Altered metabolism of mothers of young children with Autism Spectrum Disorder: a case control study

Biomarkers of mitochondrial dysfunction in autism spectrum disorder: A systematic review and meta-analysis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting 1 in 36 children and is associated with physiological abnormalities, most notably mitochondrial dysfunction, at least in a subset of individuals. This systematic review and meta-analysis discovered 204 relevant articles which evaluated biomarkers of mitochondrial dysfunction in ASD individuals. Significant elevations (all p < 0.01) in the prevalence of lactate (17%), pyruvate (41%), alanine (15%) and creatine kinase (9%) were found in ASD. Individuals with ASD had significant differences (all p < 0.01) with moderate to large effect sizes (Cohen's d' ≥ 0.6) compared to controls in mean pyruvate, lactate-to-pyruvate ratio, ATP, and creatine kinase. Some studies found abnormal TCA cycle metabolites associated with ASD. Thirteen controlled studies reported mitochondrial DNA (mtDNA) deletions or variations in the ASD group in blood, peripheral blood mononuclear cells, lymphocytes, leucocytes, granulocytes, and brain. Meta-analyses discovered significant differences (p < 0.01) in copy number of mtDNA overall and in ND1, ND4 and CytB genes. Four studies linked specific mtDNA haplogroups to ASD. A series of studies found a subgroup of ASD with elevated mitochondrial respiration which was associated with increased sensitivity of the mitochondria to physiological stressors and neurodevelopmental regression. Lactate, pyruvate, lactate-to-pyruvate ratio, carnitine, and acyl-carnitines were associated with clinical features such as delays in language, social interaction, cognition, motor skills, and with repetitive behaviors and gastrointestinal symptoms, although not all studies found an association. Lactate, carnitine, acyl-carnitines, ATP, CoQ10, as well as mtDNA variants, heteroplasmy, haplogroups and copy number were associated with ASD severity. Variability was found across biomarker studies primarily due to differences in collection and processing techniques as well as the intrinsic heterogeneity of the ASD population. Several studies reported alterations in mitochondrial metabolism in mothers of children with ASD and in neonates who develop ASD. Treatments targeting mitochondria, particularly carnitine and ubiquinol, appear beneficial in ASD. The link between mitochondrial dysfunction in ASD and common physiological abnormalities in individuals with ASD including gastrointestinal disorders, oxidative stress, and immune dysfunction is outlined. Several subtypes of mitochondrial dysfunction in ASD are discussed, including one related to neurodevelopmental regression, another related to alterations in microbiome metabolites, and another related to elevations in acyl-carnitines. Mechanisms linking abnormal mitochondrial function with alterations in prenatal brain development and postnatal brain function are outlined. Given the multisystem complexity of some individuals with ASD, this review presents evidence for the mitochondria being central to ASD by contributing to abnormalities in brain development, cognition, and comorbidities such as immune and gastrointestinal dysfunction as well as neurodevelopmental regression. A diagnostic approach to identify mitochondrial dysfunction in ASD is outlined. From this evidence, it is clear that many individuals with ASD have alterations in mitochondrial function which may need to be addressed in order to achieve optimal clinical outcomes. The fact that alterations in mitochondrial metabolism may be found during pregnancy and early in the life of individuals who eventually develop ASD provides promise for early life predictive biomarkers of ASD. Further studies may improve the understanding of the role of the mitochondria in ASD by better defining subgroups and understanding the molecular mechanisms driving some of the unique changes found in mitochondrial function in those with ASD.

Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder

Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter,V ° net, revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

Correlation between Vitamin B12 and Mental Health in Children and Adolescents: A Systematic Review and Meta-analysis

To conduct the association between vitamin B12 and mental health in children and adolescents. Five databases were searched for observational studies in any language reporting on mental health and vitamin B12 levels or intake in children and adolescents from inception to March 18, 2022. Two authors independently extracted data and assessed study quality. Qualitative and quantitative analysis of data were performed. The review was registered in the PROSPERO database (CRD42022345476). Fifty six studies containing 37,932 participants were identified in the review. Vitamin B12 levels were lower in participants with autism spectrum disorders (ASD) (standardized mean difference [SMD], -1.61; 95% confidence interval [95% CI], -2.44 to -0.79; p ＜ 0.001), attention deficit hyperactivity disorders (SMD, -0.39; 95% CI, -0.78 to -0.00; p = 0.049) compared with control group. Vitamin B12 intake were lower in participants with ASDs (SMD, -0.86; 95% CI, -1.48 to -0.24; p = 0.006) compared with control group, but showed no difference between depression group (SMD, -0.06; 95% CI, -0.15 to 0.03; p = 0.17) and the control group. Higher vitamin B12 intake were associated with lower risk of depression (odds ratio [OR], 0.79; 95% CI, 0.63-0.98; p = 0.034) and behavioral problems (OR, 0.83; 95% CI, 0.69-0.99; p = 0.04). The vast majority of included studies supported potential positive influence of vitamin B12 on mental health, and vitamin B12 deficiency may be a reversible cause for some mental health disorders in children and adolescents.

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.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Towards the Development of a Diagnostic Test for Autism Spectrum Disorder: Big Data Meets Metabolomics

Autism spectrum disorder (ASD) is defined as a neurodevelopmental disorder which results in impairments in social communications and interactions as well as repetitive behaviors. Despite current estimates showing that approximately 2.2% of children are affected in the United States, relatively little about ASD pathophysiology is known in part due to the highly heterogenous presentation of the disorder. Given the limited knowledge into the biological mechanisms governing its etiology, the diagnosis of ASD is performed exclusively based on an individual's behavior assessed by a clinician through psychometric tools. Although there is no readily available biochemical test for ASD diagnosis, multivariate statistical methods show considerable potential for effectively leveraging multiple biochemical measurements for classification and characterization purposes. In this work, markers associated with the folate dependent one-carbon metabolism and transulfuration (FOCM/TS) pathways analyzed via both Fisher Discriminant Analysis and Support Vector Machine showed strong capability to distinguish between ASD and TD cohorts. Furthermore, using Kernel Partial Least Squares regression it was possible to assess some degree of behavioral severity from metabolomic data. While the results presented need to be replicated in independent future studies, they represent a promising avenue for uncovering clinically relevant ASD biomarkers.

Multi-omic brain and behavioral correlates of cell-free fetal DNA methylation in macaque maternal obesity models

Maternal obesity during pregnancy is associated with neurodevelopmental disorder (NDD) risk. We utilized integrative multi-omics to examine maternal obesity effects on offspring neurodevelopment in rhesus macaques by comparison to lean controls and two interventions. Differentially methylated regions (DMRs) from longitudinal maternal blood-derived cell-free fetal DNA (cffDNA) significantly overlapped with DMRs from infant brain. The DMRs were enriched for neurodevelopmental functions, methylation-sensitive developmental transcription factor motifs, and human NDD DMRs identified from brain and placenta. Brain and cffDNA methylation levels from a large region overlapping mir-663 correlated with maternal obesity, metabolic and immune markers, and infant behavior. A DUX4 hippocampal co-methylation network correlated with maternal obesity, infant behavior, infant hippocampal lipidomic and metabolomic profiles, and maternal blood measurements of DUX4 cffDNA methylation, cytokines, and metabolites. We conclude that in this model, maternal obesity was associated with changes in the infant brain and behavior, and these differences were detectable in pregnancy through integrative analyses of cffDNA methylation with immune and metabolic factors.

Maternal Serum and Placental Metabolomes in Association with Prenatal Phthalate Exposure and Neurodevelopmental Outcomes in the MARBLES Cohort

Prenatal exposure to phthalates, a family of endocrine-disrupting plasticizers, is associated with disruption of maternal metabolism and impaired neurodevelopment. We investigated associations between prenatal phthalate exposure and alterations of both the maternal third trimester serum metabolome and the placental metabolome at birth, and associations of these with child neurodevelopmental outcomes using data and samples from the Markers of Autism Risk in Babies Learning Early Signs (MARBLES) cohort. The third trimester serum (n = 106) and placental (n = 132) metabolomes were investigated using 1H nuclear magnetic resonance spectroscopy. Children were assessed clinically for autism spectrum disorder (ASD) and cognitive development. Although none of the urinary phthalate metabolite concentrations were associated with maternal serum metabolites after adjustment for covariates, mixture analysis using quantile g-computation revealed alterations in placental metabolites with increasing concentrations of phthalate metabolites that included reduced concentrations of 2-hydoxybutyrate, carnitine, O-acetylcarnitine, glucitol, and N-acetylneuraminate. Child neurodevelopmental outcome was not associated with the third trimester serum metabolome, but it was correlated with the placental metabolome in male children only. Maternal phthalate exposure during pregnancy is associated with differences in the placental metabolome at delivery, and the placental metabolome is associated with neurodevelopmental outcomes in males in a cohort with high familial ASD risk.

Evidence based recommendations for an optimal prenatal supplement for women in the US: vitamins and related nutrients

The blood levels of most vitamins decrease during pregnancy if un-supplemented, including vitamins A, C, D, K, B1, B3, B5, B6, folate, biotin, and B12. Sub-optimal intake of vitamins from preconception through pregnancy increases the risk of many pregnancy complications and infant health problems. In the U.S., dietary intake of vitamins is often below recommended intakes, especially for vitamin D, choline and DHA. Many studies suggest that insufficient vitamin intake is associated with a wide range of pregnancy complications (anemia, Cesarean section, depression, gestational diabetes, hypertension, infertility, preeclampsia, and premature rupture of membranes) and infant health problems (asthma/wheeze, autism, low birth weight, congenital heart defects, intellectual development, intrauterine growth restriction, miscarriage, neural tube defects, orofacial defects, and preterm birth). The primary goal of this paper is to review the research literature and propose evidence-based recommendations for the optimal level of prenatal supplementation for each vitamin for most women in the United States. A secondary goal was to compare these new recommendations with the levels of vitamins in over 180 commercial prenatal supplements. The analysis found that prenatal supplements vary widely in content, often contained only a subset of essential vitamins, and the levels were often below our recommendations. This suggests that increasing prenatal vitamin supplementation to the levels recommended here may reduce the incidence of many pregnancy complications and infant health problems which currently occur.

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).

Metabolic Status of Lean and Obese Zucker Rats Based on Untargeted and Targeted Metabolomics Analysis of Serum

Obesity is growing worldwide epidemic. Animal models can provide some clues about the etiology, development, prevention, and treatment of obesity. We examined and compared serum metabolites between seven lean (L) and seven obese (O) female Zucker rats to investigate the individual serum metabolic profile. A combination of HPLC-UV, HPLC-ECD, and LC-MS revealed more than 400 peaks. The 50 highest quality peaks were selected as the focus of our study. Untargeted metabolomics analysis showed significantly higher mean peak heights for 20 peaks in L rats, generally distributed randomly, except for a cluster (peaks 44–50) where L showed stable dominancy over O. Only eight peaks were significantly higher in O rats. Peak height ratios between pairs of L and O rats were significantly higher at 199 positions in L rats and at 123 positions in O rats. Targeted metabolomics analysis showed significantly higher levels of methionine, cysteine, tryptophan, kynurenic acid, and cysteine/cystine ratio in L rats and significantly higher levels of cystine and tyrosine in O rats. These results contribute to a better understanding of systemic metabolic perturbations in the obese Zucker rat model, emphasizing the value of both whole metabolome and individual metabolic profiles in the design and interpretation of studies using animal models.

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 (WQS_RS)] to minimize the high multiple comparison burden. With WQS_RS, 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.