Serum metabolomics identifies dysregulated pathways and potential metabolic biomarkers for hyperuricemia and gout.

Objective To systemically profile metabolic alterations and dysregulated metabolic pathways in hyperuricemia (HU) and gout, and discover potential metabolite biomarkers to discriminate gout from asymptomatic HU. Methods Serum samples of 330 participants, 109 gout, 102 asymptomatic HU, and 119 normouricemic (NU), were analyzed by high-resolution mass spectrometry-based metabolomics. Multivariate PCA and OPLS-DA analysis were performed to explore differential metabolites and pathways. MUVR (Multivariate methods with Unbiased Variable selection in R) algorithm was performed to identify potential biomarkers and build multivariate diagnostic models using three machine learning algorithms including Random Forest, Support Vector Machine and Logistic Regressions. Results Univariate analysis demonstrated more distinct metabolic profiles between gout and NU than HU and NU, while gout and HU showed clear metabolomic differences. Pathway enrichment analysis found diverse significantly dysregulated pathways in HU and gout compared to NU, among which arginine metabolism appears to play a critical role. The multivariate diagnostic model using MUVR found thirteen metabolites as potential biomarkers to differentiate HU and gout from NU. By randomly selecting 2/3rd of the samples as training set and the remainder as validation set, receiver operating characteristic (ROC) analysis on seven metabolites yielded area under the curve of 0.83 to 0.87 in the training set and 0.78 to 0.84 in the validation set by three machine learning algorithms for distinguishing gout from asymptomatic HU. Conclusion Gout and HU have distinct serum metabolomic signatures. This diagnostic model has the potential to improve current gout care through early detection or prediction of gout progression from HU.