Multiple risk factors predict recurrence of major depressive disorder in women
Open accessAbstract
Background It is difficult to predict recurrence of depressive episodes in patients with major depression (MD): evidence for many risk factors is inconsistent and general prediction algorithms are lacking. The aim of this study was to develop a prediction model for recurrence of depressive episodes in women using improved methodology.Methods We used prospective data from a general population sample of female twins with a last-year MD episode (n=194). A rich set of baseline predictors was analyzed with Cox proportional hazards regression subject to elastic net regularization to find a model predicting recurrence of depressive episodes. Prediction accuracy of the model was assessed in an independent test sample (n=133), which was limited by the unavailability of a number of key predictors.Results A wide variety of risk factors predicted recurrence of depressive episodes in women: depressive and anxiety symptoms during the index episode, the level of symptoms at the moment of interview, psychiatric and family history, early and recent adverse life events, being unmarried, and problems with friends and finances. Kaplan Meier estimated survival curves showed that the model differentiated between patients at higher and lower risk for recurrence; estimated areas under the curve were in the range of 0.61-0.79.Limitations Despite our rich set of predictors, certain potentially relevant variables were not available, such as biological measures, chronic somatic diseases, and treatment status.Conclusions Recurrence of episodes of MD in women is highly multifactorial. Future studies should take this into account for the development of clinically useful prediction algorithms.
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Prior research has investigated a limited range of individual predictors, many of which are related to demographics and clinical history.
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- Authors
- van Loo, H. M., Aggen, S. H., Gardner, C. O., & Kendler, K. S.
- Affiliations
- Virginia Commonwealth University (US)
- Venue
- Journal of Affective Disorders
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 180
- Pages
- 52-61
- ISSN
- 0165-0327 (ISSN-L)
- PMC
- PMC4504430
- OpenAlex
- W2128173991
- Cited by
- 45
- References
- 57
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 7 · 2020: 4 · 2019: 4 · 2018: 7 · 2017: 5 · 2016: 2
- FWCI
- 3.37
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Affective Disorders (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Funders
- National Institutes of Health (UL1RR031990)
- Topics
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- major depressive disorder, depression recurrence, women, risk factors, prediction model, twin cohort, depressive symptoms, anxiety symptoms, life events, marital status, family history (medicine), psychiatric history, financial problems, social problems
- MeSH terms
- Adult, Chronic Disease, Major Depressive Disorder, Depressive Disorder, Major, Female, Humans, Interpersonal Relations, Life Change Events, Middle Aged, Predictive Value of Tests, Prospective Studies, Recurrence, Research Design, Risk Factors, Severity of Illness Index, Socioeconomic Factors, Models, Statistical, Proportional Hazards Models, Marital Status, Risk Assessment, Kaplan-Meier Estimate
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