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A longitudinal, integrated, clinical, histological and mRNA profiling study of resistance exercise in myositis.

ABSTRACT: Polymyositis and dermatomyositis are orphan, chronic skeletal muscle disorders characterized by weakness, infiltrations by mononuclear inflammatory cells, and fibrosis. Until recently, patients were advised to refrain from physical activity because of fears of exacerbation of muscle inflammation. However, recent studies have shown that moderate exercise training in combination with immunosuppressive drugs can improve muscle performance. Despite the positive effects of exercise training, the molecular mechanisms underlying the exercise-associated clinical improvements remain poorly understood. The present study was designed to define, at the molecular level, the effects of resistance exercise training on muscle performance and disease progression in myositis patients. We evaluated changes in muscle strength, histology and genome-wide mRNA profiles to determine the beneficial effects of exercise and determine the possible molecular changes associated with improved muscle performance. A total of 8 myositis patients underwent a 7-wk resistance exercise training program that resulted in improved muscle strength and increased maximal oxygen uptake (VO(2max)). Training also resulted in marked reductions in gene expression, reflecting reductions in proinflammatory and profibrotic gene networks, changes that were also accompanied by a reduction in tissue fibrosis. Consistent with the exercise-associated increase in VO(2max), a subset of transcripts was associated with a shift toward oxidative metabolism. The changes in gene expression reported in the present study are in agreement with the performance improvements induced by exercise and suggest that resistance exercise training can induce a reduction in inflammation and fibrosis in skeletal muscle.

SUBMITTER: Nader GA

PROVIDER: S-EPMC2972393 | biostudies-literature | 2010 Nov-Dec

REPOSITORIES: biostudies-literature

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A longitudinal, integrated, clinical, histological and mRNA profiling study of resistance exercise in myositis.

Nader Gustavo A GA Dastmalchi Maryam M Alexanderson Helene H Grundtman Cecilia C Gernapudi Ramkishore R Esbjörnsson Mona M Wang Zuyi Z Rönnelid Johan J Hoffman Eric P EP Nagaraju Kanneboyina K Lundberg Ingrid E IE

Molecular medicine (Cambridge, Mass.) 20100816 11-12

Polymyositis and dermatomyositis are orphan, chronic skeletal muscle disorders characterized by weakness, infiltrations by mononuclear inflammatory cells, and fibrosis. Until recently, patients were advised to refrain from physical activity because of fears of exacerbation of muscle inflammation. However, recent studies have shown that moderate exercise training in combination with immunosuppressive drugs can improve muscle performance. Despite the positive effects of exercise training, the mole ...[more]

PMID: 20809047

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Project description:BackgroundAnti-TIF1γ antibodies are a class of myositis-specific antibodies (MSAs) and are closely associated with adult cancer-associated myositis (CAM). The heterogeneity in anti-TIF1γ+ myositis is poorly explored, and whether anti-TIF1γ+ patients will develop cancer or not is unknown at their first diagnosis. Here, we aimed to explore the subtypes of anti-TIF1γ+ myositis and construct machine learning classifiers to predict cancer in anti-TIF1γ+ patients based on clinical features.MethodsA cohort of 87 anti-TIF1γ+ patients were enrolled and followed up in Xiangya Hospital from June 2017 to June 2021. Sankey diagrams indicating temporal relationships between anti-TIF1γ+ myositis and cancer were plotted. Elastic net and random forest were used to select and rank the most important variables. Multidimensional scaling (MDS) plot and hierarchical cluster analysis were performed to identify subtypes of anti-TIF1γ+ myositis. The clinical characteristics were compared among subtypes of anti-TIF1γ+ patients. Machine learning classifiers were constructed to predict cancer in anti-TIF1γ+ myositis, the accuracy of which was evaluated by receiver operating characteristic (ROC) curves.ResultsForty-seven (54.0%) anti-TIF1γ+ patients had cancer, 78.7% of which were diagnosed within 0.5 years of the myositis diagnosis. Fourteen variables contributing most to distinguishing cancer and non-cancer were selected and used for the calculation of the similarities (proximities) of samples and the construction of machine learning classifiers. The top 10 were disease duration, percentage of lymphocytes (L%), percentage of neutrophils (N%), neutrophil-to-lymphocyte ratio (NLR), sex, C-reactive protein (CRP), shawl sign, arthritis/arthralgia, V-neck sign, and anti-PM-Scl75 antibodies. Anti-TIF1γ+ myositis patients can be clearly separated into three clinical subtypes, which correspond to patients with low, intermediate, and high cancer risk, respectively. Machine learning classifiers [random forest, support vector machines (SVM), extreme gradient boosting (XGBoost), elastic net, and decision tree] had good predictions for cancer in anti-TIF1γ+ myositis patients. In particular, the prediction accuracy of random forest was >90%, and decision tree highlighted disease duration, NLR, and CRP as critical clinical parameters for recognizing cancer patients.ConclusionAnti-TIF1γ+ myositis can be separated into three distinct subtypes with low, intermediate, and high risk of cancer. Machine learning classifiers constructed with clinical characteristics have favorable performance in predicting cancer in anti-TIF1γ+ myositis, which can help physicians in choosing appropriate cancer screening programs.

Project description:BackgroundIdiopathic inflammatory myopathies (IIM) are a heterogenous group of rare muscular autoimmune diseases characterised by skeletal muscle inflammation with possible diagnostic delay. Our aim was to review the existing evidence to identify overall diagnostic delay for IIM, factors associated with diagnostic delay, and people's experiences of diagnostic delay.MethodsThree databases and grey literature sources were searched. Diagnostic delay was defined as the period between the onset of symptoms and the year of first diagnosis of IIM. We pooled the mean delay using random effects inverse variance meta-analysis and performed subgroup analyses.Results328 titles were identified from which 27 studies were included. Overall mean diagnostic delay was 27.91 months (95% CI 15.03-40.79, I2 = 99%). Subgroup analyses revealed a difference in diagnostic delay between non-inclusion body myositis (IBM) and IBM types. There was no difference in diagnostic delay between studies in which myositis specific autoantibodies (MSA) were tested or not tested. In countries with gatekeeper health systems, where primary care clinicians authorize access to specialty care, people experienced longer periods of diagnostic delay than people with IIM in countries with non-gatekeeper systems. While studies discussed factors that may influence diagnostic delay, significant associations were not identified. No qualitative studies examining people's experiences of diagnostic delay were identified.ConclusionDiagnostic delay of IIM has extensive impacts on the quality of life of people living with this disease. Understanding the experiences of people with IIM, from symptom onset to diagnosis, and factors that influence diagnostic delay is critical to inform clinical practice and training activities aimed at increasing awareness of this rare disease and expediting diagnosis.Trial registrationPROSPERO Registration number: CRD42022307236 URL of the PROSPERO registration: https://www.crd.york.ac.uk/PROSPEROFILES/307236_PROTOCOL_20220127.pdf.

Project description:ObjectiveTo identify changes in skeletal muscle microRNA expression after endurance exercise and associate the identified microRNAs with mRNA and protein expression to disease-specific pathways in polymyositis (PM) and dermatomyositis (DM) patients.MethodsFollowing a parallel clinical trial design, patients with probable PM or DM, exercising less than once a week, and on stable medication for at least one month were randomized into two groups at Karolinska University Hospital: a 12-week endurance exercise group (n = 12) or a non-exercised control group (n = 11). Using an Affymetrix microarray, microRNA expression was determined in paired muscle biopsies taken before and after the exercise intervention from 3 patients in each group. Ingenuity pathway analysis with a microRNA target filter was used to identify microRNA transcript targets. These targets were investigated at the mRNA (microarray) and protein (mass spectrometry) levels in patients.ResultsEndurance exercise altered 39 microRNAs. The microRNAs with increased expression were predicted to target transcripts involved in inflammatory processes, metabolism, and muscle atrophy. Further, these target transcripts had an associated decrease in mRNA expression in exercised patients. In particular, a decrease in the NF-κB regulator IKBKB was associated with an increase in its target microRNA (miR-196b). At the protein level, there was an increase in mitochondrial proteins (AK3, HIBADH), which were associated with a decrease in microRNAs that were predicted to regulate their expression.ConclusionImprovement in disease phenotype after exercise is associated with increasing microRNAs that target and downregulate immune processes at the transcript level, as well as decreasing microRNAs that target and upregulate mitochondrial content at the protein level. Therefore, microRNAs may improve disease by decreasing immune responses and increasing mitochondrial biogenesis.Trial registrationClinicalTrials.gov NCT01184625.

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A longitudinal, integrated, clinical, histological and mRNA profiling study of resistance exercise in myositis.

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A longitudinal, integrated, clinical, histological and mRNA profiling study of resistance exercise in myositis.

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