Jin Zhang1, Nai-Jia Liu2, Yan Zhang1, Hong Yang1, Zachary Tang2. 1. Department of Nephrology, The Fifth Affiliated Hospital of Zhengzhou University Zhengzhou, China. 2. Department of Endocrinology, Shanghai Tongji Hospital, Tongji University School of Medicine Shanghai, China.
Abstract
BACKGROUND: The objective of this study was to evaluate associations of metabolic syndrome (MetS) and chronic kidney disease (CKD) with cardiovascular autonomic neuropathy (CAN), and to estimate the extent to which interaction of MetS and CKD affects the outcome in the Chinese population. METHOD: We conducted a large-scale, population-based study to analyze the association and interaction of the two factors for CAN in a sample of 2,092 Chinese people. Univariate and multiple linear regression (MLR) analysis were employed to detect these relationships. Interaction on an additive scale can be calculated by using the relative excess risk due to interaction (RERI), the proportion attributable to interaction (AP), and the synergy index (S). RESULTS: MLR adjusted for confounding factors showed that MetS was independently associated with CAN (P < 0.001). A significant interaction effect was detected by MLR (P = 0.042). In addition, a positive interaction between MetS and CKD on CAN was estimated by using parameters of RETI = 0.119 (95% CI: 0.059-0.178), AP = 0.049 (95% CI: -0.039-0.138) and S = 1.091 (95% CI: 0.164-2.019). CONCLUSION: Our findings suggest that MetS is independently associated with CAN and offer evidence to support the hypothesis that MetS and CKD have positive interactions on CAN.
BACKGROUND: The objective of this study was to evaluate associations of metabolic syndrome (MetS) and chronic kidney disease (CKD) with cardiovascular autonomic neuropathy (CAN), and to estimate the extent to which interaction of MetS and CKD affects the outcome in the Chinese population. METHOD: We conducted a large-scale, population-based study to analyze the association and interaction of the two factors for CAN in a sample of 2,092 Chinese people. Univariate and multiple linear regression (MLR) analysis were employed to detect these relationships. Interaction on an additive scale can be calculated by using the relative excess risk due to interaction (RERI), the proportion attributable to interaction (AP), and the synergy index (S). RESULTS: MLR adjusted for confounding factors showed that MetS was independently associated with CAN (P < 0.001). A significant interaction effect was detected by MLR (P = 0.042). In addition, a positive interaction between MetS and CKD on CAN was estimated by using parameters of RETI = 0.119 (95% CI: 0.059-0.178), AP = 0.049 (95% CI: -0.039-0.138) and S = 1.091 (95% CI: 0.164-2.019). CONCLUSION: Our findings suggest that MetS is independently associated with CAN and offer evidence to support the hypothesis that MetS and CKD have positive interactions on CAN.
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