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Highly elevated polygenic risk scores are better predictors of myocardial infarction risk early in life than later
Monica Isgut1, Jimeng Sun2, Arshed A Quyyumi3
1School of Biological Sciences, Georgia Institute of Technology, 950 Atlantic Drive, EBB1 Suite 2115, Georgia Tech, Atlanta, GA, 30332, USA.
Insights
Polygenic risk scores (PRS) enhance early-life cardiovascular risk prediction when traditional factors are unknown. However, their added value diminishes by middle age as more risk factors become apparent.
Area of Science:
- Cardiovascular disease genetics
- Predictive modeling in medicine
- Genomic risk assessment
Background:
- The clinical utility of polygenic risk scores (PRS) alongside conventional risk factors for cardiovascular disease prediction remains debated.
- This study investigates the additive value of four PRS (194, 46K, 1.5M, 6M SNPs) in predicting ischemic heart disease (IHD), myocardial infarction (MI), and early MI.
Purpose of the Study:
- To assess the clinical utility of incorporating PRS with established risk factors for predicting IHD, MI, and early MI.
- To determine if PRS improve risk prediction models at different life stages.
Main Methods:
- A logistic regression (LR) model was trained using UK Biobank data from European ancestry individuals.
- Features included established-at-birth and lifespan risk factors, with and without PRS.
- The model predicted IHD, MI, and early MI.
Main Results:
- Adding PRS significantly improved the Area Under the Curve (AUC) for IHD (0.62 to 0.67) and MI (0.67 to 0.73) when only using established-at-birth risk factors.
- For early MI, AUC improved from 0.70 to 0.79 with PRS.
- When all risk factors were used, PRS only significantly increased disease prevalence in the highest risk percentiles (98th-99th).
Conclusions:
- PRS are valuable for cardiovascular risk stratification early in life, particularly when later-life risk factors are unknown.
- The incremental benefit of PRS for cardiovascular risk prediction becomes marginal in middle age when numerous risk factors are already known.
Background:
Several polygenic risk scores (PRS) have been developed for cardiovascular risk prediction, but the additive value of including PRS together with conventional risk factors for risk prediction is questionable. This study assesses the clinical utility of including four PRS generated from 194, 46K, 1.5M, and 6M SNPs, along with conventional risk factors, to predict risk of ischemic heart disease (IHD), myocardial infarction (MI), and first MI event on or before age 50 (early MI).
Methods:
A cross-validated logistic regression (LR) algorithm was trained either on ~ 440K European ancestry individuals from the UK Biobank (UKB), or the full UKB population, including as features different combinations of conventional established-at-birth risk factors (ancestry, sex) and risk factors that are non-fixed over an individual's lifespan (age, BMI, hypertension, hyperlipidemia, diabetes, smoking, family history), with and without also including PRS. The algorithm was trained separately with IHD, MI, and early MI as prediction labels.
Results:
When LR was trained using risk factors established-at-birth, adding the four PRS significantly improved the area under the curve (AUC) for IHD (0.62 to 0.67) and MI (0.67 to 0.73), as well as for early MI (0.70 to 0.79). When LR was trained using all risk factors, adding the four PRS only resulted in a significantly higher disease prevalence in the 98th and 99th percentiles of both the IHD and MI scores.
Conclusions:
PRS improve cardiovascular risk stratification early in life when knowledge of later-life risk factors is unavailable. However, by middle age, when many risk factors are known, the improvement attributed to PRS is marginal for the general population.
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