Clonal hematopoiesis of indeterminate potential contributes to accelerated chronic kidney disease progression
Bibliographic record
Abstract
Abstract Background Clonal hematopoiesis of indeterminate potential (CHIP) is a common inflammatory condition of aging that causes myriad end-organ damage. We have recently shown associations for CHIP with acute kidney injury and with kidney function decline in the general population, with stronger associations 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 examined the prospective associations between CHIP and CKD progression events in four cohorts of CKD patients (total N = 5,772). The primary outcome was a composite of 50% kidney function decline or kidney failure. The slope of eGFR decline was examined as a secondary outcome. Mendelian randomization techniques were then used to investigate potential causal effects of CHIP on eGFR decline. Finally, kidney function was assessed in adenine-fed CKD model mice having received a bone marrow transplant recapitulating Tet2 -CHIP compared to controls transplanted wild-type bone marrow. Results Across all cohorts, the average age was 66.4 years, the average baseline eGFR was 42.6 ml/min/1.73m 2 , and 24% had CHIP. Upon meta-analysis, non- DNMT3A CHIP was associated with a 59% higher relative risk of incident CKD progression (HR 1.59, 95% CI: 1.02-2.47). This association was more pronounced among individuals with diabetes (HR 1.29, 95% CI: 1.03-1.62) and with baseline eGFR ≥ 30 ml/min/1.73m (HR 1.80, 95% CI: 1.11-2.90). Additionally, the annualized slope of eGFR decline was steeper among non- DNMT3A CHIP carriers, relative to non-carriers (β -0.61 ± 0.31 ml/min/1.73m 2 , p = 0.04). Mendelian randomization analyses suggested a causal role for CHIP in eGFR decline among individuals with diabetes. In a dietary adenine mouse model of CKD, Tet2 -CHIP was associated with lower GFR as well as greater kidney inflammation, tubular injury, and tubulointerstitial fibrosis. Conclusion Non- DNMT3A CHIP is a potentially targetable novel risk factor for CKD progression.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".