Why the study?
Pooled Cohort Equations are used to predict cardiovascular disease risk, but whether the inclusion of other variables can improve risk prediction beyond recalibrated equations remains unclear.
Does the addition of personal/family medical history, blood-based markers, and genetic information improve cardiovascular risk prediction beyond recalibrated Pooled Cohort Equations in healthy adults?
Does the addition of personal/family medical history, blood-based markers, and genetic information improve cardiovascular risk prediction beyond recalibrated Pooled Cohort Equations in healthy adults?
Answers are grounded in this paper and cite their sources.
The addition of personal and family medical history, blood-based biomarkers, and genetic information significantly improves cardiovascular risk prediction beyond standard Pooled Cohort Equations.
Background Pooled Cohort Equations (PCE) are used to predict cardiovascular disease (CVD) risk. Inclusion of other variables may improve risk prediction. Objective Identify variables improving CVD risk prediction beyond recalibrated PCE. Design Prospective cohort study; sex-stratified Cox survival models with LASSO stability selection to predict CVD in non-overlapping subsets: variable selection (40%), model training (30%) and testing (30%). Setting UK population. Participants UK Biobank: 121,724 and 182,632 healthy men and women, respectively, aged 38-73 years at baseline. Measurements Personal/family medical history; lifestyle factors; genetic, biochemical, hematological, and metabolomic blood markers. Outcomes were incident hospitalization or mortality from CVD. Results There were 11,899 (men) and 9,110 (women) incident CVD cases with median 12.1 years follow-up. Variables selected for both men and women were: age, albumin, antihypertensive medication, apolipoprotein B, atrial fibrillation, C-reactive protein, current smoker, cystatin C, family history of coronary artery disease, glycated hemoglobin, polygenic risk score (PRS) for CVD and systolic blood pressure. Also selected: apolipoprotein A1, lipoprotein(a), white blood cell count, deprivation index (men); triglycerides (women). C-statistics for recalibrated PCE were 0.67 [0.66-0.68] and 0.69 [0.68-0.70] in men and women, respectively, improving to 0.71 [0.70-0.72] and 0.72 [0.71-0.73] with LASSO stably selected variables. Categorical net reclassification improvement (7.5% risk threshold) versus PCE was 0.054 [0.038-0.070] (men) and 0.081 [0.063-0.099] (women). Addition of targeted metabolomic data to LASSO stability selection did not improve predictive accuracy. Limitations Analyses were done in a single population study and require external replication. Conclusion Additional personal/family medical history, blood-based markers and genetic information improve CVD risk prediction beyond PCE. Funding source National Institute for Health Research Academic Clinical Fellowship (JE); Medical Research Council studentship (BB); European Union H2020 (MC-H).
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Elliott et al. (2023) studied this question.
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