Research Question: Endocrine and metabolic disorders (e.g., diabetes, thyroid diseases, obesity) are influenced by genetic, epigenetic, environmental, inflammatory, immune, nutritional, aging factors, lifestyle, socioeconomic status, psychological factors, microbiome, drug use, and pregnancy. This study uses the UK Biobank dataset to explore how these factors impact the onset, progression, complications, and treatment outcomes of these disorders. We aim to identify novel biomarkers and pathways through multi-omics data integration and machine learning, to develop early detection methods, improve diagnostics, tailor treatments, and inform public health policies.
Objectives:
1.Identify novel risk factors and biomarkers using multi-omics and machine learning.
2.Assess adverse outcomes and treatment efficacy related to identified risk factors.
3.Develop predictive models for disease onset, progression, and treatment response.
4.Explore gene-environment interactions, including lifestyle, socioeconomic status, psychological factors, microbiome, and drug use.
5.Validate findings and inform public health policies.
Scientific Rationale: Endocrine and metabolic disorders are complex conditions influenced by genetics, epigenetics, environmental exposures, inflammation, immune responses, nutrition, and aging. Their prevalence is increasing due to rapid lifestyle changes. Previous studies have limitations due to insufficient data integration and methodological constraints. The UK Biobank provides comprehensive multi-omics data and epidemiological information, enabling holistic analysis of risk factors. Integrating multiple omics datasets allows identification of novel biomarkers and causal pathways. Machine learning enhances detection of subtle patterns and interactions. This research aims to leverage UK Biobank data and advanced techniques to deepen understanding of these disorders, contributing to scientific knowledge and public health strategies.
