Trajectories of body mass index, from adolescence to older adulthood, and pancreatic cancer risk; a population-based case–control study in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: Pancreatic cancer has the highest fatality rate of all cancers. Adulthood obesity is an established risk factor for pancreatic cancer; however, life-course obesity is not well understood. The aim of this study was to evaluate the association between body mass index (BMI) trajectories throughout the life-course and pancreatic cancer risk. METHODS: A population-based case-control study was conducted (2011-2013) in Ontario, Canada. Cases were recruited from the Ontario pancreas cancer study (n = 310) and controls from the Ontario cancer risk factor study (n = 1258). Questionnaires captured self-reported height and weight at four timepoints (adolescence, 20 s, 30-40 s, 50-60 s). BMI trajectories were identified using latent class growth mixture modeling. Odds ratios (OR) and 95% confidence intervals (CI) were estimated from multivariable logistic regression. RESULTS: Five BMI trajectories were identified: stable-normal weight (38.9%), progressively overweight (42.2%), persistent overweight (12.6%), progressive obesity (4.2%), and persistent obesity (2.1%). The persistent overweight (OR = 1.55; 95% CI 1.02, 2.39) and progressive obesity trajectories (OR = 1.49; 95% CI 0.77, 2.87) compared to stable-normal weight were associated with increased odds of pancreatic cancer. When BMI was evaluated separately the strongest associations with pancreatic cancer emerged in young and mid-adulthood. CONCLUSION: BMI trajectories characterized by overweight in early adulthood were associated with increased pancreatic cancer risk suggesting a life-course approach to disease risk.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 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".