Approved Research

Identifying and Ranking Key Risk factors for Chronic Diseases and non-accidental Death

Lay summary

Chronic disease (e.g., cardiovascular disease, type 2 diabetes, cancer, etc.) and non-accidental death pose huge disease and economic burdens worldwide, and most of them are influenced by both genetic and environmental factors. Population-based genetic studies have been proven valuable for identifying genetic factors responsible for chronic diseases and non-accidental death and for predicting an individual's genetic risk of developing a disease. However, most identified genetic variants account for only a small proportion of the heritability in many disease outcomes. Accumulating evidence suggests that environmental factors (e.g., environmental pollutants, climate, diet, physical activity, smoke, etc.) act as critical triggers for these diseases independently, and also act as collaborators of genetic factors to affect the disease risk synergistically. Nevertheless, there is still a lack of sufficient and robust evidence on the gene-environment interactions and the causality behind environmental factors and chronic diseases/death. Therefore, it is needed for well-designed research to characterize the gene-environment interactions and examine the causality of these associations, which would be useful in investigating the etiology of specific chronic diseases and non-accidental death. In addition, it is crucial to identify and rank a range of key, robust, sensitive, and accessible risk factors (including both genetic and environmental factors) for chronic diseases and non-accidental death and yield clinically operable risk prediction models for diseases and death, which would be beneficial for taking more targeted and effective strategies to mitigate the disease burden.

This study proposes to establish a prospective cohort of natural populations by requesting the general information, core data, measures, and multi-omics data from the UK Biobank. The study aims: 1) 1) to assess the interactive effects of genetic and environmental factors on chronic and non-accidental death; 2) to investigate the causality of environmental factors with chronic diseases and non-accidental death using Mendelian randomization analysis; 3) to identify and rank major risk factors for chronic diseases and non-accidental death, as well as to develop individual risk prediction models by applying machine learning algorithms.

The duration of this project is expected to last 36 months. The findings of this project are essential for researchers to comprehensively understand chronic diseases and non-accidental death's etiology and prevention, and are also of great importance for public health.