Abstract
BACKGROUND: Statin-associated muscle symptoms (SAMS) are a significant clinical issue, and their exact cause is not well understood. Immunological mechanisms have been suggested but have not been confirmed. This study is a rare, longitudinal case-based analysis that uses transcriptomics to explore immune-related gene expression changes in peripheral blood mononuclear cells (PBMCs) in response to exercise before, during, and after the onset and resolution of SAMS. METHODS: A healthy volunteer (HV1) enrolled in an exercise immuno-fitness study underwent cardiopulmonary exercise testing (CPX) with blood collected at three timepoints: pre-exercise (TP1), peak exercise (TP2), and 1 hour post-exercise (TP3). After baseline testing (Visit 1), the participant began statin therapy on their own, developed SAMS, and had repeat CPX testing during the symptomatic phase (Visit 2) and partial recovery phase (Visit 3). RNA was extracted from PBMCs and analyzed using next-generation RNA sequencing. The data were evaluated using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Pathway and gene ontology enrichment were used to identify immunologic signatures associated with SAMS. RESULTS: The PBMC gene expression profiles showed distinct changes during SAMS compared to the baseline and recovery phases. WGCNA identified 39 co-expression modules. Several modules had high expression at peak exercise in the healthy state (V1), which was attenuated in SAMS (V2) and partially restored in recovery (V3). Gene ontology and Reactome analyses of key modules identified 16 genes that were differentially expressed at peak exercise and may be involved in specific immune pathways in SAMS pathogenesis. CONCLUSION: This case study suggests that profiling the exercise-induced immune transcriptome can reveal dynamic immunological changes related to statin-induced myopathy. These findings support the hypothesis of an immune-mediated component in SAMS and provide a basis for future studies to validate transcriptomic biomarkers for the early detection and management of SAMS.