Diabetic Cardiovascular Autonomic Neuropathy Clinical Trial
Official title:
The Risk Analysis for Diabetic Cardiovascular Autonomic Neuropathy in General Chinese Population
A large-scale, community-based, cross-sectional study was conducted to explore the extent to which risk factors associated with diabetic cardiovascular autonomic neuropathy (DCAN) in general Chinese population. A total of more than 2000 diabetic participants were recruited by using multiple stages sampling (first cluster sampling and then simply sampling). Data involved in demographic information, clinical biomarkers such as glucose and lipids profiles, medical and therapy history were collected. Every participants was complete DNA extracted and genotyped. Diabetic Cardiovascular autonomic functions were measured by using short-term heart rate variability (HRV) to evaluate the outcome of DCAN. Univariate and multiple variables analysis have been performed to examine potential environmental and genetic risk factors of CAN. In addition, clinical risk model, simply screening model and nonlinear system model such as artificial neural network was created, respectively.
Little is known about the risk factors and risk models for diabetic cardiovascular autonomic neuropathy in Chinese population. A large-scale, community-based, cross-sectional study was conducted to explore the extent to which risk factors associated with diabetic cardiovascular autonomic neuropathy (DCAN) in general Chinese population. A total of more than 2000 diabetic participants were recruited by using multiple stages sampling (first cluster sampling and then simply sampling). Data involved in demographic information, clinical biomarkers such as glucose and lipids profiles, medical and therapy history were collected. Every participants was complete DNA extracted and genotyped. Diabetic Cardiovascular autonomic functions were measured by using short-term heart rate variability (HRV) to evaluate the outcome of DCAN. Univariate and multiple variables analysis have been performed to examine potential environmental and genetic risk factors of CAN. In addition, clinical risk model, simply screening model and nonlinear system model such as artificial neural network was created, respectively. ;