ArticleEClinicalMedicine2025
Development and validation of machine learning models with blood-based digital biomarkers for Alzheimer's disease diagnosis: a multicohort diagnostic study.
Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Molecular Biomarkers for Early Detection of Alzheimer's Disease and the Complementary Role of Engineered Nanomaterials: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Enhancing Early Detection of Alzheimer's Disease: An Ensemble Model for Multi-Domain Cognitive Assessment Using Voice and Video.Sensors (Basel, Switzerland) · 2026Article
- AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques.Global epidemiology · 2026Article
- Objective diagnosis and treatment outcome prediction in major depressive disorder based on a machine learning framework with dried-serum NIRS digital biomarkers.BMC psychiatry · 2026Article
- ATN Classification and Machine-Learned Plasma Biomarker Phenotypes Reveal Distinct Alzheimer's Pathology in a Population-Based Cohort.medRxiv : the preprint server for health sciences · 2026Article
- A new paradigm for stratified cognitive assessment: fusing serum Raman spectroscopy and ensemble learning.Alzheimer's research & therapy · 2026Article
- Extracellular Vesicles in Alzheimer's Disease: Dual Roles in Pathogenesis, Promising Avenues for Diagnosis and Therapy.Pharmaceutics · 2026Review
- Blood biomarkers and artificial intelligence: toward precision medicine in Alzheimer's disease.Frontiers in aging neuroscience · 2026Review
- An in silico protocol for predicting genetic biomarkers in rare diseases: a case study in sporadic amyotrophic lateral sclerosis.Frontiers in genetics · 2026Article
- From Microbleeds to Iron: AI Prediction of Cerebrospinal Fluid Erythrocyte Load in Alzheimer's Disease.Journal of clinical medicine · 2025Article
- Article
- PPIxGPN: plasma proteomic profiling of neurodegenerative biomarkers with protein-protein interaction-based eXplainable graph propagational network.Briefings in bioinformatics · 2025Article
- The translational power of Alzheimer's-based organoid models in personalized medicine: an integrated biological and digital approach embodying patient clinical history.Frontiers in cellular neuroscience · 2025Review
- Potential Value of Plasma-Based Biomarkers for Prediction of Episodic Memory Performance and Identification of Individuals with Amnestic Mild Cognitive Impairment.Neuropsychiatric disease and treatment · 2025Article
- Article
- Glial biomarkers improve classification of cognitive impairment: an explainable artificial intelligence study using CSF biomarkers.Frontiers in neurologyArticle
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
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 ( 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.
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