Abstract PR008: Incidence of early onset colorectal, kidney, uterine and pancreatic cancer by county-level obesity prevalence in the U.S., 2010-2022
Notice bibliographique
Résumé
Abstract Background: Obesity has been implicated as a potential driving factor in rising rates of several cancers in early onset age groups, including cancers of the colorectum, kidney, uterus and pancreas. We assessed whether obesity prevalence in U.S. counties was associated with rates and trends in rates of these cancers over time in 20-49-year-olds. Methods: We used cancer incidence data from 21 cancer registries in the Surveillance, Epidemiology, and End Results (SEER) program during 2010-2022. SEER-21 data were merged with data on the 2015 county-level prevalence of obesity from the CDC Diabetes Interactive Atlas, categorized into quintiles. Average annual percentage changes (AAPCs) in age-standardized rates (ASRs) during 2010-2022 were estimated with Joinpoint regression and tested for equality across quintiles of county-level obesity among 20-49-year-olds. In addition, rate ratios of recent ASRs of cancers of the colorectum, kidney, uterus, and pancreas were compared across quintiles of county-level obesity during 2018-2022. Results: During 2010-2022, ASRs of each obesity-related cancer increased significantly among 20-49-year-olds: colorectum (AAPC=2.47%; 95% confidence interval CI 2.12, 2.89), kidney (1.69%; 95%CI 1.02, 2.77), uterus (1.94%; 95%CI 1.50, 2.63), and pancreas (2.02%; 95%CI 1.74, 2.43). There were significant increases in AAPCs during 2010-2022 for each of these cancers in each quintile of county-level obesity, with the exception of kidney cancer rates in counties with the lowest obesity prevalence. There were no significant differences in the magnitudes of the AAPCs across county-level quintiles obesity for colorectal cancer (p=0.30). While AAPCs differed significantly across quintiles for kidney (p=0.05), uterine (p=0.03) and pancreatic cancers (p=0.01), AAPCs were greatest in the highest quintile of county-level obesity only for kidney cancer. However, during 2018-2022, ASRs in the highest quintile of county-level obesity were significantly higher than in the lowest quintile of county-level obesity for colorectal cancer (rate ratio [RR]=1.21; 95%CI 1.17-.25), pancreatic cancer (RR=1.25; 95%CI 1.15-1.35), uterine cancer (RR=1.08; 95%CI 1.03-1.13) and kidney cancer (RR=1.73; 95%CI 1.65-1.80). Conclusions: Higher county-level obesity prevalence in the U.S. was associated with higher ASRs of colorectal, kidney, uterine and pancreatic cancers in early onset age groups. Age-standardized incidence rates of these cancers increased during 2010-2022, regardless of county-level obesity. Future work should examine histologic subtypes and assess whether increases over time in county-level obesity prevalence is associated with a more rapid increase in colorectal, kidney, uterine and pancreatic cancer rates. Citation Format: Meredith Shiels, Anika Haque, Ruth Pfeiffer, Constanza Camargo, Megan Clarke, Brittny Davis Lynn, Eric Engels, Neal Freedman, Gretchen Gierach, Jonathan Hofmann, Rena Jones, Erikka Loftfield, Rashmi Sinha, Lindsay Morton, Stephen Chanock. Incidence of early onset colorectal, kidney, uterine and pancreatic cancer by county-level obesity prevalence in the U.S., 2010-2022 [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 PR008.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».