Investigating the association of CD36 gene polymorphisms (rs1761667 and rs1527483) with T2DM and dyslipidemia: Statistical analysis, machine learning based prediction, and meta-analysis

Cluster of differentiation molecules in the metabolic syndrome

Metabolic syndrome (MetS) represents a significant public health concern due to its association with an increased risk of cardiovascular disease, type 2 diabetes, and other serious health conditions. Despite extensive research, the underlying molecular mechanisms contributing to MetS pathogenesis remain elusive. This review aims to provide a comprehensive overview of the molecular mechanisms linking MetS and cluster of differentiation (CD) markers, which play critical roles in immune regulation and cellular signaling. Through an extensive literature review with a systematic approach, we examine the involvement of various CD markers in MetS development and progression, including their roles in adipose tissue inflammation, insulin resistance, dyslipidemia, and hypertension. Additionally, we discuss potential therapeutic strategies targeting CD markers for the management of MetS. By synthesizing current evidence, this review contributes to a deeper understanding of the complex interplay between immune dysregulation and metabolic dysfunction in MetS, paving the way for the development of novel therapeutic interventions.

Genotypic and Allelic Distribution of the CD36 rs1761667 Polymorphism in High-Level Moroccan Athletes: A Pilot Study

Previous studies have shown that variations in the CD36 gene may affect phenotypes associated with fat metabolism as the CD36 protein facilitates the transport of fatty acids to the mitochondria for oxidation. However, no previous study has tested whether variations in the CD36 gene are associated with sports performance. We investigated the genotypic and allelic distribution of the single-nucleotide polymorphism (SNP) rs1761667 in the CD36 gene in elite Moroccan athletes (cyclists and hockey players) in comparison with healthy non-athletes of the same ethnic origin. Forty-three Moroccan elite male athletes (nineteen cyclists and twenty-four field hockey players) belonging to the national teams of their respective sports (athlete group) were compared to twenty-eight healthy, active, male university students (control group). Genotyping of the CD36 rs1761667 (G>A) SNP was performed via polymerase chain reaction (PCR) and Sanger sequencing. A chi-square (χ2) test was used to assess the Hardy-Weinberg equilibrium (HWE) and to compare allele and genotype frequencies in the "athlete" and "control" groups. The genotypic distribution of the CD36 rs1761667 polymorphism was similar in elite athletes (AA: 23.81, AG: 59.52, and GG: 16.67%) and controls (AA: 19.23, AG: 69.23, and GG: 11.54%; χ2 = 0.67, p = 0.71). However, the genotypic distribution of the CD36 rs1761667 polymorphism was different between cyclists (AA: 0.00, AG: 72.22, and GG: 27.78%) and hockey players (AA: 41.67, AG: 50.00, and GG: 8.33%; χ2 = 10.69, p = 0.004). Specifically, the frequency of the AA genotype was significantly lower in cyclists than in hockey players (p = 0.02). In terms of allele frequency, a significant difference was found between cyclists versus field hockey players (χ2 = 7.72, p = 0.005). Additionally, there was a predominance of the recessive model in cyclists over field hockey players (OR: 0.00, 95% CI: 0.00-0.35, p = 0.002). Our study shows a significant difference between cyclists and field hockey players in terms of the genotypic and allelic frequency of the SNP rs1761667 of the CD36 gene. This divergence suggests a probable association between genetic variations in the CD36 gene and the type of sport in elite Moroccan athletes.

An environment-wide association study for the identification of non-invasive factors for type 2 diabetes mellitus: Analysis based on the Henan Rural Cohort study

AIM

To explore the influencing factors of Type 2 diabetes mellitus (T2DM) in the rural population of Henan Province and evaluate the predictive ability of non-invasive factors to T2DM.

METHODS

A total of 30,020 participants from the Henan Rural Cohort Study in China were included in this study. The dataset was randomly divided into a training set and a testing set with a 50:50 split for validation purposes. We used logistic regression analysis to investigate the association between 56 factors and T2DM in the training set (false discovery rate < 5 %) and significant factors were further validated in the testing set (P < 0.05). Gradient Boosting Machine (GBM) model was used to determine the ability of the non-invasive variables to classify T2DM individuals accurately and the importance ranking of these variables.

RESULTS

The overall population prevalence of T2DM was 9.10 %. After adjusting for age, sex, educational level, marital status, and body measure index (BMI), we identified 13 non-invasive variables and 6 blood biochemical indexes associated with T2DM in the training and testing dataset. The top three factors according to the GBM importance ranking were pulse pressure (PP), urine glucose (UGLU), and waist-to-hip ratio (WHR). The GBM model achieved a receiver operating characteristic (AUC) curve of 0.837 with non-invasive variables and 0.847 for the full model.

CONCLUSIONS

Our findings demonstrate that non-invasive variables that can be easily measured and quickly obtained may be used to predict T2DM risk in rural populations in Henan Province.

A machine learning approach to personalized predictors of dyslipidemia: a cohort study

Introduction

Mexico ranks second in the global prevalence of obesity in the adult population, which increases the probability of developing dyslipidemia. Dyslipidemia is closely related to cardiovascular diseases, which are the leading cause of death in the country. Therefore, developing tools that facilitate the prediction of dyslipidemias is essential for prevention and early treatment.

Methods

In this study, we utilized a dataset from a Mexico City cohort consisting of 2,621 participants, men and women aged between 20 and 50 years, with and without some type of dyslipidemia. Our primary objective was to identify potential factors associated with different types of dyslipidemia in both men and women. Machine learning algorithms were employed to achieve this goal. To facilitate feature selection, we applied the Variable Importance Measures (VIM) of Random Forest (RF), XGBoost, and Gradient Boosting Machine (GBM). Additionally, to address class imbalance, we employed Synthetic Minority Over-sampling Technique (SMOTE) for dataset resampling. The dataset encompassed anthropometric measurements, biochemical tests, dietary intake, family health history, and other health parameters, including smoking habits, alcohol consumption, quality of sleep, and physical activity.

Results

Our results revealed that the VIM algorithm of RF yielded the most optimal subset of attributes, closely followed by GBM, achieving a balanced accuracy of up to 80%. The selection of the best subset of attributes was based on the comparative performance of classifiers, evaluated through balanced accuracy, sensitivity, and specificity metrics.

Discussion

The top five features contributing to an increased risk of various types of dyslipidemia were identified through the machine learning technique. These features include body mass index, elevated uric acid levels, age, sleep disorders, and anxiety. The findings of this study shed light on significant factors that play a role in dyslipidemia development, aiding in the early identification, prevention, and treatment of this condition.

Differential Roles of CD36 in Regulating Muscle Insulin Response Depend on Palmitic Acid Load

The possible role of fatty acid translocase (CD36) in the treatment of obesity has gained increasing research interest since researchers recognized its coordinated function in fatty acid uptake and oxidation. However, the effect of CD36 deficiency on intracellular insulin signaling is complex and its impact may depend on different nutritional stresses. Therefore, we investigated the various effects of CD36 deletion on insulin signaling in C2C12 myotubes with or without palmitic acid (PA) overload. In the present work, we reported the upregulated expression levels of CD36 in the skeletal muscle tissues of obese humans and mice as well as in C2C12 myotubes with PA stimulation. CD36 knockdown using RNA interference showed that insulin signaling was impaired in CD36-deficient C2C12 cells in the absence of PA loading, suggesting that CD36 is essential for the maintenance of insulin action, possibly resulting from increased mitochondrial dysfunction and endoplasmic reticulum (ER) stress; however, CD36 deletion improved insulin signaling in the presence of PA overload due to a reduction in lipid overaccumulation. In conclusion, we identified differential roles of CD36 in regulating muscle insulin response under conditions with and without PA overload, which provides supportive evidence for further research into therapeutic approaches to diabetes.

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.