Comparison of the Metabolic Profiles in the Plasma and Urine Samples Between Autistic and Typically Developing Boys: A Preliminary Study

Associations between amino acid levels and autism spectrum disorder severity

BACKGROUND

Autism spectrum disorder (ASD) imposes a significant burden on both patients and society. Amino acid metabolism abnormalities are particularly relevant to ASD pathology due to their crucial role in neurotransmitter synthesis, synaptic function, and overall neurodevelopment. This study aims to explore the association between amino acid metabolic abnormalities and the severity of ASD by analyzing the amino acid concentrations in the blood of children with ASD.

METHODS

Fasting peripheral blood samples were collected from 344 children with ASD, and amino acid concentrations were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) while strictly following quality control measures. The association between amino acid concentrations and ASD severity was evaluated using logistic regression and restricted cubic spline (RCS) analysis. The ROC (receiver operating characteristic) curve, decision curve analysis (DCA), and calibration curve were used to construct and validate predictive models and nomograms, thereby assessing their predictive performance.

RESULTS

Multivariate logistic regression analysis showed that aspartic acid (OR = 1.037, 95% CI: 1.009-1.068, P = 0.01), glutamic acid (OR = 1.009, 95% CI: 1.001-1.017, P = 0.03), phenylalanine (OR = 1.036, 95% CI: 1.003-1.072, P = 0.04), and leucine/isoleucine (OR = 1.021, 95% CI: 1.006-1.039, P = 0.01) were significantly positively correlated with the severity of ASD. On the other hand, tryptophan (OR = 0.935, 95% CI: 0.903-0.965, P < 0.01) and valine (OR = 0.987, 95% CI: 0.977-0.997, P = 0.01) were significantly negatively correlated with the severity of ASD. RCS analysis further revealed a nonlinear relationship between the concentrations of aspartic acid, proline, and glutamic acid and the risk of ASD. ROC curve analysis showed that the combined model achieved an AUC (area under the curve) of 0.806, indicating high diagnostic accuracy. Calibration and decision curve analysis further validated the predictive effectiveness and clinical utility of the model.

CONCLUSIONS

This study identifies potential amino acid biomarkers that may contribute to ASD severity assessment. Further research is needed to validate these findings and explore their clinical utility.

Amino Acid Patterns in Children with Autistic Spectrum Disorder: A Preliminary Biochemical Evaluation

BACKGROUND

The metabolism of plasma amino acid (AA) in children with autism spectrum disorder (ASD) has been extensively investigated, yielding inconclusive results. This study aims to characterize the metabolic alterations in AA profiles among early-diagnosed children with ASD and compare the findings with those from non-ASD children.

METHODS

We analyzed plasma AA profiles, measured by ion exchange chromatography, from 1242 ASD children (median age = 4 years; 81% male). Additionally, we studied AA profiles from 488 children, matched for age and free of ASD (control group). Principal component and cluster analysis were employed to explore potential associations within the ASD group and to identify subgroups.

RESULTS

We observed lower plasma levels of glutamine in children with ASD compared to non-ASD children (p < 0.001). Six essential, two conditionally essential, and four non-essential AA were found to be increased in children with ASD. The clustering analysis revealed two groups, labeled Neurological (NEU) and Nutritional (NUT), which included a majority of ASD children (94% and 78%, respectively). The NEU group exhibited high levels of taurine, aspartate, glutamic acid, and ornithine, while the NUT group showed elevated levels of branched-chain AA.

CONCLUSIONS

In children with ASD, we identified some heterogeneous AA patterns that may serve as biochemical signatures of neurological impairment in some individuals, while in others they may indicate nutritional dysregulation.

Integrating 16S rRNA Gene Sequencing and Metabolomics Analysis to Reveal the Mechanism of L-Proline in Preventing Autism-like Behavior in Mice

BACKGROUND/OBJECTIVES

Autism spectrum disorder (ASD) is characterized by impaired social interaction and repetitive stereotyped behavior. Effective interventions for the core autistic symptoms are currently limited.

METHODS

This study employed a valproic acid (VPA)-induced mouse model of ASD to assess the preventative effects of L-proline supplementation on ASD-like behaviors. The method of 16S rRNA sequencing and untargeted metabolomics analyses were conducted to investigate the modulation of gut microbiota and gut metabolites by L-proline.

RESULTS

The results indicated that L-proline supplementation significantly prevented ASD-like behavioral disorders, including alleviating social communication deficits and reducing repetitive behavior in the ASD mice. The 16S rRNA sequencing analysis revealed that L-proline regulated the composition and structure of gut microbiota. L-Proline supplementation enhances the abundance of the Verrucomicrobia at the phylum level and the Akkermansia at the genus level, while concurrently reducing the abundance of the Patescibacteria at the phylum level, as well as the Ileibacterium, Candidatus_Saccharimonas, and Lachnospiraceae_UCG-006 at the genus level in the VPA-induced mouse model for ASD. Additionally, the untargeted metabolomics results indicated that L-proline also modified the gut metabolite profiles. Functional analysis of the gut microbiota and KEGG pathway enrichment analysis of differential metabolites between the L-proline-supplemented and VPA groups corroborated that L-proline decreased pathways related to nucleotide metabolism, taurine and hypotaurine metabolism, and pyruvate metabolism, while increasing pathways involved in alpha-linolenic acid metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis. The integrative metabolomic and microbiome analyses showed strong connections between the gut metabolites and gut microbiota affected by L-proline. These findings suggest that the modulatory effects of L-proline on gut microbiota and its metabolites may play a crucial role in preventing autism in mice.

CONCLUSIONS

These findings suggest that dietary L-proline may represent a viable, effective option for preventing the physiological and behavioral deficits associated with ASD in mice.

Perspective: metabolomics has the potential to change the landscape of kidney transplantation diagnostics

Kidney transplantation is the most efficient renal replacement therapy. Current diagnostics for monitoring graft health are either invasive or lack precision. Metabolomics is an emerging discipline focused on the analysis of the small molecules involved in metabolism. Given the kidneys' central role in metabolic homeostasis and previous observations of altered metabolites correlating with restricted kidney graft function, metabolomics is highly promising for the discovery of novel biomarkers and the development of novel diagnostics. In this perspective, we summarize the known metabolic roles for the kidney, discuss biomarkers of graft health and immune status emerging from metabolomics research, and provide our perspective on how these and future findings can be integrated in clinical practice to enable precision diagnostics.

A Clinical Study of Urine Amino Acids in Children with Autism Spectrum Disorder

Amino acids are organic compounds that enter the protein structure, being involved in the proper functioning of the body. The role of amino acids in the onset of autism spectrum disorder (ASD) is yet to be established. Our aim was to identify correlations between urine amino acids and their derivatives and ASD.

METHODS

We designed a case-control study that consisted of 75 boys and girls, aged between 2 and 12 years. For amino acid profile, we used urine samples that were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS).

RESULTS

Descriptive analysis showed higher values for glutamine, hydroxyproline, tyrosine, aspartic acid, and tryptophan and lower values for serine in the autism group than in the control group. Also, we found that boys with autism had higher values than the boys in the control group for serine, threonine, and aspartic acid. For girls from both groups, we did not find statistically significant values. In terms of age groups, we found significantly higher values for histidine, threonine, valine, methionine, aspartic acid, glutamic acid, alpha amino-adipic acid, sarcosine, alanine, and beta-alanine and significantly lower values for proline for both the autism and control groups under 5 years.

CONCLUSIONS

The findings of this study support the assumption that amino acids may have a role in the expression of ASD.

Integration of lipidomics and metabolomics reveals plasma and urinary profiles associated with pediatric Mycoplasma pneumoniae infections and its severity

Mycoplasma pneumoniae is a significant contributor to lower respiratory infections in children. However, the lipidomics and metabolics bases of childhood M. pneumoniae infections remain unclear. In this study, lipidomics and metabolomics analyses were conducted using UHPLC-LTQ-Orbitrap XL mass spectrometry and gas chromatography-triple quadrupole mass spectrometry on plasma (n = 65) and urine (n = 65) samples. MS-DIAL software, in combination with LipidBlast and Fiehn BinBase DB, identified 163 lipids and 104 metabolites in plasma samples, as well as 208 metabolites in urine samples. Perturbed lipid species (adjusted p < 0.05) were observed, including lysophosphatidylethanolamines, phosphatidylinositols, phosphatidylcholines, phosphatidylethanol amines, and triglycerides. Additionally, differential metabolites (adjusted p < 0.05) exhibited associations with amino acid metabolism, nucleotide metabolism, and energy metabolism. Thirteen plasma metabolites, namely l-hydroxyproline, 3-phosphoglycerate, citric acid, creatine, inosine, ribitol, α tocopherol, cholesterol, cystine, serine, uric acid, tagatose, and glycine, showed significant associations with disease severity (p < 0.05) and exhibited distinct separation patterns in M. pneumoniae-infected bronchitis and pneumonia, with an area under the curve of 0.927. Nine of them exhibited either positive or negative correlations with neutrophil or lymphocyte percentages. These findings indicated significant systemic metabolic shifts in childhood M. pneumoniae infections, offering valuable insights into the associated metabolic alterations and their relationship with disease severity.

Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy

Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.

A study of genetic heterogeneity in autism spectrum disorders based on plasma proteomic and metabolomic analysis: multiomics study of autism heterogeneity

Genetic heterogeneity poses a challenge to research and clinical translation of autism spectrum disorder (ASD). In this study, we conducted a plasma proteomic and metabolomic study of children with ASD with and without risk genes (de novo mutation) and controls to explore the impact of genetic heterogeneity on the search for biomarkers for ASD. In terms of the proteomic and metabolomic profiles, the groups of children with ASD carrying and those not carrying de novo mutation tended to cluster and overlap, and integrating them yielded differentially expressed proteins and differential metabolites that effectively distinguished ASD from controls. The mechanisms associated with them focus on several common and previously reported mechanisms. Proteomics results highlight the role of complement, inflammation and immunity, and cell adhesion. The main pathways of metabolic perturbations include amino acid, vitamin, glycerophospholipid, tryptophan, and glutamates metabolic pathways and solute carriers-related pathways. Integrating the two omics analyses revealed that L-glutamic acid and malate dehydrogenase may play key roles in the pathogenesis of ASD. These results suggest that children with ASD may have important underlying common mechanisms. They are not only potential therapeutic targets for ASD but also important contributors to the study of biomarkers for the disease.

Multi-level analysis of the gut-brain axis shows autism spectrum disorder-associated molecular and microbial profiles

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

Differences of gut microbiota and behavioral symptoms between two subgroups of autistic children based on γδT cells-derived IFN-γ Levels: A preliminary study

Background

Autism Spectrum Disorders (ASD) are defined as a group of pervasive neurodevelopmental disorders, and the heterogeneity in the symptomology and etiology of ASD has long been recognized. Altered immune function and gut microbiota have been found in ASD populations. Immune dysfunction has been hypothesized to involve in the pathophysiology of a subtype of ASD.

Methods

A cohort of 105 ASD children were recruited and grouped based on IFN-γ levels derived from ex vivo stimulated γδT cells. Fecal samples were collected and analyzed with a metagenomic approach. Comparison of autistic symptoms and gut microbiota composition was made between subgroups. Enriched KEGG orthologues markers and pathogen-host interactions based on metagenome were also analyzed to reveal the differences in functional features.

Results

The autistic behavioral symptoms were more severe for children in the IFN-γ-high group, especially in the body and object use, social and self-help, and expressive language performance domains. LEfSe analysis of gut microbiota revealed an overrepresentation of Selenomonadales, Negatiyicutes, Veillonellaceae and Verrucomicrobiaceae and underrepresentation of Bacteroides xylanisolvens and Bifidobacterium longum in children with higher IFN-γ level. Decreased metabolism function of carbohydrate, amino acid and lipid in gut microbiota were found in the IFN-γ-high group. Additional functional profiles analyses revealed significant differences in the abundances of genes encoding carbohydrate-active enzymes between the two groups. And enriched phenotypes related to infection and gastroenteritis and underrepresentation of one gut-brain module associated with histamine degradation were also found in the IFN-γ-High group. Results of multivariate analyses revealed relatively good separation between the two groups.

Conclusions

Levels of IFN-γ derived from γδT cell could serve as one of the potential candidate biomarkers to subtype ASD individuals to reduce the heterogeneity associated with ASD and produce subgroups which are more likely to share a more similar phenotype and etiology. A better understanding of the associations among immune function, gut microbiota composition and metabolism abnormalities in ASD would facilitate the development of individualized biomedical treatment for this complex neurodevelopmental disorder.

Amino Acids, B Vitamins, and Choline May Independently and Collaboratively Influence the Incidence and Core Symptoms of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a developmental disorder of variable severity, characterized by difficulties in social interaction, communication, and restricted or repetitive patterns of thought and behavior. In 2018, the incidence of ASD was 2.4 times higher than estimated in 2000. Behavior and brain development abnormalities are present in the complex disorder of ASD. Nutritional status plays a key role in the incidence and severity of the core symptoms of ASD. The aim of this study was to review the available peer-reviewed studies that evaluated the relationship between amino acids, choline, B vitamins, and ASD incidence and/or severity of symptoms. Through examining plasma profiles, urine samples, and dietary intake, researchers found that low choline, abnormal amino acid, and low B vitamin levels were present in children with ASD compared to those without ASD. The evidence supports the need for future research that implements simultaneous supplementation of all essential nutrients in individuals with ASD and among prenatal mothers. Future evidence could lead to scientific breakthroughs, ultimately reducing the rates of ASD incidence and severity of symptoms by applying nutritional interventions in at-risk populations.

Multivariate Analysis of Metabolomic and Nutritional Profiles among Children with Autism Spectrum Disorder

There have been promising results regarding the capability of statistical and machine-learning techniques to offer insight into unique metabolomic patterns observed in ASD. This work re-examines a comparative study contrasting metabolomic and nutrient measurements of children with ASD (n = 55) against their typically developing (TD) peers (n = 44) through a multivariate statistical lens. Hypothesis testing, receiver characteristic curve assessment, and correlation analysis were consistent with prior work and served to underscore prominent areas where metabolomic and nutritional profiles between the groups diverged. Improved univariate analysis revealed 46 nutritional/metabolic differences that were significantly different between ASD and TD groups, with individual areas under the receiver operator curve (AUROC) scores of 0.6–0.9. Many of the significant measurements had correlations with many others, forming two integrated networks of interrelated metabolic differences in ASD. The TD group had 189 significant correlation pairs between metabolites, vs. only 106 for the ASD group, calling attention to underlying differences in metabolic processes. Furthermore, multivariate techniques identified potential biomarker panels with up to six metabolites that were able to attain a predictive accuracy of up to 98% for discriminating between ASD and TD, following cross-validation. Assessing all optimized multivariate models demonstrated concordance with prior physiological pathways identified in the literature, with some of the most important metabolites for discriminating ASD and TD being sulfate, the transsulfuration pathway, uridine (methylation biomarker), and beta-amino isobutyrate (regulator of carbohydrate and lipid metabolism).

Distinct metabolic profiles associated with autism spectrum disorder versus cancer in individuals with germline PTEN mutations

PTEN hamartoma tumor syndrome (PHTS), caused by germline PTEN mutations, has been associated with organ-specific cancers and autism spectrum disorder (ASD) and/or developmental delay (DD). Predicting precise clinical phenotypes in any one PHTS individual remains impossible. We conducted an untargeted metabolomics study on an age- and sex-matched series of PHTS individuals with ASD/DD, cancer, or both phenotypes. Using agnostic metabolomic-analyses from patient-derived lymphoblastoid cells and their spent media, we found 52 differentially abundant individual metabolites, 69 cell/media metabolite ratios, and 327 pair-wise metabotype (shared metabolic phenotype) ratios clearly distinguishing PHTS individuals based on phenotype. Network analysis based on significant metabolites pointed to hubs converging on PTEN-related insulin, MAPK, AMPK, and mTOR signaling cascades. Internal cross-validation of significant metabolites showed optimal overall accuracy in distinguishing PHTS individuals with ASD/DD versus those with cancer. Such metabolomic markers may enable more accurate risk predictions and prevention in individual PHTS patients at highest risk.

Empathy for pain in individuals with autistic traits during observation of static and dynamic stimuli

Previous studies have reported that individuals with autistic traits, like those with Autism Spectrum Disorder (ASD), may have impaired empathic responses when observing static stimuli of others' pain. However, it remains unclear whether individuals with autistic traits exhibit impaired empathy for pain in response to dynamic stimuli. The present study addressed this question by recruiting 529 individuals whose autistic traits were assessed using the autism-spectrum quotient (AQ) questionnaire. Thirty participants who scored within the top 10% and bottom 10% on the AQ were selected into High-AQ and Low-AQ groups, respectively. This study employed painful whole-body action pictures and videos as static and dynamic stimuli. Both groups were instructed to judge whether the models in the stimuli were experiencing pain, and their reaction times, accuracy and event-related potential (ERP) data were recorded. Results showed that the P2 amplitudes were larger in the High-AQ group than in the Low-AQ group when viewing painful static stimuli, while no difference between the two groups was found when viewing painful dynamic stimuli. These results suggest that autistic traits influenced the emotional processing of others' pain in response to static stimuli.

Maternal Plasma Metabolic Profile Demarcates a Role for Neuroinflammation in Non-Typical Development of Children

Maternal and cord plasma metabolomics were used to elucidate biological pathways associated with increased diagnosis risk for autism spectrum disorders (ASD). Metabolome-wide associations were assessed in both maternal and umbilical cord plasma in relation to diagnoses of ASD and other non-typical development (Non-TD) compared to typical development (TD) in the Markers of Autism risk in Babies: Learning Early Signs (MARBLES) cohort study of children born to mothers who already have at least one child with ASD. Analyses were stratified by sample matrix type, machine mode, and annotation confidence level. Dimensionality reduction techniques were used [i.e, principal component analysis (PCA) and random subset weighted quantile sum regression (WQSRS)] to minimize the high multiple comparison burden. With WQSRS, a metabolite mixture obtained from the negative mode of maternal plasma decreased the odds of Non-TD compared to TD. These metabolites, all related to the prostaglandin pathway, underscored the relevance of neuroinflammation status. No other significant findings were observed. Dimensionality reduction strategies provided confirming evidence that a set of maternal plasma metabolites are important in distinguishing Non-TD compared to TD diagnosis. A lower risk for Non-TD was linked to anti-inflammatory elements, thereby linking neuroinflammation to detrimental brain function consistent with studies ranging from neurodevelopment to neurodegeneration.