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Depression Etiology

Depression Etiology

Many factors influence whether a person will develop depression over their lifetime.  Biology plays a role in heritability of depression and predisposed vulnerability.  Environmental factors, level of social support and culture, impact the way people both experience and express emotions. Genes interact with environmental factors, and this relationship factors into the expression of pre-existing risk factors for depression. Psychological components, such as a person’s thinking patterns and cognitive style, are relevant in identifying maintaining factors and amenability to treatment.

Psychological Factors

The style and content of an individual’s thoughts may predict depressive symptomatology. For example, patients with major depressive disorder evidenced self-generated thoughts with greater negative content. Further, these thoughts were also more likely to involve content related to the past and the self. In general, this may reflect a tendency towards ruminative and anxious thinking (Hoffmann et al., 2016).  Similarly, depression was associated with future-oriented thinking, specifically, lessened expectation for the occurrence of positive events and focus on potential future negative events or threats (Miloyan et al., 2017). For women especially, negative thinking and self-doubt associated with feeling like a fraud (aptly named imposter syndrome) in turn correlated with depression (McGregor et al., 2008). Given the available data, it would be expected that an ability to maintain mental focus on the present, an expectation for positive events to occur, and an other-focused mindset would negatively correlate with depression. Internal locus of control, or the belief that one can exert control over one’s life, also negatively correlated with depression, even in the face of adversity (Wong & Anitescu, 2016).

 

Biological Factors 

Research in the area of Depression biologydepression and biology suggests a multidirectional relationship between genes and depression. For example, research suggests that molecular variation in genes correlated with a predisposition to developing depression. Some variants increased the likelihood of developing depression only in individuals who have experienced adverse events such as trauma, while the effects of other variants were not dependent on environment for expression (Peterson et al., 2018).  However, stressful events were also associated with changes in genes, and these changes were in turn correlated with depression (Park et al., 2019). Children of mothers with depression demonstrated an alteration in a marker of immune function via elevated s-IgA levels, as well as an increase in psychopathology (Ulmer-Yaniv et al., 2018).  Given this data, it is not surprising that depression and autoimmune disorders share bidirectional comorbidity and likely also share the same genetic and environmental risk factors (Euesden et al., 2017). Disturbances in the health of the gut-brain axis may also result in depression (Liang et al., 2018). This pathway is also implicated in immune health and modulating chronic inflammation in the body.

Social Factors

Social media is one of the most significant social factors affecting depression, especially for adolescents and young adults. Research has found that time spent using social media, activity level, degree of investment (i.e., including information core to one’s identity online), and addiction to social media all predicted depression (Keles et al., 2019). Social media use demonstrated a causal relationship with depression, and was not merely a coincidental behavior (Ghaemi, 2020). Social media may be a proxy for status, and a means for individuals who otherwise perceive themselves as possessing low social status to increase their social rank. Thus, an individual with a lower social status in offline relationships may be more likely to use social media to improve status. In another study, perception of social position influenced depression, such that a lower perceived social rank correlated with increased depressive symptoms, suicidal thoughts, and self-harm (Wetherall et al., 2019). Social support also carries an important role in moderating depressive symptoms. For example, social support served as a buffer to lessen the impact of environmental factors such as financial issues that can lead to depression (Viseu et al., 2018). During life-altering events such as the COVID-19 pandemic, social support not only decreased depression risk but was also correlated with reduced loneliness, irritability, and sleep problems (Grey, 2020).

Cultural Factors

The way in which depressive symptoms are expressed is dependent on cultural context.  For example, levels of positive affect, negative affect, and somatic symptoms were found to differ among European Americans, Japanese Americans, and Native Hawaiians who completed a self-report measure of depression (Kanazawa, 2007). Cultural factors also impacted conceptualization of depression. For example, South Asian immigrants were more likely to view depression through a social and moral lens, while European Americans attributed depression to either biological/medical problems or situational/life problems (Karasz, 2005). A study involving Black female participants in the U.S. found a relationship between a perceived cultural necessity of demonstrating strength and depressive symptoms (Abrams et al., 2019).

References

Abrams, J. A., Hill, A., & Maxwell, M. (2018). Underneath the mask of the Strong Black Woman Schema: Disentangling influences of strength and self-silencing on depressive symptoms among U. S. Black women. Sex Roles, 80, 517-526. https://doi.org/10.1007/s11199-018-0956-y

Euesden J., Danese, A., Lewis, C. M., & Maughan, B. (2017). A bidirectional relationship between depression and the autoimmune disorders – New perspectives from the National Child Development Study. PLOS One, 1-14. https://doi.org/10.1371/journal.pone.0173015

Ghaemi, S. N. (2020). Digital depression: A new disease of the millennium? Acta Psychiatrica Scandinavica, 141(4), 356-361. https://doi.org/10.1111/acps.13151

Hoffmann, F., Banzhaf, C., Kanske, P., Bermpohl, F., & Singer, T. (2016). Where the depressed mind wanders: Self-generated thought patterns as assessed through experience sampling as a state marker of depression. Journal of Affective Disorders, 198(1), 127-134. https://doi.org/10.1016/j.jad.2016.03.005

Kanazawa, A., White, P. M., & Hampson, S. E. (2007). Ethnic variation in depressive symptoms in a community sample in Hawai‘i. Cultural Diversity & Ethnic Minority Psychology, 13(1), 35-44. https://doi.apa.org/doi/10.1037/1099-9809.13.1.35

Karasz, A. (2005). Cultural differences in conceptual models of depression. Social Science & Medicine, 60(7), 1625-1635. https://doi.org/10.1016/j.socscimed.2004.08.011

Keles, B., McCrae, N., & Grealish, A. (2019). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. International Journal of Adolescence and Youth, 25(1), 79-93. https://doi.org/10.1080/02673843.2019.1590851

Liang, S., Wu, X., Hu, X., Wang, T., & Jin, F. (2018). Recognizing depression from the microbiota-gut-brain axis. International Journal of Molecular Sciences, 19(6), 1592. https://doi.org/10.3390/ijms19061592

Mc Gregor, L. N., Gee, D. E., & Posey, K. E. (2008). I feel like a fraud and it depresses me: The relation between the imposter phenomenon and depression. Social Behavior and Personality, 36(1), 43-48. https://doi.org/10.2224/sbp.2008.36.1.43

Miloyan, B., Pachana, N. A., & Suddendorf, T. (2017). Future-oriented thought patterns associated with anxiety and depression in lvbater life: The intriguing prospects of prospection. The Gerontologist, 57(4), 619-625. https://doi.org/10.1093/geront/gnv695

Park, C., Rosenblat, J. D., Brietzke, E., Pan, Z., Lee, Y., Cao, B., Zuckerman, H., Kalantarova, A., & McIntyre, R. S. (2019). Stress, epigenetics, and depression: A systematic review. Neuroscience & BIobehavioral Reviews, 102, 1390152. https://doi.org/10.1016/j.neubiorev.2019.04.010

Peterson, R. E., Cai, N. Dahl, A. W., Bigdeli, T. B., Edwards, A. C., Webb, B. T., Bacanu, S. A., Zaitlen, N., Flint, J., & Kendler, K. S. (2018). Molecular genetic analysis subdivided by adversity exposure suggests etiologic heterogeneity in major depression. The American Journal of Psychiatry, 175(6), 545-554. https://doi.org/10.1176/appi.ajp.2017.17060621

Ulmer-Yaniv, A., Djalovski, A., Priel, A., Zaggory-Sharon, O., & Feldman, R. (2018). Maternal depression alters stress and immune biomarkers in mother and child. Depression and Anxiety, 35(12), 1145-1157. https://doi.org/10.1002/da.22818

Viseu, J., Leal, R. Neves de Jesus, S., Pinto, P., Pechorro, P. & Greenglass, E. (2018). Relationship between economic stress factors and stress, anxiety, and depression: Moderating role of social support. Psychiatry Research, 268, 102-107. https://doi.org/10.1016/j.psychres.2018.07.008

Wetherall, K., Robb, K. A., & O’Connor, R. (2019). Social rank theory of depression: A systematic review of self-perceptinos of social rank and their relationshiop with depressive symptoms and suicide risk. Journal of Affective Disorders, 246, 300-319. https://doi.org/10.1016/j.jad.2018.12.045

Wong, H. J., & Anitescu, M. (2016). The role of health locus of control in evaluating depression and other comorbidities in patients with chronic pain conditions, a cross-sectional study. Pain Practice, 17(1), 52-61. https://doi.org/10.1111/papr.12410

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