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Abstract B035: Multilevel Insights into Obesity, Race/Ethnicity, and Survival in Early-Onset Colorectal Cancer in Georgia

2025· article· en· W4417201309 on OpenAlexaboutno aff
Meng‐Han Tsai, Marlo Vernon, Malcolm Bevel, Humberto Sifuentes, Jörge E. Cortes, Rebecca L. Siegel

Bibliographic record

VenueClinical Cancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerObesityProportional hazards modelCancerCohortMarital statusBody mass indexCohort studyRetrospective cohort study

Abstract

fetched live from OpenAlex

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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.508
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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