A combined marker of catatonia severity including autoimmune and thrombodynamic parameters in patients with autism spectrum disorder

Abstract

To verify a working hypothesis that thrombodynamic parameters of hypercoagulation and neuro-immune test correlate with the severity of catatonia in patients with autism spectrum disorder (ASD), and the combination of these indicators can predict the severity of catatonia with high accuracy and precision. Twenty-four patients with ASD (22 boys and 2 girls) with infantile psychosis in childhood autism (ICD-10 F84.02) were studied. The median age of the patients was 5,5 years. Neuro-immune and thrombodynamics tests were performed. Thrombodynamic parameters of clot growth rates from the activator (V, Vi and Vst) are significantly higher than their normal values. The values of the time of spontaneous clots occurrence (Tsp) are significantly less than the lower limit values for the norm (30 min). It was also shown that the activity of leukocyte elastase (LE) and the functional activity of the α1 protein inhibitor (α1-PI) are significantly higher than their normal values. The values of the levels of autoantibodies to S100 protein (aabS100B) and the basic myelin protein (aabOBM) are within the normal range. The initial clot growth rate (Vi) and the time of spontaneous clots occurrence (Tsp) significantly correlate with the severity of catatonia: Spearman's R is 0,55 for Vi (p=0,009) and -0,61for Tsp (p=0,002). Among the parameters of the neuro-immuno-test, only aabS100B indicator significantly correlates with the severity of catatonia. To increase the informative significance and accuracy of the contribution of the studied correlates of thrombodynamics and the neuro-immuno-test to the assessment of the severity of catatonia in children with ASD, a multivariate linear regression analysis was performed to construct a linear equation for the relationship between the severity of catatonia and correlates of thrombodynamics and a neuro-immuno-test. The determination coefficient R2, which determines the informational significance of the regression model, is 0,63. The remaining 37% is explained by unaccounted and not yet known factors.