Citations

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.

Introduction: The cardio-kidney-metabolic pathway has a crucial role in ASCVD, motivating development of the PREVENT equation. There is a need for external validation of the PREVENT equation and assessment of its performance in independent cohorts to assess generalizability across diverse populations. Aim: To compare predictive performance of the PREVENT equation with previously developed risk calculators; pooled cohort equations (PCE), and QRISK3. Methods: We used data from 500k participants of the United Kingdom Biobank (UKB). ASCVD was defined as the composite outcome of fatal and non-fatal myocardial infarction, coronary revascularization, and stroke. We ascertained demographic and clinical covariates required to calculate ASCVD risk, using the base model of PREVENT, PCE, and QRISK3, at the time of recruitment. Those with a diagnosis of ASCVD at the time of recruitment or missing data on age, sex, or ethnicity were excluded. We used the area under the ROC curve (AUC) and calibration plots for analysis. Results: Of 459,681 participants (mean age 56.3±8.1 years, 44.4% male, and 94.7% White), 24,514 (5.4%) participants had a new diagnosis of ASCVD during a 10-year follow-up. The median [interquartile range] of estimated 10-y ASCVD risk was 3.7% [1.9% - 6.1%] for PREVENT, 5.6% [2.4% - 11.3%] for PCE, and 8.0% [3.9% - 13.8%] for QRISK3 (Figure 1A). The calibration slope (95% CI) was 1.71 (1.66 – 1.76) for PREVENT, 0.64 (0.63 – 0.66) for PCE, and 0.66 (0.64 – 0.67) for QRISK3 (Figure 1B). The AUC for ASCVD prediction was 0.741 (0.738 - 0.744) for PREVENT, 0.744 (0.741 – 0.747) for PCE, and 0.751 (0.749 – 0.755) for QRISK3 (Figure 1C). Conclusion: In the UKB cohort, the PREVENT equation underpredicted while the PCE and QRISK3 overpredicted ASCVD risk, with a similar discrimination. Consequently, fewer individuals would be eligible for primary prevention with statins, based on the actionable thresholds of 7.5% or 10% when considering ASCVD risk based on PREVENT. Therefore, the external application of the PREVENT equation requires recalibration and context-specific interpretation.

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.

Abstract Current studies regarding the secondary use of electronic health records (EHR) predominantly rely on domain expertise and existing medical knowledge. Though significant efforts have been devoted to investigating the application of machine learning algorithms in the EHR, efficient and powerful representation of patients is needed to unleash the potential of discovering new medical patterns underlying the EHR. Here, we present an unsupervised method for embedding high-dimensional EHR data at the patient level, aimed at characterizing patient heterogeneity in complex diseases and identifying new disease patterns associated with clinical outcome disparities. Inspired by the architecture of modern language models—specifically transformers with attention mechanisms, we use patient diagnosis and procedure codes as vocabularies and treat each patient as a sentence to perform the patient embedding. We applied this approach to 34,851 unique medical codes across 1,046,649 longitudinal patient events, including 102,739 patients from the electronic Medical Records and GEnomics (eMERGE) Network. The resulting patient vectors demonstrated excellent performance in predicting future disease events (median AUROC = 0.87 within one year) and bulk phenotyping (median AUROC = 0.84). We then illustrated the utility of these patient vectors in revealing heterogeneous comorbidity patterns, exemplified by disease subtypes in colorectal cancer and systemic lupus erythematosus, and capturing distinct longitudinal disease trajectories. External validation using EHR data from the University of Washington confirmed robust model performance, with median AUROCs of 0.83 and 0.84 for bulk phenotyping tasks and disease onset prediction, respectively. Importantly, the model reproduced the clustering results of disease subtypes identified in the eMERGE cohort and uncovered variations in overall mortality among these subtypes. Together, these results underscore the potential of representation learning in EHRs to enhance patient characterization and associated clinical outcomes, thereby advancing disease forecasting and facilitating personalized medicine.

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 (PRS CHD ) with multiple measures of coronary artery disease (CAD) severity. We assessed the joint associations of PRS CHD 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 PRS CHD with prevalent coronary heart disease and incident myocardial infarction. RESULTS: A 1-SD increase in PRS CHD 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 PRS CHD (β, 0.21 [95% CI, 0.03–0.38]). A 1-SD increase in PRS CHD 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: PRS CHD was associated with multiple measures of CAD severity. The association of PRS CHD with incident myocardial infarction was almost fully mediated by CAD severity, indicating a considerable genetic overlap between the 2 phenotypes.

Introduction: We validated the “Familial Hypercholesterolemia (FH) Decision Aid” to promote shared decision making (SDM) about lipid lowering therapy, based on patient-clinician encounters. Methods: Validation of the DA included an assessment of whether the content and design of the tool facilitated SDM and patient engagement and to determine how well the tool integrated with clinical workflow. Validation was conducted during patient-clinician encounters in the Mayo preventive cardiology clinic. As inclusion criteria we considered an upcoming appointment in the clinic, an LDL-C level ≥ ~160 mg/dL, and no prior diagnosis of FH. Prior to the encounters, each clinician viewed a demonstration of the online, interactive DA. During the encounters, the DA was shared with patients using the clinic room computer screen, either in-person or virtually. Patient-clinician encounters were video recorded for further analysis by two design researchers and experts in SDM. Results: Ten patients were enrolled in the study (7 male; average age 51 years). The average patient LDL-C level was 207 mg/dL, range 157 - 248 mg/dL. Review and qualitative analysis of the encounter recordings yielded 4 key findings: i) clinicians primarily used the cardiovascular risk estimate in the DA to guide conversation with patients; ii) highly trained specialists needed to change their usual approach to care and conversation to integrate the DA with their clinical workflows; iii) as clinicians became more familiar with the DA, they moved through the tool at a more efficient pace; and iv) each clinician developed his/her own style of navigating the DA, either moving through it one screen at a time or focusing on one specific section for the duration of the conversation. The DA effectively guided conversation and facilitated active patient engagement in 6 of 10 encounters. Social distancing and use of telemedicine encounters, due to the COVID-19 pandemic, limited patient engagement in 2 of 10 encounters. Overall, the DA promoted SDM regarding FH management in 9 of 10 encounters. Conclusion: Using patient-clinician encounters, we found the FH DA to facilitate SDM regarding lipid-lowering therapy. The tool has potential to improve care of FH patients and increase patient engagement and knowledge.

Introduction: Somatic mutations accrue in hematopoietic stem cells with age and can lead to selective clonal expansion, a process termed Clonal Hematopoiesis of Indeterminate Potential (CHIP). CHIP has been associated with increased risk of coronary heart disease (CHD) and myocardial infarction (MI). Whether CHIP is a risk factor for incident heart failure is unclear. Methods: The presence of CHIP was assessed in exome sequence data from 200,628 UKB participants using the Genome Analysis Toolkit pipeline and filtering somatic mutations from a pre-specified list within DNMT3A , TET2 , and ASXL1 . CHIP was defined as somatic mutations in these 3 genes with variant allele fraction (VAF) >10%. We ascertained incident heart failure (overall, ischemic, and non-ischemic), death, cardiovascular death, and coronary heart disease (CHD defined as of MI and/or coronary revascularization) as determined by UKB algorithms, self-report, and diagnosis/procedure codes. Cox proportional hazard models adjusted for age, sex, self-reported race/ethnicity, diabetes, hypertension, smoking, and BMI were used to assess whether CHIP was associated with incident adverse events. Results: We identified 3,885 participants with CHIP in DNMT3A , TET2 , and ASXL1 at a VAF of >10%. Over a median follow-up of 9 years, 2,055 participants developed incident heart failure. CHIP was associated with both ischemic and non-ischemic heart failure. We replicated previously reported associations of CHIP with death and incident CHD ( Table ). Conclusions: CHIP was associated with increased risk of both ischemic and non-ischemic heart failure. The association with ischemic subtype is likely mediated by the increased risk of CHD. The basis for the increased hazard of non-ischemic heart failure is unclear but may be due to an inflammatory milieu in CHIP carriers.