Commentary - genetic susceptibility as a risk enhancer in secondary prevention: Contextualizing the CAD polygenic risk score

Coronary artery disease (CAD) remains the leading cause of mortality in the United States, and despite decades of innovation in secondary prevention, recurrent cardiovascular events continue to occur, amassing an enormous clinical and economic burden. The traditional paradigm of secondary prevention centers on aggressive modification of established risk factors, such as antiplatelet therapy, maintaining normotension, euglycemia, reducing excess adiposity, and lowering atherogenic particle number [1]. Each of these risk factors carries a well-characterized and relatively predictable incremental risk for recurrent events. However, a significant proportion of patients suffer recurrent events despite achieving guideline-directed targets across multiple domains, suggesting that conventional phenotyping does not completely capture the full spectrum of an individual’s cardiovascular risk.

The advent of polygenic risk scores (PRS) has introduced a promising but incompletely realized tool for refining cardiovascular risk stratification in secondary prevention. By aggregating the cumulative burden of millions of common genetic variants associated with CAD susceptibility, a PRS displays risk orthogonal to previously captured traditional risk factors. In this issue of the AJPC, Halford and colleagues advance this evidence base in an important and underexplored direction, using a large health system-based cohort in the U.S. They find that elevated polygenic susceptibility to CAD carries an approximately 6% increased cumulative incidence of recurrent CAD, which improved the AUC for predicting recurrent CAD by approximately 4% beyond clinical risk factors This finding has important implications for how clinicians stratify and manage the highest risk patients.

The authors find that patients with CAD who were previously deemed to not be at high clinical risk, had higher risk if they had a higher percentile CAD PRS. Specifically, each standard deviation increase in PRS confers approximately a 25% increase in the odds of CAD recurrence. This continuous dose-response relationship is clinically important, since it positions polygenic risk along a gradient rather than a discreet threshold, distinguishing it conceptually from the monogenic risk variants such as a PCSK9 gain-of function or LDLR loss-of-function variants.

It has been previously documented that a higher PRS is associated with CAD progression, but most of these findings were documented in European cohorts [2]. While foundational, genetic risk correlates closely with ancestry, so the demographic homogeneity of previous studies may limit generalizability to the racially and ethnically diverse U.S. population. The current study which utilizes the MGBB in the northeastern U.S. offers a more externally valid setting for U.S.-based practice which can be contextualized within the 2026 U.S. Dyslipidemia Guidelines on secondary prevention of CAD.

Practice implication I: optimizing therapy for those already highest risk

The 2026 U.S. Dyslipidemia guidelines articulate a “Classify, Personalize, Reclassify” framework for risk stratification in primary prevention [1]. For a patient without existing CAD, this framework recommends first initiating a calculation of risk using the PREVENT calculator [3]. Then, the framework encourages “personalization” by identifying risk enhancers, not previously accounted for including systemic inflammatory disease, cardio-kidney-metabolic syndrome, elevated lipoprotein(a), and a variety of other largely clinical or serological risk factors. Finally, the framework encourages “reclassification” of risk level accounting for these risk enhancers. The totality of this mandate raises a legitimate clinical question – if treatment is already maximized, what does an elevated PRS practically add to clinical decision-making?

Management can be seen as analogous to a patient having an elevated lipoprotein(a), a genetic risk factor with demonstrated pathogenicity for CAD independent of preexisting dyslipidemia. Despite being considered a risk enhancer, as of time of authorship, there are no FDA approved medications to treat elevated lipoprotein(a). Guidelines recommend intensification of treatment of other modifiable risk factors to reduce the burden of the elevated lipoprotein(a) [1]. An elevated PRS can be conceptualized as a risk enhancer for secondary prevention populations and can influence intensity of treatment for other comorbidities.

Looking forward, the most recent ECS/EAS Dyslipidemia Guidelines define an “extremely high risk” cohort within secondary prevention, for those patients with recurrent events despite maximal therapy or who have polyvascular disease. These guidelines recommend an LDL-C goal of <40mg/dL [4]. As U.S.-based guidelines continue to evolve toward additional granularity, a composite of clinical and genetic risk factors could reasonably inform future thresholds for LDL-C targets.

Practice implication II: the impact of PRS on non-pharmacological therapy

This contribution has downstream implications for non-pharmacological intervention as well. Cardiac rehabilitation, nutrition counseling, and structured psychosocial support are each efficacious adjunctive strategies in secondary prevention, but their allocation in resource-constrained health systems is often a question of economics not health outcomes [5]. A validated genetic risk signal has the potential to meaningfully inform triage decisions for these finite resources, by directing high-intensity and higher-frequency behavioral interventions toward secondary prevention patients with the greatest residual risk.

Practice implication III: the SMURF-less patient and the limits of clinical phenotyping

An increasingly recognized segment of those with CAD are those who lack standard modifiable risk factors (SMURFs), which account for 11% of STEMIs in a 2023 review [6]. These patients present as a fundamental challenge to conventional risk paradigms and are notoriously undertreated in primary prevention since they definitionally fall below risk detection thresholds. These patients also notoriously suffer from undertreatment with respect to medical management in the secondary setting as well [7]. In this segment, polygenic profiling may function beyond an explanatory tool, and inform treatment intensity, as well as create a rational foundation for cascade screening among first-degree relatives how may carry similar inherited risk.

Halford and colleagues advance a critical and underdeveloped evidence base in a population of clinical relevance. Their findings support that polygenic susceptibility is an independent, plausible, quantifiable risk factor for recurrent CAD in a U.S. population, with an effect size that warrants attention even within the constraints of guideline-directed maximal therapy. In the age of precision medicine, having more patient data does not always improve patient care. The vital question is how that information is operationalized: how health systems build the infrastructure needed to scale the measurement of PRS, how clinicians communicate that added risk with patients, and how guidelines evolve to incorporate genetic risk enhancers alongside their serological and image-based counterparts.

Author agreement

On behalf of all authors, I confirm that this manuscript represents original work that has not been published previously and is not under consideration elsewhere. All authors have made substantial contributions to the conception of the study and have participated in drafting or critically revising the manuscript; have approved the final submitted version; and agree to be accountable for all aspects of the work. The authorship list is accurate and complete, and any future changes will follow Elsevier’s authorship change policy, requiring written agreement from all authors. Authors have submitted the required declarations of competing interests, disclosing any relevant financial or personal relationships, or have indicated that they have nothing to declare. All funding sources supporting this work have been fully acknowledged, including the role of funders in study design, data collection, analysis, interpretation, and manuscript preparation; or, if applicable, we have noted that no specific funding was received. The study complies with ethical standards and Elsevier’s Publishing Ethics Policy. All copyrighted material has been appropriately credited, and permissions have been obtained where necessary.

CRediT authorship contribution statement

Marc Perlman: Conceptualization, Writing – original draft. Chang Liu: Writing – review & editing. Anurag Mehta: Writing – review & editing.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Anurag Mehta reports a relationship with Eli Lilly and Company that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.