Integrated multiomic scores for cardiovascular disease prediction across multiancestral populations

  1. Pascal B. Meyre
  2. Michael Chong
  3. Marie Pigeyre
  4. Shihong Mao
  5. Michael Kamal
  6. Shrikant Bangdiwala
  7. Roya Kelishadi
  8. Annika Rosengren
  9. Fernando Lanas
  10. Afzalhussein Yusufali
  11. Rita Yusuf
  12. Jephat Chifamba
  13. Patricio Lopez-Jaramillo
  14. Marc Evans M. Abat
  15. Alvaro Avezum
  16. Karen Yeates
  17. Maria Luz Diaz
  18. Hassan Mir
  19. Bernard Keavney
  20. Sonia S. Anand
  21. Koon Teo
  22. Sumathy Rangarajan
  23. Philip Joseph
  24. Hertzel Gerstein
  25. Salim Yusuf
  26. Guillaume Paré
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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.

Clinical Perspective

What is new?

  • We developed 4 cardiovascular disease-specific multiomic scores integrating proteomic signatures, polygenic scores, and DNA methylation scores to characterize risk of MI, AF, HF, and cardiovascular mortality.

  • The MEGASCOREs were associated with incident cardiovascular disease outcomes and provided prognostic information beyond conventional clinical risk factors.

  • MEGASCOREs showed consistent risk stratification across ancestry and remained associated with risk among participants without conventional cardiovascular risk factors.

What are the clinical implications?

  • Integrated multiomic scores may complement conventional cardiovascular risk assessment by capturing molecular information not fully represented by clinical variables.

  • Molecular risk profiling may help identify individuals with elevated cardiovascular risk despite having no conventional risk factors.

  • Assessment of clinical utility, evaluation of implementation and cost-effectiveness are needed before MEGASCOREs can inform preventive treatment decisions.

Article activity feed

  1. Version published to 10.64898/2026.10.23.26366372 on medRxiv