Clinical phenotypes, outcomes, and myocardial transcriptomic signatures in inflammatory myocardial disease: A prospective cohort study

Background Inflammatory myocardial disease encompasses diverse clinical phenotypes, from subtle symptoms to fulminant heart failure. Differentiating myocarditis, cardiac sarcoidosis (CS), and non-confirmed cardiomyopathy (NC) remains diagnostically and prognostically challenging. We compared clinical presentation, outcomes, and myocardial transcriptomic profiles across these groups. Methods In this prospective cohort study, 218 adults undergoing evaluation for cardiomyopathy were classified as myocarditis ( n = 81), CS ( n = 84), or NC ( n = 53). The primary composite outcome was all-cause mortality, mechanical circulatory support, or heart transplantation. RNA sequencing of endomyocardial biopsies was performed in 53 patients with differential expression analysis adjusted for age, RNA integrity number, and systolic blood pressure. Results Patients with myocarditis were younger than those with CS or NC (34.7 ± 15.8 vs 57.4 ± 8.7 vs 57.6 ± 14.0 years); 70–75% were male. Myocarditis was characterized by chest pain and elevated inflammatory biomarkers, whereas CS was associated with arrhythmias and right ventricular dysfunction. Although unadjusted Kaplan–Meier analysis suggested differences in event-free survival, adjusted Cox regression showed no significant between-group differences (myocarditis vs NC: HR 0.99, 95% CI 0.22–4.43, p = 0.985; CS vs NC: HR 1.67, 95% CI 0.63–4.43, p = 0.307). Transcriptomic profiling revealed a more pronounced inflammatory gene expression profile in myocarditis, whereas CS showed limited differential expression and substantial overlap with NC. Conclusions Inflammatory myocardial disease shows distinct clinical presentations, while adjusted analyses did not demonstrate significant differences in long-term outcomes. Myocarditis showed a stronger inflammatory transcriptional signature, whereas CS exhibited limited transcriptomic differences. These findings support further investigation of RNA-based profiling as a complementary tool.