Adipose Tissue Distribution and Cardiovascular Disease Risk Among Breast Cancer Survivors
Bibliographic record
Abstract
PURPOSE: Cardiovascular disease (CVD) is a major source of morbidity and mortality among breast cancer survivors. Although body mass index (BMI) is associated with CVD risk, adipose tissue distribution may better identify patients with a high risk of CVD after breast cancer. METHODS: Among 2,943 patients with nonmetastatic breast cancer without prior CVD, we used International Classification of Diseases (9th and 10th revisions) codes to identify incidence of nonfatal stroke, myocardial infarction, heart failure, or CVD death. From clinically acquired computed tomography scans obtained near diagnosis, we measured visceral adiposity (centimeters squared), subcutaneous adiposity (centimeters squared), and intramuscular adiposity (fatty infiltration into muscle [Hounsfield Units, scored inversely]). We estimated hazard ratios (HRs) and 95% CIs per SD increase in adiposity accounting for competing risks and adjusting for demographics, smoking, cancer treatment, and pre-existing CVD risk factors. RESULTS: Mean (SD) age was 56 (12) years. Over a median follow-up of 6 years, 328 CVD events occurred. Each SD increase in visceral or intramuscular adiposity was associated with an increase in CVD risk (HR, 1.15 [95% CI, 1.03 to 1.29] and HR, 1.21 [95% CI, 1.06 to 1.37]), respectively). Excess visceral and intramuscular adiposity occurred across all BMI categories. Among normal-weight patients, each SD greater visceral adiposity increased CVD risk by 70% (HR, 1.70 [95% CI, 1.10 to 2.62]). CONCLUSION: Visceral and intramuscular adiposity were associated with increased CVD incidence after breast cancer diagnosis, independent of pre-existing CVD risk factors and cancer treatments. The increased CVD incidence among normal-weight patients with greater visceral adiposity would go undetected with BMI alone. Measures of adipose tissue distribution may help identify high-risk patients and tailor CVD prevention strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".