OBJECTIVE: To report the impact of continuous glucose monitoring (CGM) on glycemic variability (GV) indices, factors predictive of change, and to correlate variability with conventional markers of glycemia. METHODS: Data from the JDRF study of CGM in participants with type 1 diabetes were used. Participants were randomized to CGM or self-monitored blood glucose (SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the control group were analyzed. The associations of demographic and clinical factors with change in GV indices from baseline to 26 weeks were evaluated. RESULTS: Baseline data were available for 448 subjects. GV indices were all outside normative ranges (P < 0.001). Intercorrelation between GV indices was common and, apart from coefficient of variation (CV), low blood glucose index (LBGI), and percentage of glycemic risk assessment diabetes equation score attributable to hypoglycemia (%GRADEhypoglycemia), all indices correlate positively with HbA1c. There was strong correlation between time spent in hypoglycemia, and CV, LBGI, and %GRADEhypoglycemia, but not with HbA1c. A significant reduction in all GV indices, except lability index and mean absolute glucose change per unit time (MAG), was demonstrated in the intervention group at 26 weeks compared with the control group. Baseline factors predicting a change in GV with CGM include baseline HbA1c, baseline GV, frequency of daily SMBG, and insulin pump use. CONCLUSIONS:CGM reduces most GV indices compared with SMBG in people with type 1 diabetes. The strong correlation between time spent in hypoglycemia and CV, LBGI, and %GRADEhypoglycemia highlights the value of these metrics in assessing hypoglycemia as an adjunct to HbA1c in the overall assessment of glycemia.
RCT Entities:
OBJECTIVE: To report the impact of continuous glucose monitoring (CGM) on glycemic variability (GV) indices, factors predictive of change, and to correlate variability with conventional markers of glycemia. METHODS: Data from the JDRF study of CGM in participants with type 1 diabetes were used. Participants were randomized to CGM or self-monitored blood glucose (SMBG). GV indices at baseline, at 26 weeks in both groups, and at 52 weeks in the control group were analyzed. The associations of demographic and clinical factors with change in GV indices from baseline to 26 weeks were evaluated. RESULTS: Baseline data were available for 448 subjects. GV indices were all outside normative ranges (P < 0.001). Intercorrelation between GV indices was common and, apart from coefficient of variation (CV), low blood glucose index (LBGI), and percentage of glycemic risk assessment diabetes equation score attributable to hypoglycemia (%GRADEhypoglycemia), all indices correlate positively with HbA1c. There was strong correlation between time spent in hypoglycemia, and CV, LBGI, and %GRADEhypoglycemia, but not with HbA1c. A significant reduction in all GV indices, except lability index and mean absolute glucose change per unit time (MAG), was demonstrated in the intervention group at 26 weeks compared with the control group. Baseline factors predicting a change in GV with CGM include baseline HbA1c, baseline GV, frequency of daily SMBG, and insulin pump use. CONCLUSIONS: CGM reduces most GV indices compared with SMBG in people with type 1 diabetes. The strong correlation between time spent in hypoglycemia and CV, LBGI, and %GRADEhypoglycemia highlights the value of these metrics in assessing hypoglycemia as an adjunct to HbA1c in the overall assessment of glycemia.
Authors: Elena Toschi; Christine Slyne; Kayla Sifre; Rachel O'Donnell; Jordan Greenberg; Astrid Atakov-Castillo; Sam Carl; Medha Munshi Journal: Diabetes Care Date: 2020-05-27 Impact factor: 19.112
Authors: Thomas Danne; Revital Nimri; Tadej Battelino; Richard M Bergenstal; Kelly L Close; J Hans DeVries; Satish Garg; Lutz Heinemann; Irl Hirsch; Stephanie A Amiel; Roy Beck; Emanuele Bosi; Bruce Buckingham; Claudio Cobelli; Eyal Dassau; Francis J Doyle; Simon Heller; Roman Hovorka; Weiping Jia; Tim Jones; Olga Kordonouri; Boris Kovatchev; Aaron Kowalski; Lori Laffel; David Maahs; Helen R Murphy; Kirsten Nørgaard; Christopher G Parkin; Eric Renard; Banshi Saboo; Mauro Scharf; William V Tamborlane; Stuart A Weinzimer; Moshe Phillip Journal: Diabetes Care Date: 2017-12 Impact factor: 19.112