Open AccessPredicting the Risk of Depression in the Elderly by Immunological Indicators Research
Author(s) -
А. Н. Симонов,
T P Klyushnik,
Л В Андросова,
Т. П. Сафарова
Publication year - 2020
Publication title -
psihiatriâ
Language(s) - English
Resource type - Journals
eISSN - 2618-6667
pISSN - 1683-8319
DOI - 10.30629/2618-6667-2020-18-4-26-32
Subject(s) - depression (economics) , autoantibody , medicine , logistic regression , immunology , elastase , statistical significance , myelin basic protein , correlation , antigen , psychology , gastroenterology , myelin , antibody , biology , enzyme , central nervous system , mathematics , biochemistry , geometry , economics , macroeconomics
Objective : to construct a mathematical model that predicts the state of depression by immunological parameters in the blood plasma of older people to further predict the development of the disease. Patients and methods : 55 hospitalized patients of late age (mean age 69.2 ± 6.9 years) with a depressive episode were included in the study. The control group consisted of 41 elderly people (average age 66.6 ± 6.2 years) without depressive disorders. The activity of inflammatory and autoimmune markers in the blood plasma of patients and control groups was determined: the enzymatic activity of leukocyte elastase (LE), the functional activity of the α 1 -proteinase inhibitor (α 1 -PI), the level of autoantibodies to neuro-specific antigens S100B and the myelin basic protein (MBP). Statistical data processing was performed using the R (R version 3.2.4) and STATA (version 12.1) programs. We used point-bead-correlation to measure the strength and direction of the relationship between the binary variable and continuous variables and logistic regression to predict the probability of occurrence of events of interest by the values of one or more independent variables (predictors). Results : in patients with depressive disorders, a statistically significant increase in the functional activity of α 1 -PI (p ≤ 0.05) and the level of autoantibodies to the neurospecific S100B antigen (p ≤ 0.05) was revealed compared with the control. LE activity and MBP level did not differ from the control (p = 0.12 and p = 0.1, respectively). Based on immunological parameters in elderly patients with depression, a mathematical model is constructed. The accuracy of the correct prediction of outcomes using the model as a whole was 83.33%, which indicates a high predictive efficiency of this model. Conclusion : the results of mathematical analysis obtained in this work indicate that immunological parameters such as the functional activity of α 1 -PI and S100B are statistically significantly associated with the likelihood of depression in the elderly. Indicators such as enzymatic activity of LE and the level of autoantibodies to MBP did not have a statistically significant effect on the desired probability.