Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children.
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.


OBJECTIVE
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.
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Paul R
PR
West
The M.I.N.D. Institute and Department of Psychiatry and Behavioral Sciences, University of California Davis, Davis, California, United States of America.
David G
DG
Amaral
The Jackson Laboratory for Genomic medicine, University of Connecticut Health Center, Farmington, Connecticut, United States of America.
Preeti
P
Bais
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Alan M
AM
Smith
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Laura A
LA
Egnash
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Mark E
ME
Ross
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Jessica A
JA
Palmer
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Burr R
BR
Fontaine
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Kevin R
KR
Conard
Department of Psychiatry, Psychology and Kennedy Center, Vanderbilt University, Nashville, Tennessee, United States of America.
Blythe A
BA
Corbett
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Gabriela G
GG
Cezar
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Elizabeth L R
EL
Donley
Stemina Biomarker Discovery, Madison, Wisconsin, United States of America.
Robert E
RE
Burrier
D000067877:Autism Spectrum Disorder / Q000097:blood* / Q000175:diagnosis* / Q000378:metabolism; D015415:Biomarkers / Q000097:blood*; D002648:Child; D002675:Child, Preschool; D002853:Chromatography, Liquid; D005260:Female; D008401:Gas Chromatography-Mass Spectrometry; D006801:Humans; D000069550:Machine Learning; D008297:Male; D013058:Mass Spectrometry; D055432:Metabolomics / Q000379:methods*; D015999:Multivariate Analysis; D057285:Precision Medicine / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D015415:Biomarkers