1. Campistol J, Diez-Juan M, Callejon L, Fernandez-De Miguel A, Casado M, Garcia Cazorla A, Lozano R, Artuch R. {{Inborn error metabolic screening in individuals with nonsyndromic autism spectrum disorders}}. {Dev Med Child Neurol};2016 (Mar 31)
AIM: To perform metabolic testing on 406 patients (age range 3-22y [mean 6.71, SD 4.15], 343 males and 63 females) with nonsyndromic autism spectrum disorders (ASD) to assess the diagnostic yield. In addition, we reviewed our hospital’s clinical database of 8500 patients who had undergone metabolic testing to be identified for inborn errors of metabolism (IEM), and described the characteristics of those with IEM and nonsyndromic ASD. METHOD: Neuropsychological evaluation included the Social Communication Questionnaire and Child Behavior Checklist. For metabolic testing/screening, urine samples were analyzed for the diagnosis of cerebral creatine deficiency syndromes, purine and pyrimidine disorders, amino acid metabolism defects, mucopolysaccharidoses, and organic acidurias. RESULTS: The 406 recruited participants fulfilled the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) criteria of ASD. No biochemical evidence of a metabolic disorder was detected in any of the 406 patients studied. Concerning the retrospective evaluation from the 8500 who had metabolic testing, 464 individuals had a diagnosis of an IEM (394 without the diagnosis of ASD and 70 with ASD diagnosis). Only one individual with IEM had a diagnosis of nonsyndromic ASD at the time of the metabolic study; the metabolic testing had revealed diagnosis of urea-cycle disorder. INTERPRETATION: Metabolic testing should be considered in the work-up of individuals with syndromic ASD, but metabolic testing is not cost-effective for individuals with nonsyndromic ASD.
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2. Hardan AY, Fung LK, Frazier T, Berquist SW, Minshew NJ, Keshavan MS, Stanley JA. {{A proton spectroscopy study of white matter in children with autism}}. {Prog Neuropsychopharmacol Biol Psychiatry};2016 (Apr 3);66:48-53.
White matter abnormalities have been described in autism spectrum disorder (ASD) with mounting evidence implicating these alterations in the pathophysiology of the aberrant connectivity reported in this disorder. The goal of this investigation is to further examine white matter structure in ASD using proton magnetic resonance spectroscopy ((1)H MRS). Multi-voxel, short echo-time in vivo(1)H MRS data were collected from 17 male children with ASD and 17 healthy age- and gender-matched controls. Key (1)H MRS metabolite ratios relative to phosphocreatine plus creatine were obtained from four different right and left white matter regions. Significantly lower N-acetylaspartate/creatine ratios were found in the anterior white matter regions of the ASD group when compared to controls. These findings reflect impairment in neuroaxonal white matter tissue and shed light on the neurobiologic underpinnings of white matter abnormalities in ASD by implicating an alteration in myelin and/or axonal development in this disorder.
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3. Liu W, Li M, Yi L. {{Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework}}. {Autism Res};2016 (Apr 1)
The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repeatedly discovered by previous research. The present study examined whether their face scanning patterns could be potentially useful to identify children with ASD by adopting the machine learning algorithm for the classification purpose. Particularly, we applied the machine learning method to analyze an eye movement dataset from a face recognition task [Yi et al., 2016], to classify children with and without ASD. We evaluated the performance of our model in terms of its accuracy, sensitivity, and specificity of classifying ASD. Results indicated promising evidence for applying the machine learning algorithm based on the face scanning patterns to identify children with ASD, with a maximum classification accuracy of 88.51%. Nevertheless, our study is still preliminary with some constraints that may apply in the clinical practice. Future research should shed light on further valuation of our method and contribute to the development of a multitask and multimodel approach to aid the process of early detection and diagnosis of ASD. Autism Res 2016. (c) 2016 International Society for Autism Research, Wiley Periodicals, Inc.
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4. Sato R, Shirai K, Maekawa M, Genma R, Ohki S, Morita H, Suda T, Watanabe H. {{Glycaemia and autistic traits in very low birth weight infants in adulthood}}. {Diabetes Metab};2016 (Mar 29)
