This project aims to develop a plasma protein panel to facilitate earlier detection and personalized prevention strategies for type 2 diabetes. First, we will use the UK Biobank (UKB) proteomic dataset (Olink Proteome) to identify plasma proteins associated with insulin resistance, using the triglyceride-to-HDL ratio (TG/HDL) as an alternative measure. We will account for potential confounding factors (e.g., age, sex, dietary habits, smoking, and alcohol use) to isolate proteins significantly tied to metabolic dysregulation. Next, we will perform pQTL analyses to quantify the contributions of genetic versus environmental factors for each protein. Proteins whose expression is substantially influenced by modifiable environmental factors are likely to be clinically important as targets for lifestyle interventions.
After identifying candidate proteins, we will validate them using an independent prediabetes cohort (N=215; ages 30-70 years) who had fasting blood glucose levels of 100-125 mg/dL at routine health checkups and then underwent oral glucose tolerance tests (0, 30, 120 minutes) to derive HOMA-IR. Through this validation, we will examine both cross-sectional and longitudinal associations between the candidate proteins and various lifestyle factors (diet, physical activity, sleep, etc.). Finally, we will incorporate insights from Oriental medicine by analyzing how genetic and environmental components converge on individual “constitutions,” potentially offering a more holistic approach to early-stage metabolic risk management. By integrating large-scale UKB data and additional cohorts, we aim to develop a clinically relevant, personalized plasma protein panel for earlier detection and more effective prevention of type 2 diabetes.