Rosanna Fiallo-Scharer1. 1. Jaeb Center for Health Research, 15310 Amberly Drive, Suite 350, Tampa, Florida 33647, USA. direcnet@jaeb.org
Abstract
CONTEXT: Advantages/disadvantages of continuous vs. discrete glucose monitoring are not well documented. OBJECTIVE: Compare glucose profiles from home meters vs. continuous sensors. DESIGN: Randomized clinical trial conducted by the Diabetes Research in Children Network (DirecNet) to assess the utility of the GlucoWatch G2 Biographer. SETTING: Home glucose measurements. PATIENTS: Two hundred children (age, 7 to < 18 yr) with type 1 diabetes. INTERVENTION: At baseline, subjects were asked to wear the continuous glucose monitoring system (CGMS) sensor and perform meter tests at eight prespecified times of the day (eight-point testing) each for 3 d (2 d using both, 1 d eight-point testing only, 1 d CGMS only). Hemoglobin A1c was measured in a central laboratory. MAIN OUTCOME MEASURE: Six-month hemoglobin A1c. This analysis looked at baseline glucose profiles/hemoglobin A1c. RESULTS: Only 10% of subjects completed full eight-point testing for 3 d, but median CGMS use was 70 h. Mean glucose was lower when measured by the CGMS compared with eight-point testing (183 +/- 37 vs. 188 +/- 41 mg/dl; 10.2 +/- 2.1 vs.10.4 +/- 2.3 mmol/liter; P = 0.009), especially overnight (2400-0400 h; 174 vs. 199 mg/dl; 9.7 vs. 11.1 mmol/liter; P < 0.001). Associations of hemoglobin A1c with mean glucose were similar for eight-point testing [slope 23 mg/dl per 1% (1.3 mmol/liter); correlation 0.40; P < 0.001] and CGMS [slope 19 mg/dl per 1% (1.1 mmol/liter); correlation 0.39; P < 0.001]. Postprandial excursions were lower for eight-point testing vs. CGMS, especially after dinner (mean excursion -17 vs. 63 mg/dl; -1.0 vs. 3.5 mmol/liter; P < 0.001). CONCLUSIONS: Both methods gave similar mean glucose profiles and associations with hemoglobin A1c. Advantages of the CGMS were higher density of data and better detection of postprandial peaks. However, the CGMS may overestimate the frequency of low glucose levels, especially overnight.
RCT Entities:
CONTEXT: Advantages/disadvantages of continuous vs. discrete glucose monitoring are not well documented. OBJECTIVE: Compare glucose profiles from home meters vs. continuous sensors. DESIGN: Randomized clinical trial conducted by the Diabetes Research in Children Network (DirecNet) to assess the utility of the GlucoWatch G2 Biographer. SETTING: Home glucose measurements. PATIENTS: Two hundred children (age, 7 to < 18 yr) with type 1 diabetes. INTERVENTION: At baseline, subjects were asked to wear the continuous glucose monitoring system (CGMS) sensor and perform meter tests at eight prespecified times of the day (eight-point testing) each for 3 d (2 d using both, 1 d eight-point testing only, 1 d CGMS only). Hemoglobin A1c was measured in a central laboratory. MAIN OUTCOME MEASURE: Six-month hemoglobin A1c. This analysis looked at baseline glucose profiles/hemoglobin A1c. RESULTS: Only 10% of subjects completed full eight-point testing for 3 d, but median CGMS use was 70 h. Mean glucose was lower when measured by the CGMS compared with eight-point testing (183 +/- 37 vs. 188 +/- 41 mg/dl; 10.2 +/- 2.1 vs.10.4 +/- 2.3 mmol/liter; P = 0.009), especially overnight (2400-0400 h; 174 vs. 199 mg/dl; 9.7 vs. 11.1 mmol/liter; P < 0.001). Associations of hemoglobin A1c with mean glucose were similar for eight-point testing [slope 23 mg/dl per 1% (1.3 mmol/liter); correlation 0.40; P < 0.001] and CGMS [slope 19 mg/dl per 1% (1.1 mmol/liter); correlation 0.39; P < 0.001]. Postprandial excursions were lower for eight-point testing vs. CGMS, especially after dinner (mean excursion -17 vs. 63 mg/dl; -1.0 vs. 3.5 mmol/liter; P < 0.001). CONCLUSIONS: Both methods gave similar mean glucose profiles and associations with hemoglobin A1c. Advantages of the CGMS were higher density of data and better detection of postprandial peaks. However, the CGMS may overestimate the frequency of low glucose levels, especially overnight.
Authors: Bruce Buckingham; Roy W Beck; William V Tamborlane; Dongyuan Xing; Craig Kollman; Rosanna Fiallo-Scharer; Nelly Mauras; Katrina J Ruedy; Michael Tansey; Stuart A Weinzimer; Tim Wysocki Journal: J Pediatr Date: 2007-08-24 Impact factor: 4.406
Authors: M Tansey; R Beck; K Ruedy; W Tamborlane; P Cheng; C Kollman; L Fox; S Weinzimer; N Mauras; N H White; E Tsalikian Journal: Pediatr Diabetes Date: 2014-12-11 Impact factor: 4.866
Authors: Roy W Beck; Richard M Bergenstal; Tonya D Riddlesworth; Craig Kollman; Zhaomian Li; Adam S Brown; Kelly L Close Journal: Diabetes Care Date: 2018-10-23 Impact factor: 19.112