Cardiometabolic diseases, such as coronary heart disease (CHD) and stroke are the leading cause of death and disease burden worldwide. Environmental, lifestyle, and genetic factors, as well as gene-environment interactions together contribute to the development of cardiometabolic diseases. Accurate identification of high-risk populations and then conducting specific lifestyle interventions may have great potential to reduce risk of cardiometabolic diseases. Although hundreds of genetic loci have been identified for various cardiometabolic diseases traits, they could explain a small proportion of heritability. Hence, it is essential to evaluate the roles of environmental exposures and multi-omics factors (involving germline genetic variants, somatic mutations, proteomics, metabolomics) and reveal potential pathways across different ethnicities for better risk assessment and precise prevention of cardiometabolic diseases.
Emerging evidence suggests that multi-omics factors, influenced by genetic and environmental factors, may also contribute to cardiometabolic diseases. For instance, previous studies have suggested that somatic mutations were associated with cardiometabolic diseases. In our study, in addition to the verification of the most frequently mutated genes, we first found small clones (variant allele fraction [VAF] < 2%) were nonnegligible for the elevated CHD risk and revealed that the potential interplay between germline genetic risk of inflammation and somatic mutations on CHD in the Chinese population. Therefore, it is essential to elucidate the interactions of environmental factors and multi-omics factors across different ethnicities for better understanding the heterogeneity across populations and conducting precise prevention. Additionally, an integrated risk prediction model 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 and datasets of our Chinese cohorts, we plan to explore the associations of environmental exposures and multi-omics biomarkers (including germline genetic variants, somatic mutations, et al.) as well as their interactions and potential pathways with cardiometabolic diseases. Furthermore, we will develop an integrated risk prediction model for individualized risk prediction of cardiometabolic diseases by integrating ethnic-specific multi-omics factors, environmental predictors and the interactions based on Cox regression and machine learning methods.
Our project will provide new insights into the environmental and multi-omics determinants as well as their interactions and potential pathways for cardiometabolic diseases. The integrated risk prediction model would promote individualized risk assessment and lifestyle-based primary prevention for cardiometabolic diseases.