Blood biomarkers identified with multi-omics combined with clinical and muscle imaging in oculopharyngeal muscular dystrophy

Abstract

Oculopharyngeal muscular dystrophy (OPMD) is an adult-onset, slowly progressive muscular dystrophy characterized by dysphagia, eyelid ptosis, and proximal limb weakness. Clinical trial readiness in OPMD requires development of sensitive indicators of disease progression and potential response to therapies in development. We used an untargeted multi-omics approach to identify circulating proteins, metabolites, and methylated DNA that could distinguish OPMD ( n  = 31; age 62.5 ± 10.9 years; 13 GCN repeats in PABPN1 ; 48% female) from controls ( n  = 17; age 57.2 ± 11.6 years; 71% female). Identified proteins and metabolites were evaluated against established clinical outcome measures, including muscle fat fraction in magnetic resonance imaging. Machine learning algorithms were used to assess the ability of the identified proteins and metabolites to distinguish OPMD from controls and predict clinical severity. A set of 33 highly correlated proteins (e.g., MYL11, TNNI2, MYL3) were elevated in OPMD compared to controls, and their abundance correlated strongly with clinical features including time with limb weakness (mean Pearson’s r  = 0.62) and soleus fat fraction (mean Pearson’s r  = 0.69). OPMD differed from controls on 49 metabolites (e.g., carnitine, cystine, creatine, arginine), 42 of which were highly intercorrelated and moderately correlated with time with limb weakness (mean Pearson’s r  = 0.39). Genome-wide DNA methylation analysis revealed a distinct profile for OPMD compared to controls. Machine learning revealed greater predictive power of proteins or metabolites in combination relative to single proteins or metabolites. These initial findings provide insight into disease processes of OPMD and can inform future longitudinal studies aimed at identifying biomarkers of OPMD progression and prognosis. Not applicable.