A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease

American society for preventive cardiology 2024 cardiovascular disease prevention: Highlights and key sessions

Groundbreaking strategies for preventive cardiology were showcased at the 2024 American Society for Preventive Cardiology (ASPC) Congress on Cardiovascular Disease (CVD) Prevention held in Salt Lake City, Utah, from August 2nd to 4th, 2024. The event featured 69 moderators and 13 scientific sessions comprised of 98 topics, 36 satellite events, 133 poster presentations, and 27 lifestyle classes. The conference highlighted innovative strategies focused on integrating cardiovascular, kidney, and metabolic health, presenting a cohesive approach for managing complex, interrelated conditions. Pivotal studies have addressed the role of lipid-lowering therapies, the benefits of early statin initiation, and the importance of precision medicine in preventing CVD. The ASPC's emphasis on translating this research into practical clinical tools has the potential to revolutionize preventive care strategies, making strides toward reducing the burden of CVD globally and improving long-term patient outcomes through personalized and early intervention approaches.

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.

Artificial Intelligence in Ischemic Heart Disease Prevention

PURPOSE OF REVIEW

This review discusses the transformative potential of artificial intelligence (AI) in ischemic heart disease (IHD) prevention. It explores advancements of AI in predictive modeling, biomarker discovery, and cardiovascular imaging. Finally, considerations for clinical integration of AI into preventive cardiology workflows are reviewed.

RECENT FINDINGS

AI-driven tools, including machine learning (ML) models, have greatly enhanced IHD risk prediction by integrating multimodal data from clinical sources, patient-generated inputs, biomarkers, and imaging. Applications in these various data sources have demonstrated superior diagnostic accuracy compared to traditional methods. However, ensuring algorithm fairness, mitigating biases, enhancing explainability, and addressing ethical concerns remain critical for successful deployment. Emerging technologies like federated learning and explainable AI are fostering more robust, scalable, and equitable adoption. AI holds promise in reshaping preventive cardiology workflows, offering more precise risk assessment and personalized care. Addressing barriers related to equity, transparency, and stakeholder engagement is key for seamless clinical integration and sustainable, lasting improvements in cardiovascular care.

Genetically predicted lipoprotein(a) associates with coronary artery plaque severity independent of low-density lipoprotein cholesterol

AIMS

Elevated lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerotic cardiovascular disease, but the mechanisms of risk are debated. Studies have found inconsistent associations between Lp(a) and measurements of atherosclerosis. We aimed to assess the relationship between Lp(a), low-density lipoprotein cholesterol (LDL-C), and coronary artery plaque severity.

METHODS AND RESULTS

The study population consisted of participants of the Million Veteran Program who have undergone an invasive angiogram. The primary exposure was genetically predicted Lp(a) estimated by a polygenic score. Genetically predicted LDL-C was also assessed for comparison. The primary outcome was coronary artery plaque severity categorized as normal, non-obstructive disease, one-vessel disease, two-vessel disease, and three-vessel or left main disease. Among 18 927 adults of genetically inferred European ancestry and 4039 adults of genetically inferred African ancestry, we observed consistent associations between genetically predicted Lp(a) and obstructive coronary plaque, with effect sizes trending upward for increasingly severe categories of disease. Associations were independent of risk factors, clinically measured LDL-C and genetically predicted LDL-C. However, we did not find strong or consistent evidence for an association between genetically predicted Lp(a) and risk for non-obstructive plaque.

CONCLUSION

Genetically predicted Lp(a) is positively associated with coronary plaque severity independent of LDL-C, consistent with Lp(a) promoting atherogenesis. However, the effects of Lp(a) may be greater for progression of plaque to obstructive disease than for the initial development of non-obstructive plaque. A limitation of this study is that Lp(a) was estimated using genetic markers and could not be directly assayed nor could apo(a) isoform size.

Early-onset atherosclerotic cardiovascular disease

Recent trends indicate a concerning increase in early-onset atherosclerotic cardiovascular disease (ASCVD) among younger individuals (men aged <55 years women aged <65 years). These findings highlight the pathobiology of ASCVD as a disease process that begins early in life and underscores the need for more tailored screening methods and preventive strategies. Increasing attention has been placed on the growing burden of traditional cardiometabolic risk factors in young individuals while also recognizing unique factors that mediate risk of pre-mature atherosclerosis in this demographic such as substance use, socioeconomic disparities, adverse pregnancy outcomes, and chronic inflammatory states that contribute to the increasing incidence of early ASCVD. Additionally, mounting evidence has pointed out significant disparities in the diagnosis and management of early ASCVD and cardiovascular outcomes based on sex and race. Moving towards a more personalized approach, emerging data and technological developments using diverse tools such as polygenic risk scores and coronary artery calcium scans have shown potential in earlier detection of ASCVD risk. Thus, we review current evidence on causal risk factors that drive the increase in early ASCVD and highlight emerging tools to improve ASCVD risk assessment in young individuals.

Leveraging genetic ancestry continuum information to interpolate PRS for admixed populations

The relatively low representation of admixed populations in both discovery and fine-tuning individual-level datasets limits polygenic risk score (PRS) development and equitable clinical translation for admixed populations. Under the assumption that the most informative PRS weight for a homogeneous sample varies linearly in an ancestry continuum space, we introduce a Genetic Distance-assisted PRS Combination Pipeline for Diverse Genetic Ancestries (DiscoDivas) to interpolate a harmonized PRS for diverse, especially admixed, ancestries, leveraging multiple PRS weights fine-tuned within single-ancestry samples and genetic distance. DiscoDivas treats ancestry as a continuous variable and does not require shifting between different models when calculating PRS for different ancestries. We generated PRS with DiscoDivas and the current conventional method, i.e. fine-tuning multiple GWAS PRS using the matched or similar ancestry samples. DiscoDivas generated a harmonized PRS of the accuracy comparable to or higher than the conventional approach, with the greatest advantage exhibited in admixed individuals.

From Genetic Findings to new Intestinal Molecular Targets in Lipid Metabolism

PURPOSE OF REVIEW

While lipid-lowering therapies demonstrate efficacy, many patients still contend with significant residual risk of atherosclerotic cardiovascular diseases (ASCVD). The intestine plays a pivotal role in regulating circulating lipoproteins levels, thereby exerting influence on ASCVD pathogenesis. This review underscores recent genetic findings from the last six years that delineate new biological pathways and actors in the intestine which regulate lipid-related ASCVD risk.

RECENT FINDINGS

Specifically, we detail the role of LIMA1 in cholesterol absorption within enterocytes, the function of PLA2G12B in the expansion and lipidation of chylomicrons, the involvement of SURF4 in lipoprotein secretion, and the discovery of a gut-derived hormone named CHOLESIN that modulates cholesterol homeostasis through GPR146 via a gut-liver crosstalk. We further discuss the potential of these newly identified genes and pathways as novel targets for pharmaceutical intervention. Newly identified genetic and intestinal molecular mechanisms offer promising opportunities for preventing and treating ASCVD, but careful evaluation and further research are needed to optimize their clinical application.

Evaluating Performance and Agreement of Coronary Heart Disease Polygenic Risk Scores

Importance

Polygenic risk scores (PRSs) for coronary heart disease (CHD) are a growing clinical and commercial reality. Whether existing scores provide similar individual-level assessments of disease susceptibility remains incompletely characterized.

Objective

To characterize the individual-level agreement of CHD PRSs that perform similarly at the population level.

Design, Setting, and Participants

Cross-sectional study of participants from diverse backgrounds enrolled in the All of Us Research Program (AOU), Penn Medicine BioBank (PMBB), and University of California, Los Angeles (UCLA) ATLAS Precision Health Biobank with electronic health record and genotyping data.

Exposures

Polygenic risk for CHD from published PRSs and new PRSs developed separately from testing samples.

Main Outcomes and Measures

PRSs that performed population-level prediction similarly were identified by comparing calibration and discrimination of models of prevalent CHD. Individual-level agreement was tested with intraclass correlation coefficient (ICC) and Light κ.

Results

A total of 48 PRSs were calculated for 171 095 AOU participants. The mean (SD) age was 56.4 (16.8) years. A total of 104 947 participants (61.3%) were female. A total of 35 590 participants (20.8%) were most genetically similar to an African reference population, 29 801 (17.4%) to an admixed American reference population, 100 493 (58.7%) to a European reference population, and the remaining to Central/South Asian, East Asian, and Middle Eastern reference populations. There were 17 589 participants (10.3%) with and 153 506 participants without (89.7%) CHD. When included in a model of prevalent CHD, 46 scores had practically equivalent Brier scores and area under the receiver operator curves (region of practical equivalence ±0.02). Twenty percent of participants had at least 1 score in both the top and bottom 5% of risk. Continuous agreement of individual predictions was poor (ICC, 0.373 [95% CI, 0.372-0.375]). Light κ, used to evaluate consistency of risk assignment, did not exceed 0.56. Analysis among 41 193 PMBB and 53 092 ATLAS participants yielded different sets of equivalent scores, which also lacked individual-level agreement.

Conclusions and Relevance

CHD PRSs that performed similarly at the population level demonstrated highly variable individual-level estimates of risk. Recognizing that CHD PRSs may generate incongruent individual-level risk estimates, effective clinical implementation will require refined statistical methods to quantify uncertainty and new strategies to communicate this uncertainty to patients and clinicians.

Polygenic Risk Scores in Human Disease

BACKGROUND

Polygenic risk scores (PRS) are measures of genetic susceptibility to human health traits. With the advent of large data repositories combining genetic data and phenotypic information, PRS are providing valuable insights into the genetic architecture of complex diseases and are transforming the landscape of precision medicine.

CONTENT

PRS have emerged as tools with clinical utility in human disease. Herein, details on how to develop PRS are provided, followed by 5 areas in which they can be used to improve human health: (a) augmenting risk prediction, (b) refining diagnosis, (c) guiding treatment choices, (d) making clinical trials more efficient, and (e) improving public health. Finally, some of the ongoing challenges to the clinical implementation of PRS are noted.

SUMMARY

PRS can offer valuable information for providers and patients, including identifying risk of disease earlier in life and before the onset of clinical risk factors, guiding treatment decisions, improving public health outcomes, and making clinical trials more efficient. The future of genomic-informed risk assessments of disease is through integrated risk models that combine genetic factors including PRS, monogenic, and somatic DNA information with nongenetic risk factors such as clinical risk estimators and multiomic data. However, adopting PRS in a clinical setting at scale faces some challenges, including cross-ancestry performance, standardization and calibration of risk models, downstream clinical decision-making from risk information, and seamless integration into existing health systems.

Genomic Drivers of Coronary Artery Disease and Risk of Future Outcomes After Coronary Angiography

Importance

Disease characteristics of genetically mediated coronary artery disease (CAD) on coronary angiography and the association of genomic risk with outcomes after coronary angiography are not well understood.

Objective

To assess the angiographic characteristics and risk of post-coronary angiography outcomes of patients with genomic drivers of CAD: familial hypercholesterolemia (FH), high polygenic risk score (PRS), and clonal hematopoiesis of indeterminate potential (CHIP).

Design, Setting, and Participants

A retrospective cohort study of 3518 Mass General Brigham Biobank participants with genomic information who underwent coronary angiography was conducted between July 18, 2000, and August 1, 2023.

Exposures

The presence of a genomic risk factor of CAD, defined as FH variant, high CAD PRS, or CHIP driver variation.

Main Outcomes and Measures

Coronary artery disease presentation (stable or acute), angiographic CAD characteristics (severity and burden), angiographic outcomes (repeat angiogram, revascularization, and in-stent restenosis), and clinical outcomes (heart failure and all-cause mortality).

Results

Among 3518 participants (2467 [70.1%] male; median age, 64.0 [IQR, 55.0-72.0] years), 1509 (42.9%) had at least 1 genomic driver of CAD (26 FH, 1191 high CAD PRS, and 466 CHIP) that was associated with the presentation of acute coronary syndromes (adjusted odds ratio, 2.67; 95% CI, 2.19-3.26) and with the presence, burden, and severity of angiographic CAD. This association was driven by FH and CAD PRS. One SD of CAD PRS was associated with a 12.51-point higher Gensini score. During 9 years of follow-up, there was an increased risk among FH carriers for a repeat angiogram (adjusted hazard ratio [AHR], 1.70; 95% CI, 1.02-2.83), and revascularization (AHR, 1.97; 95% CI, 1.02-3.80), and among people with high CAD PRS (repeat angiogram: AHR, 1.79; 95% CI, 1.45-2.22; revascularization: AHR, 1.85; 95% CI, 1.37-2.50; and in-stent restenosis: AHR, 3.89; 95% CI, 2.16-7.01). CHIP carriers had no significant increase in angiographic outcomes but were at higher risk of heart failure (AHR, 1.58; 95% CI, 1.04-2.40) and all-cause mortality (AHR, 1.78; 95% CI, 1.47-2.16).

Conclusions and Relevance

The findings of this study suggest that germline monogenic and polygenic risk are associated with acute coronary syndromes presentation, severity and burden of atherosclerosis, and risk of repeat angiogram, revascularization, and in-stent restenosis. CHIP variant status is associated with incident heart failure and mortality after coronary angiography.

Polygenic Risk Is Associated With Long-Term Coronary Plaque Progression and High-Risk Plaque

BACKGROUND

The longitudinal relation between coronary artery disease (CAD) polygenic risk score (PRS) and long-term plaque progression and high-risk plaque (HRP) features is unknown.

OBJECTIVES

The goal of this study was to investigate the impact of CAD PRS on long-term coronary plaque progression and HRP.

METHODS

Patients underwent CAD PRS measurement and prospective serial coronary computed tomography angiography (CTA) imaging. Coronary CTA scans were analyzed with a previously validated artificial intelligence-based algorithm (atherosclerosis imaging-quantitative computed tomography imaging). The relationship between CAD PRS and change in percent atheroma volume (PAV), percent noncalcified plaque progression, and HRP prevalence was investigated in linear mixed-effect models adjusted for baseline plaque volume and conventional risk factors.

RESULTS

A total of 288 subjects (mean age 58 ± 7 years; 60% male) were included in this study with a median scan interval of 10.2 years. At baseline, patients with a high CAD PRS had a more than 5-fold higher PAV than those with a low CAD PRS (10.4% vs 1.9%; P < 0.001). Per 10 years of follow-up, a 1 SD increase in CAD PRS was associated with a 0.69% increase in PAV progression in the multivariable adjusted model. CAD PRS provided additional discriminatory benefit for above-median noncalcified plaque progression during follow-up when added to a model with conventional risk factors (AUC: 0.73 vs 0.69; P = 0.039). Patients with high CAD PRS had an OR of 2.85 (95% CI: 1.14-7.14; P = 0.026) and 6.16 (95% CI: 2.55-14.91; P < 0.001) for having HRP at baseline and follow-up compared with those with low CAD PRS.

CONCLUSIONS

Polygenic risk is strongly associated with future long-term plaque progression and HRP in patients suspected of having CAD.

Polygenic Prediction of Recurrent Events After Early-Onset Myocardial Infarction

BACKGROUND

Myocardial infarction (MI) is a complex disease caused by both lifestyle and genetic factors. This study aims to investigate the predictive value of genetic risk, in addition to traditional cardiovascular risk factors, for recurrent events following early-onset MI.

METHODS

The Italian Genetic Study of Early-Onset Myocardial Infarction is a cohort study enrolling patients with MI before 45 years. Monogenic variants causing familial hypercholesterolemia were identified, and a coronary artery disease polygenic score (PGS) was calculated. Ten-fold cross-validated Cox proportional hazards models were fitted sequentially including all clinical variables, the PGS, and monogenic variants on the composite outcome of cardiovascular death, recurrent MI, stroke, or revascularization.

RESULTS

During a 19.9-year follow-up, 847 (50.7%) patients experienced recurrent events. Each 1-SD higher PGS was associated with a 21% higher hazard of recurrent events (hazard ratio, 1.21 [95% CI, 1.13-1.31]; P=4.04×10-6). Except for secondary prevention, PGS was the strongest determinant of recurrent event risk (C index, 0.56 [95% CI, 0.54-0.58]) compared with clinical risk factors. Overall, predictive performance of clinical risk factors (C index, 0.69 [95% CI, 0.67-0.71]) improved after adding the PGS (C index, 0.69 [95% CI, 0.68-0.71]; P=0.006). When dividing the population by PGS quintiles, the highest fifth had a 57% higher hazard of recurrent events than the lowest fifth (hazard ratio, 1.57 [95% CI, 1.26-1.96]; P=5.57×10-5).

CONCLUSIONS

When compared with other clinical risk factors, PGS was the strongest predictor of event recurrence among patients with an early-onset MI. Though the discriminative power of recurrent event prediction in this cohort was modest, the addition of PGS significantly improved discrimination.

Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and somatic traits

Co-occurring psychiatric, medical, and substance use disorders (SUDs) are common, but the complex pathways leading to such comorbidities are poorly understood. A greater understanding of genetic influences on this phenomenon could inform precision medicine efforts. We used the Yale-Penn dataset, a cross-sectional sample enriched for individuals with SUDs, to examine pleiotropic effects of genetic liability for psychiatric and somatic traits. Participants completed an in-depth interview that provides information on demographics, environment, medical illnesses, and psychiatric and SUDs. Polygenic scores (PGS) for psychiatric disorders and somatic traits were calculated in European-ancestry (EUR; n = 5691) participants and, when discovery datasets were available, for African-ancestry (AFR; n = 4918) participants. Phenome-wide association studies (PheWAS) were then conducted. In AFR participants, the only PGS with significant associations was bipolar disorder (BD), all of which were with substance use phenotypes. In EUR participants, PGS for major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), body mass index (BMI), coronary artery disease (CAD), and type 2 diabetes (T2D) all showed significant associations, the majority of which were with phenotypes in the substance use categories. For instance, PGSMDD was associated with over 200 phenotypes, 15 of which were depression-related (e.g., depression criterion count), 55 of which were other psychiatric phenotypes, and 126 of which were substance use phenotypes; and PGSBMI was associated with 138 phenotypes, 105 of which were substance related. Genetic liability for psychiatric and somatic traits is associated with numerous phenotypes across multiple categories, indicative of the broad genetic liability of these traits.

The breadth and impact of the Global Lipids Genetics Consortium

PURPOSE OF REVIEW

This review highlights contributions of the Global Lipids Genetics Consortium (GLGC) in advancing the understanding of the genetic etiology of blood lipid traits, including total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, and non-HDL cholesterol. We emphasize the consortium's collaborative efforts, discoveries related to lipid and lipoprotein biology, methodological advancements, and utilization in areas extending beyond lipid research.

RECENT FINDINGS

The GLGC has identified over 923 genomic loci associated with lipid traits through genome-wide association studies (GWASs), involving more than 1.65 million individuals from globally diverse populations. Many loci have been functionally validated by individuals inside and outside the GLGC community. Recent GLGC studies show increased population diversity enhances variant discovery, fine-mapping of causal loci, and polygenic score prediction for blood lipid levels. Moreover, publicly available GWAS summary statistics have facilitated the exploration of lipid-related genetic influences on cardiovascular and noncardiovascular diseases, with implications for therapeutic development and drug repurposing.

SUMMARY

The GLGC has significantly advanced the understanding of the genetic basis of lipid levels and serves as the leading resource of GWAS summary statistics for these traits. Continued collaboration will be critical to further understand lipid and lipoprotein biology through large-scale genetic assessments in diverse populations.

Polygenic prediction for underrepresented populations through transfer learning by utilizing genetic similarity shared with European populations

Because current genome-wide association studies are primarily conducted in individuals of European ancestry and information disparities exist among different populations, the polygenic score derived from Europeans thus exhibits poor transferability. Borrowing the idea of transfer learning, which enables the utilization of knowledge acquired from auxiliary samples to enhance learning capability in target samples, we propose transPGS, a novel polygenic score method, for genetic prediction in underrepresented populations by leveraging genetic similarity shared between the European and non-European populations while explaining the trans-ethnic difference in linkage disequilibrium (LD) and effect sizes. We demonstrate the usefulness and robustness of transPGS in elevated prediction accuracy via individual-level and summary-level simulations and apply it to seven continuous phenotypes and three diseases in the African, Chinese, and East Asian populations of the UK Biobank and Genetic Epidemiology Research Study on Adult Health and Aging cohorts. We further reveal that distinct LD and minor allele frequency patterns across ancestral groups are responsible for the dissatisfactory portability of PGS.

Precision Medicine for Cardiovascular Prevention and Population Health: A Bridge Too Far?

Precision medicine aims to provide personalized clinical care guided by tools that reflect underlying pathophysiology. The need for such an approach has never been greater in cardiovascular medicine, given the large number of guideline-directed medical therapies available. However, progress has been modest to date with few precision tools available for clinicians. Arguably, cardiovascular prevention and population health are poised for innovation to guide evaluation and management, as these areas are already informed by risk-assessment, but limited by the use of crude assessment tools with marginal performance. Risk assessment in prevention and population health may be improved with the use of genetics, circulating biomarkers, and imaging, leading to outcome-specific risk-prediction and enhanced phenotyping. Personalized management matching therapy to risk profile can be then implemented for either individuals or groups, improving cost-effectiveness and risk-benefit. Here, we explore this precision-like approach, including available tools, potential applications, and future perspectives for cardiovascular prevention and population health management.

Polygenic Risk Scores and Extreme Coronary Artery Calcium Phenotypes (CAC=0 and CAC≥1000) in Adults ≥75 Years Old: The ARIC Study

BACKGROUND

Coronary artery calcium (CAC) is heterogeneous in older age and is incompletely explained by traditional atherosclerotic cardiovascular disease risk factors. The extremes of subclinical atherosclerosis burden are strongly associated with either a low or high 10-year risk of incident atherosclerotic cardiovascular disease, respectively. However, the genetic underpinnings of differences in arterial aging remain unclear. We sought to determine the independent association of 2 polygenic scores for coronary heart disease (CHD) with CAC in adults ≥75 years of age.

METHODS

There were 1865 ARIC (Atherosclerosis Risk in Communities) participants who underwent genetic testing at visit 1 (1987-1989) and CAC scans at visit 7 (2018-2019). In the primary analysis, an externally derived multi-ancestry polygenic CHD risk score was calculated for both White and Black participants. Results were confirmed using a separate ARIC-derived polygenic CHD risk score, including ≥6 million variants computed for White participants. We used multivariable logistic regression models to assess the association of polygenic CHD risk with CAC, after adjusting for baseline, time-averaged lifestyle, traditional risk factors, and local ancestry principal components.

RESULTS

In the primary analysis, the average age was 80.6 years old, 61.6% were women, and the median CAC score was 246 (189 participants with CAC=0, 364 participants with CAC≥1000). Compared with persons below the 20th percentile of polygenic CHD risk, persons with polygenic-CHD risk above the 80th percentile had 82% lower odds of having CAC=0 (odds ratio, 0.18 [95% CI, 0.09-0.37]) and had >4-fold higher odds of CAC≥1000 (odds ratio, 4.77 [95% CI, 2.88-7.88]). On a continuous scale, each SD increment increase in the polygenic risk score was associated with a 78% higher CAC score. Results were nearly identical using a second confirmatory polygenic CHD risk score in White participants.

CONCLUSIONS

Polygenic CHD risk is robustly associated with a lower prevalence of CAC=0 and a higher prevalence of CAC≥1000 in adults ≥75 years of age, beyond lifestyle and traditional risk factors. These results suggest a heritable contribution to distinct healthy and unhealthy arterial aging phenotypes that persist throughout the life course.

Instability of high polygenic risk classification and mitigation by integrative scoring

Polygenic risk scores (PRS) continue to improve with novel methods and expanding genome-wide association studies. Healthcare and commercial laboratories are increasingly deploying PRS reports to patients, but it is unknown how the classification of high polygenic risk changes across individual PRS. Here, we assessed association and classification performance of cataloged PRS for three complex traits. We chronologically ordered all trait-related publications (Pub n ) and identified the single PRS Best(Pub n ) for each Pub n that had the strongest association with the target outcome. While each Best(Pub n ) demonstrated generally consistent population-level strengths of associations, classification of individuals in the top 10% of each Best(Pub n ) distribution varied widely. Using the PRSmix framework, which integrates information across several PRS to improve prediction, we generate corresponding ChronoAdd(Pub n ) scores for each Pub n that combine all polygenic scores from all publications up to and including Pub n . When compared with Best(Pub n ), ChronoAdd(Pub n ) scores demonstrated more consistent high-risk classification amongst themselves. This integrative scoring approach provides stable and reliable classification of high-risk individuals, and is an adaptable framework into which new scores can be incorporated as they are introduced, integrating easily with current PRS implementation strategies.

Polygenic risk, aspirin and primary prevention of coronary artery disease

AIMS

Recent aspirin primary prevention trials failed to identify a net benefit of aspirin for preventing cardiovascular disease versus the harms of bleeding. This study aimed to investigate whether a high-risk subgroup, individuals with elevated genetic predisposition to coronary artery disease (CAD), might derive more benefit than harm with aspirin, compared to those with lower genetic risk.

METHODS AND RESULTS

We performed genetic risk stratification of the Aspirin in Reducing Events in the Elderly (ASPREE) randomized controlled trial using a CAD polygenic risk score (GPSMult). For 12,031 genotyped participants (5,974 aspirin, 6,057 placebo) overall, we stratified them by GPSMult quintiles (q1-5), then examined risk of CAD (composite of myocardial infarction and coronary heart disease death) and bleeding events using Cox models. During a median 4.6 years of follow-up with randomization to 100 mg/day aspirin versus placebo, 234 (1.9%) participants had CAD and 373 (3.1%) had bleeding events. In the overall cohort, aspirin resulted in higher bleeding risk (adjusted Hazard Ratio [aHR]=1.30 [1.06-1.61], P=0.01) but no significant CAD reduction (aHR=0.84 [0.64-1.09], P=0.19). However, among the highest quintile of polygenic risk (q5, top 20% of the GPSMult distribution), there was a 47% reduction in risk of CAD events with aspirin (aHR=0.53 [0.31-0.90], P=0.02) without increased bleeding risk (aHR=1.05 [0.60-1.82], P=0.88). Interaction between the GPSMult and aspirin was significant for CAD (q5 versus q1, P=0.02) but not bleeding (P=0.80).

CONCLUSION

The balance between net benefit and harm on aspirin in the primary prevention setting shifts favourably in individuals with an elevated genetic predisposition.

Personalization of primary prevention: Exploring the role of coronary artery calcium and polygenic risk score in cardiovascular diseases

Personalized healthcare is becoming increasingly popular given the vast heterogeneity in disease manifestation between individuals. Many commonly encountered diseases within cardiology are multifactorial in nature and disease progression and response is often variable due to environmental and genetic factors influencing disease states. This makes accurate early identification and primary prevention difficult in certain populations, especially young patients with limited Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Newer strategies, such as coronary artery calcium (CAC) scans and polygenic risk scores (PRS), are being implemented to aid in the detection of subclinical disease and heritable risk, respectively. Data surrounding CAC scans have shown promising results in their ability to detect subclinical atherosclerosis and predict the risk of future coronary events, especially at the extremes; however, predictive variability exists among different patient populations, limiting the test's specificity. Furthermore, relying only on CAC scores and ASCVD risk scores may fail to identify a large group of patients needing primary prevention who lack subclinical disease and traditional risk factors, but harbor genetic variabilities strongly associated with certain cardiovascular diseases. PRS can overcome these limitations. These scores can be measured in individuals as early as birth to identify genetic variants placing them at elevated risk for developing cardiovascular disease, irrespective of their current cardiovascular health status. By applying PRS alongside CAC scores, previously overlooked patient populations can be identified and begin primary prevention strategies early to achieve optimal outcomes. In this review, we expand on the current knowledge surrounding CAC scores and PRS and highlight the future possibilities of these technologies for preventive cardiology.

Population Performance and Individual Agreement of Coronary Artery Disease Polygenic Risk Scores

Importance

Polygenic risk scores (PRSs) for coronary artery disease (CAD) are a growing clinical and commercial reality. Whether existing scores provide similar individual-level assessments of disease liability is a critical consideration for clinical implementation that remains uncharacterized.

Objective

Characterize the reliability of CAD PRSs that perform equivalently at the population level at predicting individual-level risk.

Design

Cross-sectional Study.

Setting

All of Us Research Program (AOU), Penn Medicine Biobank (PMBB), and UCLA ATLAS Precision Health Biobank.

Participants

Volunteers of diverse genetic backgrounds enrolled in AOU, PMBB, and UCLA with available electronic health record and genotyping data.

Exposures

Polygenic risk for CAD from previously published PRSs and new PRSs developed separately from the testing cohorts.

Main Outcomes and Measures

Sets of CAD PRSs that perform population prediction equivalently were identified by comparing calibration and discrimination (Brier score and AUROC) of generalized linear models of prevalent CAD using Bayesian analysis of variance. Among equivalently performing scores, individual-level agreement between risk estimates was tested with intraclass correlation (ICC) and Light's Kappa, measures of inter-rater reliability.

Results

50 PRSs were calculated for 171,095 AOU participants. When included in a model of prevalent CAD, 48 scores had practically equivalent Brier scores and AUROCs (region of practical equivalence = 0.02). Across these scores, 84% of participants had at least one score in both the top and bottom risk quintile. Continuous agreement of individual risk predictions from the 48 scores was poor, with an ICC of 0.351 (95% CI; 0.349, 0.352). Agreement between two statistically equivalent scores was moderate, with an ICC of 0.649 (95% CI; 0.646, 0.652). Light's Kappa, used to evaluate consistency of assignment to high-risk thresholds, did not exceed 0.56 (interpreted as 'fair') across statistically and practically equivalent scores. Repeating the analysis among 41,193 PMBB and 50,748 UCLA participants yielded different sets of statistically and practically equivalent scores which also lacked strong individual agreement.

Conclusions and Relevance

Across three diverse biobanks, CAD PRSs that performed equivalently at the population level produced unreliable individual risk estimates. Approaches to clinical implementation of CAD PRSs must consider the potential for discordant individual risk estimates from otherwise indistinguishable scores.

A combination of red and processed meat intake and polygenic risk score influences the incidence of hyperuricemia in middle-aged Korean adults

BACKGROUND/OBJECTIVES

The high consumption of purine-rich meat is associated with hyperuricemia. However, there is limited evidence linking the consumption of red and processed meat to the genetic risk of hyperuricemia. We investigated the relationship between various combinations of red and processed meat consumption and the polygenic risk scores (PRSs) and the incidence of hyperuricemia in middle-aged Koreans.

SUBJECTS/METHODS

We analyzed the data from 44,053 participants aged ≥40 years sourced from the Health Examinees (HEXA) cohort of the Korean Genome and Epidemiology Study (KoGES). Information regarding red and processed meat intake was obtained using a semiquantitative food frequency questionnaire (SQ-FFQ). We identified 69 independent single-nucleotide polymorphisms (SNPs) at uric acid-related loci using genome-wide association studies (GWASs) and clumping analyses. The individual PRS, which is the weighted sum of the effect size of each allele at the SNP, was calculated. We used multivariable Cox proportional hazards models adjusted for covariates to determine the relationship between red and processed meat intake and the PRS in the incidence of hyperuricemia.

RESULTS

During an average follow-up period of 5 years, 2,556 patients with hyperuricemia were identified. For both men and women, the group with the highest red and processed meat intake and the highest PRS was positively associated with the development of hyperuricemia when compared with the group with the lowest red and processed meat intake and the lowest PRS (hazard ratio [HR], 2.72; 95% confidence interval [CI], 2.10-3.53; P < 0.0001; HR, 3.28; 95% CI, 2.45-4.40; P < 0.0001).

CONCLUSION

Individuals at a high genetic risk for uric acid levels should moderate their consumption of red and processed meat to prevent hyperuricemia.

Donor and recipient genetics: Implications for the development of posttransplant diabetes mellitus

Posttransplant diabetes mellitus (PTDM) is a prevalent complication of liver transplantation and is associated with cardiometabolic complications. We studied the consequences of genetic effects of liver donors and recipients on PTDM outcomes, focusing on the diverse genetic pathways related to insulin that play a role in the development of PTDM. One thousand one hundred fifteen liver transplant recipients without a pretransplant diagnosis of type 2 diabetes mellitus (T2D) and their paired donors recruited from 2 transplant centers had polygenic risk scores (PRS) for T2D, insulin secretion, and insulin sensitivity calculated. Among recipients in the highest T2D-PRS quintile, donor T2D-PRS did not contribute significantly to PTDM. However, in recipients with the lowest T2D genetic risk, donor livers with the highest T2D-PRS contributed to the development of PTDM (OR [95% CI] = 3.79 [1.10-13.1], P = .035). Recipient risk was linked to factors associated with insulin secretion (OR [95% CI] = 0.85 [0.74-0.98], P = .02), while donor livers contributed to PTDM via gene pathways involved in insulin sensitivity (OR [95% CI] = 0.86 [0.75-0.99], P = .03). Recipient and donor PRS independently and collectively serve as predictors of PTDM onset. The genetically influenced biological pathways in recipients primarily pertain to insulin secretion, whereas the genetic makeup of donors exerts an influence on insulin sensitivity.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations

BACKGROUND

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.

OBJECTIVE AND RATIONALE

This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.

SEARCH METHODS

We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.

OUTCOMES

The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.

WIDER IMPLICATIONS

The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

Polygenic Risk and Coronary Artery Disease Severity

BACKGROUND

Coronary atherosclerotic burden and adverse coronary heart disease events are related phenotypes with likely shared genetic cause.

METHODS

We analyzed 6021 patients with available coronary angiography, genotyping, and exome sequencing data. We tested for associations of polygenic risk scores for coronary heart disease (PRSCHD) with multiple measures of coronary artery disease (CAD) severity. We assessed the joint associations of PRSCHD and pathogenic/likely pathogenic variants in 3 familial hypercholesterolemia genes, with CAD severity. We performed mediation analyses to explore whether CAD severity mediated the association of PRSCHD with prevalent coronary heart disease and incident myocardial infarction.

RESULTS

A 1-SD increase in PRSCHD was associated with multiple measures of CAD severity, including the log Gensini score (β, 0.31 [95% CI, 0.28-0.33]). Carrying a pathogenic/likely pathogenic familial hypercholesterolemia variant was associated with a higher log Gensini score after adjustment for PRSCHD (β, 0.21 [95% CI, 0.03-0.38]). A 1-SD increase in PRSCHD was associated with incident myocardial infarction over a mean follow-up of 9.2 years (hazard ratio, 1.20 [95% CI, 1.13-1.27]; P=5×10-10), and the Gensini score mediated 90% of this association.

CONCLUSIONS

PRSCHD was associated with multiple measures of CAD severity. The association of PRSCHD with incident myocardial infarction was almost fully mediated by CAD severity, indicating a considerable genetic overlap between the 2 phenotypes.

Differential prediction performance between Caribbean- and Mainland-subgroups using state-of-the-art polygenic risk scores for coronary heart disease: Findings from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Background

Coronary heart disease (CHD) is a leading cause of death for Hispanic/Latino populations in the United States. We evaluated polygenic risk scores (PRS) with incident myocardial infarction (MI) in a Hispanic/Latino study sample.

Methods

We leveraged data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) to assess four CHD-PRS from the PGS catalog, derived using multiple methods (LDpred, AnnoPred, stacked clumping and thresholding, and LDPred2). We evaluated associations between each standardized PRS and time to adjudicated incident MI, adjusted for age, sex, first 5 principal components, and weighted for survey design. Concordance statistics (c-index) compared predictive accuracy of each PRS with, and in addition to, traditional risk factors (TRF) for CHD (obesity, hypercholesterolemia, hypertension, diabetes, and smoking). Analyses were stratified by self-reported Caribbean- (Puerto Rican, Dominican or Cuban) and Mainland-(those of Mexican, Central American, or South American) heritage subgroups.

Results

After 11 years follow-up, for 9055 participants (mean age (SD) 47.6(13.1), 62.2% female), the incidence of MI was 1.0% (n = 95). Each PRS was more strongly associated with MI among Mainland participants. LDPred2 + TRF performed best among the Mainland subgroup; HR=2.69, 95% CI [1.71, 4.20], c-index = 0.897, 95% CI [0.848, 0.946]; a modest increase over TRF alone, c-index = 0.880, 95% CI [0.827, 0.933]. AnnoPred + TRF performed best among the Caribbean sample; c-index = 0.721, 95% CI [0.647, 0.795]; however, was not significantly associated with rate of MI (HR=1.14, 95% CI [0.82, 1.60]).

Conclusion

PRS performance for CHD is lacking for Hispanics/Latinos of Caribbean origin who have substantial proportions of African genetic ancestry, risking increased health disparities. AnnoPred, using functional annotations, outperformed other PRS in the Caribbean subgroup, suggesting a potential strategy for PRS construction in diverse populations. These results underscore the need to optimize cumulative genetic risk prediction of CHD in diverse Hispanic/Latino populations.

Population Heterogeneity and Selection of Coronary Artery Disease Polygenic Scores

BACKGROUND/OBJECTIVES

The identification of coronary artery disease (CAD) high-risk individuals is a major clinical need for timely diagnosis and intervention. Many different polygenic scores (PGSs) for CAD risk are available today to estimate the genetic risk. It is necessary to carefully choose the score to use, in particular for studies on populations, which are not adequately represented in the large datasets of European biobanks, such as the Italian one. This work aimed to analyze which PGS had the best performance within the Italian population.

METHODS

We used two Italian independent cohorts: the EPICOR case-control study (576 individuals) and the Atherosclerosis, Thrombosis, and Vascular Biology (ATVB) Italian study (3359 individuals). We evaluated 266 PGS for cardiovascular disease risk from the PGS Catalog, selecting 51 for CAD.

RESULTS

Distributions between patients and controls were significantly different for 49 scores (p-value < 0.01). Only five PGS have been trained and tested for the European population specifically. PGS003727 demonstrated to be the most accurate when evaluated independently (EPICOR AUC = 0.68; ATVB AUC = 0.80). Taking into account the conventional CAD risk factors further enhanced the performance of the model, particularly in the ATVB study (p-value = 0.0003).

CONCLUSIONS

European CAD PGS could have different risk estimates in peculiar populations, such as the Italian one, as well as in various geographical macro areas. Therefore, further evaluation is recommended for clinical applicability.

Associations between circulating proteins and cardiometabolic diseases: a systematic review and meta-analysis of observational and Mendelian randomisation studies

BACKGROUND

Integration of large proteomics and genetic data in population-based studies can provide insights into discovery of novel biomarkers and potential therapeutic targets for cardiometabolic diseases (CMD). We aimed to synthesise existing evidence on the observational and genetic associations between circulating proteins and CMD.

METHODS

PubMed, Embase and Web of Science were searched until July 2023 for potentially relevant prospective observational and Mendelian randomisation (MR) studies investigating associations between circulating proteins and CMD, including coronary heart disease, stroke, type 2 diabetes, heart failure, atrial fibrillation and atherosclerosis. Two investigators independently extracted study characteristics using a standard form and pooled data using random effects models.

RESULTS

50 observational, 25 MR and 10 studies performing both analyses were included, involving 26 414 160 non-overlapping participants. Meta-analysis of observational studies revealed 560 proteins associated with CMD, of which 133 proteins were associated with ≥2 CMDs (ie, pleiotropic). There were 245 potentially causal protein biomarkers identified in MR pooled results, involving 23 pleiotropic proteins. IL6RA and MMP12 were each causally associated with seven diseases. 22 protein-disease pairs showed directionally concordant associations in observational and MR pooled estimates. Addition of protein biomarkers to traditional clinical models modestly improved the accuracy of predicting incident CMD, with the highest improvement for heart failure (ΔC-index ~0.2). Of the 245 potentially causal proteins (291 protein-disease pairs), 3 pairs were validated by evidence of drug development from existing drug databases, 288 pairs lacked evidence of drug development and 66 proteins were drug targets approved for other indications.

CONCLUSIONS

Combined analyses of observational and genetic studies revealed the potential causal role of several proteins in the aetiology of CMD. Novel protein biomarkers are promising targets for drug development and risk stratification.

PROSPERO REGISTRATION NUMBER

CRD42022350327.

Incremental predictive value of genetic risk and functional brain connectivity in determining antipsychotic response in schizophrenia

We aimed to assess the incremental value of schizophrenia polygenic risk score (PgRS) and resting-state functional brain connectivity (rsFC) when added to clinical data in predicting the six-week response to oral risperidone (Risperdal) in schizophrenia. Fifty-seven, 54, and 43 individuals in a group of never-before-treated first-episode schizophrenia had good quality whole-genome sequencing (10x), rsFC, and both genomic and rsFC data, respectively, at baseline. Symptom severity ratings were obtained at baseline and six-weeks of oral risperidone (Risperdal) treatment. The primary outcome was the percentage change in the Positive and Negative Syndrome Scale Total scores after risperidone (Risperdal) treatment. Clinical, PgRS, and rsFC determinants of treatment response were first evaluated independently. Subsequently, three blocks of hierarchical multiple regression analyses with leave-one-out cross-validation (n = 43), were implemented to study clinical, clinical + PgRS and clinical + PgRS + rsFC determinants of treatment response. While the combined clinical variables did not show a statistically significant prediction of treatment response, adding PgRS (9 % R2 change) and rsFC (26 % R2 change) in hierarchical steps, significantly improved the overall proportion of variance explained in treatment response. This proof-of-concept investigation underscores the incremental benefits offered by genetic and neuroimaging metrics over clinical measures in determining prospectively-ascertained short-term treatment response in first-episode schizophrenia.

Multi-trait and multi-ancestry genetic analysis of comorbid lung diseases and traits improves genetic discovery and polygenic risk prediction

While respiratory diseases such as COPD and asthma share many risk factors, most studies investigate them in insolation and in predominantly European ancestry populations. Here, we conducted the most powerful multi-trait and -ancestry genetic analysis of respiratory diseases and auxiliary traits to date. Our approach improves the power of genetic discovery across traits and ancestries, identifying 44 novel loci associated with lung function in individuals of East Asian ancestry. Using these results, we developed PRSxtra (cross TRait and Ancestry), a multi-trait and -ancestry polygenic risk score approach that leverages shared components of heritable risk via pleiotropic effects. PRSxtra significantly improved the prediction of asthma, COPD, and lung cancer compared to trait- and ancestry-matched PRS in a multi-ancestry cohort from the All of Us Research Program, especially in diverse populations. PRSxtra identified individuals in the top decile with over four-fold odds of asthma and COPD compared to the first decile. Our results present a new framework for multi-trait and -ancestry studies of respiratory diseases to improve genetic discovery and polygenic prediction.

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

BACKGROUND AND AIMS

A cardiovascular disease polygenic risk score (CVD-PRS) can stratify individuals into different categories of cardiovascular risk, but whether the addition of a CVD-PRS to clinical risk scores improves the identification of individuals at increased risk in a real-world clinical setting is unknown.

METHODS

The Genetics and the Vascular Health Check Study (GENVASC) was embedded within the UK National Health Service Health Check (NHSHC) programme which invites individuals between 40-74 years of age without known CVD to attend an assessment in a UK general practice where CVD risk factors are measured and a CVD risk score (QRISK2) is calculated. Between 2012-2020, 44,141 individuals (55.7% females, 15.8% non-white) who attended an NHSHC in 147 participating practices across two counties in England were recruited and followed. When 195 individuals (cases) had suffered a major CVD event (CVD death, myocardial infarction or acute coronary syndrome, coronary revascularisation, stroke), 396 propensity-matched controls with a similar risk profile were identified, and a nested case-control genetic study undertaken to see if the addition of a CVD-PRS to QRISK2 in the form of an integrated risk tool (IRT) combined with QRISK2 would have identified more individuals at the time of their NHSHC as at high risk (QRISK2 10-year CVD risk of ≥10%), compared with QRISK2 alone.

RESULTS

The distribution of the standardised CVD-PRS was significantly different in cases compared with controls (cases mean score .32; controls, -.18, P = 8.28×10-9). QRISK2 identified 61.5% (95% confidence interval [CI]: 54.3%-68.4%) of individuals who subsequently developed a major CVD event as being at high risk at their NHSHC, while the combination of QRISK2 and IRT identified 68.7% (95% CI: 61.7%-75.2%), a relative increase of 11.7% (P = 1×10-4). The odds ratio (OR) of being up-classified was 2.41 (95% CI: 1.03-5.64, P = .031) for cases compared with controls. In individuals aged 40-54 years, QRISK2 identified 26.0% (95% CI: 16.5%-37.6%) of those who developed a major CVD event, while the combination of QRISK2 and IRT identified 38.4% (95% CI: 27.2%-50.5%), indicating a stronger relative increase of 47.7% in the younger age group (P = .001). The combination of QRISK2 and IRT increased the proportion of additional cases identified similarly in women as in men, and in non-white ethnicities compared with white ethnicity. The findings were similar when the CVD-PRS was added to the atherosclerotic cardiovascular disease pooled cohort equations (ASCVD-PCE) or SCORE2 clinical scores.

CONCLUSIONS

In a clinical setting, the addition of genetic information to clinical risk assessment significantly improved the identification of individuals who went on to have a major CVD event as being at high risk, especially among younger individuals. The findings provide important real-world evidence of the potential value of implementing a CVD-PRS into health systems.

Polygenic Risk Scores: The Next Step for Improved Risk Stratification in Coronary Artery Disease?

Despite significant advances in the management of coronary artery disease (CAD) and reductions in annual mortality rates in recent decades, this disease remains the leading cause of death worldwide. Consequently, there is an ongoing need for efforts to address this situation. Current clinical algorithms to identify at-risk patients are particularly inaccurate in moderate-risk individuals. For this reason, the need for ancillary tests has been suggested, including predictive genetic screening. As genetic studies rapidly expand and genomic data becomes more accessible, numerous genetic risk scores have been proposed to identify and evaluate an individual's susceptibility to developing diseases, including CAD. The field of genetics has indeed made substantial contributions to risk prediction, particularly in cases where children have parents with premature CAD, resulting in an increased risk of up to 75%. The polygenic risk scores (PRSs) have emerged as a potentially valuable tool for understanding and stratifying an individual's genetic risk. The PRS is calculated as a weighted sum of single-nucleotide variants present throughout the human genome, identifiable through genome-wide association studies, and associated with various cardiometabolic diseases. The use of PRSs holds promise, as it enables the development of personalized strategies for preventing or diagnosing specific pathologies early. Furthermore, it can complement existing clinical scores, increasing the accuracy of individual risk prediction. Consequently, the application of PRSs has the potential to impact the costs and adverse outcomes associated with CAD positively. This narrative review provides an overview of the role of PRSs in the context of CAD.

Joint impact of polygenic risk score and lifestyles on early- and late-onset cardiovascular diseases

Understanding the interactions between genetic risk and lifestyles on different types and age onsets of cardiovascular disease (CVD) risk can help identify individuals for whom lifestyle changes would be beneficial. Here we developed three polygenic risk scores, called MetaPRSs, for coronary artery disease, ischaemic stroke and intracerebral haemorrhage by combining PRSs for CVD and CVD-related risk factors in 96,400 participants from the prospective China Kadoorie Biobank. Genetic and lifestyle risks were categorized by the disease-specific MetaPRSs and the number of unfavourable lifestyles. High genetic risk and unfavourable lifestyles were found to be more strongly associated with early than late onset of CVD outcomes in men and women. Change from unfavourable to favourable lifestyles resulted in 14.7-, 2.5- and 2.6-fold greater reductions in incidence rates of early-onset coronary artery disease and ischaemic stroke and late-onset coronary artery disease in high than low genetic risk group. Young adults at high genetic risk may have larger benefits in preventing CVD from lifestyle improvements.

Polygenic risk score for blood pressure and lifestyle factors with overall and CVD mortality: a prospective cohort study in a Japanese population

Although previous polygenic risk score (PRS) studies for cardiovascular disease (CVD) focused on incidence, few studies addressed CVD mortality and quantified risks by environmental exposures in different genetic liability groups. This prospective study aimed to examine the associations of blood pressure PRS with all-cause and CVD mortality and to quantify the attributable risk by modifiable lifestyles across different PRS strata. 9,296 participants in the Japan Multi-Institutional Collaborative Cohort Study without hypertension at baseline were analyzed in this analysis. PRS for systolic blood pressure and diastolic blood pressure (PRSSBP and PRSDBP) were developed using publicly available Biobank Japan GWAS summary statistics. CVD-related mortality was defined by the International Classification of Diseases 10th version (I00-I99). Cox-proportional hazard model was used to examine associations of PRSs and lifestyle variables (smoking, drinking, and dietary sodium intake) with mortality. During a median 12.6-year follow-up period, we observed 273 all-cause and 41 CVD mortality cases. Compared to the middle PRS group (20-80th percentile), adjusted hazard ratios for CVD mortality at the top PRS group ( > 90th percentile) were 3.67 for PRSSBP and 2.92 for PRSDBP. Attributable risks of CVD mortality by modifiable lifestyles were higher in the high PRS group ( > 80th percentile) compared with the low PRS group (0-80th percentile). In summary, blood pressure PRS is associated with CVD mortality in the general Japanese population. Our study implies that integrating PRS with lifestyle could contribute to identify target populations for lifestyle intervention even though improvement of discriminatory ability by PRS alone is limited.

Fast and scalable ensemble learning method for versatile polygenic risk prediction

Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, but existing methods face several limitations, encompassing issues related to computational burden, predictive accuracy, and adaptability to a wide range of genetic architectures. To address these issues, we propose Aggregated L0Learn using Summary-level data (ALL-Sum), a fast and scalable ensemble learning method for computing PRS using summary statistics from genome-wide association studies (GWAS). ALL-Sum leverages a L0L2 penalized regression and ensemble learning across tuning parameters to flexibly model traits with diverse genetic architectures. In extensive large-scale simulations across a wide range of polygenicity and GWAS sample sizes, ALL-Sum consistently outperformed popular alternative methods in terms of prediction accuracy, runtime, and memory usage by 10%, 20-fold, and threefold, respectively, and demonstrated robustness to diverse genetic architectures. We validated the performance of ALL-Sum in real data analysis of 11 complex traits using GWAS summary statistics from nine data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen Biobank, with validation in the UK Biobank. Our results show that on average, ALL-Sum obtained PRS with 25% higher accuracy on average, with 15 times faster computation and half the memory than the current state-of-the-art methods, and had robust performance across a wide range of traits and diseases. Furthermore, our method demonstrates stable prediction when using linkage disequilibrium computed from different data sources. ALL-Sum is available as a user-friendly R software package with publicly available reference data for streamlined analysis.

Risk of adverse cardiovascular events based on common genetic variants in 8-year follow-up of the LIPIDOGEN2015 population using the polygenic risk score (PRS): study design and methodology

Introduction

Classical risk factors such as hypertension, hypercholesterolemia, pre-diabetes, diabetes and obesity can predict adverse cardiovascular events, but they are less prognostic in patients aged < 60 years. Polygenic risk scores (PRS) can be effective in predicting adverse coronary events in younger and middle-aged patients. Our main aim is to assess the utility of a new PRS created for the Polish population in predicting mortality during an 8-year follow-up in the nationwide LIPIDOGEN2015 population.

Material and methods

All DNA samples of 1779 patients were genotyped using Infinium Global Screening Array-24+ v3.0 Kit microarrays. The samples were amplified, fragmented, and hybridized to BeadChips. The BeadChips were scanned using iScan and converted to genotypes using Genome Studio 2.0.

Results

We will develop a PRS based on the identified single nucleotide polymorphisms (SNPs) in the LIPIDOGEN2015 project's studied population and determine the analyzed group's risk of death due to cardiovascular diseases (CVD) based on data obtained from 8 years of patient-follow-up. Using the developed PRS scale and biochemical analyses, we will assess the effectiveness of lipid-lowering therapy with statins in patients with high and low genetic risk of sudden CVD events (secondary endpoints).

Conclusions

The developed PRS scale, combined with clinical covariates, will facilitate the creation of an algorithm to predict long-term mortality. This will enable us to stratify CVD risk more precisely, which may result in earlier implementation of lifestyle changes and dietary adjustments and potentially initiate earlier pharmacotherapy for at-risk individuals.

Pharmacogenomic scores in psychiatry: systematic review of current evidence

In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.

All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for under-represented populations

Recent studies have demonstrated that polygenic risk scores (PRS) trained on multi-ancestry data can improve prediction accuracy in groups historically underrepresented in genomic studies, but the availability of linked health and genetic data from large-scale diverse cohorts representative of a wide spectrum of human diversity remains limited. To address this need, the All of Us research program (AoU) generated whole-genome sequences of 245,388 individuals who collectively reflect the diversity of the USA. Leveraging this resource and another widely-used population-scale biobank, the UK Biobank (UKB) with a half million participants, we developed PRS trained on multi-ancestry and multi-biobank data with up to ~750,000 participants for 32 common, complex traits and diseases across a range of genetic architectures. We then compared effects of ancestry, PRS methodology, and genetic architecture on PRS accuracy across a held out subset of ancestrally diverse AoU participants. Due to the more heterogeneous study design of AoU, we found lower heritability on average compared to UKB (0.075 vs 0.165), which limited the maximal achievable PRS accuracy in AoU. Overall, we found that the increased diversity of AoU significantly improved PRS performance in some participants in AoU, especially underrepresented individuals, across multiple phenotypes. Notably, maximizing sample size by combining discovery data across AoU and UKB is not the optimal approach for predicting some phenotypes in African ancestry populations; rather, using data from only AoU for these traits resulted in the greatest accuracy. This was especially true for less polygenic traits with large ancestry-enriched effects, such as neutrophil count (R 2: 0.055 vs. 0.035 using AoU vs. cross-biobank meta-analysis, respectively, because of e.g. DARC). Lastly, we calculated individual-level PRS accuracies rather than grouping by continental ancestry, a critical step towards interpretability in precision medicine. Individualized PRS accuracy decays linearly as a function of ancestry divergence, but the slope was smaller using multi-ancestry GWAS compared to using European GWAS. Our results highlight the potential of biobanks with more balanced representations of human diversity to facilitate more accurate PRS for the individuals least represented in genomic studies.

Investigating the Role of Neighborhood Socioeconomic Status and Germline Genetics on Prostate Cancer Risk

Background

Genetic factors play an important role in prostate cancer (PCa) development with polygenic risk scores (PRS) predicting disease risk across genetic ancestries. However, there are few convincing modifiable factors for PCa and little is known about their potential interaction with genetic risk. We analyzed incident PCa cases (n=6,155) and controls (n=98,257) of European and African ancestry from the UK Biobank (UKB) cohort to evaluate the role of neighborhood socioeconomic status (nSES)-and how it may interact with PRS-on PCa risk.

Methods

We evaluated a multi-ancestry PCa PRS containing 269 genetic variants to understand the association of germline genetics with PCa in UKB. Using the English Indices of Deprivation, a set of validated metrics that quantify lack of resources within geographical areas, we performed logistic regression to investigate the main effects and interactions between nSES deprivation, PCa PRS, and PCa.

Results

The PCa PRS was strongly associated with PCa (OR=2.04; 95%CI=2.00-2.09; P<0.001). Additionally, nSES deprivation indices were inversely associated with PCa: employment (OR=0.91; 95%CI=0.86-0.96; P<0.001), education (OR=0.94; 95%CI=0.83-0.98; P<0.001), health (OR=0.91; 95%CI=0.86-0.96; P<0.001), and income (OR=0.91; 95%CI=0.86-0.96; P<0.001). The PRS effects showed little heterogeneity across nSES deprivation indices, except for the Townsend Index (P=0.03).

Conclusions

We reaffirmed genetics as a risk factor for PCa and identified nSES deprivation domains that influence PCa detection and are potentially correlated with environmental exposures that are a risk factor for PCa. These findings also suggest that nSES and genetic risk factors for PCa act independently.

Pharmacogenomics polygenic risk score: Ready or not for prime time?

Pharmacogenomic Polygenic Risk Scores (PRS) have emerged as a tool to address the polygenic nature of pharmacogenetic phenotypes, increasing the potential to predict drug response. Most pharmacogenomic PRS have been extrapolated from disease-associated variants identified by genome wide association studies (GWAS), although some have begun to utilize genetic variants from pharmacogenomic GWAS. As pharmacogenomic PRS hold the promise of enabling precision medicine, including stratified treatment approaches, it is important to assess the opportunities and challenges presented by the current data. This assessment will help determine how pharmacogenomic PRS can be advanced and transitioned into clinical use. In this review, we present a summary of recent evidence, evaluate the current status, and identify several challenges that have impeded the progress of pharmacogenomic PRS. These challenges include the reliance on extrapolations from disease genetics and limitations inherent to pharmacogenomics research such as low sample sizes, phenotyping inconsistencies, among others. We finally propose recommendations to overcome the challenges and facilitate the clinical implementation. These recommendations include standardizing methodologies for phenotyping, enhancing collaborative efforts, developing new statistical methods to capitalize on drug-specific genetic associations for PRS construction. Additional recommendations include enhancing the infrastructure that can integrate genomic data with clinical predictors, along with implementing user-friendly clinical decision tools, and patient education. Ethical and regulatory considerations should address issues related to patient privacy, informed consent and safe use of PRS. Despite these challenges, ongoing research and large-scale collaboration is likely to advance the field and realize the potential of pharmacogenomic PRS.

Genetic variation across and within individuals

Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.

Towards Personalized TSH Reference Ranges: A Genetic and Population-Based Approach in Three Independent Cohorts

Background: Serum thyroid-stimulating hormone (TSH) measurement is the diagnostic cornerstone for primary thyroid dysfunction. There is high inter-individual but limited intra-individual variation in TSH concentrations, largely due to genetic factors. The currently used wide population-based reference intervals may lead to inappropriate management decisions. Methods: A polygenic score (PGS) including 59 genetic variants was used to calculate genetically determined TSH reference ranges in a thyroid disease-free cohort (n = 6,834). Its effect on reclassification of diagnoses was investigated when compared to using population-based reference ranges. Next, results were validated in a second independent population-based thyroid disease-free cohort (n = 3,800). Potential clinical implications were assessed in a third independent population-based cohort including individuals without thyroid disease (n = 26,321) as well as individuals on levothyroxine (LT4) treatment (n = 1,132). Results: PGS was a much stronger predictor of individual TSH concentrations than FT4 (total variance in TSH concentrations explained 9.2-11.1% vs. 2.4-2.7%, respectively) or any other nongenetic factor (total variance in TSH concentrations explained 0.2-1.8%). Genetically determined TSH reference ranges differed significantly between PGS quartiles in all cohorts, while the differences in FT4 concentrations were absent or only minor. Up to 24.7-30.1% of individuals, previously classified as having subclinical hypo- and hyperthyroidism when using population-based TSH reference ranges, were reclassified as euthyroid when genetically determined TSH reference ranges were applied. Individuals in the higher PGS quartiles had a higher probability of being prescribed LT4 treatment compared to individuals from the lower PGS quartiles (3.3% in Q1 vs. 5.2% in Q4, Pfor trend =1.7 × 10-8). Conclusions: Individual genetic profiles have the potential to personalize TSH reference ranges, with large effects on reclassification of diagnosis and LT4 prescriptions. As the currently used PGS can only predict approximately 10% of inter-individual variation in TSH concentrations, it should be further improved when more genetic variants determining TSH concentrations are identified in future studies.

Efficacy of Diagnostic Testing of Suspected Coronary Artery Disease: A Contemporary Review

BACKGROUND

Coronary artery disease (CAD) is a highly prevalent condition which can lead to myocardial ischemia as well as acute coronary syndrome. Early diagnosis of CAD can improve patient outcomes through guiding risk factor modification and treatment modalities.

SUMMARY

Testing for CAD comes with increased cost and risk; therefore, physicians must determine which patients require testing, and what testing modality will offer the most useful data to diagnose patients with CAD. Patients should have an initial risk stratification for pretest probability of CAD based on symptoms and available clinical data. Patients with a pretest probability less than 5% should receive no further testing, while patients with a high pretest probability should be considered for direct invasive coronary angiography. In patients with a pretest probability between 5 and 15%, coronary artery calcium score and or exercise electrocardiogram can be obtained to further risk stratify patients to low-risk versus intermediate-high-risk. Intermediate-high-risk patients should be tested with coronary computed tomography angiography (preferred) versus positron emission tomography or single photon emission computed tomography based on their individual patient characteristics and institutional availability.

KEY MESSAGES

This comprehensive review aimed to describe the available CAD testing modalities, detail their risks and benefits, and propose when each should be considered in the evaluation of a patient with suspected CAD.

Effect of disclosing a polygenic risk score for coronary heart disease on adverse cardiovascular events: 10-year follow-up of the MI-GENES randomized clinical trial

Background

The MI-GENES clinical trial (NCT01936675), in which participants at intermediate risk of coronary heart disease (CHD) were randomized to receive a Framingham risk score (FRSg, n=103), or an integrated risk score (IRSg, n=104) that additionally included a polygenic risk score (PRS), demonstrated that after 6 months, participants randomized to IRSg had higher statin initiation and lower low-density lipoprotein cholesterol (LDL-C).

Objectives

In a post hoc 10-year follow-up analysis of the MI-GENES trial, we investigated whether disclosure of a PRS for CHD was associated with a reduction in adverse cardiovascular events.

Methods

Participants were followed from randomization beginning in October 2013 until September 2023 to ascertain adverse cardiovascular events, testing for CHD, and changes in risk factors, by blinded review of electronic health records. The primary outcome was the time from randomization to the occurrence of the first major adverse cardiovascular event (MACE), defined as cardiovascular death, non-fatal myocardial infarction, coronary revascularization, and non-fatal stroke. Statistical analyses were conducted using Cox proportional hazards regression and linear mixed-effects models.

Results

We followed all 203 participants who completed the MI-GENES trial, 100 in FRSg and 103 in IRSg (mean age at the end of follow-up: 68.2±5.2, 48% male). During a median follow-up of 9.5 years, 9 MACEs occurred in FRSg and 2 in IRSg (hazard ratio (HR), 0.20; 95% confidence interval (CI), 0.04 to 0.94; P=0.042). In FRSg, 47 (47%) underwent at least one test for CHD, compared to 30 (29%) in IRSg (HR, 0.51; 95% CI, 0.32 to 0.81; P=0.004). IRSg participants had a longer duration of statin therapy during the first four years post-randomization and a greater reduction in LDL-C for up to 3 years post-randomization. No significant differences between the two groups were observed for hemoglobin A1C, systolic and diastolic blood pressures, weight, and smoking cessation rate during follow-up.

Conclusions

The disclosure of an IRS that included a PRS to individuals at intermediate risk for CHD was associated with a lower incidence of MACE after a decade of follow-up, likely due to a higher rate of initiation and longer duration of statin therapy, leading to lower LDL-C levels.

To Evaluate Efficiency of Various Coronary Artery Disease Risk Scores With Traditional Risk Factors in Patients Undergoing Coronary Angiography

Objective

To analyze and compare various cardiovascular disease risk scores in Western Indian patients undergoing Coronary angiogram (CAG).

Methods

In this prospective cross-sectional study, 1213 patients who underwent conventional coronary angiography; clinical risk profile and biochemical investigations were evaluated prior to undergoing CAG. Apart from the demographic information, 10-year absolute risk of having a major cardiovascular event (cardiovascular death, myocardial infarction or stroke) was calculated for each patient using various available Traditional Risk Scores (TRS). The population was divided in low, intermediate and high-risk categories for each of these scores.

Results

Traditional cardiovascular risk factors like hypertension (41.8%) and diabetes mellitus-II (26.9%) were the two most prevalent risk factors in our study population. A higher risk value for all these TRS was more likely to be associated with obstructive coronary artery disease (OCAD) on CAG. Patients with high risk (≥20% for 10-year) QRESEARCH (QRISK3) score category had higher number of patients with obstructive CAD (49.6%) as compared to high risk category of risk score for those with high Global Registry of Acute Coronary Events (GRACE) score (46.6%) or risk Framingham (FRS CHD) score (29.2%) and risk atherosclerotic cardiovascular disease (ASCVD) score (30.1%) (P < 0.0001). A higher TRS was more likely to be associated with obstructive CAD, with the highest predictability being with QRISK3 (QRISK3 score 60.9%, GRACE score 54.9%, FRS-CHD score 34% and ASCVD score 42.1% respectively; P < 0.0001). A substantial study population (27.4%) cannot be identified using any of these TRS and hence a need of indigenous or modified risk scores is proposed.

Conclusion

QRISK3 score was most efficacious for predicting obstructive CAD in our Indian study population on CAG. A higher risk score also correlated with the number of vessels involved on coronary angiogram. A substantial obstructive CAD patient could not be identified using traditional risk scores hence need for an indigenous or modified score.