The Assotiation of Il-17a Levels With Clinical Characteristics in Patients With Schizophrenia

Malashenkova, I K; Krynskiy, Sergey; Ogurtsov, Daniil; Khailov, Nikita; Chekulaeva, Ekaterina; Kostyuk, Georgiy; Didkovsky, Nikolay

doi:10.29003/m2833.sudak.ns2022-18/218-219

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“…Cytokines of innate immunity, IL-1β, IL-8, TNF-α, play an important role in triggering and maintaining the systemic inflammatory response, pyrogenic reactions, and functions of adaptive immunity [24]. The elevation of their level in the CNS, which may be observed, for example, in neuroinfections, autoimmune diseases, mental diseases, causes a damaging effect on neurons and glial cells, facilitates neurodegeneration (see, for example, review [25]). According to the data of the metaanalysis [18], the level of IL-6, TNF-α cytokines, and the receptor antagonist IL-1RA is increased in acute schizophrenia.…”

Section: Discussionmentioning

confidence: 99%

Identification of Diagnostic Schizophrenia Biomarkers Based on the Assessment of Immune and Systemic Inflammation Parameters Using Machine Learning Modeling

Malashenkova,

Krynskiy,

Ogurtsov

et al. 2023

Sovrem Tehnol Med

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Materials and Methods.We have analyzed 17 immunological parameters in 63 schizophrenia patients and 36 healthy volunteers. The parameters of humoral immunity, systemic level of the key cytokines of adaptive immunity, anti-inflammatory and pro-inflammatory cytokines, and other inflammatory markers were determined by enzyme immunoassay. Applied methods of machine learning covered the main group of approaches to supervised learning such as linear models (logistic regression), quadratic discriminant analysis (QDA), support vector machine (linear SVM, RBF SVM), k-nearest neighbors algorithm, Gaussian processes, naive Bayes classifier, decision trees, and ensemble models (AdaBoost, random forest, XGBoost). The importance of features for prediction from the best fold has been analyzed for the machine learning methods, which demonstrated the best quality. The most significant features were selected using 70% quantile threshold.Results. The AdaBoost ensemble model with ROC AUC of 0.71±0.15 and average accuracy (ACC) of 0.78±0.11 has demonstrated the best quality on a 10-fold cross validation test sample. Within the frameworks of the present investigation, the AdaBoost model has shown a good quality of classification between the patients with schizophrenia and healthy volunteers (ROC AUC over 0.70) at a high stability of the results (σ less than 0.2). The most important immunological parameters have been established for differentiation between the patients and healthy volunteers: the level of some systemic inflammatory markers, activation of humoral immunity, pro-inflammatory cytokines, immunoregulatory cytokines and proteins, Th1 and Th2 immunity cytokines. It was for the first time that the possibility of differentiating schizophrenia patients from healthy volunteers was shown with the accuracy of more than 70% with the help of machine learning using only immune parameters.The results of this investigation confirm a high importance of the immune system in the pathogenesis of schizophrenia.

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Section: Discussionmentioning

confidence: 99%