English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/101452
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Citado 58 veces en Web of Knowledge®  |  Pub MebCentral Ver citas en PubMed Central  |  Ver citas en Google académico
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar otros formatos: Exportar EndNote (RIS)Exportar EndNote (RIS)Exportar EndNote (RIS)
Título

Toward a predictive model of Alzheimer's disease progression using capillary electrophoresis-mass spectrometry metabolomics

Autor Ibáñez, Clara ; Simó, Carolina ; Martín-Álvarez, Pedro J. ; Cifuentes, Alejandro
Fecha de publicación 2012
EditorAmerican Chemical Society
Citación Analytical Chemistry 84(20): 8532-8540 (2012)
ResumenAlzheimer's disease (AD) is the most prevalent form of dementia with an estimated worldwide prevalence of over 30 million people, and its incidence is expected to increase dramatically with an increasing elderly population. Up until now, cerebrospinal fluid (CSF) has been the preferred sample to investigate central nervous system (CNS) disorders since its composition is directly related to metabolite production in the brain. In this work, a nontargeted metabolomic approach based on capillary electrophoresis-mass spectrometry (CE-MS) is developed to examine metabolic differences in CSF samples from subjects with different cognitive status related to AD progression. To do this, CSF samples from 85 subjects were obtained from patients with (i) subjective cognitive impairment (SCI, i.e. control group), (ii) mild cognitive impairment (MCI) which remained stable after a follow-up period of 2 years, (iii) MCI which progressed to AD within a 2-year time after the initial MCI diagnostic and, (iv) diagnosed AD. A prediction model for AD progression using multivariate statistical analysis based on CE-MS metabolomics of CSF samples was obtained using 73 CSF samples. Using our model, we were able to correctly classify 97-100% of the samples in the diagnostic groups. The prediction power was confirmed in a blind small test set of 12 CSF samples, reaching a 83% of diagnostic accuracy. The obtained predictive values were higher than those reported with classical CSF AD biomarkers (Aβ42 and tau) but need to be confirmed in larger samples cohorts. Choline, dimethylarginine, arginine, valine, proline, serine, histidine, creatine, carnitine, and suberylglycine were identified as possible disease progression biomarkers. Our results suggest that CE-MS metabolomics of CSF samples can be a useful tool to predict AD progression. © 2012 American Chemical Society.
URI http://hdl.handle.net/10261/101452
DOI10.1021/ac301243k
Identificadoresdoi: 10.1021/ac301243k
issn: 0003-2700
e-issn: 1520-6882
Aparece en las colecciones: (CIAL) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
accesoRestringido.pdf15,38 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo
 



NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.