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Development and validation of machine learning models with blood-based digital biomarkers for Alzheimer’s disease diagnosis: a multicohort diagnostic study
Bin Jiao, Ziyu Ouyang, Xuewen Xiao, Cong Zhang, Tianyan Xu, Qijie Yang, Yuan Zhu, Yiliang Liu et al.. EClinicalMedicine, 2025-03-01
Topic: Amyloid, tau and blood-test diagnosis
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.
Key findings
- However, the cost-effectiveness and operational complexity of method using blood-based biomarkers significantly limit its availability in clinical practice.
Why it matters
Machine learning models using plasma spectra data distinguished Alzheimer's disease from healthy controls and from other neurodegenerative diseases with areas under the curve ranging from 0.80 to 0.93. This is a diagnostic study and does not test any treatment, cure, or prevention.
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Study details
- Participants1,324
- Effect size88.2%; 84.1%; 88.8%
- Evidence strengthLow
How this study scored 52/100
Published in a peer-reviewed journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (EClinicalMedicine) |
| Journal's citation record | 2 / 4 | EClinicalMedicine: journal h-index 150 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,324 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 3 / 7 | Cited 30 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 10.0 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 4 / 6 | Cited 19.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 2 / 4 | Cited 30 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Lu Hua Shen: h-index 49 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Central South University: institution h-index 699 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40115175) |
| Free to read | 2 / 2 | Free to read |
| Total | 52 / 100 | — |
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Read the original
- DOI: 10.1016/j.eclinm.2025.103142
- PubMed: 40115175
- Original source →
- Free full text: EClinicalMedicine →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Jiao, B., Ouyang, Z., Xiao, X., Zhang, C., Xu, T., Yang, Q., et al. (2025). Development and validation of machine learning models with blood-based digital biomarkers for Alzheimer’s disease diagnosis: a multicohort diagnostic study. EClinicalMedicine. https://doi.org/10.1016/j.eclinm.2025.103142
Vancouver
Jiao B, Ouyang Z, Xiao X, Zhang C, Xu T, Yang Q, et al. Development and validation of machine learning models with blood-based digital biomarkers for Alzheimer’s disease diagnosis: a multicohort diagnostic study. EClinicalMedicine. 2025. doi:10.1016/j.eclinm.2025.103142. PMID: 40115175.
BibTeX
@article{jiao2025-40115175,
author = {Jiao, Bin and Ouyang, Ziyu and Xiao, Xuewen and Zhang, Cong and Xu, Tianyan and Yang, Qijie and Zhu, Yuan and Liu, Yiliang and others},
title = {{Development and validation of machine learning models with blood-based digital biomarkers for Alzheimer’s disease diagnosis: a multicohort diagnostic study}},
journal = {EClinicalMedicine},
year = {2025},
doi = {10.1016/j.eclinm.2025.103142},
pmid = {40115175},
url = {https://doi.org/10.1016/j.eclinm.2025.103142}
}
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