Abstract
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 α1-proteinase inhibitor (α1-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 α1-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 α1-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.
Highlights
Summary 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 with a depressive episode were included in the study
The control group consisted of 41 elderly people without depressive disorders
Summary
Резюме Цель исследования: количественная оценка взаимосвязи депрессивного состояния пожилых людей с воспалительными. 26 и аутоиммунными маркерами на основе модели бинарной логистической регрессии и использование этой модели для предсказания вероятности депрессивного состояния пожилых по этим показателям. Пациенты и методы: в исследование были включены 55 госпитализированных больных позднего возраста (средний возраст 69,2 ± 6,9 года) с депрессивным эпизодом. Контрольную группу составил 41 человек пожилого возраста (средний возраст 66,6 ± 6,2 года) без депрессивных или иных психических расстройств расстройств. На основе иммунологических показателей у пациентов пожилого возраста с депрессией построена математическая модель. Заключение: результаты математического анализа свидетельствуют о том, что такие иммунологические показатели, как функциональная активность D1-ПИ и S100B, статистически значимо связаны с вероятностью наличия депрессии у людей пожилого возраста. Как энзиматическая активность ЛЭ и уровень аутоантител к ОБМ, не оказывали статистически значимого влияния на искомую вероятность.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
