Major depressive disorder subtypes to predict long-term course

Hanna M. Van Loo, Tianxi Cai, Michael J. Gruber, Junlong Li, Peter De Jonge, Maria Petukhova, Sherri Rose, Nancy A. Sampson, Robert A. Schoevers, Klaas J. Wardenaar, Marsha A. Wilcox, Ali Obaid Al-Hamzawi, Laura Helena Andrade, Evelyn J. Bromet, Brendan Bunting, John Fayyad, Silvia E. Florescu, Oye Gureje, Chiyi Hu, Yueqin HuangDaphna Levinson, Maria Elena Medina-Mora, Yoshibumi Nakane, Jose Posada-Villa, Kate M. Scott, Miguel Xavier, Zahari Zarkov, Ronald C. Kessler

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

Abstract

Background: Variation in the course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here.

Methods: Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs), followed by k-means cluster analysis are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode, number of years with an episode lasting most of the year) and severity (hospitalization for MDD, disability due toMDD).

Results: Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity, and it was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor.

Conclusions: Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data.

Original languageEnglish
Pages (from-to)765-777
Number of pages13
JournalDepression and Anxiety
Volume31
Issue number9
DOIs
Publication statusPublished - 2014

Keywords

  • Anxiety/anxiety disorders
  • Depression
  • Epidemiology
  • Panic attacks
  • Suicide/selfharm

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