Breast Volume is an Independent Predictor of Visceral and Ectopic Fat in Premenopausal Women
Bibliographic record
Abstract
It is suggested that a large breast size among women may predict type 2 diabetes risk independent of BMI and waist circumference (WC). The purpose of this study was to determine the independent associations of breast volume with cardiometabolic risk factors and regional fat distribution. A total of 92 overweight or obese premenopausal women (age = 39.9 +/- 6.8 years) underwent full-body magnetic resonance imaging (MRI) for the assessment of breast volume, visceral adipose tissue (VAT), abdominal and lower-body subcutaneous AT (SAT), and intermuscular AT (IMAT), a 2-h oral glucose tolerance test (OGTT), and fasting phlebotomy for assessment of triglyceride, total, high-density lipoprotein-, and low-density lipoprotein-cholesterol levels. Breast volume was not associated with any of the cardiometabolic risk factors assessed (P > 0.05). However, VAT was consistently associated with a number of cardiometabolic risk factors (OGTT glucose, OGTT insulin, and triglyceride levels) after controlling for age, BMI, WC, breast volume, and the other AT depots. In univariate models, breast volume was positively associated with VAT, IMAT, and abdominal and lower-body SAT (P < 0.05). After controlling for age, BMI, and WC level, breast volume remained positively associated with VAT and IMAT (P < 0.05), such that women with the highest breast volume had approximately 1.1 and 1.3 kg more VAT and IMAT, respectively, but no more abdominal or lower-body SAT, by comparison to women with the smallest breast volume. Thus, the previously documented association between breast size and type 2 diabetes risk may be in part explained by excess VAT and/or IMAT deposition.
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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 | 0.002 |
| 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) | 0.001 | 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".