Taxonicity and network structure of generalized anxiety disorder and major depressive disorder: An admixture analysis and complex network analysis

Background: Recent years have witnessed sustained efforts to delineate the nosology of generalized anxiety disorder (GAD), especially in light of its substantial comorbidity with major depressive disorder (MDD). Traditional diagnostic conceptualizations regard these disorders as categorically distinct; however, extant literature attests to appreciable similarities. The application of admixture analyses and complex network analyses has become more prevalent in recent years to investigate the presence of meaningful subgroups in mental disorders and to address qualitative similarity in network structure across disorders. To date, no studies have extended these analytic techniques to determine whether GAD and MDD constitute independent syndromes. The current study used a clinical sample comprising individuals diagnosed with primary GAD or primary MDD to examine potential subgroups and network structure using symptoms of each disorder as indicators. Methods: The current sample comprised 111 individuals who received primary diagnoses of either GAD or MDD and completed a battery of assessments related to anxiety and depression. Results: Results of the admixture analyses converged on a single class solution, suggesting that individuals with GAD derive from the same population as those with MDD. Furthermore, results of the complex network analyses did not reveal differences in centrality parameters across disorders, suggesting qualitative similarity. Limitations: The cross-sectional nature of this study precludes conclusions about the temporal and causal dynamics of these disorders. Conclusion: GAD and MDD exhibit robust similarities, as evidenced by the converging results of the admixture analyses and complex network analyses. This conclusion complements the findings of transdiagnostic research, which has identified common mechanisms underlying multiple emotional disorders. (C) 2016 Elsevier B.V. All rights reserved.