The aromatic compound p-cresol (4-methylphenol) has been found elevated in the urines of Italian autistic children up to 8 years of age. The present study aims at replicating these initial findings in an ethnically distinct sample and at extending them by measuring also the three components of urinary p-cresol, namely p-cresylsulfate, p-cresylglucuronate and free p-cresol. Total urinary p-cresol, p-cresylsulfate and p-cresylglucuronate were significantly elevated in 33 French autism spectrum disorder (ASD) cases compared with 33 sex-and age-matched controls (p50.05). This increase was limited to ASD children aged 8 years (p50.01), and not older (p ¼ 0.17). Urinary levels of p-cresol and p-cresylsulfate were associated with stereotypic, compulsive/repetitive behaviors (p50.05), although not with overall autism severity. These results confirm the elevation of urinary p-cresol in a sizable set of small autistic children and spur interest into biomarker roles for p-cresol and p-cresylsulfate in autism.

No abstract

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with no clinical biomarker. The aims of this study were to characterize a metabolic signature of ASD and to evaluate multiplatform analytical methodologies in order to develop predictive tools for diagnosis and disease follow-up. Urine samples were analyzed using 1 H and 1 H− 13C NMR-based approaches and LC−HRMS-based approaches (ESI+ and ESI− on HILIC and C18 chromatography columns). Data tables obtained from the six analytical modalities on a training set of 46 urine samples (22 autistic children and 24 controls) were processed by multivariate analysis (orthogonal partial least-squares discriminant analysis, OPLS-DA). The predictions from each of these OPLS-DA models were then evaluated using a prediction set of 16 samples (8 autistic children and 8 controls) and receiver operating characteristic curves. Thereafter, a data fusion block-scaling OPLS-DA model was generated from the 6 best models obtained for each modality. This fused OPLS-DA model showed an enhanced performance (R 2 Y(cum) = 0.88, Q 2 (cum) = 0.75) compared to each analytical modality model, as well as a better predictive capacity (AUC = 0.91, p-value = 0.006). Metabolites that are most significantly different between autistic and control children (p < 0.05) are indoxyl sulfate, N-α-acetyl-L-arginine, methyl guanidine, and phenylacetylglutamine. This multimodality approach has the potential to contribute to find robust biomarkers and characterize a metabolic phenotype of the ASD population.

Background: Cranio-facial anomalies frequently occur in neurodevelopmental disorders because both face and brain are derived from neuroectoderm. The identification of differences in the facial phenotype of children with Autism Spectrum Disorders (ASD) may reflect alterations in embryologic brain development in children with ASD. Methods: we evaluated 33 caucasian children with ASD using a 2D computerized photogrammetry. Anthropometric euclidean measurements and landmarks located on the soft tissue of the face and head, were based on five cranio-facial indexes. Relationships between anthropometric z-scores and participant characteristics (i.e., age, Global IQ, severity of autistic symptoms measured using the CARS checklist) were assessed. Results: Cephalic index z-score differed significantly from 0 in our ASD group (p = 0.019). Moreover, a significant negative correlation was found between Facial Index z-score and CARS score (p = 0.003); conversely, a positive correlation was found between Interchantal Index z-score and CARS score (p = 0.028). Conclusion: our measurements shows a dolichocephalic head shape which is not correlated with autism severity. Importantly, two craniofacial markers were significantly correlated with autism severity: increased orbital hyperthelorism and decrease of height of the facial midline. These data support previous findings of craniofacial anomalies in autism spectrum disorder suggesting an “ASD facial phenotype” that could be used to improve ASD diagnoses.