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Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer's Disease and Other Neurological Disorders
Bellomo G, Indaco A, Chiasserini D, Maderna E, Paolini Paoletti F, Gaetani L, Paciotti S, Petricciuolo M et al.. Frontiers in neuroscience, 2021-03-31
Topic: Amyloid, tau and blood-test diagnosis
Amyloid-beta (Aβ) 42/40 ratio, tau phosphorylated at threonine-181 (p-tau), and total-tau (t-tau) are considered core biomarkers for the diagnosis of Alzheimer's disease (AD). The use of fully automated biomarker assays has been shown to reduce the intra- and inter-laboratory variability, which is a critical factor when defining cut-off values. The calculation of cut-off values is often influenced by the composition of AD and control groups. Indeed, the clinically defined AD group may include patients affected by other forms of dementia, while the control group is often very heterogeneous due to the inclusion of subjects diagnosed with other neurological diseases (OND). In this context, unsupervised machine learning approaches may overcome these issues providing unbiased cut-off values and data-driven patient stratification according to the sole distribution of biomarkers. In this work, we took advantage of the reproducibility of automated determination of the CSF core AD biomarkers to compare two large cohorts of patients diagnosed with different neurological disorders and enrolled in two centers with established expertise in AD biomarkers. We applied an unsupervised Gaussian mixture model clustering algorithm and found that our large series of patients could be classified in six clusters according to their CSF biomarker profile, some presenting a typical AD-like profile and some a non-AD profile. By considering the frequencies of clinically defined OND and AD subjects in clusters, we subsequently computed cluster-based cut-off values for Aβ42/Aβ40, p-tau, and t-tau. This approach promises to be useful for large-scale biomarker studies aimed at providing efficient biochemical phenotyping of neurological diseases.
Key findings
- In this context, unsupervised machine learning approaches may overcome these issues providing unbiased cut-off values and data-driven patient stratification according to the sole distribution of biomarkers.
- In this work, we took advantage of the reproducibility of automated determination of the CSF core AD biomarkers to compare two large cohorts of patients diagnosed with different neurological disorders and enrolled in two centers with established expertise in AD biomarkers.
- We applied an unsupervised Gaussian mixture model clustering algorithm and found that our large series of patients could be classified in six clusters according to their CSF biomarker profile, some presenting a typical AD-like profile and some a non-AD profile.
- By considering the frequencies of clinically defined OND and AD subjects in clusters, we subsequently computed cluster-based cut-off values for Aβ42/Aβ40, p-tau, and t-tau.
- This approach promises to be useful for large-scale biomarker studies aimed at providing efficient biochemical phenotyping of neurological diseases.
Why it matters
This study used an unsupervised machine learning clustering algorithm on cerebrospinal fluid biomarker data from two large patient cohorts. It found patients could be classified into six clusters by biomarker profile, which may help with diagnosis but does not treat or prevent Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 45/100
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 (Frontiers in neuroscience) |
| Journal's citation record | 2 / 4 | Frontiers in Neuroscience: journal h-index 226 (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 | 3 / 5 | 2021 — within 6 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 | 4 / 7 | Cited 58 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 8.3 citations a year over 7 year(s) since 2021 |
| Impact for its field and year | 3 / 6 | Cited 5.1× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 2 / 4 | Cited 41 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Giuseppe Di Fede: h-index 36 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Perugia: institution h-index 747 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 33867925) |
| Free to read | 2 / 2 | Free to read |
| Total | 45 / 100 | — |
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Read the original
- DOI: 10.3389/fnins.2021.647783
- PubMed: 33867925
- Original source →
- Free full text: Frontiers in Neuroscience →
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
Bellomo, G., Indaco, A., Chiasserini, D., Maderna, E., Paolini Paoletti, F., Gaetani, L., et al. (2021). Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer's Disease and Other Neurological Disorders. Frontiers in neuroscience. https://doi.org/10.3389/fnins.2021.647783
Vancouver
Bellomo G, Indaco A, Chiasserini D, Maderna E, Paolini Paoletti F, Gaetani L, et al. Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer's Disease and Other Neurological Disorders. Frontiers in neuroscience. 2021. doi:10.3389/fnins.2021.647783. PMID: 33867925.
BibTeX
@article{bellomo2021-33867925,
author = {Bellomo, G. and Indaco, A. and Chiasserini, D. and Maderna, E. and Paolini Paoletti, F. and Gaetani, L. and Paciotti, S. and Petricciuolo, M. and others},
title = {{Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer's Disease and Other Neurological Disorders}},
journal = {Frontiers in neuroscience},
year = {2021},
doi = {10.3389/fnins.2021.647783},
pmid = {33867925},
url = {https://doi.org/10.3389/fnins.2021.647783}
}
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