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The Association of Mixed Mineral Intake Exposure With Gout: A National Cross-Sectional National Health and Nutrition
Rui Lai1, Huilin Liu1, Shiqi Wang1
1Chengdu University of Traditional Chinese Medicine School of Acupuncture and Tuina Chengdu China.
Abstract:
Minerals influence urate production, renal clearance, oxidative stress, and systemic inflammation, yet epidemiologic evidence linking mineral intake to gout is limited and rarely considers co-exposures. We analyzed six NHANES cycles (2007-2018) comprising 22,661 adults ≥ 20 years. After 1:1 propensity-score matching, usual intakes of nine dietary minerals were divided into quartiles. Survey-weighted multivariable logistic regression quantified single-mineral associations with gout. Weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) evaluated combined effects and potential interactions. In logistic regression analysis, only calcium showed a consistent inverse, dose-dependent relation with gout. For the highest versus lowest calcium quartile, the ORs were 0.55 (95% CI 0.37-0.81, p = 0.003) in Model 1, 0.52 (95% CI 0.36-0.77, p = 0.001) in Model 2, and 0.51 (95% CI 0.36-0.74, p < 0.001) in Model 3. No significant associations were observed for phosphorus, magnesium, zinc, iron, copper, potassium, selenium, or sodium (all p > 0.05). In mixture analyses, the mineral index was inversely associated with gout: WQS-OR = 0.88 (95% CI 0.78-0.99) and qgcomp-OR = 0.87 (95% CI 0.79-0.97). BKMR confirmed an overall inverse exposure-response function for the combined minerals and identified calcium as the dominant contributor (posterior inclusion probability 0.72). Pair-wise response surfaces showed no consistent synergistic or antagonistic interactions. Greater calcium intake was associated with reduced gout prevalence in US adults. These findings highlight calcium-rich foods, particularly low-fat dairy products, as potential components of gout-preventive dietary strategies and underscore the utility of mixture modeling for clarifying complex nutritional exposures. Prospective studies are warranted to confirm causality and elucidate underlying mechanisms.
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