A NEW genomic model has improved prediction of coronary artery disease (CAD) risk by integrating multiple genetic factors into a single, comprehensive score.
CAD remains one of the leading causes of death worldwide, driven by a complex interplay of genetic and environmental factors. While polygenic risk scores have advanced risk stratification, they typically capture only inherited (germline) variation and overlook other important biological contributors. This latest research addressed that gap by combining both germline and somatic genomic data into a unified CAD risk framework.
Integrated CAD Risk Model Shows Broader Insight
The researchers developed an integrated genomic model incorporating six distinct drivers of CAD risk, including polygenic risk scores, genetically proxied proteomic and metabolomic risk scores, and clonal haematopoiesis of indeterminate potential. The model was evaluated in 391,536 participants from the UK Biobank and validated in a further 34,177 individuals from the TOPMed program.
Findings showed a wide gradient of 10-year CAD risk. In the UK Biobank cohort, risk ranged from 1.1% to 15.5%, while in the TOPMed cohort it extended from 3.8% to 33.0%. Notably, the gradient was more pronounced in males than females, highlighting potential sex-based differences in genomic risk expression.
Importantly, the model captured the cumulative effect of multiple genetic drivers. This allowed identification of individuals at high risk despite lacking any single major genetic factor, as well as those at low risk even when carrying known high-risk variants.
Identifying Hidden Risk Beyond Traditional Scores
Although the integrated genomic model provided only modest improvements over polygenic risk scores at a population level, its clinical value became clearer at the individual level. The model identified approximately 13% of high-risk individuals who would not have been detected using polygenic risk scores alone.
In middle-aged populations, the model also enhanced the predictive performance of existing clinical tools, such as standard cardiovascular risk calculators. This suggests a potential role in refining early prevention strategies, particularly for individuals whose risk may otherwise be underestimated.
However, the authors noted that the incremental benefit remains relatively small in broad population screening, and further work is needed to determine how best to implement such models in routine clinical care.
Overall, the findings highlight a shift towards more comprehensive genomic profiling in cardiovascular medicine, where integrating diverse biological signals may enable earlier and more precise identification of CAD risk.
Reference
Yang X et al. An integrated germline and somatic genomic model for coronary artery disease. Nat Commun. 2026;DOI:10.1038/s41467-026-70379-2.
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