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Record W4397045661 · doi:10.1681/asn.20233411s156a

Associations Between Clonal Hematopoiesis of Indeterminate Potential and Cardiovascular Disease in Three Prospective CKD Patient Cohorts

2023· article· en· W4397045661 on OpenAlexaffabout
Yang Pan, Caitlyn Vlasschaert, Varun S. Rao, James E. Hixson, Md Mesbah Uddin, Zhi Yu, Dokyun Kim, Alexander G. Bick, Bryan Kestenbaum, 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, Pradeep Natarajan, Tanika N. Kelly, Cassianne Robinson‐Cohen, Matthew B. Lanktree

Bibliographic record

VenueJournal of the American Society of Nephrology · 2023
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsMcMaster UniversityUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsDiseaseMedicineProspective cohort studyIndeterminateInternal medicineHaematopoiesisIntensive care medicineStem cellBiologyGenetics

Abstract

fetched live from OpenAlex

Background: Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related condition characterized by the clonal expansion of blood cells carrying somatic mutations to specific driver genes. Although CHIP has been established as an important contributor to cardiovascular diseases (CVD) in the general population, its association with CVD in a pro-inflammatory chronic kidney disease (CKD) setting has not been examined. Methods: We examined prospective associations between CHIP status and CVD events in three cohorts that included a total of 3,414 CKD patients: the Chronic Renal Insufficiency Cohort (CRIC), the African American Study of Kidney Disease (AASK), and the Canadian study of prediction of death, dialysis and interim cardiovascular events (CanPREDDICT). Primary analyses tested associations between CHIP status and a composite CVD endpoint of myocardial infarction (MI), stroke, congestive heart failure (CHF), or peripheral artery disease (PAD). Cox proportional hazards regression models were used, adjusting for demographic, lifestyle, and clinical covariables, including cardiovascular risk factors. Secondary analyses investigated individual CVD endpoints. Random-effect meta-analyses were employed to combine effects across studies. Results: Study participants had an average age of 68.8 years and a mean eGFR of 40.2 ml/min/1.73m2. As expected, participants had a high frequency of hypertension (95%) and diabetes (49%), with CHIP identified in 25% of participants. Those with large CHIP clone size (VAF≥10%) and a non-DNMT3A CHIP gene mutation exhibited 38% (95% CI:2%-85%) and 42% (95% CI: 6%-90%) higher risks of the composite CVD endpoint, respectively, compared to noncarriers. Compared to non-CHIP status, large clone size was further associated with incident CHF (HR: 1.53, 95% CI: 1.13-2.00). Conclusions: CHIP carrier status may be an important risk factor for CVD among CKD patients, with associations mirroring those observed in the general population. Funding: NIDDK Support, Government Support - Non-U.S.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.007
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

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