Autism spectrum disorders (ASDs) are a group of mental illnesses highly correlated with gut microbiota. Recent studies have shown that some abnormal aromatic metabolites in autism patients are presumably derived from overgrown Clostridium species in gut, which may be used for diagnostic purposes. In this paper, a GC/MS based metabolomic approach was utilized to seek similar biomarkers by analyzing the urinary information in 62 ASDs patients compared with 62 non-ASDs controls in China, aged 1.5–7. Three compounds identified as 3-(3-hydroxyphenyl)-3-hydroxypropionic acid (HPHPA), 3-hydroxyphenylacetic acid (3HPA), and 3-hydroxyhippuric acid (3HHA) were found in higher concentrations in autistic children than in the controls (p < 0.001). After oral vancomycin treatment, urinary excretion of HPHPA (p < 0.001), 3HPA (p < 0.005), and 3HHA (p < 0.001) decreased markedly, which indicated that these compounds may also be from gut Clostridium species. The sensitivity and specificity of HPHPA, 3HPA, and 3HHA were evaluated by receiver-operating characteristic (ROC) analysis. The specificity of each compound for ASDs was very high (>96%). After two-regression analysis, the optimal area under the curve (AUC, 0.962), sensitivity (90.3%), and specificity (98.4%) were obtained by ROC curve of Prediction probability based on the three metabolites. These findings demonstrate that the measurements of the three compounds are strong predictors of ASDs and support the potential clinical utility for identifying a subgroup of ASDs subjects.

Although the phenylalanine/tyrosine ratio in blood has been the gold standard for diagnosis of phenylketonuria (PKU), the disadvantages of invasive sample collection and false positive error limited the application of this discriminator in the diagnosis of PKU to some extent. The aim of this study was to develop a new standard with high sensitivity and specificity in a less invasive manner for diagnosing PKU. In this study, an improved oximation-silylation method together with GC/MS was utilized to obtain the urinary metabolomic information in 47 PKU patients compared with 47 non-PKU controls. Compared with conventional oximation-silylation methods, the present approach possesses the advantages of shorter reaction time and higher reaction efficiency at a considerably lower temperature, which is beneficial to the derivatization of some thermally unstable compounds, such as phenylpyruvic acid. Ninety-seven peaks in the chromatograms were identified as endogenous metabolites by the National Institute of Standards and Technology (NIST) mass spectra library, including amino acids, organic acids, carbohydrates, amides, and fatty acids. After normalization of data using creatinine as internal standard, 19 differentially expressed compounds with p values of <0.05 were selected by independent-sample t test for the separation of the PKU group and the control group. A principal component analysis (PCA) model constructed by these differentially expressed compounds showed that the PKU group can be discriminated from the control group. Receiver-operating characteristic (ROC) analysis with area under the curve (AUC), specificity, and sensitivity of each PKU marker obtained from these differentially expressed compounds was used to evaluate the possibility of using these markers for diagnosing PKU. The largest value of AUC (0.987) with high specificity (0.936) and sensitivity (1.000) was obtained by the ROC curve of phenylacetic acid at its cutoff value (17.244 mmol/mol creatinine), which showed that phenylacetic acid may be used as a reliable discriminator for the diagnosis of PKU. The low false positive rate (1-specificity, 0.064) can be eliminated or at least greatly reduced by simultaneously referring to other markers, especially phenylpyruvic acid, a unique marker in PKU. Additionally, this standard was obtained with high sensitivity and specificity in a less invasive manner for diagnosing PKU compared with the Phe/Tyr ratio. Therefore, we conclude that urinary metabolomic information based on the improved oximation-silylation method together with GC/MS may be reliable for the diagnosis and differential diagnosis of PKU.

ObjectiveRecent studies have provided insights into the gut microbiota in autism spectrum disorder (ASD); however, these studies were restricted owing to limited sampling at the unitary stage of childhood. Herein, we aimed to reveal developmental characteristics of gut microbiota in a large cohort of subjects with ASD combined with interindividual factors impacting gut microbiota.DesignA large cohort of 773 subjects with ASD (aged 16 months to 19 years), 429 neurotypical (NT) development subjects (aged 11 months to 15 years) were emolyed to determine the dynamics change of gut microbiota across different ages using 16S rRNA sequencing.ResultIn subjects with ASD, we observed a distinct but progressive deviation in the development of gut microbiota characterised by persistently decreased alpha diversity, early unsustainable immature microbiota, altered aboudance of 20 operational taxonomic units (OTUs), decreased taxon detection rate and 325 deregulated microbial metabolic functions with age-dependent patterns. We further revealed microbial relationships that have changed extensively in ASD before 3 years of age, which were associated with the severity of behaviour, sleep and GI symptoms in the ASD group. This analysis demonstrated that a signature of the combination of 2 OTUs, Veillonella and Enterobacteriaceae, and 17 microbial metabolic functions efficiently discriminated ASD from NT subjects in both the discovery (area under the curve (AUC)=0.86), and validation 1 (AUC=0.78), 2 (AUC=0.82) and 3 (AUC=0.67) sets.ConclusionOur large cohort combined with clinical symptom analysis highlights the key regulator of gut microbiota in the pathogenesis of ASD and emphasises the importance of monitoring and targeting the gut microbiome in future clinical applications of ASD.

To investigate the emotional problems (depressive and anxiety symptoms) of mothers of children with autism spectrum disorder (ASD) and explore the role of the mother's socioeconomic status (SES) and the core symptoms of the child on the mother's emotional problems. This cross-sectional survey was performed in 180 mothers of children with ASD in Chang Sha city of China. The 7-item Generalized Anxiety Disorder Scale (GAD-7) and the 9-item Patient Health Questionnaire (PHQ-9) were used to assess the anxiety and depressive symptoms of the mothers of the autistic children. The education level and annual family income, as well as occupation, were be selected as components of the mother's SES. Autism Behaviour Checklist (ABC) and Social Responsiveness Scale (SRS) were used for the evaluation of the core symptoms of the children. A general information questionnaire was also used. The ordinal regression was used to examine the effect of the SES and children's core symptoms on maternal emotional problems. The valid response rate was 92.7% (167 of 180 questionnaires were returned). Of the mothers studied, 72.5% and 80.2% had depressive and anxiety symptoms, respectively, and 67.1% suffered from both symptoms. Mother's SES was observed to be unrelated to maternal anxiety symptoms ( P >.05). Only 1 component of the SES (junior high school education level) was related to depressive symptoms (OR = 0.31, 95% CI 0.12–0.80). SRS score under 115 (OR = 0.38, 95% CI 0.16–0.93) of autistic children was a protective factor against maternal anxiety symptoms. The borderline and mild behavioral problems (OR = 0.43, 95% CI 0.19–0.99; OR = 0.45, 95% CI 0.22–0.94, respectively) of autistic children were protective factors against maternal depressive symptoms. Mothers of autistic children generally exhibited high levels of anxiety and depressive symptoms. The core symptoms of the autistic children were observed to be strongly associated with both maternal anxiety and depressive symptoms. Improvements in the core symptoms of children with ASD may help reduce maternal anxiety and depressive symptoms to some extent.

Background. There has been significant research on the genetic and environmental factors of congenital heart defects (CHDs), but few causes of teratogenicity, especially teratogenic mechanisms, can be clearly identified. Metabolomics has a potential advantage in researching the relationship between external factors and CHD. Objective. To find and identify the urinary potential biomarkers of pregnancy (including in the second and third trimesters) for fetuses with CHD based on modified gas chromatograph-mass spectrometer (GC-MS), which could reveal the possibility of high-risk factors for CHD and lay the foundation for early intervention, treatment, and prevention. Methods. Using a case-control design, we measured the urinary potential biomarkers of maternal urine metabolomics based on GC-MS in a population-based sample of women whose infants were diagnosed with CHD (70 case subjects) or were healthy (70 control subjects). SIMCA-P 13.0 software, principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), Wilcoxon-Mann-Whitney test, and logistics regression were used to find significant potential biomarkers. Result. The 3D score graph of the OPLS-DA showed that the CHD and control groups were fully separated. The fitting parameters were R2x=0.78 and R2y=0.69, and the forecast rate was Q2=0.61, indicating a high forecast ability. According to the ranking of VIPs from the OPLS-DA models, we found 34 potential metabolic markers with a VIP > 1, and after two pairwise rank sum tests, we found 20 significant potential biomarkers, which were further used in multifactor logistic regressions. Significant substances, including 4-hydroxybenzeneacetic acid (OR=4.74, 95% CI: 1.06-21.06), 5-trimethylsilyloxy-n-valeric acid (OR=15.78, 95% CI: 2.33-106.67), propanedioic acid (OR=5.37, 95% CI: 1.87-15.45), hydracrylic acid (OR=6.23, 95% CI: 1.07-36.21), and uric acid (OR=5.23, 95% CI: 1.23-22.32), were associated with CHD. Conclusion. The major potential biomarkers in maternal urine associated with CHD were 4-hydroxybenzeneacetic acid, 5-trimethylsilyloxy-n-valeric acid, propanedioic acid, hydracrylic acid, and uric acid, respectively. These results indicated that the short chain fatty acids (SCFAs) and aromatic amino acid metabolism may be relevant with CHD.