Identification of Biomarkers of Impaired Sensory Profiles among Autistic Patients
Autor: | Wail M. Hassan, Afaf El-Ansary, Undurti N. Das, Hanan Qasem |
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Rok vydání: | 2016 |
Předmět: |
0301 basic medicine
Oncology Male Autism Spectrum Disorder Prostaglandin Autism MAP Kinase Kinase 1 lcsh:Medicine Social Sciences Dinoprost Biochemistry Correlation 0302 clinical medicine Neurodevelopmental disorder Mathematical and Statistical Techniques MAP2K1 Medicine and Health Sciences Psychology Lipid Hormones lcsh:Science Child Prostaglandin-E Synthases Principal Component Analysis Multidisciplinary NF-kappa B Cognition Interleukin-12 Interleukin-10 Enzymes Monte Carlo method Neurology Autism spectrum disorder Child Preschool Physical Sciences Phosphatidylcholines Population study Female Sensory Perception Statistics (Mathematics) Research Article medicine.medical_specialty Adolescent Sensory system Phosphatidylserines Research and Analysis Methods Dinoprostone 03 medical and health sciences Developmental Neuroscience Internal medicine medicine Humans Autistic Disorder Statistical Methods Psychiatry business.industry lcsh:R Biology and Life Sciences Proteins medicine.disease Hormones Phospholipases A2 030104 developmental biology Cyclooxygenase 2 Neurodevelopmental Disorders Developmental Psychology Multivariate Analysis Enzymology lcsh:Q business Cognition Disorders Protein Kinases 030217 neurology & neurosurgery Biomarkers Mathematics Neuroscience |
Zdroj: | PLoS ONE PLoS ONE, Vol 11, Iss 11, p e0164153 (2016) |
ISSN: | 1932-6203 |
Popis: | Background Autism is a neurodevelopmental disorder that displays significant heterogeneity. Comparison of subgroups within autism, and analyses of selected biomarkers as measure of the variation of the severity of autistic features such as cognitive dysfunction, social interaction impairment, and sensory abnormalities might help in understanding the pathophysiology of autism. Methods and Participants In this study, two sets of biomarkers were selected. The first included 7, while the second included 6 biomarkers. For set 1, data were collected from 35 autistic and 38 healthy control participants, while for set 2, data were collected from 29 out of the same 35 autistic and 16 additional healthy subjects. These markers were subjected to a principal components analysis using either covariance or correlation matrices. Moreover, libraries composed of participants categorized into units were constructed. The biomarkers used include, PE (phosphatidyl ethanolamine), PS (phosphatidyl serine), PC (phosphatidyl choline), MAP2K1 (Dual specificity mitogen-activated protein kinase kinase 1), IL-10 (interleukin-10), IL-12, NFκB (nuclear factor-κappa B); PGE2 (prostaglandin E2), PGE2-EP2, mPGES-1 (microsomal prostaglandin synthase E-1), cPLA2 (cytosolic phospholipase A2), 8-isoprostane, and COX-2 (cyclo-oxygenase-2). Results While none of the studied markers correlated with CARS and SRS as measure of cognitive and social impairments, six markers significantly correlated with sensory profiles of autistic patients. Multiple regression analysis identifies a combination of PGES, mPGES-1, and PE as best predictors of the degree of sensory profile impairment. Library identification resulted in 100% correct assignments of both autistic and control participants based on either set 1 or 2 biomarkers together with a satisfactory rate of assignments in case of sensory profile impairment using different sets of biomarkers. Conclusion The two selected sets of biomarkers were effective to separate autistic from healthy control subjects, demonstarting the possibility to accurately predict the severity of autism using the selected biomarkers. The effectiveness of the identified libraries lied in the fact that they were helpful in correctly assigning the study population as control or autistic patients and in classifying autistic patients with different degree of sensory profile impairment. |
Databáze: | OpenAIRE |
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