Circulating leptin and risk of pancreatic cancer: a pooled analysis of three cohorts (LB369)
Bibliographic record
Abstract
Background: Diabetes, overweight and obesity are consistently associated with increased pancreatic cancer risk; however the underlying mechanism(s) is uncertain. Leptin is an adipokine important in metabolic regulation. Methods: We conducted a pooled nested case‐control study of participants from the Prostate, Lung, Colorectal, and Ovarian Cancer Trial (PLCO), Alpha‐Tocopherol, Beta‐Carotene Cancer Prevention Study (ATBC), and Cancer Prevention Study II Nutrition (CPS‐II) cohorts to investigate whether pre‐diagnostic circulating leptin concentrations were associated with pancreatic cancer. In total, 759 incident pancreatic adenocarcinoma cases were included in this analysis (average follow‐up 8.3 years, up to 20 years). Incidence density selected controls (n=1069) were matched to cases by cohort, age, sex, race, and date of blood draw. We used conditional logistic regression analysis to calculate adjusted odds ratios (OR) and 95% confidence intervals (CI) and sex‐specific quintiles (Q) based on the distribution of the controls. Results: Overall, we did not observe an association between leptin and pancreatic cancer (compared to Q1, Q5 OR=1.13, 95% CI 0.75‐1.70, p‐trend=0.39). There was a statistically significant interaction by follow‐up time (p‐interaction=0.003) such that significantly elevated risk was apparent only among cases occurring 10 or more years after blood draw (compared to Q1, Q4 OR=2.21, 95% CI 1.18‐4.13, Q5 OR=2.55, 95% CI 1.23‐5.27, p‐trend=0.004). There were no significant interactions by cohort, sex, smoking, age, baseline BMI or diabetes. Conclusion: The lack of association during early follow‐up might be explained by subclinical disease. Our results support the hypothesis that leptin plays a role in pancreatic carcinogenesis, however long follow‐up is necessary to observe the association.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".