Integrated multiomic scores for cardiovascular disease prediction across multiancestral populations

Background: Genomic, epigenomic, and proteomic data capture complementary aspects of cardiovascular disease biology, but the value of integrating these data for predicting multiple cardiovascular disease outcomes across ancestry groups remains uncertain. Methods: We derived disease-specific proteomic scores using 999 plasma proteins in 50,673 UK Biobank participants and validated them in 9,358 participants from the Prospective Urban and Rural Epidemiological (PURE) study. In PURE, we integrated these proteomic scores with 2,279 polygenic scores and 479 DNA methylation scores to develop 4 multiomic scores (MEGASCOREs) for myocardial infarction (MI), atrial fibrillation (AF), heart failure (HF), and cardiovascular mortality. Associations with incident cardiovascular disease, discrimination, calibration, and performance across ancestry groups were evaluated in PURE and 2 additional cohorts. Results: In pooled analyses, each standard deviation increase in the corresponding MEGASCORE was associated with MI (hazard ratio [HR], 2.13; 95% CI, 1.86-2.43), AF (HR, 1.94; 95% CI, 1.68-2.24), HF (HR, 3.33; 95% CI, 2.75-4.05), and cardiovascular mortality (HR, 3.28; 95% CI, 2.33-4.61). MEGASCOREs improved discrimination compared with clinical models, with strongest improvements for HF and cardiovascular mortality. MEGASCORE-CV-Death had a higher area under the receiver operating characteristic curve (AUC) than SCORE2 for cardiovascular mortality (0.83 [95% CI, 0.82-0.85] vs. 0.78 [95% CI, 0.77-0.80]). Its discrimination was also higher than that of SCORE2 across all 4 ancestry groups, with better calibration across non-European ancestry groups. MEGASCOREs remained associated with cardiovascular outcomes among participants without conventional cardiovascular risk factors. Conclusions: Integrated multiomic MEGASCOREs were associated with multiple cardiovascular outcomes and provided prognostic information beyond conventional clinical variables in PURE.

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Journal
medRxiv
Published
2026-10-28
DOI
https://doi.org/10.64898/2026.10.23.26366372
Primary Topic
Genetic Associations and Epidemiology
Type
preprint

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preprint

Integrated multiomic scores for cardiovascular disease prediction across multiancestral populations

Marie Pigeyre, Michael J. Kamal, Fernando Tomas Lanas, Karen Yeates et al.
6 links
medRxiv
Genetic Associations and Epidemiology
preprint

Integrated multiomic scores for cardiovascular disease prediction across multiancestral populations

Marie Pigeyre, Michael J. Kamal, Fernando Tomas Lanas, Karen Yeates, Shihong Mao, Rita Yusuf, Marc Evans M. Abat, Álvaro Avezum Júnior, Hertzel C. Gerstein, Patricio A López-Jaramillo, Shrikant I. Bangdiwala, Bernard D. Keavney, Hassan R. Mir, Marı́a Luz Dı́az, Roya Kelishadi, Pascal B. Meyre, Guillaume Paré, Annika Rosengren, Sonia Savitri Anand, Sumathy Rangarajan, Michael Chong, Jephat Chifamba, Koon Teo, Philip Joseph, Afzalhussein M Yusufali, Salim Yusuf
preprint en

Abstract

Background: Genomic, epigenomic, and proteomic data capture complementary aspects of cardiovascular disease biology, but the value of integrating these data for predicting multiple cardiovascular disease outcomes across ancestry groups remains uncertain. Methods: We derived disease-specific proteomic scores using 999 plasma proteins in 50,673 UK Biobank participants and validated them in 9,358 participants from the Prospective Urban and Rural Epidemiological (PURE) study. In PURE, we integrated these proteomic scores with 2,279 polygenic scores and 479 DNA methylation scores to develop 4 multiomic scores (MEGASCOREs) for myocardial infarction (MI), atrial fibrillation (AF), heart failure (HF), and cardiovascular mortality. Associations with incident cardiovascular disease, discrimination, calibration, and performance across ancestry groups were evaluated in PURE and 2 additional cohorts. Results: In pooled analyses, each standard deviation increase in the corresponding MEGASCORE was associated with MI (hazard ratio [HR], 2.13; 95% CI, 1.86-2.43), AF (HR, 1.94; 95% CI, 1.68-2.24), HF (HR, 3.33; 95% CI, 2.75-4.05), and cardiovascular mortality (HR, 3.28; 95% CI, 2.33-4.61). MEGASCOREs improved discrimination compared with clinical models, with strongest improvements for HF and cardiovascular mortality. MEGASCORE-CV-Death had a higher area under the receiver operating characteristic curve (AUC) than SCORE2 for cardiovascular mortality (0.83 [95% CI, 0.82-0.85] vs. 0.78 [95% CI, 0.77-0.80]). Its discrimination was also higher than that of SCORE2 across all 4 ancestry groups, with better calibration across non-European ancestry groups. MEGASCOREs remained associated with cardiovascular outcomes among participants without conventional cardiovascular risk factors. Conclusions: Integrated multiomic MEGASCOREs were associated with multiple cardiovascular outcomes and provided prognostic information beyond conventional clinical variables in PURE.

medRxiv
Isfahan University of Medical Sciences (IR), Universidad de La Frontera (CL), University of Zimbabwe (ZW), Queen's University (CA), Philippine General Hospital (PH), University Hospital of Basel (CH), Population Health Research Institute (CA), University of Manchester (GB), Independent University, Bangladesh (BD), Hospital Alemão Oswaldo Cruz (BR), Estudios Clínicos Latinoamérica (AR), Universidad De Santander (CO), Ottawa Heart Institute (CA), University of Gothenburg (SE), McMaster University (CA)
AstraZeneca, GlaxoSmithKline, Sanofi, Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), Public Health Agency of Canada, Ontario Ministry of Health and Long-Term Care, South Africa Netherlands research Programme on Alternatives in Development, Forskningsrådet för Arbetsliv och Socialvetenskap, AFA Försäkring, Vetenskapsrådet, North-West University, Universidad de La Frontera, Servier, Independent University, Bangladesh, Canadian Institutes of Health Research, Medical Research Council
Good health and well-being
Genetic Associations and Epidemiology
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