Clonal Hematopoiesis and Risk of Stroke: Evidence from Over 800,000 Individuals Across Three Cohorts
Bibliographic record
Abstract
Abstract Clonal hematopoiesis of indeterminate potential (CHIP) is a common age-related condition that increases risk for cardiovascular disease. However, its relationship with stroke remains uncertain: some studies have reported a significant association between CHIP and stroke risk, while others, including large biobank analyses, found no association after adjustment. To resolve these conflicting findings, we analyzed genomic and clinical data from 800,160 participants with genetic sequencing and medical records across the Vanderbilt BioVU, NIH AllofUs , and UK Biobank. Stroke events were identified and classified as ischemic or hemorrhagic using ICD codes. Results from the three cohorts were meta-analyzed with previously published results. Subgroup analyses were conducted by driver gene, clone size, sex and menopausal status. In addition, genetically predicted levels of 27 plasma cytokines were assessed as potential modifiers of CHIP-associated stroke risk. CHIP was associated with increased risk of incident stroke in each cohort and in the meta-analysis (HR = 1.20, 95% CI 1.13–1.27; P = 2.21 × 10 ⁻10 ). This association was observed for both ischemic (HR = 1.18) and hemorrhagic (HR = 1.30) stroke subtypes. Gene-specific analyses showed strong associations for JAK2 (HR = 2.46) and TET2 (HR = 1.40). DNMT3A demonstrated weak but significant associations (HR = 1.11). CHIP was associated with stroke risk in both sexes; however, among women, the association was evident in postmenopausal (HR = 1.49, 95% CI 1.16–1.92; P = 1.91 × 10 ⁻3 ) but not in premenopausal participants (HR = 0.70, 95% CI 0.36–1.43, P = 0.33). Among participants with CHIP, but not among participants without CHIP, genetically predicted levels of IL-1RAP were predictive of risk for stroke, suggesting IL-1RAP as a modifier of the CHIP-associated risk for stroke. Collectively, this large-scale, multi-cohort study establishes CHIP as an important determinant of incident stroke risk and IL-1-mediated inflammation as a targetable pathway to reduce this risk.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".