Association of total adiposity and computed tomographic measures of regional adiposity with incident cancer risk: a prospective population-based study of older adults
Bibliographic record
Abstract
Obesity is associated with increased risk of many types of cancer. Less is known regarding associations between adipose depots and cancer risk. We aimed to explore relationships between adipose depots, risk of cancer, and obesity-related cancer (per NCI definition) in participants initially aged 70-79 years without prevalent cancer (1179 men, 1340 women), and followed for incident cancer for 13 years. Measures included body mass index (BMI), total adipose tissue from dual-energy X-ray absorptiometry, and computed tomography measures of visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue, thigh intermuscular adipose tissue, and thigh muscle attenuation (Hounsfield unit, HU), where low HU indicates fatty infiltration. Hazard ratios (HR) and 95% confidence intervals (CIs) were estimated by Cox proportional hazards models adjusted for demographics, lifestyle variables, and medical conditions. During follow-up, 617 participants developed cancer of which 224 were obesity-related cancers. Total adipose tissue and VAT were positively associated with cancer risk among women (HR 1.14, 95% CI 1.01-1.30 per SD increase; HR 1.15, 95% CI 1.02-1.30 per SD increase). There were no associations with cancer risk among men. Total adipose tissue was positively associated with obesity-related cancer risk among women (HR 1.23, 95% CI 1.03-1.46 per SD increase). VAT was positively associated with obesity-related cancer risk among men (HR 1.30, 95% CI 1.06-1.60 per SD increase) and remained associated even with adjustment for BMI (HR 1.40, 95% CI 1.08-1.82 per SD increase). These findings provide insight into relationships between specific adipose depots and cancer risk and suggest differential relationships among men and women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".