Population frequency and risk factors for depression in Eastern Ukraine
Abstract
The research is aimed to analysis of age-specific and gender-based risk factors for affective pathologies. Based on the population of the Kharkiv region, the population frequency of affective disorders was determined. It was calculated as probability for an individual to be affected throughout life and can be used for scientific purposes and genetic counseling. The age-specific cumulative frequencies were calculated, reflecting the risk for an individual to be affected in a specific period of life. They can be reference points in genealogical analysis. It has been shown that, despite a higher frequency in women, depressive disorders manifest earlier and tend to be more severe in men. That is, the female sex is a factor of increased risk, while affected men have a high background of genetic predisposition. To analyze risk factors, statistical material from specialized medical institutions of the Kharkiv region from 2010 to 2016 was used. The data were obtained from 1,199 patients who were hospitalized at the Institute of Neurology, Psychiatry and Narcology of the Academy of Medical Sciences of Ukraine, that is, they had extremely severe degrees of affective disorder and, probably, had a more significant genetic component in the structure of individual predisposition. The proportion of hospitalized women (74.9 %) was three times higher than the proportion of men (25.1 %) that significantly differs from the population sex ratio and indicates that the female sex is a factor of increased risk for affective pathology. The age of onset for affective disorders was lower in men than in women: bipolar disorder in males manifests 6 years earlier than in females, depressive episode – 2 years, recurrent depression – 5 years, chronic mood disorders – 4 years. On average, the difference between age of onset in women (46.6 years, 95% CI 45.7–47.5) and men (42.7 years, 95% CI 41.0–44.3) is 4 years. The maximum risk of affective disorder in women is between the age of 50 and 60 years, in men there are two peaks – at 20–30 and 45–60 years. The population frequency, that is an indicator of the risk for an affective disorder in the population of the Kharkiv region, is 0.21 %. The probability of affective disorder for men is 0.15 %, for women this indicator is 1.7 times higher – 0.26 %.
Downloads
References
Ayub M., Irfan M., Maclean A. et al. (2008). Linkage analysis in a large family from Pakistan with depression and a high incidence of consanguineous marriages. Human Heredity, 66, 190–198. https://doi.org/10.1159/000135265
Bierut L.J., Heath A.C., Bucholz K.K. et al. (1999). Major depressive disorder in a community-based twin sample: are there different genetic and environmental contributions for men and women? Arch. Gen. Psychiatry, 56(6), 557–563. https://doi.org/10-1001/pubs.ArchGenPsychiatry-ISSN-0003-990x-56-6-yoa8229
Gilliam F.G., Barry J.J., Hermann B.P. et al. (2006). Rapid detection of major depression in epilepsy: a multicentre study. Lancet Neurology, 5(5), 399–405. https://doi.org/10.1016/S1474-4422(06)70415-X
Hou L., Bergen S.E., Akula N. et al. (2016). Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Human molecular genetics, 25(15), 3383–3394. https://doi.org/10.1093/hmg/ddw181
International Classification of Diseases (10th revision). Classification of mental and behavioural disorders (symptom description and diagnostics recommendations). (1994). Saint Petersburg: Publisher house ADIS. 300 p.
Kang H.J., Park Y., Yoo K.H. et al. (2020). Sex differences in the genetic architecture of depression. Scientific reports, 10(1), 9927. https://doi.org/10.1038/s41598-020-66672-9
Levinson D.F., Mostafavi S., Milaneschi Y. (2014). Genetic studies of major depressive disorder: why are there no genome-wide association study findings and what can we do about it? Biol. Psychiatry, 76(7), 510–512. https://doi.org/10.1016/j.biopsych.2014.07.029
Lim G.Y., Tam W.W., Lu Y. et al. (2018). Prevalence of depression in the community from 30 countries between 1994 and 2014. Sci. Rep., 8(1), 2861. https://doi.org/10.1038/s41598-018-21243-x
McGuffin P., Katz R., Watkins S., Rutherford J. (1996). A hospital-based twin register of the heritability of DSM-IV unipolar Depression. Arch. Gen. Psychiatry, 53(2), 129–136. https://doi.org/10.1001/ archpsyc.1996.01830020047006
Salk R.H., Hyde J.S., Abramson L.Y. (2017). Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychological bulletin, 143(8), 783–822. https://doi.org/10.1037/bul0000102
Schwabe I., Milaneschi Y., Gerring Z. et al. (2019). Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies. Psychological medicine, 49(16), 2646–2656. https://doi.org/10.1017/S0033291719002502
Serretti A., Kato M., De Ronchi D., Kinoshita T. (2007). Meta-analysis of serotonin transporter gene promoter polymorphism (5-HTTLPR) association with selective serotonin reuptake inhibitor efficacy in depressed patients. Molecular psychiatry, 12(3), 247–257. https://doi.org/10.1038/sj.mp.4001926
Shimizua E., Hashimotoa K., Okamura N. (2003). Alterations of serum levels of brain-derived neurotrophic factor (BDNF) in depressed patients with or without antidepressants. Biological Psychiatry, 54(1), 70–75. https://doi.org/10.1016/S0006-3223(03)00181-1
Stahl E.A., Breen G., Forstner A.J. et al. (2019). Genome-wide association study identifies 30 loci associated with bipolar disorder. Nature genetics, 51(5), 793–803. https://doi.org/10.1038/s41588-019-0397-8
Wilkie M.J.V., Smith G., Day R.K. et al. (2009). Polymorphisms in the SLC6A4 and HTR2A genes influence treatment outcome following antidepressant therapy. Pharmacogenomics Journal, 9, 61–70. https://doi.org/10.1038/sj.tpj.6500491
Wray N.R., Ripke S., Mattheisen M. et al. (2018). Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature genetics, 50(5), 668–681. https://doi.org/10.1038/s41588-018-0090-3
Zhao L., Han G., Zhao Y. et al. (2020). Gender differences in depression: evidence from genetics. Frontiers in genetics, 11, 562316. https://doi.org/10.3389/fgene.2020.562316
Authors retain copyright of their work and grant the journal the right of its first publication under the terms of the Creative Commons Attribution License 4.0 International (CC BY 4.0), that allows others to share the work with an acknowledgement of the work's authorship.