Clonal Hematopoiesis Associates with Prevalent and Incident Cardiometabolic Disease in High-Risk Individuals

Clonal Hematopoiesis Associates with Prevalent and Incident Cardiometabolic Disease in High-Risk Individuals | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Clonal Hematopoiesis Associates with Prevalent and Incident Cardiometabolic Disease in High-Risk Individuals View ORCID Profile Jessica A. Regan , Lydia Coulter Kwee , Navid A. Nafissi , Alexander G. Bick , William E. Kraus , View ORCID Profile Pradeep Natarajan , Sidd Jaiswal , Svati H. Shah doi: https://doi.org/10.1101/2025.01.14.25320566 Jessica A. Regan 1 Division of Cardiology, Department of Medicine, Duke University , Durham, NC, USA 2 Duke Molecular Physiology Institute , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica A. Regan Lydia Coulter Kwee 2 Duke Molecular Physiology Institute , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Navid A. Nafissi 1 Division of Cardiology, Department of Medicine, Duke University , Durham, NC, USA 2 Duke Molecular Physiology Institute , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexander G. Bick 3 Division of Genetic Medicine, Department of Medicine, Vanderbilt University School of Medicine , Nashville, TN, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site William E. Kraus 1 Division of Cardiology, Department of Medicine, Duke University , Durham, NC, USA 2 Duke Molecular Physiology Institute , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pradeep Natarajan 4 Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital , Boston, MA 5 Program in Medical & Population Genetics, Broad Institute of Harvard & MIT , Cambridge, MA 6 Department of Medicine, Harvard Medical School , Boston, MA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pradeep Natarajan Sidd Jaiswal 7 Department of Pathology, Stanford University School of Medicine , Stanford, CA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Svati H. Shah 1 Division of Cardiology, Department of Medicine, Duke University , Durham, NC, USA 2 Duke Molecular Physiology Institute , Durham, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: svati.shah{at}duke.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Clonal hematopoiesis of indeterminate potential (CHIP) is the age-related presence of expanded somatic clones secondary to leukemogenic driver mutations and is associated with cardiovascular (CV) disease and mortality. We sought to evaluate relationships between CHIP with cardiometabolic diseases and incident outcomes in high-risk individuals. Methods CHIP genotyping was performed in 8469 individuals referred for cardiac catheterization at Duke University (CATHGEN study) to identify variants present at a variant allele fraction (VAF) ≥2%. Associations were tested among any CHIP variant, large CHIP clones (VAF ≥10%) and individual CHIP genes with prevalent cardiometabolic traits. Cox proportional hazard models tested CHIP associations with time-to-overall mortality and Fine-Gray analyses tested CHIP associations with incident cardiovascular outcomes. Results We identified 463 CHIP variants in 427 individuals (5.0%) of which 268 (3.2%) harbored large CHIP clones. CHIP and large CHIP were associated with lower odds of obesity (OR 0.79 [95% CI 0.65-0.98], p=0.03; OR 0.76 [95% CI 0.57-0.99], p=0.04, respectively). CHIP was associated with prevalent HF (OR 1.25 [95% CI 1.01 - 1.55], p=0.04; especially for non- DNMT3A CHIP (OR 1.38 [95% CI 1.04-1.82], p=0.02). CHIP was also associated with incident events: Non- DNMT3A CHIP was associated with increased risk of time-to-HF hospitalization (HR 1.29 [95% CI 1.02-1.63], p=0.03). Conclusions In high-risk individuals referred for cardiac catheterization, large CHIP and non- DNTM3A CHIP were associated with obesity, prevalent HF, incident CV events. These findings strengthen the importance of CHIP as a biomarker for CV disease and highlight the contributing risk of large CHIP clones and non- DNMT3A CHIP variants. Condensed Abstract CHIP, the presence of somatic expanded mutations in myeloid driver genes in hematopoietic cells, is an emerging CVD biomarker. Using whole exome sequencing of peripheral blood derived DNA from participants in the CATHGEN cohort, we identified significant associations with obesity, prevalent HF, incident mortality, HF hospitalization and AF after adjusting for established clinical risk factors. These findings add strength to the growing literature of CHIP as a CVD biomarker, emphasizing large CHIP and non- DNMT3A CHIP variants for driving risk. Future studies should aim to further elucidate gene-specific risk and the inflammatory and metabolic mechanisms possibly mediating these relationships. What Is New? In a cohort with high prevalence of CAD, CHIP is inversely associated with obesity and associated with higher odds of prevalent HF and subsequent mortality, even after adjustment for relevant clinical comorbidities. Risk of incident events of mortality, HF hospitalization and AF were driven by large CHIP variants (VAF≥10%) and CHIP variants in genes other than DNMT3A . What are the Clinical Implications? Though more research is needed, as the evidence around the risk associated with specific CHIP variants continues to grow, clinicians should be prepared to provide gene- specific counseling for cardiometabolic disease risk. Introduction Genetic epidemiology studies have identified hundreds of variants associated with cardiovascular disease (CVD); however, these variants typically have small effect sizes and incompletely predict CVD risk 1 . While work has focused on germline mutations, which occur with formation of the embryo and are static over the lifetime, somatic mutations have increasingly been shown to contribute to CVD. Inherent in cancers, somatic mutations have been identified in diverse non-tumor tissues including in the atria of patients with atrial fibrillation (AF) 2 . Clonal hematopoiesis (CH) is the age-related expansion of somatic clones in hematopoietic stem cells (HSCs). CH of indeterminate potential (CHIP) can be detected in peripheral blood DNA and is defined as having a clone size (variant allele fraction [VAF]) ≥2% in genes associated with the development of myeloid malignancies and myelodysplastic syndrome (MDS) 3 – 5 . The most frequently mutated CHIP genes are DNMT3A , TET2 and ASXL1 , where mutant cells have a competitive proliferative advantage over native HSCs. Germline contributors to acquisition of CHIP have been identified 6 – 8 , and smoking has been associated with increased odds of ASXL1 -mutated CHIP 6 . The presence of CHIP is associated with increased risk of overall mortality, which appears to be driven by CVD events more so than by malignancy-related mortality. CHIP is associated with increased risk of incident coronary artery disease (CAD) and accelerated atherosclerosis, with heightened levels of inflammation in both clinical and pre-clinical models 9 , 10 . Large CHIP clones (VAF >10%) in DNMT3A and TET2 contribute greater risk for incident CVD 11 . CH is also associated with incident heart failure (HF) 8 , 12 , 13 including incident heart failure with preserved ejection fraction (HFpEF) 14 , 15 , as well as AF 8 , 16 – 18 . Variants in particular CHIP genes, have been identified as drivers of phenotype-specific risk such as TP53 and atherosclerosis 19 and TET2 and HF 13 – 15 . Knowledge gaps remain in our understanding of CHIP in patients with high cardiometabolic risk and existing cardiovascular disease. Although DNMT3A is the most commonly mutated CHIP gene, these variants may not be the strongest drivers of cardiometabolic risk 13 . Given the evolving CVD phenotypic associations, we sought to identify novel findings of CHIP, large CHIP and mutations in genes other than DNMT3A (non- DNMT3A ) and cardiometabolic traits and to assess further validation for CVD events, HF and AF in a high-risk population. Here we leverage whole exome sequencing (WES) to explore the role of CHIP as an emerging biomarker for intermediate cardiometabolic risk factors and prevalent and incident CVD in a medically complex, but clinically relevant cohort of patients referred for cardiac catheterization. Methods Study Population The CATHGEN biorepository is comprised of 9334 individuals who underwent cardiac catheterization at Duke University Medical Center (Durham, NC) between January 2001 and December 2010 20 . Femoral arterial blood samples were collected at the time of catheterization and whole blood was stored in ethylenediaminetetraacetic acid (EDTA) tubes. All study participants gave written informed consent for participation and use of their stored biospecimens for future use. The study was approved by the Duke University Institutional Review Board. Demographics and comorbidities were collected through medical record review at study enrollment, and yearly follow-up was conducted for events and vital status through 2020. These data were supplemented with electronic health records, including International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes. Patients with a diagnosis of hematologic malignancy prior to or within 6 months of study enrollment were excluded for this study (n=99, Supplemental Table 1, “Prevalent Hematologic Malignancy” phenotype). Whole Exome Sequencing and Somatic Variant Calling WES was performed by Regeneron Genetics on DNA extracted from EDTA whole blood using the HiSeq 2500 platform (Illumina, San Diego, California). Following genomic sequence alignment and quality control, the resultant BAM files were used for somatic variant calling using the GATK 21 Mutect2 pipeline on the Terra platform 22 , 23 . A panel of normals (PON) created from 40 young, healthy participants was used to eliminate sequencing artifacts. Functional annotation was performed for identified somatic variants and output into a variant call format (VCF) file. VCF files were parsed to regions of interest in 68 previously described CHIP genes and filtered to specific single-nucleotide variants (SNVs) and indels ( Supplemental Table 2 ). CHIP was defined as presence of a variant at a VAF ≥2% and large CHIP clones at a VAF >10%. An experienced hematopathologist performed manual curation and review to refine the final variant call set. Additional details on sequencing and variant calling details can be found in the Supplemental Methods . After WES quality control, 8469 participants had high quality somatic variant calls and were included for analyses. For participants with variants in multiple CHIP genes identified, analyses are categorized by the largest CHIP clone. Clinical Data and Outcomes Baseline cardiometabolic comorbidities were assessed by the enrolling physician and medical record review. Obesity was defined as BMI ≥30kg/m 2 . Prevalent CAD was defined as a prior history of MI, coronary artery bypass surgery, coronary percutaneous intervention, or cardiac catheterization with the presence of more than one major epicardial coronary vessel with 50-74% stenosis or one vessel with ≥75% stenosis. Prevalent CAD, HF and AF diagnoses were supplemented by ICD-9 and ICD-10 codes within six months or prior to index catheterization. All-cause mortality was determined using the Social Security Death Index (SSDI), National Death Index (NDI) and follow-up calls through the Duke Information System for Cardiovascular Care; cause of death was available on a subset of patients. ICD-9 and ICD-10 codes for emergency room visits or hospitalizations between 2001 and 2020 were used to define incident outcomes: MI and HF hospitalization ( Supplemental Table 1 , “Myocardial Infarction” and “Heart Failure / Cardiomyopathy” phenotype, respectively). Patients with MI within 30 days of their HF outcome were excluded to avoid new diagnoses of acute heart failure secondary to MI 24 . Incident AF was defined by ICD-9 or ICD-10 codes ( Supplemental Table 1 , “Atrial Fibrillation” phenotype). GRACE Score Although not all CATHGEN participants presented with acute coronary syndrome, given the high prevalence of baseline CAD (64.6%) in a cardiac catheterization cohort we used the Global Registry of Acute Coronary Events (GRACE) score to determine the prognostic significance of CHIP for overall mortality prediction in addition to a validated clinical score 25 ( Supplemental Methods ). Statistical Analyses A summary of the analytic approach is shown in Supplemental Table 3 . Associations of age with CHIP and clone size were tested using two sample t-test. Associations of CHIP, large CHIP and specific CHIP genes and obesity were tested using logistic regression in univariate and multivariate models. Across all multivariate analyses the basic covariates adjusted for were: age, genetic ancestry, sex, smoking. Analyses of CHIP and other prevalent cardiometabolic factors CAD, HF and AF were additionally adjusted for diabetes, BMI, hypertension and hyperlipidemia. Sensitivity models for prevalent CAD included statin therapy (available in 61.2% of participants). HF and AF models were additionally adjusted for prevalent CAD and history of malignancy given the known associations of chemotherapy exposure with both CHIP and cardiomyopathy 24 , and AF was also adjusted for prevalent HF. Stratified sensitivity analyses tested the association between HF phenotypes and CHIP: HFpEF (LVEF≥50%) and HF with reduced ejection fraction (HFrEF, LVEF<50%) using ANOVA and chi-square tests to test CHIP proportions in these groups compared to participants without HF 26 . These HF phenotype sensitivity analyses excluded participants with a history of congenital heart disease, valvular disease, heart transplant or end-stage renal disease requiring dialysis. Associations of CHIP and time-to-overall mortality were tested using Cox proportional hazard models and compared to the GRACE score. Fine-Gray competing risk regression models were used to test associations for incident outcomes with overall mortality as a competing risk: composite of MI or CV death, HF hospitalization and AF. Incident outcome analyses were adjusted for the same covariates as above including prevalent CAD, HF and AF. Given the long length of follow-up sensitivity analyses also truncated follow-up duration at ten years. For each clinical outcome of interest, analyses were performed to test associations with the presence of any CHIP variant and large CHIP (VAF>10%). To determine gene-specific risk, the most frequently mutated CHIP genes: DNMT3A , TET2 , ASXL1 and non- DNMT3A CHIP were also tested, requiring a minimum of ten cases or events for gene level analyses. Significance was considered at p<0.05. Analyses were performed in R version 4.4.2. Results Baseline Clinical Characteristics and CHIP Variants Identified Baseline participant characteristics are shown in Table 1 . We identified an overall prevalence of CHIP of 5.0% with 463 CHIP variants in 427 unique participants, including 210 DNMT3A , 100 TET2 , and 45 ASXL1 variants ( Supplemental Table 3, Supplemental Figure 1a ). The median VAF across all CHIP variants was 13.2% (IQR 7.9-22.5%), including 289 large CHIP clones in 268 individuals ( Supplemental Figure 1b ). As expected, participants with CHIP were older (mean 69.5±10.3 years) than those without CHIP (60.8±12.0 years, p<2×10 - 16 , Supplemental Figure 2 ). Individuals with large CHIP clones were on average 2.9 years older (mean 70.6±9.7 years) than those with small CHIP clones (VAF<10%, 67.7±11.0 years, p=0.006). View this table: View inline View popup Download powerpoint Table 1: Baseline Participants Characteristics Stratified by CHIP Status View this table: View inline View popup Download powerpoint Table 2. Summary of Significant Associations of CHIP with Prevalent and Incident Cardiometabolic Disease in CATHGEN Participants with CHIP were more often female (44.3%) than those without CHIP (37.7%, p=0.007). DNMT3A CHIP carriers were mostly female (50.5%), whereas TET2 and ASXL1 CHIP carriers were predominantly male (55.8% and 81.3%, respectively). Participants with CHIP had a higher percentage of European ancestry (81.0%) than those without CHIP (75.8%, p=0.045). Participants with CHIP also had a lower BMI (28.9±6.6 kg/m 2 ) than those without CHIP (30.1±7.2 kg/m 2 p=0.001). There were no differences in history of smoking, dyslipidemia, diabetes or hypertension for overall CHIP, though 72.1% of ASXL1 CHIP carriers had a history of smoking. Thirty-two individuals had greater than one CHIP variant, five of which had two TET2 variants, five had TET2 and DNMT3A co-mutations and three had two DNMT3A variants ( Supplemental Figure 3 ). Individuals with multiple CHIP variants were older (mean 72.5±8.2 years, p=0.045) and had greater history of MI (46.9% vs. 26.1%, p=0.02) and coronary artery bypass surgery (46.9% vs. 20.5%, p=0.001) than those with one CHIP variant. CHIP is Associated with Prevalent Cardiometabolic Disease CHIP and large CHIP were inversely associated with obesity in univariate models and multivariate models (adjusted OR [aOR] CHIP 0.79 [95% CI 0.64-0.98], p=0.03; aOR large CHIP 0.76 [95% CI 0.57-0.99], p=0.04, Supplemental Figure 4, Supplemental Table 5 ). Non- DNMT3A , DNMT3A and ASXL1 CHIP were inversely associated with obesity in univariate models, but the relationship was attenuated in multivariate models. There were no significant associations with CHIP or large CHIP in univariate or multivariate analyses with prevalent CAD ( Supplemental Table 5 ). ASXL1 CHIP was associated with CAD in univariate models (OR 5.36 [95% CI 2.16-17.89], p=0.001), but was not significant in multivariate models ( Supplemental Table 5 ). There were no significant relationships in sensitivity models adjusting for baseline statin therapy. CHIP and large CHIP were associated with prevalent HF in univariate models, though only overall CHIP remained significant in multivariate analyses (aOR 1.25 [95% CI 1.01 - 1.55], p=0.04, Figure 1 , Supplemental Table 5 ). Gene level analysis revealed that non- DNMT3A, and ASXL1 CHIP carriers had a higher odds of prevalent HF in univariate analyses, only non- DNMT3A remained significant in multivariate analyses (aOR 1.38 [95% CI 1.04-1.82], p=0.02). Sensitivity analyses of CHIP and HF phenotypes suggested the highest prevalence of CHIP in HFrEF (6.1%) vs. HFpEF (5.0%) vs. No HF (4.5%; ANOVA p=0.04, chi-squared p=0.04 for HFrEF vs. No HF, Supplemental Figure 5 ). Download figure Open in new tab Figure 1. CHIP and non- DNMT3A CHIP Associate with Prevalent HF The presence of any CHIP variant and non -DNMT3A was associated with higher odds of prevalent HF in CATHGEN. Multivariate models are adjusted for age, sex, genetic ancestry, smoking, diabetes, body-mass index, hypertension, hyperlipidemia, prevalent coronary artery disease and history of malignancy. CHIP and large CHIP were associated with higher odds of prevalent AF in univariate models (OR CHIP 1.72 [95% CI 1.37-2.14], p=1.8×10 -6 , OR large CHIP 1.75 [95% CI 1.32-2.30], 6.6×10 -5 ), but the relationship was not significant in multivariate models ( Supplemental Table 6 ). Similarly, non- DNMT3A , DNMT3A , TET2 and ASXL1 CHIP were associated with higher odds of prevalent AF in univariate, but not multivariate models. CHIP Associates with Overall Mortality in CATHGEN The median follow-up time was 9.97 years (IQR 5.57-12.95 years) and there were 4197 total deaths. The presence of any CHIP variant and large CHIP were associated with time-to-death in univariate, but only large CHIP remained associated with time-to-death in multivariate models (aHR 1.17 [95% CI 1.01-1.36], p=0.04, c-statistic=0.675, Supplemental Table 6, Supplemental Figure 6 ). The presence of CHIP in non- DNMT3A , DNMT3A , TET2 and ASXL1 were strongly associated with time-to-death in univariate models, only non- DNMT3A remained associated in multivariate models (aHR 1.31 [95% CI 1.12-1.54], p=0.001, c-statistic=0.676, Supplemental Table 6, Supplemental Figure 6 ). Given the high prevalence of baseline CAD in a cohort referred for catheterization we used the GRACE score to determine the prognostic value of CHIP in addition to a validated clinical score for predicting mortality. Mean GRACE Score was higher in participants with CHIP, non- DNTM3A and large CHIP (92.0±21.2, 93.1±21.9 and 94.5±20.7 points, respectively) versus those without CHIP (77.4±22.0 points). The GRACE Score was strongly associated with mortality (aHR 1.02 [95% CI 1.01-1.02], p<2.0×10 - 16 , c-statistic=0.682), where an increase in HR is associated with the risk associated with every additional GRACE score point. The addition of either large CHIP or non- DNMT3A CHIP to the GRACE score did not improve the c-statistic. In sensitivity models truncated at ten years, non- DNMT3A CHIP remained associated with time-to-death, but large CHIP was not. There were no significant differences in the predictive capability of the GRACE score or addition of non- DNMT3A CHIP at ten years. CHIP Associates with Incident Cardiovascular Disease Over a median follow-up of 9.50 years (IQR 5.02-12.74 years), a total of 2286 participants suffered a composite outcome of time-to-first incident MI or CV death. While overall CHIP was not associated with the composite CV outcome, large CHIP clones were associated in univariate models (HR 1.37 [95% CI 1.11-1.68], p=0.003) but this relationship was attenuated in multivariate models (aHR 1.21 [95% CI 1.01-1.44], p=0.056, Supplemental Table 7 ). Similarly non- DNMT3A and ASXL1 CHIP were associated with higher risk of the composite CV outcome in univariate models but were no longer significant in multivariate models ( Supplemental Table 7 ). Over a median follow-up of 8.08 years (IQR 3.21-12.00 years), there were 2541 participants with incident HF hospitalization. There were no significant associations for overall CHIP or large CHIP in univariate or multivariate models for incident HF hospitalization. Non- DNMT3A and DNMT3A CHIP were associated with incident HF hospitalization in both univariate and multivariate models, but with opposite direction of effect. Non- DNMT3A CHIP was associated with higher risk of HF hospitalization (aHR 1.29 [95% CI 1.02-1.63], p=0.03), whereas the presence of DNMT3A CHIP was associated with lower risk of incident HF hospitalization (aHR 0.65 [95% CI 0.48-0.88], p=0.005, Supplemental Table 7, Figure 2 ). Non- DNMT3A and DNMT3A CHIP remained associated with time-to-HF hospitalization in sensitivity models truncated at ten years. Download figure Open in new tab Figure 2. Non- DNMT3A and DNMT3A CHIP Associate with Incident HF Hospitalization Adjust Kaplan-Meier curves for incident HF Hospitalization. Non- DNMT3A CHIP was associated with increased risk of incident HF hospitalization, whereas DNMT3A was associated with lower risk. Fine-Gray models are adjusted for age, sex, ancestry, smoking, diabetes, hypertension, hyperlipidemia, body-mass index, prevalent coronary artery disease, HF and atrial fibrillation with overall mortality as a competing risk. HF, heart failure. After restricting the population to participants without prevalent AF, there were 1398 subjects with incident AF over a median follow-up of 8.13 years (IQR 3.40-11.96 years). There were no significant associations with CHIP or large CHIP for time-to-incident AF diagnosis. Non- DNMT3A and ASXL1 CHIP were associated with time-to-incident AF in univariate models, but only ASXL1 CHIP remained associated in multivariate models (aHR 2.15 [95% CI 1.15-4.04], p=0.02, Supplemental Table 7, Supplemental Figure 7 ). ASXL1 CHIP remained associated with time-to-AF diagnosis in sensitivity models truncated at ten years. Discussion For the first time to date in a large, single-center study of 8469 high-risk participants referred for cardiac catheterization, we observed pleiotropic effects of large clones and non- DNMT3A CHIP on cardiometabolic traits and CV outcomes ( Central Illustration ). We identified associations of CHIP with obesity, prevalent HF and incident HF hospitalization and AF. Additionally, we recapitulated the known age and mortality associations of CHIP. This study expands the existing CHIP literature on the effect of CHIP across cardiometabolic traits in a comorbid but clinically relevant cohort of high-risk participants referred for cardiac catheterization. Importantly, we show that though DNMT3A is the most frequently mutated CHIP gene, variants in non- DNMT3A genes drive cardiovascular risk associated with CHIP. Although we expected to find a greater CHIP prevalence in this cohort enriched for CVD, we found a similar prevalence (5.0%) to previously published population-based CHIP cohorts 6,7,9, 27 , but lower than in a recent analysis of five TIMI trials (8.2%) with high baseline CVD 28 . CHIP prevalence in our study was lower than in recent work with a prevalence of 18.4% in 1142 individuals referred for cardiac catheterization at Vanderbilt University Medical Center, however Heimlich et al. used a targeted sequencing approach, with higher sensitivity than the WES used here 29 . With respect to the association between CHIP and demographic characteristics, we found higher prevalence of CHIP in female participants in our cohort; this is discordant with the initial 2014 work by Jaiswal et al., where men had a higher odds of carrying a CHIP mutation 3 . However, our findings are similar to data from other cohorts showing that the effect on sex depends on the driver gene, where there is an increased prevalence of DNMT3A mutations in females, but other CHIP driver genes including TET2 and ASXL1 are more prevalent in males. We replicated similar findings as have been published between smoking history and ASXL1 CHIP, but there was no difference for smoking history and overall CHIP 6,7, 30 , 31 . Interestingly, with respect to cardiometabolic risk factors, we observed an inverse association between CHIP, large CHIP and obesity. To date, there have been conflicting findings between CHIP and obesity. For example, Jaiswal et al. found an inverse relationship between CHIP and BMI 3 , however in 8709 postmenopausal women from the Women’s Health Initiative (WHI), normal and overweight BMI participants had a lower odds of DNMT3A and TET2 CHIP than obese participants 32 . In 47,466 UK Biobank participants, CHIP was associated with higher waist-to-hip-ratio, though only TET2 CHIP was associated with high BMI 33 . Though we have described for the first time the association of large CHIP clones and obesity, we observed no significant associations for gene-specific analyses and obesity. Conversely, obesity may exacerbate CHIP. TET2 and DNMT3A murine CH models crossed with genetic obesity murine models have shown expansion of CHIP clones and pre-leukemic stem and progenitor cells with heightened levels of pro-inflammatory cytokines 33 . In our cohort there was a strong inverse relationship between age and BMI; this may reflect sarcopenia and frailty. There may be a complex and potentially parabolic relationship between CHIP and BMI, with higher prevalence of CHIP at both extremes of BMI. Future studies should further explore gene-specific metabolic underpinnings of the obesity paradox seen with CHIP in our present work and consider the role of CHIP in organ-specific adiposity. Interestingly, we did not identify significant associations for CHIP or large CHIP with prevalent CAD. The lack of association may be related to survival bias, the high prevalence of comorbidities and CAD in the CATHGEN cohort, including with the highly sensitive detection of CAD with invasive assessment of coronary lesions in the majority of patients studied. In a recent analysis of five TIMI trials, there was similar baseline prevalence of atherosclerotic CVD between those with and without CHIP and, similar to our observation here, a lower percentage with prior MI in those with CHIP 28 Taken together, these findings support CHIP as a stronger risk marker for incident disease than prevalent cardiometabolic traits. To the first time to our knowledge, we identified higher odds of prevalent HF in CHIP carriers in the CATHGEN cohort, with strong associations for non- DNMT3A variants. CHIP prevalence was greatest in those with HFrEF, while HFpEF prevalence was intermediate to those without HF. Here we provide key associations for CHIP and prevalent HF, even after adjustment for CAD, adding further support to the growing literature of pathophysiologic mechanisms linking CHIP and HF independent of atherosclerosis. In recent work using ultradeep error-corrected sequencing, prevalence of TET2 CH (VAF>0.5%) was enriched in HFpEF cases compared to controls 15 . Concordant with prior studies, we also identified greater risk for incident HF hospitalization. Although not significant for overall CHIP, we found associations for non- DNMT3A CHIP and incident HF hospitalization. Interestingly, DNMT3A CHIP was associated with lower risk of incident HF hospitalization. In a meta-analysis of over 56,000 participants, CHIP was associated with incident HF, specifically ASXL1 , TET2 and JAK2 variants, but the authors did not find significant associations with DNMT3A CHIP 13 . More recently, TET2 CHIP, but not overall CHIP was associated with incident HFpEF-specific hospitalization in 8090 participants from the Jackson Health Study and WHI 14 . In murine models, TET2 CH exacerbated features of HFpEF compared to wild-type bone marrow. Taken together, these findings suggest that variants in CHIP genes other than DNMT3A drive both prevalent and incident HF risk and ongoing work is needed to decipher gene and variant specific risk in clinical and pre-clinical research. Given the complex systemic and metabolic changes in HFpEF, the role of CHIP and its associated inflammatory should be assessed more deeply in future work. Only large and non- DNMT3A CHIP were associated with overall mortality in CATHGEN after adjustment for relevant clinical covariates, however there was no improvement in risk prediction when added to the GRACE score 25 . Unlike prior work, CHIP was not associated with time-to-incident MI or CV death in competing risk regression models in CATHGEN. Large CHIP and non- DNMT3A , including ASXL1 were significant in univariate models though the relationship was attenuated in multivariate models. Interestingly, large CHIP was not associated with HF hospitalization even in univariate models, suggesting risk associated with clone size may be more specific for atherosclerotic phenotypes. These findings are in contrast to data from 424,651 UK Biobank participants, in whom CHIP was associated with incident CVD events (composite MI, CAD or revascularization, stroke or death) with strong associations for large CHIP variants in each gene assessed, including ASXL1 34 . In 13,129 individuals in the UK Biobank with established CVD, CHIP and large CHIP were associated with a primary outcome of CVD events, with TET2 and spliceosome CHIP ( SF3B1 , SRSF2 , U2AF1 ) having the strongest association with adverse outcomes 35 . In CATHGEN, ASXL1 CHIP was associated with incident MI or CV death, as well as HF hospitalization in univariate, but not multivariate models. In 235 participants with established MDS, ASXL1 variants were highly prevalent (40%) and associated with risk of vascular events 36 . The work presented here supports the need for ongoing mutation-specific analyses of CHIP-risk phenotypes and underlying mechanisms, particularly for non- DNTM3A CHIP. We did not identify any significant findings between CHIP and prevalent AF risk, including when looking at large CHIP clones. Concordant with prior work, ASXL1 CHIP was associated with risk of incident AF. A phenome-wide association study (PheWAS) found baseline CHIP to be associated with incident AF in the UK Biobank 8 . More recently, large TET2 and ASXL1 CHIP clones, but not any CHIP or DNMT3A CHIP were associated with incident AF in nearly 200,000 participants from the UK Biobank and Atherosclerosis Risk in Communities (ARIC) studies 17 . Further murine models of AF and TET2 CHIP suggest the therapeutic potential of NLPR3 inflammasome blockade in arrhythmia 18 . Future studies should continue to explore the connection between arrhythmias, CHIP and dysregulated inflammation. Despite multiple strengths, including long duration of follow-up in a comorbid cohort of diverse participants with ability for detailed EHR review at the individual-participant level, this work has limitations. First, as all participants had at least concern for baseline CVD prompting catheterization referral, the study sample from a cardiac catheterization biorepository limits the generalizability to a broader patient population. As a tertiary referral center, not all participants were followed in Duke University Health system long term. This limited incident data from ICD codes is linked to inpatient and emergency room encounters. We recognize the study may suffer from survival bias for those individuals who may have had CHIP and survived to get catheterization. Likely secondary to limited coverages of the exons of interest in this region, the lower than expected number of JAK2 V617F variants in our WES data restricted the ability to analyze for JAK2 -specific associations in CATHGEN. In summary, in a cohort of participants referred for cardiac catheterization, CHIP and large CHIP were inversely associated with obesity. CHIP and non- DNMT3A CHIP were associated with prevalent HF, even in patients with a history of malignancy or chemotherapy exposure. We also identified risk of mortality and incident outcomes for large, non- DNMT3A and ASXL1 CHIP. Further work is warranted to understand the mechanistic underpinnings of the relationships of specific CHIP mutations, inflammation and cardiometabolic disease. Ultimately, utilization and refinement of CHIP as a CVD biomarker may allow for improved precision medicine approaches to patient care. Data Availability All data produced in the present study are available upon reasonable request to the authors Author Contributions J.A.R., L.C.K and S.H.S. designed the project and statistical analysis plan. W.E.K provided expertise on the CATHGEN biorepository and clinical data, reviewed and edited the manuscript. N.A.N. designed statistical analysis plan for testing associations with atrial fibrillation. A.G.B., P.N. and S.J. provided expertise on CHIP calling methods and quality control. S.J. provided detailed variant review and hematopathologic expert consultation for identification of putative CHIP variants. J.A.R, L.C.K and N.A.N. performed statistical analyses. All authors contributed to manuscript development and critically reviewed the final manuscript. Central Illustration. CHIP Associates with Prevalent and Incident Cardiometabolic Disease in CATHGEN Download figure Open in new tab Funding Acknowledgements J.A.R. is supported by a grant from the National Heart, Lung and Blood Institute (1R38HL143612) and the Edna and Fred L. Mandel Jr. Foundation. Footnotes Disclosures: P.N. reports research grants from Allelica, Amgen, Apple, Boston Scientific, Genentech / Roche, and Novartis, personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Creative Education Concepts, CRISPR Therapeutics, Eli Lilly & Co, Esperion Therapeutics, Foresite Capital, Foresite Labs, Genentech / Roche, GV, HeartFlow, Magnet Biomedicine, Merck, Novartis, Novo Nordisk, TenSixteen Bio, and Tourmaline Bio, equity in Bolt, Candela, Mercury, MyOme, Parameter Health, Preciseli, and TenSixteen Bio, and spousal employment at Vertex Pharmaceuticals, all unrelated to the present work. S.HS. reports research grants from National Institutes of Health, American Heart Association, Verily, AstraZeneca, Lilly and unlicensed patents to Duke University. All other authors report no relevant relationships with industry to disclosure. Abbreviations CHIP clonal hematopoiesis of indeterminate potential VAF variant allele fraction CATHGEN Catheterization Genetics CAD coronary artery disease MI myocardial infarction CV cardiovascular CVD cardiovascular disease HF heart failure AF atrial fibrillation References 1. ↵ Consortium CAD , Deloukas P , Kanoni S , et al. Large-scale association analysis identifies new risk loci for coronary artery disease . Nat Genet . Jan 2013 ; 45 ( 1 ): 25 – 33 . doi: 10.1038/ng.2480 OpenUrl CrossRef PubMed 2. ↵ Park HW , Ahn Y , Jeong MH , et al. Chronic atrial fibrillation associated with somatic mitochondrial DNA mutations in human atrial tissue . J Clin Pathol . Aug 2007 ; 60 ( 8 ): 948 – 50 . doi: 10.1136/jcp.2007.047134 OpenUrl FREE Full Text 3. ↵ Jaiswal S , Fontanillas P , Flannick J , et al. Age-related clonal hematopoiesis associated with adverse outcomes . N Engl J Med. Dec 25 2014 ; 371 ( 26 ): 2488 – 98 . doi: 10.1056/NEJMoa1408617 OpenUrl CrossRef PubMed 4. Genovese G , Kahler AK , Handsaker RE , et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence . N Engl J Med. Dec 25 2014 ; 371 ( 26 ): 2477 – 87 . doi: 10.1056/NEJMoa1409405 OpenUrl CrossRef PubMed 5. ↵ Xie M , Lu C , Wang J , et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies . Nat Med . Dec 2014 ; 20 ( 12 ): 1472 – 8 . doi: 10.1038/nm.3733 OpenUrl CrossRef PubMed 6. ↵ Bick AG , Weinstock JS , Nandakumar SK , et al. Inherited causes of clonal haematopoiesis in 97,691 whole genomes . Nature . Oct 2020 ; 586 ( 7831 ): 763 – 768 . doi: 10.1038/s41586-020-2819-2 OpenUrl CrossRef 7. Kessler MD , Damask A , O’Keeffe S , et al. Common and rare variant associations with clonal haematopoiesis phenotypes . Nature . Dec 2022 ; 612 ( 7939 ): 301 – 309 . doi: 10.1038/s41586-022-05448-9 OpenUrl CrossRef PubMed 8. ↵ Kar SP , Quiros PM , Gu M , et al. Genome-wide analyses of 200,453 individuals yield new insights into the causes and consequences of clonal hematopoiesis . Nat Genet . Aug 2022 ; 54 ( 8 ): 1155 – 1166 . doi: 10.1038/s41588-022-01121-z OpenUrl CrossRef 9. ↵ Jaiswal S , Natarajan P , Silver AJ , et al. Clonal Hematopoiesis and Risk of Atherosclerotic Cardiovascular Disease . N Engl J Med. Jul 13 2017 ; 377 ( 2 ): 111 – 121 . doi: 10.1056/NEJMoa1701719 OpenUrl CrossRef PubMed 10. ↵ Fuster JJ , MacLauchlan S , Zuriaga MA , et al. Clonal hematopoiesis associated with TET2 deficiency accelerates atherosclerosis development in mice . Science. Feb 24 2017 ; 355 ( 6327 ): 842 – 847 . doi: 10.1126/science.aag1381 OpenUrl Abstract / FREE Full Text 11. ↵ Bick AG , Pirruccello JP , Griffin GK , et al. Genetic Interleukin 6 Signaling Deficiency Attenuates Cardiovascular Risk in Clonal Hematopoiesis . Circulation. Jan 14 2020 ; 141 ( 2 ): 124 – 131 . doi: 10.1161/CIRCULATIONAHA.119.044362 OpenUrl CrossRef PubMed 12. ↵ Pascual-Figal DA , Bayes-Genis A , Diez-Diez M , et al. Clonal Hematopoiesis and Risk of Progression of Heart Failure With Reduced Left Ventricular Ejection Fraction . J Am Coll Cardiol. Apr 13 2021 ; 77 ( 14 ): 1747 – 1759 . doi: 10.1016/j.jacc.2021.02.028 OpenUrl CrossRef PubMed 13. ↵ Yu B , Roberts MB , Raffield LM , et al. Supplemental Association of Clonal Hematopoiesis With Incident Heart Failure . J Am Coll Cardiol. Jul 6 2021 ; 78 ( 1 ): 42 – 52 . doi: 10.1016/j.jacc.2021.04.085 OpenUrl CrossRef PubMed 14. ↵ Schuermans A , Honigberg MC , Raffield LM , et al. Clonal Hematopoiesis and Incident Heart Failure With Preserved Ejection Fraction . JAMA Netw Open. Jan 2 2024 ; 7 ( 1 ): e2353244 . doi: 10.1001/jamanetworkopen.2023.53244 OpenUrl CrossRef 15. ↵ Cochran JD , Yura Y , Thel MC , et al. Clonal Hematopoiesis in Clinical and Experimental Heart Failure With Preserved Ejection Fraction . Circulation. Oct 10 2023 ; 148 ( 15 ): 1165 – 1178 . doi: 10.1161/CIRCULATIONAHA.123.064170 OpenUrl CrossRef 16. ↵ Ninni S , Dombrowicz D , Kuznetsova T , et al. Hematopoietic Somatic Mosaicism Is Associated With an Increased Risk of Postoperative Atrial Fibrillation . J Am Coll Cardiol. Apr 4 2023 ; 81 ( 13 ): 1263 – 1278 . doi: 10.1016/j.jacc.2023.01.036 OpenUrl CrossRef 17. ↵ Saadatagah S , Naderian M , Uddin M , et al. Atrial Fibrillation and Clonal Hematopoiesis in TET2 and ASXL1 . JAMA Cardiol. Apr 10 2024 ; doi: 10.1001/jamacardio.2024.0459 OpenUrl CrossRef 18. ↵ Lin AE , Bapat AC , Xiao L , et al. Clonal Hematopoiesis of Indeterminate Potential With Loss of Tet2 Enhances Risk for Atrial Fibrillation Through Nlrp3 Inflammasome Activation . Circulation. Feb 15 2024 ; doi: 10.1161/CIRCULATIONAHA.123.065597 OpenUrl CrossRef 19. ↵ Zekavat SM , Viana-Huete V , Matesanz N , et al. TP53-mediated clonal hematopoiesis confers increased risk for incident atherosclerotic disease . Nat Cardiovasc Res. Jan 16 2023 ; 2 : 144 – 158 . doi: 10.1038/s44161-022-00206-6 OpenUrl CrossRef 20. ↵ Kraus WE , Granger CB , Sketch MH , Jr. , et al. A Guide for a Cardiovascular Genomics Biorepository: the CATHGEN Experience . J Cardiovasc Transl Res . Nov 2015 ; 8 ( 8 ): 449 – 57 . doi: 10.1007/s12265-015-9648-y OpenUrl CrossRef PubMed 21. ↵ McKenna A , Hanna M , Banks E , et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data . Genome Res . Sep 2010 ; 20 ( 9 ): 1297 – 303 . doi: 10.1101/gr.107524.110 OpenUrl Abstract / FREE Full Text 22. ↵ Cibulskis K , Lawrence MS , Carter SL , et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples . Nat Biotechnol . Mar 2013 ; 31 ( 3 ): 213 – 9 . doi: 10.1038/nbt.2514 OpenUrl CrossRef PubMed 23. ↵ Benjamin D , Sato T , Cibulskis K , Getz G , Stewart C , Lichtenstein L. Calling Somatic SNVs and Indels with Mutect2. bioRxiv . 2019 ; doi: 10.1101/861054 . OpenUrl Abstract / FREE Full Text 24. ↵ Coombs CC , Zehir A , Devlin SM , et al. Therapy-Related Clonal Hematopoiesis in Patients with Non-hematologic Cancers Is Common and Associated with Adverse Clinical Outcomes . Cell Stem Cell. Sep 7 2017 ; 21 ( 3 ): 374 – 382 e4. doi: 10.1016/j.stem.2017.07.010 OpenUrl CrossRef PubMed 25. ↵ Fox KA , Dabbous OH , Goldberg RJ , et al. Prediction of risk of death and myocardial infarction in the six months after presentation with acute coronary syndrome: prospective multinational observational study (GRACE) . BMJ. Nov 25 2006 ; 333 ( 7578 ):1091. doi: 10.1136/bmj.38985.646481.55 OpenUrl Abstract / FREE Full Text 26. ↵ Heidenreich PA , Bozkurt B , Aguilar D , et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines . Circulation. May 3 2022 ; 145 ( 18 ): e895 – e1032 . doi: 10.1161/CIR.0000000000001063 OpenUrl CrossRef PubMed 27. ↵ Uddin MDM , Nguyen NQH , Yu B , et al. Clonal hematopoiesis of indeterminate potential , DNA methylation, and risk for coronary artery disease. Nat Commun. Sep 12 2022 ; 13 ( 1 ): 5350 . doi: 10.1038/s41467-022-33093-3 OpenUrl CrossRef PubMed 28. ↵ Marston NA , Pirruccello JP , Melloni GEM , et al. Clonal hematopoiesis, cardiovascular events and treatment benefit in 63,700 individuals from five TIMI randomized trials . Nat Med. Aug 6 2024 ; doi: 10.1038/s41591-024-03188-z OpenUrl CrossRef 29. ↵ Heimlich JB , Raddatz MA , Wells J , et al. Invasive Assessment of Coronary Artery Disease in Clonal Hematopoiesis of Indeterminate Potential . Circ Genom Precis Med. Jun 28 2024 : e004415 . doi: 10.1161/CIRCGEN.123.004415 OpenUrl CrossRef 30. ↵ Dawoud AAZ , Tapper WJ , Cross NCP . Clonal myelopoiesis in the UK Biobank cohort: ASXL1 mutations are strongly associated with smoking . Leukemia . Oct 2020 ; 34 ( 10 ): 2660 – 2672 . doi: 10.1038/s41375-020-0896-8 OpenUrl CrossRef 31. ↵ van Zeventer IA , Salzbrunn JB , de Graaf AO , et al. Prevalence, predictors, and outcomes of clonal hematopoiesis in individuals aged >/=80 years . Blood Adv. Apr 27 2021 ; 5 ( 8 ): 2115 – 2122 . doi: 10.1182/bloodadvances.2020004062 OpenUrl CrossRef PubMed 32. ↵ Haring B , Reiner AP , Liu J , et al. Healthy Lifestyle and Clonal Hematopoiesis of Indeterminate Potential: Results From the Women’s Health Initiative . J Am Heart Assoc . Feb 2021 ; 10 ( 5 ): e018789 . doi: 10.1161/JAHA.120.018789 OpenUrl CrossRef PubMed 33. ↵ Pasupuleti SK , Ramdas B , Burns SS , et al. Obesity-induced inflammation exacerbates clonal hematopoiesis . J Clin Invest. Jun 1 2023 ; 133 (11) doi: 10.1172/JCI163968 OpenUrl CrossRef 34. ↵ Yu Z , Fidler TP , Ruan Y , et al. Genetic modification of inflammation- and clonal hematopoiesis-associated cardiovascular risk . J Clin Invest. Sep 15 2023 ; 133 (18) doi: 10.1172/JCI168597 OpenUrl CrossRef 35. ↵ Gumuser ED , Schuermans A , Cho SMJ , et al. Clonal Hematopoiesis of Indeterminate Potential Predicts Adverse Outcomes in Patients With Atherosclerotic Cardiovascular Disease . J Am Coll Cardiol. May 23 2023 ; 81 ( 20 ): 1996 – 2009 . doi: 10.1016/j.jacc.2023.03.401 OpenUrl CrossRef 36. ↵ Faber MG , Lloyd DR , Singh A , et al. Predictors of vascular disease in myelodysplastic syndromes . EJHaem . Nov 2020 ; 1 ( 2 ): 467 – 472 . doi: 10.1002/jha2.101 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 15, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Clonal Hematopoiesis Associates with Prevalent and Incident Cardiometabolic Disease in High-Risk Individuals Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Clonal Hematopoiesis Associates with Prevalent and Incident Cardiometabolic Disease in High-Risk Individuals Jessica A. Regan , Lydia Coulter Kwee , Navid A. Nafissi , Alexander G. Bick , William E. Kraus , Pradeep Natarajan , Sidd Jaiswal , Svati H. Shah medRxiv 2025.01.14.25320566; doi: https://doi.org/10.1101/2025.01.14.25320566 Share This Article: Copy Citation Tools Clonal Hematopoiesis Associates with Prevalent and Incident Cardiometabolic Disease in High-Risk Individuals Jessica A. Regan , Lydia Coulter Kwee , Navid A. Nafissi , Alexander G. Bick , William E. Kraus , Pradeep Natarajan , Sidd Jaiswal , Svati H. Shah medRxiv 2025.01.14.25320566; doi: https://doi.org/10.1101/2025.01.14.25320566 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cardiovascular Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (541) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9232) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00af3874c454807',t:'MTc3OTYxMTU2Mg=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();