Integrated analysis of plasma and urine reveals unique metabolomic profiles in idiopathic inflammatory myopathies subtypes. Issue 5 (21st July 2022)

• Record Type: Journal Article
• Authors: Liu, Di; Zhao, Lijuan; Jiang, Yu; Li, Liya; Guo, Muyao; Mu, Yibing; Zhu, Honglin
• Is Part Of: Journal of cachexia, sarcopenia and muscle. Volume 13:Issue 5(2022)
• Page Start: 2456
• Page End: 2472
• Publication Date: 2022-07-21
• Subjects: Idiopathic inflammatory myopathies -- Machine learning algorithm -- Metabolomics -- Biomarkers; Cachexia -- Periodicals; Muscles -- Aging -- Periodicals; Muscles -- Periodicals; Cachexia; Sarcopenia; Muscles; Cachexia; Muscles; Muscles -- Aging; Periodicals; Periodicals; 616
• DOI: 10.1002/jcsm.13045
• Languages: English
• ISSNs: 2190-5991

Abstract

Abstract: 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.