Chromatographic methods in the study of autism

Żurawicz, Ewa; Kałużna–Czaplińska, Joanna; Rynkowski, Jacek

doi:10.1002/bmc.2911

Cited by 13 publications

(11 citation statements)

References 48 publications

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“…Assessing classification performances of each model by an independent set appears to be necessary in order to minimize this overvaluation. 1 13 C HSQC NMR ROC curve was 0.03. The sensitivity of ROC curve analysis for independent validation was found better compared to the 1 H NMR (75% vs 62.5%) with an equal specificity between them (87.5%).…”

Section: −4mentioning

confidence: 94%

Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology

et al. 2015

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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.

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Section: −4mentioning

confidence: 94%

Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology

et al. 2015

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“…Twin studies show that if one of a pair of identical twins meets the diagnostic criteria for ASD, the probability that the other will have the disorder is as high as 58% for boys and 60% for girls, while in the case of fraternal twins the probability falls to 21% for boys and 27% for girls (Hallmayer et al 2011 ). In order to get a thorough understanding of causes of ASD, numerous factors and their interactions have been examined (Szatmari et al 2012 ), including a number of genetic disorders (Xu et al 2012 ), anatomical brain abnormalities (cerebellum, brain stem, hippocampus, amygdale, and frontal lobes) (Belmonte et al 2004 ) and the comorbidities such as psychiatric, neurological, metabolic and gastrological disorders (Żurawicz et al 2013 ; Kałużna-Czaplińska et al 2013 , 2014 ; McElhanon et al 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Focus on the Social Aspect of Autism

Kałużna–Czaplińska

Żurawicz

Jóźwik-Pruska

2017

J Autism Dev Disord

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Autism spectrum disorder (ASD) describes a set of neurodevelopmental disorders. Despite extensive ASD research lasting more than 60 years, its causes are still unknown. Without indicating the etiology, its development cannot be stopped. Over the years, both the definition and diagnostic criteria have developed. The number of ASD incidence is rising. The economical aspect should also be highlighted. This disorder presents particular challenges to affected children, their parents and physicians. The research of ASD, physician activities, policy making and raising the level of awareness must be coordinated. Special attention should be paid to the problem among adults with ASD.

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“…A promising new method that is designed to increase specificity of biomarkers in ASD is the multiplex immunoassay, a method that analyzes sets of biomarkers to create a diagnostic profile (182, 183). Furthermore, advances in chromatographic and proteomic techniques are also contributing to the progress of the field, allowing easier assessment of several substances (184, 185). …”

Section: Future Research Directionsmentioning

confidence: 99%

Biomarkers in Autism

et al. 2014

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Autism spectrum disorders (ASDs) are complex, heterogeneous disorders caused by an interaction between genetic vulnerability and environmental factors. In an effort to better target the underlying roots of ASD for diagnosis and treatment, efforts to identify reliable biomarkers in genetics, neuroimaging, gene expression, and measures of the body’s metabolism are growing. For this article, we review the published studies of potential biomarkers in autism and conclude that while there is increasing promise of finding biomarkers that can help us target treatment, there are none with enough evidence to support routine clinical use unless medical illness is suspected. Promising biomarkers include those for mitochondrial function, oxidative stress, and immune function. Genetic clusters are also suggesting the potential for useful biomarkers.

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Chromatographic methods in the study of autism

Cited by 13 publications

References 48 publications

Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology

Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology

Focus on the Social Aspect of Autism

Biomarkers in Autism

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