Alcohol Use, Coronary Heart Disease and Hypertension Modify the Predictive Accuracy of Pre-Operative CEA for TNM Staging in Chinese Colorectal Cancer Patients

饮酒、冠心病和高血压会影响术前CEA对中国结直肠癌患者TNM分期的预测准确性

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Abstract

OBJECTIVE: To evaluate the effects of comorbidities and lifestyle factors on the prognostic value of preoperative carcinoembryonic antigen (CEA) for tumor-node-metastasis (TNM) staging in Chinese patients with colorectal cancer (CRC). METHODS: A retrospective cohort study of 307 patients with CRC from Beijing Luhe Hospital (2020-2024) was performed. Clinicopathological data, including TNM and Numerical staging (AJCC 8th edition), serum CEA levels, and covariates (comorbidities and lifestyle factors), were analyzed using univariate and multivariate logistic regression. Multivariable logistic regression with multiplicative interaction terms (CEA × modifier) was used to test for effect modification. RESULTS: Elevated CEA levels were significantly associated with advanced TNM staging (Stage III-IV vs stage I-II, p < 0.001). Multivariate analysis confirmed that CEA was an independent predictor of T stage progression (HR = 1.15, p= 0.017), lymph node metastasis (N stage: HR = 1.17, p = 0.046), and distant metastasis (M stage: HR = 1.06, p = 0.018). Formal interaction analysis revealed that alcohol use significantly amplified the CEA-stage association (HR = 3.11, 95% CI 1.11-8.74, p = 0.031), whereas coronary heart disease attenuated the relationship (HR = 0.40, 95% CI 0.18-0.87, p = 0.022), yielding a paradoxical inverse association in affected patients. In addition, hypertension nullified the predictive utility of CEA, with a significant stage association observed only in the nonhypertensive subgroup. CONCLUSION: Preoperative CEA exhibits robust predictive accuracy for TNM staging in Chinese patients with colorectal cancer; however, this performance is critically modulated by alcohol use, coronary heart disease, and hypertension. Systematic incorporation of these three effect modifiers into preoperative risk-stratification algorithms will refine staging accuracy and enable patient-tailored therapeutic strategies.

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