Serum C-peptide, Total and High Molecular Weight Adiponectin, and Pancreatic Cancer: Do Associations Differ by Smoking?
Bibliographic record
Abstract
Abstract Background: Studies examining associations between circulating concentrations of C-peptide and total adiponectin, two biomarkers related to obesity and insulin secretion and sensitivity and pancreatic ductal adenocarcinoma (PDA) risk have shown inconsistent results and included limited numbers of smokers. Methods: We examined associations of these biomarkers and high molecular weight (HMW) adiponectin with PDA, overall, and by smoking status. We conducted a pooled nested case–control analysis in 3 cohorts (Prostate, Lung, Colorectal, and Ovarian Cancer Trial, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, and Cancer Prevention Study-II), with 758 cases (435 current smokers) and 1,052 controls (531 smokers) matched by cohort, age, sex, race, blood draw date and follow-up time. We used conditional logistic regression adjusted for age, smoking, diabetes, and body mass index to calculate ORs and 95% confidence intervals (CI). Results: Circulating C-peptide concentration was not associated with PDA in never or former smokers, but was inversely associated with PDA in current smokers (per SD OR = 0.67; 95% CI, 0.54–0.84; Pinteraction = 0.005). HMW adiponectin was inversely associated with PDA in never smokers (OR = 0.43; 95% CI, 0.23–0.81), not associated in former smokers, and positively associated in smokers (OR = 1.23; 95% CI, 1.04–1.45; Pinteraction = 0.009). Total adiponectin was not associated with PDA in nonsmokers or current smokers. Conclusions: Associations of biomarkers of insulin secretion and sensitivity with PDA differ by smoking status. Smoking-induced pancreatic damage may explain the associations in smokers while mechanisms related to insulin resistance associations in nonsmokers. Impact: Future studies of these biomarkers and PDA should examine results by smoking status. Cancer Epidemiol Biomarkers Prev; 26(6); 914–22. ©2017 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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".