Coffee, Tea, and Sugar-Sweetened Carbonated Soft Drink Intake and Pancreatic Cancer Risk: A Pooled Analysis of 14 Cohort Studies
Bibliographic record
Abstract
BACKGROUND: Coffee has been hypothesized to have pro- and anticarcinogenic properties, whereas tea may contain anticarcinogenic compounds. Studies assessing coffee intake and pancreatic cancer risk have yielded mixed results, whereas findings for tea intake have mostly been null. Sugar-sweetened carbonated soft drink (SSB) intake has been associated with higher circulating levels of insulin, which may promote carcinogenesis. Few prospective studies have examined SSB intake and pancreatic cancer risk; results have been heterogeneous. METHODS: In this pooled analysis from 14 prospective cohort studies, 2,185 incident pancreatic cancer cases were identified among 853,894 individuals during follow-up. Multivariate (MV) study-specific relative risks (RR) and 95% confidence intervals (CI) were calculated using Cox proportional hazards models and then pooled using a random-effects model. RESULTS: No statistically significant associations were observed between pancreatic cancer risk and intake of coffee (MVRR = 1.10; 95% CI, 0.81-1.48 comparing ≥900 to <0 g/d; 237g ≈ 8oz), tea (MVRR = 0.96; 95% CI, 0.78-1.16 comparing ≥400 to 0 g/d; 237g ≈ 8oz), or SSB (MVRR = 1.19; 95% CI, 0.98-1.46 comparing ≥250 to 0 g/d; 355g ≈ 12oz; P value, test for between-studies heterogeneity > 0.05). These associations were consistent across levels of sex, smoking status, and body mass index. When modeled as a continuous variable, a positive association was evident for SSB (MVRR = 1.06; 95% CI, 1.02-1.12). CONCLUSION AND IMPACT: Overall, no associations were observed for intakes of coffee or tea during adulthood and pancreatic cancer risk. Although we were only able to examine modest intake of SSB, there was a suggestive, modest positive association for risk of pancreatic cancer for intakes of SSB.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.023 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".