Psychiatric disorders such as major depressive disorder, anxiety, schizophrenia, and bipolar disorder are complex conditions influenced by a combination of genetic, metabolic, environmental, and behavioral factors. However, the integrated, multimodal pathways underlying disease onset and progression remain poorly understood. This project aims to systematically investigate how life history trait, behavioral patterns, plasma metabolites, environmental exposures, and genetic risk jointly contribute to the risk and course of psychiatric disorders.
Research questions:
1. What are the individual and combined effects of metabolomic, genetic, behavioral, and environmental factors on the incidence of depression, anxiety, schizophrenia, and bipolar disorder?
2. How do these multimodal risk factors interact (e.g., gene-environment and metabolite-behavior interactions)?
3. Can predictive models incorporating multi-omics and life-course variables improve risk stratification for psychiatric disorders?
Objectives:
* To identify novel metabolomic biomarkers associated with psychiatric diagnoses using prospective data.
* To construct polygenic risk scores (PRS) for psychiatric conditions and examine their predictive utility in the general population.
* To examine mediation and interaction effects among PRS, metabolites, and lifestyle/environmental factors.
* To build multimodal risk prediction models for psychiatric disorders using machine learning approaches.
Scientific rationale:
The UK Biobank offers an unparalleled opportunity to explore these questions using its deep phenotyping, rich biomarker data.detailed lifestyle/environmental exposures, and robust mental health outcomes. This research will enhance our understanding of complex etiology, support the development of predictive tools, and inform early prevention strategies in precision psychiatry.
