Özet:

The background. Nowadays many efforts are taken in searching for Parkinson's disease biomarkers, especially for a early recognition of the disease. The intestinal microbiota is one of the potential sources of biomarkers, changes in the composition of which in PD are actively studied. The aim of this study is to identify microbiota biomarkers in the Parkinson's disease with an estimated accuracy of the diagnostics, including differential diagnostics, relative to other neurological diseases for patients of the Russian population. Materials and methods. One hundred ninety-two metagenomics profiles from patients with Parkinson's disease (n = 93), people with other neurological diagnoses (n = 33), and healthy controls (n = 66) were included in this study. These profiles were obtained with amplicon sequencing of bacterial 16S rRNA genes. Classifying models were made using the naive Bayes classifier, the artificial neural network, support vector machine, generalized linear model, and partial least squares regression. As a result we established that an optimal classification by the composition of the gut microbiota on the validation sample (sensitivity 91.30%, specificity 91.67% at 91.49% accuracy) amid patients was demonstrated with a naive Bayes classifier using the representation of the following gener as predictors: Christensenella, Methanobrevibacter, Leuconostoc, Enterococcus, Catabacter, Desulfovibrio, Sphingomonas, Yokenella, Atopobium, Fusicatenibacter, Cloacibacillus, Bulleidia, Acetanaerobacterium, and Staphylococcus. and conclusions. Information of the intestinal microbiota taxonomic composition may be used in differential diagnosis of Parkinson's disease.