Miao Huang1, Jingyuan Chen1, Guangyu Yan1, Yiping Yang1, Dan Luo2, Xiang Chen3, Meian He4, Hong Yuan5, Zhijun Huang6, Yao Lu7. 1. Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha 410013, China. 2. Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha 410078, China. 3. Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410008, China. 4. Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. 5. Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha 410013, China; National-Local Joint Engineering Laboratory of Drug Clinical Evaluation Technology, Changsha 410000, China. 6. Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha 410013, China. Electronic address: huangzj@csu.edu.cn. 7. Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha 410013, China; National-Local Joint Engineering Laboratory of Drug Clinical Evaluation Technology, Changsha 410000, China. Electronic address: luyao0719@163.com.
Abstract
OBJECTIVE: Several heavy metals have been reported to be associated with metabolic syndrome(MetS) in general population, while effects of multiple metals exposure on MetS in residents living in heavy metal polluted regions have not been investigated. We aimed to assess the association of 23 metal levels and MetS among population living in China's heavy metal polluted regions. METHODS: From August 2016 to July 2017, a total of 2109 eligible participants were consecutively enrolled in our study in Hunan province, China. The levels of plasma and urine metals were measured by inductively coupled plasma mass spectrometer (ICP-MS). MetS was defined by the criteria of the International Diabetes Federation. Multivariable regression models were applied to analysis the potential relationship. RESULTS: In the overall population, crude model showed positive relationship of plasma titanium (Ti) with MetS and negative association of urine vanadium, iron, and selenium with MetS. After adjusted for potential confounders, only plasma Ti was positive associated with MetS (adjusted OR for Q4 versus Q1: 1.46; 95% CI: 1.06-1.99), and this positive correlation was explained by abdominal obesity (OR = 1.84, 95% CI: 1.41-2.39) and high triglycerides (OR = 2.23, 95% CI: 1.68-2.96). Further linear regression analysis revealed significant association of plasma Ti levels with waist circumference (β = 0.0056, 95% CI: 0.0004-0.0109, P = 0.036) and triglycerides (β = 0.0012, 95% CI: 0.0006-0.0019, P < 0.001), respectively. CONCLUSION: High plasma Ti level was associated with increased risk of MetS via increasing waist circumference and triglycerides in people under high metal exposure.
OBJECTIVE: Several heavy metals have been reported to be associated with metabolic syndrome(MetS) in general population, while effects of multiple metals exposure on MetS in residents living in heavy metal polluted regions have not been investigated. We aimed to assess the association of 23 metal levels and MetS among population living in China's heavy metal polluted regions. METHODS: From August 2016 to July 2017, a total of 2109 eligible participants were consecutively enrolled in our study in Hunan province, China. The levels of plasma and urine metals were measured by inductively coupled plasma mass spectrometer (ICP-MS). MetS was defined by the criteria of the International Diabetes Federation. Multivariable regression models were applied to analysis the potential relationship. RESULTS: In the overall population, crude model showed positive relationship of plasma titanium (Ti) with MetS and negative association of urine vanadium, iron, and selenium with MetS. After adjusted for potential confounders, only plasma Ti was positive associated with MetS (adjusted OR for Q4 versus Q1: 1.46; 95% CI: 1.06-1.99), and this positive correlation was explained by abdominal obesity (OR = 1.84, 95% CI: 1.41-2.39) and high triglycerides (OR = 2.23, 95% CI: 1.68-2.96). Further linear regression analysis revealed significant association of plasma Ti levels with waist circumference (β = 0.0056, 95% CI: 0.0004-0.0109, P = 0.036) and triglycerides (β = 0.0012, 95% CI: 0.0006-0.0019, P < 0.001), respectively. CONCLUSION: High plasma Ti level was associated with increased risk of MetS via increasing waist circumference and triglycerides in people under high metal exposure.