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Record W4409290429 · doi:10.1681/asn.0000000671

Association of Clonal Hematopoiesis of Indeterminate Potential with Cardiovascular Events in Patients with CKD

2025· article· en· W4409290429 on OpenAlexafffund
Yang Pan, Caitlyn Vlasschaert, Varun Rao, Elvis A. Akwo, James E. Hixson, Md Mesbah Uddin, Zhi Yu, Do-Kyun Kim, Alexander G. Bick, Bryan Kestenbaum, Michael Chong, Guillaume Paré, Michael J. Rauh, Adeera Levin, James P. Lash, Manjula Kurella Tamura, Debbie L. Cohen, Jiang He, L. Lee Hamm, Rajat Deo, Zeenat Bhat, Panduranga S. Rao, Dawei Xie, Pradeep Natarajan, Tanika N. Kelly, Cassianne Robinson‐Cohen, Matthew B. Lanktree

Bibliographic record

VenueJournal of the American Society of Nephrology · 2025
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsQueen's UniversityMcMaster UniversityPopulation Health Research InstituteImpactSt. Joseph’s Healthcare HamiltonUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Human Genome Research InstituteCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsMedicineInternal medicineProspective cohort studyStroke (engine)PopulationMyocardial infarctionHeart failureCardiologyDiseaseOncology

Abstract

fetched live from OpenAlex

Key Points In patients with CKD, non- DNMT3A clonal hematopoiesis of indeterminate potential (CHIP) was associated with higher risk of cardiovascular events. There was no significant difference in the CHIP–cardiovascular disease association based on baseline eGFR, diabetes status, or race. The protective effect of IL6R p.Asp358Ala on non- DNMT3A CHIP cardiovascular risk was similar to the general population. Background Patients with CKD are at higher risk of cardiovascular disease. Clonal hematopoiesis of indeterminate potential (CHIP) has been associated with cardiovascular disease in the general population, with a causal role observed in animal models. In the general population, the effect of CHIP is greater for somatic mutations in predefined CHIP driver genes other than DNMT3A (referred to as non- DNMT3A CHIP). We sought to assess the prospective association between CHIP and cardiovascular events in patients with CKD. Methods CHIP was measured by high-depth targeted sequencing. The primary analysis tested the association of somatic mutations in non- DNMT3A CHIP driver genes with a composite cardiovascular disease end point of myocardial infarction, stroke, congestive heart failure, and peripheral artery disease in 5043 patients with CKD in four prospective cohorts. Sensitivity analyses examined the effect of CHIP subtypes, race, baseline comorbidities, APOL1 risk alleles, and IL6R p.Asp358Ala genotype. Results At baseline, patients had a mean age of 66±12 years and eGFR of 43±18 ml/min per 1.73 m 2 . CHIP was present in 24% of patients, with 13% of all patients carrying acquired non- DNMT3A mutations. Non- DNMT3A CHIP was associated with a 36% higher risk of the composite cardiovascular end point (95% confidence interval [CI], 6% to 76%). Among composite components, non- DNMT3A CHIP was associated with a higher risk of stroke (hazard ratio, 1.65; 95% CI, 1.10 to 2.47). Baseline eGFR, diabetes status, or race did not alter the association of non- DNMT3A CHIP with cardiovascular risk. Those without genetically reduced IL-6 signaling (noncarriers of IL6R p.Asp358Ala) had worse disease (hazard ratio, 1.46; 95% CI, 1.17 to 1.83; P subgroup difference = 0.05). Conclusions In patients with CKD, non- DNMT3A CHIP was associated with cardiovascular disease with an effect size similar to that reported in the general population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.226
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2025
Admission routes2
Has abstractyes

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