abundant metal and, differently from the trend of other contaminants, the highest and lowest levels of such metal were found respectively in the PGI Euphorbia honey from Azilal (5.56 mg/Kg, $p \leq 0.05$) and in the jujube honey produced in Khénifra (1.69 mg/Kg, $p \leq 0.05$).
Aluminum may be related to the soil acidification caused by elevated industrial emissions and poor mining practices occurring in such Moroccan regions, and it may be increasingly bioavailable to organisms through plant roots. Inevitably, the metal can then spread up the food chain through pollen, and nectar collection [73].
To follow, equal amounts of Pb and As (0.06–0.16 mg/Kg, $p \leq 0.05$) were determined in all samples, while Cd was lower than LOQ (0.003 µg/Kg) in any case. Regulation (EU) No. $\frac{2015}{1005}$, amending Regulation (EC) No. $\frac{1881}{2006}$ for the ML of Pb in certain foods [74], has introduced a ML of 0.1 mg/Kg in honey. Accordingly, 3 out of the 4 types of Moroccan honey, namely the sweet orange, the PGI Euphorbia and the G. alypum honeys, were characterized by a Pb content higher than the regulatory limit.
Anthropogenic activities, including industrial applications and agricultural chemicals, are responsible for the heavy metal contamination of surroundings. As with other contaminants, bees and bee products including honey are exposed to these contaminants via polluted pollen, water and air. The role of honeybees as “filters” of heavy metals and their protective function against honey contamination have been commonly accepted [75,76]. In contrast, honey is still considered as a typical indicator of heavy metal pollution related not only to anthropogenic activities (e.g., agriculture, industry etc.) but also to the entire production process, as poor beekeeping practices may be also source of heavy metal residues in honey [77,78].
In the last decade, the issue of potentially toxic trace elements in honey from Mediterranean countries has been approached and, as expected, great data variability was pointed out. For example, carob honey from different Moroccan areas and the PGI Euphorbia honey did not display heavy metals such as Cd and Pb, probably because of the high instrumental LOQ values [41,42]. Honey from several Libyan locations had very high levels of Pb (2.42–10.98 mg/Kg), Cd (0.125–0.150 mg/Kg), but low As contents (0.006–0.018 mg/Kg) [79]. Despite honeys from different Italian regions generally showing Cd and Al at higher levels than Moroccan honeys (respectively, 0.003–0.02 mg/Kg and 0.52–26 mg/Kg) [80,81,82,83,84], Pb was always under the regulatory limit (0.05–0.06 mg/Kg) [79,80] and As at lower levels than those detected in this study (0.01 mg/Kg) [81,84]. On the other hand, different varieties of Spain honeys were marked by lower levels of As (0.004–0.010 mg/Kg) and Pb (0.011–0.041 mg/Kg) [85].
## 3.5. PCA Analysis
In the present study, PCA provided information on the most significant variables describing the whole data set, enabling data reduction at the same time with a minimum loss of original information.
Four principal components (PCs) with eigenvalues exceeding one (325.627, 18.195, 7.575 and 1.257) were extracted according to the Kaiser Criterion, and they explained up to $97.51\%$ of total variance (i.e., $47.457\%$, $33.695\%$, $14.029\%$ and $2.327\%$, respectively). Figure 2 illustrates the bidimensional score and loading plots. Defined by the first two PCs accounting for more than $81\%$ of the variability of the system, the score plot (Figure 2left) showed four distinguished clusters of honey samples. Such clusters correspond to the four types of Moroccan honey investigated, which differed from each other not only in their botanical origin (i.e., jujube, sweet orange, Euphorbia and G. alypum honeys) but also for the production area (i.e., provinces of Khénifra, Béni Mellal, Azilal and Fquih Ben Salah). Due to such a sample arrangement in the study, it is somewhat troublesome to define whether the clustering of honeys occurred according the botanical or geographic origin. However, based on the array of variables investigated, the obtained data and the provided considerations, it may be argued that both factors noticeably contributed to such sample differentiation. Indeed, the loading plot (Figure 2right) shows that, in accordance with the results of the Kruskal–Wallis test, almost all investigated parameters (i.e., physicochemical indicators, minerals, organic and inorganic contaminants), each related to the floral and/or geographical origin, contributed significantly to the differentiation of honey samples, except for pH, DEHT, DEHA, BPAF, Pb and As.
By overlapping the loading and score plots, it becomes clear that variables such as pesticides, PAHs, plasticizers and BPs weighed more on the jujube honey from Khénifra and the sweet orange honey from Béni Mellal which, in fact, were the honey samples most affected by organic contamination.
The PGI Euphorbia honey was in general less contaminated than the above honeys, but it was still characterized by the highest content of pesticides, such as diazinon and metalaxyl-M, and potentially toxic metals, such as Al.
On the other hand, the G. alypum honey was the least contaminated Moroccan product and, moreover, it was marked by the most convenient physicochemical traits (i.e., TSS, acidity and conductivity), as well as precious contents of minerals and essential trace elements.
## 3.6. Dietary Exposure to Contaminants
The quality of Moroccan honeys and the potential health risk to consumers were assessed by calculating the EDI and the non-carcinogenic risk (HQ) of organic and inorganic contaminants (Table 6).
EDIs and HQs were calculated by considering the amount of honey consumed daily in the diet by an adult consumer (70 Kg) from Europe (1.8 g/day) and North Africa (0.3 g/day), according to FAO [86], as well as guideline values recommended by international organizations (Table S5).
As shown in Table 6, the EDIs calculated were well below the intake levels of relative pollutants recommended by international regulatory bodies, thus indicating that Moroccan honey can be safely consumed through the provided dietary amounts. For the non-carcinogenic risk assessment, HQ did not exceed the threshold value of 1 for each contaminant potentially ingested by adults through honey in both European and North-African diets, thus indicating that non-carcinogenic health effects derived from the consumption of these Moroccan honeys were not significant.
## 4. Conclusions
For the first time, a comprehensive characterization of the physicochemical traits and contaminants of four monofloral honeys from different provinces of the Moroccan region Béni Mellal-Khénifra was carried out, thus corroborating the scarce literature on Moroccan honey. According to the physicochemical parameters, all honeys under analysis were in line with those EU standards established for assuring the authenticity of such bee products. However, a critical contamination pattern was outlined, with several toxicants often exceeding the EU regulatory limits available for honey. Specifically, the jujube honey from Khénifra and the sweet orange honey from the Béni Mellal province were the most contaminated products, as opposed to the G. alypum honey from the Fquih Ben Salah province, which was shown to be the least contaminated one. In this arrangement, the PGI Euphorbia honey from the *Azilal area* had an intermediate contamination degree. However, the dietary exposure assessment highlighted that small amounts of all honeys can be safely introduced both in European and North African diets on a daily basis.
Hopefully, findings from this study should not only encourage the enforcement and harmonization of the international regulatory framework on the chemical safety of honey, which is still suffering from evident flaws and shortcomings, but also serve as a “wake-up call” for Moroccan governmental bodies to strengthen monitoring activity in beekeeping and to find suitable solutions for accomplishing more sustainable agricultural practices and modernize the beekeeping sector.
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|
---
title: Auditory Electrophysiological and Perceptual Measures in Student Musicians
with High Sound Exposure
authors:
- Nilesh J. Washnik
- Ishan Sunilkumar Bhatt
- Alexander V. Sergeev
- Prashanth Prabhu
- Chandan Suresh
journal: Diagnostics
year: 2023
pmcid: PMC10000734
doi: 10.3390/diagnostics13050934
license: CC BY 4.0
---
# Auditory Electrophysiological and Perceptual Measures in Student Musicians with High Sound Exposure
## Abstract
This study aimed to determine (a) the influence of noise exposure background (NEB) on the peripheral and central auditory system functioning and (b) the influence of NEB on speech recognition in noise abilities in student musicians. Twenty non-musician students with self-reported low NEB and 18 student musicians with self-reported high NEB completed a battery of tests that consisted of physiological measures, including auditory brainstem responses (ABRs) at three different stimulus rates (11.3 Hz, 51.3 Hz, and 81.3 Hz), and P300, and behavioral measures including conventional and extended high-frequency audiometry, consonant–vowel nucleus–consonant (CNC) word test and AzBio sentence test for assessing speech perception in noise abilities at −9, −6, −3, 0, and +3 dB signal to noise ratios (SNRs). The NEB was negatively associated with performance on the CNC test at all five SNRs. A negative association was found between NEB and performance on the AzBio test at 0 dB SNR. No effect of NEB was found on the amplitude and latency of P300 and the ABR wave I amplitude. More investigations of larger datasets with different NEB and longitudinal measurements are needed to investigate the influence of NEB on word recognition in noise and to understand the specific cognitive processes contributing to the impact of NEB on word recognition in noise.
## 1. Introduction
Musical training involves processing auditory features in challenging situations while performing complex cognitive tasks [1]. Structured musical experiences can systematically shape the auditory–cognitive processes of professional musicians. Recent studies suggest that professional musicians have an “advantage” while processing suprathreshold sounds and can outperform non-musicians in a wide range of auditory perceptual tasks [2,3,4]. It is suggested that musical training can improve the coding precision of the acoustic features (e.g., frequency, intensity, rhythm, and duration) in the classical auditory pathway [5,6]. Moreover, musicians exhibit enhanced cognitive abilities, such as better attention and extended auditory working memory, which are necessary for auditory processing in background noise [7,8]. The musicians’ advantage is also reflected in auditory coding precision at the brainstem and cortical levels, measured through auditory evoked potentials [4,9,10,11].
A large body of literature indicates musicians’ advantage while processing speech in noise (SIN) [4,12,13]. Parbery-Clark et al. [ 4] studied the performance of age-matched young musicians and non-musicians with normal hearing sensitivity and similar non-verbal intelligence quotients. They reported that the performance of young musicians was significantly better than non-musicians in the QuickSIN and hearing in noise test (HINT), which are widely used clinical measures for assessing SIN perception. Musicians revealed better working memory (WM) capacity than non-musicians. The study further showed that WM was a significant predictor for QuickSIN scores, contributing to about one third of variability in the dependent variable. The number of years of musical training accounted for an additional $6\%$ of the variability in the QuickSIN scores. Similar results were reported in older professional musicians when their speech-in-noise perception, WM, and auditory temporal acuity were compared to age-matched non-musicians [14]. Slater and Kraus [15] attributed the musicians’ advantage to heightened rhythm sensitivity, while other researchers attributed it to better pitch processing [12] and temporal resolution [16]. Taken together, recent evidence highlights that structural musical experiences can improve speech and music coding in the auditory pathway and improve auditory cognitive processes [17].
Professional musicians are routinely exposed to loud traumatic sound levels. A large body of literature suggests that hazardous sound levels encountered during musical training and performances can put them at higher risk of noise-induced hearing loss (NIHL) than non-musicians [18,19,20,21,22,23]. Collegiate students and music faculty members are exposed to sound levels that range from 80 dBA to 104.5 dBA during solo and group rehearsals and performances [24,25]. Gopal et al. [ 19] measured sound exposure levels among collegiate musicians during 50 min jazz ensemble activities and reported that the equivalent continuous noise level (Leq) during the ensemble ranged from 95 dBA to 105.8 dBA. Recent research indicates that about $40\%$ of musicians report hearing difficulties due to high sound exposure [26]. Musicians have a $57\%$ higher hazard ratio for tinnitus and an approximately four-fold higher hazard ratio for NIHL when compared to the general population [26]. In conclusion, recent evidence shows that professional musicians are at a higher risk of acquiring NIHL than their non-musical counterparts.
NIHL is typically characterized by an audiometric notch at frequencies 3, 4, and 6 kHz with recovery at 8 kHz [27]. Phillips et al. [ 28] reported that $45\%$ of young music students aged 18–25 showed a notched audiogram with 15 dB or greater notch depth in at least one ear. Recent studies suggest that conventional hearing thresholds are not sensitive enough to detect subtle hearing deficits induced by noise exposure, and a substantial amount of synaptic loss can remain “hidden” from behavioral audiograms [29]. Research on various animal models, including rodents [29], guinea pigs [30,31], and rhesus monkeys [32] have revealed that short-term exposure to medium to high-intensity noise can inflict irreversible damage to the synaptic connections between the inner hair cells (IHSs) of the cochlea and spiral ganglion neurons, even when hearing threshold recuperate and hair cell recovers. These studies examined the auditory functions before and after a complete recovery from temporary threshold shift (TTS) using distortion-product otoacoustic emissions (DPOAEs) and auditory brainstem responses (ABRs). The histopathological results of these studies showed abrupt and permanent loss of up to $50\%$ of afferent nerve terminal connections between IHCs and auditory nerve fibers. The DPOAE amplitudes and ABR thresholds showed complete recovery to pre-noise exposure levels. However, ABR wave I amplitudes at high stimulus levels were significantly more reduced in noise-exposed animals than in the controls and the pre-exposure baseline. A similar loss of synaptic ribbons is observed in aging ears [33,34]. The loss of synaptic connections between inner hair cells and auditory nerve fibers without damaging hair cells or permanent threshold shift is referred to as noise-induced cochlear synaptopathy (NICS), also known as hidden hearing loss (HHL) [35]. Conventional hearing thresholds could substantially underestimate NICS [36]. There is evidence to suggest that the damage caused to auditory nerve fibers in NICS might also have functional consequences while processing suprathreshold stimuli, suggesting that suprathreshold measures might be more sensitive than conventional audiograms for detecting early staged NIHL [37,38,39].
In recent years, various attempts have been made to extend the findings of NICS in animal models to the normal-hearing human population. Recent studies on noise-induced HHL in humans showed a correlation between [1] auditory evoked potential measures and noise exposure history, [2] psychoacoustic measures and noise exposure, and [3] speech-in-noise measures and noise exposure history. Investigations in normal-hearing adults with high noise exposure have sometimes shown a correlation with ABR responses [40,41,42,43,44,45,46,47,48] and sometimes have not [49,50,51]. The studies showing an association between noise exposure and electrophysiological measures utilized different metrics of ABR. For example, Valderrama et al. [ 48] and Stamper and Johnson [46] showed a negative association between wave I amplitude and noise exposure; Grose et al. [ 41] showed reduced wave I/V amplitude ratio in the high noise exposure group; and Liberman et al. [ 43] reported an enhanced summating potential/action potential (SP/AP) ratio in their high-risk group.
Recent studies examined the possible perceptual consequences of cochlear synaptopathy using psychoacoustic tests. Grose et al. [ 41] compared temporal and spectral modulation detection acuity and sensitivity to phase interaural phase differences between high- and low-risk groups of collegiate students. They found no significant differences between the groups on any psychoacoustic tests. Similarly, Fullgrabe et al. [ 52] investigated the influence of noise exposure on the ability to process temporal cues using a few psychoacoustic tests and found no significant difference between the high- and low-noise exposure groups on temporal acuity measures.
In addition to ABR and psychoacoustic measures, researchers have used various speech-in-noise perception measures to study the functional changes associated with suspected primary neural degeneration due to high noise exposure history in individuals with normal hearing [41,43,48,49,53]. Liberman et al. [ 43] found that collegiate students at high risk of NIHL due to frequent exposure to noisy events/activities had poorer speech recognition in noise scores compared to age-matched controls. On the contrary, some studies did not find any significant association between speech-in-noise performance and noise exposure history [41,49,53,54].
Most investigations on cochlear synaptopathy in humans have focused on ABR measurements which provide information on the peripheral auditory system. In the present study, ABRs were recorded at low (11.3/s), medium (51.3/s), and high (81.3/s) rates, and at each rate, ABR wave amplitude and latency were obtained. ABRs obtained at low, medium, and high rates provide a way to look into the temporal dynamics of synaptic activity and neural conduction as the auditory system is strained [55]. High click rates are associated with prolonged ABR absolute and interpeak latencies and decreased ABR amplitudes [55]. The ABR protocol utilized in this study mainly targets the peripheral adaptive processes which capture the inefficiencies in synaptic processing associated with NICS. It is also important to consider that any damage to the peripheral auditory system may cause dysfunction in the central auditory system [56,57]. Hence, the influence of peripheral pathology such as NICS on the central auditory system could be examined by incorporating a test battery that includes tests sensitive enough to identify subtle electrophysiological changes in the peripheral and higher auditory centers and associated functional changes such as speech-in-noise perception.
The higher auditory centers can be analyzed using non-invasive long latency auditory evoked potentials such as P300. P300 is one of the auditory evoked late latency responses generated by the reticulothalamus, frontal cortex, and medial septal area and occurs approximately 250–350 ms after the stimulus onset. P300 is typically elicited by an instructed and infrequently presented target stimulus [58,59]. The P300 response is associated with stimulus assessment and allocation of attentional resources while updating working memory [60]. Thus, the inclusion of the P300 measure in the test battery for evaluating NICS can help in determining the influence of noise exposure on the central auditory system.
The goal of this study was to determine [1] the effects of noise exposure on the peripheral and central auditory nervous system (CANS) functioning using electrophysiological measures among young musicians and non-musicians, and [2] the effects of noise exposure history on speech recognition in noise at the word level and sentence level among young musicians and non-musicians. To address these objectives, we evaluated ABR waveforms (waves I and V obtained at low, medium, and high stimulus repetition rates), P300 measures, and speech-in-noise performance in young musicians and non-musicians.
## 2.1. Participants
The study was approved (IRB number—18-X-247) by Ohio University’s Institutional Review Board (IRB). A total of 38 students aged 18–30 years were enrolled from Ohio University’s School of Music and non-music disciplines. Student musicians were selected because of their routine exposure to loud sounds during ensemble and solo rehearsals, and their noise exposure is expected to be higher than non-musicians. Participants’ inclusion criteria were (a) no history of hearing, tinnitus, balance, or language impairments and (b) no history of previous developmental, cognitive, neurological, and attention-related disorders. All the participants were recruited via emails and flyers posted across the Ohio University campus.
The enrolled participants were asked to complete an online noise exposure questionnaire [61], which would quantify their annual noise exposure background (NEB). The noise exposure questionnaire includes questions on the duration and frequency of noise exposure and provides a quantitative estimate of annual noise exposure. Based on the responses to the online questionnaire, 20 non-musicians (10 males and 10 females) and 18 musicians (11 males and 7 females) of European descent were shortlisted and recruited for the study. Participants of European descent were selected in this study, as previous investigations indicate that people of European ethnicity are more prone to NIHL than people of African ethnicity [62,63]. The recruited student musicians were percussion, brass, and saxophone majors. Participants were contacted via email to schedule appointments for the testing session.
The data were collected in two sessions. The first session includes a brief case history, a battery of hearing tests, a consonant–vowel nucleus–consonant (CNC) test, and an AzBio sentence test. The second session was composed of DPOAE and electrophysiological (ABR and P300) tests. Both data collection sessions occurred within 15 days of each other.
## 2.2. Noise Exposure Questionnaire
Before the scheduling of appointments for the testing sessions, each participant’s noise exposure history was measured using an online noise exposure screening questionnaire. This noise exposure questionnaire was developed by Johnson et al. [ 61]. This questionnaire has been validated for estimating the overall annual acoustic exposure and used in previous investigations to quantify noise exposure in young adult populations [46,64,65].
Participants were required to submit their responses to the online questionnaire (Supplementary Material S1) at least a week before the first testing session. This questionnaire was used to estimate participants’ annual noise exposure background (NEB). The first part of the questionnaire has nine sections targeting different types of noise exposure such as aircraft, firearms, heavy equipment, power tools, music through speakers, and headphones. The second part of the questionnaire consisted of nine questions related to the musical instruments played by the participant. The questionnaire also includes questions on the duration and frequency of noise exposure. The participants’ responses were elicited using a forced-choice method and then rated by category to calculate the noise dose of last 12 months. The noise dose was calculated via these responses for each area of high noise exposure. Time spent in routine or mundane activities performed in quiet environments was calculated by subtracting overall time spent in noisy activities from 8760 h (365 days/year × 24 h/day). Questionnaire responses were further used to calculate the activity-related noise dose and overall annual noise dose, reported as LAeq8760h. Here, “L” represents the sound pressure level measured in decibels (dB), “A” indicates application of A-weighted frequency response; “eq” represents the sound pressure level (in dB) equivalent to the total acoustic energy over a given amount of time; and “8760 h” represents the overall duration of the noise exposure in hours over one year (365 days/year × 24 h/day). LAeq8760h was extracted from the questionnaire responses utilizing the 3-dB exchange rate for calculation of the time/intensity level relation. Details of the questionnaire are reported in Stamper and Johnson [46] [2015] and Johnson et al. [ 61].
Non-musician participants who reported playing any instrument including voice on a daily basis were excluded from the non-musician group. For the purpose of this study, non-musician participants with LAeq8760h values of 76 or greater were not included in the study. All the student musician participants had LAeq8760h values higher than 76. Potential participants were contacted via email to schedule appointments for the testing sessions. Participants were also informed through email to avoid loud sound exposure for at least 12 h before the testing appointment time. Before administering the tests, the participants were asked to confirm that they had abstained from loud events or activities as requested. Participants who reported exposure to loud sounds in the last 12 h were rescheduled.
## 2.3. First Session
The session I started with obtaining informed consent, followed by a brief case history, which comprised questions related to health, hearing, head trauma, and balance. After completing the brief case history, an otoscopy was performed on both ears of each participant, followed by a middle ear examination (tympanometry), pure tone audiometry and extended high-frequency audiometry. Tympanograms of both ears were obtained over a pressure range of +400 to −400 daPa using a 226 Hz probe tone presented through a GSI 39 (GSI, Eden Prairie, MN, USA) middle ear analyzer. All the participants had a normal type “A” tympanogram. Hearing sensitivity was measured in an audiometric testing booth meeting ANSI standards (ANSI S3.1e1999). Air conduction thresholds were obtained for both ears at 250, 500, 1000, 2000, 3000, 4000, 6000, and 8000 Hz using an audiometer (AVANT MedRx, Largo, FL, USA) with ER-3A insert earphones (Etymotic Research. Inc., Elk Grove Village, IL, USA). The pure tone average of the hearing thresholds at 3000, 4000, and 6000 Hz (PTA346) was also calculated because hearing sensitivity at these frequencies is typically affected in individuals with high noise exposure history. Normal hearing of participants was defined as audiometric thresholds of ≤15 dBHL for frequencies between 0.5 and 8 kHz, and this was one of the inclusion criteria for the study. Extended high-frequency audiometry was carried out using circumaural earphones (Sennheiser, HDA 200) at 10, 12.5, and 16 kHz. At these extended high frequencies, the hearing thresholds were averaged to obtain the extended high-frequency pure-tone average (EHFPTA).
After high frequency audiometry, word and sentence recognition in noise was tested binaurally using the CNC and AzBio tests, respectively. The CNC test assesses open-set monosyllabic word recognition in quiet and noise. A customized MATLAB program for controlling the stimulus presentation was utilized to administer the CNC test, which consists of 10 lists. Each list includes 50 monosyllabic words. Each participant was seated at the center of the double-walled audiometric booth, meeting ANSI standards (ANSI S3.1e1999). Participants were asked to listen to the word through circumaural headphones (Sennheiser HD 280; Sennheiser, Wedemark, Hanover, Germany). Participants were instructed to type what they heard on the LCD monitor in front of them. If they were unsure of the word, they took their best guess or typed ellipses to indicate that they did not know. A practice test was performed for each participant before the actual test. The practice test consisted of three separate words that the participant could see on the LCD monitor after entering what they heard. The actual test included 250 words from five randomized lists. The CNC word lists were prerecorded by one male talker. These words were presented at 65 dB SPL in the presence of two-talker babble at five signal-to-noise ratios (SNRs) (−9, −6, −3, 0, and +3 dB); one list was administered per condition. Lists number 2, 3, 4, 7, and 10 were used. Responses were scored based on the entire word (% correct; CNC-Word) and the number of phonemes (% correct; CNC-Phoneme) repeated correctly. The AzBio test assesses sentence recognition in quiet and in noise. The AzBio test was also administered in the same acoustic environment as the CNC test, and a similar customized MATLAB program was used for conducting the AzBio test. The AzBio test consists of 33 different sentence lists. Each list has 20 different sentences. Participants were asked to listen to the word through circumaural headphones (Sennheiser HD280). A practice test was administered before the actual test. The practice test had three sentences wherein the participant could look at the actual sentence after typing in what they heard. The actual test consisted of 100 sentences from 5 randomized lists spoken by two male and two female talkers in a randomized manner. These sentences were presented at 65 dB SPL in the presence of two-talker babble at five SNRs (−9, −6, −3, 0, and +3 dB); one list was administered per condition. Lists number 2, 3, 4, 5, and 10 were used. Responses were scored based on the percentage of words repeated correctly for sentences at different SNRs. After the AzBio test, the session I was terminated. An appointment for the second session was scheduled at the lab. Both testing sessions occurred within one or two weeks of each other. Participants were also instructed to avoid loud sound exposure at least 12 h before their second testing session.
## 2.4. Second Session
The second session started with the DPOAE test for evaluating the outer hair cell functioning of the cochlea (inner ear). DPOAEs of all participants were measured using a commercial system (Smart DPOAE—Intelligent Hearing Systems, Miami, FL, USA) connected to an ER- 10 D probe (Etymotic Research. Inc., Elk Grove Village, IL, USA) across the range of frequencies from 500 Hz to 6 kHz. DPOAEs at 2F1-F2 were obtained for F2 values ranging from 500 to 6000 Hz in two data points per octave. A stimulus-level combination of $\frac{65}{55}$, sound pressure level (SPL), and stimulus frequency ratio of 1.22 were used. The DPOAE test was followed by the ABR test. The ABR test was conducted using a commercial system (Duet, Intelligent Hearing Systems, Miami, FL, USA) in the same environment as in session I. ABRs were obtained using a one-channel electrode montage with a mastoid-placed electrode from the left ear. The stimulus and acquisition parameters set to record ABRs are shown in Table 1. The left ear was selected for ABR because the noise-induced damage is more prevalent in the left ear than in the right ear [28,66,67].The non-inverting and ground electrodes were placed on the participant’s forehead (Fz) and low forehead (Fpz), while the inverting electrode was placed on the mastoid of the left ear. These areas were prepped using alcohol wipes and a Nuprep skin prep gel to effectively reduce the inter-electrode impedance values. Impedance values at each electrode site were monitored to remain below 3 kOhms with an inter-impedance value of less than 2 kOhms. These impedance values were monitored throughout the testing procedure. ABR stimuli were presented with alternating polarity at rates 11.3, 51.3, and 81.3/using insert earphones (ER-3A, St. Paul, MN, USA). ABR responses were obtained using 100 ms click stimuli presented at 80 dB nHL (85.7 ± 0.3 dB SPL, calibration in an IEC-711ear simulator). At each stimulus rate, two replications of 2000 sweeps were collected for analysis. Recording parameters included a gain of 100,000 and band-pass filtering from 100 Hz to 3000 Hz. The artifact rejection threshold was set at 31 mV. ABRs were collected with a pre-stimulus window of 12.5 ms, a post-stimulus window of 12.5 ms, and a sampling frequency of 40,000 Hz.
The ABR test was followed by the P300 test. P300 testing was done with the same system (Duet, Intelligent Hearing Systems, Miami, FL, USA). A two-channel montage was used, with channel A assigned for P300 measures and channel B for measuring and monitoring eye movements and eye blink artifacts. The stimulus and acquisition parameters set to record P300 are shown in Table 1. The prerecorded speech tokens stimuli used were the consonant–vowel /ba/as frequent stimuli ($80\%$) and/ta/as infrequent stimuli ($20\%$). The stimuli sequence was presented monoaurally to the left ear of each participant at 80 dB SPL. In total, 500 stimuli were used (100 rare and 400 frequent) to obtain the P300 responses.
## 2.5. Electrophysiological Waveform Analysis
After completing the ABR test, the two replications at each rate were averaged, and the averaged waveforms were utilized for evaluating the ABR I and V waveforms. The amplitude of ABR waves I and waves V was calculated from the voltage difference between the identified positive peak and the following trough. Similarly, the P300 amplitude was calculated from the voltage difference between the identified positive peak and the following trough. Two audiologists identified the waveforms separately. Any disagreement pertaining to the peak measurements between two audiologists was resolved by them reviewing the data together.
## 2.6. Statistical Analysis
Both descriptive and inferential statistical analyses for this study were performed using IBM SPSS (version 26.0; IBM Corp.: Armonk, NY, USA). Multivariable linear regression modeling was used to estimate the relationship between NEB and peripheral auditory electrophysiological measures (wave I and V amplitude). To estimate the influence of NEB on ABR wave I and V amplitudes while controlling the effect of gender, the ABR wave I and V amplitudes were included as continuous dependent variables, and gender and NEB as independent variables. Similar analyses were performed to estimate the relationship between NEB and central auditory electrophysiological measures (P300 amplitude and latency). The relationship between NEB and speech-in-noise measures at the word level (CNC) and sentence level (AzBio test) at five SNRs was also estimated using linear regression modeling.
Mixed effect linear regression models were used to study the effects of non-musician/musician groups, gender, and SNR and interaction between these factors on CNC and AzBio measures. The subjects were considered a random variable in these analyses.
## 3.1. Descriptive Statistics
A total of 38 participants (17 females, 21 males) from 18–30 years were included in this study (mean age 21.9 years). These 38 participants were divided into two groups, non-musicians (10 females, 10 males) and musicians (7 females, 11 males). The group means audiograms of musicians and non-musicians are shown in Figure 1 (Panel A). As specified by the inclusion criteria, both musician and non-musician groups had thresholds within clinically normal limits (≤15 dB HL) for the octave frequencies 500 to 8000 Hz. There was increased variability at the extended high frequencies, particularly at 16 kHz. However, there were no significant group differences at any frequencies from 500 to 16 kHz (Supplementary Material S2). The average hearing thresholds at 3, 4, 6 kHz (PTA346) were calculated because the effect of noise exposure is higher on these frequencies [66]. Likewise, the average of EHF hearing thresholds (PTA 101216) was also calculated. PTA346 and PTA101216 were not statistically different between non-musicians and musician groups (See Supplementary Material S2). Outer hair cell functioning was evaluated by recording DPOAEs at frequencies 0.5 to 6 kHz. DPOAE amplitudes (Figure 1, panel B) were not statistically significant between the two groups (see Supplementary Material S2). An independent sample t-test revealed that the mean NEB between non-musicians and musicians groups was significantly different. The mean NEB was higher for musicians compared to the mean for non-musicians (mean (musician)-mean (non-musician) = 8.65 LAeq8760h, $p \leq 0.001$). The mean NEB of non-musicians and musician groups was 70.46 and 79.11 LAeq8760h, respectively. Figure 2 shows NEB data as a function of experimental and control groups. The mean differences in NEB between non-musician and musician groups were attributed to our sampling scheme. The results of the linear regression revealed no statistically significant linear association between NEB and PTA 101216 (r[36] = −0.068, $$p \leq 0.680$$). Similar analysis showed no linear association between NEB and PTA 346 kHz (r[36] = −0.045, $$p \leq 0.780$$).
## 3.2. Electrophysiological Measures
Table 2 shows means and standard deviations for latencies and amplitude of ABR waves I and V obtained at rates of 11.3, 51.3, and 81.3 clicks per second according to gender. The means and standard deviations for latencies and amplitudes of P300 of 34 participants [18 non-musicians (9 females, 9 males); 16 musicians (6 females, 10 males)] are shown in Table 3. The P300 data of four participants were not included due to poor wave morphology and artifacts. The grand average ABR and P300 waveforms of musicians and non-musicians are shown in Figure 3 and Figure 4, respectively. The wave I amplitude is highest at rate 11.3/s, and decreases at higher stimulus rates. The results of the regression analyses for examining the relationship between NEB and ABR measures are shown in Table 4. The relationship between the NEB and the amplitude of wave I and between the NEB and wave V at three stimulus rates was investigated while controlling the effects of gender. The NEB revealed no significant association with wave I and wave V amplitudes at all three stimulus rates (See Figure S1 in Supplementary Material S3). Similar regression analyses were also performed to investigate the relationship between the group and ABR measures. The group revealed no significant association with wave I and wave V’s amplitude at all three stimulus rates.
Regression analyses were also performed to study the relationship between NEB and P300 amplitude and latency while controlling the effect of gender. There was no significant association between NEB and P300 amplitude (R2 = −0.043, F[2,31] = 0.315, $$p \leq 0.732$$), and latency measure (R2 = 0.034, F[2,31] =1.579, $$p \leq 0.222$$), (See Figure S2 in Supplementary Material S3. The relationship between groups and P300 amplitude and latency were examined using regression analyses while controlling the effect of gender. There was no significant association between groups and P300 amplitude (R2 = −0.052, F[2,31] = 0.184, $$p \leq 0.833$$) and latency measure (R2 = 0.015, F[2,31] =1.250, $$p \leq 0.300$$).
## 3.3. Word Recognition in Noise
Word recognition in noise was examined in all the participants using the CNC test at +3, 0, −3, −6, and −9 dB SNRs. The relationship between NEB and the performance on CNC test at different SNRs was investigated while controlling the confounding effect of gender. NEB showed a significant association with performance on CNC measures at all SNRs. Table 5 shows the results of regression analyses of NEB and CNC measures. The adjusted R2 values for the models ranged from 0.079 to 0.245, suggesting that a small portion of the variance in the dependent variables was exclusively attributed to NEB. Figure 5 (left panel) reveals a significant negative relationship between NEB and performance in the CNC test at all five SNRs. In addition, the effect of groups, gender, SNRs and interaction between these variables on CNC measures were evaluated using mixed model linear regression. The results of this analysis are shown in Supplementary Material S4. The main effects of groups (F[1,34] = 8.630; $$p \leq 0.006$$) and SNRs (F[4,140] = 526.737; $$p \leq 0.000$$) were statistically significant, while the main effect of gender (F[1,34] = 1.623; $$p \leq 0.211$$) was not statistically significant. Furthermore, there was not strong evidence of interaction between groups and gender (F[1,34] = 0.772; $$p \leq 0.386$$), gender and SNRs (F[4,140] = 0.210; $$p \leq 0.932$$), and between groups and SNRs (F[4,140] = 0.684; $$p \leq 0.604$$). This finding shows that the overall performance of non-musicians on the CNC test was significantly better than that of musicians.
## 3.4. Sentence Recognition in Noise
Table 6 shows the results of regression analyses for examining the relationship between NEB and performance on the AzBio test at +3, 0, −3, −6, and −9 dB SNRs. The relationship between NEB and the performance on the AzBio test at different SNRs was investigated while controlling the confounding effect of gender. NEB showed a significant association with performance on the AzBio test at 0 dB SNR. At all other four SNR conditions, there was no significant association between NEB and performances on the AzBio test. Figure 5 (right panel) displays the scatter plots between the NEB and performance on the AzBio test of non-musicians and musicians at +3, 0, −3, −6, and −9 dB SNRs.
The results of a mixed model linear regression analysis examining the effects of group, gender, SNRs and interaction between these variables on AzBio measures are shown in Supplementary Material S4. The main effect of SNRs was significant (F[4,140] = 1002.669; $$p \leq 0.000$$). The main effect of groups (F[1,34] = 0.302; $$p \leq 0.586$$) and gender (F[1,34] = 0.082; $$p \leq 0.776$$) were not statistically significant. Similarly, the interaction between groups and gender (F[1,34] = 0.310; $$p \leq 0.581$$), gender and SNRs (F[4,140] = 0.763; $$p \leq 0.551$$) and between groups and SNRs (F[4,140] = 1.268; $$p \leq 0.285$$) were also not statistically significant. The results of this analysis indicates that the performance of non-musicians in the AzBio test was not significantly different from that of musicians.
## 4. Discussion
The present study aimed to investigate the effect of noise exposure history on the peripheral and central auditory system, and on performance on speech-in-noise tests. It was hypothesized that the influence of high noise exposure on peripheral and central auditory systems would be manifested in the form of compromised electrophysiological and speech-in-noise measures in normal-hearing collegiate students with high NEB. We obtained supporting evidence for this hypothesis, suggesting that musicians with high NEB exhibit poorer speech-in-noise performance than their non-musicians counterparts.
## 4.1. The Relationship between NEB and Performances on Speech-in-Noise Tasks
We recruited musicians with high NEB and non-musicians with low NEB. We obtained a significant main effect for groups in the CNC test and a negative relationship between NEB and CNC scores at –9, −6, −3, 0, and +3 dB SNRs. As shown in Figure 5, the relationship between NEB and performance on the CNC test is consistent at all five SNRs, indicating that high NEB might compromise suprathreshold speech perception abilities among young musicians. Similarly, the result of mixed linear regression indicates that musicians perform poorer compared to non-musicians. The difference between the groups does not reach the conventional $p \leq 0.05$ level of statistical significance at any SNR, which could possibly be due to the smaller sample size of our study. Further research with a larger sample size is warranted to clarify more definitively the implications of these findings.
We obtained no association between NEB and sentence recognition in noise performance in the AzBio test at –9, −6, −3, and +3 dB SNRs. Similarly, the main effect for groups in the mixed model linear regression analysis was also not statistically significant. We found a significant negative relationship between NEB and AzBio test performance at 0 dB SNR, as shown in Figure 5. The discrepancy between the findings of the CNC and AzBio tests could be attributed to the stimuli used in these two tests. Cognitive and linguistic factors might influence the performance on AzBio tests, but they might exhibit a lower influence on CNC scores. Although a sentence may be a realistic stimulus with better face validity, the contextual cues contribute heavily to intelligibility and make basic auditory functions difficult to determine [68]. A few studies have reported an approximate difference of 6–7 dB SNR in the speech recognition performance of words and sentences among adults, with sentences always requiring lower SNR than words [69,70].
The observed negative trend between NEB and word recognition in noise performance on the CNC test might be influenced by the effect of noise exposure on central auditory structures. The results of the CNC tests are consistent with the findings of previous studies on normal-hearing adults with high noise exposure histories [43,44,51]. Some studies on adults with high noise exposure have found no association between speech-in-noise performance and noise exposure history [42,54,71,72]. Further research is required to quantify the influence of cochlear synaptopathy on suprathreshold speech perceptions.
## 4.2. The Relationship between NEB and Electrophysiological Measures
The findings of the present study showed no relationship between NEB and ABR wave I amplitude obtained at low (11.3/s), medium (51.3/s), and high (81.3/s) stimulus repetition rates. We could not find any difference between ABR wave I between musicians and non-musicians. As is apparent in Figure 3, there is no significant association between NEB and ABR wave I amplitude and between NEB and ABR wave V amplitude. Our past study indicated a modest association between NEB and ABR wave I amplitude in young musicians and non-musicians [65]. The present study could not replicate these findings, possibly due to our smaller sample size and the high inter-subject variability in audiological measures. We observed that the standard error (SE) of a mean for ABR wave I amplitude obtained at rate 11.3 (SE11.3wave $I = 0.019$ µV) was higher than the SE of a mean for ABR wave I obtained at rate 51.3 (SE51.3wave $I = 0.013$ µV) and 81.3 (SE81.3wave $I = 0.012$ µV). A similar trend was observed for ABR wave V amplitude (SE11.3wave $I = 0.021$ µV, SE51.3wave $I = 0.19$ µV, SE81.3wave $I = 0.020$ µV). These findings correspond with the results of other studies investigating the association between noise exposure and electrophysiological measures [49,50,51,54,73]. This result is in accordance with some previous studies on different study populations [49,53,54,74]. The first possible explanation for this insignificant finding could be the higher variability of auditory evoked potentials, particularly ABR wave I in humans. In a study by Prendergast et al. [ 50], the coefficient of variation for ABR waves I amplitude was $25\%$ in the low noise exposure group, and this may indicate a substantial degree of variability compared to the effect being measured. Washnik et al. [ 65] also reported higher variability in ABR wave I amplitude obtained at 90, 75, and 60 dB nHL. The differences in adult head size and geometry might also contribute to the inter-subject variability and reduced statistical power to identify differences in auditory electrophysiological measures in the human population [75,76]. In addition, there is another possibility that noise exposure induces cochlear synaptopathy only in selected portions of the cochlea [29,30,38], and therefore, the effect of cochlear synaptopathy is enshrouded when ABRs are evoked by transient click stimuli, which present energy in a broad frequency range.
Furthermore, no significant influence of NEB on P300 amplitude and latency was found in the present study. The P300 measures are reflective of attentional capacity. Many studies have reported that musical training enhances neural coding to discriminate subtle differences, leading to enhanced discrimination abilities of the brain; this is manifested in the form of shorter P300 latencies and higher P300 amplitude among musicians when compared to non-musicians [77,78]. Our P300 amplitude and latency findings are consistent with other studies on the human population with high noise exposure history [79,80]. Thakur and Banerjee [79] studied the influence of high noise exposure on the central auditory pathway using P300 among ground crew members of an airport. They found no significant difference in P300 amplitude and latency between the experimental and control groups. One reason for the lack of significant association between NEB and P300 could be the sample size. Future research is needed to investigate the influence of noise exposure on auditory–cognitive responses such as P300.
## 4.3. Speech-in-Noise and Electrophysiological Measures in Musicians
Several studies have shown musicians’ advantage in speech-in-noise (SIN) perception [4,14,81,82]. In contrast, others reported no significant difference in SIN performances between musicians and non-musicians [13,83,84,85]. A possible factor influencing these mixed findings is inter-subject variability in noise exposure among musicians. Musicians are regularly exposed to high sound levels during large and small ensemble rehearsals, individual practice sessions, music performances, and listening to music pieces through speakers or headphones. Skoe et al. [ 86] found that noise exposure among musicians suppresses the musicians’ SIN perception advantage. The result of our study indicates that noise exposure is negatively associated with SIN performance at the word level among musicians. Though our speech-in-noise findings are in line with the investigation by Liberman et al. [ 43] and Hope et al. [ 87], other researchers found no significant relationship between noise exposure history and SIN performance [48,49,53,72]. The null results of the SIN measures in the above studies could be related to methodological factors, such as the complexity of stimuli and their difficulty levels. Valderrama et al. [ 48] and Yeend et al. [ 72] used sentences in the listening in spatialized noise—sentences high cue condition (LiSN-S) test, which may be influenced by cognitive factors. Other studies on humans with history of high noise exposure utilized SIN measures such as word-in noise (WIN) tests [49,53]; however, these studies administered WIN tests at SNRs ranging from 0 to 30 dB, which was comparatively higher than the SNRs used for CNC test in our study. Le Prell [88] suggested that studies incorporating the most difficult SIN tasks may show greater sensitivity to the detection of the relationship between noise exposure and SIN performance. In a recent systematic review, DiNino et al. [ 89] mentioned that speech-in-noise tests that use low SNRs and maximize minute sensory details by using stimuli that offer minimal lexical, syntactic, or semantic cues are more likely to show an interest in the relationship between human studies and HHL.
Speech-in-noise measures, particularly the CNC test in our study, have shown that NEB is negatively related to speech-in-noise performance, and that the overall performance of musicians as a group is significantly poorer than non-musicians. On the other hand, the outcomes of the electrophysiological measures, such as amplitude and latency of P300 and ABR wave I and V, showed no association with NEB. The insignificant findings in the ABR measures of this study could also be associated with the possibility that noise exposure induces synaptopathy only in certain regions of the cochlea [29,30,38]; hence, the effect of synaptopathy becomes obscured when ABR are evoked by broad-range frequency stimuli such as clicks.
The results of the current study show that despite similar peripheral processing (DPOAE responses and ABR wave I amplitude), speech-in-noise performance with CNC words was reduced in individuals with high NEB. Recent investigations have revealed the negative influence of noise exposure on human cognition [90,91,92,93]. Patel et al. [ 2022] suggested that high-level cognitive tasks and their corresponding brain regions are not equally susceptible to high noise exposure [93]. Thus, it can be hypothesized that the central processing (except the central processes involved in P300 generation) involved in understanding speech in highly demanding situations, such as CNC words in noise, might be more prone to the negative effects of noise exposure, and this might be manifested in the form of reduced performance in speech-in-noise tasks at the word level. Unfortunately, the current study did not measure specific cognitive domains such as working memory and other executive functions contributing to speech-in-noise performance. The specific central processes involved in the reduced speech-in-noise performance of individuals with high NEB need to be further explored in future studies.
## 4.4. Study Limitations and Future Directions
There are a few methodological factors that may have influenced the outcomes of this study, and these should be considered when interpreting the results. Firstly, noise exposure was measured using a retrospective noise exposure questionnaire developed by Johnson et al. [ 61], and this questionnaire estimates the amount of noise exposure in one year. Although many studies have used this questionnaire [49,51,53,73], it does not include a comprehensive list of noise exposure areas and does not account for noise exposure beyond an individual’s last 12 months of noise exposure. On the other hand, other studies measured noise exposure across a lifetime [40,48,50], and performed noise dosimetry measurements [45,86] to obtain real and more accurate noise exposure data. As the effect of noise exposure is cumulative, an estimate of noise exposure will be more accurate if noise dosimetry measurements are performed along with the administration of a lifetime noise exposure questionnaire. Future studies should include noise dosimetry measurements and a lifetime noise exposure questionnaire to obtain more reliable and accurate noise exposure data which can be compared with auditory electrophysiological and behavioral measures.
In addition, there is no widely accepted standard protocol for evaluating cochlear synaptopathy in the human population, and it can be argued that the evoked potential metrics other than the one used by the present study might be more sensitive in detecting cochlear synaptopathy in humans [94]. The inclusion of non-musicians and musicians with different ranges of noise exposure is another critical factor. All the student musician participants had LAeq8760h values higher than 76, while all the non-musicians had LAeq8760h values below 76. Such division in the noise exposure range between musicians and non-musicians may influence the findings of this study.
Cognitive factors such as working memory and non-verbal IQ that have been linked to speech recognition in noise abilities were not measured in the current study. Previous investigations have revealed that greater WM capacity is associated with enhanced speech-in-noise perception abilities [4,84,95] Recent findings indicate that the cognitive abilities of the individual may be the crucial factor in their speech recognition in noise ability, rather than musicianship [83,84]. Hence, the reduced performance on the CNC test among student musicians cannot be solely attributed to noise exposure, because the effect of non-verbal IQ and WM cannot be ruled out.
With regard to the set of tests utilized in the current study, there are a few other sensitive auditory tests, such as the threshold equalizing nose (TEN) test and contralateral OAE suppression, whose inclusion into the test battery might have provided better insight into the association between noise exposure and speech-in-noise deficits. Lastly, the participants in the current study were of European ethnicity. Thus, the results of this study should not be generalized beyond individuals of European ethnicity. Future studies can address the influence of noise exposure on auditory electrophysiological and perceptual measures among other ethnic groups.
## 5. Conclusions
The findings of several studies investigating noise-induced HHL in humans have been mixed [40,41,42,43,49,50,51,52,53,54,72]. The present study obtained a significant association between noise exposure and word-level speech-in-noise measures. However, we did not find any association between NEB and any electrophysiological measures used in the present study. These findings indicate that noise exposure may affect the central auditory structures. We found a negative relationship between NEB and speech recognition in noise at the word level. More interestingly, we found that musicians perform more poorly than non-musicians on word-level tasks, but not on sentence-level tasks. Collectively, these results suggest that musicians with high NEB could lose their perceptual advantage for processing words in background noise over non-musicians. The null findings in the AzBio test suggest that the deficit in speech processing at the word level was compensated at the sentence level. These results might indicate that musicians with high NEB exhibit a cognitive advantage which influences speech processing at the sentence level [96]. Future research is needed to test the influence of high noise exposure on auditory–cognitive measures in musicians and non-musicians.
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|
---
title: An Innovative Mei-Gin Formula Exerts Anti-Adipogenic and Anti-Obesity Effects
in 3T3-L1 Adipocyte and High-Fat Diet-Induced Obese Rats
authors:
- Hsin-Lin Cheng
- Wei-Tang Chang
- Jiun-Ling Lin
- Ming-Ching Cheng
- Shih-Chien Huang
- Shiuan-Chih Chen
- Yue-Ching Wong
- Chin-Lin Hsu
journal: Foods
year: 2023
pmcid: PMC10000739
doi: 10.3390/foods12050945
license: CC BY 4.0
---
# An Innovative Mei-Gin Formula Exerts Anti-Adipogenic and Anti-Obesity Effects in 3T3-L1 Adipocyte and High-Fat Diet-Induced Obese Rats
## Abstract
Background: To investigate the potential anti-obesity properties of an innovative functional formula (called the Mei-Gin formula: MGF) consisting of bainiku-ekisu, Prunus mume ($70\%$ ethanol extract), black garlic (water extract), and Mesona procumbens Hemsl. ( $40\%$ ethanol extract) for reducing lipid accumulation in 3T3-L1 adipocytes in vitro and obese rats in vivo. Material and Methods: The prevention and regression of high-fat diet (HFD)-induced obesity by the intervention of Japan Mei-Gin, MGF-3 and -7, and positive health supplement powder were investigated in male Wistar rats. The anti-obesity effects of MGF-3 and -7 in rats with HFD-induced obesity were examined by analyzing the role of visceral and subcutaneous adipose tissue in the development of obesity. Results: The results indicated that MGF-1-7 significantly suppressed lipid accumulation and cell differentiation through the down-regulation of GPDH activity, as a key regulator in the synthesis of triglycerides. Additionally, MGF-3 and MGF-7 exhibited a greater inhibitory effect on adipogenesis in 3T3-L1 adipocytes. The high-fat diet increased body weight, liver weight, and total body fat (visceral and subcutaneous fat) in obese rats, while these alterations were effectively improved by the administration of MGF-3 and -7, especially MGF-7. Conclusion: This study highlights the role of the Mei-Gin formula, particularly MGF-7, in anti-obesity action, which has the potential to be used as a therapeutic agent for the prevention or treatment of obesity.
## 1. Introduction
Obesity is characterized by a defective body fat storage capacity caused by a chronic imbalance of energy due to excess dietary consumption and insufficient physical activity [1]. The prevalence of obesity is still rising globally and has become a pervasive public health threat. Obesity precedes type 2 diabetes mellitus, dyslipidemia, fatty liver injury, hypertension, and cancer, and is greatly associated with a higher premature disability and mortality rate [2]. Excess calorie intake is accompanied by less energy expenditure, leading to adipogenesis in both the liver and the adipose tissue, and subsequently promoting the development of metabolic disorders [3,4].
White adipose tissue (WAT) represents a key reservoir for energy storage such as triglycerides (TG) in adipose tissue and expands via increasing individual size (hypertrophy) or number (hyperplasia) of differentiated mature adipocytes to allow adequate tissue expansion in response to high-fat dietary consumption or overnutrition [5,6,7]. The adipocyte differentiation process begins from adipocyte progenitors’ mitogenic expansion in the determination phase, and later gains the characteristics of mature adipocytes in the terminal differentiation phase. Anatomically, subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) are considered the two main types of WAT; the enlargement of the SAT and VAT enlargement are considered to mediate obesity development and its related metabolic complications [8,9,10,11]. In particular, rodents were fed a high-fat diet with the consequence of a significant imbalance in energy storage and expenditure capacity between WAT and brown adipose tissue (BAT) depots [12,13]. Evidence from animal and human studies indicates that the inability of SAT and VAT to expand when faced with dietary phytochemicals is pharmacologically beneficial to the metabolic health status of obesity [14,15,16]. Lipid metabolism is often considered a complex mechanism with the involvement of regulatory elements such as glycerol-3-phosphate dehydrogenase (GPDH), which mediate the rate-determining reaction in the synthesis of triglycerides and serve as a marker of adipogenesis [17]. Numerous studies indicated that inhibition of GPDH expression or activity can suppress lipid accumulation in 3T3-L1 adipocytes and may act as an anti-obesity target in adipocytes [17,18,19,20].
Bainiku-ekisu is the fruit juice concentrate of Prunus mume and has been proposed to have pharmacological properties that might benefit the treatment of dyspepsia and diarrhea. An in vitro study has shown that bainiku-ekisu exhibited immediate bacteriostatic activity on serious strains of H. pylori at a concentration of $0.3\%$ in 15 min [21]. Furthermore, an in vivo pilot study reported that H. pylori-positive patients received $1\%$ bainiku-ekisu solutions for 2 weeks, resulting in a slightly decreased in the urea breath test (UBT) values [22]. Yang et al. demonstrated that bainiku-ekisu has a higher total phenolic content (1.9-fold) and flavonoid content (1.4-fold) than fresh Japanese apricot juice, which may have direct effects on improving metabolic disorders, and, therefore, improve metabolic diseases such as type 2 diabetes and hyperlipidemia [23]. Black garlic, a fermented product of fresh garlic, is generated by the application of controlled high temperature under high humidity over 10 days [24,25]. A previous study suggested that fermented black garlic exhibited bioprotective properties against vascular disease through the downregulation of the MAPK pathway in a model of zebrafish vascular lesions [26]. Extracts from black garlic decreased body weight ($7.69\%$) and lumbar subcutaneous fat mass ($16.88\%$) in a high-fat diet-fed rodents model [27]. Treatment with black garlic extract was founded to increase cellular oxygen uptake and alter UCP-1-based thermogenesis in human adipose-derived stem cells [28]. Mesona procumbens Hemsl., also called Hsian-tsao, is consumed as folk medicine and is considered to have therapeutic potential for the treatment of liver disease, heat shock, and metabolic disorders. The anti-adipogenic activity of Mesona procumbens in 3T3-L1 cells was also reported to decrease lipid droplet accumulation by transcriptionally inhibiting peroxisome proliferator-activated receptor γ (PPARγ) and transcription factors CCAAT/enhancer-binding protein (C/EBP) β expressions [29]. We have previously shown that in vivo and in vitro, the Mesona procumbens Hemsl. extract could decrease xanthine oxidase activity and prevent the overproduction of serum uric acid, which is suggested to be a novel hypouricemic agent [30]. In addition, extracts rich in phenolic compounds from Hsian-tsao have been demonstrated to exhibit antioxidant properties and served as free radical scavengers [31].
Based on the accumulated experimental evidence, we have developed an innovative bainiku-ekisu-based functional combination of ethanol extracts from Prunus mume, Mesona procumbens Hemsl., and water extract from black garlic, called the Mei-*Gin formula* (MGF). However, due to the bioavailability of individual substances in the complex mixture and differences in the pharmacokinetics of the active substance, a deeper understanding of the beneficial effect of the Mei-*Gin formula* in the prevention or treatment of metabolic parameters in obesity is now an urgent need. In the present study, our aim is to investigate the efficacy of the Mei-*Gin formula* in pre-adipose cell differentiation and a high-fat diet-fed rat model.
## 2.1. Composition Analysis of the Mei-Gin Formula (MGF)
MGF-1-7 capsules containing mixed extract of functional powder, consisting of bainiku-ekisu (decompress concentration process), Prunus mume ($70\%$ ethanol extract), black garlic (water extract), and Hsian-tsao ($40\%$ ethanol extract) were provided by Dr. Ming-Ching Cheng, Department of Healthy Food, Chung Chou University of Science and Technology. In brief, analysis of the phenolic content of the Mei-Gin formulas was carried out using a high-performance liquid chromatography system (L-2130 pump and L-2400 UV detector, Hitachi, Tokyo, Japan), and permeation data were recorded on a computer with the LC solution 1.25 sp1 software. Elution was carried out with two buffers: buffer A ($0.1\%$ formic acid in water) and buffer B ($0.1\%$ formic acid in acetonitrile); the flow rate was set at 1 mL/min. The absorption spectra of the samples were detected at a UV wavelength of 280 nm, and all phenolic acid identification was carried out by comparing their retention time with known reference standards [32].
## 2.2. Cell Culture and Treatment
3T3-L1 preadipocytes (BCRC No. 60159) obtained from the Bioresource Collection and Research Center (BCRC, Food Industry Research and Development Institute, Hsinchu, Taiwan) were cultured in high glucose Dulbecco’s modified *Eagle medium* (high glucose DMEM) supplemented with $9\%$ newborn calf serum (NBCS), NaHCO3 (1.5 g/L) and $1\%$ penicillin-streptomycin (PSN) in an incubator with an atmosphere of $5\%$ CO2. For the differentiation of adipocytes, 3T3-L1 cells were cultured in the differentiation medium containing 0.5 mM 3-isobutyl-1-methylxanthine (IBMX), 1 μM dexamethasone (DEX), 1 μM insulin, 1.5 g/L NaHCO3, and $1\%$ PSN. After 4 days of incubation, the medium was changed to high glucose DMEM containing $8\%$ fetal bovine serum (FBS), 1 μM insulin, 1.5 g/L NaHCO3, and $1\%$ PSN for 2 days. The mature 3T3-L1 adipocytes were treated with appropriate doses of MGF-1-7 solution (0, 10, 25, 50, 100, and 250 μg/mL) and then incubated for 48 h.
## 2.3. Animals and Diet
Six-week-old male Wistar rats were purchased from BioLASCO Taiwan Co., Ltd. (Taipei, Taiwan) and supplied with a pelletized commercial laboratory diet (Purina Lab Chow) and water ad libitum. The rats were maintained under an air-conditioned environment (23 ± 2 ℃ with $60\%$ relative humidity) with a 12 h light/dark cycle at the Experimental Animal Center of Chung Shan Medical University. All the experimental manipulations involving animals were strictly implemented according to ethical guidelines for animal experiments and were approved by the Laboratory Animals Center of Chung Shan Medical University (IACUC No. 1664). After 1 week of acclimatization, the rats were randomly divided into nine groups of 12 rats (Figure 1). Rats in the control group were fed an AIN-93G control diet ($7\%$ fat); rats in the experimental groups were fed an AIN-93G-based high-fat diet containing $32\%$ lipids ($7\%$ soybean oil and $25\%$ lard). For the positive control treatment, a health supplement containing hydroxycitric acid (HCA) and chlorogenic acid (CGA) was obtained from Taiyen Biotech Co., Ltd. (Tainan, Taiwan), which has been proven to have antibody fat accumulation effects by the Taiwan Food and Drug Administration (TFDA, License NO. A00274). Furthermore, the experimental groups were distributed into eight subgroups: (a) continued received HFD throughout the period (HFD), (b) HFD supplemented with 100 mg/kg body weight Japan Mei-Gin (low-dose Japan Mei-Gin, HFD + JMG-LD), (c) HFD supplemented with 300 mg/kg body weight Japan Mei-Gin (high-dose Japan Mei-Gin, HFD + JMG-HD), (d) HFD supplemented with 100 mg/kg body weight Mei-Gin formula-3 (low-dose Mei-Gin formula-3, HFD + MGF-3-LD), (e) HFD supplemented with 300 mg/kg body weight Mei-Gin formula-3 (high-dose Mei-Gin formula-3, HFD + MGF-3-HD), (f) HFD supplemented with 100 mg/kg body weight Mei-Gin formula-7 (low-dose Mei-Gin formula-7, HFD + MGF-7-LD), (g) HFD supplemented with 300 mg/kg body weight Mei-Gin formula-7 (high-dose Mei-Gin formula-7, HFD + MGF-7-HD), and (h) HFD supplemented with 140.6 mg/kg body weight HCA + GCA powder capsules (HFD + PC). During the experiment period, the body weight, daily feed, and water were measured and used to calculate the feeding efficiency. After 8 weeks, animals were fasted overnight, euthanized by carbon dioxide, and whole blood was collected from the abdominal aorta. The heart, liver, spleen, lung, kidney, visceral adipose tissue (perirenal fat, epididymal fat, and mesenteric fat), and subcutaneous adipose tissue (retroperitoneal fat and inguinal fat) were dissected, rinsed, weighed, and stored at −80 °C.
## 2.4. Oil Red O Lipid Staining
For measuring intracellular lipid accumulation, oil red O staining was performed to determine the effect of MGF-1-7 on lipid synthesis. Briefly, mature 3T3-L1 adipocytes were harvested and then washed by PBS and fixed in $10\%$ neutral buffered formalin for 20 min at room temperature. Subsequently, cells were placed in $100\%$ propylene glycol for 3 min and then stained with a mixture of oil red O working solution (oil red O solution/water 3:2, v/v) for 60 min. The lipid droplets were visualized and photographed by microscopy (Motic AE$\frac{30}{31}$), and the oil red O was solubilized in isopropanol and measured spectrophotometrically at 510 nm.
## 2.5. Determination of Glycerol-3-Phosphate Dehydrogenase (GPDH) Activity
Using a GPDH activity colorimetric assay kit (Cat No. K640-100, BioVision, Milpitas, CA, USA), mature 3T3-L1 adipocytes were harvested 48 h after MGF-1-7. Based on the manufacturer’s instructions, protein concentration was determined spectrophotometrically according to the reduced nicotinamide adenine dinucleotide (NADH) standard. GPDH activity (%) was pressed as a percentage change against control ($100\%$).
## 2.6. Statistical Analysis
For all experiments, the quantitative data are expressed as mean ± SEM. The analysis of variance was followed by one-way ANOVA with Duncan’s multiple range test by using the SPSS software program, version22.0 (APSS Inc., Chicago, IL, USA). A statistically significant difference was established only if the p-value < 0.05.
## 3.1. Determination of Phenolic Contents of the Mei-Gin Formulas
The content of the main phenolic compounds in MGF-1-7 was identified using HPLC analysis and as follows: MGF-1 (chlorogenic acid: 0.46, caffeic acid: 0.19, and p-coumaric acid: 0.09 mg/g), MGF-2 (chlorogenic acid: 0.26, caffeic acid: 0.24, and p-coumaric acid: 0.07 mg/g), MGF-3 (chlorogenic acid: 0.34, caffeic acid: 0.18, and p-coumaric acid: 0.08 mg/g), MGF-4 (chlorogenic acid: 0.32, caffeic acid: 0.26, and p-coumaric acid: 0.08 mg/g), MGF-5 (chlorogenic acid: 0.54, caffeic acid: 0.11, and p-coumaric acid: 0.08 mg/g), MGF-6 (chlorogenic acid: 0.70, caffeic acid: 0.19, and p-coumaric acid: 0.07 mg/g), and MGF-7 (chlorogenic acid: 0.72, caffeic acid: 0.11, and p-coumaric acid: 0.08 mg/g). The data indicated that MGF-7 had higher chlorogenic acid, MGF-4 had higher contents of caffeic acid content, and the contents of p-coumaric acid content was similar in each group.
## 3.2. Effects of Mei-Gin Formulas on Lipid Accumulation in 3T3-L1 Adipocytes
To determine the effects of the Mei-*Gin formula* on intracellular lipid accumulation in adipocyte cells, the effects of serially diluted MGF-1-7 on 3T3-L1 adipocytes were visualized using oil red O staining. MGF-1-7 effectively reduced lipid accumulation in mature 3T3-L1 adipocytes. To validate the observation of reduced lipid accumulation, a TG quantification assay was performed to confirm the changes. As shown in Figure 2A,B, similar to the reduction of lipid accumulation, MGF-1-7 significantly decreased the cellular level of TG in 3T3-L1 adipocytes. The 250 μg/mL MGF-3 and -7 treatments were observed to show a significant 31.4 and $35.9\%$ reduction in TG content compared to untreated control cells, respectively (Figure 2B), indicating that in the presence of high-dose MGF-3 and -7, the intracellular level of TG levels was dramatically decreased. Additionally, a further detailed analysis of the effects of MGF-1-7 on GPDH activity is shown in Figure 2C. The alteration of intracellular GPDH activity was observed in 3T3-L1 adipocytes treated with MGF-1-7. In particular, GPDH activity was decreased in the same manner as previously observed in the intracellular TG level in cells treated with MGF-3 and -7. The results suggested that MGF-3 and -7 appeared to have the most potency in reducing 3T3-L1 adipocytes. Therefore, we used MGF-3 and -7 in subsequent experiments involving animal models.
## 3.3. Effect of the Mei-Gin Formula on Body Weight, Feed Intake, Energy Intake, Feed Efficiency, and Mass of Selected Organs in HFD-Induced Obesity
As shown in Figure 3 and Table 1, the body weight during the experimental period progressively decreased among the JMG, MGF, and PC intervention groups, as compared to the HFD group. In detail, consumption of low-dose Japanese MG, high (300 mg/kg) MGF-3, and both low and high (100 mg/kg and 300 mg/kg) MGF-7 significantly decreased body weight change and weight gain compared to that of the HFD group ($p \leq 0.05$). The feed intake and energy intake of the JMG-fed rats were significantly lower than those of the HFD-fed rats. Although the feed efficiency of rats that consumed MGF-3 (300 mg/kg), MGF-7 (300 mg/kg), and HCA + GCA powder capsules (140.6 mg/kg) was significantly lower than that of the HFD group (Table 2), no significant differences were observed in the weights of the heart, spleen, lungs, and kidneys among each group. A significant reversal in increased liver weight was observed in rats that ate low (100 mg/kg) JMG, high (300 mg/kg) MGF-3, low and high (100 mg/kg and 300 mg/kg) MGF-7, and powder capsules (140.6 mg/kg) (Table 3).
## 3.4. Effect of the Mei-Gin Formula on Body Fat Mass and Adipose Tissue in Rates with HFD-Induced Obesity Rates
As shown in Table 4 and Table 5, the rats fed a high-fat diet exhibited persistent higher total body fat mass compared to the counterpart ND group, and this was significantly attenuated by consuming low (100 mg/kg) JMG, high (300 mg/kg) MGF-3, both low and high (100 mg/kg and 300 mg/kg) MGF-7, and HCA + GCA powder capsules (140.6 mg/kg). Similarly, a trend of reduction in subcutaneous adipose tissue was also observed among those five groups as the result of increased retroperitoneal and inguinal adipose tissue. Although the effect of low-dose JMG on visceral adipose tissue was not statistically different, it still showed potential to lower the mass of adipose tissue at week 8. Among visceral (perirenal, epididymal, and mesenteric) adipose tissue, a significant reduction was found in perirenal and mesenteric adipose tissue after administration of low-dose JMG, high-dose MGF-3, both high- and low-dose MGF-7, and powder capsules; while the weight of epididymal adipose tissue did not show a significant difference between each group after dietary intervention.
## 4. Discussion
Obesity is characterized by defective excess body fat content, including the determination of size and body fat distribution. Recently, emerging evidence has focused on dietary phenolic compounds that provide a therapeutic strategy for people with obesity, as naturally occurring plant products reduced the potential for side effects [33,34,35,36]. Concerning the risk of adverse medication reactions, various options for obesity management and treatments have been constantly conducted in various biological properties of natural phenolic compounds that exhibited a preventive or therapeutic potential to improve lipid and glucose dysregulation [37,38,39]. Existing anti-obesity medications including orlistat and sibutramine, with very modest efficacy, can cause clinically adverse drug reactions. Therefore, there is a strong need to exploit and discover naturally occurring foods and substances as safe and acceptable alternatives to designer drugs. Based on Chinese herbology, we developed a novel Mei-*Gin formula* to exert a powerful synergistic effect to target the development of obesity [40,41]. In this study, our aim was to determine the anti-obesity effect of the Mei-*Gin formula* on the 3T3-L1 cells in vitro and in vivo HFD-induced rat model by monitoring the regulation of cell lipid accumulation and adipose tissue. In vitro cell models, particularly 3T3-L1 preadipocyte differentiation and adipogenesis of 3T3-L1 preadipocytes, with the main characteristic of intracellular triglyceride accumulation, are associated with the development of obesity [42,43]. In this regard, we demonstrated that the inhibitory effects of the Mei-*Gin formula* on 3T3-L1 adipocyte differentiation were due to the downregulation of GPDH activity and this subsequently led to a reduction in cellular triglyceride production. As shown in Figure 2, including the serial tested Mei-Gin formulas (MGF-1-7), all are capable of altering the differentiation capacity of the 3T3-L1 preadipocyte into the 3T3-L1 adipocyte. On the basis of the HPLC analysis, we determined the phenolic compounds in the Mei-*Gin formula* which were most abundant in p-coumaric acid, caffeic acid, and chlorogenic acid. Among food phenolic compounds, hydroxycinnamic acid represents a major class of phenolic acid available in fruits, seeds, and vegetables [44,45]. A diet supplement of hydroxycinnamic acid can easily reach levels of 0.5–1 g or even higher in humans. Previously, evidence indicated that hydroxycinnamic acid, including p-coumaric acid caffeic acid, ferulic acid, and chlorogenic acid, can serve as the primary antioxidant [46], have powerful anti-inflammatory [47] and anti-cancer activity [48,49], and are involved in improving insulin resistance [50]. The study reveals that caffeic acid phenethyl ester effectively prevents body weight gain and the gain of epididymal adipose tissue [51]. The result of oil red O staining indicated that caffeic acid phenethyl ester significantly reduced adipogenesis in 3T3-L1 preadipocytes, which is in accordance with our in vivo results. Dietary consumption of chlorogenic acid markedly altered the plasma lipid profile and attenuated the fatty liver by increasing hepatic PPAR-α expression in hypercholesterolemic rats [52]. Furthermore, p-coumaric acid-induced activation of the AMPK pathway subsequently leads to the inhibition of adipogenesis in 3T3-L1 adipocytes [53]. To understand the synergistic effects for each of the natural plants in our new formula, different ratios of each individual plant were used to form MGF-1-7 in the context. Interestingly, quantitative results from intracellular triglycerides in 3T3-L1 adipocytes indicated that MGF-3, -4, and -7 exhibited predominant inhibitory effects on lipid accumulation, while the inhibitory capacity among Mei-Gin formulas on GPDH activity was found mostly in MGF-3, -5, and -7. Taken together, MGF-3 and -7 appeared to be the most potent in regulating the development of obesity. Therefore, we examined the effectiveness of MGF-3 and -7 to ameliorate weight gain and further identified the contribution of adipose tissues to obesity in the HFD-induced rat model.
Previously, rats fed a high-fat/high-energy diet have been shown to have significantly increased body weight compared to normal diet-fed rats, and are widely used in diet-induced obesity studies [54,55]. In the rat model induced by a high-fat diet, excessive lipid accumulation in subcutaneous and visceral adipose tissue is a key feature of obesity. In the present study, we observed that the administration of the Mei-*Gin formula* in obese rats for 8 weeks significantly reduced final body weight and weight gain. Furthermore, both low- and high-dose MGF-7 effectively suppressed body weight compared to MGF-3. Importantly, diet-induced obesity is associated with lipid burden in adipose tissue, as well as in non-adipose tissue. The increased fat deposition was observed in the liver and eventually leads to weight gain in the obese animal model [36,56,57,58]. Our data reveal a similar trend of reduction of liver weight in HFD-induced rats after administration of the Mei-Gin formula. Interestingly, a similar result was shown in 3T3-L1 adipocytes in vitro and obese mice in vivo as a supplement of plant resin [59]. Recently, the complete analysis of Prunus mume extracts was reported to identify the phytochemical composition, including chlorogenic acid, lupeol, mumefural, and ursolic acid, which are proposed to have anti-cancer properties [60,61,62,63]. A pilot study conducted on 18 H. pylori-positive participants in the stomach proposed an anti-bacterial activity of bainiku-ekisu therapy [22]. Bainiku-ekisu is a Prunus mume juice concentrate and was previously reported to exhibit strong anti-bacterial activity in vitro [23]. Due to the concentration process, the phytochemical contents such as phenolic acid and flavonoids were higher in bainiku-ekisu than those in Prunus mume juice [64,65]. Herein, we are the first to verify the effects of the Mei-Gin-based plant mixture in regulating body weight gain of obese rats. High-performance liquid chromatography analysis confirmed the phenolic acid and flavonoid constituents in the thermal process and was found to increase markedly in black garlic as compared to that of fresh garlic [66,67,68]. The HPLC isolation and identification of black garlic showed variable quantities of phenolic acid, including garlic acid, vanillic acid, chlorogenic acid, caffeic acid, p-coumaric acid, and ferulic acid. Hung et al. have investigated the antioxidant activity and active components of Hsian-tsao [69]. Several phenolic acids were identified from the water extract of Hsian-tsao, including apigenin, caffeic acid, vanillic acid, and kaempferol [70]. The authors also determined that the amounts of caffeic acid and kaempferol were the highest among those phenolic acids and are considered the main functional components, and may contribute to the antioxidant properties of Hsian-tsao. The diet supplement of phenolic compounds from natural plants that have served as potent anti-obesity agents has been well documented. However, the effects of natural plants on obesity are not yet defined. The composition of the innovative plant formula was based on the hypothesis that combining the candidates of the plants with particular reference mentioned above can modulate the development of obesity in vivo and in vitro. Our data demonstrated that low-dose JMG, high-dose Mei-Gin formula, both high-and low-dose Mei-Gin formula, and HCA + GCA powder capsules effectively reduced total body fat in HFD-induced rats, which is consistent with the initial observation in body weight gain and liver weight. In addition, we identified the contribution of adipose tissue in obese rats. Along the same lines, it leads to a significant reduction in visceral (perirenal and mesenteric adipose tissue) and subcutaneous (retroperitoneal and inguinal adipose tissue) adipose compartments in HFD-induced rats compared to HFD controls. These data demonstrated that the administration of the Mei-*Gin formula* caused weight loss, which affected both visceral and subcutaneous adipose tissue. However, no differences were established regarding low-dose JMG and HFD controls.
JMG is a condensed extract obtained using traditional thermal condensation methods. Accumulated studies have shown that JMG inhibits the proliferation of hepatocellular carcinoma cells [71] and improves hyperglycemia [72], which can serve as a dietary intervention as adjuvant therapy. However, phytochemical compounds such as phenolic acid have been reported to be destroyed after heat treatment at high temperatures for a long period of time [64,65]. In the present study, high-dose MGF-3 and high- and low-dose MGF-7 administration in HFD-induced rats results in a more efficient reduction in body weight gain, liver weight, and total body fat compared to JMG groups, which may be attributed to our decompression-processed Mei-Gin, which kept more bioactive components. Among each group, the optimal level of each MG formula group has been determined in the corresponding HFD-induced rat model. The results suggested that high-dose MGF-7 exerts the most potent anti-obesity activity.
## 5. Conclusions
In conclusion, this is the first study to verify the effects of a Mei-Gin-based plant formula on obesity both in vivo and in vitro. MGF-1-7 reduced 3T3-L1 adipocyte differentiation and lipid accumulation by suppressing GPDH activity. Furthermore, the anti-obesity effects can reduce body fat accumulation in adipose tissue, which caused a reduction in body weight gain. Therefore, the Mei-*Gin formula* is a promising candidate for the treatment of obesity; however, further investigation on the mechanistic pathway of lipolysis, oxidation, synthesis of fatty acid, and thermogenesis in adipose tissues in the future may provide deeper insight into the molecular mechanisms underlying obesity and help develop emerging therapeutic options.
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|
---
title: Insights into the Chemical Composition and In Vitro Bioactive Properties of
Mangosteen (Garcinia mangostana L.) Pericarp
authors:
- Bianca R. Albuquerque
- Maria Inês Dias
- José Pinela
- Ricardo C. Calhelha
- Tânia C. S. P. Pires
- Maria José Alves
- Rúbia C. G. Corrêa
- Isabel C. F. R. Ferreira
- Maria Beatriz P. P. Oliveira
- Lillian Barros
journal: Foods
year: 2023
pmcid: PMC10000740
doi: 10.3390/foods12050994
license: CC BY 4.0
---
# Insights into the Chemical Composition and In Vitro Bioactive Properties of Mangosteen (Garcinia mangostana L.) Pericarp
## Abstract
The industrial processing of mangosteen (*Garcinia mangostana* L.) generates high amounts of waste, as ~$60\%$ of the fruit is formed by an inedible pericarp. However, its pericarp has been explored as a source of xanthones; nevertheless, studies addressing the recovery of other chemical compounds from such biomass are still scarce. Hence, this study intended to elucidate the chemical composition of the mangosteen pericarp, including fat-soluble (tocopherols and fatty acids) and water-soluble (organic acids and phenolic compound non-xanthones) compounds present in the following extracts: hydroethanolic (MT80), ethanolic (MTE), and aqueous (MTW). In addition, the antioxidant, anti-inflammatory, antiproliferative and antibacterial potentials of the extracts were assessed. The mangosteen pericarp showed a composition with seven organic acids, three tocopherol isomers, four fatty acids and fifteen phenolic compounds. Regarding the extraction of phenolics, the MT80 was the most efficient (54 mg/g extract), followed by MTE (19.79 mg/g extract) and MTW (4.011 mg/g extract). All extracts showed antioxidant and antibacterial activities; however, MT80 and MTE extracts were more efficient than MTW. Only MTW did not show anti-inflammatory properties, whereas MTE and MT80 showed inhibitory activities towards tumor cell lines. Notwithstanding, MTE showed cytotoxicity towards normal cells. Our findings support the idea that the ripe mangosteen pericarp is a source of bioactive compounds, although their recovery is dependent on the extraction solvent.
## 1. Introduction
The global agri-food sector generates significant volumes of food waste each year. For instance, the processing of fruit yields significant quantities of biowaste, which account for between 25 and $60\%$ of the fruit’s weight. In turn, this biowaste is mainly composed of peels and seed that have a chemical composition rich in bioactive compounds, that can be recovered and used to create health supplements or added to food products to improve their nutritional content [1].
Garcinia mangostana L. is a tropical shrub belonging to the family Clusiaceae, native to South Asia, which can also be found in other tropical territories such as South America [2]. Due to its unique form and flavor, mangosteen fruit is acknowledged as “the queen of fruit” (Figure 1). Mangosteen is a spherical fruit; when immature, it has a green color, and when entirely mature, its color is completely purple. This fruit holds many edible sweet petals (pulp), with delicious and widely appreciated unique flavor and aroma—so much that mangosteen is considered a delicacy worldwide [3].
Indeed, the edible part of the mangosteen is small, as more than $60\%$ of the whole fruit comprises an inedible tick dark purple or reddish pericarp, which leads to a high amount of residue production after consumption/fruit processing. It is estimated that approximately 30.8 million tons of mangosteen pericarp waste are produced per year around the planet [4,5]. On the other hand, in traditional medicine, the mangosteen fruit shell has been used for the treatment of several ailments, such as skin infections, diarrhea, and fever [5]; in addition, in some regions of South America, it is employed in the preparation of digestive and energetic teas [6]. In the past few years, mangosteen fruit, including its pericarp, has been exploited for the acquisition of manifold dietary supplements, essentially capsules and functional drinks. Such products normally claim to improve the immune system, protect against free radicals, reduce allergic reactions and weight loss, among other health-promoting properties [7,8,9]. Notwithstanding, most of these potential health effects are not scientifically supported [10] for the pharmaceutical use of mangosteen. Moreover, the greater part of existing scientific evidence on mangosteen fruit and its by-products, including original articles and reviews, only addresses its xanthones compounds and corresponding bioactivities [3,11,12]. In addition, the correlation between the mangosteen chemical composition and its bioactive profile has not been completely elucidated yet [7,13,14].
Considering all of the above, this study aimed to elucidate the phytochemical profile of the mangosteen pericarp, including lipophilic compounds such as tocopherols and fatty acids, and hydrophilic compounds, namely organic acids, and phenolic compounds, including anthocyanins. Furthermore, diverse in vitro assays were performed to evaluate the antioxidant, anti-inflammatory, antiproliferative, and antibacterial potential of the hydroethanolic (MT80), ethanolic (MTE) and aqueous (MTW) extracts obtained from this by-product.
## 2.1. Fruit Material
Ripe mangosteen fruits with completely purple pericarp were acquired locally in Bragança, Portugal. Fruits were washed in current water and the pericarp and pulp were manually separated. Then, the pericarps were frozen (−20 °C), lyophilized, and ground into uniform particles, which were kept frozen until analysis.
## 2.2.1. Organic Acids
Organic acids were recovered from the sample (1 g) by maceration (room temperature for 20 min) with 25 mL metaphosphoric acid ($4.5\%$ (w/v)). The extract obtained was injected in a Shimadzu 20A series UFLC-PDA. A C18 column was used to separate the compounds, and sulphuric acid (3.6 mM) was used for the elution, with a flow rate of 0.8 mL/min. The preferred wavelengths for detection in a PDA were 215 and 245 nm (for ascorbic acid). Compounds were identified by comparing the area of extract peaks with calibration curves produced from commercial standards. Data were presented in mg per 100 g of dry pericarp (dw).
## 2.2.2. Tocopherols
Tocopherols were extracted from the sample (500 mg) through successive homogenization and centrifugation (4000× g at 10 °C for 5 min, three times) with methanol and hexane, and the supernatant phase was gathered, dried in a flow of nitrogen, re-diluted in hexane (2 mL), and analyzed using a HPLC-FL [15]. For quantification, genuine standards of α-, β-, γ-, and δ-tocopherol, as well as tocol (internal standard) were used. The results were reported in mg per 100 g of dw.
## 2.2.3. Fatty Acids
Fatty acids were obtained from the lipid fraction assisted by Soxhlet extraction, followed by methylation with 5 mL of methanol, sulphuric acid, and toluene 2:1:1 (v:v:v), for at least 12 h at 50 °C and 160 rpm. Next, 3 mL of deionized water were added to obtain phase separation. The FAME were recovered with 3 mL of diethyl ether by shaking in vortex. A gas–liquid chromatography with flame ionization detection was performed using a YOUNG IN Crhomass 6500 GC System instrument equipped with a split/splitless injector set at 250 °C with a split ratio of 1:50, a flame ionization detector (FID) set at 260 °C, and a Zebron-Fame column (30 m). It was set to the following oven temperature program: Initial temperature of 100 °C, maintained for 2 min, increase of 10 °C/min to 140 °C, then a ramp of 3 °C/min to 190 °C, and a final ramp of 30 °C/min to 260 °C. At 250 °C, the carrier gas (hydrogen) flow rate was 1.2 mL/min. The results were described as a relative percentage of each fatty acid.
## 2.2.4. Phenolic Compounds
Three distinct extracts were prepared by adding 1 g of dry sample (mangosteen lyophilized pericarp) to 30 mL of solvent, which contained [1] solution composed of $80\%$ ethanol and $20\%$ water for hydroethanolic extraction (MT80); [2] distilled water for aqueous extraction (MTW); and [3] ethanol ($100\%$) for ethanolic extraction (MTE). For the extraction of anthocyanin compounds, the same solvents were acidified with citric acid ($0.1\%$, 1 µM). The samples were extracted for 1 h under stirring at room temperature and filtrated (qualitative filter paper of 20–25 µm). After that, the residues were subsequently extracted a second time for 1 h under the same circumstances using additional 30 mL of the same solvent. The ethanol presented in the extracts was evaporated (at 40 °C) under vacuum condition, and aqueous phases of the hydroethanolic and aqueous extracts were lyophilized to produce dry extracts.
The freeze-dried extracts were resuspended (5 mg/mL) in of ethanol/water (20:80 v/v) and were analyzed using an HPLC-DAD-MSn, working under optimized conditions as in other studies [16,17]. The results were expressed as mg per g of extract (E) and mg per g of dw.
## 2.3.1. Antioxidant Potential
Two cell-based assays that measure the capacity to [1] prevent the generation of thiobarbituric acid reactive substances (TBARS) [15] and [2] postpone the oxidative hemolysis (OxHLIA) [18] were used to examine the antioxidant potential of the extracts (3.12–400 µg/mL). The TBARS results were defined as EC50 values (µg/mL), which represents the extract concentration that suppresses TBARS by $50\%$. The OxHLIA results were defined as IC50 values (µg/mL) for a Δt of 60 min, which is the amount of extract needed to maintain $50\%$ of the sheep erythrocyte population for 60 min. In both experiments, Trolox acted as a positive control.
## 2.3.2. Anti-Inflammatory Potential
The ability of the extracts (6.25–400 g/mL) to prevent lipopolysaccharide-stimulated murine macrophage RAW 264.7 cells from producing nitric oxide (NO) was used to assess their anti-inflammatory potential [19]. A positive control was applied, which was Dexamethasone (50 μM). The results were represented as IC50 values (µg/mL), which correspond to the amount of the extract that causes $50\%$ of the NO generation to be inhibited.
## 2.3.3. Antiproliferative Potential
The evaluation of the antiproliferative potential of the extracts (6.25–400 µg/mL) was performed following the protocol for the sulforhodamine B (SRB) assay [20]. Four human tumor cell lines, namely NCI-H460 (non-small lung carcinoma cells); Caco-2 (colon adenocarcinoma cells); MCF-7 (breast carcinoma cells); and AGS (gastric adenocarcinoma cells), besides one non-tumor cell line obtained from African green monkey kidney (Vero), were used. The positive control employed was Ellipticine. The results were expressed as GI50 values (µg/mL), which correspond to the extract’s concentration required to inhibit $50\%$ of cell proliferation.
## 2.3.4. Antibacterial Potential
In order to evaluate the antibacterial potential of the extracts on pathogenic bacteria commonly causing nosocomial infections, six Gram-negative (Escherichia coli, Klebsiella pneumoniae, Morganella morganni, Proteus mirabilis, and Pseudomonas aeruginosa) and three Gram-positive (Enterococcus faecalis, Listeria monocytogenes, and Methicillin-resistant *Staphylococcus aureus* (MRSA)) bacteria were selected. Following a protocol established [21], the extracts were re-dissolved in water (20 mg/mL) and successive dilutions were carried out in a 96-well plate until 0.15 mg/mL. The minimum inhibitory concentrations (MIC), were determined by the rapid p-iodonitrotetrazolium chloride (INT) colorimetric assay. The minimum bactericidal concentrations (MBC) were determined by transferring a portion (10 µL) of each well that showed no color change to a blood agar medium ($7\%$ sheep blood) and incubated at 37 °C/24 h. MBC was determined as the lowest concentration capable of eradicating bacteria. Streptomycin and ampicillin were used as positive controls.
## 2.4. Statistical Analysis
The results of the analysis were expressed as mean value ± standard deviation, which were all carried out in triplicate. Statistical analyses were carried out using the R software (version 11). Student’s t-test was applied to detect statistical differences ($p \leq 0.05$) between two samples; for three samples, the analyses of variance (ANOVA) were used for detecting significant differences ($p \leq 0.05$) between them. The Tukey’s honest significant difference (HSD) test at the $5\%$ of significance was applied to discriminate the samples.
## 3.1.1. Organic Acids
The organic acid profile of mangosteen pericarp is presented in Table 1. Citric acid ($56.72\%$) was the major compound detected in the sample, followed by quinic acid ($17.99\%$). A low concentration of ascorbic acid was detected, whereas only traces of shikimic and fumaric acids were observed. Other studies reported different organic acid profiles for mangosteen pericarp samples. For instance, Mamat et al. [ 22] detected five organic acids by chromatography-mass spectrometry (GC-MS), namely malic, L-(+)-tartaric, citraconic, malonic and succinic acids when investigating mangosteen ripened pericarp. More recently, the same research team assessed the mangosteen metabolites throughout all ripening process (from green fruit (stage 0) to purple dark fruit (stage 6)) and detected traces of aspartic acid until stage 4 (brownish red), and the presence of ascorbic acid-2-glucoside, 2-butynedioic acid, and quinic acid was detected only in stage 2. Indeed, organic acid composition can be associated with the stage of fruit ripening [23]. In our investigation, only purple dark pericarps were analysed.
## 3.1.2. Tocopherols
Tocopherol isomers present in the mangosteen pericarp were identified by HPLC-FD, and the results are shown in Table 1. In total, three tocopherol isomers were detected and quantified. β-tocopherol was the most abundant isomer, γ-tocopherol was detected in the lowest concentration, and α-tocopherol was detected in a median amount. Mangosteen pericarp showed a considerable amount of tocopherol isomers (9.9 mg/100 g dw).
Isabelle et al. [ 24] identified γ-, α-, and δ-tocopherol in samples of mangosteen flesh, α-tocopherol being the most abundant (5.74 mg/g fw) within a total tocopherol content of 9.9 mg/100 g dw. In another study concerning the G. mangostana pulp, the amount of vitamin E (α-tocopherol) quantified was 0.18 mg/100 g fw [25]. As expected, such composition of tocopherol is distinct from the one herein reported, which addresses only the mangosteen pericarp. Until the publication of this study, to the best of our knowledge, there was no previous report on the tocopherol profile of this part of the fruit.
## 3.1.3. Fatty Acids
Mangosteen pericarp showed a low content of lipid (2.7 ± 0.6 g/100 g dw), and this lipid fraction was evaluated regarding its fatty acid composition, the results of which are presented in Table 1. Oleic acid was the most abundant fatty acid detected, followed by palmitic and linoleic acid, whereas stearic acid was the minor fatty acid constituent. In total, mangosteen pericarp showed $40.6\%$ saturated, $34.1\%$ monounsaturated, and $25.2\%$ polyunsaturated fatty acids. The ratio between PUFA and SFA verified for the mangosteen shell of 0.62 can be considered appropriate for maintaining good health [26]. As far as we are aware, this is also the first research on the fatty acid profile of mangosteen pericarp. However, the fatty acid composition of the mangosteen seed has been described in the literature [27]. Similar to the pericarp, the mangosteen seed shows a higher amount of palmitic and oleic acid, besides linoleic acid in low concentrations [27].
## Non-Anthocyanin Compounds
The non-anthocyanin compounds present in the different extracts of mangosteen pericarp were assessed by LS-MS and the identification of the compounds was conducted considering their main characteristics in the mass spectral (mass fragmentation (MS²), maximum UV absorption (λmax)) and based on information obtained from the literature. The identification and quantification of phenolic compounds detected in the extracts are presented in Table 2 and Table 3, respectively.
Compound 1 ([M-H]− at m/z 353) showed four fragment ions at MS² and comparing its mass spectrum with commercial standard, this compound was tentatively identified as a 5-O-caffeoylquinic acid. No data in the literature was discovered related to the presence of such compound in mangosteen fruit samples. Compounds 2, 4, and 11 ([M-H]− at m/z 577) also showed the presence of ion fragment at m/z 289 after the loss of 288 Da, and based on its chromatographic characteristics and literature data, this compound was tentatively identified as procyanidin dimer [28]. Compounds 3, 6, and 10 ([M-H]− at m/z 865) released three fragment ions at MS², corresponding to the successive breaking of bonds between monomers of epicatechin/catechin molecule (m/z 289). According to the mass spectrum and previous studies regarding mangosteen, these compounds have been identified as procyanidin trimer isomers [28]. The mass characteristics of compound 5 ([M-H]− at m/z 289) were compared with commercial standards, and this compound was tentatively identified as a (+)-epicatechin. Regarding compounds 7 and 8 ([M-H]− at m/z 1153), the analysis of MS² allowed the detection of four epicatechin/catechin molecules; however, these compounds were identified as procyanidin tetramer isomers, compounds previously identified in mangosteen pericarp by Zhou et al. [ 28]. Compound 9 ([M-H]− at m/z 863) showed five fragment ions at MS²; according to the literature, this molecule has been discovered in mangosteen pericarp and identified as a procyanidin-A-like linkage [28]. Compound 12 ([M-H]− at m/z 609) released a unique fragment ion MS² at m/z 301; the mass spectrum of this compound allowed its identification as a quercetin-3-O-rutinoside through DAD-MSn data. Compound 13 ([M-H]− at m/z 449) showed two fragment ions at m/z 303 and m/z 285. Comparing its spectrum characteristics with those of standard compounds, this compound was identified as taxifolin-O-rhamnoside. These last two compounds have not been detected in mangosteen fruit before.
Most studies on the chemical composition of mangosteen fruit focus on xanthones, a restricted polyphenol class present in a small group of plants and fungi, as mangosteen fruit has been considered one of the major sources of such phytochemicals [9,31]. However, the present study focused on other classes of biocompounds. As a result, among non-anthocyanin compounds, condensed tannins were the most abundant polyphenols present in all extracts of mangosteen pericarp. In addition, one phenolic acid, two flavonoids, one flavonol and one flavanonol, were detected in this by-product.
According to Table 3, the hydroethanolic extract (MT80) showed the higher amount of phenolics among samples (MT80 > MTE > MTW). However, proanthocyanidins were the most abundant compound class present in all extracts, accounting for $95\%$, $94\%$ and $90\%$ of the total phenolic compounds non-anthocyanin in MT80, MTE, and MTW, respectively. 5-O-caffeoylquinic and traces of quercetin-3-O-rutinoside (Compound 12) were detected only in MT80, whereas taxifolin-O-rhamnoside (Compound 13) was detected in low concentration in all extracts, which, as far as we know, is an unprecedented information regarding the phenolic profile of mangosteen fruit. MTW showed the lowest total phenolic compound non-anthocyanin, and the concentration of each compound detected in it is also lower than the concentration present in the other extracts. Such result can be associated with the poor solubility of proanthocyanidins in water [32].
Only few works have described in detail the phenolic composition of the mangosteen pericarp. For example, Zarena and Sankar [33] identified the presence of thirteen phenolic acids in the fractionated and hydrolysed extract of mangosteen fruit shell. These authors concluded that most of the phenolic acids naturally present in this bioresidue are bound to glucoside molecules [33]. Zhou et al. [ 28] identified proanthocyanidins from purified extracts of mangosteen pericarp that showed high antioxidant activity by chemimal methods, namely ferric reduncing antioxidant power (FRAP), Trolox equivalent antioxidant capacity (TEAC), and DPPH radiacal scavenging capacity.
## Anthocyanin Compounds
According to the anthocyanin composition data shown in Table 2, only two anthocyanin compounds were detected in the MT80 extract. The first one detected was compound 14 ([M]+ at m/z 611) that released MS² fragment at m/z 287 (−324 u, loss of two hexose units), suggesting the presence of a cyanidin-O-dihexoside. The same fragmentation behaviour was previously described in the identification of cyanidin-O-sophoroside in this part of mangosteen fruit [29,30]. The other anthocyanin compound (Compound 15 ([M]+ at m/z 435) was identified as delphinidin-O-pentoside due to the loss −132 u, which revealed the MS² fragmentation m/z 303, characteristic of an aglycone delphinidin. Following our knowledge, this is the first time that a delphinidin derivate is detected in the mangosteen fruit.
Zarena and Sankar [30] reported the identification of other two anthocyanins in mangosteen pericarp, namely pelargonidin-3-O-glucoside and cyanidin-3-O-glucoside. On the other hand, Yenrina et al. [ 34] did not detect such components in their mangosteen pericarp samples. Palapol et al. [ 29], which evaluated the anthocyanin composition of mangosteen fruit during repining, also reported the presence of cyanidin-3-O-glucoside in this bioresidue, besides other anthocyanins, such as cyanidin-glucoside-pentoxide and other three cyanidin derivates. In their studies, cyanidin-O-sophoroside was the most abundant anthocyanin compound detected, which corroborates our findings [29,30].
According to Table 3, the hydroethanolic extract (MT80) showed a 3.66 ± 0.02 mg/g E value equivalent to 1.062 ± 0.007 mg/g dw. This result is slightly lower than the one reported by Cheok et al. [ 35] when analyzing extracts obtained by conventional extraction with ethanol $70\%$ (1.62 mg/g dw); on the other hand, the same authors registered the amount of 2.92 mg/g dw of anthocyanin for the extract produced via ultrasound-assisted extraction with methanol $70\%$. Another study performed by Muzykiewicz et al. [ 36] shows that anthocyanin extraction by ultrasound process from mangosteen epicarp is dependent on the solvent concentration, time of extraction, and initial condition of the pericarp (whether fresh or frozen (−20 °C)). According to the authors, the highest anthocyanin recovery yield (±24 mg cyanidin-3-O-glucoside/L) was obtained with extraction time of 60 min, ethanol $70\%$ as solvent and fresh pericarp. Interestingly, the authors registered the lowest amounts of anthocyanin recovered when using the minimal and the maximum ethanol concentration ($20\%$ and $96\%$, respectively) [36], which is similar to what happened in our study, where no anthocyanin compounds were detected for minimal and maximum ethanol concentrations (Table 3). Therefore, selective methods and optimized conditions can improve the recovery of this specific class of color compounds present in mangosteen pericarp.
## 3.2.1. Antioxidant Potential
The different extracts obtained from mangosteen pericarp were evaluated regarding their antioxidant potential. According to the results presented in Figure 2, all extracts have the capability to prevent lipid oxidation and oxidative hemolysis. However, the MT80 and MTE, which showed similar activities, were more efficient in inhibiting lipid oxidation than MTW, although no sample demonstrated a Trolox-like potential (IC50 value = 5.8 ± 0.6). Regarding the preservation of the blood erythrocytes, the MTE showed the best activity, being more protective than the positive control (Trolox, IC50 value = 19.6 ± 0.7 µg/mL), whereas MT80 had an antioxidant activity equivalent to that of the control, and a higher concentration of MTW was necessary to keep $50\%$ of erythrocytes intact. In the study performed by Muzykiewicz et al. [ 36], extracts obtained by ultrasound showed better antioxidant activity when the solvent used had an ethanol concentration greater than $20\%$. Some studies suggested that the mangosteen pericarp has more antioxidant activity than the corresponding edible part, which has been correlated with its higher amount of phenolic compounds [12]. Among the phenolic compound classes, tannin and phenolic acid fractions of mangosteen have shown scavenging free radical capacity and anti-lipid peroxidation [28,33]. Some studies show that the tannin fraction has more free radical scavenging activity than the xanthone fraction. However, young fruits (rich in tannins, IC50 = 5.56 µg/mL) are more antioxidant than mature fruit (rich in xanthones, IC50 > 150 mg/mL) due to the change in phenolic composition occurring throughout ripening. In another study, xanthones isolated from mangosteen pericarp have shown less antioxidant activity than a crude extract of this by-product. In the study by Ngawhirunpat et al. [ 32], isolated α-mangostin showed lower antioxidant potential than isolated epicatechin and tannin. For instance, α-mangostin, the major compound of this class present in mangosteen, did not show free radical scavenging ability in DPPH assay (EC50 > 150 µg/mL) [2]. According to previous reports, phenolic compounds are mainly responsible for the antioxidant activity of mangosteen, especially ellagitannin derivatives [32]. In our study, MT80 had the highest concentration of phenolic compounds, which could justify its potent lipid oxidation inhibition. However, MTE was the most antioxidant extract in the OxHLIA system, what indicates that other classes of bioactive compounds, not identified in this study (such as xanthones) may also contribute for its antioxidant potential.
As demonstrated herein, the antioxidant potential of the extracts depends on the ethanol concentration (Figure 2). Several studies on diverse vegetal matrices have demonstrated that the antioxidant potentials of extracts obtained with the binary solvent water + ethanol tends to increase with the ethanol concentration between 60 and $80\%$, which is also correlated with the amounts of phenolic acids and flavanols recovered, as well as with the total phenolic content [37,38,39,40,41].
## 3.2.2. Anti-Inflammatory Potential
Mangosteen pericarp extracts were evaluated regarding their ability to inhibit NO production on RAW 264.7 cells. Only MT80 and MTE had moderate anti-inflammatory potential, once their IC50 values, 85 ± 9 and 341 ± 2 μg/mL, respectively, were more than 5-fold higher than the concentration required for the positive control (Dexamethasone, IC50 value = 16 ± 1 μg/mL). On the other hand, the aqueous extract did not show inhibition of NO production at the highest concentration tested, which is likely related to the low concentration of bioactivity detected in this extract. Moreover, although the determination of xanthone compounds was not carried out in this study, it is known that this compound class has low solubility in water [11]. Hence, the combination of the factors cited above may justify the low anti-inflammatory potential verified for the MTW extract. Other studies have reported the potential of mangosteen extracts and their isolated compounds as anti-inflammatory agents. For instance, a low concentration (IC50 = 1 µg/mL) of an ethanolic extract obtained from exhaustive maceration of mangosteen pericarp was required to inhibit the NO production by RAW 264.7 cells [42]. In the same work, isolated xanthones, namely α- and γ-mangostin, showed IC50 values of 3.1 and 6.0 µM, respectively. Likewise, the proanthocyanidins present in the mangosteen pericarp showed the ability to bind LPS and neutralize its cytotoxicity [43]. Furthermore, the administration of silver nanoparticle biosynthesized with mangosteen pericarp extract to mice a dosage of 5 mg/mL/day for one week was able to inhibit the development of Listeria-induced infection [44]. The body of evidence mentioned above indicates that perhaps, after more specific studies, the pericarp of mangosteen and its isolated compounds may become a natural alternative to the traditional medicines used to control inflammation.
## 3.2.3. Antiproliferative Potential
Numerous studies have proven the anticancer properties of the xanthone extracts and isolated compounds from the mangosteen fruit. These compounds, mainly α-mangostin, have shown high antiproliferative activity on diverse tumor cell lines [10]. However, the present study focused on the determination of the antiproliferative activity of crude extracts poor in xanthone compounds. The results obtained are presented in Figure 3. All extracts showed cytotoxicity on NCI-H460, AGS and Caco-2 cell lines: for both lines, lower MT80 concentrations were required (GI50 values = 19–74 µg/mL), whereas higher MTW concentrations were needed (GI50 values = 93–141 µg/mL). MTE showed the highest antiproliferative activity on MCF-7 cells among samples, while MTW did not show antiproliferative action at the highest concentration tested. According to the literature, compounds from mangosteen fruit belonging to the xanthone class have shown anticancer proprieties against several malignant cell lines [45,46,47]. Moreover, mangosteen pericarp extracts displayed inhibitory activities against hepatocellular carcinoma in an animal model [47]. The action against the proliferation of HeLa cells was determined in a crude hydroethanolic extract (GI50 value of 18.087 μg/mL) of mangosteen pericarp, whereas the isolated α-mangosteen was highly cytotoxic on this cancer cell line (GI50 values of 6.5 μg/mL) [48]. MTW showed considerable antiproliferative potential against Caco-2 and AGS tumor-cell lines. Such bioactivity can be related, inter alia, to the presence of proanthocyanidin compounds detected in this extract [49].
Regarding the antiproliferative potential of our extracts on the Vero cell line, the MT80 and MTW extracts did not show toxicity in the highest concentration tested. Although the MTE extract was harmful to the proliferation of VERO cells, it only happened in a concentration (GI50 value of 76 ± 7 µg/mL) higher than the one required to inhibit the proliferation of tumor cell lines (GI50 values 17–73 µg/mL). Similar results were reported in the study performed by Ngawhirunpat et al. [ 32] using water, methanol and hexane as solvents. According to the authors, the first extract did not show toxicity in human keratinocyte cells (HaCat) in the maximal level tested, whereas the other extracts and isolated compounds were harmful to cell viability (GI50 values were 72, 30 and 2.5 μg/mL, respectively). Their aqueous extract did not show α-mangostin in its composition, while this compound was quantified in high amounts in their other extracts (15.5 and $18.7\%$ (w/w), in methanol and hexane extracts, respectively).
Finally, it is worth noting that in the balance between the antiproliferative potential on tumor and non-tumor cell lines tested, the hydroethanolic extract could be considered safe for the development of anticancer drugs. In addition, previous in vitro studies have also verified the toxicity of different extracts obtained from mangosteen pericarp and their isolated compounds, namely xanthones and proanthocyanidins, towards diverse non-cancerous human cell lines.
## 3.2.4. Antibacterial Potential
The minimal inhibitory concentration (MIC) of the extracts required for each bacteria tested is shown in Table 4. The pericarp samples exhibited antibacterial potential against all the bacteria tested, except against P. aeruginosa to which no inhibition was observed in the highest concentration of extract tested (20 mg/mL). However, no extract showed bactericidal effect at the highest concentration tested. Lower extract concentrations were required to inhibit the proliferation of Gram-positive bacteria (0.625–1.25 mg/mL) rather than of Gram-negative bacteria (2.5–10 mg/mL). The same tendency was reported by Taokaew et al. [ 50] when investigating the antibacterial potential of a multifunctional cellulosic nanofiber enhanced with mangosteen pericarp extract. They believe that this activity is related to the ability of α-mangostin to diffuse through the cell membrane of Gram-positive bacteria. In another study, aqueous extracts obtained from mangosteen by-products did not show antibacterial potential against Gram-positive bacteria, as their extracts had a low concentration of bioactive compounds, namely tartaric acid and flavonols, and likely less ability to cause damage to the cell membrane of the Gram-positive bacteria [51]. Furthermore, according to the extensive review performed by Lima et al. [ 52], fruit extracts in methanol and ethanol are more effective in inhibiting pathogenic bacteria than fruit extracts obtained with water as solvent.
According to the literature, xanthones from mangosteen hold high antibacterial activity towards several bacterial strains, such as Propionibacterium acnes, Staphylococcus epidermidis, and *Streptococcus mutans* [2,53]. On the other hand, proanthocyanidins from mangosteen have been shown to contribute to the inhibition of L. monocytogenes growth [54]. Regarding potential applications, the antibacterial activity of mangosteen pericarp extract and its derivatives have been explored for the development of dye cotton with properties against S. aureus and E. coli [55], self-nanoemulsifying drug delivery with action against *Staphylococcus epidermidis* [56], and for the production of medical glove films with antimicrobial activity [57].
Compared to other fruit by-products, mangosteen pericarp has greater antibacterial potential against E. coli, S. aureus, and L. monocytogenes than *Punica granatum* L. pericarp (MIC= 50–60 mg/mL), *Sambucus nigra* L. peels and seeds (MIC = 7.81–15.63 mg/mL), and *Prunus domestica* L. peels (MIC = 7.81–15.63 mg/mL) [52]. As a result, it might be a fascinating source of antimicrobial substances.
## 4. Conclusions
Mangosteen is an exotic fruit with cultural and economic relevance in Asia countries. Its industrial exploitation generates huge amount of by-products, as the inedible pericarp can constitute more than $50\%$ of the whole fruit. Some researchers have focused on the xanthone composition of the mangosteen residue; however, our study showed that other interesting bioactive compounds can be recovered from this biomass. Among them, proanthocyanidins are the most abundant, the successful recovery of which depends on solvent extraction. In the conditions tested in this work, the hydroethanolic solvent ($80\%$) was the most efficient for proanthocyanidin recovery, as anthocyanin compounds and a quercetin derivative were only detected in this extract (MT80). Moreover, the MT80 extract showed good bioactivity, such as high antioxidant, antiproliferative, anti-inflammatory and antibacterial potential. However, it is worth nothing that a good bioactive potential was also verified for the ethanol extract, although this extract showed some cytotoxic at a low concentration. Taken together, our results support the suggestion that the ripe mangosteen pericarp is a source of proanthocyanidins with promising biological activities, with potential to be upcycled into multifunctional ingredients for the food and pharmacological industries.
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|
---
title: On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a
Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories
authors:
- Abdelghani Moussaid
- Nabila Zrira
- Ibtissam Benmiloud
- Zineb Farahat
- Youssef Karmoun
- Yasmine Benzidia
- Soumaya Mouline
- Bahia El Abdi
- Jamal Eddine Bourkadi
- Nabil Ngote
journal: Healthcare
year: 2023
pmcid: PMC10000749
doi: 10.3390/healthcare11050662
license: CC BY 4.0
---
# On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories
## Abstract
The identification and characterization of lung diseases is one of the most interesting research topics in recent years. They require accurate and rapid diagnosis. Although lung imaging techniques have many advantages for disease diagnosis, the interpretation of medial lung images has always been a major problem for physicians and radiologists due to diagnostic errors. This has encouraged the use of modern artificial intelligence techniques such as deep learning. In this paper, a deep learning architecture based on EfficientNetB7, known as the most advanced architecture among convolutional networks, has been constructed for classification of medical X-ray and CT images of lungs into three classes namely: common pneumonia, coronavirus pneumonia and normal cases. In terms of accuracy, the proposed model is compared with recent pneumonia detection techniques. The results provided robust and consistent features to this system for pneumonia detection with predictive accuracy according to the three classes mentioned above for both imaging modalities: radiography at $99.81\%$ and CT at $99.88\%$. This work implements an accurate computer-aided system for the analysis of radiographic and CT medical images. The results of the classification are promising and will certainly improve the diagnosis and decision making of lung diseases that keep appearing over time.
## 1. Introduction
The world was gripped by the COVID-19 pandemic over the first half of 2020, it’s spread had severe and damaging sociological impacts and led to a significant slowdown in economic activities. In this context, it is mentioned that the respiratory virus SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2) became a major pandemic because of its easy spread in the air and contact with contaminated objects and people. In less than a year, it has plunged humanity into an unprecedented crisis that has spared no area of life [1]. So far, this pandemic has affected more than 200 countries, with more than 591 million coronavirus pneumonia cases and more than 6.4 million deaths directly linked to this coronavirus, officially counted on 19 August 2022 [2,3]. This enormous number of cases has led the medical community to take a greater interest in this kind of disease. Over time, the emergence of respiratory viruses has placed humanity in a context of uncertainty and fear, often accentuated by the urgency of the response. They affect all people of both sexes and all age groups, including smokers and non-smokers, making them one of the most widespread health problems worldwide [4]. Among the most common lung diseases are asthma, bronchitis, emphysema, lung cancer, common pneumonia, and coronavirus pneumonia. It should be noted that pneumonia caused by coronaviruses may be misdiagnosed as common pneumonia. It is an infection that affects one or both lungs. It can be caused by bacteria, viruses, or fungi. Symptoms can range from mild to severe and may include coughing either with or without mucus (a slimy substance), fever, chills, and breathing difficulty [5]. Since pneumonia caused by respiratory viruses causes the same symptoms, additional high-precision methods are needed to confirm this pneumonia’s etiology. One of the most reliable tests is the Polymerase Chain Reaction (PCR) which is recommended by the WHO [6,7]. Among the instrumental methods used, as an alternative or confirmation approach and recommended by the WHO to diagnose lung lesions, imaging can be done either by radiography, Computed Tomography (CT) or ultrasound [8]. Coronavirus pneumonia infection is not so rare in everyday life. Over time, several coronavirus cases have manifested as common pneumonia, since they both have very similar symptoms. Therefore, the main objective is to relieve the medical community and facilitate the differentiation between these diseases. Given the increasing number of cases, the use of such methods shows limitations. A computer science resource will be relevant. Besides, since the 1990s, the Artificial Intelligence (AI) industry has made significant progress. It was first described by John McCarthy in 1956 as “The science and engineering of making intelligent machines.” [ 9]. Nevertheless, early models’ flaws hindered widespread adoption and medical application. With the rise of Machine Learning (ML) in the 1980s and Deep Learning (DL) in 2010, many of these limitations were overcome. Thus, the ability of AI to exploit significant associations in a dataset has started to be used for diagnosis, therapy, and outcome prediction in many clinical contexts [10]. In 2013, Maoling Zhu et al. used the computer-aided diagnosis to differentiate Pancreatic Cancer (PC) from normal tissue [11]. In 2017, Gargeya et al. used DL for diabetic retinopathy screening, their model achieved $94\%$ sensitivity and $98\%$ specificity [12]. By the same year, AI was also applied to reliably predict cardiovascular risk, failing to recognize a large number of individuals who would benefit from preventative care while others underwent unneeded intervention. Machine Learning was used to improve accuracy by taking advantage of the complex interactions between risk factors [13]. Pneumology was no exception to this trend since many AI techniques were used both to classify and detect lung diseases. In this context, the emergence of pneumonia coronavirus around the world has challenged researchers to provide rapid and effective diagnostic tools to make healthcare systems intelligent in the fight against pandemics caused by respiratory viruses [14]. Thus, the use of AI remains mandatory, and the development of convolutional networks and DL algorithms that researchers, specialists, and companies around the world are deploying, has enabled a revolution in the rapid processing of hundreds of X-ray and CT scan images. Several algorithms to enhance, accelerate, and make an accurate diagnosis of different cases of pneumonia and aid in decision-making were developed [15]. For that, this work will present a Post-Pandemic Classification Algorithm (PPCA) in three classes: common pneumonia, coronavirus pneumonia, and healthy lungs; on two different modalities, chest X-rays and chest CT scans. This will be done using artificial intelligence tools such as the Convolution Neural Network (CNN) called EfficientNet and preprocessing techniques. The major contributions of this paper are:Using preprocessing techniques to enhance the quality of images;Training DL model to distinguish between common pneumonia, coronavirus pneumonia and normal cases;Performing classification on two different modalities including chest X-ray and chest CT scan.
Many studies have been devoted to deep learning-based solutions for detecting lung diseases. Among the studies already mentioned in the related word section, we cite: (i) Nishio et al. [ 16] who used pre-trained models, (VGG16, Resnet-50, MobileNet, DenseNet-121 and EfficientNet) for X-ray image classification between COVID-19 pneumonia, non-COVID-19 pneumonia and healthy individuals with an accuracy of $83.6\%$ and an average sensitivity of COVID-19 pneumonia of $90.9\%$; (ii) Ucar and Korkmaz [17] proposed a Bayes-SqueezeNet based rapid diagnostic system; an accuracy performance was $98.3\%$ (among normal, pneumonia and Covid cases), and $100\%$ for unique COVID-19 recognition; (iii) Maftouni et al [18] presented a robust COVID-19 classifier on chest CT images by proposing a deep learning model of pre-trained Residual Attention-92 and DenseNet architectures ensemble. The results were $97.93\%$ and $98.32\%$ accuracy respectively with COVID-19 pneumonia sensitivity of $96.72\%$ and $98.06\%$. Regarding the present study, comparing to the above mentioned studies the proposed model based on EfficientNet B7 architecture showed its effectiveness in providing better results for hybrid CT Scan and X-ray classification between COVID-19 pneumonia, non-COVID-19 pneumonia and healthy lungs, with an accuracy successively of $99.81\%$ and $99.88\%$ and a sensitivity to COVID-19 pneumonia of $100\%$ for both imaging modalities.
The following is the structure of the research paper. Section 2 summarizes previous work in the field of pneumonia classification. Section 3 covers all stages of the proposed approach, from image preprocessing to image classification. Section 4 discusses the research’s implementation specifics and the experimental findings. Section 5 depicts a discussion, and finally, Section 6 closes the paper with a conclusion.
## 2. Related Work
Deep Learning (DL) technology is currently being implemented in various subfields of medicine, including diagnostics, bioinformatics, and education. Since the onset of the pandemic, many researchers have shown the effectiveness of using the concept of transfer learning in DL frameworks, even is still limited. Part of the challenge deals with distinguishing between common pneumonia, coronavirus pneumonia, and normal cases. Several DL models can be implemented in medical image classification and analysis to support speed and correct decision-making.
## 2.1. X-ray-Based Approaches
Tripathi et al. [ 19] proposed and evaluated a deep Convolutional Neural Network (CNN) designed to classify thoracic diseases. The proposed model consists of convolutional layers, ReLU activations, a pooling layer, and a fully connected layer. An open-source dataset called Chest X-ray 14 was used. It consists of fifteen categories called atelectasis, cardiomegaly, effusion, infiltration, mass, nodule, pneumonia, pneumothorax, consolidation, edema, emphysema, fibrosis, pleural thickening, and hernia. This model gives an average accuracy of $89.77\%$ obtained for the classification of different diseases. The comparative analysis shows the effectiveness of the proposed model. Zhang et al. [ 20] developed a deep model to identify coronavirus pneumonia infection from radiological images. This model was trained on a dataset comprising radiological images from 1008 patients with common pneumonia and 70 patients with coronavirus pneumonia. It achieved a sensitivity of $96.0\%$ and a specificity of $70.7\%$ with an AUC of $95.2\%$. Sarki et al. [ 21] have developed a CNN-based system trained from scratch for persuasive classification and reliable detection of coronavirus pneumonia using a public X-ray database for training and validation. The data collected consisted of 1341 healthy images, 296 images with positive and suspect coronavirus pneumonia, and 3875 images with positive viral and bacterial pneumonia. Therefore, an imbalance in the collected data can be observed, which can lead to misleading classification results. Therefore, they examined all images manually and removed overexposed and underexposed images. Finally, they selected 140 images from each category for their experiments. Tuncer et al. [ 22] used the novel fuzzy tree classification approach for X-ray images in three classes (normal cases, common pneumonia, and coronavirus pneumonia). They applied Multi-Kernel Local Binary Pattern (MKLBP) to generate features, which were selected using the interactive neighborhood component (INCA) feature selector. INCA selected 616 features, which were forwarded to 16 conventional classifiers in five groups: Decision Tree (DT), Linear Discriminant (LD), Support Vector Machine (SVM), Ensemble Learning (EL), and K-Nearest Neighbor (K-NN). The best classifier was the cubic SVM which achieved $97.01\%$ classification accuracy. The application was applied using the MATLAB (2019b) software, with the MATLAB Classification Learner Toolbox (MCLT) for classification. Nishio et al. [ 16] aimed at developing and validating a computer-aided diagnostic system for the classification of a total of 1248 chest X-ray images, including 215 with coronavirus pneumonia, 533 with common pneumonia, and 500 healthy. They used 4 pre-trained models for transfer learning. VGG16 was the most accurate for category classification with a ratio of $83.6\%$. As the study dataset was relatively small [1248], it was necessary to improve the robustness of the CNN models by building an accurate CNN model using both transfer learning with VGG16 and a combination of data augmentation methods. Asif et al. [ 5] aimed to automatically detect patients with coronavirus pneumonia using chest X-ray images while maximizing detection accuracy using deep convolutional neural networks (DCNN) on 864 cases of coronavirus pneumonia, 1345 cases of viral pneumonia, and 1341 cases of normal pneumonia. The authors used Inception-v3 with transfer learning. The classification accuracy reached more than $98\%$, with a training accuracy of $97\%$ and a validation accuracy of $93\%$. Here, DCNN performed better with a larger dataset than with a smaller one. Shelke et al. [ 23] tended to apply a specific method. From an X-ray screening machine, the chest X-ray image was run through the VGG16 model, with the results bracketed as normal, pneumonia, and TB. The pneumonia images were then run through the DenseNet-161 model and classified as normal pneumonia and coronavirus pneumonia. These coronavirus pneumonia images were run through a ResNet-18 model and classified as severe, moderate, and mild Coronavirus pneumonia. The VGG16 achieved an accuracy rate of $96\%$, denseNet-161 reached $98.9\%$, and $76\%$ for ResNet-18. Ucar and Korkmaz [17] handled the problem of imbalanced data of the public dataset by using a multiscale offline augmentation technique. After that, the authors trained the augmented data with SqueezeNet architecture. The approach achieved an accuracy of $98.3\%$. Elaraby et al. [ 24] designed a new Gray-Scale Spatial Exploitation Net (GSEN) to classify patients with coronavirus pneumonia. This approach used web page crawling across cloud computing environments. The approach achieved an accuracy of $92.76\%$ for three-class labels and $95.60\%$ for two-class labels. Table 1 shows a summary of the comparison between the different studies that discussed X-ray-based approaches.
## 2.2. CT-Scan Based Approaches
Saba et al. [ 25] used six models, namely K-NN and RF based on traditional machine learning, VGG19, and Inception-v3 based on transfer learning. Then, they used CNN and iCNN based on personalized deep learning to address the classification between coronavirus pneumonia and common pneumonia from CT images. The dataset used was 2758 coronavirus pneumonia CT scans and 990 non-coronavirus pneumonia CT scans. The customized deep learning models of CNN and iCNN gave good results, with a ratio of $99.53\%$ for CNN and $99.69\%$ for iCNN. Shi et al. [ 26] used CT scan images of 1658 patients with coronavirus pneumonia and 1027 patients with common pneumonia. The authors proposed the infection-size-aware random forest (iSARF) method. Subjects were automatically classified with different ranges of infected lesion sizes, followed by random forests within each group for classification. The results were: acc: $87\%$, AUC: $94\%$, sensitivity: $90\%$ and specificity: $83\%$. The proposed method has been integrated into the Ucloud platform as an online service and is available to more than 20 clinical facilities in China. Maftouni et al. [ 18] presented a coronavirus pneumonia classifier on chest CT images with noisy labels by proposing an ensemble deep-learning model of pre-trained residual attention and DenseNet architectures. The novelty of this method is that the features extracted from the two deep networks (core learners) are stacked together and processed by a meta-learner to provide the final, robust prediction. The results in terms of precision were respectively $97.93\%$ and $98.32\%$ for the two proposed models. Nguyen et al. [ 27] conducted an evaluation of DL classification models trained to identify patients with positive coronavirus pneumonia on 3D computed tomography (CT) datasets from different countries: CC-CCII Dataset (China), COVID-CTset (Iran) and MosMedData (Russia) in addition to a dataset at UT Southwestern (UTSW). Models trained on a single dataset achieved receiver operating accuracy (AUC) values of $\frac{0.87}{0.826}$ (UTSW), $\frac{0.97}{0.988}$ (CC-CCCI) and $\frac{0.86}{0.873}$ (COVID-CTset). Apart from this, models trained on multiple datasets and evaluated on a test set from one of the datasets used for training performed better. However, performance dropped by almost an AUC of 0.5 (random estimate) for all models when evaluated on a different dataset outside of its training datasets, including the MosMedData. Pathan et al. [ 28] proposed two models for *Coronavirus pneumonia* detection: (i) based on a transfer learning approach and (ii) using a novel strategy to optimize CNN hyperparameters using the BAT algorithm based on Whale optimization + AdaBoost classifier built using dynamic ensemble selection techniques. The proposed system achieved $96\%$ classification accuracy in detecting *Coronavirus pneumonia* using chest CT scans. Table 2 shows a summary of the comparison between the different studies that discussed CT-Scan-based approaches.
Through our approach, we aim to improve the results obtained by other authors. The main objective is to create a lung disease classification model with higher accuracy that will be trained to recognize both X-ray images and CT scans.
## 3. Materials and Methods
To carry out this work, image processing was done to improve the quality of the used images. Followed by the choice and enhancement of the classification network to automatically classify pneumonia.
## 3.1. Image Preprocessing
The preprocessing pipeline is habitually used for preparing the input layer to satisfy the CNN requirements. In this work, image preprocessing was performed in four steps (Figure 1). The first step concerns only the CT scan dataset. The second step represents the image quality improvement, which is separated into two different methods: Contrast Limited Adaptive Histogram Equalization for X-ray modality and standard Histogram Equalization for CT scan modality. The third and the last steps are common for both modalities.
## 3.1.1. Cropping the Region of Interest (RoI)
CT scan images contain a high number of unwanted and insignificant pixels. For this reason, the RoI is cropped using some image processing techniques. First, Otsu’s *Method is* applied to perform unsupervised image thresholding. This step provides two different thresholds that are taken into consideration for the quantization of the images. The second step is quantizing the image using two quantization levels (i.e., two values of the threshold) to obtain only two regions. Third, binarizing the image to keep only the RoI that contains the lungs as well as the surrounding thoracic tissue. As shown in Figure 2, the RoI is cropped to extract only the efficient pulmonary regions.
## 3.1.2. Improving the Image Quality
Histogram *Equalization is* an image processing technique that adjusts image intensities to enhance the contrast. In this method, the probability density function of a given image is modified into a uniform probability density function which spreads out from the lowest pixel value to the highest one.
Contrast Limited Adaptive Histogram Equalization (CLAHE) aims to enhance the local contrast of an image [29]. CLAHE calculates the contrast transform function for each region individually. The contrast of each tile is enhanced so that the histogram of the output region approximately matches the histogram specified by the ’Distribution’ value. Neighboring regions are then combined using bilinear interpolation to remove artificially induced boundaries. The contrast, especially in homogeneous areas, can be limited so as not to amplify any noise present in the image. Figure 3 depicts the image quality improvement after applying the CLAHE technique, in which some lung details are more discriminant.
## 3.1.3. Image Resizing
Deep Learning models generally train more quickly on small images. For this reason, it is necessary to resize all the images into 256×256 dimensions, to provide the most adequate dataset for the used model which will allow obtaining the best results. The method takes the input image as input and a scaling factor and scales the input image with that factor.
## 3.1.4. Data Augmentation
To get good performance, the used model should be trained on a proportional number of examples. To both increase the number of training images and avoid problems with unbalanced data, the Augmentor tool was used. Augmentor is a Python package designed to generate artificial images for machine and deep-learning problems. For this purpose, rotational, vertical, and horizontal mirror transformations were used to generate 600 images per class for the X-ray dataset. Since the CT scan dataset is large enough to train the proposed model, the data augmentation step was skipped.
## 3.2. Classification Network Architecture
A deep learning architecture based on EfficientNetB7 which is known as the most advanced architecture in convolutional networks was built [30]. EfficientNetB7 shows a particularity in using a scaling strategy that employed a compound coefficient to equitably scale all the architecture parameters including resolution, depth, and width. First, a convolutional network is used to learn feature maps, while the second is used to classify the input images. Each convolutional layer was followed by an activation layer (ReLU- Rectified Linear Unit). By measuring a weighted sum, activation determines whether a neuron needs to be activated or not. It is used to introduce nonlinearity into the output of a neuron. In the following steps, max-pooling is performed to downsample the input image, reduce dimensionality, and prepare it for processing. The pre-trained model was used on the ImageNet dataset and the last layers of each model (1000 classes). Afterward, batch normalization, fully connected, and dropout layers were applied. As shown in Figure 4, the dropout layers are used to prevent overfitting. Initialization of the weight kernel with orthogonal weights was also performed. During the forward pass through a CNN, this initialization prevents the layers’ activation outputs from exploding or disappearing. Lastly, a fully-connected layer with three neurons was added to represent the class scores (i.e., output layer).
## 3.3.1. X-ray Dataset
The used dataset is composed of the publicly available datasets named MOMA Dataset and was published in June 2020 [31]. It is composed of 603 X-ray images in JPEG format, downloaded from Mendeley. 234 of these images were normal, 221 were positive for coronavirus pneumonia and 148 of them were positive for pneumonia. The pneumonia images were completed by 100 images recovered from the Cheikh Zaid International University Hospital.
## 3.3.2. CT Scan Dataset
MosMedData [32] was acquired from 1 March 2020 to 25 April 2020 at municipal hospitals in Moscow, Russia. It consists of anonymized lung CT scans with COVID-19 signs, as well as CT scans without such findings. It contains several CT scans for 1110 patients, of whom $42\%$ were males, $56\%$ were females and $2\%$ were not identified. The patient’s age ranges from 18 to 97 years old. Every exam has been saved in NIFTI (Neuroimaging Informatics Technology Initiative) format and archived in GZIP format. During this process, only every 10th instance was maintained in the final file. To complete the missing pneumonia cases for classification on 03 classes, a database named the largest COVID-19 CT dataset was sollicted [18] from [(https://www.kaggle.com/datasets/maedemaftouni/large-covid19-ct-slice-dataset) (accessed on 17 February 2022)].
## 3.3.3. Cheikh Zaid Data
Validation images were obtained on one CT system (Somatom Def AS, Siemens Healthineers, Germany). The main scanning parameters were as follow: Tube voltage: 120 kV, pitch factor $\frac{1}{4}$ 0.3–1.5 mm, recon matrix $\frac{1}{4}$ 512×512, slice thickness $\frac{1}{4}$ 1 mm. The patients were positioned toward the front of the imaging equipment where the face is in an upward direction (i.e., Head FirstSupine). The patient’s dataset is saved in DICOM (Digital Imaging and Communication in Medicine) 3.0 format. This study is a prospective analysis approved by the ethics committee of the Cheikh Zaid International University Hospital. 127 patients suspected of pneumonia including coronavirus pneumonia on a base of clinical symptomatology or an uncontrasted CT scan with suspicious images, were admitted to isolation departments at Cheikh Zaid International University Hospital in Rabat. Details of the clinical characteristics of these patients are summarized in Table 3.
## 3.4. Experimental Settings
The network was trained on Google Colab. All training and testing phases were performed in the same environment, using Keras deep learning framework and Python 3.5 as the programming language. The network training is performed with the hyperparameters illustrated in Table 4. Both datasets are divided into a training set and a testing set with a ratio of 0.7:0.3, respectively.
## 4. Experimental Results
In this section, experimental results on both public and private datasets are illustrated. The performance of the classification model was evaluated based on different metrics including, accuracy, confusion matrix, precision, recall and F1-score.
## 4.1. Results on X-ray Modality
The confusion matrix is used to determine the performance of the classification models for a given set of test data. It can only be determined if the true values for test data are known. According to Figure 5, the outcomes of the proposed model demonstrated the highest performance of the EfficientNet model, in which only one image of pneumonia is misclassified as the normal lung. Also, Table 5 shows that this model can efficiently classify the three classes with the highest ratio of precision for common pneumonia and coronavirus pneumonia is $100\%$ and $99\%$ for normal cases. This result assures that the classification is performed correctly for the three classes on the X-ray modality.
ResNet125V2 [33], DenseNet121 [34], and EfficientNetB7 [30] are all deep neural network architectures that have been used for various computer vision tasks such as image classification, object detection, and semantic segmentation. To validate the use of EfficientNetB7 in pneumonia classification, we perform different experiments in terms of accuracy and execution time. Each of these architectures has its own strengths and weaknesses, and the choice of which one to use depends on the specific task and available computational resources. As shown in Table 6, EfficientNetB7 is the best architecture in terms of accuracy, but it takes more time to train and produce a classification. Training a deep learning model typically requires a significant amount of computational resources and can take a long time to complete, depending on the size and complexity of the model and the amount of training data. Although training time is important, we can save the model once the results are very significant. Then the model can be used for inference. The inference time of EfficientNetB7 is about 2.51 s, and it refers to the classification of 540 X-ray images. On average, each image can be classified in 4 ms.
## 4.2. Results on CT Scan Modality
Furthermore, training and testing of the suggested network were done on the CT scan dataset. The confusion matrix is an important metric for the performance evaluations of a classification model. As depicted in Figure 6, only three normal images were confused with coronavirus pneumonia classes. From the values of Table 7, a precision of $99\%$ was achieved for coronavirus pneumonia and $100\%$ for both normal and common pneumonia. Also, this model attained $100\%$ of recall for coronavirus pneumonia and common pneumonia classes. In this work, one of the main findings is that the proposed model can distinguish correctly between the lungs infected with coronavirus pneumonia and all the patients infected with common pneumonia.
Table 8 depicts comparison results by CT scan modality. We can assert that EfficienNetB7 is the best architecture for pneumonia classification. The inference time of EfficientNetB7 on CT scan images is about 41.50 s, and it refers to the classification of 2953 X-ray images. On average, each CT scan can be classified in 14 ms.
## 4.3. Results on Cheikh Zaid Data
All validation DICOM images taken at the Cheikh Zaid International University Hospital were analyzed and reviewed by a radiologist, who overlooked epidemiological history and clinical symptoms. He classified chest CT as normal, positive for coronavirus pneumonia, or positive for common pneumonia.
All Cheikh Zaid images were treated following the preprocessing pipeline. As shown in Figure 7, cropping of the original image was performed to remove the unwanted regions, then to improve the image quality, the histogram equalization technique was used. After that, the image was resized to be suitable for the input model. To perform classification, the trained model was loaded and the class of each Cheikh Zaid image was predicted. As a result, shown in Table 9, the proposed model achieved an accuracy of $95\%$ on Moroccan CT scan images. Thus, it will help the radiologist to classify pneumonia diseases for further diagnosis.
## 4.4. Comparison with the State-of-the-Art
Table 10 compares the proposed model based on the EfficientNet architecture as well as the preprocessing pipeline with the state-of-the-art. Previous approaches have used the X-ray dataset or CT scan dataset to attain coronavirus pneumonia classification with data augmentation techniques and transfer learning. A high accuracy Was achieved on both chest X-ray and CT scan modalities. These results are very encouraging and can be used by radiologists to diagnose pneumonia diseases early and correctly.
## 5. Discussion
The proposed EfficientNet model showed higher classification performance on the three classes with the highest accuracy ratio on public datasets. The results of the present work only show that the proposed CADx system achieved high accuracy in public datasets for both X-ray and CT scans. For radiographic modality, Table 5 shows that the model efficiently and correctly classified the three classes with the highest accuracy ratio, which is $100\%$ for common pneumonia and coronavirus pneumonia and $99\%$ for normal cases. Regarding the CT scan modality and based on Table 6 values, an accuracy of $99\%$ was obtained for coronavirus pneumonia and $100\%$ for normal and common pneumonia. Similarly, this model achieved $100\%$ recall for coronavirus pneumonia and common pneumonia classes. Therefore, the proposed model can correctly distinguish lungs infected by coronavirus pneumonia from those infected by common pneumonia. As a result, the proposed model achieved an accuracy of $95\%$ on Moroccan CT images. This may occur in the presence of low-quality images that contain artifacts and noise similar to the opacity of patients’ lungs. Moreover, the characteristics of public datasets may be different from those of Moroccan clinical data. In such a case, an overfit may have occurred during external validation. In addition, the specificity of the proposed model can be summed up in the fact that it has achieved very satisfactory accuracy rates on two imaging modalities based on public data. This shows its robustness when compared with models cited in related works that have treated a single imaging modality. Therefore, its use will be of great help to radiologists in classifying pneumonia diseases for further diagnosis.
## 6. Conclusions
In this work, a Post-Pandemic Classification Algorithm (PPCA) based on the EfficientNet architecture is proposed in the context of pneumonia virus classification. The proposed approach consists of two major phases. In the first phase, preprocessing is performed on both X-ray and CT scan images. Whereas, in the second phase, the used architecture is trained on two public datasets as well as the Cheikh Zaid dataset. In sum, this work was able to implement an accurate computer-aided system for the analysis of both radiographic and CT medical images that led to more promising classification results and higher accuracy. Thus, this system will provide valuable support to radiologists and physicians for the early detection and diagnosis of respiratory viruses.
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---
title: CD24 Gene Expression as a Risk Factor for Non-Alcoholic Fatty Liver Disease
authors:
- Mona A. Amin
- Halla M. Ragab
- Nabila Abd El Maksoud
- Wafaa Abd Elaziz
journal: Diagnostics
year: 2023
pmcid: PMC10000766
doi: 10.3390/diagnostics13050984
license: CC BY 4.0
---
# CD24 Gene Expression as a Risk Factor for Non-Alcoholic Fatty Liver Disease
## Abstract
In light of increasing NAFLD prevalence, early detection and diagnosis are needed for decision-making in clinical practice and could be helpful in the management of patients with NAFLD. The goal of this study was to evaluate the diagnostic accuracy of CD24 gene expression as a non-invasive tool to detect hepatic steatosis for diagnosis of NAFLD at early stage. These findings will aid in the creation of a viable diagnostic approach. Methods: This study enrolled eighty individuals divided into two groups; a study group included forty cases with bright liver and a group of healthy subjects with normal liver. Steatosis was quantified by CAP. Fibrosis assessment was performed by FIB-4, NFS, Fast-score, and Fibroscan. Liver enzymes, lipid profile, and CBC were evaluated. Utilizing RNA extracted from whole blood, the CD24 gene expression was detected using real-time PCR technique. Results: It was detected that expression of CD24 was significantly higher in patients with NAFLD than healthy controls. The median fold change was 6.56 higher in NAFLD cases compared to control subjects. Additionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients but without significant difference ($$p \leq 0.588$$). ROC curve analysis showed that CD24 ∆CT had significant diagnostic accuracy in the diagnosis of NAFLD ($$p \leq 0.034$$). The optimum cutoff for CD24 was 1.83 for distinguishing patients with NAFLD from healthy control with sensitivity $55\%$ and specificity $74.4\%$; and an area under the ROC curve (AUROC) of 0.638 ($95\%$ CI: 0.514–0.763) was determined. Conclusion: In the present study, CD24 gene expression was up-regulated in fatty liver. Further studies are required to confer its diagnostic and prognostic value in the detection of NAFLD, clarify its role in the progression of hepatocyte steatosis, and to elucidate the mechanism of this biomarker in the progression of disease.
## 1. Introduction
NAFLD is a clinico-pathologic syndrome that encompasses various medical entities, including simple fatty liver or simple steatosis, nonalcoholic steatohepatitis (NASH), cirrhosis, and its complications [1]. NAFLD now affects up to $25\%$ of people around the world. The highest prevalence rate is in the Middle East ($32\%$), followed by South America ($30\%$), while the lowest is in Africa ($13\%$). It also accounts for $2\%$ of total deaths [2]. The increase in NAFLD prevalence parallels the rise in obesity and is tightly associated with metabolic comorbidities (diabetes, hypertension, insulin resistance, and dyslipidemia). It also places patients at higher risk for progressive liver disease [3].
It became clear that, as with different complex multisystem disorders, NAFLD is triggered by a variety of underlying mechanisms; the most important one of them is the alterations in hepatic and extra-hepatic lipid metabolism [4]. The study of genetic factors in NAFLD is a rapidly growing field, as they determine not only the response of different individuals to excess caloric consumption, but also the resulting metabolic derangements [5].
Cluster of differentiation 24 (CD24) is a glycophosphatidylinositol (GPI)-anchored mucin-like cell surface glycoprotein, encoded by a gene located on chromosome 6. It is expressed on mature granulocytes and B cells and regulates growth and differentiation signals to these cells. Accumulating evidence showed that abnormal over-expression of this protein is a prognostic factor in many types of cancers, resulting in cancer cell growth, proliferation, and metastasis [6]. The expression of the cell surface molecule CD24 has previously been shown to identify a subset of adipocyte progenitor cells that is crucial for the reconstitution of white adipose tissue (WAT) function in vivo, as well as a particular regulator of adipogenesis in vitro [7]. Recently, CD24 has been identified as a possible biomarker for distinguishing NAFLD/NASH. It was concluded that the mRNA expression of CD24 is upregulated in the fatty liver [8]. Additionally, Feng et al., [ 2021] detected that CD24 was positively associated with NAFLD severity, and it could also differentiate mild NAFLD patients from severe NAFLD patients [9].
Therefore, the present study aimed to identify the association between gene expression of CD24 and early stage of NAFLD.
## 2. Subjects and Methods
The present study is a prospective study that was carried out on 80 subjects who attended outpatient clinics of the Internal Medicine Department of Kasr Al Ainy Hospital Cairo, Egypt during the period from May 2019 to December 2020 either for general health checks or to identify and treat the complications of other metabolic disorders such as diabetes or obesity.
The selected subjects were divided into two groups according to the sonographic findings of steatosis: 40 NAFLD patients with bright liver echogenicity and 40 healthy subjects with normal liver echogenicity. All cases have age ranging between 19 to 56 years old.
Those with clinical, biochemical, or histological evidence of cirrhosis, those with known causes of liver disease [viral hepatitis B and C, autoimmune hepatitis, primary biliary cirrhosis, haemochromatosis or Wilson disease], those with history of current or past excessive alcohol drinking as defined by an average daily consumption of more than 20 g alcohol, drug-induced liver disease, pregnant women and patients on hormonal contraceptive drugs (oral, parenteral), hormone replacement therapy were excluded from the study. The study was approved by Medical Research Ethical Committee of the National Research Center, Cairo, Egypt (Approval No.19-001), and informed consent was obtained from all patients.
All patients were evaluated by history and clinical examination and measurement of anthropometric parameters, such as weight (kg), height (m), body mass index (BMI; kg/m2), waist circumference (cm), and mid-arm circumference (cm).
Body mass index (BMI) was determined by dividing weight by square height (kg/m2). BMI is calculated as weight in kilograms divided by the height in metres squared. According to WHO, People with BMI = 18.5–24.9 have normal weight, people with BMI = 25.0–29.9 were classified overweight, while people with BMI ≥ 30 kg/m2 defines obese. BMI is calculated as weight in kilograms divided by the height in metres squared. According to WHO, in adults, overweight is defined as a BMI of 25–29.9, while a BMI ≥ 30 kg/m2 defines obesity.
Waist circumference (WC) was obtained from each subject by measuring at the midpoint between the lower rib margin and the iliac crest using a conventional tape graduated in centimeters (cm). Mid-arm circumference was measured as the right upper arm measured at the midpoint between the tip of the shoulder and the tip of the elbow (olecranon process and the acromium).
Cases were divided according to their previous diagnosis or levels of fasting blood sugar: a fasting blood sugar level less than 115 mg/dL is considered normal or prediabetes. While, if the fasting blood sugar level is 126 mg/dL or higher, the patient was diagnosed diabetic.
Complete blood count was determined using the automated hematology analyzer SF-300 (Sysmex Corporation, Japan). Additionally, liver enzymes (ALT, AST, ALP, GGT), serum albumin, prothrombin time, INR, serum creatinine, lipid profile, and fasting blood sugar were measured to all individuals according to the manufacture instructions. The reagents were purchased from Spectrum Company, Cairo, Egypt.
NAFLD fibrosis score (NFS), FIB-4, and Fast score were calculated as mentioned previously by Angulo et al. [ 2007] and Calès et al. [ 2009] [10,11] to assess fibrosis of the NAFLD patients’ group.
NFS score = −1.675 + 0.037 × age [y] + 0.094× BMI [kg/m2] + 1.13 × IFG/diabetes [yes = 1, no = 0] + 0.99 × AST/ALT ratio − 0.013 × platelet count [×109/L] − 0.66 × albumin [g/dL] FIB-4 score = Age [y] × AST [U/L]/platelet [×109/L] × ALT [U/L] FAST score was calculated according to Newsome et al., [ 2020] [12] as: FAST = {exp (–1.65 + 1.07 × ln (LSM) + 2.66 × 10–8 × CAP3 – 63.3 × AST–1)}/{1 + exp (–1.65 + 1.07 × ln (LSM) + 2.66 × 10–8 × CAP3 – 63.3 × AST–1)}[1] Abdominal ultrasonography was performed to all individuals using the 3.5 MHz probe of Logic 6 of a General Electric machine.
## 2.1. Liver Stiffness Measurement (LSM) and Controlled Attenuation Parameter (CAP)
Fibroscan (M probe, Echosens, Paris) was carried out by an experienced examiner in all patients (with at least 6 h of fasting) in left lateral position and the median liver stiffness of the 10 successful measurements fulfilling the criteria (success rate of greater than $60\%$ and interquartile range/median ratio of <$30\%$) were noted (in kPa).
The final CAP value, which ranges from 100 to 400 (dB/m), is the median of individual measurements. As an indicator of variability, the ratio of the IQR of CAP values to the median (IQR/MCAP) was calculated. The operator was blinded to the patients’ clinical data. According to the manufacturer’s instructions, in addition to previous studies, the stages of fibrosis (F0: 1–6, F1: 6.1–7, F2: 7–9, F3: 9.1–10.3, and F4: ≥10.4) were defined in kPa [13]. Moreover, steatosis stages (S0: <215, S1: 216– 252, S2: 253–296, S3: >296) were defined in dB/m [13].
## 2.2. Sample Collection
10 mL venous blood were drawn from all study participants in the morning after a 12 h fast; a portion of the blood was collected on EDTA tube for the extraction of RNA and for the determination of routine blood pictures (CBC) by Sysmex, the automated hematology analyzer SF-300, which was produced by Sysmex Corporation, Japan. The other portion was left to clot at room temperature. Serum was separated by centrifuging for 10 min at 3000 rpm. Sera were used immediately for other biochemical investigations including aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin, serum albumin, fasting blood glucose, cholesterol, triglycerides, HDL-C, and LDL-C according to the manufacturer’s instructions. The reagents were purchased from Spectrum Company, Cairo, Egypt.
## 2.3. CD24 Gene Expression by Quantitative Real Time-PCR (qRT-PCR):
Total RNA was isolated from whole blood using GeneJET Whole Blood RNA Purification Mini Kit (Thermo Scientific, Lithuania) following the manufacturer’s suggestions.
## 2.4. Reverse Transcription for cDNA Synthesis and Quantitative Real-Time PCR (RTqPCR)
Reverse transcription (RT) was performed to obtain cDNA from 400 ng of purified RNA using the High-Capacity cDNA Reverse Transcription Kits (Applied Biosystem, Lithuania) with random hexamers according to the manufacturer’s suggestions. A value of 10 µL of the 2X-RT master mix was pipetted into each tube and then 10 µL of RNA sample was added to it and mixed well. The tubes were centrifuged to spin down the content and to eliminate any air bubbles. After that, the tubes were placed on the PCR machine (Cleaver Scientific, UK) programmed as follows: 25 °C, 10 min, 37 °C, 120 min, and 85 °C, 5 min. After detection of cDNA concentration and purity, they were stored in −20 °C until carryover quantitative real-time PCR (QRT-PCR).
CD-24 gene expression for enrolled samples was quantified using PowerUp SYBR Green master mix (2×) (ThermoFisher Scientific, Lithuania). The sequences for used primers were as follows: PrimerPrimer SequenceCD24 Forward primer5′-ACC CAC GCA GAT TTA TTC CA-3′CD24 Reverse primer5′-ACC ACG AAG AGA CTG GCT GT-3′β-actin Forward primer5′-TGA GCG CGG CTA CAG CTT-3′β-actin Reverse primer5′-TCC TTA ATG TCA CGC ACG ATT T-3′ PCR amplification was carried out in 20 μL reaction volume containing 1 µL cDNA, 10 µL PowerUp SYBR Green master mix, 7 μL nuclease-free water, and 1 µL of gene-specific forward and reverse primers as mentioned in table. The reaction was run in the Rotor-Gene Q instrument, (QIAGEN).
Fluorescence measurements were made in every cycle, and the thermal profile was used as the follows: The amplification program included a UDG activation at 50 °C with a 2-min hold, and a dual-lock DNA polymerase at 94 °C with a 3-min hold, followed by 45 cycles with denaturation at 94 °C for 30-s, annealing at 55 °C for 30-s, and extension at 72 °C for 30-s. The expression levels of CD-24 in tested samples were expressed in the form of ∆∆CT (cycle threshold) value, which was calculated based on threshold cycle (Ct) values, corrected by β-actin expression, with the following equation.
The relative amount of CD-24 = 2–ΔΔCt; ΔΔCt = [ΔCt of cases − ΔCt of control]; [ΔCt = Ct (CD-24) − Ct (β-actin)]. The following primers were used in the quantitative real-time PCR analyses.
## 2.5. Statistical Analysis
SPSS version 16.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis with a two-side significant criterion at $p \leq 0.05.$ The clinical data were expressed as mean ± SD (continuous, normally distributed variables). Categorical data were summarized as percentages. The significance for the difference between groups was determined by using a two-tailed Student’s t-test. Additionally, qualitative variables were assessed by chi-squared χ2-test. Correlations between different parameters were performed using Pearson’s and spearman’s correlation coefficients. A receiver operating characteristic (ROC) curve was plotted to assess the diagnostic power of CD24 in NAFLD and controls, and the area under the curve (AUC) greater than 0.5 considered to be statistically significant. The probability (p) values of ≤0.05 were considered statistically significant and indicated, while $p \leq 0.05$ was considered statistically not significant and indicated NS.
## 3. Results
The present study is a case-control study recruited 80 adult subjects, (28 males and 52 females). Their age ranged from 19 to 56 years.
The demographic, anthropometric, clinical, and biochemical characteristics of both groups (NAFLD and controls) are summarized in Table 1. Patients with NAFLD were significantly older than controls (mean age 42.18 ± 11.1 4 y vs. 29.65 ± 6.63 y, $p \leq 0.0001$). There were more males in the control group ($45\%$), but the majority was females in the NAFLD group ($75\%$). NAFLD patients exhibited a higher mean BMI (31.8 ± 2.9 kg/m2) than the control group (23.76 ± 1.4 kg/m2) ($p \leq 0.001$).
Patients with NAFLD had a higher prevalence of hypertension and diabetes mellitus in comparison to healthy controls ($p \leq 0.001$) (Table 1).
Among studied NAFLD patients, $22.5\%$ had a family history of diabetes, and $30\%$ had family history of liver disease, and $62.5\%$ of NAFLD cases ($$n = 25$$) have enlarged liver size on ultrasound.
The mean serum fasting blood glucose was significantly higher in NAFLD patients than that in controls (122.6 ± 40.97 vs. 96.03 ± 7.77); ($p \leq 0.001$). In addition, hemoglobin levels were lower in NAFLD cases (11.56 ± 1.4 (g/dL) than in healthy controls (12.81 ± 1.06 (g/dL), ($p \leq 0.001$). No significant difference was observed in total leucocytic count (TLC) and platelet count between the NAFLD and control groups ($p \leq 0.05$).
NAFLD patients had significantly higher serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT) compared to healthy controls ($p \leq 0.001$). On the other hand, the mean albumin level was almost normal (3.8 ± 0.38 g/dL) in the NAFLD group.
There was a significant elevation in total cholesterol, triglycerides, and LDL-cholesterol among NAFLD patients compared to controls, while there was significant decrement in HDL in the NAFLD group as opposed to controls ($p \leq 0.05$).
Table 2 shows clinical and biochemical characteristics of participants stratified by sex and presence/absence of NAFLD. In both sexes, participants with NAFLD were older, had a higher BMI, as well as a higher prevalence of diabetes. Levels of hemoglobin was significantly lower in female cases compared to male cases in NAFLD group ($$p \leq 0.001$$). However, ALT and AST levels were significantly higher in male NAFLD cases compared to female NAFLD casess ($$p \leq 0.009$$ and $$p \leq 0.038$$; respectively) (Table 2).
The mean Fibroscan value in all NAFLD patients was 5.1 ± 0.99 (kPa), indicating that all patients had mild fibrosis with a stage less than 2. Thirty patients had fibrosis belonging to stage 0, while the rest had fibrosis stage 1. Mean Fibroscan values for cases with fibrosis stages 0 and 1 were 4.7 ± 0.67 and 6.5 ± 0.3 (kPa), respectively. There was a statistically significant difference in liver stiffness measurements in patients with stage 0 fibrosis as compared to stage 1 fibrosis ($p \leq 0.001$). In addition, there was a stepwise increase in Cap score parallel to the increase in severity of liver fibrosis ($p \leq 0.001$) (Table 3).
This study showed that both NFS and FIB-4 score were similar in patients with fibrosis stages 0 and those with fibrosis stages 1 ($p \leq 0.05$). This may be due to that all cases included in our study have mild fibrosis. Additionally, performances of FIB-4 and NFS to rule in advanced fibrosis are rather inadequate, meaning that further assessment with another test is needed in case of positive results.
According to the RT-PCR results, it was detected that expression of CD24 was significantly higher in patients with NAFLD than healthy controls. The median fold change in the expression of CD24 was 6.56 higher in NAFLD cases compared to control subjects (Figure 1).
The present study showed higher expression of CD24 in female cases with NAFLD compared to male cases (fold change was 6.9 in females vs 4.4 in males, but without significant difference; $$p \leq 0.262$$) (Figure 2).
Additionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients, but without significant difference ($$p \leq 0.588$$). Furthermore, there was no difference in CD24 fold change between overweight patients (median fold change = 9) and obese cases (median fold change = 5.89) ($$p \leq 0.447$$) (Figure 3). Additionally, the median fold change in CD24 in diabetic cases was seven compared to 5.13 in non-diabetic cases ($$p \leq 0.609$$) (Figure 4).
## 3.1. Evaluation of the Diagnostic Accuracy of CD24 Gene Expression for Distinguishing Patients with NAFLD from Healthy Controls
Figure 5 illustrates the ROC plots to assess the diagnostic accuracy of CD24 ∆CT to distinguish patients with NAFLD from healthy controls.
ROC curve analysis showed that CD24 ∆CT had significant diagnostic accuracy in the diagnosis of NAFLD ($$p \leq 0.034$$). ROC curve showed the optimum cutoff for CD24 was 1.83 for distinguishing patients with NAFLD from healthy control with sensitivity $55\%$ and specificity $74.4\%$; and an area under the ROC curve (AUROC) 0.638 ($95\%$ CI: 0.514–0.763).
## 3.2. Correlation between Different Non-Invasive Fibrosis Markers and CD24 Gene Expression
Table 4 shows the correlation of Kpa, CAP, FAST, NFS, and FIB-4 with CD 24 gene expression. Pearson’s correlation test showed positive significant correlation between CD24 and NFS ($r = 0.356$, $$p \leq 0.001$$).
By binary logistic regression analysis, none of the examined parameters found to be significant determinant of NAFLD after adjusting the effects of potential cofounders of age, gender, suffering of diabetes, and BMI, respectively (Table 5).
## 4. Discussion
NAFLD is known nowadays as the most common liver disorder in the 21st century. It is diagnosed by the presence of more than $5\%$ fat accumulation in liver cells without excess alcohol consumption or secondary causes of fat accumulation in the background. Approximately $25\%$ of the world’s adult’s population has NAFLD, and the prevalence is still increasing [13].
NAFLD may eventually deteriorate to HCC as a result of excessive lipid accumulation, liver cell damage, immune system dysfunction, which leads to scarring, and permanent liver damage [14]. In light of increasing NAFLD prevalence, early detection and diagnosis are needed for decision-making in clinical practice and could be helpful in the management of patients with NAFLD.
The present study showed a significant trend of elder age with the progression of non-alcoholic fatty liver disease. This finding substantiates previous findings in the literature, which suggested that the prevalence of NAFLD increases with increasing age [15].
The present study showed that, regarding gender distribution, there were more males in the control group ($45\%$) compared to the NAFLD group ($25\%$), but the majority was females in the NAFLD group ($75\%$). These results revealed that there was no statistically significant difference between both studied groups according to gender as $$p \leq 0.061.$$ The explanation for the gender difference is different distributions of fat mass by gender, e.g., more abdominal visceral adipose tissue in male and more subcutaneous adipose tissue mass in female. Additionally, previous results showed that Hispanic women having a higher risk for NAFLD compared to men, whereas, for the non-Hispanic population, the prevalence of NAFLD is more frequent in males [16]. Additionally, Lonardo et al. mentioned that gender is one of the main cause of variation in NAFLD risk factors. They also detected that NAFLD is more common and more severe in men than women. However, it is more common in women after menopause, indicating that estrogen may be beneficial [17].
In the current study, the incidence of NAFLD has been increasing in concert with the presence of multiple metabolic disorders, such as dyslipidemia, diabetes, hypertension, and visceral obesity. As expected, the incidence of diabetes and hypertension was significantly higher in patients suffering from NAFLD. This is in good agreement with previous studies that mentioned impaired glucose tolerance as an independent risk factor for the progression of NAFLD [18,19].
According to the International Diabetes Federation (IDF), the prevalence of DM among Egyptian adults is $15.2\%$, which may be an underestimation [20]. Lonardo et al. reported that patients with T2DM had $80\%$ higher liver fat contents compared to non-diabetic patients [21].
Additionally, Lee, et al., [ 2019], mentioned that compared to the general population (around $25\%$), $50\%$ to $70\%$ of people with diabetes have NAFLD, and NAFLD severity (including fibrosis) tends to be worsened by the presence of diabetes [22].
Additionally, another study carried out on the Egyptian college students showed that around 1 in 3 had steatosis, and 1 in 20 had fibrosis. The prevalence of NAFLD in young adults was estimated to be $31.6\%$, which is perfectly in line with the $31.8\%$ prevalence rate found in a meta-analysis of numerous epidemiological studies across general Middle Eastern populations. It is known that the Middle East and North Africa region has one of the highest prevalence rates of NAFLD globally, and that Egypt ranked among the highest 10 nations with obesity prevalence. Combing both may explain our unexpected observation. In our cohort, 59 ($49.2\%$) of participants were overweight or obese [23].
NAFLD is caused by a variety of different molecular pathways and cellular alterations. The molecular pathways of NAFLD pathogenesis in the liver have been identified in several studies. The major genes linked to illness development and the underlying functional pathways are yet unknown, and whether the differentially expressed CD24 is involved in hepatic lipid metabolism is still unclear.
Microarray technologies have revealed a large number of new molecular markers (DNA, RNA, and protein) in recent years. Further research is needed to confirm the clinical utility of these impending novel indicators in relation to hepatic steatosis. CD24 is one of these markers, which was recently reported by Huang et al. as a possible biomarker in the course of hepatocyte steatosis [8].
Various studies have recently discovered that CD24 expression is relatively high in many human malignancies, including HCC [24,25,26,27,28]. Additionally, CD24 overexpression has been correlated with increased invasiveness, proliferation, and metastasis [29]. It was previously identified that a subpopulation of adipocyte progenitor cells with the expression of the cell surface molecule CD24 being necessary for reconstitution of white adipose tissue function in vivo as well as being a key regulator of adipogenesis in vitro [30].
In our study, we investigated the association between CD24 gene expression and the prevalence of NAFLD.
The current study found that CD24 gene expression was considerably greater in NAFLD cases compared to controls, and the normalized CD24 gene expression in NAFLD was up-regulated 6.56-fold. These findings suggest that the CD24 gene is important in the development of NAFLD. This could be related to CD24 gene expression’s impact on the immune/inflammatory response via T-cell activation [31]. Several immune cell-mediated inflammatory processes are involved in NAFLD and its progression to NASH. They also influence the generation of cytokines by necrotic liver cells [32].
This confirms the previous results detected by Feng et al., who observed the up-regulation of CD24 gene expression in the livers of HFD-induced NAFLD mice and in cultured HepG2 cells exposed to glucolipotoxicity (palmitic acid or advanced glycation end products) [9]. Up until now, the precise role and the underlying mechanisms of CD24 in NAFLD progression remain unclear. However, Huang and his colleague identified the prominent correlation between CD24 and NAFLD/NASH. They mentioned that CD24 could play a key role in one of the pathways that may cause IR and may induce NAFLD/NASH in humans including [“glycolysis/gluconeogenesis”, “p53 signaling pathway” and “glycine”, serine and threonine metabolism [8].
Additionally, CD24 expression was higher in cases with fibrosis stage F1 compared to those with fibrosis stage F0, as the mean expression level of CD24 was 7.19 in F0 cases as compared to 8.65 in F1 patients, but without significant difference ($$p \leq 0.588$$). This may be because that all cases included in the present study have mild fibrosis. This results most be confirmed by other studies based on large number of samples and on patients with severe stage of fibrosis.
The changes in liver tissue-transcriptome in a subset of 25 mild-NAFLD and 20 NASH biopsies were examined in a cross-sectional study. CD24 was revealed to be one of five differentially expressed genes (DEGs) positively linked with disease severity and to be main classifiers of mild and severe NAFLD [33].
Additionally, CD24-positive cells isolated from hepatocellular carcinoma cell lines exhibited stemness properties, such as self-renewal, chemotherapy resistance, metastasis, and tumorigenicity [34]. These results indicate that CD24 may play a role in hepatocyte injury and promote regeneration during the development and progression of NAFLD.
Another Egyptian study detected that CD24 polymorphism 170 CT/TT may affect the incidence of infection with CHC, as well as HCC [35]. They revealed that the P170T allele, which is expressed at a higher level than P170C, encodes a certain protein, which is responsible for the progression of chronic HCV infection by affecting the efficiency of cleavage of posttranslational GPI. Additionally, Robert and Pelletier [2018] showed that the P170T allele affects the progression of chronic HCV infection through posttranslational mechanisms [36]. Another study by Kristiansen et al. [ 2010] also suggested that CD24 SNPs are prognostic markers for hepatic carcinoma [37].
Interestingly, CD24 was also up-regulated in the NAFLD patients with type 2 diabetes than its expression in non-diabetic cases, but without significant difference.
Another study carried out by Shapira et al. [ 2021] reported that CD24 may negatively regulate peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in male mice. *This* gene is a regulator of adipogenesis that plays a role in insulin sensitivity, lipid metabolism, and adipokine expression in adipocytes. Furthermore, they concluded the association between the CD24 and insulin sensitivity, suggesting its possible mechanism for diabetes [38].
## 5. Conclusions
The current study found CD24 gene expression was considerably greater in NAFLD cases compared to controls. This could indicate that CD24 may contribute to hepatic steatosis, but a current study showed that it cannot be used as an independent predictor of NAFLD.
Further studies are required to confer its diagnostic and prognostic value in the detection of NAFLD, as well as to clarify its role in the progression of hepatocyte steatosis in patients with advanced stage of fibrosis and to elucidate the mechanism of this biomarker in the progression of disease. However, our study is limited because of the small sample size, because all participants in this study have early stage of NAFLD, and because accurate diagnosis of liver fibrosis or hepatocellular injury are invasive and very expensive. Although abdominal ultrasonography has low sensitivity for detecting mild-NAFLD as reported in the previous literature, it is the best low-cost available non-invasive technique to detect NAFLD. Because of ethical considerations, we did not rely on the liver biopsy for diagnosis, as none of the patients had clinical manifestations. Moreover, the studied patients considered themselves healthy and refused to undergo further invasive investigations, including pathological examinations via liver biopsy to detect fibrosis.
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|
---
title: The Adaptive Force as a Potential Biomechanical Parameter in the Recovery Process
of Patients with Long COVID
authors:
- Laura V. Schaefer
- Frank N. Bittmann
journal: Diagnostics
year: 2023
pmcid: PMC10000769
doi: 10.3390/diagnostics13050882
license: CC BY 4.0
---
# The Adaptive Force as a Potential Biomechanical Parameter in the Recovery Process of Patients with Long COVID
## Abstract
Long COVID patients show symptoms, such as fatigue, muscle weakness and pain. Adequate diagnostics are still lacking. Investigating muscle function might be a beneficial approach. The holding capacity (maximal isometric Adaptive Force; AFisomax) was previously suggested to be especially sensitive for impairments. This longitudinal, non-clinical study aimed to investigate the AF in long COVID patients and their recovery process. AF parameters of elbow and hip flexors were assessed in 17 patients at three time points (pre: long COVID state, post: immediately after first treatment, end: recovery) by an objectified manual muscle test. The tester applied an increasing force on the limb of the patient, who had to resist isometrically for as long as possible. The intensity of 13 common symptoms were queried. At pre, patients started to lengthen their muscles at ~$50\%$ of the maximal AF (AFmax), which was then reached during eccentric motion, indicating unstable adaptation. At post and end, AFisomax increased significantly to ~$99\%$ and $100\%$ of AFmax, respectively, reflecting stable adaptation. AFmax was statistically similar for all three time points. Symptom intensity decreased significantly from pre to end. The findings revealed a substantially impaired maximal holding capacity in long COVID patients, which returned to normal function with substantial health improvement. AFisomax might be a suitable sensitive functional parameter to assess long COVID patients and to support therapy process.
## 1. Introduction
Long term sequelae of SARS-CoV-2 infections increasingly challenge medical, social and economic systems worldwide. Different terms are used to define persisting post-infectious symptoms, such as ‘long COVID’, ‘post-COVID syndrome’, ‘post-acute COVID’ or ‘persistent post-COVID’, mostly depending on the duration of symptoms after acute infection. For simplification, the term ‘long COVID’ will be used in the following for patients suffering from symptoms at least 4 weeks after acute infection. Reports on the amount of patients with at least one persistent symptom after SARS-CoV-2 infection range from $10\%$ to $57\%$, or even up to $87\%$ in hospitalized patients, depending on the time span after acute infection or hospitalization vs. non-hospitalization [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19]. Long COVID occurs in $10\%$ to $35\%$ of non-hospitalized patients [1,18], which is most important, since only $5\%$ to $7\%$ of patients are hospitalized [20]. Current data show a lower rate of long COVID after infection with omicron variants than with delta ($4.5\%$ vs. $10.8\%$) [21]. Infection severity is considered to not be a major factor for the development of long COVID [16,22]. According to ‘COVID-19 data Explorer’ from Johns Hopkins University, more than 570 million SARS-CoV-2 cases were confirmed worldwide (Europe, Asia, North America, South America, Africa, Australia) from 22 January 2020 to 28 July 2022. Assuming that $10\%$ of them develop long term sequelae, more than 57 million people suffer or suffered from long COVID. The socioeconomic relevance becomes clear.
The medical community is mainly describing the characteristics of long COVID, but the pathomechanisms or causality are not sufficiently known [6,23]. Furthermore, there is a lack of diagnostic and therapeutic approaches, which are urgently needed to intercept the large amount of sick leave [1,24].
Post-infectious syndromes have been studied for decades and they are known to emerge after different viral infections [25,26,27,28,29,30]. They partly result in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) [11,25,26,30,31,32,33], which is closely connected to long COVID. Symptoms of long COVID range from fatigue, tiredness, muscle weakness, joint/muscle pain, cognitive impairments (‘brain fog’), depression, anxiety, dyspnoea, chest pain/tightness, cough, loss of taste/smell, headache, cardiac symptoms, insomnia, diarrhoea and more [1,2,11,13,14,19,34,35,36]. As can be seen, different systems are involved, including the respiratory, cardiovascular, musculoskeletal, integumentary, gastrointestinal, endocrine and neurological systems [14]. A dysfunction of the autonomous nervous system (ANS) has been discussed as a cause for the symptoms [25,26,30,31,37,38,39]. However, the diagnosis of post-infectious syndromes is difficult and is usually based on a diagnosis of exclusion [24,40]. Patients frequently report that they are not taken seriously by their doctors [34], which even increases the helplessness and anxiety.
A possible supportive diagnostic approach could be to investigate the neuromuscular system since muscle weakness and musculoskeletal pain occur frequently in long COVID patients. Some researchers examined the maximal voluntary isometric contraction (MVIC, e.g., hand grip force) in patients with post-infectious syndromes [41,42,43,44]. Two studies reported non-significant differences between patients and controls regarding the MVIC of the quadriceps femoris muscle (90° knee flexion) or of elbow flexors (90° elbow flexion, maximal supination forearm), respectively [41,42]. Two further studies revealed a significantly reduced hand grip force in ME/CFS [43,44]. However, in Meeus et al., gender effects were not considered [44]. Females were overrepresented in ME/CFS patients vs. controls ($96\%$ vs. $62\%$) [44], which might explain the lower strength. The findings are inconclusive and highlight that common maximal strength assessments might not be that appropriate to investigate muscle dysfunction in post-infectious states.
The Adaptive Force (AF) was inaugurated as a special neuromuscular function, which was found to be sensitive to stimuli [45,46,47,48,49,50,51,52]. The AF characterizes the capacity of the neuromuscular system to adapt to external varying forces in an isometric holding manner [45,46,47,48,49,50,51,52]. It can be assessed by a technical device using pneumatics [45,46,47] or by an objectified manual muscle test (MMT) using a handheld device which measures dynamics and kinematics during the MMT [49,50,51,52]. For the latter, it was shown that the maximal isometric AF (AFisomax; maximal holding capacity) was significantly reduced in reaction to negative stimuli, such as unpleasant emotional imagery or odors in healthy participants [50,51,52]. AFisomax immediately decreased by perceiving the negative stimulus and switched back instantaneously to baseline values by applying the positive stimulus. The peak value (maximal AF; AFmax) was reached during the subsequent eccentric action and was similarly high as for the baseline and positive stimuli. For baseline or under positive stimuli, the muscles remained stable during the whole force increase up to AFmax (AFisomax ≥ $99\%$ of AFmax). Thereby, AFisomax was similar to AFmax of unstable muscles. Hence, the maximal force was not influenced by the stimuli but the isometric holding function. In other words, under disturbing influences, the isometric holding capacity broke down to a significantly low level but the maximal strength was not affected. This was interpreted as a high sensitivity of AFisomax with respect to possibly impairing stimuli [49,50,51,52]. Neurophysiological explanations were given previously.
This longitudinal study aimed to investigate the AF in patients with long COVID in a non-clinical setting. For that, AF parameters were assessed at three time points in the course of long COVID: [1] in the long COVID state (pre), [2] after the first treatment (post) and [3] with substantial health improvement (recovery; end). The individual treatments received were not part of the study. It was not a clinical trial; therefore, it was not aimed to measure treatment efficacy. However, the treatments were queried and described to gain an impression of potentially helpful approaches without any claim of evidence.
Based on the current scientific knowledge of AF and of therapeutical experience, the main hypotheses were [1] the holding capacity would be significantly reduced in patients with long COVID and then it would stabilize, thus increase, during the recovery process. [ 2] AFmax would show no significant differences between the time points. [ 3] AF at onset of oscillations (AFosc) would be significantly higher in long COVID state compared to post and end.
The study provides early data on AF in long COVID patients. If the hypotheses are positively verified, AF might be used as a supportive biomechanical parameter to examine patients with long COVID. Furthermore, AF could help to find the appropriate treatment approach, which will be explained and discussed.
## 2. Materials and Methods
This longitudinal non-clinical study investigated patients in a long COVID state and in the course of their recovery process. Patients were not approached directly. They consulted the practice for Integrative Medicine (Potsdam, Germany; complementary medicine) out of their own personal initiative. If they were diagnosed with post-COVID syndrome or long COVID, they were invited to participate in the study. Regardless of their response, AF data were measured anyway for diagnostic purposes in daily practice. The treatments were neither subject nor part of the investigation. We only aimed to investigate the AF in those patients and its behavior during the recovery process. Therefore, a control group was not included. The measurements took place at the practice of Integrative Medicine and were conducted by researchers from the University of Potsdam (Potsdam, Germany).
## 2.1. Patients
Until July 2022, 37 patients diagnosed with long COVID attended the above-mentioned practice for consultation and the AF was measured initially using a handheld device. The only inclusion criterion was the medical diagnosis ‘post-COVID-syndrome’ or ‘long COVID’, which was received from medical doctors before the patients visited the practice. Exclusion criteria were pre-existing complaints of arm, shoulder, hip or knee of the measured side. Seventeen patients were included in this study since they reported a substantially improved or regained health state by July 2022. The remaining 20 patients were still in therapy or cancelled further therapy because of various reasons (long distance between home and the practice, difficulties in finding appointments, other ongoing treatments/rehabilitation or unknown reasons).
Of the 17 included patients, 14 were female (age: 44.43 ± 14.78 yrs., body height: 168.75 ± 5.23 cm, body mass: 69.93 ± 13.18 kg) and three were male (49.00 ± 7.94 yrs., 187.5 ± 3.54 cm, 94.75 ± 0.35 kg). Further information is given in the Results section (intensity of acute infection, duration from acute SARS-CoV-2 infection to input measurement, symptoms and others).
The study was conducted according to the Declaration of Helsinki and permission from the local ethics committee of the University of Potsdam (Germany) was given (no. $\frac{70}{2021}$, date: 16 February 2022). Each participant gave written informed consent.
## 2.2. Questionnaires
The patients filled out two questionnaires. The first one assessed information with respect to acute SARS-CoV-2 infection: duration, medical diagnosis and examination, symptoms and degree of severity (0 = symptom free, 1 = mild, 2 = moderate, 3 = severe but without hospitalization, 4 = hospitalization without intensive care, 5 = hospitalization with intensive care without invasive ventilation, 6 = intensive care with invasive ventilation); as well as concerning long COVID state: period between acute infection and onset of long COVID, periods of improvement, symptoms, diagnosis, medical examinations, experiences with health care, treatments and their effects.
The second questionnaire queried the intensity of common symptoms during long COVID using a scale from 0 (no) to 10 (very strong). The assessed symptoms were fatigue, breathing difficulties, cough, chest pain, chest tightness, memory/concentration problems, headache, muscle pain, fast/strong heartbeat, loss of smell/taste, depression/anxiety, fever, dizziness and post-exertion malaise. Professional and personal stress levels were also queried. The questionnaire was filled out for the following time points: [1] retrospectively for the pre-COVID baseline (before acute SARS-CoV-2 infection), [2] in long COVID state (time of input measurement; pre), [3] 1 day after first treatment (post) and [4] after recovery/with substantial health improvement (output measurement; end).
## 2.3. Handheld Device to Measure the Adaptive Force
The AF of the hip and elbow flexors of one side was assessed by the objectified MMT using the handheld device which was used in previous studies [49,50,51,52]. ( Figure 1a; development funded by the Federal Ministry of Economic Affairs and Energy; project no. ZF4526901TS7). It records force and position simultaneously and has proven to be reliable and valid [49]. Strain gauges (co. sourcing map, model: a14071900ux0076, precision: 1.0 ± $0.1\%$, sensitivity: 0.3 mV/V) and kinematic sensor technology (Bosch BNO055, 9-axis absolute orientation sensor, sensitivity: ±$1\%$) are implemented inside the device. The reaction force between tester and the patient’s limb, as well as the linear accelerations and angular velocity were captured during the MMT. The sampling rate was 180 Hz. The data were buffered, A/D converted and sent via Bluetooth 5.0 to a tablet. An app (Sticky Notes, comp.: StatConsult) saved the transmitted data [49,50,51,52].
## 2.4. Manual Muscle Test to Assess the Adaptive Force: Procedure and Setting
For testing the AF, the MMT in the form of a ‘break test’ was performed [53], since it enables a flexible and time-saving approach. This is especially necessary in fatigued long COVID patients. The MMT aims to assess the patient’s neuromuscular capacity to adapt to an external force increase. It does not test the maximal strength of the patient in the sense of MVIC. MMT characteristics were described previously [49,50,51,52] (Figure 1b). The starting positions of MMT of elbow and hip flexors, including the application of the handheld device, are illustrated in Figure 1c,d (according to [50,51,52]). The patient laid supine. The starting position for the elbow flexor test was 90° elbow flexion and maximal supination of the forearm (Figure 1c). For the hip flexor test, hip and knee angles were ~90° (Figure 1d). The contact with the handheld device was at the distal forearm or thigh, respectively. The contact points were marked and the lever was measured from the lateral epicondyle of the humerus and trochanter major, respectively, to the respective contact point for the standardization of retests. The tester applied a smoothly increasing force (Figure 1b) on the participant’s limb in the direction of muscle lengthening until a considerably high force level was reached. The patient had the task to maintain the starting position in an isometric holding manner for as long as possible. The patient is supposed to react and adapt to the applied force, but the patient was not allowed to push against the tester (for explanation see [50,51,52]). The whole MMT lasted ~4 s.
The MMTs were rated subjectively by the tester: ‘unstable’: the muscle started to lengthen during the force increase, hence, the patient was not able to maintain the isometric position. In that case, the maximal holding capacity (AFisomax) was lower than AFmax, which was then reached during eccentric muscle action. ‘ Stable’: the patient was able to maintain the isometric position until an oscillating force equilibrium occurred at a considerably high force level; in that case, the maximal AF (AFmax) was reached under isometric conditions (AFmax = AFisomax). Healthy persons usually show such stable adaptation (AFisomaxAFmax ≥ $99\%$) [50,51,52]. ‘ Unclear’: the muscle was neither completely stable nor unstable; slight suspensions were present.
A reproducible force application is a necessary precondition for valid data. Experienced testers are able to perform reliable force profiles over time [49]. Both testers (female, 36 years, 168 cm, 55 kg, 9 yrs. of MMT experience; male, 65 years, 185 cm, 87 kg, 26 yrs. of MMT experience) who assessed the AF of the patients in the present study, had previously proven their ability to test reproducibly [49]. Moreover, the force profiles over time matched precisely between both testers [49].
## 2.5. Procedure
At the first appointment, the patient was examined by means of the MMT by one of the two testers. This tester also conducted all subsequent MMTs of the same patient. Four muscle groups of the lower and upper extremities on both sides were assessed manually (without handheld device), respectively, to obtain an overall impression of the neuromuscular functionality. Then, the input measurements (pre) were performed: AF of hip and elbow flexors of one side was recorded utilizing the handheld device for objectification during the MMT. Patients chose the side to measure, in case of complaints, the complaint-free side was used. Both muscle groups were measured consecutively in alternating order three times, each starting with hip flexors (~1 min resting period between trials). The subjective assessment of the performed MMT by the tester was noted (0 = unstable; 1 = stable, 2 = unclear). Subsequently, the patients received their individual treatment which was not part of the study. Following this treatment (~1 h after input measurements), the AF of hip and elbow flexors was measured again (post) according to the procedure of the pre-measurements. A treatment period of varying duration and number of treatments were prescribed for each patient. During this phase the patients received their individual treatments, which they would have received anyway regardless of the study. The patients were prompted to contact the tester as soon as they felt substantially better or recovered. Then, a final appointment was scheduled for the end measurements (end), that followed the same measuring procedure as for the pre/post measurements. It should be emphasized that no treatment was given at the final appointment prior to the end measurements.
## 2.6. Data Processing and Statistical Analyses
Data processing and evaluation were performed according to Schaefer et al. [ 50,51,52] in NI DIAdem 2020 (National Instruments, Austin, TX, USA). The recorded data (force and gyrometer signals) were transferred from the measuring app to NI DIAdem. They were interpolated (1 kHz) and filtered (Butterworth, filter degree 5, cut-off frequency 20 Hz). For visualization proposed, the angular velocity was additionally filtered (degree: 3, cut-off: 10 Hz) to smoothen the oscillations. The following AF parameters were captured for further evaluation:Maximal Adaptive Force (AFmax): AFmax (N) refers to the peak value of a trial. This could have been reached either during isometric or eccentric muscle action.
AFisomax stands for the highest force value under isometric conditions. It was defined as the force at the moment in which the gyrometer signal increased above zero (breaking point). This indicated a yielding of the limb and, accordingly, muscle lengthening. If the gyrometer signal oscillated around zero during the entire trial, AFmax = AFisomax. Thus, the muscle length stayed stable during the whole MMT until the peak force value was reached (stable MMT). If the muscle started to lengthen in the course of MMT, AFisomax was reached during the force increase prior to the peak value. This points out that the position of the limb has to be considered to assess AFisomax. In 1 of 256 trials, AFisomax could not be determined because of peculiarities in the curve shape (excluded from evaluation). The ratio of AFisomax to AFmax (%) was additionally calculated per trial.
AFosc (N) characterizes the force at the moment in which oscillations start to appear regularly (onset of oscillations). Previous studies [50,51,52] showed that both interacting partners develop an oscillating force equilibrium, especially during stable MMTs. This was indicated by oscillations which arose in the force signal mostly in phase 3 of MMT (linear increase) showing a clearly distinguishable regular oscillatory behavior. During unstable MMTs, this oscillatory up swing was missing or occurred attenuated on a considerably higher force level. To evaluate AFosc, the force signal was checked for oscillations (force maxima) appearing sequentially during the force increase. If four maxima with a time distance dx < 0.15 s appeared consecutively, the force value of the first maximum was defined as AFosc. Time delta dx < 0.15 s was chosen due to the knowledge that mechanical muscle oscillations occur ~10 Hz [54,55,56,57,58,59,60,61,62,63]. In case no such oscillatory onset occurred, AFosc = AFmax. In 2 of 256 trials, AFosc could not be clearly determined, hence, they were excluded from evaluation. Ratios of AFosc to AFmax (%), as well as AFosc to AFisomax (%) were calculated per trial. The latter is based on previous findings that for stable MMTs, AFosc arose on a lower level than AFisomax, and for unstable MMTs, oscillations occurred—if at all—after that breaking point.
The slope of force increase prior to the breaking point (AFisomax) of all trials was evaluated to control the force application by the tester. This has to be similar between the trials for a valid comparison. The difference quotient m = y2−y1x2−x1 was used to calculate the slope, whereby x refers to time and y to the respective force values. The reference points (time, force) were $70\%$ and $100\%$ of the averaged AFisomax of all of the assessed unstable MMTs of one patient. The decadic logarithm was taken from the slope values (lg(N/s)) since the force rise was exponential. In 11 of 256 trials, the slope could not be determined since oscillations occurred too intensively which would have distorted the slope value.
Arithmetic means (M), standard deviation (SD), coefficient of variation (CV) and $95\%$ confidence intervals (CIs) were calculated per parameter, muscle and time point (pre, post, end) in Microsoft Excel (Microsoft 365, Redmond, WA, USA, Microsoft Corp).
Statistical evaluation was performed with IBM SPSS Statistics 28 (Windows, Armonk, NY, USA, IBM Corp). All parameters (AFmax, AFisomax, AFosc, their ratios and slope) were checked for normal distribution by a Shapiro–Wilk test. In case of normal distribution, a repeated measures ANOVA (RM ANOVA) was used to compare the three time points (pre, post, end). In case the Mauchly test of sphericity was significant, the Greenhouse–Geisser correction was chosen (FG). For post-hoc tests, a Bonferroni correction was applied (adjusted p values are given by padj). Effect size eta squared (η2) was given for the RM ANOVA. For pairwise comparisons, the effect size Cohen’s dz was used, which was interpreted as small (0.2), moderate (0.5), large (0.80) or very large (1.3) [64,65]. Since the RM ANOVA is known to be robust against violation of the normal distribution [66,67], a Friedman test was only executed to compare the three time points if more than one dataset (pre, post or end) was not normally distributed (applied for the ratio of AFisomax to AFmax). The Bonferroni post-hoc test was used for pairwise comparisons (padj) and the effect size Pearson’s r was calculated by r = zn in Microsoft Excel. Significance level was α = 0.05.
In addition to the AF parameters, the intensities of the different queried symptoms were evaluated by calculating M and SD. Those values were also compared between the three time points using a Friedman test. Furthermore, the percentage of patients who stated their respective symptoms with an intensity of at least 2 was calculated for descriptive purposes.
## 3.1. Number of Trials and Subjective MMT Ratings by the Testers
The hip flexors were measured in all 17 patients at the three time points (pre, post, end). The measurements of the elbow flexors were only completed in 14 patients due to reasons such as lack of time, shoulder complaints, too exhausted or similar. In total, 144 MMTs were performed using the handheld device for hip flexors and 118 for elbow flexors. One patient was only tested twice for both muscles because of a lack of time. In two other patients, hip and elbow flexors were only measured twice at pre and post because of tiredness. The hip flexors of another patient were assessed only once at end because of hip pain. In total, 141 valid trials were gathered for the evaluation of hip flexors and 115 for elbow flexors, since technical issues occurred in six trials (3× hip, 3× elbow). In one patient, four trials of hip flexors were performed at the end because the device indicated an error, but the data were nevertheless transferred and, therefore, used for evaluation.
The female tester assessed ten patients and the male tester assessed seven. The number of MMTs rated as ‘unstable’, ‘stable’ and ‘unclear’ by the testers is given in Table 1.
As can be seen, all MMTs ($100\%$) were rated as ‘unstable’ at pre. This reflects that elbow and hip flexors started to lengthen during the force increase, thus, the patients were not able to adapt their muscle length and force adequately in an isometric position during the external force increase. At post and end, the majority of MMTs were rated as ‘stable’ for both muscles (elbow: $92.1\%$ and $97.6\%$, respectively; hip: $89.4\%$ and $95.9\%$, respectively). This indicates that the patients were able to adapt to the external force increase in an isometric holding position in the vast majority of MMTs, and the muscles did not yield during the force rise. In total, six MMTs were rated as ‘unclear’ (elbow: $2.6\%$ at post; hip: $6.4\%$ at post, $4.1\%$ at end). This highlights that the MMTs could not be rated as completely stable. The testers mostly described that the muscle showed stronger suspensions than usual for stable MMTs or that the muscle started to yield at a very high force level (especially in comparison to the pre trials). Those subjective ratings should be verified using the data from the handheld device.
## 3.2. Parameters of Adaptive Force in the Course of Long COVID
Figure 2 exemplifies the force and gyrometer signals of three MMTs of elbow and hip flexors, respectively, of one female patient (24 years, 168 cm, 65 kg) tested by the male tester at the pre, post and end time points. As can be seen in the uppermost graphs of Figure 2, force rises at pre, post and end can be regarded as similar except for one curve at pre. The single values of each parameter and patient are provided in the Supplementary Materials. Table 2 displays the group averages and statistical results.
## 3.2.1. Slope of Force Increase
Slope was similar for elbow and hip flexors (Table 2, Figure 3) and did not differ significantly between the three time points, neither for elbow, nor for hip flexors. For the latter, the RM ANOVA was close to significant. The lowest slope was present for pre. Thus, at post and end, the challenge for patients to adapt to the external load can be assumed as even higher. The slope can be interpreted as statistically similar between the time points, which is the prerequisite for comparison of the AF parameters.
## 3.2.2. Maximal Adaptive Force and Maximal Isometric Adaptive Force
The AFmax did not differ significantly between the three time points for both muscles (Table 2, Figure 4a,d). As can be seen in Table 2, AFmax was considerably high in the pre measurements. One female patient (outlier) showed an extremely low AFmax in the pre trials, with only 61.43 ± 4.86 N for elbow and 67.38 ± 8.66 N for hip flexors. At post, she could increase her AFmax immediately to 146.44 ± 22.05 N for elbow and to 162.58 ± 26.01 N for hip flexors. In her case, the AFmax at pre amounted to only $42\%$ of AFmax at post for elbow and $41\%$ for hip flexors, respectively. This is usually not expected and will be discussed later. The other patients showed AFmax values between 145.83 and 295.05 N for elbow and 124.10 and 257.27 N for hip flexors. For timepoints post and end the AFmax was considerably high for all patients (Table 2, Figure 4a,d). The AFmax at pre related to post amounted averagely 100 ± $14\%$ for elbow and 106 ± $24\%$ for hip flexors, respectively (excl. outlier). Similar for post vs. end with 100 ± $10\%$ for elbow and 102 ± $15\%$ for hip, respectively. Hence, AFmax seems to not be appropriate to reflect the testers’ MMT ratings adequately, which showed clear differences between pre and post and pre and end, as well as similar ratings between post and end (Table 1). It has to be mentioned that for all pre trials, AFmax was reached during muscle lengthening, whereby for the majority of the post and end trials, AFmax arose during isometric muscle action. Therefore, the suggested main parameter to quantify the manually found difference is the maximal force during the isometric muscle action (holding capacity; AFisomax).
The AFisomax showed clearly lower values at pre vs. post/end, with a significant main effect in the RM ANOVA (Table 2, Figure 4b,e). The pairwise comparisons revealed significantly lower values for pre vs. post (elbow: t[13] = −11.144, padj < 0.0001, $d = 2.978$; hip: t[16] = −10.228, padj < 0.0001, $d = 2.481$; one-tailed) and for pre vs. end (elbow: t[13] = −12.140, padj < 0.0001, $d = 3.245$; hip: t[16] = −10.007, padj < 0.0001, $d = 2.427$, one-tailed). Post vs. end did not differ significantly (elbow: padj = 1.000; hip: padj = 1.000).
For elbow flexors, four patients showed an AFisomax below 60 N at pre (range 20.56 to 58.66 N), which has to be considered as extremely low. Hereby, the outlier mentioned above, showed the lowest value. Another patient showed a very high AFisomax = 229.94 N. The others ranged from 62.21 to 156.30 N. All patients showed considerably high AFisomax values at post and end (Table 2, Figure 4).
For hip flexors, three patients showed AFisomax < 60 N at pre (range: 27.36 to 52.00 N), whereby the mentioned outlier again showed the lowest value. The highest AFisomax for pre was 166.73 N, which was reached by the same patient who showed the highest value for elbow flexors. At post and end, AFisomax was considerably high for all patients.
This is mirrored by the ratio of AFisomax to AFmax (Table 2, Figure 4c,f). For elbow flexors, it ranged from $23\%$ to $78\%$ at pre, $89\%$ to $100\%$ at post and $97\%$ to $100\%$ at end; for hip flexors, it ranged from $28\%$ to $69\%$ at pre, $87\%$ to $100\%$ at post and $98\%$ to $100\%$ at end. The patients started to lengthen their muscles already at 47 ± $16\%$ of their maximal force (AFmax) for elbow flexors and at 49 ± $12\%$ for hip flexors in the pre trials. Some patients showed an extremely low ratio in single MMTs. The lowest ratios were $14\%$ for elbow and $15\%$ for hip flexors. In 15 of 36 MMTs (elbow) and 13 of 46 MMTs (hip), the ratio amounted less than $40\%$. In 11 and 13 MMTs, respectively, it was >$60\%$. The others were in-between $40\%$ and $60\%$.
At post, already 12 of 14 patients were able to generate at least $98\%$ of AFmax for elbow flexors, two patients reached lower values ($89\%$ and $96\%$). It was similar for hip flexors, whereby 14 of 17 patients were able to demand at least $98\%$ of AFmax, three showed lower values ($87\%$, $93\%$ and $97\%$). In all end trials, the patients were able to reach $100\%$ of AFmax in an isometric holding position, except for two patients who showed values of ~$97\%$ or ~$98\%$ for elbow flexors and one who reached ~$98\%$ for hip flexors.
## 3.2.3. Onset of Oscillations during Force Increase
The AFosc revealed a significant main effect in the RM ANOVA for both muscles (Table 2). For elbow flexors, oscillations started at an $18\%$ and $17\%$ higher force level comparing pre vs. post and pre vs. end, respectively. The pairwise comparisons missed significance after the Bonferroni correction (Figure 5a,d). For hip flexors, oscillations occurred at a $43\%$ and $52\%$ higher force level comparing pre vs. post and pre vs. end, respectively. Pairwise comparisons were highly significant (pre vs. post: t[16] = 5.997, padj < 0.0001, $d = 1.454$; pre vs. end: t[16] = 5.892, padj < 0.0001, $d = 1.429$). Post vs. end did not differ significantly (padj = 1.000) (Figure 5a,d).
The above-mentioned outlier regarding AFmax showed an extremely low AFosc for both muscles at pre with 60.84 (elbow) and 66.48 N (hip). For elbow flexors, AFosc was 100.94 N at post and 100.90 N at end. For hip flexors AFosc was similarly low comparing pre (66.48 N), post (62.88 N) and end (69.89 N). The other patients showed AFosc of elbow flexors in the range of 145.69 to 272.70 N at pre, 62.98 to 217.69 N at post and 52.00 to 236.84 N at end. For hip flexors, it ranged from 116.90 to 242.17 N at pre, from 62.88 to 188.86 N at post and from 29.86 to 191.76 N at end. The between-patients CV for pre, post and end was large with ~31 ± $2\%$ for elbow and ~32 ± $6\%$ for hip flexors. The intraindividual CV was considerably lower with 5.8 ± $2.8\%$ (pre), 12.1 ± $8.3\%$ (post) and 12.8 ± $5.3\%$ (end) for elbow flexors and 6.1 ± $4.1\%$, 12.3 ± $7.03\%$ and 16.5 ± $15.1\%$, respectively, for hip flexors.
The ratio of AFosc to AFmax was clearly and significantly higher at pre vs. post and pre vs. end for both muscles (Table 2, Figure 5b,e) (elbow: pre vs. post: t[13] = 5.455, padj < 0.0001, $d = 1.458$; pre vs. end: t[13] = 5.863, padj < 0.0001, $d = 1.567$; hip: pre vs. post: t[16] = 8.306, padj < 0.0001, $d = 2.014$; pre vs. end: t[16] = 9.876, padj < 0.0001, $d = 2.395$). No significant differences were present comparing post vs. end.
Displayed by the ratio of AFosc to AFisomax, the oscillations arose consistently after the breaking point (AFisomax) at pre. At post and end, they occurred before AFisomax (Table 2, Figure 5c,f). Only in one MMT of elbow flexors at post, the oscillations arose just with AFisomax (AFosc/AFisomax = $100\%$). The RM ANOVA revealed a significant main effect for both muscles (Table 2). Pairwise comparisons were highly significant for pre vs. post (elbow: t[13] = 5.918, padj < 0.0001, $d = 1.582$; hip: t[16] = 8.905, padj < 0.0001, $d = 2.160$) and pre vs. end (elbow: t[13] = 5.892, padj < 0.0001, $d = 1.575$; hip: t[17] = 8.979, padj < 0.0001, $d = 2.178$). Post vs. end showed no significant differences (padj = 1.000 for both muscles).
## 3.3. Patients Characteristics Regarding Long COVID
The patients’ professions were teachers/educators [6] students/trainees [2], IT specialist [1], editor [1], lawyer [1], filmmaker [1], social insurance clerk [1], physiotherapist [1], business economist [1], manager of a coronavirus test center [1] and pensioner [1]. From the 16 employed patients, 14 were unable to work because of long COVID at the first appointment (pre), one had just started to work again and one had no sick leave at all. At timepoint end, eight of the 14 incapacitated patients were working again and six wanted to return to work again soon. One was still on sick leave.
The acute SARS-CoV-infection lasted on average 15.29 ± 9.40 days (range: 7 to 40, $$n = 17$$). The median of acute infection severity was 2.25 ($$n = 16$$). One patient was admitted to hospital due to vertigo, another because of a suspected heart attack (nevertheless, she rated the intensity with 1). Overall, infections could be interpreted as mild.
The duration from acute infection to input measurement (pre) was on average 274.88 ± 210.70 days (range: 32 to 688). From pre to end, the duration amounted to 71.06 ± 44.43 days (range: 26 to 166 days). The patients had on average 3.29 ± 1.79 (range: 1 to 7, $$n = 17$$) treatment appointments at the practice from pre to end. At end, four patients were completely recovered and required no further appointments. Thirteen patients reported to feel sustainably better but wanted to receive further treatments. For 10 of those 13 patients the therapy phase was completed after an average of 2.80 ± 1.99 further treatments. Three patients were still in therapy (July 2022), since they had not regained their full quality of life back or they wanted to stabilize their health further, especially with regard to mental stability.
The patients reported a large variety of symptoms in the long COVID state, which were not all regarded in the questionnaire. Beyond the queried symptoms, the patients reported recurrent ‘crashes’, (muscle) weakness, joint stiffness, limb heaviness, feeling of whole body paralysis, brain fog to black outs, aphasia, forgetfulness, slowed reaction, sensitivity to stimuli, such as light/noise, hypersomnia or sleeping problems, vertigo, nausea, diarrhea, sore throat, ague, strong sweating, impaired vision, olfactory hallucination, body aches (back/shoulder/neck/heart/lung/tooth/eyes), tingles in the nerves/limbs/head/tongue, cold hands/feet, increased convulsion tendency, internal vibrating, inner restlessness, being phlegmatic, high level of irritability, fast overload, mental imbalance, depression, tachycardia, extrasystoles, hair loss, eczema, herpes, reactivated Epstein–*Barr virus* infection and tinnitus.
Figure 6a illustrates the percentage of patients who stated the respective symptoms with an intensity of at least 2. As can be seen, no patient reported to have chest pain/tightness, cough, dizziness, loss of smell/taste or fever with such an intensity before COVID. However, the majority of symptoms was already present in some patients—at least slightly—before COVID infection ($7.14\%$ to $35.71\%$, $$n = 14$$). Depression/anxiety showed the highest percentage before COVID. This is in line with the statements regarding job-related and personal stress before COVID (Table 3), which were rated with an intensity of ≥2 in $90.91\%$ and $100\%$, respectively ($$n = 11$$). Those values declined in long COVID state to $85.71\%$ ($$n = 14$$, three patients did not check the boxes because of sick leave) and $88.24\%$ ($$n = 17$$), respectively; at timepoint end, they amounted to $30.33\%$ and $50.00\%$ ($$n = 12$$), respectively.
Absolute symptom intensities are displayed in Table 3 and Figure 6b. The Friedman test was significant for each symptom. From ‘before COVID’ to ‘long COVID state’, the intensities increased significantly for each symptom ($p \leq 0.001$ to 0.024), except for fever ($$p \leq 0.202$$). In long COVID state, all patients reported to suffer from fatigue, post-exertion malaise and breathing difficulties with an intensity of at least 1. Most prominent were post-exertion-malaise and fatigue with an intensity of 8.1 and 7.8, respectively. The other symptoms did not occur in each patient, whereby fever was the rarest (four patients).
Comparing ‘long COVID state’ vs. end, the intensity of all symptoms declined, most of them significantly ($p \leq 0.001$ to 0.031), except for fever ($$p \leq 0.281$$) and loss of smell/taste ($$p \leq 0.062$$) (Table 3, Figure 6).
The symptom intensities did not differ significantly between ‘before COVID’ and end ($$p \leq 0.202$$ to 0.922), whereby for concentration/memory problems, significance was just missed with $$p \leq 0.050.$$ The latter was still present in 11 of 13 patients (three of them stated an intensity of 1, one patient of 9). Another patient stated that an elevated temperature was partly still present at timepoint end when he was physically active (rated fever with intensity 3); after one further treatment this was resolved, too.
Although the individual treatments were not part of the evaluation, they should be reported briefly. Fifteen of 17 patients filled out the questionnaire with respect to their diagnosis and therapies prior to the first appointment. At least seven patients had large diagnostic assessments in centers or rehabilitation facilities for long COVID, the others stated to have received assessments from medical specialists (pulmonologists, internists and similar). The initiated treatments ranged from rehabilitation measures, such as physiotherapy including lymph drainage, manual therapy/massage, reflective breathing therapy, hot role and exercise therapy to (hyperbaric) oxygen therapy, ergotherapy, psychotherapy or psychological guidance, behavioral therapy, speech therapy, concentration training, pharmacological approaches (antibiotics, cortisone, asthma inhaler) to dietary changes. The most common advice from medical specialists was ‘pacing’. According to the patients’ statements, this was very frustrating. Four patients stated that they received no advice or arranged therapy from medical specialists. They were told to rest or were not taken seriously. The majority of patients reported having the impression that conventional medicine had no treatment approach and some reported that medical specialists were ‘clueless’. Others stated that as soon as no somatic reason for their condition could be found, the patients were diagnosed with a psychosomatic disorder. Nevertheless, some patients reported positive effects regarding reflective breathing therapy and for psychological guidance to cope with the condition. Regarding physiotherapeutic measures, the effects varied widely. Some patients reported at least a supporting effect regarding manual therapy which helped to reduce some pain in the short-term. Others stated that lymph drainage worsened the condition. Some also stated that exercise therapy helped for their musculature and cardiovascular system, however, others reported a deterioration after low-intensity exercise therapy.
The mentioned helplessness and—if at all—mostly short-term supportive therapies led the patients to try interventions on their own initiative. Those included supplements (mostly vitamins, trace elements), walks, relaxation techniques or meditation, planning of daily routine, concentration training and rest. At least two patients went to naturopaths or specialists in traditional Chinese medicine. However, none of those measures led to the desirable improvement of their health condition. That is why the patients were seeking other approaches and made an appointment at the practice, where the AF measurements took place. Two of the 17 patients were transferred from a pulmonologist, the others came via other ways. The patients were still partly undergoing therapies elsewhere. The additive treatment at the practice for integrative medicine involved an individual approach based on the muscular holding capacity. Some regularities regarding the applied treatments were found. For each patient, an individualized pulsed electromagnetic field therapy (PEMF) was applied. Based on several studies [31,69,70,71,72], an influence on the ANS is assumed. For a single case, the PEMF therapy in long COVID was recently described [68]. Moreover, 11 of 17 patients were treated for mental stress (persisting or post traumatic situations) using an individualized treatment approach. The lymphatic system was treated in seven of 17 patients using manual methods, as well as individualized PEMF. In some cases, osteopathic and chiropractic techniques for the cranial and/or the musculoskeletal system were applied.
## 4. Discussion
The present pilot study investigated the AF of elbow and hip flexors via an objectified MMT in patients with long COVID at three time points: during long COVID state (pre), after the first treatment (post) and after recovery/substantial health improvement (end). The additionally received individual treatments of the patients were not part of the study and were only included descriptively. The evaluation of the slope of applied force rise by the tester revealed a non-significant difference between the three time points. Therefore, the results are based on reproducible force increases and can be regarded as valid. The results supported the hypotheses and will be discussed with regard to different physiological and practical aspects.
## 4.1. Comparison of the Subjective Ratings of the Manual Muscle Test and Measured AF
The MMT was comprehensibly criticized to be subjective. The applied force increase as well as the ratings of MMTs are based on the manual ability and ‘feeling’ of the tester. By measuring the dynamics and kinematics during the MTT, the force increase and breaking point can be objectified. The question remains whether the measured AF parameters support the subjective MMT ratings of the tester. Since the results of elbow and hip flexors showed similar characteristics, they will be considered together. All 84 MMTs at pre were assessed as ‘unstable’ by the testers. The MMTs at post and end were rated as ‘stable’ in the majority of trials (164 of 173), as ‘unstable’ in four of 173 cases and as ‘unclear’ in six of 173 cases. Regarding MMTs rated as either ‘unstable’, ‘stable’ or ‘unclear’, the ratio of AFisomax to AFmax amounted to 50.27 ± $13.15\%$, 99.69 ± $0.64\%$ or 97.95 ± $2.61\%$, respectively. It can be concluded that the testers’ MMT ratings were in accordance with the measured AF. Under unstable conditions, AFisomax was only half as high as for the stable tests. The AF values rated as unclear in the MMTs were rather in accordance with the stable ones. Obviously, they showed a high AFisomax. However, during the MMT, the testers felt higher suspensions and the muscle resistance felt ‘softer’. The values of stable MMTs support the previous findings, in which the ratio of AFisomax to AFmax was ≥$99\%$ [50,51,52]. Unstable MMTs previously revealed values of ~$56\%$, which is slightly higher than the ~$50\%$ found here. This might be attributable to the fact that the previous studies were performed on healthy participants who were affected temporarily by unpleasant odors or imagery. Unhealthy individuals with long COVID seem to show—at least in part—even stronger impairment of muscular adaptation. Some patients showed extremely low AFisomax values; the lowest was $15\%$ for AFmax for hip flexors and $14\%$ for elbow flexors. This is interpreted as a—partly extremely—impaired muscular adaptation, probably due to the long COVID state. However, it cannot be stated whether their muscular adaptation was already impaired before SARS-CoV-2 infection. Based on the findings, it can be concluded that the MMT ratings of both experienced testers were strongly in accordance with the measured AF values. Therefore, AF measured by the objectified MMT seems to be a suitable biomechanical parameter to evaluate the muscular function in adaptation to an external increasing force.
## 4.2. Adaptive Force in the Recovery Process of Long COVID
Fatigue is considered as the main symptom of long COVID [73,74,75]. The link between fatigue and muscle weakness has already been raised previously [11,15,30,76,77,78,79]. That is why maximal strength is partly investigated in post-infectious syndromes or ME/CFS. As was mentioned in the introduction, the findings have been inconclusive until now [41,42,43,44]. In the presented study, AFmax did not differ significantly between the three time points, as was assumed. Since AFmax was previously found to be similar to MVIC [45,46,47,48,49,50,51,52], the assumption that maximal forces (eccentric/MVIC) might not be suitable parameters to investigate patients in post-infectious states is supported. However, one outlier existed here, who showed extremely low AFmax values at pre. This could be a hint that some individuals suffering from post-infectious syndromes or ME/CFS may also have significantly reduced common maximal strengths, as was found in [43,44]. Nevertheless, the results of AFmax can also explain, why other investigations did not find such differences [41,42].
The findings for AFisomax suggest that the holding capacity seems to be a more sensitive biomechanical parameter to assess muscle function. AFisomax was significantly lower with very large effect sizes for pre vs. post and pre vs. end, whereby post vs. end did not differ significantly, according to the hypothesis. In the long COVID state (pre), the patients were not able to maintain an isometric position while trying to adapt to the increasing applied force. Muscles gave way at less than half of the maximal AF. Hence, patients could not exert their maximal strengths at this stage. This was further supported by the ratio of AFisomax to AFmax, which was significantly reduced at pre.
As the main result of the study, AFisomax turned out to be a sensitive parameter for a long COVID state, because $100\%$ of the patients showed initially clear instability (this was also the case for the other 20 patients measured in long COVID state, but who were not included in the study). To the authors’ knowledge, only one study assessed muscle strength in SARS-CoV-2 patients [80]. MVIC was measured directly at the discharge of elderly hospitalized patients. Thereby, $73\%$ and $86\%$ of patients showed a ‘weakness’ for biceps brachii and quadriceps femoris muscles, respectively. Muscle weakness was defined as strength which “was inferior to $80\%$ of the predicted normal value” based on Andrews et al. [ 81]. However, those patients are not comparable with those included in the study, since measurements were executed at the end of acute infection following a period of hospitalization (averagely 20.7 days). More than $90\%$ received oxygen supply and all of them were pharmacologically treated. Jäkel et al. reported a sensitivity of ~$70\%$ and ~$86\%$ for maximal hand grip strength in CFS/ME patients aged 20–39 years and 50–59 years, respectively (prior to the COVID-19 pandemic) [43].
AFisomax of all long COVID patients responded immediately following the treatment at the first appointment with a clear and significant increase. This instant change leads to the assumption that AFisomax does not reflect the maximal strength capacity but a functional aspect of motor control that can be influenced by stimuli. It can switch immediately from instability to stability or vice versa. This was shown in previous studies involving healthy participants [50,51,52]. The health condition of the long COVID patients in this study was not improved directly after the first treatment (except for one patient), but the motor control already clearly responded. It is hypothesized that the motor reaction could have been a first hint at a helpful intervention, at least in a share of subjects. The actual causality remains unclear. There could have been helpful treatment methods, but also possible mental factors, such as an empathetic atmosphere or the like.
The significant differences between pre and end revealed that the holding capacity was not only substantially improved, but even fully normalized until recovery. This result has to be discussed independently of the possible causations of the improvement. Because the study was non-clinical, no control group was included. Therefore, the reasons for improvement of health conditions and AF parameters remain unclear.
Considering the queried symptoms, it was visible that they behave inversely proportional to the holding capacity: at pre, the symptom intensity was significantly higher in most items compared to timepoint end ($p \leq 0.001$ to 0.031; except for fever ($$p \leq 0.281$$) and loss of smell/taste ($$p \leq 0.062$$)), whereby AFisomax and the ratio of AFisomax to AFmax was significantly lower at pre vs. end with large effect sizes of > 2.42. This indicates an inverse correlation of the health condition and holding capacity. The directionality and causation of this connection can only be assumed. Since the holding capacity was improved already directly after the first treatment (post), the holding capacity cannot be a direct indicator for the improvement in health. Moreover, it remains unclear whether that observed instant improvement was sustainable. It seems to be likely that the motor response was a more transitional phenomenon at the beginning. MMTs at following treatment appointments showed a fallback to muscular instability for the most patients; however, this was not verified by objective measures. Because the output-measurements (end) were not carried out after an immediately preceding treatment, the observed stability could be interpreted as a part of the improved health state. We assume that the holding capacity is regained prior to the decrease of symptom intensity. Hence, after suitable treatments, the functionality is first restored. A probable improvement in the health condition is time-delayed and might possibly depend on the sustainability of this regained functionality, mirrored by the stable muscle function.
## 4.3. Neurophysiological Considerations with Respect to the Reaction of AF in Long COVID
The discussion on the etiology of long COVID should not be opened here in detail. Brain stem dysfunction [36], a reduced cerebral blood flow [31,82] and the involvement of the ANS [25,26,30,31,37,38,39] were discussed. Recently, preinfection psychological distress was reported as a risk factor for long COVID [75,83]. This is in line with the self-reported stress prior to acute infection regarding the patients in the present study. Central structures, such as the brain stem, thalamus, basal ganglia, cerebellum, inferior olivary nucleus, cingulate cortex and more are involved in processing and controlling nociception, emotions and motor control [84,85,86,87,88]. Hence, the influence of possibly interfering inputs in the complex control circuitries of motor function are conceivable. The adaptive holding capacity in reaction to an external increasing force was suggested to be especially vulnerable regarding such stimuli. The length-tension control with respect to an increasing external load challenges the regulation and control processes of motor control in a specific way (for detailed discussion see [49,50,51,52]). Therefore, it is conceivable that a health state, such as long COVID, can influence the holding capacity. Based on the findings of previous studies on the influence of emotions on AF in healthy participants [50,51,52] and on long-term practical experience that mental stress can reduce the holding capacity, we assume that the motor output in the sense of AF could have been impaired already prior to SARS-CoV-2 infection because of mental stress. This might have affected the functionality of the human system on different levels. Especially an impairment of the immune system is known to be associated with mental distress [83,89,90,91,92,93,94]. Hence, the individually perceived mental stress could have diminished the resilience of the individual with regard to the virus and, probably, could have impeded the recovery of the acute infection, resulting in long COVID. Wang et al. [ 83] highlighted that the findings that psychological distress is a risk factor for long COVID “should not be misinterpreted as supporting a hypothesis that post-COVID-19 conditions are psychosomatic”. We concur with this statement. From our point of view, mental stress might lead to disbalances of different bodily systems, e.g., the immune system [83,89,90] or the ANS [95]. This, in turn, could lower the resilience and might favor long COVID. We interpret the long COVID state rather as a sign of dysfunction. The found instability of the holding muscle function might be a part of the complex physiological functional disbalance in long COVID patients.
The onset of oscillation (AFosc) might also reflect an impaired functionality. The neuromuscular system is known to be characterized by oscillations. AFosc was significantly higher for pre vs. post and pre vs. end, as was hypothesized. Moreover, in all of the 84 MMTs of elbow and hip flexors at pre (rated as unstable), oscillations arose—if at all—after the breaking point, thus during muscle lengthening. For the remaining 173 MMTs at post and end (mostly rated as stable), the up-swing of oscillations arose regularly during isometric actions. Those findings support the previous ones that, in case of stability, oscillations occur during isometric muscle action; in case of instability, they do not arise. This indicates that oscillations might be a prerequisite for the stable adaptation in the sense of AF, as was suggested previously [50,51,52]. The evidence consolidates that oscillations are playing a major role in the neuromuscular adaptation with respect to external forces.
Based on the connection of physiological disbalances and motor control, the AF might be a suitable biomechanical parameter to check for such functional impairments. Due to the immediate response of the holding capacity to supporting or disrupting inputs, the recovery process of long COVID could also be controlled, and a potentially supportive therapy approach might be ascertained by assessing the holding capacity.
## 4.4. Limitations
One limitation was the non-standardized duration from post to end measurements. Due to the individual recovery process, this limitation is difficult to resolve. The duration depended on the self-reported health state of the patients. This self-report is another limitation. Further studies could include a more quantitative assessment of the health state. However, the individual feeling of health is the most important one, also for return to work. Furthermore, the study was not blinded. The testers were aware of the patients’ health state. However, the evaluation of the slope and AFmax revealed statistically similar values between the three time points. Only AFisomax, as well as AFosc, showed significant differences between pre vs. post and pre vs. end. This strongly indicates that the AF assessment was not influenced by lack of blinding.
## 5. Conclusions
The investigation of the AF in patients with long COVID and in the course of their recovery process revealed that the holding capability was significantly reduced in long COVID state and was stabilized after the first treatment and with substantial health improvement. AFmax did not reflect this difference. The holding capacity seems to be sensitive but is assumed to be not specific for long COVID. Nonetheless, its assessment might support the diagnostics of long COVID and especially the choice of the individual helpful therapy approach, since the holding function can switch immediately from instability to stability. This should be used to identify a treatment tailored to the patient’s individual conditions and requirements. It is concluded that the assessment of AFisomax could be a supportive biomechanical parameter to assess the functional health state, follow up and recovery process in patients with long COVID. The next step should be to investigate the mentioned treatment approaches in a clinical design. Based on the present study, it cannot be judged whether the treatments were the reason for the recovery. Possibly, other received treatments or a spontaneous recovery over time could have led to the improved health state. In case the treatment approaches are verified positively, this would be a big step towards diagnostics and therapy with regard to long COVID. This would have major socioeconomic implications.
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|
---
title: Multiparametric Profiling of Neutrophil Function via a High-Throughput Flow
Cytometry-Based Assay
authors:
- Kyle D. Timmer
- Daniel J. Floyd
- Allison K. Scherer
- Arianne J. Crossen
- Johnny Atallah
- Adam L. Viens
- David B. Sykes
- Michael K. Mansour
journal: Cells
year: 2023
pmcid: PMC10000770
doi: 10.3390/cells12050743
license: CC BY 4.0
---
# Multiparametric Profiling of Neutrophil Function via a High-Throughput Flow Cytometry-Based Assay
## Abstract
Neutrophils are a vital component of the innate immune system and play an essential function in the recognition and clearance of bacterial and fungal pathogens. There is great interest in understanding mechanisms of neutrophil dysfunction in the setting of disease and deciphering potential side effects of immunomodulatory drugs on neutrophil function. We developed a high throughput flow cytometry-based assay for detecting changes to four canonical neutrophil functions following biological or chemical triggers. Our assay detects neutrophil phagocytosis, reactive oxygen species (ROS) generation, ectodomain shedding, and secondary granule release in a single reaction mixture. By selecting fluorescent markers with minimal spectral overlap, we merge four detection assays into one microtiter plate-based assay. We demonstrate the response to the fungal pathogen, Candida albicans and validate the assay’s dynamic range using the inflammatory cytokines G-CSF, GM-CSF, TNFα, and IFNγ. All four cytokines increased ectodomain shedding and phagocytosis to a similar degree while GM-CSF and TNFα were more active in degranulation when compared to IFNγ and G-CSF. We further demonstrated the impact of small molecule inhibitors such as kinase inhibition downstream of Dectin-1, a critical lectin receptor responsible for fungal cell wall recognition. Bruton’s tyrosine kinase (Btk), Spleen tyrosine kinase (Syk), and Src kinase inhibition suppressed all four measured neutrophil functions but all functions were restored with lipopolysaccharide co-stimulation. This new assay allows for multiple comparisons of effector functions and permits identification of distinct subpopulations of neutrophils with a spectrum of activity. Our assay also offers the potential for studying the intended and off-target effects of immunomodulatory drugs on neutrophil responses.
## 1. Introduction
Neutrophils possess a repertoire of functions as the first line of defense in controlling invading pathogens. While the absolute neutrophil count is of obvious importance, the functional capacity of neutrophils to properly execute these functions is also critical for the prevention of disease [1,2,3]. Despite having normal or greater than normal neutrophil counts, studies suggest select patient cohorts face an increased risk of infection due to inherited or acquired defects in neutrophil function, including those with diabetes [4,5], cirrhosis [6,7], chronic granulomatous disease [8,9], and recipients of organ transplants [10]. Interrogating neutrophil functions can help to explain why neutrophils may be ineffective in controlling pathogens in these patients. For example, neutrophils from patients with diabetes have decreased reactive oxygen species (ROS) production, chemotaxis, phagocytosis, and neutrophil recruitment. Shedding light on these impairments may explain patients’ increased vulnerability to infection [11].
In addition to genetic and metabolic causes, many approved therapeutics can have unintended side effects leading to neutrophil dysfunction. Bruton’s tyrosine kinase (Btk) inhibitors, such as ibrutinib, a backbone in treating chronic lymphocytic leukemia [12], impairs neutrophil function [13]. Patients on treatment with ibrutinib have neutrophils with diminished phagocytosis, ROS generation, and cytokine production, which may explain the increased incidence of invasive aspergillosis in this patient population [13]. Developing an assay that rapidly and efficiently analyzes a diverse array of neutrophil functions could improve our understanding of neutrophil dysfunction in various disease settings and highlight potential off-target effects of medications on neutrophil function.
Flow cytometry is commonly used for the concurrent measurement of multiple parameters on large populations of cells. Reactive dyes, fluorescent pathogens, analyte capture beads, and antibody labeling of cell surface proteins allow one to quantify various neutrophil functions (Figure 1) rapidly and can be accomplished within hours of blood collection [14]. Flow cytometry-based multifunctional assays have identified aberrant neutrophil activity in specific subpopulations of patients such as those with community-acquired pneumonia [14], HIV [15], or severe-injury-related trauma [16]. By quickly identifying patients with neutrophil dysfunction, interventions can be promptly employed to manage at-risk patients. To date, the breadth of measurements in such assays has been limited to two functions: phagocytosis and oxidative burst [14,15,16]. Specific protocols increase the study dimensions with parallel assays or downstream secondary analyses of supernatant to measure the release of soluble factors [17,18]. A high throughput assay that measures multiple functions remains an unmet need for granulocyte phenotyping. We present a microtiter plate assay for simultaneously measuring four neutrophil functions, including phagocytosis, ROS generation, ectodomain shedding, and degranulation.
The selected functions are cardinal features of neutrophil response. Ectodomain shedding, an often-overlooked neutrophil function, refers to the cleavage of specific motif-bearing surface proteins by selective proteases, most often of the a disintegrin and metalloproteases (ADAMs) family [19]. These changes to the neutrophil surface (ectodomain) influence various neutrophil activities such as cell-cell adhesion and rolling through the cleavage of L-selectin (CD62L) [20], opsonin recognition through the cleavage of the Fc receptor CD16b [21], paracrine signaling through the cleavage of tumor necrosis factor alpha (TNFα) and the TNFα receptors [22,23], and triggering neutrophil activation through the cleavage of the receptor TREM-1 [24,25]. CD62L is rapidly shed within minutes of stimulation [26] and is a classic and early indicator of neutrophil activation [27]. Additionally, CD62L, CD16b, TNFα, and the TNFα receptor are cleaved by the same metalloprotease, ADAM17 [28]. Thus, CD62L is an ideal marker for measuring ectodomain shedding because it acts as a metric for other surface changes and is an early sign of neutrophil activation.
Degranulation is another critical function in neutrophil pathogen response. Neutrophils possess granules that release preformed enzymes into the extracellular environment during degranulation [29]. These granules contain antimicrobials such as myeloperoxidase (MPO), lysozyme, lactoferrin, mixed metalloprotease 9, and neutrophil elastase [30]. Granule subsets are categorized based on their contents and are released in a regulated order: secretory vesicles, tertiary, secondary, and finally primary granules [31]. We chose to analyze secondary granule release, through expression of CD66b, since it requires greater activation than secretory vesicles and tertiary granules and because these granules contain multiple antimicrobial peptides such as lactoferrin, neutrophil gelatinase-associated lipocalin (NGAL), and prodefensin [30,32].
Here, we describe a new high-throughput assay that quantifies four neutrophil functions simultaneously from a single sample. By selecting compatible fluorescence read-outs, we minimize spectral overlap and demonstrate the potential to detect increases and decreases in neutrophil activity following treatment with cytokines, kinase inhibitors, and common immunomodulatory drugs. Our assay provides a convenient workflow for multiple rounds of priming or inhibition, pathogen co-culture, antibody labeling, and data acquisition within the same reaction well.
## 2.1. Reagents
Flow cytometry staining buffer (FACS buffer) was prepared with $2\%$ heat-inactivated fetal bovine serum (FBS) (Life Technologies, Carlsbad, CA, USA) and 1 mM EDTA (Life Technologies) in phosphate-buffered saline (PBS) without calcium and magnesium (Corning, Corning, New York, NY, USA). Common immunosuppressive drugs were used to demonstrate the ability to test off-target therapeutic effects on neutrophil functions including cyclosporine A (10 μg/mL, Selleckchem, Houston, TX, USA) and mycophenolic acid (30 μM, Selleckchem). Cytokines used included G-CSF (100 ng/mL, Peprotech, Cranbury, NJ, USA), IFNγ (100 ng/mL, Peprotech), TNFα (10 ng/mL, Peprotech), and GM-CSF (10 ng/mL, Peprotech). Chemical inhibitors included diphenyleneiodonium for ROS inhibition (DPI, 10 μM, Selleckchem), TMI-005 for inhibition of CD62L shedding (2.5 μM, Cayman Chemical, Ann Arbor, MI, USA), PP1 for Src inhibition (10 μM, Cayman Chemical), R406 for Syk inhibition (20 μM, Selleckchem), and IBT for Btk inhibition (1 μM, Cayman Chemical). Lipopolysaccharide (LPS) from E. coli strain K12 was purchased from InvivoGen (San Diego, CA, USA). Complete RPMI (cRPMI) was prepared from RPMI (Corning), $10\%$ FBS, 2 mM L-glutamine (Life Technologies), and $1\%$ penicillin-streptomycin (Thermo Fisher Scientific, Waltham, MA, USA).
## 2.2. Preparation of Human Neutrophils
Healthy blood donors were consented under the Massachusetts General Hospital Institutional Review Board-approved protocol (2019P002840). Whole blood was collected in EDTA-coated vacutainers (Beckton Dickinson, Franklin Lakes, NJ, USA) and subsequently centrifuged at 1500xg for 15 min. Buffy coat was collected, and neutrophil isolation was performed using the negative selection EasySep Direct Human Neutrophil Isolation Kit, according to the manufacturer’s instructions (STEMCELL Technologies, Seattle, WA, USA). Wright-Giemsa staining was performed after the isolation process to confirm neutrophil purity from the isolation kit. Flow cytometry was also used to verify a high neutrophil purity from the isolation procedure (≥$94\%$ neutrophil purity). Cell concentration and viability were measured by staining the cells with a 1:10 dilution of acridine orange/propidium iodide followed by automatic cell counting using the LUNA fl Dual Fluorescence Cell Counter (Logos Biosystems, Annandale, VA, USA) (≥$99\%$ live). Neutrophils were resuspended in cRPMI at a concentration of 2 × 106 cells/mL.
## 2.3. Preparation of C. albicans
Wildtype strain SC5314 Candida albicans was purchased from the American Type Culture Collection (American Type Culture Collection, Manassas, VA, USA). SC5314 constitutively expressing far-red fluorescent protein (C. albicans iRFP) was kindly donated by Robert Wheeler (University of Maine, Orono, ME) [33]. C. albicans was grown in YPD liquid media (yeast extract, peptone, dextrose) containing $1\%$ yeast extract (Acros Organics, Fair Lawn, NJ, USA), $2\%$ peptone (BD Biosciences, San Jose, CA, USA), and $2\%$ dextrose (Sigma-Aldrich). C. albicans was cultured overnight at 30 °C on a rotating culture wheel (Thermo Fisher Scientific). The following day, C. albicans was removed from the wheel, washed twice with PBS and resuspended in PBS. C. albicans was counted using the LUNA automatic cell counter and kept on ice until the time of the assay.
## 2.4. Neutrophil-Candida Co-Incubation
Drugs, cytokines, or appropriate vehicles were prepared at a 2X concentration in cRPMI, with a maximum DMSO concentration of $0.1\%$ v/v. In a 96-well V bottom polypropylene plate (Corning) the perimeter and outer wells were moated with 200 μL sterile PBS. Next, 50 μL of cRPMI or 50 μL of 2X drug or cytokine was mixed with 50 μL of neutrophil stock in a reaction well. The plate was sealed with breathable film (VWR, Radnor, PA) and placed in an incubator at 37 °C, $5\%$ CO2 for 30 min to one hour. A 30-min cytokine incubation was chosen based on previous time courses. Neutrophils incubated with immunomodulatory drugs for 1 h, as this was the midpoint seen in similar published studies [34,35,36]. After the neutrophils were incubated, 20 μL of C. albicans at the desired multiplicity of infection (MOI) was added to the appropriate wells and mixed by pipetting. Immediately following, 30 μL of dihydrorhodamine 123 (DHR123) (5 μM, Thermo Fisher Scientific) was added to the wells and mixed well by pipetting. The plate was sealed and returned to the incubator for 30 min for coincubation with C. albicans. After the 30 min, the plate was placed on ice for 10 min in the dark and prepared for flow cytometry. During rescue studies, neutrophils were first treated with kinase inhibitors for 30 min [37] in an incubator. LPS (400 ng/mL) was then added to the well and the plate was returned to the incubator for an additional 45 min [37] prior to co-culturing neutrophils with C. albicans for 30 min.
## 2.5. Flow Cytometry
Cells were pelleted at 4 °C and stained in 50 μL of cold FACS buffer containing (BV605) anti-human CD62L antibody (1:200 dilution; clone DREG-56; BioLegend, San Diego, CA, USA) and (BV421) anti-CD66b (1:200 dilution; clone $\frac{6}{40}$c; BioLegend). The cells were incubated for 30 min at 4 °C in the dark. Cells were rinsed with 150 μL cold FACS buffer, centrifuged at 4 °C and resuspended in 150 μL cold FACS buffer. The 96 well plate was left on ice until just prior to data acquisition on a BD FACSCelesta (BD Biosciences, San Jose, CA, USA) with a blue, violet, red (BVR) laser configuration with specific wavelengths at 488 nm, 405 nm, 640 nm respectively. Bandpass filters for the Celesta include $\frac{450}{40}$, $\frac{525}{50}$, $\frac{610}{20}$, $\frac{660}{20}$, $\frac{780}{60}$ for the 405 nm laser; $\frac{530}{30}$, $\frac{575}{26}$, $\frac{610}{20}$, $\frac{695}{40}$ for the 488 nm laser; and $\frac{670}{30}$, $\frac{730}{45}$, $\frac{780}{60}$ for the 640 nm laser. Before recording data, gates were prepared so that 7000 neutrophil events could be collected. Compensation was performed with single color controls and was calculated using BD FACSDiva Software (BD Biosciences). A compensation matrix demonstrating the spectral overlap values can be seen in Supplementary Table S1. FCS files were exported from BD FACSDiva Software in a 3.0 format. Analysis of FCS files was performed using FlowJo v.10 software (BD Biosciences). T-distributed stochastic neighbor embedding (tSNE) was performed in FlowJo on gated neutrophils and calculated with the four neutrophil function flow cytometer parameters. Heat map statistics for iRFP (phagocytosis), DHR123 (ROS), CD62L expression (shedding), and CD66b expression (degranulation) were then overlayed on tSNE plots. Fluorescence minus one (FMO) controls were performed to ensure minimal spectral overlap. For each FMO control, the multiparametric assay was run equivalently but the flow cytometry preparation excluded one fluorescent marker while retaining the others. Supplementary Table S2 provides further information on the fluorescence reagents and detection.
## 2.6. Statistical Analysis
All statistical analyses for normality and significance were performed on GraphPad Prism 9 (San Diego, CA) using ordinary one-way ANOVA. A p-value greater than 0.05 was considered nonsignificant (ns).
## 3. Results
Neutrophils employ a variety of effector functions to combat pathogens including phagocytosis, oxidative burst, degranulation, cytokine release, ectodomain shedding, and NETosis as illustrated in Figure 1. Here, we demonstrate a simple multiparametric assay to examine four neutrophil functions to Candida albicans challenge. Our assay is performed in a 96 well plate format and requires 1 × 105 cells per well. Neutrophils isolated from healthy donors or patients can be pre-incubated with test biologics or small molecules to assess their influence on neutrophil functions. Following pre-incubation, fluorescent C. albicans are introduced along with DHR123 as an indicator for ROS production. Following co-incubation with C. albicans, neutrophils are immunostained for CD62L and CD66b. CD62L allows for the detection of ectodomain changes and elevation of CD66b serves as a metric for secondary granule release (Figure 2A).
A stepwise flow cytometric gating strategy identified neutrophils. Total events were gated by forward and side scatter properties to target live cells with the correct size and granularity. This gate removed most free-floating C. albicans and non-granulocytes (Figure 2B). It is important to note that this initial gate was generous because activated neutrophils engaging in the various functions tend to increase in size (FSC) and granularity (SSC). Single cells were identified, and neutrophils were selected by their expression of the neutrophil-specific surface protein CD66b (Figure 2B). Gates were set by comparing fluorescence minus one control to unstained as well as unstimulated stained samples. Phagocytosing neutrophils were gated as iRFP(+) events, ROS producing neutrophils were gated as DHR123(hi) events, shedding neutrophils were gated as CD62L[-] events, and degranulating neutrophils were gated as CD66b(hi) events. Neutrophil functions were analyzed simultaneously (Figure 2A) or individually (Figure 2B).
Increasing the ratio of C. albicans to neutrophils in the assay format increased the frequency of cells responding in terms of ectodomain shedding, phagocytosis, degranulation, and ROS production (Figure 2C–F). The multiplicity of infection (MOI) of 1, 2, 4, and 8 were statistically different in the frequencies of neutrophils engaging in phagocytosis, ROS generation, and secondary granule release (Figure 2C,D,F), while CD62L shedding plateaued between MOI or 4 and MOI of 8 (Figure 2E). Vastly different levels of neutrophil activity were achieved simply by increasing the ratio of C. albicans to neutrophils. Using an MOI of 8 triggered robust neutrophil responses resulting in $77\%$ phagocytosis and $79\%$ secondary granule release compared to $33\%$ and $29\%$, respectively, at an MOI of 2 (Figure 2C,F).
Many neutrophil functional studies have examined phagocytosis and ROS production [14,15], while our assay simultaneously measures four canonical neutrophil functions. The multiparametric nature of the assay reveals that not all neutrophils perform the exact coordination of functional responses (Figure 3A). For example, shedding and degranulation may occur regardless of whether a neutrophil is phagocytosing and generating ROS. Further, some neutrophils may be performing all four simultaneous functions (population b) while others have not begun any of the measured responses (population a). We additionally, determined the overlap of neutrophils that could participate in more than one function. Figure 3B summarizes the proportion of individual neutrophils engaging in multiple simultaneous functions after co-culture with C. albicans. This presentation of the data reveals relationships between neutrophil functions. For example, there was great overlap between phagocytosing and ROS-generating neutrophils. Most phagocytosing neutrophils also generated ROS; however, only about $60\%$ of degranulating neutrophils were simultaneously phagocytosing C. albicans. Furthermore, our assay allows one to measure the frequency of neutrophils participating in four concurrent functions. At an MOI of 1, on average, $40\%$ of phagocytosing neutrophils participate in all four functions simultaneously compared to $82\%$ at MOI of 4.
Each flow cytometer is equipped with multiple lasers and filters that allow different fluorescent panel possibilities. The cytometer configuration is carefully paired with fluorescent markers to minimize spectral overlap and the possibility of false positives. To ensure our multiparametric assay accurately detected individual functions, gates were set using fluorescence minus one (FMO) controls. In the degranulation (BV421), ROS (DHR123), and phagocytosis (iRFP) FMO controls, there were fewer than $0.4\%$ positive events for each function (Figure 4A,B). In the ectodomain shedding (BV605) FMO control, $99.8\%$ of neutrophil events were negative for CD62L expression (Figure 4A,B). Our assay reliably measures individual functions, and results are not confounded by spectral overlap.
An essential application of the multiparametric assay is to screen potential bioactive small molecule drugs for neutrophil-modulating effects. We selected a panel of compounds known to influence immune function. These chemicals are inhibitors of ROS production (DPI) [38], and ectodomain shedding (TMI-005) [20], as well as two clinically relevant compounds used for immune suppression in transplant patients, mycophenolate (MPA) [39] and cyclosporine A (CSA) [40]. Treatment with compounds for one hour before C. albicans co-culture did not significantly affect neutrophil phagocytic ability. DPI was the only chemical to profoundly affect ROS generation, decreasing the number of ROS-positive cells by $87\%$ (Figure 5). Ectodomain shedding was influenced by many of the small molecule compounds tested. The ADAM17 inhibitor TMI-005, nearly abolished ectodomain shedding. Treatment with MPA did not cause significant impairments to neutrophil functions. CSA however did cause a striking $55\%$ reduction in ectodomain shedding and $45\%$ reduction in secondary granule release. The small molecules tested resulted in unique changes to specific neutrophil behaviors and highlight the value of determining the precise functional area of neutrophil impairment.
To demonstrate the ability to measure neutrophil augmentation, we pre-stimulated neutrophils with cytokines, including granulocyte colony-stimulating factor (G-CSF), interferon-gamma (IFNγ), granulocyte-macrophage colony-stimulating factor (GM-CSF), and tumor necrosis factor-alpha (TNFα). These four inflammatory cytokines significantly increased the frequency of neutrophils engaging in phagocytosis, ROS generation, ectodomain shedding, and secondary granule release relative to the vehicle (Figure 6A–D). The response was nonuniform and cytokine-specific; IFNγ elicited the least improvement in neutrophil function. While IFNγ increased secondary granule release, the response was less than that of G-CSF, GM-CSF, or TNFα stimulation. Additionally, some of these cytokines began to trigger neutrophil responses in the absence of C. albicans co-culture. TNFα and GM-CSF produced significantly more ectodomain shedding at rest (Figure 6C). Interestingly, GM-CSF and TNFα encouraged degranulation in approximately $80\%$ of neutrophils cultured with C. albicans (Figure 6D), nearly a sevenfold increase over the vehicle control. TNFα stimulation was unique in that it induced significant secondary granule release in the absence of a pathogen, a result seen in other studies [41].
We also sought to determine how the assay might be used to study the recovery of functional responses in dysfunctional or attenuated neutrophils. Inhibition of downstream kinases (e.g., Btk, Syk, Src) within the Dectin-1 signal transduction pathway can render neutrophils unresponsive to fungal pathogens such as C. albicans or *Aspergillus fumigatus* [42,43]. Syk and Btk inhibition can completely block neutrophil functions, including swarming, phagocytosis, oxidative burst, and cytokine production, even when challenged with C. albicans or A. fumigatus [37,44,45]. In the context of our assay, the inhibition of Btk, Syk, and Src, prevented phagocytosis, ROS production, ectodomain shedding, and secondary granule release (Figure 7A–D, respectively).
Neutrophil inhibition by ibrutinib (IBT) or R406 can be overcome by alternate pathway stimulation (e.g., TLR stimulation using LPS) before challenging with C. albicans [37]. LPS stimulation alone did not trigger ROS production in pre-treatment conditions when C. albicans was not present (Figure 7B). However, when inhibited neutrophils were stimulated with LPS and challenged with C. albicans, phagocytosis, and ROS production were significantly restored (Figure 7A,B). All LPS-treated neutrophil conditions had near unanimous shedding of CD62L (Figure 7C), and secondary granule release was improved with LPS stimulation (Figure 7D). These results suggest that the multiparametric assay can measure inhibition, augmentation, and subsequent pathogen challenge in the presence of crucial kinase inhibitors.
## 4. Discussion
While the absolute number of circulating neutrophils is critical for pathogen control, the neutrophil function is another crucial factor that is less easily quantified. Microbial killing assays provide information on the overall ability of neutrophils to recognize and eliminate pathogens, though they do not explain which specific functions contribute to pathogen detection and containment. In studying how specific disease states and small molecules may influence pathogen killing, we wished to quantify multiple neutrophil functions simultaneously. Here, we have demonstrated a simple high-throughput multiparametric flow cytometer-based assay that permits studying four canonical neutrophil functions: phagocytosis, ROS production, ectodomain shedding, and secondary granule release. Furthermore, we demonstrated our assay’s clinical relevance and sensitivity in detecting improvements and diminution of neutrophil activities by pre-stimulating neutrophils with biological and small molecule agents.
Our study does have limitations. There is growing evidence of neutrophil heterogeneity in peripheral blood [46], suggesting there may be populations with distinct functional potentials [47]. Our multiparametric assay did not include markers, such as CD10, to identify left shift neutrophils, an immature subset known to behave differently than mature circulating neutrophils [48,49]. Our study focused on healthy individuals who tend to contain scarce populations of immature neutrophils in circulation [48]. While it is unlikely that a left-shift neutrophil population significantly influenced the results demonstrated here, future work with the multiparametric assay should incorporate heterogeneity markers to fractionate results into additional unique subsets.
Our assay is not exhaustive of all neutrophil functions, and future iterations could be expanded to define additional neutrophil responses (Figure 1). The most versatile flow cytometers can be equipped with up to nine lasers and corresponding detectors, allowing for the theoretical recognition of ~40 different parameters. Our assay did not include a measurement of NETosis, a well-defined mechanism for pathogen control. During NETosis, neutrophils ensnare the microbe in a conglomerate of nuclear material laced with high concentrations of antimicrobial proteins [50,51]. NETosis can be quantified by flow cytometry using cell impermeable nucleic acid dyes such as SYTOX and fluorochrome-conjugated antibodies targeting MPO and/or citrullinated histone 3 [52,53] (Figure 1). In addition to pathogen elimination, measuring relative neutrophil extracellular trap formation is also essential for understanding potential tissue damage in the host. Excessive NET formation is implicated in ongoing endothelial tissue damage [54] and can contribute to microthromboses [55,56].
Further adaptations to the panel could also include readouts of other granule types. For example, primary (also known as azurophilic) granule release results in the extracellular expulsion of toxic proteins such as proteinase 3, neutrophil elastase, and MPO [30]. Primary granule release can be measured using fluorochrome conjugated antibodies against CD63 which is not traditionally expressed on the neutrophil surface but is deposited on the cellular membrane upon primary granule fusion (Figure 1) [57,58,59]. In developing future multiparametric assays to measure neutrophil function, primary granules are of key interest in bacterial pathogenesis. Some pathogens inhibit primary granule release as an immune evasion strategy [60]. Furthermore, the recognition of excessive primary degranulation of neutrophil elastase has been implicated in host tissue damage [61]. Alternatively, secretory granule release can be measured by similarly detecting increases in the surface level expression of CD11b or CD35, though these responses can be non-specifically provoked making them markers of general neutrophil activation [32,62].
The discarded reaction media from the neutrophil-pathogen co-culture contains informative metabolites and biomolecules that could shed light on specific neutrophil responses including the measurement of cytokines involved in cell-cell communication. Advancements in multiplex cytokine panels could allow for multiple soluble analyte measurements from microtiter volumes, making it compatible with the miniaturized design of our assay. Shed compounds such as CD62L, TREM-1 or the release of factors like G-CSF, or other pro and anti-inflammatory cytokines could be measured by bead capture and quantified by flow cytometry [63,64].
In our assay, we used live C. albicans marked by the constitutive expression of a fluorescent protein. Enforced expression of fluorescent proteins by pathogens are useful tools however, genetic manipulation may introduce additional changes from the parental strain [65]. In our FMO experiment, lower rates of all functions were observed, when the nonfluorescent parental strain of C. albicans was incubated with neutrophils. This result was obtained on multiple separate occasions and highlights a limitation in utilizing fluorescent protein expressing pathogen. Live fluorescent pathogen culture is not feasible in all lab settings and instead, uncolored pathogens may be conveniently FITC stained [66] or surface labeled using succinimidyl ester-based reactions [37]. Live pathogens may also not be feasible, or approved, for use in cytometer or within every flow cytometry core facility. If so, the quantification of phagocytosis can be adapted to measure the uptake of alternative targets such as fixed or heat-killed samples. Additionally, inert fluorescent bioparticles or fluorescent beads coated with microbial antigens could also substitute the use of live pathogens [17,67,68].
The multiparametric assay accurately measures augmented neutrophil functions. Cytokine therapies have been explored for multiple patient diagnoses, such as cirrhosis [10,69] and cases of neutrophil dysfunction [70]. Cytokine screens in our multiparametric assay could identify the most effective treatments for enhancing or attenuating neutrophil activity for specific diagnoses and therefore increase patient outcomes. Our assay design may also be applied to studies investigating the role of endogenous cytokine levels in tissue-specific microenvironments in the setting of diseases such as cystic fibrosis [71] or cancer [71,72].
Newly evolving biologic and small molecules are constantly being developed for the treatment of a variety of autoimmune disorders and malignancies. Small molecule kinase inhibitors have revolutionized such treatments; however, they can have off-target effects and can leave the patient vulnerable to invasive fungal disease [73]. We showed that three kinase inhibitors (IBT, R406, and PP1) eliminate neutrophil responses to C. albicans but that function can be restored with cytokines and growth factors. In the development of novel drugs, our assay could be used to detect negative consequences of kinase inhibiting therapeutics on neutrophil function and potential ways to overcome function inhibition.
In addition to kinase inhibitors, we tested two FDA-approved drugs, CSA and MPA, that are frequently used to suppress T and B cells [74,75] in transplant patients. The calcineurin inhibitor, CSA, suppressed innate immune cell function including neutrophil ectodomain shedding and secondary granule release. Other studies have also observed inhibitory effects of CSA on neutrophil function in vitro and in vivo, including reductions in primary granule release, neutrophil migration, and ROS production [34,35]. Our assay did not detect a decrease in ROS production though it appears that CSA diminishes ROS production in response to some but not all neutrophil-stimulating molecules [34]. This distinction may also reflect the type of ROS detected, specifically those from external release versus internal signaling [76,77].
As our assay detects functions simultaneously, one can observe changes to coupled mechanisms. Figure 3 shows a substantial overlap between individual cells capable of phagocytosis and ROS production. The strong association between phagocytosis and ROS-producing cells can be explained by the fact that NOX2 is activated upon phagosome formation [78,79]. When neutrophils were treated with the ROS inhibitor DPI, phagocytosis was relatively unchanged, but ROS was nearly abolished, highlighting the ability to investigate uncoupled functional processes.
Our assay yields reproducible results and possesses numerous applications for studying functional deficits. Larger donor enrollment could establish a healthy neutrophil function range from which aberrant neutrophil activity could be identified for clinical applications. We showed that our multiparametric assay could detect function improvement upon pre-incubation with inflammatory cytokines. Our assay may therefore be compatible with studies looking to augment neutrophil responses in patients experiencing neutrophil dysfunction or recurrent opportunistic infections [10,70].
In conclusion, our multiparametric assay demonstrates the utility of simultaneous measurement of several neutrophil functions that can be used for patient and population cohort immune profiling, assessment of biological or small molecule impact on granulocyte function as well as determination of novel approaches to augmentation of patients with dysfunctional neutrophils for improved health outcomes. The potential addition of metrics such as NETosis and cytokine release can render these assays more informative.
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|
---
title: 'The Influence of Ethnicity on Survival from Malignant Primary Brain Tumours
in England: A Population-Based Cohort Study'
authors:
- Hiba A. Wanis
- Henrik Møller
- Keyoumars Ashkan
- Elizabeth A. Davies
journal: Cancers
year: 2023
pmcid: PMC10000771
doi: 10.3390/cancers15051464
license: CC BY 4.0
---
# The Influence of Ethnicity on Survival from Malignant Primary Brain Tumours in England: A Population-Based Cohort Study
## Abstract
### Simple Summary
Previous reports using broad ethnic group classifications have suggested that patient outcomes may vary. This study examined survival differences in malignant primary brain tumours of various morphologies between well-recorded and detailed ethnic groups for the whole of England. An ethnic difference in brain tumour survival was found with patients of an Indian background, Any Other White, Other Ethnic Group, and Unknown/Not Stated Ethnicity Groups having better one-year survival than the White British Group, following adjustment for known prognostic factors. By investigating the ethnic variations associated with better brain tumour survival, we may begin to better understand any ethnic inequalities that exist and possibly identify subgroups of patients that could benefit from personalised medicine.
### Abstract
Background: In recent years, the completeness of ethnicity data in the English cancer registration data has greatly improved. Using these data, this study aims to estimate the influence of ethnicity on survival from primary malignant brain tumours. Methods: Demographic and clinical data on adult patients diagnosed with malignant primary brain tumour from 2012 to 2017 were obtained ($$n = 24$$,319). Univariate and multivariate Cox proportional hazards regression analyses were used to estimate hazard ratios (HR) for the survival of the ethnic groups up to one year following diagnosis. Logistic regressions were then used to estimate odds ratios (OR) for different ethnic groups of [1] being diagnosed with pathologically confirmed glioblastoma, [2] being diagnosed through a hospital stay that included an emergency admission, and [3] receiving optimal treatment. Results: After an adjustment for known prognostic factors and factors potentially affecting access to healthcare, patients with an Indian background (HR 0.84, $95\%$ CI 0.72–0.98), Any Other White (HR 0.83, $95\%$ CI 0.76–0.91), Other Ethnic Group (HR 0.70, $95\%$ CI 0.62–0.79), and Unknown/Not Stated Ethnicity (HR 0.81, $95\%$ CI 0.75–0.88) had better one-year survivals than the White British Group. Individuals with Unknown ethnicity are less likely be diagnosed with glioblastoma (OR 0.70, $95\%$ CI 0.58–0.84) and less likely to be diagnosed through a hospital stay that included an emergency admission (OR 0.61, $95\%$ CI 0.53–0.69). Conclusion: The demonstrated ethnic variations associated with better brain tumour survival suggests the need to identify risk or protective factors that may underlie these differences in patient outcomes.
## 1. Background
Each year, over 5000 new cases of primary brain tumours are diagnosed in the United Kingdom (UK) [1]. In particular England has become both more multicultural in recent decades [2] and seen a steady increase in the incidence of malignant primary brain tumours [3]. One study considering broad ethnic groups found higher incidence rates of 4.8 per 100,000 population from 2001 to 2007 for people from White ethnic group compared to those from South Asian, Black, and Chinese ethnic groups (respective rates of 3.1, 2.8, and 2.7 per 100,000 population) [4].
English population-based studies have reported ethnic differences in the incidence of most cancers with individuals from non-White groups generally having a lower cancer risk than the White Group [5,6]. Survival for the four common cancers has been widely reported [6], but not examined in detail for brain tumours using the well-defined ethnicity information now available. A small study of high-grade gliomas in South-East England 2000–2009 [7], reported that patients of White and Not Known ethnicities had the worse survival for all tumour groups after adjusting for sex, age, morphology, socio-economic deprivation, and co-morbidity.
The improved and detailed National Health Service (NHS) data on ethnicity captured by the National Disease Registration Service, which is part of NHS England, provided an opportunity to explore the impact of ethnicity on brain tumour survival. This resulted from major efforts across the NHS to increase self-reporting of this variable. With data collected on over 300,000 cancer cases in England each year, this is also the first English study to consider the more detailed classifications for malignant primary brain tumours including all gliomas, primary central nervous system lymphoma (PCNSL), as well as unclassified malignant brain neoplasms. It aims to examine the possible effect of ethnicity on the route or pathway taken to diagnosis and of receiving optimal treatment. A better understanding of any ethnic inequalities in brain cancer could potentially lead to improved treatment or services for these patients.
## 2.1. Study Population
Data on all adult patients diagnosed with a malignant primary brain tumour during 2012–2017, who are resident in England and registered with a general practitioner (GP), were extracted from the English cancer registration data.
## 2.2. Selection of Cases
Cases for this study were identified using the International Classification of Diseases [version 10] (ICD-10) tumour site C71. For those with PCNSL, ICD-10 code site was used along with the morphology codes for lymphoma. Other inclusion criteria were cases having a complete tumour registration and known sex. The brain tumour morphological subtypes considered in this study were based on the 2016 WHO Classification of Tumours of the Central Nervous System [8]. WHO updated this classification in 2021; however, the changes have minimal effect on the analysis of this data. Due to sample sizes, histological tumour subtypes were grouped as follows: glioblastoma, anaplastic astrocytoma, astrocytoma NOS, oligodendroglioma, PCNSL, malignant glioma, and unclassified malignant. Data on all tumours were extracted from the English cancer registration irrespective of their pathological confirmation—gliomas without a specified classification or as unclassified malignant neoplasms were included. In addition, glioblastomas with a pathological confirmation were included but tumours of benign, uncertain, and metastatic nature were not included. Molecular data are not available for this study cohort.
Inpatient hospital episodes statistics (HES) data were linked to the cancer registration data from 2012. These records include ethnicity data that are almost always self-reported upon admission to NHS hospitals. The categories of ethnicities were as follows: White British, Bangladeshi, Indian, Pakistani, Chinese, Black African, Black Caribbean, and Unknown/Not Stated, and due to small numbers in these groups—White Irish and Any Other White were grouped together as Any Other White, and Mixed Ethnic Groups and Any Other Ethnic Group were grouped as Other Ethnic Group.
Socio-economic deprivation was measured using the income domain of the index of multiple deprivation (IMD) 2015, divided into quintiles across England and Wales, and assigned to cases using postcode of residence at diagnosis. Charlson co-morbidity score was based on conditions occurring within one year of the cancer diagnosis date. The conditions were weighted according to their severity and scores were grouped as 0 (where none were recorded), 1, and 2 or more. Route to *Diagnosis is* defined as the sequence of interactions between the patient and the NHS, leading to a cancer diagnosis [9]. This is identified using an algorithm linking various sources based on the setting of diagnosis, and the pathway and referral route into secondary care.
Information on surgical resections, chemotherapy, and radiotherapy treatments received within the first 18 months following diagnosis were also extracted. Treatment options were categorised to reflect clinical practice as: radiotherapy only, chemotherapy only, surgical resection only, all three treatments given as surgical resection followed by radiotherapy and chemotherapy (optimal treatment), radiotherapy plus chemotherapy, surgical resection plus radiotherapy, surgical resection plus chemotherapy, and no treatment. Surgical resections did not include cases with biopsies only.
## 2.3. Data Analysis
We first extracted 27,934 records, cleaning the dataset to exclude duplicated cases, those without the required brain tumour morphology, with unknown vital status, or who were registered by death certificate only (DCO) (Figure 1). Survival time was calculated from the date of diagnosis until date of death with a survival period of up to one year. To retain 145 patients who died on their date of diagnosis, we added half a day to their survival time. The final study population included 24,319 cases.
Initially, we examined the distribution of patients by demographic factors (age, sex, area of residence, and socio-economic status), co-morbidity, tumour morphology, route to diagnosis, and treatment factors. Univariate and multivariate Cox proportional hazards regressions were then used to estimate hazard ratios (HR) and their $95\%$ confidence intervals ($95\%$ CI) for the survival of each ethnic group up to one year following diagnosis. The follow-up period ended on 31 December 2018. χ2 Tests estimated the p-values for trend and heterogeneity, excluding unknown categories. We then carried out a sensitivity analysis in which each variable was adjusted to identify how much variation it contributed to the model, and as a result we finally focused the analysis on age, sex, co-morbidity, socio-economic deprivation, tumour morphology, route to diagnosis, and treatment received. Due to the high fatality of malignant primary brain tumours, cancer-specific survival was not studied, as this is similar to overall survival.
Logistic regression was used to generate odds ratios (OR) (and their $95\%$ CI) for each ethnic group of [1] being diagnosed with pathologically confirmed glioblastoma, [2] being diagnosed during a hospital stay that included an emergency admission, and [3] receiving optimal treatment (surgical resection followed by radiotherapy and chemotherapy). ORs were adjusted for age, sex, socio-economic deprivation, co-morbidity, morphology, route to diagnosis (patient’s pathway to diagnosis), and treatment. All analyses were performed using Stata Software, version 16 (StataCorp, TX, USA).
## 2.4. Ethical Approval
Data for this study were collected and analysed under the National Disease Registries Directions 2021, made in accordance with sections 254[1] and 254[6] of the 2012 Health and Social Care Act. Further ethical approval for this study was not required per the definition of research according to the UK Policy Framework for Health and Social Care Research.
## 3. Results
Data from 24,319 patients with a malignant primary brain tumour diagnosed between 2012 and 2017 in England were included. Table 1 displays the distribution of patient, tumour and clinical characteristics, and univariate and mutually adjusted HRs. Brain tumour diagnosis increased with age, peaking at 65–74 years with most patients being men ($58.0\%$ $$n = 14$$,094). In absolute numbers, it was more frequent in people living in Southeast England, an area that is highly populated and more ethnically diverse. Overall, the most aggressive morphology, glioblastoma, was the most common type ($60.7\%$ $$n = 14$$,768). The Kaplan–*Meier analysis* for brain tumour morphology (Figure 2) demonstrates glioblastoma as having a very high mortality, followed by malignant glioma ($7.0\%$ of all cases, $$n = 1709$$) and unclassified malignant tumours ($11.1\%$ of all cases, $$n = 2707$$) (log-rank test, $p \leq 0.001$). Over one half of cases ($53.2\%$ $$n = 12$$,926) were diagnosed during a hospital stay that included an emergency admission, with most patients receiving either the optimal treatment ($23.0\%$ $$n = 5585$$), or no treatment ($34.9\%$ $$n = 8483$$). In the univariate analysis, each of the covariates was correlated with survival. The effects of age, sex, and co-morbidity were attenuated in the mutually adjusted analyses.
Almost all patients ($95.6\%$) were recorded as having a known ethnicity. The most common ethnic group representing $85.5\%$ ($$n = 20$$,795) of the patients was the White British Group, followed by $4.2\%$ ($$n = 1018$$) from the Any Other White Group and $2.8\%$ ($$n = 674$$) from Other Ethnic Group. The more specific ethnic groups were less common with $1.3\%$ ($$n = 321$$) of patients defining themselves as Indian, $0.8\%$ ($$n = 186$$) as Pakistani, and less than $0.4\%$ as Bangladeshi ($$n = 30$$), Chinese ($$n = 37$$), Black African ($$n = 84$$), and Black Caribbean ($$n = 94$$) (Table 2). The univariate model for ethnicity showed a survival difference and the mutually adjusted model demonstrated that patients with Other Ethnic Group and Unknown/Not Stated Ethnicity had a $18\%$ and $23\%$ decreased risk of death from any cause, respectively, compared to the White British Group.
In a sensitivity analysis, the association of survival with age seemed to disappear in most non-white ethnic groups. This could be explained by the younger age of these groups leading to a lower median age at diagnosis than for the White British population (Table 2). The effect on survival in the Unknown/Not Stated Group was less sensitive to statistical adjustment by age, as the median age was older than for the White British Group. After fully adjusting for age, sex, co-morbidity, socio-economic deprivation, tumour morphology, route to diagnosis and treatment received, patients from the Indian Group (HR 0.84, $95\%$ CI 0.72–0.98), Any Other White (HR 0.83, $95\%$ CI 0.76–0.91), Other Ethnic Group (HR 0.70, $95\%$ CI 0.62–0.79) and Unknown/Not Stated Ethnicity (HR 0.81, $95\%$ CI 0.75–0.88), had better one-year survivals than the White British Group (Table 3). There was no difference between the White British Group and the remaining Bangladeshi, Pakistani, Chinese, Black Caribbean, and Black African Ethnic minority groups.
The ethnic difference in survival was further explored by investigating whether there was any interaction between ethnicity and glioblastoma diagnosis, route or pathway to diagnosis, and optimal treatment received (Table 4). The Any Other White Group were more likely to be diagnosed through a hospital stay that included an emergency admission (OR 1.16, $95\%$ CI 1.02–1.33). The Other Ethnic Group were nearly a third more likely to receive the diagnosis of glioblastoma (OR 1.28, $95\%$ CI 1.04–1.56) than the White British Group. However, individuals with Unknown/Not Stated Ethnicity had the most favourable prognosis and were less likely to be diagnosed with a glioblastoma (OR 0.70, $95\%$ CI 0.58–0.84), less likely to be diagnosed through a hospital stay that included an emergency admission (OR 0.61, $95\%$ CI 0.53–0.69), and more likely to receive the optimal treatment option for their other-than-glioblastoma diagnosis (OR 0.39, $95\%$ CI 0.31–0.49).
## 4.1. Main Findings
This study of 24,319 people residing in England and diagnosed with a brain tumour between 2012 and 2017 shows better one-year survival for patients from Indian, Any Other White, Other Ethnic Groups, and Unknown/Not Stated Ethnic Groups than for the White British Group (HR 0.84 ($95\%$ CI 0.72–0.98), HR 0.83 ($95\%$ CI 0.76–0.91), HR 0.70 ($95\%$ CI 0.62–0.79), and HR 0.81 ($95\%$ CI 0.75–0.88), respectively). The survival analysis was adjusted for age, sex, co-morbidity, socio-economic deprivation, tumour morphology, route to diagnosis, and treatment received. Individuals with Unknown/Not Stated Ethnicity had the best prognoses and as a group were less likely be diagnosed with glioblastoma or to be diagnosed through a hospital stay, including an emergency admission.
## 4.2. Comparisons to Other Findings
In comparison to the smaller study by Ratneswaren et al. [ 2014], which was limited to high-grade glioma patients living in South East England [7], our current study was able to incorporate additional factors that may influence the impact of ethnicity on survival. In this larger national dataset, the heterogenous ethnicities were categorised into better defined groups for a precise analysis. We also found the Indian and Other Ethnic Group had a better survival than the White British Group. However, we identified the Unknown/Not Stated Ethnic Group having a better one-year survival than the White British Group, in contrast to the reverse finding in the earlier study. This could be due to a higher proportion of unknown ethnicity data, which was $21.7\%$ compared to only $4.4\%$ in the current study.
US population-based studies have also reported racial and ethnic variations in brain tumour incidence and survival. Most have shown that Caucasian people have poorer survival outcomes compared to Black/African Americans and Asian/Pacific Islander Americans [10,11,12,13,14,15]. Other work, however, has reported that African Americans have an increased risk of death from malignant brain tumour compared to Caucasians and other race and ethnicities [16], which was explained further by an interaction between race and surgery type.
## 4.3. Interpretations and Implications
In this study, we have demonstrated that the White British Ethnic Group has a poorer survival compared to other ethnic groups. An English paper by Maile et al. [ 2016] has reported incidence data broadly similar to the US finding that patients from White Ethnic Groups were significantly more likely to develop glioblastoma than other racial/ethnic groups [4,17]. They did not evaluate survival; however, their results could help explain the association between White British ethnicity and a higher risk of mortality from high-grade glioma.
Increasing age is known to be a poor prognostic factor for patients with malignant brain tumour [17]. The demography of ethnic minorities in England reflects the fact that people from these groups are younger and congregate in major cities, such as London, compared to other areas of England. A cohort study from the US also identified that patients of Hispanic background were diagnosed at a younger age compared to non-Hispanic Whites and had an improved overall survival [18]. A report by The King’s Fund suggested that, overall, people from ethnic minorities have poorer access to UK healthcare compared to the White British Groups [19], and this could correlate with fewer individuals from these being registered with the NHS. As a result, the probability of White people being diagnosed with a glioma could be increased due to their greater use of diagnostic tools, even from a young age, and therefore, they have a greater risk of ionising radiation exposure [20]. Since our finding of better survival for patients from Any Other White and Unknown/Not Stated Ethnic *Group is* new, it needs further exploration. One explanation could be that these individuals travel to their countries of origin for better healthcare and social support following their diagnosis, which could mean that their deaths abroad were not formally registered in the English system [21].
Brain tumours are considered difficult to diagnose [22], as these cancers tend to [1] involve 3 or more GP visits before diagnosis [23,24] and [2] are likely to present as an emergency [9,25]; both could lead to poorer outcomes [9]. The potential impact of family support on outcome might also differ between ethnic groups. People from minority ethnic groups, particularly those from Asian backgrounds, are more likely to be surrounded by extended families compared to the nuclear family structure, typical of the White British Group; it is possible that extended families may be more likely to recognise subtle signs of a brain tumour, including neuro-cognitive changes, as well as possible recurrences and encourage earlier diagnosis.
The current standard therapy for gliomas consists of surgical resection followed by adjuvant chemotherapy and radiation, prolonging median overall survival to 15 months for glioblastomas [26,27], and is represented in this study by the optimal treatment option. From our results, we demonstrated that the Unknown/Not Stated Ethnic Group are less likely to receive optimal treatment, but that could be due to the lower chance of being diagnosed with a glioblastoma.
Studies investigating possible factors explaining brain tumour occurrence have identified genes that could be associated with glioma development and tumours carrying the worst prognosis [28,29]. The presence of such genes and their specific alterations could perhaps explain the differences in prognosis by ethnicity. Epigenetic age acceleration, which is the difference between age predicted by DNA methylation and chronological age, has been linked with many cancers [30]. A recent study by Crimmins et al. [ 2021], which examined epigenetic clocks to evaluate a linkage with race/ethnicity, found that the majority of clocks indicated slower epigenetic ageing among Hispanic and African American individuals compared to White individuals [31]. The reports of differing incidence and survival by racial/ethnic groups, make it important to explore possible genetic alterations and variation in signalling pathways to identify and compare polymorphisms between ethnic minority and White individuals [13]. For example, one study found a $42\%$ reduction of risk of glioma in patients with a history of diabetes [32], and a recent meta-analysis confirmed this inverse relationship where elevated blood sugar, or a previous history of diabetes, are inversely associated with risk of glioma [33]. In England, people of Black (African and Caribbean) and South Asian (Bangladeshi, Indian, and Pakistani) backgrounds are at higher risk of developing type 2 diabetes from a younger age compared to those of a White background [34], and this could possibly be associated with the decreased glioma mortality observed here.
The better survival for Indian individuals after adjusting for other factors was of particular interest and may suggest that there are other more specific influencing factors. As there were no significant differences between Indian and White British Groups in terms of patient characteristics, tumour morphology, and route or pathway to diagnosis, further explanation is needed to justify the difference when treatment received is added to the Cox proportional regression model. Due to curcumin’s anti-tumoural effects on glioma cells in preclinical in vitro and in vivo [35], we speculate that an Indian diet that usually includes curcumin might play a role in their prolonged brain tumour survival, perhaps linked to a better response to treatment. A recent study in India evaluated the molecular biomarkers of brain tumours in Indian patients [36] and reported a high prevalence of isocitrate dehydrogenase 1 (IDH1) mutation in astrocytoma and glioblastoma in these patients [37]. The presence of IDH1 mutation correlates with a survival benefit and is more common among glioblastomas progressing from a lower grade glioma compared with 5–$10\%$ of de novo glioblastomas [38,39]. Consequently, the association between individuals of an Indian background and improved survival could be related in some way to this mutation, in addition to, or perhaps independent of, the therapeutic potential of curcumin. Obtaining detailed information on molecular, genetics, and lifestyle or environmental factors could enable us to further compare outcomes with other populations.
## 4.4. Strengths and Limitations
The extended period of time covered by this study, along with the greatly improved cancer registration data in recent years for England, have meant that more detailed ethnic groups can be analysed. The previously reported NCIN incidence data for the years 2002 to 2006, and other related studies, found a quarter of cancer patients had unknown (i.e., missing) ethnicity information [4,6,7]. While cancer registration acknowledges that ethnicity data may not be self-reported and may possibly have been derived from already held information, the very large increase in completeness means we can be confident in these analyses. In these new data, only $4\%$ of patients had unknown/not stated ethnicity information and were analysed separately to get a better understanding of this individual group. Our study also considered the importance of analysing more defined ethnic groups, as significant heterogeneity of risk for many cancers can be seen particularly among those from Black and South Asian Groups [4]. Another strength of this study is the adjustment for many prognostic variables that could vary by ethnicity and prognosis. However, other and unknown factors could still be relevant including patient’s performance status, tumour location within the brain, extent of tumour excision, and more interestingly, molecular biomarkers and genetic information. Our study had some limitations. Reporting patterns of incidence for the histological subtypes by ethnicity would have strengthened the study; however, this was restricted by the small number of patients in each subgroup. In addition, limited numbers of patients in the mixed ethnic groups and those from a White Irish background meant they had to be combined within Any Other Ethnic and Any Other White Groups respectively. Additionally, cancer registration did not collate information that could possibly influence survival, such as recurrences, glioblastoma progressing from a low-grade glioma, or biopsy-only—and hence, we observed a high proportion of patients ($34.9\%$) who had no surgical resection but could possibly just had a diagnostic biopsy.
## 5. Conclusions
To obtain a better understanding of potential ethnic differences in malignant primary brain tumour survival, we carried out a detailed evaluation of factors, including age at diagnosis, sex, co-morbidity, socio-economic deprivation, histologic tumour subtype (which is correlated with tumour grade), route or pathway to diagnosis, and treatment options that might affect prognosis. After controlling for these variables, we found that patients from Indian, Any Other White, Other Ethnic Groups, and Unknown/Not Stated Ethnic Groups, had better one-year survival compared to the White British Group. To determine whether biological, behavioural, or clinical factors are driving these survival differences, more data on patients’ clinicopathological characteristics are therefore needed. This will help us better understand any ethnic inequalities in brain cancer and identify improvements to the health service for specific groups.
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|
---
title: 'Sleep-Disordered Breathing among Saudi Primary School Children: Incidence
and Risk Factors'
authors:
- Saleh H. Alwadei
- Suliman Alsaeed
- Ahmed Ibrahim Masoud
- Farhan Alwadei
- Khalid Gufran
- Abdurahman Alwadei
journal: Healthcare
year: 2023
pmcid: PMC10000777
doi: 10.3390/healthcare11050747
license: CC BY 4.0
---
# Sleep-Disordered Breathing among Saudi Primary School Children: Incidence and Risk Factors
## Abstract
This study aimed to identify the incidence and risk factors of sleep-disordered breathing (SDB) using an Arabic version of the pediatric sleep questionnaire (PSQ). A total of 2000 PSQs were circulated to children aged 6–12 years who were randomly selected from 20 schools in Al-Kharj city, Saudi Arabia. The questionnaires were filled out by the parents of participating children. The participants were further divided into two groups (younger group: 6–9 years and older group: 10–12 years). Out of 2000 questionnaires, 1866 were completed and analyzed ($93.3\%$ response rate), of which $44.2\%$ were from the younger group and $55.8\%$ were from the older group. Among all the participants, a total of 1027 participants were female ($55\%$) and 839 were male ($45\%$) with a mean age of 9.67 ± 1.78 years. It showed that $13\%$ of children were suffering from a high risk of SDB. Chi-square test and logistic regression analyses within this study cohort showed a significant association between SDB symptoms (habitual snoring; witnessed apnea; mouth breathing; being overweight; and bedwetting) and risk of developing SDB. In conclusion: habitual snoring; witnessed apnea; mouth breathing; being overweight; and bedwetting strongly contribute the to development of SDB.
## 1. Introduction
Many airway dysfunctions, including obstructive sleep apnea (OSA) and primary snoring, are caused by sleep-disordered breathing (SDB) [1]. OSA is a severe form of SDB characterized by upper airway obstruction, either partial or intermittent, that interrupts the normal sleeping pattern [2]. Among children, SDB is linked with growth retardation, behavioral problems, disturbances in cognitive development, failure to thrive, and attention deficit/hyperactivity disorder [2,3]. The myriad of SDB symptoms in children also include abnormal breathing, snoring, sweating, aggressiveness, irritability, hyperactivity, sleepiness, excessive fatigue, memory impairments, and poor school performance, among many others [1,2,3,4,5]. Furthermore, the craniofacial morphology associated with SDB in children includes retrognathic mandible, increased lower facial height, narrow maxillary arch with a high vault, posterior crossbite, anterior open bite, and restriction in the upper airway space [5,6].
The prevalence of pediatric OSA ranges from 1–$6\%$, while habitual snoring varies by definition and ranges from 4–$17\%$ [1,7,8,9,10]. A previous study in southern Italy found that $4.9\%$ school children were possessed habitual snoring and among them only $1\%$ had OSA [11]. Another Turkish study stated that the prevalence of 6–13 years children’s snoring habit is $7\%$ [12]. Moreover, $11.4\%$, $12\%$ and $27.6\%$ prevalence rates of snoring habit were observed in the Indian, Chinese and Brazilian populations, respectively [13,14,15].
In Saudi Arabia, the prevalence among adults and children leans toward the higher end of the range reported in the literature [16,17,18]. However, the majority of children with OSA remain undiagnosed [19]. In order to diagnose OSA in children, nocturnal sleep-based polysomnography (PSG) is widely used and is considered the gold standard to diagnose OSA [8]. Regardless of its diagnostic advantages, PSG presents challenges related to its cost, time, complexity, and availability/access which necessitates the use of an efficient screening test that may prompt early detection and treatment [9,19,20]. As dentists and selected dental specialists obtain radiographic x-rays regularly and examine children daily, they could effectively identify the children who are at risk of SDB [9,21]. This is especially true given the previously mentioned association between SDB and craniofacial growth and development, as well as the proven positive responses to various treatment modalities ranging from interceptive orthodontics to orthognathic surgery [22,23,24].
Although thorough clinical history and physical examination are an integral part of best practice in any healthcare profession including assessment of SDB, they may not be sufficient to identify children suspected (or at high risk) of OSA [19]. It is important to consider efficient, reliable, and accurate screening tests for timely diagnosis and management (including referral) of children with SDB since it might prevent associated comorbidities [20]. Pediatric sleep questionnaires have been developed for the screening of SDB in children and adolescents, and epidemiological studies have shown them to be clinically significant and relevant [25,26]. As subjective parent report tools, sleep questionnaires are apprehensive about the risk factors and sign symptoms of SDB and OSA [27]. Among these questionnaires, the pediatric sleep questionnaire-22 (PSQ-22) has been validated in groups of referred snoring children and controls, showing excellent specificity and sensitivity for identifying children with OSA [2]. Therefore, this study aimed to determine the incidence of SDB and identify related risk factors among primary school children in Al-Kharj, Saudi Arabia.
## 2. Materials and Methods
This was a cross-sectional study that was conducted from September 2018 to December 2018 in Al-Kharj city, Saudi Arabia. Al-Kharj city is situated in the Riyadh province of Saudi Arabia with about 376,325 residents (https://www.stats.gov.sa (accessed on 21 November 2017)). Ethical approval was obtained from the College of Dentistry Research Centre at Prince Sattam Bin Abdulaziz College of Dentistry (Registration No: 1439-03-003). In addition, permission to conduct this research was also obtained from the authority of the Ministry of Education. A stratified randomization technique was used to list the schools to be included in this study to ensure the proper sample representation in the Al-Kharj. A free online randomization software (http://www.randomization.com (accessed on 21 November 2017)) was used to select 20 schools (10 boys’ schools and 10 girls’ schools). A formal letter was sent to the principal of each school to obtain permission by explaining the purpose of the study. Once the school authorities agreed to participate in the current research, they were requested to provide a list of primary school students with an assigned number corresponding to each student. Then, a randomization table was used to select the students.
A required sample size of 454 subjects was estimated based on a statistical power calculation described by Pourhoseingholi and colleagues [2013] [28], considering 0.20 non-responses, a confidence level of $95\%$, a precision of 0.04, and $20\%$ prevalence SBD as reported in Saudi-based literature using PSQ [17,18].
Each child was given a folder in order to obtain permission from the parents which included: [1] a cover page explaining the aim of the study, the significance of the study, and the confidentiality measures taken to protect collected information, [2] a consent form, and [3] the PSQ. Parents/guardians were asked to observe their child’s sleep pattern for one week before filling out the PSQ to improve response accuracy. Children aged 6–12 years who presented a consent form signed by their parents were included in the current study. The participants were divided into two groups by age: the younger group (age 6–9 years) and the older group (age 10–12 years).
A total of 2000 folders containing a cover letter, a consent form, and the PSQ were distributed to randomly selected children, and the folders were collected a week later.
The PSQ was previously validated by Chervin et al. [ 2000]; therefore, no validation was required for this study. Moreover, the Arabic version of PSQ was validated by Baidas et al. [ 2019]; therefore, the Arabic form of PSQ was used in this study in order to explain the PSQ properly by the parents [17]. The PSQ contains 22 items, and each item consists of three options to respond to with the following options: ‘yes’ = 1; ‘no’ = 0; and ‘don’t know’ = missing. If participants scored ≥8 items to ‘yes’, they would be considered at high risk of SDB, whilst if they scored <8 items to ‘yes’, they would be considered at a low risk of SDB.
## Statistical Analysis
Statistical analysis was performed using SPSS software version 28 (IBM Corp. Armonk, NY, USA). The demographic of the children and the prevalence rate of SDB was assessed with descriptive statistics. A Chi-square test was performed to identify the differences in demographic variables and SDB symptoms related to the risk factors. For the Chi-square test (2 × 2), demographic variables were dichotomized as (male/female) for gender and (older/younger) for age, when the test was performed to identify differences in being at high risk of developing SBD (yes/no). Moreover, possible risk factors for the SDB were assessed by binary logistic regression. The p-value was set to <0.05 as statistically significant.
## 3. Results
A total of 1866 parents out of 2000 agreed to participate and complete the questionnaire ($93.3\%$). The mean age of the participants was 9.67 ± 1.78 years. Among all the participants, a total of 1027 children were female ($55\%$) and 839 were male ($45\%$). The younger group and older group consisted of a total of $44.2\%$ and $55.8\%$ of participants, respectively. The outcome of the PSQ scoring among all the participants was presented in Table 1. Based on the PSQ scores, a total of 243 children ($13\%$) were categorized as high risk of SBD.
Table 2 showed that the most prevalent symptom of SBD is mouth breathing ($14.4\%$) and the least prevalent symptom is witnessed apnea ($6.6\%$). The Chi-square test showed that there were significant differences between gender (females/males) and age (younger/males) in relation to being at high risk of developing SBD ($p \leq 0.05$). Moreover, there was a significant difference between high and low-risk children concerning habitual snoring, mouth breathing, witnessed apnea, being overweight, and bedwetting (Table 2).
Table 3 presented that binary logistic regression exhibited no significant association with gender in terms of developing SDB. However, the younger age group is significantly associated with the risk of developing SDB. The risks of developing SDB were 1.43 times higher in younger children compared to older children. In addition, children with habitual snoring, witnessed apnea, mouth breathing, being overweight, and bedwetting were at 8.9 times, 2.15 times, 6.6 times, 4.57 times, and 4.81 times higher risk of developing SDB, respectively (Table 3).
## 4. Discussion
The current study determined the incidence of SDB and identified related risk factors among primary school children in Al-Kharj, Saudi Arabia with the Arabic version of PSQ, which is the most used pediatric sleep questionnaire [17,27]. The original PSQ was translated into the Arabic language by Baidas et al. [ 2018], which assessed 1350 Saudi children, with $91\%$ of the questionnaire reporting good concordance [17].
The risk of undiagnosed and untreated pediatric OSA could result in significant medical comorbidities including, but not limited to, cardiovascular, cognitive, metabolic, and growth hormone dysfunction [29]. The lack of awareness of pediatric SDB is one of the main barriers for families to seeking proper care, which starts with a formal diagnosis by a sleep physician using PSG followed by proper treatment [1]. This is the first population-based study in Al-Kharj which determined the incidence of SDB among school-going children. The response rate of the current study is $93.3\%$ from a total of 1866 participants which is relatively large compared to previous studies [17,30], and somewhat comparable to a recent Saudi study [13]. Such a high response rate ($93.3\%$) might be attributed to the way the questionnaire was distributed through school principals who are figures of authority at their schools.
The prevalence of SDB varies by definition and ranges from 4–$17\%$ [1,7,8,9,10]. The main finding of the study showed that $13\%$ of participants with an age limit of 6–12 years were at a high risk of SDB. Additionally, $14.4\%$ were considered mouth breathing, and $6.6\%$ had witnessed OSA. In comparison to previous Saudi studies that used PSQ and included children with similar age ranges and comparable gender distribution exhibited $21\%$ and $23\%$ of high-risk SBD which is higher compared to the current study [17,18]. Moreover, samples from the previous studies reported participants with habitual snoring are more reported ($10.7\%$ and $15.9\%$) and witnessed apneas reported to a lesser extent ($3.4\%$ and $4\%$) compared to the present study [17,18]. The variation between our findings and Baidas et al. [ 2018] is due to differences in the operational definition of what constitutes habitual snoring. Moreover, Al Ehaideb et al. [ 2021] conducted a PSQ-based survey study of 285 Saudi children seeking orthodontic treatment and found that $47.7\%$ were at high risk of developing SDB [31]. They also reported $11.3\%$ and $11.6\%$ of their sample to have habitual snoring and witnessed apnea, respectively [31]. These larger numbers could be because their sample was collected from an orthodontic clinic at a tertiary public hospital that receives referrals of cases with moderate or severe forms of malocclusions. Globally, studies that used the PSQ reported the prevalence of children at high risk of developing SDB to range from $7.9\%$ to $12.8\%$ [1,32,33].
The current study shows that there is a significant difference ($$p \leq 0.009$$) in SBD risk factors between younger and older groups. Additionally, it also showed that younger children were 1.43 times more likely to be at high risk of developing SDB compared to older children. In children, enlarged adenoids are the main reason for developing SDB and adenoidectomy/tonsillectomy is considering the primary treatment of SDB in children [9,34]. Adenoids reach their maximum size between the ages of 5 and 7 and begin to shrink afterward [35]. Therefore, enlarged adenoids were more likely to be found in the younger age group in our sample aged 6 to 9 years, and this might be the reason why the younger age group exhibited increased risks of developing SDB. In addition to enlarged adenoids, obesity has also been considered a possible cause of OSA. This study showed that there was a significant association between the high-risk group and children being overweight as perceived by their parents, which agrees with other studies conducted in Saudi Arabia [17,18,31].
In terms of gender distribution, this study showed a significant association between females and OSA, which is opposite to the reported male predilection of pediatric OSA in other studies [1,11,12,14,17]. The reason why there are usually more males affected by OSA is suggested to be due to the differences in the puberty age between males and females, as females enter puberty first. This variation in OSA prevalence between males and females usually increases as they age [1]. This finding reinforced the information from the previous studies where a significant difference between gender and OSA was observed, including children older than 12–13 years [36,37], while studies that did not show a significant association between gender and OSA were mostly limited to the younger age group [1,10]. This might explain why the current study, which was limited to children younger than 12 years old, was not in line with other studies in terms of the association between males and OSA. Nonetheless, it is important to note that the unique contribution of gender in the regression model was not significant in relation to being at high risks of developing SBD, controlling for other variables (including age, habitual snoring, witnessed apnea, parent perception of the child being overweight, and bedwetting). Therefore, cautious interpretation is warranted regarding this study findings in terms of gender association with, and contribution to, being at high risk of developing SBD.
It is not surprising that this study showed a significant association between high risks of developing SBD and snoring as snoring is one of the main symptoms of SDB and OSA [1]. Similar findings were observed in other previous studies [6,17,31]. Hence, parents need to appreciate the importance of seeking medical care when their child snores during sleeping. In the future, it would be worthwhile to explore whether the parents who participated in the study have considered medical care for their children, especially those with a high risk of pediatric SDB.
One of the common misapplications of PSQ questionnaires in the literature is the interpretation of a “yes” response to question no. 6 in the first domain (Table 1), as many authors have interpreted this as the presence of OSA [17,29]. It is recommended that authors use the term “witnessed apnea” for “yes” responses to this question instead of the inaccurate assumption that the child has OSA or is considered at high risk to develop SDB. The current study also showed that the high-risk group ($19.3\%$) is more associated with OSA compared to the low-risk group ($4.7\%$). Additionally, children with witnessed apneas were 2.84 times more likely to be at high risk for SDB. However, not every child with witnessed apnea was at high risk for SDB. There were 77 children who had witnessed apneas yet were considered low risk for SDB based on the answers to the remaining questions.
This study consists of some risk of bias due to the nature of its methodology. Moreover, being overweight was assessed by the parent’s perception. Using the body mass index would have been more objective yet was much harder to do especially given the large sample used in the current study. Additionally, having a PSG would have given a definitive OSA diagnosis. A future study could include a subgroup of the total sample to assess the prevalence more accurately. Although it is not the aim of the study, public awareness programs regarding snoring for children and the risk of pediatric SDB and its symptoms need to be implemented in schools as many local studies have reported similar findings of high rates of snoring and witnessed apnea.
## 5. Conclusions
In conclusion, $13\%$ of the school-going children in Al-Kharj Saudi Arabia are at high risk of developing SDB at a younger age. Moreover, habitual snoring, mouth breathing, being overweight, bedwetting, and witnessed apnea were more prevalent in children with a high risk of SDB. Thus, the importance of further exploration of SDB among Saudi school-going children needs to be recognized, strategized, and materialized.
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---
title: Comprehensive Analysis of Prognosis and Immune Landscapes Based on Lipid-Metabolism-
and Ferroptosis-Associated Signature in Uterine Corpus Endometrial Carcinoma
authors:
- Pusheng Yang
- Jiawei Lu
- Panpan Zhang
- Shu Zhang
journal: Diagnostics
year: 2023
pmcid: PMC10000778
doi: 10.3390/diagnostics13050870
license: CC BY 4.0
---
# Comprehensive Analysis of Prognosis and Immune Landscapes Based on Lipid-Metabolism- and Ferroptosis-Associated Signature in Uterine Corpus Endometrial Carcinoma
## Abstract
[1] Background: The effect of tumor immunotherapy is influenced by the immune microenvironment, and it is unclear how lipid metabolism and ferroptosis regulate the immune microenvironment of uterine corpus endometrial carcinoma (UCEC). [ 2] Methods: Genes associated with lipid metabolism and ferroptosis (LMRGs-FARs) were extracted from the MSigDB and FerrDb databases, respectively. Five hundred and forty-four UCEC samples were obtained from the TCGA database. The risk prognostic signature was constructed by consensus clustering, univariate cox, and LASSO analyses. The accuracy of the risk modes was assessed through receiver operating characteristic (ROC) curve, nomogram, calibration,, and C-index analyses. The relationship between the risk signature and immune microenvironment was detected by the ESTIMATE, EPIC, TIMER, xCELL, quan-TIseq, and TCIA databases. The function of a potential gene, PSAT1, was measured by in vitro experiments. [ 3] Results: A six-gene (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) risk signature based on MRGs-FARs was constructed and evaluated with high accuracy in UCEC. The signature was identified as an independent prognostic parameter and it divided the samples into high- and low-risk groups. The low-risk group was positively associated with good prognosis, high mutational status, upregulated immune infiltration status, high expression of CTLA4, GZMA and PDCD1, anti-PD-1 treatment sensitivity, and chemoresistance. [ 4] Conclusions: We constructed a risk prognostic model based on both lipid metabolism and ferroptosis and evaluated the relationship between the risk score and tumor immune microenvironment in UCEC. Our study has provided new ideas and potential targets for UCEC individualized diagnosis and immunotherapy.
## 1. Introduction
Uterine corpus endometrial carcinoma (UCEC) is one of the most common gynecologic malignancies, with an increasing incidence of about $1\%$ per year [1]. Approximately $15\%$ of UCEC patients are diagnosed at an advanced stage, and approximately 15–$20\%$ of patients will experience relapse after primary surgical treatment [2,3]. Although surgery, carboplatin/paclitaxel systemic chemotherapy, and hormone therapy are effective treatments, patients with advanced disease, recurrence, or drug resistance still have poor prognoses [4,5]. In recent years, it has been reported that patients with advanced endometrial cancer may benefit from immunotherapy. The main immunotherapy approaches include immune checkpoint inhibitors (ICIs), adoptive cell transfer (ACT), cancer vaccines, and lymphocyte-promoting cytokines. For example, dostarlimab, a drug that inhibits the programmed cell death 1 and programmed cell death ligand 1 pathway, can improve the prognosis of patients receiving platinum chemotherapy or progressive mismatch repair deficiency endometrial cancer [6]. However, the effect of immunotherapy is not ideal due to the complexity of the immune microenvironment and differences in the response to immunotherapy [7,8]. Therefore, it is vital to identify potential diagnostic and prognostic targets or risk signatures and to tailor individualized immunotherapy strategies for improving the outcomes of UCEC patients.
Obesity is an independent risk factor for UCEC [9]. Almost all UCEC patients with obesity have altered lipid metabolism [10]. Tan et al. built an 11 lipid metabolism gene (LMG) signature to reflect the prognosis of UCEC patients [11]. Lipids are susceptible to oxidation by oxygen free radicals. Overproduction and elimination failure of lipid peroxidation are the main reasons for the novel iron-dependent cell death ferroptosis [12,13,14]. Liu et al., Wang et al., and Wei et al. constructed a ferroptosis-related gene signature to predict the prognosis of UCEC patients [15,16,17]. Lipid synthesis, storage, and degradation processes can be regulated by ferroptosis [18,19]. Iron depletion leads to a large amount of lipid accumulation in breast cancer cells [20]. Iron accumulation is due to altered lipid metabolism associated with increased oxidative stress in myelodysplastic syndromes [21]. Ferroptosis is closely associated with lipid metabolism pathways [22,23]. Inhibiting β-oxidation can restore tumor cell sensitivity to ferroptosis [24]. Upregulating stearoyl CoA desaturase 1 (SCD1), the rate-limiting enzyme in fatty acid synthesis, increases the resistance of tumor cells to ferroptosis. Increasing evidence suggests that lipid metabolism and ferroptosis closely affect each other [25,26]. However, the interaction and shared role of ferroptosis and lipid metabolism in UCEC remains unclear.
In the present study, we aimed to construct a prognostic risk signature based on both lipid metabolism and ferroptosis to comprehensively analyze their combined effects on UCEC. We screened six risk genes (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) as reliable diagnostic and prognostic biomarkers and divided UCEC patients into high- and low-risk groups based on their risk score. Then, we estimated differences in immune score, immune infiltration, immune checkpoint, immunotherapy, and chemotherapy response between the high- and low-risk groups. The findings provide a new idea for individualized therapy strategies to improve the prognosis of UCEC patients.
## 2.1. Dataset Information
Sequencing RNA data (HTSeq-FPKM) and clinical information were obtained from The Cancer Genome Atlas (TCGA) database, and 579 cases were selected for study, including 544 UCEC samples and 35 normal samples. The detailed clinical information of the UCEC patients is shown in Table S1.
## 2.2. Extraction of Lipid-Metabolism-Related and Ferroptosis-Associated Genes
Lipid-metabolism-related genes (LMRGs) were collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Molecular Signatures Database (MSigDB), including the GSEA, HALLMARK, and REACTOME databases [27]. The detailed gene sets are shown in Table S2. A total of 1457 genes were selected for analyses after removing duplicate genes (Table S3). In addition, we downloaded 288 ferroptosis-associated genes (FAGs) from the FerrDb database (http://zhounan.org/ferrdb/legacy/index.html, accessed on 1 June 2022). After removing the replicates, 259 individual FAGs were used for further investigation.
## 2.3. Construction of the LMRG and FAR Prognostic Signature
The evaluation of the differentially expressed LMRGs (DE-LMRGs) was performed using the default settings for the “lmFit”, “eBayes”, and “topTable” functions in the “limma” R package. The screening criteria were $p \leq 0.05$, |Log2 Fold Change (FC)| > 1, and a false discovery rate (FDR) < 0.05. Then, univariate Cox regression analysis was applied to determine LMRGs with overall survival (OS) in UCEC by using the coxph function in the “survival” R package at $p \leq 0.05.$ The molecular classification of DE-LMRGs in UCEC was analyzed by the “ConsensusClusterPlus” R package. Principal component analysis (PCA) was performed to identify the grouping ability of our model with the R package “stats”. Then, the FAGs interacted with the results of the consensus clustering approach, and the genes of interaction were selected for further study.
We performed univariate cox and least absolute shrinkage and selection operator (LASSO) analyses to identify significant prognostic genes based on both LMRGs and FARs with a threshold of $p \leq 0.05.$ Then, a risk score signature was created by considering the estimated cox regression correlation coefficients and the expression values of the optimized LMRGs and FARs. The formula is risk score = Σi1expGenei*coeffi. According to the median value of the calculated risk scores from the TCGA-UCEC, the patients were divided into low- and high-risk groups. The prognostic ability and stability of the signature was measured by the Kaplan–Meier (K–M) analysis, multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve with the “Survival” and “sevivalROC” R package ($p \leq 0.05$).
## 2.4. Functional Enrichment Analysis
To examine the distinction between the high- and low-risk group of our model, we further carried out gene set variation analysis (GSVA) using the “GSVA” function with method parameters (min.sz = 10, max.sz = 500, verbose = TRUE) of the “GSVA” R package, and conducted KEGG pathway analysis and Gene Ontology (GO) analysis via the “clusterProfiler (version 3.14.3)” R package ($p \leq 0.05$).
## 2.5. Tumor Mutational Burden (TMB) Analysis
We downloaded the somatic mutation data from TCGA. Using Perl, we calculated the TMB value of each sample and divided all samples into high- and low-TMB groups based on the median TMB [28]. Then, K–M analysis was used to assess survival differences between the groups. We also calculated the expression differences in TMB between the high- and low-risk groups and analyzed the relationship between TMB and the risk score ($p \leq 0.05$)
## 2.6. Immune Infiltration of the Prognostic Model
The CIBERSORT algorithm was utilized to evaluate the 22 types of immune fractions between the high- and low-risk groups, and the results were visualized with the “vioplot” R package. Then, we used the Tumor Immune Estimation Resource (TIMER) to evaluate correlations between expression of six model genes and the immune infiltration level of tumor-infiltrating immune cells. We also analyzed the relationship between innovative targeted therapy and risk prognostic models. The Wilcoxon test was used to detect expression of potential immune checkpoints between the high-risk and low-risk groups ($p \leq 0.05$). Furthermore, we downloaded clinical data from The Cancer Immunome Atlas (TCIA) to predict the response to immune checkpoint blockade (CTLA-4 and PD-1) in patients in the high- and low-risk groups by the immunophenoscore. In addition, according to the Genomics of Drug Sensitivity in Cancer (GDSC) database, the R package “pRRophetic” was used to measure the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs.
## 2.7. Cell Culture
The UCEC cell lines Ishikawa, HEC-1A, HEC-1B, and ECC-1 were obtained from the American Type Culture Collection (ATCC). The HEC-1A cell lines were cultured in McCoy’s 5A (Gibco, New York, NY, USA) supplemented with $10\%$ fetal bovine serum (FBS, Biological Industries, Kibbutz Beit-Haemek, Israel) and $1\%$ penicillin/streptomycin (P/S); the others were cultured in RPMI 1640 culture medium with $10\%$ FBS and $1\%$ P/S. All of the cells were cultured at 37 °C in a humidified incubator under $5\%$ CO2.
## 2.8. Small Interfering RNA (siRNA) Transfection
The siRNA PSAT1 and scrambled control sequences were obtained from Gene Pharma (Shanghai, China). The details of the sequences are as follows: si-PSAT1-1: forward 5′-CAGUGUUGUUAGAGAUACAdTdT-3′, reverse 5′-UGUAUCUCUAACAACACUGdTdT-3′; si-PSAT1-2: forward 5′-GCUGUUCCAGACAACUAUAdTdT-3′, reverse 5′-UAUAGUUGUCUGGAACAGCdTdT-3′. siRNA transfection was carried out using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA).
## 2.9. Quantitative Real-Time PCR (qRT–PCR)
Total RNA was extracted using TRIzol reagent (Sangon Biotech, Shanghai, China) after transfecting siRNA for 48 h, and reverse transcription was performed using PrimeScriptTM RT Reagent Kit (TAKARA, RR047A). QRT–PCR was conducted with the SYBR Green qPCR Supermix kit (Invitrogen). The primers used were purchased from Tsingke Biotechnology Co (Beijing, China), as follows: PSAT1 Forward 5′-ACTTCCTGTCCAAGCCAGTGGA-3′; PSAT1 Reverse 5′-CTGCACCTTGTATTCCAGGACC-3′; GAPDH Forward 5′-GGAGCGAGATCCCTCCAAAAT-3′; GAPDH Reverse 5′-GGCTGTTGTCATACTTCTCATGG-3′.
## 2.10. Western Blot Analysis
Total proteins were obtained from cells using PIPA buffer (New Cell & Molecular Biotech, Suzhou, China) at 72 h after siRNA transfection, separated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE), and transferred to polyvinylidene fluoride (PVDF) membranes (Millipore, New York, NY, USA). The membranes were blocked using $5\%$ BSA for at least 1 h at room temperature and incubated with PSAT1 (10501-1-AP, Proteintech, Wuhan, China) or GAPDH (10494-1-AP, Proteintech) at 4 °C overnight. The next day, the membranes were incubated with secondary antibody (GB23303, Servicebio, Shanghai, China) for 1 h at room temperature, and bands were detected by chemiluminescence.
## 2.11. Cell Proliferation Assay
Cell proliferation was detected by the Cell Counting Kit-8 assay (CCK-8) and colony formation assay. For CCK-8, the cells were seeded into 96-well plates at a density of 2000 cells/well after 72 h of transfection. At the indicated time, CCK-8 solution (10 μL) was added to each well of the culture medium. Cell viability was measured using an automatic enzyme-linked immune detector after incubation for 1 h. For the colony formation assay, 1000 transfected cells were seeded into six-well plates for 10–14 days, and the culture medium was changed every three days. After staining with $0.1\%$ crystal violet and photographing, cell colonies were statistically analyzed by the t-test.
## 2.12. Cell Migration and Invasion Assay
Cell migration and invasion were assessed using 24-well transwell chambers (8 μm; Millipore). In brief, a sample of 4 × 104 cells suspended in 200 μL serum-free medium was seeded in the upper chamber, and the lower chamber contained 600 μL medium with $10\%$ FBS. After 48 h, the chambers were fixed with $4\%$ paraformaldehyde and stained with $0.1\%$ crystal violet dye for 30 min. The upper chamber cells were wiped off and then photographed and counted under a microscope. For the invasion assays, Matrigel (BD, biocoat, #358248) was used to coat the upper chamber, after which the cells were seeded; the next step was the same as above.
## 2.13. Statistical Analysis
Bioinformatic statistical analyses were performed using R (v.3.6.1) software. Pearson correlation analysis was employed for correlation analysis between TMB and the risk model. All of the in vitro experiments were independently performed in triplicate and analyzed by the t-test. Data were analyzed using the IBM SPSS Statistics 22 and visualized in GraphPad Prism 9. The values were presented as the mean ± standard deviation (SD). $p \leq 0.05$ was considered statistically significant.
## 3.1. Identification and Clustering of LMRGs
A brief workflow of this research is presented in Figure S1. We screened 1457 LMRGs for differential expression analysis and identified 88 differentially expressed LMRGs (DE-LMRGs) with the “limma” R package based on 544 UCEC samples and 35 normal samples from TCGA. The boxplot of the expression patterns of the 88 DE-LMRGs is shown in Figure 1A. KEGG analysis and GO analysis showed that these significant genes mainly participate in lipid metabolic processes (Figure S2A,B). Then, univariate cox hazards regression and Kaplan–Meier (K–M) analyses were utilized to screen out prognostic LMRGs based on TCGA, and we obtained six risk genes and three protective genes for survival (Figure S2C,D). The consensus clustering approach was used to divide the UCEC samples with the non-negative matrix factorization (NMF) algorithm. Based on LMRGs expression, the optimal clustering stability was confirmed when $K = 3$ (Figure 1B and Figure S2E,F). We also performed principal component analysis (PCA), which showed the good grouping ability of our clustering (Figure 1C). Therefore, all of the UCEC samples were divided into three clusters, and the heatmap showed lower expression for the DE-LMRG genes in Cluster A (Figure S2G). Moreover, K–M analysis indicated a significant difference in OS among the three subgroups, with the patients in Cluster A having the best prognosis (Figure 1D, $p \leq 0.05$). By further analyzing the clinical characteristics among the three clusters, we found that patients in Cluster C had an older age and a higher grade and stage (Figure S2H–J, $p \leq 0.05$).
## 3.2. Signature Construction Based on LMRGs and FAGs
Differentially expressed genes among the three clusters were obtained from consensus clustering analysis and intersected with FAGs. Then, we obtained both lipid metabolism-related and ferroptosis-associated genes (LMG-FAGs) (Figure 2A). We performed overall-survival-based univariate regression analysis on the lipid-metabolism-related and ferroptosis-associated genes (LMG-FAGs) obtained through consensus clustering analysis. This approach revealed 211 LMG-FAGs associated with the prognosis of endometrial cancer, and we classified them into 87 risk genes and 124 protective genes according to the hazard ratio (HR) and p value (Table S4, $p \leq 0.05$). To avoid overfitting and bias, the results of univariate regression analysis were subjected to LASSO regression analysis using the “glmnet” R package, and the accuracy of the model was tested by cross-validation (Figure 2B,C). Hence, a six-gene prognostic risk model was established by the following formula: risk score = [CDKN1A expression × (−0.02353)] + [CDKN2A expression × (0.11554)] + [ESR1 expression × (−0.05874)] + [PGR expression × (−0.11493)] + [PSAT1 expression × (0.05505)] + [RSAD2 expression × (0.01431)]. We analyzed the relationship between different risk scores and patient follow-up times, events, and expression changes of individual genes, and it was observed that with an increase in the risk score, the survival rate of patients decreased significantly. CDKN1A, ESR1, and PGR were found to be protective factors that showed downregulated expression with increased risk scores; CDKN2A, PSAT1, and RSAD2 showed the opposite result (Figure 2D $p \leq 0.05$). Furthermore, we detected expression levels and performed multivariate Cox regression and K-M survival analyses on the six independent prognostic genes. The results indicated that high expression of CDKN1A, ESR1, and PGR was related to better prognosis, whereas high expression of CDKN2A, PSAT1, and RSAD2 was not (Figure S3A–C, $p \leq 0.05$). According to the median cut-off value of the risk score, the high- and low-risk groups were established to differentiate the UCEC patients in TCGA, and the high-risk patients had a worse prognosis than the low-risk patients (Figure 2E, $p \leq 0.05$). Then, time-dependent ROC analysis was applied to evaluate the prediction capacity of the signature, with an area under the receiver operating characteristic curve (AUC) of 0.67, 0.70, and 0.70 at 365, 1905, and 1825 days, respectively (Figure 2F, $p \leq 0.05$).
## 3.3. Prognosis and Validation of the LMRG- and FAG-Based Signature
To assess the accuracy of the model, we evaluated the performance of this signature with regard to pathological features (age, grade, and stage). The results indicated that high risk was significantly associated with older age and higher grade and stage (Figure 3A–C, $p \leq 0.05$). Then, the pathological features were added for univariate and multivariate cox regression, and the forest plot showed that age, grade, and stage were still independent prognostic factors, which means that the signature had high accuracy (Figure 3D, $p \leq 0.05$). In addition, we built a nomogram to predict the 1-year, 3-year, and 5-year survival probability of UCEC patients based on all of the above prognostic elements (Figure 3E, $p \leq 0.05$), and the calibration plot showed a C-index of 0.767 (0.741–0.793), indicating that the nomogram had good predictive ability (Figure 3F, $p \leq 0.05$).
## 3.4. DEG and Functional Enrichment Analyses of the Signature
To investigate the relationship between the six genes in the risk model, we constructed a protein–protein interaction (PPI) network (Figure S4A) and analyzed the correlations (Figure S4B). The results showed that PSAT1 and RSAD2 were more independent and less associated with other genes. Next, a volcano plot and heatmap showed the DEGs between the two risk groups; 81 genes were upregulated and 195 genes were downregulated (Figure 4A,B). The PPI network of the DEGs is depicted in Figure S4C. To reveal the underlying biological characteristics associated with the risk scores, KEGG and GO analyses were performed based on DEGs between the high- and low-risk groups. The results indicated that pathways such as kinase and peptidase regulation, apparatus morphogenesis, cell cycle regulation, viral infection, and antiviral innate immune response were highly enriched (Figure 4C,D, $p \leq 0.05$). In addition, we performed GSVA to probe differences in pathways between the two risk groups. As illustrated in the heatmap in Figure 4E, pathways related to lipid metabolism and ferroptosis, such as “tyrosine metabolism”, “fatty acid metabolism”, “alpha linolenic acid metabolism”, and “DNA replication”, were significantly enriched ($p \leq 0.05$).
## 3.5. Relationship between the Tumor Mutational Burden (TMB) and the Risk Model
TMB, the somatic coding errors, is generally considered high when >10 or >16 mutations/megabase DNA are present [28]. Recently, TMB is thought to be closely related to the survival prognosis of tumor patient [29]. To examine in more depth how well the risk-prognosis model predicts tumor development, we investigated its relationship with TMB. First, correlation analysis showed that the TMB level had a negative association with the LMRG-FAG risk score (Figure 5A, $p \leq 0.05$), and the high-risk group showed lower TMB levels (Figure 5B, $p \leq 0.05$). We also investigated the survival of patients with different TMB statuses by K-M analysis, and the results demonstrated that the patients in the low-TMB group had poor prognostic outcomes (Figure 5C, $p \leq 0.05$). In addition, mutation information of the genes in the low- and high-TMB groups was explored using a waterfall chart, and PTEN ($58.2\%$), PIK3CA ($48.7\%$), TTN ($44.5\%$), ARID1A ($43.5\%$), and TP53 ($36.4\%$) were the top five mutated genes (Figure 5D). We further studied and classified the mutation information, variant type, and SNV class, and the results demonstrated that missense mutations, single nucleotide polymorphism (SNP), and C > T accounted for the largest proportion (Figure S5A–C). The number of altered bases in each sample and the mutation types in different colors are shown in Figure S5D,E; mutation information for the six risk genes [PGR ($37\%$), ESR1 ($33\%$), RSAD2 ($27\%$), PSAT1 ($18\%$), CDKN1A ($14\%$), and CDKN2A ($5\%$)] is provided in Figure S5F. Recently, multiple pieces of research have illustrated that TMB is closely associated with tumor immune cell infiltration and affects the efficacy of immunotherapy [30,31]. Therefore, we evaluated the value of TBM in the complexity of the tumor immune microenvironment. We discovered that most immune cells had a positive correlation with the TMB level, especially T cells CD8+, T cells CD4+, and B cells (Figure S5G). In addition, T cells CD8+, T cells CD4+ memory activated, T cells CD4+ memory resting, and T cells regulatory had higher expression in the high-TMB group compared to the low-TMB group (Figure S5F, $p \leq 0.05$), suggesting that TMB may have an effect on the immune response.
## 3.6. Immune Infiltration Associated with the LMRG-FAG-Based Signature
Recent studies have shown that lipid metabolism and ferroptosis are important components of the tumor microenvironment and are strongly associated with tumor immune activities [32,33,34,35]. We first used ESTIMATE to determine the relationship of tumor immune infiltration between the two risk groups. The stromal, immune score, and ESTIMATE score were significantly downregulated in the high-risk group (Figure 6A–C, Wilcoxon $p \leq 0.05$). Then, the CIBERSORT algorithm was applied to detect the composition of the 22 immune cells in UCEC patients (Figure S6A). A boxplot demonstrated that the difference in the distribution of the 10 immune-infiltrating cells between the two risk groups was significant. The naive B cells, memory B cells, resting CD4 memory T cells, regulatory T cells (Tregs), and resting dendritic cells had low expression in the high-risk group compared to the low-risk group. Meanwhile, the follicular helper T cells, monocytes, M1 macrophages, activated dendritic cells, and M2 macrophages were significantly upregulated in the high-risk group compared to the low-risk group (Figure 6D, $p \leq 0.05$). We also analyzed immune infiltration using the EPIC, TIMER, xCELL, and quanTIseq databases, which fully confirmed the six-gene prognostic risk signature to be closely related to immune activity (Figure S6B–E). In addition, the TIMER database was utilized to assess the relationship between the six risk genes and tumor-infiltrating immune cells. The results showed that only RSAD2 correlated positively with B cells (cor = 0.1858, $$p \leq 0.0015$$); except for RSAD2, the other genes were significantly associated with CD8+ T cells (Figure S7, $p \leq 0.05$).
## 3.7. Immunotherapy and Chemotherapy in Different Risk Groups
Recently, immune checkpoints have been identified as key targets of immunotherapy, and immune checkpoint inhibitors (ICIs) are regarded as an effective therapeutic strategy for patients with advanced disease [36,37]. Therefore, we identified potential relationships between the expression of immune checkpoint molecules and our risk model. The results showed that IDO1 and LAG3 expression was significantly increased in the high-risk group compared with the low-risk group, while the expression of CTLA4, GZMA and PDCD1 was obviously decreased in the high-risk group compared with the low-risk group (Figure 6E, $p \leq 0.05$). Then, we conducted immunophenoscore (IPS) analysis to predict immunotherapy response. As shown in Figure 6F, low-risk patients were more sensitive to anti-PD-1 therapy ($p \leq 0.05$), suggesting that immunotherapy of blocking CTLA-4 and PDCD1 may be more beneficial for patients in the low-risk group. Since chemotherapy is the main treatment for advanced and recurrent UCEC, we evaluated the response of chemotherapeutics to UCEC patients using the pRRophetic algorithm based on our signature and found that the estimated IC50 of typical chemotherapy drugs (cisplatin, paclitaxel, doxorubicin, and etoposide, etc.) were significantly higher in the low-risk group (Figure 6G, $p \leq 0.05$). For the other 40 chemotherapy and small molecule drugs, such as lenalidomide, gefitinib, AMG.706, and JNK inhibitor VIII, patients in the high-risk group were identified as being more sensitive (Figure S8, $p \leq 0.05$). Thus, we indicated that patients with low risk scores were more resistant to chemotherapy than those with high risk scores, but they were more sensitive to anti-PD-1 therapy. In addition, patients in the high-risk group were better suited for chemotherapy. These results may have important implications for individualized immunotherapy in patients with advanced UCEC.
## 3.8. In Vitro Function of the Risk Gene PSAT1 in UCEC Cells
To further validate the ability of risk signatures to predict prognosis, we investigated protein expression of the six risk genes between normal and UCEC tissues with the CPTAC and HPA (Human Protein Atlas) databases (Figure S9A,B, $p \leq 0.05$), and the results corresponded with previous analysis. Combined with prognostic analysis and literature searches, we selected PSAT1 for further in vitro functional assays. We identified the mRNA and protein expression of PSAT1 in four UCEC cell lines (Ishikawa, HEC-1A, HEC-1B, and ECC1), and Ishikawa and HEC-1B cells were selected for subsequent studies (Figure 7A,B). Next, we knocked down PSAT1 with siRNA, and the efficiency was verified by qPCR (Figure 7C, $p \leq 0.05$) and Western blot analysis (Figure 7D). CCK-8 and colony formation assays showed that knockdown of PSAT1 significantly suppressed the proliferation of Ishikawa and HEC-1B cells (Figure 7E,F, $p \leq 0.05$). In addition, the migration and invasion of the two cell lines were also apparently inhibited after PSAT1 knockdown, as determined by transwell assays (Figure 7G). These results demonstrate that the risk gene PSAT1 significantly promotes progression of UCEC and may affect the prognosis of UCEC patients.
## 4. Discussion
UCEC is one of the most lethal gynecological malignancies. Although many studies over the past decades have sought to improve treatment efficacy, patients with advanced and recurrent disease still have poor prognosis [38]. With the rise and application of immunotherapy, it is insufficient to estimate the prognosis of UCEC patients based on traditional clinicopathological stage [39]. Therefore, our study included the tumor immune microenvironment and immunotherapy in UCEC based on both lipid metabolism and ferroptosis to select more effective prognostic targets and guide individualized treatment of patients.
Previous studies have established prognostic models of lipid metabolism or ferroptosis in UCEC [11,15,16,17]. However, they only took a single influencing factor into account, and the complex tumor microenvironment was not considered. In our study, we comprehensively considered the interrelationship between lipid metabolism and ferroptosis, based on which a prognostic model of six genes was constructed. We deeply explored the relationship between the model risk score and the tumor immune microenvironment. We found that infiltration of B cells, T cells, and NK cells and expression of the immune checkpoints (CTLA4, GZMA, and PDCD1), as well as sensitivity and chemotherapy resistance to anti-PD-1 treatment in UCEC patients were closely related to the risk scores of the prognostic model. Moreover, in vitro experiments demonstrated that one of the potential targets, PSAT1, promoted the proliferation, migration, and invasion of UCEC cells. Our experiments provide new ideas and a basis for individualized immunotherapy for UCEC patients and provide a potential target for UCEC therapy.
In the present study, we obtained genes associated with both lipid metabolism and ferroptosis by consensus clustering analysis. After LASSO Cox regression, we constructed a prognostic signature containing six risk genes (CDKN1A, ESR1, PGR, CDKN2A, PSAT1, and RSAD2) based on LMG-FAGs. K-M survival analysis, ROC curves, a nomogram, and calibration identified that the signature had high predictive ability. Estrogen receptor 1 (ESR1) and a progesterone receptor (PGR) were reported to participate in lipid metabolism by encoding estrogen or steroid receptors to promote tumor progression [40,41,42]. Cyclin-dependent kinase inhibitors 1A and 2A (CDKN1A and CDKN2A) have been identified as ferroptosis-related genes in recent studies and can be regarded as biomarkers that influence the tumor microenvironment [43,44,45]. Radical s-adenosyl methionine domain containing 2 (RSAD2) is an interferon-stimulated gene that exerts antiviral effects by dysregulating cellular lipid metabolism [46,47]. Phosphoserine aminotransferase 1 (PSAT1) has been reported to affect the progression of various cancers by participating in lipid metabolism processes [48,49,50]. In conclusion, the six-gene prognostic model showed a significant correlation with lipid metabolism or ferroptosis. In our study, these six genes were used for risk scoring, and each UCEC patient was categorized into two risk groups according to the risk score. We then explored the pathological features of the risk signature, with the high-risk group being related to older age and higher grade and stage. We also found that knockdown of PSAT1 inhibited the proliferation, migration, and invasion of UCEC cells, enhancing the reliability of our model.
Subsequently, we comprehensively analyzed the impact of the risk signature on UCEC. A total of 276 genes were identified to be closely related to the risk score. GO, KEGG, and GSVA analyses based on the signature demonstrated that pathways associated with lipid metabolism and ferroptosis were significantly enriched, which also confirmed the accuracy of our signature. TMB is reported to correlate highly with tumor progression; for example, gastrointestinal tumor patients with low TMB have lower objective response rates and shorter progression-free survival [51], and high TMB is a poor prognostic factor for non-small cell lung cancer [52]. We found that TMB levels had a negative relationship with the LMRG-FAG risk model, which means that patients with low risk and high a mutational burden have a better prognosis in UCEC.
Because surgery and chemoradiotherapy have limited effects in patients with advanced and recurrent UCEC and traditional pathological staging has an insufficient ability to estimate prognosis, we focused on the relationship of the LMRG-FAG-based risk model with immunotherapy. Stromal, immune, and ESTIMATE scores were significantly downregulated in the high-risk group, indicating that lipid metabolism and ferroptosis are significantly associated with the immune status of UCEC. CIBERSORT algorithm analysis showed that the distribution of 10 immune cells varied between the high- and low-risk groups, with antitumor cells (B cells, T cell CD8, and monocytes, etc.) present at higher abundance in the low-risk group. According to the results, we suggest that the risk score is associated with immune infiltration and immune status in UCEC.
Adverse T cell regulatory pathways tend to be overactive when cancer occurs. CTLA-4 inhibits the immune response at the early stage of T cell induction, and PDCD1 prevents T cell function in peripheral tissues in the later stages [53,54]. Recently, immune checkpoint blockade, one of the major immunotherapy methods, has proven to be an effective strategy for enhancing the effector activity and clinical impact of anti-tumor T cells [55]. Among the ICIs, blocking CTLA-4 and PDCD1 are the two most eminent approaches. CTLA-4 and PDCD1 blockade could induce tumor immunity by improving effector T cell activity or consuming Treg [56]. In 2011, Ipilimumab, a CTLA-4 inhibitor, was approved for melanoma [57]. In 2017, the PDCD1 inhibitor pembrolizumab was approved for UCEC patients with microsatellite instability, and half of the patients benefited from it [58]. Since the predictive value of immune checkpoints has been demonstrated in a variety of human malignancies, we then explored immune checkpoint expression between the two risk groups to guide individualized immunotherapy for UCEC patients. The expression of CTLA4, GZMA and PDCD1 was significantly upregulated in patients with low risk scores, and IDO1 and LAG3 were increased in the high-risk group. Therefore, we indicated that specially blocking CTLA-4 and PDCD1 immunotherapy would be more effective for patients in the low-risk group. Meanwhile, we detected the difference in sensitivity to PD-1 and CTLA-4 inhibitors, and the results indicated that low-risk patients were more sensitive to anti-PD-1 therapy, meaning that immunotarget therapy was more effective in low-risk patients. Accordingly, our risk signature has a certain guiding role in the anti-PD-1 immunotherapy of UCEC patients. Interestingly, high-risk patients were more sensitive to traditional chemotherapeutic agents and small molecule inhibitors such as cisplatin, paclitaxel, AMG.706, and ABT.888. Hence, patients in the high-risk group were more likely to benefit from chemotherapy and our signature can be used to guide personalized treatment of UCEC patients.
However, it is undeniable that our study also has some limitations. First, the study data were obtained from only TCGA, and we did not verify the accuracy of our model with more cohorts. Second, immunotherapy and chemosensitivity analyses were only derived from the transcriptome, and we still need to obtain more prospective experimental data to support the findings. Finally, as a potential therapeutic target, the molecular mechanism underlying the risk-related gene PSTA1 needs to be further clarified.
## 5. Conclusions
Consequently, we constructed a risk prognostic model based on both lipid metabolism and ferroptosis to deeply analyze the relationship between lipid metabolism, ferroptosis and gene mutation, immune infiltration, immunotherapy, and chemotherapy in UCEC patients and provided potential biomolecules and a preliminary basis for individualized treatment of patients.
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|
---
title: Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying
High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast
Cancer
authors:
- Shayan Shafiee
- Jaidip Jagtap
- Mykhaylo Zayats
- Jonathan Epperlein
- Anjishnu Banerjee
- Aron Geurts
- Michael Flister
- Sergiy Zhuk
- Amit Joshi
journal: Cancers
year: 2023
pmcid: PMC10000786
doi: 10.3390/cancers15051460
license: CC BY 4.0
---
# Dynamic NIR Fluorescence Imaging and Machine Learning Framework for Stratifying High vs. Low Notch-Dll4 Expressing Host Microenvironment in Triple-Negative Breast Cancer
## Abstract
### Simple Summary
Breast cancer is a disease that is affected by both the tumor cells and the host environment. It is well known that the tumor blood vessels are aberrant in structure and function due to rapid angiogenesis, and this aberrant vasculature plays a major role in drug delivery and therapy response of breast cancer. Dll4 is a protein that helps control the growth of blood vessels in tumors. This study used near-infrared optical imaging and a novel machine learning framework to determine if Dll4 levels can be predicted from simple noninvasive imaging assays. The eventual results of this study may help physicians decide if a given triple-negative breast cancer patient will benefit from a Dll4 targeted therapy.
### Abstract
Delta like canonical notch ligand 4 (Dll4) expression levels in tumors are known to affect the efficacy of cancer therapies. This study aimed to develop a model to predict Dll4 expression levels in tumors using dynamic enhanced near-infrared (NIR) imaging with indocyanine green (ICG). Two rat-based consomic xenograft (CXM) strains of breast cancer with different Dll4 expression levels and eight congenic xenograft strains were studied. Principal component analysis (PCA) was used to visualize and segment tumors, and modified PCA techniques identified and analyzed tumor and normal regions of interest (ROIs). The average NIR intensity for each ROI was calculated from pixel brightness at each time interval, yielding easily interpretable features including the slope of initial ICG uptake, time to peak perfusion, and rate of ICG intensity change after reaching half-maximum intensity. Machine learning algorithms were applied to select discriminative features for classification, and model performance was evaluated with a confusion matrix, receiver operating characteristic curve, and area under the curve. The selected machine learning methods accurately identified host Dll4 expression alterations with sensitivity and specificity above $90\%$. This may enable stratification of patients for Dll4 targeted therapies. NIR imaging with ICG can noninvasively assess Dll4 expression levels in tumors and aid in effective decision making for cancer therapy.
## 1. Introduction
Breast cancer heterogeneity has been extensively studied and it has enabled the classification and categorization of tumors into molecular subtypes depending on the overexpression of antigens or hormone receptors on tumor cells [1]. Identification of tumor subtypes improves cancer patients’ care and prognosis by tailoring therapies to the subtypes [2,3,4]. Breast and many other cancers are highly heritable, yet most causative variants are unknown, and most of the known risk variants are considered tumor-cell-autonomous, with far less emphasis placed on identifying the role of germline variants impacting the tumor microenvironment (TME). The TME is a complex and dynamic system that includes cancer cells, stromal cells, blood vessels, and extracellular matrix [5] and plays a significant role both in tumor cell proliferation and in chemo- or radiotherapy delivery and response [5,6,7]. There is growing evidence that heritable modifiers of the tumor microenvironment can profoundly impact tumor behavior and response to diagnostic and therapeutic interventions [8,9,10,11,12].
Tumor blood vessels have abnormal structure and function, which leads to heterogeneity in blood perfusion both temporally and spatially [13]. This heterogeneity has multiple adverse consequences, including limiting the access of blood-borne drugs and effector immune cells to poorly perfused regions of tumors [14]. As a result, these areas become hypoxic and have low extracellular pH [15]. Hypoxia has been shown to play a significant role in tumor progression and metastasis by inducing genetic instability, angiogenesis, immunosuppression, inflammation, and resistance to cell death by apoptosis and autophagy [16,17]. Anti-angiogenic drugs are designed to target the vasculature in order to starve tumors and prevent them from growing. However, recent studies have shown that the efficacy of these drugs may be limited by specific biomarkers and pathways associated with resistance [15]. For example, it has been shown that some patients may not benefit from anti-VEGF therapies if they have elevated levels of plasma sVEGFR1 [18]. Similar outcomes have been observed with increased levels of SDF1α and anti-VEGF therapies [19]. Further the vascular TME and therapy response differs in primary tumor and metastasis sites [20] and the anatomic location [21,22,23]. Thus, characterizing angiogenesis in tumors holistically may have therapeutic implications.
The process of tumor angiogenesis is closely regulated by a balance between promoting and suppressing angiogenic factors [24,25]. Delta like canonical notch ligand 4 (Dll4) is a protein-coding gene that provides instructions for making a protein part of a signaling pathway known as the notch pathway, which is essential for the normal development of many tissues throughout the body, affecting cell functions [26,27], modulating tumor angiogenesis [28], promoting vessel maturation, and inhibiting vessel sprouting by inducing apoptosis of tip endothelial cells (TECs) [28,29,30]. Dll4 is overexpressed in various types of cancer, including breast, ovarian, and colorectal cancer, and has been shown to promote tumor angiogenesis, growth, and metastasis by interacting with receptors on endothelial cells (ECs) [31,32,33,34]. Blockade of Dll4 activity results in enhanced vessel sprouting and increased vascular permeability [29,30,35], but anti-Dll4 therapy has not been universally successful, as Dll4 has been shown to have both pro-tumorigenic and anti-tumorigenic effects depending on the context of its expression [34,36,37]. We recently reported that Dll4 expression on the host TME rather than on tumor cells determines the EPR or enhanced permeation and retention effect in breast tumor xenografts and thus governs nanomedicine delivery and therapy response [38].
Despite the increasing evidence about the function of germline genetic modifiers, such as Dll4, in TME heterogeneity and enhanced permeability and retention (EPR) effects, the underlying influencers have mainly remained unexplored because of the lack of a systematic approach to studying them. Therefore, we developed the Consomic Xenograft Model (CXM) as a strategy for mapping heritable modifiers of TME heterogeneity. In the CXM, human breast cancer cells are orthotopically implanted into consomic xenograft host strains. These strains are derived from two parental strains with different susceptibilities to breast cancer. Salt-sensitive (SS) rats were employed as a tumor promoting strain, while Brown Norway (BN) rats were used as a tumor suppressing strain. A sequence of consomic strains were generated with chromosomes of SS rats replaced by those of BN rats one at a time and used for breast tumor xenograft studies [7,38,39]. Because the host backgrounds genetically differ by one chromosome, whereas the tumor cells are unvaried, any observed phenotypic differences are due to TME modifier(s) and can be linked to a single chromosome. These modifiers can be further localized by congenic mapping (inbred strains containing a given sub-chromosomal region in their genome). By combining CXM with dynamic epifluorescence near-infrared (DE-NIR) imaging, systemic injections of indocyanine green (ICG) through a tail vein in tumor-bearing rats, and multiparametric analysis of pharmacokinetic modeling, we localized and identified the function of the vascular-specific Dll4 allele on rat chromosome 3 (RNO3) as a heritable host TME modifier of EPR [38]. The SS.BN3IL2Rγ− CXM strain with low-level expression of Dll4 (referred to as Dll4−) had significant tumor growth inhibition compared with the parental SSIL2Rγ− strain with higher expression of Dll4 (Dll4+), despite a paradoxical increase in tumor blood vessel density in Dll4+. Further analysis revealed that the changes in the Dll4+ tumors were accompanied by altered expression of Dll4, which was previously linked with nonproductive angiogenesis. Additionally, Dll4 was found to be co-localized within a host TME modifier locus (Chr3: 95–131 Mb) identified by congenic mapping and correlated with the phenotypic differences observed at the consomic level [7,39,40].
The inheritance of functionally different Dll4 alleles can influence the efficacy of nanoparticle (NP) therapy, and previous results indicate that inherited microvascular distribution patterns, rather than overall NP uptake, ultimately determine the effectiveness of NP-mediated photothermal therapy (PTT). Consequently, patients with high endothelial Dll4 expression can be selected for treatment with anti-Dll4 targeted nanoparticles as opposed to patients with low Dll4 expression, where PEGylated nanoparticles will provide sufficient therapy response [38]. Recent advances in dynamic vascular imaging techniques, such as DCE-MRI and perfusion computed tomography, have facilitated the investigation of the time kinetics of a contrast agent to extract multiple vascular parameters and have been successfully applied in clinical trials of anti-angiogenic drugs [41,42]. However, these techniques have certain drawbacks, including a lack of high temporal resolution and the need for a heavy hardware system with sophisticated analysis software. Dynamic NIR fluorescence imaging, on the other hand, offers a sufficient and effective alternative to other dynamic vascular imaging techniques for characterizing germline-dependent vascular phenotypes [7,43]. This has led to the combination of these modalities, such as in the paired agent MRI-coupled fluorescence tomography approach for noninvasive quantification of paired-agent uptake in response to anti-angiogenesis therapy in vivo [44].
As the field of artificial intelligence continues to advance, researchers are increasingly utilizing AI techniques, particularly machine learning, to develop predictive models that can support effective decision making in various domains including cancer therapy selection [45,46]. Previous research has investigated the use of machine learning algorithms to analyze near-infrared (NIR) signal intensity and perfusion patterns to differentiate between healthy, benign, and malignant tissue [47]. This work demonstrated that the signal intensity time course of an FDA-cleared near-infrared dye ICG inflow during the wash-in phase and ICG outflow during the wash-out phase could serve as significant markers for tissue distinction. This finding offers a new method for noninvasive tissue distinction and has prognostic potential [48,49]. However, there remains a need for further exploration of the use of machine learning for classifying host genetic tumor microenvironment (TME) modifiers and predicting therapy responses based on dynamic contrast-enhanced imaging of tumors, particularly DE-NIR fluorescence imaging data [47].
## 2. Hypothesis and Objective
We hypothesize that the observation of subtle differences in vasculature structure and perfusion patterns characterized by ICG inflow and outflow using DE-NIR imaging could be used to differentiate between inherited tumor vascular microenvironment differences, such as Dll4 expression levels. We propose an experimental framework to noninvasively assess Dll4 expression levels in tumors based on the NIR signal intensity time course of perfusion patterns characterized by ICG time kinetics to develop a predictive model to support effective decision making in cancer therapy. Herein, we used two rat-based CXM strains of breast cancer, SSIL2Rγ−(Dll4+) and SS.BN3IL2Rγ− (Dll4−) [7,38,50], as well as eight congenic xenograft strains, CG1–CG8 (Figure 1a,b), to assess the impact of germline TME vascular heterogeneity on the signal intensity of DE-NIR imaging with systemically delivered ICG. Principal component analysis (PCA)-based decomposition of time-dependent epifluorescence videos (image stacks) was used for visualization and anatomical segmentation of tumors, liver, lungs, and fat pads [7]. In addition, we utilized modified principal component analysis (PCA)-based anatomical segmentation techniques to identify and analyze regions of interest (ROIs) representing potential tumors within the current dataset. To gather further information, we calculated the average NIR intensity for each ROI by analyzing the brightness of individual pixels at each time interval, resulting in a series of intensity measurements for each ROI. From this analysis, several easily interpretable features were extracted, including the slope of the initial uptake of ICG, the time it takes to reach peak perfusion, and the rate of ICG intensity changes once the half-maximum intensity is reached (which, to the best of our knowledge, has not been previously reported in the literature). We then applied a subset of machine learning algorithms, including Support Vector Machines (SVMs), Naive Bayesian Classifiers (NBCs), Generalized Additive Models (GAMs), Decision Trees (DTs), Nearest Neighbors (NN), and Logistic Regression (LR) to select the most discriminative features for classification. The performance of the model was evaluated using confusion matrix, receiver operating characteristic curve (ROC), and the area under the curve. To further evaluate our hypothesis of detecting Dll4 expression levels from DE-NIR imaging and test the generalizability of our framework, we conducted a secondary performance evaluation method using congenic groups with high and low Dll4 expression levels. The classification models were trained based on the selected features, and the performance of the model was tested on the remaining congenic groups. We demonstrate that robust ML methods can identify the alterations in host Dll4 expression from the tumor dynamic imaging datasets, and thus these methods can potentially stratify patients for Dll4 targeted therapies.
## 3. Materials and Methods
All methods have been carried out in accordance with relevant guidelines and regulations. Approved protocols by the Medical College of Wisconsin Institutional Biosafety Committee (IBC) and Institutional Animal Care and Use Committee (IACUC) were followed. All live animal experiments are reported per the ARRIVE guidelines’ recommendations [51]. All results were rigorously adjusted for multiple comparisons.
## 3.1. Animals
All animal protocols employed in this study were approved by the Institutional Animal Care and Use Committee (IACUC), Medical College of Wisconsin (MCW). The MCW has an Animal Welfare Assurance (Assurance number D16-00064 (A3102-01)) on file with the Office of Laboratory Animal Welfare, National Institutes of Health (NIH). Animal experiments were performed according to the relevant guidelines and regulations and in compliance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health (NIH publication no.85–23, revised 1996). SS.BN3 rats were developed as part of the Consomic Xenograft Model at the Medical College of Wisconsin (MCW) [40]. SS and SS.BN3 rats were purchased from the Rat Research Models Service Center at the Medical College of Wisconsin [52]. All rats were provided reverse osmosis hyper-chlorinated water ad libitum. All animal experiments were performed on anesthetized animals. The animal was placed in a transparent induction chamber to induce anesthesia. Isoflurane was delivered through a precision vaporizer and compressed O2 to the chamber. For induction, the percentage of isoflurane was up to $5\%$. Once the animal was unconscious, it was removed from the chamber. The unconscious animal was then placed on a warm surface and fitted with a nose cone attached to the vaporizer in the presence of a scavenging system and oxygen source. At this point, the concentration of isoflurane was reduced to this level that maintained the correct anesthesia plane, usually between 0.5 and $3\%$. After the end of the experiment, or when other criteria for animal protocols were justified, rats were euthanized. Rats were placed in an approved euthanasia chamber and exposed to CO2 from a compressed gas cylinder until the animal was no longer breathing. To ensure death in rats, a pneumothorax was created via thoracotomy for rats weighing more than 200 g. For rats weighing less than 200 g, a pneumothorax was created, or a cervical dislocation was performed.
As previously described [39,40], consomic strains (SS and SS.BN3 rats) were generated by sequentially replacing SS chromosomes with the outbred wild-type and tumor-resistant strain of Brown Norway (BN) rats referred to as SS.BN#, which are reported for their tumorigenic potential, where # refers to the chromosome number. These parental SS and consomic SS.BN# strains were genetically ablated by knocking down the IL2Rγ gene to allow the grafting and growth of human cancer cell lines. Such immunocompromised strains are labeled as SSIL2Rγ−(Dll4+) and SS.BN#IL2Rγ−(Dll4−).
Previous research has localized inherited modifier(s) of TME vascular heterogeneity to RNO3 by CXM mapping and further narrowed by congenic mapping to a 36 *Mb locus* containing Dll4 alleles with distinct vascular expression patterns in the SS.BN3IL2Rγ− consomic (Dll4−) and SSIL2Rγ− (Dll4+) rat strains, and verified via species-specific RNA sequencing and immunohistochemistry that strains inheriting the SS Dll4 allele on chromosome 3 have higher Dll4 expression on tumor-associated endothelium and that the blood vessel tortuosity and dysfunction increased in Dll4− strains [38,39]. Since there are many other candidate alleles on chromosome 3 that could also potentially account for the observed differences in therapeutic efficacy between the SS.BN3IL2Rγ− and SSIL2Rγ− strains [38], to further investigate the potential contribution of Dll4 to inherited tumor vascular heterogeneity, eight novel SS.BN3IL2Rγ− congenic xenograft host strains (CG1 to CG8) were constructed by introgression of the F1 progeny and F2 generation to capture different regions of RNO3 by marker-assisted selection, as described previously [50,53]. The exclusion congenic mapping localized a 7.9 Mb candidate region (marked by the SSLP marker D3Mgh11) that was associated with inherited tumor vascular heterogeneity and contained the Dll4 locus. As a result, eight new congenic strains CGN(s-e) were generated, where N (1 to 8) refers to the congenic group, while s and e refer to the starting and ending of Simple Sequence Length Polymorphism (SSLP) marker regions, respectively. This resulted in generating CG1(D3Rat26-D3Mgh30), CG2(D3Rat222-D3Got42), CG3(D3Rat222-D3Mco33), CG4(D3Rat164-D3Rat218), CG5(D3Rat26-D3Mco218), CG6(D3Rat86-D3Rat218), CG7(D3Mgh13-D3Rat218), and CG8(D3Rat160-D3Rat218) congenic groups (Figure 1a,b).
## 3.2. Cell Culture and Triple-Negative Breast Cancer Xenografts
As previously described [38], triple-negative breast cancer MDA-MB-231 cells were maintained in DMEM media (Sigma, Burlington, MA, USA) supplemented with $10\%$ FBS (Gibco, New York, NY, USA) and $1\%$ penicillin and streptomycin (Lonza, Cohasset, MN, USA) and incubated in $5\%$ CO2 at 37 °C. These cells (6 × 106) in $50\%$ Matrigel were orthotopically implanted into the mammary fat pads (MFP) of 4- to 6-week-old female Dll4+ ($$n = 8$$) and Dll4– ($$n = 17$$) rats and eight congenic strains CG1 ($$n = 19$$), CG2 ($$n = 2$$), CG3 ($$n = 26$$), CG4 ($$n = 12$$), CG5 ($$n = 28$$), CG6 ($$n = 12$$), CG7 ($$n = 5$$), and CG8 ($$n = 4$$) (Figure 1c) [54]. Tumors were treated after 10 days of implantation at an approximate size of 600 mm3, consistent across all rat strains.
## 3.3. In Vivo NIR Fluorescence Imaging
A customized NIR imaging system was assembled for imaging the rats. A bifurcated optical fiber bundle was used to deliver 785 nm excitation light (~5 mW/cm2 power at the surface, diode laser, ThorLabs Inc., Newton, NJ, USA) from two positions for uniform illumination of the entire rat body surface. A 16-bit deep-cooled intensified charge-coupled device camera (PIMAX4 ICCD, Princeton Instruments, Trenton, NJ, USA) equipped with 830 nm long-pass filter positioned following a holographic notch rejection filter in the optical path (ThorLabs Inc.) was used to image the rats through computer-controlled LightField® software (Teledyne Princeton Instruments, Trenton, NJ, USA) (Figure 1d).
Dynamic contrast-enhanced NIR fluorescence imaging was performed on anesthetized rats, as reported previously for 800 nm NIR imaging [7]. In this study, the setup was used for imaging the whole body. A total of 133 rats (Dll4+ ($$n = 8$$), Dll4– ($$n = 17$$)) and eight congenic strains CG1 ($$n = 19$$), CG2 ($$n = 2$$), CG3 ($$n = 26$$), CG4 ($$n = 12$$), CG5 ($$n = 28$$), CG6 ($$n = 12$$), CG7 ($$n = 5$$), and CG8 ($$n = 4$$) were imaged. NIR imaging was performed for approximately 6 min following ICG injection with the CCD array hardware binned to 256 × 256 with a frame rate of 10.6 fps. A total of 3000 frames were captured for each imaging case, including about 50 frames for background correction. ICG (MP Biomedicals) was delivered in an intravenous bolus of 1200 µM ICG/200 g body weight into the tail vein via a catheter with a 32-gauge needle tip connected to a syringe pump (Harvard Apparatus PHD 2000 syringe pump, Holliston, MA, USA) operated at a speed of 0.2 mL/s. The injected volume was calibrated to provide a body-weight-equilibrated dose to each rat.
## 3.4. Denoising and Motion Correction
Image processing and data analysis were performed in MATLAB (R2021b MATHWORKS Inc.) software. The time-dependent image frames were assembled as 3D arrays (two spatial and one temporal dimensions) for all animals. A custom-designed breathing correction method with a low-pass temporal filter combined with a 1D wavelet-based denoising was used to filter the high-frequency jitter generated by the animal’s respiratory motion from the fluorescence kinetic sequences of each pixel, as described previously [7]. An average of pre-ICG injection frames (acquired in the ~5 s before ICG injection) was used as background, incorporating contributions from CCD noise and excitation light leakage from emission filters and subtracted from all the frames. ( Refer to Video S1 for respiratory motion corrected time course images of ICG biodistribution).
## 3.5. Principal Component Analysis for Extraction of Spatial Patterns of Internal Organs
First, motion correction and background subtraction were performed on the imaging data described in the previous steps. This was carried out to remove any potential artifacts or noise that could affect the accuracy of the principal component analysis. Next, the data were decomposed by PCA along the time dimension using MATLAB software following the previously published methods [55,56]. This resulted in converting the imaging data to a k-component vector for each pixel, where k is the number of time-frames in the original dataset. The PCA on the dynamic fluorescence image was used to extract spatial patterns of internal organs linked to statistically similar kinetic behaviors. This was carried out by comparing the k-component vectors for each pixel and identifying those that displayed similar patterns over time. The contribution of the first six principal components on a time basis is illustrated in Figure S1.
## 3.6. PCA Ranking Tumor Detection
The ROI detection module employed three steps in order to identify the tumor area in images (Figure 2a): [1] spatial alignment, [2] PCA ranking and selection, and [3] ROI selection and masking. First, images were registered to a reference image using a rigid body transformation in order to ensure consistent spatial alignment for the detection of the tumor area in subsequent steps. The variability in the visual appearance of internal organs or tumors within certain principal components necessitated the adaptation of existing methods for ranking normalized components based on their two-dimensional cross-correlation (2DCC) with a reference image containing the tumor. This was achieved by generating a stack of the first ten normalized principal components and applying a two-dimensional cross-correlation function (xcorr2 in MATLAB) to each component. The principal components were ranked according to the correlation scores obtained and a semi-automatic algorithm was then utilized to carry out the subsequent two steps for tumor ROI generation.
To select the appropriate principal component for tumor identification, the algorithm prioritized the PC with the highest likelihood of containing the tumor tissue based on the ranking from the previous step. Once the proper PC was selected, we used 2DCC to estimate the tumor’s location in the frame. This was performed by creating a stack of four reference images using PCs that clearly visualized the tumor and generated a bounding box around the tumor. Next, we applied basic morphological dilation and image arithmetic operations to emphasize the tumor’s boundaries. A threshold was then applied to separate the tumor from the background. Finally, we used Blob Analysis (Computer Vision Toolbox™, MATLAB, MathWorks, MA, USA) to extract the centroid and exact location of the tumor in the frame, generating a region of interest (ROI). The use of a graphic user-interface-enabled semi-automatic platform allowed for real-time evaluation and adjustment of the algorithm’s performance if necessary.
## 3.7. Video Processing
The underlying assumption for the NIR fluorescence intensity video analysis is that intensity was proportional to the ICG concentration in the tissue, which has been shown in the literature [57].
From each available video, p, between 4 and 5 fluorescence time series are extracted, one based on the tumor ROI generated from earlier steps, and between three and four from mammary fat pads (MFPs) (Figure 2b). For each ROI, r, and each included time step, t, the mean brightness of the pixels inside that ROI in the NIR video is stored as Ip,r(t). The result is a collection of time series Ip,r(t), where p ranges from 1 to N (the number of animals in each group in our dataset), r ranges from 4 to the number of ROIs generated in the specific animal (up to 5, 1 tumor and 3 or 4 fat pads), and t from 0 to 3000 (Figure 2b).
## 3.8. Peak and Latency Estimation
First, we started with smoothing the data to exclude any potential noise from motion artifacts. The smoothing was conducted using a Savitzky–Golay filter of order 3 with window length 31 [58]. The parameters for this filter were determined through manual annotation of peak and latency in a subset of the data, with the goal of minimizing average estimation error. Subsequently, we used a MATLAB function to identify the time point of maximum intensity, which corresponded to the peak of the fluorescence signal. We employed a custom latency detector script that analyzed the smoothed derivative of the curves and identified the first “robust” zero crossing as the latency point (Lp,r(t)) [48].
## 3.9. Feature Design
The features were chosen in two steps as previously described (Figure 3) [47,48]: first, the following characteristic numbers were chosen for each normalized time series individually.
The time to peak (TTPp,r) is simply the time difference between the peak intensity time point and the latency Lp,r.
The upslope (Up,r) is computed as [1]Up,$r = 1$−Ip,r(Lp,r)TTPp,r, which is the average slope between initial ICG arrival and the peak.
The downslopes (DS) are the downslopes between the peak and S seconds further:[2]Dp,$r = 1$−Ip,r(Lp,r+TTPp,r+S)S,S∈{2,4,5,6,8,10,12,14,16,18,20,23,25,30,35,40,50,60,70,80}.
The time ratio (TR) is the ratio between TTPp and when Ip(t) reaches half the peak values.
The half intensity forward (HIF) is the average slopes between when Ip,r(t) reaches half the peak values (T$\frac{1}{2}$p,r) and S seconds further, so [3]HIFp,$r = 1$−Ip,r(Lp,r+T$\frac{1}{2}$p,r+S)I,S∈{2,4,5,6,8,10,12,14,16,18,20,23,25,30,35,40,50,60,70,80}.
To increase the robustness of estimated downslope-based and HIF-based features, we introduced a window around Lp,r+TTPp,r+S and Lp,r+T$\frac{1}{2}$p,r+S time steps taking the median of those downslopes:[4]DSp,r,avg=median s∈Window D(S+s)p,r, [5]HIFp,r,avg=median s∈Window D(S+s)p,r,Window={−1.5,−1.3,…,1.3,1.5}seconds.
These features provide insight into the tumor’s ICG uptake and decay. U and TTP relate to the initial uptake of ICG, while DS relates to the decay of ICG fluorescence [48]. TR is a measure of the temporal inhomogeneity of the initial uptake, and HIF is a measure of the temporal inhomogeneity of both the initial uptake and decay of ICG fluorescence.
To address inter-animal variation, we propose a feature design that relates the features of tumor intensity (Fr=tumor) to the features of healthy tissue intensity (Fr=fatpad) in the same animal. The median value of a feature across the healthy tissue (fat pad) is chosen as a reference value, and each feature is defined as its percentage difference to that reference value. This results in a single normalized feature for each animal. By using the median as an average that is robust to outliers, our feature design allows for a more accurate comparison of tumor intensity across different animals. [ 6]Fn =Ftumor−median(Ffat pad)median(Ffat pad),
## 3.10. Classification Algorithms and Feature Selection
The feature extraction and design required specialized knowledge of the tumor microenvironment (TME) and its impact on near-infrared intensity. However, the classification based on the inheritances of Dll4 can be considered a standard binary classification problem with feature selection as a sub-problem: given the features of an animal for which the Dll4 inheritance is unknown, we want to assign the label “Dll4high” or “Dll4low” to it. We also want to investigate which small subset of features performs best [44]. We restricted ourselves to a subset of available ML algorithms which were reported to perform best with intensity time series classification. [ 48], We excluded neural networks from consideration and evaluated Support Vector Machines (SVMs), Naive Bayesian Classifiers (NBs), Generalized Additive Models (GAMs), Decision Trees (DTs), Nearest Neighbors (NN), and Logistic Regression (LR).
## 3.11. Primary Classification
DTs and SVMs using the full set of 86 features were trained. Each ML model was tuned to the training set in an internal cross-validation procedure of 10-fold. This process was repeated 20 times, and the performance metrics are reported as classifier performance. One of the key metrics used to evaluate the performance of a classification algorithm is accuracy, which measures the proportion of correctly classified instances in the dataset. Other important metrics include sensitivity, which measures the proportion of positive instances that were correctly classified, and specificity, which measures the proportion of negative instances that were correctly classified. In our case, we are interested in classifying animals as either Dll4high or Dll4low, and we use the average metric (Ascore) for each pair of groups to evaluate the performance of the different classification algorithms.
The Ascore for a given pair of groups is calculated as the average of accuracy, sensitivity, and specificity, as follows:[7]Ascore (Dll4high| Dll4low)=Accuracy+Sensitivity+Specificity3,
## 3.12. Congenic Pair Selection
In this study, we describe a two-step method for selecting congenic pairs with high and low Dll4 expression levels for feature selection and hypothesis testing. This method was used to identify well-behaved pairs for binary classification and to identify the pair with the highest classification performance: The primary selection of congenic pairs is based on their primary classification scores (Ascore) and their performance against parental strains using all available features. The secondary selection is based on the classification performance using the 2 best-performing features.
The congenic pair selection process involved the selection of all possible combinations of congenic pairs with high and low Dll4 expression levels with n > 5, resulting in a total of 12 pairs: Dll4+|Dll4−, Dll4+|CG3−, Dll4+|CG4−, Dll4−|CG1+, Dll4−|CG5+, Dll4−|CG6−, CG1+|CG3−, CG1+|CG4−, CG3−|CG5+, CG3−|CG6+, CG4−|CG5+, CG4−|CG6+. It should be noted that we only included subgroups with n > 5 in our analysis, to avoid introducing noise into the feature selection process. This is because smaller sample sizes can be more prone to variability and may not represent the larger population [59]. Therefore, we focused on more significant subgroups to ensure our feature selection process was robust and reliable. These 12 pairs have gone through our primary classification algorithm with a split of $75\%$–$25\%$ for testing and training with seeded randomization and proportional distribution of each group in training and testing datasets. Each ML model was tuned to the training set in an internal cross-validation procedure of 10-fold and evaluated by its performance on the test set. This process was repeated 20 times, and the best Ascore was reported as the metric of classifier performance. The result of this step was used to identify well-behaved congenic pairs for binary classification by calculating the separation score (Sscore) for each pair of congenic groups with CG#+ and CG#− as below, which is bound between 0 and 1 and reported in Table 1:[8]Sscore (CG#+| CG#−)=Ascore (Dll4+| CG#−)+Ascore (CG#+| Dll4−)+2∗ Ascore (CG#+| CG#−)4, The congenic pairs with Sscore above $80\%$ were selected for secondary congenic pair selection.
For the secondary selection, we evaluated the classification performance (Ascore) of each pair determined this time by using the two most effective features as described in Section 3.13. We used a $75\%$–$25\%$ split for testing and training, with randomization to ensure a proportionate representation of each group in both datasets.
The pair with the highest classification performance, Ascore, based on the two most effective features was selected for the final classification model training. To ensure a high-quality classification model, we set a minimum threshold for the Ascore of 0.70 for inclusion in the feature selection process. Congenic groups with an Ascore below 0.70 were considered to have an insignificant contribution to the classification model and were excluded from further consideration in the feature selection process. This approach allowed us to focus on the most informative feature pairs, improving the overall classification performance of our machine learning model. Each machine learning model was tuned to the training set using a 10-fold cross-validation procedure and evaluated based on its performance on the test set. We repeated this process 20 times and reported the best Ascore as the classifier’s performance.
## 3.13. Feature Selection and Secondary Classification
We selected the best pair of features in terms of achieved sensitivity, specificity, and accuracy by a two-step procedure as previously reported: DTs and SVMs using the full set of 86 features were trained, and recursive feature elimination (RFE) was performed to refine a much smaller set of best-performing features [60]. RFE is a widely used machine learning classification algorithm that helps in reducing the dimensionality of feature space and selecting a small subset of features that yield the best classification performance. This was achieved through an iterative procedure that uses a ranking criterion to eliminate features one or more at a time. The RFE algorithm started by selecting a subset of features and training a model on this subset. The features were then ranked based on their contribution to the model’s performance, and the least important feature was eliminated. The process was then repeated with the remaining features, and the best subset of features was selected based on a model selection criterion [61].
One of the main advantages of RFE is that it helps to reduce the risk of overfitting when the number of features is large, and the number of training patterns is comparatively small [62]. This is because the algorithm selects only a subset of features that are relevant to the classification task, and this helps to avoid the inclusion of irrelevant and redundant features. RFE can be used in conjunction with other techniques such as regularization and support vector machines (SVMs) to further improve the performance of the classification model. In addition, projection methods such as principal component analysis can reduce the feature space’s dimensionality before applying RFE [55].
We used a k-fold cross-validation strategy to assess the performance of our model. We also reserved a portion of the training data for primary testing of the model after hyperparameter optimization. Our experiments were conducted with 20 random splits of the training and test datasets, and the mean performance metrics were reported for sensitivity, specificity, and accuracy as Ascore. To facilitate the interpretation of our results, we limited the number of final features to two. Furthermore, given the small size of our dataset, there was no justification for using high-dimensional feature spaces.
## 3.14. Data Augmentation
The use of data augmentation has become a popular technique in machine learning and deep learning, especially in the field of computer vision. Data augmentation involves applying random transformations to the training dataset to increase its diversity and improve the performance of a model. In this study, we used data augmentation on raw near-infrared (NIR) image stacks to evaluate the robustness of a classification model. We used a dataset of 3000 frames of the original raw 256 × 256 NIR images for this part of our study. These images were augmented using TensorFlow and the Keras API, which allowed us to apply random transformations to the dataset. The transformations included random rotation followed by a horizontal flipping, and up to $2\%$ rescaling.
## 3.15. Training and Testing Dataset
The final training and testing dataset for the machine learning models was determined by the outcome of the congenic pair selection and feature selection steps. This dataset included all congenic groups except for Dll4+ and Dll4−, as well as the selected CG#+ and CG#− groups in the previous step. This step was conducted separately for the original dataset and augmented dataset. The models were trained using 10-fold cross-validation and a portion of the training data was reserved for testing after hyperparameter optimization, with $25\%$ for the original dataset and $20\%$ for the augmented dataset. The performance of the models was assessed using a confusion matrix, receiver operating characteristic curve (ROC), and the area under the curve.
## 3.16. Statistical Analysis
Repeated measures models are a powerful tool in statistical analysis that allow researchers to study the effects of different factors on a given outcome while accounting for the inherent dependence of multiple measurements taken on the same subject. In this study, a mixed effects model with appropriate time varying covariates was used to analyze the average fluorescence intensity of indocyanine green (ICG) in the tumor with multiple measurements per subject, with the subject number serving as the repeated measure indicator and the rat strain serving as a covariate. This allows for flexible time-based modeling when using multiple measures, likely dependent from the same animal [63,64]. Customized scripts in MATLAB were used to generate the fitted coefficients, covariance parameters, design matrix, error degrees of freedom, and between- and within-subjects factor names for the repeated measures model. The output was then analyzed with a multiple comparison of the estimated marginal means based on the variable strain, using the Tukey–Kramer test statistic [65]. This allowed estimation of multiplicity-adjusted p-values for the post hoc comparisons, which indicate whether the groups significantly differed with respect to strain. The data were then visualized as a p-value matrix, providing a clear illustration of the significant differences between groups.
## 3.17. Data Availability
This study employed the established consomic rat models SS and SS.BN3 as well as our congenic strains CG1 to CG8. The publicly accessible and NIH-supported Rat Genome Database (rgd.mcw.edu) catalogs has tools to explore the genotype and phenotype information for the SS (Dll4+) and SS.BN3 (Dll4−) and congenic strains under strain records: Dll4+ (RGDID:61499), Dll4− (RGDID:1358154), CG1 (RGDID:155782881), CG2 (RGDID:155782883), CG3 (RGDID:155782884), CG4 (RGDID:155791428), CG5 (RGDID:155791426), CG6 (RGDID:155791430), CG7 (RGDID:155791429), and CG8 (RGDID:155791427).
## 4.1. Dynamic Contrast-Enhanced NIR Fluorescence Imaging and Tumor Detection
Dynamic contrast-enhanced NIR fluorescence imaging has been widely used for tumor detection in various studies [66,67,68]. The use of NIR imaging allows for the visualization of internal organs and tissues without the need for invasive procedures, which can be particularly useful in detecting tumors due to their vascular heterogeneity compared to surrounding healthy tissues.
In previous studies, the use of principal component analysis (PCA) on the time domain of dynamic fluorescence images was utilized to extract spatial patterns of internal organs linked to statistically similar kinetic behaviors, such as liver, kidneys, lungs, and various tumors [7,56]. However, this technique required manual inspection and selection of proper principal components, which was time consuming and prone to human error and bias.
In order to overcome the limitations present in the current dataset, we implemented a modified method that utilizes near-infrared imaging and principal component analysis to detect tumors with high accuracy and without the need for manual correction (Figure 2 and Figure S1). The use of principal component analysis in this context not only allows for dimensionality reduction and noise removal but also enhances the robustness and efficiency of the method.
Our study also implemented a novel method of ranking PCA components based on the 2D cross-correlation of a reference image containing the tumor. This added to the simplicity and computational efficiency of the framework. However, it should be noted that this method may not be effective for detecting tumors with random locations. On the other hand, it could be useful for detecting tumors or tissues of interest with high localization, such as the lungs, liver, and kidney, and lesions in breast tissue. Overall, our method shows potential for improving the accuracy and efficiency of tumor detection using NIR imaging and PCA (Figure 4a). However, further experimentation is needed to expand the framework to a general tumor detection algorithm.
## 4.2. Dll4 and Its Effect on the NIR Time Series
The analysis of the average fluorescence intensity of indocyanine green (ICG) in the tumor tissue of Dll4+ and Dll4− rats bearing triple-negative breast cancer (TNBC) tumors revealed that ICG uptake occurred more rapidly in Dll4− tissues and was retained for longer periods of time compared to Dll4+ hosts (Figure 4b). This indicates systemic differences in vascular function between the two rat strains. Our previous histological data showed that Dll4+ tumors have a higher vascular density and tortuosity, indicating a genetic microenvironment that promotes nonproductive angiogenesis [38]. This is further supported by the slower ICG wash-out observed in the Dll4+ tumors. These findings provide insight into the effects of host genetics on tumor angiogenesis and suggest potential therapeutic targets for TNBC.
In order to further investigate the role of Dll4 in vascular function in tumors, we divided chromosome 3 into regions with and without the *Dll4* gene in congenic rat strains (Figure 1b) and then examined the ICG fluorescence intensity of tumors in Dll4-high and Dll4-low rats (Video S2) (Figure 4c). Our findings reveal significant systemic differences in vascular function between tumors in Dll4+ and Dll4− rats (parental strains), indicating the critical role of the *Dll4* gene in tumor angiogenic response [38]. However, analysis of the ICG fluorescence intensity of tumors for individual strains (Figure 4d) reveals more complex behavior than the obvious differences in wash-in and wash-out patterns observed between Dll4+ and Dll4−. This supports the need for further investigation into the impact of Dll4 on NIR time series signatures and the potential use of Dll4-directed therapies for cancer treatment.
It is worth noting that although there are significant differences in Dll4-low vs. Dll4-high rat strains (when all the strains of Dll4 expression levels are combined), they are inconsistent with the observations made in Dll4+ and Dll4− rats. These results have significant implications for developing novel therapies that target Dll4 and other host TME modifiers involved in angiogenesis, as they demonstrate the critical role of these genes in tumor vascular function and angiogenic response.
Additionally, our research further highlights the capricious nature of the NIR signal, which is influenced by various heritable tumor microenvironments across different groups, as shown in Figure 4b,c. We aim to illustrate and categorize the impact of the Dll4 expression level on the NIR signal through this erratic behavior.
We used a repeated measures model to analyze the average fluorescence intensity of ICG in the tumor over time, with the rat strain serving as a covariate. Figure 5a,b show the estimated response covariances matrix, which is the covariance of the repeated measures. The higher values in this matrix indicate the time points at which groups experience the greatest differences. By projecting the diagonal of the covariance matrix onto the time axis (Figure 5c), we were able to visualize the amount of difference between groups over time.
This projection, when compared to the average fluorescence intensity of ICG in the tumor (Figure 4b,d), showed the strongest differences between groups at the points where the NIR signal regions from half of its peak value to the peak value and at the tail of the curve, which are measures of the temporal inhomogeneity of the initial uptake and the decay of ICG fluorescence, were found to be particularly useful in discriminating between groups with different levels of Dll4 expression. This projection of the diagonal of the estimated response covariances matrix on the time curve can be used in feature design to focus on regions with the maximum amount of useful information for discriminating between groups and, subsequently, between classes with different levels of Dll4 expression. This could potentially improve the accuracy of tumor classification and ultimately improve therapy outcomes.
Our repeated measures model, which included responses as measurements and strains as predictor variables, allowed us to conduct multiple comparisons of estimated marginal means between groups. The resulting p-value matrix (Figure 5d) revealed significant differences in estimated marginal means between the Dll4+ and Dll4− groups, with a p-value of 4.71 × 10−7. In addition, we observed significant differences between Dll4+ and CG3, CG4, and CG8, with p-values of 1.67 × 10−5, 7.03 × 10−7, and 2.18 × 10−3, respectively.
For each group pair with high and low Dll4 expression levels, the separation score was calculated. First each of Dll4+|CG#−, CG#+|Dll4−, and CG#+|CG#− went through our classification algorithm with 10-fold cross-validation using Nearest Neighborhood, Linear SVM, RBF SVM, Decision Tree, Naive Bayes, and Logistic Regression models. The highest average classification metrics (Accuracy + Specificity + Sensitivity)/3 for Dll4+|CG#−, CG#+|Dll4− and CG#+|CG#−) was used to calculate the separation score (Score Dll4+|CG#− + Score CG#+|Dll4− + 2 × Score CG#+|CG#−)/4.
Furthermore, our analysis showed significant differences between Dll4− and CG5 and CG6, with p-values of 8.70 × 10−3 and 2.58 × 10−4, respectively. This supports the hypothesis that Dll4 expression levels can act as a heritable TME modifier on NIR time series intensity. However, the smallest p-value between Dll4+ and Dll4− suggests that there are other factors on chromosome 3, in addition to Dll4, that contribute to the observed differences in the NIR time series signature between these groups.
In contrast, no significant differences were found between Dll4− and the congenic strains with low levels of Dll4 expression (CG2, CG3, CG4, CG7, and CG8). This further supports the notion that Dll4 plays a crucial role in determining tumor vascular function and NIR time series intensity.
Among the congenic groups, the most significant differences were observed between CG5, CG6, and CG3, CG4 from the Dll4-high and Dll4-low groups, respectively. Notably, the differences were most significant between CG4 and CG6, with a p-value of 0.0003. This suggests that very narrow regions of differences on chromosome 3 between these two groups, one containing Dll4 and the other lacking it, have a significant effect on the NIR time series signature.
## 4.3. Primary Classification and Congenic Dissimilarity
The relationship between Dll4 expression and classification performance was analyzed using a total of 12 congenic pairs with n > 5 based on their levels of Dll4 expression (Dll4+|Dll4−, Dll4+|CG3, Dll4+|CG4, Dll4−|CG1, Dll4−|CG5, Dll4−|CG6, CG1|CG3, CG1|CG4, CG3|CG5, CG3|CG6, CG4|CG5, and CG4|CG6). The pairs were then subjected to a primary classification algorithm and their mean performance metrics, the Ascore, were calculated and reported in Table 1. The congenic pairs with low levels of Dll4 expression showed a mean Ascore of 0.91 +/− 0.01, indicating a high level of classification performance when compared to the Dll4+ parental strain. In contrast, the congenic pairs with high levels of Dll4 expression showed a mean Ascore of 0.8 +/− 0.05 when classified against the Dll4− consomic strain. Among the congenic pairs, the CG5|CG4 pair demonstrated the highest Ascore of 0.8, followed by the CG6|CG4 and CG6|CG3 pairs with Ascore values of 0.78 and 0.77, respectively. The results of the Ascore calculation are visualized in Figure 5e through a Sankey diagram.
To account for potential differences between the congenic pairs and the parental pairs, the Sscore was calculated. The CG5|CG4, CG6|CG3, and CG6|CG4 pairs showed the highest Sscore values of 0.84, 0.84, and 0.85, respectively, and were selected for the feature selection step. These results align with the multiple comparison of estimated marginal means between groups, indicating that CG5|CG4, CG6|CG3, and CG6|CG4 show the strongest differences in classification performance.
## 4.4. Feature Selection
RFE is a wrapper method that evaluates the entire classification algorithm and has shown improved classification accuracy and reduced overfitting compared to other feature selection methods [69]. However, RFE can be sensitive to noise and irrelevant features, leading to suboptimal feature subsets and reduced classification performance. Additionally, RFE is computationally intensive, which can pose a challenge for large datasets with a high number of features. Despite these limitations, RFE remains a valuable tool for selecting an optimal subset of features that maximizes classification performance [70,71].
To address these limitations, we performed feature selection in two steps to optimize the selection process and improve the performance of the classifier. First, we used RFE to select only two features out of the 86 available features for congenic pairs Dll4+|Dll4−, Dll4+|CG4, CG5|Dll4−, CG5|CG4, CG6|Dll4−, and CG6|CG4. The CG3 and its combinations (CG6|CG3, CG5|CG3, and Dll4+|CG3) were dropped from the feature selection process as the performances of the classifiers, the Ascore, using only two features were below 0.70, and lower than the other strains. The congenic pairs CG5|CG4 and CG6|CG4 as well as the parental and consomic group Dll4+|Dll4− went through our feature selection algorithm, and for each pair the two best-performing classification algorithms based on Ascore and associated feature pair were reported (Table 2).
The highest Ascore for the two best-performing models for CG5|CG4 was 0.78 ± 0.04 compared to CG6|CG4 with an Ascore of 0.72 ± 0.19 and 0.72 ± 0.22, resulting in the selection of CG5|CG4 for final congenic pair selection.
Finally, from each pair of Dll4+|Dll4−, Dll4+|CG4, CG5|Dll4, and CG4|CG5, four of the best performing features regardless of the ML model were chosen and were used as a collection of features for the final feature selection (Table 2). A combination of parental and consomic groups and the final selected congenic pair (Dll4+, Dll4−, CG5, and CG4) was used to select the final feature pair out of the 16 selected features, resulting in the selection of HIF5_avg and HIF50_avg as the best-performing features.
## 4.5. Performance of the Classification Models Based on the Selected Features
To evaluate the performance of the selected features, we trained datasets consisting of all the remaining congenic groups excluding the Dll4+, Dll4−, CG4, and CG5 (CG1 to CG3 and CG5 to CG8) using 10-fold cross-validation and keeping $25\%$ of the dataset for testing the trained models. This allowed us to assess the generalizability of our model and test it on previously unseen datasets. The results of this step are reported as a confusion matrix, ROC curve, and AUC (Figure 6), as well as general classification metrics (Table 3). The best-performing models based on the selected features were SVM and KNN, with sensitivity and specificity of 1.00 and 0.81 and 1.00 and 0.75, respectively.
In order to further assess the effectiveness of our model, the selected features, and the generated congenic pair, we generated an augmented dataset consisting of all remaining congenic pairs excluding Dll4+, Dll4−, CG4 and CG5 (CG1 to CG3 and CG5 to CG8, with random variations in rotation, horizontal flip, and limited scaling (up to ±$2\%$) to increase the diversity of the dataset. This resulted in a total of 606 data points. The performance of the models was evaluated using 10-fold cross-validation and a $20\%$ hold out. The results of this step were reported as a confusion matrix, ROC curve, AUC (Figure 7), and overall classification metrics (Table 4). The best-performing models based on the selected features were SVM and KNN, with sensitivity and specificity of 0.97 and 0.91, and 0.97 and 0.92, respectively. These results align closely with the performance of the models over the original dataset, indicating the generalizability of our framework.
It is noteworthy that of the 16 most contributing features used to select the final feature pair, 12 were the newly proposed HIF features, and the other 4 were DS features, which we previously reported [48]. Additionally, the HIF features were amongst the best features for identifying genetic TME modifiers.
The relationship between covariance of the repeated measures and optimal feature design in machine learning classification algorithms is an essential factor in developing effective classification algorithms. Combined with our recent report [47], our analysis found that the DS and HIF features, which are generated in regions where the NIR signal varies from half of its peak value to the peak value and at the tail of the curve, were particularly effective in discriminating between benign/malignant tumors (DS features) and groups with different levels of Dll4 expression (HIF features). Furthermore, the projection of the covariance matrix onto the time axis revealed similar regions, indicating a relationship between this projection and optimal feature design. These findings have significant implications for feature design in machine learning classification algorithms. By focusing on the regions with the greatest amount of useful information for discrimination, we can design features specifically to capture these differences and improve the accuracy of tumor classification. This can ultimately lead to better therapy outcomes for patients. It is worth noting that this relationship between the covariance of the repeated measures and optimal feature design is not limited to HIF features and the specific context of our analysis. *In* general, considering the covariance of repeated measures can provide valuable information for identifying key regions and designing effective features for machine learning classification algorithms.
## 5. Conclusions
Dynamic vascular imaging techniques such as DCE-MRI and perfusion CT are used to extract multiple vascular parameters and have been used in clinical trials of anti-angiogenic drugs. However, these techniques have limitations, such as low temporal resolution and the need for specialized hardware and software [41,42]. To overcome these limitations, dynamic near-infrared (NIR) fluorescence imaging can serve as an effective alternative for characterizing germline-dependent vascular phenotypes. It can be combined with other modalities, such as in a paired-agent or multimodal MRI and fluorescence tomography approaches for noninvasive quantification of response to anti-angiogenesis therapy and classifying in vivo vascular phenotypes [7,43,44,67]. Furthermore, DE-NIR imaging, as a potential alternative for characterizing germline-dependent vascular phenotypes in preclinical models, can be extended to clinical modalities upon validation with cross-sectional dynamic contrast enhanced imaging.
The present study proposes that by combining DE-NIR imaging and machine learning algorithms with consomic xenograft models with human tumors, the role of inherited notch protein Dll4 (rat variant of delta like canonical ligand 4) expression specifically in the host vascular microenvironment can be studied. Specifically, in the context of breast cancer, where different genetic subtypes can impact treatment outcomes, identifying patients with high or low DLL4 (human variant of delta like canonical ligand 4) expression levels through noninvasive imaging could assist in selecting personalized treatment options. Nonetheless, the study authors acknowledge notable differences between the rat model utilized in the study and the human system, which could affect the generalization of the findings to human cases, as in human tumors DLL4 expression maybe be both on tumor cells and host vasculature, whereas in our CXM model, we focused specifically on the inherited variation in rat-derived host vasculature Dll4 expression in human xenograft tumors. Such differences may result in amplification or suppression of vascular phenotype responses if both tumor cells and the host microenvironment express high levels or contrasting levels of DLL4. However, even in that case, dynamic imaging will be useful in identifying patients likely to respond better to DLL4 targeted therapies.
Future studies will be necessary to validate this study’s findings, to assess the reliability and validity of the developed imaging and machine learning algorithms in a large and diverse patient population to determine if contrast agent kinetic profiles observed in human DCE-MRI or dyna-CT imaging datasets for primary and/or metastatic disease differ in human patients with high vs. low DLL4 expression. In the metastatic setting, where surgery is no longer an option, a machine-learning-enabled analysis of dynamic contrast-enhanced imaging will be valuable to assess the expression levels of DLL4 and guide therapy selection, especially in cases where a biopsy is not taken or if biopsy results are inconclusive [72,73]. Human anti-DLL4 antibodies have been reported for cancer treatment [54,74,75,76]. In one study, immunotoxin DLL4Nb-PE was developed, potentially as a cell cytotoxic agent and angiogenesis maturation inhibitor [76]. Another study successfully developed a bispecific monoclonal antibody that targets both human DLL4 and VEGF and showed efficacy in inhibiting proliferation, migration, and tube formation of human umbilical vein endothelial cells (HUVEC) [74]. In a phase 1a trial, navicixizumab, a bispecific antibody that inhibits DLL4 and VEGF, was tested in refractory solid tumor patients and showed the potential to inhibit tumor growth [75]. While DLL4 blockade is an attractive therapy, long-term extended use of DLL4 mAbs has demonstrated concerning off-target effects [77,78]. Pharmacokinetic modulation of DLL4 mAbs may reduce off-target effects [77], such as via short-term administration or by focusing on patients where dynamic contrast imaging indicates a high DLL4 vascular phenotype. As we have shown in prior work, high vascular DLL4 expressing tumors may also be susceptible to DLL4 targeted nanomedicine [38] or as combination therapy with anti-DLL4 monoclonal antibodies with nanomedicine drugs such as nab-paclitaxel (Abraxane) or Liposomal Doxorubicin (DoxilTM).
The use of noninvasive DE-NIR imaging to detect heritable TME modifiers is significant for several reasons. First, this method allows for the identification of potential modifiers without the need for invasive procedures, reducing the potential for discomfort and complications for patients. Second, the use of machine learning and DE-NIR imaging to develop a predictive model for cancer nanomedicine therapy can support effective decision making in the treatment process. While data processing and preparation and algorithm training can be complex, the resulting algorithms are simple and allow for the prediction of heterogeneity in a single step using ROI brightness measurements. Interestingly, traditional features such as time-to-peak and upslope do not appear in our selection of the most discriminative features. However, two novel features derived from HIF (HIF5_avg and HIF50_avg), which is a measure of the temporal inhomogeneity of both the initial uptake and decay of ICG fluorescence, were identified.
It is important to note that the training and testing sets used in this study are minimal, and therefore the high accuracy rates obtained should be interpreted with caution. Further research with larger datasets will be necessary to assess the reliability and validity of these findings with confidence.
We have reported novel dynamic enhanced near-infrared (NIR) fluorescence imaging and machine learning algorithms to noninvasively assess Dll4 expression levels in tumors. Our results showed that observation of subtle differences in vasculature structure and perfusion patterns characterized by ICG time kinetics could be used to differentiate between inherited tumor vascular microenvironment differences, such as Dll4 expression levels. Additionally, our analysis demonstrated the importance of considering the covariance of the repeated measures in the design of features for machine learning classification algorithms. By utilizing this information, we can improve the accuracy of tumor classification and ultimately improve therapy outcomes for patients.
To summarize, based on our recent study, we investigated the impact of genetically heterogeneous notch-Dll4 inheritance on the contrast agent uptake and clearance in triple-negative breast cancer xenografts. The differences in Dll4 inheritance have been shown to impact nanomedicine biodistribution, pharmacokinetics, and therapy response in our prior work. Thus, our results indicated that imaging can be potentially employed for selecting patients for Dll4-directed therapies by identify host microenvironments with high- or low-expressing Dll4 inheritance. This further suggests that the success of nanomedicine might depend on hereditary tumor microenvironment genes, regardless of tumor type. Additionally, host genes such as Dll4 can affect individual differences in NP uptake and response to NP-mediated therapies, providing the potential for more effective personalized Dll4 targeted nanomedicine for therapy-resistant hosts. Further studies are needed to validate these findings and explore the potential clinical applications of this approach.
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|
---
title: Influence of Pig Genetic Line and Salt Reduction on Peptide Production and
Bioactivity of Dry-Cured Hams
authors:
- Beatriz Muñoz-Rosique
- Noelia Hernández-Correas
- Adela Abellán
- Estefanía Bueno
- Rafael Gómez
- Luis Tejada
journal: Foods
year: 2023
pmcid: PMC10000787
doi: 10.3390/foods12051022
license: CC BY 4.0
---
# Influence of Pig Genetic Line and Salt Reduction on Peptide Production and Bioactivity of Dry-Cured Hams
## Abstract
Ham (Jamón) is a product of great value in Spanish gastronomy, although experts have recommended reducing its consumption due to its high salt content and its relationship with cardio-vascular diseases due to the increase in blood pressure it may cause. Therefore, the objective of this study was to evaluate how the reduction of salt content and the pig genetic line influence bioactivity in boneless hams. For this purpose, 54 hams were studied, 18 boneless Iberian hams (RIB), 18 boneless white hams from commercial cross-bred pigs (RWC), and 18 salted and traditionally processed Iberian hams (TIB) to check if the pig genetic line (RIB vs. RWC) or the processing (RIB vs. TIB) affect the peptide production and bioactivity of the hams. The pig genetic line significantly affected the activity of ACE-I and DPPH, with RWC having the highest ACE-I activity and RIB having the highest antioxidative activity. This coincides with the results obtained in the identification of the peptides and the bioactivity analysis performed. Salt reduction positively affected the different hams, influencing their proteolysis and increasing their bioactivity in traditionally cured hams.
## 1. Introduction
In recent years, due to the increasing concern of the population for health and nutrition, there is a tendency to investigate and demonstrate the added value and nutritional properties of certain foods. Therefore, the term “Functional Foods” is becoming increasingly prevalent and these foods could prevent the appearance or improve symptoms of certain chronic diseases [1].
Meat products have also been studied for this bifunctionality [2], and numerous proposals have been reformulated to reduce or even eliminate certain components such as salt [3] or fat, seeking to improve the nutritional profile of the food. One of the most important challenges in the food industry is the development of functional foods that maintain the organoleptic characteristics necessary to meet consumer demands, especially regarding flavor and texture [4,5] Bioactive peptides are a group of biological molecules normally buried in the structure of parent proteins that become active after the cleavage of the proteins. During the ham curing process, the action of endopeptidases (capthesins and calpains) and exoproteases is crucial for the formation of peptides. Capthepsins and calpeins begin their action during the cold phase of the dryer (post-salting stage), but the increase in salt concentration, together with drying, causes their activity to decrease. However, exoprotease activity is highly favored when the temperature is increased in the dryer above 25 °C (temperature-dependent activity) and is maintained until the most advanced stages of the process. Intense proteolysis is thus triggered, leading to the hydrolysis of proteins and the release of non-protein nitrogenous compounds (NPNs). Due to this process, free amino acids and small peptides accumulate. These would comprise 2–20 amino acids (AA) with a molecular mass less than 6000 Da [6].
The natural generation of these bioactive peptides is a consequence of the intense proteolysis of muscle peptidases produced during the processing of cured ham [4,7]. However, there is still little information on the amount of these peptides in the final product. Due to the large number of bioactive peptides, the low abundance of each one and their presence within a complex matrix such as dry-cured ham makes their extraction and analysis difficult [7,8,9]. Peptides modify the texture of the cured meat and influence the aroma at the end of the processing [4,10,11].
Numerous studies have described the beneficial properties that these peptides may have on health. The influence they may have on certain diseases of chronic evolution has been evaluated, exerting antioxidant activity [12,13,14], antihypertensive activity [15,16], immunomodulatory [17], appetite regulator activity [18], or antidiabetogenic activity [19]; with good results and evidence for the ability to inhibit the angiotensin-I converting enzyme (ACE-I) [15,20,21].
The direct or indirectly prescription of these peptides, which are naturally derived from food, could treat certain diseases and thus circumvent adverse effects secondary to the use of artificial drugs. Other authors described strategies to increase the consumption of meat products in meals, to add precursor proteins of these functional peptides to certain meals or to directly add the peptides, after studying their encapsulation [22].
Salt plays a fundamental role during the salting period of cured ham. Water retention capacity, texture, flavor, pH, and proteolysis are parameters directly influenced by salt content [23,24]. Despite being one parameter that defines the quality of cured ham, and being physiologically necessary for the proper function of an organism [25], the current excessive consumption of high salt products, such us ultra-processed foods has caused current human populations to exceed the necessary daily intake of this mineral, causing hypertension, directly related to cardiovascular diseases and other diseases of risk [26,27]. Therefore, numerous studies are being conducted on various foods to reduce their salt content.
In the case of ham, salt reduction directly influences proteolysis, which is greaterwhen the salt content is lower, and which, together with water activity, will result in a softer texture of the ham, which, as a consequence, will alter the quality of the final product [28]. The increase in proteolysis leads to protein degradation, resulting in the generation of free AA and peptides through the proteolytic action of endopeptidases that can have biological activity, which can counteract and/or prevent diseases [29].
Scant research has been performed on meat-derived peptides, and the degree to which salt reduction has influenced the generation of peptides or the generation of bioactive peptides has not been investigated.
Numerous authors have identified peptides with antihypertensive and antioxidant activity in different types of cured ham [29] that show in vitro ACE inhibitory activity, most important in the peptides Ala-Ala-Pro-Leu-Ala-Pro and Ile-Ala-Gly-Arg-Pro [30]. Some of the identified peptides showed multifunctional activity, i.e., some showed antioxidant or anti-inflammatory activity besides antihypertensive activity [29].
Some trials have been conducted with peptides found in ham to test whether they had bioactivity. A study conducted in hypertensive rats showed a significant reduction in systolic pressure after administering a peptide with antihypertensive activity (ACE inhibitor) eight hours earlier [31]. Furthermore, this multifunctional activity had been recognized from bioactive peptides in other foods [32], most commonly generated from hydrophobic waste [33].
Despite advances made in this field, the effect of salt reduction in ham on peptide generation and bioactivity has not been studied. Furthermore, no studies have been found comparing the peptides present in Iberian ham with those present in white ham, and no studies have attempted to identify peptides in boneless hams. Therefore, the objective of this study was to evaluate the influence of the reduction of salt content and pig genetic line on peptide production and bioactivity in boneless cured hams.
## 2. Materials and Methods
For this study, 54 hams were selected from different genetic lines of Iberian pigs (thirty-six hams with a racial percentage of $50\%$ Iberian and $50\%$ Duroc) and white pigs (eighteen hams from crossbreeding Landrace x Large White or Hampshire).
The hams from the Iberian pork genetic line were distributed in six batches of six hams. Half were processed on bone traditionally (18 TIB hams) and were the control batches. The other half of the Iberian hams (18 RIB hams) and the three batches of white hams (18 RWC hams) were freshly deboned and subjected to the new process developed to achieve salt reduction (RIB and RWC, respectively). To evaluate the effect of processing (deboning and salt reduction), TIB and RIB hams were compared, so only salt reduction in the Iberian hams was compared. To study the effect of the genetic line, RIB and RWC hams were compared.
To compare hams from different genetic lines, the percentage of loss was taken as a reference. During the processing phases, each sample was weighed in triplicate to determine the percentage weight loss of each ham respect to the initial fresh weight of each piece. We considered the optimal curing moment, or the final product, when the ham reached $38\%$ of weight loss. The experimental design is shown in Figure 1.
Fresh hams were deboned and salted using sea salt and nitrifying salts. These hams were kept in a cold room at 3 °C for an established period (0.8 days per kilogram of ham weight). The hams were then removed and washed with water, following the normal curing process [34]. The next stage (rest or post-salting) was conducted at 3 °C, gradually increasing this temperature to 6 °C until the percentage of weight loss of the hams rose to $18\%$. When this phase ended, the temperature was increased to 28 °C, reaching its completion when the hams reached a loss of $38\%$. Once the hams were cured, 18 hams were selected (six TIB hams, six RIB hams and six RWC hams). The samples were taken during the drying stage ($33\%$ weight loss) and final product stage ($38\%$ weight loss). All samples were taken when the ham reached the required percentage of weight loss. To avoid damage to the piece during the sampling process, for all the analysis all samples were taken from the femoral muscle (biceps) using a stainless steel cylinder with a diameter of 2 cm. After, samples were kept refrigerated until their analysis.
## 2.1. Non-Protein Nitrogenous Compounds
To prepare the extracts, 2 g of sample were weighed in an Erlenmeyer flask, after which 30 mL of distilled water were added, and agitated for 15–20 min in a magnetic stirrer. Then, 15 mL of $20\%$ trichloroacetic acid was added and shaken for 10 min. The content of the Erlenmeyer was filtered using a funnel and filter paper in a 50 mL volumetric flask. After filtration, the flask was filled with distilled water [35]. Finally, 10 mL of the extract were used for the Kjeldahl method [36].
## 2.2. Antioxidant Activity
The determination of antioxidant activity was conducted following the method of Bersuder et al. [ 1998] [37] with minor variations. First, a standard was created with the TROLOX reagent (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid), which is an analog of vitamin E with antioxidant capacity, used to react with the DPPH radical (2,2-diphenyl-1-picrylhydrazyl). The stock solution of TROLOX (2 Mm) was prepared by weighing 12.5 mg of TROLOX and diluting it in 25 mL of ethanol. A second 0.1 mM stock solution of 10 mL was then prepared from the first stock solution of 2 Mm. This second stock solution was prepared with 0.5 mL of the first stock solution and 9.5 mL of ethanol. The TROLOX concentrations of the straight standard were 5, 10, 15, 20, 40, and 50 µM. Two milliliters of each of the concentrations were prepared from the second 0.1 mM stock solution to prepare the standard line, obtaining the following linear equation: $y = 1.01$x + 10.224 (R2 = 0.9988).
The antioxidant activity of the samples was then determined using a $0.02\%$ (w/v) solution of the DPPH radical in ethanol. In Eppendorf tubes, 500 µL of ethanol, 500 µL of sample, and 125 µL of $0.02\%$ (w/v) DPPH solution were added. For the blank, 500 µL of ethanol, 500 µL of water, and 125 µL of DPPH were used. Samples were incubated for 1 h in the dark at room temperature. The samples were then centrifuged at 10,000× g for 2 min and their absorbance was measured at 517 nm. % DPPH RSA = (Abs Blank − Abs Sample)/Abs Blank × 100 where, % DPPH RSA is % DPPH radical scavenging activity. Abs blank is the absorbance of DPPH with water instead of hydrolysate and Abs sample is the absorbance of DPPH radical in the presence of hydrolysate. The analyses for the different hydrolysates were performed three times.
To calculate the IC50 (concentration of our sample that inhibits $50\%$ of the DPPH radical) of our sample, we use the equation of the TROLOX standard we performed previously.
## 2.3. Angiotensin-I-Converting Enzyme Inhibitory Activity
The ACE-I activity of the hydrolysates was conducted according to the spectro-photometric method of Cushman and Cheung [1971] [38], modified by Miguel et al. [ 2004] [39].
For this purpose, 40 µL of each hydrolysate was incubated at 37 °C with 100 µL of 5 mM HHL dissolved in 0.1 M borate buffer and 0.3 M NaCl (pH 8.3). Next, 2 mU of ECA was added to the substrate. Thirty minutes later, 150 µL of 1 M HCl was added. The formed hippuric acid was recovered with 1000 µL ethyl acetate and centrifuged 10 min at 4000× g and the organic phase (800 µL) was collected. The ethyl acetate was removed by bringing the temperature to 95 °C. The resulting hippuric acid was resuspended in 1000 µL of distilled water and the absorbance was measured at 228 nm. ACE inhibitory activity was determined by the following equation [1]ACE inhibitory activity (%)=(Acontrol−Ablank)−(Asample−Ablank)(Acontrol−Ablank)×100 where, *Acontrol is* the measure of hippuric acid produced by the action of uninhibited ACE, *Asample is* the measure of hippuric acid produced by the action of ACE with the sample, and *Ablank is* the measure of unreacted HHL.
To calculate the IC50, (the peptide concentration required for inhibit $50\%$ of ACE activity), dilutions were prepared at different concentrations of the hydrolysate and the percentage inhibition was calculated for each concentration tested. Subsequently, the percentage of ACE-I activity versus the concentration of hydrolysate used (µg peptides/mL) was plotted. The equation of the line (y = ax + b) was obtained and the concentration of the hydrolysate giving an inhibition activity of $50\%$, i.e., IC50 = (50 − b)/a, was calculated.
## 2.4. Peptide Identification
The identification was determined using tandem mass spectroscopy (MS) analysis using non-liquid chromatography from the NPN fraction obtained in Section 2.1. The methodology used was described by Bueno-Gavilá et al. [ 2019]. The identification of the peptide sequences of the hydrolysates of the different hams was conducted at the Proteomics and Bioinformatics Unit of the University of Córdoba, Spain. MS2 spectra were found with SEQUEST HT against the UnitProtKB database.
The peptides of each hydrolysate were identified and quantified using the peptide spectral matches (PSM). Quantification values were normalized, focusing on the total PSM for all peptides in the sample. Thus, the quantification of a single peptide was comparable between those of the different samples. Additionally, we performed a search for each of the identified peptides in the BIOPEP-UWM database [40]. Two types of searches were performed: identification of activated biopeptides in the sample and identification of potential biopeptides containing fragments of bioactive sequences in their primary structure. Data analysis was performed with R (version 3.4.1; https://www.r-project.org, accessed on 17 December 2022).
## 2.5. Statistical Analysis
All analyses of our samples were performed in triplicate. Statistical analysis of our samples was performed using SPSS software (version 21.0, IBM Corporation, Armonk, NY, USA). To evaluate whether salt reduction and deboning affected the different analyses, a one-way ANOVA between RIB and TIB hams was performed. The effect of breed was also evaluated using a one-way ANOVA between RIB and RWC hams. When the effect of transformation or breed was significant ($p \leq 0.05$), the results were compared using a Fisher’s LSD test.
## 3.1. Evaluation of Antihypertensive Activity (ACE-I) in Hams with Different Curing Losses (33% and 38%)
Angiotensin-I-converting enzyme (ACE-I) is one of the key enzymes in the regulation of blood pressure, given its participation in the renin angiotensin–aldosterone system (RAAS) [41]. Figure 2 indicates the evolution of ACE inhibitory activity throughout the assay as a function of peptide concentration in hams with different curing loss.
Table 1 and Table 2 indicate the effect of pig genetic line and processing on ACE inhibitory activity, represented as the concentration of peptides necessary (mg/mL) to inhibit $50\%$ of this activity (IC50). All samples showed ACE inhibitory activity, which increased with increasing concentration of the peptides. This could be due to the presence of small peptides smaller than 3 kDa [15,42,43]. The ham that showed the highest ACE-I activity (IC50) was RIB33, having a greater potential to control diseases associated with the cardiovascular system [44].
The IC50 in the final product was lower (higher activity) in Iberian hams than in white hams (Table 1), probably due to the longer curing time used in Iberian hams, consistent with what has been observed in other studies [15]. However, when the weight loss is $33\%$, no significant differences were observed between genetic lines (Table 2).
The processing method did not significantly influence ACE-I activity in Iberian hams (p ≥ 0.05), although it was slightly higher in salt-reduced hams (RIB38) (Table 1).
A study conducted at the Catholic University of Murcia (UCAM) showed that the consumption of cured ham rich in bioactive peptides has a positive influence on the regulation of glycaemia and cholesterolemia in healthy patients, so that far from being a restricted food, its regular consumption has a positive effect on modifiable risk factors associated with premature cardiovascular disease [20].
Table 3 presents the results of the effect of processing time on the production of ACE inhibitory activity. In RWC, hams with a weight loss of $33\%$ have greater antihypertensive activity than those with a $38\%$ weight loss (p ≤ 0.05). However, in Iberian hams, processing time does not imply greater ACE-I activity. In Serrano and Panxian hams, some have observed that this activity increases significantly in the last curing phase [30,42]. Furthermore, other authors have also observed this behavior for dipeptide AA, which increases its activity by $40\%$ from 6 months to 12 months of ham curing [45]. Because ACE-I has been detected in the hams studied, it could counteract the harmful effects of sodium in the body [46].
## 3.2. Antioxidant Activity
The DPPH radical study to evaluate the antioxidant activity of samples has been described as a suitable procedure for this purpose [47].
Cured ham has been identified as a source of peptides with antioxidant activity [48]. Despite this, no studies have evaluated antioxidant activity in salt-reduced hams. DPPH scavenging activity is also a commonly used technique to evaluate antioxidant capacity. This activity is directly associated with hydrophobic AA in peptides, so these AA will exist in antioxidant peptides [49,50].
Figure 3 indicates the evolution of in vitro antioxidant activity as a function of the peptide concentration of the hams with different processing; all samples show higher antioxidant activity as the concentration of peptides increases. RWC38 has higher antioxidant activity, reaching $75\%$ inhibition. In RIB38, we also observed an increase in antioxidant activity as the curing process progressed, higher than the healing process, higher than in RIB33. TIB38 shows lower antioxidant activity than RWC38 and is like RIB38. The ham with the lowest antioxidant activity was RIB33 in all the peptide concentrations we studied.
Table 4 and Table 5 indicate the in vitro DPPH radical (antioxidant) scavenging activity of the ham in the drying and final phases, respectively. The concentration (mg/mL) of each NPN needed to inhibit $50\%$ of the antioxidant activity (IC50) was evaluated. All the samples studied showed antioxidant activity both in the drying phase and in the final product.
Table 4 indicates the IC50 values obtained for each sample and the effect of pig genetic line and processing on the antioxidant activity of the TIB, RIB, and RWC hams. Genetic line significantly influenced the uptake of the DPPH radical in these samples (p ≤ 0.05), as did RIB33 and RWC33 (Table 5). However, salt reduction and deboning did not influence the antioxidant activity of the samples, although it was higher in TIB38. The RWC38 hams have the highest antioxidant activity because they reached the IC50 with a lower peptide concentration (0.155 ± 0.013 mg/mL). These data coincide with the higher proteolysis index obtained in white hams in a previous study [3] due to the higher activity of cathepsins and calpains of this breed [4]. In Serrano hams, peptides have been identified with an IC50 at a concentration of 1.5 mg/mL [46]. Furthermore, Jinhua hams in eastern China, managed an IC50 at a lower concentration of 1 mg/mL [51]. However, in subsequent studies, Jinhua hams achieved an IC50 at 2.5 mg/mL, whereas Xuanwei hams required a concentration of 4.5 mg/mL [52]. In contrast to this study, others have shown that meat from purebred and Duroc-crossed Iberian pigs would be less predisposed to oxidation than those from white pig breeds [53]. Others claim that meat products such as Iberian ham have a greater antioxidant capacity than fresh ham products before being cured, or other foods such as red wine [54].
Table 6 shows that, for both salt-reduced Iberian and white hams, the increase in curing time significantly affects the antioxidant capacity of the samples (p ≤ 0.05), being higher in RIB38 and RWC38. This could be due to the increase observed in proteolytic activity in the later stages of curing [3], often related to the increase in temperature [23].
The results show that the antioxidant capacity of the hams increases as the curing process progresses and is not affected by the reduction in the Iberian ham. Therefore, cured hams would be a good source of antioxidant activity despite containing pro-oxidant agents such as salt and heme and even reactive oxygen species (ROS), which can cause cell damage [55,56].
## 3.3. Bioactive Peptide Sequencing
The peptides present in the samples of the hams from the five batches studied (RIB38, RIB33, RWC38, RWC33, and TIB38) were sequenced by LC-MS/MS analysis. Table 7 indicates the number of sequenced peptides per sample. The ham with the highest number of sequenced peptides was RWC38, RIB33 had the lowest number of peptides sequenced, and RIB38 presented a greater number of peptides than TIB38. This coincides with the values of non-protein nitrogen and the proteolysis index (PI) obtained in a previous study in hams with a loss of $38\%$, where the highest and lowest NPN and PI were found in salt-reduced white hams (RWC38) and traditionally cured hams (TIB38), respectively [3].
In this study, no peptides already obtained from the database were found among the peptides obtained in the proteomic study. Therefore, their bioactivity has not been demonstrated in previous studies.
In other studies, identical sequences were found in cured ham, for example, KAAAAP, AAPLAP, and KPVAAP, with origin in different types of myosin protein, were identified as the peptides with the highest ACE-I activity in Teruel PDO ham [30], and are also present in Serrano ham [20].
Their stability and their retention of bioactivity during processing and after in vitro digestion were examined. In vivo studies showed that the AAATP peptide had the highest antihypertensive activity, lowering systolic blood pressure with a short-term effect [46]. Furthermore, other sequences with antihypertensive activity were identified, such as ASGPINFT and DVITGA (both also derived from myosin protein). In another study, AAATP with the KA dipeptide had DPP4 inhibitory activity that would contribute to improving the concentration of glucose in the blood [20].
The antioxidant power is another bioactivity studied in traditional Serrano ham [46]. The SAGNPN peptide has been identified to have the greatest capacity to donate electrons, neutralizing the oxidative capacity, even more than the peptides synthesized [46,57]; furthermore, the peptide GLAGA had the highest reducing power [58]. Moreover, SNAAC and AEEEYPDL, identified in the cured ham, had high antioxidant activity [59].
Numerous bioactive peptides with a high antihypertensive power have been identified in Iberian ham, which are higher than those in Serrano ham. The sequences that are repeated most frequently, which coincide with the BIOPEP database, are PPK, PAP, and AAP [60]. However, the following dipeptides, such as EA, with ACE-I activity, or PP and VE, which showed ACE- and DPP4-inhibitory activity have also been sequenced [61].
Dipeptides with anti-inflammatory and cardiovascular protective activity (PA, GA, VG, EE, ES, DA, and DG) have been identified in hams with reduced salt content, besides contributing to the product aroma and flavor [62]. However, no studies have been found in fresh deboned and salt-reduced Iberian or white ham.
## Study of Putative Activity Peptide Sequences
A search has been conducted for peptide precursors that may contain biopeptides in their sequence and could theoretically be activated after digestion. This technique is useful for very small sequences (less than seven Amino Acids) and by using the proteomics procedure, it is impossible to detect them.
To contextualize the type of bioactivity of the samples, a Z-scoring was performed to plot the variation between samples regarding the mean of the different activities (heatmap). The results are shown in Figure 4; a higher intensity red color means that this activity will be over-represented regarding the mean of the five samples. Bioactivities are grouped according to the intensity of occurrence in each sample. In addition, the succession of rows and columns is rearranged to avoid intersection of the dendrogram lines. Blue lines represent the value of the coefficient. Individually, we have represented in which sample each group of activities stands out for each group or clusters (Figure 5), each corresponding to a group of bioactivities. In RWC33, the main activity is immunostimulatory. No studies have been found on the presentation of this bioactivity in cured ham.
RWC38 showed the highest antioxidant activity. These results coincide with the activity observed in vitro using DPPH (Table 4). Other bioactivities that stand out in this sample are those of neuropeptide activation, hypolipemic, anti-inflammatory, anti-cancer, and hypotensive activities. Antioxidant and hypotensive activity have also been well studied in white pig hams.
Several studies confirm the occurrence of these bioactivities in ham [48]. Recently, peptides with anti-inflammatory activity have been identified in Xuanwei hams, showing reduced symptoms of inflammatory bowel disease in mice, and it has been pro-posed that these peptides could be a functional drug in patients suffering from this disease [63].
In RIB33 hams, the predominant activities are stimulatory, immunomodulatory, a CaMPDE inhibitor, a DPP4 inhibitor, antithrombotic, and ACE-I, consistent with our results for antihypertensive activity (Table 2), where RIB33 had the highest ACE-I. Likewise, the Iberian ham showed greater ACE-I activity compared to the traditional Serrano hams [60].
Antihypertensive activity is well studied in ham [20,21,60]. There are studies that would claim that Serrano ham would be a good source of DPP4 and that these peptides could be an adjunct in the treatment of type 2 diabetes [64].
RIB38 hams have HMG-CoA reductase inhibitory, regulatory, and immunological activity. HMG-CoA reductase inhibitors play an important role in the control of hyper-cholesterolemia and, indirectly, in the control of the onset of cardiovascular disease. Other studies have found dipeptides such as DA, DD, EE, ES, and LL in cured ham, which have been identified as the main inhibitors of this coenzyme [65]. Furthermore, TIB38 hams stand out for their binding, ubiquitin mediator protein activator, renin inhibitor, dipeptidyl peptidase III inhibitor, and embryotoxic activity, bioactivities that have not yet been studied.
Each group of bioactivities was represented by a color (Figure 4). In Figure 5, we can observe six clusters, one for each group of bioactivities, where the values of that group of bioactivities are quantified for each sample.
Because bioactive sequence fragments are found in the samples, a spider web plot with normalized quantification of the peptide precursors of the five hams is shown in Figure 6. This distribution allows differentiation between hams according to activity. The potential bioactivity of the peptides identified in each sample is reflected by using the same scale and amplitude and the same scale and width of the axis, allowing comparison between them.
Cured ham is considered a good source of different bioactive peptides that have important functional activities, such as the inhibition of the angiotensin converting enzyme, hypoglycemic, and anti-inflammatory activities [29].
## 3.4. Bioactivity Analysis Based on Amino Acid Composition
The composition of Amino Acid (AA) used to analyze the bioactivity of the samples was conducted on $38\%$ cured hams, as these had the best organoleptic characteristics and, therefore, would be destined for the end consumer.
In bioactivity studies, it is important to consider the structural properties of sequences [66]. Certain characteristics, such as size, hydrophobicity, and composition, may influence the stability or bioavailability of the peptides. Approximately 20 sequences were selected from those identified in each ham with less than 1.5 kDa and with a maximum of 12 AA in their chain. Processing time causes the size of the peptides to decrease and increases the antioxidant activity of the peptides [49], as short AA sequences are more likely to be bioactive [62,67]. In addition, over $50\%$ of the AAs in the chain should be hydrophobic, as this contributes to antioxidant activity [68]. The presence of AAs A, D, E, G, L, P, and V confers antioxidant and antihypertensive activity on the peptide sequence [68,69,70], and this activity is directly related to the molecular weight of the peptide sequence [71]. However, the presence of H, Y, W, F, M, and C could inhibit free radicals by direct electron transfer [67]. The amino acid sequences of the peptides identified from salt-reduced Iberian hams (RIB) are shown in Table 7.
## 3.5. Identification of Peptides Present in RIB Hams
The AA sequences of the peptides identified from the hydrolysates of salt-reduced Iberian hams (RIB) are shown in Table 8.
Antioxidant activity is highly present among the selected sequences. Some have over $50\%$ of the peptides that provide antioxidant activity. The LDLALEKD, AAFPPDVGGN, AGNPDLVLPV, and AFGPGLEGGL peptides stand out for having over $80\%$ of AAs that would favor antioxidant activity, with AFGPGLEGGL having the highest antioxidant activity ($90\%$ of its AAs).
The AAFPPDVGGN and AFPPDVGGN have been identified as present in pork [72] and six sequences containing them have been found (AFPPDVGGN, AAFPPDVGGN, AFPPDVGGNV, AAFPPDVGGGGNV, AFPPDVGGGGNVD, and AAFPPDVGGGGNVD). The peptides FPPDVGGN and FPPDVGGNVD originating from the protein could also be derived from these sequences, identified as myosin [46]. From the action of the enzyme, dipeptidyl peptidase [73] could be released from some sequences as the VD dipeptide, which would have DPP4 inhibitory activity and, therefore, anti-diabetic activity [64,74].
The most prominent sequence is CLFVCR, as it has $83\%$ of hydrophobic AAs, $67\%$ of AAs conferring ACE-I activity, and $50\%$ of AAs scavenging free radicals. ACE-I activity would be more present in sequences containing hydrophobic AA residues in the three C-terminal positions [75]. For this sample, the sequence AGNPDLVLPV has three hydrophobic AAs at the C-terminus. The dipeptide WK could be extracted from longer peptides originating from β-enolase, such as DGADFAKW (Table 8). This dipeptide has been identified as an inhibitor of DPP4 [76,77]. Likewise, the sequences LIGIEVPH, IDLIEKPM, FDKIEDMA, WNDEIAPQ, and DLDISAPQ originate from the IE and SI dipeptides of the α-enolase protein; they have been described as ACE- and DPP4-inhibitory peptides, respectively [74,78]. These dipeptides could be responsible for the high antihypertensive activity observed in this study for sample RIB38 (Table 1).
Recently, some dipeptides related to anti-inflammatory activity, which could confer cardiovascular protection, have been identified in salt-reduced cured hams [62]. These dipeptides are PA, GA, DA, and DG and could be derived from sequences found in RIB38 (ALQPALKF, WNDEIAPQ, MADTFLEH, DLDISAPQ, DGADFAKW, MADTFLEH, and AGNPDLVLPV), with GA being mainly identified in the study.
Table 9 indicates the prominent peptide sequences detected in the ham samples of traditionally cured Iberian ham (TIB). In these samples, six of the selected sequences presented over $80\%$ of the AAs that could provide antioxidant activity to the product (AFPPDVGGNV, AAFPPDVGGN, DVVLPGGNL, VAVGDKVPAD, DIAVDGEPLG AGNPDLVLPV, and AFGPGLEGGL). RIB38 has the highest antioxidant activity. However, the sequence that stands out for having the highest amount of hydrophobic peptides is ILPGPAPW. This peptide comprises the Pro-Ala-Pro sequence, one of the most repeated sequences among the bioactive peptides described in the literature [15], which would confer good antioxidant activity to the sample [68]. Furthermore, these sequences could contribute to the bioactivity described for TIB38 ham (Figure 4).
Four sequences (ILPGPAPW, VMGAPGAPM, GDLGIEIPA, and AGNPDLVLPV) have three hydrophobic AAs at the C-terminus, and are therefore more likely to develop ACE-I activity [75]. The AGNPDLVLPV sequence matches that found in RIB38. In the GDLGIEIPA and IELIEKPM sequences, we can find the dipeptide IE dipeptide related to ACE-I bioactivity [74]. The same six sequences identified in RIB38 have also been found in TIB38 (AAFPPDVGGNV, AAFPPDVGGN, AFPPDVGGNVD, AAFPPDVGG-NVD, AFPPDVGGN, and AFPPDVGGNV), have been identified in pork, and could have inhibited DPP4 derived from the dipeptide DV [61].
From a comparison study between traditional and salt-reduced cured hams, di-peptides such as DA, PA, and VG would be present in a higher proportion in traditional cured hams [62]. The last two sequences of Table 9 could derive from the peptides found in sample TIB38 and could contribute to its anti-inflammatory and antihypertensive activity. Other sequences, which were identified in this study, are GA (ACE and DPP4 inhibitory activities) and DG (ACE-I activity).
The selected AA sequences of salt-reduced white hams (RWC) are shown in Table 10. The sequences that stand out for having over $80\%$ of AAs and confer antioxidant activity are DLAEDAPW and AEVIALPVE. The latter sequence is also present in RIB38, with the highest antioxidant activity.
The sequence that stands out for having the highest amount of hydrophobic AAs is ILPGPAPW, the same as TIB38, and has one of the most repeated sequences among bio-active peptides (PAP) [15]. Furthermore, this sequence has $75\%$ of AAs that confer antioxidant activity, three hydrophobic AAs at the C-terminus that confer ACE-I activity, and $13\%$ of AAs that could inhibit free radicals. However, six peptide sequences with three hydrophobic AAs at the C-terminus (ILPGPAPW, AVIGPSLPL, VMGAPGAPM, ISAPSADAPM, DLAEDAPW, and GDLGIEIPA) were found in the RWC38 samples, which conferred ACE-I activity. However, in RIB38, of the sequences selected, none had over two hydrophobic AAs at the C-terminus. The sequences ILPGPAPW, VMGAP-GAPM, and GDLGIEIPA match TIB38. Furthermore, the LKGADPEDVITGA and GADPEDVITGA would contain the bioactive peptide DVITGA in their chain, related to high ACE-I activity due to the presence of AA alanine at the C-terminus [39,46]. Despite this, the RWC38 ham showed the least antihypertensive activity (Figure 4); it would be necessary to study if these peptides confer ACE-I activity and in what quantity they are present. There are more sequences from which the dipeptide IE could be derived, already described as a precursor of this bioactivity [74].
The sequence identified in RWC38, FKAEEEYPDLS, once digested, could cause the peptide AEEEYPDL, derived from protein creatine kinase and identified as a potent antioxidant [59]. Using multiple reaction monitoring (MRM), it was quantified at a concentration of 0.148 fg/g in cured ham [79]. This could explain why the RWC38 hams showed the highest antioxidant activity (Figure 4) and the highest rate of proteolysis obtained in white hams [3].
In RWC38 hams, the same six sequences described in RIB38 and TIB38 (AAFPPDVGGNV, AAFPPDVGGNV, AFPPDVGGNVD, AAFPPDVGGNVD, AFPPDVGGNV, and AFPPDVGGNV) have been identified. However, this would explain the difference in proteolytic activity [3] between different pig genetic lines and influence of processing, because salt-reduced Iberian hams (RIB38 and RIB33) had the highest DPP4 inhibitory activity, lower than white hams (RWC38 and RWC33) and traditional Iberian ham (TIB38).
In a recent study of salt-reduced white ham, hydrophobic PA dipeptides (related to bitter taste and with ACE-I and anti-inflammatory activity) and VG (related to bitter and umami taste and with ACE-I activity) were identified that could be derived from the sequences identified in our sample [62].
Different unique and common sequences that could act as peptide precursors and that have been identified in the samples would be responsible for the bioactivities found in the different types of ham. The results show that in RIB38 ham, the precursors found could be responsible for its high antihypertensive capacity, noting that the change in processing varies the sequences identified in both samples (RIB38 and TIB38). Furthermore, peptides already referenced in the literature have been found in the RWC38 ham, including a sequence that gives rise to a potent antioxidant peptide (AEEEYPDL) that would explain its increased bioactivity. However, none of these co-inciding peptides are found in Iberian hams.
## 4. Conclusions
Salt-reduced boneless hams presented a higher concentration of peptides and higher bioactivity compared to traditionally cured hams, due to higher proteolysis.
The salt-reduced white ham had the highest antioxidant activity and the salt-reduced Iberian ham the highest antihypertensive activity. Antioxidant activity was significantly influenced by pig genetic line and antihypertensive activity was modified by pig genetic line in the final product. In addition, salt-reduced white ham had the greatest hypolipidemic, anti-inflammatory, and anticarcinogenic activity. However, salt-reduced Iberian ham stood out for its significant HMG-CoA reductase inhibitory, regulatory, and immunological activity.
Salt reduction has had a positive influence on the bioactivity of the hams, with salt-reduced hams, both Iberian hams and hams from white pigs, having the highest bioactivity compared to traditionally cured Iberian hams.
In all types of hams, peptide precursors sequenced could give rise to sequences identified as bioactive in literature, with white pig hams showing the highest quantity. The dipeptide DV is present among the precursors of all hams and the bioactive peptides DVITGA and AEEEYPDL in the precursors of the reduced white hams.
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|
---
title: A New Artificial Intelligence Approach Using Extreme Learning Machine as the
Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia
authors:
- Dimas Chaerul Ekty Saputra
- Khamron Sunat
- Tri Ratnaningsih
journal: Healthcare
year: 2023
pmcid: PMC10000789
doi: 10.3390/healthcare11050697
license: CC BY 4.0
---
# A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia
## Abstract
The procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed $99.21\%$ accuracy, $98.44\%$ sensitivity, $99.30\%$ precision, and an F1 score of $98.84\%$.
## 1. Introduction
The main task of the circulatory system is to allow the flow of blood, oxygen, and nutrients to all cells and tissues in the body [1,2]. However, there are disorders of the circulatory system, better known as blood disorders, in which blood circulation is obstructed [3,4] Blood disorders commonly experienced by humans include anemia, hemophilia [5,6], and blood clots [7,8], as well as blood cancers such as leukemia [9,10], lymphoma [11,12], and myeloma [13,14]. A blood disorder is a condition that affects the ability of blood to function properly in humans [15], with most of these disorders having the capacity to reduce the number of cells, proteins, platelets, or nutrients in the blood, thereby impairing its function [16,17]. It is important to note that most of these problems are caused by abnormalities in certain genes and can be passed down via families [18]. Some medical issues such as drug use and lifestyle also lead to blood abnormalities [19]. It has been reported that anemia is the most common blood disorder seen in humans [20].
Anemia has been defined as a decrease in red blood cells, hemoglobin, and the blood’s ability to carry oxygen throughout the body [21,22]. It is a serious and persistent issue affecting individuals worldwide [23,24]. The prevalence of anemia among Indonesian women of reproductive age is shown in Figure 1 to exceed the global incidence [25]. A previous study also noted that iron deficiency is the major cause of anemia in every part of the globe [26]. As previously stated, this disease is the substantial reduction in the number of red blood cells circulating inside the body [27], thereby leading to a great decrease in the ability of the blood to transport oxygen [28]. The diagnosis of anemia is usually confirmed by the concentration of hemoglobin in the blood or the hematocrit, which is the ratio of the number of red blood cells to the total volume of a blood sample [29]. A patient with hemoglobin or hematocrit values that are more than two standard deviations below the normal range is believed to have anemia [30]. Meanwhile, the blood’s hemoglobin and hematocrit levels may not adequately reflect the severity of the anemia in a patient with a low RBC mass who is also suffering from hypovolemia-caused dehydration-induced plasma volume loss because the values are likely to fall within the normal range [31].
There are many people suffering from anemia in Indonesia [33]. Iron is believed to be an essential component of several enzymes; it has a role in the formation of hemoglobin in the human body [34]. This means its deficiency can cause anemia [35]. A survey conducted showed that the frequency of anemia in the Indonesian population was anticipated to grow by $0.8\%$ in 2019 reaching $31.20\%$ of the population [32]. The prevalence of anemia sufferers worldwide increased by $0.3\%$ in 2019 to $29.90\%$ [32]. It has also been noted that abnormal production of alpha (α)- or beta (β)-globin chains is the root cause of thalassemia, which is a hematological disease that runs in families, usually passed down from one generation to another [36,37]. Serum ferritin levels [38], serum iron [39], total iron binding capacity, and transferrin saturation percentage are the tests most often used to confirm the presence of IDA, as presented in Table 1 [40,41]. The identification of BTT and HbE is normally performed through Hb tests using high-performance liquid chromatography, capillary/hemoglobin electrophoresis, or DNA analysis [42,43]. The application of DNA analysis is not accessible in normal labs due to the need for specialized equipment; in addition, it is time-consuming and expensive [44]. When a patient is assumed to be anemic, doctors often prescribe a hematology test cassette that includes the diagnosis [45]. The high cost of this laboratory test can exhaust patients’ resources and government funds, thereby precipitating a financial crisis in the national healthcare systems of low- and middle-income nations. Therefore, a web-based application is proposed in this study to assist doctors in prescribing cost-effective and sensible laboratory tests for the diagnosis of anemia to aid in the rational use of laboratories by clinicians.
Anemia is a significant threat to public health on a global scale, and its incidence is disproportionately higher among young children and pregnant women. According to the World Health Organization (WHO), $42\%$ of children under the age of five and $40\%$ of pregnant women worldwide are affected by anemia [46]. Indonesia Family Life Surveys (IFLS) reported that the prevalence among Indonesian children, adolescents, women, and men continued to fall from 1997 to 2008 as indicated in Figure 2 [47]. The anticipated decline among non-pregnant women in Southeast Asia between 1995 and 2011 was predicted to be $8\%$ less than the $9.4\%$ decline among non-pregnant women between 1997 and 2008 as reported by IFLS [47]. It was also discovered that the reduction among pregnant women during the period was comparable to IFLS ($7.8\%$) and Southeast Asian ($9\%$) estimates [47]. However, the decline among children under the age of five was much greater in the IFLS ($14.6\%$) than in Southeast Asia ($4\%$) [47].
The Fick equation is normally used to determine the flow of oxygen to a certain bodily region [48] using three independent variables, including the blood flow [49], the concentration of red blood cells [50], and the portion of hemoglobin that has released oxygen on its journey from the arteries to the veins [51]. The oxygen-carrying capacity of anemic individuals’ blood was discovered to be diminishing while the remaining two variables underwent compensatory modifications as illustrated in Equation [1] and in the further discussion in [52]. [ 1] where DPG is diphosphoglycerate, *Asat is* arterial blood, and *Vsat is* venous blood.
In anemic individuals, the blood flow to crucial organs such as the heart, brain, liver, and kidneys is boosted, while the blood supply to less vital organs is diminished [53]. The anemic patient appears pale because blood is taken from the skin to ensure the vital organs continue to get sufficient oxygen [54]. Moreover, cardiac output is projected to be lower at rest and greater during exercise in individuals with mild or severe anemia compared to healthy individuals [55]. It has also been reported that severe anemia has the ability to increase resting cardiac output in individuals with coronary artery disease or other preexisting cardiovascular diseases, thereby increasing their risk of developing high-output heart failure [55].
It was discovered, as shown in Table 1, that indices and discriminant formulas had promising prediction outcomes in several investigations, but the forecast findings for different populations remain unsatisfactory, particularly when assessing the efficacy of the methodologies used. Some research was observed to be contentious due to gender, age, or ethnic variances [56,57,58]. Meanwhile, machine learning-based strategies are believed to have a short-term translational effect. This is indicated by their most significant applications in the field of biomedicine such as medical diagnostics, radiological diagnostics, and medication synthesis. Therefore, it is feasible to create discriminant models using machine learning software approaches to undertake large-scale assessments of laboratory data. It is pertinent to state that machine learning is one of the subfields under the umbrella of artificial intelligence [59].
Artificial intelligence (AI) is defined as the effort of computers to simulate human cognitive processes [60]. This is observed in signal processing, voice recognition, expert systems, and natural language processing. The continuous development of interest in AI has enhanced competition among businesses to showcase the AI features of their goods and services [61]. It is important to note that the creation and training of machine learning and deep learning algorithms require the application of specialized hardware and software [62]. This is mainly due to the fact that the operation of AI systems entails the intake of massive quantities of labeled training data as well as the analysis of the data for correlations and patterns to predict future states [61]. AI programming focuses on three cognitive skills, which include learning [59], reasoning [63], and self-correction [64].
This aspect of AI programming involves the collection of data and the development of rules to translate the data into actionable knowledge. Algorithms provide computer systems with detailed instructions to perform a certain task [65]. It is important to state that AI technology is experiencing a time of rapid expansion, but few are aware it has several subfields, one of which is deep learning [65]. The subfields and branches were developed to reduce the very large scope of AI for the purpose of development or research [66]. It is anticipated that AI has the ability to expedite the process of discovering human issues. For example, Laengsri et al. [ 67] classified 6935 data obtained from the Medical Laboratory Service Centre, Faculty of Medical Technology, Mahidol University between July 2014 and September 2016 as either thalassemia trait or iron deficiency anemia using k-nearest neighbor, decision tree, random forest, artificial neural network, and support vector machine methods. It was discovered that the decision tree algorithm attained a maximum degree of accuracy of $98.03\%$. The study used seven hematology analyzer-generated features to determine the existence of anemia in individuals. This is necessary because it is difficult to determine the kind of anemia present in a patient using only a blood sample. The findings are expected to allow medical personnel to conduct further diagnostic tests without difficulty and also to ensure a more precise and specific diagnosis [67].
Another study also applied extreme learning machines and regularized extreme learning machines to anemia cases [58]. A total of 342 patients, including 152 with beta-thalassemia-type anemia obtained from the Elazig Public Health Laboratory between 1 December 2016 and 23 May 2019 and 190 with iron deficiency anemia obtained from the Elazig City Hospital Biochemistry Laboratory between 1 March 2018 and 31 July 2018, were studied. The investigation considered a large number of other variables such as gender, in addition to the findings of the clinical pathology test. The regularized extreme learning machine approach produced a $95.59\%$accuracy rate by combining the k-nearest neighbor, support vector machine, extreme learning machine, and regularized extreme learning machine methods [58].
This present research focuses on establishing an AI model to rapidly, precisely, and reliably diagnose anemia. The process involves classifying anemia into four types, which include beta thalassemia trait (BTT), hemoglobin E (HbE), iron deficiency anemia (IDA), and combination (BTT and IDA or HbE and IDA) using the extreme learning machine approach. Previous studies [23,52,57,66] have shown the relevance of data mining and increasing computing capacity in several biological applications. Therefore, this research seeks to develop a trustworthy and interpretable computational model through the following: (a) collection of clear and dependable laboratory datasets for training and validation, (b) demonstration of dataset characteristics or descriptors to predict the intrinsic properties, and (c) development of a simple and interpretable model.
## 2. Related Work
Support vector machines (SVM), naive Bayes (NB), decision trees (DT), k-nearest neighbor (KNN), multilayer perceptron (MLP), hybrid classifier machine learning, average ensemble (AE), genetic algorithm convolutional neural network (GA-CNN), genetic algorithm stacked-encoder (GA-SAE), support vector machines (SVM), and random forest (RF) are different types of AI. Several studies have been published on the application of machine learning to categorize different kinds of anemia, as indicated by [68,69,70], which forecasted data in the form of a complete blood count (CBC) and constructed a model to identify anemia.
Hemoglobin level estimation is an important step in any task related to blood analysis [50], and it also determines whether a person is anemic. A study [68] used blood test characteristics and applied a machine learning model to calculate hemoglobin levels and identify anemia. The dataset used consists of 9004 data with $75\%$, or 6753, for training and $25\%$, or 2251, for testing. A total of three machine learning algorithms—including DT, NB, and NN, as well as a combination of all three approaches known as a hybrid classifier—were applied. Moreover, the MAE and RMSE methodologies were used to assess the performance of the approach, and the MAE results showed that the hybrid classifier had 0.083, the best RMSE value of 0.015, and an accuracy of $0.996\%$ [68].
Tremendous advances in the healthcare industry have resulted in the production of significant data in everyday life [71]. There is a need to extract information for analysis, prediction, recommendation, and decision-making purposes. It was discovered in the realm of medical research that the prediction of disease is essential to design effective prevention and treatment methods. The presentation of wrong information occasionally leads to death. Therefore, a recent study applied 200 CBC data fields obtained from the Pathology Centre and Laboratory Test Centre, as well as RF, C4.5, and NB, which are considered three distinct types of machine learning. K-fold cross-validation and mean absolute error were both used at different stages of the model evaluation process. It was discovered that the C4.5 approach produced the most precise answers, with a precision percentage of 96.0909 and an absolute mean error of 0.0333 [70].
Anemia was also found to be a severe public health problem, particularly for children, in Bangladesh [69]. Thus, the prediction of illness is essential to formulate community and healthcare policy as well as to forecast resource planning. The study used the common risk variables to determine the appropriate machine learning method to predict anemia status in children (under five years) [69]. The 2013 data containing all relevant characteristics for the children, obtained through a nationally representative cross-sectional study conducted by the Bangladesh Demographic and Health Survey (BDHS) in 2011, were used. The investigation employed six techniques, which included the LDA, CART, KNN, SVM, RF, and LR, and they were assessed using the confusion matrix, accuracy, sensitivity, and specificity. The findings showed that the CART approach yielded the greatest evaluation scores of $62.35\%$, $71.54\%$, and $53.52\%$ [69].
It is important to note that “deep learning” and “machine learning” are interchangeable when discussing artificial intelligence (AI) [62]. Deep learning is established based on the concept of creating learning algorithms or models that can simulate the human brain [65]. Humans use neurons in their brains to process information, while deep learning algorithms utilize artificial neural networks to perform the same function [72]. Some recent studies [73,74,75] used deep learning to enhance the process of identifying anemia in patients. The single red blood cell count imaging data of 130 individuals with sickle cell anemia (SCA) were surveyed and discovered to exceed 9000 single red blood count image data of patients [73]. SCA is a severe hematological illness that often leads to lifelong hospitalization and, in some circumstances, death [73]. It is important to note that the manual location and classification of aberrant cells in the blood films of SCA patients is time-consuming, difficult, and error-prone, and it requires the skill of a hematologist. The study used the AlexNet deep learning model, and the accuracy was recorded to be $95.92\%$, sensitivity was $77\%$, specificity was $98.82\%$, and precision was $90\%$ based on the assessment conducted using the confusion matrix [73].
Deep learning algorithms are gaining importance in the prognosis and prediction of diseases using patients’ data [76]. It is pertinent to state that the lack of prompt diagnosis and treatment of anemia can lead to a life-threatening illness [51]. Therefore, several artificial intelligence algorithms have been employed to forecast nutritional anemia cases, especially those related to iron deficiency [35,53]. Each algorithm was observed to be optimized for a certain subset of data, and this means there is a need to develop new processing techniques. The trend was identified in a previous study where the properties of each dataset are unique, and the size was governed by the number of records and variables specific to the dataset [74]. The strategy blends machine and deep learning to improve the identification process. These were observed to be in the form of genetic algorithm (GA), stacked autoencoder (SAE), and convolutional neural network (CNN) methods, which were used to predict the HGB, nutritional or iron deficiency, B12 deficiency, and folate deficiency anemia as well as to examine individuals without the illness [74]. Moreover, a confusion matrix was used to assess the model, and the greatest level of accuracy for the GA-CNN algorithm was recorded to be $98.5\%$; the F1 score was $98.8\%$, sensitivity was $98.7\%$, and precision was $99.00\%$ [74].
Hemoglobin, a protein contained in red blood cells, is important for the transport and storage of oxygen throughout the body [77]. It has been reported to have the ability to preserve its elasticity, spherical form, and stability in healthy individuals [78]. This is the reason it can float above the red blood cells, but its structure does not ameliorate the symptoms of sickle cell disease [22]. The phenomenon is associated with red blood cells that are twisted and blocked with fluid. It is also important to note that blood circulation is hindered by dysfunctional cells. This is dangerous and has the ability to lead to a range of symptoms, including excruciating pain, organ damage, and even heart attacks [49]. It also has the potential to reduce the average human lifespan. Sickle cell disease identified at an early stage can be treated with antibiotics, vitamins, blood transfusions, painkillers, and other medications. However, the manual grading, diagnosis, and cell counts are time-intensive, and this poses a risk of inaccurate data and misclassification because a single sample usually comprises millions of red blood cells. This is the reason the application of data mining techniques is considered effective and efficient in determining the status of sickle cells inside the human body [75]. An example of this is the adoption of a robust and rapid MLP (multilayer perceptron) classification algorithm to separate sickle cell anemia (SCA) patients into three groups, and the method was observed to surpass the constraints of the manual methods. It was discovered that there are three different types of red blood cells, which include normal, sickle, and thalassemic cells [75]; this discovery was followed by the application of the confusion matrix to analyze the performance of the MLP approach. The results obtained using the 1387 datasets gathered between August 2017 and August 2019 showed a correctness score of $96.04\%$ [75] while the 100 most recent datasets obtained from the Thalassemia and Sickle Cell Society (TSCS) in Rajendra Nagar, Hyderabad, Telangana, India [75] from September 2019 to August 2020 showed $99\%$ [75].
## 3.1. Data Collection
This research was conducted using 165 females and 25 males between the ages of 15 and 41 diagnosed with different kinds of anemia. The data used were compiled by the Clinical Pathology Laboratory at Dr. Sardjito General Hospital in Yogyakarta, Indonesia, and the Department of Clinical Pathology and Laboratory Medicine of the Faculty of Medicine, Public Health, and Nursing at Universitas Gadjah Mada. Moreover, a hematological measure was generated from patients with BTT, IDA, HbE, and a combination of BTT and IDA or HbE and IDA. It is important to note that the Medical and Health Research Ethics Committee (MHREC) of the Faculty of Medicine, Public Health, and Nursing at Dr. Sardjito General Hospital, Universitas Gadjah Mada, issued an ethical letter for the conduct of this research, with the identifier KE/FK/1255/EC/2021. The parameters used include the RBC, Hb, HCT, MCV, MCH, and MCHC in addition to RDW. A total of 24 patients were diagnosed with BTT, 41 with HbE, 104 with IDA, and 21 with the combination method. The definitions of several acronyms used during the investigation are presented in Table 2.
## 3.2. Research Flow
The data derived from the results of a full blood count performed in the laboratory using Advia and Sysmex hematology analyzers produced seven primary characteristics. Moreover, serum ferritin was applied to acquire the gold standard from IDA while hemoglobin electrophoresis was used for BTT and HbE, and the data obtained were examined further and placed in the database based on the flow presented in Figure 3. The seven characteristics previously identified were processed in the database, and the data were labeled by clinical pathology physicians. The data put into the database were preprocessed through cleansing, deletion, the MinMax scaler, and the LabelEncoder. The remaining data were divided into $67\%$training and $33\%$testing. Furthermore, the ELM algorithm was used to train the data, which were subsequently applied as the standard to grade the test data. The doctor was involved in the process to provide training courses based on the findings from the laboratory tests. This was followed by the application of the ELM algorithm to classify the data, and its performance was also evaluated. The performance results were further used by the clinical pathology doctor to analyze the data once more to ensure transparency and accountability of the categorization.
## 3.3. Extreme Learning Machine
The research on the predictive capacities of feedforward neural networks has been mathematically centered on two aspects. The first is the simultaneous estimate of the number of inputs while the second is the estimation within a certain period. Thus, the focus of several studies has been on the feedforward neural networks, as indicated by those conducted on the extensive approximation capabilities of typical multilayer feedforward neural networks [79,80,81,82,83]. Due to their benefits, these networks have been extensively adopted across a variety of commercial sectors over the last few decades. These benefits include the capability to predict complex nonlinear mappings using the available input samples, as well as to provide models for an extraordinarily high number of natural and artificial occurrences, which are considered problematic for standard parametric techniques designed for such events [84]. The single hidden layer feedforward networks, also known as SLFNs, are among the most well-known feedforward neural networks, and their learning and fault tolerance properties have been the topic of discussion in both theoretical and practical studies [85,86,87,88]. The recent development of the extreme learning machine (ELM) neural algorithm for SLFNs [81,88] was used to improve their performance. It is a novel training method that is exceedingly efficient and effective as indicated in Figure 4. The SLFNs were used in this research to analyze anemia data. It is pertinent to note that the behavior of a linear function as a sum of all linear functions in the network is identical to that of a perceptron regardless of the number of layers comprising the neural network [89,90,91]. Thus, a linear function can be described, but there is a possibility of obtaining a nonlinear outcome when an attempt is made to imitate reality. Therefore, a nonlinear activation function was included in the model. It is also pertinent to note that when a network with several layers fails to provide the desired output, the weights and biases need to be modified. The absence of an activation function can cause a change, such as a switch in the neuron signal from 0 to 1, a huge shift, with each neuron feeding a few neurons in the next layer causing a few more neurons to flip. This means minute modifications to the weights and biases used can have a dramatic effect on the end conclusions. Therefore, an activation function was applied to the neuron’s output, and small changes in the function’s weights can lead to moderate changes in the output. Moreover, the sigmoid function receives any number between -infinity and +infinity, but its output is always between 0 and 1. The Adam optimization method is used. This method is the optimal method for these research data in order to obtain optimal performance results. Table 3 explains the mathematical notation used in the ELM formula.
In Equation [2], xi represents the input vector, oj is the output vector, βj=[βj1,βj2,…,βjm]T indicates the output layer’s density, wj=[wj1,wj2,…,wjn]T represents the difference in weight between the input and hidden layers, bj is the function’s threshold, and g(.) is the function of activation. Moreover, the output matrix of hidden layers H and output-hidden layer weights b for the given input-output sample pairs allows the ELM to obtain an output calculated as Hβ=O as indicated in Equation [3], [2]oj=∑$j = 1$Nβjg(∑$j = 1$Nwjxi+bj), where wj and bj are randomly generated learning parameters of hidden jth nodes, βj are the links connecting hidden jth nodes and output nodes, and g is the sigmoid activation function for ELM.
The wj. xi part becomes the product of the parts of wj and xi. Equation [3] is, therefore, presented as follows:[3]Hβ=O where [4]H=[g(w1x1+b1)⋯g(wNx1+bN)⋮⋱⋮g(w1xn+b1)⋯g(wNxn+bN)]n×N [5]β=[β1T⋮βNT]N×m, and [6]O=[O1T⋮ONT]N×m, and H is referred to here as the output matrix of the hidden layer, [7]β^=HTt where HT is the generalized Moore–Penrose inverse of H and t is the target class/data label. Therefore, the output weights were calculated using a mathematical transformation that eliminates the need for a lengthy training phase requiring repeated updates of the network’s parameters through suitable learning parameters such as learning rate and iteration.
It is possible to implement the ELM method in two simple steps, which include the training and testing steps.
## 3.4. Blood
All blood cells in the body, as shown in Table 4, are derived from pluripotent stem cells located in bone marrow [52]. It is important to note that one of the basic activities of red blood cells is to transfer oxygen from the lungs to the tissues and also to move carbon dioxide in the opposite direction [78]. Moreover, the platelets, which are vital to hemostasis, circulate for just ten days, but red blood cells have a lifetime of four months [91,92]. It has also been stated that different kinds of phagocytes—including neutrophils, eosinophils, basophils, monocytes, and lymphocytes—comprise white blood cells [93]. The B cells are responsible for the creation of antibodies while the T cells are in charge of immunological responses and defending against viruses and other foreign cells [94]. The white blood cells are present in the blood’s white component to combat illnesses caused by bacteria and fungus. Furthermore, the lymphocytes are responsible for the generation of antibodies. Previous studies have also shown that white blood cells have a relatively lengthy lifetime [95,96].
Red blood cells are the most numerous blood cells [98], and they appear as biconcave discs densely packed with cytoplasm rich in the oxygen-carrying protein hemoglobin on smears of human peripheral blood [68]. They have a clever structure that allows them to perform their primary functions of transporting oxygen from the lungs to the tissues in the body’s periphery and transporting carbon dioxide from the tissues in the body’s periphery to the lungs, where it can be expelled via respiration. This means red blood cells facilitate the exchange of oxygen and carbon dioxide between the lungs and peripheral tissues of the body [99]. They also have an average lifespan of 120 days [93]. Meanwhile, platelets which are also known as thrombocytes are microscopic, fully nucleated, and granular-colored cell fragments. They are usually released by the megakaryocytes in bone marrow [98] and play a key part in the control of hemostasis together with the clotting factors of plasma [99,100]. Platelets have a seven- to ten-day lifespan [101]. It has also been discovered that there are several varieties of white blood cells [94]. These include the granulocytes, which are bone marrow-derived, short-lived cells that look identical on a peripheral smear [101,102,103,104]. Neutrophils, sometimes referred to as polymorphonuclear leukocytes, are the most prevalent kind of white blood cell, which possess between three and five lobes on their nucleus and an abundance of light purple granules in their cytoplasm [105]. They are phagocytes that provide defense against a variety of acute pathogens [105].
Monocytes are the biggest white blood cells, ranging from 12 to 20 μm in diameter [105]. They have a folded or kidney-shaped nucleus and an abundance of light blue cytoplasm with a modest number of extremely tiny granules [106]. Monocytes, like neutrophils, are extremely phagocytic, although they vary from neutrophils in a crucial aspect [107]. They primarily develop into relatively long-lived macrophages capable of recognizing “danger” signals created by infection or tissue damage upon emigration into tissues [108]. Meanwhile, eosinophils with a diameter of 12 to 15 have two nuclear lobes and an abundance of red cytoplasmic granules (as befits the cell named after Eos, goddess of the dawn) [109]. They have a crucial role in some chronic immunological responses, including those linked with worm infections, asthma, and certain forms of allergic reactions [110].
The rarest of the granulocytes is basophil, and its nucleus is enveloped by numerous dark blue cytoplasmic granules [111]. They have a diameter of 12 to 15 μm [110], and many of the circumstances linked with an increase in eosinophil counts are also related to a small rise in basophil numbers [112]. It was also discovered that the mononuclear cells, another kind of white blood cell, lack the segmented nucleus typical of granulocytes [113]. Furthermore, lymphocytes are an essential part of the adaptive immune system [114] and are found to be approximately the same size, 7 to 9 μm in diameter, as a typical red blood cell while at rest and feature a spherical, compact nucleus with minimal cytoplasm [115]. However, the active cells have the potential to grow to a maximum size of 20 μm and also have a small number of granules in addition to the expanded nucleus and copious cytoplasm [115]. Unless the cells are evaluated for the presence of certain lineage-specific markers, it is impossible to tell with absolute certainty whether circulating lymphocytes are B cells, T cells, or natural killer cells. This is due to the fact that the lymphocytes circulating in the blood can be any of these three types [116]. The immune system also has the ability to “remember” the pathogen exposures from many years ago since it has the necessary foundation due to its longevity [117].
## 3.5. Anemia
Anemia is usually defined through the blood hemoglobin level which is below what is considered normal for a person’s age and gender, as indicated in Table 5. The results can vary across labs, but the average values for adult men and women are fewer than 135 g/L and 115 g/L, respectively [118]. The existence of less than 110 g/L for children between the ages of 2 and puberty implies anemia [119], and because newborns have high hemoglobin levels, the minimum acceptable threshold at birth is 140 g/L [120,121]. The World Health Organization classifies individuals as having anemia when their hemoglobin levels fall below 130 g/L for males and 120 g/L for women [122]. This scenario shows that approximately $40\%$ of the world’s population was expected to suffer from anemia in 2019. There is a higher prevalence in females than males of any age, and in children less than five years old. Moreover, the greatest occurrence throughout the globe has been reported in Sub-Saharan South Asia, and Central, West, and East Africa [23]. The primary causes were found to be iron deficiency (hookworms, schistosomiasis), sickle cell disease, thalassemia, malaria, and chronic diseases [123].
Physicians usually inquire about the patient’s medical and family history, conduct a physical examination, and perform some tests including a full blood count to diagnose anemia [124]. More concern is usually placed on the hematocrit and hemoglobin levels, as well as the total number of red blood cells present in the patient’s blood, as indicated in Figure 5a. The natural differences between the quantity of blood components present in males and females are presented in Table 4 [97]. It is important to note that the blood counts can possibly be lower in those engaging in or those who have engaged in significant physical activity, particularly in pregnant women or the elderly [125,126]. Smoking and being at higher altitudes can also increase the number [124,125]. The testing process usually requires analyzing the size and content of red blood cells [127,128] as well as the shape and color deviations. A doctor can also prescribe further tests to establish the underlying reason and occasionally examine a sample of bone marrow to determine the existence of anemia, as indicated in Figure 5b [129].
Some patients can exhibit symptoms such as shortness of breath (particularly during physical exercise), weakness, tiredness, palpitations, and headaches [121,122]. Other symptoms—such as heart failure, angina pectoris, intermittent claudication, and disorientation—are more prevalent among the elderly [132,133,134]. Moreover, vision impairment due to retinal hemorrhages can be a serious consequence of anemia, particularly when it develops rapidly [135]. These signals can be classified as either generic or particular. It is also important to note that pallid mucous membranes and nail beds, as shown in Figure 6, are prominent indicators of a hemoglobin concentration below 90 g/L. It is pertinent to state that the color of a person’s skin is not a reliable indicator, but tachycardia, pulse rate, cardiomegaly, and a systolic flow murmur indicate hyperdynamic circulation, particularly at the apex. The symptoms of congestive heart failure can also manifest at any age, but they are more prevalent in older people. Furthermore, certain symptoms are linked to each subtype of anemia, such as koilonychia, sometimes referred to as “spoon nails”, with iron deficiency, jaundice with hemolytic or megaloblastic anemia, foot ulcers with sicklecell and other hemolytic anemias, and skeletal abnormalities with thalassemia major [136]. Koilonychia is usually caused by the deficiency of iron in the body and is classified as a disorder characterized by inwardly curled nails resembling spoons [137]. It is important to note that megaloblastic anemia is the most prevalent kind.
The conjunction of anemic symptoms with severe infection or spontaneous bruising shows the presence of neutropenia or thrombocytopenia, potentially due to bone marrow failure [137]. People with blood hemoglobin levels below the values considered normal for their age and gender are believed to have anemia. Moreover, individual cell size can be used to assess when red blood cells are macrocytic, normocytic, or microcytic. It is also pertinent to state that the cause of anemia can be diagnosed in part by examining the reticulocyte count, the red blood cell shape, and any changes to the white blood cell and/or platelet count [123]. The common clinical manifestations include exertional dyspnea, pale mucous membranes, and tachycardia [138], while the other symptoms associated with some forms of anemia include jaundice and leg ulcers [139]. The aspiration or trephine biopsy of bone marrow can also be used to investigate anemia and a variety of other hematological disorders [140]. It is also possible to conduct specialized examinations such as immunology and cytogenetics on the cells recovered [140].
## 4. Experimental Results
The experiment involved using an ELM model to identify and categorize illness in a dataset of individuals with beta thalassemia trait, iron deficiency anemia, hemoglobin E, and the combinations previously defined. The model parameters utilized are listed in Table 2. The real anemic dataset was used and split into training and testing sets during the experiment, and the classification method applied was evaluated using a Python-written confusion matrix. The process was conducted on an Apple M1 machine with 512GB internal memory and 8GB RAM.
## 4.1. Evaluation Model
The data used were classified into test and training data, and they were both evaluated using the confusion matrix model. This was necessary due to the feasibility of determining the accuracy of classification algorithms using an industry-standard technique. It was discovered that the dataset had five separate classifications, which included the BTT, IDA, HbE, and their combinations. The training data accounted for $67\%$ of the entire data set for the inquiry while test data made up the remaining $33\%$. The assessment conducted was based on the accuracy, precision, sensitivity, and F1 score of the classification algorithm as listed in Equations [12]–[15]. The method of value distribution is highlighted in Table 6.
Table 6 shows that positive data correctly classified by the system are referred to as the “true positive” (TP), negative data correctly identified as negative are referred to as the “true negative” (TN), negative data incorrectly perceived as positive are known as “false negatives” (FN), and “positive” data incorrectly recognized as “positive” are “false positives” (FP).
These values were further used to determine the accuracy, precision, recall, and F1 scores through the following formulas. [ 12]Accuracy: TP+TNTP+TN+FP+FN [13]Precision: TPTP+FP [14]Sensitivity: TPTP+FN [15]F1-Score: 2×Recall × PrecisionRecall + Precision
## 4.2. Experimental Results of Extreme Learning Machine
The ELM classification model applied to categorize the anemia dataset used a single feedforward network with a hidden layer implementation (SLFNs). This strategy reduced the processing time required for the concealed layer. The usefulness of the model was assessed based on accuracy, precision, sensitivity, and the F1 score in classifying anemic datasets. Table 7 shows the model used in the ELM. The findings of the ELM performance model are presented in Table 8, and it was discovered that it performed best on the four-class anemia dataset, with $99.21\%$ accuracy, $99.30\%$ precision, $98.44\%$ sensitivity, and $98.84\%$ F1 score.
The confusion matrix for the model is presented in Table 9, with each row representing an instance of the prediction class while each column indicates an instance of the actual class. The RF approach use n estimators = 400, max features = auto, and entropy. Although entropy is more sophisticated than the Gini index, entropy provides ideal results. In contrast, the KNN technique employs several experiments, including Euclidean distance to determine the distance between classes, and $K = 15$, which is derived from the K error rate calculation. Several tests were conducted by employing polynomial kernels, RBF kernels, and linear kernels in the SVM approach. In linear kernels, optimal outcomes were obtained. The ELM model, which is the approach described in this work, employed the sigmoid activation function, with the number of hidden layers [9] modified based on the number of inputs and outputs, followed by gradient descent to optimize the weights.
ELM was used to optimize the classification process for anemic datasets in order to improve the success rate of the approach as indicated in Table 9. The performance index of each class and the recommended strategy with the highest rate of success are, therefore, presented in Table 10. It was discovered that the random forest, k-nearest neighbor, support vector machine, and extreme learning machine techniques provided the most accurate predictions for the beta thalassemia trait and iron deficiency anemia classes. Moreover, the forecasts for the hemoglobin E and the combination classes were rather correct.
## 5. Discussion
It is very dangerous in the field of medicine to erroneously identify healthy individuals with sickness and vice versa due to the possibility of severe repercussions. This has led to an increase in the usage of data mining technologies for a reliable diagnosis. Therefore, this research used a model of an extreme learning machine to reliably detect and diagnose anemia as well as construct a decision support system to aid clinicians in making decisions.
A total of 127 training and 63 test data were employed, and it was discovered that the ELM approach performed much better than RF, KNN, and SVM as indicated by its $99.21\%$ accuracy, $98.44\%$ sensitivity, $99.30\%$ precision, and $98.84\%$ F1 score compared to RF’s $77.01\%$ accuracy, $90.83\%$ precision, $78.40\%$ recall, and $80.99\%$ F1 score as well as KNN’s $65.42\%$, $59.40\%$, $62.81\%$, and $51.74\%$, respectively. A previous study by [67] used 6935 data with 986 variables and applied two of the five techniques, KNN ($92.36\%$) and RF ($94.16\%$), to classify BTT and IDA into two groups. Another study by [69] predicted the risk of childhood anemia using several machine learning techniques including KNN and RF. The results showed that KNN had a classification accuracy of $61.95\%$, a sensitivity of $65.85\%$, and a specificity of $58.20\%$while RF had $68.5\%$, $70.7\%$, and $66.4\%$, respectively. This means the overall performance of RF was better than KNN in all three aspects. Another research conducted in 2020 [58] showed that RELM had an accuracy of $95.59\%$ when applied to separate 342 patient records into two types of anemia, IDA and BTT. It is important to note that the ELM method was also applied in the research.
Thus, studies have been conducted on the ELM approach, and the concept has progressed to the point where it has shifted from a single hidden layer to a 100-node multilayer hidden layer. This is known as the enhanced improved multilayer extreme learning machine (IML-ELM) with the neural activity occurring both during and after training in the proposed network architecture. Moreover, each layer of the first IML-ELM (IML-ELM1) network was assigned an orthonormal random connection weight while only the very first layer of the second iteration of the IML-ELM contained the random orthonormal connection weights (IML-ELM2). The output weight matrix of the layer was used to calculate the connection weights. The application of the IML-ELM2 assignment method considerably reduced the amount of time required for calculations, and the root mean square error test was observed to have produced 0.627977, 0.104272 ($83\%$), and 0.092685 ($85\%$) [143].
The three studies conducted by [58,67,69] used several machine and deep learning techniques, and RELM was reported to have the highest level of performance with $95.59\%$ in distinguishing two forms of anemia. The outcomes of this experiment conducted using the ELM method have been encouraging, with the anemia classified into four separate subtypes, thereby increasing the diagnostic accuracy to $99.21\%$, precision to $99.30\%$, sensitivity to $98.44\%$, and F1 score to $98.84\%$.
Table 11 compares the findings of this research with those from previous studies based on the accuracy metric. It is important to note that this research divided the patients into four distinct groups, including BTT, IDA, HbE, and combinations, and the differences between these groups were categorized with a greater degree of precision than previous approaches, as indicated by the $99.21\%$ recorded for each class. Future studies are expected to focus on analyzing the characteristics considered to be the most important components of anemia to ensure an easier diagnosis process for physicians using a more ideal system. There is also the need for a technique to identify and recommend appropriate anemia datasets using deep learning.
## 6. Conclusions
It is difficult to distinguish between BTT, IDA, and HbE, as well as combinations of these three variables, due to the variability of the anemia-afflicted population. The introduction of computer models was observed to have ensured the rapid screening of anemia at a lower cost. This research provided a summary of the findings of the health system analysis as well as the challenges and barriers encountered throughout the globe in treating anemia patients by using a thorough analysis. Therefore, an ELM approach was applied to expedite the identification of different kinds of anemia. The method using 190 data and seven parameters was found to have an accuracy, sensitivity, and precision of $99.21\%$, $98.44\%$, and $99.30\%$, respectively, as well as an F1 score of $98.84\%$ using a confusion matrix. This means it has a high performance and can be applied quickly and at a cheaper cost. The ELM approach is believed to have the capacity to supplement current indices and formulas developed to aid in the operations of healthcare professionals.
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|
---
title: Hypergravity Increases Blood–Brain Barrier Permeability to Fluorescent Dextran
and Antisense Oligonucleotide in Mice
authors:
- David Dubayle
- Arnaud Vanden-Bossche
- Tom Peixoto
- Jean-Luc Morel
journal: Cells
year: 2023
pmcid: PMC10000817
doi: 10.3390/cells12050734
license: CC BY 4.0
---
# Hypergravity Increases Blood–Brain Barrier Permeability to Fluorescent Dextran and Antisense Oligonucleotide in Mice
## Abstract
The earliest effect of spaceflight is an alteration in vestibular function due to microgravity. Hypergravity exposure induced by centrifugation is also able to provoke motion sickness. The blood–brain barrier (BBB) is the crucial interface between the vascular system and the brain to ensure efficient neuronal activity. We developed experimental protocols of hypergravity on C57Bl/6JRJ mice to induce motion sickness and reveal its effects on the BBB. Mice were centrifuged at 2× g for 24 h. Fluorescent dextrans with different sizes (40, 70 and 150 kDa) and fluorescent antisense oligonucleotides (AS) were injected into mice retro-orbitally. The presence of fluorescent molecules was revealed by epifluorescence and confocal microscopies in brain slices. Gene expression was evaluated by RT-qPCR from brain extracts. Only the 70 kDa dextran and AS were detected in the parenchyma of several brain regions, suggesting an alteration in the BBB. Moreover, Ctnnd1, Gja4 and Actn1 were upregulated, whereas Jup, Tjp2, Gja1, Actn2, Actn4, Cdh2 and *Ocln* genes were downregulated, specifically suggesting a dysregulation in the tight junctions of endothelial cells forming the BBB. Our results confirm the alteration in the BBB after a short period of hypergravity exposure.
## 1. Introduction
Astronauts are exposed to successive phases of hypergravity phases during the takeoff and landing of spaceflights and due to microgravity in space. The most important and earliest reported symptom, related to days 1–3 of the spaceflight, is space motion sickness due to vestibular dysfunction [1,2,3,4,5,6,7]. The fluid shift is the shift in the distribution of human body fluids due to microgravity exposure. It was proposed to be responsible for space motion sickness. Several ground devices and protocols were rapidly developed to reproduce this phenomenon, such as centrifugation and parabolic flights [8,9]. Moreover, the decreases in plasma volume and cardiac performance, and the increase in intracranial blood pressure participate in vascular deterioration, as recently reviewed [10,11,12]. Furthermore, the alterations in gravity induce cardiovascular adaptations via the modifications in endothelial and smooth muscle vascular cell functions [13,14,15,16,17]. It is noticeable that the effects of centrifugation are only partially described in humans [18,19,20,21,22]. Like during microgravity exposure, hypergravity exposure, from 1.5 to 5 g, affects the vestibular functions [23,24,25,26] and modifies gene expression in the brain [27,28,29] and cognitive performances [30,31,32]. The use of hypergravity by centrifugation is required to qualify the biological effects of space motion sickness.
Likewise, centrifugation, close to 2× g, is also proposed as a countermeasure against the deleterious effects of microgravity seen in humans [33,34]. Therefore, before the exposure of humans to centrifugation, it is important to study its biological impacts.
The cerebral blood vessels are crucial in brain functions regarding oxygen supply and exchanges of nutrients and wastes. The endothelial cells of brain capillaries are organized to form the blood–brain barrier (BBB), assuming the fine-tuning of these exchanges to maintain brain homeostasis [35,36]. The efficacy of the BBB is regulated by the nychthemeral rhythms [37,38,39,40]. BBB alterations are clearly implicated in stroke and neurodegenerative disorders [41,42,43,44].
Gravity changes are able to modify endothelial cell functions [45]. Many in vitro models have been developed to reproduce the BBB [46], and experiments that exposed endothelial cells to gravity modifications revealed miscellaneous results, as reviewed [47]. Depending on hypergravity levels from 3 g to 20 g, endothelial cells modify their gene expression, angiogenesis, cytoskeleton architecture and tube formation [48,49,50,51]. Moreover, in devices that reproduce the barrier function, the effects of short-term exposure to hypergravity remain unclear. In fact, exposure (2 g and 4 g) increases the barrier efficacy, shown by resistance measurements of the endothelial cell culture [52], whereas a higher level (10 g) decreases it, as shown by the increase in fluorescent molecules passing through the culture monolayer [53].
The effects of hypergravity on the capacity of endothelial cells to form a barrier in vitro are insufficient to interpret the modifications in the BBB observed in vivo. More information should be collected in vivo.
In mice exposed to hypergravity at 2 g for 24 h, we measured the transit through the BBB of different fluorescent molecules with different sizes, such as dextrans and antisense oligonucleotides (AS). We also investigated the regulation of expression of genes involved in junctions between endothelial cells.
## 2.1. Animals and Centrifugation
In accordance with the principles of the European community, the experimental protocols were validated by the local ethics committee (CEEA-Loire, APAFIS #38819), the animal welfare committee of PLEXAN (PLateforme d’EXpérimentations et d’ANalyses, Faculty of Medicine, Université Jean Monnet, Saint Etienne, France, agreement n°42-18-0801) and the French Ministry of Research. In this study, 86 male C57BL/6JRJ mice (8 weeks old, 22.5 ± 0.1 g, Janvier Labs, France) were used. The animals were housed (3 mice per cage) in standard conditions (22 °C, humidity $55\%$; 12 h/12 h day/night cycle; unlimited access to food and water). They were familiarized with the centrifugation room the week before the experiments and monitored by video in the centrifuge. In order to expose all the animals to the same environmental conditions, the mice were centrifuged at 2× g for 24 h, and the control mice in normogravity at 1 g for 24 h were placed simultaneously in the experimental room. The centrifugation protocol was detailed in our previous publication.
## 2.2. In Vivo Injection of Antisense Oligonucleotide and Dextrans
All the fluorescent molecules were diluted in saline solution (sodium chloride 9 g/L) and retro-orbitally injected in the blood, under isoflurane anesthesia ($5\%$). In our hands, this route of administration is safer (more rapid, efficient and reproducible) than other routes of i.v. administration. Sham mice were injected with vehicle solution. Mice received only one injection with one fluorescent tracer. Phosphorothioate antisense oligonucleotide directed against angiopoietin-2 (Angpt2, named AS, GCG-TTA-GAC-ATG-TAG-GG, 6084.9 g/mol, Eurogentec) was coupled to 5-carboxyfluorescein (excitation: 492 nm, blue light; emission: 518 nm, green light) and injected (18 mg/Kg). Fluorescein isothiocyanate-dextrans D40, D70 and D150 (FD40-100MG, FD70S-100MG, FD150S-1G, respectively, Sigma-Aldrich, St. Louis, MI, USA) were solubilized in vehicle (2× g/100 mL to be injected retro-orbitally at 150 mg/Kg, near 200 µL/mouse). Fluorescein isothiocyanate-dextrans were maximally excited at 490 nm (blue) and maximally emitted at 525 nm (green).
## 2.3. Collection of Biological Samples
Mice were randomly killed by lethal intraperitoneal injection of sodium pentobarbital (Euthasol, 175 mg/Kg, i.p.), within 2 h after stopping the centrifuge. Before intracardiac perfusion, a catheter was introduced in the right atrium, and blood samples were collected and placed in microtubes. Finally, mice were perfused intracardiacally (5 mL/min) with 30 mL of phosphate-buffered saline (0.01 M PBS, pH: 7.4) to discard blood cells and residual fluorescence of the injected tracers into vessel lumen. This step was followed by 30 mL formalin solution ($10\%$, Merck, HT501128) to fix the tissues. Brains and the left lobe of livers were dissected and post-fixed for 24 h in a formalin solution at room temperature, placed for 48 h in a $30\%$ sucrose–PBS solution at 4 °C and cryopreserved before being sliced.
## 2.4. Corticosterone Assay
The microtubes containing blood samples were centrifuged (10 min at 2000× g) and 20 µL of serum was collected. Some serum samples were excluded due to hemolysis. The others ($$n = 60$$) were used for corticosterone assay (ELISA kit, K014, Arbor Assays, Ann Arbor, MI, USA), following the protocol of the supplier.
## 2.5. Histology
Using a freezing microtome (frigomobil, Reichert-Jung), coronal sections of the brain (40 μm thick) were made. Olfactory bulbs were removed, and 3.2 mm after beginning the rostro-caudal slicing, the new slices were collected and placed individually in 48-well plates. To ensure reproducibility, we anatomically selected three similar brain slices for each mouse. Using a binocular device, the slices corresponding to interaural 1.98 mm; Begma −1.82 mm of the Atlas of the mouse brain in stereotaxic coordinates [54] were retained. Indeed, the medial habenular nuclei and mammillothalamic tract were anatomical landmarks, as well as the form and volume of the hippocampus. In the same manner, the left lobes of the liver were sliced (40 µm), and three slices per mouse were mounted. All the floating sections were incubated for 10 min in DAPI (4′,6-diamidino-2-phenylindole, 1:250,000, Interchim, Mannheim, Germany) and rinsed twice in PBS (10 and 20 min, respectively). Finally, they were mounted on glass slides (Superfrost) with a handmade medium based on Mowiol. All slices were DAPI-labeled and mounted on the same day. Slices presenting red blood cells in capillaries in ROI were excluded to reduce experimental bias [55].
## 2.6. Image Acquisition
The fluorescence of labeled brain slices was observed by confocal microscopy (SP5, Leica Microsystems, Wetzlar, Germany) and the slide scanner Nanozoomer (2.0 HT, Hamamatsu Photonics, Shizuoka Prefecture, Japan). The Nanozoomer contains a fluorescence imaging module using objective UPS APO 20X NA 0.75 combined with an additional lens 1.75X. Virtual slides were acquired with a TDI-3 CCD camera. The fluorescent acquisitions were conducted with a mercury lamp (LX2000 200W—Hamamatsu Photonics, Massy, France), and the set of filters adapted for DAPI and FITC/FAM fluorescence were usable for both fluorescein isothiocyanate-dextrans and 5-carboxyfluorescein antisense oligonucleotide. The DAPI labeling, revealing the double strain of DNA in the cell nuclei, was used for the automated focus required for Nanozoomer imaging. To reduce bias, all images (slices from control and centrifuged mice) were performed randomly in one batch. To localize antisense oligonucleotides in the brain and liver tissues, some images were acquired with SP5 confocal microscope. In this case, fluorescent molecules were excited with the 488 nm line of Argon laser and all acquisition parameters were kept constant.
## 2.7. Fluorescence Analyses
Several types of fluorescence analyses were double-blindly performed on Nanozoomer images. To evaluate the intensity level of fluorescence, the ndpi files generated by Nanozoomer were converted into tiff images with NDPI software (version 2). The tiff files were opened with Fiji software 2.9.0, and the intensity levels were measured in regions of interest (ROI defined as red circle of 960 µm2 in Figures and placed on the hippocampus (HPC), dorsal thalamic nuclei (THAL) and the retrosplenial and ectorhinal cortices (DCx and LCx, respectively) on both hemispheres of the three slices. No filter settings were applied to the images and we checked that the images did not have any saturated dots. The mean of fluorescence was calculated for each mouse and reported in the statistical analysis. A similar analysis was performed in three liver slices. Five ROI were randomly placed on each slice. Moreover, the image analysis of fluorescent spots was performed with QuPath directly on the ndpi files. The software is able to identify and localize fluorescent spots. We empirically determined parameters to segregate fluorescent spots in brain slices from 5 mice (control and centrifuged mice) and we applied these parameters to the project containing the entire sample. The parameters were: pixel size 0.5 µm, background radius 30 µm, median filter radius 0, sigma 1, minimum area 5 µm2, maximal area 1000 µm2 and threshold 7. The collected data were attributed to experimental groups (2 g vs. 1 g) and compared statistically. The analyses, reported, were performed on the ROI anatomically defined as HPC (hippocampus), THAL (grouping all medio-dorsal and lateral thalamic nuclei), DCx (containing retrosplenial cortices), SoCx (containing somatosensorial cortices) and PirCx (containing piriform cortices). A similar analysis was performed on the left lobe liver slices.
## 2.8. Gene Expression by RT-qPCR
For this experiment, 16 mice were used (8 were exposed to 2 g and 8 to 1 g, as described before). They were anesthetized with isoflurane $5\%$ and decapitated, and the brains were directly frozen and stored at −80 °C. Hippocampus were dissected on ice and placed in 2 mL tubes containing 500 µL of Tri-reagent (MRCgene) and 10 ceramic beads (diameter 1.5 mm). Samples were mashed in a Beadbug6 shaker (Benchmark, 3 cycles, level of speed 4350 and 60 s time). RNA was isolated, following the instruction of the protocol elaborated by MRCgene. The concentration of RNA was measured with Nanodrop (Thermoscientific, Waltham, MA, USA) and adjusted close to 100 ng/µL. The cDNA was produced with the RT-i-script gDNA clear cDNA synthesis kit (Bio-Rad’s reference 1725035), using 100 ng of RNA and following the protocol from the supplier. The qPCR was performed using the endothelial cell contacts by junction M96 (predesigned for use with SYBR green; Bio-Rad’s plate reference 10029202) and the Sso-advanced universal SYBR green PCR kit (Bio-Rad’s reference 1725275). The qPCR was performed with CFX96 thermocycler (Bio-Rad). Samples were allocated randomly in plates, and some of them were tested twice to verify the quality of the experiment. The validation of Hprt and Gapdh as reference genes was evaluated with CFX Maestro software (Bio-Rad). The analysis of gene expression was performed on Actb, Actg1, Actn1, Actn2, Actn4, Cdh2, Cdh5, Cldn1, Cldn3, Cldn5, Ctnna1, Ctnnb1, Ctnnd1, Dsp, F11r, Gja1, Gja4, Gja5, Jam2, Jup, Ocln, Tjp1, Tjp2 and Vim. The threshold of the regulation by hypergravity on gene expression was chosen at 1.5. To discuss the RT-qPCR results, we checked the brain localization, cell types expressing genes and function of proteins encoded by these genes in endothelial cells using databases: https://www.proteinatlas.org; http://mousebrain.org; http://betsholtzlab.org and https://www.informatics.jax.org (accessed on 26 January 2023).
## 2.9. Statistical Analysis
The data were statistically compared using paired t-tests, non-parametric Mann–Whitney test, or one- and two-way ANOVA with post hoc comparisons when applicable. The normogravity (1 g) is the control condition. The software used was GraphPad Prism V9, which calculated the p value as the probability of observing two identical conditions. If $p \leq 0.05$, the two compared conditions were considered statistically different.
## 3.1. Effects of Centrifugation on Mice
The body weight gain, expressed as the difference in weight in a 24 h period (Figure 1), is the difference in body weight measured before and after exposure to centrifugation (2× g) or control conditions (1 g). As expected, the exposure to centrifugation induced a decrease in body weight (Figure 1A, $p \leq 0.0001$).
More precisely, the decrease in body weight was similar in mice injected with saline solution (Sham) and solution containing fluorescent antisense oligonucleotide directed against angiopoietin-2 (AS) (two-way ANOVA coupled with Sidak post hoc test, interaction $$p \leq 0.0009$$; 1 g vs. 2 g: $p \leq 0.0001$; sham vs. AS $$p \leq 0.589$$; Figure 1B). The decrease in body weight gain due to centrifugation was similarly observed in mice injected with dextrans (D40, D70 and D150, one-way ANOVA coupled with Sidak post hoc test, 1 g vs. 2 g: $p \leq 0.0001$; $p \leq 0.05$ for comparison of 1 g groups as well as for 2 g groups, Figure 1C). The effects observed in mice injected with AS or dextrans (D40, D70 and D150) were similar (statistical analysis shown in Figure 1C). In conclusion, the injection of fluorescent tracers did not influence the effect of centrifugation on body weight gain. To explain the decrease in body weight, we also measured the food and water consumption. As shown in Figure 1D and 1E, the comparison of food and water consumption, respectively, during the day before the centrifugation with the consumption during the 24 h of centrifugation exposure showed that both food and water consumption specifically decreased in the group exposed to the centrifugation (two-way ANOVA, time x gravity $$p \leq 0.0001$$ for both parameters).
The stress was evaluated by the concentration of corticosterone in plasma. The comparison between 1 g and 2 g conditions, including all the samples, did not reveal a variation in corticosterone concentration (Figure 2A, Mann–Whitney test, $$p \leq 0.255$$). We also separately analyzed the corticosterone concentration in each experimental group. In mice injected with saline solution (Sham), AS, D40, D70 and D150, the centrifugation had no significant effect on the corticosterone concentration (Figure 2B, one-way ANOVA, $$p \leq 0.278$$). In conclusion, centrifugation at 2× g did not modify the plasma concentration of corticosterone in mice injected with fluorescent tracers.
## 3.2. Effects of Centrifugation on Extravasation of Fluorescent Dextrans in Brain
The extravasation of dextrans through the BBB were firstly evaluated by the analysis of fluorescence intensities of several brain areas. To minimize local variations, we performed all analyses on slices containing similar anatomical landmarks. The regions of interest were distributed in different cerebral areas (red ROI in thalamus, hippocampus and dorsal and lateral cortices, Figure 3).
The centrifugation was not able to modify the fluorescent levels in THAL (Figure 3A, Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40, $$p \leq 0.93$$; for D70 $$p \leq 0.29$$ and for D150 $$p \leq 0.069$$), HPC (Figure 3B, Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40 $$p \leq 0.53$$; for D70 $$p \leq 0.76$$ and for D150 $$p \leq 0.089$$) and LCx (Figure 3C, Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40 $$p \leq 0.50$$; for D70 $$p \leq 0.08$$ and for D150 $$p \leq 0.16$$). In DCx, the hypergravity can increase the level of fluorescence only in D70 (Mann–Whitney tests comparing 1 g vs. 2 g conditions for D40 $$p \leq 0.051$$; for D70 $$p \leq 0.040$$ and for D150 $$p \leq 0.050$$; Figure 3D). The differences in D70 fluorescence across brain sections are illustrated in Figure 3E. More marked fluorescence diffusion is observed in the DCx of 2 g-exposed mice. In conclusion, these sets of data analysis suggested that centrifugation significantly increased the presence of D70 in DCx.
## 3.3. Effects of Centrifugation on Extravasation of Fluorescent AS in Liver
We tested the ability of hypergravity to promote the passage of a molecule that can be captured by liver parenchyma cells. To test this hypothesis, we injected mice with fluorescent antisense oligonucleotides and compared the 1 g and 2 g conditions. The same quantification methods used for dextrans were applied on images obtained with Nanozoomer (Figure 4A).
A significant increase in fluorescence in liver parenchyma was revealed in mice exposed to hypergravity (Figure 4B, Mann–Whitney tests, 1 g vs. 2 g $$p \leq 0.0291$$). Moreover, the number of areas containing fluorescence evaluated with QuPath was higher in 2 g in comparison with 1 g (Figure 4C, Mann–Whitney tests, 1 g vs. 2 g $p \leq 0.0001$). With confocal microscopy, the presence of AS was qualitatively revealed as spots of fluorescence close to vessel walls in the liver parenchyma. Taken together, these results strongly suggest that hypergravity increased the AS extravasation in the liver parenchyma.
## 3.4. Effects of Centrifugation on Extravasation of Fluorescent AS in Brain
The qualitative analysis of images obtained with Nanozoomer and confocal SP5 showed fluorescent spots in the brain parenchyma only in slices from 2 g-exposed mice (Figure 5A and Figure 6A). The confocal images also revealed that these fluorescent spots were more localized in the brain parenchyma close to the vessel walls (Figure 5A, right panel).
The quantitative analyses of images from Nanozoomer showed an increase in fluorescence level in HPC and DCx due to hypergravity exposure (Mann–Whitney tests, 1 g vs. 2 g in THAL $$p \leq 0.369$$, in HPC $$p \leq 0.033$$, in DCx $$p \leq 0.016$$ and in LCx $$p \leq 0.265$$; Figure 5B).
The analysis with QuPath software was used to segregate fluorescent areas from the background in several brain regions (Figure 6A) using the same filtering parameters in both 1 g and 2 g conditions. The analyses confirmed that the exposure to hypergravity increased the number of fluorescent spots in HPC and DCx, but not in THAL (Mann–Whitney tests, 1 g vs. 2 g in THAL $$p \leq 0.0536$$, in HPC $$p \leq 0.0003$$ and in DCx $p \leq 0.0001$; Figure 6B). Moreover, it also revealed an increase in the number of fluorescent spots in SoCx and PirCx (Mann–Whitney tests, 1 g vs. 2 g $p \leq 0.0001$ and $$p \leq 0.0024$$, respectively; Figure 6B).
In conclusion, our data suggest that hypergravity induced a BBB leakage able to increase the presence of AS in brain parenchyma.
## 3.5. Effects of Centrifugation on Expression of Genes Involved in Endothelial Cells Interactions
Using Hprt and Gapdh as reference genes, the RT-qPCR analysis of the expression of genes involved in the regulation of endothelial cells interactions revealed that Gja4, Ctnnd1 and Actn1 were upregulated. Cdh2 was downregulated more than 2-fold and Ocln, Actn2, Jup, Actn4, Tjp2 and Gja1 were downregulated between 1.5- and 2-fold (Figure 7). The expressions of Actb, Actg1, Cdh5, Cldn1, Cldn5, Ctnna1, Ctnnd1, Dsp, F11r, Gja5, Jam2, Tjp1 and Vim were considered not altered (less than 1.5-fold modification), and Cldn3 appeared not expressed. The names, functions and cell types expressing these genes are summarized in Table 1 and supplementary Table S1.
## 4. Discussion
In the present study, our results suggest that hypergravity induces an increase in BBB permeability, allowing the passage of antisense oligonucleotides as well as dextran from blood to brain parenchyma. Moreover, the RT-qPCR experiments suggested an alteration in the expression of genes involved in endothelial cell junctions.
In a ground model of hindlimb unloaded animals without [56] or in combination with radiation [57], as well as during spaceflight [58], the BBB was altered, suggesting that vestibular regulations were involved. As reviewed recently, the increase in gravity by centrifugation modifies vestibular function and induces motion sickness [59]. Our experiments confirm the decrease in body weight generated by centrifugation [18,60]. It is linked with the decrease in food intake [61], and probably linked to vestibular impairments [18,62].
Hypergravity exposure at 2 g increases the corticosterone concentration when it is measured during the first hour of exposure [63]. The increase in the hypergravity level can transiently increase the plasma corticosterone level [64]. Nevertheless, as our data showed, after 24 h of weak exposure at 2 g, the corticosterone levels were not altered in the first hour following the stop of the centrifuge [65]. The stress induced by the centrifugation is controversial and probably depends on the design of the centrifuge and experimental procedure with animals [18,27,63]. Moreover, our data showed a large spread of individual values of corticosterone concentration, confirming other studies [18,23,65].
In motion sickness, the relationship between brain and intestinal functions were known and clearly demonstrated, including microgravity and hypergravity models [66,67,68]. The most probable link is hypophagia. In mice and rats, the decrease in food intake was observed at the beginning of the 2 g exposure (first two days) and depended on the vestibular organ [18,69].
The hypophagia could have several causes, including: 1. modifications in microbiota [70] that can decrease the gastric acid synthesis [71], 2. metabolism dysregulation, such as decreases in leptin and insulin plasma concentrations [60] and 3. modifications in the expression of the starvation-induced genes [72]. Moreover, the serotonin pathways are involved in this phenomenon [69,73]. In conclusion, our results also confirm that the hypophagia induced a decrease in body weight. This is more related to the hypergravity and not related to an increase in corticosterone levels [30,60,62,65].
Fluorescent polysaccharides such as dextrans are safe at low concentrations, available in sizes from 3 to 2000 kDa. They can be used to study BBB permeability [74,75,76] and to determine the size of a BBB leak [77,78,79]. After 24 h exposure to 2 g hypergravity, our results demonstrated that 70 kDa dextran can be exported in cortex parenchyma, but not 40 or 150 kDa dextran. The lower molecular weight dextrans, the faster they are excreted. In fact, in less than one hour, dextrans between 30 and 40 kDa were excreted in urine, whereas the 62 kDa dextran was always present in the blood circulation and not highly present in urine [80]. This suggests that after 24 h, the 40 kDa dextran would be excreted. Thereby, the BBB leak required more than one hour of hypergravity exposure, confirming our previous data showing that short exposure (1–9 min at 5 g) was not efficient in destabilizing the BBB [65]. Because we cannot exclude an alteration in urine excretion of dextran in the hypergravity context, our data should be completed by the evaluation of the kinetics of dextran excretion in centrifuged mice.
Because of the molecule shape, 150 kDa dextran was unable to flow from the circulation to the tissues in physiological conditions [80]. Our results showed that the BBB leak is not sufficient for 150 kDa dextran extravasation, suggesting that this leak was not comparable to the BBB disruption induced by stroke or acute hypertension [81]. In our previous study [65], the extravasation of IgG (around 150 kDa) was measured, suggesting that the nature of the molecule is also a crucial parameter. Moreover, our data showing the extravasation of antisense oligonucleotide in the cortex and hippocampus confirm that the BBB properties depend on the brain areas and the chemical nature of the markers [82,83,84]. In conclusion, our results showed an increase in the transfer of fluorescent molecules from blood to tissues, suggesting a global modification in effluxes due to hypergravity.
To assess the alterations in the BBB in centrifuged mice, we focused the molecular investigation on gene expression using a set of primers targeting consensual genes involved in BBB efficacy. As reviewed recently [85,86], all of the proteins encoded by the genes studied here are involved in the scaffoldings required to maintain endothelial cell interactions to create the BBB, as well as in the initiation of angiogenesis and/or vascular repair. The database queries concerning the expression level in non-neuronal cells of the brain indicated that the proteins encoded by studied genes are also expressed in endothelial cells, but not exclusively (Supplementary Table S2). As expected, the modifications in gene expression are related to the durations of centrifugation and the levels of hypergravity, as suggested by the comparison between this current study and the RNAseq performed previously on the same device and the same mouse strains [29]. Moreover, the regulation of gene expression is not comparable to acute and chronic stress (Supplementary Table S3). Globally, the observed modifications could be interpreted as a specific dysregulation of gene expression that can alter the turnover and replacement of proteins involved in BBB efficacy as observed in BBB disruption models such as stroke, middle cerebral artery occlusion or hypoxia.
## 5. Conclusions
This work suggests that the modification in gravity, which is accompanied by a modification in the vestibular functions, leads to an alteration in the BBB via a modification in the expression of genes which code the proteins in the junctions between the endothelial cells. As now studied, an alteration in the BBB, and not its destruction, allows the passage of molecules defined by their sizes and their chemical natures. Our work insists on this point; an alteration in the BBB is characterized according to the means of study, i.e., markers and measurement methods. This can be considered in two antagonistic ways, either as a minimally invasive physical means of crossing the BBB by molecules of therapeutic interest or, on the contrary, as something deleterious that can be found in the pathology of alterations in vestibular functions during spaceflight. The most important limit of this study is that the RT-qPCR was performed on RNA extracted from whole brain, and the query of hipposeq.janelia.org indicated that we cannot exclude the alteration in molecular scaffolding of synapses also implicating these genes. Finally, our study can be considered an extension of studies relating to the effectiveness of molecules to modulate the passage across the BBB. In a hypergravity context, but also in other models of alteration in vestibular functions, the transduction pathways involved in alterations in the BBB should also be investigated. For example, the angiopoietin-2 pathway is crucial for endothelial cell disassembly [87], and GPCR internalization in endothelial cells [88] should be considered in the context of centrifugation. The last topic that we can investigate is the effects of gravity modulation on angiogenesis, which is required to renovate the endothelium and form new brain capillaries. In fact, experiments on cultured endothelial cells have suggested that hypergravity reduces their capacity to form tubes and alters their responses to angiogenic factors [48,49,50,51]. In centrifugation as well as during parabolic flights, the in vivo responses to angiogenic factors have not yet been investigated. Moreover, it has been shown that during the takeoff and landing of a space module (BION-M 1), hypergravity induces cardiovascular changes [89]. More experiments should be conducted to precise how these cardiovascular changes can modify the structure of the BBB and neurovascular unit functions. To restore physiological functions after spaceflight or bed-rest in humans, a daily sequence of short exposure to centrifugation close to 2× g has been proposed. It is crucial to verify if this protocol has any effect on the BBB. Recently, biomarkers of BBB alteration have been listed [90], and their investigation in the spaceflight context should be performed. Finally, centrifugation can be considered to potentiate vectorization and should be used to investigate cell functions with antisense oligonucleotides in pathophysiological contexts.
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