Association of Continuous Glucose Monitoring-Derived Glycemia Risk Index With Cardiovascular Autonomic Neuropathy in Patients With Type 1 Diabetes Mellitus: A Cross-sectional Study.
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引用次数: 0
Abstract
Background: The glycemia risk index (GRI) is a new composite continuous glucose monitoring (CGM) metric for weighted hypoglycemia and hyperglycemia. We evaluated the association between the GRI and cardiovascular autonomic neuropathy (CAN) and compared the effects of the GRI and conventional CGM metrics on CAN.
Methods: For this cross-sectional study, three-month CGM data were retrospectively analyzed before autonomic function tests were performed in 165 patients with type 1 diabetes. CAN was defined as at least two abnormal results of parasympathetic tests according to an age-specific reference.
Results: The overall prevalence of CAN was 17.1%. Patients with CAN had significantly higher GRI scores, target above range (TAR), coefficient of variation (CV), and standard deviation (SD) but significantly lower time in range (TIR) than those without CAN. The prevalence of CAN increased across higher GRI zones (P for trend <.001). A multivariate logistic regression analysis, adjusted for covariates such as HbA1c, demonstrated that the odds ratio (OR) of CAN was 9.05 (95% confidence interval [CI]: 2.21-36.96, P = .002) per 1-SD increase in the GRI. TIR and CV were also significantly associated with CAN in the multivariate model. The area under the curve of GRI for the prediction of CAN (0.85, 95% CI: 0.76-0.94) was superior to that of TIR (0.80, 95% CI: 0.71-0.89, P for comparison = .046) or CV (0.71, 95% CI: 0.57-0.84, P for comparison = .049).
Conclusions: The GRI is significantly associated with CAN in patients with type 1 diabetes and may be a better CGM metric than TIR for predicting CAN.
期刊介绍:
The Journal of Diabetes Science and Technology (JDST) is a bi-monthly, peer-reviewed scientific journal published by the Diabetes Technology Society. JDST covers scientific and clinical aspects of diabetes technology including glucose monitoring, insulin and metabolic peptide delivery, the artificial pancreas, digital health, precision medicine, social media, cybersecurity, software for modeling, physiologic monitoring, technology for managing obesity, and diagnostic tests of glycation. The journal also covers the development and use of mobile applications and wireless communication, as well as bioengineered tools such as MEMS, new biomaterials, and nanotechnology to develop new sensors. Articles in JDST cover both basic research and clinical applications of technologies being developed to help people with diabetes.