Abstract

Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as latent profile analysis, are useful for the taxonomy of psychopathology, they provide little implications for targeting specific symptoms in each cluster. To overcome these limitations, we introduced Gaussian graphical mixture model (GGMM)-based clustering, a method developed in mathematical statistics to integrate clustering and network statistical approaches. To illustrate the technical details and clinical utility of the analysis, we applied GGMM-based clustering to a Japanese sample of 1,521 patients (Mage = 42.42 years), who had diagnostic labels of major depressive disorder (MDD; n = 406), panic disorder (PD; n = 198), social anxiety disorder (SAD; n = 116), obsessive-compulsive disorder (OCD; n = 66), comorbid MDD and any anxiety disorder (n = 636), or comorbid anxiety disorders (n = 99). As a result, we identified the following four transdiagnostic clusters characterized by i) strong OCD and PD symptoms, and moderate MDD and SAD symptoms; ii) moderate MDD, PD, and SAD symptoms, and weak OCD symptoms; iii) weak symptoms of all four disorders; and iv) strong symptoms of all four disorders. Simultaneously, a covariance symptom network within each cluster was visualized. The discussion highlighted that the GGMM-based clusters help us generate clinical hypotheses for transdiagnostic clusters by enabling further investigations of each symptom network, such as the calculation of centrality indexes.

Highlights

  • Forty-five percent of individuals with mental disorders suffer from comorbidity, or multiple mental disorders [1]

  • A total of 2,459 Japanese anonymous disease panelists 18 years or older participated in the survey, and their labels were as follows: major depressive disorder (MDD; n = 619), panic disorder (PD; n = 619), social anxiety disorder (SAD; n = 576), obsessive-compulsive disorder (OCD; n = 645)

  • The results of a series of confirmatory factor analyses on symptom measures were detailed in the R Markdown file on Open Science Framework (OSF), and the factor loadings obtained were used to calculate factor scores of MDD, PD, SAD, and OCD symptoms

Read more

Summary

Introduction

Forty-five percent of individuals with mental disorders suffer from comorbidity, or multiple mental disorders [1]. Comorbidity predicts poorer prognosis and healthrelated quality of life [2, 3]. There is a substantial clinical need to establish treatment guidelines. GGMM-based clustering of depression and anxiety https://osf.io/6jnf4/), along with the preregistered data analysis plan and the R script

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call