The relative and interactive effects of urinary multiple metals exposure on hyperuricemia among urban elderly in China

Bibliographic Data

ID22073141
AuthorsChao Huang (0000-0003-1097-0679, Shenzhen Center for Disease Control and Prevention), Erwei Gao (Shenzhen Center for Disease Control and Prevention), Feng Xiao (0000-0003-3412-5816, Shenzhen Academy of Inspection and Quarantine), Qiongzhen Wu (Shenzhen Academy of Inspection and Quarantine), Wei Liu (0000-0002-5949-0302, Shenzhen Center for Disease Control and Prevention), Yi Luo (0000-0002-6343-4127, Shenzhen Luohu People's Hospital), Xiaohu Ren (0000-0003-4181-3887, Shenzhen Center for Disease Control and Prevention), Xiao Chen (0000-0001-5474-0205, Shenzhen Center for Disease Control and Prevention), Kaiwu He (0000-0002-8131-5850, Shenzhen Center for Disease Control and Prevention), Haiyan Huang (0009-0001-6180-6249, Shenzhen Center for Disease Control and Prevention), Qian Sun (0000-0001-8556-2464, Shenzhen Center for Disease Control and Prevention), Desheng Wu (0000-0002-4446-0194, Shenzhen Center for Disease Control and Prevention), Jianjun Liu (0000-0002-3255-3019, Shenzhen Center for Disease Control and Prevention, corresponding author)
Year2023
Volume11
Pages1015202-1015202
Publication date2023-02-13
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1015202
PMID36860398
OpenAlexW4320495851
LanguageEN
Citations received1
References cited57

Objective Independent and interactive effects of multiple metals levels in urine on the risk of hyperuricemia (HUA) in the elderly were investigated. Methods A 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. Results Unconditional 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). Conclusion Urinary vanadium, iron, nickel, zinc, or arsenic levels were associated with HUA risk, and the additive interaction of low-iron (<78.56 μg/L) and high-zinc (≥385.39 μg/L) levels may lead to a higher risk of HUA

Absolute risk reduction · Confidence interval · Linear regression · Logistic regression · Statistics · Stepwise regression · Urinary system · Urine · Zinc · Chemistry · Gout, Hyperuricemia, Uric Acid · Heavy Metal Exposure and Toxicity · Magnesium in Health and Disease · Mathematics · Medicine · Internal Medicine

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