Metabolic profiling in children with autism spectrum disorder with and without mental regression: preliminary results from a cross-sectional case–control study

Rangel-Huerta, Óscar Daniel; Gómez-Fernández, Antonio; Torre-Aguilar, María José de la; Gil, Ángel; Pérez-Navero, Juan Luís; Flores-Rojas, Katherine; Martín-Borreguero, Pilar; Gil-Campos, Mercedes

doi:10.1007/s11306-019-1562-x

Cited by 38 publications

(35 citation statements)

References 40 publications

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“…There have been many other studies of metabolites in plasma or serum of children with ASD [37][38][39][40][41][42][43][44][45][46]. Several of these studies attempted to classify ASD versus TD children, with some of the most successful studies including Anwar et al [37] (four-metabolite model with sensitivity/specificity of 92%/84%) and Momeni et al [40] (three-peptide model with sensitivity/specificity of 95%/85%).…”

Section: Discussionmentioning

confidence: 99%

Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy

et al. 2019

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Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from the TD − GI cohort with 94% sensitivity and 100% specificity after leave-one-out cross-validation. After the ASD + GI participants underwent Microbiota Transfer Therapy with significant improvement in GI and ASD-related symptoms, their metabolic profiles shifted significantly to become more similar to the TD − GI group, indicating potential utility of this combination of plasma metabolites as a biomarker for treatment efficacy. Two of the metabolites, sarcosine and inosine 5′-monophosphate, improved greatly after treatment. The third metabolite, tyramine O-sulfate, showed no change in median value, suggesting it and correlated metabolites to be a possible target for future therapies. Since it is unclear whether the observed differences are due to metabolic abnormalities associated with ASD or with GI symptoms (or contributions from both), future studies aiming to classify ASD should feature TD participants with GI symptoms and have larger sample sizes to improve confidence in the results.

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Section: Discussionmentioning

confidence: 99%

Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy

et al. 2019

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“…Thus, metabolomics provides a powerful tool to map these perturbations and their relationship to disease and response to therapy. In fact, some studies have been carried out to investigate metabolomics biomarkers in the brain [ 65 , 66 ], plasma [ 67 , 68 , 70 ], dried blood [ 79 ] and urine [ 72 , 73 , 74 , 75 , 76 , 77 , 78 ] samples of ASD patients ( Table 2 ). Yap and coworkers used a proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy method that showed increased levels of taurine and low content of glutamate in urine samples of ASD patients [ 72 ].…”

Section: Metabolomicsmentioning

confidence: 99%

“…The metabolic intermediates were detected in the aspartate, beta-alanine, glucose–alanine cycle, malate–aspartate shuttle, urea cycle, and tryptophan breakdown pathways showing significant statistical differences between controls and ASD patients. In addition, within the two subgroups, significant statistical differences were also highlighted in the levels of the fatty acids decanoylcarnitine, laurate, arachidate, myristate, octanoylcarnitine, quinate, and 7-methylurate [ 70 ]. Rachel S. Kelly et al highlighted differences in tyrosine metabolism, tryptophan biosynthesis and endocannabinoid metabolism in a cohort of children, 13% of whom had ASDs.…”

Section: Metabolomicsmentioning

confidence: 99%

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Ristori

Mortera

Marzano

et al. 2020

IJMS

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Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

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“…Regarding physical health, individuals with ASD have been reported to be particularly at risk for immune dysregulation, gastrointestinal (GI) symptoms, and neurological complications (Alabaf et al 2018 ; Kohane et al 2012 ). Additional research investigates disease risks in ASD and the general population for other physical systems, while the results are rather inconsistent or the evidence is rather modest, such as infectious diseases (Rosen et al 2007 ; Sabourin et al 2019 ), disturbances in metabolism (Orozco et al 2019 ; Rangel-Huerta et al 2019 ), obesity (de Vinck-Baroody et al 2015 ; Dreyer Gillette et al 2015 ), and mitochondrial dysfunctions (Griffiths and Levy 2017 ; Hollis et al 2017 ). The co-existence of somatic health problems in ASD is associated with increased autism symptom severity and lower quality of life (Aldinger et al 2015 ; Kuhlthau et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

The Association Between Somatic Health, Autism Spectrum Disorder, and Autistic Traits

Pan¹,

Tammimies

Bölte³

2019

Behav Genet

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This study used a twin cohort to investigate the association of autism spectrum disorder (ASD) and autistic traits with somatic health. A total of 344 twins (172 pairs; mean age 15.56 ± 5.62 years) enriched for ASD and other neurodevelopmental conditions were examined. Medical history and current physical problems were collected with a validated questionnaire to determine twin's somatic health. The Social Responsiveness Scale (SRS-2) was used to measure the participant's severity of autistic traits. Identified somatic health issues with significant within-twin pair differences were tested in relation to both ASD diagnosis and autistic traits in a co-twin control model. Twins with ASD exhibited more neurological and immunological health problems compared to those without ASD (p = 0.005 and p = 0.004, respectively). The intra-pair differences of neurological conditions and SRS-2 score were significantly correlated in monozygotic twins differing for autism traits (r = 0.40, p = 0.001), while the correlation was not found for immunological problems. In addition, a conditional model for analysis of within-twin pair effects revealed an association between neurological problems and clinical ASD diagnosis (Odds ratio per neurological problem 3.15, p = 0.02), as well as autistic traits (β = 10.44, p = 0.006), after adjusting for possible effects of co-existing attention deficit hyperactivity disorder and general intellectual abilities. Our findings suggest that neurological problems are associated with autism, and that non-shared environmental factors contribute to the overlap for both clinical ASD and autistic traits. Further population-based twin studies are warranted to validate our results and examine in detailed the shared genetic and environmental contributions of neurological problems and ASD.

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Metabolic profiling in children with autism spectrum disorder with and without mental regression: preliminary results from a cross-sectional case–control study

Cited by 38 publications

References 40 publications

Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy

Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

The Association Between Somatic Health, Autism Spectrum Disorder, and Autistic Traits

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