| Title: | Large-Scale Epidemiological Analysis of Endocrine System Diseases: Risk Factors and Genetic Determinants |
| Lead Institution: | Sichuan University |
| Principal investigator: |
Application 1054039
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About
Research Questions: What are the genetic and environmental risk factors for major endocrine diseases (diabetes mellitus, thyroid disorders, metabolic syndrome, hyperuricemia) in the UK population? How do gene-environment interactions contribute to disease development and can we develop accurate prediction models for clinical use? Objectives: This study aims to conduct comprehensive genome-wide association studies (GWAS) to identify novel genetic loci and validate known associations for endocrine diseases, investigate environmental risk factors including lifestyle, dietary, and socioeconomic determinants, examine gene-environment interactions affecting disease susceptibility, develop and validate integrated risk prediction models incorporating genetic, clinical, and lifestyle factors, and analyze longitudinal disease trajectories and comorbidity patterns to understand disease progression pathways. Scientific Rationale: Endocrine diseases represent a growing global health burden with substantial healthcare costs and patient morbidity. Current understanding of disease etiology remains incomplete, particularly regarding gene-environment interactions and risk prediction in diverse populations. Previous studies have been limited by small sample sizes insufficient for detecting rare variants and complex interactions. The UK Biobank's unprecedented scale (>500,000 participants), comprehensive phenotyping, genome-wide genotyping, and longitudinal follow-up provide unique opportunities to address these limitations. This research will advance personalized medicine approaches, inform clinical practice guidelines for primary care, and contribute to evidence-based prevention strategies, directly supporting my PhD dissertation in General Practice while generating valuable resources for the broader research community.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 21041 | TyG-Biomarker discordance profiling identifies metabolically heterogeneous phenotypes with divergent cardiovascular and diabetes risk: metabolomic characterization from the UK biobank | Yuhao Yang (+5) | 2026 | Lipids in Health and Disease |
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