Abstract B029: Clinical and Sociodemographic Associations Between Early-Onset and Late-Onset Colorectal Cancer in a U.S.–Mexico Border Population
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC) is the second leading cause of cancer mortality for both men and women in the United States. While the general incidence of CRC among older adults has decreased since the 1990s, CRC incidence in younger adults has increased during the same time span. Surveillance data specifically reveal a hotspot of CRC incidence along the U.S.-Mexico border near El Paso, Texas, with an upward trend even as statewide rates have plateaued. Despite these observed disparities, there is limited data on early-onset CRC patterns in border communities. This study is a secondary analysis of data derived from a case-control study conducted between 2011 and 2023 to describe the health status of an outpatient population in El Paso, Texas. The sample population (n = 818) was identified from patients in the parent study who were cases and had a CRC diagnosis. This study investigated differences in the health status, comorbidities, and biomarkers in individuals diagnosed with CRC before age 50 (early-onset, EO-CRC) and those diagnosed at or after age 50 (late-onset, LO-CRC) in a predominantly Hispanic population along the U.S.-Mexico border. Results indicated that EO-CRC patients had higher rates of being uninsured (22.6%), having private insurance (54.1%), and reporting higher household incomes (p = 0.047). They were also more likely to have metastatic cancer (OR: 0.650; 95% CI: 0.457, 0.925; p = 0.016) and to have a family history of cancer (OR: 0.276; 95% CI: 0.117, 0.652; p = 0.002). In contrast, LO-CRC patients exhibited greater comorbidity burden, including significantly higher prevalence of heart failure, hypertension, renal failure, and type II diabetes. LO-CRC patients were also more likely to have been prescribed medications such as aspirin (OR: 5.472; 95% CI: 2.819, 10.618; p < 0.001) and statins (OR: 3.470; 95% CI: 2.092, 5.758; p < 0.001). Biomarker analysis revealed that EO-CRC patients had higher aspartate aminotransferase, alanine aminotransferase, high-density lipoprotein, and alkaline phosphatase levels compared to LO-CRC patients, while LO-CRC patients had higher blood urea nitrogen, creatinine, glucose, and potassium levels. These findings underscore meaningful clinical and demographic distinctions between EO-CRC and LO-CRC that may reflect unique etiologic pathways, access challenges, and behavioral factors. Younger CRC patients tended to have lower Elixhauser risk scores and fewer comorbidities, which aligns with previous studies suggesting that EO-CRC patients may otherwise be relatively healthy at diagnosis. The higher metastatic burden among younger patients adds urgency to improving symptom recognition in younger adults. It suggests that existing screening and diagnostic models may miss warning signs in younger populations. As EO-CRC incidence increases, particularly among Hispanic populations, targeted, age-sensitive approaches to prevention, diagnosis, and care are essential for improving outcomes and reducing disparities. Citation Format: Atharva Railkar, Amir Hernandez, Jennifer Molokwu. Clinical and Sociodemographic Associations Between Early-Onset and Late-Onset Colorectal Cancer in a U.S.–Mexico Border Population [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 B029.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".