Traduction: G.M.
PLoS One. 2014 Nov 7;9(11):e112445. doi: 10.1371/journal.pone.0112445. eCollection 2014.
Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children
West PR1, Amaral DG2, Bais P3, Smith AM1, Egnash LA1, Ross ME1, Palmer JA1, Fontaine BR1, Conard KR1, Corbett BA4, Cezar GG1, Donley EL1, Burrier RE1.
Author information
- 1Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
- 2The M.I.N.D. Institute and Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, California, United States of America.
- 3The Jackson Laboratory for Genomic medicine, University of Connecticut Health Center, Farmington, Connecticut, United States of America.
- 4Department of Psychiatry, Psychology and Kennedy Center, Vanderbilt University, Nashville, Tennessee, United States of America.
Abstract
BACKGROUND:
The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age.Le diagnostic de trouble du spectre autistique (TSA) au plus jeune âge possible est important pour démarrer une intervention d'une efficacité optimale. Aux États-Unis, l'âge moyen du diagnostic est de 4 ans. Identifier les signatures de biomarqueurs métaboliques de TSA à partir d'échantillons de sang offre une opportunité pour le développement de tests de diagnostic pour la détection de TSA à un âge précoce.
OBJECTIVES:
To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment.METHODS:
Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD.RESULTS:
A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set.Un ensemble de 179 caractéristiques statistiquement significatives résultant de l'analyse univariée ont été utilisées pour la modélisation multivariée. Les sous-ensembles de ces traits classe correctement les échantillons de TSA et TD dans l'ensemble de la formation 61échantillons avec une précision moyenne de 84% et 86%, et avec un maximum de précision de 81% dans en ensemble indépendant de 21 échantillons de validation.
CONCLUSIONS:
This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment recommendations.Cette analyse des métabolites plasmatiques de sang a permis la découverte de biomarqueurs qui peuvent être utiles dans le diagnostic des jeunes enfants avec TSA.
Les résultats serviront de base pour la découverte supplémentaire et la recherche de validation pour:
- la détermination de biomarqueurs pour développer des tests de diagnostic pour détecter le TSA plus tôt et d'améliorer les résultats des patients,
- obtenir un nouvel éclairage sur les mécanismes biochimiques de différents sous-types de TSA
- l'identification de cibles biomoléculaires pour de nouveaux modes de traitement, et
- fournir la base des recommandations de traitement individualisé.
- PMID: 25380056