Study on the correlation and interaction between urinary metals level and diabetes:A case-control study of community-dwelling elderly

Abstract Background 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. The present case-control study of 188 diabetic 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. Methods 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).Logistic regression and restricted cubic spline (RCS) analysis were used to explore the associations and dose-response relationships of urine metals with diabetes. To analysis of multi-metal exposures and diabetes risk, weighted quantile sum regression Models (WQS) and the Bayesian Kernel Machine Regression (BKMR) model were applied. Results 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 Zn and Tl were the major contributors to the combined effect. Conclusion 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 Zn and Tl on diabetes.