Subtyping depression in the rheumatic diseases by cluster analysis.

Area of Science:

• Rheumatology
• Psychiatry
• Clinical Medicine

Background:

• Major depressive disorder (MDD) and rheumatic diseases (RD) frequently coexist, negatively impacting patient outcomes.
• Understanding the interplay between RD and MDD is crucial for effective management.

Purpose of the Study:

• To determine the prevalence and associated factors of depression in patients with rheumatic diseases.
• To identify predictors of mental health disorders in RD patients.
• To apply cluster analysis to stratify RD patients with MDD for tailored interventions.

Main Methods:

• A cohort of 205 patients with rheumatic diseases was assessed.
• The Profile of Mood State (POMS) and Patient Health Questionnaire-9 (PHQ-9) were used to evaluate depressive symptoms.
• Cluster analysis based on clinical and serological data identified patient subgroups.

Main Results:

• The prevalence of depression among RD patients was 22.9%.
• Risk factors for depression included female sex, longer disease duration, and higher disease activity; albumin levels showed a protective effect.
• Cluster analysis identified two distinct RD-MDD patient groups: one older with milder symptoms and lower socioeconomic status, and another younger, female-dominated group with higher disease activity and more severe depression.

Conclusions:

• Rheumatic disease with comorbid depression exhibits diverse clinical phenotypes, necessitating individualized treatment approaches.
• Identifying distinct patient clusters aids in the precise management and early prevention of complications in RD-MDD.
• The study highlights the importance of considering mental health in rheumatic disease care.