Pancreatic Cancer Risk Associated with Prediagnostic Plasma Levels of Leptin and Leptin Receptor Genetic Polymorphisms
Bibliographic record
Abstract
Abstract Leptin is an adipokine involved in regulating energy balance, which has been identified as a potential biologic link in the development of obesity-associated cancers, such as pancreatic cancer. In this prospective, nested case–control study of 470 cases and 1,094 controls from five U.S. cohorts, we used conditional logistic regression to evaluate pancreatic cancer risk by prediagnostic plasma leptin, adjusting for race/ethnicity, diabetes, body mass index, physical activity, plasma C-peptide, adiponectin, and 25-hydroxyvitamin D. Because of known differences in leptin levels by gender, analyses were conducted separately for men and women. We also evaluated associations between 32 tagging SNPs in the leptin receptor (LEPR) gene and pancreatic cancer risk. Leptin levels were higher in female versus male control participants (median, 20.8 vs. 6.7 ng/mL; P < 0.0001). Among men, plasma leptin was positively associated with pancreatic cancer risk and those in the top quintile had a multivariable-adjusted OR of 3.02 [95% confidence interval (CI), 1.27–7.16; Ptrend = 0.02] compared with men in the bottom quintile. Among women, circulating leptin was not associated with pancreatic cancer risk (Ptrend = 0.21). Results were similar across cohorts (Pheterogeneity = 0.88 for two male cohorts and 0.35 for three female cohorts). In genetic analyses, rs10493380 in LEPR was associated with increased pancreatic cancer risk among women, with an OR per minor allele of 1.54 (95% CI, 1.18–2.02; multiple hypothesis-corrected P = 0.03). No SNPs were significantly associated with risk in men. In conclusion, higher prediagnostic levels of plasma leptin were associated with an elevated risk of pancreatic cancer among men, but not among women. Cancer Res; 76(24); 7160–7. ©2016 AACR.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".