Abstract 10075: Hematopoietic Mosaic Chromosomal Alterations and Cardiovascular Disease-Related Deaths Among Cancer Survivors: An Analysis of the UK Biobank
Bibliographic record
Abstract
Introduction: Cancer survivors are at an increased risk for cardiovascular (CV)-related deaths. Recently, clonal hematopoiesis (CH) was described as a novel risk factor for both cancer and CV disease. However, the influence of CH on CV outcomes in cancer survivors has not been explored. Hypothesis: We hypothesized that CH captured by mosaic chromosomal alterations (mCA) could predict the risk of CV outcomes and death following cancer diagnosis. Methods: In the UK Biobank, we identified patients with a history of selected cancer types known for their high CV risk. mCAs were detected using DNA genotyping array intensity data. The association of mCA with CV-death, coronary artery disease (CAD)-death, and any cause death was performed using Cox proportional hazards regression, adjusting for age, sex, smoking, chemotherapy, radiotherapy, and ancestry. Exploratory analyses also included incident CV phenotypes. The specificity of the effect was assessed using a model with an interaction term between mCA and cancer history. Results: Among 48 919 unrelated individuals with a cancer diagnosis, 10 070 patients (20.6%) carried ≥1 mCA clone. Of those, 2432 were autosomal carriers and 1910 had a mCA in ≥10% of peripheral leukocytes. mCA prevalence increased with age: from 4.8% among those aged <49 years old to 30.4% for those aged ≥70 years old. mCA was associated with an increased risk of CAD-death (HR 1.37, 95% CI 1.09-1.71, P= 0.006) and overall mortality (HR 1.07, 95% CI 1.02-1.11, P =0.006). In exploratory analyses, mCA was associated with higher risks of incident STEMI (HR 1.18, 95% CI 1.02-1.37, P =0.022) and peripheral vascular disease (HR 1.17, 95% CI 1.04-1.31, P =0.007). The effect of mCA was specific to cancer survivors for any cause of death ( P interaction <0.001) but not for CAD-death ( P interaction =0.96). Conclusions: Our findings indicate that among cancer survivors, CH captured by mCA was associated with a higher risk of CAD death and overall mortality. Cancer survivors with CH may benefit from targeted CV screening and preventive measures to better manage individual CV 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".