Untargeted Metabolomics for Autism Spectrum Disorders: Current Status and Future Directions

Metabolomic changes in children with autism

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication and repetitive behaviors. Metabolomic profiling has emerged as a valuable tool for understanding the underlying metabolic dysregulations associated with ASD.

AIM

To comprehensively explore metabolomic changes in children with ASD, integrating findings from various research articles, reviews, systematic reviews, meta-analyses, case reports, editorials, and a book chapter.

METHODS

A systematic search was conducted in electronic databases, including PubMed, PubMed Central, Cochrane Library, Embase, Web of Science, CINAHL, Scopus, LISA, and NLM catalog up until January 2024. Inclusion criteria encompassed research articles (83), review articles (145), meta-analyses (6), systematic reviews (6), case reports (2), editorials (2), and a book chapter (1) related to metabolomic changes in children with ASD. Exclusion criteria were applied to ensure the relevance and quality of included studies.

RESULTS

The systematic review identified specific metabolites and metabolic pathways showing consistent differences in children with ASD compared to typically developing individuals. These metabolic biomarkers may serve as objective measures to support clinical assessments, improve diagnostic accuracy, and inform personalized treatment approaches. Metabolomic profiling also offers insights into the metabolic alterations associated with comorbid conditions commonly observed in individuals with ASD.

CONCLUSION

Integration of metabolomic changes in children with ASD holds promise for enhancing diagnostic accuracy, guiding personalized treatment approaches, monitoring treatment response, and improving outcomes. Further research is needed to validate findings, establish standardized protocols, and overcome technical challenges in metabolomic analysis. By advancing our understanding of metabolic dysregulations in ASD, clinicians can improve the lives of affected individuals and their families.

Label free quantitative proteomic profiling of serum samples of intellectually disabled young patients revealed dysregulation of complement coagulation and cholesterol cascade systems

Intellectual disability is a heterogeneous disorder, diagnosed using intelligence quotient (IQ) score criteria. Currently, no specific clinical test is available to diagnose the disease and its subgroups due to inadequate understanding of the pathophysiology. Therefore, current study was designed to explore the molecular mechanisms involved in disease perturbation, and to identify potential biomarkers for disease diagnosis and prognosis. A total of 250 participants were enrolled in this study, including 200 intellectually disabled (ID) subjects from the subgroups (mild, moderate, and severe) with age and gender matched healthy controls (n = 50). Initially, IQ testing score and biochemical profile of each subject was generated, followed by label-free quantitative proteomics of subgroups of IQ and healthy control group through nano-LC/MS- mass spectrometry. A total of 310 proteins were identified, among them198 proteins were common among all groups. Statistical analysis (ANOVA) of the subgroups of ID showed 142 differentially expressed proteins, in comparison to healthy control group. From these, 120 proteins were found to be common among all subgroups. The remaining 22 proteins were categorized as exclusive proteins found only in disease subgroups. Furthermore, the hierarchical cluster analysis (HCL) of common significant proteins was also performed, followed by PANTHER protein classification and GO functional enrichment analysis. Results provides that the datasets of differentially expressed proteins, belong to the categories of immune / defense proteins, transfer carrier proteins, apolipoproteins, complement proteins, protease inhibitors, hemoglobin proteins etc., they are known to involvein immune system, and complement and coagulation pathway cascade and cholesterol metabolism pathway. Exclusively expressed 22 proteins were found to be disease stage specific and strong PPI network specifically those that have significant role in platelets activation and degranulation, such as Filamin A (FLNA). Furthermore, to validate the mass spectrometric findings, four highly significant proteins (APOA4, SAP, FLNA, and SERPING) were quantified by ELISA in all the study subjects. AUROC analysis showed a significant association of APOA4 (0.830), FLNA (0.958), SAP (0.754) and SERPING (0.600) with the disease. Apolipoprotein A4 (APOA4) has a significant role in cholesterol transport, and in modulation of glucose and lipid metabolism in the CNS. Similarly, FLNA has a crucial role in the nervous system, especially in the functioning of synaptic network. Therefore, both APOA4, and FLNA proteins represent good potential for candidate biomarkers for the diagnosis and prognosis of the intellectual disability. Overall, serum proteome of ID patients provides valuable information of proteins/pathways that are altered during ID progression.

Etiologic Evaluation of Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is clinically and etiologically heterogeneous. A causal genetic variant can be identified in approximately 20% to 25% of affected individuals with current clinical genetic testing, and all patients with an ASD diagnosis should be offered genetic etiologic evaluation. We suggest that exome sequencing with copy number variant coverage should be the first-line etiologic evaluation for ASD. Neuroimaging, neurophysiologic, metabolic, and other biochemical evaluations can provide insight into the pathophysiology of ASD but should be recommended in the appropriate clinical circumstances.

Des-Arg(9) bradykinin as a causal metabolite for autism spectrum disorder

BACKGROUND

Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder (ASD). However, the etiology of ASD is not completely understood. The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD.

AIM

To estimate and interpret the causal relationship between ASD and metabolite profile, taking into consideration both genetic and environmental influences.

METHODS

A two-sample Mendelian randomization (MR) analysis was conducted using summarized data from large-scale genome-wide association studies (GWAS) including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls. Metabolites in plasma were set as exposures with ASD as the main outcome. The causal relationships were estimated using the inverse variant weight (IVW) algorithm. We also performed leave-one-out sensitivity tests to validate the robustness of the results. Based on the drafted metabolites, enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome, SwissTargetPrediction, STRING, and Metascape.

RESULTS

Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD (β = 0.262, SE = 0.064, PIVW = 4.64 × 10-5). The association was robust, with no significant heterogeneity among instrument variables (PMR Egger = 0.663, PIVW = 0.906) and no evidence of pleiotropy (P = 0.949). Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9) bradykinin and ASD.

CONCLUSION

Through the application of MR, this study provides practical insights into the potential causal association between plasma metabolites and ASD. These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical practice in treating ASD.

A scoping review of multigenerational impacts of grandparental exposures on mental health in grandchildren

PURPOSE OF REVIEW

The multigenerational effects of grandparental exposures on their grandchildren's mental health and neurodevelopment are gaining research attention. We conducted a scoping review to summarize the current epidemiological studies investigating pregnancy-related and environmental factors that affected grandparental pregnancies and mental health outcomes in their grandchildren. We also identified methodological challenges that affect these multigenerational health studies and discuss opportunities for future research.

RECENT FINDINGS

We performed a literature search using PubMed and Embase and included 18 articles for this review. The most investigated grandparental pregnancy-related factors were the grandparental age of pregnancy (N = 6), smoking during pregnancy (N = 4), and medication intake (N = 3). The most frequently examined grandchild outcomes were autism spectrum disorder (N = 6) and attention-deficit/hyperactivity disorder (N = 4). Among these studies, grandparental smoking and the use of diethylstilbestrol were more consistently reported to be associated with neurodevelopmental disorders, while the findings for grandparental age vary across the maternal or paternal line. Grandmaternal weight, adverse delivery outcomes, and other spatial-temporal markers of physical and social environmental stressors require further scrutiny. The current body of literature has suggested that mental and neurodevelopmental disorders may be outcomes of unfavorable exposures originating from the grandparental generation during their pregnancies. To advance the field, we recommend research efforts into setting up multigenerational studies with prospectively collected data that span through at least three generations, incorporating spatial, environmental, and biological markers for exposure assessment, expanding the outcome phenotypes evaluated, and developing a causal analytical framework including mediation analyses specific for multigenerational research.

Using urine FTIR spectra to screen autism spectrum disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder caused by multiple factors, lacking clear biomarkers. Diagnosing ASD still relies on behavioural and developmental signs and usually requires lengthy observation periods, all of which are demanding for both clinicians and parents. Although many studies have revealed valuable knowledge in this field, no clearly defined, practical, and widely acceptable diagnostic tool exists. In this study, 26 children with ASD (ASD+), aged 3-5 years, and 26 sex and age-matched controls are studied to investigate the diagnostic potential of the Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy. The urine FTIR spectrum results show a downward trend in the 3000-2600/cm region for ASD+ children when compared to the typically developing (TD) children of the same age. The average area of this region is 25% less in ASD+ level 3 children, 29% less in ASD+ level 2 children, and 16% less in ASD+ level 1 children compared to that of the TD children. Principal component analysis was applied to the two groups using the entire spectrum window and five peaks were identified for further analysis. The correlation between the peaks and natural urine components is validated by artificial urine solutions. Less-than-normal levels of uric acid, phosphate groups, and ammonium ([Formula: see text]) can be listed as probable causes. This study shows that ATR-FTIR can serve as a practical and non-invasive method to screen ASD using the high-frequency region of the urine spectrum.

Cord Blood Metabolite Profiles and Their Association with Autistic Traits in Childhood

Autism Spectrum Disorder (ASD) is a diverse neurodevelopmental condition. Gene-environmental interactions in early stages of life might alter metabolic pathways, possibly contributing to ASD pathophysiology. Metabolomics may serve as a tool to identify underlying metabolic mechanisms contributing to ASD phenotype and could help to unravel its complex etiology. In a population-based, prospective cohort study among 783 mother-child pairs, cord blood serum concentrations of amino acids, non-esterified fatty acids, phospholipids, and carnitines were obtained using liquid chromatography coupled with tandem mass spectrometry. Autistic traits were measured at the children's ages of 6 (n = 716) and 13 (n = 648) years using the parent-reported Social Responsiveness Scale. Lower cord blood concentrations of SM.C.39.2 and NEFA16:1/16:0 were associated with higher autistic traits among 6-year-old children, adjusted for sex and age at outcome. After more stringent adjustment for confounders, no significant associations of cord blood metabolites and autistic traits at ages 6 and 13 were detected. Differences in lipid metabolism (SM and NEFA) might be involved in ASD-related pathways and are worth further investigation.

Metabolomic biomarkers in autism: identification of complex dysregulations of cellular bioenergetics

Autism Spectrum Disorder (ASD or autism) is a phenotypically and etiologically heterogeneous condition. Identifying biomarkers of clinically significant metabolic subtypes of autism could improve understanding of its underlying pathophysiology and potentially lead to more targeted interventions. We hypothesized that the application of metabolite-based biomarker techniques using decision thresholds derived from quantitative measurements could identify autism-associated subpopulations. Metabolomic profiling was carried out in a case-control study of 499 autistic and 209 typically developing (TYP) children, ages 18-48 months, enrolled in the Children's Autism Metabolome Project (CAMP; ClinicalTrials.gov Identifier: NCT02548442). Fifty-four metabolites, associated with amino acid, organic acid, acylcarnitine and purine metabolism as well as microbiome-associated metabolites, were quantified using liquid chromatography-tandem mass spectrometry. Using quantitative thresholds, the concentrations of 4 metabolites and 149 ratios of metabolites were identified as biomarkers, each identifying subpopulations of 4.5-11% of the CAMP autistic population. A subset of 42 biomarkers could identify CAMP autistic individuals with 72% sensitivity and 90% specificity. Many participants were identified by several metabolic biomarkers. Using hierarchical clustering, 30 clusters of biomarkers were created based on participants' biomarker profiles. Metabolic changes associated with the clusters suggest that altered regulation of cellular metabolism, especially of mitochondrial bioenergetics, were common metabolic phenotypes in this cohort of autistic participants. Autism severity and cognitive and developmental impairment were associated with increased lactate, many lactate containing ratios, and the number of biomarker clusters a participant displayed. These studies provide evidence that metabolic phenotyping is feasible and that defined autistic subgroups can lead to enhanced understanding of the underlying pathophysiology and potentially suggest pathways for targeted metabolic treatments.

Metabolomics: Perspectives on Clinical Employment in Autism Spectrum Disorder

Precision medicine is imminent, and metabolomics is one of the main actors on stage. We summarize and discuss the current literature on the clinical application of metabolomic techniques as a possible tool to improve early diagnosis of autism spectrum disorder (ASD), to define clinical phenotypes and to identify co-occurring medical conditions. A review of the current literature was carried out after PubMed, Medline and Google Scholar were consulted. A total of 37 articles published in the period 2010-2022 was included. Selected studies involve as a whole 2079 individuals diagnosed with ASD (1625 males, 394 females; mean age of 10, 9 years), 51 with other psychiatric comorbidities (developmental delays), 182 at-risk individuals (siblings, those with genetic conditions) and 1530 healthy controls (TD). Metabolomics, reflecting the interplay between genetics and environment, represents an innovative and promising technique to approach ASD. The metabotype may mirror the clinical heterogeneity of an autistic condition; several metabolites can be expressions of dysregulated metabolic pathways thus liable of leading to clinical profiles. However, the employment of metabolomic analyses in clinical practice is far from being introduced, which means there is a need for further studies for the full transition of metabolomics from clinical research to clinical diagnostic routine.

Investigating the Urinary Metabolome in the First Year of Life and Its Association with Later Diagnosis of Autism Spectrum Disorder or Non-Typical Neurodevelopment in the MARBLES Study

Developmental disabilities are often associated with alterations in metabolism. However, it remains unknown how early these metabolic issues may arise. This study included a subset of children from the Markers of Autism Risks in Babies-Learning Early Signs (MARBLES) prospective cohort study. In this analysis, 109 urine samples collected at 3, 6, and/or 12 months of age from 70 children with a family history of ASD who went on to develop autism spectrum disorder (ASD n = 17), non-typical development (Non-TD n = 11), or typical development (TD n = 42) were investigated by nuclear magnetic resonance (NMR) spectroscopy to measure urinary metabolites. Multivariate principal component analysis and a generalized estimating equation were performed with the objective of exploring the associations between urinary metabolite levels in the first year of life and later adverse neurodevelopment. We found that children who were later diagnosed with ASD tended to have decreased urinary dimethylamine, guanidoacetate, hippurate, and serine, while children who were later diagnosed with Non-TD tended to have elevated urinary ethanolamine and hypoxanthine but lower methionine and homovanillate. Children later diagnosed with ASD or Non-TD both tended to have decreased urinary 3-aminoisobutyrate. Our results suggest subtle alterations in one-carbon metabolism, gut-microbial co-metabolism, and neurotransmitter precursors observed in the first year of life may be associated with later adverse neurodevelopment.

Biochemical, Genetic and Clinical Diagnostic Approaches to Autism-Associated Inherited Metabolic Disorders

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders characterized by impaired social interaction, limited communication skills, and restrictive and repetitive behaviours. The pathophysiology of ASD is multifactorial and includes genetic, epigenetic, and environmental factors, whereas a causal relationship has been described between ASD and inherited metabolic disorders (IMDs). This review describes biochemical, genetic, and clinical approaches to investigating IMDs associated with ASD. The biochemical work-up includes body fluid analysis to confirm general metabolic and/or lysosomal storage diseases, while the advances and applications of genomic testing technology would assist with identifying molecular defects. An IMD is considered likely underlying pathophysiology in ASD patients with suggestive clinical symptoms and multiorgan involvement, of which early recognition and treatment increase their likelihood of achieving optimal care and a better quality of life.

A Comprehensive NMR Analysis of Serum and Fecal Metabolites in Familial Dysautonomia Patients Reveals Significant Metabolic Perturbations

Central metabolism has a profound impact on the clinical phenotypes and penetrance of neurological diseases such as Alzheimer’s (AD) and Parkinson’s (PD) diseases, Amyotrophic Lateral Sclerosis (ALS) and Autism Spectrum Disorder (ASD). In contrast to the multifactorial origin of these neurological diseases, neurodevelopmental impairment and neurodegeneration in Familial Dysautonomia (FD) results from a single point mutation in the ELP1 gene. FD patients represent a well-defined population who can help us better understand the cellular networks underlying neurodegeneration, and how disease traits are affected by metabolic dysfunction, which in turn may contribute to dysregulation of the gut–brain axis of FD. Here, 1H NMR spectroscopy was employed to characterize the serum and fecal metabolomes of FD patients, and to assess similarities and differences in the polar metabolite profiles between FD patients and healthy relative controls. Findings from this work revealed noteworthy metabolic alterations reflected in energy (ATP) production, mitochondrial function, amino acid and nucleotide catabolism, neurosignaling molecules, and gut-microbial metabolism. These results provide further evidence for a close interconnection between metabolism, neurodegeneration, and gut microbiome dysbiosis in FD, and create an opportunity to explore whether metabolic interventions targeting the gut–brain–metabolism axis of FD could be used to redress or slow down the progressive neurodegeneration observed in FD patients.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Cannabis-Responsive Biomarkers: A Pharmacometabolomics-Based Application to Evaluate the Impact of Medical Cannabis Treatment on Children with Autism Spectrum Disorder

Introduction: Autism spectrum disorder (ASD) is a group of neurodevelopmental conditions that impact behavior, communication, social interaction, and learning abilities. Treatment of ASD with medical cannabis (MC) shows promising results in reducing the severity of certain behavioral aspects. The goals of this observational study are to demonstrate the potential of metabolic biomarkers to (1) objectively determine the impact on metabolites of MC treatment and (2) suggest the metabolic pathways of children with ASD, who respond to MC treatment. Materials and Methods: The impact of effective physician-supervised MC treatment on children with ASD (n=15), compared with an age-matched group of typically developing (TD; n=9) children, was evaluated in an observational study design. Each child followed a unique MC regimen determined by their specific response over at least 1 year of treatment, which included the following: tetrahydrocannabinol-dominant MC (dosing range 0.05-50 mg per dose) in 40% of children and cannabidiol-dominant MC (dosing range 7.5-200 mg per dose) in 60% of children. Samples from the ASD group collected pre-MC treatment and at time of maximal impact, and from the TD group, were subjected to salivary metabolomics analysis. Ten minutes before saliva sampling, parents filled out behavioral rating surveys. Results: Sixty-five potential cannabis-responsive biomarkers exhibiting a shift toward the TD physiological levels were identified in children with ASD after MC treatment. For each biomarker, the physiological levels were determined based on the values detected in the TD group. A similar qualitative improvement trend in children with ASD treated with MC was also observed in the behavioral surveys. Twenty-three potential Cannabis-Responsive biomarkers exhibiting change toward TD mean were categorized as anti-inflammatory, bioenergy associated, neurotransmitters, amino acids, and endocannabinoids. The changes in the levels of the Cannabis-Responsive biomarkers N-acetylaspartic acid, spermine, and dehydroisoandrosterone 3-sulfate have been previously linked to behavioral symptoms commonly observed in individuals with ASD. Conclusions: Our results suggest Cannabis-Responsive biomarkers shift toward the TD mean after MC treatment and can potentially quantify benefit at the metabolic level. These changes appear to be similar to the trend described in behavior surveys. Larger trials are needed to confirm these preliminary findings.

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

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.

Urinary organic acids spectra in children with altered gut microbiota composition and autistic spectrum disorder

Introduction: Alteration of human gut microbiota is described in a number of neuro-developmental and cognitive disorders including autistic spectrum disorder (ASD). Along with the changes in the gut microbiota, children with ASD are also reported to have changes in urinary organic acid spectra implying these metabolites as potential biomarkers for gastrointestinal dysbiosis.Aim: Identify urinary metabolites that would indicate specific changes in the gut microbiota and could be useful as biomarkers.Methods: The study group consisted of 44 children with ASD. Urinary organic acids spectra and composition of gut microbiota were analysed.Results: Any significant deviation in quantified metabolites compared to the reference values were not confirmed. The main variations were detected in concentration of p-cresol and 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA), but we cannot confirm the presence of HPHPA in urine as a biomarker for Clostridium sp. overgrowth in the gut. The acquired results indicate higher relative abundance of Firmicutes phylum alone may be attributed to increased concentration of p-cresol in urine. Decreased Bacteroidetes/Firmicutes ratio was found in the group with the presence of HPHPA in urine.Conclusions: Metabolites of human urine can be used as biomarkers for alterations of gut microbiota with caution, guided treatment should be administrated only based on gut microbiota analysis results or in combination with urinary organic acid results, but not solely based on organic acid biomarkers.

NMR-Based Metabolomics of Rat Hippocampus, Serum, and Urine in Two Models of Autism

Autism spectrum disorders (ASDs) are increasingly diagnosed as developmental disabilities of unclear etiology related to genetic, epigenetic, or environmental factors. The diagnosis of ASD in children is based on the recognition of typical behavioral symptoms, while no reliable biomarkers are available. Rats in whom ASD-like symptoms are due to maternal administration of the teratogenic drugs valproate or thalidomide on critical day 11 of pregnancy are widely used models in autism research. The present studies, aimed at detecting changes in the levels of hydrophilic and hydrophobic metabolites, were carried out on 1-month-old rats belonging to the abovementioned two ASD models and on a control group. Analysis of both hydrophilic and hydrophobic metabolite levels gives a broader view of possible mechanisms involved in the pathogenesis of autism. Hippocampal proton magnetic resonance (MRS) spectroscopy and ex vivo nuclear magnetic resonance (NMR) analysis of serum and urine samples were used. The results were analyzed using advanced statistical tests. Both the results of our present MRS studies of the hippocampus and of the NMR studies of body fluids in both ASD models, particularly from the THAL model, appeared to be consistent with previously published NMR results of hippocampal homogenates and data from the literature on autistic children. We detected symptoms of disturbances in neurotransmitter metabolism, energy deficit, and oxidative stress, as well as intestinal malfunction, which shed light on the pathogenesis of ASD and could be used for diagnostic purposes. These results confirm the usefulness of the noninvasive techniques used in ASD studies.

Biallelic variants in SLC38A3 encoding a glutamine transporter cause epileptic encephalopathy

The solute carrier (SLC) superfamily encompasses >400 transmembrane transporters involved in the exchange of amino acids, nutrients, ions, metals, neurotransmitters and metabolites across biological membranes. SLCs are highly expressed in the mammalian brain; defects in nearly 100 unique SLC-encoding genes (OMIM: https://www.omim.org) are associated with rare Mendelian disorders including developmental and epileptic encephalopathy and severe neurodevelopmental disorders. Exome sequencing and family-based rare variant analyses on a cohort with neurodevelopmental disorders identified two siblings with developmental and epileptic encephalopathy and a shared deleterious homozygous splicing variant in SLC38A3. The gene encodes SNAT3, a sodium-coupled neutral amino acid transporter and a principal transporter of the amino acids asparagine, histidine, and glutamine, the latter being the precursor for the neurotransmitters GABA and glutamate. Additional subjects with a similar developmental and epileptic encephalopathy phenotype and biallelic predicted-damaging SLC38A3 variants were ascertained through GeneMatcher and collaborations with research and clinical molecular diagnostic laboratories. Untargeted metabolomic analysis was performed to identify novel metabolic biomarkers. Ten individuals from seven unrelated families from six different countries with deleterious biallelic variants in SLC38A3 were identified. Global developmental delay, intellectual disability, hypotonia, and absent speech were common features while microcephaly, epilepsy, and visual impairment were present in the majority. Epilepsy was drug-resistant in half. Metabolomic analysis revealed perturbations of glutamate, histidine, and nitrogen metabolism in plasma, urine, and CSF of selected subjects, potentially representing biomarkers of disease. Our data support the contention that SLC38A3 is a novel disease gene for developmental and epileptic encephalopathy and illuminate the likely pathophysiology of the disease as perturbations in glutamine homeostasis.

Association between EEG Paroxysmal Abnormalities and Levels of Plasma Amino Acids and Urinary Organic Acids in Children with Autism Spectrum Disorder

Abnormalities in the plasma amino acid and/or urinary organic acid profile have been reported in autism spectrum disorder (ASD). An imbalance between excitatory and inhibitory neuronal activity has been proposed as a mechanism to explain dysfunctional brain networks in ASD, as also suggested by the increased risk of epilepsy in this disorder. This study explored the possible association between presence of EEG paroxysmal abnormalities and the metabolic profile of plasma amino acids and urinary organic acids in children with ASD. In a sample of 55 children with ASD (81.8% male, mean age 53.67 months), EEGs were recorded, and 24 plasma amino acids and 56 urinary organic acids analyzed. EEG epileptiform discharges were found in 36 (65%) children. A LASSO regression, adjusted by age and sex, was applied to evaluate the association of plasma amino acids and urinary organic acids profiles with the presence of EEG epileptiform discharges. Plasma levels of threonine (THR) (coefficient = −0.02, p = 0.04) and urinary concentration of 3-Hydroxy-3-Methylglutaric acid (HMGA) (coefficient = 0.04, p = 0.02) were found to be associated with the presence of epileptiform discharges. These results suggest that altered redox mechanisms might be linked to epileptiform brain activity in ASD.

Clinical metabolomics for inborn errors of metabolism

Metabolism is a highly regulated process that provides nutrients to cells and essential building blocks for the synthesis of protein, DNA and other macromolecules. In healthy biological systems, metabolism maintains a steady state in which the concentrations of metabolites are relatively constant yet are subject to metabolic demands and environmental stimuli. Rare genetic disorders, such as inborn errors of metabolism (IEM), cause defects in regulatory enzymes or proteins leading to metabolic pathway disruption and metabolite accumulation or deficiency. Traditionally, the laboratory diagnosis of IEMs has been limited to analytical methods that target specific metabolites such as amino acids and acyl carnitines. This approach is effective as a screening method for the most common IEM disorders but lacks the comprehensive coverage of metabolites that is necessary to identify rare disorders that present with nonspecific clinical symptoms. Fortunately, advancements in technology and data analytics has introduced a new field of study called metabolomics which has allowed scientists to perform comprehensive metabolite profiling of biological systems to provide insight into mechanism of action and gene function. Since metabolomics seeks to measure all small molecule metabolites in a biological specimen, it provides an innovative approach to evaluating disease in patients with rare genetic disorders. In this review we provide insight into the appropriate application of metabolomics in clinical settings. We discuss the advantages and limitations of the method and provide details related to the technology, data analytics and statistical modeling required for metabolomic profiling of patients with IEMs.

Environmental exposures to pesticides, phthalates, phenols and trace elements are associated with neurodevelopment in the CHARGE study

OBJECTIVE

To determine if higher exposures measured in early childhood to environmental phenols, phthalates, pesticides, and/or trace elements, are associated with increased odds of having a diagnosis of Autism Spectrum Disorder (ASD), Developmental Delay (DD), or Other Early Concerns (OEC) compared to typically developing children (TD).

METHODS

This study included 627 children between the ages of 2-5 who participated in the Childhood Autism Risks from Genetics and Environment (CHARGE) study. Urine samples were collected at the same study visit where diagnostic assessments to confirm diagnosis indicated during the recruitment process were performed. Adjusted multinomial regression models of each chemical with diagnosis as the outcome were conducted. Additionally, two methods were used to analyze mixtures: repeated holdout multinomial weighted quantile sum (WQS) regression for each chemical class; and a total urinary mixture effect was assessed with repeated holdout random subset WQS.

RESULTS

Many urinary chemicals were associated with increased odds of ASD, DD or OEC compared to TD; however, most did not remain significant after false discovery rate adjustment. Repeated holdout WQS indices provided evidence for associations of both a phenol/paraben mixture effect and a trace element mixture effect on DD independently. In analyses adjusted for confounders and other exposures, results suggested an association of a pesticide mixture effect with increased risk for ASD. Results also suggested associations of a total urinary mixture with greater odds of both ASD and DD separately.

CONCLUSION

Higher concentrations of urinary biomarkers were associated with ASD, DD, and OEC compared to TD, with consistency of the results comparing single chemical analyses and mixture analyses. Given that the biospecimens used for chemical analysis were generally collected many months after diagnoses were made, the direction of any causal association is unknown. Hence findings may reflect higher exposures among children with non-typical development than TD children due to differences in behaviors, metabolism, or toxicokinetics.

Recent Advances in Understanding the Structure and Function of the Human Microbiome

Trillions of microbes live within our bodies in a deep symbiotic relationship. Microbial populations vary across body sites, driven by differences in the environment, immunological factors, and interactions between microbial species. Major advances in genome sequencing enable a better understanding of microbiome composition. However, most of the microbial taxa and species of the human microbiome are still unknown. Without revealing the identity of these microbes as a first step, we cannot appreciate their role in human health and diseases. A shift in the microbial balance, termed dysbiosis, is linked to a broad range of diseases from simple colitis and indigestion to cancer and dementia. The last decade has witnessed an explosion in microbiome research that led to a better understanding of the microbiome structure and function. This understanding leads to potential opportunities to develop next-generation microbiome-based drugs and diagnostic biomarkers. However, our understanding is limited given the highly personalized nature of the microbiome and its complex and multidirectional interactions with the host. In this review, we discuss: (1) our current knowledge of microbiome structure and factors that shape the microbial composition, (2) recent associations between microbiome dysbiosis and diseases, and (3) opportunities of new microbiome-based therapeutics. We analyze common themes, promises, gaps, and challenges of the microbiome research.

Maternal inflammatory and omega-3 fatty acid pathways mediate the association between socioeconomic disadvantage and childhood cognition

Poor cognitive outcomes in early childhood predict poor educational outcomes and diminished health over the life course. We sought to investigate (i) whether maternal metabolites predict child cognition, and (ii) if maternal metabolomic profile mediates the relationship between environmental exposures and child cognition. Metabolites were measured using nuclear magnetic resonance-based metabolomics in pregnant women from a population-derived birth cohort. Child cognition was measured at age 2 years. In 662 mother-child pairs, elevated inflammatory markers (β = -2.62; 95% CI -4.10, -1.15; P = 0.0005) and lower omega-3 fatty acid-related metabolites (β = 0.49; 95% CI 0.09, 0.88; P = 0.02) in the mother were associated with lower child cognition and partially mediated the association between lower child cognition and multiple risk factors common to socioeconomic disadvantage. Modifying maternal prenatal metabolic pathways related to inflammation and omega-3 fatty acids may offset the adverse associations between prenatal risk factors related to socioeconomic disadvantage and low child cognition.

Alteration of the Intestinal Permeability Are Reflected by Changes in the Urine Metabolome of Young Autistic Children: Preliminary Results

Several metabolomics-based studies have provided evidence that autistic subjects might share metabolic abnormalities with gut microbiota dysbiosis and alterations in gut mucosal permeability. Our aims were to explore the most relevant metabolic perturbations in a group of autistic children, compared with their healthy siblings, and to investigate whether the increased intestinal permeability may be mirrored by specific metabolic perturbations. We enrolled 13 autistic children and 14 unaffected siblings aged 2–12 years; the evaluation of the intestinal permeability was estimated by the lactulose:mannitol test. The urine metabolome was investigated by proton nuclear magnetic resonance (1H-NMR) spectroscopy. The lactulose:mannitol test unveiled two autistic children with altered intestinal permeability. Nine metabolites significantly discriminated the urine metabolome of autistic children from that of their unaffected siblings; however, in the autistic children with increased permeability, four additional metabolites—namely, fucose, phenylacetylglycine, nicotinurate, and 1-methyl-nicotinamide, strongly discriminated their urine metabolome from that of the remaining autistic children. Our preliminary data suggest the presence of a specific urine metabolic profile associated with the increase in intestinal permeability.

Urinary Metabolomic Study in a Healthy Children Population and Metabolic Biomarker Discovery of Attention-Deficit/Hyperactivity Disorder (ADHD)

OBJECTIVES

Knowledge of the urinary metabolomic profiles of healthy children and adolescents plays a promising role in the field of pediatrics. Metabolomics has also been used to diagnose disease, discover novel biomarkers, and elucidate pathophysiological pathways. Attention-deficit/hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in childhood. However, large-sample urinary metabolomic studies in children with ADHD are relatively rare. In this study, we aimed to identify specific biomarkers for ADHD diagnosis in children and adolescents by urinary metabolomic profiling.

METHODS

We explored the urine metabolome in 363 healthy children aged 1-18 years and 76 patients with ADHD using high-resolution mass spectrometry.

RESULTS

Metabolic pathways, such as arachidonic acid metabolism, steroid hormone biosynthesis, and catecholamine biosynthesis, were found to be related to sex and age in healthy children. The urinary metabolites displaying the largest differences between patients with ADHD and healthy controls belonged to the tyrosine, leucine, and fatty acid metabolic pathways. A metabolite panel consisting of FAPy-adenine, 3-methylazelaic acid, and phenylacetylglutamine was discovered to have good predictive ability for ADHD, with a receiver operating characteristic area under the curve (ROC-AUC) of 0.918. A panel of FAPy-adenine, N-acetylaspartylglutamic acid, dopamine 4-sulfate, aminocaproic acid, and asparaginyl-leucine was used to establish a robust model for ADHD comorbid tic disorders and controls with an AUC of 0.918.

Metabolomics in Clinical Practice: Improving Diagnosis and Informing Management

BACKGROUND

Metabolomics is the study of small molecules to simultaneously identify multiple low molecular weight molecules in a system. Broadly speaking, metabolomics can be subdivided into targeted and untargeted types of analysis, each type having advantages and drawbacks. Targeted metabolomics can quantify analytes but only looks for known or expected analytes related to particular disease(s), whereas untargeted metabolomics is typically nonquantitative but can detect thousands of analytes from an agnostic or nonhypothesis driven perspective, allowing for novel discoveries.

CONTENT

One application of metabolomics is the study of inborn errors of metabolism (IEM). The biochemical hallmark of IEMs is decreased concentrations of analytes distal to the enzymatic defect and buildup of analytes proximal to the defect. Metabolomics can detect these changes with one test and is effective in screening for and diagnosis of IEMs. Metabolomics has also been used to study many nonmetabolic diseases such as autism spectrum disorder, various cancers, and multiple congenital anomalies syndromes. Metabolomics has led to the discovery of many novel biomarkers of disease. Recent publications demonstrate how metabolomics can be useful clinically in the diagnosis and management of patients, as well as for research and clinical discovery.

SUMMARY

Metabolomics has proved to be a useful tool clinically for screening and diagnostic purposes and from a research perspective for the detection of novel biomarkers. In the future, metabolomics will likely become a routine part of the evaluation for many diseases as either a supplementary test or it may simply replace historical analyses that require several individual tests and sample types.

Profiles of urine and blood metabolomics in autism spectrum disorders

Early diagnosis and treatment for autism spectrum disorder (ASD) pose challenges. The current diagnostic approach for ASD is mainly clinical assessment of patient behaviors. Biomarkers-based identification of ASD would be useful for pediatricians. Currently, there is no specific treatment for ASD, and evidence for the efficacy of alternative treatments remains inconclusive. The prevalence of ASD is increasing, and it is becoming more urgent to find the pathogenesis of such disorder. Metabolomic studies have been used to deeply investigate the alteration of metabolic pathways, including those associated with ASD. Metabolomics is a promising tool for identifying potential biomarkers and possible pathogenesis of ASD. This review comprehensively summarizes and discusses the abnormal metabolic pathways in ASD children, as indicated by evidence from metabolomic studies in urine and blood. In addition, the targeted interventions that could correct the metabolomic profiles relating to the improvement of autistic behaviors in affected animals and humans have been included. The results revealed that the possible underlying pathophysiology of ASD were alterations of amino acids, reactive oxidative stress, neurotransmitters, and microbiota-gut-brain axis. The potential common pathways shared by animal and human studies related to the improvement of ASD symptoms after pharmacological interventions were mammalian-microbial co-metabolite, purine metabolism, and fatty acid oxidation. The content of this review may contribute to novel biomarkers for the early diagnosis of ASD and possible therapeutic paradigms.

DNA Methylation of PGC-1α Is Associated With Elevated mtDNA Copy Number and Altered Urinary Metabolites in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex disorder that is underpinned by numerous dysregulated biological pathways, including pathways that affect mitochondrial function. Epigenetic mechanisms contribute to this dysregulation and DNA methylation is an important factor in the etiology of ASD. We measured DNA methylation of peroxisome proliferator-activated receptor-gamma coactivator-1 alpha (PGC-1α), as well as five genes involved in regulating mitochondrial homeostasis to examine mitochondrial dysfunction in an ASD cohort of South African children. Using targeted Next Generation bisulfite sequencing, we found differential methylation (p < 0.05) at six key genes converging on mitochondrial biogenesis, fission and fusion in ASD, namely PGC-1α, STOML2, MFN2, FIS1, OPA1, and GABPA. PGC-1α, the transcriptional regulator of biogenesis, was significantly hypermethylated at eight CpG sites in the gene promoter, one of which contained a putative binding site for CAMP response binding element 1 (CREB1) (p = 1 × 10^–6). Mitochondrial DNA (mtDNA) copy number, a marker of mitochondrial function, was elevated (p = 0.002) in ASD compared to controls and correlated significantly with DNA methylation at the PGC-1α promoter and there was a positive correlation between methylation at PGC-1α CpG#1 and mtDNA copy number (Spearman’s r = 0.2, n = 49, p = 0.04) in ASD. Furthermore, DNA methylation at PGC-1α CpG#1 and mtDNA copy number correlated significantly (p < 0.05) with levels of urinary organic acids associated with mitochondrial dysfunction, oxidative stress, and neuroendocrinology. Our data show differential methylation in ASD at six key genes converging on PGC-1α-dependent regulation of mitochondrial biogenesis and function. We demonstrate that methylation at the PGC-1α promoter is associated with elevated mtDNA copy number and metabolomic evidence of mitochondrial dysfunction in ASD. This highlights an unexplored role for DNA methylation in regulating specific pathways involved in mitochondrial biogenesis, fission and fusion contributing to mitochondrial dysfunction in ASD.

Alterations in Gut Vitamin and Amino Acid Metabolism are Associated with Symptoms and Neurodevelopment in Children with Autism Spectrum Disorder

Metabolic disturbance may be implicated in the pathogenesis of autism. This study aimed to investigate the gut metabolomic profiles of autistic children and to analyze potential interaction between gut metabolites with autistic symptoms and neurodevelopment levels. We involved 120 autistic and 60 neurotypical children. Autistic symptoms and neurodevelopment levels were assessed. Fecal samples were analyzed using untargeted liquid chromatography-tandem mass spectrometry methods. Our results showed the metabolic disturbances of autistic children involved in multiple vitamin and amino acid metabolism pathways, with the strongest enrichment identified for tryptophan metabolism, retinol metabolism, cysteine-methionine metabolism, and vitamin digestion and absorption. Differential gut metabolites were correlated to autistic symptoms and neurodevelopment levels. Our findings improved the understanding of the perturbations of metabolome networks in autism.

Studying Autism Using Untargeted Metabolomics in Newborn Screening Samples

Main risk factors of autism spectrum disorder (ASD) include both genetic and non-genetic factors, especially prenatal and perinatal events. Newborn screening dried blood spot (DBS) samples have great potential for the study of early biochemical markers of disease. To study DBS strengths and limitations in the context of ASD research, we analyzed the metabolomic profiles of newborns later diagnosed with ASD. We performed LC-MS/MS-based untargeted metabolomics on DBS from 37 case-control pairs randomly selected from the iPSYCH sample. After preprocessing using MZmine 2.41, metabolites were putatively annotated using mzCloud, GNPS feature-based molecular networking, and MolNetEnhancer. A total of 4360 mass spectral features were detected, of which 150 (113 unique) could be putatively annotated at a high confidence level. Chemical structure information at a broad level could be retrieved for 1009 metabolites, covering 31 chemical classes. Although no clear distinction between cases and controls was revealed, our method covered many metabolites previously associated with ASD, suggesting that biochemical markers of ASD are present at birth and may be monitored during newborn screening. Additionally, we observed that gestational age, age at sampling, and month of birth influence the metabolomic profiles of newborn DBS, which informs us on the important confounders to address in future studies.

Towards a biochemical approach to occupational stress management

Given the immense and growing cost of occupational stress to society through lost productivity and the burden to healthcare systems, current best practices for detecting, managing and reducing stress in the workplace are clearly sub-optimal and substantially better methods are required. Subjective, self-reported psychology and psychiatry-based instruments are prone to biases whereas current objective, biology-based measures produce conflicting results and are far from reliable. A multivariate approach to occupational stress research is required that reflects the broad, coordinated, physiological response to demands placed on the body by exposure to diverse occupational stressors. A literature review was conducted to determine the extent of application of the emerging multivariate technology of metabolomics to occupational stress research. Of 170 articles meeting the search criteria, three were identified that specifically studied occupational stressors using metabolomics. A further ten studies were not specifically occupational or were of indirect or peripheral relevance. The occupational studies, although limited in number highlight the technological challenges associated with the application of metabolomics to investigate occupational stress. They also demonstrate the utility to evaluate stress more comprehensively than univariate biomarker studies. The potential of this multivariate approach to enhance our understanding of occupational stress has yet to be established. This will require more studies with broader analytical coverage of the metabolome, longitudinal sampling, combination with experience sampling methods and comparison with psychometric models of occupational stress. Progress will likely involve combining multi-omic data into a holistic, systems biology approach to detecting, managing and reducing occupational stress and optimizing workplace performance.

Inborn Errors of Metabolism Associated With Autism Spectrum Disorders: Approaches to Intervention

Increasing evidence suggests that the autism spectrum disorder (ASD) may be associated with inborn errors of metabolism, such as disorders of amino acid metabolism and transport [phenylketonuria, homocystinuria, S-adenosylhomocysteine hydrolase deficiency, branched-chain α-keto acid dehydrogenase kinase deficiency, urea cycle disorders (UCD), Hartnup disease], organic acidurias (propionic aciduria, L-2 hydroxyglutaric aciduria), cholesterol biosynthesis defects (Smith-Lemli-Opitz syndrome), mitochondrial disorders (mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes-MELAS syndrome), neurotransmitter disorders (succinic semialdehyde dehydrogenase deficiency), disorders of purine metabolism [adenylosuccinate lyase (ADSL) deficiency, Lesch-Nyhan syndrome], cerebral creatine deficiency syndromes (CCDSs), disorders of folate transport and metabolism (cerebral folate deficiency, methylenetetrahydrofolate reductase deficiency), lysosomal storage disorders [Sanfilippo syndrome, neuronal ceroid lipofuscinoses (NCL), Niemann-Pick disease type C], cerebrotendinous xanthomatosis (CTX), disorders of copper metabolism (Wilson disease), disorders of haem biosynthesis [acute intermittent porphyria (AIP)] and brain iron accumulation diseases. In this review, we briefly describe etiology, clinical presentation, and therapeutic principles, if they exist, for these conditions. Additionally, we suggest the primary and elective laboratory work-up for their successful early diagnosis.

Metabolomic Characterization of Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS)

Introduction

PANS is a controversial clinical entity, consisting of a complex constellation of psychiatric symptoms, adventitious changes, and expression of various serological alterations, likely sustained by an autoimmune/inflammatory disease. Detection of novel biomarkers of PANS is highly desirable for both diagnostic and therapeutic management of affected patients. Analysis of metabolites has proven useful in detecting biomarkers for other neuroimmune-psychiatric diseases. Here, we utilize the metabolomics approach to determine whether it is possible to define a specific metabolic pattern in patients affected by PANS compared to healthy subjects.

Design

This observational case-control study tested consecutive patients referred for PANS between June 2019 to May 2020. A PANS diagnosis was confirmed according to the PANS working criteria (National Institute of Mental Health [NIMH], 2010). Healthy age and sex-matched subjects were recruited as controls.

Methods

Thirty-four outpatients referred for PANS (mean age 9.5 years; SD 2.9, 71% male) and 25 neurotypical subjects matched for age and gender, were subjected to metabolite analysis. Serum samples were obtained from each participant and were analyzed using Nuclear Magnetic Resonance (NMR) spectroscopy. Subsequently, multivariate and univariate statistical analyses and Receiver Operator Curves (ROC) were performed.

Results

Separation of the samples, in line with the presence of PANS diagnosis, was observed by applying a supervised model (R²X = 0.44, R²Y = 0.54, Q² = 0.44, p-value < 0.0001). The significantly altered variables were 2-Hydroxybutyrate, glycine, glutamine, histidine, tryptophan. Pathway analysis indicated that phenylalanine, tyrosine, and tryptophan metabolism, as well as glutamine and glutamate metabolism, exhibited the largest deviations from neurotypical controls.

Conclusion

We found a unique plasma metabolic profile in PANS patients, significantly differing from that of healthy children, that suggests the involvement of specific patterns of neurotransmission (tryptophan, glycine, histamine/histidine) as well as a more general state of neuroinflammation and oxidative stress (glutamine, 2-Hydroxybutyrate, and tryptophan-kynurenine pathway) in the disorder. This metabolomics study offers new insights into biological mechanisms underpinning the disorder and supports research of other potential biomarkers implicated in PANS.

Role of Pediatrician in Early Risk Evaluation, Diagnosis and Management of Children with Autism Spectrum Disorders

The article discusses recent ideas about autism: classification approaches, incidence, etiology and pathogenesis, clinical manifestations and diagnosis, comorbid medical conditions, early detection approaches and medical care for children with autism spectrum disorders. The focus is on the information needed for pediatricians in their practice to provide effective medical care for children with neurodevelopmental disorders.

Metabolomics analysis of maternal serum exposed to high air pollution during pregnancy and risk of autism spectrum disorder in offspring

BACKGROUND

Previously, numerous epidemiologic studies reported an association between autism spectrum disorder (ASD) and exposure to air pollution during pregnancy. However, there have been no metabolomics studies investigating the impact of pregnancy pollution exposure to ASD risk in offspring.

OBJECTIVES

To identify differences in maternal metabolism that may reflect a biological response to exposure to high air pollution in pregnancies of offspring who later did or did not develop ASD.

METHODS

We obtained stored mid-pregnancy serum from 214 mothers who lived in California's Central Valley and experienced the highest levels of air pollution during early pregnancy. We estimated each woman's average traffic-related air pollution exposure (carbon monoxide, nitric oxides, and particulate matter <2.5 μm) during the first trimester using the California Line Source Dispersion Model, version 4 (CALINE4). By utilizing liquid chromatography-high resolution mass spectrometry, we identified the metabolic profiles of maternal serum for 116 mothers with offspring who later developed ASD and 98 control mothers. Partial least squares discriminant analysis (PLS-DA) was employed to select metabolic features associated with air pollution exposure or autism risk in offspring. We also conducted extensive pathway enrichment analysis to elucidate potential ASD-related changes in the metabolome of pregnant women.

RESULTS

We extracted 4022 and 4945 metabolic features from maternal serum samples in hydrophilic interaction (HILIC) chromatography (positive ion mode) and C18 (negative ion mode) columns, respectively. After controlling for potential confounders, we identified 167 and 222 discriminative features (HILIC and C18, respectively). Pathway enrichment analysis to discriminate metabolic features associated with ASD risk indicated various metabolic pathway perturbations linked to the tricarboxylic acid (TCA) cycle and mitochondrial function, including carnitine shuttle, amino acid metabolism, bile acid metabolism, and vitamin A metabolism.

CONCLUSION

Using high resolution metabolomics, we identified several metabolic pathways disturbed in mothers with ASD offspring among women experiencing high exposure to traffic-related air pollution during pregnancy that were associated with mitochondrial dysfunction. These findings provide us with a better understanding of metabolic disturbances involved in the development of ASD under adverse environmental conditions.

Metabolomic and lipidomic characterization of an X-chromosome deletion disorder in neural progenitor cells by UHPLC-HRMS

INTRODUCTION

Intellectual disorders involving deletions of the X chromosome present a difficult task in the determination of a connection between symptoms and metabolites that could lead to treatment options. One specific disorder of X-chromosomal deletion, Fragile X syndrome, is the most frequently occurring of intellectual disabilities. Previous metabolomic studies have been limited to mouse models that may not have sufficiently revealed the full biochemical diversity of the disease in humans.

OBJECTIVES

The primary objective of this study was to elucidate the human biochemistry in X-chromosomal deletion disorders through metabolomic and lipidomic profiling, using cells from a X-deletion patient as a representative case.

METHODS

Metabolomic and lipidomic analysis was performed by UHPLC-HRMS on neural progenitor (NP) cells isolated from an afflicted female patient versus normal neural progenitor cells.

RESULTS

Results showed perturbations in several metabolic pathways, including those of arginine and proline, that significantly impact both neurotransmitter generation and overall brain function. Coincidently, dysregulation was observed for lipids involved in both cellular structure and membrane integrity. The trends of observed metabolomic changes, as well as lipidomic profiling from identified features, are discussed.

CONCLUSION

The lipidomic and metabolomic profiles of NP cell samples exhibited significant differentiation associated with partial deletion of the X chromosome. These findings suggest that rare X-chromosomal deletion disorders are not only a mental disorder limited to alterations in local neuronal functions, but are also metabolic diseases.

Oxytocin-induced increase in N,N-dimethylglycine and time course of changes in oxytocin efficacy for autism social core symptoms

Background

Oxytocin is expected as a novel therapeutic agent for autism spectrum disorder (ASD) core symptoms. However, previous results on the efficacy of repeated administrations of oxytocin are controversial. Recently, we reported time-course changes in the efficacy of the neuropeptide underlying the controversial effects of repeated administration; however, the underlying mechanisms remained unknown.

Methods

The current study explored metabolites representing the molecular mechanisms of oxytocin’s efficacy using high-throughput metabolomics analysis on plasma collected before and after 6-week repeated intranasal administration of oxytocin (48 IU/day) or placebo in adult males with ASD (N = 106) who participated in a multi-center, parallel-group, double-blind, placebo-controlled, randomized controlled trial.

Results

Among the 35 metabolites measured, a significant increase in N,N-dimethylglycine was detected in the subjects administered oxytocin compared with those given placebo at a medium effect size (false discovery rate (FDR) corrected P = 0.043, d = 0.74, N = 83). Furthermore, subgroup analyses of the participants displaying a prominent time-course change in oxytocin efficacy revealed a significant effect of oxytocin on N,N-dimethylglycine levels with a large effect size (P_FDR = 0.004, d = 1.13, N = 60). The increase in N,N-dimethylglycine was significantly correlated with oxytocin-induced clinical changes, assessed as changes in quantifiable characteristics of autistic facial expression, including both of improvements between baseline and 2 weeks (P_FDR = 0.006, r = − 0.485, N = 43) and deteriorations between 2 and 4 weeks (P_FDR = 0.032, r = 0.415, N = 37).

Limitations

The metabolites changes caused by oxytocin administration were quantified using peripheral blood and therefore may not directly reflect central nervous system changes.

Conclusion

Our findings demonstrate an association of N,N-dimethylglycine upregulation with the time-course change in the efficacy of oxytocin on autistic social deficits. Furthermore, the current findings support the involvement of the N-methyl-D-aspartate receptor and neural plasticity to the time-course change in oxytocin’s efficacy.

Trial registration: A multi-center, parallel-group, placebo-controlled, double-blind, confirmatory trial of intranasal oxytocin in participants with autism spectrum disorders (the date registered: 30 October 2014; UMIN Clinical Trials Registry: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000017703) (UMIN000015264).

A Comparative Evaluation of Tools to Predict Metabolite Profiles From Microbiome Sequencing Data

Metabolomic analyses of human gut microbiome samples can unveil the metabolic potential of host tissues and the numerous microorganisms they support, concurrently. As such, metabolomic information bears immense potential to improve disease diagnosis and therapeutic drug discovery. Unfortunately, as cohort sizes increase, comprehensive metabolomic profiling becomes costly and logistically difficult to perform at a large scale. To address these difficulties, we tested the feasibility of predicting the metabolites of a microbial community based solely on microbiome sequencing data. Paired microbiome sequencing (16S rRNA gene amplicons, shotgun metagenomics, and metatranscriptomics) and metabolome (mass spectrometry and nuclear magnetic resonance spectroscopy) datasets were collected from six independent studies spanning multiple diseases. We used these datasets to evaluate two reference-based gene-to-metabolite prediction pipelines and a machine-learning (ML) based metabolic profile prediction approach. With the pre-trained model on over 900 microbiome-metabolome paired samples, the ML approach yielded the most accurate predictions (i.e., highest F1 scores) of metabolite occurrences in the human gut and outperformed reference-based pipelines in predicting differential metabolites between case and control subjects. Our findings demonstrate the possibility of predicting metabolites from microbiome sequencing data, while highlighting certain limitations in detecting differential metabolites, and provide a framework to evaluate metabolite prediction pipelines, which will ultimately facilitate future investigations on microbial metabolites and human health.

The Urine Metabolome of Young Autistic Children Correlates with Their Clinical Profile Severity

Autism diagnosis is moving from the identification of common inherited genetic variants to a systems biology approach. The aims of the study were to explore metabolic perturbations in autism, to investigate whether the severity of autism core symptoms may be associated with specific metabolic signatures; and to examine whether the urine metabolome discriminates severe from mild-to-moderate restricted, repetitive, and stereotyped behaviors. We enrolled 57 children aged 2–11 years; thirty-one with idiopathic autism and twenty-six neurotypical (NT), matched for age and ethnicity. The urine metabolome was investigated by gas chromatography-mass spectrometry (GC-MS). The urinary metabolome of autistic children was largely distinguishable from that of NT children; food selectivity induced further significant metabolic differences. Severe autism spectrum disorder core deficits were marked by high levels of metabolites resulting from diet, gut dysbiosis, oxidative stress, tryptophan metabolism, mitochondrial dysfunction. The hierarchical clustering algorithm generated two metabolic clusters in autistic children: 85–90% of children with mild-to-moderate abnormal behaviors fell in cluster II. Our results open up new perspectives for the more general understanding of the correlation between the clinical phenotype of autistic children and their urine metabolome. Adipic acid, palmitic acid, and 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid can be proposed as candidate biomarkers of autism severity.

The role of glutathione redox imbalance in autism spectrum disorder: A review

The role of glutathione in autism spectrum disorder (ASD) is emerging as a major topic, due to its role in the maintenance of the intracellular redox balance. Several studies have implicated glutathione redox imbalance as a leading factor in ASD, and both ASD and many other neurodevelopmental disorders involve low levels of reduced glutathione (GSH), high levels of oxidized glutathione (GSSG), and abnormalities in the expressions of glutathione-related enzymes in the blood or brain. Glutathione metabolism, through its impact on redox environment or redox-independent mechanisms, interferes with multiple mechanisms involved in ASD pathogenesis. Glutathione-mediated regulation of glutamate receptors [e.g., N-methyl-d-aspartate (NMDA) receptor], as well as the role of glutamate as a substrate for glutathione synthesis, may be involved in the regulation of glutamate excitotoxicity. However, the interaction between glutathione and glutamate in the pathogenesis of brain diseases may vary from synergism to antagonism. Modulation of glutathione is also associated with regulation of redox-sensitive transcription factors nuclear factor kappa B (NF-κB) and activator protein 1 (AP-1) and downstream signaling (proinflammatory cytokines and inducible enzymes), thus providing a significant impact on neuroinflammation. Mitochondrial dysfunction, as well as neuronal apoptosis, may also provide a significant link between glutathione metabolism and ASD. Furthermore, it has been recently highlighted that glutathione can affect and modulate DNA methylation and epigenetics. Review analysis including research studies meeting the required criteria for analysis showed statistically significant differences between the plasma GSH and GSSG levels as well as GSH:GSSG ratio in autistic patients compared with healthy individuals (P = 0.0145, P = 0.0150 and P = 0.0202, respectively). Therefore, the existing data provide a strong background on the role of the glutathione system in ASD pathogenesis. Future research is necessary to investigate the role of glutathione redox signaling in ASD, which could potentially also lead to promising therapeutics.

Plasma metabolites in treatment-requiring retinopathy of prematurity: Potential biomarkers identified by metabolomics

Retinopathy of prematurity (ROP) is a potentially blinding condition caused by disruption of retinal vascularization and metabolism. This study aims to identify altered metabolites from plasma in patients with treatment-requiring ROP (TR-ROP) compared with controls. An untargeted metabolomics analysis was performed to reveal the metabolomic profiles of the plasma between TR-ROP patients (n = 38) and age-matched infants (n = 23). The Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the potential signaling pathways of the changed metabolites. Under positive ion mode, a total of 29 metabolites were significantly altered in plasma between TR-ROP patients and controls, and 23 altered metabolites were identified under negative ion mode. KEGG analyses indicated that "protein digestion and absorption" and "aminoacyl-tRNA biosynthesis" were the most enriched pathways of the altered metabolites. These results demonstrated that metabolomic profiles changed in plasma of TR-ROP, and the altered metabolites could be served as potential biomarkers for the diagnosis and prognosis of TR-ROP patients. Besides, the metabolomic profiles might provide clues to discover novel therapeutic strategies in ROP treatment.

A Metabolomics Approach to Screening for Autism Risk in the Children's Autism Metabolome Project

Autism spectrum disorder (ASD) is biologically and behaviorally heterogeneous. Delayed diagnosis of ASD is common and problematic. The complexity of ASD and the low sensitivity of available screening tools are key factors in delayed diagnosis. Identification of biomarkers that reduce complexity through stratification into reliable subpopulations can assist in earlier diagnosis, provide insight into the biology of ASD, and potentially suggest targeted interventions. Quantitative metabolomic analysis was performed on plasma samples from 708 fasting children, aged 18 to 48 months, enrolled in the Children's Autism Metabolome Project (CAMP). The primary goal was to identify alterations in metabolism helpful in stratifying ASD subjects into subpopulations with shared metabolic phenotypes (i.e., metabotypes). Metabotypes associated with ASD were identified in a discovery set of 357 subjects. The reproducibility of the metabotypes was validated in an independent replication set of 351 CAMP subjects. Thirty-four candidate metabotypes that differentiated subsets of ASD from typically developing participants were identified with sensitivity of at least 5% and specificity greater than 95%. The 34 metabotypes formed six metabolic clusters based on ratios of either lactate or pyruvate, succinate, glycine, ornithine, 4-hydroxyproline, or α-ketoglutarate with other metabolites. Optimization of a subset of new and previously defined metabotypes into a screening battery resulted in 53% sensitivity (95% confidence interval [CI], 48%-57%) and 91% specificity (95% CI, 86%-94%). Thus, our metabolomic screening tool detects more than 50% of the autistic participants in the CAMP study. Further development of this metabolomic screening approach may facilitate earlier referral and diagnosis of ASD and, ultimately, more targeted treatments. LAY SUMMARY: Analysis of a selected set of metabolites in blood samples from children with autism and typically developing children identified reproducible differences in the metabolism of about half of the children with autism. Testing for these differences in blood samples can be used to help screen children as young as 18 months for risk of autism that, in turn, can facilitate earlier diagnoses. In addition, differences may lead to biological insights that produce more precise treatment options. We are exploring other blood-based molecules to determine if still a higher percentage of children with autism can be detected using this strategy. Autism Res 2020, 13: 1270-1285. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC.

How could metabolomics change pediatric health?

In the last years, 'omics' technologies, and especially metabolomics, emerged as expanding scientific disciplines and promising technologies in the characterization of several pathophysiological processes.In detail, metabolomics, able to detect in a dynamic way the whole set of molecules of low molecular weight in cells, tissues, organs, and biological fluids, can provide a detailed phenotypic portray, representing a metabolic "snapshot."Thanks to its numerous strength points, metabolomics could become a fundamental tool in human health, allowing the exact evaluation of individual metabolic responses to pathophysiological stimuli including drugs, environmental changes, lifestyle, a great number of diseases and other epigenetics factors.Moreover, if current metabolomics data will be confirmed on larger samples, such technology could become useful in the early diagnosis of diseases, maybe even before the clinical onset, allowing a clinical monitoring of disease progression and helping in performing the best therapeutic approach, potentially predicting the therapy response and avoiding overtreatments. Moreover, the application of metabolomics in nutrition could provide significant information on the best nutrition regimen, optimal infantile growth and even in the characterization and improvement of commercial products' composition.These are only some of the fields in which metabolomics was applied, in the perspective of a precision-based, personalized care of human health.In this review, we discuss the available literature on such topic and provide some evidence regarding clinical application of metabolomics in heart diseases, auditory disturbance, nephrouropathies, adult and pediatric cancer, obstetrics, perinatal conditions like asphyxia, neonatal nutrition, neonatal sepsis and even some neuropsychiatric disorders, including autism.Our research group has been interested in metabolomics since several years, performing a wide spectrum of experimental and clinical studies, including the first metabolomics analysis of human breast milk. In the future, it is reasonable to predict that the current knowledge could be applied in daily clinical practice, and that sensible metabolomics biomarkers could be easily detected through cheap and accurate sticks, evaluating biofluids at the patient's bed, improving diagnosis, management and prognosis of sick patients and allowing a personalized medicine. A dream? May be I am a dreamer, but I am not the only one.

Biomarkers in Psychiatry: Concept, Definition, Types and Relevance to the Clinical Reality

During the last years, an extraordinary effort has been made to identify biomarkers as potential tools for improving prevention, diagnosis, drug response and drug development in psychiatric disorders. Contrary to other diseases, mental illnesses are classified by diagnostic categories with a broad variety list of symptoms. Consequently, patients diagnosed from the same psychiatric illness present a great heterogeneity in their clinical presentation. This fact together with the incomplete knowledge of the neurochemical alterations underlying mental disorders, contribute to the limited efficacy of current pharmacological options. In this respect, the identification of biomarkers in psychiatry is becoming essential to facilitate diagnosis through the developing of markers that allow to stratify groups within the syndrome, which in turn may lead to more focused treatment options. In order to shed light on this issue, this review summarizes the concept and types of biomarkers including an operational definition for therapeutic development. Besides, the advances in this field were summarized and sorted into five categories, which include genetics, transcriptomics, proteomics, metabolomics, and epigenetics. While promising results were achieved, there is a lack of biomarker investigations especially related to treatment response to psychiatric conditions. This review includes a final conclusion remarking the future challenges required to reach the goal of developing valid, reliable and broadly-usable biomarkers for psychiatric disorders and their treatment. The identification of factors predicting treatment response will reduce trial-and-error switches of medications facilitating the discovery of new effective treatments, being a crucial step towards the establishment of greater personalized medicine.