Relationship between phthalates exposures and hyperuricemia in U.S. general population, a multi-cycle study of NHANES 2007-2016

Yuxuan Tan; Yingyin Fu; Huojie Yao; Xiaomei Wu; Zhiyu Yang; Huixian Zeng; Zurui Zeng; Huanzhu Liang; Yexin Li; Chunxia Jing

doi:10.1016/j.scitotenv.2022.160208

Relationship between phthalates exposures and hyperuricemia in U.S. general population, a multi-cycle study of NHANES 2007-2016

Sci Total Environ. 2023 Feb 10;859(Pt 1):160208. doi: 10.1016/j.scitotenv.2022.160208. Epub 2022 Nov 15.

Authors

Affiliations

¹ Department of Epidemiology, School of Medicine, Jinan University, No.601 Huangpu Ave West, Guangzhou 510632, Guangdong, China.
² 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. Electronic address: jcxphd@gmail.com.

PMID: 36400295
DOI: 10.1016/j.scitotenv.2022.160208

Abstract

Background: Phthalates exposure might cause kidney damage and a potential risk for hyperuricemia. However, direct evidence on phthalates and hyperuricemia is somewhat limited.

Objective: To examine associations between 10 phthalates metabolites and hyperuricemia in a large-scale representative of the U.S.

Methods: 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.

Results: The multivariable logistics regression showed that MECPP, MEOHP, MEHHP, MBzP, and MiBP were generally positively associated with hyperuricemia (P_FDR < 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.

Conclusions: 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.

Keywords: BKMR (Bayesian kernel machine regression); Hyperuricemia; Multivariable logistics regression; Phthalates; RCS (restricted cubic spline).

MeSH terms

Adult
Bayes Theorem
Cross-Sectional Studies
Environmental Exposure / analysis
Environmental Pollutants* / analysis
Humans
Nutrition Surveys
Phthalic Acids* / metabolism
Quality of Life
Uric Acid / analysis

Substances

phthalic acid
Environmental Pollutants
Uric Acid
Phthalic Acids