Diabetes, Obesity, and Endometrial Cancer: A Review
Bibliographic record
Abstract
Endometrial cancer is the fourth most diagnosed cancer in U.S. women. Diabetes and obesity are established independent risk factors for EC, but their combined effect is less defined. This review investigates the literature on these comorbidities as risk factors and modifiers of EC. Multiple cohort and case–control investigations have shown an increased relative risk (RR) and odds ratio (OR) when diabetes and obesity coexist. In one prospective cohort, the RR of EC in diabetic women was 1.94 [95% CI, 1.23–3.08], but increased to 6.39 [95% CI, 3.28–12.06] with obesity; with low physical activity added, RR rose to 9.61 [95% CI, 4.66–19.83]. Case–control studies similarly show an OR of 1.4 [95% CI, 0.9–2.4] for diabetes alone, vs. 5.1 [95% CI, 3.0–8.7] with BMI > 30 and diabetes. Mechanistically, both conditions promote a pro-cancerous microenvironment through metabolic and inflammatory pathways. They also worsen treatment outcomes, with greater surgical complications, thromboembolic events (p < 0.01), prolonged hospitalizations 6.2 days versus 4.5 days (p < 0.03), and poorer survival with an elevated cancer-specific mortality (HR = 2.65, 95% CI 1.60–4.40). These findings underscore the urgent need for targeted interventions and translational research on how these comorbidities impact the pathophysiologic processes of EC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".