scholarly journals Altered Glutamate and Glutamine Kinetics in Autism Spectrum Disorder

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 845-845
Author(s):  
Sofie De Wandel ◽  
Marielle PKJ Engelen ◽  
Raven Wierzchowska-McNew ◽  
Julie Thompson ◽  
Sarah K Kirschner ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Amino Acid ◽  
Pearson Correlation ◽  
Plasma Concentrations ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Word Fluency ◽  
Attention Switching ◽  
Neurocognitive Dysfunction ◽  
Mood Changes

Abstract Objectives Recent studies suggest that glutamate (GLU) signaling abnormalities are involved in the etiology of Autism Spectrum Disorder (ASD), suggesting perturbations in GLU and glutamine (GLN) metabolism. Although GLU and GLN plasma concentrations have been linked to cognitive decline, the actual production of GLU and GLN have never been measured in ASD and linked to ASD severity score and neurocognitive and mood changes. Methods 19 young adults with high functioning ASD (age 24.3 ± 1.0y, Autism Quotient (AQ): 31.4 ± 1.7), and 46 control subjects (age 23.4 ± 0.3y)) were enrolled. ASD severity and subscores as well as cognitive function and mood were assessed (MOCA, TMT, word fluency, Stroop, HADS). Postabsorptive amino acid kinetics (production of GLU and GLN, and its interconversion rates (GLU > >GLN and GLN > >GLU)) were measured by pulse stable isotope administration and subsequent blood sampling for 4 hours. Plasma amino acid enrichments and concentrations were analyzed by LC-MS/MS. Stats were done by ANCOVA, post hoc analysis and Pearson correlation. Significance was set at p < 0.05. Results No differences were present in word fluency, but higher values in ASD for TMTb (p = 0.066), depression (p = 0.0004), anxiety (p = 0.0006) and lower values for MOCA (0.0054). The ASD group was characterized by lower plasma GLN and GLU concentrations (p < 0.05). Although the production rates of GLN and GLU were not different between the groups, in ASD GLN > >GLU was higher (p = 0.0018) and GLU > >GLN lower (p = 0.016) and a higher GLN clearance rate (p = 0.006). Significant relationships were found in the ASD group between several GLU and GLN kinetic markers and AQ subscores (e.g., attention to detail, attention switching and/or communication) and word fluency (p < 0.05). Conclusions Disturbances in GLN and GLU metabolism in ASD are associated with changes in ASD subscores but less with neurocognitive dysfunction or mood changes. Funding Sources None.

Plasma concentrations of the trace elements copper, zinc and selenium in Brazilian children with autism spectrum disorder

2018 ◽  
Vol 106 ◽  
pp. 605-609 ◽  
Author(s):  
Paula Fabiana Saldanha Tschinkel ◽  
Geir Bjørklund ◽  
Lourdes Zélia Zanoni Conón ◽  
Salvatore Chirumbolo ◽  
Valter Aragão Nascimento
Keyword(s):  
Autism Spectrum Disorder ◽  
Trace Elements ◽  
Plasma Concentrations ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Copper Zinc ◽  
Brazilian Children

Detection of CYP2D6 polymorphism using Luminex xTAG technology in autism spectrum disorder: CYP2D6 activity score and its association with risperidone levels

2016 ◽  
Vol 33 (S1) ◽  
pp. S93-S94
Author(s):  
C. Sukasem ◽  
S. Santon
Keyword(s):  
Autism Spectrum Disorder ◽  
Plasma Concentration ◽  
Plasma Concentrations ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Activity Score ◽  
Cyp2d6 Polymorphism ◽  
Cyp2d6 Activity ◽  
Luminex Assay ◽  
Competing Interest

IntroductionThe determination of the accurate CYP2D6 genotyping is essential in the clinical setting and individualization of drug therapy.ObjectivesIn this study, was to apply the Luminex xTAG technology to detect significant CYP2D6 polymorphisms and copy number variation, including assessment the relationship of CYP2D6 polymorphisms and risperidone plasma concentration in autism spectrum disorder children (ASD) treated with risperidone.MethodsAll 84 ASD patients included in this study had been receiving risperidone at least for 1 month. The CYP2D6 genotypes were determined by luminex assay. Plasma concentrations of risperidone and 9-hydroxyrisperidone were measured using LC/MS/MS.ResultsAmong the 84 patients, the most common genotype was CYP2D6*1/*10 (26.19%). The most common allele was CYP2D6*10 (51.79%) and the second most allele was CYP2D6*1 (27.98%). There were 46 (55.42%) classified as EM, 33 (39.76%) as IM, and 4 (4.82%) as UM. The plasma concentration of risperidone and risperidone/9-hydroxyrisperidone ratio in the patients were significant differences among the CYP2D6 predicted phenotype group (P = 0.001 and P < 0.0001, respectively). Moreover, the plasma concentration of risperidone and risperidone/9-hydroxyrisperidone ratio in the patients with CYP2D6 activity score 0.5 were significantly higher than those with the CYP2D6 activity score 2.0 (P = 0.004 and P = 0.002, respectively).DiscussionsThe present study suggests that it would be ideal to identify the CYP2D6 genotype of patients before prescribing and administering risperidone. Furthermore, the use of CYP2D6 gene scoring system to determine an individual's metabolic capacity may become an essential tool for a more rational and safer drug administration.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

2020 ◽  
Author(s):  
Fleming C. Peck ◽  
Laurel J. Gabard-Durnam ◽  
Carol L. Wilkinson ◽  
William Bosl ◽  
Helen Tager-Flusberg ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Language Learning ◽  
Predictive Accuracy ◽  
Pearson Correlation ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Phoneme Discrimination ◽  
Co Morbidity

AbstractEarly identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved outcomes. Use of electroencephalography (EEG) in infants has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment in ASD, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly within the first postnatal year, so altered neural substrates either during or after the first year may serve as early, accurate indicators of later autism diagnosis. Using longitudinal EEG data collected during a passive phoneme task in infants with high familial risk for ASD, we compared predictive accuracy at 6-months (during phoneme learning) versus 12-months (after phoneme learning). Samples at both ages were matched in size and diagnoses (n=14 with later ASD; n= 40 without ASD). Using Pearson correlation feature selection and support vector machine with radial basis function classifier, 100% predictive diagnostic accuracy was observed at both ages. However, predictive features selected at the two ages differed and came from different scalp locations. We also report that performance across multiple machine learning algorithms was highly variable and declined when the 12-month sample size and behavioral heterogeneity was increased. These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes in order to develop clinically relevant classification algorithms.

scholarly journals Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Fleming C. Peck ◽  
Laurel J. Gabard-Durnam ◽  
Carol L. Wilkinson ◽  
William Bosl ◽  
Helen Tager-Flusberg ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Language Learning ◽  
Predictive Accuracy ◽  
Pearson Correlation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Phoneme Discrimination ◽  
Co Morbidity

Abstract Background Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. Methods Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). Results Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. Conclusions These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.

Serum amino acid spectrum in children with autism spectrum disorder (ASD)

2020 ◽  
Vol 77 ◽  
pp. 101605
Author(s):  
Anatoly V. Skalny ◽  
Andrey A. Skalny ◽  
Yulia N. Lobanova ◽  
Tatiana V. Korobeinikova ◽  
Olga P. Ajsuvakova ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Amino Acid ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Amino Acid Spectrum

Relationship Between Autism Spectrum Disorder Characteristics and Humor Appreciation in Typically Developing Individuals

2018 ◽  
Vol 122 (6) ◽  
pp. 2282-2297
Author(s):  
Kai Nagase
Keyword(s):  
Autism Spectrum Disorder ◽  
Autistic Traits ◽  
Japanese Version ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Autism Spectrum Quotient ◽  
Typically Developing ◽  
Attention Switching ◽  
Humor Appreciation ◽  
The Relationship

Extant research regarding humor appreciation in individuals with autism spectrum disorder has been equivocal. This study aims to clarify the relationship between the severity of autism spectrum disorder characteristics and humor appreciation in typically developing individuals. We hypothesized that the severity of autistic traits would have a U-shaped linear relationship with humor appreciation. Eighty typically developing undergraduates between the ages of 18 and 22 years ( Mage = 20.20; SDage = 1.08) were recruited for this study. They were asked to answer 24 statements, devised to measure humor appreciation, in response to a joke stimulus comprising 12 typically funny daily life occurrences (two statements per episode). The participants also responded to the Japanese version of the Autism-Spectrum Quotient. A significant U-shaped relationship was observed between the severity of autistic traits and appreciation of humor. A similar significant U-shaped relationship was seen between humor appreciation and the Autism-Spectrum Quotient subscales of attention switching, communication, and imagination. Humor appreciation showed no significant U-shaped relationship with the Autism-Spectrum Quotient subscales of social skills and local details. This study identified ways that autistic traits may influence how people appreciate humor. These findings are discussed in relation to cognitive processes underlying humor appreciation.

scholarly journals Abnormalities in the genes that encode Large Amino Acid Transporters increase the risk of Autism Spectrum Disorder

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Lauren Cascio ◽  
Chin‐Fu Chen ◽  
Rini Pauly ◽  
Sujata Srikanth ◽  
Kelly Jones ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Amino Acid ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Amino Acid Transporters
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