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Published on: September 26, 2018
Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk
Adam S Chan1,2,3, Songhua Wu4, Stephen T Vernon5
1School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia.
Insights
Identifying distinct patient groups within coronary artery disease (CAD) using metabolomic data improves disease prediction. This approach aids in discovering novel biomarkers for early detection and personalized prevention strategies for cardiovascular disease (CVD).
Area of Science:
- Cardiovascular research
- Metabolomics
- Biomarker discovery
Background:
- Cardiovascular disease (CVD) is a major global health burden, affecting half a billion people.
- Detecting links between pathophysiology and coronary plaque phenotypes in diverse populations with coronary artery disease (CAD) is complex.
- Cohort heterogeneity in CAD presents challenges for identifying specific disease mechanisms and risk factors.
Purpose of the Study:
- To develop and illustrate methods for identifying subcohorts within CAD populations.
- To find distinct metabolomic signatures associated with subclinical CAD.
- To demonstrate how identifying subcohorts can enhance subclinical CAD prediction and biomarker discovery.
Main Methods:
- Employed both knowledge-guided and data-driven approaches to analyze complex multi-omic datasets.
- Focused on identifying distinct subcohorts of individuals with subclinical CAD.
- Utilized metabolomic signatures to characterize identified subcohorts.
Main Results:
- Successfully identified subcohorts within the CAD population with distinct metabolomic profiles.
- Demonstrated that utilizing these identified subcohorts improves the prediction accuracy of subclinical CAD.
- Facilitated the discovery of potential novel biomarkers for subclinical cardiovascular disease.
Conclusions:
- Acknowledging and utilizing cohort heterogeneity is crucial for advancing CVD understanding.
- Identifying specific patient subcohorts can lead to more effective and personalized preventative treatments.
- This approach holds promise for reducing the societal and individual burden of cardiovascular disease.
Abstract:
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 multi-omic 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.
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