Details

Autor(en) / Beteiligte

• El-Ansary, Afaf
• Al-Onazi, Mona
• Alhowikan, Abdulrahman M
• Alghamdi, Mashael A
• Al-Ayadhi, Laila

Titel

Assessment of a combination of plasma anti-histone autoantibodies and PLA2/PE ratio as potential biomarkers to clinically predict autism spectrum disorders

Ist Teil von

• Scientific reports, 2022-08, Vol.12 (1), p.13359-13359, Article 13359

Ort / Verlag

London: Nature Publishing Group

Erscheinungsjahr

2022

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• Abstract Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficiencies in social interaction and repetitive behaviors. Multiple studies have reported abnormal cell membrane composition and autoimmunity as known mechanisms associated with the etiopathogenesis of ASD. In this study, multiple regression and combined receiver operating characteristic (ROC) curve as statistic tools were done to clarify the relationship between phospholipase A2 and phosphatidylethanolamine (PE) ratio (PLA2/PE) as marker of lipid metabolism and membrane fluidity, and antihistone-autoantibodies as marker of autoimmunity in the etiopathology of ASD. Furthermore, the study intended to define the linear combination that maximizes the partial area under an ROC curve for a panel of markers. Forty five children with ASD and forty age- and sex-matched controls were enrolled in the study. Using ELISA, the levels of antihistone-autoantibodies, and PLA2 were measured in the plasma of both groups. PE was measured using HPLC. Statistical analyses using ROC curves and multiple and logistic regression models were performed. A notable rise in the area under the curve was detected using combined ROC curve models. Additionally, higher specificity and sensitivity of the combined markers were documented. The present study indicates that the measurement of the predictive value of selected biomarkers related to autoimmunity and lipid metabolism in children with ASD using a ROC curve analysis should lead to a better understanding of the pathophysiological mechanism of ASD and its link with metabolism. This information may enable the early diagnosis and intervention.