Polygenic risk score adds to a clinical risk score in the prediction of cardiovascular disease in a clinical setting

Translation of genomics into routine cardiological practice: insights from a European Society of Cardiology Cardiovascular Round Table

Cardiovascular diseases (CVD) remain the leading cause of death globally and there is an urgent need for innovative approaches to treatment. One emerging avenue is genetic therapies, which hold particular promise for diseases with a monogenic basis. Gene silencing techniques using antisense oligonucleotides or ribonucleic acid interference strategies are currently at the forefront of genetic therapies in CVD, with several ribonucleic acid-targeted therapies already approved for the treatment of conditions such as familial hypercholesterolaemia and transthyretin amyloidosis. For diseases caused by loss-of-function genetic variants, there is growing interest in gene therapy, applying either gene replacement strategies using adeno-associated virus vectors or gene editing strategies using tools such as the clustered regularly interspaced short palindromic repeats and clustered regularly interspaced short palindromic repeats-associated protein-9 system. Preclinical studies have highlighted the potential of this technology in CVD and promising data are beginning to emerge from early-phase clinical trials. During a European Society of Cardiology Cardiovascular Round Table workshop, the challenges of translating these novel therapeutic strategies to the routine cardiology clinic were discussed. Several key priorities were identified, including the need for disease-specific preclinical models, precision diagnostics, adequately powered clinical trials with meaningful endpoints, and enhanced education of healthcare professionals and patients. The Cardiovascular Round Table also considered the role of polygenic risk scores in risk stratification and how these can potentially be implemented in clinical practice.

Clinical utility and implementation of polygenic risk scores for predicting cardiovascular disease

Genome-wide association studies have revealed hundreds of genetic variants associated with cardiovascular diseases (CVD). Polygenic risk scores (PRS) can capture this information in a single metric and hold promise for use in CVD risk prediction. Importantly, PRS information can reflect the causally mediated risk to which the individual is exposed throughout life. Although European Society of Cardiology guidelines do not currently advocate their use in routine clinical practice, PRS are commercially available and increasingly sought by clinicians, health systems, and members of the public to inform personalized health care decision-making. This clinical consensus statement provides an overview of the scientific basis of PRS and evidence to date on their role in CVD risk prediction for the purposes of disease prevention. It provides the reader with a summary of the opportunities and challenges for implementation and identifies current gaps in supporting evidence. The document also lays out a potential roadmap by which the scientific and clinical community can navigate any future transition of PRS into routine clinical care. Finally, clinical scenarios are presented where information from PRS may hold most value and discuss organizational frameworks to enable responsible use of PRS testing while more evidence is being generated by clinical studies.

New Approaches to Lipoproteins for the Prevention of Cardiovascular Events

Atherosclerotic cardiovascular disease (ASCVD) is a leading global cause of mortality, and recent research has underscored the critical role of lipoproteins in modulating cardiovascular (CV) risk. Elevated low-density lipoprotein cholesterol (LDL-C) levels have been linked to increased CV events, and while numerous trials have confirmed the efficacy of lipid-lowering therapies (LLT), significant gaps remain between recommended LDL-C targets and real-world clinical practice. This review addresses care gaps in LLT, emphasizing the necessity for innovative approaches that extend beyond LDL-C management. It explores combination therapy approaches such as statins combined with ezetimibe or PCSK9 inhibitors, which have shown promise in enhancing LDL-C reduction and improving outcomes in high-risk patients. Additionally, this review discusses new approaches in lipid modification strategies, including bempedoic acid, inclisiran, and drugs that lower Lp(a), highlighting their potential for CV risk reduction. Furthermore, it emphasizes the potential of polygenic risk scores to guide LLT and lifestyle changes despite challenges in implementation and genetic testing ethics. This article discusses the current guidelines and proposes innovative approaches for optimizing lipoprotein management, ultimately contributing to improved patient outcomes in ASCVD prevention.

Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit

The transferability of polygenic scores across population groups is a major concern with respect to the equitable clinical implementation of genomic medicine. Since genetic associations are identified relative to the population mean, inevitably differences in disease or trait prevalence among social strata influence the relationship between PGS and risk. Here we quantify the magnitude of PGS-by-Exposure (PGSxE) interactions for seven human diseases (coronary artery disease, type 2 diabetes, obesity thresholded to body mass index and to waist-to-hip ratio, inflammatory bowel disease, chronic kidney disease, and asthma) and pairs of 75 exposures in the White-British subset of the UK Biobank study (n=408,801). Across 24,198 PGSxE models, 746 (3.1%) were significant by two criteria, at least three-fold more than expected by chance under each criterion. Predictive accuracy is significantly improved in the high-risk exposures and by including interaction terms with effects as large as those documented for low transferability of PGS across ancestries. The predominant mechanism for PGS×E interactions is shown to be amplification of genetic effects in the presence of adverse exposures such as low polyunsaturated fatty acids, mediators of obesity, and social determinants of ill health. We introduce the notion of the proportion needed to benefit (PNB) which is the cumulative number needed to treat across the range of the PGS and show that typically this is halved in the 70th to 80th percentile. These findings emphasize how individuals experiencing adverse exposures stand to preferentially benefit from interventions that may reduce risk, and highlight the need for more comprehensive sampling across socioeconomic groups in the performance of genome-wide association studies.