Case–Control Study of the Metabolic Syndrome and Metabolic Risk Factors for Endometrial Cancer
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome may predict endometrial cancer risk better than diabetes, hypertension, dyslipidemia, dysglycemia, or weight alone, but few studies have examined this issue. METHODS: We conducted a population-based case-control study in Alberta, Canada (2002-2006) that included 515 incident endometrial cancer cases and 962 frequency age-matched controls. Data were collected through in-person interviews, anthropometric measurements, and 8-hour fasting bloods drawn either pre- or postsurgery. Bloods were analyzed using quantitative colorimetric or absorbance-based assays (ELISA), specific to metabolic syndrome markers. Metabolic syndrome was defined using harmonized guidelines requiring presence of ≥ 3 of the following risk factors: waist circumference ≥ 88 cm, triglycerides ≥ 150 mg/dL, high-density lipoprotein cholesterol <50 mg/dL, treatment of previously diagnosed hypertension, and fasting blood glucose ≥ 100 mg/dL. OR and 95% CIs for endometrial cancer risk with presence of metabolic syndrome and individual metabolic syndrome components were estimated using logistic regression analysis. RESULTS: Metabolic syndrome was significantly more prevalent among cases (62%) than controls (38%). A statistically significant increased risk for endometrial cancer was observed for metabolic syndrome (OR = 1.53; 95% CI: 1.17-2.00), as well as for some of the individual components of metabolic syndrome including waist circumference ≥ 88 cm (OR = 1.57; 95% CI: 1.18-2.08), hypertension (OR = 1.57; 95% CI: 1.18-2.09), and fasting blood glucose ≥ 100 mg/dL (OR = 1.31; 95% CI: 1.03-1.67). Some evidence for effect modification by menopausal status and body mass index was also found. CONCLUSION: Metabolic syndrome is clearly associated with increased endometrial cancer risk. IMPACT: Targeting the entire metabolic syndrome may optimize endometrial cancer risk reduction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".