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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Diabetesarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Diabetes
Article . 2018 . Peer-reviewed
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Glucose Variability and Flash Glucose Monitoring in the Real World

Authors: SUJIT JANGAM; YONGJIN XU; GARY HAYTER; TIMOTHY DUNN;

Glucose Variability and Flash Glucose Monitoring in the Real World

Abstract

High glucose variability is associated with increased hypoglycemia. Previous analyses of real-world data have shown that increased testing using flash glucose monitoring (FreeStyle LibreTM system) is associated with lower hypoglycemia and hyperglycemia. Here we studied: 1) The relationship between glucose variability and hypoglycemia/hyperglycemia during flash glucose monitoring 2) The relationship between increased glucose testing and glucose variability. De-identified glucose data is collected from individuals using the system when they upload data from their readers to the desktop software. Data from 237,747 individuals was analyzed. To understand the relationship between variability and hypoglycemia/hyperglycemia, individuals were divided into groups of high and low glucose variability using CV=36% as the threshold. Hypoglycemia (time below 70mg/dL) and hyperglycemia (time above 180mg/dL) metrics were then analyzed in the two groups. To understand the impact of testing frequency on glycemic variability, individuals were divided into 20 equal sized groups spanning the full range of daily testing frequency. Average CV was then calculated for each group. It was observed that individuals with high variability showed 35% more hyperglycemia (10.1±5.9 hours/day) compared to individuals with low variability (7.5±7.0 hours/day). Similarly, individuals with high variability showed 167% more hypoglycemia (144±281 minutes/day) compared to those with low variability (54±185 minutes/day). It was observed that increased testing with the system was associated with lower glucose variability. CV decreased from 40.6% to 34.5% from the lowest to highest testing frequency groups. High variability is associated with both increased hyperglycemia and hypoglycemia in a large sample population from the real world. Increased testing is associated with decreased glucose variability. Disclosure S. Jangam: Employee; Self; Abbott. Y. Xu: Employee; Self; Abbott. G. Hayter: Employee; Self; Abbott. T. Dunn: Employee; Self; Abbott.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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