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Wang H, Zhang Y, Sun L, Guo X, Liu Q, Li J, Tian Z, Cheng X, Wang Y, Li H, Hu B, Sheng J, Qu G, Chen G, Liu X, Lin W, Tao F, Yang L. Associations of toxic metals and their mixture with hyperuricemia in Chinese rural older adults. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:256. [PMID: 38884822 DOI: 10.1007/s10653-024-02035-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/14/2024] [Indexed: 06/18/2024]
Abstract
Previous studies have related single toxic metals (TMs) to hyperuricemia (HUA) among the general population, however, the association of the TM mixture with HUA, especially in older adults, remains poorly understood. We aimed to examine the relationships between individual TMs and their mixture and HUA in Chinese rural older adults. This study consisted of 2075 rural older adults aged 60 years or over. Blood concentrations of aluminum (Al), arsenic (As), barium (Ba), cadmium (Cd), cesium (Cs), gallium (Ga), mercury (Hg), lead (Pb), thallium (Tl), and uranium (U) were detected using inductively coupled plasma mass spectrometry. The associations of single TMs with HUA were assessed using logistic regression and restricted cubic spline (RCS) models, and the association of TM mixture with HUA was explored using the elastic net with environmental risk score (ENET-ERS), quantile g-computation (QGC), and Bayesian kernel machine regression (BKMR) models, respectively. Adjusted logistic regression model showed that Cs (OR = 1.65, 95% CI 1.37-1.99) and Pb (OR = 1.46, 95% CI 1.28-1.67) were positively related to HUA, and RCS model exhibited a positive linear association of Cs and Pb with HUA. ENET-ERS and QGC models quantified a positive correlation between the TM mixture and the odds of HUA, with estimated ORs of 1.15 (95% CI 1.11-1.19) and 1.84 (95% CI 1.37-2.47), respectively, and Cs and Pb had the most weight. BKMR model demonstrated a significant linear association between the TM mixture and increased odds of HUA, with the posterior inclusion probabilities (PIPs) of both Cs and Pb being 1.00. Moreover, we observed a positive interaction between Cs and Pb on HUA. The TM mixture is associated with increased odds of HUA in rural older adults, which may mainly be driven by Cs and Pb. Subsequent studies are warranted to confirm these findings and clarify the mechanisms linking multiple TMs with HUA.
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Affiliation(s)
- Hongli Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China
- Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, 230032, Anhui, China
- Scientific Research Center in Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Yan Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China
- Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, 230032, Anhui, China
- Scientific Research Center in Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Liang Sun
- Fuyang Center for Diseases Prevention and Control, Fuyang, 236069, Anhui, China
| | - Xianwei Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China
| | - Qiang Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China
- Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Junzhe Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China
- Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Ziwei Tian
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China
- Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Xuqiu Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China
- Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Yuan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China
- Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Huaibiao Li
- Fuyang Center for Diseases Prevention and Control, Fuyang, 236069, Anhui, China
| | - Bing Hu
- Fuyang Center for Diseases Prevention and Control, Fuyang, 236069, Anhui, China
| | - Jie Sheng
- Scientific Research Center in Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Guangbo Qu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China
| | - Guimei Chen
- School of Health Services Management, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Xuechun Liu
- Department of Neurology, The Second People's Hospital of Hefei, Hefei, 230011, Anhui, China
| | - Wenbo Lin
- Second School of Clinical Medicine, Anhui Medical University, Hefei, 230032, Anhui, China
| | - Fangbiao Tao
- Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, 230032, Anhui, China
- Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei, 230032, Anhui, China
| | - Linsheng Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Meishan Road 81, Hefei, 230032, Anhui, China.
- Center for Big Data and Population Health of IHM, Anhui Medical University, Hefei, 230032, Anhui, China.
- Scientific Research Center in Preventive Medicine, School of Public Health, Anhui Medical University, Hefei, 230032, Anhui, China.
- Anhui Provincial Key Laboratory of Population Health and Aristogenics, Hefei, 230032, Anhui, China.
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Han X, Lv Z, He M, Cheng J, Zhang Y, Wang T, Chen J, Liu Y, Hu D, Wu X, Zhai R, Huang H, Huang S. Effects of multiple metals exposure on abnormal liver function: The mediating role of low-density lipoprotein cholesterol. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 276:116283. [PMID: 38574647 DOI: 10.1016/j.ecoenv.2024.116283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 03/09/2024] [Accepted: 03/30/2024] [Indexed: 04/06/2024]
Abstract
Equilibration of metal metabolism is critical for normal liver function. Most epidemiological studies have only concentrated on the influence of limited metals. However, the single and synergistic impact of multiple-metal exposures on abnormal liver function (ALF) are still unknown. A cross-sectional study involving 1493 Chinese adults residing in Shenzhen was conducted. Plasma concentrations of 13 metals, including essential metals (calcium, copper, cobalt, iron, magnesium, manganese, molybdenum, zinc, and selenium) and toxic metals (aluminum, cadmium, arsenic, and thallium) were detected by the inductively coupled plasma spectrometry (ICP-MS). ALF was ascertained as any observed abnormality from albumin, alanine transaminase, aspartate transaminase, γ-glutamyl transpeptidase, and direct bilirubin. Diverse statistical methods were used to evaluate the single and mixture effect of metals, as well as the dose-response relationships with ALF risk, respectively. Mediation analysis was conducted to evaluate the role of blood lipids in the relation of metal exposure with ALF. The average age of subjects was 59.7 years, and 56.7 % were females. Logistic regression and the least absolute shrinkage and selection operator (LASSO) penalized regression model consistently suggested that increased levels of arsenic, aluminum, manganese, and cadmium were related to elevated risk of ALF; while magnesium and zinc showed protective effects on ALF (all p-trend < 0.05). The grouped weighted quantile sum (GWQS) regression revealed that the WQS index of essential metals and toxic metals showed significantly negative or positive relationship with ALF, respectively. Aluminum, arsenic, cadmium, and manganese showed linear whilst magnesium and zinc showed non-linear dose-response relationships with ALF risk. Mediation analysis showed that LDL-c mediated 4.41 % and 14.74 % of the relationship of plasma cadmium and manganese with ALF, respectively. In summary, plasma aluminum, arsenic, manganese, cadmium, magnesium, and zinc related with ALF, and LDL-c might underlie the pathogenesis of ALF associated with cadmium and manganese exposure. This study may provide critical public health significances in liver injury prevention and scientific evidence for the establishment of environmental standard.
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Affiliation(s)
- Xu Han
- School of Public Health, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China; Department of Occupational and Environmental Health and Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Ziquan Lv
- Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong 518055, China
| | - Mei'an He
- Department of Occupational and Environmental Health and Key Laboratory of Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Jinquan Cheng
- Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong 518055, China
| | - Yanwei Zhang
- Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong 518055, China
| | - Tian Wang
- Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong 518055, China
| | - Jiaxin Chen
- Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong 518055, China
| | - Yuewei Liu
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Dongsheng Hu
- School of Public Health, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China
| | - Xuli Wu
- School of Public Health, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China
| | - Rihong Zhai
- School of Public Health, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China
| | - Hui Huang
- Department of Cardiology, Joint Laboratory of Guangdong-Hong Kong-Macao Universities for Nutritional Metabolism and Precise Prevention and Control of Major Chronic Diseases, the Eighth Affiliated Hospital, Shenzhen 518303, China
| | - Suli Huang
- School of Public Health, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong 518055, China; Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong 518055, China.
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Mo M, Yin L, Wang T, Lv Z, Guo Y, Shen J, Zhang H, Liu N, Wang Q, Huang S, Huang H. Associations of essential metals with the risk of aortic arch calcification: a cross-sectional study in a mid-aged and older population of Shenzhen, China. MedComm (Beijing) 2024; 5:e533. [PMID: 38745853 PMCID: PMC11091022 DOI: 10.1002/mco2.533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/10/2024] [Accepted: 03/12/2024] [Indexed: 05/16/2024] Open
Abstract
Vascular calcification is a strong predictor of cardiovascular events. Essential metals play critical roles in maintaining human health. However, the association of essential metal levels with risk of aortic arch calcification (AoAC) remains unclear. We measured the plasma concentrations of nine essential metals in a cross-sectional population and evaluated their individual and combined effects on AoAC risk using multiple statistical methods. We also explored the mediating role of fasting glucose. In the logistic regression model, higher quartiles of magnesium and copper were associated with the decreased AoAC risk, while higher quartile of manganese was associated with higher AoAC risk. The least absolute shrinkage and selection operator penalized regression analysis identified magnesium, manganese, calcium, cobalt, and copper as key metals associated with AoAC risk. The weighted quantile sum regression suggested a combined effect of metal mixture. A linear and positive dose-response relationship was found between manganese and AoAC in males. Moreover, blood glucose might mediate a proportion of 9.38% of the association between manganese exposure and AoAC risk. In summary, five essential metal levels were associated with AoAC and showed combined effect. Fasting glucose might play a significant role in mediating manganese exposure-associated AoAC risk.
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Affiliation(s)
- Mingxing Mo
- Department of CardiologyJoint Laboratory of Guangdong‐Hong Kong‐Macao Universities for Nutritional Metabolism and Precise Prevention and Control of Major Chronic Diseasesthe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Li Yin
- Department of CardiologyJoint Laboratory of Guangdong‐Hong Kong‐Macao Universities for Nutritional Metabolism and Precise Prevention and Control of Major Chronic Diseasesthe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Tian Wang
- School of Public HealthShenzhen University Medical SchoolShenzhen UniversityShenzhenGuangdongChina
- Department of Central LaboratoryShenzhen Center for Disease control and PreventionShenzhenChina
| | - Ziquan Lv
- Department of Central LaboratoryShenzhen Center for Disease control and PreventionShenzhenChina
| | - Yadi Guo
- Department of CardiologyJoint Laboratory of Guangdong‐Hong Kong‐Macao Universities for Nutritional Metabolism and Precise Prevention and Control of Major Chronic Diseasesthe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Jiangang Shen
- School of Chinese MedicineLi Ka Shing Faculty of MedicineThe University of Hong KongHong Kong SARChina
- State Key Laboratory of Pharmaceutical BiotechnologyThe University of Hong KongHong Kong SARChina
| | - Huanji Zhang
- Department of CardiologyJoint Laboratory of Guangdong‐Hong Kong‐Macao Universities for Nutritional Metabolism and Precise Prevention and Control of Major Chronic Diseasesthe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Ning Liu
- Department of Central LaboratoryShenzhen Center for Disease control and PreventionShenzhenChina
| | - Qiuling Wang
- Department of Central LaboratoryShenzhen Center for Disease control and PreventionShenzhenChina
| | - Suli Huang
- School of Public HealthShenzhen University Medical SchoolShenzhen UniversityShenzhenGuangdongChina
- Department of Central LaboratoryShenzhen Center for Disease control and PreventionShenzhenChina
| | - Hui Huang
- Department of CardiologyJoint Laboratory of Guangdong‐Hong Kong‐Macao Universities for Nutritional Metabolism and Precise Prevention and Control of Major Chronic Diseasesthe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
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Gan H, Xing Y, Tong J, Lu M, Yan S, Huang K, Wu X, Tao S, Gao H, Pan Y, Dai J, Tao F. Impact of Gestational Exposure to Individual and Combined Per- and Polyfluoroalkyl Substances on a Placental Structure and Efficiency: Findings from the Ma'anshan Birth Cohort. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6117-6127. [PMID: 38525964 DOI: 10.1021/acs.est.3c09611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Prenatal exposure to perfluoroalkyl and polyfluoroalkyl substances (PFASs) is inevitable among pregnant women. Nevertheless, there is a scarcity of research investigating the connections between prenatal PFAS exposure and the placental structure and efficiency. Based on 712 maternal-fetal dyads in the Ma'anshan Birth Cohort, we analyzed associations between individual and mixed PFAS exposure and placental measures. We repeatedly measured 12 PFAS in the maternal serum during pregnancy. Placental weight, scaling exponent, chorionic disc area, and disc eccentricity were used as the outcome variables. Upon adjusting for confounders and implementing corrections for multiple comparisons, we identified positive associations between branched perfluorohexane sulfonate (br-PFHxS) and 6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA) with placental weight. Additionally, a positive association was observed between br-PFHxS and the scaling exponent, where a higher scaling exponent signified reduced placental efficiency. Based on neonatal sex stratification, female infants were found to be more susceptible to the adverse effects of PFAS exposure. Mixed exposure modeling revealed that mixed PFAS exposure was positively associated with placental weight and scaling exponent, particularly during the second and third trimesters. Furthermore, br-PFHxS and 6:2 Cl-PFESA played major roles in the placental measures. This study provides the first epidemiological evidence of the relationship between prenatal PFAS exposure and placental measures.
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Affiliation(s)
- Hong Gan
- Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
- Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei 230032 Anhui, China
- Anhui Provincial Key Laboratory of Population Health and Aristogenics, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
| | - Yanan Xing
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Juan Tong
- Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
- Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei 230032 Anhui, China
- Anhui Provincial Key Laboratory of Population Health and Aristogenics, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
| | - Mengjuan Lu
- Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
- Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei 230032 Anhui, China
- Anhui Provincial Key Laboratory of Population Health and Aristogenics, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
| | - Shuangqin Yan
- Ma'anshan Maternal and Child Health Care Hospital, Ma'anshan 243011 Anhui, China
| | - Kun Huang
- Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
- Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei 230032 Anhui, China
- Anhui Provincial Key Laboratory of Population Health and Aristogenics, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
| | - Xiaoyan Wu
- Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
- Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei 230032 Anhui, China
- Anhui Provincial Key Laboratory of Population Health and Aristogenics, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
| | - Shuman Tao
- Department of Ophthalmology, The Second Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Hui Gao
- Department of Pediatrics, The First Affiliated Hospital of Anhui Medical University, Hefei 230022 Anhui, China
| | - Yitao Pan
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jiayin Dai
- State Environmental Protection Key Laboratory of Environmental Health Impact Assessment of Emerging Contaminants, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Fangbiao Tao
- Department of Maternal, Child and Adolescent Health, School of Public Health, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
- Key Laboratory of Population Health Across Life Cycle (Anhui Medical University), Ministry of Education of the People's Republic of China, No 81 Meishan Road, Hefei 230032 Anhui, China
- Anhui Provincial Key Laboratory of Population Health and Aristogenics, Anhui Medical University, No 81 Meishan Road, Hefei 230032 Anhui, China
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Hong X, Wang W, Huang L, Yuan J, Ding X, Wang H, Ji Q, Zhao F, Wang B. Associations between multiple metal exposure and fertility in women: A nested case-control study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 272:116030. [PMID: 38310826 DOI: 10.1016/j.ecoenv.2024.116030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/06/2024]
Abstract
Metal pollution can cause a decline in female fertility, however, previous studies have focused more on the effect of a single metal on fertility. In this study, we evaluated the effect of metal mixtures on female fertility based on nested case-control samples. The plasma levels of 22 metal elements from 180 women were determined by an inductively coupled plasma mass spectrometer (ICP-MS). Minimum absolute contraction and selection operator (LASSO) penalty regression selected metals with the greatest influence on clinical outcome. Logistic regression was used to analyze the correlation between single metals and fertility while a Bayesian kernel function regression (BKMR) model was used to analyze the effect of mixed metals. Eight metals (Calcium (Ca), Chromium (Cr), Cobalt (Co), Copper (Cu), Zinc (Zn), Rubidium (Rb), Strontium (Sr) and Zirconium (Zr)) were selected by LASSO regression for subsequent analysis. After adjusting for covariates, the logistic model showed that Cu (Odds Ratio(OR):0.33, 95% CI: 0.13 - 0.84) and Co (OR:0.38, 95% CI: 0.15 -0.94) caused a significant reduction in fertility, and identified the protective effect of Zn (OR: 2.96, 95% CI:1.21 -7.50) on fertility. Trend tests showed that increased Cr, Cu, and Rb levels were associated with reduced fertility. The BKMR model showed that Cr, Co, Cu, and Rb had a nonlinear relationship with fertility decline when controlling for the concentrations of other metals and suggested that Cu and Cr might exert an influence on fertility. Analysis showed a negative correlation between Cu, Cr, Co, Rb, and fertility, and a positive correlation between Zn and fertility. Furthermore, we found evidence for the interaction between Cu and Cr. Our findings require further validation and may identify new mechanisms in the future.
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Affiliation(s)
- Xiang Hong
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Wei Wang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Lingling Huang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Jinhua Yuan
- Nanjing Municipal Centre for Disease Control and Prevention, Nanjing, China
| | - Xiaoling Ding
- Maternal and Child Health Center of Gulou District, Nanjing, China
| | - Hao Wang
- Nanjing Municipal Centre for Disease Control and Prevention, Nanjing, China
| | - Qian Ji
- Nanjing Municipal Centre for Disease Control and Prevention, Nanjing, China
| | - Fanqi Zhao
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu, China
| | - Bei Wang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, Jiangsu, China.
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Yu Y, Chen R, Li Z, Luo K, Taylor MP, Hao C, Chen Q, Zhou Y, Kuang H, Hu G, Chen X, Li H, Dong C, Dong GH. Associations of urinary zinc exposure with blood lipid profiles and dyslipidemia: Mediating effect of serum uric acid. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168951. [PMID: 38042193 DOI: 10.1016/j.scitotenv.2023.168951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 11/25/2023] [Accepted: 11/25/2023] [Indexed: 12/04/2023]
Abstract
The relationship between zinc (Zn) exposure and abnormal blood lipids including dyslipidemia is contentious. Serum uric acid (SUA) has been reported to be correlated to both Zn exposure and dyslipidemia. The underlying mechanisms of Zn exposure associated with blood lipids and the mediating effects of SUA remain unclear. Therefore, this study analyzed the data from Chinese 2110 adults (mean age: 59.0 years old) in rural areas across China to explore the associations of Zn exposure with blood lipid profiles and dyslipidemia, and to further estimate the mediating effects of SUA in these relationships. The study data showed that urinary Zn was associated with increased levels of blood lipid components triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C). Moreover, an increased risk of dyslipidemia was observed in the study participants who had higher urinary Zn levels. Compared with the first quartile, the fourth quartile of urinary Zn concentration corresponded to the increase of TG (β = 0.20, 95 % CI: 0.12, 0.28), LDL-C (β = 0.06, 95 % CI: 0.01, 0.10) and dyslipidemia risk (OR = 2.16, 95 % CI: 1.50, 3.10), respectively. Elevated urinary Zn was also associated with higher levels of SUA and hyperuricemia risk. The SUA levels were positively related to total cholesterol (TC), TG, LDL-C levels and dyslipidemia risk. Mediation analyses revealed that SUA mediated 31.75 %, 46.16 % and 19.25 % of the associations of urinary Zn with TG, LDL-C levels and dyslipidemia risk, respectively. The subgroup and sensitivity analyses confirmed the positive associations between urinary Zn and blood lipid profiles and the mediating effect of SUA. The national population-based study further enhanced our understanding of the associations between Zn exposure and blood lipid profiles and mediating effect of SUA among generally healthy, middle-aged, and elderly individuals.
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Affiliation(s)
- Yunjiang Yu
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China.
| | - Runan Chen
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Zhenchi Li
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Kai Luo
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York 10461, USA
| | - Mark Patrick Taylor
- Environment Protection Authority Victoria, Centre for Applied Sciences, Melbourne, Victoria 3085, Australia
| | - Chaojie Hao
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Qian Chen
- Ministry of Education-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
| | - Yang Zhou
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Hongxuan Kuang
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Guocheng Hu
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Xichao Chen
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Hongyan Li
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China
| | - Chenyin Dong
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou 510655, China.
| | - Guang-Hui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
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Zhang Z, Xiao Y, Long P, Yu Y, Liu Y, Liu K, Yang H, Li X, He M, Wu T, Yuan Y. Associations between plasma metal/metalloid mixtures and the risk of central obesity: A prospective cohort study of Chinese adults. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 270:115838. [PMID: 38128312 DOI: 10.1016/j.ecoenv.2023.115838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023]
Abstract
Central obesity has increased rapidly over the past decade and posed a substantial disease burden worldwide. Exposure to metals/metalloids has been acknowledged to be involved in the development of central obesity through regulation of cortisol, insulin resistance, and glucocorticoid receptor reduction. Despite the importance, it is lack of prospective study which comprehensively evaluate the relations between multiple metals exposure and central obesity. We explored the prospective associations of plasma metal concentrations with central obesity in a prospective study of the Dongfeng-Tongji cohort. The present study included 2127 participants with a 6.87-year mean follow-up duration. We measured 23 plasma metal/metalloid concentrations at baseline. The associations between metals and incident central obesity were examined utilizing the Cox proportional hazard regression in single and multiple metals models. Additionally, we applied elastic net (ENET), Bayesian kernel machine regression (BKMR), plasma metal score (PMS), and quantile-based g-computation (Qgcomp) models to explore the joint associations of metal mixtures with central obesity. After adjusting potential confounders, we found significant associations of plasma manganese (Mn) and thallium (Tl) concentrations with a higher risk of central obesity, whereas plasma rubidium (Rb) concentration was associated with a lower risk of central obesity both in single and multiple metals models (all FDR <0.05). The ENET and Qqcomp models verified similar metals (Mn, Rb, and Tl) as important predictors for central obesity. The results of both BKMR model and PMS suggested cumulative exposure to metal mixtures was associated with a higher risk of central obesity. Our findings suggested that co-exposure to metals was associated with a higher risk of central obesity. This study expands our knowledge that the management of metals/metalloids exposure may be beneficial for the prevention of new-onset central obesity, which may subsequently alleviate the disease burden of late-life health outcomes.
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Affiliation(s)
- Zirui Zhang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Xiao
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pinpin Long
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanqiu Yu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiyi Liu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kang Liu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Handong Yang
- Department of Cardiovascular Diseases, Dongfeng Central Hospital, Hubei University of Medicine, Shiyan, China
| | - Xiulou Li
- Department of Cardiovascular Diseases, Dongfeng Central Hospital, Hubei University of Medicine, Shiyan, China
| | - Meian He
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Yuan
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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8
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Chen H, Wang M, Zhang C, Li J. A methodological study of exposome based on an open database: Association analysis between exposure to metal mixtures and hyperuricemia. CHEMOSPHERE 2023; 344:140318. [PMID: 37775054 DOI: 10.1016/j.chemosphere.2023.140318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 09/08/2023] [Accepted: 09/26/2023] [Indexed: 10/01/2023]
Abstract
BACKGROUND Exposome recognizes that humans are constantly exposed to multiple environmental factors, and elucidating the health effects of complex exposure mixtures places greater demands on analytical methods. OBJECTS We aimed to explore the association between mixed exposure to metals and hyperuricemia (HUA), and highlight the potential of explainable machine learning (EML) and causal mediation analysis (CMA) for application in the analysis of exposome data. METHODS Pre-pandemic data from the National Health and Nutrition Examination Survey (NHANES) 2011-2020 and a total of 13780 individuals were included. We first used traditional statistical models (multiple logistic regression (MLR) and restricted cubic spline regression (RCS)) and EML to explore associations between mixed metals exposures and HUA, followed by the CMA using the 4-way decomposition method to analyze the interaction and mediation effects among BMI or estimated glomerular filtration rate (eGFR), metals and HUA. RESULTS The prevalence of HUA was 18.91% (2606/13780). The MLR showed that mercury (Q4 vs Q1: OR = 1.08, 95% CI:1.02-1.14) and lead (Q4 vs Q1: OR = 1.23, 95% CI:1.13-1.34) were generally positively associated with HUA. Higher concentrations of lead, mercury, selenium and manganese were associated with the increased odds of HUA, and BMI and eGFR were the top two variables attributable to the risk of developing HUA in the EML. Subgroup analyses from the MLR and EML consistently demonstrated the positive relationship between exposure to lead, mercury and selenium in participants with BMI <25 kg/m2 and BMI ≥30 kg/m2. BMI mediated 32.12% of the association between lead exposure and HUA, and the interaction between BMI and lead accounted for 3.88% of the association in the CMA. CONCLUSIONS Heavy metals can increase the HUA risk and BMI or eGFR can mediate and interact with metals to cause HUA. Future studies based on exposome can attempt to utilize the EML and CMA.
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Affiliation(s)
- Haoran Chen
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China
| | - Min Wang
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China
| | - Chongyang Zhang
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China
| | - Jiao Li
- Institute of Medical Information/Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100020, China.
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9
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Du G, Song X, Zhou F, Ouyang L, Li Q, Ruan S, Yang S, Rao S, Wan X, Xie J, Feng C, Fan G. Association between multiple metal(loid)s exposure and renal function: a cross-sectional study from southeastern China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:94552-94564. [PMID: 37532974 DOI: 10.1007/s11356-023-29001-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/22/2023] [Indexed: 08/04/2023]
Abstract
In the real world, humans are exposed to multiple metal(loid)s (designated hereafter metals) that contain essential metals as well as toxic metals. Exposure to the metal mixture was assumed to be associated with renal function impairment; however, there is no consensus on available studies. Therefore, we here explored the association between multiple metals exposure and indicators of renal function in the general population from southeastern China. A total of 11 metals with 6 human essential metals and 5 toxic metals were determined in the selected 720 subjects. In addition, serum uric acid (SUA), serum creatinine (SCR), and the estimated glomerular filtration rate (eGFR) were measured or calculated as indicators of renal function. Using multiple flexible statistical models of generalized linear model, elastic net regression, and Bayesian kernel machine regression, the joint as well as the individual effect of metals within the mixture, and the interactions between metals were explored. When exposed to the metal mixture, the statistically non-significantly increased SUA, the significantly increased SCR, and the significantly declined eGFR were observed. In addition, the declined renal function may be primarily attributed to lead (Pb), arsenic (As), and nickel (Ni) exposure. Finally, interactions, such as the synergistic effect between Pb and Mo on SUA, whereas the antagonistic effect between Ni and Cd on SCR and eGFR were identified. Our finding suggests that combined exposure to multiple metals would impair renal function. Therefore, reducing exposure to toxic heavy metals of Pb, As, and Cd and limiting exposure to the human essential metal of Ni would protect renal function.
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Affiliation(s)
- Guihua Du
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Xiaoguang Song
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Province Center for Disease Control and Prevention, Institute of Environmental Health, 555 Beijingdong Road, Qingshanhu District, Nanchang, Jiangxi, 330046, People's Republic of China
| | - Fankun Zhou
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Lu Ouyang
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Qi Li
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Shiying Ruan
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- First Affiliated Hospital of Nanchang University, 17 Yongwaizheng Stress, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Shuo Yang
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Shaoqi Rao
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Xin Wan
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Jie Xie
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Chang Feng
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China
| | - Guangqin Fan
- Department of Occupational Health and Toxicology, School of Public Health, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China.
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, 461 Bayi Road, Donghu District, Nanchang, Jiangxi, 330006, People's Republic of China.
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10
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Wang D, Li Y, Duan H, Zhang S, Liu L, He Y, Chen X, Jiang Y, Ma Q, Yu G, Liu S, Yao N, Liang Y, Lin X, Liu L, Wan H, Shen J. Associations between blood essential metal mixture and serum uric acid: a cross-sectional study. Front Public Health 2023; 11:1182127. [PMID: 37670835 PMCID: PMC10476669 DOI: 10.3389/fpubh.2023.1182127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/02/2023] [Indexed: 09/07/2023] Open
Abstract
Introduction Although several studies have explored the associations between single essential metals and serum uric acid (SUA), the study about the essential metal mixture and the interactions of metals for hyperuricemia remains unclear. Methods We performed a cross-sectional study to explore the association of the SUA levels with the blood essential metal mixture, including magnesium (Mg), calcium (Ca), iron (Fe), copper (Cu), zinc (Zn), manganese (Mn) in Chinese community-dwelling adults (n=1039). The multivariable linear regression, the weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were conducted to estimate the associations of blood essential metals with SUA levels and the BKMR model was also conducted to estimate the interactions of the essential metals on SUA. Results In the multivariable linear regression, the association of blood Mg, Mn, and Cu with SUA was statistically significant, both in considering multiple metals and a single metal. In WQS regression [β=13.59 (95%CI: 5.57, 21.60)] and BKMR models, a positive association was found between the mixture of essential metals in blood and SUA. Specifically, blood Mg and Cu showed a positive association with SUA, while blood Mn showed a negative association. Additionally, no interactions between individual metals on SUA were observed. Discussion In conclusion, further attention should be paid to the relationship between the mixture of essential metals in blood and SUA. However, more studies are needed to confirm these findings.
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Affiliation(s)
- Dongmei Wang
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Yue Li
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Hualin Duan
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Shuting Zhang
- Department of Endocrinology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Lingling Liu
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Yajun He
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Xingying Chen
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Yuqi Jiang
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Qintao Ma
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Genfeng Yu
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Siyang Liu
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Nanfang Yao
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Yongqian Liang
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Xu Lin
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Lan Liu
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Heng Wan
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
| | - Jie Shen
- Department of Endocrinology and Metabolism, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, Guangdong, China
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11
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Lin Z, Chen H, Lan Q, Chen Y, Liao W, Guo X. Composite Dietary Antioxidant Index Is Negatively Associated with Hyperuricemia in US Adults: An Analysis of NHANES 2007-2018. Int J Endocrinol 2023; 2023:6680229. [PMID: 37636314 PMCID: PMC10449592 DOI: 10.1155/2023/6680229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/29/2023] Open
Abstract
Hyperuricemia and its complications are severe risks to human health. Dietary intervention is considered an essential part of the management of hyperuricemia. Studies have reported that the intake of antioxidants has a positive effect on hyperuricemia. Here, we collected data from 8761 participants of the National Health and Nutrition Examination Survey for this analysis. Daily intakes of vitamins A, C, and E; manganese; selenium; and zinc were calculated as the composite dietary antioxidant index (CDAI). The participants were divided into four groups (Q1, Q2, Q3, and Q4) according to the CDAI. Univariate analysis was used to assess the association of covariates with hyperuricemia. The association between the CDAI and hyperuricemia was evaluated using multinomial logistic regression, and its stability was determined by stratified analysis. Our results revealed that the CDAI has a significant negative association with hyperuricemia (Q2: 0.81 (0.69, 0.95); Q3: 0.75 (0.62, 0.90); Q4: 0.65 (0.51, 0.82); P < 0.01). The results of stratified analysis emphasize that this association between CDAI and hyperuricemia is stable. In conclusion, this study suggested a negative association between the CDAI and hyperuricemia.
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Affiliation(s)
- Zhenzong Lin
- Department of Clinical Laboratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Haokai Chen
- Department of Clinical Laboratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiwen Lan
- Department of Medical Imageology, The Second Clinical School of Guangzhou Medical University, Guangzhou 511436, China
| | - Yinghan Chen
- Department of Medical Imageology, The Second Clinical School of Guangzhou Medical University, Guangzhou 511436, China
| | - Wanzhe Liao
- Department of Clinical Medicine, The Nanshan College of Guangzhou Medical University, Guangzhou 511436, China
| | - Xuguang Guo
- Department of Clinical Laboratory Medicine, Guangdong Provincial Key Laboratory of Major Obstetric Diseases, Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
- Department of Clinical Medicine, The Third Clinical School of Guangzhou Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Major Obstetric Diseases, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases, KingMed School of Laboratory Medicine, Guangzhou Medical University, Guangzhou, China
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12
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Wang T, Zhang L, Liu Y, Li J, Chen G, Zhou H, Yu L, Wan Z, Dong C, Qin L, Chen J. Combined Exposure to Multiple Metals and Kidney Function in a Midlife and Elderly Population in China: A Prospective Cohort Study. TOXICS 2023; 11:toxics11030274. [PMID: 36977039 PMCID: PMC10051264 DOI: 10.3390/toxics11030274] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/01/2023] [Accepted: 03/15/2023] [Indexed: 06/10/2023]
Abstract
[Background] Metal exposure is suspected to be correlated to kidney function. However, the combined effects of co-exposing to multiple metals, especially both toxic and protective metals, have not been completely evaluated. [Method] A prospective cohort study was conducted with the "135" cohort for the evaluation of how plasma metal levels are correlated to kidney function in a midlife and elderly community in southern China. An amount of 1368 subjects without kidney disease at baseline were enrolled in the final analysis. By using linear regression and logistic regression models, the correlation of individual metal values with renal function parameters was assessed. Measuring of the multiple metal exposure level was performed by principal component analysis (PCA). [Results] Diminished renal function, as evaluated based on fast kidney function decline, or estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2, was positively associated with the plasma concentrations of chromium and potassium, but it was negatively associated with selenium and iron (p < 0.05). In multiple-metal analyses, linear and logistic regression models showed that the iron and chromium exposure pattern had a protective effect on renal function, whereas the sodium and potassium exposure pattern and the cadmium and lead exposure pattern increased the risk for fast kidney function decline, and eGFR < 60 mL/min/1.73 m2. [Conclusions] Certain metals, including chromium, potassium, selenium, and iron, were correlated with kidney function in a midlife and elderly community in China. In addition, the potential combined influences of co-exposing to multiple metals were observed.
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Affiliation(s)
- Tianci Wang
- Department of Nutrition and Food Hygiene, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
| | - Liming Zhang
- Suzhou Municipal Center for Disease Control and Prevention, Suzhou 215007, China
| | - Yujie Liu
- Department of Nutrition and Food Hygiene, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
| | - Jian Li
- Suzhou Municipal Center for Disease Control and Prevention, Suzhou 215007, China
| | - Guochong Chen
- Department of Nutrition and Food Hygiene, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
| | - Hui Zhou
- Suzhou Industrial Park Centers for Disease Control and Prevention, Suzhou 215021, China
| | - Lugang Yu
- Suzhou Industrial Park Centers for Disease Control and Prevention, Suzhou 215021, China
| | - Zhongxiao Wan
- Department of Nutrition and Food Hygiene, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
| | - Chen Dong
- Department of Nutrition and Food Hygiene, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
| | - Liqiang Qin
- Department of Nutrition and Food Hygiene, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
| | - Jingsi Chen
- Department of Nutrition and Food Hygiene, School of Public Health, Suzhou Medical College of Soochow University, Suzhou 215123, China
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13
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Mei P, Zhou Q, Liu W, Huang J, Gao E, Luo Y, Ren X, Huang H, Chen X, Wu D, Huang X, Yu H, Liu J. Correlating metal exposures and dietary habits with hyperuricemia in a large urban elderly cohort by artificial intelligence. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:41570-41580. [PMID: 36633743 DOI: 10.1007/s11356-022-24824-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
Epidemiological studies using conventional statistical methods have reported an association between individual metal exposure and hyperuricemia (HUA). There is also evidence that diet may influence HUA development, although the available data are inconsistent. We therefore used an elastic net regression (ENR) model to screen the usefulness of various environmental and dietary factors as predictors of HUA in a large sample cohort. This study included 6217 subjects drawn from the Shenzhen Aging Related Disorder Cohort. We obtained information on the subjects' dietary habits via face-to-face interviews and used inductively coupled plasma mass spectrometry (ICP-MS) to measure the urinary concentrations of 24 metals to which elderly persons in large urban areas may be exposed. An elastic net regression (ENR) model was generated to screen the utility of the metals and dietary factors as predictors of HUA, and we demonstrated the superiority of the ENR model by comparing it to a traditional logistic regression model. The identified predictors were used to create a clinically usable nomogram for identifying patients at risk of developing HUA. The area under curve (AUC) value of the final model was 0.692 for the training set and 0.706 for the test set. Important predictors of HUA were Zn, As, V, and Fe as well as consumption of wheat, beans, and rice; the corresponding estimated odds ratios and 95% confidence intervals were 1.091 (0.932,1.251), 1.190 (1.093,1.286), 0.924 (0.793,1.055), 0.704 (0.626,0.781), 0.998 (0.996,1.001), 0.993 (0.989,0.998), and 1.001 (0.998,1.002), respectively. In contrast to previous studies, we found that both urinary metal concentrations and dietary habits are important for predicting HUA risk. Exposure to specific metals and consumption of specific foods were identified as important predictors of HUA, indicating that the incidence of this disease could be reduced by reducing exposure to these metals and promoting improved dietary habits.
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Affiliation(s)
- Pengcheng Mei
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410006, Hunan, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Qimei Zhou
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410006, Hunan, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Wei Liu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Jia Huang
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410006, Hunan, China
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Erwei Gao
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
- Key Laboratory of Environment & Health (Huazhong University of Science and Technology), Ministry of Education, State Environmental Protection Key Laboratory of Environment and Health (Wuhan) and State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, Hubei, China
| | - Yi Luo
- Shenzhen Luohu Hospital Group, Shenzhen Luohu Hospital for Traditional Chinese Medicine, Shenzhen, 518020, Guangdong, China
| | - Xiaohu Ren
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Haiyan Huang
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Xiao Chen
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Desheng Wu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Xinfeng Huang
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China
| | - Hao Yu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, Guangdong, China.
| | - Jianjun Liu
- Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, 410006, Hunan, China.
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020-2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, Guangdong, China.
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14
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Huang C, Gao E, Xiao F, Wu Q, Liu W, Luo Y, Ren X, Chen X, He K, Huang H, Sun Q, Wu D, Liu J. The relative and interactive effects of urinary multiple metals exposure on hyperuricemia among urban elderly in China. Front Public Health 2023; 11:1015202. [PMID: 36860398 PMCID: PMC9969194 DOI: 10.3389/fpubh.2023.1015202] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 01/13/2023] [Indexed: 02/15/2023] Open
Abstract
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.
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Affiliation(s)
- Chao Huang
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Erwei Gao
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Feng Xiao
- Food Inspection and Quarantine Technology Center of Shenzhen Customs, Shenzhen, Guangdong, China
| | - Qiongzhen Wu
- Food Inspection and Quarantine Technology Center of Shenzhen Customs, Shenzhen, Guangdong, China
| | - Wei Liu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Yi Luo
- Shenzhen Luohu Hospital for Traditional Chinese Medicine, Shenzhen Luohu Hospital Group, Shenzhen, Guangdong, China
| | - Xiaohu Ren
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Xiao Chen
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Kaiwu He
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Haiyan Huang
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Qian Sun
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Desheng Wu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Jianjun Liu
- Shenzhen Key Laboratory of Modern Toxicology, Shenzhen Medical Key Discipline of Health Toxicology (2020–2024), Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong, China,*Correspondence: Jianjun Liu ✉
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15
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Wu S, Huang H, Ji G, Li L, Xing X, Dong M, Ma A, Li J, Wei Y, Zhao D, Ma W, Bai Y, Wu B, Liu T, Chen Q. Joint Effect of Multiple Metals on Hyperuricemia and Their Interaction with Obesity: A Community-Based Cross-Sectional Study in China. Nutrients 2023; 15:nu15030552. [PMID: 36771259 PMCID: PMC9921062 DOI: 10.3390/nu15030552] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 01/14/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
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.
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Affiliation(s)
- Shan Wu
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China
| | - Huimin Huang
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China
| | - Guiyuan Ji
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Lvrong Li
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China
| | - Xiaohui Xing
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China
| | - Ming Dong
- Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou 510399, China
| | - Anping Ma
- Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou 510399, China
| | - Jiajie Li
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China
| | - Yuan Wei
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China
| | - Dongwei Zhao
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China
| | - Yan Bai
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China
| | - Banghua Wu
- Guangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou 510399, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510630, China
- Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou 510632, China
- Correspondence: (T.L.); (Q.C.)
| | - Qingsong Chen
- Guangdong Provincial Engineering Research Center of Public Health Detection and Assessment, School of Public Health, Guangdong Pharmaceutical University, Guangzhou 510310, China
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance, Guangdong Pharmaceutical University, 283 Jianghai Avenue, Guangzhou 510300, China
- Correspondence: (T.L.); (Q.C.)
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16
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Zhang Y, Huang B, Jin J, Xiao Y, Ying H. Recent advances in the application of ionomics in metabolic diseases. Front Nutr 2023; 9:1111933. [PMID: 36726817 PMCID: PMC9884710 DOI: 10.3389/fnut.2022.1111933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
Abstract
Trace elements and minerals play a significant role in human health and diseases. In recent years, ionomics has been rapidly and widely applied to explore the distribution, regulation, and crosstalk of different elements in various physiological and pathological processes. On the basis of multi-elemental analytical techniques and bioinformatics methods, it is possible to elucidate the relationship between the metabolism and homeostasis of diverse elements and common diseases. The current review aims to provide an overview of recent advances in the application of ionomics in metabolic disease research. We mainly focuses on the studies about ionomic or multi-elemental profiling of different biological samples for several major types of metabolic diseases, such as diabetes mellitus, obesity, and metabolic syndrome, which reveal distinct and dynamic patterns of ion contents and their potential benefits in the detection and prognosis of these illnesses. Accumulation of copper, selenium, and environmental toxic metals as well as deficiency of zinc and magnesium appear to be the most significant risk factors for the majority of metabolic diseases, suggesting that imbalance of these elements may be involved in the pathogenesis of these diseases. Moreover, each type of metabolic diseases has shown a relatively unique distribution of ions in biofluids and hair/nails from patients, which might serve as potential indicators for the respective disease. Overall, ionomics not only improves our understanding of the association between elemental dyshomeostasis and the development of metabolic disease but also assists in the identification of new potential diagnostic and prognostic markers in translational medicine.
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Affiliation(s)
- Yan Zhang
- Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China,*Correspondence: Yan Zhang ✉
| | - Biyan Huang
- Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Jiao Jin
- Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Yao Xiao
- Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Huimin Ying
- Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, China,Huimin Ying ✉
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Xu P, Feng L, Xu D, Wu L, Chen Y, Xiang J, Cheng P, Wang X, Lou J, Tang J, Lou X, Chen Z. Ribosomal DNA copy number associated with blood metal levels in school-age children: A follow-up study on a municipal waste incinerator in Zhejiang, China. CHEMOSPHERE 2022; 307:135676. [PMID: 35842053 DOI: 10.1016/j.chemosphere.2022.135676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/15/2022] [Accepted: 07/09/2022] [Indexed: 06/15/2023]
Abstract
To evaluate the body burdens of heavy metals and explore the impact of environmental metal exposure on ribosomal DNA (rDNA) or mitochondrial DNA (mtDNA) copy number (CN) variation in school-age children living near a municipal waste incinerator (MWI), we conducted a follow-up study in 2019. A total of 146 sixth-grade children from a primary school located 1.2 km away from the MWI were recruited for our study. Metals, including vanadium (V), chromium (Cr), manganese (Mn), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), arsenic (As), selenium (Se), cadmium (Cd), stannum (Sn), stibium (Sb), thallium (Tl), and lead (Pb), were determined by an inductively coupled plasma mass spectrometer method. Real-time qPCR was used to measure the rDNA and mtDNA CN. The blood metal levels followed this order: Zn > Cu > Se > Pb > Mn > Sb > As > Ni > Cd > Co > Cr > Sn > V > Tl. Blood Cr level was significantly correlated with 18 S, 2.5 S, and 45 S CN (β = -0.25, -0.22, -0.26, p < 0.05); Ni was correlated with 5 S (β = -0.36, p < 0.01); Cu was correlated with 28 S, 18 S, and 5.8 S (β = -0.24, -0.24, -0.23, p < 0.05); while Zn was correlated with 18 S, 5.8 S, and 45 S (β = -0.28, -0.32, -0.26, p < 0.05). In conclusion, school-age children living near the MWI had lower blood metal levels compared to children recruited in 2013, while rDNA CN loss was found to be correlated to several heavy metals in these children.
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Affiliation(s)
- Peiwei Xu
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China
| | - Lingfang Feng
- School of Public Health, Hangzhou Medical College, 8 Yi Kang Street, Lin'an District, 311399, Hangzhou, Zhejiang, China
| | - Dandan Xu
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China
| | - Lizhi Wu
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China
| | - Yuan Chen
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China
| | - Jie Xiang
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China
| | - Ping Cheng
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China
| | - Xiaofeng Wang
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China
| | - Jianlin Lou
- School of Public Health, Hangzhou Medical College, 8 Yi Kang Street, Lin'an District, 311399, Hangzhou, Zhejiang, China
| | - Jun Tang
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China
| | - Xiaoming Lou
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China
| | - Zhijian Chen
- Zhejiang Provincial Center for Disease Control and Prevention, 3399 Bin Sheng Road, Binjiang District, Hangzhou 310051, China.
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18
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A systematic review and meta-analysis of the hyperuricemia risk from certain metals. Clin Rheumatol 2022; 41:3641-3660. [DOI: 10.1007/s10067-022-06362-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/30/2022]
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19
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Ma Y, Hu Q, Yang D, Zhao Y, Bai J, Mubarik S, Yu C. Combined exposure to multiple metals on serum uric acid in NHANES under three statistical models. CHEMOSPHERE 2022; 301:134416. [PMID: 35490746 DOI: 10.1016/j.chemosphere.2022.134416] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 03/21/2022] [Accepted: 03/22/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND There are rare researches on the correlations between metals exposure and serum uric acid (SUA), and existing research has only investigated the single metal effect. This study aimed to investigate the combined effects of metal mixtures on SUA and hyperuricemia using three statistical models. METHODS In this study, the data were extracted from three cycle years of the National Health and Nutrition Examination Survey (NHANES). Subsequently, generalized linear regression, weighted quantile regression (WQS) and Bayesian kernel machine regression (BKMR) models were fitted to evaluate the correlations between metal mixtures and both SUA and hyperuricemia. RESULTS Of 3926 participants included, 19.13% participants had hyperuricemia. It was found using multi-metals generalized linear regression models that there were positive correlations of arsenic and cadmium with both outcomes. The negative correlations were identified in cobalt, iodine, and manganese with SUA concentration, whereas only cobalt was negatively correlated with hyperuricemia. Based on the WQS regression model fitted in positive direction, it was suggested that the WQS indices were significantly correlated with SUA (β = 6.64, 95% CI: 3.14-10.13) and hyperuricemia (OR = 1.25, 95% CI: 1.08-1.44); however, the result achieved by using the model fitted in negative direction indicated that the WQS indices were only significantly correlated with SUA (β = -5.29, 95%CI: 8.02 ∼ -2.56). With the use of the BKMR model, a significant increasing trend between metal mixtures and hyperuricemia was found, while no significant overall effect of metal mixtures on SUA was identified. The predominant roles of arsenic, cadmium, and cobalt in the change of SUA and hyperuricemia risk were found using all three models. CONCLUSION The finding of this study revealed that metal mixtures might have a positive combined effect on hyperuricemia. The mutual verification of two outcomes using the three different models provided strong public health implications for protecting people from heavy metal pollution and preventing hyperuricemia.
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Affiliation(s)
- Yudiyang Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, No. 115, Dong-hu Road, Wuhan 430071, China
| | - Qian Hu
- Department of Public Health, Tongji Medical College, Huazhong University of Science and Technology, China
| | - Donghui Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, No. 115, Dong-hu Road, Wuhan 430071, China
| | - Yudi Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, No. 115, Dong-hu Road, Wuhan 430071, China
| | - Jianjun Bai
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, No. 115, Dong-hu Road, Wuhan 430071, China
| | - Sumaira Mubarik
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, No. 115, Dong-hu Road, Wuhan 430071, China
| | - Chuanhua Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, No. 115, Dong-hu Road, Wuhan 430071, China.
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20
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Li A, Zhou Q, Mei Y, Zhao J, Zhao M, Xu J, Ge X, Xu Q. Novel Strategies for Assessing Associations Between Selenium Biomarkers and Cardiometabolic Risk Factors: Concentration, Visit-to-Visit Variability, or Individual Mean? Evidence From a Repeated-Measures Study of Older Adults With High Selenium. Front Nutr 2022; 9:838613. [PMID: 35711534 PMCID: PMC9196882 DOI: 10.3389/fnut.2022.838613] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 05/11/2022] [Indexed: 12/23/2022] Open
Abstract
Background and Aims Previous studies have focused only on the cardiometabolic effects of selenium concentrations. We explored whether selenium levels and their visit-to-visit variability (VVV) and individual mean (IM) are independently associated with cardiometabolic risk factors. Methods A three-wave repeated-measures study of older adults with high selenium (n = 201) was conducted in Beijing from 2016 to 2018. Whole blood selenium and urinary selenium concentrations were measured. VVV and IM were used to profile the homeostasis of the selenium biomarkers. Four indicators, namely standard deviation, coefficient of variation, average real variability, and variability independent of the mean, were employed to characterize VVV. We considered 13 cardiometabolic factors: four lipid profile indicators, three blood pressure indices, glucose, uric acid, waistline, hipline, waist-hip ratio, and sex-specific metabolic syndrome score. Linear mixed-effects regression models with random intercepts for the participants were employed to explore the associations of the selenium concentrations, VVV, and IM with the cardiometabolic factors. Results The geometric mean whole blood and urinary selenium levels were 134.30 and 18.00 μg/L, respectively. Selenium concentrations were significantly associated with numerous cardiometabolic factors. Specifically, whole blood selenium was positively associated with total cholesterol [0.22, 95% confidence interval (CI): 0.12, 0.33], low-density lipoprotein cholesterol (LDL-C; 0.28, 95% CI: 0.13, 0.42), glucose (0.22, 95% CI: 0.10, 0.34), and uric acid (0.16, 95% CI: 0.04, 0.28). After adjustment for VVV, the IM of whole blood selenium was positively correlated with total cholesterol (0.002, 95% CI: 0.001, 0.004), triglycerides (0.007, 95% CI: 0.004, 0.011), and LDL-C (0.002, 95% CI: 0.000, 0.004). However, we did not observe any robust associations between the VVV of the selenium biomarkers and cardiometabolic risk factors after adjustment for IM. Conclusion Our findings suggest that selenium concentrations and their IMs are significantly associated with cardiometabolic risk factors among older adults with high selenium. Longer repeated-measures studies among the general population are required to validate our findings and elucidate the relevant underlying mechanisms.
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Affiliation(s)
- Ang Li
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.,Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Quan Zhou
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.,Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Yayuan Mei
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.,Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jiaxin Zhao
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.,Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Meiduo Zhao
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.,Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Jing Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.,Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Xiaoyu Ge
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.,Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Qun Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.,Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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Xu J, Zhu X, Hui R, Xing Y, Wang J, Shi S, Zhang Y, Zhu L. Associations of metal exposure with hyperuricemia and gout in general adults. Front Endocrinol (Lausanne) 2022; 13:1052784. [PMID: 36531480 PMCID: PMC9755211 DOI: 10.3389/fendo.2022.1052784] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Accepted: 11/21/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Epidemiological evidence of the associations between metal exposure and gout-related outcomes (including serum uric acid [SUA], hyperuricemia and gout) is scarce. The aim of the study is to investigate the associations of metal exposure with SUA, hyperuricemia and gout in general adults. METHODS In this study, the exposure to five blood metals (mercury, manganese, lead, cadmium and selenium) of general adults was analyzed based on the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018 (n = 14,871). Linear, logistic and weighted quantile sum (WQS) regression models were applied to examine the associations of blood metals with gout-related outcomes. Possible dose-response relationships were analyzed through restricted cubic spline regression. RESULTS Compared with the lowest quartile of blood metals, mercury (quartile 2 and 4), lead (quartile 2, 3, and 4) and selenium (quartile 2 and 4) were found to be positively correlated with SUA and hyperuricemia. Higher levels of mercury and lead were associated with gout, but only those in the fourth quartile had statistical significance (OR [95%CI]: 1.39 [1.10-1.75] and 1.905 [1.41-2.57]) respectively). The WQS index of the blood metals was independently correlated with SUA (β [95%CI]: 0.17 [0.13-0.20]), hyperuricemia (OR [95%CI]: 1.29 [1.16-1.42]) and gout (OR [95%CI]: 1.35 [1.15-1.58]). Among them, lead was the most heavily weighted component (weight = 0.589 for SUA, 0.482 for hyperuricemia, and 0.527 for gout). In addition, restricted cubic spline regression models showed a linear association of lead with the prevalence of hyperuricemia and gout. CONCLUSION Our results suggested that blood metal mixtures were positively associated with gout-related outcomes, with the greatest effect coming from lead.
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Affiliation(s)
- Jing Xu
- Department of Cardiology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
| | - Xu Zhu
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Rutai Hui
- Department of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yujie Xing
- Department of Cardiology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
| | - Junkui Wang
- Department of Cardiology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
| | - Shuang Shi
- Department of Cardiology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
- *Correspondence: Shuang Shi, ; Yong Zhang, ; Ling Zhu,
| | - Yong Zhang
- Department of Cardiology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
- *Correspondence: Shuang Shi, ; Yong Zhang, ; Ling Zhu,
| | - Ling Zhu
- Department of Cardiology, Shaanxi Provincial People’s Hospital, Xi’an, Shaanxi, China
- Department of Cardiology, The Third Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- *Correspondence: Shuang Shi, ; Yong Zhang, ; Ling Zhu,
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