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

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.

Keywords: Biomarkers; Idiopathic inflammatory myopathies; Machine learning algorithm; Metabolomics.

Figure 1

Study design flow chart for metabolite‐based model. An untargeted metabolomics analysis was carried out in the plasma and urine cohort of 79 newly diagnosed idiopathic inflammatory myopathies (IIM) patients and 52 normal control (NC) samples. Orthogonal partial least‐squares discriminate analysis (OPLS‐DA) and fold change analysis were performed to measure the significance of metabolites. Then, the differentially expressed metabolites were included in 10 machine learning models to identify potential biomarkers in each IIM subtype. Pathway enrichment analysis was conducted based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) human metabolic pathways. The correlation between the shared metabolites in the plasma and urine samples of the IIM patients and clinical parameters was conducted by Pearson's or Spearman's correlation.

Figure 2

Plasma metabolomic profiles in main IIM subtypes. (A and B) The orthogonal partial least‐squares discriminate analysis (OPLS‐DA) score plots of plasma metabolomics data compared dermatomyositis (DM), anti‐synthetase syndrome (ASS), and immune‐mediated necrotizing myopathy (IMNM) to normal control (NC) samples in positive (A) and negative (B) ion mode, respectively. (C) The UpSet plot analysis based on the selected important metabolites in plasma [variable importance in the projection (VIP) > 1, fold change (FC) > 1.2 or <0.83]. (D) KEGG pathway analysis of exclusively important metabolites in each main IIM subtype. (E) The linear discriminant analysis (LDA) plot based on the Top 30 metabolites in random forest machine model. (F) The most specific metabolite identified by the random forest machine model to classify plasma samples from NC, DM, ASS, and IMNM, respectively (P value compared with NC, *, <0.05; **, <0.01; ***, <0.001, ****, <0.0001).

Figure 3

Plasma metabolomic profiles in the MSA‐defined IIM subtypes. (A and B) The Orthogonal partial least‐squares discriminate analysis (OPLS‐DA) score plots of plasma metabolomics data compared anti‐EJ+, anti‐Jo1+, anti‐MDA5+, anti‐Mi2+, anti‐TIF1γ+, anti‐PL12+, and anti‐SRP + IIM patients to normal control (NC) samples in positive (A) and negative (B) ion mode, respectively. (C) The UpSet plot analysis based on the selected important metabolites in plasma (VIP > 1, FC > 1.2 or <0.83). (D) KEGG pathway analysis of exclusively important metabolites in each IIM subtype. (E) The linear discriminant analysis (LDA) plot based on the Top 12 metabolites in random forest machine model. (F) The most specific metabolite identified by the random forest machine model to classify plasma samples from NC, anti‐EJ+, anti‐Jo1+, anti‐MDA5+, anti‐Mi2+, anti‐TIF1γ+, anti‐PL12+, and anti‐SRP + IIM patients, respectively (P value compared with NC, *, <0.05; **, <0.01; ***, <0.001, ****, <0.0001).

Figure 4

Urine metabolomic profiles in main IIM subtypes. (A and B) The orthogonal partial least‐squares discriminate analysis (OPLS‐DA) score plots of urine metabolomics data compared dermatomyositis (DM), anti‐synthetase syndrome (ASS), and immune‐mediated necrotizing myopathy (IMNM) to normal control (NC) samples in positive (A) and negative (B) ion mode, respectively. (C) The UpSet plot analysis based on the selected plasma important metabolites (VIP > 1, FC > 1.2 or <0.83). (D) KEGG pathway analysis of exclusively important metabolites in each IIM subtype. (E) The linear discriminant analysis (LDA) plot based on the Top 30 metabolites in the AdaBoost machine model. (F) The most specific metabolites used by the AdaBoost machine model to classify urine samples from NC, DM, ASS, and IMNM, respectively (P value compared with NC, *, <0.05; **, <0.01; ***, <0.001, ****, <0.0001).

Figure 5

Urine metabolomic profiles in the MSA‐defined IIM subtypes. (A and B) The orthogonal partial least‐squares discriminate analysis (OPLS‐DA) score plots of urine metabolomics data compared anti‐EJ+, anti‐Jo1+, anti‐MDA5+, anti‐Mi2+, anti‐TIF1γ+, anti‐PL12+, anti‐PL7+, and anti‐SRP + IIM patients to normal control (NC) samples in positive (A) and negative (B) ion mode, respectively. (C) The UpSet plot analysis based on the selected urine important metabolites (VIP > 1, FC > 1.2 or <0.83). (D) KEGG pathway analysis of exclusively important metabolites in each IIM subtype. (E) The linear discriminant analysis (LDA) plot based on the top 12 metabolites in the AdaBoost machine model. (F) The most specific metabolites identified by the AdaBoost machine model to classify urine samples from NC, anti‐EJ+, anti‐Jo1+, anti‐MDA5+, anti‐Mi2+, anti‐TIF1γ+, anti‐PL12+, anti‐PL7+, and anti‐SRP+ myositis patients, respectively (P value compared with NC, *, <0.05; **, <0.01; ***, <0.001, ****, <0.0001).

Figure 6

Common pathways and metabolites in the plasma and urine of main IIM subtypes. (A) KEGG pathway analysis of the dysregulated pathways in both the plasma and urine of dermatomyositis (DM), anti‐synthetase syndrome (ASS), and immune‐mediated necrotizing myopathy (IMNM) patients compared with normal control (NC) samples based on important metabolites (VIP > 1). (B) The important metabolites altered in both plasma and urine of DM, ASS, and IMNM patients, respectively (VIP > 1, FC > 1.2 or <0.83).

Figure 7

Common pathways and metabolites in the plasma and urine of the MSA‐defined IIM subtypes. (A) KEGG pathway analysis of the dysregulated pathways in both the plasma and urine of the seven pairs (anti‐EJ+ vs. NC, anti‐Jo1+ vs. NC, anti‐MDA5+ vs. NC, anti‐Mi2+ vs. NC, anti‐PL12+ vs. NC, anti‐SRP+ vs. NC, and anti‐TIF1γ+ vs. NC) (VIP > 1). (B) The important metabolites altered in both plasma and urine of anti‐EJ+, anti‐Jo1+, anti‐MDA5+, anti‐Mi2+, anti‐PL12+, anti‐SRP+, and anti‐TIF1γ + IIM patients, respectively (VIP > 1, FC > 1.2 or <0.83).

Figure 8

Correlations between metabolites and clinical parameters.(A and B) Spearman's or Pearson's correlation analysis was carried out to assess the associations between clinical parameters and the shared metabolites in the plasma (A–C) and urine (D–F) samples of dermatomyositis (DM), anti‐synthetase syndrome (ASS), immune‐mediated necrotizing myopathy (IMNM), and myositis‐specific autoantibody (MSA)‐defined IIM subtypes (VIP > 1, FC > 1.2 or <0.83).