Multi-marker discovery for mild cognitive impairment in metabolomics using machine learning with a global surrogate model via partial least squares.

ABSTRACT:

Introduction

Dementia can be prevented through early intervention; hence, there is an urgent need for biomarkers to help diagnose mild cognitive impairment (MCI).

Objectives

We aimed to develop a multi-marker panel composed of plasma metabolites to aid in the diagnosis of MCI.

Methods

We performed an analysis of a multi-marker panel of MCI metabolites using a random forest algorithm with variable selection methods and a global surrogate with principal component analysis and partial least squares (PLS).

Results

By incorporating variable selection methods, we constructed a predictive model that demonstrated robust performance, with an AUC of approximately 0.85 in both cross-validation and test evaluations, using only five metabolites (methionine, quinic acid, hyp

SUBMITTER: Tatara Y 

PROVIDER: S-EPMC12619728 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature