Abstract B035: Multilevel Insights into Obesity, Race/Ethnicity, and Survival in Early-Onset Colorectal Cancer in Georgia
Notice bibliographique
Résumé
Abstract Background: Early-onset colorectal cancer (EOCRC) is rising among adults aged 18–49, with Georgia showing particularly high mortality rates. Racial minorities (e.g., Black or Hispanic adults) in the state are more likely to face higher obesity rates and live in areas with limited access to healthy food and safe spaces for physical activity- factors that may hinder healthy lifestyle adoption and worsen cancer outcomes. However, most research on obesity and EOCRC mortality relies on either ecological or individual-level data and rarely examines changes in survival over time. By integrating county-level obesity rates into individual-level data, we evaluated how structural barriers affect cause-specific EOCRC survival across racial groups and time intervals in Georgia. Methods: We conducted a retrospective cohort study using data from the 2010-2020 Georgia Cancer Registry, linked with County Health Rankings. The primary exposures were race/ethnicity (White, Black, Hispanic/Other) and log-transformed county-level obesity rates (body mass index, [BMI] ≥ 30), categorized as low vs. high based on the median value. Outcome was survival time from diagnosis to 12, 36, and 60 months, censored at death from other causes or at the date of last contact. Traditional and piecewise Cox regression models were used, adjusting for sociodemographic characteristics (sex, age at diagnosis, marital status, insurance status, county-level rurality, and poverty), stage at diagnosis, and diagnosis year. Results: Among 6,291 EOCRC patients, 63.4% lived in high-obesity areas, and 53.3% were White patients. White patients living in high-obesity areas had significantly lower 3-year (76.6% vs. 81.1%; p=0.002) and 5-year (71.3% vs. 75.7%; p =0.001) survival rates compared to those in low-obesity areas. Survival differences were not observed for Black and Hispanic/Other patients. Adjusted analysis showed that patients living in high-obesity areas were 14% more likely to die from CRC than those living in low-obesity areas at both 3- (HR,1.14; 95% CI, 1.02-1.28) and 5-year (HR,1.14; 95% CI, 1.03-1.27) intervals, whereas White patients specifically were 32% (HR,1.32; 95% CI, 1.11-1.54) and 33% (HR,1.33; 95% CI, 1.13-1.50) more likely to die from CRC, respectively. Piecewise models revealed a 29% increased risk of CRC mortality within 1–3 years (HR,1.29; 95% CI, 1.11–1.50), with subgroup analysis showing an even higher 51% risk for White patients during the same interval (HR,1.51; 95% CI, 1.21–1.89). Conclusions: Distinct results between traditional and piecewise models suggest that mortality risk varies over time, with elevated risk in the first 1–3 years for White patients in high-obesity areas. These findings support targeted efforts to promote healthy lifestyles and invest in infrastructure that fosters healthier living to reduce early mortality. Finally, our study did not observe similar disparities among racial minorities due to limited sample size that could reduce the power to detect survival differences. Future research incorporating more diverse datasets is warranted. Citation Format: Meng-Han Tsai, Marlo Vernon, Malcolm Bevel, Humberto Sifuentes, Jorge Cortes, Rebecca L. Siegel. Multilevel Insights into Obesity, Race/Ethnicity, and Survival in Early-Onset Colorectal Cancer in Georgia [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 B035.
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 | 0,005 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 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 | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».