Shared molecular mechanisms between colorectal cancer and cardiovascular disease drive perioperative neurological vulnerability.

Background: Colorectal cancer (CRC) and cardiovascular disease (CVD) share overlapping molecular pathogenic axes-including chronic systemic inflammation, immune dysregulation, metabolic reprogramming, and cellular senescence-that collectively amplify perioperative neurological vulnerability in elderly patients. Postoperative delirium (POD), the most prevalent acute neuropsychiatric complication following major oncological surgery, emerges at the intersection of these shared pathogenic pathways: tumor-driven neuroinflammation, anesthesia-induced cerebral hypoperfusion, cardio-metabolic derangements, and frailty-associated multi-system reserve failure collectively prime the aging brain for acute cognitive disruption. Despite this mechanistic convergence, clinically actionable risk stratification tools anchored in this cardio-oncological molecular framework remain absent. Objective: To construct and validate a precision nomogram for POD prediction in elderly CRC patients by integrating preoperative frailty phenotyping, cardio-metabolic biomarkers, and anesthesia-related intraoperative indicators, positioning each predictor within the shared CRC-CVD molecular landscape and translational omics evidence. Methods: A retrospective cohort of 283 elderly patients (≥65 years) undergoing elective radical resection for CRC (January 2021-June 2024) was analyzed. Preoperative frailty was assessed using the Fried Frailty Phenotype across five physiological dimensions. POD was diagnosed by the Confusion Assessment Method (CAM). Data encompassed demographic characteristics, cardio-metabolic comorbidities, inflammatory and nutritional biomarkers, frailty dimensions, and anesthesia-related indicators. Multivariate logistic regression identified independent predictors, and a nomogram was constructed using the rms package (R 4.3.0). Model performance was evaluated by C-index, AUC, Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA), with Bootstrap internal validation (1,000 iterations). Each predictor is mechanistically contextualized within existing multi-omics, proteomics, and translational CRC-CVD molecular biology evidence. Results: POD developed in 53/283 patients (18.7%). Seven independent predictors were identified: advanced age (OR = 1.125, 95%CI: 1.058-1.196), low education level (OR = 2.341, 95%CI: 1.287-4.259), frailty score ≥3 items (OR = 4.862, 95%CI: 2.154-10.975), intraoperative hypotension (OR = 3.524, 95%CI: 1.723-7.209), intraoperative hypothermia (OR = 3.187, 95%CI: 1.542-6.589), anesthesia duration ≥200 min (OR = 2.678, 95%CI: 1.328-5.400), and albumin <35 g/L (OR = 2.814, 95%CI: 1.245-6.358). The nomogram achieved C-index 0.842 (95%CI: 0.792-0.892), Bootstrap-corrected C-index 0.835, AUC 0.842 (95%CI: 0.791-0.893), sensitivity 79.2%, specificity 82.6%, and NPV 92.7%. DCA demonstrated robust net clinical benefit across threshold probabilities of 0.10-0.70. Conclusion: This nomogram operationalizes the convergent CRC-CVD molecular pathogenic landscape into a clinically deployable perioperative precision medicine tool. Its predictors-spanning neuroinflammatory priming, vascular homeostatic failure, metabolic-nutritional derangement, and phenotypic frailty-map directly onto shared molecular axes of CRC and CVD, validating the cardio-oncological framework as a foundation for POD risk stratification. Future multi-omics integration-incorporating inflammatory proteomics, metabolomic signatures, and machine-learning models-will further advance individualized perioperative management in elderly CRC patients.