Machine learning models of Alzheimer's disease spectrum using blood tests.

ABSTRACT:

Introduction

The diagnosis of Alzheimer's disease (AD) traditionally relies on cerebrospinal fluid and plasma levels of amyloid beta and phosphorylated tau. Although informative, these biomarkers represent a narrow, hypothesis-driven approach to intercept the disease.

Methods

Data-driven analysis was applied on demographic data, apolipoprotein E (APOE) ε4 allele, and 82 biomarkers obtained from blood tests of healthy controls (HC), mild cognitive impairment that remained stable within 36 months following blood collection (sMCI), and patients with AD.

Results

Statistical analyses revealed differences among groups in many cholesterol-related analytes. APOE ε4 and analytes such as amino acids, lipoproteins, and fatty acids emerged as the most influential fe

SUBMITTER: Falasca NW 

PROVIDER: S-EPMC12689267 | biostudies-literature | 2025 Oct-Dec

REPOSITORIES: biostudies-literature