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Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer’s disease in cerebrospinal fluid
Xiaosen Hou, Yunjie Qiu, Hui Li, Yan Yan, Dongxu Zhao, Simei Ji, Junjun Ni, Jun Zhang et al.. Alzheimer s Research & Therapy, 2025-07-04
Topic: Amyloid, tau and blood-test diagnosis
Utilizing machine learning, we identify a 12-protein panel from cerebrospinal fluid proteomic datasets. The universal diagnosis model demonstrated strong diagnostic capability and high accuracy across ten different AD cohorts across different countries and different detection technologies. These proteins involved in various biological processes related to AD and shows a tight correlation with established AD pathogenic biomarkers, including amyloid-β, tau/p-tau, and the Montreal Cognitive Assessment score. The high accuracy in the model may due to multiple protein combination based on comprehensive pathogenesis and different AD progress. Furthermore, it effectively differentiates AD from mild cognitive impairment (MCI) and other neurodegenerative disorders, especially the frontotemporal dementia (FTD), which share similar pathogenesis as AD. This study highlights a high accuracy, robustness and compatibility model of 12-protein panel whose detection is even based on label-free, TMT and DIA mass spectrometry or ELISA technologies, implicating its potential prospect in clinical application.
Key findings
- Utilizing machine learning, we identify a 12-protein panel from cerebrospinal fluid proteomic datasets.
- The universal diagnosis model demonstrated strong diagnostic capability and high accuracy across ten different AD cohorts across different countries and different detection technologies.
- These proteins involved in various biological processes related to AD and shows a tight correlation with established AD pathogenic biomarkers, including amyloid-β, tau/p-tau, and the Montreal Cognitive Assessment score.
- The high accuracy in the model may due to multiple protein combination based on comprehensive pathogenesis and different AD progress.
- Furthermore, it effectively differentiates AD from mild cognitive impairment (MCI) and other neurodegenerative disorders, especially the frontotemporal dementia (FTD), which share similar pathogenesis as AD.
Why it matters
Using machine learning on cerebrospinal fluid proteomic data, this study identified a 12-protein panel that distinguished Alzheimer's disease from mild cognitive impairment and other neurodegenerative disorders across ten cohorts and different detection technologies. It is a diagnostic biomarker study and does not report a treatment, cure or prevention.
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Study details
- Evidence strengthLow
How this study scored 39/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Alzheimer s Research & Therapy) |
| Journal's citation record | 1 / 4 | Alzheimer s Research & Therapy: journal h-index 146 (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 | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 10 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 6.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 10 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Zhenzhen Quan: h-index 21 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Central South University: institution h-index 699 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40616179) |
| Free to read | 2 / 2 | Free to read |
| Total | 39 / 100 | — |
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Read the original
- DOI: 10.1186/s13195-025-01789-5
- PubMed: 40616179
- Original source →
- Free full text: Alzheimer s Research & Therapy →
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
Hou, X., Qiu, Y., Li, H., Yan, Y., Zhao, D., Ji, S., et al. (2025). Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer’s disease in cerebrospinal fluid. Alzheimer s Research & Therapy. https://doi.org/10.1186/s13195-025-01789-5
Vancouver
Hou X, Qiu Y, Li H, Yan Y, Zhao D, Ji S, et al. Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer’s disease in cerebrospinal fluid. Alzheimer s Research & Therapy. 2025. doi:10.1186/s13195-025-01789-5. PMID: 40616179.
BibTeX
@article{hou2025-40616179,
author = {Hou, Xiaosen and Qiu, Yunjie and Li, Hui and Yan, Yan and Zhao, Dongxu and Ji, Simei and Ni, Junjun and Zhang, Jun and others},
title = {{Machine-learning based strategy identifies a robust protein biomarker panel for Alzheimer’s disease in cerebrospinal fluid}},
journal = {Alzheimer s Research \& Therapy},
year = {2025},
doi = {10.1186/s13195-025-01789-5},
pmid = {40616179},
url = {https://doi.org/10.1186/s13195-025-01789-5}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.