Objective: This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength.
Methods: We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.
Results: In the multimetal linear regression, Cu (β = -2.119), As (β = -1.318), Sr (β = -2.480), Ba (β = 0.781), Fe (β = 1.130) and Mn (β = -0.404) were significantly correlated with grip strength ( P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95% confidence interval: -1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn ( P interactions of 0.003 and 0.018, respectively).
Conclusion: In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.
Keywords: Bayesian kernel machine regression; Handgrip strength; Quantile g-computation; Urinary metals.
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