Noninvasive predictive models based on lifestyle analysis and risk factors for early‐onset colorectal cancer
Jiawen Deng, Yi‐Lu Zhou, Wei‐Xing Dai, Hui‐Min Chen, Cheng‐Bei Zhou, Chunqi Zhu, Xinyue Ma, Si‐Yuan Pan, Yun Cui, Jia Xu, Enhao Zhao, Ming Wang, Jinxian Chen, Zheng Wang, Qiang Liu, Jilin Wang, Guo‐Xiang Cai, Yingxuan Chen, Jing‐Yuan Fang
Abstract
BACKGROUND: Colorectal cancer (CRC) incidence has increased among patients aged <50 years. Exploring high-risk factors and screening high-risk populations may help lower early-onset CRC (EO-CRC) incidence. We developed noninvasive predictive models for EO-CRC and investigated its risk factors. METHODS: This retrospective multicenter study collected information on 1756 patients (811 patients with EO-CRC and 945 healthy controls) from two medical centers in China. Sociodemographic features, clinical symptoms, medical and family history, lifestyle, and dietary factors were measured. Patients from one cohort were randomly assigned (8:2) to two groups for model establishment and internal validation, and another independent cohort was used for external validation. Multivariable logistic regression, random forest, and eXtreme Gradient Boosting (XGBoost) were performed to establish noninvasive predictive models for EO-CRC. Some variables in the model influenced EO-CRC occurrence and were further analyzed. Multivariable logistic regression analysis yielded adjusted odd ratios (ORs) and 95% confidence intervals (CIs). RESULTS: All three models showed good performance, with areas under the receiver operator characteristic curves (AUCs) of 0.82, 0.84, and 0.82 in the internal and 0.78, 0.79, and 0.78 in the external validation cohorts, respectively. Consumption of sweet (OR 2.70, 95% CI 1.89-3.86, P < 0.001) and fried (OR 2.16, 95% CI 1.29-3.62, P < 0.001) foods ≥3 times per week was significantly associated with EO-CRC occurrence. CONCLUSION: We established noninvasive predictive models for EO-CRC and identified multiple nongenetic risk factors, especially sweet and fried foods. The model has good performance and can help predict the occurrence of EO-CRC in the Chinese population.