Can network analysis shed light on predictors of lithium response in bipolar I disorder?. (15th March 2020)

• Record Type: Journal Article
• Authors: Scott, J.; Bellivier, F.; Manchia, M.; Schulze, T.; Alda, M.; Etain, B.
• Other Names: Cervantes Pablo investigator.; Garnham Julie investigator.; Nunes Abraham investigator.; O'Donovan Claire investigator.; Slaney Claire investigator.; Bauer Michael investigator.; Pfennig Andrea investigator.; Reif Andreas investigator.; Kittel‐Schneider Sarah investigator.; Veeh Julia investigator.; Zompo Maria del investigator.; Ardau Raffaella investigator.; Chillotti Caterina investigator.; Severino Giovanni investigator.; Kato Tadafumi investigator.; Ozaki Norio investigator.; Kusumi Ichiro investigator.; Hashimoto Ryota investigator.; Akiyama Kazufumi investigator.; Kelso John investigator.
• Is Part Of: Acta psychiatrica Scandinavica. Volume 141:Number 6(2020)
• Page Start: 522
• Page End: 533
• Publication Date: 2020-03-15
• Subjects: lithium response -- phenotype -- predictors -- network analysis; Psychiatry -- Periodicals; 616.89
• DOI: 10.1111/acps.13163
• Languages: English
• ISSNs: 0001-690X

Abstract

Abstract : Objective: To undertake a large‐scale clinical study of predictors of lithium (Li) response in bipolar I disorder (BD‐I) and apply contemporary multivariate approaches to account for inter‐relationships between putative predictors. Methods: We used network analysis to estimate the number and strength of connections between potential predictors of good Li response (measured by a new scoring algorithm for the Retrospective Assessment of Response to Lithium Scale) in 900 individuals with BD‐I recruited to the Consortium of Lithium Genetics. Results: After accounting for co‐associations between potential predictors, the most important factors associated with the good Li response phenotype were panic disorder, manic predominant polarity, manic first episode, age at onset between 15–32 years and family history of BD. Factors most strongly linked to poor outcome were comorbid obsessive–compulsive disorder, alcohol and/or substance misuse, and/or psychosis (symptoms or syndromes). Conclusions: Network analysis can offer important additional insights to prospective studies of predictors of Li treatment outcomes. It appears to especially help in further clarifying the role of family history of BD (i.e. its direct and indirect associations) and highlighting the positive and negative associations of different subtypes of anxiety disorders with Li response, particularly the little‐known negative association between Li response and obsessive–compulsive disorder.