Machine Learning-Based Early Screening of Mild Cognitive Impairment Using Nutrition-Related Biomarkers and Functional Indicators

Ling Yuan¹Yao Zhang²Yiwen Wu³Anke Zhang³He Bai¹Mengyao He¹Zhaoxin Wang^4*Liqiang Zheng^1*

• ¹School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
• ²Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, China
• ³Department of Neurosurgery, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, China
• ⁴School of Management, Hainan Medical University, Haikou, China

Objectives: Mild cognitive impairment (MCI), an early stage of cognitive decline preceding dementia, poses a growing public health concern, especially in aging populations. Early identification of individuals at risk is essential for implementing timely interventions to delay or prevent progression to dementia. Nutritional factors and related biomarkers have emerged as promising targets for developing convenient, scalable screening strategies, particularly in resource-limited rural settings. This study aimed to develop and validate a machine learning (ML) model that integrates diet-related metabolites, physical examination indicators, lifestyle behaviors, and sleep quality to predict MCI risk and to evaluate the biological and predictive relevance of trimethylamine N-oxide (TMAO) and its dietary precursors among older adults in rural China. Methods: Data were derived from a large-scale epidemiological survey in Fuxin County, Liaoning Province, , including 907 participants, of whom 270 were classified as MCI based on the Montreal Cognitive Assessment-Basic. Seven ML models were trained and evaluated using accuracy, sensitivity, and the area under the receiver operating characteristic curve (AUC).. The best model’s predictors were interpreted using Shapley Additive Explanation (SHAP) values. Results: The random forest model showed the bestperformance (AUC = 0.74, 95% CI: 0.677–0.801; sensitivity = 0.72). SHAP analysis identified age, choline, carnitine, betaine, TMAO, daily intake of fruit and vegetables, body mass index, hip circumference, and daytime dysfunction as key predictors.. Conclusion: TMAO-related metabolites consistently contributed positive SHAP effects, suggesting biologically relevant links between dietary metabolism and early cognitive decline. This interpretable ML framework offers a feasible, sensitive, and biologically informed approach for early MCI screening and supports the integration of nutritional biomarkers into cognitive health surveillance.