Abstract
BACKGROUND: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is an autoimmune systemic disease, and the majority of AAV patients have renal involvement presenting as rapid progressive glomerulonephritis (GN). Currently, the clinically available AAV markers are limited, and some of the newly reported markers are still in the nascent stage. The particular mechanism of the level changes of various markers and their association with the pathogenesis of AAV are not well defined. With the help of metabolomics analysis, this study aims to explore metabolic changes in AAV patients with renal involvement and lay the foundation for the discovery of novel biomarkers for AAV-related kidney damage. METHODS: We performed liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based on serum samples from patients with AAV (N = 33) and healthy controls (N = 33) in order to characterize the serum metabolic profiling. The principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were used to identify the differential metabolites. Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) analysis were further conducted to identify the potential diagnostic biomarker. A receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of the identified potential biomarker. RESULTS: A total of 455 metabolites were detected by LC-MS analysis. PCA and OPLS-DA demonstrated a significant difference between AAV patients with renal involvement and healthy controls, and 135 differentially expressed metabolites were selected, with 121 upregulated and 14 downregulated. Ninety-two metabolic pathways were annotated and enriched based on the KEGG database. N-acetyl-L-leucine, Acetyl-DL-Valine, 5-hydroxyindole-3-acetic acid, and the combination of 1-methylhistidine and Asp-phe could accurately distinguish AAV patients with renal involvement from healthy controls. And 1-methylhistidine was found to be significantly associated with the progression and prognosis of AAV with renal impairment. Amino acid metabolism exhibits significant alternations in AAV with renal involvement. CONCLUSION: This study identified metabolomic differences between AAV patients with renal involvement and non-AAV individuals. Metabolites that could accurately distinguish patients with AAV renal impairment from healthy controls in this study, and metabolites that were significantly associated with disease progression and prognosis were screened out. Overall, this study provides information on changes in metabolites and metabolic pathways for future studies of AAV-related kidney damage and lays a foundation for the exploration of new biomarkers of AAV-related kidney damage.