Abstract 3592: Hospital-treated infectious diseases and pancreatic cancer risk: Findings from a large population-based cohort
Bibliographic record
Abstract
Abstract Rationale: Inflammation has been established as a mechanism for pancreatic cancer development. The longitudinal relationship between infection, and the resulting inflammation, and pancreatic cancer risk has yet to be elucidated. A large population-based cohort, such as the United Kingdom (UK) Biobank, offers a unique opportunity to assess these relationships with long follow-up time and a large number of infections. Objectives: Estimate the effect of hospital-treated infection on pancreatic cancer risk by infection type and infection burden. Methods: UK Biobank participants with no record of pancreatic cancer at cohort entry were included. Follow-up was to February 29, 2020 and January 31, 2021 for participants from England and Wales, and Scotland, respectively. Hospital-treated infection status was obtained through linkage to hospital inpatient data and allowed for characterization of exposure to over 900 infectious diseases using the International Classification of Diseases and Related Health Problems 10th Revision. Incident pancreatic cancer diagnosis was obtained through linkage to national cancer registries. Relative risk (RR) and associated 95% confidence intervals (CI) were estimated for infectious disease groups and infectious disease burden categories using negative binomial regression models. Results: From cohort entry to the end of follow up, 502, 219 people were included in the cohort. 93, 123 participants had at least one recorded exposure to hospital-treated infection preceding pancreatic cancer diagnosis and 1, 255 were diagnosed with pancreatic cancer throughout the follow up period. Exposure to any hospital-treated infection was associated with increased risk of pancreatic cancer (aRR 2.56 [95% CI 2.27, 2.88]). When considering infection burden, a dose-response relationship was seen (ptrend < 0.001) where a greater number of infections was associated with a greater risk of pancreatic cancer. When compared to those with no infection, participants had increasing risk with 1 infection (aRR 1.99 [95% CI 1.69, 2.35]), 2 infections (aRR 2.84 [95% CI 2.34, 3.43]), and >3 infections (aRR 3.21 [95% CI 2.72, 3.78]). Conclusions: Infections which require treatment in hospital are associated with risk of pancreatic cancer. This association was strongest when considering bacterial infection and its impact on longitudinal risk of pancreatic cancer. Further, the dose-response relationship observed when investigating infection burden may suggest a cumulative risk of pancreatic cancer conferred with severe infections. Citation Format: Kiera R. Murison, Rayjean J. Hung. Hospital-treated infectious diseases and pancreatic cancer risk: Findings from a large population-based cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3592.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".