Age and Gender-Dependent Comorbidities in UAE Population identifies Coronary Artery Disease with Gene-Environment Interactions

This study investigated the age and sex-dependent comorbidities contributing to coronary artery disease (CAD) within the UAE population. A cohort of 3,000 individuals was analyzed by integrating genetic data, environmental stressors (e.g., PM2.5 exposure), and demographic profiles to identify CAD and nine other diseases with heterogeneous comorbidity patterns. Key genetic markers, including APOE rs429358, PCSK9, and LPA were significantly associated with CAD risk, amplified by environmental exposure and metabolic conditions such as diabetes and obesity. Notably, APOE rs429358 carriers exposed to high PM2.5 levels exhibited a 2.8-fold increase in CAD risk (p < 0.001), emphasizing the synergistic effects of gene-environment interactions. Monte Carlo and Markov Chain Monte Carlo simulations validated the results, enabling the identification of high-risk genetic profiles across various environmental and demographic conditions. Kaplan-Meier survival analyses revealed accelerated disease progression in high-risk groups, whereas Principal Component Analysis and hierarchical clustering identified distinct genetic clusters stratified by age and sex. This study further identified demographic-specific disease subtypes with implications for public health strategies, such as addressing higher environmental susceptibility in males and targeted management of metabolic comorbidities in females such as obesity, diabetes, and stroke. These findings support precision medicine strategies tailored to regional populations, promoting targeted interventions to mitigate CAD risk. This study synthesizes observational findings and computational simulations to establish a comprehensive framework for elucidating the pathogenesis of coronary artery disease (CAD) and enhancing public health interventions in the United Arab Emirates (UAE). The actionable outcomes include the development of sex-specific health interventions and environmental policies to reduce CAD risk in high-susceptibility groups.