Predicting the Risk of Depression in the Elderly by Immunological Indicators Research

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.

1. Gavrilova SI, Kalyn YaB. Social and environ mental factors and the state of mental health of the elderly (clinical and epidemiological study). Vestnik RAMN. 2002;9:15–20. (In Russ.).

2. Bаldwin R, Wild R. Management of depression in later life. Advances in Psychiatric Treatment. 2004;10:131–139. DOI: 10.1192/apt.10.2.131

3. Blazer D. Depression in late life: review and commentary. J. Gerontol. Biol. Sci. Med. Sci. 2003;58(3):249–265. DOI: 10.1093/gerona/58.3.m249

4. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLOS Med. 2013;10(11):e1001547.DOI: 10.1371/journal.pmed.1001547

5. Kok RM, Reynolds CF. 3rd. Management of depression in older adults: A review. JAMA. 2017;317(20):2114–2122. DOI: 10.1001/jama.2017.5706

6. Smulevich AB. Depression with somatic and mental illness. M.: Medical Informational Agency, 2015. (In Russ.).

7. Alamo C, Lopez-Munoz F, Garсia-Garcia P. Treatment of depression in elderly: the challenge to success. Int. J. Clin. Psychiaty Ment. Health. 2014;2:77–88. DOI: 10.12970/2310-8231.20/4.02.01.8

8. Howren MB, Lamkin DM, Jerry S. Associations of depression with C-reactive protein, IL-1 and IL-6: a Meta-analysis. Psychosom. Med. 2009;71(2):171–186. DOI: 10.1097/PSY.0b013e3181907c1b. Epub 2009 Feb 2.

9. Ting EYi-C, Yang AC, Tsai S-J. Role of Interleukin-6 in depressive disorder. Int. J. Mol. Sci. 2020;21(6):2194. DOI: 10.3390/ijms21062194

10. Subbotskaya NV, Sarmanova ZV, Barkhatova AN, Kliushnik TP, Tiganov AS. Clinical and immunological correlations in endogenous depression. Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova. 2015;115(4):49–53. DOI: 10.17116/jnevro20151154149-53 (In Russ.).

11. Alexopoulos GS, Morimoto SS. The inflammation hypothesis in geriatric depression. Int. J. Geriatr. Psychiatry. 2011;26(11):1109–1118. DOI: 10.1002/gps.2672

12. Alexopoulos GS. Mechanisms and treatment of latelife depression. Translational Psychiatry. 2019;9(1):1–16. DOI: 10.1038/s41398-019-0514-6

13. Safarova TP, Yakovleva OB, Androsova LV, Simonov AN, Klyushnik TP. Some inflammation factors and immunophenotypes of depression in elderly patients. Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova. 2020;120(2):53–58. DOI: 10.17116/jnevro202012002153 (In Russ.).

14. Dotsenko VL, Neshkova EA, Yarovaya GA. Detection of leukocyte elastase from complex with plasma a1-protease inhibitor by it enzymatic activity with a synthetic substrate. Vopr. Med. Khimii. 1994;40(3):20–25. (In Russ.).

15. Nartikova VF, Paskhina TS. A unified method for assay of alpha-1-antitrypsin and alpha-2-macroglobulin activity in human serum (plasma). Vopr. Med. Khimii. 1979;25(4):494–499. (In Russ.).

16. Kendall MG, Stuart A. The advanced theory of statistics. London: Published by Charles Griffin & Co.1952.

17. Linacre J. The Expected Value of a Point-Biserial (or Similar) Correlation. Rasch Measurement Transactions. 2008;22(1):1154.

18. Fleiss JL, Williams JB, Dubro AF. The logistic regression analysis of psychiatric data. J. Psychiatr. Res. 1986;20(3):195–209.

19. Hosmer DW, Lemeshow S. Applied Logistic Reg 19. Hosmer DW, Lemeshow S. Applied Logistic Regression. New York. Wiley–Interscience.1989.