- Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk
- Journal of Affective Disorders
- Pages (from-to)
- Document type
- Faculty of Social and Behavioural Sciences (FMG)
- Psychology Research Institute (PsyRes)
BACKGROUND: Genetic risk and environmental adversity-both important risk factors for major depression (MD)-are thought to differentially impact on depressive symptom types and associations. Does heterogeneity in these risk factors result in different depressive symptom networks in patients with MD?
METHODS: A clinical sample of 5784 Han Chinese women with recurrent MD were interviewed about their depressive symptoms during their lifetime worst episode of MD. The cases were classified into subgroups based on their genetic risk for MD (family history, polygenic risk score, early age at onset) and severe adversity (childhood sexual abuse, stressful life events). Differences in MD symptom network structure were statistically examined for these subgroups using permutation-based network comparison tests.
RESULTS: Although significant differences in symptom endorsement rates were seen in 18.8% of group comparisons, associations between depressive symptoms were similar across the different subgroups of genetic and environmental risk. Network comparison tests showed no significant differences in network strength, structure, or specific edges (P-value > 0.05) and correlations between edges were strong (0.60-0.71).
LIMITATIONS: This study analyzed depressive symptoms retrospectively reported by severely depressed women using novel statistical methods. Future studies are warranted to investigate whether similar findings hold in prospective longitudinal data, less severely depressed patients, and men.
CONCLUSIONS: Similar depressive symptom networks for MD patients with a higher or lower genetic or environmental risk suggest that differences in these etiological influences may produce similar symptom networks downstream for severely depressed women.
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- Copyright © 2017. Published by Elsevier B.V.
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