Machine learning-based early screening of mild cognitive impairment using nutrition-related biomarkers and functional indicators.

ABSTRACT:

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) an

SUBMITTER: Yuan L 

PROVIDER: S-EPMC12714913 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature