Dataset Information

Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder.

ABSTRACT: BACKGROUND:Autism spectrum disorder (ASD) is a complex group of heterogeneous neurodevelopmental disorders the prevalence of which has been in the rise in the past decade. In an attempt to better target the basic causes of ASD for diagnosis and treatment, efforts to identify reliable biomarkers related to the body's metabolism are increasing. Despite an increase in identifying biomarkers in ASD, there are none so far with enough evidence to be used in routine clinical examination, unless medical illness is suspected. Promising biomarkers include those of mitochondrial dysfunction, oxidative stress, energy metabolism, and apoptosis. METHODS AND PARTICIPANTS:Sodium (Na+), Potassium (K+), glutathione (GSH), glutathione-s-transferase (GST), Creatine kinase (CK), lactate dehydrogenase (LDH), Coenzyme Q10, and melatonin (MLTN) were evaluated in 13 participants with ASD and 24 age-matched healthy controls. Additionally, five ratios, which include Na+/K+, GSH:GST, CK:Cas7, CoQ10: Cas 7, and Cas7:MLTN, were tested to measure their predictive values in discriminating between autistic individuals and controls. These markers, either in absolute values, as five ratios, or combined (9 markers + 5 ratios) were subjected to a principal component analysis and multidimensional scaling (MDS), and hierarchical clustering, which are helpful statistical tools in the field of biomarkers. RESULTS:Our data demonstrated that both PCA and MDS analysis were effective in separating autistic from control subjects completely. This was also confirmed through the use of hierarchical clustering, which showed complete separation of the autistic and control groups based on nine biomarkers, five biomarker ratios, or a combined profile. Excellent predictive value of the measured profile was obtained using the receiver operating characteristics analysis, which showed an area under the curve of 1. CONCLUSION:The availability of an improved predictive profile, represented by nine biomarkers plus the five ratios, inter-related different etiological mechanisms in ASD and would be valuable in providing greater recognition of the altered biological pathways in ASD. Our predictive profile could be used for the diagnosis and intervention of ASD.

SUBMITTER: El-Ansary A

PROVIDER: S-EPMC6964874 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder.

El-Ansary Afaf A Hassan Wail M WM Daghestani Maha M Al-Ayadhi Laila L Ben Bacha Abir A Ben Bacha Abir A

PloS one 20200116 1

<h4>Background</h4>Autism spectrum disorder (ASD) is a complex group of heterogeneous neurodevelopmental disorders the prevalence of which has been in the rise in the past decade. In an attempt to better target the basic causes of ASD for diagnosis and treatment, efforts to identify reliable biomarkers related to the body's metabolism are increasing. Despite an increase in identifying biomarkers in ASD, there are none so far with enough evidence to be used in routine clinical examination, unless ...[more]

PMID: 31945130

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Project description:BackgroundAutism is a neurodevelopmental disorder that is usually diagnosed in early childhood. Timely diagnosis and early initiation of treatments such as behavioral therapy are important in autistic people. Discovering critical genes and regulators in this disorder can lead to early diagnosis. Since the contribution of miRNAs along their targets can lead us to a better understanding of autism, we propose a framework containing two steps for gene and miRNA discovery.MethodsThe first step, called the FA_gene algorithm, finds a small set of genes involved in autism. This algorithm uses the WGCNA package to construct a co-expression network for control samples and seek modules of genes that are not reproducible in the corresponding co-expression network for autistic samples. Then, the protein-protein interaction network is constructed for genes in the non-reproducible modules and a small set of genes that may have potential roles in autism is selected based on this network. The second step, named the DMN_miRNA algorithm, detects the minimum number of miRNAs related to autism. To do this, DMN_miRNA defines an extended Set Cover algorithm over the mRNA-miRNA network, consisting of the selected genes and corresponding miRNA regulators.ResultsIn the first step of the framework, the FA_gene algorithm finds a set of important genes; TP53, TNF, MAPK3, ACTB, TLR7, LCK, RAC2, EEF2, CAT, ZAP70, CD19, RPLP0, CDKN1A, CCL2, CDK4, CCL5, CTSD, CD4, RACK1, CD74; using co-expression and protein-protein interaction networks. In the second step, the DMN_miRNA algorithm extracts critical miRNAs, hsa-mir-155-5p, hsa-mir-17-5p, hsa-mir-181a-5p, hsa-mir-18a-5p, and hsa-mir-92a-1-5p, as signature regulators for autism using important genes and mRNA-miRNA network. The importance of these key genes and miRNAs is confirmed by previous studies and enrichment analysis.ConclusionThis study suggests FA_gene and DMN_miRNA algorithms for biomarker discovery, which lead us to a list of important players in ASD with potential roles in the nervous system or neurological disorders that can be experimentally investigated as candidates for ASD diagnostic tests.

Project description:While several recent evaluation studies have shown the efficacy of parent training programs for children with neurodevelopmental disorders, manual-based training in German is still scarce. To address this gap, we developed a specific modularized training program for parents of children from preschool to pre-adolescent age with Autism Spectrum Disorder (FETASS). The overarching purpose of the FETASS intervention is to enhance social communication behavior and quality of life of the child by coaching parents. As a proximal target, the FETASS training aims to provide families with behavior management and communication strategies. The development of the training was influenced by published behavioral parent trainings and autism-specific interventions. The training comprises eight weekly sessions and targets families whose children have a diagnosis of Autism Spectrum Disorder (ASD) without intellectual and language impairments. As a preliminary pilot study, the purpose was to evaluate the acceptability of the training. Furthermore, the study aimed at initially evaluating social communication behavior, quality of life of the child, parental stress level, and parenting after training in comparison to a treatment as usual (TAU) group. Exploratively, long-term effects were investigated after 6 months of training as well. In total, 57 families participated (n[TAU] = 29, n[FETASS] = 28). Questionnaires about social communication behavior and quality of life of the child, parental stress, and parenting were administered at three time points (t1: baseline TAU/FETASS, t2: post TAU/FETASS; and t3: 6-month follow-up after FETASS). Primary outcome measures were the social communication behavior of the child and the parent's proxy report on quality of life of the child. Secondary outcome measures were changes in parental stress and parenting behavior. Acceptability of the training was very high and we had almost no dropouts during training. Results for the primary outcome measure of social communication behavior, overall quality of life of the child, and long-term effects on social communication behavior were not significant. While long-term findings for parent stress reduction and for the quality of life of the child are promising, further research has to be done in a future randomized controlled trial.

Dataset Information

Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder.

Publications

Preliminary evaluation of a novel nine-biomarker profile for the prediction of autism spectrum disorder.

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