Urate-lowering effects of polyphenolic compounds in animal models: systematic review and meta-analysis

Recent research underscores the critical role of uric acid (UA) in the pathogenesis and progression of various diseases. However, the effects of polyphenolic compounds on uric acid levels remain poorly defined.

This review aims to assess the impact of five specific polyphenolic compounds on uric acid levels in animal models.

We performed an exhaustive literature search through October 30, 2024, utilizing databases including Wanfang, VIP, Cochrane Library, CNKI, Embase, and PubMed. The methodological quality of the included animal studies was evaluated using the SYRCLE (Systematic Review Centre for Laboratory animal Experimentation) risk of bias tool. Data analysis was conducted using R software, with meta-analyses performed via RevMan 5.3, adhering to the Cochrane Handbook for Systematic Reviews of Interventions.

Our analysis integrated data from 49 studies, revealing that the selected polyphenolic compounds significantly lowered serum uric acid (SUA) levels across various animal models (standardized mean difference (SMD) = -2.33, 95% CI [-2.73, -1.93]) and increased urinary uric acid (UUA) levels (SMD = 2.53, 95% CI [1.38, 3.69]). Subgroup analyses demonstrated consistent SUA reduction across different disease models. Detailed meta-analyses for each polyphenol disclosed distinct contributions to SUA reduction: resveratrol (RES) (SMD = -1.86, 95% confidence interval (CI) [-2.28, -1.45]), chlorogenic acid (CGA) (SMD = -2.31, 95% CI [-2.89, -1.73]), ferulic acid (FA) (SMD = -2.82, 95% CI [-4.46, -1.19]), punicalagin (PU) (SMD = -3.87, 95% CI [-5.99, -1.75]), and bergenin (BER) (SMD = -8.51, 95% CI [-10.30, -6.73]).

This meta-analysis supports the proposition that polyphenols such as RES, CGA, FA, PU, and BER effectively reduce serum uric acid in animal models. Notably, RES exhibited an inverted U-shaped nonlinear trend. However, the high heterogeneity and methodological constraints, including small sample sizes, ambiguous randomization practices, and potential publication bias, necessitate cautious interpretation. Further high-quality research is essential to substantiate these findings and facilitate their translation into clinical practice.