Abstract
It has been reported that metal exposure is associated with the risk of diabetes, but the results are inconsistent. The relationship between diabetes and a single metal might be attenuated or strengthened due to the complex interactions of metals and the chronic diseases comorbidity (especially in the elderly). However, the evidence of multiple metal exposure effect in participants with diabetes only is limited, particularly in the elderly. This cross-sectional study used a case-control method, involving 188 diabetes patients and 376 healthy participants aimed to evaluate the potential relationships between the concentrations of 9 metals in urine and the risk of diabetes and to access the interactive effects of metals in Chinese community-dwelling elderly. The urine levels of 9 metals (cobalt, zinc, copper, arsenic, molybdenum, cadmium, tellurium, thallium, lead) were detected by inductively coupled plasma mass spectrometry (ICP-MS) in 564 adults recruited from Yinchuan Community Health Service Center (Yinchuan, China). During the baseline survey, the demographic information of the subjects was collected through questionnaire survey, the indexes such as blood pressure, blood lipid and liver function were measured through physical examination. Logistic regression and restricted cubic spline (RCS) analysis were used to explore the associations and dose–response relationships of urine metals with diabetes. To the analysis of multi-metal exposures and diabetes risk, weighted quantile sum (WQS) regression model and the Bayesian Kernel machine regression (BKMR) model were applied. The concentrations of cobalt, zinc, copper, arsenic, molybdenum, cadmium, tellurium, thallium, and lead were higher in the diabetes group (p < 0.05). In logistic regression analysis, we found that the OR values of urinary cobalt, zinc, copper, arsenic, molybdenum, cadmium, tellurium, thallium, and lead quartiles showed an increasing trend. In the single-metal model, the adjusted ORs(95% CI) in the highest quartiles were 2.94(1.72,5.05) for cobalt,5.05 (2.85,8.93) for zinc, 2.28(1.32,3.91) for copper, 1.99(1.15,3.43) for arsenic, 2.61(1.54,4.43) for molybdenum, 2.89(1.68,4.96) for cadmium, 2.52(1.44,4.41) for tellurium, 3.53(2.03,6.12) for thallium, and 2.18(1.27,3.75) for lead compared with the lowest quartile. And in the RCS model, the concentrations of cobalt, zinc, copper, arsenic, molybdenum, cadmium, tellurium, thallium, and lead showed a nonlinear dose–response relationship with diabetes risk (P-overall < 0.05,P-nonlinear < 0.05). The results from multi-pollutant models all indicated that metal mixture was positively associated with the risk of diabetes, and zinc and thallium were the major contributors to the combined effect. Elevated levels of urine cobalt, zinc, copper, arsenic, molybdenum, cadmium, tellurium, thallium, and lead were associated with increased risk of diabetes. There is a positive interaction between zinc and thallium on diabetes.
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The datasets generated during and/or analyzed during the current study are not publicly available due to limited authorizations from the authors but are available from the corresponding author on reasonable request.
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Acknowledgements
We sincerely thank the laboratory staff and data collection personnel who participated in our basic work. We are particularly grateful to all the participants in this study for their questionnaires and donated biological samples.
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This project was supported by the Natural Science Foundation Project of Ningxia, China (2022AAC05028), and the Special talents start-up funding of Ningxia Medical University (XT2019013, XZ2020008, and XT2019003). This work was also supported by the 2022 "Light of the West" Talent Training Plan Project of Chinese Academy of Sciences (XAB2022YM18) and the Key Research and Development Project of Ningxia (Grant No. 2021BEG02026).
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RW contributed to conceptualization, methodology, investigation, writing—original draft, and data curation. PH contributed to conceptualization, methodology, formal analysis, writing—original draft, and data curation. SD contributed to conceptualization, methodology, investigation, formal analysis, and data curation. ZZ contributed to methodology, formal analysis, and data Curation. YD contributed to methodology, formal analysis, and data curation. ML contributed to investigation. ZS: Investigation. XL: Writing—review & editing. YS: Investigation. YS contributed to investigation. RZ contributed to conceptualization, investigation, formal analysis, and writing—original draft, and supervision. JS contributed to conceptualization, investigation, formal analysis, writing—original draft, and supervision. HY contributed to conceptualization, investigation, formal analysis, writing – original draft, and supervision.
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Wang, R., He, P., Duan, S. et al. Correlation and interaction between urinary metals level and diabetes: A cross sectional study of community-dwelling elderly. Expo Health 16, 559–574 (2024). https://doi.org/10.1007/s12403-023-00577-6
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DOI: https://doi.org/10.1007/s12403-023-00577-6