Development and validation of machine learning models with blood-based digital biomarkers for Alzheimer's disease diagnosis: a multicohort diagnostic study.

ABSTRACT:

Background

Alzheimer's disease (AD) involves complex alterations in biological pathways, making comprehensive blood biomarkers crucial for accurate and earlier diagnosis. However, the cost-effectiveness and operational complexity of method using blood-based biomarkers significantly limit its availability in clinical practice.

Methods

We developed low-cost, convenient machine learning-based with digital biomarkers (MLDB) using plasma spectra data to detect AD or mild cognitive impairment (MCI) from healthy controls (HCs) and discriminate AD from different types of neurodegenerative diseases. Retrospective data were gathered for 1324 individuals, including 293 with amyloid beta positive AD, 151 with mild cognitive impairment (MCI), 106 with Lewy body dementia (DLB), 106 with f

SUBMITTER: Jiao B 

PROVIDER: S-EPMC11925590 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature