Natural history of circulating miRNAs in Duchenne disease: Association with muscle injury and metabolic parameters

The Role of MicroRNA in the Pathogenesis of Duchenne Muscular Dystrophy

Duchenne muscular dystrophy (DMD) is an X-linked progressive disorder associated with muscle wasting and degeneration. The disease is caused by mutations in the gene that encodes dystrophin, a protein that links the cytoskeleton with cell membrane proteins. The current treatment methods aim to relieve the symptoms of the disease or partially rescue muscle functionality. However, they are insufficient to suppress disease progression. In recent years, studies have uncovered an important role for non-coding RNAs (ncRNAs) in regulating the progression of numerous diseases. ncRNAs, such as micro-RNAs (miRNAs), bind to their target messenger RNAs (mRNAs) to suppress translation. Understanding the mechanisms involving dysregulated miRNAs can improve diagnosis and suggest novel treatment methods for patients with DMD. This review presents the available evidence on the role of altered expression of miRNAs in the pathogenesis of DMD. We discuss the involvement of these molecules in the processes associated with muscle physiology and DMD-associated cardiomyopathy.

Circulating Nrf2, Glutathione, and Malondialdehyde Correlate with Disease Severity in Duchenne Muscular Dystrophy

Oxidative stress (OS) plays an essential role in the pathophysiology of Duchenne muscular dystrophy (DMD). However, the actors that regulate OS need to be better studied. We aimed to evaluate whether NFE2-like bZIP transcription factor 2 (Nrf2), glutathione, malondialdehyde (MDA), and protein carbonyl concentrations change according to the disease severity in DMD patients. Moreover, we assessed whether OS correlated with muscle injury, clinical characteristics, physical activity, and antioxidant food consumption (AFC). A total of 28 DMD patients participated in this study. OS markers, metabolic indicators, and enzymatic markers of muscle injury were measured in circulation. Muscle injury was measured with clinical scales, and physical activity and AFC were evaluated with questionnaires. Nrf2 concentration was lower (p ≤ 0.01), and malondialdehyde concentration was higher (p < 0.05) in non-ambulatory patients than in ambulatory patients. Nrf2 correlated with age (rho = -0.387), Vignos scale (rho = -0.328), GMFCS scale (rho = -0.399), and Brooke scale scores (rho = -0.371) (p < 0.05). MDA correlated with Vignos (rho = 0.317) and Brooke scale scores (rho = 0.414) (p ≤ 0.05). In conclusion, DMD patients with the worst muscle function had more significant oxidative damage and lower antioxidant function than DMD patients with better muscle function.

A Predictive Model to Evaluate the HbeAg Positivity of Chronic Hepatitis B Virus Patients in Clinics: A Cross-Sectional Study

Background and Objective: The study aims to investigate the correlation between Hepatitis B ‘e’ antigen (HBeAg) and HBV DNA levels, and to find a convenient tool to estimate the HBV DNA level for clinicians. Materials and Methods: We enrolled 1020 patients in this cross-sectional study and divided them into four groups: an HbeAg-positive and -negative group, and high and low HBV DNA levels groups. Results: Alanine aminotransferase (ALT), Albumin (ALB) and HBeAg are independent risk factors for CHB patients. When the level of HBeAg is higher than 16.15 S/CO, it is four times more likely that the patients will have high levels of HBV DNA than those who do not. The ALT and TB are independent risk factors in HBeAg-negative patients with a high HBV DNA level. We have drawn three predictive models to estimate the HBV DNA levels for those with the chronic hepatitis B virus (CHB), and those that are HBeAg-positive and HBeAg-negative (Y1 = 0.004 × ALT(IU/L) + 1.412 × HBeAg (S/CO) − 0.029 × ALB (g/L) + 0.779, the AUC is 0.672, and the cutoff value is −0.072, there the sensitivity is 0.615, the specificity is 0.648, PPV is 65.182% and NPV is 60.837%; Y2 = 0.007 × HBeAg (S/CO) − 0.016 × HGB (g/L) + 3.070, the AUC is 0.724, and the cutoff value is 1.216, where the sensitivity is 0.626, the specificity is 0.897, PPV is 94.118% and NPV is 34.437%; Y3 = −0.005 × ALT(IU/L) + 0.006 × TB (umol/L) + 0.385, the AUC is 0.661, and the cutoff value is 0.263, where the sensitivity is 0.677, the specificity is 0.587, PPV is 66.820% and NPV is 40.774%, respectively). We propose that HBeAg is the most important risk factor for the patient with a high HBV DNA level, however, it is not as important in the HBeAg-positive group. Conclusions: HBeAg is an independent risk factor that reflects the level of HBV DNA with a strong correlation. Patient with HBeAg (−) should combine TB and ALT to estimate the level of HBV DNA.