The altered metabolism profile in pathogenesis of idiopathic inflammatory myopathies

Immunometabolic shifts in autoimmune disease: Mechanisms and pathophysiological implications

Autoimmune diseases occur when the immune system abnormally attacks the body's normal tissues, causing inflammation and damage. Each disease has unique immune and metabolic dysfunctions during pathogenesis. In rheumatoid arthritis (RA), immune cells have different metabolic patterns and mitochondrial/lysosomal dysfunctions at different disease stages. In systemic lupus erythematosus (SLE), type I interferon (IFN) causes immune cell metabolic dysregulation, linking activation to metabolic shifts that may worsen the disease. In systemic sclerosis (SSc), mitochondrial changes affect fibroblast metabolism and the immune response. Idiopathic inflammatory myopathies (IIMs) patients have mitochondrial and metabolic issues. In primary Sjögren's syndrome (pSS), immune cell metabolism is imbalanced and mitochondrial damage can lead to cell/tissue damage. Metabolic reprogramming links cellular energy needs and immune dysfunctions, causing inflammation, damage, and symptoms in these diseases. It also affects immune cell functions like differentiation, proliferation, and secretion. This review discusses the potential of targeting metabolic pathways to restore immune balance, offering directions for future autoimmune disease research and treatment.

Methodology Development for Investigating Pathophysiological [18F]-FDG Muscle Uptake in Patients with Rheumatic Musculoskeletal Diseases

Objectives: This retrospective study explored the qualitative and quantitative assessment of F18-fluordeoxyglucose ([18F]-FDG) positron emission tomography and computed tomography (PET/CT) scans to assess pathophysiological muscle glucose uptake in patients with a rheumatic musculoskeletal disease (RMD). [18F]-FDG PET/CT detects metabolic activity via glucose uptake in tissues. This study aimed to determine the feasibility of quantitative assessment of [18F]-FDG uptake in muscles across three different RMDs compared to controls. Methods: In this study we analysed whole-body [18F]-FDG PET/CT scans from patients with rheumatoid arthritis (RA; n = 11), osteoarthritis (OA; n = 10), and idiopathic inflammatory myositis (IIM; n = 10), and non-RMD controls (n = 11), focusing on muscle-tracer uptake in specific muscle groups. Qualitative assessment visually identified regions with high [18F]-FDG uptake, followed by quantitative assessment using two methods: fixed volume-of-interest (VOI) and hotspot VOI. In the fixed VOI method, a VOI was placed in the respective muscle at a fixed position (50% height from proximal to distal end) on PET/CT images. In the hotspot VOI method, the VOI was placed at the site of the highest [18F]-FDG uptake observed during qualitative assessment. Standardised uptake values (SUVs) were determined for different muscle groups between RMDs and controls. Results: Qualitative assessment revealed a heterogenous uptake pattern of [18F]-FDG that was found in 93% of quadriceps and hamstring muscles, while other muscles displayed either heterogenous or homogenous patterns. A Bland-Altman analysis showed that the hotspot VOI method had a higher sensitivity in detecting differential [18F]-FDG uptake in muscles. Across all muscle groups, patients with IIM had the highest [18F]-FDG uptake, followed by patients with OA and RA, respectively. Conclusions: [18F]-FDG PET/CT enables qualitative and quantitative differentiation of muscle glucose uptake in patients with RA, OA, and IIM, at both individual muscle and patient group levels. The hotspot method and SUVpeak are recommended for quantitative assessment. High [18F]-FDG uptake in multiple muscle groups suggests pathophysiological glucose metabolism in RMD-affected muscles.

Plasma proteomic profiling reveals KRT19 could be a potential biomarker in patients with anti-MDA5+ dermatomyositis

OBJECTIVE

To investigate the immune response-related protein profiling in plasma of patients with idiopathic inflammatory myopathies (IIMs), especially in anti-MDA5+ dermatomyositis (DM).

METHODS

A total of 166 IIM patients and 107 healthy controls (HCs) were enrolled in our study. Ninety-two plasma immune response-related proteins were detected by Olink proteomics in 36 IIM patients and 25 HCs. The expression of plasma KRT19 was validated in another 130 IIM patients, 82 HCs, and 55 other rheumatic diseases.

RESULTS

A total of 46 differentially expressed proteins were detected, including 12 upregulated proteins and 34 downregulated proteins in IIM patients compared with HCs. Pathway analysis revealed lactoferrin danger signal response pathway, TLR4 signaling and tolerance, infection, and IL-10 signaling pathway were activated. The immune response-related protein profiling significantly altered in anti-MDA5+ DM patients, with LAMP3, HSD11B1, and KRT19 significantly increased, while SH2D1A, ITGA11, TRIM21, CD28, ITGB6, and HEXIM1 tremendously decreased. In addition, KRT19 was significantly increased in IIM patients, especially in anti-MDA5+ DM patients with the diagnostic value of a significant area under the ROC curve of 0.881.

CONCLUSION

Immune response-related proteins are significantly altered in patients with anti-MDA5+ DM patients. KRT19 could be a potential biomarker for anti-MDA5+ DM patients. Key Points • What is already known on this topic? Anti-MDA5+ DM is a distinctive subtype of IIM. Plasma immune response-related proteins panel needs to be investigated. • What this study adds? Plasma protein profiling of immune response-related proteins significantly altered in patients with idiopathic inflammatory myopathies (IIM), especially in anti-MDA5+ DM patients. • How this study might affect research, practice, or policy? KRT19 could be a potential biomarker in patients with anti-MDA5+ dermatomyositis.

Identification of Metabolic Signature Associated with Idiopathic Inflammatory Myopathy Reveals Polyamine Pathway Alteration in Muscle Tissue

Idiopathic inflammatory myopathy (IIM) is hard to diagnose without a muscle biopsy. We aimed to identify a metabolite panel for IIM detection by metabolomics approach in serum samples and to explore the metabolomic signature in tissue samples from a mouse model. We obtained serum samples from IIM patients, ankylosing spondylitis (AS) patients, healthy volunteers and muscle tissue samples from IIM murine model. All samples were subjected to a targeted metabolomic approach with various statistical analyses on serum and tissue samples to identify metabolic alterations. Three machine learning methods, such as logistic regression (LR), support vector machine (SVM), and random forest (RF), were applied to build prediction models. A set of 7 predictive metabolites was calculated using backward stepwise selection, and the model was evaluated within 5-fold cross-validation by using three machine algorithms. The model produced an area under the receiver operating characteristic curve values of 0.955 (LR), 0.908 (RF) and 0.918 (SVM). A total of 68 metabolites were significantly changed in mouse tissue. Notably, the most influential pathways contributing to the inflammation of muscle were the polyamine pathway and the beta-alanine pathway. Our metabolomic approach offers the potential biomarkers of IIM and reveals pathologically relevant metabolic pathways that are associated with IIM.

Integrated analysis of plasma and urine reveals unique metabolomic profiles in idiopathic inflammatory myopathies subtypes

OBJECTIVES

Idiopathic inflammatory myopathies (IIM) are a class of autoimmune diseases with high heterogeneity that can be divided into different subtypes based on clinical manifestations and myositis-specific autoantibodies (MSAs). However, even in each IIM subtype, the clinical symptoms and prognoses of patients are very different. Thus, the identification of more potential biomarkers associated with IIM classification, clinical symptoms, and prognosis is urgently needed.

METHODS

Plasma and urine samples from 79 newly diagnosed IIM patients (mean disease duration 4 months) and 52 normal control (NC) samples were analysed by high-performance liquid chromatography of quadrupole time-of-flight mass spectrometry (HPLC-Q-TOF-MS)/MS-based untargeted metabolomics. Orthogonal partial least-squares discriminate analysis (OPLS-DA) were performed to measure the significance of metabolites. Pathway enrichment analysis was conducted based on the KEGG human metabolic pathways. Ten machine learning (ML) algorithms [linear support vector machine (SVM), radial basis function SVM, random forest, nearest neighbour, Gaussian processes, decision trees, neural networks, adaptive boosting (AdaBoost), Gaussian naive Bayes and quadratic discriminant analysis] were used to classify each IIM subtype and select the most important metabolites as potential biomarkers.

RESULTS

OPLS-DA showed a clear separation between NC and IIM subtypes in plasma and urine metabolic profiles. KEGG pathway enrichment analysis revealed multiple unique and shared disturbed metabolic pathways in IIM main [dermatomyositis (DM), anti-synthetase syndrome (ASS), and immune-mediated necrotizing myopathy (IMNM)] and MSA-defined subtypes (anti-Mi2+, anti-MDA5+, anti-TIF1γ+, anti-Jo1+, anti-PL7+, anti-PL12+, anti-EJ+, and anti-SRP+), such that fatty acid biosynthesis was significantly altered in both plasma and urine in all main IIM subtypes (enrichment ratio > 1). Random forest and AdaBoost performed best in classifying each IIM subtype among the 10 ML models. Using the feature selection methods in ML models, we identified 9 plasma and 10 urine metabolites that contributed most to separate IIM main subtypes and MSA-defined subtypes, such as plasma creatine (fold change = 3.344, P = 0.024) in IMNM subtype and urine tiglylcarnitine (fold change = 0.351, P = 0.037) in anti-EJ+ ASS subtype. Sixteen common metabolites were found in both the plasma and urine samples of IIM subtypes. Among them, some were correlated with clinical features, such as plasma hypogeic acid (r = -0.416, P = 0.005) and urine malonyl carnitine (r = -0.374, P = 0.042), which were negatively correlated with the prevalence of interstitial lung disease.

CONCLUSIONS

In both plasma and urine samples, IIM main and MSA-defined subtypes have specific metabolic signatures and pathways. This study provides useful clues for understanding the molecular mechanisms, searching potential diagnosis biomarkers and therapeutic targets for IIM.

Muscle imaging in myositis: MRI, US, and PET

Imaging is an important tool in the evaluation of idiopathic inflammatory myopathies. It plays a role in diagnosis, assessment of disease activity and follow-up, and as a non-invasive biomarker. Among the different modalities, nuclear magnetic resonance imaging (MRI), ultrasound (US), and positron emission tomography (PET) may have the most clinical utility in myositis. MRI is currently the best modality to evaluate skeletal muscle and provides excellent characterization of muscle edema and fat replacement through the use of T1-weighted and T2-weighted fat suppressed/STIR sequences. Although MRI can be read qualitatively for the presence of abnormalities, a more quantitative approach using Dixon sequences and the generation of water T2 parametric maps would be preferable for follow-up. Newer protocols such as diffusion-weighted imaging, functional imaging measures, and spectroscopy may be of interest to provide further insights into myositis. Despite the advantages of MRI, image acquisition is relatively time-consuming, expensive, and not accessible to all patients. The use of US to evaluate skeletal muscle in myositis is gaining interest, especially in chronic disease, where fat replacement and fibrosis are detected readily by this modality. Although easily deployed at the bedside, it is heavily dependent on operator experience to recognize disease states. Further, systematic characterization of muscle edema by US is still needed. PET provides valuable information on muscle function at a cellular level. Fluorodeoxyglucose (FDG-PET) has been the most common application in myositis to detect pathologic uptake indicative of inflammation. The use of neurodegenerative markers is now also being utilized for inclusion body myositis. These different modalities may prove to be complementary methods for myositis evaluation.