Impaired lipid metabolism markers to assess the risk of neuroinflammation in autism spectrum disorder

Abstract

Autism spectrum disorder (ASD) is a multifactorial disorder caused by an interaction between environmental risk factors and a genetic background. It is characterized by impairment in communication, social interaction, repetitive behavior, and sensory processing. The etiology of ASD is still not fully understood, and the role of neuroinflammation in autism behaviors needs to be further investigated. The aim of the present study was to test the possible association between prostaglandin E2 (PGE2), cyclooxygenase-2 (COX-2), microsomal prostaglandin E synthase-1 (mPGES-1), prostaglandin PGE2 EP2 receptors and nuclear kappa B (NF-κB) and the severity of cognitive disorders, social impairment, and sensory dysfunction. PGE2, COX-2, mPGES-1, PGE2-EP2 receptors and NF-κB as biochemical parameters related to neuroinflammation were determined in the plasma of 47 Saudi male patients with ASD, categorized as mild to moderate and severe as indicated by the Childhood Autism Rating Scale (CARS) or the Social Responsiveness Scale (SRS) or the Short Sensory Profile (SSP) and compared to 46 neurotypical controls. The data indicated that ASD patients have remarkably higher levels of the measured parameters compared to neurotypical controls, except for EP2 receptors that showed an opposite trend. While the measured parameter did not correlate with the severity of social and cognitive dysfunction, PGE2, COX-2, and mPGES-1 were remarkably associated with the dysfunction in sensory processing. NF-κB was significantly increased in relation to age. Based on the discussed data, the positive correlation between PGE2, COX-2, and mPGES-1 confirm the role of PGE2 pathway and neuroinflammation in the etiology of ASD, and the possibility of using PGE2, COX-2 and mPGES-1 as biomarkers of autism severity. NF-κB as inflammatory inducer showed an elevated level in plasma of ASD individuals. Receiver operating characteristic analysis together with predictiveness diagrams proved that the measured parameters could be used as predictive biomarkers of biochemical correlates to ASD.

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