Untargeted metabolomic and lipidomic profiling in a hyperuricemic rabbit model: a pilot study

The incidence of hyperuricemia is rising globally; however, its molecular mechanisms and reliable progression biomarkers remain unclear. This study characterized temporal changes in serum metabolites and lipids in hyperuricemic rabbit models using untargeted metabolomic and lipidomic profiling with ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS/MS). Serum samples collected at baseline (0 h), 48 h, and 84 h (n = 3 per time point) were analyzed using both hydrophilic interaction chromatography (HILIC) and reversed-phase C18 columns to achieve broad metabolite and lipid coverage. HILIC analysis yielded 9813 peaks with 65 annotated compounds, whereas C18 analysis identified 7333 peaks with 122 annotated compounds across both positive and negative ion modes. Significant monotonic increases were observed in the normalized feature intensities of 4-aminohippuric acid, L-alanine, N-isobutyrylglycine, and uric acid, whereas the normalized feature intensity of inosine decreased relative to baseline. These metabolites satisfied two selection criteria, [|log₂(fold change, FC)| ≥ 0.58, Nemenyi p < 0.05] and variable importance in projection (VIP) > 1.0 using orthogonal partial least squares-discriminant analysis (OPLS-DA). Among them, inosine and 4-aminohippuric acid, which showed a strong correlations with uric acid, emerged as the most exploratory candidate biomarkers for hyperuricemia progression. The C18-based lipidomic profiling identified the phosphatidylinositols, PI(16:0_20:4) and PI(18:1_18:2), as the only lipid species that met the two selection criteria. In particular, PI(16:0_20:4) was strongly correlate with uric acid. Collectively, our study reveals the metabolomic and lipidomic changes for hyperuricemia and suggests exploratory candidate biomarkers for its early detection and monitoring.