Clonal Hematopoiesis of Indeterminate Potential and the Risk of Cognitive Impairment in the Women’s Health Initiative Memory Study

Clonal Hematopoiesis of Indeterminate Potential and the Risk of Cognitive Impairment in the Women’s Health Initiative Memory Study | 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 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Uddin , David W Fardo , Pradeep Natarajan , Alexander G Bick , Jacob O Kitzman , Michael C Honigberg , Kathleen M Hayden , JoAnn E Manson , Siddhartha Jaiswal , Eric A Whitsel , Alexander P Reiner doi: https://doi.org/10.1101/2025.08.02.25332871 Yasminka A Jakubek 1 Department of Internal Medicine, College of Medicine, University of Kentucky , 760 Press Ave, Lexington, KY 40508, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yasminka A Jakubek For correspondence: yaj2{at}cornell.edu ya.jakubek{at}uky.edu Aaron P Smith 1 Department of Internal Medicine, College of Medicine, University of Kentucky , 760 Press Ave, Lexington, KY 40508, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiaoyan I Leng 2 Department of Biostatistics and Data Sciences, Wake Forest School of Medicine , 475 Vine St, Winston-Salem, NC 27101, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Megan E Hall 3 Department of Biostatistics, College of Public Health, University of Kentucky , 760 Press Ave, Lexington KY 40508, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Ezzat 4 Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard , 415 Main St, Cambridge, MA 02142, USA 5 Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital , 55 Fruit Street, Boston, MA 02114, USA 6 Faculty of Medicine, KU Leuven , Oude Markt 13, 3000 Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yash Pershad 7 Department of Medicine, Vanderbilt University School of Medicine , 1161 21st Ave S, Nashville, TN 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jason M Collins 8 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , 135 Dauer Drive, Chapel Hill, NC 27599, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Md Mesbah Uddin 4 Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard , 415 Main St, Cambridge, MA 02142, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site David W Fardo 3 Department of Biostatistics, College of Public Health, University of Kentucky , 760 Press Ave, Lexington KY 40508, USA 9 Sanders-Brown Center on Aging, University of Kentucky , 789 S Limestone, Lexington, KY 40536, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pradeep Natarajan 4 Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard , 415 Main St, Cambridge, MA 02142, USA 5 Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital , 55 Fruit Street, Boston, MA 02114, USA 10 Cardiology Division, Massachusetts General Hospital , 55 Fruit St, Boston, MA 02114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexander G Bick 7 Department of Medicine, Vanderbilt University School of Medicine , 1161 21st Ave S, Nashville, TN 37232, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jacob O Kitzman 11 Department of Human Genetics, University of Michigan , 1241 Catherine St, Ann Arbor, MI 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael C Honigberg 4 Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard , 415 Main St, Cambridge, MA 02142, USA 5 Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital , 55 Fruit Street, Boston, MA 02114, USA 10 Cardiology Division, Massachusetts General Hospital , 55 Fruit St, Boston, MA 02114, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathleen M Hayden 12 Department of Social Sciences and Health Policy, Wake Forest University School of Medicine , 475 Vine Street, Winston-Salem, NC 27101, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site JoAnn E Manson 13 Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School , 75 Francis Street, Boston MA 02115, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Siddhartha Jaiswal 14 Department of Pathology, Stanford University School of Medicine , 300 Pasteur Drive, Stanford, CA 94305, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eric A Whitsel 8 Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina , 135 Dauer Drive, Chapel Hill, NC 27599, USA 15 Department of Medicine, School of Medicine, University of North Carolina , 125, 333 S Columbia St MacNider Hall, Chapel Hill, NC 27516, NC, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Alexander P Reiner 16 Division of Public Health Sciences, Fred Hutchinson Cancer Center , 1100 Fairview Ave N, Seattle, WA 98109, USA 17 Department of Epidemiology, University of Washington , 1959 NE Pacific St # F262, Seattle, WA 98195, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT INTRODUCTION Clonal hematopoiesis of indeterminate potential (CHIP) confers an increased risk of several chronic aging-related diseases. Paradoxically, CHIP was associated with lower risk of dementia in recent studies. METHODS We examined associations between baseline CHIP and incident mild cognitive impairment (MCI) and/or probable dementia in the Women’s Health Initiative Memory Study. CHIP was detected using blood-based targeted sequencing. Cox proportional hazards models examined time to onset of cognitive impairment, adjusting for traditional risk factors. RESULTS Using a conventional variant allele fraction (VAF) threshold of 2%, CHIP was not associated with incident cognitive impairment. The presence of larger CHIP clone (VAF≥8%) was associated with a lower incidence of adjudicated probable dementia (HR=0.62 [95% CI=0.41-0.94], p =0.025), while the association with the composite outcome MCI/probable dementia was weaker and overlapped 1.0. DISCUSSION The association of CHIP with lower risk of cognitive impairment in postmenopausal women may be dependent on VAF and impairment severity. Introduction Clonal hematopoiesis occurs when a hematopoietic cell acquires a somatic mutation and then undergoes clonal expansion, resulting in a genetically distinct leukocyte subpopulation. Clonal hematopoiesis of indeterminate potential (CHIP), a common age-related condition, is a type of clonal hematopoiesis defined by mutations in genes commonly mutated in myeloid leukemia[ 1 ]. Mutations in CHIP genes are associated with alterations in DNA methylation, DNA damage response, inflammation, malignant transformation, and other aging-related processes[ 2 ]. Emerging evidence suggests that CHIP is associated with acceleration of vascular disease processes including atherosclerosis, risk of cardiovascular events, and death[ 3 – 6 ]. Risk factors for Alzheimer’s disease (AD) and other forms of dementia include conditions associated with CHIP, such as inflammation, cardiovascular disease (CVD), metabolic disorders, and stroke[ 7 ]. Because CHIP appears to be an independent and potent CVD risk factor, it was originally hypothesized that CHIP would increase the risk for cognitive decline and dementia, either directly or indirectly, through cardiovascular pathways. Surprisingly, a recent set of detailed analyses of multiple cohorts suggested that CHIP is protective against AD, contrary to the original hypothesis[ 8 ]. A subsequent longitudinal analysis among patients with chronic kidney disease (CKD) similarly showed CHIP was associated with a lower risk of cognitive impairment[ 9 ]. The Women’s Health Initiative Memory Study (WHIMS) provides an opportunity to examine potential associations of CHIP with cognitive outcomes over as long as twenty-five years of observation. Herein we examine the association between CHIP and mild cognitive impairment (MCI) and probable dementia from WHIMS. We hypothesized that CHIP would be inversely associated with the risk of incident cognitive impairment. Secondary objectives were to explore the associations between specific driver mutations for CHIP and the incidence of cognitive impairment. Methods Participants and Setting WHIMS was designed to evaluate the effects of conjugated equine estrogens alone (E-alone; in women with hysterectomy), or estrogen (E) in combination with medroxyprogesterone acetate (progestin; [E + P]), compared to placebo groups on the incidence of MCI or probable dementia[ 10 ]. All women provided written informed consent to participate. In total, 7,429 women from 39 sites of the Women’s Health Initiative (WHI) randomized trial for hormone therapy were enrolled in WHIMS between May 28, 1996, and December 13, 1999. All WHIMS participants were aged 65+ at baseline and met the inclusion criteria for the WHI hormone trials. The trials ended in 2002 (E + P) and 2004 (E-alone). In 2008, data collection transitioned from face-to-face clinic visits to telephone based cognitive assessments in the WHI Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO)[ 11 ]. WHIMS-ECHO data collection continued to 2021 allowing for up to 26 years of observation (1995-2021). Cognitive status was adjudicated as MCI, probable dementia, or no impairment. Details of the cognitive assessment protocols and adjudication for WHIMS and WHIMS-ECHO are described in the Supplement . In the current CHIP study, the cognitive impairment outcome was defined in two ways: (1) a combined outcome that includes incident MCI, adjudicated probable dementia, and self-reported dementia (i.e., combined MCI/dementia) during follow-up or (2) a more restrictive definition of dementia that includes only adjudicated probable dementia during follow-up (i.e., adjudicated probable dementia only), excluding individuals with MCI and those with dementia based only on self-report. CHIP assay and Definition of CHIP Status at Baseline CHIP was assayed from blood in a subset of 4,949 WHIMS women as described in the Supplement . CHIP targeted sequencing and variant calling of the WHIMS samples were performed in two distinct batches with median read depths 5,791x (n=3,012) and 4,609x (n=1,955), respectively. Therefore, a covariate indicating sequencing batch was included in all CHIP association analyses. In our primary analyses, CHIP was analyzed in two ways. (1) We defined CHIP as VAF ≥2% in one or more CHIP driver genes. (2) We also performed analyses in which CHIP carriers were limited to those with large CHIP clones (defined as VAF≥8%), to allow greater comparability to prior analyses of CHIP and AD conducted by Bouzid et al[ 8 ]. We also conducted sensitivity analyses modeling CHIP status as a categorical variable (8%). In additional exploratory analyses, we defined CHIP status according to driver gene subtype ( DNMT3A, TET2 , other, or multiple), with each category being mutually exclusive. Covariates Covariates included demographic and design factors at baseline: age, race and ethnicity, education, treatment assignment (E-alone, E+P, or placebo), and U.S. recruitment region (Midwest, Northeast, South, and West). Clinical covariates included body mass index (BMI; kg/m 2 ), alcohol use (≥1 drinks/month), smoking history (never/former/current), self-reported history of diabetes, cholesterol-lowering medication, antihypertensive medication, and history of CVD (including myocardial infarction, stroke, heart failure, transient ischemic attack or TIA, coronary revascularization, peripheral artery disease, and carotid artery disease). APOE genotypes were based on two SNPs, rs429358 and rs7412 that were available in a subsample of 4,508 participants. Genetic data were imputed and harmonized across WHI genome-wide association studies using the 1000 Genomes Project reference panel and MaCH algorithms implemented in Minimac[ 12 ]. Both SNPs had high imputation quality (R 2 >0.97). Statistical Analysis Baseline age was defined as age at blood draw. For both cognitive impairment outcomes, those who self-reported moderate or severe memory problems, dementia, AD, or blood cancer at or before the time of the blood draw used for CHIP analysis were excluded from downstream analyses (N = 8). The association of baseline CHIP status (present versus absent) with cognitive impairment during follow-up was analyzed using Cox proportional hazards models with age as the underlying timescale. For the survival analyses, the onset of cognitive impairment was defined as time to MCI or time to dementia, as some participants did not have MCI prior to dementia. Each combination of CHIP VAF (exposure) and time to cognitive impairment (outcome) were tested with a set of three Cox regression models with increasing numbers of covariates. The first model (M1) included age at blood draw, sequencing batch, hormone therapy treatment assignment, race/ethnicity, and US region. The second model (M2) included all covariates from M1 as well as education level, baseline alcohol use, smoking history, BMI, cholesterol levels, hypertension, and CVD. CVD was a categorical variable with 3 groups: no CVD, stroke/TIA, or other CVD. The third model (M3) included all covariates from M1 and M2, as well as the presence of one or more APOE2 alleles, and the presence of one or more APOE4 alleles. For each model, individuals were included in the analysis if they had complete information for all covariates (n = 4934 for M1, n = 4655 for M2, and n = 4240 for M3). Baseline covariate comparisons between individuals with and without CHIP utilized Fisher’s exact test for categorical variables, t-tests with unequal variance for continuous variables, and normal approximated rate-ratio tests for crude incidence rates. Adjusted hazard ratios (HR) and 95% confidence intervals (CI) are reported for survival analyses. In additional sensitivity analysis, a Fine-Gray competing risk regression model was used to evaluate the association between CHIP and cognitive outcomes with death as the competing risk and adjusting for covariates as in the survival analysis. All statistical tests were two-sided with a significance threshold of 0.05 and computed in R version 4.2.0. Results Table 1 shows the demographic characteristics of the sample by CHIP status for 4,934 women (mean age 70 +/-3.8 years; 91% White), defined by a VAF threshold of 2% (standard CHIP) or 8% (large CHIP). 20.4% of women had CHIP using the VAF ≥2% definition while 6.0% had CHIP using the VAF ≥8% definition. The most common CHIP type for both definitions was DNMT3A followed by TET2 ( Table 1 ). For both the VAF ≥2% and VAF ≥8% CHIP definitions, participants with CHIP were older and tended to have less follow-up time. During follow-up, 1,776 women developed the composite MCI/dementia outcome with a mean (standard deviation [SD]) follow-up time of 12.0 (6.7) years, and 660 developed the more restrictive outcome of adjudicated dementia with a mean (SD) follow-up time of 11.6 (6.6) years. Notably the cumulative incidence proportion and cumulative incidence rates of adjudicated dementia were lower among large CHIP VAF ≥8% carriers than large CHIP non-carriers ( Table 1 ). View this table: View inline View popup Table 1: Demographic/Clinical characteristics and CHIP When CHIP was defined using VAF ≥2%, there was no significant difference in risk of cognitive impairment for either the broader definition of MCI/dementia or the narrower definition of adjudicated dementia (all p- values ≥0.1) ( Table 2 ). By contrast, we observed a suggestive association of the presence of larger CHIP clone size (VAF ≥8%) with lower incidence of adjudicated dementia, whereas the HR using the broader definition of cognitive impairment was closer to 1.0 and not significant ( Table 2 ). In the fully adjusted model (M3), the lower risk of developing adjudicated dementia associated with large CHIP (VAF ≥8%) was nominally significant (M3 HR = 0.62, 95%CI 0.41 - 0.94, p = 0.025). The protective association between large CHIP and probable dementia was weaker and not significant in the models that did not include APOE genotypes as covariates (M1, M2, Table 2 ), even when the M1 and M2 analyses were restricted to the same subset of participants included in M3 ( Table S1 ). View this table: View inline View popup Download powerpoint Table 2: Association cognitive phenotypes and CHIP Sensitivity analyses with death as a competing risk showed similar results to the survival model, confirming the association between CHIP and adjudicated probable dementia for larger CHIP clones VAF ≥8% (M3 HR = 0.61, p = 0.023; Table S2 ). Next, CHIP was modeled as a 3-category variable using VAF thresholds of <2%, 2% to 8%, and ≥8% (No CHIP, low and large CHIP clones). In fully adjusted M3, large CHIP clones (VAF ≥8%) had a nominally significant association with adjudicated probable dementia, (HR = 0.64, p = 0.036; Table S3 ), but there was no evidence of a protective association for the smaller clone category. To explore differences in the protective association of large CHIP clones on probable dementia by APOE genotype, the large CHIP VAF ≥8% analysis was repeated stratifying participants into three APOE risk categories [neutral ( APOE ε3ε3), low-risk ( APOE ε2ε2 and ε2ε3) and high-risk (any APOE ε4 allele)]. The apparent protective association of CHIP was strongest in the high-risk group (HR = 0.58, 95% CI 0.31-1.10, p = 0.094; Table 3 ) and progressively closer to the null in the neutral and low-risk APOE groups. View this table: View inline View popup Download powerpoint Table 3: Association cognitive phenotypes and CHIP 8% with stratified APOE risk Next, we explored the association between baseline CHIP and incident cognitive impairment according to CHIP driver gene subtype. In the fully adjusted model ( Table 4 ), the protective association of large CHIP (VAF ≥8%) on risk of incident adjudicated dementia was apparent among carriers of DNMT3A mutations (M3 HR = 0.48, 95% CI 0.27-0.86, p = 0.014) but not among TET2 mutation carriers (M3 HR = 1.11, 95% CI 0.55-2.26, p = 0.77). However, these results are exploratory and not corrected for multiple comparisons. Hazards for developing the composite MCI/dementia outcome using the 2% VAF CHIP definition trended in the same direction, but did not reach significance ( Table S4 ). View this table: View inline View popup Download powerpoint Table 4: Association of adjudicated probable dementia and CHIP mutations Discussion Utilizing data from the prospective, community-based WHIMS, we found that larger CHIP clones (≥8% VAF) were nominally associated with lower risk of incident adjudicated dementia in postmenopausal women when adjusting for traditional risk factors including APOE genotype. The direction of effect is consistent with two recent observational studies indicating a protective association of CHIP on AD[ 13 ] or impairment of attention and executive cognitive function among CKD patients[ 9 ]. Given the well-established relationship between CHIP and increased risk of other chronic aging-related and inflammatory diseases (atherosclerotic CVD, stroke, and mortality), the protective association of CHIP on neurodegenerative outcomes initially appears counter-intuitive. However, largely consistent findings of a protective association have now been observed across at least three human cohort studies and are further supported by Mendelian randomization and functional studies of postmortem brain tissue demonstrating the role of mutant, marrow-derived cells in reducing neuritic plaques and neurofibrillary tangles[ 13 ]. Other age-related conditions characterized by non-malignant clonal expansion of hematopoietic cells driven by myeloid malignancy-associated somatic driver mutations (e.g., myelodysplastic syndrome) have similarly been associated with reduced risk of neurodegenerative diseases including dementia[ 14 ]. In WHIMS, the protective association of CHIP on adjudicated dementia was observed for large CHIP clones (VAF ≥8%), but there was little evidence for association when CHIP was defined using the traditional VAF threshold of ≥2%. Moreover, the association of large CHIP clones with the combined outcome that includes both dementia and milder cognitive deficits was weaker and not statistically significant. These findings are analogous to those reported in the Alzheimer’s Disease Sequencing Project (ADSP)[ 13 ], in which CHIP with VAF >8% was associated with a stronger protective association of AD dementia (OR = 0.66, P = 5.5 × 10 −4 ) than using a CHIP VAF cutoff of 2% (OR = 0.79, P = 0.024), whereas a VAF ≤8% had no association with dementia in ADSP (OR = 1.25, P = 0.23). CHIP clone size has previously been shown to be an important predictor of risk for other aging-related disorders including hematologic malignancy and cardiovascular outcomes[ 3 , 4 , 15 ]. In contrast, the lower risk of cognitive impairment phenotypes among CKD patients was confined to those with small CHIP clone size, whereas no association was observed for large CHIP clone size[ 9 ]. The reasons for the lack of dose-response relationship in the latter CKD study are unclear. It is possible that differences in study design, participant characteristics (including presence of underlying CKD), sample sizes, VAF distribution, or cognitive outcome definitions may contribute to some of the differences in findings observed across the three studies. When CHIP was analyzed according to major driver gene subtypes in WHIMS, associations with cognitive impairment were stronger across DNMT3A than TET2 CHIP, a pattern that was also observed by Xiao et al in CKD patients[ 9 ]. In contrast, consistent protective associations of CHIP driver genes, including DNMT3A, TET2, ASXL1 , and SF3B1 , were observed on AD[ 13 ]. When stratified by APOE risk alleles, we observed a trend toward more protective associations among those without any e2 alleles, which is also consistent with previous observations[ 9 , 13 ]. Additional studies with larger sample sizes and sensitive CHIP detection assays are needed to clarify the role of both CHIP clone size, individual CHIP driver gene subtype, and APOE genotype on risk of cognitive impairment. A strength of the current study is the length of follow-up in a community-based sample of older women who are more likely than men to experience adverse cognitive outcomes later in life. However, one limitation of this study is the lack of generalization of findings to men. Mosaic loss of chromosome Y is another type of clonal hematopoiesis common in older men, and has been associated with increased AD risk[ 16 ]. Larger studies including both men and women are needed to elucidate the combined effects of CHIP and other somatic mutations in hematopoietic cells on the risk of adverse cognitive outcomes. A second limitation of this study is the inability to distinguish between different etiologies of cognitive impairment, such as vascular dementia, AD, and other neurodegenerative conditions. In the UK Biobank, CHIP was associated with increased risk of vascular, but not with primary, neurodegenerative diseases[ 17 ]. In another study, CHIP with TET2 mutations was associated with increased risk of Parkinson’s disease[ 18 ]. Therefore, the protective association of CHIP on cognitive impairment may be specific to certain neurodegenerative conditions such as AD. Additional clinical and population studies that include brain imaging and AD biomarkers are needed to better understand the specificity and mechanisms underlying these relationships. Data Availability Data needs to be requested through the Womens Health Initiative Repository. Sequencing data used in this study is deposited in dbGaP (phs000200.v12.p3). Conflicts of Interest A.G.B. has received honoraria for advisory board membership from, and holds equity in, TenSixteen Bio. P.N. reports research grants from Allelica, Amgen, Apple, Boston Scientific, Genentech / Roche, and Novartis, personal fees from Allelica, Apple, AstraZeneca, Blackstone Life Sciences, Bristol Myers Squibb, 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. The remaining authors have nothing to disclose. Acknowledgments/Funding This work was supported by NIH R01 grants HL148565 and HL148565-02S1 (A.P.R., E.A.W). 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Share Clonal Hematopoiesis of Indeterminate Potential and the Risk of Cognitive Impairment in the Women’s Health Initiative Memory Study Yasminka A Jakubek , Aaron P Smith , Xiaoyan I Leng , Megan E Hall , Daniel Ezzat , Yash Pershad , Jason M Collins , Md Mesbah Uddin , David W Fardo , Pradeep Natarajan , Alexander G Bick , Jacob O Kitzman , Michael C Honigberg , Kathleen M Hayden , JoAnn E Manson , Siddhartha Jaiswal , Eric A Whitsel , Alexander P Reiner medRxiv 2025.08.02.25332871; doi: https://doi.org/10.1101/2025.08.02.25332871 Share This Article: Copy Citation Tools Clonal Hematopoiesis of Indeterminate Potential and the Risk of Cognitive Impairment in the Women’s Health Initiative Memory Study Yasminka A Jakubek , Aaron P Smith , Xiaoyan I Leng , Megan E Hall , Daniel Ezzat , Yash Pershad , Jason M Collins , Md Mesbah Uddin , David W Fardo , Pradeep Natarajan , Alexander G Bick , Jacob O Kitzman , Michael C Honigberg , Kathleen M Hayden , JoAnn E Manson , Siddhartha Jaiswal , Eric A Whitsel , Alexander P Reiner medRxiv 2025.08.02.25332871; doi: https://doi.org/10.1101/2025.08.02.25332871 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 Neurology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (381) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5431) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) 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:'9ff01b613e2edf88',t:'MTc3OTMzMDA3OA=='};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())}}}})();