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Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease
Blanco K, Salcidua S, Orellana P, Sauma-Pérez T, León T, Steinmetz LCL, Ibañez A, Duran-Aniotz C et al.. Alzheimer's research & therapy, 2023-10-14
Topic: Amyloid, tau and blood-test diagnosis
Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.
Key findings
- After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information.
- Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI.
Why it matters
Working out whether mild memory problems are caused by Alzheimer's usually needs spinal-fluid tests or brain scans, which the authors note only "a limited percentage of the population" can access. This review of 29 studies found that researchers increasingly combine blood, spinal-fluid or saliva markers with brain imaging, memory tests and genetics in machine-learning models, an approach the authors say "has shown promise" but which is not yet a proven diagnostic test. They call for cheaper, long-term data from culturally diverse populations and for "white-box" (interpretable) models.
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Study details
- Study designsystematic review
- Evidence strengthHigh
How this study scored 63/100
Published in a peer-reviewed journal, and it is a systematic review of all the studies on the question. Scored zero for sample size, effect reported and studied in people 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 | 12 / 12 | A systematic review of all the studies on the question |
| Strength of evidence | 5 / 5 | Rated “high” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2023 — 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 | A review of other studies — randomisation is not credited here |
| Times cited | 4 / 7 | Cited 73 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 14.6 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 3 / 6 | Cited 4.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 3 / 4 | Cited 73 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Rolando de la Cruz: h-index 17 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of California, San Francisco: institution h-index 1,915 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 13 institution(s) in 5 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37838690) |
| Free to read | 2 / 2 | Free to read |
| Total | 63 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1186/s13195-023-01304-8
- PubMed: 37838690
- 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
Blanco, K., Salcidua, S., Orellana, P., Sauma-Pérez, T., León, T., Steinmetz, L. C. L., et al. (2023). Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease. Alzheimer's research & therapy. https://doi.org/10.1186/s13195-023-01304-8
Vancouver
Blanco K, Salcidua S, Orellana P, Sauma-Pérez T, León T, Steinmetz LCL, et al. Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease. Alzheimer's research & therapy. 2023. doi:10.1186/s13195-023-01304-8. PMID: 37838690.
BibTeX
@article{blanco2023-37838690,
author = {Blanco, K. and Salcidua, S. and Orellana, P. and Sauma-Pérez, T. and León, T. and Steinmetz, L. C. L. and Ibañez, A. and Duran-Aniotz, C. and others},
title = {{Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease}},
journal = {Alzheimer's research \& therapy},
year = {2023},
doi = {10.1186/s13195-023-01304-8},
pmid = {37838690},
url = {https://doi.org/10.1186/s13195-023-01304-8}
}
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