Key result
Cardiovascular risk prediction is advancing by pairing molecular proteomic predictors with earlier subclinical atherosclerosis and later heart failure outcomes to capture residual risk missed by conventional clinical scores.
Captured external expert commentary on this paper, strongest first. Original sources are linked where available.
“Given that preventive therapies like statins are typically targeted according to predicted risk, these apparent low-risk individuals may be systematically excluded from interventions that could otherwise alter their disease trajectory, underscoring the real-world consequences of residual risk misclassification.”
Cardiovascular risk prediction is evolving beyond traditional clinical risk scores by incorporating molecular omics and dynamic monitoring to predict both early subclinical atherosclerosis and later heart failure, though external validation and proof of clinical utility remain necessary.
Current risk equations miss many cardiovascular events in people classified as low risk. Prediction is simultaneously advancing on two axes: broadening predictors toward molecular omics, and expanding outcomes toward earlier subclinical atherosclerosis and later heart failure.
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Tang et al. (2026) conducted an editorial in Cardiovascular disease risk prediction (from subclinical atherosclerosis to heart failure). Emerging molecular (proteomic/polygenic) and dynamic (wearable/EHR) cardiovascular risk prediction models vs. Traditional multivariable clinical risk equations (e.g., Framingham, PCE, PREVENT, SCORE2, QRISK4, China-PAR) was evaluated on Cardiovascular risk discrimination and reclassification across the disease trajectory (from subclinical atherosclerosis progression to incident ASCVD and heart failure). Cardiovascular risk prediction is advancing by pairing molecular proteomic predictors with earlier subclinical atherosclerosis and later heart failure outcomes to capture residual risk missed by conventional clinical scores.
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