Kullo, Iftikhar

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.

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 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.

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.

Introduction: We investigated the predictive power of a machine learning (ML) algorithm for 5-year mortality in patients with atherosclerotic cardiovascular disease (ASCVD). Candidate features included clinical variables from the electronic health record (EHR) as well as self-reported health and lifestyle data. Methods: The study cohort included 4,689 patients (66±11 years, 62% male) with ASCVD enrolled in the Vascular Disease Biorepository, which recruits patients referred to noninvasive vascular evaluation at Mayo Clinic, Rochester, MN. Clinical variables including comorbidities, lab measurements, and medication use were obtained from the EHR using validated electronic phenotyping algorithms. Information regarding social habits, physical activity levels, and personal health was obtained from a survey filled by the participants. Mortality was ascertained from the national death index. Clinical variables (n=52) and self-reported variables related to health and lifestyle habits (n=48) were added as individual features to reinforcement learning trees (RLT), a sparse decision tree algorithm, to construct the prediction model. We assessed the impact of each feature on prediction accuracy by applying feature selection and computing variable importance (VI). Results: Features with trivial VI that made no contribution to prediction were excluded. A model with 15 features had an area under the ROC curve (AUC) of 0.82 [95% CI 0.79 - 0.83] in the test set. Among the top 10 predictors of 5-year mortality, 6 were self-reported health variables (Table) , including outlook on personal health, limitations on routine daily activities, and physical activity levels. Conclusions: A ML approach had robust discrimination (AUC 0.82) in predicting 5-year mortality in patients with ASCVD. Self-reported health variables were among the top predictive features in the final model.

Introduction: Chronic coronary disease (CCD) is a leading cause of death worldwide. Whole blood (WB) transcriptomic (RNA-seq) and methylation ‘omics’ data can discern molecular underpinnings of CCD and create molecular subtypes to aid risk prediction. Methods: Of 1,064 participants with moderate-severe ischemia (Age: 67.1±6.2, 19.4% Female, 17% non-White) from the ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) biorepository, 646 participants had available WB RNA-seq and 737 had WB methylation data ( Fig 1A ). Unsupervised clustering and differential analyses identified omics-derived molecular subtypes. We identified co-regulated gene modules of each subtype, performed subtype-level enrichment of clinical variables, and performed Kaplan-Meier analyses for time to cardiovascular (CV) death or myocardial infarction (MI). Results: Multi-omic analysis revealed 4 RNA-seq-derived subtypes (RS) and 3 methylation-derived subtypes (MS). RNA-seq Subtype 1 (RS1) was enriched for molecular drivers indicative of an adaptive immune response, had clinical enrichment of female sex, statin use, and greater ischemia, and had decreased risk of CV death or MI ( Fig 1B ). RNA-seq Subtype 2 (RS2) was enriched for innate immune response pathway, and patients who were older and White, and was associated with increased risk of CV death or MI. Similarly, methylation subtypes also yielded a high-risk subtype (MS3) and a low-risk subtype (MS2) ( Fig 1C ). Integration of transcriptomic and methylomic subtypes led to further distinction of resilient and vulnerable subgroups. Conclusions: WB RNA-seq and methylation data identified molecularly-defined subtypes that informed the underlying disease pathology of CCD patients in ISCHEMIA. These subgroups were differentially enriched for clinical characteristics and event outcomes, demonstrating the potential use for blood-based omics tests to aid in the prognosis of CCD.

Introduction: Fibromuscular dysplasia (FMD) is a non-atherosclerotic arteriopathy of unknown etiology, afflicting mostly middle-aged women. It is characterized by stenotic lesions of the vascular wall in middle-size arteries, sometimes associated with dissection, aneurysm or tortuosity. Renovascular hypertension is a major presentation of FMD and 50–85% of FMD patients are hypertensive. Methods & Results: We report results from the first genome-wide association meta-analysis of six studies including 1556 FMD cases and 7100 controls. We identified four robustly associated loci (PHACTR1, LRP1, ATP2B1, and LIMA1). Using colocalization and transcriptome-wide association analysis with arterial expression datasets, we found PHACTR1, LRP1 and ATP2B1 as target genes at FMD loci and identified one additional locus (SLC24A3). We analysed chromatin accessibility in artery-derived primary cells and found that FMD associated variants were located in arterial specific regulatory elements active in smooth muscle cells. Target genes were broadly involved in mechanisms related to actin cytoskeleton and intracellular calcium homeostasis, central to vascular contraction. All FMD loci were also associated to at least one blood pressure trait. Colocalization analysis supported the same genetic variants to cause association to FMD and systolic blood pressure at PHACTR1, ATP2B1 and LIMA1 loci, and for FMD and pulse pressure at PHACTR1, LRP1 and ATP2B1 loci. Using genome-wide genetic correlation, we found a significant association between FMD and blood pressure. Mendelian Randomization analysis supported genetically determined systolic blood pressure to significantly increase the risk of FMD. However, we confirmed using adjusted and stratified analyses that hypertension status alone was not driving the association with FMD at top loci. We also report an important genetic overlap of FMD with migraine, intracranial aneurysm, coronary artery disease and myocardial infarction. Conclusion: Our study confirms FMD as a polygenic disease following a complex genetic mode of inheritance. We find that FMD shares an important part of its genetic basis with blood pressure regulation, involving in particular several genes related to intracellular calcium homeostasis and vascular smooth muscle contraction. These results are consistent with previous studies showing systemic alterations in vascular contraction in FMD patients, even outside of affected arterial beds. While the mechanisms through which FMD may cause renovascular hypertension are well known, further studies will be required to determine how alterations in vascular contraction may lead to the arterial remodeling observed in FMD.