Abstract B026: Pre-diagnostic disease and medication history in early-onset pancreatic cancer from large-scale registry and EHR data
Bibliographic record
Abstract
Abstract Introduction Early-onset cancers are emerging globally, and an alarming upward trend of young adults below 50 are being diagnosed with cancers in the gastrointestinal tract, including pancreatic cancer. It has been proposed that the rising incidence of early-onset gastrointestinal cancers appears to be related to nonhereditary factors, such as lifestyle, environmental exposures, and individual host mechanisms. Nevertheless, the specific risk factors, molecular characteristics, and genomic patterns driving the development of early-onset pancreatic cancer (EOPC) remain unknown. In our study, we investigated whether there are differences between EOPC and LOPC (late-onset pancreatic cancer) in their disease and medication history. Method We extracted pancreatic cancer patients from the period 1978–2022 using the Danish Cancer Registry (DCR) and included their disease history from the Danish National Patient Registry as well as their medication use from the Danish National Prescription Registry. Single disease differences between EOPC and LOPC were identified using a logistic regression model, and pairwise directional co-occurrences were identified using a disease-disease trajectory software program. This program calculates significant disease pairs and the strength of their directionality, i.e., whether one of the diseases occurs significantly more before or after the other, among pancreatic cancer patients compared to over 8 million Danish controls. Subsequently, the pancreatic cancer disease patterns were analyzed according to the age at disease onset for all disease pairs found. We compare these patterns and investigate whether pancreatic cancer progression differs for EOPC and LOPC according to disease history. Finally, we investigate the medication history prior to pancreatic cancer diagnoses. Results From the DCR, we identified 35,263 pancreatic cancer patients, comprising 1,612 EOPC (≤50 years) and 33,651 LOPC (>50 years) cases. EOPC was significantly associated with infertility diagnoses, mental disorders, and injuries compared to LOPC, although subgroups of LOPC patients also had these conditions. LOPC was significantly more associated with heart diseases and diabetes mellitus. LOPC had unique disease pairs related to older age, which were not observed in EOPC. EOPC patients redeemed painkillers such as paracetamol, tramadol, and codeine closer to their pancreatic cancer diagnosis compared to LOPC. Conclusion There are differences in certain disease and medication patterns prior to pancreatic cancer diagnosis between EOPC and LOPC, although some of these patterns may also be attributed to age differences. Pancreatic cancer patients with mental disorders were diagnosed at a younger age, suggesting that lifestyle factors or an unknown shared exposure may play a role in this patient group. Citation Format: Sif Ingibergsdóttir. Novitski, Sidsel Christy. Lindgaard, Jessica Xin. Hjaltelin, Kevin Zi Ming. Lim, Isabella Friis. Jørgensen, Troels Dreier. Christensen, Charlotte Vestrup. Rift, Claus Fristrup, Inna Markovna. Chen, Julia Sidenious. Johansen2, Søren Brunak. Pre-diagnostic disease and medication history in early-onset pancreatic cancer from large-scale registry and EHR data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B026.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".