Literature DB >> 31886733

Glucose Time In Range, Time Above Range, and Time Below Range Depend on Mean or Median Glucose or HbA1c, Glucose Coefficient of Variation, and Shape of the Glucose Distribution.

David Rodbard1.   

Abstract

Background: Examine the expected relationships between time in range (%TIR), time above range (%TAR), and time below range (%TBR) with median glucose (or %HbA1c) and %coefficient of variation (%CV) of glucose for various shapes of the glucose distribution.
Methods: We considered several thresholds defining hypoglycemia and hyperglycemia and examined wide ranges of median glucose and %CV using three models for the glucose distribution: gaussian, log-gaussian, and a modified log-gaussian distribution.
Results: There is a linear relationship between %TIR and median glucose for any specified %CV when median glucose is well removed from the threshold for hypoglycemia. %TIR reaches a peak when median glucose is close to 120 mg/dL and declines both at higher and lower median glucose values. There is a nearly linear relationship for %TAR and median glucose for a wider range of glucose (80-220 mg/dL). Risk of hypoglycemia is minimal when %CV is below 20%, but rises exponentially as %CV increases or as median glucose decreases. Similar results were obtained for a wide range of possible shapes of glucose distribution. These simulations are consistent with results from clinical studies.
Conclusion: Both %TIR and %TAR are approximately linearly related to mean and median glucose (or %HbA1c). %TAR provides linearity over a wider range than %TIR. Risk of hypoglycemia (%TBR) is critically dependent on both glycemic variability (%CV) and mean or median glucose. These relationships support the use of %TIR, %TAR, and %TBR as metrics of quality of glycemic control for clinical, research, and regulatory purposes.

Entities:  

Keywords:  %Coefficient of variation (%CV); Glucose distribution; Glycemic variability; Hypoglycemia; Modelling; Statistics; Time above range (%TAR); Time below range (%TBR); Time in range (%TIR); Transformation of the glucose scale

Year:  2020        PMID: 31886733     DOI: 10.1089/dia.2019.0440

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


  12 in total

1.  Estimation of Hemoglobin A1c from Continuous Glucose Monitoring Data in Individuals with Type 1 Diabetes: Is Time In Range All We Need?

Authors:  Chiara Fabris; Lutz Heinemann; Roy Beck; Claudio Cobelli; Boris Kovatchev
Journal:  Diabetes Technol Ther       Date:  2020-07       Impact factor: 6.118

2.  Hemoglobin A1c modifies the association between triglyceride and time in hypoglycemia determined by flash glucose monitoring in adults with type 1 diabetes: implications for individualized therapy and decision-making.

Authors:  Yiwen Liu; Jie Yu; Chifa Ma; Shuli He; Fan Ping; Huabing Zhang; Wei Li; Lingling Xu; Xinhua Xiao; Yuxiu Li
Journal:  Ann Transl Med       Date:  2021-04

3.  HbA1c and Glucose Management Indicator Discordance: A Real-World Analysis.

Authors:  Jordan E Perlman; Theodore A Gooley; Bridget McNulty; Jedidiah Meyers; Irl B Hirsch
Journal:  Diabetes Technol Ther       Date:  2020-12-01       Impact factor: 6.118

4.  Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes.

Authors:  Petra Augstein; Peter Heinke; Lutz Vogt; Klaus-Dieter Kohnert; Eckhard Salzsieder
Journal:  J Diabetes Sci Technol       Date:  2021-05-18

5.  Thresholds of Glycemia and the Outcomes of COVID-19 Complicated With Diabetes: A Retrospective Exploratory Study Using Continuous Glucose Monitoring.

Authors:  Yun Shen; Xiaohong Fan; Lei Zhang; Yaxin Wang; Cheng Li; Jingyi Lu; Bingbing Zha; Yueyue Wu; Xiaohua Chen; Jian Zhou; Weiping Jia
Journal:  Diabetes Care       Date:  2021-02-11       Impact factor: 19.112

6.  The association between hypoglycemia and glycemic variability in elderly patients with type 2 diabetes: a prospective observational study.

Authors:  Takahisa Handa; Akinobu Nakamura; Aika Miya; Hiroshi Nomoto; Hiraku Kameda; Kyu Yong Cho; So Nagai; Narihito Yoshioka; Hideaki Miyoshi; Tatsuya Atsumi
Journal:  Diabetol Metab Syndr       Date:  2021-04-01       Impact factor: 3.320

7.  Association of Body Fat Percentage with Time in Range Generated by Continuous Glucose Monitoring during Continuous Subcutaneous Insulin Infusion Therapy in Type 2 Diabetes.

Authors:  Yuting Ruan; Jiana Zhong; Rongping Chen; Zhen Zhang; Dixing Liu; Jia Sun; Hong Chen
Journal:  J Diabetes Res       Date:  2021-05-28       Impact factor: 4.011

8.  Management of glucose profile throughout strict COVID-19 lockdown by patients with type 1 diabetes prone to hypoglycaemia using sensor-augmented pump.

Authors:  Clara Viñals; Alex Mesa; Daria Roca; Merce Vidal; Irene Pueyo; Ignacio Conget; Marga Giménez
Journal:  Acta Diabetol       Date:  2020-10-30       Impact factor: 4.280

9.  Impaired insulin secretion predicting unstable glycemic variability and time below range in type 2 diabetes patients regardless of glycated hemoglobin or diabetes treatment.

Authors:  Aika Miya; Akinobu Nakamura; Takahisa Handa; Hiroshi Nomoto; Hiraku Kameda; Kyu Yong Cho; So Nagai; Hideaki Miyoshi; Tatsuya Atsumi
Journal:  J Diabetes Investig       Date:  2020-11-09       Impact factor: 4.232

10.  Effects of Teneligliptin on HbA1c levels, Continuous Glucose Monitoring-Derived Time in Range and Glycemic Variability in Elderly Patients with T2DM (TEDDY Study).

Authors:  Ji Cheol Bae; Soo Heon Kwak; Hyun Jin Kim; Sang-Yong Kim; You-Cheol Hwang; Sunghwan Suh; Bok Jin Hyun; Ji Eun Cha; Jong Chul Won; Jae Hyeon Kim
Journal:  Diabetes Metab J       Date:  2021-06-16       Impact factor: 5.376

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