Subtyping depression in the rheumatic diseases by cluster analysis

Major depressive disorder (MDD) and rheumatic diseases (RD) interact to exacerbate disease outcomes. The purpose of this study was to assess the prevalence and associated factors of depression in RD patients in order to identify independent predictors of mental health disorders risk and apply cluster analysis to identify homogeneous groups in a population of approximately 47 patients with RD-MDD to achieve precise treatment and early prevention of complications.

In total, 205 RD patients were included in this study. We used the Profile of Mood State (POMS) and Patient Health Questionnaire-9 (PHQ-9) to assess the patients' state of mind. A cluster analysis was applied according to six clinical and serological variables to define different subgroups of patients.

The rate of depression in RD patients in our study was 22.9%. Sex (female), disease duration, and disease activity are risk factors for the development of depression. Albumin is a protective factor for MDD. RD-MDD patients were clustered in two groups. Cluster 1 (n = 30, 63.8%): patients were of older age, lower education and income levels, low disease activity, and mild depressive symptoms. Cluster 2 (n = 17, 36.2%): Young women with higher education and income levels, high disease activity, and more severe depressive symptoms.

Our findings provide evidence indicating that RD-MDD presents varying clinical phenotypes and the treatment varies accordingly, suggesting the need for individualized treatment. Key Points • Depression is often comorbid in patients with rheumatic diseases. The two interact and aggravate the patient's condition. • The rate of depression in RD patients in our study was 22.9%. Sex (female), disease duration, and disease activity are risk factors for the development of depression. Albumin is a protective factor for MDD. • RD-MDD patients were clustered in two groups through cluster analysis in order to guide individualized treatment.