High-sensitivity C-reactive protein is associated with clonal hematopoiesis of indeterminate potential
Bibliographic record
Abstract
Clonal hematopoiesis of indeterminate potential (CHIP) is predictive of hematological cancers and cardiovascular diseases, but the etiology of CHIP initiation and clonal expansion is unknown. Several lines of evidence suggest that proinflammatory cytokines may favor mutated hematopoietic stem cell expansion. To investigate the potential link between inflammation and CHIP, we performed targeted deep sequencing of 11 genes previously implicated in CHIP in 1887 subjects aged >70 years from the Montreal Heart Institute Biobank, of which 1359 had prior coronary artery disease (CAD), and 528 controls did not. We assessed association of CHIP with log transformed high-sensitivity C-reactive protein (hs-CRP), a validated biomarker of inflammation. CHIP was identified in 427 of the 1887 subjects (22.6%). CHIP mutations were more frequently identified in DNMT3A (11.6%) and TET2 (6.1%), with a higher proportion of TET2 mutations occurring in controls than in patients with CAD (9.0% vs 4.9%, P < .001). CHIP carriers had 21% higher hs-CRP levels compared with their noncarrier counterparts (eβ = 1.21, 95% confidence interval [CI]: 1.08 to 1.36; P = .001). A similar effect was observed in the subgroup of patients with known CAD (eβ = 1.22, 95% CI: 1.06 to 1.41; P = .005). These findings confirm the association between inflammation and CHIP. This association may open investigational avenues aimed at documenting mechanisms linking inflammation to clonal progression and ultimately supports prevention interventions to attenuate CHIP's impact on cardiovascular disease and cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".