Literature DB >> 32163723

Assessment of Glucose Control Metrics by Discriminant Ratio.

Vanessa Moscardó1, Pau Herrero2, Monika Reddy3, Nathan R Hill4, Pantelis Georgiou2, Nick Oliver3.   

Abstract

Objective: Increasing use of continuous glucose monitoring (CGM) data has created an array of glucose metrics for glucose variability, temporal patterns, and times in ranges. However, a gold standard metric has not been defined. We assess the performance of multiple glucose metrics to determine their ability to detect intra- and interperson variability to determine a set of recommended metrics.
Methods: The Juvenile Diabetes Research Foundation data set, a randomized controlled study of CGM and self-monitored blood glucose conducted in children and adults with type 1 diabetes (T1D), was used. To determine the ability of the evaluated glycemic metrics to discriminate between different subjects and attenuate the effect of within-subject variation, the discriminant ratio was calculated and compared for each metric. Then, the findings were confirmed using data from two other recent randomized clinical trials.
Results: Mean absolute glucose (MAG) has the highest discriminant ratio value (2.98 [95% confidence interval {CI} 1.64-3.67]). In addition, low blood glucose index and index of glycemic control performed well (1.93 [95% CI 1.15-3.44] and 1.92 [95% CI 1.27-2.93], respectively). For percentage times in glucose target ranges, the optimal discriminator was percentage time in glucose target 70-180 mg/dL. Conclusions: MAG is the optimal index to differentiate glucose variability in people with T1D, and may be a complementary therapeutic monitoring tool in addition to glycated hemoglobin and a measure of hypoglycemia. Percentage time in glucose target 70-180 mg/dL is the optimal percentage time in range to report.

Entities:  

Keywords:  Discriminant ratio; Glucose variability; Type 1 diabetes; Variability metrics

Year:  2020        PMID: 32163723      PMCID: PMC7591377          DOI: 10.1089/dia.2019.0415

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


  40 in total

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Authors:  J SCHLICHTKRULL; O MUNCK; M JERSILD
Journal:  Acta Med Scand       Date:  1965-01

Review 2.  Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control.

Authors:  David Rodbard
Journal:  Diabetes Technol Ther       Date:  2009-06       Impact factor: 6.118

3.  Evaluation of a new measure of blood glucose variability in diabetes.

Authors:  Boris P Kovatchev; Erik Otto; Daniel Cox; Linda Gonder-Frederick; William Clarke
Journal:  Diabetes Care       Date:  2006-11       Impact factor: 19.112

4.  Intermittent high glucose enhances ICAM-1, VCAM-1 and E-selectin expression in human umbilical vein endothelial cells in culture: the distinct role of protein kinase C and mitochondrial superoxide production.

Authors:  Lisa Quagliaro; Ludovica Piconi; Roberta Assaloni; Roberto Da Ros; Amabile Maier; Gianni Zuodar; Antonio Ceriello
Journal:  Atherosclerosis       Date:  2005-12       Impact factor: 5.162

5.  Normal reference range for mean tissue glucose and glycemic variability derived from continuous glucose monitoring for subjects without diabetes in different ethnic groups.

Authors:  Nathan R Hill; Nick S Oliver; Pratik Choudhary; Jonathan C Levy; Peter Hindmarsh; David R Matthews
Journal:  Diabetes Technol Ther       Date:  2011-06-29       Impact factor: 6.118

6.  Relating mean blood glucose and glucose variability to the risk of multiple episodes of hypoglycaemia in type 1 diabetes.

Authors:  E S Kilpatrick; A S Rigby; K Goode; S L Atkin
Journal:  Diabetologia       Date:  2007-09-19       Impact factor: 10.122

7.  The Minimum Duration of Sensor Data From Which Glycemic Variability Can Be Consistently Assessed.

Authors:  Orla M Neylon; Peter A Baghurst; Fergus J Cameron
Journal:  J Diabetes Sci Technol       Date:  2014-02-09

8.  Non-HDL-cholesterol as valid surrogate to apolipoprotein B100 measurement in diabetes: Discriminant Ratio and unbiased equivalence.

Authors:  Michel P Hermans; Frank M Sacks; Sylvie A Ahn; Michel F Rousseau
Journal:  Cardiovasc Diabetol       Date:  2011-02-28       Impact factor: 9.951

9.  Switching from Flash Glucose Monitoring to Continuous Glucose Monitoring on Hypoglycemia in Adults with Type 1 Diabetes at High Hypoglycemia Risk: The Extension Phase of the I HART CGM Study.

Authors:  Monika Reddy; Narvada Jugnee; Sinthuka Anantharaja; Nick Oliver
Journal:  Diabetes Technol Ther       Date:  2018-09-28       Impact factor: 6.118

Review 10.  Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range.

Authors:  Tadej Battelino; Thomas Danne; Richard M Bergenstal; Stephanie A Amiel; Roy Beck; Torben Biester; Emanuele Bosi; Bruce A Buckingham; William T Cefalu; Kelly L Close; Claudio Cobelli; Eyal Dassau; J Hans DeVries; Kim C Donaghue; Klemen Dovc; Francis J Doyle; Satish Garg; George Grunberger; Simon Heller; Lutz Heinemann; Irl B Hirsch; Roman Hovorka; Weiping Jia; Olga Kordonouri; Boris Kovatchev; Aaron Kowalski; Lori Laffel; Brian Levine; Alexander Mayorov; Chantal Mathieu; Helen R Murphy; Revital Nimri; Kirsten Nørgaard; Christopher G Parkin; Eric Renard; David Rodbard; Banshi Saboo; Desmond Schatz; Keaton Stoner; Tatsuiko Urakami; Stuart A Weinzimer; Moshe Phillip
Journal:  Diabetes Care       Date:  2019-06-08       Impact factor: 19.112

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  3 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.  Mitigation of Rebound Hyperglycemia With Real-Time Continuous Glucose Monitoring Data and Predictive Alerts.

Authors:  Giada Acciaroli; John B Welsh; Halis Kaan Akturk
Journal:  J Diabetes Sci Technol       Date:  2021-01-05

3.  Effectiveness of real-time continuous glucose monitoring to improve glycaemic control and pregnancy outcome in patients with gestational diabetes mellitus: a study protocol for a randomised controlled trial.

Authors:  Evelyn Annegret Huhn; Tina Linder; Daniel Eppel; Karen Weißhaupt; Christine Klapp; Karen Schellong; Wolfgang Henrich; Gülen Yerlikaya-Schatten; Ingo Rosicky; Peter Husslein; Kinga Chalubinski; Martina Mittlböck; Petra Rust; Irene Hoesli; Bettina Winzeler; Johan Jendle; T Fehm; Andrea Icks; Markus Vomhof; Gregory Gordon Greiner; Julia Szendrödi; Michael Roden; Andrea Tura; Christian S Göbl
Journal:  BMJ Open       Date:  2020-11-30       Impact factor: 2.692

  3 in total

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