Obesity-related hormones and endometrial cancer among postmenopausal women: a nested case–control study within the B∼FIT cohort
Bibliographic record
Abstract
Endometrial cancer risk is strongly influenced by obesity, but the mechanisms of action remain unclear. Leptin and adiponectin, secreted from adipose tissue, reportedly play a role in such carcinogenic processes as cell proliferation, angiogenesis, and insulin regulation. In this case-control study, nested within the Breast and Bone Follow-up of the Fracture Intervention Trial (n=15,595), we assessed pre-diagnostic serum leptin, total adiponectin, and high-molecular-weight (HMW) adiponectin in relation to endometrial cancer among postmenopausal women. During the 10-year follow-up, 62 incident endometrial cases were identified and matched to 124 controls on age, geographical site, time of fasting blood draw at baseline (1992-1993), and trial participation status. Adipokines and C-peptide were measured by ELISA. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated via conditional logistic regression, with exposures categorized in tertiles (T). Multivariable models considered C-peptide, BMI (kg/m(2)), and estradiol (E2) as potential confounders. Endometrial cancer risk was significantly associated with higher leptin levels, adjusted for E2 and C-peptide (OR(T3 vs T1)=2.96; 95% CI, 1.21-7.25; P trend <0.01). After further adjustment for BMI, the estimates were attenuated and the positive trend was no longer statistically significant (OR(T3 vs T1)=2.11; 95% CI, 0.69-6.44; P trend=0.18). No significant associations were observed with adiponectin or HMW adiponectin and endometrial cancer. Our findings with leptin suggest that the leptin-BMI axis might increase endometrial cancer risk through mechanisms other than estrogen-driven proliferation. Continued exploration of these pathways in larger prospective studies may help elucidate mechanisms underlying observed obesity-endometrial cancer associations.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".