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Record W4397041788 · doi:10.1681/asn.20233411s1473c

Clonal Hematopoiesis of Indeterminate Potential Is Associated with Kidney Disease Progression in a Multi-Cohort Meta-Analysis of Individuals with CKD

2023· article· en· W4397041788 on OpenAlexaffabout
Caitlyn Vlasschaert, Yang Pan, Varun S. Rao, James E. Hixson, Michael Chong, Elvis A. Akwo, Md Mesbah Uddin, Zhi Yu, Dokyun Kim, Manjula Kurella Tamura, Debbie L. Cohen, He Jiang, Changwei Li, Zeenat Bhat, Panduranga S. Rao, Alexander G. Bick, Bryan Kestenbaum, Guillaume Paré, Michael J. Rauh, Adeera Levin, Pradeep Natarajan, James P. Lash, Cassianne Robinson‐Cohen, Matthew B. Lanktree, Tanika N. Kelly

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
KeywordsMeta-analysisIndeterminateCohortKidney diseaseMedicineInternal medicineDiseaseHaematopoiesisCohort studyOncologyImmunologyBiologyStem cellGenetics

Abstract

fetched live from OpenAlex

Background: Clonal hematopoiesis of indeterminate potential (CHIP) is a common inflammatory condition of aging caused by acquired mutations in blood stem cells. CHIP causes myriad end-organ damage, including a doubling of the risk of cardiovascular disease independent of traditional risk factors. We have recently shown associations for CHIP with acute kidney injury and with kidney function decline in the general population, with a greater effect for CHIP driven by mutations in genes other than DNMT3A (non-DNMT3A CHIP). Longitudinal kidney function endpoints in individuals with pre-existing chronic kidney disease (CKD) and CHIP have been examined in two previous studies, which reported conflicting findings and were limited by small sample sizes. Methods: In this study, we examine the prospective associations between CHIP and CKD progression events in four cohorts of CKD patients: the Chronic Renal Insufficiency Cohort (CRIC), the African American Study of Kidney Disease (AASK), the Canadian study of prediction of death, dialysis and interim cardiovascular events (CanPREDDICT), and BioVU (total N = 4853). The primary outcome was CKD progression (composite of 50% kidney function decline or end-stage kidney disease). Analyses were adjusted for age, age2, sex, self-reported race, and the following baseline parameters: eGFR, proteinuria, smoking status, BMI, diabetes status, hypertension, and cardiovascular disease history. Results: Across all cohorts, the average age was 67.4 years, the average baseline eGFR was 41.2 ml/min/1.73m2, and 25% had CHIP. In a random-effects meta-analysis, non-DNMT3A CHIP was associated with a 59% increased risk of incident CKD progression (HR 1.59, 95% CI: 1.01-2.51). This effect was slightly more pronounced in the subgroup with baseline eGFR ≥ 30 ml/min/1.73m2 (HR 1.77, 95% CI: 1.06-2.98). Conclusions: Non-DNMT3A CHIP is a potentially targetable novel risk factor for CKD progression in a multi-cohort meta-analysis. Funding: NIDDK Support, Other NIH Support - Canadian Institutes of Health Research Project Grant (application # 427810), R01DK132155, R01DK125782, 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)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.015
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.328
Teacher spread0.291 · 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 designMeta-analysis
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

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