Patent Application
20240118291
The invention relates generally to methods of diagnosing and treating individuals with autism spectrum disorders.
The prevalence of Autism Spectrum Disorders (ASD) is high and growing rapidly. According to a 2018 report from the Centers for Disease Control and Prevention (CDC), the incidence of ASD in children in the United States more than doubled from 1 in 125 in 2008 to 1 in 59 in 2018. ASD includes a range of neurodevelopmental disorders that affect social and communication skills. Raising children with ASD places huge demands on parents and school systems, and adults with ASD are often have difficulty developing social relationships, maintaining jobs, and performing daily tasks.
The underlying basis of ASD is poorly understood, making ASD difficult both to diagnose and to treat. Although certain risk factors, such as high parental age and gestational diabetes, are associated with ASD, specific causes have not been identified. For example, autism displays a strong heritability component, but most cases cannot be linked to individual mutations. Thus, ASD is thought to result from multiple mutations that have low penetrance. In addition, many mutations that are associated with autism are not inherited from a parental genome but appear to have occurred during embryonic development. Therefore, ASD cannot be reliably predicted at an early stage from genetic data alone. Moreover, because the molecular mechanisms of ASD are not known, drugs to treat them are lacking. Existing pharmacological approaches are limited to the use of psychoactive or anticonvulsant medications to treat symptoms, such as irritability, self-injury, aggression, and tantrums, associated with ASD. However, such drugs do not remedy the social and communication impairments at the core of ASD. Consequently, the tools to diagnose and treat ASD remain woefully inadequate even as increasing numbers of people are affected by these disorders.
The invention provides methods of diagnosing and treating individuals having or at risk of developing neurodevelopmental disorders, such as ASD, by identification of altered metabolic pathways in such individuals. The invention is based on the discovery that analysis of ratios of concentrations of metabolites in individuals having or at risk of developing ASD reveals alterations in specific metabolic pathways associated with ASD. By pinpointing specific metabolic defects, physicians can identify ASD patients even before abnormalities in speech and behavior are evident. For example, the invention allows patients to be classified into specific metabolic subtypes associated with ASD or development delay (DD), and prognoses and recommended treatments may differ from one subtype to another. In addition, by enabling identification of metabolic deficiencies, the methods of the invention provide guidance on interventions that will correct those deficiencies.
A critical factor to success in treatment of ASD is early intervention. The diagnostic methods of the invention enable detection of ASD much earlier than is possible with prior methods. For example, altered metabolic pathways can be detected shortly after birth or even in utero. Therefore, the methods allow initiation of treatment at an early stage to promote normal neurological development.
In an aspect, the invention provides methods of providing guidance for treating a subject that has or is at risk of developing a neurodevelopmental disorder. The methods include receiving results of an assay in which concentrations of two or more metabolites are measured in a sample from a subject that has or is at risk of developing a neurodevelopmental disorder, and based on the results, providing guidance for treating the subject that has or is suspected of having a neurodevelopmental disorder. The metabolites include two or more of 2-hydroxybutyrate, 2-hydroxyisobutyrate, 2-hydroxyisocaproic acid, 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid, 3-hydroxy-3-methylbutyric acid, 3-hydroxybutrylcarnitine, 3-hydroxyisobutyrate, 3-indoxyl sulfate, 3-methylhistidine, 3-methylxanthine, 4-ethylphenyl sulfate, 4-hydroxyproline, acetylcarnitine, alanine, alpha-hydroxyisovalerate, alpha-ketoglutarate, alpha-ketoisovaleric acid, arginine, asparagine, aspartic acid, beta-aminoisobutyric acid, beta-hydroxybutyrate, butyric acid, butyrylcarnitine, carnitine, cis-aconitic acid, citrate, citrulline, cortisone, cystine, decanoylcarnitine, decenoylcarnitine, dodecanedioic acid, dodecanoylcarnitine, elaidic carnitine, ethanolamine, gamma-aminobutyric acid, glutamic acid, glutamine, glutarylcarnitine, glyceraldehyde, glyceric acid, glycine, glycolic acid, hexadecenoylcarnitine, hexanoylcarnitine, histidine, homocitrulline, homoserine, hypoxanthine, indoleacetic acid, indoleacrylic acid, indolelactic acid, inosine, isoleucine, isovalerylcarnitine, kynurenine, lactate, leucine, linoleylcarnitine, lysine, malate, methionine, N-acetylglutamic acid, N-acetylneuraminic acid, nicotinamide, octadecanedioic acid, octanoylcarnitine, ornithine, palmitoylcarnitine, para-cresol sulfate, phenylalanine, pipecolic acid, proline, propionic acid, propionylcarnitine, pyroglutamic acid, pyruvate, S-adenosylhomocysteine, S-adenosylmethionine, sarcosine, serine, serotonin, succinate, taurine, tetradecadienylcarnitine, tetradecanoylcarnitine, tetradecenoylcarnitine, threonine, tryptophan, tyrosine, urate, valine, and xanthine.
The results include at least one ratio of concentrations of the metabolites, a reference level that provides an indication as to whether the ratio is imbalanced, and identification of a metabolic pathway that includes at least one of the metabolites.
The ratio of concentrations may include one or more of 4-hydroxyproline to xanthine; alanine to 4-hydroxyproline; alanine to carnitine; alanine to kynurenine; alanine to lactate; alanine to lysine; alanine to phenylalanine; alanine to succinate; alanine to tyrosine; alanine to valine; alpha-ketoglutarate to alanine; alpha-ketoglutarate to ethanolamine; alpha-ketoglutarate to glycine; alpha-ketoglutarate to lactate; alpha-ketoglutarate to lysine; alpha-ketoglutarate to ornithine; alpha-ketoglutarate to pyruvate; alpha-ketoglutarate to taurine; alpha-ketoglutarate to tryptophan; alpha-ketoglutarate to valine; arginine to 4-hydroxyproline; arginine to carnitine; arginine to citrate; arginine to glycine; arginine to lactate; arginine to leucine; arginine to phenylalanine; arginine to succinate; arginine to tyrosine; asparagine to glycine; asparagine to lactate; asparagine to succinate; aspartic acid to lactate; aspartic acid to pyruvate; aspartic acid to succinate; carnitine to citrulline; carnitine to ethanolamine; carnitine to glycine; carnitine to homoserine; carnitine to hypoxanthine; carnitine to lactate; carnitine to leucine; carnitine to malate; carnitine to methionine; carnitine to ornithine; carnitine to pyruvate; carnitine to succinate; carnitine to taurine; carnitine to xanthine; citrate to ethanolamine; citrate to glycine; citrate to homoserine; citrate to lactate; citrate to ornithine; citrate to phenylalanine; citrate to serine; citrate to taurine; citrulline to lactate; citrulline to succinate; ethanolamine to 4-hydroxyproline; ethanolamine to kynurenine; ethanolamine to lactate; ethanolamine to malate; ethanolamine to taurine; ethanolamine to urate; gamma-aminobutyric acid to succinate; glutamic acid to 4-hydroxyproline; glutamic acid to lactate; glutamic acid to pyruvate; glutamic acid to succinate; glutamine to lactate; glutamine to lysine; glycine to isoleucine; glycine to lactate; glycine to leucine; glycine to lysine; glycine to malate; glycine to methionine; glycine to phenylalanine; glycine to succinate; glycine to valine; histidine to lactate; histidine to leucine; histidine to xanthine; homocitrulline to lactate; homocitrulline to pyruvate; homocitrulline to succinate; homoserine to isoleucine; homoserine to lactate; homoserine to leucine; homoserine to malate; homoserine to pyruvate; hypoxanthine to 4-hydroxyproline; isoleucine to lactate; isoleucine to serine; kynurenine to glutamate; kynurenine to lactate; kynurenine to ornithine; kynurenine to pyruvate; lactate to 4-hydroxyproline; lactate to leucine; lactate to lysine; lactate to malate; lactate to methionine; lactate to ornithine; lactate to phenylalanine; lactate to proline; lactate to sarcosine; lactate to serine; lactate to taurine; lactate to threonine; lactate to tyrosine; lactate to urate; lactate to valine; lactate to xanthine; leucine to methionine; leucine to serine; leucine to succinate; leucine to valine; lysine to ornithine; lysine to phenylalanine; malate to 4-hydroxyproline; malate to proline; malate to taurine; methionine to succinate; ornithine to phenylalanine; ornithine to succinate; phenylalanine to pyruvate; phenylalanine to taurine; phenylalanine to taurine; proline to pyruvate; proline to succinate; pyruvate to 4-hydroxyproline; pyruvate to sarcosine; serine to succinate; serine to urate; succinate to 4-hydroxyproline; succinate to taurine; taurine to 4-hydroxyproline; threonine to valine; and xanthine to urate.
The ratio of concentrations may include a measured metabolite and an internal standard. The measured metabolite may be any of the metabolites listed above. The internal standard may be labeled. The internal standard may be labeled with an isotope, such as a radioisotope.
The results may include one or more ratios of concentrations of metabolites. The results may include at least two ratios, at least three ratios, at least four ratios, at least five ratios, at least six ratios, at least seven ratios, at least eight ratios, at least nine ratios, at least ten ratios, at least twelve ratios, at least fifteen ratios, at least twenty ratios, at least twenty-five ratios, at least thirty ratios, at least thirty-five ratios, at least forty ratios, at least forty-five ratios, or at least fifty ratios.
The results may include ratios that fall into different clusters. The results may include at least one ratio from multiple different clusters. The results may include at least one ratio from two, three, four, five, six, seven, eight, nine, ten, or more different clusters. The results may include multiple ratios from an individual cluster. The results may include at least two, at least three, at least four, at least five, or at least six ratios from an individual cluster. The results may include multiple ratios from an individual cluster and ratios from multiple individual clusters.
One cluster of ratios of concentrations may include 4-hydroxyproline to xanthine; ethanolamine to 4-hydroxyproline; histidine to xanthine; hypoxanthine to 4-hydroxyproline; lactate to 4-hydroxyproline; malate to 4-hydroxyproline; pyruvate to 4-hydroxyproline; succinate to 4-hydroxyproline; and taurine to 4-hydroxyproline.
Another cluster of ratios of concentrations may include alpha-ketoglutarate to alanine; alpha-ketoglutarate to lysine; alpha-ketoglutarate to ornithine; alpha-ketoglutarate to tryptophan; and alpha-ketoglutarate to valine.
Another cluster of ratios of concentrations may include alanine to carnitine; arginine to carnitine; carnitine to citrulline; carnitine to ethanolamine; carnitine to glycine; carnitine to homoserine; carnitine to hypoxanthine; carnitine to lactate; carnitine to leucine; carnitine to malate; carnitine to methionine; carnitine to ornithine; carnitine to pyruvate; carnitine to succinate; carnitine to taurine; and carnitine to xanthine.
Another cluster of ratios of concentrations may include arginine to citrate; citrate to ethanolamine; citrate to homoserine; citrate to ornithine; citrate to phenylalanine; and citrate to serine.
Another cluster of ratios of concentrations may include alpha-ketoglutarate to ethanolamine; ethanolamine to urate; and serine to urate.
Another cluster of ratios of concentrations may include glutamine to lysine; and lysine to phenylalanine.
Another cluster of ratios of concentrations may include alanine to kynurenine; alanine to lysine; alanine to phenylalanine; alanine to tyrosine; alanine to valine; alpha-ketoglutarate to glycine; arginine to glycine; arginine to leucine; arginine to phenylalanine; arginine to tyrosine; asparagine to glycine; citrate to glycine; glycine to isoleucine; glycine to leucine; glycine to lysine; glycine to malate; glycine to methionine; glycine to phenylalanine; glycine to valine; histidine to leucine; homoserine to isoleucine; homoserine to leucine; isoleucine to serine; leucine to methionine; leucine to serine; and threonine to valine.
Another cluster of ratios of concentrations may include alanine to lactate; alpha-ketoglutarate to lactate; alpha-ketoglutarate to pyruvate; arginine to lactate; asparagine to lactate; aspartic acid to lactate; aspartic acid to pyruvate; aspartic acid to succinate; citrate to lactate; citrulline to lactate; ethanolamine to lactate; glutamic acid to lactate; glutamic acid to pyruvate; glutamic acid to succinate; glutamine to lactate; glycine to lactate; histidine to lactate; homocitrulline to lactate; homocitrulline to pyruvate; homoserine to lactate; homoserine to pyruvate; isoleucine to lactate; kynurenine to lactate; kynurenine to pyruvate; lactate to leucine; lactate to lysine; lactate to malate; lactate to methionine; lactate to ornithine; lactate to phenylalanine; lactate to proline; lactate to sarcosine; lactate to serine; lactate to taurine; lactate to threonine; lactate to tyrosine; lactate to urate; lactate to valine; lactate to xanthine; phenylalanine to pyruvate; proline to pyruvate; and pyruvate to sarcosine.
Another cluster of ratios of concentrations may include ethanolamine to malate; homoserine to malate; and malate to proline.
Another cluster of ratios of concentrations may include lysine to ornithine; and ornithine to phenylalanine.
Another cluster of ratios of concentrations may include arginine to 4-hydroxyproline; ethanolamine to kynurenine; and leucine to valine.
Another cluster of ratios of concentrations may include alanine to succinate; arginine to succinate; asparagine to succinate; citrulline to succinate; gamma-aminobutyric acid to succinate; glycine to succinate; homocitrulline to succinate; leucine to succinate; methionine to succinate; ornithine to succinate; proline to succinate; and serine to succinate.
Another cluster of ratios of concentrations may include alpha-ketoglutarate to taurine; citrate to taurine; ethanolamine to taurine; glutamic acid to 4-hydroxyproline; malate to taurine; phenylalanine to taurine; phenylalanine to taurine; and succinate to taurine.
Another cluster of ratios of concentrations may include succinate to citrulline and succinate to glycine.
Another cluster of ratios of concentrations may include lactate to 4-hydroxyproline; lactate to alanine; lactate to arginine; lactate to asparagine; lactate to citrulline; lactate to glutamate; lactate to glutamine; lactate to histidine; lactate to kynurenine; lactate to leucine; sarcosine; lactate to tyrosine; pyruvate to kynurenine; and pyruvate to phenylalanine.
Another cluster of ratios of concentrations may include ornithine to leucine; ornithine to lysine; and ornithine to phenylalanine.
Another cluster of ratios of concentrations may include glycine to asparagine; glycine to isoleucine; glycine to lysine; and glycine to phenylalanine.
Another cluster of ratios of concentrations may include alanine to 4-hydroxyproline; and arginine to 4-hydroxyproline.
Another cluster of ratios of concentrations may include α-ketoglutarate to phenylalanine; and alanine to α-ketoglutarate.
The neurodevelopmental order may be an autism spectrum disorder. For example, the neurodevelopmental disorder may be autism, Asperger syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), or childhood disintegrative disorder.
The reference level may be from a defined population of subjects. For example, the population may be a subset of autism spectrum disorder (ASD) subjects. The subset may include subjects that have an alteration in a metabolic pathway in comparison to other ASD subjects, typically developing subjects, or in both. In such embodiments, a similar metabolic alteration in the subject may indicate that the subject from whom the sample was obtained has or is likely to develop ASD, and the absence of such an alteration may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD.
The population may include typically developing subjects. In such embodiments, a metabolic similarity or lack of alteration between the subject and the reference may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD, and metabolic dissimilarity or alteration may indicate that the subject from whom the sample was obtained has or is likely to develop ASD.
The population may include subjects that have a non-ASD developmental disorder.
The method may include receiving the sample from the subject. The method may include performing the assay. The assay may include mass spectrometry.
The sample may be a body fluid sample. For example, the body fluid may be blood, plasma, urine, sweat, tears, or saliva.
The results may include additional data about the subject. The additional data may include demographic factors such as age, sex, race and ethnicity of the subject, medical history of the subject, medical history of a family member of the subject, or genetic data from the subject.
The methods may include distinguishing whether a subject has an ASD and or a non-ASD developmental disorder. Thus, the methods may include comparing a metabolic profile or metabotype of the subject with the metabolic profile or metabotype of a reference population of subjects that have a non-ASD developmental disorder. In such embodiments, a similar metabolic profile or metabotype may indicate that the subject has or is likely to develop a non-ASD developmental disorder or that the subject does not have or is not likely to develop an ASD developmental disorder. Conversely, in such embodiments, a dissimilar metabolic profile or metabotype may indicate that the subject has or is likely to develop an ASD developmental disorder.
The guidance may include a recommendation for applied behavior analysis therapy, behavioral therapy, dietary modification, a drug, medical grade food, occupational therapy, physical therapy, speech-language therapy, or a supplement for the subject. The dietary modification may include supplementation with a source of metabolites or amino acids. The dietary modification may include supplementation with specific amino acids. For example, the dietary modification may include supplementation with one or more branched chain amino acids, such as isoleucine, leucine, or valine. The dietary modification may include supplementation with a source of metabolites or amino acids that is substantially free of phenylalanine, such as glycomacropeptide. The dietary modification may include decreasing the intake of specific metabolites or amino acids, such as phenylalanine.
The guidance may include a recommendation that the subject consult with a specialist, such as a neurodevelopment specialist or nutritionist.
The guidance may be provided in a report. The report may contain additional information about the subject, such as age, sex, weight, height, genetic data, genomic data, and dietary preferences.
The subject may be a human. The test subject may be a child. For example, the test subject may be a child of less than about 18 years of age, less than about 16 years of age, less than about 14 years of age, less than about 13 years of age, less than about 12 years of age, less than about 10 years of age, less than about 9 years of age, less than about 8 years of age, a child of less than about 7 years of age, a child of less than about 6 years of age, a child of less than about 5 years of age, a child of less than about 4 years of age, a child of less than about 3 years of age, a child of less than about 2 years of age, a child of less than about 18 months of age, a child of less than about 12 months of age, a child of less than about 9 months of age, a child of less than about 6 months of age, or a child of less than about 3 months of age.
The methods may include distinguishing whether a subject has an ASD and or a non-ASD developmental disorder. Thus, the methods may include comparing a metabolic profile or metabotype of the subject with the metabolic profile or metabotype of a reference population of subjects that have a non-ASD developmental disorder. In such embodiments, a similar metabolic profile or metabotype may indicate that the subject has or is likely to develop a non-ASD developmental disorder or that the subject does not have or is not likely to develop an ASD developmental disorder. Conversely, in such embodiments, a dissimilar metabolic profile or metabotype may indicate that the subject has or is likely to develop an ASD developmental disorder.
In an aspect, the invention provides methods of analyzing a sample from a subject by receiving a sample from a subject that has or is at risk of developing a neurodevelopmental disorder, measuring a concentration in the sample of at least two metabolites, determining at least one ratio of concentrations of the at least two metabolites, and generating a report that includes at least one ratio of concentrations in the sample from the subject and at least one reference ratio of concentrations of the at least two metabolites. The metabolites include two or more of 4-hydroxyproline, alanine, arginine, asparagine, citrulline, ethanolamine, glutamate, glutamine, glycine, histidine, isoleucine, kynurenine, lactate, leucine, lysine, ornithine, phenylalanine, proline, pyruvate, sarcosine, succinate, tyrosine, uric acid, and α-ketoglutarate.
The ratio of concentrations may include one or more of α-ketoglutarate to phenylalanine, alanine to 4-hydroxyproline, alanine to α-ketoglutarate, alanine to lysine, arginine to 4-hydroxyproline, ethanolamine to uric acid, glycine to asparagine, glycine to isoleucine, glycine to lysine, glycine to phenylalanine, histidine to leucine, lactate to 4-hydroxyproline, lactate to alanine, lactate to arginine, lactate to asparagine, lactate to citrulline, lactate to glutamate, lactate to glutamine, lactate to histidine, lactate to kynurenine, lactate to leucine, lactate to lysine, lactate to ornithine, lactate to phenylalanine, lactate to proline, lactate to sarcosine, lactate to tyrosine, ornithine to leucine, ornithine to lysine, ornithine to phenylalanine, pyruvate to kynurenine, pyruvate to phenylalanine, succinate to citrulline, and succinate to glycine.
The results may include one or more ratios of concentrations of metabolites. The results may include at least two ratios, at least three ratios, at least four ratios, at least five ratios, at least six ratios, at least seven ratios, at least eight ratios, at least nine ratios, at least ten ratios, at least twelve ratios, at least fifteen ratios, at least twenty ratios, at least twenty-five ratios, at least thirty ratios, at least thirty-five ratios, at least forty ratios, at least forty-five ratios, or at least fifty ratios.
The results may include one or more ratios of concentrations of a measured metabolite and an internal standard. The measured metabolite may be any of the metabolites listed above. The internal standard may be labeled. The internal standard may be labeled with an isotope, such as a radioisotope.
The results may include ratios that fall into different clusters, such as the clusters described above. The results may include at least one ratio from multiple different clusters. The results may include at least one ratio from two, three, four, five, six, or more different clusters. The results may include multiple ratios from an individual cluster. The results may include at least two, at least three, at least four, at least five, or at least six ratios from an individual cluster. The results may include multiple ratios from an individual cluster and ratios from multiple individual clusters.
The results may include additional data about the subject, such as the types of data described above.
The neurodevelopmental order may be an autism spectrum disorder, such any of those described above.
The reference ratio may include concentrations of metabolites in samples from a defined population of subjects, such as any of those described above. The reference ratio may be defined in relation to a subset of autism spectrum disorder (ASD) subjects. The subset may include subjects that have a ratio of concentrations of two or more metabolites that is different from the ratio of concentrations of the two or more metabolites in other ASD subjects, in typically developing subjects, or in both. The reference ratio may be representative of subjects in the subset. In such embodiments, a match between the ratio obtained from the sample and the reference ratio may indicate that the subject from whom the sample was obtained has or is likely to develop ASD, and a mismatch may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD. Alternatively, the reference ratio may be representative of typically developing subjects. In such embodiments, a match between the ratio obtained from the sample and the reference ratio may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD, and a mismatch may indicate that the subject from whom the sample was obtained has or is likely to develop ASD.
The reference subject or population may be selected to have one or more characteristics the same as, similar to, or different from, those of the test subject. For example, the reference subject or population may be the same as, similar to, or different from, the test subject in age, sex, weight, height, genetic profile, cooccurring medical conditions, or genomic profile.
A reference ratio may be or include an average value or a range of values. Thus, a match may be present if the ratio of concentration of metabolites in a sample obtained from a subject (1) falls above or below a threshold defined by the reference ratio, (2) falls within a range defined by a reference ratio, or (3) is otherwise similar to the ratio by some quantitative measure, and a mismatch may be present if the ratio of concentration of metabolites in sample obtained from a subject (1) does not fall above or below a threshold defined by the reference ratio, (2) does not fall within a range defined by a reference ratio, or (3) is otherwise different from the ratio by some quantitative measure. Likewise, two ratios may be deemed similar to each other by the same criteria for determining matching ratios, and two ratios may be different from each other by the same criteria for determining mismatched ratios.
The report may indicate that the test subject has or is at risk of developing a neurodevelopmental disorder if the test ratio is imbalanced compared to the reference ratio. The report may indicate a likelihood or probability that the test subject will develop a neurodevelopmental disorder. The report may indicate a likelihood or probability that the test subject will develop a neurodevelopmental disorder if the test subject goes untreated. The report may indicate a likelihood or probability that the test subject will develop a neurodevelopmental disorder if the test subject undergoes a particular course of treatment, such as a dietary modification.
The report may include guidance for treating the subject. The guidance may include a recommendation for a dietary modification for the subject, such as one or more of the dietary modifications described above. The guidance may include a recommendation for a drug, a medical grade food, or a supplement. The guidance may include a recommendation for therapy, such as applied behavior analysis therapy, behavioral therapy, occupational therapy, physical therapy, or speech-language therapy. The guidance may include a recommendation that the subject consult with a specialist, such as a neurodevelopment specialist or nutritionist.
The report may contain additional information about the subject, such as age, sex, weight, height, genetic data, genomic data, and dietary preferences. The report may include additional data about the subject, such as the types of data described above.
The sample may be a body fluid sample, such as those described above.
Measuring the concentrations of the two or more metabolites may include mass spectrometry. Measuring the concentrations of the two or more metabolites may be performed without derivatizing the metabolites.
The methods may include distinguishing whether a subject has an ASD and or a non-ASD developmental disorder, as described above.
In an aspect, the invention provides methods of providing guidance for treating a subject that has or is at risk of developing a neurodevelopmental disorder. The methods include receiving results of an assay in which concentrations of two or more metabolites are measured in a sample from a subject that has or is at risk of developing a neurodevelopmental disorder, and based on the results, providing guidance for treating the subject that has or is suspected of having a neurodevelopmental disorder. The results include at least one ratio of concentrations of the metabolites, a reference level that provides an indication as to whether the ratio is imbalanced, and identification of a metabolic pathway that includes at least one of the metabolites,
The metabolic pathway may be an amine metabolic pathway, a metabolic pathway related to a gut microbiome, a mitochondrial energy homeostasis pathway, a neurotransmission pathway, a neurotransmitter synthesis pathway, a purine degradation pathway, or a reactive oxidative species metabolic pathway.
The neurodevelopmental order may be an autism spectrum disorder, such any of those described above.
Either of the metabolites may be alanine, asparagine, aspartic acid, glycine, histidine, hypoxanthine, inosine, kynurenine, lactate, leucine, lysine, ornithine, phenylalanine, pyruvate, succinate, taurine, uric acid, xanthine, or α-ketoglutarate.
The ratio of concentrations of metabolites may include asparagine to glycine; glycine to phenylalanine; histidine to leucine; kynurenine to ornithine; lactate to alanine; lactate to phenylalanine; lysine to ornithine; xanthine to uric acid; α-ketoglutarate to alanine; or α-ketoglutarate to lactate. The ratio may be ethanolamine to (glutamate and kynurenine); glutamine to isoleucine; glutamine to leucine; glutamine to valine; glycine to asparagine; glycine to glutamate; glycine to isoleucine; glycine to leucine; glycine to lysine; glycine to phenylalanine; glycine to valine; hypoxanthine to uric acid; lactate to phenylalanine; ornithine to isoleucine; ornithine to kynurenine; ornithine to leucine; ornithine to lysine; ornithine to phenylalanine; ornithine to valine; pyruvic acid to phenylalanine; serine to isoleucine; serine to leucine; serine to valine; or xanthine to hydroxyproline.
The ratio of concentrations may be a group of ratios of a first metabolite, such as an amino-containing compound, to branched amino acids, in which the branched chain amino acids are isoleucine, leucine, or valine. For example, the group of ratios of concentrations may be (A) glutamine to isoleucine; glutamine to leucine; and glutamine to valine, (B) glycine to isoleucine; glycine to leucine; and glycine to valine, (C) ornithine to isoleucine; ornithine to leucine; and ornithine to valine, (D) serine to isoleucine; serine to leucine; and serine to valine, or (E) hypoxanthine to uric acid; and xanthine to uric acid. Other groups include ratios of concentrations in which the first analyte in each ratio is the same and the second analyte in each ratio is different, i.e., groups of the general formula X:A, X:B, X:C, etc. Such groups or panels may include two, three, four, five, or more ratios. The second analytes in such groups may have a common feature or be members of a common class of compounds. For example, the second analytes in such groups may be branched chain amino acids, hydrophobic amino acids, polar amino acids, negatively charged amino acids, positively charged amino acids, or metabolites in a common metabolic pathway, e.g., the citric acid cycle or fatty acid oxidation.
In certain embodiments, the metabolic pathway is purine degradation, and the metabolites are two or more of hypoxanthine, inosine, taurine, uric acid, and xanthine. In certain embodiments, the metabolic pathway is purine degradation, and the ratio is xanthine to uric acid.
In certain embodiments, the metabolic pathway is a mitochondrial energy homeostasis pathway, and the metabolites are two or more of alanine, lactate, phenylalanine, pyruvate, succinate, and α-ketoglutarate. In certain embodiments, the metabolic pathway is a mitochondrial energy homeostasis pathway, and the ratio is α-ketoglutarate to alanine; α-ketoglutarate to lactate, lactate to alanine; or lactate to phenylalanine.
In certain embodiments, the metabolic pathway is an amine metabolic pathway, a neurotransmission pathway, or a neurotransmitter synthesis pathway, and the metabolites are two or more of asparagine, glycine, histidine, kynurenine, leucine, lysine, ornithine, and phenylalanine. In certain embodiments, the metabolic pathway is an amine metabolic pathway, a neurotransmission pathway, or a neurotransmitter synthesis pathway, and the ratio is asparagine to glycine; glycine to phenylalanine; histidine to leucine; kynurenine to ornithine; or lysine to ornithine.
The reference level may include one or more ratios obtained from a reference population. The reference population may be a subset of autism spectrum disorder (ASD) subjects. The subset may include subjects that have an alteration in a metabolic pathway in comparison to other ASD subjects, typically developing subjects, or in both. In such embodiments, a similar metabolic alteration in the subject may indicate that the subject from whom the sample was obtained has or is likely to develop ASD, and the absence of such an alteration may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD. The reference population may include typically developing subjects. In such embodiments, a metabolic similarity or lack of alteration between the subject and the reference may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD, and metabolic dissimilarity or alteration may indicate that the subject from whom the sample was obtained has or is likely to develop ASD. The reference population may include subjects that have a non-ASD developmental disorder.
The sample may be a body fluid sample, such as those described above.
The method may include receiving the sample from the subject. The method may include performing the assay. The assay may include mass spectrometry.
The results may include additional data about the subject, such as the types of data described above.
The methods may include distinguishing whether a subject has an ASD and or a non-ASD developmental disorder, as described above.
The guidance may include a recommendation, such as any of the recommendations described above.
The guidance may be provided in report, which may contain additional information about the subject, as described above.
The subject may be a human, such as a child of any age range described above.
In an aspect, the invention provides methods of determining whether a test subject has or is at risk of developing a neurodevelopmental disorder. The methods include receiving a sample from a test subject; conducting a mass spectrometry analysis on the sample to generate mass spectral data; performing, via a computer, one or more algorithmic analyses on the mass spectral data to determine concentrations of two or more metabolites in the sample; generating, via the computer, a test ratio of the concentrations of the at least two metabolites in the sample from the test subject; identifying a metabolic pathway containing at least one of the metabolites; and outputting the test ratio to a report that includes a reference ratio of concentrations of the metabolites in one or more samples from one or more typically developing subjects and indicates if the metabolic pathway is altered in the test subject compared to typically developing subject.
The neurodevelopmental order may be an autism spectrum disorder, such any of those described above.
The methods may include the use of multiple test ratios and multiple reference ratios. The test and reference ratios of concentrations of metabolites may be one or more of ethanolamine to (glutamate and kynurenine); glutamine to isoleucine; glutamine to leucine; glutamine to valine; glycine to Asparagine; glycine to glutamate; glycine to isoleucine; glycine to leucine; glycine to lysine; glycine to phenylalanine; glycine to valine; His to leucine; hypoxanthine to uric acid; lactic acid to phenylalanine; ornithine to isoleucine; ornithine to kynurenine; ornithine to leucine; ornithine to lysine; ornithine to phenylalanine; ornithine to valine; pyruvic acid to phenylalanine; serine to isoleucine; serine to leucine; serine to valine; xanthine to hydroxyproline; and xanthine to uric acid.
The ratio of concentrations may be a group of ratios of a first metabolite to branched amino acids, in which the branched chain amino acids are isoleucine, leucine, or valine. For example, the group of ratios of concentrations may be (A) glutamine to isoleucine; glutamine to leucine; and glutamine to valine, (B) glycine to isoleucine; glycine to leucine; and glycine to valine, (C) ornithine to isoleucine; ornithine to leucine; and ornithine to valine, (D) serine to isoleucine; serine to leucine; and serine to valine, or (E) hypoxanthine to uric acid; and xanthine to uric acid. Other groups include ratios of concentrations in which the first analyte in each ratio is the same and the second analyte in each ratio is different, i.e., groups of the general formula X:A, X:B, X:C, etc. Such groups may include two, three, four, five, or more ratios. The second analytes in such groups may have a common feature or be members of a common class of compounds. For example, the second analytes in such groups may be branched chain amino acids, hydrophobic amino acids, polar amino acids, negatively charged amino acids, positively charged amino acids, or metabolites in a common metabolic pathway, e.g., the citric acid cycle or fatty acid oxidation.
The reference ratio may include concentrations of metabolites in samples from a defined population of subjects, such as any of those described above. The reference ratio may be defined in relation to a subset of autism spectrum disorder (ASD) subjects, as described above.
The reference subject or population may be selected to have one or more characteristics the same as, similar to, or different from, those of the test subject, as described above.
A reference ratio may be or include an average value or a range of values, as described above. A match to the reference ratio may be determined as described above.
The methods may include identifying whether a subject has metabolic dysregulation. For example, a subject may be identified as having metabolic dysregulation if results indicate an imbalance in one or more of the following ratios of concentrations: ethanolamine to (glutamate and kynurenine); glutamine to isoleucine; glutamine to leucine; glutamine to valine; glycine to Asparagine; glycine to glutamate; glycine to isoleucine; glycine to leucine; glycine to lysine; glycine to phenylalanine; glycine to valine; histidine to leucine; hypoxanthine to uric acid; lactic acid to phenylalanine; ornithine to isoleucine; ornithine to kynurenine; ornithine to leucine; ornithine to lysine; ornithine to phenylalanine; ornithine to valine; pyruvic acid to phenylalanine; serine to isoleucine; serine to leucine; serine to valine; xanthine to hydroxyproline; and xanthine to uric acid.
The ratio of concentrations may be a group of ratios of a first metabolite, such as an amine-containing compound, to branched amino acids, in which the branched chain amino acids are isoleucine, leucine, or valine. For example, the group of ratios of concentrations may be (A) glutamine to isoleucine; glutamine to leucine; and glutamine to valine, (B) glycine to isoleucine; glycine to leucine; and glycine to valine, (C) ornithine to isoleucine; ornithine to leucine; and ornithine to valine, (D) serine to isoleucine; serine to leucine; and serine to valine, or (E) hypoxanthine to uric acid; and xanthine to uric acid. Other groups include ratios of concentrations in which the first analyte in each ratio is the same and the second analyte in each ratio is different, i.e., groups of the general formula X:A, X:B, X:C, etc. Such groups may include two, three, four, five, or more ratios. The second analytes in such groups may have a common feature or be members of a common class of compounds. For example, the second analytes in such groups may be branched chain amino acids, hydrophobic amino acids, polar amino acids, negatively charged amino acids, positively charged amino acids, or metabolites in a common metabolic pathway, e.g., the citric acid cycle or fatty acid oxidation.
The methods may include distinguishing whether a subject has an ASD and or a non-ASD developmental disorder. Thus, the methods may include comparing the ratio of concentrations of the two or more metabolites in the sample obtained from the subject with a ratio of concentrations of the two or more metabolites in samples from subjects that have a non-ASD development disorder. Thus, a ratio of concentrations of the two or more metabolites in the sample obtained from the subject that is different from, or does not match, a ratio of concentrations of the two or more metabolites in samples from subjects that have a non-ASD developmental disorder may indicate that the subject from whom the sample was obtained has or is likely to develop ASD and/or that the subject from whom the sample was obtained does not have or is not likely to develop a developmental disorder. Conversely, a ratio of concentrations of the two or more metabolites in the sample obtained from the subject that is similar to, or matches, a ratio of concentrations of the two or more metabolites in samples from subjects that have a non-ASD developmental disorders may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD and/or that the subject from whom the sample was obtained has or is likely to develop a non-ASD developmental disorder. For example, the results may indicate that the subject has or is likely to develop an ASD developmental disorder if each of the ratios in one of the following groups of ratios indicates an imbalance: (A) glutamine to isoleucine; glutamine to leucine; and glutamine to valine, (B) glycine to isoleucine; glycine to leucine; and glycine to valine, (C) ornithine to isoleucine; ornithine to leucine; and ornithine to valine, (D) serine to isoleucine; serine to leucine; and serine to valine, or (E) hypoxanthine to uric acid; and xanthine to uric acid.
The report may indicate that the test subject has or is at risk of developing a neurodevelopmental disorder if the test ratio is imbalanced compared to the reference ratio, as described above.
The report may include guidance for treating the subject, as described above.
The report may contain additional information about the subject, such as age, sex, weight, height, genetic data, genomic data, and dietary preferences. The report may include additional data about the subject, such as the types of data described above.
The sample may be a body fluid sample, such as those described above.
In another aspect, the invention provides methods of providing guidance for treating a subject that has or is at risk of developing a neurodevelopmental disorder. The methods include receiving results of an assay in which a concentration of a metabolite is measured in a sample from a subject that has or is at risk of developing a neurodevelopmental disorder, and based on the results, providing guidance for treating the subject that has or is suspected of having a neurodevelopmental disorder. The metabolite is 2-hydroxybutyrate, 2-hydroxyisobutyrate, 2-hydroxyisocaproic acid, 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid, 3-hydroxy-3-methylbutyric acid, 3-hydroxybutrylcarnitine, 3-hydroxyisobutyrate, 3-indoxyl sulfate, 3-methylhistidine, 3-methylxanthine, 4-ethylphenyl sulfate, 4-hydroxyproline, acetylcarnitine, alanine, alpha-hydroxyisovalerate, alpha-ketoglutarate, alpha-ketoisovaleric acid, arginine, asparagine, aspartic acid, beta-aminoisobutyric acid, beta-hydroxybutyrate, butyric acid, butyrylcarnitine, carnitine, cis-aconitic acid, citrate, citrulline, cortisone, cystine, decanoylcarnitine, decenoylcarnitine, dodecanedioic acid, dodecanoylcarnitine, elaidic carnitine, ethanolamine, gamma-aminobutyric acid, glutamic acid, glutamine, glutarylcarnitine, glyceraldehyde, glyceric acid, glycine, glycolic acid, hexadecenoylcarnitine, hexanoylcarnitine, histidine, homocitrulline, homoserine, hypoxanthine, indoleacetic acid, indoleacrylic acid, indolelactic acid, inosine, isoleucine, isovalerylcarnitine, kynurenine, lactate, leucine, linoleylcarnitine, lysine, malate, methionine, N-acetylglutamic acid, N-acetylneuraminic acid, nicotinamide, octadecanedioic acid, octanoylcarnitine, ornithine, palmitoylcarnitine, para-cresol sulfate, phenylalanine, pipecolic acid, proline, propionic acid, propionylcarnitine, pyroglutamic acid, pyruvate, S-adenosylhomocysteine, S-adenosylmethionine, sarcosine, serine, serotonin, succinate, taurine, tetradecadienylcarnitine, tetradecanoylcarnitine, tetradecenoylcarnitine, threonine, tryptophan, tyrosine, urate, valine, or xanthine.
The results include the concentration of the metabolite, a reference level, and identification of a metabolic pathway comprising the metabolite.
The subject may be determined to have or be at risk of developing the neurodevelopmental disorder if the concentration is above or below the reference level.
The results may include the concentrations of more than one metabolite in the sample. For example, the results may include the concentrations of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more metabolites in the sample.
The reference level may be range of concentrations. The reference level may include upper and lower limits that deviate from a value, such as average value, by a defined amount. For example, the reference level may be a range that includes upper and lower limits that are about 1 standard deviation, about 1.5 standard deviations, about 2 standard deviations, about 2.5 standard deviations, or about 3 standard deviations, from a value. The value may be an average value from a defined population of subjects. For example, the population may be a subset of autism spectrum disorder (ASD) subjects. The subset may include subjects that have an alteration in a metabolic pathway in comparison to other ASD subjects, typically developing subjects, or in both. The value may be an average value from a reference population. The reference population may be a subset of autism spectrum disorder (ASD) subjects. The subset may include subjects that have an alteration in a metabolic pathway in comparison to other ASD subjects, typically developing subjects, or in both. The reference population may include typically developing subjects. In such embodiments, a metabolic similarity or lack of alteration between the subject and the reference may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD, and metabolic dissimilarity or alteration may indicate that the subject from whom the sample was obtained has or is likely to develop ASD. The reference population may include subjects that have a non-ASD developmental disorder.
The invention provides methods of diagnosing and treating autism spectrum disorders (ASD) by identification of altered ratios of metabolite concentrations in such individuals. ASD, such as autism, Asperger syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), includes neurodevelopmental disorders that impair an individual's social and communication skills. Children with ASD are typically not diagnosed until 2-4 years of age, the age range at which their deficiencies in such skills become apparent. Although evidence suggests that certain environmental and genetic factors contribute to the development of ASD, no specific cause has yet been identified. Consequently, it is currently difficult to predict whether a given individual will develop as ASD prior to the onset of symptoms.
The invention is based on the insight that some ASD are associated with alterations in the ratios of concentrations of certain metabolites. By analyzing ratios of concentrations of metabolites, metabolic alterations can be detected at a very early age, well before the manifestation of behavioral symptoms. The early detection of an alteration in a metabolic pathway allows metabolic dysregulation to be treated or mitigated before it leads to neurodevelopmental abnormalities. Consequently, the effects of environmental and genetic factors that put individuals at risk of developing ASD can be alleviated, allowing normal or near-normal development in at-risk individuals.
The autism spectrum includes a range of neurodevelopmental disorders associated with problems with social communication, social interaction, and restrict, repetitive patterns of behavior, interests, or activities. Disorders on the autism spectrum include autism, Asperger syndrome, pervasive developmental disorder not otherwise specified (PDD-NOS), and childhood disintegrative disorder.
The specific causes of ASD are not known. However, risk factors that contribute the development of ASD have been identified. For example, genetic factors play a role in the heritability of ASD, particular genetic conditions include variants of PTEN and SHANK3 and fragile X syndrome. Nonetheless, ASD cannot be attributed to specific mutations, and it is believed that a confluence of genetic variants is required for development of ASD. Advanced parental age is also associated with ASD. Other factors include gestational diabetes, bleeding after the first trimester of pregnancy, and the use of prescription medication such valproate during pregnancy.
ASD displays elevated rates of comorbidity with other disorders, such as seizure disorder, epilepsy, tuberous sclerosis, fragile X syndrome, Down syndrome, Prader-Willi and Angelman syndromes, Williams syndrome, learning disabilities, anxiety disorders, depression, and sensory processing disorder.
Metabolic Pathways Associated with ASD and Neurodevelopmental Disorders
The invention provides methods of identifying aberrant metabolic pathways associated with ASD and neurodevelopmental disorder by analysis of metabotypes. Ratios of concentrations of metabolites in a particular pathway may reveal alterations in activity of that pathways. Thus, any pathway that is dysregulated in an ASD or neurodevelopmental disorder may manifest a metabotype that can be used for methods of diagnosis and/or treatment.
One metabolic pathway that can be altered in ASD subjects is the purine degradation pathway. Flow of metabolites through the purine degradation pathway is shown below:
Xanthine oxidoreductase (XOR) is required for catabolism of purines. XOR catalyzes conversion of hypoxanthine to xanthine and xanthine to uric acid.
Changes in mitochondrial energy production pathways may also be associated with ASD subjects. Mitochondrial energy production pathways include the citric acid cycle (also called the tricarboxylic acid cycle or Krebs cycle) and oxidative phosphorylation (also called the electron transport chain). Key metabolites in these pathways include α-ketoglutarate, lactate, pyruvate, glutamate, and alanine, which are interconverted according to relation shown below:
Alterations in amine synthesis pathways are also associated with ASD subjects. Amine synthesis pathways are involved in synthesis of neurotransmitters, such as glutamate, aspartate, γ-aminobutyric acid (GABA), and glycine. Thus, defects in neurotransmitter synthesis or neurotransmission may be contribute to the clinical symptoms of ASD.
Dysregulation of other metabolic pathways may be associated with ASD subjects. For example, changes in metabolic pathways related to the gut microbiome or reactive oxidative species may also be identified in ASD subjects.
Various metabolites may be used in a concentration ratio. Concentration ratios compare a concentration of a first metabolite with that of another metabolite (for example, a “biological normalizer”). For example and without limitation, suitable metabolites include amino acids, purine degradation metabolites, and carboxylic acids. Amino acids represent a class of amine containing metabolites that include both proteinogenic and non-proteinogenic compounds. Purine metabolites include molecules involved in the synthesis and breakdown of purines. Carboxylic acids, such as lactate and citrate, include intermediates in carbon utilization pathways, such as the citric acid cycle.
Exemplary metabolites include 2-hydroxybutyrate, 2-hydroxyisobutyrate, 2-hydroxyisocaproic acid, 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid, 3-hydroxy-3-methylbutyric acid, 3-hydroxybutrylcarnitine, 3-hydroxyisobutyrate, 3-indoxyl sulfate, 3-methylhistidine, 3-methylxanthine, 4-ethylphenyl sulfate, 4-hydroxyproline, acetylcarnitine, alanine, alpha-hydroxyisovalerate, alpha-ketoglutarate, alpha-ketoisovaleric acid, arginine, asparagine, aspartic acid, beta-aminoisobutyric acid, beta-hydroxybutyrate, butyric acid, butyrylcarnitine, carnitine, cis-aconitic acid, citrate, citrulline, cortisone, cystine, decanoylcarnitine, decenoylcarnitine, dodecanedioic acid, dodecanoylcarnitine, elaidic carnitine, ethanolamine, gamma-aminobutyric acid, glutamic acid, glutamine, glutarylcarnitine, glyceraldehyde, glyceric acid, glycine, glycolic acid, hexadecenoylcarnitine, hexanoylcarnitine, histidine, homocitrulline, homoserine, hypoxanthine, indoleacetic acid, indoleacrylic acid, indolelactic acid, inosine, isoleucine, isovalerylcarnitine, kynurenine, lactate, leucine, linoleylcarnitine, lysine, malate, methionine, N-acetylglutamic acid, N-acetylneuraminic acid, nicotinamide, octadecanedioic acid, octanoylcarnitine, ornithine, palmitoylcarnitine, para-cresol sulfate, phenylalanine, pipecolic acid, proline, propionic acid, propionylcarnitine, pyroglutamic acid, pyruvate, S-adenosylhomocysteine, S-adenosylmethionine, sarcosine, serine, serotonin, succinate, taurine, tetradecadienylcarnitine, tetradecanoylcarnitine, tetradecenoylcarnitine, threonine, tryptophan, tyrosine, urate, valine, and xanthine.
Each metabolite may be used in combination with one or more additional metabolites. For example, in some embodiments, one or more of the metabolites may be used in combination with one additional metabolite, two additional metabolites, three additional metabolites, four additional metabolites, five additional metabolites, six additional metabolites, seven additional metabolites, eight additional metabolites, nine additional metabolites, ten additional metabolites, or more.
For example, when the relationship of the metabolites of the ASD subjects was evaluated by correlation analysis and hierarchical clustering, six reproducible clusters of metabolites were identified following permutation-based analysis of the hierarchical clustering. Five clusters contain ratios that include one of the following metabolites: succinate, glycine, ornithine, 4-hydoxyproline, or α-ketoglutarate). A sixth cluster contains ratios that included lactate or pyruvate.
Examples of amine-containing metabolites that may be used as analytes include 4-hydroxyproline, alanine, arginine, asparagine, aspartic acid, beta-alanine, beta-aminoisobutyric acid, citrulline, ethanolamine, gamma-aminobutyric acid, glutamic acid, glutamine, glycine, histidine, homocitrulline, homoserine, hypoxanthine, inosine, isoleucine, kynurenine, leucine, lysine, methionine, ornithine, phenylalanine, proline, sarcosine, serine, serotonin, taurine, threonine, tryptophan, tyrosine, uric acid, valine, and xanthine. Examples of non-amine-containing metabolites that may be used as analytes include alpha-ketoglutaric acid, lactic acid, pyruvic acid, and succinic acid.
Metabotypes include ratios of levels of specific metabolites. The ratios may be ratios of levels of individual metabolites, or the ratio may include the level of a class of metabolites, such as branched chain amino acids, e.g., leucine, isoleucine, and valine. For example and without limitation, the ratios of concentrations may be or include one or more of 4-hydroxyproline to xanthine; alanine to 4-hydroxyproline; alanine to carnitine; alanine to kynurenine; alanine to lactate; alanine to lysine; alanine to phenylalanine; alanine to succinate; alanine to tyrosine; alanine to valine; alpha-ketoglutarate to alanine; alpha-ketoglutarate to ethanolamine; alpha-ketoglutarate to glycine; alpha-ketoglutarate to lactate; alpha-ketoglutarate to lysine; alpha-ketoglutarate to ornithine; alpha-ketoglutarate to pyruvate; alpha-ketoglutarate to taurine; alpha-ketoglutarate to tryptophan; alpha-ketoglutarate to valine; arginine to 4-hydroxyproline; arginine to carnitine; arginine to citrate; arginine to glycine; arginine to lactate; arginine to leucine; arginine to phenylalanine; arginine to succinate; arginine to tyrosine; asparagine to glycine; asparagine to lactate; asparagine to succinate; aspartic acid to lactate; aspartic acid to pyruvate; aspartic acid to succinate; carnitine to citrulline; carnitine to ethanolamine; carnitine to glycine; carnitine to homoserine; carnitine to hypoxanthine; carnitine to lactate; carnitine to leucine; carnitine to malate; carnitine to methionine; carnitine to ornithine; carnitine to pyruvate; carnitine to succinate; carnitine to taurine; carnitine to xanthine; citrate to ethanolamine; citrate to glycine; citrate to homoserine; citrate to lactate; citrate to ornithine; citrate to phenylalanine; citrate to serine; citrate to taurine; citrulline to lactate; citrulline to succinate; ethanolamine to 4-hydroxyproline; ethanolamine to kynurenine; ethanolamine to lactate; ethanolamine to malate; ethanolamine to taurine; ethanolamine to urate; gamma-aminobutyric acid to succinate; glutamic acid to 4-hydroxyproline; glutamic acid to lactate; glutamic acid to pyruvate; glutamic acid to succinate; glutamine to lactate; glutamine to lysine; glycine to isoleucine; glycine to lactate; glycine to leucine; glycine to lysine; glycine to malate; glycine to methionine; glycine to phenylalanine; glycine to succinate; glycine to valine; histidine to lactate; histidine to leucine; histidine to xanthine; homocitrulline to lactate; homocitrulline to pyruvate; homocitrulline to succinate; homoserine to isoleucine; homoserine to lactate; homoserine to leucine; homoserine to malate; homoserine to pyruvate; hypoxanthine to 4-hydroxyproline; isoleucine to lactate; isoleucine to serine; kynurenine to glutamate; kynurenine to lactate; kynurenine to ornithine; kynurenine to pyruvate; lactate to 4-hydroxyproline; lactate to leucine; lactate to lysine; lactate to malate; lactate to methionine; lactate to ornithine; lactate to phenylalanine; lactate to proline; lactate to sarcosine; lactate to serine; lactate to taurine; lactate to threonine; lactate to tyrosine; lactate to urate; lactate to valine; lactate to xanthine; leucine to methionine; leucine to serine; leucine to succinate; leucine to valine; lysine to ornithine; lysine to phenylalanine; malate to 4-hydroxyproline; malate to proline; malate to taurine; methionine to succinate; ornithine to phenylalanine; ornithine to succinate; phenylalanine to pyruvate; phenylalanine to taurine; phenylalanine to taurine; proline to pyruvate; proline to succinate; pyruvate to 4-hydroxyproline; pyruvate to sarcosine; serine to succinate; serine to urate; succinate to 4-hydroxyproline; succinate to taurine; taurine to 4-hydroxyproline; threonine to valine; and xanthine to urate.
Metabotypes may be defined by clusters of ratios of metabolite concentrations. For example, a metabotype may include multiple ratios within a cluster, or a metabotype may include one or more ratios from each of one or more different clusters.
One cluster of ratios of concentrations may include 4-hydroxyproline to xanthine; ethanolamine to 4-hydroxyproline; histidine to xanthine; hypoxanthine to 4-hydroxyproline; lactate to 4-hydroxyproline; malate to 4-hydroxyproline; pyruvate to 4-hydroxyproline; succinate to 4-hydroxyproline; and taurine to 4-hydroxyproline.
Another cluster of ratios of concentrations may include alpha-ketoglutarate to alanine; alpha-ketoglutarate to lysine; alpha-ketoglutarate to ornithine; alpha-ketoglutarate to tryptophan; and alpha-ketoglutarate to valine.
Another cluster of ratios of concentrations may include alanine to carnitine; arginine to carnitine; carnitine to citrulline; carnitine to ethanolamine; carnitine to glycine; carnitine to homoserine; carnitine to hypoxanthine; carnitine to lactate; carnitine to leucine; carnitine to malate; carnitine to methionine; carnitine to ornithine; carnitine to pyruvate; carnitine to succinate; carnitine to taurine; and carnitine to xanthine.
Another cluster of ratios of concentrations may include arginine to citrate; citrate to ethanolamine; citrate to homoserine; citrate to ornithine; citrate to phenylalanine; and citrate to serine.
Another cluster of ratios of concentrations may include alpha-ketoglutarate to ethanolamine; ethanolamine to urate; and serine to urate.
Another cluster of ratios of concentrations may include glutamine to lysine; and lysine to phenylalanine.
Another cluster of ratios of concentrations may include alanine to kynurenine; alanine to lysine; alanine to phenylalanine; alanine to tyrosine; alanine to valine; alpha-ketoglutarate to glycine; arginine to glycine; arginine to leucine; arginine to phenylalanine; arginine to tyrosine; asparagine to glycine; citrate to glycine; glycine to isoleucine; glycine to leucine; glycine to lysine; glycine to malate; glycine to methionine; glycine to phenylalanine; glycine to valine; histidine to leucine; homoserine to isoleucine; homoserine to leucine; isoleucine to serine; leucine to methionine; leucine to serine; and threonine to valine.
Another cluster of ratios of concentrations may include alanine to lactate; alpha-ketoglutarate to lactate; alpha-ketoglutarate to pyruvate; arginine to lactate; asparagine to lactate; aspartic acid to lactate; aspartic acid to pyruvate; aspartic acid to succinate; citrate to lactate; citrulline to lactate; ethanolamine to lactate; glutamic acid to lactate; glutamic acid to pyruvate; glutamic acid to succinate; glutamine to lactate; glycine to lactate; histidine to lactate; homocitrulline to lactate; homocitrulline to pyruvate; homoserine to lactate; homoserine to pyruvate; isoleucine to lactate; kynurenine to lactate; kynurenine to pyruvate; lactate to leucine; lactate to lysine; lactate to malate; lactate to methionine; lactate to ornithine; lactate to phenylalanine; lactate to proline; lactate to sarcosine; lactate to serine; lactate to taurine; lactate to threonine; lactate to tyrosine; lactate to urate; lactate to valine; lactate to xanthine; phenylalanine to pyruvate; proline to pyruvate; and pyruvate to sarcosine.
Another cluster of ratios of concentrations may include ethanolamine to malate; homoserine to malate; and malate to proline.
Another cluster of ratios of concentrations may include lysine to ornithine; and ornithine to phenylalanine.
Another cluster of ratios of concentrations may include arginine to 4-hydroxyproline; ethanolamine to kynurenine; and leucine to valine.
Another cluster of ratios of concentrations may include alanine to succinate; arginine to succinate; asparagine to succinate; citrulline to succinate; gamma-aminobutyric acid to succinate; glycine to succinate; homocitrulline to succinate; leucine to succinate; methionine to succinate; ornithine to succinate; proline to succinate; and serine to succinate.
Another cluster of ratios of concentrations may include alpha-ketoglutarate to taurine; citrate to taurine; ethanolamine to taurine; glutamic acid to 4-hydroxyproline; malate to taurine; phenylalanine to taurine; phenylalanine to taurine; and succinate to taurine.
Another cluster of ratios of concentrations may include succinate to citrulline and succinate to glycine.
Another cluster of ratios of concentrations may include lactate to 4-hydroxyproline; lactate to alanine; lactate to arginine; lactate to asparagine; lactate to citrulline; lactate to glutamate; lactate to glutamine; lactate to histidine; lactate to kynurenine; lactate to leucine; sarcosine; lactate to tyrosine; pyruvate to kynurenine; and pyruvate to phenylalanine.
Another cluster of ratios of concentrations may include ornithine to leucine; ornithine to lysine; and ornithine to phenylalanine.
Another cluster of ratios of concentrations may include glycine to asparagine; glycine to isoleucine; glycine to lysine; and glycine to phenylalanine.
Another cluster of ratios of concentrations may include alanine to 4-hydroxyproline; and arginine to 4-hydroxyproline.
Another cluster of ratios of concentrations may include α-ketoglutarate to phenylalanine; and alanine to α-ketoglutarate.
Other Representative metabotypes are indicated in Table 1.
Metabotypes are described in co-owned, co-pending International Publication No. WO 2019/148189, the contents of which are incorporated herein by reference.
Methods of the invention include comparing concentrations of metabolites or ratios of concentrations of metabolites to reference levels. A reference level may be a discrete value or a range of values. The range of values may include all values that differ from a discrete value by a defined amount. For example and without limitation, the upper and lower limits of a range of values may differ from a discrete value by 1 standard deviation, 1.5 standard deviations, 2 standard deviations, 2.5 standard deviations, 3 standard deviations, 3.5 standard deviations, 4 standard deviations, 4.5 standard deviations, or 5 standard deviations.
The reference level may be from a defined population of subjects. For example, the population may be a subset of autism spectrum disorder (ASD) subjects. The subset may include subjects that have an alteration in a metabolic pathway in comparison to other ASD subjects, typically developing subjects, or in both. In such embodiments, a similar metabolic alteration in the subject may indicate that the subject from whom the sample was obtained has or is likely to develop ASD, and the absence of such an alteration may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD.
The reference level may include one or more ratios obtained from a reference population. The reference population may be a subset of autism spectrum disorder (ASD) subjects. The subset may include subjects that have an alteration in a metabolic pathway in comparison to other ASD subjects, typically developing subjects, or in both. In such embodiments, a similar metabolic alteration in the subject may indicate that the subject from whom the sample was obtained has or is likely to develop ASD, and the absence of such an alteration may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD. The reference population may include typically developing subjects. In such embodiments, a metabolic similarity or lack of alteration between the subject and the reference may indicate that the subject from whom the sample was obtained does not have or is not likely to develop ASD, and metabolic dissimilarity or alteration may indicate that the subject from whom the sample was obtained has or is likely to develop ASD. The reference population may include subjects that have a non-ASD developmental disorder.
Samples may be obtained from any of a variety of mammalian subjects. In preferred embodiments, a sample is from a human subject.
A sample may be from an individual clinically diagnosed with ASD. ASD may be diagnosed by any of a variety of well-known clinical criteria. For example, diagnosis of autism spectrum disorder may be based on the DSM-V criteria determined by an experienced neuropsychologist and/or the Autism Diagnostic Observation Schedule-Generic (ADOS-G) which provides observation of a child's communication, reciprocal social interaction, and stereotyped behavior including an algorithm with cutoffs for autism and autism spectrum disorders. A sample may be obtained from an individual previously diagnosed with autism spectrum disorder (ASD) and/or is undergoing treatment.
A sample may be obtained from an individual with a neurodevelopmental disorder.
A sample may be obtained from an individual determined to be developmentally delayed (DD), for example, demonstrating impairment in physical learning, language, and/or behavior
A sample may be obtained from an individual determined to be at some risk for ASD (for example by family history) with little or no current ASD symptoms. A sample may be a suitable reference or control sample from an individual not suffering from ASD with or without a family history of ASD. A sample may be obtained from a typically developing (TD) individual.
A sample may be obtained from a member of a subset of ASD subjects. The subset of ASD subjects may have a metabotype that is different from that of other ASD subjects, typically developing subjects, or both. The subset of ASD ratios may have a ratio of metabolites that is different from ratios in other ASD subjects, in typically developing subjects, or in both.
A sample may be obtained from a phenotypic subpopulation of autism subjects, such as, for example, high functioning autism (HFA) or low functioning autism (LFA). A sample may be from an adult subject. A sample may be from a teenager. A sample may be from a child. A subject may be less than about 18 years of age, less than about 16 years of age, less than about 14 years of age, less than about 13 years of age, less than about 12 years of age, less than about 10 years of age, less than about 9 years of age, less than about 8 years of age, a child of less than about 7 years of age, a child of less than about 6 years of age, a child of less than about 5 years of age, a child of less than about 4 years of age, a child of less than about 3 years of age, a child of less than about 2 years of age, a child of less than about 18 months of age, a child of less than about 12 months of age, a child of less than about 9 months of age, a child of less than about 6 months of age, or a child of less than about 3 months of age, about 1 to about 6 years of age, about 1 to about 5 years of age, about 1 to about 4 years of age, about 1 to about 2 years of age, about 2 to about 6 years of age, about 2 to about 4 years of age, or about 4 to about 6 years of age.
In accordance with the methods disclosed herein, any type of biological sample that originates from anywhere within the body of a subject may be tested, including, but not limited to, blood (including, but no limited to serum or plasma), dried blood spots, cerebrospinal fluid (CSF), pleural fluid, urine, stool, sweat, tears, hair, mucus, breath condensate, saliva, vitreous humour, a tissue sample, amniotic fluid, a chorionic villus sampling, brain tissue, a biopsy of any solid tissue including tumor, adjacent normal, smooth and skeletal muscle, adipose tissue, liver, skin, hair, brain, kidney, pancreas, lung, colon, stomach, or the like may be used. A blood sample may include, for example, a whole blood sample, a blood serum sample, a blood plasma sample, or other blood components, such as, for example, a subfraction or an isolated cellular subpopulation of whole blood. In some aspects, a sample may be a cellular membrane preparation. A sample may be from a live subject. In some applications, samples may be collected postmortem. A sample includes for example, cerebrospinal fluid, brain tissue, amniotic fluid, blood, serum, plasma, amniotic fluid, urine, breath condensate, sweat, saliva, tears, hair, cell membranes, and/or vitreous humour. In some aspects, a sample includes plasma.
When a blood sample is drawn from a subject, it can be processed in any of many known ways. The range of processing can be from little to none (such as, for example, frozen whole blood) or as complex as the isolation of a particular cell type. Common and routine procedures include the preparation of either serum or plasma from whole blood. All blood sample processing methods, including spotting of blood samples onto solid-phase supports, such as filter paper or other immobile materials, are contemplated by the present invention.
Samples may be collected repeatedly from a subject. For example, samples may be collected according to a schedule or at defined intervals, such as daily, weekly, biweekly, monthly, every two months, every three months, every four months, every six months, or annually.
Samples may be collected after a wash-out period, i.e., a period following a change in diet, medication, or other therapeutic program. The wash-out period allows the body to adapt to a new course of treatment and manifest an effect of the new treatment. The wash-out period may be about one day, about two days, about three days, about four days, about five days, about one week, about two weeks, about three weeks, about four weeks, about six weeks, about eight weeks, about twelve weeks, or more
With the preparation of samples for analysis, metabolites may be extracted from their biological source using any number of extraction/clean-up procedures that are typically used in quantitative analytical chemistry.
The metabolic markers and signatures described herein may be utilized in tests, assays, methods, kits for diagnosing, predicting, modulating, or monitoring ASD, including ongoing assessment, monitoring, susceptibility assessment, carrier testing and prenatal diagnosis.
Metabolic biomarkers may be identified by their unique molecular mass and consistency, thus the actual identity of the underlying compound that corresponds to the biomarker is not required for the practice of this invention. Biomarkers may be identified using, for example, Mass Spectrometry such as MALDI/TOF (time-of-flight), SELDI/TOF, liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography-mass spectrometry (HPLC-MS), capillary electrophoresis mass spectrometry, nuclear magnetic resonance spectrometry, tandem mass spectrometry (e.g., MS/MS, MS/MS/MS, ESI-MS/MS etc.), secondary ion mass spectrometry (SIMS), and/or ion mobility spectrometry (e.g. GC-IMS, IMS-MS, LC-IMS, LC-IMS-MS etc.).
Metabolites as set forth herein can be detected using any of the methods described herein. Metabolites, as set forth herein, can be detected using alternative spectrometry methods or other methods known in the art, in addition to any of those described herein.
In some aspects, the determination of a metabolite may be by a methodology other than a physical separation method, such as for example, a colorimetric, electrochemical, enzymatic, immunological methodology, and gene expression analysis, including, for example, real-time PCR, RT-PCR, Northern analysis, and in situ hybridization.
In some aspects, the quantification of one or more small molecule metabolites of a metabolic signature of autism may be assayed using a physical separation method, such as, for example, one or more methodologies selected from gas chromatography mass spectrometry (GCMS), C8 liquid chromatography coupled to electrospray ionization in positive ion polarity (C8pos), C8 liquid chromatography coupled to electrospray ionization in negative ion polarity (C8neg), hydrophilic interaction liquid chromatography coupled to electrospray ionization in positive ion polarity (HILICpos), and/or hydrophilic interaction liquid chromatography coupled to electrospray ionization in negative ion polarity (HILICneg).
With any of the methods described herein, any combination of one or more gas chromatography-mass spectrometry (GC-MS) methodologies and/or one or more liquid chromatography-high resolution mass spectrometry (LC-HRMS) methodologies may be used. In some aspects, a GC-MS method may be targeted. In some aspects, a LC-HRMS method may be untargeted. Subsequently, in some embodiments, tandem mass spectrometry (MS-MS) methods may be employed for the structural confirmation of metabolites. LC-HRMS methodologies may include C8 chromatography and/or Hydrophilic Interaction Liquid Chromatography (HILIC) chromatography. Either of C8 chromatography or HILIC chromatography may be coupled to electrospray ionization in both positive and negative ion polarities, resulting in multiple data acquisitions per sample.
In some aspects of the methods described herein, concentrations of one or more metabolites, including, but not limited to CMPF, may be determined using C18 (reverse phase) LC coupled with a triple quadrupole (QqQ) MS using electrospray ionization in the positive ion mode with analyte detection in the multiple reaction monitoring (MRM) mode. This may include a stable label internal standard and CMPF concentrations are measured distributed over a linear range of 0.05 to 100 μM.
In some embodiments, levels of metabolites are measured by mass spectrometry, optionally in combination with liquid chromatography. Molecules may be ionized for mass spectrometry by any method known in the art, such as ambient ionization, chemical ionization (CI), desorption electrospray ionization (DESI), electron impact (EI), electrospray ionization (ESI), fast-atom bombardment (FAB), field ionization, laser ionization (LIMS), matrix-assisted laser desorption ionization (MALDI), paper spray ionization, plasma and glow discharge, plasma-desorption ionization (PD), resonance ionization (RIMS), secondary ionization (SIMS), spark source, or thermal ionization (TIMS). Methods of mass spectrometry are known in the art and described in, for example, U.S. Pat. Nos. 8,895,918; 9,546,979; 9,761,426; Hoffman and Stroobant, Mass Spectrometry: Principles and Applications (2nd ed.). John Wiley and Sons (2001), ISBN 0-471-48566-7; Dass, Principles and practice of biological mass spectrometry, New York: John Wiley (2001) ISBN 0-471-33053-1; and Lee, ed., Mass Spectrometry Handbook, John Wiley and Sons, (2012) ISBN: 978-0-470-53673-5, the contents of each of which are incorporated herein by reference.
In certain embodiments, samples are derivatized prior to analysis by liquid chromatography and/or mass spectrometry. In certain embodiments, samples are not derivatized prior to analysis by liquid chromatography and/or mass spectrometry.
In certain embodiments, a sample can be directly ionized without the need for use of a separation system. In other embodiments, mass spectrometry is performed in conjunction with a method for resolving and identifying ionic species. Suitable methods include chromatography, capillary electrophoresis-mass spectrometry, and ion mobility. Chromatographic methods include gas chromatography, liquid chromatography (LC), high-pressure liquid chromatography (HPLC), and reversed-phase liquid chromatography (RPLC). In a preferred embodiment, liquid chromatography-mass spectrometry (LC-MS) is used. Methods of coupling chromatography and mass spectrometry are known in the art and described in, for example, Holcapek and Brydwell, eds. Handbook of Advanced Chromatography/Mass Spectrometry Techniques, Academic Press and AOCS Press (2017), ISBN 9780128117323; Pitt, Principles and Applications of Liquid Chromatography-Mass Spectrometry in Clinical Biochemistry, The Clinical Biochemist Reviews. 30(1): 19-34 (2017) ISSN 0159-8090; Niessen, Liquid Chromatography-Mass Spectrometry, Third Edition. Boca Raton: CRC Taylor & Francis. pp. 50-90. (2006) ISBN 9780824740825; Ohnesorge et al., Quantitation in capillary electrophoresis-mass spectrometry, Electrophoresis. 26 (21): 3973-87 (2005) doi:10.1002/elps.200500398; Kolch et al., Capillary electrophoresis-mass spectrometry as a powerful tool in clinical diagnosis and biomarker discovery, Mass Spectrom Rev. 24 (6): 959-77. (2005) doi:10.1002/mas.20051; Kanu et al., Ion mobility-mass spectrometry, Journal of Mass Spectrometry, 43 (1): 1-22 (2008) doi:10.1002/jms.1383, the contents of which are incorporated herein by reference.
In some embodiments of the assays and/or methods described herein, the assay/method comprises or consists essentially of a system for doing one or more of the following steps: analyzing data, such as mass spectrometry data, to determine levels of metabolites in a sample; determining a ratio of two or more levels; and comparing a ratio from a sample to a reference ratio. If the comparison system, which may be a computer implemented system, indicates that the ratio in the sample is statistically higher or lower than the reference ratio, the subject from which the sample is collected may be identified as having or likely to develop an ASD.
The computer systems of the invention may include one or more of the following: (a) at least one memory containing at least one computer program adapted to control the operation of the computer system to implement a method that includes (i) a determination module configured to measure the levels of two or more metabolites in a test sample obtained from a subject; (ii) a storage module configured to store output data from the determination module; (iii) a computing module adapted to identify from the output data whether the ratio of levels in the test sample is statistically different from a reference ratio, and to provide a retrieved content; (iv) a display module for displaying for retrieved content (e.g., the ratio in the test sample and whether the test ratio is higher or lower than the reference ratio in a statistically significant manner); and (b) at least one processor for executing the computer program.
Embodiments may be described through functional modules, which are defined by computer executable instructions recorded on computer readable media and which cause a computer to perform method steps when executed. The modules are segregated by function for the sake of clarity. However, it should be understood that the modules/systems need not correspond to discreet blocks of code and the described functions may be carried out by the execution of various code portions stored on various media and executed at various times. Furthermore, it should be appreciated that the modules may perform other functions, thus the modules are not limited to having any particular functions or set of functions.
The computer-readable storage media may be any available tangible media that can be accessed by a computer. Computer readable storage media includes volatile and nonvolatile, removable and non-removable tangible media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer readable storage media includes, but is not limited to, RAM (random access memory), ROM (read only memory), EPROM (erasable programmable read only memory), EEPROM (electrically erasable programmable read only memory), flash memory or other memory technology, CD-ROM (compact disc read only memory), DVDs (digital versatile disks) or other optical storage media, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage media, other types of volatile and non-volatile memory, and any other tangible medium which can be used to store the desired information and which can accessed by a computer including and any suitable combination of the foregoing.
Computer-readable data embodied on one or more computer-readable media may define instructions, for example, as part of one or more programs that, as a result of being executed by a computer, instruct the computer to perform one or more of the functions described herein, and/or various embodiments, variations and combinations thereof. Such instructions may be written in any of a plurality of programming languages, for example, Java, J #, Visual Basic, C, C #, C++, R, Python, Fortran, Pascal, Eiffel, Basic, COBOL assembly language, and the like, or any of a variety of combinations thereof. The computer-readable media on which such instructions are embodied may reside on one or more of the components of either of a system, or a computer readable storage medium described herein, may be distributed across one or more of such components.
The computer-readable media may be transportable such that the instructions stored thereon may be loaded onto any computer resource to implement the aspects of the technology discussed herein. In addition, it should be appreciated that the instructions stored on the computer-readable medium, described above, are not limited to instructions embodied as part of an application program running on a host computer. Rather, the instructions may be embodied as any type of computer code (e.g., software or microcode) that can be employed to program a computer to implement aspects of the technology described herein. The computer executable instructions may be written in a suitable computer language or combination of several languages. Basic computational biology methods are known to those of ordinary skill in the art and are described in, for example, Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000); and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2nd ed., 2001), the contents of each of which are incorporated herein by reference.
The functional modules of certain embodiments may include at minimum a determination module, a storage module, a computing module, and a display module. The functional modules may be executed on one, or multiple, computers, or by using one, or multiple, computer networks. The determination module has computer executable instructions to provide e.g., levels of expression products in computer readable form.
The determination module may comprise any system for detecting a signal resulting from the ratio of levels of metabolites in a biological sample. In some embodiments, such systems may include an instrument, e.g., a plate reader for measuring absorbance. In some embodiments, such systems may include an instrument, e.g., the Cell Biosciences NANOPRO 1000™ System (Protein Simple; Santa Clara, CA) for quantitative measurement of proteins.
The information determined in the determination system may be read by the storage module. As used herein the “storage module” is intended to include any suitable computing or processing apparatus or other device configured or adapted for storing data or information. Examples of electronic apparatus suitable for use with the technology described herein include stand-alone computing apparatus, data telecommunications networks, including local area networks (LAN), wide area networks (WAN), Internet, Intranet, and Extranet, and local and distributed computer processing systems. Storage modules also include, but are not limited to: magnetic storage media, such as floppy discs, hard disc storage media, magnetic tape, optical storage media such as CD-ROM, DVD, electronic storage media such as RAM, ROM, EPROM, EEPROM and the like, general hard disks and hybrids of these categories such as magnetic/optical storage media. The storage module is adapted or configured for having recorded thereon, for example, sample name, patient name, and numerical value of the ratio. Such information may be provided in digital form that may be transmitted and read electronically, e.g., via the Internet, on diskette, via USB (universal serial bus) or via any other suitable mode of communication. Those skilled in the art can readily adopt any of the presently known methods for recording information on known media to generate manufactures comprising expression level information.
In one embodiment of any of the systems described herein, the storage module stores the output data from the determination module. In additional embodiments, the storage module stores the reference information such as ratios of levels of metabolites samples obtained from typically developing subjects. In some embodiments, the storage module stores the information such as ratios of levels of metabolites in samples obtained from the same subject in earlier time points.
The computing module may use a variety of available software programs and formats for computing the ratios of levels of metabolites. Such algorithms are well established in the art. A skilled artisan is readily able to determine the appropriate algorithms based on the size and quality of the sample and type of data. The data analysis may be implemented in the computing module. In one embodiment, the computing module further comprises a comparison module, which compares the ratios of levels of metabolites in the test sample obtained from a subject as described herein with the reference ratio. For example, when the ratio in the test sample obtained from a subject is determined, a comparison module may compare or match the output data, e.g. with the reference ratio. In certain embodiments, the reference level has been pre-stored in the storage module. During the comparison or matching process, the comparison module may determine whether the ratio in the test sample obtained from a subject is higher or lower than the reference ratio to a statistically significant degree. In various embodiments, the comparison module may be configured using existing commercially-available or freely-available software for comparison purpose, and may be optimized for particular data comparisons that are conducted.
The computing and/or comparison module, or any other module, may include an operating system (e.g., UNIX) on which runs a relational database management system, a World Wide Web application, and a World Wide Web server. World Wide Web application includes the executable code necessary for generation of database language statements (e.g., Structured Query Language (SQL) statements). Generally, the executables will include embedded SQL statements. In addition, the World Wide Web application may include a configuration file which contains pointers and addresses to the various software entities that comprise the server as well as the various external and internal databases which must be accessed to service user requests. The Configuration file also directs requests for server resources to the appropriate hardware, as may be necessary should the server be distributed over two or more separate computers. In one embodiment, the World Wide Web server supports a TCP/IP protocol. Local networks such as this are sometimes referred to as “Intranets.” An advantage of such Intranets is that they allow easy communication with public domain databases residing on the World Wide Web (e.g., the GenBank or Swiss Pro World Wide Web site). Thus, in a particular preferred embodiment, users can directly access data (via Hypertext links for example) residing on Internet databases using a HTML, interface provided by Web browsers and Web servers.
The computing and/or comparison module provides a computer readable comparison result that can be processed in computer readable form by predefined criteria, or criteria defined by a user, to provide content based in part on the comparison result that may be stored and output as requested by a user using an output module, e.g., a display module.
In some embodiments, the content displayed on the display module may be the relative ratio in the test sample obtained from a subject as compared to a reference ratio. In certain embodiments, the content displayed on the display module may indicate whether the ratio is found to be statistically significantly higher in the test sample obtained from a subject as compared to a reference ratio. In some embodiments, the content displayed on the display module may show the ratios from the subject measured at multiple time points, e.g., in the form of a graph. In some embodiments, the content displayed on the display module may indicate whether the subject has an ASD. In certain embodiments, the content displayed on the display module may indicate whether the subject is in need of treatment for an ASD.
In one embodiment, the content based on the computing and/or comparison result is displayed on a computer monitor. In one embodiment, the content based on the computing and/or comparison result is displayed through printable media. The display module may be any suitable device configured to receive from a computer and display computer readable information to a user. Non-limiting examples include, for example, general-purpose computers such as those based on Intel PENTIUM-type processor, Motorola PowerPC, Sun UltraSPARC, Hewlett-Packard PA-RISC processors, any of a variety of processors available from Advanced Micro Devices (AMD) of Sunnyvale, California, or any other type of processor, visual display devices such as flat panel displays, cathode ray tubes and the like, as well as computer printers of various types.
In one embodiment, a World Wide Web browser is used for providing a user interface for display of the content based on the computing/comparison result. It should be understood that other modules may be adapted to have a web browser interface. Through the Web browser, a user can construct requests for retrieving data from the computing/comparison module. Thus, the user will typically point and click to user interface elements such as buttons, pull down menus, scroll bars and the like conventionally employed in graphical user interfaces.
Systems and computer readable media described herein are merely illustrative embodiments of the technology relating to determining the ratios of level of metabolites, and therefore are not intended to limit the scope of the invention. Variations of the systems and computer readable media described herein are possible and are intended to fall within the scope of the invention.
The modules of the machine, or those used in the computer readable medium, may assume numerous configurations. For example, function may be provided on a single machine or distributed over multiple machines.
In some aspects, the minimum percentage sensitivity required for the determination of a hypothetical diagnostic includes about 1%, about 2%, about 3%, about 4%, about 5%, about 7%, about 8%, about 9%, about 10%, about 11%, about 12%, about 13%, about 14%, about 15%, about 16%, about 17%, about 18%, about 19%, or about 20%, rather than about 6%; the ratio diagnostics perform with greater than at least about 95% specificity, at least about 96% specificity, at least about 97% specificity, at least about 98% specificity, or at least about 99% specificity; and/or the ratio diagnostics perform with at least about 75% specificity, at least about 80% specificity, at least about 85% specificity, at least about 86% specificity, at least about 87% specificity, at least about 88% specificity, or at least about 89% specificity, rather than greater than about 90% specificity.
Data collected during analysis may be quantified for one or more than one metabolite. Quantifying data may be obtained by measuring the levels or intensities of specific metabolites present in a sample. The quantifying data may be compared to corresponding data from one or more than one reference sample. For example, a reference sample may be a sample from a control individual, i.e., a person not suffering from ASD with or without a family history of ASD (also referred to herein as a “typically developing individual” (TD), or “normal” counterpart). A reference sample may also be a sample obtained from a patient clinically diagnosed with ASD. A reference sample may be a sample from a member of a subset of ASD subjects. Subjects in the subset may have a ratio of concentrations of two or more metabolites that is different from the ratio of concentrations of the two or more metabolites in other ASD subjects, in typically developing subjects, or in both. For example, the subset may include ASD subjects of a particular metabotype. As would be understood by a person of skill in the art, more than one reference sample may be used for comparison to the quantifying data.
Sensitivity and specificity are statistical measures of the performance of a binary classification test. Sensitivity measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition). Specificity measures the proportion of negatives which are correctly identified (e.g. the percentage of healthy people who are correctly identified as not having the condition). These two measures are closely related to the concepts of type I and type II errors. A theoretical, optimal prediction can achieve 100% sensitivity (i.e. predict all people from the sick group as sick) and 100% specificity (i.e. not predict anyone from the healthy group as sick). A specificity of 100% means that the test recognizes all actual negatives—for example, in a test for a certain disease, all disease-free people will be recognized as disease free. A sensitivity of 100% means that the test recognizes all actual positives—for example, all sick people are recognized as being ill. Thus, in contrast to a high specificity test, negative results in a high sensitivity test are used to rule out the disease. A positive result in a high specificity test can confirm the presence of disease. However, from a theoretical point of view, a 100%-specific test standard can also be ascribed to a ‘bogus’ test kit whereby the test simply always indicates negative. Therefore, the specificity alone does not tell us how well the test recognizes positive cases. Knowledge of sensitivity is also required. For any test, there is usually a trade-off between the measures. For example, in a diagnostic assay in which one is testing for people who have a certain condition, the assay may be set to overlook a certain percentage of sick people who are correctly identified as having the condition (low specificity), in order to reduce the risk of missing the percentage of typically developing people who are correctly identified as not having the condition (high sensitivity). Eliminating the systematic error improves accuracy but does not change precision. This trade off can be represented graphically using a receiver operating characteristic (ROC) curve.
The accuracy of a measurement system is the degree of closeness of measurements of a quantity to its actual (true) value. The “precision” of a measurement system, also called reproducibility or repeatability, is the degree to which repeated measurements under unchanged conditions show the same results. Although the two words can be synonymous in colloquial use, they are deliberately contrasted in the context of the scientific method. A measurement system can be accurate but not precise, precise but not accurate, neither, or both. For example, if an experiment contains a systematic error, then increasing the sample size generally increases precision but does not improve accuracy.
Predictability (also called banality) is the degree to which a correct prediction or forecast of a system's state can be made either qualitatively or quantitatively. Perfect predictability implies strict determinism, but lack of predictability does not necessarily imply lack of determinism. Limitations on predictability could be caused by factors such as a lack of information or excessive complexity.
Data analysis may include comparing a ratio of levels of metabolites in a sample from a test subject to a corresponding ratio from one or more reference subjects. The latter number may be referred to as a reference ratio. The reference ratio may be defined based on clinical trials that determine the ratio of levels of metabolites that that optimally defines a cut-off point above which the likelihood of occurrence of an ASD is high and below which the likelihood of occurrence of an ASD is low.
The reference ratio may be defined by a statistic describing the distribution of ratios in typically developing subjects. The reference ratio may be above the highest observed ratio in a sample from a typically developing subject or a population of typically developing subjects, or the reference ratio may be below the lowest observed ratio in a sample from a typically developing subject or a population of typically developing subjects. Any ratio above or below the reference ratio may be deemed to be significantly different from the average ratio in a sample from a typically developing subject or a population of typically developing subjects. The reference ratio may be greater than 95% of the ratios observed in samples from a typically developing subject or a population of typically developing subjects, or it may be above the lower limit of the highest decile, quartile or tertile of the ratios observed in samples from a typically developing subject or a population of typically developing subjects. Alternatively, the reference ratio may be less than 95% of the ratios observed in samples from a typically developing subject or a population of typically developing subjects, or it may be lower than the upper limit of the lowest decile, quartile or tertile of the ratios observed in samples from a typically developing subject or a population of typically developing subjects.
The reference ratio may be defined by a statistic describing the distribution of ratios in a subset of ASD subjects. The reference ratio may be above the highest observed ratio in a sample from a subset of ASD subjects, or the reference ratio may be below the lowest observed ratio in a sample from a subset of ASD subjects. Any ratio above or below the reference ratio may be deemed to be significantly different from the average ratio in a sample from a member of a subset of ASD subjects. The reference ratio may be greater than 95% of the ratios observed in samples from a subset of ASD subjects, or it may be above the lower limit of the highest decile, quartile or tertile of the ratios observed in samples from a subset of ASD subjects. Alternatively, the reference ratio may be less than 95% of the ratios observed in samples from a subset of ASD subjects, or it may be lower than the upper limit of the lowest decile, quartile or tertile of the ratios observed in samples from a subset of ASD subjects.
The reference ratio may be at least one standard deviation, at least two standard deviations, or at least three standard deviations above or below the average ratio in a sample from a typically developing subject or a population of typically developing subjects. Any ratio above or below the reference ratio may be deemed to be significantly different from the average ratio in a sample from a typically developing subject or a population of typically developing subjects.
The reference ratio may be at least one standard deviation, at least two standard deviations, or at least three standard deviations above or below the average ratio in a sample from a subset of ASD subjects. Any ratio above or below the reference ratio may be deemed to be significantly different from the average ratio in a sample from a subset of subjects.
The reference ratio may be a ratio in a sample of the same subject measured at an earlier time point. The reference level may be a ratio in a sample obtained from the same subject before commencement of a therapeutic program, such as an altered diet and/or course of medication. The reference ratio may be from a sample obtained 1 hour, 2 hours, 4 hours, 6 hours, 8 hours, 12 hours, 24 hours, 36 hours, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days or more before commencing the course of therapy.
The reference ratio may be at least one standard deviation, at least two standard deviations, or at least three standard deviations above or below a ratio in a sample obtained from the same subject at an earlier time point. Any ratio above or below the reference ratio may be deemed to be significantly different from the ratio in the earlier sample.
In some embodiments, the ratio of level of metabolites measured in a sample from a subject identified as having an ASD may be at least 5%, at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100%, at least 150%, at least 200%, or at least 300% higher or lower than the reference ratio.
The reference ratio may be adjusted to account for variables such as sample type, gender, age, weight, and ethnicity. Thus, reference ratios accounting for these and other variables may provide added accuracy for the methods described herein.
With any of the methods described herein, the method may further include providing guidance for individualized treatment to the one or more individuals identified as belonging to an autism subpopulation. In some aspects, individualized treatment includes one or more of a modified diet, dietary supplements, probiotic therapy, medical grade food, pharmacological therapy, applied behavior analysis therapy, behavioral therapy, occupational therapy, physical therapy, and speech-language therapy. In some aspects, the level of the one or more metabolites indicative of ASD and/or an ASD subset returns to TD levels after initiation of treatment. The methods may include providing the treatment to the subject.
The dietary modification may include supplementation with a source of amine containing compounds. For example, the dietary modification may be a protein-rich diet. The dietary modification may include supplementation with specific amine containing compounds or amino acids. For example, the dietary modification may include supplementation with one or more branched chain amino acids, such as isoleucine, leucine, or valine. Providing additional branched chain amino acids in the diet may alter ratios of levels of amino acids that are associated with ASD and therefore may prevent development of an ASD or mitigate the severity of an ASD.
The dietary modification may include a source of amine containing compounds or amino acids that is substantially free of phenylalanine. For example, patients with phenylketonuria are unable to metabolize phenylalanine, which can lead to intellectual disability, seizures, behavioral problems, and mental disorders. The milk peptide casein glycomacropeptide (CGMP) is naturally free of pure phenylalanine. Therefore, dietary supplementation with glycomacropeptide provides other amine containing compounds or amino acids s but not phenylalanine. The use of glycomacropeptide for preparation of medical foods is known in the art and described in, for example, U.S. Pat. Nos. 5,968,586; 6,168,823; and 8,604,168, the contents of each of which are incorporated herein by reference.
The guidance may provide recommendations for dietary modification. For example, the guidance may include specific formulations, such as beverages, powder, mixes, protein shakes, and the like, to provide one or more amine containing compounds or amino acids. The guidance may include a recommended quantity of one or more therapeutic dietary supplements.
The guidance may provide recommendations for specific medications to treat the ASD or one or more symptoms associated with an ASD.
The guidance may provide a recommendation for therapy, such as applied behavior analysis therapy, behavioral therapy, occupational therapy, physical therapy, or speech-language therapy.
The guidance may include a recommended schedule for a course of treatment, such as a dietary modification or medication regimen. For example, the guidance may recommend taking a supplement or medication once per day, twice per day, three times per day, or more. The guidance may include a recommended duration for a course of treatment. The duration may be one week, two weeks, three weeks, one month, two months, three months, four months, six months, one year, two years, three years, four year, five years, or more.
The guidance may include a recommendation that the subject be evaluated by a specialist. For example, the guidance may include a recommendation that the subject consult with a neurodevelopment specialist or nutritionist.
The guidance may include metrics or criteria for evaluating developmental progress of the subject. For example and without limitation, the metrics may include measures of growth, such as height and weight, hyperactivity, irritability, communication skills, socialization, or academic performance of the subject.
The guidance may be communicated in any suitable manner. For example, the guidance may be provided in a written report. The guidance may be shown on a display device, such as a computer monitor, telephone, portable electronic device, or the like.
The report may contain additional information about the subject, such as age, sex, weight, height, genetic data, genomic data, and dietary preferences.
The report may indicate that the test subject has or is at risk of developing a neurodevelopmental disorder if the test ratio is imbalanced compared to the reference ratio. The report may indicate a likelihood or probability that the test subject will develop a neurodevelopmental disorder. The report may indicate a likelihood or probability that the test subject will develop a neurodevelopmental disorder if the test subject goes untreated. The report may indicate a likelihood or probability that the test subject will develop a neurodevelopmental disorder if the test subject undergoes a particular course of treatment, such as a dietary modification.
The present invention includes kits for identifying and/or measuring one or more metabolites associated with a neurodevelopmental disorder, such as ASD or a subset of ASD. In some aspects, the kit may be for the determination of a metabolite by a physical separation method. In some aspects, the kit may be for the determination of a metabolite by a methodology other than a physical separation method, such as for example, a colorimetric, enzymatic, immunological methodology. In some aspects, an assay kit may also include one or more appropriate negative controls and/or positive controls. Kits of the present invention may include other reagents such as buffers and solutions needed to practice the invention are also included. Optionally associated with such container(s) can be a notice or printed instructions. As used herein, the phrase “packaging material” refers to one or more physical structures used to house the contents of the kit. The packaging material is constructed by well-known methods, preferably to provide a sterile, contaminant-free environment. As used herein, the term “package” refers to a solid matrix or material such as glass, plastic, paper, foil, and the like. Kits of the present invention may also include instructions for use. Instructions for use typically include a tangible expression describing the reagent concentration or at least one assay method parameter, such as the relative amounts of reagent and sample to be admixed, maintenance time periods for reagent/sample admixtures, temperature, buffer conditions, and the like. In some aspects, a kit may be a packaged combination including the basic elements of a first container including, in solid form, a specific set of one or more purified metabolites, as described herein, and a second container including a physiologically suitable buffer for resuspending or dissolving the specific subset of purified metabolites. Such a kit may be used by a medical specialist to determine whether or not a subject is at risk for ASD. Appropriate therapeutic intervention may be prescribed or initiated upon the determination of a risk of ASD. One or more of the metabolites described herein may be present in a kit.
Autism Spectrum Disorder (ASD) is characterized by core symptoms of altered social communication and repetitive behaviors or circumscribed interests and has a prevalence of 1:59 children in the United States. Affected individuals vary enormously in the severity of their autistic characteristics as well as in the occurrence of many co-morbid conditions. Co-morbid conditions include intellectual disability which affects at least 40% of individuals with autism; anxiety in approximately 50%; epilepsy in approximately 25%; and gastrointestinal disorders in approximately 25% of autistic individuals. Twin studies have indicated that genetic factors play a prominent role in the etiology of ASD although the genetics of autism appears to be extremely complex. There has been enormous progress in establishing the genetic architecture of ASD and there are at least 100 genes known to confer risk of ASD. There is also increasingly strong evidence that environmental factors, alone or in conjunction with genotype, can contribute to the risk for ASD. These findings have led to a widespread consensus that there are different biological forms of ASD that may necessitate different diagnostic, preventative, and treatment strategies.
ASD is currently diagnosed based on behavioral characteristics exhibited by an affected child. While specialist clinicians are able to confidently diagnose children as young as 24 months, the average age of diagnosis in the United States is over 4 years. Families often experience long waits to receive a definitive diagnosis due to the paucity of trained clinicians able to perform diagnostic assessment. Early diagnosis is important because intensive behavioral therapies are not only effective in reducing disability in many children with autism, but the benefit of early intervention is greater the earlier the intervention is started.
Unfortunately, there is currently no reliable biomarker that can be used to identify children at risk for ASD. Because of the genetic complexity of ASD, there is currently no clinically meaningful genotyping carried out to detect ASD. There have been recent intriguing neuroimaging studies indicating that alterations of brain function or structure as early as 6 months may be valuable indicators of a higher risk for autism. However, it is unlikely that comprehensive structural and functional magnetic resonance imaging is a practical approach to detecting ASD in young children. Other, more cost effective and widely applicable biomarker strategies must be discovered.
We previously demonstrated that a metabolomics approach for the detection of autism risk holds substantial promise. In our preliminary study, we identified a subset of 179 features that classified ASD and TYP children with 81% accuracy. Metabolism-based analysis has the merit of being sensitive to interactions between the genome, gut microbiome, diet, and environmental factors that contribute to the unique metabolic signature of an individual. Metabolic testing can provide important biomarkers toward identifying the perturbations of biological processes underlying an individual's ASD. Past studies have been underpowered to identify metabolic perturbations that lead to actionable metabolic subtypes.
To test for metabolic imbalances that can reveal subtypes of ASD subjects, we conducted the Children's Autism Metabolome Project (CAMP, ClinicalTrials.gov Identifier NCT02548442). CAMP recruited 1,100 young children (18 to 48 months) with ASD, intellectual disability or typical development. Research reliable clinicians confirmed the child's diagnosis and blood samples were collected under protocols designed specifically for metabolomics analyses. The CAMP study is the largest metabolomics study of ASD to date.
The current study was motivated by observations of AA dysregulation in West et al. and in preliminary analysis of the CAMP samples. The relevance of AA dysregulation to ASD is reinforced by Novarino who demonstrated loss of function mutations in the gene BCKDK (Branched Chain Ketoacid Dehydrogenase Kinase) resulting in reductions of BCKDK messenger RNA and protein, Ela phosphorylation, and plasma branched-chain AAs in consanguineous families with autism, epilepsy, and intellectual disability. Follow on studies by Tarlungeanu demonstrated that altered transport of BCAAs across the blood brain barrier led to dysregulation of AA levels and neurological impairments. We sought to determine whether dysregulation of AAs was a more pervasive phenomenon in individuals with ASD. Thus, we set out to identify metabotypes indicating the dysregulation of AAs in individuals with ASD and to determine whether these metabotypes might be diagnostic of subsets of individuals. A metabotype is a subpopulation defined by a common metabolic signature that can be differentiated from other members of the study population. Metabotypes of ASD can be useful in stratifying the broad autism spectrum into more biochemically homogeneous and clinically significant subtypes. Stratification of ASD based on distinct metabolism can inform pharmacological and dietary interventions that prevent or ameliorate clinical symptoms within a metabotype.
CAMP Participants
The CAMP study recruited children, ages 18 to 48 months, from 8 centers across the United States as shown in Table 2.
Informed consent of a parent or legal guardian was obtained for each participant. The study protocol was approved and monitored by local IRBs at each of the sites. Enrollment was limited to one child per household to minimize genetic or family environmental effects. Children participating in other clinical studies could not have used any investigational agent within 30 days of participation. Children were excluded from the study if they were previously diagnosed with a genetic condition such as fragile X syndrome, Rett syndrome, Down syndrome, tuberous sclerosis, or inborn errors of metabolism. Subjects that had fetal alcohol syndrome, or other serious neurological, metabolic, psychiatric, cardiovascular, or endocrine system disorders were also excluded. In addition, children exhibiting signs of illness within 2 weeks of enrollment, including vomiting, diarrhea, fever, cough, or ear infection were rescheduled. Each participant underwent physical, neurological and behavioral examinations. Metadata was obtained about the children's birth, developmental, medical and immunization histories, dietary supplements and medications. Parents' medical histories were also obtained.
The Autism Diagnostic Observations Schedule-Second Version (ADOS-2) was performed by research reliable clinicians to confirm ASD diagnoses. The Mullen Scales of Early Learning (MSEL) was administered to establish a developmental quotient (DQ) for all children in the study. A prior ADOS-2 or MSEL was accepted if performed within 90 days of enrollment by qualified personnel. Children without ASD receiving a clinical diagnosis of developmental delay were not included in the current study. Children entering the study as TYP were not routinely administered the ADOS-2. The Social Communications Questionnaire (SCQ) was administered for a subset of TYP children as a screen for ASD.
Training and Test Sets
A training set was used to identify metabotypes associated with ASD and a test set was used to evaluate the reproducibility of the metabotypes. The sample size of the training set was designed to detect metabotypes with a sensitivity (metabotype prevalence)>3% and specificity >85% with a power of 0.90. The power analysis and minimum sample size requirements for metabotype identification are shown in Table 3.
The training set (N=338, ASD=253, TYP=85) was established and analyzed, then as recruitment continued, the test set (N=342, ASD=263, TYP=79) was established when sufficient subjects were available to match the training set demographics. Subject composition of training and tests are shown in Table 4.
Phlebotomy Procedures
Blood was collected from subjects after at least a 12 hour fast by venipuncture into 6 ml sodium heparin tubes on wet ice. A minimum of a 2 ml blood draw was required for sample inclusion in the computational analyses. The plasma was obtained by centrifugation (1200×G for 10 minutes) and frozen to −70° C. within 1 hour.
Triple Quadrupole LC-MS/MS Method for Quantitative Analysis of Biological Amines
The Waters AccQTag™ Ultra kit (Waters Corporation, Milford, MA), which employs derivatization of amine moieties with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate was employed for all samples prior to multiple reaction monitoring (MRM) on a liquid chromatography (LC) mass spectrometry (MS) system consisting of an Agilent 1290 ultra-high performance liquid chromatograph (UHPLC) coupled to an Agilent G6490 Triple Quadrupole Mass Spectrometer (Agilent Technologies Santa Clara, CA). Endogenous metabolites chemical reference standards, ions used for quantitation, and the stable isotope labeled (SIL) internal standard used for normalization are shown in Table 5.
Stable isotope labeled (SIL) chemical reference standards and ions used for quantification are shown in Table 6.
Bioinformatic Analysis
The concentration values of each metabolite were log base 2 transformed and Z-score normalized prior to analyses. Analysis of covariance (ANCOVA) and pairwise Pearson correlation analysis were performed on each amine compound. False discovery rates were controlled for multiple testing using the Benjamin and Hochberg (27) method of p-value correction. A comparison was considered significant if the corrected p-value was less than 0.05. Dissimilarity measurements of 1−the absolute value of the Pearson correlation coefficients (ρ) was used to calculate distances for clustering. Wards' method was utilized for hierarchical clustering. Bootstrap analysis of the clustering result was performed using the pvclust package in R (28). Clusters were considered significant when the unbiased p-value was ≥0.95. The non-linear iterative partial least squares (NIPALS) algorithm was used for principal component analysis (PCA) and confidence intervals drawn at 95th percentile of the PCA scores using Hotelling's T2 statistic using the package PCAmethods (29). Welch T-tests were used to test for differences in study populations. The independence of subject metadata relative to the metabotypes was tested using the Fisher Exact test statistic with an alpha of 0.05 to reject the null hypothesis. These analyses were conducted with R (version 3.4.3).
Establishing and Assessing Diagnostic Thresholds
A heuristic algorithm was developed to identify individual biomarkers able to discriminate ASD subpopulations, indicative of a metabotype, using a threshold for metabolite abundance or ratios. Diagnostic thresholds were established in the training set to generate a subpopulation with at least 10% of the ASD population while minimizing the number of TYP subjects in the subpopulation. A subject exceeding the diagnostic threshold was scored as a metabotype-positive ASD subject and the remaining subjects as metabotype-negative. Diagnostic performance metrics of specificity (detection of TYP), sensitivity (detection of ASD) and positive predictive value (PPV, percent of metabotype-positives that are ASD) were calculated based on metabotype status (positive or negative) and ADOS-2 diagnosis (ASD or TYP).
Permutation analysis was performed to test the probability that the observed diagnostic performance values from threshold setting and subpopulation prediction could be due to chance. Chance was assessed using 1000 permutations of subject diagnoses in the training set for threshold setting and subpopulation prediction or test set following subpopulation prediction. In both permutation procedures, the probability that observed biomarker performance metrics were due to chance was calculated based on the frequency that the observed sensitivity, specificity, and PPV were met or exceeded in the random permutation set.
When the diagnostic ratios were combined into panels of ratios to test for ASD associated metabotypes, the minimum performance required to consider a metabotype as reproducible were a sensitivity ≥5%, a specificity ≥95%, and a PPV ≥90% in both training and test sets.
Children's Autism Metabolome Project (CAMP) Study Demographics
The training and test sets of subjects were chosen with appropriate power and randomization. The ASD prevalence, DQ, and gender composition between the training and test sets are equivalent (p value >0.05). However, the ASD population contains 16.5% more males than the TYP population (p value <0.01). The ASD population is slightly older than the TYP by 3.3 months (p value <0.01) and the ASD subjects within the training set are 1.4 months older than ASD subjects in the test set (p value <0.01).
Analysis of Amine-Containing Metabolites Between ASD and TYP Study Populations
Analysis of covariance (ANCOVA) was performed on 31 amine containing metabolites in the training set of subjects to test the effect of gender or diagnoses controlling for subject age on metabolite means. No significant differences were identified in metabolite abundance values for diagnosis, age, sex, gender or their interactions. Analysis of covariance (ANCOVA) of diagnosis and sex controlling for subject age is shown in Table 7.
These results suggest that within the demographic ranges in this study, the differences in subject age or sex have little impact on metabolite abundance. Therefore, the differences in the composition of ASD and TYP study populations are unlikely to have significant impact on study results.
Metabolite Correlations within ASD Reveal Distinct Clusters of Amine Metabolites
We then examined the relationship among the amine metabolites in the training set of ASD subjects by pairwise Pearson correlation analysis and hierarchical clustering to identify metabolites with co-regulated metabolism. Three clusters of metabolites with positive correlations were identified. Cluster 1 contains the metabolites serine, glycine, ornithine, 4-hydroxyproline, alanine, glutamine, homoserine, and proline (i.e., the glycine cluster-mean ρ 0.45±0.02). Cluster 2 contains the BCAAs (leucine, isoleucine, and valine) and phenylalanine where the BCAAs are more highly correlated with each other (mean pairwise p of 0.86±0.02) than the BCAAs are with phenylalanine (mean pairwise p of 0.56±0.02) (i.e., the BCAA cluster red boxes,
Identification of Amino Acid:BCAA Imbalance Metabotypes Associated with ASD
The negative correlation between the BCAA and glycine cluster led us to evaluate ratios of these AA as predictors of ASD diagnosis. Ratios can uncover biological properties not evident with individual metabolites and increase the signal when two metabolites with a negative correlation are evaluated. This strategy, for example, formed the basis of the standard phenylketonuria (PKU) diagnostic using a ratio of phenylalanine and tyrosine (30). Based on analysis of boxplots, we created ratios with one of the BCAAs in the denominator and one of the glycine cluster metabolites in the numerator. Thresholds for the ratios were set in the training set and evaluated in the test set of subjects. The BCAA ratios of glutamine, glycine, ornithine and serine identified subpopulations of subjects associated with an ASD diagnosis at a rate higher than chance in both training and test sets. Diagnostic performance of the branched chain amino acid (BCAA) ratios used in development of the Amino Acid Dysregulation Metabotypes (AADM) diagnostic panels as measured in the training set of subjects are shown in Table 8.
Performance metrics of the diagnostic branched chain amino acid (BCAA) ratios used to identify subpopulations of ASD in the test set of subjects are shown in Table 9.
Table 10 shows diagnostic performance metrics of amine ratios to discriminate subpopulations of ASD subjects in the training and test sets of subjects.
aThe observed diagnostic performance occurred in less than 5% of 1000 permutations of subject diagnosis in both training and test sets.
The correlation of the BCAAs with each other (ρ=0.86±0.02) and the overlap of affected-subjects (
However, requiring that the subject be positive for all three Glycine:BCAA ratios, results in a specificity of 98.8% and PPV of 96.0%. Through this process, we identified groups of subjects that exhibited an Amino Acid Dysregulation Metabotype (AADM). Subjects were identified by AADM when they exceeded an established threshold for all three AA:BCAA ratios. Since the nomenclature for these biomarkers can quickly become confusing, we have designated different AADMs using the numerator metabolite e.g. AADM glutamine Not all ratios of AAs to BCAA resulted in diagnostic differences between the ASD and TYP groups. We focused, therefore, on those AA:BCAA ratios that had the greatest predictive power including glutamine AADM (AADMglutamine), glycine AADM (AADMglycine) and ornithine AADM (AADMornithine).
Each circle represents the subjects identified by the diagnostic threshold for a given ratio. The intersection of the Venn diagram indicates the subjects called AADMalanine positive (red dots in scatter plots). Performance metrics above the Venn diagram represent entire study population (training and test sets).
Table 12 shows diagnostic performance metrics of Amino Acid Dysregulation Metabotypes (AADM).
aAADMs are a reproducible metabotype that is identified across training and test populations with a sensitivity greater than 5% and a positive predictive value greater than 90%.
AADMs Define a Diagnostic for BCAA Dysregulation Associated with ASD
The ASD subjects identified by each AADM were evaluated to assess the extent of overlap. We found that there is substantial overlap of the subjects identified by each of the metabotypes. However, each of the metabotypes also identifies a unique group of subjects. The AADM glutamine identified 7.9% of the ASD subjects in the total CAMP population, AADMglycine 9.7%, and the AADMornithine 9.1%, with PPVs of 97.6%, 94.3% and 92.2% respectively. Combining all three AADM subtypes together (AADMtotal) identified 16.7% of ASD subjects in the CAMP population with a specificity of 96.3% and a PPV of 93.5%. Principal component analysis (PCA) of the metabolite ratios utilized in AADMglycine, AADMglutamine, and AADMornithine was performed to test if an unsupervised method could identify subjects with AA dysregulation. A majority (80%, 74/92) of the AADM-positive subjects were separated from the unaffected subjects.
AADMornithine and AADMGlutamine are More Sensitive at Detecting Females with ASD.
Since the composition of subject sex and age differed between the ASD and TYP populations, the impact of these variables was evaluated in the AADM positive and negative populations. Differential analysis of reproducible AADM positive and negative subjects' metabolite levels with respect to age or sex did not identify statistically significant changes in abundance. Differential analysis of age bins and correlation of age in the branched chain amino acid (BCAA) metabotype-positive population from the entire study population is shown in Table 13.
Differential analysis of the metabolite and ratio abundance values comparing Amino Acid Dysregulation Metabotypes (AADMtotal)-positive and AADMtotal-negative populations from the entire study population is shown in Table 14.
Females with ASD were 2.1-fold (odds ratio 2.8, p value 0.002) more likely to be identified by AADMornithine and AADMglutamine than would be expected by chance.
Fisher exact test for gender bias in each panel and across reproducible Amino Acid Dysregulation Metabotypes (AADMs) is shown in Table 15.
The AADMglycine did not demonstrate a predictive sex bias.
CAMP is the largest study of the metabolism of children with autism spectrum disorder and age-matched typically developing children carried out to date. Metabolomics offers the opportunity to examine associations between small molecule abundance levels and the presence of a disorder such as ASD as well as influences such as sex, severity of the disorder, comorbid conditions, diet, supplements and other environmental factors. Given the known heterogeneity of ASD, the size of CAMP offers the prospect of identifying metabolically defined subtypes (or metabotypes) that can identify groups with a prevalence as low as 5%. Diagnostic tests for metabotypes of ASD create an opportunity for earlier diagnosis and the potential to inform more targeted treatment.
Our goal is to analyze data from the CAMP population to identify metabotypes associated with ASD that could enable stratification of the disorder based on shared metabolic characteristics. Based on our own observations and growing literature reporting a dysregulation of amino acid metabolism associated with ASD, we began our analysis by studying free plasma amine levels. A simple analysis of the mean concentrations of free plasma amines did not reveal meaningful differences between the ASD and TYP populations of children. However, scatterplots of amine levels indicated that there were subsets of children with ASD with amine levels at the extreme upper or lower end of the abundance distribution. Moreover, correlation analyses revealed two negatively correlated clusters of related metabolites. We tested if ratios of these metabolites could identify subpopulations that exhibit dysregulation of AA metabolism associated with ASD. Diagnostic thresholds established in the training set of subjects using ratios of glutamine, glycine, ornithine and serine with leucine, isoleucine and valine (BCAAs) reproducibly detected subpopulations in an independent test set. Three AADMs based on an imbalance of glutamine, glycine, or ornithine with the BCAAs were reproduced across training and test sets of subjects. Separately, each AADM identified ASD subjects with 7-10% sensitivity and 92-98% PPVs. Taken together, all AADMs identified an altered metabolic phenotype of imbalanced BCAA metabolism in 16.7% of CAMP ASD subjects with a specificity of 96.3% and PPV of 93.5%.
Identification of ASD children with altered AADMs represents an important step toward understanding the etiology of one form of ASD. Imbalances in BCAAs in plasma have been shown to alter not only brain levels of BCAAs, but also other amino acids important for key metabolic processes including intermediary metabolism, protein synthesis, and neurotransmission. For example, when plasma BCAA levels are reduced due to a rare genetic defect in branched chain ketoacid dehydrogenase kinase (BCKDK) (24) leading to accelerated BCAA degradation, the transporters that are normally responsible for their import into the brain transport an excess of other amino acids instead. And, this condition is associated with ASD. Similarly, Tarlungeanu demonstrated that rare disruption of amino acid transport associated with defects in the LAT1 transporter reduced the uptake of BCAAs into the brain; again this was associated with ASD-like symptoms. Interestingly, neither study reported elevated plasma levels of glycine, ornithine, or glutamine. The imbalance of amino acid levels in CAMP strongly suggests that other perturbations in BCAA metabolism may be a risk factor for the development of ASD. Importantly, the metabolomic results reported here provide a mechanism for stratifying the larger group of children with ASD into an AADM positive subgroup to enable a more targeted approach to understanding the etiology of this form of ASD. For example, the AADMs we identified may reveal a disruption of the mTORC1 system which could be an underlying reason for lower free plasma BCAA levels. Cellular levels of BCAA as well as other amino acids are maintained through signaling associated mTORC1 and the transcription factor ATF4 (33). Dysregulation of the mTOR pathway is an underlying cause of amino acid dysregulation that is associated with ASD and tuberous sclerosis.
The AADMs provide one pathway to much earlier diagnosis of a substantial subset of children with ASD. Earlier diagnosis may also provide the opportunity for earlier biological intervention. BCAA supplementation or high protein diet has been used in mouse models and human patients with BCKDK deficiency to successfully reduce ASD symptoms and improve cognitive function. Defining a group of AADM positive children may enable stratification of the autistic population as a precursor to targeted intervention through dietary supplementation or specialized diet. Currently, clinical trials of common therapies such as vitamin and mineral supplements, carnitine and gluten-free casein-free diets, apply these therapies to all participants. Metabotyping subjects prior to treatment and monitoring metabolite levels provides the opportunity to assess patient compliance and response, and to adjust treatment based on objective measurement of the metabolic profile of the individual subject. It is likely that this strategy would substantially improve positive treatment outcomes.
This study does have some limitations. The levels of blood plasma amine metabolites are not directly relatable to brain levels making direct association of changes in plasma levels to changes in brain levels difficult. The CAMP study focused on recruitment of a large sample of children with ASD and age-matched typically developing controls. Logistical and financial constraints precluded our ability to recruit a large enough sample of children with developmental delays without ASD. Thus, the specificity of ADDM for ASD relative to other neurodevelopmental disorders is currently unclear. This is an important issue that will need to be resolved in future studies. In addition, longitudinal samples are not available to analyze whether AADMs are stable over time. Finally, this study lacks animal models or tissue samples that could be used to dissect enzymatic and expression analysis to identify the molecular mechanisms underlying AADM. While we cannot explain the alterations in metabolism, we have demonstrated that our approach provides stratification of subjects for which future studies and perhaps targeted treatments could be carried out.
This study demonstrates one approach to analyzing the metabolism of ASD to successfully identify reproducible metabotypes. Analysis of the CAMP study samples is ongoing and there will be additional metabotypes which will be diagnostic for subsets of children with ASD. Stratifying ASD based on metabotypes offers an opportunity to identify efficacious interventions within metabotypes that can lead to more precise and individualized treatment. The hope is that by combining the established metabotypes into a more comprehensive diagnostic system, that a substantial percentage of children at risk for ASD will be identifiable at a very early age.
Mass Spectrometry
Mass spectroscopy (MS) was performed using electrospray ionization in positive ion mode with an Agilent QqQ 6490 triple quadrupole mass spectrometer. Analyte selectivity used a combination of product/precursor mass transitions and retention time. Agilent MassHunter Quantitative Analysis software (version B.06.00) was used for quantitation of liquid chromatography (LC) MS/MS data. Dynamic Multiple Reaction Monitoring (MRM) was utilized to assign optimal dwell times for each analyte.
Stable isotope labeled (SIL) internal standards were used to normalize the signal for each analyte to account for variations in the matrix and sample preparation. For analytes in which no SIL internal standard was available, a surrogate SIL internal standard was chosen based on the work of Gray et al. (1) using a structurally similar analyte. Chromatographic separation was performed using reverse-phase chromatography on a
HSS T3 2.1×150 mm, 1.8 μm column (Waters). Column temperature was maintained at 45° C. The mobile phase was composed of 0.1% formic acid in water and 0.1% formic acid in acetonitrile. A gradient elution was performed which separates the analytes over the course of 7.5 minutes per injection using a flow rate of 0.6 ml/min.
Samples were evaluated relative to calibration standards measured in each analysis batch. Samples that measured below the lowest concentration level of the calibration standard were reported as having a concentration of 0.00 μM. Samples with an analyte(s) that quantified above the highest concentration level calibration standard were diluted and reanalyzed to obtain a measurement within the range of valid quantification for that analyte.
Abundance of Metabolite Ratios and Metabolites Used in the Ratios are not Changed in AADM Positive and AADM Negative Subjects with Respect to Age and Sex
The mean levels of metabolite ratios or metabolites in the AADM positive and ADDM negative populations were not different (FDR >0.05) in males and females indicating that the sex bias in detection of the AADMglutamine, AADMornithine and AADMtotal positive populations is not evident in the levels of metabolites within AADM positive and negative populations. Differential analysis of subject sex in the Amino Acid Dysregulation Metabotypes (AADM)-positive population from the entire study population is shown in Table 16.
Since there are slight differences between the age of TYP and ASD subjects (3.3 months) and between the age of ASD subjects in the training and test set (1.4 months), we tested if the age of the subject within AADM positive population was associated with the ratio of metabolite abundance levels. No differences in mean (FDR >0.05) or correlations (FDR >0.05) of abundance levels of metabolite ratios or individual metabolites of AADM positive populations were found in association with the age of the subjects.
Metabolite Ratios and Metabolites Used in the Ratios are Differentially Abundant in the AADMtotal Positive Population
Differential analysis of the AADMtotal positive and negative populations was performed to test if differences in the metabolites are present. The mean levels of metabolite ratios used to identify the AADMtotal population were increased by 47-82% (FDR <0.001) in the AADMtotal positive population when compared to the AADMtotal negative population. The mean levels of numerator metabolites glutamine, glycine, and ornithine were increased by 16-38% (FDR <0.001) and BCAA metabolites were decreased by 23-26% (FDR <0.001) in the AADMtotal positive population compared to the AADMtotal negative population.
A sample test report is provided below.
Patient and Sample Data
The following information about the patient and sample is provided: patient's name, patient's date of birth, patient's sex, specimen type (e.g., plasma), date of specimen collection, date specimen was received, test panel used, and date and time of test report.
Results Summary
The algorithmic analysis indicates patient has form(s) of amino acid dysregulation associated with autism spectrum disorder (ASD). Specific details of findings are listed below.
Metabotype 8: An imbalance between the plasma concentrations of Ornithine and Phenylalanine was detected. This imbalance includes above average Ornithine.
Metabotype 12: An imbalance between the plasma concentrations of Ornithine and branched chain amino acids (BCAA) was detected. This imbalance includes above average Ornithine and below average BCAA.
Metabotype 15: An imbalance between the plasma concentrations of Ornithine and Kynurenine was detected. This imbalance generally indicates plasma concentrations of Ornithine which are above average and kynurenine which is below average.
Metabotype 16: An imbalance between the plasma concentrations of Ornithine and Lysine was detected. This imbalance generally indicates plasma concentrations of Ornithine which are above average.
Additional Findings: Levels of individual analytes tested are all within the normal range. The following analytes were tested: Alanine, Arginine, Asparagine, Aspartic Acid, B-Alanine, B-Aminoisobutyric Acid, Citrulline, Ethanolamine, B-Aminoisobutyric Acid, Glutamic Acid, Glutamine, Glycine, Histidine, Homocitrulline, Homoserine, Isoleucine, Kynurenine, Leucine, Lysine, Methionine, Ornithine, Phenylalanine, Proline, Sarcosine, Serine, Serotonin, Taurine, Threonine, Tryptophan, Tyrosine, Valine, and 4-Hydroxyproline. Levels of individual analytes are provided in Table 17.
Exemplary Guidance
Recommend follow up with neurodevelopment/ASD specialist for further evaluation. Some studies indicate dietary modification may be beneficial for patients with metabolic dysregulation. May want to refer patient to a registered dietitian nutritionist (RDN) for an evaluation of his/her diet and supplement intake.
Metabotypes may include combinations or groups of ratios of concentrations of metabolites. Groups may include ratios in which a concentration of a first metabolite is compared to concentrations of various second metabolites. The concentration of the constant first metabolite may be in the numerator of the ratio and the concentrations of the various second metabolites may be in the denominator of the ratio. Alternatively, the concentration of the constant first metabolite may be in the denominator of the ratio and the concentrations of the various second metabolites may be in the numerator. The second metabolites within a group may have a common feature, or they be members of a common class of compounds. For example, the second analytes in such groups may be branched chain amino acids, hydrophobic amino acids, polar amino acids, negatively charged amino acids, positively charged amino acids, or metabolites in a common metabolic pathway, e.g., the citric acid cycle or fatty acid oxidation. single metabolite is used for the numerator and various metabolites are used for the denominator, or vice versa.
Additional ratios of metabolite concentrations that are indicative of ASD are provided in Table 18.
A sample test report is provided below.
Patient and Sample Data
The following information about the patient and sample is provided: patient's name, patient's date of birth, patient's sex, specimen type (e.g., plasma), date of specimen collection, date specimen was received, test panel used, and date and time of test report.
Results Summary
The algorithmic analysis indicates patient has form(s) of amino acid dysregulation associated with autism spectrum disorder (ASD). Specific details of findings are listed below.
Metabotype 3: An imbalance between the plasma concentrations of Glycine and Asparagine was detected. This imbalance includes above average Glycine
Metabotype 5: An imbalance between the plasma concentrations of Glycine and Isoleucine was detected. This imbalance includes above average Glycine and below average Isoleucine.
Metabotype 11: An imbalance between the plasma concentrations of Glycine and branched chain amino acids (BCAA) was detected. This imbalance includes above average Glycine and below average BCAA.
Additional Findings: Level of individual amines indicates 1 is out of range. The following analytes were tested: Alanine, Arginine, Asparagine, Aspartic Acid, β-Alanine, β-Aminoisobutyric Acid, Citrulline, Ethanolamine, γ-Aminoisobutyric Acid, Glutamic Acid, Glutamine, Glycine, Histidine, Homocitrulline, Homoserine, Isoleucine, Kynurenine, Leucine, Lysine, Methionine, Ornithine, Phenylalanine, Proline, Sarcosine, Serine, Serotonin, Taurine, Threonine, Tryptophan, Tyrosine, Valine, and 4-Hydroxyproline. Levels of individual analytes measured by LC-MS/MS are provided in Table 19.
Exemplary Guidance
Recommend follow up with neurodevelopment/ASD specialist for further evaluation. Some studies indicate dietary modification may be beneficial for patients with metabolic dysregulation.
Metabotypes indicative of altered purine degradation were identified. Plasma metabolites in CAMP subjects were measured by quantitative LC-MS/MS, and statistical analysis of metabolites was used to identify metabotypes. Samples were divided into a training set and an independent validation set, i.e., test set. The following metabolites were analyzed: xanthine, hypoxanthine, inosine, uric acid, and taurine. Hemolyzed samples with hemoglobin levels >200 mg/dL were excluded from analysis due to interference.
A single reproducible metabotype was identified in 6.3% of CAMP ASD subjects as shown in Table 20.
Elevated xanthine is correlated with taurine (ρ=0.64), an amine which reduces XOR activity and is evaluated in molybdenum cofactor required by XOR (MOCOS) deficiency. The data suggest that biomarkers of defective sulfite metabolism may provide a link to understanding the biology in a subset of children with ASD. The data further suggest that altered activity of purine degradation is associated with a subset of ASD subjects and that the xanthine to uric acid metabotype can be used to identify individuals who belong to this subset.
Metabotypes indicative of altered energy homeostasis were identified. Plasma metabolites in CAMP subjects were measured by quantitative LC-MS/MS, and statistical analysis of metabolites was used to identify metabotypes. Samples were divided into a training set and an independent validation set, i.e., test set. The following metabolites were analyzed: α-ketoglutarate, lactate, pyruvate, succinate, alanine, and phenylalanine. Lactate, pyruvate, and alanine are commonly used to assess mitochondrial bioenergetic function.
22.3% of CAMP ASD subjects were identified by an energy related metabotype. Results are summarized in Table 21.
The data suggest that altered activity of mitochondrial energy homeostasis pathways is associated with a subset of ASD subjects and that specific metabotypes can be used to identify individuals who belong to this subset.
Metabotypes indicative of altered amine metabolism were identified. Plasma metabolites in CAMP subjects were measured by quantitative LC-MS/MS, and statistical analysis of metabolites was used to identify metabotypes. Samples were divided into a training set and an independent validation set, i.e., test set. Thirty-two amine-containing metabolites were analyzed.
Five amine metabotypes identified 21.5% of CAMP ASD subjects. Results are summarized in Table 22.
Altered neurotransmission, mitochondrial biology, nitrogen metabolism may be associated with amine metabotypes.
The data suggest that altered activity of pathways involved in one or more of amine metabolism, neurotransmission, and neurotransmitter synthesis are associated with a subset of ASD subjects and that specific metabotypes can be used to identify individuals who belong to this subset.
Overall, 41% of CAMP ASD subjects were identified in the training and test sets. Metabotype positive subjects that share multiple metabotypes may have more complex metabolic phenotypes, i.e., may have alterations in multiple metabolic pathways. Conversely, subjects identified by a single amine, purine, or energy process metabotype may have a specific metabolic dysregulation.
Data from the large ASD CAMP cohort indicate that metabolomic analysis allows detection of reproducible metabotypes with a prevalence >5%. Biological processes, including mitochondrial biology/energy metabolism, amino acid metabolism and homeostasis, and purine catabolism, were associated metabotypes were identified in 41% of CAMP ASD subjects. Thus, the results demonstrate that subsets of ASD are associated with alterations of specific metabolic pathways and that such pathways can be identified through metabolomic analysis.
Autism Spectrum Disorder (ASD) is a clinically and etiologically heterogeneous neurodevelopmental condition. The average age of ASD diagnosis in the United States is over 4 years and is based on behaviorally assessed alterations in social interaction and persistent repetitive behaviors or circumscribed interests. There is substantial evidence that earlier diagnosis of ASD improves outcomes by expediting behavioral therapy that leads to higher cognitive and social function. This has the added benefit of reducing the financial and emotional burden on families and society.
As a result of the high global population prevalence (1-2%) of ASD, its substantial impact on affected individuals and their families, and the potential benefit of early intervention, screening for ASD is recommend for children at 18 and 24 months during routine pediatric visits. Additional assessment is carried out if a child is deemed to be at high risk for ASD. The American Academy of Pediatrics recommends that children who fail a screening test should be referred to specialists who are trained to make a diagnosis of ASD.
While parental questioning is widely used as a screen for ASD, a number of studies have indicated that this strategy is not optimal. The Modified Checklist for Autism in Toddlers with Follow-Up (M-CHAT/F), for example, is reported to have a sensitivity of only 38.8% and positive predictive value of 14.6%. Thus, this widely used screening tool detects less than 40% of children who will go on to attain a diagnosis of ASD and less than 15% of the children that are positive on the test actually end up with a diagnosis of ASD. Failure to identify a child with risk for ASD during screening will lead to delayed diagnosis.
There has been intense interest in discovering easily implementable biomarkers that support screening, diagnosis and targeted intervention of ASD. Diverse modalities of biomarkers have been investigated including neuroimaging, EEG, eye tracking, pupillary reflex, and transcriptomic, proteomic, and metabolomics markers. Potential metabolic biomarkers of ASD have been identified, mainly in blood or urine, using a variety of analytical approaches that have suggested that a range of metabolic processes are altered in ASD.
Metabotyping is subtyping based on shared metabolic phenotypes identified using metabolic biomarkers. Metabotyping using metabolic biomarkers associated with ASD can enable stratification of the disorder into distinct subpopulations based on a common metabolic dysregulation identified by the biomarker. Stratification of ASD using metabotype based tests can lead to underlying biological differences among those with ASD and, in turn, potentially to targeted therapeutic intervention for individuals with a specific metabotype.
We conducted the Children's Autism Metabolome Project (CAMP) to identify metabolic dysregulations associated with ASD. The CAMP study, the largest metabolomics study of children with ASD to date, was designed to reproducibly identify metabotypes associated with ASD. We recruited 1,102 children between the ages of 18 months and 4 years from 8 clinical sites spread across the United States. Of these, 708 had a diagnosis of autism spectrum disorder or were typically developing and were able to contribute blood samples that met quality control standards for metabolic analyses. Previous analysis of CAMP metabolomics data identified a group of plasma metabolites in autistic children that were negatively correlated with plasma branched chain amino acids (BCAAs). Imbalances in the concentrations of the amino acids glycine, glutamine, and ornithine relative to the BCAAs identified ASD-associated amino acid metabotypes (AADMs) that were present in 17% of the ASD subjects.
In the current study, we quantitatively assessed 39 metabolites associated with amino acid and energy metabolism in an attempt to expand the identification of metabolic subpopulations of children with ASD. This set of metabolites was chosen based on our pilot studies and published research related to abnormalities of biochemical processes noted in ASD related to purine metabolism and mitochondrial bioenergetics. The current work presents the results of this metabolomic analysis and explores the potential of these metabotype tests as another step toward creating a metabolomic screening platform to determine risk for ASD in young children.
CAMP Participants
The case-control CAMP study consented 1,102 children, ages 18 to 48 months, from 8 centers across the United States from August 2015-January 2018 (ClinicalTrials.gov Identifier: NCT02548442). The 8 centers included: Children's Hospital of Philadelphia, Cincinnati Children's Hospital, The Lurie Center at Massachusetts General Hospital, The Melmed Center, The MIND Institute, University of California—Davis, Nationwide Children's Hospital, The University of Arkansas for Medical Sciences, and Vanderbilt University Medical Center. Children were excluded from the study if they were previously diagnosed with a genetic condition. Subjects that had recognized serious neurological, metabolic, psychiatric, cardiovascular, or endocrine system disorders were also excluded. Children exhibiting signs of illness within 2 weeks of enrollment were rescheduled for blood collection. All participants underwent medical and behavioral examinations. Metadata were obtained about the child's gestational history, birth, developmental, medical and immunization histories, dietary supplements and medications. Brief parental medical histories were also obtained. The Autism Diagnostic Observation Schedule-Second Version (ADOS-2) assessment was performed by research reliable clinicians to confirm ASD diagnoses. A developmental quotient (DQ) was derived from The Mullen Scales of Early Learning (MSEL) which was administered to all children. A child was diagnosed as ASD if the ADOS-2 comparison severity score was 4 or higher. A child was designated as typical if their developmental quotient was greater than 70 and was not diagnosed by a clinician as having a developmental disorder. Specimens of plasma were collected and processed as previously described. The study protocol was approved and monitored by institutional review boards at each of the clinical centers. Written informed consent from a parent or legal guardian was obtained and a small monetary stipend was provided for each participant. Of the 1,102 consented children, 645 had a diagnosis of ASD and 255 were typically developing (TYP). Of the 900 subjects receiving these diagnoses, 708 provided plasma samples meeting study and quality control criteria for inclusion in this analysis.
Assignment of Subjects to Discovery and Replication Sets.
The discovery set was established to measure metabotype positive populations with a sensitivity of 8% with a lower confidence limit of 3% and specificity of 95% with a lower confidence limit of 85% under an alpha of 5% and a power of at least 0.90. The replication set of subjects was established and analyzed once enough subjects were recruited to match the demographic composition of the discovery set. Randomization of available CAMP participants was performed within study sets to maintain a prevalence of ASD of approximately 70%. Randomization was restricted by age, DQ, and sex to maintain discovery set demographic values in the replication set.
Phlebotomy Procedures
Blood was collected by venipuncture into 6 mL sodium heparin tubes placed on wet ice from subjects who had not eaten for at least 12 hours. Plasma was obtained by centrifugation (1200×G for 10 minutes) and frozen to −70° C. within 1 hour. Hemolysis of blood samples was measured spectrophotometrically in plasma (Noe, Weedn, & Bell, 1984). Plasma samples with hemoglobin levels >600 mg/dL were excluded from analyses. Values for the analytes xanthine, uric acid, or hypoxanthine (which are more sensitive to hemoglobin interference) were omitted when hemoglobin levels exceeded 200 mg/dL.
Quantitative Analysis of Candidate Metabolites Using Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS)
Three quantitative LC-MS/MS methods measuring a total of 39 unique endogenous metabolites and 37 stable isotope-labeled internal standards were analytically validated in compliance with FDA and CLSI guidance for bioanalytical method validation. Following analytical validation, the quantitative assays were used to measure biological amines, purines, and carboxylic acid-containing analytes in participant samples. Information about chemical reference standards, isotopically labeled internal standards, vendors, detection on analytes, and MS detection, including retention times, analyte transitions, cone voltages and collision energies, is provided in Table 23.
Analyte quantification was performed using an Agilent Technologies G6490 Triple Quadrupole Mass Spectrometer and a Waters Xevo TQ-S micro, IVD mass spectrometer with appropriate internal standards, calibration ranges, and quality control samples.
Chemicals
Optima- and HPLC-grade reagents (water, acetonitrile (ACN), methanol (MeOH), isopropanol, acetic acid, formic acid) were purchased from Fisher Scientific. Ammonium acetate was purchased from Sigma-Aldrich. The AccQTag Ultra derivatization kit was purchased from Waters and SeraSub was from CST Technologies, Inc.
Triple Quadrupole LC MS Method for Quantitative Analysis of Biologic Amines
Protein precipitation with cold methanol was employed for all plasma, calibration standards (CAL) and quality control (QC) samples. Samples were derivatized as described previously (Smith et al., 2019) using the AccQTag Ultra kit (Waters). In brief, samples were thawed at room temperature, and 50 μl sample was prepared by adding 25 μl internal standard solution and 150 μl methanol (−20° C.) to precipitate plasma protein. Samples were vortex-mixed for 5 min and spun at 18,500×g for 5 min at 4° C. Derivatization of sample extracts was carried out by transferring 10 μl of the supernatant onto a 96 well plate containing 70 μl of AccQTag Ultra Borate Buffer, followed by an addition of 20 μl of AccQTag Ultra Derivatization Reagent. Samples were briefly mixed and heated to 55° C. for 10 minutes then transferred to the autosampler (4° C.) for injection. Analysis was performed using 2 μl derivatized sample on an Agilent 1290 ultra-high-performance liquid chromatography system (UHPLC) coupled to an Agilent G6490 Triple Quadrupole Mass Spectrometer (Agilent Technologies Santa Clara, CA) run in dynamic Multiple-Reaction-Monitoring (dMRM) mode. Analyte separation was achieved on an Acquity UPLC HSS T3, 1.8 μm, 2.1×150 mm (Waters) column using water and ACN both with 0.1% formic acid as mobile phases A and B, respectively. The chromatographic gradient is shown in Table 24.
MS detection was carried out using electrospray ionization in positive ion mode. Agilent MassHunter Quantitative Analysis software (version B.06.00) was used to quantify analytes based on area-under-the-response-curve. Stable isotope labeled internal standards were used for each analyte to account for variations in the matrix. Samples with analytes below the lowest calibration level standard were reported as 0.00 concentration. Samples with analytes above the highest calibration level standard were reanalyzed at an appropriate dilution using water:methanol (1:1).
Triple Quadrupole LC-MS Method for Quantitative Analysis of Purine Degradation Metabolites
Protein precipitation with cold methanol (−20° C.) was employed for all plasma, CAL and QC samples. Samples were thawed on ice, and 50 μl sample was used for the analysis. A 25 μl internal standard mix aliquot was added to the sample, and proteins were precipitated by addition of 200 μl MeOH (20° C.). Samples were vortex-mixed for 5 min followed by centrifugation at 18,500×g for 5 min at 4° C. The supernatant (200 μl) was transferred to a 96 well plate for injection (5 μl). Multiple reaction monitoring (MRM) analysis was performed on a liquid chromatography (LC) mass spectrometry (MS) system consisting of an Agilent 1290 ultra-high-performance liquid chromatography system (UHPLC) coupled to an Agilent G6490 Triple Quadrupole Mass Spectrometer (Agilent Technologies Santa Clara, CA). Agilent MassHunter Quantitative Analysis software (version B.06.00) was used for the quantitative LC-MS data analysis. Chromatographic separation was performed using a BEH Amide 2.1×50 mm, 1.7 μm column (Waters). Column temperature was maintained at 35° C. The mobile phase was composed of A) 0.1% formic acid in water and B) 0.1% formic acid in acetonitrile. The details for the gradient elution are shown in Table 25.
The seven-minute chromatographic gradient ran at a flow rate of 0.5 ml/min, and MS detection was carried out using electrospray ionization in both positive and negative ion modes. To account for matrix effects, stable isotope labeled (SIL) internal standards were used for each analyte. Samples with analytes below the lowest calibration level standard were reported as 0.00 concentration. Samples with analytes above the highest calibration level standard were reanalyzed at an appropriate dilution using SeraSub (CST Technologies, Inc.).
Triple Quadrupole LC-MS Method for Quantitative Analysis of Carboxylic Acids
Protein precipitation with ACN:MeOH (9:1) at −20° C. was used for all plasma, CAL and QC samples. Samples were thawed on ice, and 50 μl sample was used for the analysis. A 25 μl internal standard mix aliquot was added to the sample and proteins were precipitated by addition of 200 μl ACN:MeOH (9:1, −20° C.) and vortex-mixed for 5 min followed by centrifugation at 18,500×g for 5 minutes at 4° C. The supernatant (200 μl) was transferred to a 96 well plate, and 5 μl was injected onto a 2.1×150 mm BEH Amide column (Waters). Chromatographic separation and analyte detection were achieved on a Waters Xevo TQ-S micro, IVD mass spectrometer hyphenated with an ACQUITY I-Class System, IVD instrument. Mobile phase A was 20 mM ammonium acetate (pH 9.2) in water with 5% ACN and mobile phase B was 10 mM ammonium acetate (pH 9.2) in 95% ACN. Stepped gradient elution was performed at a flow-rate of 0.6 ml/min with the column kept at 30° C. as shown in Table 26.
MS detection was carried out using electrospray ionization in both positive and negative ion modes. To mitigate matrix effects, stable isotope labeled internal standards were used for each analyte. Analytes were quantified as area-under-the-response curve using TargetLynx v4.1 (Waters). Samples with analytes below the lowest calibration level standard were reported as 0.00 concentration. Samples with analytes above the highest calibration level standard were reanalyzed at an appropriate dilution using water.
Bioinformatic Analyses
The values of each metabolite or ratio of metabolites were log base 2 transformed and Z-score normalized prior to analyses. Imputation was not performed, and missing data were omitted from analysis, reducing the number of samples analyzed for a test statistic. Analysis of covariance (ANCOVA), analysis of variance (ANOVA), Welch T-tests and pairwise Pearson correlation analyses were performed on each metabolite or ratio of metabolites. Effect sizes were reported using Cohen's d for T-tests or generalized eta squared for analysis of variance. Dissimilarity measurements (1 minus the absolute value of the pairwise Pearson correlation coefficient (ρ) of metabolite ratios) were used to calculate distances for clustering. Hierarchical clustering was performed using the unweighted pair group method with arithmetic mean (UPGMA). Bootstrap analysis of the clustering result was performed using the pvclust package. Clusters were considered significant, and therefore stably identified within repeated sampling, when the unbiased p-value was ≥0.95. The independence of subject metadata relative to the metabotypes was tested using the Fisher Exact test statistic and effect sizes were estimated with Crammer's V. Post-hoc evaluation of the responses within metadata variables was performed using an exact binomial test. False discovery rate corrections of p-values were performed to control for multiple testing. Analyses were conducted using R version 3.6.1.
Metabotyping Analysis
A metabotype is a subpopulation of individuals with a shared metabolic characteristic or phenotype that can be distinguished from the larger population. We carried out this study in an attempt to identify metabolic features (i.e. an individual metabolite or ratio of metabolites) that are able to distinguish subpopulations (or metabotypes) of ASD subjects. Potential metabotypes associated with ASD were identified by using a heuristic algorithm that tested whether a metabolite or ratio of metabolites identified a subpopulation of primarily ASD subjects above (or below) a particular quantity of the metabolite or above the threshold in a ratio of metabolites. These thresholds were then used to create metabotype tests that identified subjects exceeding the threshold as metabotype-positive and subjects that did not as metabotype-negative. The metabotype tests were established in the discovery set. Diagnostic performance and reproducibility of the metabotype tests were evaluated in the replication set.
Diagnostic performance metrics of sensitivity (detection of ASD) and specificity (detection of TYP) were calculated based on the percentage of ASD or TYP subjects who were positive or negative for a metabotype test. The criteria utilized to accept a metabotype test as being associated with ASD was based on both diagnostic performance and a permutation test to determine if the diagnostic performance values were due to chance. The minimum diagnostic criteria required for a metabotype in the discovery set to be further evaluated in the replication set were: sensitivity at least 5% (indicating that at least 5% of the ASD participants were metabotype-positive), specificity at least 95% (indicating that 95% of the TYP participants were metabotype-negative), and the metabotype-positive population was at least 90% ASD. A permutation test was used to determine whether or not each metabotype was due to chance. 1000 random permutations of CAMP subjects were performed to test how frequently the diagnostic performance of a metabotype was observed in the random permutations. A metabotype test was considered valid (i.e. not considered to be a result found only by chance), if the combined diagnostic performance of sensitivity at least 5%, specificity at least 95%, and percent of ASD positive subjects in the metabotype-population at least 90% were met or exceeded with a frequency of 5% or less in the permutation test. A metabotype test was considered to be reproducible if it also met the diagnostic performance and permutation test criteria required for the discovery set in the replication set. We made a strategic choice to maximize specificity in order to reduce the number of false positives associated with the combination of metabotype tests. Fewer false positives per metabotype test allows multiple tests to be combined into a test battery without significant loss of overall specificity.
As described below, we discovered a number of metabotypes associated with ASD in this study. Test batteries were generated by combining multiple metabotypes into a single test. If an individual was positive for any one of the metabotype tests within the test battery, it indicated that the individual is at higher risk for a diagnosis of ASD. In the current study, this test battery approach was used to determine the diagnostic performance of closely related tests within a metabotype cluster and for the development of an optimized screening test battery.
Study Population
The CAMP study population was divided into two independent subject sets of children: (1) a discovery set of 357 subjects to establish metabotypes and (2) a replication set of 351 subjects to establish the reproducibility of metabotypes and diagnostic performance A description of the primary demographics of the CAMP study population by study set is shown in Table 27.
The primary demographic values of age, sex, and developmental quotient (DQ) were balanced between discovery and replication sets. However, the percentage of male subjects, as well as age, and subject DQ differed between the ASD and TYP populations within the sets. The ASD population contained 17.3% and 22% more male subjects in the discovery and replication sets, respectively, which were 2.5 and 3 months older than the TYP populations. Due to the prevalence of co-occurring cognitive and developmental delays in the ASD population, the DQ was lower in the ASD group compared to the TYP population.
Differential Analysis of Metabolite Levels in ASD and TYP Subjects
Individual metabolites, and all unique combinations of the ratios of metabolites, were evaluated as potential screens for ASD. The ratios of metabolites were evaluated since this type of analysis can uncover biologically relevant changes not evident when evaluating each metabolite independently. When the metabolite and ratio values were adjusted for age, no differences in mean values were identified for the age, sex, or diagnosis of the subjects or their interactions. Thus, the mean levels of the metabolites and their ratios are similar between ASD and TYP subjects regardless of age or sex. This indicates that demographic differences in age and sex between ASD and TYP populations are not likely to impact the conclusions of this study.
Metabotype-Based Test Development
Metabotype analysis of the discovery set identified 250 potential metabotype tests that met established diagnostic performance criteria. Metabotype tests meeting minimum performance metrics in the discovery set of subjects are shown in Table 28.
These tests were then evaluated in the replication set and 34 metabolite ratios reproducibly identified ASD metabotypes. Metabolite ratios that identify metabotypes of ASD meeting minimum performance criteria in both discovery and replication sets are shown in Table 29.
Alanine/4-
hydroxyproline
a-ketoglutarate/
Phenylalanine
Glycine/
Asparagine
Glycine/
Phenylalanine
Lactate/
Citrulline
Lactate/
Glutamate
Pyruvate/
Kynurenine
Ornithine/
Phenylalanine
Ethanolamine/
Uric acid
Histidine/
Leucine
Succinate/
Citrulline
Succinate/
Glycine
Among these 34, there were two that were previously reported, while the remaining 32 ratios were novel. Taken together, the 34 metabotypes identified 57% (95% CI, 52%-61%) of the CAMP ASD population with a total specificity of 83% (95% CI, 95% CI, 77%-88%).
Diagnostic performance of metabotype tests that meet minimum performance criteria in both the discovery and replication sets are shown in Table 30.
Clusters of Metabotypes Identify Metabolically Distinct Subpopulations
Correlation analysis and hierarchical clustering of the 34 reproducible amino acid and energy metabolism metabotypes were used to understand the relationships between the metabotype tests. We wanted to determine, for example, whether different metabotype tests identified the same groups of participants. We used hierarchical clustering for the metabotype-positive subject population to test for clusters of related metabotypes. Following bootstrap resampling analysis, the metabolite ratios formed 6 reproducible clusters of metabotype tests. Five of these clusters contain ratios that include one of the following metabolites: succinate, glycine, ornithine, 4-hydoxyproline, or α-ketoglutarate. A sixth cluster contains ratios that included either lactate or pyruvate.
Diagnostic performance of the significant clusters of metabotype tests is shown in Table 31.
Each of the clusters consists of several metabotype tests. Metabotype positive subjects are generally identified by multiple metabotypes within the cluster. For example, numerous subjects within the lactate and pyruvate cluster (purple text) are positive for multiple metabotypes. The closer relationship of metabotypes within a cluster is also evident in the increased probability of being positive in more than one metabotype test within a cluster. The newly identified metabotype clusters identify between 10% and 28% of the CAMP ASD population, with specificity greater than or equal to 95%. The sensitivity of the clusters is greater than any of the individual metabotype tests within a cluster.
The succinate, 4-hydoxyproline, α-ketoglutarate and lactate/pyruvate clusters identify novel metabotypes associated with ASD that have not been previously reported. The branched chain amino acid (BCAA) dysregulation metabotype (AADM) that we had previously described identifies subpopulations of autistic individuals with elevated levels of the metabolites glycine, ornithine, or glutamine and lower levels of the BCAAs. The glycine and ornithine clusters reported here contain the AADM associated metabolite ratios glycine/isoleucine and ornithine/leucine, respectively. These two clusters identify 70% of the subjects in the previously reported AADM metabotype-positive population indicating that the metabotype tests in the glycine and ornithine clusters identify AADM metabotypes related to ornithine and leucine.
Association Analysis of ASD Subjects by Metabolic Cluster
The metabotype clusters were analyzed for associations with phenotypic information gathered on the ASD subjects related to medical history, behavioral testing, diet, supplements, and medications. Interestingly, the ornithine cluster identified a higher proportion of females (Fisher's Exact Test odds ratio 3.3 (95% CI, 1.83 to 6.00), FDR=0.00068). The α-ketoglutarate cluster metabotype-positive subjects were more likely to be delivered by Cesarean section (Fisher's Exact Test odds ratio 2.23 (95% CI, 1.27 to 3.86), FDR=0.044). The metabotype-positive population identified by the succinate cluster had 14% lower receptive language scores than the metabotype negative population (−0.1432%; 95% CI, −0.229 to −0.057), T-test FDR=0.024).
Optimized Metabotype Test Battery
The fundamental goal of this research is to develop a metabolomics-based test battery that can be used as a screen for autism risk. As indicated above, the metabotype tests within each cluster redundantly identified a similar group of ASD subjects. Similarity of metabotype ratio tests based on conditional probability of metabotype positive results is shown in Tables 32a-32d.
Similarity of Metabotype Clusters Based on Conditional Probability of Metabotype Positive Results is shown in Table 33.
We sought to create an optimized test battery based on selecting a subset of the 32 novel metabotype tests that 1) maximized sensitivity while maintaining a specificity of at least 90% to provide more diagnostic value to a positive test result, 2) contained at least one metabotype test from each of the 6 clusters to represent biological information from each cluster in the final test battery, and 3) eliminated redundant tests. To reduce the number of redundant tests, a subset of tests from each cluster were selected that identified the ASD participants identified by all of the tests within a cluster. This process led to the selection of 19 metabotype tests that captured the total sensitivity identified by each of the clusters. We then created test batteries containing 7 to 18 metabotype tests using combinations of the 19 tests. The test combinations were filtered by diagnostic performance in the combined discovery and replication sets. The maximum observed sensitivity of test combinations was 50% at specificities of at least 90%. The optimal combination selected for the final test battery contained 14 metabotype tests that represented each cluster and yielded the highest sensitivity in the discovery and replication sets with a specificity greater than 90%. This optimized test battery identified CAMP subjects with a sensitivity of 50% (95% CI, 45%-54%) and specificity of 92% (95% CI, 88%-96%). Addition of the AADMs test predictions to the optimized test battery increased the overall sensitivity to 53% (95% CI, 48%-57%) with a specificity of 91% (95% CI 86-94%). When compared to the diagnostic performance of the combination of the 34 metabotype tests, the optimization process led to a reduction in the number of tests, and importantly, to the reduction of false positives, thereby increasing the specificity by 8%. Total sensitivity was reduced from 57% to 53% due to the elimination of tests that contributed an unacceptable number of false positive results to the overall test battery.
The CAMP study was designed to reproducibly identify subpopulations of autistic children as small at 5% who share common metabolic differences from typically developing children (i.e metabotypes). The study involved 499 children that had a diagnosis of autism spectrum disorder and 208 children that were typically developing and were able to contribute blood samples that met quality control standards for metabolic analyses. We quantitatively measured 39 metabolites associated with amino acid and energy metabolism. This set of metabolites was initially chosen for analysis based on pilot studies and published research related to abnormalities of purine metabolism and mitochondrial bioenergetics. We observed that: (1) analysis of ratios of plasma metabolite concentrations revealed 34 metabotype tests that reproducibly identified metabolic differences associated with ASD; and (2) these metabotypes formed 6 distinct clusters related to the underlying metabolic dysregulation. A battery of 14 metabotype tests, when integrated with previously identified metabotypes, identified ASD subjects within CAMP with a sensitivity of 53% (95% CI, 48%-57%) and a specificity of 91% (95% CI 86-94%).
Our Strategy for Metabotype Analysis
There has been intense interest in discovering effective and practical metabolite assays for the identification of children at risk for ASD. Disappointingly, most previously described “diagnostic tests” have generally not been reproduced in subsequent studies. Lack of reproducibility is likely due to several issues including the etiological and phenotypic heterogeneity of ASD, and the small number of cases vs controls in most previous studies. Our metabotyping approach starts from the premise that different subgroups of individuals with autism will have different metabolic signatures. Our analytic approach quantitatively explores domains of metabolites to find those that identify homogenous subpopulations of individuals with ASD. We explicitly do not attempt to create a single, broad-based predictive signature of autism spectrum disorder, i.e., we acknowledge the heterogeneity of ASD. Moreover, the size of the CAMP study population provides sufficient power to enable both a discovery and an independent replication set of subjects each larger than the total number of subjects in most previously published metabolism studies of autism.
The autism literature provides clues to which metabolic anomalies should be investigated. However, the design attributes of this study (eg, large cohort size with replication set, validated analytical methods, and subtyping approaches) allow for a significant extension of prior work. For example, altered metabolism among individuals with ASD has been observed related to biochemical pathways including oxidative phosphorylation, branched chain amino acid metabolism and others. The current work draws from the earlier studies to reproducibly identify and stratify metabolic alterations common in specific groups of subjects such that they can be used to begin further work toward therapies that are specific to defined metabotypes.
Ratios of metabolites can increase diagnostic efficacy by detecting metabolic associations and biochemical pathways not apparent in the analysis of single metabolites. For example, metabolite ratios of bloodspot-derived amino acids and acylcarnitines have been successfully used in newborn screening for metabolic disorders such as phenylketonuria, maple syrup urine disease, and certain disorders of mitochondrial fatty acid beta-oxidation. Prenatal serum metabolite ratios can predict fetal growth restriction. In view of the value of metabolite ratio analysis, we analyzed all possible combinations of the 39 plasma metabolite pairs related to amino acid, purine catabolism and energy metabolism in a supervised approach to identify potential metabolic subpopulations associated with ASD. Whereas none of the levels of individual metabolites met the diagnostic criteria required in the discovery set, ratios of these metabolites led to 34 metabotype tests that reproducibly identified metabotypes.
Alterations in Metabolite Ratios May Provide Insight into Pathophysiology
While the primary goal of this research program is to establish reliable metabolomic screens, a related aim is to provide insight into metabolic disturbances that may lead to more targeted treatments. Hierarchical clustering of metabotypes established six clusters of metabotype tests related to amino acid and mitochondrial energy metabolism. The metabolic clusters are comprised of ratios containing: (1) lactate or pyruvate, (2) succinate, (3) α-ketoglutarate, (4) glycine, (5) ornithine, and (6) 4-hydroxyproline in combination with other metabolites. These clusters highlight potential dysregulation in amino acid and energy metabolism in ASD when compared to TYP. It is important to point out that the dysregulation that we report occurs at quantitative metabolite levels that that for any of the studied metabolites are not diagnostic of specific clinical disorders. But, when evaluated as ratios, they identify changes that are outside the normal range of values observed in the vast majority of typically developing children.
Alterations in succinate, lactate, and pyruvate concentrations and their ratios are often associated with disturbances of mitochondrial bioenergetics and these disturbances occur with increased frequency in people with ASD. The overlap of ASD subjects identified by metabotype tests in the lactate/pyruvate cluster suggests that they may all experience similar dysregulation and underlying pathophysiology. While one might expect that the succinate and α-ketoglutarate clusters would be closely related to the lactate and pyruvate cluster as intermediates of the tricarboxylic acid (TCA) or Krebs cycle, they actually identify largely different subsets of ASD cases. Subjects identified by the α-ketoglutarate cluster were only infrequently positive in the succinate (10%), or pyruvate and lactate (29%) clusters. This raises the possibility that these two groups of autistic individuals have different underlying pathophysiologies. Without wishing to be bound by a theory, this may be due to complex biological roles that succinate and α-ketoglutarate play in signaling outside of the TCA cycle.
Metabotype-positive ASD participants in clusters containing ornithine, glycine, α-ketoglutarate, and 4-hydroxyproline are mostly (67-94%) metabotype negative for ratios containing succinate, lactate, or pyruvate, again suggesting differences in the underlying metabolism of these two groups. The metabotype ratios fall into two larger clusters, one comprised of ratios containing α-ketoglutarate, glycine, ornithine, and 4-hydroxyproline and a second containing ratios with lactate, pyruvate and succinate. The metabotype-positive subjects in the first group of clusters may be related to dysregulation of amino acid metabolism and the urea cycle. While metabotype-positive participants in a second group of clusters may have dysregulation related to energy metabolism or mitochondrial function. Further, the ASD participants who are metabotype-positive for ornithine and glycine clusters are very similar to the previously described AADM metabotype population with increased ornithine and glycine and decreased levels of BCAAs. Individuals that are metabotype-positive for 4-hydroxyproline do not have much overlap with those who are metabotype-positive for the ornithine, glycine, or AADM populations, and are more similar to the α-ketoglutarate cluster. Thus, the 6 clusters of metabotype tests that we have discovered highlight a diversity of underlying metabolic alterations. Although the pathophysiological basis of these alterations is not understood at this time, our approach provides a stratification mechanism to facilitate research into the underlying biology related to each of these metabotypes.
Functional Associations of the Metabotypes
Analysis of phenotypic data of the autistic subjects revealed some intriguing, albeit very preliminary, associations between subjects with a certain metabotype and biological or behavioral features of the ASD cohort. For example, there was an over representation of female subjects identified by the ornithine-related metabotypes. Ornithine aminotransferase, ornithine decarboxylase, and arginase-2 are regulated by testosterone, which could explain sex-specific differences observed in ASD. Interestingly, subjects in the α-ketoglutarate metabotype-positive cluster were more likely to have had a Cesarean delivery (CD). Children born by CD tend to have an increased body mass index, altered microbiome, and immune function, each of which is associated with increased risk of ASD. Lastly, subjects in the succinate cluster had decreased receptive language scores compared to metabotype-negative subjects. These preliminary observations need to be replicated and extended in future studies, but they highlight the potential that subtle, yet reliable, metabolic alterations may be associated with functional outcomes.
How would a Metabolomics-Based Screen be Deployed?
Metabotype-based tests can support earlier diagnosis by identifying subsets of children having metabolic differences associated with ASD. A metabolomic-based test may be used as both an additional screening modality to detect children who are at risk for a diagnosis of ASD and as a stratification tool for individuals who are already diagnosed.
A child for whom there may be grounds for evaluation based on family history, or because of clinical or parental concerns would be a candidate for metabotype-based screening. A positive metabotype result could lead to a prioritized referral to a specialist for diagnostic assessment of ASD. A metabotype-negative result would follow the American Academy of Pediatrics (AAP) standard of care for further behavioral and developmental assessment at periodic intervals in early childhood. Individuals already diagnosed with ASD, may benefit in the future from metabotype screening for insight into metabolic dysregulation that could potentially lead to a refined, personalized intervention plan.
Deployment of a Metabolomics-Based Screen
Metabotype-based tests can support earlier diagnosis by identifying subsets of children having metabolic differences associated with ASD. In practice, we envision a metabolomic-based test as both an additional screening modality to detect children who are at risk for a diagnosis of ASD and as a stratification tool for individuals who are already diagnosed. A child for whom there may be grounds for evaluation based on family history, or because of clinical or parental concerns would be a candidate for metabotype-based screening. A positive metabotype result could lead to a prioritized referral to a specialist for diagnostic assessment of ASD. A metabotype-negative result would follow the American Academy of Pediatrics (AAP) standard of care for further behavioral and developmental assessment at periodic intervals in early childhood. Individuals already diagnosed with ASD, may benefit in the future from metabotype screening for insight into metabolic dysregulation that could potentially lead to a refined, personalized intervention plan.
Conclusions
The CAMP study has produced a unique repository of samples from children with autism and age-matched typically developing controls that will enable an ongoing exploration of small molecule signatures of risk for ASD. Our first study, which focused on branched chain amino acid metabolism enabled the detection of 17% of the CAMP ASD cohort. The current study, which focused on 39 metabolites associated with amino acid and energy metabolism, has enabled the detection of 50% of the autistic subjects. Taken together, the current test battery can detect 53% of the children with ASD in CAMP.
Ratios of metabolites can increase diagnostic and screening efficacy by detecting metabolic associations and biochemical pathways not apparent in the analysis of single metabolites. In this analysis, 94 metabolites measured using two quantitative LC-MS/MS and three semiquantitative LC-MS/MS methods were evaluated to identify metabolic changes associated with ASD. The metabolites and all unique combination of ratios of these metabolites were evaluated as potential biomarkers able to identify metabolic subpopulations associated with risk ASD and aid in stratifying ASD subjects into subpopulations with shared metabolic phenotypes also known as metabotypes. Each metabolite and ratio of metabolites was evaluated to determine if a diagnostic threshold could identify metabolic subpopulations with acceptable diagnostic performance that can be used as a metabotype-based metabolic test for risk of ASD. The metabolites and ratios of metabolites associated with ASD metabotypes were tested for clusters of metabotypes and if these clusters were associated metabolic processes. Subsets of the metabotype tests were also examined to determine diagnostic performance in tests batteries containing multiple metabotype tests.
The diagnostic performance of the metabotype positive populations was based on a repeated cross-validation using Children's Autism Metabolome Project (CAMP) ASD and typical developing (TYP) subjects. The demographics of CAMP subjects used in cross-validation are shown in Table 34.
The cross-validation technique provides estimates of the test's diagnostic performance in the training set of subject samples. Cross-validation utilizes all of the available CAMP samples to be utilized in both model training and model assessment. Metabotypes capable of discriminating ASD from TYP CAMP participants were selected based on the average performance of the cross-validation hold out set. Diagnostic metabotype thresholds utilized for future tests were based on the final model trained on all subject samples used in the cross-validation process. In this example of metabotyping, utilizing all of the samples allows for a more accurate threshold to discriminate ASD in the final model since all of the TYP samples are evaluated producing a better estimate typical metabolism and when metabolism is atypical.
The cross-validation process was executed by partitioning subject samples into training and hold-out sets using 4-fold cross-validation repeated 50 times stratified by subject sex, age, and diagnosis. The following procedure was performed for each cross-validation resampling iteration: diagnostic thresholds were set on a training set of samples comprised of ASD and TYP subjects. A heuristic algorithm based on receiver operator curve (ROC) analysis was applied to identify individual biomarkers able to discriminate ASD subpopulations, indicative of a metabotype, using a diagnostic threshold for metabolite abundance or the values of metabolite ratios. Diagnostic thresholds were assessed to determine if the threshold could identify subpopulations of ASD subjects when subject values exceed the threshold (greater than) or subject values are lower than the threshold (less than). The threshold and direction of threshold assessment (greater than or less than) that maximized PPV at the greatest sensitivity was selected as the threshold for the metabotype test. These thresholds were then used to create metabotype tests that identified subjects exceeding the threshold as metabotype-positive and subjects that did not as metabotype-negative. The tests were then applied to predict the metabotype status of the hold set of samples. A hold out sample was scored as being part of an affected metabotype population (metabotype positive) while the remaining subjects were scored as normal or the unaffected population (metabotype negative). The diagnostic performance of the metabotype was based confusion matrix generated from the subjects scored as being part of the metabotype and by their diagnosis. A true positive was defined as metabotype positive subject with a diagnosis of ASD, a false negative was a metabotype negative results for a subject with a diagnosis of ASD, a false positive was a metabotype positive subject with a diagnosis of TYP, and a true negative was a metabotype negative subject with diagnosis of TYP. The performance metrics of specificity, positive predictive value (PPV) and sensitivity (subtype prevalence) were based on ASD as the positive class. Diagnostic performance metrics of sensitivity (detection of ASD) and specificity (detection of TYP) were calculated based on the percentage of ASD or TYP subjects who were positive or negative for a metabotype test in the hold set.
The average performance of the holdout set from repeated cross-validation was used to select diagnostic ratio and panels for use in metabotype based diagnostics. Test were considered to identify a metabolic subpopulation associated with ASD and not due to a chance association if the average performance in the holdout set had a sensitivity at least 4.5% (indicating that at least 4.5% of the ASD participants were metabotype-positive), specificity at least 95% (indicating that 95% of the TYP participants were metabotype-negative), and the metabotype-positive population was at least 90% ASD (equivalent to the positive predictive value (PPV) within the CAMP study set population). The final metabotype diagnostic thresholds were set using the entire study set of CAMP ASD and TYP subjects.
The metabotyping analyses was divided into two different approaches. One approach utilized only metabolites with quantitative measurements and the other approach utilized metabolites measured by both quantitative and semiquantitative approaches. Both approaches used the same methodology to evaluate metabotypes. Metabotype tests meeting minimum diagnostic performance criteria in the holdout set were clustered based on the pairwise spearman correlation of plasma values as well as the pairwise conditional probability that a subject will test positive given they had already tested positive in another test. The pairwise conditional probability of tests was determined and used an adjacency matrix to group tests using the infomap clustering algorithm to cluster tests using conditional probability to weight edges between metabotype tests (vertices). Hierarchical clustering was utilized to cluster metabotype test based on the pairwise plasma correlations using average linkage. Test groups or clusters represent biological domains identified by metabotype tests within the clusters. Tests were then optimized into test batteries to determine potential overall diagnostic performance of the metabotype tests.
Analysis of the metabolites with quantitative measurements identified 143 metabolites and ratios of metabolites meeting minimum diagnostic performance criteria indicative of a metabotype associated with ASD. The 143 metabotypes could be utilized as metabotype tests to identify individual at risk for ASD. The metabotype tests formed 13 metabolic clusters that identified ASD subjects sensitivities 10.4% to 36.2% and specificities of 91% to 99%. The quantitative metabotype grouping and holdout set average diagnostic performance are shown in Table 35.
These 143 tests could subset into test batteries of 17 to 42 metabotype tests that identify CAMP ASD subjects with sensitivities of 67% to 68% and a specificity of 90%. The metabolites measured by both quantitative and semiquantitative approaches identified 609 metabolites and ratios of metabolites meeting minimum diagnostic performance criteria indicative of metabotype associated with ASD. A representative subset of the metabotypes, one for each metabolite measured by semiquantitative methods, meeting minimum diagnostic performance are shown in Table 36.
These metabotype tests could be clustered into 31 groups of metabotypes with sensitivities from 6.8% to 60% and specificities of 83% to 99%. The 609 metabotypes could be subset into test batteries of 12 to 74 tests that identified CAMP ASD subjects with sensitivities of 73% to 91% and specificities of 90%.
References and citations to other documents, such as patents, patent applications, patent publications, journals, books, papers, web contents, have been made throughout this disclosure. All such documents are hereby incorporated herein by reference in their entirety for all purposes.
Various modifications of the invention and many further embodiments thereof, in addition to those shown and described herein, will become apparent to those skilled in the art from the full contents of this document, including references to the scientific and patent literature cited herein. The subject matter herein contains important information, exemplification, and guidance that can be adapted to the practice of this invention in its various embodiments and equivalents thereof.
This application claims the benefit of, and priority to, U.S. Provisional Patent Application No. 62/914,111, filed Oct. 11, 2019, the contents of which are incorporated by reference.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US20/55186 | 10/12/2020 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 62914111 | Oct 2019 | US |
