Associations of insulin resistance and inflammatory biomarkers with endometrial cancer survival: The Alberta endometrial cancer cohort study
Bibliographic record
Abstract
BACKGROUND: Metabolic dysfunction and inflammation have been associated with endometrial cancer risk; however, their influence on endometrial cancer survival is less understood. METHODS: A prospective cohort study of 540 endometrial cancer cases diagnosed between 2002 and 2006 in Alberta were followed for survival outcomes to 2019. Baseline blood samples collected either pre- or post-hysterectomy were analyzed for glucose, insulin, adiponectin, leptin, tumor necrosis factor-α, interleukin-6, and C-reactive protein. Covariates were obtained during in-person interviews and via medical chart abstraction. Cox proportional hazard regression models were used to estimate multivariable-adjusted hazard ratios (HR) and 95% confidence intervals (95% CI) for the association between each biomarker and disease-free and overall survival. RESULTS: Blood samples were collected from 520 of the 540 participants (presurgical n = 235; postsurgical n = 285). During the median follow-up of 14.3 years (range 0.4-16.5 years), there were 125 recurrences, progressions, and/or deaths with 106 overall deaths. None of the biomarkers were associated with disease-free or overall survival in multivariable-adjusted analyses. In an exploratory stratified analysis, the highest level of presurgical adiponectin, compared to the lowest level, was associated with improved disease-free (HR = 0.42, 95% CI = 0.20-0.85) and overall (HR = 0.41, 95% CI = 0.18-0.92) survival, whereas no statistically significant associations were noted for postsurgical measures of adiponectin. CONCLUSIONS: Overall, there was no evidence of an association between biomarkers of insulin resistance and inflammation with mortality outcomes in endometrial cancer survivors. Future cohort studies with serial blood samples are needed to understand the impact of changes in insulin resistance and inflammatory markers on endometrial cancer survival.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".