Sex differences in the relationship between social support and risk for major depression: a longitudinal study of opposite-sex twin pairs
Abstract
OBJECTIVE: Compared to men, women have larger and more intimate social networks and higher rates of major depression. Prior studies have suggested that women are more sensitive to the depressogenic effects of low social support, but most of these studies had substantial methodologic limitations. METHOD: In two interview waves at least 1 year apart, 1,057 pairs of opposite-sex dizygotic twin pairs ascertained from a population-based register were assessed. The authors predicted risk of major depression in the year before the wave 2 interview from levels of social support assessed at wave 1. RESULTS: Women reported higher levels of global social support than their twin brothers. Global social support at wave 1 predicted risk for major depression at wave 2 significantly more strongly in female than in male members of these pairs, and the same effect was seen when the analysis controlled for the history of major depression in the year prior to wave 1. Women were more sensitive than men to the depressogenic effects of low levels of social support, particularly from the co-twin, other relatives, parents, and spouses. Levels of social support did not explain the sex difference in risk for major depression. CONCLUSIONS: Emotionally supportive social relationships are substantially more protective against major depression for women than for men. While these effects cannot explain sex effects on the prevalence of major depression, they do suggest important sex differences in pathways of risk. Clarification of the nature of the causal links between low social support and depression in women is needed.
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Insufficient social support and stressful life events pose a greater risk for depression among women compared with men.
“insufficient social support and stressful life events have been found to pose a greater risk for depression among women compared with men.26, 27”
From Association of depressive disorders, depression characteristics and antidepressant medication with inflammation (2012) · cites this paper as [26]
Gender and perceived social support interact to predict depression, such that only among females does perceived social support predict the disorder.
“Similarly, gender and perceived social support interact to predict depression, such that only among females does perceived social support predict the disorder.32,34”
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- Authors
- Kendler, K. S., Myers, J., & Prescott, C. A.
- Affiliations
- Virginia Commonwealth University (US)
- Venue
- American Journal of Psychiatry
- Published
- 2005-02-01
- Volume
- 162
- Issue
- 2
- Pages
- 250-256
- ISSN
- 0002-953X (ISSN-L)
- 1535-7228
- OpenAlex
- W2145197998
- Cited by
- 544
- References
- 32
- Citations by year
- 2026: 12 · 2025: 17 · 2024: 28 · 2023: 33 · 2022: 28 · 2021: 24 · 2020: 29 · 2019: 26 · 2018: 26 · 2017: 30 · 2016: 29 · 2015: 36 · 2014: 33 · 2013: 24 · 2012: 18
- FWCI
- 23.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- American Journal of Psychiatry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Alcohol Abuse and Alcoholism (AA-49492)
- National Institute of Mental Health (MH-49492, R01MH049492)
- National Institute on Drug Abuse (/DA-49492)
- Topics
- Social Sciences › Health › Health disparities and outcomes
- Social Sciences › Psychology › Clinical Psychology › Health and Well-being Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- sex differences, major depressive disorder, social support, depression risk, opposite-sex twins
- MeSH terms
- Adult, Major Depressive Disorder, Diagnosis, Computer-Assisted, Diseases in Twins, Female, Humans, Interviews as Topic, Longitudinal Studies, Male, Middle Aged, Psychiatric Status Rating Scales, Surveys and Questionnaires, Risk Factors, Sex Factors, Social Support, Sociometric Techniques, Twins, Dizygotic, Virginia, Logistic Models, Sex Distribution, Principal Component Analysis
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