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Approved Research

Identification of causal relationship and interaction between metabolic and environmental risk factor with chronic diseases and mortality

Principal Investigator: Dr Jiayuan Wu
Approved Research ID: 97101
Approval date: January 31st 2023

Lay summary

  1. Objectives

This study aims to identify how strongly metabolic and environmental factors are related to the development and outcomes of common chronic diseases. We will also investigate the interaction between the genes, metabolism, and environmental exposures on chronic diseases and their outcomes.

  1. Scientific rationale

Both environmental and metabolic factors affect the development of chronic non-communicable diseases and early health damage. However, neither metabolic nor environmental risk factors for major chronic diseases act in isolation and that their combined effects must be considered. Previous studies have shown that adverse environmental factors can amplify the effects of individual metabolic factors on the development of chronic diseases.

At present, many susceptible genes affecting metabolism level have been found by using genome-wide association study (GWAS) technology. The combination of GWAS and metabolic risk factors, together with environmental exposure (such as diet, environmental pollutants, lifestyle) and the determination of key exposure biomarkers, opens the opportunity to further clarify the pathogenesis of chronic diseases and to find new biomarkers of chronic non-communicable diseases.

  1. Project duration

The project is planning to take 3 years after the data has been downloaded.

  1. Public health impact

Based on the concepts of systems epidemiology and systems biology, we integrate genomics with traditional epidemiological designs (e.g., cross-section study, case - control study, and cohort study) to investigate the effects of environmental risk factors, metabolic risk factors, and their interaction. Thus, we can make a more comprehensive explanation and elucidation of the potential mechanisms of chronic diseases, early health damage, and mortality. Accordingly, more perfect prediction models and intervention programs can be constructed, and more scientific prevention strategies can be formulated, so as to make more contribution to the prevention of chronic diseases and reduce the disease burden.