Clonal hematopoiesis and inflammation: Partners in leukemogenesis and comorbidity
Bibliographic record
Abstract
Clonal hematopoiesis (CH) of indeterminate potential (CHIP), defined as the presence of a somatic mutation in the peripheral blood at a variant allele frequency (VAF) ≥2%, affects at least 10% of individuals older than 65, but low-VAF clones can be detected in 95% of individuals older than 50. CHIP associates with a wide range of comorbidities from atherosclerosis to pulmonary disease. A growing body of evidence, primarily from studies involving Tet2-knockout and stem cell transplant models of CH, suggest that dysregulated inflammation contributes to clonal expansion and associated comorbidities. Mutant leukocytes from animal models contribute to an inflammatory milieu that may confer a selective advantage to the clone, thus perpetuating a cycle of inflammation and expansion. Although it is unclear whether inflammation or expansion sets this cycle in motion, some evidence suggests that inflammation from infections or pre-existing comorbidities initiates this cycle. The pro-inflammatory phenotypes of macrophages from mutant clones and their contributions to disease are well characterized in murine models, but have not yet been confirmed in humans. Furthermore, the roles of other cell types that can carry mutations of CHIP are not fully understood. We propose a rationale for further investigation of neutrophils, other granulocytes and T, B, and NK cells as they may play a role in CHIP-associated comorbidities. As the understanding of CH has advanced, potential interventions, especially those targeting aberrant inflammation, have been proposed. We are hopeful that as studies continue to unravel the complex links between CHIP, inflammation, and leukocyte dysfunction, CHIP-related comorbidities may be more effectively managed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".