Distinct characteristics of lymphoid and myeloid clonal hematopoiesis in World Trade Center first responders
Bibliographic record
Abstract
Abstract Clonal hematopoiesis of indeterminate potential (CHIP) represents the presence of clonal somatic mutations in blood cells in otherwise healthy individuals. While CHIP is known to increase risk for hematologic malignancies and cardiovascular disease, its association with airborne carcinogens remains largely unknown. We investigated CHIP mutations in 9/11 World Trade Center (WTC) disaster responders (n=350), who experienced substantial exposure to a complex mix of airborne carcinogens. Ultra-deep whole-exome sequencing at 250X was performed on banked blood samples. We characterized CHIP mutations and their associations with clinical factors (age, ancestry, gender, body mass index, cardiovascular disease, stroke), laboratory parameters (peripheral blood counts), mental and cognitive assessments, exposure data, and HLA zygosity. Statistical methods included Fisher’s exact test, Wilcoxon rank sum test, and multivariate logistic regression. Findings were compared to unexposed controls (n=293) analyzed using identical methods. Among WTC participants, CHIP prevalence was 34.2%, with myeloid-CHIP (M-CHIP) at 16.2% and lymphoid-CHIP (L-CHIP) at 21.4%. M-CHIP prevalence correlated positively with age ( p =0.02), smoking history ( p= 0.01), and lower platelet counts ( p =0.03). The most frequent M-CHIP mutations were in DNMT3A, TET2, PPM1D , while L-CHIP mutations were in EEF1A1, DDX11 and KMT2D . Notably, DDX11 mutations were associated with lower Montreal Cognitive Assessment scores ( p =6.57e-03). Comparison with unexposed controls demonstrated higher CHIP prevalence in WTC responders, particularly in those 55 or younger. Study highlights the potential utility of deep sequencing for CHIP detection with clinical, laboratory and exposure data to develop personalized risk-adapted screening programs for cancer and other CHIP-related conditions in individuals exposed to airborne carcinogens.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".