Genotype characterization and delayed loss of ambulation by glucocorticoids in a large cohort of patients with Duchenne muscular dystrophy

Abstract

Background: Duchenne muscular dystrophy (DMD) is the most common genetic muscle disease in human. We aimed to describe the genotype distribution in a large cohort of Chinese DMD patients and their delayed loss of ambulation by glucocorticoid (GC) treatments. This is to facilitate protocol designs and outcome measures for the emerging DMD clinical trials. Results: A total of 1163 patients with DMD were recruited and genotyped. Genotype variations were categorized as large deletions, large duplications, and small mutations. Large deletions were further analyzed for those amenable to exon-skipping therapies. Participants aged 5 years or older were grouped into GC-treated and GC-naïve groups. Clinical progression among different genotypes and their responses to GC treatments were measured by age at loss of ambulation (LOA). Among the mutation genotypes, large deletions, large duplications, and small mutations accounted for 68.79%, 7.14%, and 24.07%, respectively. The mean age at diagnosis was 4.59 years; the median ages at LOA for the GC-naïve, prednisone/prednisolone-treated, and deflazacort-treated groups were 10.23, 12.02, and 13.95 years, respectively. The “deletion amenable to skipping exon 44” subgroup and the nonsense-mutation subgroup had older ages at LOA than the “other deletions” subgroup. Subgroups were further analyzed by both genotypes and GC status. All genotypes showed significant beneficial responses to GC treatment. Deletions amenable to skipping exon 44 showed a lower hazard ratio (0.155). The mean age at death was 18.57 years in this DMD group. Conclusion: Genotype variation influences clinical progression in certain DMD groups. Beneficial responses to GC treatment were observed among all DMD genotypes. Compared with other genotypes, deletions amenable to skipping exon 44 had a lower hazard ratio, which may indicate a stronger protective effect of GC treatments on this subgroup. These data are valuable for designing future clinical trials, as clinical outcomes may be influenced by the genotypes.