Identification of Biomarkers of Impaired Sensory Profiles among Autistic Patients

Autor: Wail M. Hassan, Afaf El-Ansary, Undurti N. Das, Hanan Qasem
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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