Tan Yuxuan, Fu Yingyin, Yao Huojie, Wu Xiaomei, Yang Zhiyu, Zeng Huixian, Zeng Zurui, Liang Huanzhu, Li Yexin, Jing Chunxia
Department of Epidemiology, School of Medicine, Jinan University, No.601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
Department of Epidemiology, School of Medicine, Jinan University, No.601 Huangpu Ave West, Guangzhou 510632, Guangdong, China; Guangdong Key Laboratory of Environmental Exposure and Health, Jinan University, Guangzhou 510632, Guangdong, China.
Sci Total Environ. 2023 Feb 10;859(Pt 1):160208. doi: 10.1016/j.scitotenv.2022.160208. Epub 2022 Nov 15.
Phthalates exposure might cause kidney damage and a potential risk for hyperuricemia. However, direct evidence on phthalates and hyperuricemia is somewhat limited.
To examine associations between 10 phthalates metabolites and hyperuricemia in a large-scale representative of the U.S.
A cross-sectional study of 6865 participants aged over 20 from NHANES 2007-2016 was performed. All participants had complete data on ten phthalate metabolites (MECPP, MnBP, MEHHP, MEOHP, MiBP, cx-MiNP, MCOP, MCPP, MEP, MBzP), hyperuricemia, and covariates. We used multivariable logistics regression, restricted cubic splines (RCS) model, and Bayesian kernel machine regression (BKMR) models to assess single, nonlinear, and mixed relationships between phthalate metabolites and hyperuricemia. As a complement, we also assessed the relationship between phthalate metabolites and serum uric acid (SUA) levels.
The multivariable logistics regression showed that MECPP, MEOHP, MEHHP, MBzP, and MiBP were generally positively associated with hyperuricemia (P < 0.05), especially in MiBP (Q3 (OR (95 %): 1.31 (1.02, 1.68)) and Q4 (OR (95 %): 1.68 (1.27, 2.24)), compared to Q1). All ten phthalate metabolites had a linear dose-response relationship with hyperuricemia in the RCS model (P for non-linear >0.05). BKMR showed that mixed phthalate metabolites were associated with a higher risk of hyperuricemia, with MBzP contributing the most (groupPIP = 0.999, condPIP = 1.000). We observed the consistent results between phthalate metabolites and SUA levels in three statistical models. The relationship between phthalate metabolites and hyperuricemia remained in the sensitivity analysis.
The present study suggests that exposure to phthalates, individually or jointly, might increase the risk of hyperuricemia. Since hyperuricemia influences on the quality of life, more explorations are needed to confirm these findings.
邻苯二甲酸盐暴露可能导致肾脏损伤以及高尿酸血症的潜在风险。然而，关于邻苯二甲酸盐与高尿酸血症的直接证据较为有限。
在美国大规模代表性人群中研究10种邻苯二甲酸盐代谢物与高尿酸血症之间的关联。
对2007 - 2016年美国国家健康与营养检查调查（NHANES）中6865名20岁以上参与者进行横断面研究。所有参与者均有关于10种邻苯二甲酸盐代谢物（MECPP、MnBP、MEHHP、MEOHP、MiBP、cx - MiNP、MCOP、MCPP、MEP、MBzP）、高尿酸血症及协变量的完整数据。我们使用多变量逻辑回归、受限立方样条（RCS）模型和贝叶斯核机器回归（BKMR）模型来评估邻苯二甲酸盐代谢物与高尿酸血症之间的单一、非线性和混合关系。作为补充，我们还评估了邻苯二甲酸盐代谢物与血清尿酸（SUA）水平之间的关系。
多变量逻辑回归显示，MECPP、MEOHP、MEHHP、MBzP和MiBP通常与高尿酸血症呈正相关（P < 0.05），尤其是MiBP（与第一四分位数相比，第三四分位数（OR（95%）：1.31（1.02，1.68））和第四四分位数（OR（95%）：1.68（1.27，2.24）））。在RCS模型中，所有10种邻苯二甲酸盐代谢物与高尿酸血症均呈线性剂量反应关系（非线性P > 0.05）。BKMR显示，混合邻苯二甲酸盐代谢物与高尿酸血症风险较高相关，其中MBzP的贡献最大（组PIP = 0.999，条件PIP = 1.000）。在三种统计模型中，我们观察到邻苯二甲酸盐代谢物与SUA水平之间的结果一致。在敏感性分析中，邻苯二甲酸盐代谢物与高尿酸血症之间的关系依然存在。
本研究表明，单独或联合接触邻苯二甲酸盐可能会增加高尿酸血症的风险。由于高尿酸血症会影响生活质量，因此需要更多研究来证实这些发现。
