| Title: | Improving risk prediction of cardiometabolic diseases by integrating ethnic-specific genetic variants and environmental predictors |
| Lead Institution: | Fuwai Hospital Chinese Academy of Medical Sciences |
| Principal investigator: | Dr Xiangfeng Lu |
Application 69815
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About
Cardiometabolic diseases, such as coronary heart diseases, stroke, hypertension, dyslipidemia, and diabetes, are the leading cause of death and disease burden worldwide. With the development of the social economy, unhealthy lifestyles are becoming more prominent and have caused tremendous health burden. Environmental, lifestyle, and genetic factors, as well as gene-environment interactions together, contribute to the development of cardiometabolic diseases. To reduce the disease burden caused by cardiometabolic diseases, it is essential to further elucidate the role of lifestyle, environmental, and genetic factors in the development of cardiometabolic diseases. Moreover, although hundreds of genetic variants have been discovered for various cardiometabolic traits, only a small proportion of the heritability could be explained. Integrating the ethnic-specific genetic variants and their interactions with environmental factors could further reveal the genetic determinants of cardiometabolic diseases. Finally, with the increased availability of genetic testing, a polygenic risk score for cardiometabolic diseases could play a significant role in improving risk stratification and identifying potential high-risk individuals beyond currently available clinical risk scores. Aims: Using UK Biobank datasets and our Chinese cohorts, we plan to explore the associations of environmental factors, genetic variants, and their interactions with cardiometabolic diseases. We also aim to identify the causal effects of environmental factors or biomarkers on cardiometabolic diseases by mendelian randomization. Furthermore, we will develop a polygenic risk prediction model for individualized cardiometabolic disease risk prediction by integrating ethnic-specific genetic variants and environmental predictors based on Cox regression and machine learning methods. Our project will provide new insights into the environmental and genetic determinants and their interactions for cardiometabolic diseases. The improved polygenic risk score would promote individualized risk assessment and lifestyle-based primary prevention of cardiometabolic diseases.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11331 | Efficacy and safety of low levels of low-density lipoprotein cholesterol: trans-ancestry linear and non-linear Mendelian randomization analyses | Hongwei Liu (+11) | 2023 | European Journal of Preventive Cardiology |
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