Literature DB >> 21299401

Optimal sampling intervals to assess long-term glycemic control using continuous glucose monitoring.

Dongyuan Xing1, Craig Kollman, Roy W Beck, William V Tamborlane, Lori Laffel, Bruce A Buckingham, Darrell M Wilson, Stuart Weinzimer, Rosanna Fiallo-Scharer, Katrina J Ruedy.   

Abstract

AIMS AND HYPOTHESIS: The optimal duration and frequency of short-term continuous glucose monitoring (CGM) to reflect long-term glycemia have not been determined. The Juvenile Diabetes Research Foundation CGM randomized trials provided a large dataset of longitudinal CGM data for this type of analysis.
METHODS: The analysis included 185 subjects who had 334 3-month intervals of CGM data meeting specific criteria. For various glucose indices, correlations (r²) were computed for the entire 3-month interval versus selected sampling periods ranging from 3 to 15 days. Other computed agreement measures included median relative absolute difference, values within ± 10% and ± 20% of full value, and median absolute difference.
RESULTS: As would be expected, the more days of glucose data that were sampled, the higher the correlation with the full 3 months of data. For 3 days of sampling, the r² value ranged from 0.32 to 0.47, evaluating mean glucose, percentage of values 71-180 mg/dL, percentage of values > 180 mg/dL, percentage of values ≤ 70 mg/dL, and coefficient of variation; in contrast, for 15 days of sampling, the r² values ranged from 0.66 to 0.75. The results were similar when the analysis intervals were stratified by age group (8-14, 15-24, and ≥ 25 years), by baseline hemoglobin A1c level (< 7.0% and ≥ 7.0%), and by CGM device type. CONCLUSIONS AND
INTERPRETATION: Our data suggest that a 12-15-day period of monitoring every 3 months may be needed to optimally assess overall glucose control. Shorter periods of sampling can be useful, but the correlation with 3-month measures of glycemic control is lower.

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Year:  2011        PMID: 21299401      PMCID: PMC6468940          DOI: 10.1089/dia.2010.0156

Source DB:  PubMed          Journal:  Diabetes Technol Ther        ISSN: 1520-9156            Impact factor:   6.118


  27 in total

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Authors:  Enrico Longato; Giada Acciaroli; Andrea Facchinetti; Alberto Maran; Giovanni Sparacino
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2.  The Fallacy of Average: How Using HbA1c Alone to Assess Glycemic Control Can Be Misleading.

Authors:  Roy W Beck; Crystal G Connor; Deborah M Mullen; David M Wesley; Richard M Bergenstal
Journal:  Diabetes Care       Date:  2017-08       Impact factor: 19.112

3.  Improved Accuracy of Continuous Glucose Monitoring Systems in Pediatric Patients with Diabetes Mellitus: Results from Two Studies.

Authors:  Lori Laffel
Journal:  Diabetes Technol Ther       Date:  2016-02       Impact factor: 6.118

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Journal:  Clin Diabetes       Date:  2020-10

Review 5.  Continuous Glucose Monitoring: A Review of Recent Studies Demonstrating Improved Glycemic Outcomes.

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Journal:  Diabetes Technol Ther       Date:  2017-06       Impact factor: 6.118

Review 6.  Clinical Use of Continuous Glucose Monitoring in Adults with Type 2 Diabetes.

Authors:  Anders L Carlson; Deborah M Mullen; Richard M Bergenstal
Journal:  Diabetes Technol Ther       Date:  2017-05       Impact factor: 6.118

7.  Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials.

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

8.  Correlation Among Hypoglycemia, Glycemic Variability, and C-Peptide Preservation After Alefacept Therapy in Patients with Type 1 Diabetes Mellitus: Analysis of Data from the Immune Tolerance Network T1DAL Trial.

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Review 9.  Strategies for clinical trials in type 1 diabetes.

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Journal:  J Autoimmun       Date:  2016-04-05       Impact factor: 7.094

10.  Beyond A1C: A Practical Approach to Interpreting and Optimizing Continuous Glucose Data in Youth.

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Journal:  Diabetes Spectr       Date:  2021-05-25
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