ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Machine learning models of Alzheimer's disease spectrum using blood tests.
Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Beyond Feature Selection: Interpretable Machine Learning for Mechanistic Insights in Metabolomics.Biology · 2026Review
- Machine learning models of Alzheimer's disease spectrum using blood tests.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
6 authors.
Funding
Abstract
introductionThe diagnosis of Alzheimer's disease (AD) traditionally relies on cerebrospinal fluid and plasma levels of amyloid beta and phosphorylated tau. Although informative, these biomarkers represent a narrow, hypothesis-driven approach to intercept the disease.
methodsData-driven analysis was applied on demographic data, apolipoprotein E (
resultsStatistical analyses revealed differences among groups in many cholesterol-related analytes. DISCUSSION: These findings support the hypothesis that systemic alterations also occur during the preclinical stages of dementia, which can be detected by ML models on blood biomarkers. Highlights: Machine learning on blood tests detects preclinical cognitive decline.Glycolysis metabolites are predictive for distinguishing stable MCI and AD from HC.Amino acids, lipoproteins, and fatty acids are the most predictive features.Inflammatory and metabolic biomarkers represent a biosignature of cognitive health.
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Registered trials
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