Joint Effect of Multiple Metals on Hyperuricemia and Their Interaction with Obesity: A Community-Based Cross-Sectional Study in China

Abstract

Metal exposures have been inconsistently related to the risk of hyperuricemia, and limited research has investigated the interaction between obesity and metals in hyperuricemia. To explore their associations and interaction effects, 3300 participants were enrolled from 11 districts within 1 province in China, and the blood concentrations of 13 metals were measured to assess internal exposure. Multivariable logistic regression, restricted cubic spline (RCS), Bayesian kernel machine regression (BKMR), and interaction analysis were applied in the single- and multi-metal models. In single-metal models, five metals (V, Cr, Mn, Co, and Zn) were positively associated with hyperuricemia in males, but V was negatively associated with hyperuricemia in females. Following the multi-metal logistic regression, the multivariate-adjusted odds ratios (95% confidence intervals) of hyperuricemia were 1.7 (1.18, 2.45) for Cr and 1.76 (1.26, 2.46) for Co in males, and 0.68 (0.47, 0.99) for V in females. For V and Co, RCS models revealed wavy and inverted V-shaped negative associations with female hyperuricemia risk. The BKMR models showed a significant joint effect of multiple metals on hyperuricemia when the concentrations of five metals were at or above their 55th percentile compared to their median values, and V, Cr, Mn, and Co were major contributors to the combined effect. A potential interaction between Cr and obesity and Zn and obesity in increasing the risk of hyperuricemia was observed. Our results suggest that higher levels of Cr and Co may increase male hyperuricemia risk, while higher levels of V may decrease female hyperuricemia risk. Therefore, the management of metal exposure in the environment and diet should be improved to prevent hyperuricemia.

The mediation effect of Systemic Immunity Inflammation Index between urinary metals and TOFAT among adults in the NHANES dataset

“…Model 3 was further adjusted for education, smoking, drinking, physical activity, sedentary, energy intake, carbohydrate intake, fat intake, and protein intake. Finally, the criteria under which mediation analysis can be performed are: "statistically signi cant correlation between X and M" and "statistically signi cant correlation between M and Y" [31]. Once the conditions for mediation analysis were met, we used the R language package "bruceR" to analyze the mediating role of SII in the relationship between metals and TOFAT.…”

Relationship Between High Sodium Chloride Intake and Obesity in Hypertensive Population A Cross-Sectional Study

“…Although numerous studies have suggested that limiting salt intake to moderate levels lowers the risk of cardiovascular events, prospective randomized controlled trials have not yet offered conclusive evidence regarding the ideal sodium intake to reduced cardiovascular events and death 22 . A recent meta-analysis revealed that the best antihypertensive medications for lowering blood pressure in salt-sensitive people were calcium channel blockers and hydrochlorothiazide 23 .…”

The mediation effect of Systemic Immunity Inflammation Index between urinary metals and TOFAT among adults in the NHANES dataset

“…We also used g-computation analysis to explore the mixture effect of metals on TOFAT and SII. Finally, the criteria under which mediation analysis can be performed are: “statistically significant correlation between X and M” and “statistically significant correlation between M and Y” 31 . Once the conditions for mediation analysis were met, we used the R language package “bruceR” to analyze the mediating role of SII in the relationship between metals and TOFAT.…”