ArticleJournal of diabetes science and technology2010
The minimum frequency of glucose measurements from which glycemic variation can be consistently assessed.
Article in Journal of diabetes science and technology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Association of Glycemic Variability in Type 1 Diabetes With Progression of Microvascular Outcomes in the Diabetes Control and Complications Trial.Diabetes care · 2017Trial
- Heterogeneity of responses to real-time continuous glucose monitoring (RT-CGM) in patients with type 2 diabetes and its implications for application.Diabetes care · 2013Trial
- Minding the gaps in continuous glucose monitoring: a method to repair gaps to achieve more accurate glucometrics.Journal of diabetes science and technology · 2013Trial
- Glycemic variability: Measurement, target, impact on complications of diabetes and does it really matter?Journal of diabetes investigation · 2024Review
- Complications of Diabetes and Metrics of Glycemic Management Derived From Continuous Glucose Monitoring.The Journal of clinical endocrinology and metabolism · 2022Review
- Glycaemic Variability and Hyperglycaemia as Prognostic Markers of Major Cardiovascular Events in Diabetic Patients Hospitalised in Cardiology Intensive Care Unit for Acute Heart Failure.Journal of clinical medicine · 2022Article
- Continuous glucose monitoring in pregnant women with type 1 diabetes: an observational cohort study of 186 pregnancies.Diabetologia · 2019Article
- Glucose Management Technologies for the Critically Ill.Journal of diabetes science and technology · 2019Review
- A Simplified Approach Using Rate of Change Arrows to Adjust Insulin With Real-Time Continuous Glucose Monitoring.Journal of diabetes science and technology · 2017Article
- Glycemic Variability: How Do We Measure It and Why Is It Important?Diabetes & metabolism journal · 2015 · on this mapReview
- Utility of different glycemic control metrics for optimizing management of diabetes.World journal of diabetes · 2015Review
- The Minimum Duration of Sensor Data From Which Glycemic Variability Can Be Consistently Assessed.Journal of diabetes science and technology · 2014Article
- Increased glycemic variability is independently associated with length of stay and mortality in noncritically ill hospitalized patients.Diabetes care · 2013Article
- Glycemic variability in hospitalized patients: choosing metrics while awaiting the evidence.Current diabetes reports · 2013Review
- Effects of fluctuating glucose levels on neuronal cells in vitro.Neurochemical research · 2012Article
- Hypoglycemia, but not glucose variability, relates to vascular function in children with type 1 diabetes.Diabetes technology & therapeutics · 2012Article
- The challenges of measuring glycemic variability.Journal of diabetes science and technology · 2012Review
- Translating glucose variability metrics into the clinic via Continuous Glucose Monitoring: a Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©).Diabetes technology & therapeutics · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsWhile there has been much debate about the clinical importance of glycemic variation (GV), little attention has been directed to the properties of data sets from which it is measured. The purpose of this study is to assess the minimum frequency of glucose measurements from which GV can be consistently and meaningfully measured.
methodsForty-eight 72 h continuous glucose monitoring traces from children with type 1 diabetes were assessed. Measures of GV included standard deviation (SD), mean amplitude of glycemic excursion (MAGE), and continuous overlapping net glycemic action (CONGA1-4). Measures of GV calculated using 5 min sampling were designated as the 100% or "best estimate" value. Calculations were then repeated for each patient using glucose values spaced at increasing intervals. For each of the specified sampling frequencies, the ratio (%) of the between-subject SD based on the reduced subset of data to the estimate of the SD based on the full 5 min sampling data set was calculated.
resultsAs the interval between observations increased, so did the variability of the estimators of GV. Standard deviation exhibited the least systematic change at all measurement intervals, and MAGE exhibited the greatest systematic change.
conclusionsIn patients with type 1 diabetes, GV as measured by SD or CONGA4, becomes unreliable if observations are more than 2-4 h apart, and estimates of MAGE become unreliable if glucose measurements are more than 1 h apart. MAGE is more unstable and prone to random measurement error than either SD or CONGA. The frequency of glycemic measurements is thus pivotal when selecting a parameter for measurement of GV.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.