Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children
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{"title"=>"Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children", "type"=>"journal", "authors"=>[{"first_name"=>"Paul R.", "last_name"=>"West", "scopus_author_id"=>"16937928300"}, {"first_name"=>"David G.", "last_name"=>"Amaral", "scopus_author_id"=>"7007019852"}, {"first_name"=>"Preeti", "last_name"=>"Bais", "scopus_author_id"=>"39862735300"}, {"first_name"=>"Alan M.", "last_name"=>"Smith", "scopus_author_id"=>"7406758359"}, {"first_name"=>"Laura A.", "last_name"=>"Egnash", "scopus_author_id"=>"8658912300"}, {"first_name"=>"Mark E.", "last_name"=>"Ross", "scopus_author_id"=>"56423048000"}, {"first_name"=>"Jessica A.", "last_name"=>"Palmer", "scopus_author_id"=>"54420856600"}, {"first_name"=>"Burr R.", "last_name"=>"Fontaine", "scopus_author_id"=>"54419839600"}, {"first_name"=>"Kevin R.", "last_name"=>"Conard", "scopus_author_id"=>"12241493900"}, {"first_name"=>"Blythe A.", "last_name"=>"Corbett", "scopus_author_id"=>"9334785400"}, {"first_name"=>"Gabriela G.", "last_name"=>"Cezar", "scopus_author_id"=>"8751761300"}, {"first_name"=>"Elizabeth L.R.", "last_name"=>"Donley", "scopus_author_id"=>"25642520700"}, {"first_name"=>"Robert E.", "last_name"=>"Burrier", "scopus_author_id"=>"57189823316"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"600483524", "sgr"=>"84911466852", "issn"=>"19326203", "pmid"=>"25380056", "scopus"=>"2-s2.0-84911466852", "doi"=>"10.1371/journal.pone.0112445", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"c7a04c45-9213-36d4-9b81-e112d4fc058c", "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. 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. 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.", "link"=>"http://www.mendeley.com/research/metabolomics-tool-discovery-biomarkers-autism-spectrum-disorder-blood-plasma-children", "reader_count"=>87, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>25, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>21, "Student > Postgraduate"=>1, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>6, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>3, "Librarian"=>1, "Researcher"=>25, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>21, "Student > Postgraduate"=>1, "Student > Master"=>11, "Other"=>2, "Student > Bachelor"=>6, "Lecturer"=>3, "Lecturer > Senior Lecturer"=>3, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>9, "Agricultural and Biological Sciences"=>22, "Arts and Humanities"=>1, "Chemistry"=>9, "Computer Science"=>2, "Engineering"=>1, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>8, "Nursing and Health Professions"=>1, "Medicine and Dentistry"=>18, "Neuroscience"=>4, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Psychology"=>8, "Social Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>18}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>8}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>1}, "Chemistry"=>{"Chemistry"=>9}, "Neuroscience"=>{"Neuroscience"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>22}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}}, "reader_count_by_country"=>{"United States"=>4, "Spain"=>2}, "group_count"=>5}

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  • {"files"=>["https://ndownloader.figshare.com/files/1783601"], "description"=>"<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>", "links"=>[], "tags"=>["orthogonally measure", "Autism Spectrum Disorder", "treatment.MethodsBlood plasma", "United States", "TD samples", "biomarker signatures", "validation research", "univariate analysis", "Blood Plasma", "patient outcomes", "blood samples", "asd", "blood plasma metabolites", "biomolecular targets", "multivariate modeling", "4 years", "treatment recommendations", "Children BackgroundThe diagnosis", "plasma samples"], "article_id"=>1232333, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Paul R. West", "David G. Amaral", "Preeti Bais", "Alan M. Smith", "Laura A. Egnash", "Mark E. Ross", "Jessica A. Palmer", "Burr R. Fontaine", "Kevin R. Conard", "Blythe A. Corbett", "Gabriela G. Cezar", "Elizabeth L. R. Donley", "Robert E. Burrier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112445.t003", "stats"=>{"downloads"=>3, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classifier_performance_metrics_based_on_predictions_on_the_independent_21_sample_validation_set_showing_the_feature_sets_with_the_highest_accuracy_/1232333", "title"=>"Classifier performance metrics based on predictions on the independent 21-sample validation set, showing the feature sets with the highest accuracy.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-07 02:46:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1783600"], "description"=>"<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>", "links"=>[], "tags"=>["orthogonally measure", "Autism Spectrum Disorder", "treatment.MethodsBlood plasma", "United States", "TD samples", "biomarker signatures", "validation research", "univariate analysis", "Blood Plasma", "patient outcomes", "blood samples", "asd", "blood plasma metabolites", "biomolecular targets", "multivariate modeling", "4 years", "treatment recommendations", "Children BackgroundThe diagnosis", "plasma samples"], "article_id"=>1232332, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Paul R. West", "David G. Amaral", "Preeti Bais", "Alan M. Smith", "Laura A. Egnash", "Mark E. Ross", "Jessica A. Palmer", "Burr R. Fontaine", "Kevin R. Conard", "Blythe A. Corbett", "Gabriela G. Cezar", "Elizabeth L. R. Donley", "Robert E. Burrier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112445.t002", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_breakdown_of_the_numbers_of_features_resulting_from_filtering_and_annotation_processes_based_on_molecular_formula_/1232332", "title"=>"A breakdown of the numbers of features resulting from filtering and annotation processes, based on molecular formula.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-07 02:46:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1783597"], "description"=>"<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>", "links"=>[], "tags"=>["orthogonally measure", "Autism Spectrum Disorder", "treatment.MethodsBlood plasma", "United States", "TD samples", "biomarker signatures", "validation research", "univariate analysis", "Blood Plasma", "patient outcomes", "blood samples", "asd", "blood plasma metabolites", "biomolecular targets", "multivariate modeling", "4 years", "treatment recommendations", "Children BackgroundThe diagnosis", "plasma samples"], "article_id"=>1232329, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Paul R. West", "David G. Amaral", "Preeti Bais", "Alan M. Smith", "Laura A. Egnash", "Mark E. Ross", "Jessica A. Palmer", "Burr R. Fontaine", "Kevin R. Conard", "Blythe A. Corbett", "Gabriela G. Cezar", "Elizabeth L. R. Donley", "Robert E. Burrier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112445.g003", "stats"=>{"downloads"=>2, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_the_SVM_and_PLS_models_/1232329", "title"=>"Performance of the SVM and PLS models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 02:46:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1783595"], "description"=>"<p>The top 179 features were compared for rank between SVM and PLS modeling methods. The lowest rank scores represent the most important features.</p>", "links"=>[], "tags"=>["orthogonally measure", "Autism Spectrum Disorder", "treatment.MethodsBlood plasma", "United States", "TD samples", "biomarker signatures", "validation research", "univariate analysis", "Blood Plasma", "patient outcomes", "blood samples", "asd", "blood plasma metabolites", "biomolecular targets", "multivariate modeling", "4 years", "treatment recommendations", "Children BackgroundThe diagnosis", "plasma samples"], "article_id"=>1232327, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Paul R. West", "David G. Amaral", "Preeti Bais", "Alan M. Smith", "Laura A. Egnash", "Mark E. Ross", "Jessica A. Palmer", "Burr R. Fontaine", "Kevin R. Conard", "Blythe A. Corbett", "Gabriela G. Cezar", "Elizabeth L. R. Donley", "Robert E. Burrier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112445.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Feature_Importance_Rankings_/1232327", "title"=>"Feature Importance Rankings.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 02:46:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1783604", "https://ndownloader.figshare.com/files/1783605", "https://ndownloader.figshare.com/files/1783606", "https://ndownloader.figshare.com/files/1783607", "https://ndownloader.figshare.com/files/1783608"], "description"=>"<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>", "links"=>[], "tags"=>["orthogonally measure", "Autism Spectrum Disorder", "treatment.MethodsBlood plasma", "United States", "TD samples", "biomarker signatures", "validation research", "univariate analysis", "Blood Plasma", "patient outcomes", "blood samples", "asd", "blood plasma metabolites", "biomolecular targets", "multivariate modeling", "4 years", "treatment recommendations", "Children BackgroundThe diagnosis", "plasma samples"], "article_id"=>1232336, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Paul R. West", "David G. Amaral", "Preeti Bais", "Alan M. Smith", "Laura A. Egnash", "Mark E. Ross", "Jessica A. Palmer", "Burr R. Fontaine", "Kevin R. Conard", "Blythe A. Corbett", "Gabriela G. Cezar", "Elizabeth L. R. Donley", "Robert E. Burrier"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0112445.s001", "https://dx.doi.org/10.1371/journal.pone.0112445.s002", "https://dx.doi.org/10.1371/journal.pone.0112445.s003", "https://dx.doi.org/10.1371/journal.pone.0112445.s004", "https://dx.doi.org/10.1371/journal.pone.0112445.s005"], "stats"=>{"downloads"=>5, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Metabolomics_as_a_Tool_for_Discovery_of_Biomarkers_of_Autism_Spectrum_Disorder_in_the_Blood_Plasma_of_Children_/1232336", "title"=>"Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-11-07 02:46:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1783602"], "description"=>"<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>", "links"=>[], "tags"=>["orthogonally measure", "Autism Spectrum Disorder", "treatment.MethodsBlood plasma", "United States", "TD samples", "biomarker signatures", "validation research", "univariate analysis", "Blood Plasma", "patient outcomes", "blood samples", "asd", "blood plasma metabolites", "biomolecular targets", "multivariate modeling", "4 years", "treatment recommendations", "Children BackgroundThe diagnosis", "plasma samples"], "article_id"=>1232334, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Paul R. West", "David G. Amaral", "Preeti Bais", "Alan M. Smith", "Laura A. Egnash", "Mark E. Ross", "Jessica A. Palmer", "Burr R. Fontaine", "Kevin R. Conard", "Blythe A. Corbett", "Gabriela G. Cezar", "Elizabeth L. R. Donley", "Robert E. Burrier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112445.t004", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Confirmed_metabolites_/1232334", "title"=>"Confirmed metabolites.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-07 02:46:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1783599"], "description"=>"<p>Patient demographic information.</p>", "links"=>[], "tags"=>["orthogonally measure", "Autism Spectrum Disorder", "treatment.MethodsBlood plasma", "United States", "TD samples", "biomarker signatures", "validation research", "univariate analysis", "Blood Plasma", "patient outcomes", "blood samples", "asd", "blood plasma metabolites", "biomolecular targets", "multivariate modeling", "4 years", "treatment recommendations", "Children BackgroundThe diagnosis", "plasma samples"], "article_id"=>1232331, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Paul R. West", "David G. Amaral", "Preeti Bais", "Alan M. Smith", "Laura A. Egnash", "Mark E. Ross", "Jessica A. Palmer", "Burr R. Fontaine", "Kevin R. Conard", "Blythe A. Corbett", "Gabriela G. Cezar", "Elizabeth L. R. Donley", "Robert E. Burrier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112445.t001", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Patient_demographic_information_/1232331", "title"=>"Patient demographic information.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-11-07 02:46:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1783598"], "description"=>"<p>The average of 100 iterations of the classifier for the best performing feature sets following recursive feature elimination comparing ASD vs. TD samples (Black and Grey Lines). The blue (PLS) and red (SVM) lines are ROC curves of the best performing validation feature subsets. Vertical bars represent the standard error of the mean.</p>", "links"=>[], "tags"=>["orthogonally measure", "Autism Spectrum Disorder", "treatment.MethodsBlood plasma", "United States", "TD samples", "biomarker signatures", "validation research", "univariate analysis", "Blood Plasma", "patient outcomes", "blood samples", "asd", "blood plasma metabolites", "biomolecular targets", "multivariate modeling", "4 years", "treatment recommendations", "Children BackgroundThe diagnosis", "plasma samples"], "article_id"=>1232330, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Paul R. West", "David G. Amaral", "Preeti Bais", "Alan M. Smith", "Laura A. Egnash", "Mark E. Ross", "Jessica A. Palmer", "Burr R. Fontaine", "Kevin R. Conard", "Blythe A. Corbett", "Gabriela G. Cezar", "Elizabeth L. R. Donley", "Robert E. Burrier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112445.g004", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curve_performance_of_the_classification_models_from_the_training_and_validation_sets_/1232330", "title"=>"ROC curve performance of the classification models from the training and validation sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 02:46:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1783593"], "description"=>"<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>", "links"=>[], "tags"=>["orthogonally measure", "Autism Spectrum Disorder", "treatment.MethodsBlood plasma", "United States", "TD samples", "biomarker signatures", "validation research", "univariate analysis", "Blood Plasma", "patient outcomes", "blood samples", "asd", "blood plasma metabolites", "biomolecular targets", "multivariate modeling", "4 years", "treatment recommendations", "Children BackgroundThe diagnosis", "plasma samples"], "article_id"=>1232325, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Paul R. West", "David G. Amaral", "Preeti Bais", "Alan M. Smith", "Laura A. Egnash", "Mark E. Ross", "Jessica A. Palmer", "Burr R. Fontaine", "Kevin R. Conard", "Blythe A. Corbett", "Gabriela G. Cezar", "Elizabeth L. R. Donley", "Robert E. Burrier"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0112445.g001", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_modeling_process_/1232325", "title"=>"Classification modeling process.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-11-07 02:46:25"}

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Relative Metric

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