The urate oxidase (Uox) gene encodes uricase that in the rodent liver degrades uric acid into allantoin, forming an obstacle for establishing stable mouse models of hyperuricemia. The loss of uricase in humans during primate evolution causes their vulnerability to hyperuricemia. Thus, we generated a Uox-knockout mouse model on a pure C57BL/6J background using the transcription activator-like effector nuclease (TALEN) technique. These Uox-knockout mice spontaneously developed hyperuricemia (over 420 μmol/l) with about 40% survival up to 62 weeks. Renal dysfunction (elevated serum creatinine and blood urea nitrogen) and glomerular/tubular lesions were observed in these Uox-knockout mice. Male Uox-knockout mice developed glycol-metabolic disorders associated with compromised insulin secretion and elevated vulnerability to streptozotocin-induced diabetes, whereas female mice developed hypertension accompanied by aberrant lipo-metabolism. Urate-lowering drugs reduced serum uric acid and improved hyperuricemia-induced disorders. Thus, uricase knockout provides a suitable mouse model to investigate hyperuricemia and associated disorders mimicking the human condition, suggesting that hyperuricemia has a causal role in the development of metabolic disorders and hypertension.

Gout is one of the most common types of inflammatory arthritis, caused by the deposition of monosodium urate crystals in and around the joints. Previous genome-wide association studies (GWASs) have identified many genetic loci associated with raised serum urate concentrations. However, hyperuricemia alone is not sufficient for the development of gout arthritis. Here we conduct a multistage GWAS in Han Chinese using 4,275 male gout patients and 6,272 normal male controls (1,255 cases and 1,848 controls were genome-wide genotyped), with an additional 1,644 hyperuricemic controls. We discover three new risk loci, 17q23.2 (rs11653176, P=1.36 × 10−13, BCAS3), 9p24.2 (rs12236871, P=1.48 × 10−10, RFX3) and 11p15.5 (rs179785, P=1.28 × 10−8, KCNQ1), which contain inflammatory candidate genes. Our results suggest that these loci are most likely related to the progression from hyperuricemia to inflammatory gout, which will provide new insights into the pathogenesis of gout arthritis.

Objective To systematically profile metabolic alterations and dysregulated metabolic pathways in hyperuricemia and gout, and to identify potential metabolite biomarkers to discriminate gout from asymptomatic hyperuricemia. Methods Serum samples from 330 participants, including 109 with gout, 102 with asymptomatic hyperuricemia, and 119 normouricemic controls, were analyzed by high‐resolution mass spectrometry–based metabolomics. Multivariate principal components analysis and orthogonal partial least squares discriminant analysis were performed to explore differential metabolites and pathways. A multivariate methods with Unbiased Variable selection in R (MUVR) algorithm was performed to identify potential biomarkers and build multivariate diagnostic models using 3 machine learning algorithms: random forest, support vector machine, and logistic regression. Results Univariate analysis demonstrated that there was a greater difference between the metabolic profiles of patients with gout and normouricemic controls than between the metabolic profiles of individuals with hyperuricemia and normouricemic controls, while gout and hyperuricemia showed clear metabolomic differences. Pathway enrichment analysis found diverse significantly dysregulated pathways in individuals with hyperuricemia and patients with gout compared to normouricemic controls, among which arginine metabolism appeared to play a critical role. The multivariate diagnostic model using MUVR found 13 metabolites as potential biomarkers to differentiate hyperuricemia and gout from normouricemia. Two‐thirds of the samples were randomly selected as a training set, and the remainder were used as a validation set. Receiver operating characteristic analysis of 7 metabolites yielded an area under the curve of 0.83–0.87 in the training set and 0.78–0.84 in the validation set for distinguishing gout from asymptomatic hyperuricemia by 3 machine learning algorithms. Conclusion Gout and hyperuricemia have distinct serum metabolomic signatures. This diagnostic model has the potential to improve current gout care through early detection or prediction of progression to gout from hyperuricemia.