Abstract B026: Pre-diagnostic disease and medication history in early-onset pancreatic cancer from large-scale registry and EHR data
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,009 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».