Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

Cardiovascular disease remains a leading cause of mortality with an estimated half a billion people affected in 2019. However, detecting signals between specific pathophysiology and coronary plaque phenotypes using complex multiomic discovery datasets remains challenging due to the diversity of individuals and their risk factors. Given the complex cohort heterogeneity present in those with coronary artery disease (CAD), we illustrate several different methods, both knowledge-guided and data-driven approaches, for identifying subcohorts of individuals with subclinical CAD and distinct metabolomic signatures. We then demonstrate that utilizing these subcohorts can improve the prediction of subclinical CAD and can facilitate the discovery of novel biomarkers of subclinical disease. Analyses acknowledging cohort heterogeneity through identifying and utilizing these subcohorts may be able to advance our understanding of CVD and provide more effective preventative treatments to reduce the burden of this disease in individuals and in society as a whole.

Automated summary

Cardiovascular disease remains a leading cause of mortality, affecting an estimated half a billion people in 2019. However, detecting signals between specific pathophysiology and coronary plaque phenotypes using complex multiomic discovery datasets is challenging due to individual diversity and risk factors. Various methods, including knowledge-guided and data-driven approaches, can help identify subcohorts with distinctive metabolomic signatures and subclinical CAD, improving prediction and facilitating the discovery of new biomarkers. Analyzing cohort heterogeneity through these subcohorts can advance our understanding of CVD and provide more effective preventative treatments.