Polygenic Prediction of Recurrent Events After Early-Onset Myocardial Infarction.

Area of Science:

• Cardiovascular Genetics
• Precision Medicine
• Epidemiology

Background:

• Myocardial infarction (MI) is a complex condition influenced by both lifestyle and genetic factors.
• Early-onset MI (before age 45) presents unique challenges for risk prediction and management.
• Understanding genetic contributions is crucial for improving risk stratification in MI survivors.

Purpose of the Study:

• To evaluate the predictive value of genetic risk, specifically polygenic scores (PGS), for recurrent cardiovascular events after early-onset MI.
• To compare the predictive power of PGS against traditional clinical risk factors in this patient cohort.
• To assess the incremental benefit of incorporating PGS into existing risk prediction models.

Main Methods:

• The Italian Genetic Study of Early-Onset Myocardial Infarction enrolled patients with MI before age 45.
• Monogenic variants and a coronary artery disease polygenic score (PGS) were assessed.
• Cox proportional hazards models were used to analyze the association of clinical variables and PGS with recurrent cardiovascular events over a 19.9-year follow-up.

Main Results:

• A higher PGS was strongly associated with an increased hazard of recurrent events (HR, 1.21 per SD increase; P=4.04×10⁻⁶).
• The PGS emerged as the strongest determinant of recurrent event risk, outperforming clinical factors.
• Adding PGS to clinical risk factors significantly improved the predictive performance (C-index) for recurrent events.

Conclusions:

• Polygenic scores are powerful predictors of event recurrence in patients with early-onset MI.
• Incorporating genetic risk information, via PGS, enhances the discrimination of cardiovascular event prediction models.
• These findings support the potential of genetic profiling for personalized risk assessment and secondary prevention strategies in younger MI patients.