Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

Adam S. Chan; Songhua Wu; Stephen T. Vernon; Owen Tang; Gemma A. Figtree; Tongliang Liu; Jean Y.H. Yang; Ellis Patrick

iScience (May 2023)

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

Adam S. Chan,
Songhua Wu,
Stephen T. Vernon,
Owen Tang,
Gemma A. Figtree,
Tongliang Liu,
Jean Y.H. Yang,
Ellis Patrick

Affiliations

Adam S. Chan: School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia
Songhua Wu: School of Computer Science, The University of Sydney, Sydney, NSW, Australia
Stephen T. Vernon: Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia
Owen Tang: Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia
Gemma A. Figtree: Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia
Tongliang Liu: Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia; School of Computer Science, The University of Sydney, Sydney, NSW, Australia
Jean Y.H. Yang: School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia; Corresponding author
Ellis Patrick: School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, Australia; Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, Australia; Westmead Medical Institute, Sydney, NSW, Australia; Corresponding author

Journal volume & issue: Vol. 26, no. 5
p. 106633

Abstract

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Summary: 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.

Published in iScience

ISSN: 2589-0042 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Science
Website: http://www.cell.com/iscience/home

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