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Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model
Lin CH, Chiu SI, Chen TF, Jang JR, Chiu MJ.. International journal of molecular sciences, 2020-09-21
Topic: Amyloid, tau and blood-test diagnosis
Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples ( n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.
Key findings
- We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls.
- The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition.
- We found that the random forest (RF) was the best classifier to separate different dementia syndromes.
- Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively.
Why it matters
This study used blood biomarker levels and machine learning to classify different neurodegenerative disorders, with the random forest classifier best separating dementia syndromes. It is a diagnostic approach, not a treatment or cure for Alzheimer's disease.
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Study details
- Participants377
- Evidence strengthLow
How this study scored 49/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (International journal of molecular sciences) |
| Journal's citation record | 3 / 4 | International Journal of Molecular Sciences: journal h-index 426 (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 | 2 / 5 | 2020 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 377 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 4 / 7 | Cited 59 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.4 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 4.5× 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 31 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Ming‐Jang Chiu: h-index 51 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | National Taiwan University: institution h-index 752 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32967146) |
| Free to read | 2 / 2 | Free to read |
| Total | 49 / 100 | — |
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Read the original
- DOI: 10.3390/ijms21186914
- PubMed: 32967146
- Original source →
- Free full text: International Journal of Molecular Sciences →
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. Check the citation against the original before you use it.
APA 7th
Lin, C. H., Chiu, S. I., Chen, T. F., Jang, JR., & Chiu, M. J. (2020). Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model. International journal of molecular sciences. https://doi.org/10.3390/ijms21186914
Vancouver
Lin CH, Chiu SI, Chen TF, Jang JR, Chiu MJ. Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model. International journal of molecular sciences. 2020. doi:10.3390/ijms21186914. PMID: 32967146.
BibTeX
@article{lin2020-32967146,
author = {Lin, C. H. and Chiu, S. I. and Chen, T. F. and Jang, JR. and Chiu, M. J.},
title = {{Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model}},
journal = {International journal of molecular sciences},
year = {2020},
doi = {10.3390/ijms21186914},
pmid = {32967146},
url = {https://doi.org/10.3390/ijms21186914}
}
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