Profiles of urine and blood metabolomics in autism spectrum disorders

Likhitweerawong, Narueporn; Thonusin, Chanisa; Boonchooduang, Nonglak; Louthrenoo, Orawan; Nookaew, Intawat; Chattipakorn, Nipon

doi:10.1007/s11011-021-00788-3

Cited by 31 publications

(29 citation statements)

References 89 publications

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“…Similarly, in depression, metabolomic techniques have been used to identify differential metabolites and mechanisms of action for antidepressants 15 . In the context of ASD, researchers have observed notable disparities in the abundance of metabolites present in the serum and urine of individuals with ASD compared to typically developing children 16–18 . Using mass spectrometry techniques, Needham et al.…”

Section: Introductionmentioning

confidence: 99%

Metabolomic analysis of plasma biomarkers in children with autism spectrum disorders

Liu,

Tan,

Zhang

et al. 2024

MedComm

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Autism spectrum disorder (ASD) presents a significant risk to human well‐being and has emerged as a worldwide public health concern. Twenty‐eight children with ASD and 33 healthy children (HC) were selected for the quantitative determination of their plasma metabolites using an ultraperformance liquid chromatography‐tandem mass spectrometry (UPLC‐MS/MS) platform. A total of 1997 metabolites were detected in the study cohort, from which 116 metabolites were found to be differentially expressed between the ASD and HC groups. Through analytical algorithms such as least absolute shrinkage selection operator (LASSO), support vector machine (SVM), and random forest (RF), three potential metabolic markers were identified as FAHFA (18:1(9Z)/9‐O‐18:0), DL‐2‐hydroxystearic acid, and 7(S),17(S)‐dihydroxy‐8(E),10(Z),13(Z),15(E),19(Z)‐docosapentaenoic acid. These metabolites demonstrated superior performance in distinguishing the ASD group from the HC group, as indicated by the area under curves (AUCs) of 0.935, 0.897, and 0.963 for the three candidate biomarkers, respectively. The samples were divided into training and validation sets according to 7:3. Diagnostic models were constructed using logistic regression (LR), SVM, and RF. The constructed three‐biomarker diagnostic model also exhibited strong discriminatory efficacy. These findings contribute to advancing our understanding of the underlying mechanisms involved in the occurrence of ASD and provide a valuable reference for clinical diagnosis.

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Section: Introductionmentioning

confidence: 99%

Metabolomic analysis of plasma biomarkers in children with autism spectrum disorders

Liu,

Tan,

Zhang

et al. 2024

MedComm

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“…Several dozen autism gut metagenomics studies have revealed many, albeit inconsistent, variations in microbial diversity in individuals with ASD compared with neurotypical individuals [18, 19, 17]. Similarly, metagenome-based functional reconstructions and metabolic analyses have also shown strong, albeit inconclusive differences between ASD and neurotypical individuals [20, 21, 22]. Comparative analyses at other omic levels have further shown little agreement across studies [23] raising the question of whether the results obtained so far reflect intrinsic biological differences among cohorts, insufficient statistical power, or experimental biases that preclude meaningful comparisons [24].…”

Section: Introductionmentioning

confidence: 99%

Multi-omic analysis along the gut-brain axis points to a functional architecture of autism

Morton

Jin

Mills³

et al. 2022

Preprint

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Autism is a highly heritable neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in autism, with dozens of cross-sectional microbiome and other omic studies revealing autism-specific profiles along the GBA albeit with little agreement in composition or magnitude. To explore the functional architecture of autism, we developed an age and sex-matched Bayesian differential ranking algorithm that identified autism-specific profiles across 10 cross-sectional microbiome datasets and 15 other omic datasets, including dietary patterns, metabolomics, cytokine profiles, and human brain expression profiles. The analysis uncovered a highly significant, functional architecture along the GBA that encapsulated the overall heterogeneity of autism phenotypes. This architecture was determined by autism-specific amino acid, carbohydrate and lipid metabolism profiles predominantly encoded by microbial species in the genera Prevotella, Enterococcus, Bifidobacterium, and Desulfovibrio, and was mirrored in brain-associated gene expression profiles and restrictive dietary patterns in individuals with autism. Pro-inflammatory cytokine profiling and virome association analysis further supported the existence of an autism-specific architecture associated with particular microbial genera. Re-analysis of a longitudinal intervention study in autism recapitulated the cross-sectional profiles, and showed a strong association between temporal changes in microbiome composition and autism symptoms. Further elucidation of the functional architecture of autism, including of the role the microbiome plays in it, will require deep, multi-omic longitudinal intervention studies on well-defined stratified cohorts to support causal and mechanistic inference.

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“…As for ASD, serum and urine metabolomics have identified biomarkers attributable to gut dysmicrobism, amino acid metabolism, and mitochondrial dysfunction [ 19 , 20 ] while, regarding PANS and metabolomics, this field appears to not be sufficiently explored, with little reported evidence [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

1H-NMR-Based Metabolomics in Autism Spectrum Disorder and Pediatric Acute-Onset Neuropsychiatric Syndrome

Gagliano

Murgia

Capodiferro

et al. 2022

JCM

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We recently described a unique plasma metabolite profile in subjects with pediatric acute-onset neuropsychiatric syndrome (PANS), suggesting pathogenic models involving specific patterns of neurotransmission, neuroinflammation, and oxidative stress. Here, we extend the analysis to a group of patients with autism spectrum disorder (ASD), as a consensus has recently emerged around its immune-mediated pathophysiology with a widespread involvement of brain networks. This observational case-control study enrolled patients referred for PANS and ASD from June 2019 to May 2020, as well as neurotypical age and gender-matched control subjects. Thirty-four PANS outpatients, fifteen ASD outpatients, and twenty-five neurotypical subjects underwent physical and neuropsychiatric evaluations, alongside serum metabolomic analysis with 1H-NMR. In supervised models, the metabolomic profile of ASD was significantly different from controls (p = 0.0001), with skewed concentrations of asparagine, aspartate, betaine, glycine, lactate, glucose, and pyruvate. Metabolomic separation was also observed between PANS and ASD subjects (p = 0.02), with differences in the concentrations of arginine, aspartate, betaine, choline, creatine phosphate, glycine, pyruvate, and tryptophan. We confirmed a unique serum metabolomic profile of PANS compared with both ASD and neurotypical subjects, distinguishing PANS as a pathophysiological entity per se. Tryptophan and glycine appear as neuroinflammatory fingerprints of PANS and ASD, respectively. In particular, a reduction in glycine would primarily affect NMDA-R excitatory tone, overall impairing downstream glutamatergic, dopaminergic, and GABAergic transmissions. Nonetheless, we found metabolomic similarities between PANS and ASD that suggest a putative role of N-methyl-D-aspartate receptor (NMDA-R) dysfunction in both disorders. Metabolomics-based approaches could contribute to the identification of novel ASD and PANS biomarkers.

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Profiles of urine and blood metabolomics in autism spectrum disorders

Cited by 31 publications

References 89 publications

Metabolomic analysis of plasma biomarkers in children with autism spectrum disorders

Metabolomic analysis of plasma biomarkers in children with autism spectrum disorders

Multi-omic analysis along the gut-brain axis points to a functional architecture of autism

1H-NMR-Based Metabolomics in Autism Spectrum Disorder and Pediatric Acute-Onset Neuropsychiatric Syndrome

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