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Record W4397046247 · doi:10.1681/asn.20223311s11a

Clonal Hematopoiesis of Indeterminate Potential Is Associated With a Higher Risk of Incident AKI

2022· article· en· W4397046247 on OpenAlexaff
Caitlyn Vlasschaert, Alexander G. Bick, Michael J. Rauh, Matthew B. Lanktree, Morgan E. Grams, Bruce M. Psaty, Anna Köttgen, Nora Franceschini, Holly Kramer, Bryan Kestenbaum, Cassianne Robinson‐Cohen

Bibliographic record

VenueJournal of the American Society of Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsMcMaster UniversityQueen's University
Fundersnot available
KeywordsIndeterminateMedicineHaematopoiesisIntensive care medicineInternal medicineStem cellBiologyGenetics

Abstract

fetched live from OpenAlex

Background: Clonal hematopoiesis of indeterminate potential (CHIP) is a common, age-related process wherein an acquired driver mutation in a hematopoietic stem cell produces a resilient clonal leukocyte population with dysregulated inflammatory signaling. The presence of CHIP has been associated with the progression of chronic kidney disease. We tested whether CHIP is a novel risk factor for acute kidney injury (AKI) in two community-based cohorts. Methods: We evaluated participants from the Atherosclerosis Risk in Community (ARIC; N = 10,570) and Cardiovascular Health Study (CHS; N = 2,792). We identified somatic DNA mutations in peripheral leukocytes that met established criteria for CHIP using whole exome and whole genome data. AKI events were previously ascertained in both cohorts based on hospitalization codes with additional validation by manual chart review in CHS. We used proportional hazards regression to test associations of CHIP with AKI after adjustment for relevant confounders. Results: CHIP was identified in 7.6% of ARIC participants (median age: 58) and 14.5% of CHS participants (median age: 72). The incidence rate of AKI was higher among persons with CHIP in both cohorts: 12.6 vs. 10.4 events per 1000 person-years in ARIC and 6.6 vs. 4.4 events per 1000 person-years in CHS. In a fixed-effects meta-analysis adjusted for age, age2, sex, and baseline eGFR, the presence of CHIP was associated with an estimated 18% greater risk of AKI (HR 1.18, 95% CI: 1.02 - 1.37). The risk for AKI was greatest for mutations in driver genes other than DNMT3A (non-DNMT3A CHIP; HR 1.29, 95% CI: 1.07 - 1.55). Conclusions: CHIP is associated with a greater risk of incident AKI in two large community-based cohorts. Non-DNMT3A CHIP mutations demonstrate the strongest associations with incident AKI. CHIP may therefore be a novel risk factor for AKI that could partially explain the strong age dependency of this condition. Future studies will elucidate which subtypes of CHIP pose the highest risk of kidney sequelae and in which scenarios emerging treatments for CHIP would be beneficial. Funding: Other NIH Support - NHLBI Trans-Omics for Precision Medicine

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.007
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.257
Teacher spread0.247 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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