Work

Discovery of novel metabolic biomarkers in blood serum for diagnosis of Alzheimer's disease

Year: 2024

Type: article

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.

Source: Journal of Alzheimer s Disease

DOI: 10.3233/jad-240280

Authors: Yingxin Zhao, Alejandro Villasante-Tezanos, Ernesto G Miranda-Morales, Miguel A Pappolla, Xiang Fang

Institution: The University of Texas Medical Branch at Galveston

Language: English

Topic: Dementia and Cognitive Impairment Research

Subfield: Psychiatry and Mental health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: Texas Alzheimer's Research and Care Consortium, National Institutes of Health, National Institute on Aging