Abstract
BACKGROUND: Blood metabolites have emerged as promising candidates in the search for biomarkers for Alzheimer's disease (AD), as evidence shows that various metabolic derangements contribute to neurodegeneration in AD. OBJECTIVE: We aim to identify metabolic biomarkers for AD diagnosis. METHODS: We conducted an in-depth analysis of the serum metabolome of AD patients and age, sex-matched cognitively unimpaired older adults using ultra-high-performance liquid chromatography-high resolution mass spectrometry. The biomarkers associated with AD were identified using machine learning algorithms. RESULTS: Using the discovery dataset and support vector machine (SVM) algorithm, we identified a panel of 14 metabolites predicting AD with a 1.00 area under the curve (AUC) of receiver operating characteristic (ROC). The SVM model was tested against the verification dataset using an independent cohort and retained high predictive accuracy with a 0.97 AUC. Using the random forest (RF) algorithm, we identified a panel of 13 metabolites that predicted AD with a 0.96 AUC when tested against the verification dataset. CONCLUSIONS: These findings pave the way for an efficient, blood-based diagnostic test for AD, holding promise for clinical screenings and diagnostic procedures.
Original language | English (US) |
---|---|
Pages (from-to) | 237-253 |
Number of pages | 17 |
Journal | Journal of Alzheimer's disease : JAD |
Volume | 102 |
Issue number | 1 |
DOIs | |
State | Published - Nov 1 2024 |
Keywords
- Alzheimer's disease
- biomarker
- diagnosis
- neurodegenerative disease
- serum metabolome
ASJC Scopus subject areas
- General Neuroscience
- Clinical Psychology
- Geriatrics and Gerontology
- Psychiatry and Mental health