Highlights
- The novel AHA PREVENT equations adequately predict 10-year ASCVD risk across diverse populations, including those with elevated lipoprotein(a) [Lp(a)].
- Elevated Lp(a) (≥125 nmol/L) independently associates with increased risk of coronary heart disease (CHD), ASCVD, heart failure (HF), and total cardiovascular disease, particularly in low- and borderline-risk individuals.
- Incorporation of Lp(a) into existing PREVENT equations modestly improves risk discrimination and reclassification, with the greatest benefit observed among borderline- and low-risk groups.
- Genetic and biomarker analyses affirm Lp(a) as a distinct and clinically relevant risk factor complementary to traditional lipids and calcium scoring in cardiovascular risk assessment.
Background
Cardiovascular disease (CVD) remains a leading cause of morbidity and mortality globally. Accurate risk stratification is essential for primary prevention and personalized management strategies. The American Heart Association’s PREVENT equations represent an evolution in risk assessment, incorporating traditional risk factors to estimate 10-year risk of ASCVD events. However, Lp(a), a genetically determined lipoprotein particle linked to atherosclerosis and thrombosis, is not included despite robust evidence associating elevated Lp(a) with increased cardiovascular risk. This omission reflects ongoing challenges in integrating emerging biomarkers into widely validated clinical tools. Recent large-scale cohort analyses, such as those leveraging the Multi-Ethnic Study of Atherosclerosis (MESA) and UK Biobank databases, provide critical data on whether and how Lp(a) measurement may refine ASCVD risk prediction beyond current standards.
Key Content
1. Cohort Studies Evaluating PREVENT Equations and Lp(a)
The pivotal 2025 study by Bhatia et al. examined 314,783 participants from MESA (n=6,670) and UK Biobank (n=308,113) without known CVD, stratifying subjects by elevated Lp(a) levels (≥125 nmol/L). Participants were categorized by PREVENT-estimated 10-year risk (<5%, 5%-<7.5%, 7.5%-<20%, ≥20%) for CHD, ASCVD, HF, and total CVD outcomes, with endpoint events ascertained over follow-up.
Results demonstrated that the observed ASCVD event rates largely conformed to predicted risk categories across Lp(a) strata. Nonetheless, elevated Lp(a) conferred approximately 30% higher hazard for ASCVD (HR 1.30; 95% CI, 1.22-1.38) and similar relative risk increases for CHD, HF, and total CVD. Interestingly, the strongest association for CHD occurred within individuals classified as low risk, underscoring Lp(a)'s potential to unmask underappreciated risk.
Incorporation of elevated Lp(a) into the PREVENT equations yielded statistically significant though modest improvements in risk reclassification metrics, particularly the category-free net reclassification improvement (NRI ~0.058). This enhancement was most impactful for borderline-risk individuals when Lp(a) was dichotomized, and among low-risk individuals when analyzed as a continuous variable. For CHD risk prediction, the most pronounced NRI gain was observed in low- and high-risk subgroups.
2. Lp(a) Beyond PREVENT: Genetic and Biomarker Perspectives
Complementary evidence from genetic subtyping studies in severe hypercholesterolemia (e.g., UK Biobank exome analyses) confirms that elevated Lp(a) identifies individuals with substantially increased incident coronary artery disease (CAD) risk independent of LDL cholesterol levels. Monogenic familial hypercholesterolemia and ‘two-hit’ phenotypes combining polygenic hypercholesterolemia with elevated Lp(a) show the highest CAD event rates, underscoring Lp(a)’s pathogenic role.
Furthermore, lipid biomarker studies within MESA reveal that unlike LDL-C or apolipoprotein B, Lp(a) correlates only modestly with coronary artery calcium progression, suggesting Lp(a) mediates risk via distinct mechanisms beyond calcific atherosclerosis detectable by imaging.
3. Lp(a) and Broader Cardiovascular Outcomes
Large prospective cohort data, including from UK Biobank, implicate elevated Lp(a) not only in ASCVD but also in atrial fibrillation risk, independent of systemic inflammation markers such as hs-CRP. Additionally, analyses explore Lp(a)’s interaction with other lipid traits in relation to bone health outcomes, highlighting the complex pleiotropic biology of Lp(a).
4. Biomarker Integration and Secondary Prevention Implications
Emerging data from secondary prevention cohorts highlight the nuanced role of Lp(a) alongside other biomarkers such as cystatin C and HbA1c in refining risk for recurrent ASCVD events. However, in some analyses, Lp(a) does not significantly improve model discrimination beyond robust clinical predictors, suggesting contextual utility dependent on population and risk setting.
Expert Commentary
The integration of Lp(a) into cardiovascular risk prediction models like the PREVENT equations represents a pivotal step toward precision medicine in cardiology. Despite the current modest improvement in metrics such as NRI, the independent and additive risk conveyed by Lp(a) is biologically plausible and clinically relevant. Elevated Lp(a) is a genetically determined, largely nonmodifiable lipoprotein particle promoting proatherogenic and prothrombotic pathways via oxidized phospholipids and enhanced LDL-like particle function.
Guidelines have yet to universally endorse routine Lp(a) screening in primary prevention, partly due to assay standardization challenges and limited therapeutic options. However, novel Lp(a)-lowering agents, including antisense oligonucleotides and RNA interference therapeutics, are emerging and may alter future clinical paradigms.
Methodological challenges remain in optimal risk categorization incorporating Lp(a), particularly regarding threshold choice, continuous versus categorical modeling, and interaction with other risk factors such as LDL-C and inflammation. The current evidence supports the selective assessment of Lp(a) in individuals with borderline or intermediate risk, family history of premature CVD, or severe hypercholesterolemia.
Additionally, the disconnect between Lp(a) and coronary artery calcium suggests complementary assessment strategies combining biomarker profiling and imaging may better capture individual risk phenotypes.
Conclusion
The combined data from large-scale cohort analyses affirm that the AHA PREVENT equations provide robust ASCVD risk prediction across diverse populations, including those with elevated Lp(a). Elevated Lp(a) independently augments cardiovascular risk, and its addition modestly enhances risk classification, especially in borderline- and low-risk individuals. Future research should focus on refining Lp(a) measurement incorporation, characterizing subgroup-specific benefits, and evaluating integration with emerging Lp(a)-lowering therapies.
Clinicians should consider Lp(a) testing in selected populations to personalize risk assessment and guide preventive strategies. As therapeutic landscapes evolve, Lp(a) may transition from a primarily prognostic biomarker to a modifiable risk factor central to cardiovascular disease prevention.
References
- Bhatia HS, Ambrosio M, Razavi AC, et al. AHA PREVENT Equations and Lipoprotein(a) for Cardiovascular Disease Risk: Insights From MESA and the UK Biobank. JAMA Cardiol. 2025;10(8):810-818. doi:10.1001/jamacardio.2025.1603. PMID:40465279.
- Xing Y, Yu S, et al. Lipoprotein(a) elevation independently associates with incident atrial fibrillation irrespective of inflammatory status. Heart Rhythm. 2025;22(12):e1146-e1154. doi:10.1016/j.hrthm.2025.08.001. PMID:40780691.
- Dangas G, et al. Subtyping severe hypercholesterolemia by genetic determinant to stratify risk of coronary artery disease. Arterioscler Thromb Vasc Biol. 2023;43(10):2058-2067. doi:10.1161/ATVBAHA.123.319341. PMID:37589137.
- Nasir K, et al. Lipoprotein(a) and coronary artery calcium in comparison with other lipid biomarkers: The multi-ethnic study of atherosclerosis. J Clin Lipidol. 2023;17(4):538-548. doi:10.1016/j.jacl.2023.06.002. PMID:37357049.
- Aragam KG, et al. Genetic, sociodemographic, lifestyle, and clinical risk factors of recurrent coronary artery disease events: a population-based cohort study. Eur Heart J. 2023;44(36):3456–3465. doi:10.1093/eurheartj/ehad380. PMID:37350734.
- Gencer B, et al. Adding traditional and emerging biomarkers for risk assessment in secondary prevention: a prospective cohort study of 20 656 patients with cardiovascular disease. Eur J Prev Cardiol. 2025;32(7):585-595. doi:10.1093/eurjpc/zwae352. PMID:39474888.
