Kullo, Iftikhar J.

BACKGROUND: In the Myocardial Infarction Genes clinical trial (URL: https://www.clinicaltrials.gov ; Unique identifier: NCT01936675), participants at intermediate risk of coronary heart disease (CHD) were randomized to receive a Framingham risk score (Framingham risk score group, n=103) or an integrated risk score (integrated risk score group [IRS g ], n=104) that additionally included a polygenic risk score. After 6 months, IRS g participants had higher statin initiation and lower low-density lipoprotein cholesterol. We conducted a post hoc 10-year follow-up analysis to investigate whether disclosure of a polygenic risk score for CHD was associated with a reduction in major adverse cardiovascular events (MACE). METHODS: Participants were followed from randomization in October 2013 to September 2023 to ascertain MACE, testing for CHD, and changes in risk factors. The primary outcome was time to first MACE, defined as cardiovascular death, nonfatal myocardial infarction, coronary revascularization, and nonfatal stroke. Statistical analyses included Cox proportional hazards regression and linear mixed-effects models. RESULTS: We followed all participants who completed the trial, 100 in Framingham risk score group and 103 in IRS g (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 Framingham risk score group and 2 in IRS g (hazard ratio, 0.20 [95% CI, 0.04–0.94]; P =0.042). In Framingham risk score group, 47 (47%) underwent at least 1 diagnostic test for CHD, compared with 30 (29%) in IRS g (hazard ratio, 0.51 [95% CI, 0.32–0.81]; P =0.004). A higher proportion of IRS g participants were on statin therapy during the first 4 years postrandomization and had a greater reduction in low-density lipoprotein cholesterol for up to 3 years postrandomization. No significant differences were observed between 2 groups in other traditional cardiovascular risk factors during follow-up. CONCLUSIONS: Disclosure of an integrated risk score that included a polygenic risk score to individuals at intermediate risk for CHD was associated with lower MACE incidence after 10 years, likely due to higher statin initiation, leading to lower low-density lipoprotein cholesterol levels.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Introduction: The MI-GENES randomized clinical trial (NCT01936675) assessed the effect of disclosing a polygenic risk score (PRS) for coronary heart disease (CHD), in addition to a clinical risk based on Framingham risk score (FRS), on LDL-C levels. The trial enrolled participants from Olmsted County, Minnesota, without cardiovascular disease, at intermediate CHD risk (10-y risk: 5-20%), and not on statins. There was a significant LDL-C reduction in the integrated risk score group (IRS g ; received PRS information in addition to the FRS) compared to the FRS group (FRS g ; received their risk based on FRS), due to more frequent statin initiation. Research Question: Does disclosure of an IRS for CHD lead to a lower rate of major adverse cardiovascular events (MACE)? Methods: Participants were followed from randomization beginning in October 2013 until September 2023 to ascertain cardiovascular events, testing for CHD, and risk factor changes, by blinded review of electronic health records. The primary outcome was time from randomization to the first MACE— defined as cardiovascular death, nonfatal MI, coronary revascularization, and nonfatal stroke. Analyses were done using Cox and linear mixed-effects models. Results: We followed all 203 participants, who completed the MI-GENES trial, 100 in FRS g and 103 in IRS g (mean age at the end of follow-up 68.2±5.2 years, 48% male). During a median follow-up of 9.5 years, 9 MACE occurred in FRS g and 2 in IRS g (HR, 0.20; 95% CI, 0.04 to 0.94; P =0.042, Figure 1A). In FRS g , 47 (47%) underwent at least one test for CHD, compared to 30 (29%) in IRS g (HR, 0.51; 95% CI, 0.32 to 0.81; P =0.004, Figure 1B). IRS g participants had a longer duration of statin therapy during the first four years post-randomization (Figure 1C) and a greater reduction in LDL-C for up to 3 years post-randomization (Figure 1D). No significant differences between the two groups were observed for hemoglobin A1C, blood pressure, weight, and smoking cessation rate during follow-up. Conclusion: The disclosure of 10-year CHD risk that included a PRS to those at intermediate risk was associated with lower incidence of MACE after a decade, likely due to more frequent and prolonged statin use, leading to lower LDL-C levels.

Background: Coronary heart disease (CHD) results from environmental and genetic factors. Lifestyle-psychosocial factors that capture environmental risk and polygenic risk scores (PRS) that capture genetic risk have been associated with CHD in prior studies. Aim: To assess whether higher CHD risk traditionally attributed to non-White race/ethnicty is mediated by PSS CHD or PRS CHD . Methods: In UK Biobank, we ascertained incident CHD (MI/coronary revascularization) and calculated an ancestry-specific PRS for CHD (PGS000018). Based on self-reported race/ethnicity, participants were classified as White and non-White. An age, sex, and ethnicity-adjusted polysocial score for CHD (PSS CHD ) was derived from 100 self-reported lifestyle-psychosocial factors across nine domains: Adulthood Relationship, Education, Entertainment, Housing, Income, Occupation, Physical Activity, Psychological Health, and Sleep. Those with CHD or statin use at recruitment or missing age, sex, race/ethnicity or genotype were excluded. Machine learning (elastic net model) was used to develop PSS CHD . Cox and mediation analyses were performed. Results: In 398,403 participants (56±8 y, 43% male, 96.3% White, 1.8% Asian, 1.6% Black, and 0.3% Chinese), 10,033 had an incident CHD event during a 10-year follow-up. The HRs (95% CIs) for a 1 SD increase in PSS CHD and PRS CHD for CHD risk were 1.5 (1.4–1.5, P <10 -16 ), and 1.6 (1.5 - 1.6, P <10 -16 ), respectively (Figure 1A-B). Asians and Blacks had the highest PSS CHD (mean±SD: 0.5±1, and 0.5±1, respectively), followed by Chinese (0.1±0.9), and Whites (0.0±1) (Figure 1C). After adjustment for age, sex, diabetes, hypertension, LDL-C, and smoking, non-Whites had higher CHD risk: HR (95% CI): 1.2 (1.0–1.4, P <10 -16 ), and the higher risk was mediated more substantially by PSS CHD than PRS CHD (63% (38%-100%) vs. 16% (8%-41%), both P <10 -16 ). Conclusions: The higher CHD risk observed in non-Whites in UK Biobank was mainly due to unfavorable lifestyle-psychosocial factors, rather than PRS, consistent with the paradigm that race/ethnicity is not a biological but rather a social construct. Our findings support substituting race/ethnicity with lifestyle-psychosocial factors and social determinants of health in CHD risk equations.

Introduction: The overall amount and the distribution of body fat, proxied by body-mass index (BMI) and waist-to-hip ratio (WHR), are two important measures of obesity and have significant polygenic heritability. We aimed to investigate whether polygenic scores (PGS) for these traits have additional predictive utility, beyond phenotypic characterization of obesity, for cardiovascular-kidney-metabolic (CKM) health. Methods: We derived PGS for BMI (PGS BMI ) and WHR adjusted for BMI (PGS WHRadjBMI ) using PRS-CS-auto on summary statistics from ~700k individuals of European ancestry (EA) in the GIANT consortium and selecting ~1M common variants for each score. We conducted association analyses in the Atherosclerosis Risk in Communities study, a prospective cohort including 8,631 EA individuals with ascertainment of incident diabetes, hypertension, chronic kidney disease, atrial fibrillation, coronary heart disease, ischemic stroke, and heart failure. We used nested Cox models to measure the added predictive utility of PGS to BMI and WHR for incident CKM phenotypes. Results: The study cohort (age 54.4±5.7 years, 52.4% women, follow-up 24.5±7.6 years) had a baseline BMI of 26.4 kg/m 2 (median [IQR]; 21.6-29.6) and WHR of 0.94 (0.88-0.98). PGS BMI and PGS WHRadjBMI were associated with their phenotypes (partial R 2 ; BMI 0.11 and WHR 0.04, p <0.001), although with a large phenotypic variability across the distribution of PGS (IQR for 1 st and 10 th deciles of PGS; BMI 19.6-28.3 and 22.6-35.9, WHR 0.79-1.03 and 0.87-1.05). Anthropometric measures of obesity were associated with a wider gradient of risk compared to PGS for obesity–most notable for diabetes and cardiovascular phenotypes (Table 1a). Addition of PGS BMI and PGS WHRadjBMI to BMI and WHR resulted in limited improvement in risk discrimination across incident CKM phenotypes (Table 1b). Conclusions: Observed anthropometric measures of obesity are sufficient, and for certain conditions far superior, to capture the risk of incident CKM phenotypes. These findings suggest PGS for adiposity traits likely have limited clinical utility to refine risk stratification for CKM syndrome among middle aged adults.

Introduction: CHIP is associated with CAD and mortality. The prognostic relevance of CHIP for high-risk patients with confirmed CAD is unknown. Hypothesis: CHIP variants are associated with cardiovascular (CV) events and mortality in high-risk patients with known CAD in the ISCHEMIA Trials Biorepository. Methods: 895 ISCHEMIA and ISCHEMIA-CKD (hereafter, ISCHEMIA Trials) participants with moderate-severe ischemia and next-generation sequencing performed for CHIP variant allele fractions of ≥2% (CHIP) and ≥10% (large CHIP) were included. Unadjusted and multivariable adjusted (age, sex, diabetes, eGFR not on HD, HD, and LVEF) associations of CHIP and large CHIP with a) ISCHEMIA Trials initial phase primary endpoint (CV death, myocardial infarction (MI), or hospitalization for unstable angina, heart failure, or resuscitated cardiac arrest, RCA) and b) ISCHEMIA Trials combined initial and extended follow-up phase (hereafter, Cumulative) endpoint of all-cause death . Results: Median (IQR) age of sequenced participants was 67 (56 – 79) years, 19% were female, 83% white, and 6% Hispanic. Hypertension (84%), diabetes (45%) and obesity (47%) were common, 26% had an eGFR <60 ml/min/1.73m 2 . The prevalence of CHIP and large CHIP was 23% and 7%, and both increased with age ( Figure 1a ). CHIP mutations in DNMT3A were detected in 106 (12%), TET2 CHIP in 44 (5%), and ASXL1 CHIP in 21 (2%) participants ( Figure 1b ). DNMT3A, TET2, and CREBBP mutations collectively comprised 199 (52%) of overall CHIP mutations, including individuals with multiple mutations in the same gene (N overall = 385); 67 participants (7%) had mutations in >1 gene. Over 3.1 years of RCT follow-up there were 135 (20%), 32 (16%) and 15 (18%) primary endpoints for no CHIP, CHIP and large CHIP, respectively. Over 6.8 years of EXTEND follow-up, there were 126 (18%), 47 (23%) and 24 (29%) deaths in the no CHIP, CHIP and large CHIP groups, respectively. After multivariable adjustment there was no association between CHIP or large CHIP and CV events or mortality ( Figure 1c ). Conclusion: Neither CHIP nor large CHIP was associated with adverse outcomes in 895 high-risk individuals with confirmed CAD, despite a high prevalence of these mutations.

Precision medicine, which among other aspects includes an individual's genomic data in diagnosis and management, has become the standard‐of‐care for Mendelian cardiovascular disease (CVD). However, early identification and management of asymptomatic patients with potentially lethal and manageable Mendelian CVD through screening, which is the promise of precision health, remains an unsolved challenge. The reduced costs of genomic sequencing have enabled the creation of biobanks containing in‐depth genetic and health information, which have facilitated the understanding of genetic variation, penetrance, and expressivity, moving us closer to the genotype‐first screening of asymptomatic individuals for Mendelian CVD. This approach could transform health care by diagnostic refinement and facilitating prevention or therapeutic interventions. Yet, potential benefits must be weighed against the potential risks, which include evolving variant pathogenicity assertion or identification of variants with low disease penetrance; costly, stressful, and inappropriate diagnostic evaluations; negative psychological impact; disqualification for employment or of competitive sports; and denial of insurance. Furthermore, the natural history of Mendelian CVD is often unpredictable, making identification of those who will benefit from preventive measures a priority. Currently, there is insufficient evidence that population‐based genetic screening for Mendelian CVD can reduce adverse outcomes at a reasonable cost to an extent that outweighs the harms of true‐positive and false‐positive results. Besides technical, clinical, and financial burdens, ethical and legal aspects pose unprecedented challenges. This review highlights key developments in the field of genotype‐first approaches to Mendelian CVD and summarizes challenges with potential solutions that can pave the way for implementing this approach for clinical care.