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Myostatin is a myokine which acts upon skeletal muscle to inhibit growth and regeneration. Myostatin is endogenously antagonised by follistatin. This study assessed serum myostatin and follistatin concentrations as monitoring or prognostic biomarkers in dysferlinopathy, an autosomal recessively inherited muscular dystrophy. Myostatin was quantified twice with a three-year interval in 76 patients with dysferlinopathy and 38 controls. Follistatin was quantified in 62 of these patients at the same timepoints, and in 31 controls. Correlations with motor function, muscle fat fraction and contractile cross-sectional area were performed. A regression model was used to account for confounding variables. Baseline myostatin, but not follistatin, correlated with baseline function and MRI measures. However, in individual patients, three-year change in myostatin did not correlate with functional or MRI changes. Linear modelling demonstrated that function, serum creatine kinase and C-reactive protein, but not age, were independently related to myostatin concentration. Baseline myostatin concentration predicted loss of ambulation but not rate of change of functional or MRI measures, even when relative inhibition with follistatin was considered. With adjustment for extra-muscular causes of variation, myostatin could form a surrogate measure of functional ability or muscle mass, however myostatin inhibition does not form a promising treatment target in dysferlinopathy.

[Background & Aims] Non-invasive scoring systems (NSS) are used to identify patients with non-alcoholic fatty liver disease (NAFLD) who are at risk of advanced fibrosis, but their reliability in predicting long-term outcomes for hepatic/extrahepatic complications or death and their concordance in cross-sectional and longitudinal risk stratification remain uncertain., [Methods] The most common NSS (NFS, FIB-4, BARD, APRI) and the Hepamet fibrosis score (HFS) were assessed in 1,173 European patients with NAFLD from tertiary centres. Performance for fibrosis risk stratification and for the prediction of long-term hepatic/extrahepatic events, hepatocarcinoma (HCC) and overall mortality were evaluated in terms of AUC and Harrell’s c-index. For longitudinal data, NSS-based Cox proportional hazard models were trained on the whole cohort with repeated 5-fold cross-validation, sampling for testing from the 607 patients with all NSS available., [Results] Cross-sectional analysis revealed HFS as the best performer for the identification of significant (F0-1 vs. F2-4, AUC = 0.758) and advanced (F0-2 vs. F3-4, AUC = 0.805) fibrosis, while NFS and FIB-4 showed the best performance for detecting histological cirrhosis (range AUCs 0.85-0.88). Considering longitudinal data (follow-up between 62 and 110 months), NFS and FIB-4 were the best at predicting liver-related events (c-indices>0.7), NFS for HCC (c-index = 0.9 on average), and FIB-4 and HFS for overall mortality (c-indices >0.8). All NSS showed limited performance (c-indices <0.7) for extrahepatic events., [Conclusions] Overall, NFS, HFS and FIB-4 outperformed APRI and BARD for both cross-sectional identification of fibrosis and prediction of long-term outcomes, confirming that they are useful tools for the clinical management of patients with NAFLD at increased risk of fibrosis and liver-related complications or death., [Lay summary] Non-invasive scoring systems are increasingly being used in patients with non-alcoholic fatty liver disease to identify those at risk of advanced fibrosis and hence clinical complications. Herein, we compared various non-invasive scoring systems and identified those that were best at identifying risk, as well as those that were best for the prediction of long-term outcomes, such as liver-related events, liver cancer and death.

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA – a NF-kB subunit, master regulator of the response to infection – under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes.