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J Alzheimers Dis. 2015;45(4):1157-73. doi: 10.3233/JAD-142925.

A blood-based, 7-metabolite signature for the early diagnosis of Alzheimer's disease.

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Servicio de Neurología, HGU Gregorio Marañón, Alzheimer Center Reina Sofía Foundation-CIEN Foundation, Madrid, Spain.
BIOCROSS, S.L., Boecillo, Valladolid, Spain.
CIEN Foundation, Carlos III Institute of Health, Alzheimer Center Reina Sofia Foundation, Madrid, Spain.
Hospital Clínico Universitario Virgen de la Arrixaca de Murcia, Unidad de Demencias, El Palmar, Murcia, Spain.
IdiPaz Hospital Universitario La Paz. Paseo de la Castellana n° 261, Subdirección médica, Edificio Maternidad, Madrid, Spain.
Servicio de Neurología; Servicio de Geriatría, Hospital Universitario de Getafe, Getafe, Madrid, Spain.
Unidad de Medicina y Cirugia Experimental, Hospital General Universitario Gregorio Marañon, Madrid, Spain.
Head Neurology Department U. H. "12 de Octubre", Madrid, Spain; CIBERNED, Carlos III National Research Institute, Madrid, Spain; Medicine Department, Complutense University, Madrid, Spain.
Research Fellow at the Madrid-MIT M+Vision Consortium, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Hospital Universitario Infanta Cristina, Sección de Neurología, Parla-Madrid, Spain.
Exploristics, Titanic Suites Belfast, Belfast, UK.
Departamento de Neuropatología y Banco de Tejidos, Fundación CIEN, Instituto de Salud Carlos III c/ Valderrebollo, Madrid, Spain.
Chronic Disease Programme, CIBERNED and Alzheimer Disease Research Unit, CIEN Foundation, Alzheimer Center Reina Sofía Foundation, Carlos III Institute of Health, Madrid, Spain.


Accurate blood-based biomarkers of Alzheimer's disease (AD) could constitute simple, inexpensive, and non-invasive tools for the early diagnosis and treatment of this devastating neurodegenerative disease. We sought to develop a robust AD biomarker panel by identifying alterations in plasma metabolites that persist throughout the continuum of AD pathophysiology. Using a multicenter, cross-sectional study design, we based our analysis on metabolites whose levels were altered both in AD patients and in patients with amnestic mild cognitive impairment (aMCI), the earliest identifiable stage of AD. UPLC coupled to mass spectrometry was used to independently compare the levels of 495 plasma metabolites in aMCI (n = 58) and AD (n = 100) patients with those of normal cognition controls (NC, n = 93). Metabolite alterations common to both aMCI and AD patients were used to generate a logistic regression model that accurately distinguished AD from NC patients. The final panel consisted of seven metabolites: three amino acids (glutamic acid, alanine, and aspartic acid), one non-esterified fatty acid (22:6n-3, DHA), one bile acid (deoxycholic acid), one phosphatidylethanolamine [PE(36:4)], and one sphingomyelin [SM(39:1)]. Detailed analysis ruled out the influence of potential confounding variables, including comorbidities and treatments, on each of the seven biomarkers. The final model accurately distinguished AD from NC patients (AUC, 0.918). Importantly, the model also distinguished aMCI from NC patients (AUC, 0.826), indicating its potential diagnostic utility in early disease stages. These findings describe a sensitive biomarker panel that may facilitate the specific detection of early-stage AD through the analysis of plasma samples.


Alzheimer's disease; biomarkers; diagnosis; mild cognitive impairment; plasma

[Indexed for MEDLINE]

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