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Abstract Objective To describe the development and implementation of an automated platform for genomic risk prediction that integrates multiple data types. Materials and methods Using the REDCap infrastructure, we constructed the R4 (Recruitment, Results, and Risk Reduction) platform to intake data from clinical sites, partner laboratories, participant surveys, and electronic health record (EHR) data across 13 institutions. Results The R4 Portal successfully integrated data to generate genome-informed risk assessments (GIRAs) across 11 conditions for a 23 840 person cohort. Testing phases and quality control led to network-wide protocols ensuring consistency and accuracy. Discussion As the science of estimating disease risk evolves, standardized and high-throughput methods of collecting and manipulating complex data are required. Platforms should be open-source, modular, and reusable, ensuring flexibility, security, and integration across healthcare environments. Conclusion The electronic MEdical Records and GEnomics (eMERGE) network successfully generated and returned comprehensive risk profiles using logic and data specific to 11 conditions in a secure and semi-automated fashion employing a customized REDCap database.

BACKGROUND: Performance and transferability of contemporary polygenic risk scores (PRS) for atherosclerotic cardiovascular disease phenotypes may vary across PRS methods, training data, and trait ascertainment. METHODS: We aimed to investigate the performance and transferability of contemporary PRS for atherosclerotic cardiovascular disease subtypes: coronary heart disease (CHD), abdominal aortic aneurysm (AAA), ischemic stroke (IS), and peripheral artery disease (PAD), using the All of Us Workbench, which consists of a large, diverse cohort with whole-genome sequence data. We also developed and evaluated a multi-trait PRS for each subtype. Performance of PRS for 4 atherosclerotic cardiovascular disease subtypes was compared across genetic similarity groups in 245 388 All of Us participants. Groups genetically similar to European, African, admixed American, and remaining groups (combined as other) were used to assess PRS for CHD, IS, AAA, PAD, and multi-trait. RESULTS: PRS for CHD and AAA performed better than IS and PAD. For CHD, CHD PGS003725 performed the best (hazard ratio per SD increase [95% CI]), across genetic ancestry groups, European, and African (1.72 [1.67–1.78], 1.23 [1.17–1.29]), with CHD PGS004696 being best for admixed American (1.91 [1.70–2.15]), and CHD PGS003356 for other (1.75 [1.58–1.95]). The best performing PRS for AAA was AAA Multi for European, other, and admixed American (1.71 [1.52–1.92], 1.59 [1.07–2.37], 1.50 [0.90–2.52]) and AAA PGS003972 for African (1.39 [1.19–1.63]). For IS, IS Multi performed best for other and European (1.49 [1.17–1.89], 1.33 [1.25–1.42]), and IS PGS000039 performed best in admixed American and African (1.17 [1.07–1.27], 1.09 [1.04–1.15]). For PAD, PAD Multi performed best for all groups (other, 1.51 [1.19–1.92]; European, 1.32 [1.24–1.41]; admixed American, 1.23 [1.05–1.45]; and African, 1.18 [1.04–1.34]). CONCLUSIONS: Multi-trait and multi-ancestry PRS performed better than individual trait and/or single ancestry PRS for each atherosclerotic cardiovascular disease phenotype across ancestrally diverse and admixed individuals, with minimal change including adjustment for conventional risk factors.

Abstract Background Contemporary prevalence and penetrance of familial hypercholesterolemia (FH) and the associated coronary heart disease (CHD) risk across diverse genetic ancestries in the United States are unclear. Purpose To estimate prevalence of individuals harboring pathogenic/likely pathogenic (P/LP) variants in LDLR, APOB, and PCSK9, distribution of low-density-lipoprotein cholesterol (LDL-C) levels, and associated CHD risk for carriers in an ancestrally diverse cohort. Methods In 242,167 All of Us (AoU) participants with available whole-genome sequencing data, we obtained LDL-C measurements, statin prescriptions, and ascertained P/LP variants in FH-related genes and presence of CHD based on electronic-health record data. P/LP variants in LDLR, APOB, and PCSK9 were identified in AoU data by filtering for high-impact (frameshift, canonical splice site, indels, and stop gain) and predicted-deleterious missense variants, then selecting those classified as P/LP in ClinVar. Untreated LDL-C levels were estimated assuming a 30% reduction if there was a record of a statin prescription in the preceding 12 months and the highest value among multiple measurements for each individual were selected. Penetrance was defined as LDL-C>155 mg/dl. CHD was determined using a validated phenotyping algorithm based on diagnostic and procedural billing codes. Logistic regression models were used to obtain prevalence and penetrance estimates and odds ratios for CHD, along with associated 95% confidence intervals (95% CI). Results We identified 221 FH-related P/LP variants (LDLR=169, APOB=50, PCSK9=2) in the study cohort. Of 125,841 European (EUR), 53,367 African (AFR), 40,189 admixed American (AMR), and 22,770 other ancestry individuals, 1:218 (0.46%, 95% CI 0.42%-0.50%), 1:307 (0.33%,0.28%-0.38%), 1:350 (0.29%, 0.24%-0.34%), and 1:201 (0.50%, 0.41%-0.59%) were carriers of a P/LP variant. In 92,616 individuals with at least one LDL-C measurement, carriers of a P/LP variant had a higher distribution of LDL-C across all ancestries with similarly high penetrance (Figure 1). Harboring a P/LP variant in LDLR, APOB, and PCSK9 was associated with similarly increased odds of CHD across all the different ancestry groups (Figure 2). Conclusions Monogenic FH was prevalent, penetrant, and significantly associated with CHD similarly across diverse ancestry groups in the AoU cohort. These findings support a ‘genotype first’ approach to detection of FH to enable early intervention across diverse ancestry groups.Figure 1 Figure 2

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.

Background: We investigated the impact of genetic risk factors for coronary heart disease (CHD) -polygenic risk score (PRS), familial hypercholesterolemia (FH), and family history (FamHx)- on CHD risk estimates, across the age spectrum, in two diverse cohorts of US adults -eMERGE IV (eIV) and All of Us (AoU). Methods: CHD was defined as myocardial infarction, unstable angina, and coronary revascularization. Self-identified race/ethnicity (SIRE) was used as a population descriptor. We calculated a PRS for CHD (PRS CHD , PGS004698), ascertained FH as the presence of pathogenic/likely pathogenic variants in FH genes, and defined FamHx as early-onset CHD in first-degree relatives. We employed Pooled Cohort Equations (PCE) to estimate the 10y risk of CHD for adults ≥40y and modeled the association of conventional risk factors with CHD in adults <40y. AoU served as the training set, and eIV as the testing set. We analyzed the impact of PRS CHD and FamHx on CHD risk estimates by a) using multivariable logistic regression and Cox proportional hazard models, assessing discrimination and the extent of risk reclassification; and b) net benefit analysis and decision curves to assess the performance of prediction models across actionable thresholds. Results: We analyzed data for 19348 participants from eIV (age 50±15, 68% female, 41% non-White) and 239645 participants from AoU (age 55±17, 61% female, 48% non-White). Genetic risk factors were significantly associated with CHD. PRS CHD performance varied by SIRE groups, while FamHx was consistent. The effects of PRS CHD and FamHx on CHD were independent and additive (Figure 1). In adults ≥40y, incorporating PRS CHD and FamHx into PCE improved discrimination (C-statistic increased from 0.719 to 0.753; P -diff=9.1×10 -3 , Figure 2) and reclassified risk in 19% and 20% of participants at the 7.5% and 10% 10y CHD risk thresholds, respectively. Between the 7.5% and 10% 10y CHD risk thresholds, incorporating PRS CHD and FamHx into the PCE improved the net benefit of the risk prediction models across White, Black, and Hispanic/Latino groups (Figure 3). Conclusion: PRS CHD and FamHx were independently and additively associated with CHD in two large diverse cohorts in the US. Incorporating PRS CHD and FamHx into PCE improved risk discrimination, reclassified risk in a significant portion of participants, and improved net benefit of the PCE across all three major SIRE groups, motivating the addition of these factors to clinical risk calculators.

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.

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.

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: Lp(a) is a causal risk factor for ASCVD that is often elevated in patients with personal or family history (FHx) of ASCVD. The 2019 ESC guidelines recommend universal Lp(a) testing, while the 2019 AHA/ACC guidelines include Lp(a) as a ‘risk enhancer’. We used electronic health records (EHR) to assess utilization of Lp(a) testing in patients with ASCVD, premature ASCVD, and FHx of ASCVD at Mayo Clinic sites in Rochester MN, Phoenix AZ and Jacksonville FL and the Mayo Clinic Health System (MCHS) from 1999-2021. Methods: We ascertained Lp(a) testing using LOINC codes and ASCVD status using validated ICD/CPT-based algorithms. ASCVD included coronary heart disease (CHD), cerebrovascular disease (CVD) (ischemic stroke and/or carotid artery stenosis), peripheral artery disease, and abdominal aortic aneurysm. Premature ASCVD was defined as occurring in males < 55 years and females < 65 years. We ascertained FHx using EHR survey data from 2017-2021. We estimated the proportion of patients with ASCVD, premature ASCVD, or FHx of ASCVD who underwent Lp(a) testing. Results: Between 1999-2021, we identified 310,760 patients with at least 1 ASCVD subtype of whom 66,710 had premature disease. FHx data was available starting 2017. For the timeframe 2017-2021, we identified 267,177 patients with FHx of ASCVD and 90,269 ASCVD patients. As summarized in Table 1, Lp(a) was tested only in a small proportion of patients; 7% of ASCVD patients, 11% of premature ASCVD patients, and 6% of patients with FHx of ASCVD. Premature CHD patients had the highest testing proportion (17%). Conclusions: Our results demonstrate underutilization of Lp(a) testing in patients with personal or FHx of ASCVD. Lp(a) is one of the mediators of “ASCVD residual risk” in patients on statins, and with new Lp(a)-lowering medications in phase III clinical trials, patient and provider education will be needed to increase awareness of Lp(a) as a modulator of ASCVD risk.

Introduction. We assessed the effects of a polygenic risk score (PRS), monogenic variants (Mono + ), and family history (FamHx) on the risk of coronary heart disease (CHD) and whether incorporating a PRS into the Pooled Cohort Equations (PCE) improves risk prediction. Method. In 199,997 UK Biobank participants, we calculated a PRS CHD (metaGRS, based on ~1.6 million single nucleotide variants), identified individuals with monogenic etiology (presence of a pathogenic variant in LDLR , APOB , or PCSK9 ), and ascertained family history of CHD (CHD in a first-degree relative). Odds of CHD (myocardial infarction/ coronary revascularization/ cardiovascular death) associated with PRS CHD , Mono + , and FamHx at age 55 were computed using logistic regression analyses adjusted for age, sex, and first 4 principal components of ancestry. We combined PRS CHD and PCE into an integrated score (IS) and assessed net reclassification using an actionable risk threshold of 7.5%. Results. The odds ratio (OR) for CHD in the top 5 th percentile of PRS CHD (PRS P95 ), Mono + , and FamHx were 3.37 (2.98 - 3.81), 2.68 (1.67 - 4.07), and 1.61 (1.47 - 1.76), respectively (all p<0.001). These effects were independent and similar in different subgroups. ( Figure 1-A ) Compared to PRS Q2-4 Mono - (3 middle quintiles of PRS CHD and no monogenic variants), the OR for CHD ranged from 1.73 (0.28 - 5.48, p = 0.446) in PRS Q1 Mono + to 5.10 (2.14 - 10.24, p <0.001) in PRS Q5 Mono + . A similar pattern was seen for other combinations, indicating additive effects ( Figure 1-B ). Incorporating PRS CHD into PCE resulted in a net reclassification improvement of 4.56%. Compared to those with high PCE and low IS, the hazard ratio for CHD in those with low PCE and high IS was 1.48 (1.34 - 1.64, p <0.001). Conclusion. The CHD risk associated with PRS CHD , monogenic variants, and family history is independent and additive. Combining PRS CHD and PCE improved risk prediction, that could improve further by adding monogenic variants and family history.