GARS Study Raw Data

Raw data for Metabolic derangements in the GARS+/- transgenic mous

Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children

<div><p>Background</p><p>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.</p><p>Objectives</p><p>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.</p><p>Methods</p><p>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.</p><p>Results</p><p>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.</p><p>Conclusions</p><p>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.</p></div

Classifier performance metrics based on predictions on the independent 21-sample validation set, showing the feature sets with the highest accuracy.

<p>Feature No. corresponds to the number of the ordered, ranked VIP features that were evaluated. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112445#pone.0112445.s003" target="_blank">Table S3</a> shows the results for all feature sets.</p><p>Classifier performance metrics based on predictions on the independent 21-sample validation set, showing the feature sets with the highest accuracy.</p

Performance of the SVM and PLS models.

<p>Average AUC and accuracy of the (a) SVM and (b) PLS models containing different numbers of features. The bar graphs show the number of optimal models which were derived from recursive feature elimination process that was included in the resampling process for the indicated number of features.</p

A breakdown of the numbers of features resulting from filtering and annotation processes, based on molecular formula.

<p>This table also helps to illustrate the orthogonality and contribution of each of the 5 analytical platforms. Molecular formulae are being used here only to approximate the method orthogonality, since any given molecular formula may be associated with multiple chemical structures. *These annotations were confirmed in the GCMS platform and the formula were confirmed by using the KEGG database instead of the FBF procedure used in the 4 LCMS platforms.</p><p>A breakdown of the numbers of features resulting from filtering and annotation processes, based on molecular formula.</p

Confirmed metabolites.

<p>Statistically significant metabolites from the 61-sample training set with chemical structures confirmed by LC-HRMS-MS or GC-MS.</p><p>Confirmed metabolites.</p

Classification modeling process.

<p>A three-layer nested cross-validation approach was applied using both PLS-DA and SVM modeling methods to determine significant features capable of classifying children with ASD from TD children. The 179 features of the training set were analyzed using a leave-one-group-out cross-validation loop as described. The results from this cross-validation process were used to estimate model performance and create a robust feature VIP score index to rank the ASD vs TD classification importance of each of the 179 features. These feature ranks were used to evaluate the performance of the molecular signature using an independent validation set.</p

Patient demographic information.

<p>Patient demographic information.</p