Incorporating a polygenic risk score and a polysocial score into a comprehensive prediction model improved the performance of clinical risk calculators for incident coronary heart disease.
Cohort
Does a prediction model incorporating a polygenic risk score and a polysocial score improve incident coronary heart disease risk prediction compared to standard clinical risk calculators in UK Biobank participants?
Incorporating polygenic and polysocial risk scores into standard clinical risk calculators improves the prediction of incident coronary heart disease.
BACKGROUND: Clinical risk calculators for coronary heart disease (CHD) do not include genetic, social, and lifestyle-psychological risk factors. OBJECTIVE: To improve CHD risk prediction by developing and evaluating a prediction model that incorporated a polygenic risk score (PRS) and a polysocial score (PSS), the latter including social determinants of health and lifestyle-psychological factors. DESIGN: Cohort study. SETTING: United Kingdom. PARTICIPANTS: UK Biobank participants recruited between 2006 and 2010. MEASUREMENTS: from 100 related covariates. Machine-learning and time-to-event analyses and model performance indices. RESULTS: into PREVENT and QRISK3. LIMITATION: A predominantly White cohort; possible healthy participant effect and ecological fallacy. CONCLUSION: improved the performance of clinical risk calculators. PRIMARY FUNDING SOURCE: National Human Genome Research Institute.
Naderian et al. (Mon,) conducted a cohort in Coronary heart disease. Prediction model incorporating polygenic risk score (PRS) and polysocial score (PSS) vs. Clinical risk calculators (PREVENT and QRISK3) was evaluated on Model performance for incident coronary heart disease. Incorporating a polygenic risk score and a polysocial score into a comprehensive prediction model improved the performance of clinical risk calculators for incident coronary heart disease.