Multivariate techniques enable a biochemical classification of children with autism spectrum disorder versus typically-developing peers: A comparison and validation study (original) (raw)
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Research in Autism Spectrum Disorders, 2020
Background: Previous studies have found plasma measurements of metabolites from the folatedependent one-carbon metabolism (FOCM) and transsulfuration (TS) pathways to be useful for differentiating individuals with autism spectrum disorder (ASD) from their typically developing peers. However, ASD is heterogeneous due to wide variation in the presence of co-occurring behavioral and medical conditions, and it is unknown how these conditions influence the ability to identify ASD based on FOCM/TS metabolites. Method: This study employs a previously developed multivariate model that makes use of five FOCM/TS measurements (S-adenosylmethionine/S-adenosylhomocysteine, glutamylcysteine, glutathione disulfide, free cystine/free cysteine, and percent oxidized glutathione) to distinguish children with ASD from typically developing children. The model is used here to evaluate an independent cohort of individuals having ASD with diagnosed co-occurring conditions (age range 2-17 years old) and assess classifier performance in the presence/absence of these conditions. The four categories of co-occurring conditions considered were allergic disorders, gastrointestinal disorders, immune/metabolic disorders, and neurological disorders. All data were collected and retrospectively analyzed from previous clinical studies. Results: The model was able to identify 124 of 131 participants with ASD (94.7 %) correctly regardless of co-occurring condition status. Model performance was generally not sensitive to the absence or presence of most co-occurring conditions, with the exceptions of ever/never having allergies or gastrointestinal symptoms, or currently (not) having any condition, all of which had minor impacts on model prediction accuracy. Conclusion: The results of this exploratory study suggest that a FOCM/TS-based classifier for diagnosing ASD may potentially be robust to variations in co-occurring conditions and potentially applicable across ASD subtypes. Larger, more comprehensive follow-up studies with typically developing and/or developmentally delayed control groups are required to provide a more conclusive assessment of classifier robustness to co-occurring conditions.
Journal of Proteome Research, 2015
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with no clinical biomarker. The aims of this study were to characterize a metabolic signature of ASD and to evaluate multiplatform analytical methodologies in order to develop predictive tools for diagnosis and disease follow-up. Urine samples were analyzed using 1 H and 1 H− 13 C NMR-based approaches and LC−HRMS-based approaches (ESI+ and ESI− on HILIC and C18 chromatography columns). Data tables obtained from the six analytical modalities on a training set of 46 urine samples (22 autistic children and 24 controls) were processed by multivariate analysis (orthogonal partial least-squares discriminant analysis, OPLS-DA). The predictions from each of these OPLS-DA models were then evaluated using a prediction set of 16 samples (8 autistic children and 8 controls) and receiver operating characteristic curves. Thereafter, a data fusion block-scaling OPLS-DA model was generated from the 6 best models obtained for each modality. This fused OPLS-DA model showed an enhanced performance (R 2 Y(cum) = 0.88, Q 2 (cum) = 0.75) compared to each analytical modality model, as well as a better predictive capacity (AUC = 0.91, p-value = 0.006). Metabolites that are most significantly different between autistic and control children (p < 0.05) are indoxyl sulfate, N-α-acetyl-L-arginine, methyl guanidine, and phenylacetylglutamine. This multimodality approach has the potential to contribute to find robust biomarkers and characterize a metabolic phenotype of the ASD population.
Potential serum biomarkers from a metabolomics study of autism
Journal of psychiatry & neuroscience : JPN, 2015
Early detection and diagnosis are very important for autism. Current diagnosis of autism relies mainly on some observational questionnaires and interview tools that may involve a great variability. We performed a metabolomics analysis of serum to identify potential biomarkers for the early diagnosis and clinical evaluation of autism. We analyzed a discovery cohort of patients with autism and participants without autism in the Chinese Han population using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF MS/MS) to detect metabolic changes in serum associated with autism. The potential metabolite candidates for biomarkers were individually validated in an additional independent cohort of cases and controls. We built a multiple logistic regression model to evaluate the validated biomarkers. We included 73 patients and 63 controls in the discovery cohort and 100 cases and 100 controls in the validation cohort. Metabolomic analysis of ...
Frontiers in Psychiatry
Autism spectrum disorder (ASD) is currently diagnosed according to behavioral criteria. Biomarkers that identify children with ASD could lead to more accurate and early diagnosis. ASD is a complex disorder with multifactorial and heterogeneous etiology supporting recognition of biomarkers that identify patient subsets. We investigated an easily testable blood metabolic profile associated with ASD diagnosis using high throughput analyses of samples extracted from dried blood spots (DBS). A targeted panel of 45 ASD analytes including acyl-carnitines and amino acids extracted from DBS was examined in 83 children with ASD (60 males; age 6.06 ± 3.58, range: 2-10 years) and 79 matched, neurotypical (NT) control children (57 males; age 6.8 ± 4.11 years, range 2.5-11 years). Based on their chronological ages, participants were divided in two groups: younger or older than 5 years. Two-sided T-tests were used to identify significant differences in measured metabolite levels between groups. Näive Bayes algorithm trained on the identified metabolites was used to profile children with ASD vs. NT controls. Of the 45 analyzed metabolites, nine (20%) were significantly increased in ASD patients including the amino acid citrulline and acyl-carnitines C2, C4DC/C5OH, C10, C12, C14:2, C16, C16:1, C18:1 (P: < 0.001). Näive Bayes algorithm using acylcarnitine metabolites which were identified as significantly abnormal showed the highest performances for classifying ASD in children younger than 5 years (n: 42; mean age 3.26 ± 0.89) with 72.3% sensitivity (95% CI: 71.3;73.9), 72.1% specificity (95% CI: 71.2;72.9) and a diagnostic odds ratio 11.25 (95% CI: 9.47;17.7). Re-test analyses as a measure of validity showed an accuracy of 73% in children with ASD aged ≤5 years. This easily testable, non-invasive profile in DBS may support recognition of metabolic ASD individuals aged ≤5 years and represents a potential complementary tool to improve diagnosis at earlier stages of ASD development.
Untargeted Metabolomics for Autism Spectrum Disorders: Current Status and Future Directions
Frontiers in Psychiatry
Autism spectrum disorders (ASDs) are a group of neurodevelopment disorders characterized by childhood onset deficits in social communication and interaction. Although the exact etiology of most cases of ASDs is unknown, a portion has been proposed to be associated with various metabolic abnormalities including mitochondrial dysfunction, disorders of cholesterol metabolism, and folate abnormalities. Targeted biochemical testing like plasma amino acid and acylcarnitine profiles have demonstrated limited utility in helping to diagnose and manage such patients. Untargeted metabolomics has emerged, however, as a promising tool in screening for underlying biochemical abnormalities and managing treatment and as a means of investigating possible novel biomarkers for the disorder. Here, we review the principles and methodology behind untargeted metabolomics, recent pilot studies utilizing this technology, and areas in which it may be integrated into the care of children with this disorder in the future.
Seminars in Pediatric Neurology, 2020
An ever-evolving understanding of autism spectrum disorder (ASD) pathophysiology necessitates that diagnostic standards also evolve from being observation-based to include quantifiable clinical measurements. The multisystem nature of ASD motivates the use of multivariate methods of statistical analysis over common univariate approaches for discovering clinical biomarkers relevant to this goal. In addition to characterization of important behavioral patterns for improving current diagnostic instruments, multivariate analyses to date have allowed for thorough investigation of neuroimaging-based, genetic, and metabolic abnormalities in individuals with ASD. This review highlights current research using multivariate statistical analyses to quantify the value of these behavioral and physiological markers for ASD diagnosis. A detailed discussion of a blood-based diagnostic test for ASD using specific metabolite concentrations is also provided. The advancement of ASD biomarker research promises to provide earlier and more accurate diagnoses of the disorder.
Folate metabolism abnormalities in autism: potential biomarkers
Biomarkers in Medicine, 2017
Autism spectrum disorder (ASD) has been linked to abnormalities in folate metabolism. Polymorphisms in folate genes may act in complex polygenic ways to increase the risk of developing ASD. Autoantibodies that block folate transport into the brain have been associated with ASD and children with ASD and these autoantibodies respond to high doses of a reduced form of folate known as folinic acid (leucovorin calcium). Some of the same abnormalities are also found in mothers of children with ASD and supplementing folate during preconception and gestational periods reduces the risk to the offspring from developing ASD. These data suggest that folate pathway abnormalities may be a major metabolic disturbance underlying ASD that can be leveraged as biomarkers to improve symptoms and prevent ASD.
Autism-Open Access, 2016
Autism spectrum disorder (ASD) is a pervasive broad-spectrum disorder that involves multifaceted delays in the development of many basic, social, and communication skills. The etiology of ASD is multifactorial and involves interplay among genetic, neurologic, hormonal, metabolic, immune-inflammatory, and environmental factors; which further complicate both the diagnosis and management in affected individuals. This review delineates the underpinnings of clinical challenges posed by ASD, like clinical heterogeneity of patient presentation, unresolved ASD genomic map and deficient drug therapy. Besides, it addresses the emerging pathogenic, etiologic, and diagnostic clues of diverse origins, encompassing genetic-, neurotransmitter-, androgenic-and immunoinflammatory-anomalies in ASD patients, as availed by advances in Omics technologies (like genomics and proteomics). Moreover, as unravelled by metabolomics studies, metabolic flaws that pertain to amino acids, fatty acids, mitochondrial anomalies, and oxidative-stress are highlighted and further correlated with the extent of clinical picture and malbehaviors coherent with ASD patients. Lastly, future directions are underlined to promote and rationalize ASD research so as to help translate its findings into remedy.