Multimodal risk phenotyping of early-onset colorectal cancer.

Abstract

10561 Background: Early-onset colorectal cancer (EOCRC) is rising rapidly, yet its risk profile is incompletely defined. Prior studies have largely focused on isolated metabolic or lifestyle factors and have rarely integrated broader sociodemographic context or objective functional measures. We performed multimodal phenotyping to characterize integrated clinical, behavioral, cardiometabolic, and wearable-derived profiles among individuals with EOCRC compared with average-onset CRC (AOCRC). Methods: Adults with CRC in the NIH All of Us Controlled Tier Dataset v8 were identified (≥2 EHR diagnosis codes ≥3 months apart). The primary exposure was age at diagnosis, defined as EOCRC (<50 years) vs AOCRC (≥50 years). Covariates included sociodemographic factors (sex, race/ethnicity, education, employment, health insurance, household income, marital status, neighborhood deprivation). Outcomes included health behaviors (smoking, alcohol use), anthropometrics (BMI, waist circumference), cardiometabolic conditions (dyslipidemia, metabolic syndrome, diabetes, hypertension), laboratory measures (lipids), and wearable-derived (Fitbit) activity, sleep, and heart rate, anchored to the most recent pre-diagnosis timestamp. Results: Among 3,915 CRC participants, 710 (18.1%) had EOCRC. The cohort was predominantly male (54.5%), White (64.4%), unemployed (68.6%), with annual income <$50k (45.5%), and married (53.6%). Participants with EOCRC were less often female (38.7% vs 47.0%) and White (52.7% vs 67.0%) (both p <0.001). EOCRC was associated with higher obesity (42.0% vs 37.0%), higher LDL (101 vs 86 mg/dL), and lower HDL (51 vs 54 mg/dL) (all p <0.001). Alcohol use (60.6% vs 50.5%, p <0.001) and heavy drinking (22.0% vs 16.1%, p =0.003) were more common among participants with EOCRC. In the wearable sub-cohort, EOCRC participants had higher heart rate (76.8 vs 73.8 bpm , p =0.03) and lower sedentary time (773 vs 894 min/day , p =0.016) with no significant differences in sleep metrics or heart rate variability. Female sex was associated with lower odds of EOCRC (OR 0.45, 95% CI 0.25–0.83; p =0.01), while Hispanic ethnicity was associated with higher odds (OR 4.04, 95% CI 1.51–10.86; p =0.006). Conclusions: EOCRC is characterized by higher obesity, dyslipidemia, and alcohol use, as well as differences in wearable-derived physiologic and behavioral profiles. Integrating real-world clinical data with digital biomarkers offers a scalable framework for refined risk stratification and precision prevention in EOCRC. Multivariable logistic regression examining sociodemographic predictors for EOCRC. Variable OR (95% CI) P value Sex Male Ref Female 0.45 (0.25–0.83) 0.010 Race/ethnicity White Ref Black 2.43 (0.99–5.97) 0.052 Hispanic 4.04 (1.51–10.86) 0.006 Employment Unemployed Ref Employed 3.38 (1.87–6.10) <0.001