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Cited by 73Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsCerebrospinal fluid and hydrocephalus

Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer’s disease

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Blanco, Kevin, Salcidua, Stefanny, Orellana, Paulina, Sauma-Pérez, Tania, León, Tomás, Steinmetz, Lorena Cecilia López, Ibañez, Agustín, Duran-Aniotz, Claudia +1 more·2023·Alzheimer's Research & Therapy
DOI 10.1186/s13195-023-01304-8PMC PMC10576366
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Abstract

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.

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  • AI-enabled platforms for biomarker discovery, which integrate large-scale cohort data with machine learning algorithms, have facilitated the identification, validation, and clinical implementation of blood-based biomarkers.

    “AI-enabled platforms for biomarker discovery, which integrate large-scale cohort data with machine learning algorithms, have facilitated the identification, validation, and clinical implementation of blood-based biomarkers90,91.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [91]

    The Introduction (‘Machine learning for medical diagnosis’; page not provided) says ML can integrate biomarkers and analyze large datasets, while the Discussion identifies blood-derived biomarkers as promising for diagnosis. But ‘Biomarkers in MCI’ says validation studies are still needed before miRNAs can be used clinically; the review does not establish that AI-enabled platforms have facilitated clinical implementation of blood-based biomarkers. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Authors
Blanco, K., Salcidua, S., Orellana, P., Sauma-Pérez, T., León, T., Steinmetz, L. C. L., Ibañez, A., Duran-Aniotz, C., & de la Cruz, R.
Affiliations
  • Adolfo Ibáñez University (CL)
  • Trinity College Dublin (IE)
  • Consejo Nacional de Investigaciones Científicas y Técnicas (AR)
Corresponding author
Duran-Aniotz, Claudia
ORCID
Venue
Alzheimer's Research & Therapy
Publisher
Springer Science and Business Media LLC
Published
2023-10-14
Volume
15
Issue
1
Pages
176-176
ISSN
1758-9193 (ISSN-L)
DOI
10.1186/s13195-023-01304-8
PMC
PMC10576366
OpenAlex
W4387639790
Cited by
73
References
148
Citations by year
2026: 28 · 2025: 32 · 2024: 13
FWCI
3.33
Type
journal article
Language
en
Open access
Licence
Locations
  • Alzheimer s Research & Therapy (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Universidad de Chile (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Alzheimer's Association (20-725707, SG-20- 725707, ACT210096, SG-20-, R01 AG057234)
  • Agencia Nacional de Investigación y Desarrollo (ACT210096, FONDAP-15150012, 15150012, R01 AG057234, FONDEF ID20I10152, ID20I10152, ANID/FONDAP/15150012, SG-20-725707, ID22I10029, 1210195, 1210622, 1210176, 1220995)
  • National Institutes of Health (FONDAP 15150012, SG-20-725707, 15150012)
  • National Institute on Aging (R01AG057234)
  • Fondo de Fomento al Desarrollo Científico y Tecnológico (ID22I10029, 15150012, ANID/FONDAP/15150012, CW2680521, ID20I10152, 1210622)
  • Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias (15150012, /FONDAP/15150012, ANID/FONDAP/15150012)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Cerebrospinal fluid and hydrocephalus
Keywords
mild cognitive impairment, Alzheimer's disease, fluid biomarkers, machine learning, dementia diagnosis, cerebrospinal fluid biomarkers, peripheral blood biomarkers, salivary biomarkers, multimodal biomarker integration, neuroimaging, long-term monitoring
MeSH terms
Machine Learning, Alzheimer Disease, Cross-Sectional Studies, Humans, Biomarkers, Amyloid beta-Peptides, tau Proteins, Disease Progression, Cognitive Dysfunction

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