title: Major depressive disorder subtypes to predict long-term course.
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 to MDD).
  
  
  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.
authors.affiliation: Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
authors.forename: Hanna M
authors.initials: HM
authors.lastname: van Loo
authors.forename: Tianxi
authors.initials: T
authors.lastname: Cai
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Gruber
authors.forename: Junlong
authors.initials: J
authors.lastname: Li
authors.forename: Peter
authors.initials: P
authors.lastname: de Jonge
authors.forename: Maria
authors.initials: M
authors.lastname: Petukhova
authors.forename: Sherri
authors.initials: S
authors.lastname: Rose
authors.forename: Nancy A
authors.initials: NA
authors.lastname: Sampson
authors.forename: Robert A
authors.initials: RA
authors.lastname: Schoevers
authors.forename: Klaas J
authors.initials: KJ
authors.lastname: Wardenaar
authors.forename: Marsha A
authors.initials: MA
authors.lastname: Wilcox
authors.forename: Ali Obaid
authors.initials: AO
authors.lastname: Al-Hamzawi
authors.forename: Laura Helena
authors.initials: LH
authors.lastname: Andrade
authors.forename: Evelyn J
authors.initials: EJ
authors.lastname: Bromet
authors.forename: Brendan
authors.initials: B
authors.lastname: Bunting
authors.forename: John
authors.initials: J
authors.lastname: Fayyad
authors.forename: Silvia E
authors.initials: SE
authors.lastname: Florescu
authors.forename: Oye
authors.initials: O
authors.lastname: Gureje
authors.forename: Chiyi
authors.initials: C
authors.lastname: Hu
authors.forename: Yueqin
authors.initials: Y
authors.lastname: Huang
authors.forename: Daphna
authors.initials: D
authors.lastname: Levinson
authors.forename: Maria Elena
authors.initials: ME
authors.lastname: Medina-Mora
authors.forename: Yoshibumi
authors.initials: Y
authors.lastname: Nakane
authors.forename: Jose
authors.initials: J
authors.lastname: Posada-Villa
authors.forename: Kate M
authors.initials: KM
authors.lastname: Scott
authors.forename: Miguel
authors.initials: M
authors.lastname: Xavier
authors.forename: Zahari
authors.initials: Z
authors.lastname: Zarkov
authors.forename: Ronald C
authors.initials: RC
authors.lastname: Kessler
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000569:Americas / Q000453:epidemiology; D001208:Asia / Q000453:epidemiology; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D003865:Major Depressive Disorder / Q000145:classification* / Q000453:epidemiology; D005060:Europe / Q000453:epidemiology; D006801:Humans; D008875:Middle Aged; D009520:New Zealand / Q000453:epidemiology; D009549:Nigeria / Q000453:epidemiology; D011379:Prognosis; D012720:Severity of Illness Index; D055815:Young Adult
keywords: anxiety/anxiety disorders; depression; epidemiology; panic attacks; suicide/self-harm