| Title: | Risk evaluation using multi-environmental factors [natural environmental, social, behavioral, dietary determinants, etc.] of health and genetic susceptibility for prevention of complex diseases. |
| Lead Institution: | Chengdu University of Traditional Chinese Medicine. |
| Principal investigator: |
Application 578405
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
About
Globally, the prevalence of complex diseases such as cancer, cardiometabolic diseases, mental illnesses, asthma, and multiple sclerosis is steadily increasing, posing significant threats to public health. The combination of genetic predisposition and environmental factors (pollution, lifestyle habits, dietary patterns, socioeconomic factors, etc.), which is considered as key drivers of these diseases. However, the extent to which genetic susceptibility interacts with these environmental factors to influence disease risk remains poorly understood, which limits effective preventive strategies. This research's primary aim is to investigate how environmental factors and genetic susceptibility jointly contribute to the risk of complex diseases. Genetic data will be used to quantify inherited risk of disease via polygenic risk scores (PRS). By combining PRS with environmental factors, we will develop models to predict likelihood of developing specific diseases. These models will utilize both traditional statistical approaches and advanced machine learning algorithms to improve accuracy. In addition to prediction, we will employ Mendelian randomization (MR), a powerful method that uses genetic variants as natural experiments to explore causal relationships between environmental factors and diseases. By analyzing genetic data and leveraging published genome-wide association studies (GWAS), MR will provide robust evidence on whether specific exposures are causally linked to diseases. It can guide targeted interventions by identifying which environmental factors are most critical to address. Our research, expected to take at least three years, aims to identify modifiable risk factors for complex diseases and provide evidence for personalized prevention strategies. By identifying high-risk populations based on genetic and environmental profiles, this project will contribute to early diagnosis, targeted interventions, and effective prevention strategies for public health.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 17823 | Association of Metabolic Vulnerability Index With Incident Type 2 Diabetes in General Population and All-Cause Mortality in Patients With Diabetes: A Cohort Study Based on UK Biobank | Dongqi Zhou (+5) | 2026 | Diabetes Obesity and Metabolism |
Enabling scientific discoveries that improve human health