Evidence map›Paper›PMID 37838690›Full record

SynthesisAlzheimer's research & therapy2023

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

Kevin Blanco, Stefanny Salcidua, Paulina Orellana, Tania Sauma-Pérez, Tomás León, Lorena Cecilia López Steinmetz, Agustín Ibañez, Claudia Duran-Aniotz, Rolando de la Cruz

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in Alzheimer's research & therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
11.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Article
  6. Vascular cognitive impairment and dementia: Prevention, treatments, mechanisms and management options for the future.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
    Review
  7. Article
  8. Review
  9. Review
  10. Review
  11. Detection of emergency department patients at risk of dementia through artificial intelligence.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Observational
  12. Article
  13. Review
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Diabetes accelerates Alzheimer's disease progression in the first year post mild cognitive impairment diagnosis.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 5 countries.

Kevin BlancoCenter for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile.
Stefanny SalciduaLatin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
Paulina OrellanaCenter for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile.
Tania Sauma-PérezLatin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
Tomás LeónGlobal Brain Health Institute, Trinity College, Dublin, Ireland.
Lorena Cecilia López SteinmetzLatin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
Agustín IbañezLatin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
Claudia Duran-AniotzCenter for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile. Claudia.Duran@uai.cl.
Rolando de la CruzLatin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile. rolando.delacruz@uai.cl.
Adolfo Ibáñez University · CLHospital del Salvador · CLTrinity College Dublin · IE

Funding

US-South American Initiative for Genetic-Neural-Behavioral Interactions in Human Neurodegenerative ResearchR01AG057234 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Claudia Duran-Aniotz, Agustin M. Ibanez · 2019 to 2026
$6.1M
6 · The paper itself

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.

Indexed as

Alzheimer DiseaseCognitive DysfunctionAmyloid beta-PeptidesBiomarkersCross-Sectional StudiesDisease ProgressionHumansMachine Learningtau ProteinsAmyloid beta-PeptidesBiomarkerstau ProteinsAlzheimer’s diseaseArtificial intelligenceFluid biomarkerMachine learningMild cognitive impairment

Identifiers

PMID37838690
PMCPMC10576366
OpenAlexW4387639790

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.