Uncovering genetic and biological causes of cardio-metabolic disease and traits by integrated genetic, functional genomic, clinical, and imaging data

Uncovering genetic and biological causes of cardio-metabolic disease and traits by integrated genetic, functional genomic, clinical, and imaging data

Lay summary

Cardio-metabolic diseases, such as heart attack, stroke, diabetes and obesity are the leading cause of death worldwide. Previous research regarding risk assessment models for cardio-metabolic conditions has mostly examined routine clinical predictors such as smoking, LDL-C, glucose, UA, HbA1c, Apolipoprotein, and HcY, using mainly conventional statistical models to understand the relationship between risk factors and complications. We intend to use machine learning methods such as ensemble methods and other prediction models to illustrate the predictive ability of various novel risk factors of cardio-metabolic disease.

Moreover, our goal is to explore which risk factor that lead to the development and progression of additional cardio-metabolic traits.  Phenotypic cluster groups will be identified and examined with various dimensionality reduction techniques and cluster analyses to investigate if any specific clusters are associated with increased risk of cardio-metabolic disease. We aim to run an MR-PheWAS which integrates PheWAS and MR analysis, to investigate the associations between risk factors and multiple disease outcomes. Through this approach, we expect to explore novel, potentially causal effects of risk factors. MR (Mendelian Randomization) analysis utilize genetic variants of known functions as exposures and estimate its causal effect on specific disease outcomes. Our models will result in novel definitions of cardiometabolic disease and risk factor importance.