Integrated gut microbiome and serum metabolomics identify candidate metabolic signatures of uric acid nephropathy

Objective To characterize the clinical, gut microbial, and serum metabolic features of uric acid nephropathy (UAN) and identify candidate metabolic signatures distinguishing UAN from uncomplicated hyperuricemia (HUA). Methods This observational study enrolled 108 patients with hyperuricemia, including 57 with HUA and 51 with UAN. Clinical characteristics, metabolic comorbidities, lipid profiles, and renal function indices were compared, and multivariable linear regression identified factors independently associated with estimated glomerular filtration rate (eGFR). Fecal 16S rRNA gene sequencing was performed in 72 participants (HC, n = 10; HUA, n = 30; UAN, n = 32), whereas serum untargeted metabolomic profiling was conducted in a nested subset of 30 participants ( n = 10 per group). Differential taxa and metabolites were identified using LEfSe and combined multivariate and univariate analyses. Spearman correlation and MIMOSA2 analyses assessed microbiota–metabolite–clinical associations and predicted microbial contributions to candidate metabolites. Results Patients with UAN were older and more frequently had hyperlipidemia than those with HUA. They also had higher serum creatinine, blood urea nitrogen, total cholesterol, low-density lipoprotein cholesterol, and urinary albumin-to-creatinine ratio, together with lower eGFR, despite comparable serum uric acid concentrations. Age and total cholesterol were independently associated with lower eGFR, whereas body mass index was positively associated with eGFR. Neither microbial α-diversity nor overall β-diversity differed significantly among groups. Exploratory microbiome analyses suggested higher Faecalibacterium abundance in HC, whereas selected taxa including Streptococcus and Dialister differed between HUA and UAN. OPLS-DA separated HUA from UAN, with 217 differential metabolites identified using VIP > 1.0 and p < 0.05. Exploratory multi-omics integration further highlighted nine candidate metabolic signatures. These metabolites were enriched mainly in glutathione metabolism, arginine and proline metabolism, histidine metabolism, glycerolipid metabolism, bile secretion, and ABC transporter pathways. Integrated analyses identified nine candidate metabolites: citric acid, succinic acid, deoxycytidine, uridine, guanosine, betaine, nicotinamide, glycerol, and stearic acid. Citric acid, deoxycytidine, succinic acid, and uridine showed the broadest predicted microbial contribution profiles. Conclusion UAN was characterized by renal dysfunction, lipid metabolic abnormalities, selective gut microbial alterations, and extensive serum metabolic remodeling rather than by a further increase in circulating uric acid. The nine metabolites represent exploratory host–microbial metabolic signatures associated with hyperuricemia-related renal involvement. Their biological relevance and clinical discriminatory value require validation in larger, independent prospective cohorts.