Dikilitas, Ozan

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: 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.

ImportanceClonal hematopoiesis of indeterminate potential (CHIP) may contribute to the risk of atrial fibrillation (AF) through its association with inflammation and cardiac remodeling.ObjectiveTo determine whether CHIP was associated with AF, inflammatory and cardiac biomarkers, and cardiac structural changes.Design, Setting, and ParticipantsThis was a population-based, prospective cohort study in participants of the Atherosclerosis Risk in Communities (ARIC) study and UK Biobank (UKB) cohort. Samples were collected and echocardiography was performed from 2011 to 2013 in the ARIC cohort, and samples were collected from 2006 to 2010 in the UKB cohort. Included in this study were adults without hematologic malignancies, mitral valve stenosis, or previous mitral valve procedure from both the ARIC and UKB cohorts; additionally, participants without hypertrophic cardiomyopathy and congenital heart disease from the UKB cohort were also included. Data analysis was completed in 2023.ExposuresCHIP (variant allele frequency [VAF] ≥2%), common gene-specific CHIP subtypes (DNMT3A, TET2, ASXL1), large CHIP (VAF ≥10%), inflammatory and cardiac biomarkers (high-sensitivity C-reactive protein, interleukin 6 [IL-6], IL-18, high-sensitivity troponin T [hs-TnT] and hs-TnI, N-terminal pro–B-type natriuretic peptide), and echocardiographic indices.Main Outcome MeasureIncident AF.ResultsA total of 199 982 adults were included in this study. In ARIC participants (4131 [2.1%]; mean [SD] age, 76 [5] years; 2449 female [59%]; 1682 male [41%]; 935 Black [23%] and 3196 White [77%]), 1019 had any CHIP (24.7%), and 478 had large CHIP (11.6%). In UKB participants (195 851 [97.9%]; mean [SD] age, 56 [8] years; 108 370 female [55%]; 87 481 male [45%]; 3154 Black [2%], 183 747 White [94%], and 7971 other race [4%]), 11 328 had any CHIP (5.8%), and 5189 had large CHIP (2.6%). ARIC participants were followed up for a median (IQR) period of 7.0 (5.3-7.7) years, and UKB participants were followed up for a median (IQR) period of 12.2 (11.3-13.0) years. Meta-analyzed hazard ratios for AF were 1.12 (95% CI, 1.01-1.25; P = .04) for participants with vs without large CHIP, 1.29 (95% CI, 1.05-1.59; P = .02) for those with vs without large TET2 CHIP (seen in 1340 of 197 209 [0.67%]), and 1.45 (95% CI, 1.02-2.07; P = .04) for those with vs without large ASXL1 CHIP (seen in 314 of 197 209 [0.16%]). Large TET2 CHIP was associated with higher IL-6 levels. Additionally, large ASXL1 was associated with higher hs-TnT level and increased left ventricular mass index.Conclusions and RelevanceLarge TET2 and ASXL1, but not DNMT3A, CHIP was associated with higher IL-6 level, indices of cardiac remodeling, and increased risk for AF. Future research is needed to elaborate on the mechanisms driving the associations and to investigate potential interventions to reduce the risk.

BACKGROUND: Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRS CHD ) for 5 genetic ancestry groups. METHODS: We derived ancestry-specific and multi-ancestry PRS CHD based on pruning and thresholding (PRS PT ) and ancestry-based continuous shrinkage priors (PRS CSx ) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRS CHD in 176,988 individuals across 9 diverse cohorts. RESULTS: Multi-ancestry PRS PT and PRS CSx outperformed ancestry-specific PRS PT and PRS CSx across a range of tuning values. Two best-performing multi-ancestry PRS CHD (ie, PRS PTmult and PRS CSxmult ) and 1 ancestry-specific (PRS CSxEUR ) were taken forward for validation. PRS PTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41–3.14], 1.65 [1.59–1.72]), followed by East Asian ancestry (1.56 [1.50–1.61]), Hispanic/Latino ancestry (1.38 [1.24–1.54]), and African ancestry (1.16 [1.11–1.21]). PRS CSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38–3.00]) and European ancestry (1.65 [1.59–1.71]), lower in East Asian ancestry (1.59 [1.54–1.64]), Hispanic/Latino ancestry (1.51 [1.35–1.69]), and the lowest in African ancestry (1.20 [1.15–1.26]). CONCLUSIONS: The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRS CHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRS CHD .

Introduction: Hospitalization for COVID-19 is often associated with myocardial injury (elevated cardiac troponin [cTn]). In the initial pandemic waves, patients with myocardial injury had increased likelihood of cardiovascular complications and inpatient mortality; however, the differences in impact of distinct subtypes of myocardial injury on mortality is unclear. In addition, little is known about how these outcomes compare with the subsequent, milder, Omicron wave. Methods: Utilizing Mayo Clinic enterprise patients we developed algorithms based on diagnostic/procedural codes, cTn levels, and temporal relationships to positive COVID-19 PCR tests, to determine 5 common subtypes of myocardial injury: stress cardiomyopathy, myocarditis, pericarditis, pulmonary embolism, and myocardial infarction. We examined hospitalized adults with COVID-19; those with elevated cTn designated as cases and others as controls. We fitted Cox models using the following covariates: spline transformed age, sex, race, ethnicity, index date and clinical comorbidities. Patients were analyzed based on admission dates coinciding with either the Alpha-Delta or Omicron waves. Results: Across both wave cohorts, hospitalized patients with acute COVID-19 complicated by myocardial injury had an increased risk of mortality compared to those without ( Figure ). Patients with one of the 5 subtypes examined tended to have higher risk than those in the “other myocardial injury” category. Admission during the Omicron wave, compared to the Alpha-Delta waves, tended to have higher mortality risk in all subtypes except myocarditis. Conclusions: Our algorithms successfully identified subgroups of myocardial injury in patients hospitalized with acute COVID-19 leading to stratification of mortality risk. Surprisingly, among hospitalized patients during the Omicron wave, myocardial injury of most subtypes was tended to have increased risk of mortality compared to the Alpha-Delta waves.

Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) is associated with an increased risk of heart failure (HF) and stroke, particularly HF with preserved ejection fraction and hemorrhagic stroke (HS). Hypothesis: We hypothesized that CHIP is associated with incident hypertension and this association mediates the effect of CHIP on HF and HS. Methods: In 200K UK-Biobank participants with exome sequencing, we ascertained CHIP as variant allele fraction (VAF) ≥2% and large CHIP as VAF ≥10%. Hypertension, HF, and HS diagnosis were determined based on the ICD-9 and ICD-10 codes. Cox proportional hazards models were used to test the association of both CHIP and the CHIP subtypes based on the most implicated driver genes (i.e., DNMT3A , TET2 , and ASXL1 ) with incident hypertension. Logistic regression models were used for mediation analyses, quantifying the extent to which hypertension mediates the effects of CHIP. Results: After excluding those with hematological malignancy (n=1,025) and pre-existing hypertension (n=14,754), 184,760 participants (56.14±8.12 years, 44.25% male, and 95.33% white ethnicity) were included. During a median follow-up of 12.2 years, 42,049 (22.76%) participants developed hypertension. CHIP mutations were significantly associated with incident hypertension (Table). Mutations in DNMT3A and TET2 but not ASXL1 drove this association. Mediation analysis revealed 17.9% (9.4% - 38.2%, P<0.001) of the effect of CHIP on HF and 5.7% (2.4% - 22.4%, P<0.001) of the effect on HS were mediated through the increased risk of hypertension. Conclusion: CHIP is associated with an increased risk of hypertension. The association of CHIP with HF and HS is partially mediated by hypertension. These findings suggest a new mechanistic pathway for the association of CHIP with cardiovascular diseases.

Background: Heart failure (HF) is a complex trait influenced by environmental and genetic factors. Genome-wide association studies (GWAS) have found common genetic variants associated with HF risk, but the extent that common and rare variants explain risk and implicate similar genes and pathways in HF remains less understood. Methods: For common variants, we meta-analyze existing GWAS of all-cause HF with data from the Million Veteran Program, including 207,346 individuals with HF and 2,151,210 without in diverse populations. We examine rare coding variants in the Penn Medicine BioBank (PMBB) using gene-collapsing association studies and burden heritability regression. We test for an interaction between a polygenic risk score (PRS) based on our GWAS meta-analysis and truncating variants in TTN (TTNtv). Results: GWAS meta-analysis identified 176 risk loci (146 previously unreported), implicating known risk factors for HF and genes involved in cardiac development. New signals at GIPR/ GLP1R highlight potential contributions of dysregulated glucose homeostasis and metabolism in HF. Gene collapsing studies identified associations between HF and rare loss-of-function variants in TTN (OR 2.94, 95% CI 2.43-3.56) and MYBPC3 (OR 7.71, 95% CI 3.97-14.97) at exome-wide significance. HF heritability mediated through common variants (4.5%) was significantly higher than for rare variants (1.8%), but rare variant heritability was further enriched in cardiomyopathy genes compared to common variants (27-fold vs. 3.4-fold). We found a significant interaction between a new multi-ancestry HF-associated PRS (AUC 0.72, 95% CI 0.72-0.73) and TTNtv carrier status (interaction OR 1.32, 95% CI 1.06-1.64). Conclusions: Common and rare variants contribute to HF heritability. Common variant HF heritability is highly polygenic, while rare variant burden heritability appears more concentrated in core cardiomyopathy genes. HF risk conferred by TTNtv carrier status is further increased by an individual’s polygenic contribution. Our results support the continued inclusion of cardiomyopathy genes in rare-variant genetic testing panels for HF but suggest a meaningful polygenic component to HF exists that is not currently captured by clinical genetic testing.

Introduction: Aging and inflammation are associated with risk of atrial fibrillation (AF), to which clonal hematopoiesis of indeterminate potential (CHIP) may contribute. We investigated whether CHIP was associated with AF, inflammatory and cardiac biomarkers and cardiac structural changes. Methods: We ascertained CHIP (variant allele frequency, VAF ≥2%) in the Atherosclerosis Risk in Community Study (ARIC visit-5, n = 4,131) and the UK Biobank (UKBB, n = 195,851) participants. Using multivariable Cox regression models, we studied the association of CHIP and the top three implicated genes (i.e., DNMT3A , TET2 , ASXL1 ) with incident AF and conducted a meta-analysis on both cohorts. We also ascertained high-sensitivity C-reactive protein (hs-CRP), interleukin 6 (IL6), IL18, high-sensitivity troponin T and I (hs-TnT and hs-TnI), NT-proBNP, and echocardiography indices in ARIC. We first standardized the natural log of all markers (st-ln) and used the adjusted quantile regression to compare the median in those with and without CHIP. Results: In ARIC, the mean ± SD age was 75.8 ± 5.2 years, 41% were men, 23% were Black, 25% had CHIP, and 11.6% had large CHIP (VAF ≥10%). These measures in UKBB were 56.4 ± 8.1 years, 45%, 2%, 5.8%, and 2.6%, respectively. In the meta-analysis, compared to those without CHIP, the hazard ratios (95% confidence interval) for AF were 1.12 (1.01 - 1.25, P =0.035), 1.31 (1.07 - 1.60, P =0.009), and 1.48 (1.05 - 2.08, P =0.026) for Large-CHIP, Large- TET2, and Large- ASXL1 , respectively. hs-CRP and IL18 levels were similar in those with and without CHIP as well as gene-specific subgroups. Large- TET2 and ASXL1 were associated with higher IL6, which was separately associated with AF. Additionally, Large- ASXL1 was associated with increased left ventricle mass index and NT-proBNP (coefficients in Figure 1 ). Conclusion: Large TET2 and ASXL1 but not DNMT3A CHIP is associated with higher IL6 levels, indices of cardiac remodeling, and an increased risk of AF.

Introduction: Previous studies have shown inconsistent changes in the rates of adverse cardiovascular events after daylight savings time (DST) transitions. In this large-scale, U.S.-based nationwide study, we sought to investigate the association of DST transitions with the rates of adverse cardiovascular events. Methods: The study cohort included 36,116,951 unique individuals from de-identified administrative claims data of the OptumLabs Data Warehouse®. There were 74,722 total adverse cardiovascular events during the week of DST transition and the control weeks (two weeks prior and after) in spring and autumn of 2015-2019. We used Bayesian hierarchical Poisson regression models to estimate the event rate ratios (ERR) representing the ratio of composite adverse cardiovascular event rates (acute myocardial infarction, stroke, cardiogenic shock, cardiac arrest, and sudden death) between DST transition weeks and control weeks. Results: Our results showed an average increase of 3% [95% uncertainty interval; -3% to 10%] and 4% [-2% to 12%] in the rate of adverse cardiovascular events during Monday and Friday of the spring DST transition week, respectively. The probability of spring DST transition being associated with a moderate-to-large increase in the event rates (ERR>1.10) was estimated to be <6% for Monday and Friday, and <1% for the remaining days. During autumn DST transition, the probability of any decrease in adverse cardiovascular event rates was estimated to be <46% and a moderate-to-large decrease in the event rates to be <4% across all days of the week. Conclusions: Spring DST transition week had a suggestive association with a minor increase in adverse cardiovascular event rates on Monday and Friday. The probability of this association to be of clinically meaningful magnitude was estimated to be very low. We did not observe any evidence suggestive of a change in adverse cardiovascular event rates immediately following autumn DST transition. Taken together, our findings, based on a large, diverse cohort from across the United States, suggest that DST transitions are unlikely to significantly impact the rate of cardiovascular events.