Background
Metabolic diseases represent a growing global health burden, with complex inter-organ interactions and frequent co-occurrence with mental health disorders. Understanding the progression patterns of multi-organ metabolic dysfunction and their relationship with multimorbidity is crucial for developing targeted prevention and intervention strategies.
Objectives & Research Questions
This study aims to:
1. Map the natural progression of metabolic diseases across multiple organ systems (liver, pancreas, adipose tissue, cardiovascular system) and identify critical transition points and multimorbidity clusters.
2. Investigate bidirectional associations between metabolic diseases and mental health disorders, including shared risk factors and potential causal pathways.
3. Quantify the individual and combined effects of modifiable lifestyle factors-including dietary patterns, nutrient intake, and physical activity-on multi-organ metabolic disease risk and progression.
4. Identify genetic variants associated with multi-organ metabolic disease susceptibility through genome-wide association studies and explore gene-environment interactions.
5. Develop and validate machine learning-based risk prediction models integrating clinical, imaging, lifestyle, and genetic data for early detection and personalized risk stratification.
6. Translate findings into actionable insights for opportunistic screening protocols and evidence-based lifestyle intervention strategies.
MethodsWe will leverage the comprehensive phenotypic data available in the UK Biobank, including demographic data, imaging data, biomarkers, genetic data, mental health questionnaires, dietary assessments, and physical activity records. Artificial intelligence methods will be employed for longitudinal analysis to assess disease progression over time. Genetic analyses will utilize whole-genome sequencing data to identify novel susceptibility loci.
