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 frontotemporal dementia (FTD), 135 with progressive supranuclear palsy (PSP) and 533 healthy controls (HCs) between July 2017 and August 2023.

Findings: Random forest classifier and feature selection procedures were used to select digital biomarkers. MLDB achieved area under the curves (AUCs) of 0.92 (AD vs. HC, Sensitivity 88.2%, specificity 84.1%), 0.89 (MCI vs. HC, Sensitivity 88.8%, specificity 86.4%), 0.83 (AD vs. DLB, Sensitivity 77.2%, specificity 74.6%), 0.80 (AD vs. FTD, sensitivity 74.2%, specificity 72.4%), and 0.93 (AD vs. PSP, sensitivity 76.1%, specificity 75.7%). Digital biomarkers distinguishing AD from HC were negatively correlated with plasma p-tau217 (r = -0.22, p < 0.05) and glial fibrillary acidic protein (GFAP) (r = -0.09, p < 0.05).

Interpretation: The ATR-FTIR (Attenuated Total Reflectance-Fourier Transform Infrared) plasma spectra features can identify AD-related pathological changes. These spectral features serve as digital biomarkers, providing valuable support in the early screening and diagnosis of AD.

Funding: The National Natural Science Foundation of China, STI2030-Major Projects, National Key R&D Program of China, Outstanding Youth Fund of Hunan Provincial Natural Science Foundation, Hunan Health Commission Grant, Science and Technology Major Project of Hunan Province, Hunan Innovative Province Construction Project, Grant of National Clinical Research Center for Geriatric Disorders, Xiangya Hospital and Postdoctoral Fellowship Program of CPSF.