ObjectiveIndependent and interactive effects of multiple metals levels in urine on the risk of hyperuricemia (HUA) in the elderly were investigated. MethodsA total of 6,508 individuals from the baseline population of the Shenzhen aging-related disorder cohort were included in this study. We detected urinary concentrations of 24 metals using inductively coupled plasma mass spectrometry, fitted unconditional logistic regression models, and the least absolute shrinkage and selection operator regression models for the selection of metals as well as unconditional stepwise logistic regression models and restricted cubic spline logistic regression models for assessing the associations of urinary metals and HUA risk, and finally applied generalized linear models to determine the interaction with urinary metals on the risk of HUA. ResultsUnconditional stepwise logistic regression models showed the association between urinary vanadium, iron, nickel, zinc, or arsenic and HUA risk (all P < 0.05). We revealed a negative linear dose-response relationship between urinary iron levels and HUA risk (P-overall < 0.001, P-nonliner = 0.682), a positive linear dose-response relationship between urinary zinc levels and HUA risk (P-overall < 0.001, P-nonliner = 0.513), and an additive interaction relationship between urinary low-iron and high-zinc levels and HUA risk (RERI = 0.31, 95% CI: 0.03-0.59; AP = 0.18, 95%CI: 0.02-0.34; S = 1.76, 95%CI: 1.69-3.49). ConclusionUrinary vanadium, iron, nickel, zinc, or arsenic levels were associated with HUA risk, and the additive interaction of low-iron (<78.56 mu g/L) and high-zinc (>= 385.39 mu g/L) levels may lead to a higher risk of HUA.