Can network analysis shed light on predictors of lithium response in bipolar I disorder?

Abstract

investigators involved in the ConLiGen collaboration. Can network analysis shed light on predictors of lithium response in bipolar I disorder?.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 interrelationships 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.• Using network modelling may help clarify predictors of lithium response and understand any differences associated with two commonly employed definitions of outcome: change in illness activity or change in illness activity after consideration of potential cofounders.• The network model highlighted that whilst family history of bipolar disorders has a weak direct connection with good lithium response, it is a highly influential node in the network and is linked to other predictors such as age at onset of illness.• This study identified that obsessive-compulsive disorder was one of the most robust predictors of poor lithium response, a finding that may have been obscured in previous studies due to lack of statistical power of other analytic approaches. Limitations• We focused only on bipolar I disorder, measured treatment outcome using only one rating scale and used a strict definition good response to lithium. Using these approaches, the overall rate of good outcome was about 14%.• The original data set available for this study lacks information on some factors previously identified as being associated with good response to lithium.• We created a binary network model, which may have over-or under-estimated some co-associations between putative predictors. As such, findings will need replicating in another large-scale, independent data set.

Clinical correlates of seasonality in bipolar disorder: A specifier that needs specification?

“…Seasonality-related aspects were recollected as further specified. Predominant polarity was defined according to the standard definition created in our Unit and repeatedly validated [32][33][34][35] : depressive or manic predominant polarity was defined based on, respectively, depressions or mania representing two-thirds of the total affective relapses. A family history of mood disorders was defined as having a first-degree relative diagnosed with and/or treated for any mood disorder, including major depression, cyclothymia, and dysthymia.…”

Metacognition and emotion regulation as treatment targets in binge eating disorder: a network analysis study

“…Development of the NA approach over the past decade has provided a theoretical framework that was adopted to identify the central symptoms of different psychiatric disorders, such as bipolar disorder [15], depression [16], obsessive compulsive disorder [17] and schizophrenia [18]. More recently, researchers in the field of eating disorders have applied NA to examine the symptoms of anorexia nervosa [19][20][21][22] and bulimia nervosa [23][24][25].…”

Pharmacogenomics of Lithium Response in Bipolar Disorder

“…Pharmacogenetic studies are attempting to identify response or tolerability biomarkers, but the process of cherry-picking candidate genes has been proven not to be ideal considering the size of the human genome [2]. Favorable response is more likely for patients with a BD-I diagnosis, few comorbidities, manic/hypomanic-depression-euthymic interval cycle pattern, early age of symptom onset and treatment onset, family history of BD, and adequate drug adherence [8][9][10]. Familial clusters may be a prognosticator for recurrent mood episodes due to patients with good lithium response tending to cluster in families, but alone this datum holds little weight [11].…”

“…Figure 2. Genetic, Biological, and Clinical Predictors of Lithium Response in Bipolar Disorder[5,[8][9][10][11][15][16][17][18][19][20][21]40,44,[49][50][51][52][53][54][55][56][57][58][59][60][61][62].…”