Epigenetic associations of GPNMB rs199347 variant with alcohol consumption in Parkinson's disease.

Introduction: Alcohol consumption can induce a neuroinflammatory response and contribute to the progression of neurodegeneration. However, its association with Parkinson's disease (PD), the second most common neurodegenerative disorder, remains undetermined. Recent studies suggest that the glycoprotein non-metastatic melanoma protein B (GPNMB) is a potential biomarker for PD. We evaluated the association of rs199347, a variant of the GPNMB gene, with alcohol consumption and methylation upstream of GPNMB. Methods: We retrieved genetic and DNA methylation data obtained from participants enrolled in the Taiwan Biobank (TWB) between 2008 and 2016. After excluding individuals with incomplete or missing information about potential PD risk factors, we included 1,357 participants in our final analyses. We used multiple linear regression to assess the association of GPNMB rs199347 and chronic alcohol consumption (and other potential risk factors) with GPNMB cg17274742 methylation. Results: There was no difference between the distribution of GPNMB rs199347 genotypes between chronic alcohol consumers and the other study participants. A significant interaction was observed between the GPNMB rs199347 variant and alcohol consumption (p = 0.0102) concerning cg17274742 methylation. Compared to non-chronic alcohol consumers with the AA genotype, alcohol drinkers with the rs199347 GG genotype had significantly lower levels (hypomethylation) of cg17274742 (p = 0.0187). Conclusion: Alcohol consumption among individuals with the rs199347 GG genotype was associated with lower levels of cg17274742 methylation, which could increase expression of the GPNMB gene, an important neuroinflammatory-related risk gene for PD.

Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors.

OBJECTIVE: To develop and validate machine learning models for predicting coronary artery disease (CAD) within a Taiwanese cohort, with an emphasis on identifying significant predictors and comparing the performance of various models. METHODS: This study involved a comprehensive analysis of clinical, demographic, and laboratory data from 8,495 subjects in Taiwan Biobank (TWB) after propensity score matching to address potential confounding factors. Key variables included age, gender, lipid profiles (T-CHO, HDL_C, LDL_C, TG), smoking and alcohol consumption habits, and renal and liver function markers. The performance of multiple machine learning models was evaluated. RESULTS: The cohort comprised 1,699 individuals with CAD identified through self-reported questionnaires. Significant differences were observed between CAD and non-CAD individuals regarding demographics and clinical features. Notably, the Gradient Boosting model emerged as the most accurate, achieving an AUC of 0.846 (95% confidence interval [CI] 0.819-0.873), sensitivity of 0.776 (95% CI, 0.732-0.820), and specificity of 0.759 (95% CI, 0.736-0.782), respectively. The accuracy was 0.762 (95% CI, 0.742-0.782). Age was identified as the most influential predictor of CAD risk within the studied dataset. CONCLUSION: The Gradient Boosting machine learning model demonstrated superior performance in predicting CAD within the Taiwanese cohort, with age being a critical predictor. These findings underscore the potential of machine learning models in enhancing the prediction accuracy of CAD, thereby supporting early detection and targeted intervention strategies. TRIAL REGISTRATION: Not applicable.

Epigenetic regulation of Parkinson's disease risk variant GPNMB cg17274742 methylation by sex and exercise from Taiwan Biobank.

Background: Parkinson's disease (PD) is a complex neurodegenerative disease with an elusive etiology that involves the interaction between genetic, behavioral, and environmental factors. Recently, epigenetic modifications, particularly DNA methylation, have been recognized to play an important role in the onset of PD. Glycoprotein non-metastatic melanoma protein B (GPNMB), a type I transmembrane protein crucial for immune cell activation and maturation, has emerged as a potential biomarker for the risk of PD. This research aims to investigate the influence of exercise and gender on the regulation of methylation levels of GPNMB cg17274742 in individuals. Methods: We analyze data from 2,474 participants in the Taiwan Biobank, collected from 2008 and 2016. Methylation levels at the GPNMB cg17274742 CpG site were measured using Illumina Infinium MethylationEPIC beads. After excluding individuals with incomplete data or missing information on possible risk factors, our final analysis included 1,442 participants. We used multiple linear regression models to assess the association between sex and exercise with adjusted levels of GPNMB cg17274742 for age, BMI, smoking, drinking, coffee consumption, serum uric acid levels, and hypertension. Results: Our results demonstrated that exercise significantly influenced the methylation levels of GPNMB cg17274742 in males (ß = -0.00242; p = 0.0026), but not in females (ß = -0.00002362; p = 0.9785). Furthermore, male participants who exercised showed significantly lower levels of methylation compared to the reference groups of the female and non-exercising reference groups (ß = -0.00357; p = 0.0079). The effect of the interaction between gender and exercise on the methylation of GPNMB cg17274742 was statistically significant (p = 0.0078). Conclusion: This study suggests that gender and exercise can modulate GPNMB cg17274742, with hypomethylation observed in exercise men. More research is needed to understand the underlying mechanisms and implications of these epigenetic changes in the context of risk and prevention strategies.

Risk of gout among Taiwanese adults with ALDH-2 rs671 polymorphism according to BMI and alcohol intake.

BACKGROUND: Gout stems from both modifiable and genetic sources. We evaluated the risk of gout among Taiwanese adults with aldehyde dehydrogenase-2 (ALDH2) rs671 single nucleotide polymorphism (SNP) according to body mass index (BMI) and alcohol drinking. METHODS: We obtained information of 9253 individuals having no personal history of cancer from the Taiwan Biobank (2008-2016) and estimated the association between gout and independent variables (e.g., rs671, BMI, and alcohol drinking) using multiple logistic regression. RESULTS: Alcohol drinking and abnormal BMI were associated with a higher risk of gout whereas the rs671 GA+AA genotype was associated with a lower risk. The odds ratios (ORs) and 95% confidence intervals (CIs) were 1.297 and 1.098-1.532 for alcohol drinking, 1.550 and 1.368-1.755 for abnormal BMI, and 0.887 and 0.800-0.984 for GA+AA. The interaction between BMI and alcohol on gout was significant for GG (p-value = 0.0102) and GA+AA (p-value = 0.0175). When we stratified genotypes by BMI, alcohol drinking was significantly associated with gout only among individuals with a normal BMI (OR; 95% CI = 1.533; 1.036-2.269 for GG and 2.109; 1.202-3.699 for GA+AA). Concerning the combination of BMI and alcohol drinking among participants stratified by genotypes (reference, GG genotype, normal BMI, and no alcohol drinking), the risk of gout was significantly higher in the following categories: GG, normal BMI, and alcohol drinking (OR, 95% CI = 1.929, 1.385-2.688); GG, abnormal BMI, and no alcohol drinking (OR, 95% CI, = 1.721, 1.442-2.052); GG, abnormal BMI, and alcohol drinking (OR, 95% CI = 1.941, 1.501-2.511); GA+AA, normal BMI, and alcohol drinking (OR, 95% CI = 1.971, 1.167-3.327); GA+AA, abnormal BMI, and no alcohol drinking (OR, 95% CI = 1.498, 1.256-1.586); and GA+AA, abnormal BMI, and alcohol drinking (OR, 95% CI = 1.545, 1.088-2.194). CONCLUSIONS: Alcohol and abnormal BMI were associated with a higher risk of gout, whereas the rs671 GA+AA genotype was associated with a lower risk. Noteworthy, BMI and alcohol had a significant interaction on gout risk. Stratified analyses revealed that alcohol drinking especially among normal-weight individuals might elevate the risk of gout irrespective of the genotype.

Interactive Effect of IGF2BP2 rs4402960 Variant, Smoking and Type 2 Diabetes.

PURPOSE: Genetic and environmental factors are related to type 2 diabetes (T2D). Genetic modifiers of T2D have not been widely determined among smoking individuals. In this population-based study, we investigated the interactive association between rs4402960 polymorphism of the insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) gene and smoking with T2D among Taiwanese adults. MATERIALS AND METHODS: We obtained genetic data collected between 2008 and 2018 for 22,039 participants (aged 30-70 years) from the Taiwan Biobank (TWB) database. These data were analyzed using the t-test, Chi-square (χ 2) test, and multiple logistic regression. RESULTS: The mean ages for participants with and without diabetes were 58.11±8.75 and 48.58±11, respectively. Compared with the rs4402960 GG genotype, the odds ratio (OR) for T2D was 1.261 among GT and 1.545 among TT genotype individuals (p<0.05). Current smokers compared to nonsmokers were associated with a higher risk of T2D (OR=1.266, p=0.0404). There was a significant interaction between the IGF2BP2 rs4402960 variant and smoking on T2D (p = 0.0497). After stratification by rs4402960 genotypes and smoking status, the OR was substantial only in current smokers with GG genotype (OR, 1.663, p = 0.0008). CONCLUSION: This population-based study indicated that the risk for T2D was stronger among current smoking rs4402960 GG individuals recruited between 2008 and 2019 in Taiwan.