Machine learning models of Alzheimer's disease spectrum using blood tests
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.
Authors
- Nicola Walter Falasca
- Stefano Luca Sensi (ORCID: https://orcid.org/0000-0002-0710-2574)
- Alberto Granzotto (ORCID: https://orcid.org/0000-0001-9181-4373)
- Antonio Ferretti (ORCID: https://orcid.org/0000-0002-8459-0129)
- Raffaella Franciotti (ORCID: https://orcid.org/0000-0002-8819-1061)
Institutions
- University of Chieti-Pescara (IT)
Publication Details
- Journal
- Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1002/dad2.70228
- Primary Topic
- Alzheimer's disease research and treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00