Diabetes, especially type 2, is a global health challenge with rising prevalence and significant complications. While existing studies focus on isolated risk factors like genetics or lifestyle, they often fail to integrate multi-dimensional data. Key aspects, such as environmental exposures, genetic predispositions, and body composition metrics, remain underexplored in predicting outcomes like macrovascular or microvascular complications. This study aims to bridge these gaps using the UK Biobank dataset, offering a holistic analysis to enhance personalized risk prediction and prevention. This doctoral research is academic, with no commercial intent, advancing knowledge in diabetes pathophysiology and modeling. The primary objective of this study is to identify and quantify risk factors influencing prognostic outcomes in patients with diabetes and prediabetes, including macrovascular/microvascular complications, diabetes-related death, and all-cause mortality. Specific aims include: 1. To evaluate the independent and interactive effects of environmental pollution, lifestyle factors (sleep, smoking, alcohol consumption, micronutrient intake), baseline genetic and proteomic features, post-diagnosis medication use, and body composition on these outcomes. 2. To develop predictive models incorporating these factors for individualized prognosis assessment. Hypotheses: 1. Environmental pollutants (e.g., air pollution) and unhealthy lifestyle behaviors (e.g., poor sleep, smoking) will exacerbate genetic and proteomic risks, leading to higher rates of vascular complications and mortality. 2. Favorable post-diagnosis medication adherence and optimal body composition at baseline will mitigate adverse outcomes, with interactions modulated by genetic/proteomic profiles. This project is student-led as part of my PhD thesis, where I will take the dominant role in design, analysis, and interpretation, serving as the first author on resulting publications.
