Biomarkers of autism spectrum disorder
Inventors
West, Paul • Burrier, Robert E. • Egnash, Laura • Smith, Alan • Bais, Preeti
Assignees
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Publication Number
US-10060932-B2
Publication Date
2018-08-28
Expiration Date
Abstract
Methods for identifying metabolic signatures in blood plasma which are unique to autism are described herein. Samples are analyzed using multiple chromatographic-mass spectrometry-based techniques to orthogonally measure a broad range of small molecular weight metabolites differentially produced in autistic patient samples versus non-autistic control samples. These individual metabolites or a panel of such metabolites serve as metabolic signatures of autism. Such metabolic signatures are used in diagnostic methods to accurately identify individuals with autism spectrum disorder (ASD).
Core Innovation
The disclosure describes identifying autism-spectrum-disorder (ASD) metabolic biomarker signatures in blood or plasma biosamples by assaying small molecule metabolites with mass spectrometry. A plurality of small molecule metabolites is quantified in a biosample from a subject, and orthogonal high-resolution mass spectrometry approaches include GC-MS and untargeted LC-HRMS.
The disclosure further specifies metabolite measurements using liquid chromatography and electrospray ionization modes, including C8 chromatography with positive and negative electrospray ionization and HILIC chromatography with positive and negative electrospray ionization. Metabolites with statistically significant abundance differences between autistic and typically developing controls are identified and combined into a training set, and a statistically selected subset forms an ASD metabolomic signature.
The disclosure describes training and validating classification models using selected metabolomic features, including PLS-DA and SVM, with reported performance metrics such as accuracy and AUC. Chemical identities of key signature features are confirmed using LC-HRMS-MS/MS and database comparison, and the signature features are linked to risk assessment and diagnosis, including indications for high-functioning autism versus low-functioning autism and metabolite abundance directionality.
Claims Coverage
The partial claims include two independent method claims, each covering a mass-spectrometry-based assay of a biosample to quantify a defined plurality of small molecule metabolites.
Quantifying a defined plurality of small molecule metabolites in a biosample for ASD-associated biomarker use
Assaying a biosample from a subject for a plurality of small molecule metabolites by mass spectrometry; and quantifying the amount of 2-aminooctanoic acid, acesulfame, asymmetric dimethylarginine (ADMA), choline, 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), cysteine, cystine, dehydroepiandrosterone sulfate (DHEAS), glycine, glycocholic acid, hypoxanthine, indoleacrylic acid, indoxyl sulfate, LysoPC(16:1(9Z)), LysoPE(0:0/18:1(9Z)), LysoPE(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0), methionine, p-cresol sulfate, phenylalanine, phenyllactic acid, proline, serotonin, tryptophan, uric acid, and valine in the biosample.
Quantifying a distinct defined plurality of small molecule metabolites in a biosample for ASD-associated biomarker use
Assaying a biosample from a subject for a plurality of small molecule metabolites by mass spectrometry; and quantifying the amount of homocitrulline, 2-hydroxyvaleric acid, cystine, aspartic acid, isoleucine, creatinine, serine, 4-hydroxyphenyllactic acid, citric acid, glutamic acid, lactic acid, dehydroepiandrosterone sulfate (DHEAS), glutaric acid, 5-hydroxynorvaline, heptadecanoic acid, 5-aminovaleric acid lactam, succinic acid, myristic acid, methylhexadecanoic acid, and 3-aminoisobutyric acid in the biosample.
Across the two independent claims, coverage centers on assaying a biosample by mass spectrometry and quantifying a specified plurality of metabolites, with dependent refinements narrowing mass spectrometry modality and ionization configurations and restricting subject age and biosample type to plasma under two years of age.
Stated Advantages
Supports risk assessment and diagnosis through ASD metabolomic signature features.
Enables discrimination between autistic and typically developing controls using classification models trained on statistically selected metabolomic features.
Documented Applications
ASD risk assessment and diagnosis using metabolomic signature features derived from quantified metabolites in blood or plasma biosamples.
Subtype indications for high-functioning autism and low-functioning autism based on metabolomic signature features and metabolite abundance directionality.
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