To Örebro University

Background: Idiopathic inflammatory myopathies (IIM) encompass a group of disorders characterized by muscle inflammation, weakness, and varying levels of systemic involvement. These conditions profoundly impact patients’ quality of life (QoL) due to their chronic nature, physical impairments, and accompanying mental health challenges. Recent literature has highlighted the prevalence of comorbidities in IIM, with cardiovascular damage and depression being particularly prominent and significantly influencing on patient’s QoL.

Objectives: This study aims to explore the influence of mental health disorders (MHDs) on the QoL of in patients with IIM.

Methods: We examined the prevalence of MHDs in IIM adult patients using the COVAD-2 data. Mental Health Multimorbidity (MHM) was defined as the presence of ≥2 MHDs. PROMIS global physical health (PGP), mental health (PMH), fatigue 4a (F4a), and physical function (SF10) were analyzed using descriptive statistics and linear regression. Hierarchical Clustering on Principal Components was performed to delineate the grouping of IIM patients.

Results: Of the 10,740 COVAD participants, 1,523 reported a diagnosis of IIM. The IIM cohort included 470 (30.8%) patients with dermatomyositis (DM), 217 (14.2%) with polymyositis (PM), 375 (24.6%) with Inclusion body inclusion myositis (IBM), 103 (6.7%) with antisynthetase syndrome (ASS), 96 (6.3%) with immune-mediated necrotizing myopathy (IMNM), and 239 (15.6%) with overlap myositis (OM), while the remaining patients were considered “unclassified myositis.” The demographics and characteristics of the study cohort are shown in Table 1A. MHD was present in 34% of the IIM patients, predominantly manifesting as anxiety, with no significant difference across subgroups, except for insomnia, which was notably more prevalent in the OM subgroup (P=0.001).No significant differences were found in the distribution of MHM across the subgroups. It was also noted that patients with MHDs exhibited poorer physical function, as evidenced by lower PGP, PGM, and SF10 scores, and higher F4a scores (all P<0.001). Furthermore, the presence of MHM and active disease were significant predictors of all assessed PROMIS domains (all P<0.001). Age and gender were not found to be associated with physical function scores.Three distinct clusters were identified (Table 2A). Cluster 1 appears to be characterized by patients without a significant multimorbidity burden, generally healthier scores, and an older average age. Cluster 2, with the youngest average age, shows adequate health scores and fewer multimorbidities. Cluster 3, with both single and multiple morbidities, exhibits more mental health issues, poorer health scores, and a middle-range average age. In Cluster 1, DM and IBM were the most common subtypes, whereas OM was predominant in Cluster 2. In Cluster 3, the distribution was more varied, but DM and OM were relatively more common than other subtypes.

Conclusion: Our study indicates that patients with a multitude of coexisting conditions and MHDs tend to report poorer outcomes in various aspects of health, such as overall physical and mental health, physical functioning, and fatigue, especially in certain demographic with DM or OM. Recognizing the complex needs of these patients, our findings suggest a need for tailored care approaches. These may encompass specialized medical services or prioritized follow-up strategies to minimize treatment delays. Additionally, we must be cognizant of the increased demand these patients may place on healthcare resources.