Associations of mixed urinary metals exposure with metabolic syndrome in the US adult population

Abstract

Background: Metals are harmful to human health in many ways. However, the association between metals and metabolic syndrome (MetS) remains unclear. Aims of this study is to discuss the relationship between urinary metal and MetS.

Methods: This study included 3419 adult participants from the National Health and Nutrition Examination Survey (NHANES) (2005-2018). Logistic regression analysis, Bayesian kernel machine regression (BKMR), weighted quantile sum (WQS), and restricted cubic spline (RCS) were used to explore the associations of nine urinary metal and MetS.

Results: BKMR and WQS showed the effects of combined nine urinary metal were negatively correlated with MetS. Logistic regression analysis, WQS, and BKMR all suggested that cesium (Cs) and lead (Pb) were negatively correlated with MetS (all P_FDCR <0.05). And RCS suggested log₂-transformed Cs (χ2 = 20, P < 0.001) and log₂-transformed Pb (χ2 = 19.9, P < 0.001) were negatively and linearly associated with MetS.

Conclusion: Existing evidence suggests that urine metal content is related to MetS. Cs and Pb are negatively related to MetS. It is still necessary to study and further discuss the causal relationship and mechanism.

Keywords: Bayesian kernel machine regression; Metabolic syndrome; National health and nutrition examination survey; Restricted cubic spline; Urinary metals; Weighted quantile sum.