Deciphering Clonal Hematopoiesis of Indeterminate Potential
Bibliographic record
Abstract
CKD afflicts over 10% of US adults, with its prevalence increasing sharply with age. Clonal hematopoiesis of indeterminate potential (CHIP) is a common, genetically heterogeneous blood cell disorder characterized by the age-related clonal expansion of hematopoietic cells driven by leukemogenic somatic mutations yet without hematologic malignancy or dysplasia. While CHIP is a strong risk factor of future hematologic malignancy (estimated at approximately 0.5% per year, compared with <0.1% for those without CHIP), it is also linked to two-fold higher cardiovascular disease in epidemiologic, cell-based, and murine studies. However, more recent work has implicated CHIP with kidney outcomes, such as CKD as well as AKI, independent of traditional risk factors. This review covers the observations and proposed hypotheses linking CHIP and kidney disease. The review also underscores the need for further research to elucidate the distinct pathways through which CHIP may contribute to CKD and its comorbidities, considering the heterogeneity within CKD stages and etiologies, as well as whether CHIP is a causal driver of kidney disease or a marker of aging and comorbidity. Finally, we discuss the potential of anti-inflammatory treatments to mitigate CHIP's adverse effects on kidney health, aiming to improve management strategies for patients with CHIP-associated kidney diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".