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Record W4376867172 · doi:10.1101/2023.05.16.23290051

Clonal Hematopoiesis of Indeterminate Potential is Associated with Acute Kidney Injury

2023· preprint· en· W4376867172 on OpenAlexaff
Caitlyn Vlasschaert, Cassianne Robinson‐Cohen, Bryan Kestenbaum, Samuel A. Silver, Jianchun Chen, Elvis A. Akwo, Pavan K. Bhatraju, Ming‐Zhi Zhang, Shirong Cao, Ming Jiang, Yinqiu Wang, Aolei Niu, Edward D. Siew, Holly Kramer, Anna Köttgen, Nora Franceschini, Bruce M. Psaty, Russell P. Tracy, Álvaro Alonso, Dan E. Arking, Josef Coresh, Christie M. Ballantyne, Eric Boerwinkle, Morgan E. Grams, Matthew B. Lanktree, Michael J. Rauh, Raymond C. Harris, Alexander G. Bick

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversityPopulation Health Research InstituteImpactQueen's University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsAcute kidney injuryMedicineInternal medicineKidney diseaseDialysisDiseaseCohortOncologyMyeloid

Abstract

fetched live from OpenAlex

Abstract Age is a predominant risk factor for acute kidney injury (AKI), yet the biological mechanisms underlying this risk are largely unknown and to date no genetic mechanisms for AKI have been established. Clonal hematopoiesis of indeterminate potential (CHIP) is a recently recognized biological mechanism conferring risk of several chronic aging diseases including cardiovascular disease, pulmonary disease and liver disease. In CHIP, blood stem cells acquire mutations in myeloid cancer driver genes such as DNMT3A, TET2, ASXL1 and JAK2 and the myeloid progeny of these mutated cells contribute to end-organ damage through inflammatory dysregulation. We sought to establish whether CHIP causes acute kidney injury (AKI). To address this question, we first evaluated associations with incident AKI events in three population-based epidemiology cohorts (N = 442,153). We found that CHIP was associated with a greater risk of AKI (adjusted HR 1.26, 95% CI: 1.19–1.34, p<0.0001), which was more pronounced in patients with AKI requiring dialysis (adjusted HR 1.65, 95% CI: 1.24–2.20, p=0.001). The risk was particularly high in the subset of individuals where CHIP was driven by mutations in genes other than DNMT3A (HR: 1.49, 95% CI: 1.37–1.61, p<0.0001). We then examined the association between CHIP and recovery from AKI in the ASSESS-AKI cohort and identified that non- DNMT3A CHIP was more common among those with a non-resolving pattern of injury (HR 2.3, 95% CI: 1.14–4.64, p = 0.03). To gain mechanistic insight, we evaluated the role of Tet2 -CHIP to AKI in ischemia-reperfusion injury (IRI) and unilateral ureteral obstruction (UUO) mouse models. In both models, we observed more severe AKI and greater post-AKI kidney fibrosis in Tet2 -CHIP mice. Kidney macrophage infiltration was markedly increased in Tet2 -CHIP mice and Tet2 -CHIP mutant renal macrophages displayed greater pro-inflammatory responses. In summary, this work establishes CHIP as a genetic mechanism conferring risk of AKI and impaired kidney function recovery following AKI via an aberrant inflammatory response in CHIP derived renal macrophages.

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.313
Teacher spread0.287 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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