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Clinical Implications of Glucose Variability: Chronic Complications of Diabetes.

Jung HS - Endocrinol Metab (Seoul) (2015)

Bottom Line: However, there remains no generally accepted gold standard for assessing glucose variability.Representative indices for measuring intraday variability include calculation of the standard deviation along with the mean amplitude of glycemic excursions (MAGE).MAGE is used to measure major intraday excursions and is easily measured using continuous glucose monitoring systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea. junghs@snu.ac.kr.

ABSTRACT
Glucose variability has been identified as a potential risk factor for diabetic complications; oxidative stress is widely regarded as the mechanism by which glycemic variability induces diabetic complications. However, there remains no generally accepted gold standard for assessing glucose variability. Representative indices for measuring intraday variability include calculation of the standard deviation along with the mean amplitude of glycemic excursions (MAGE). MAGE is used to measure major intraday excursions and is easily measured using continuous glucose monitoring systems. Despite a lack of randomized controlled trials, recent clinical data suggest that long-term glycemic variability, as determined by variability in hemoglobin A1c, may contribute to the development of microvascular complications. Intraday glycemic variability is also suggested to accelerate coronary artery disease in high-risk patients.

No MeSH data available.


Related in: MedlinePlus

Continuous glucose monitoring in a patient with type 1 diabetes mellitus. Qualifying excursions are shown as blue arrows (only the inflection components in this case). Each inflection incorporates several excursions smaller than 1 standard deviation (SD) within a given day (44 mg/dL for day 1 and 65 mg/dL for day 2). The averaged excursion (that is, mean amplitude of glycemic excursion [MAGE]) is (A) 85.0 mg/dL for day 1 and (B) 156.5 mg/dL for day 2. MAGE calculated from the entire 48-hour time course (SD, 56.5 mg/dL) was 131.5 mg/dL; this level was similar across each day of the study period (120.7 mg/dL). Similar MAGE values could also be calculated from the descending limbs.
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Figure 2: Continuous glucose monitoring in a patient with type 1 diabetes mellitus. Qualifying excursions are shown as blue arrows (only the inflection components in this case). Each inflection incorporates several excursions smaller than 1 standard deviation (SD) within a given day (44 mg/dL for day 1 and 65 mg/dL for day 2). The averaged excursion (that is, mean amplitude of glycemic excursion [MAGE]) is (A) 85.0 mg/dL for day 1 and (B) 156.5 mg/dL for day 2. MAGE calculated from the entire 48-hour time course (SD, 56.5 mg/dL) was 131.5 mg/dL; this level was similar across each day of the study period (120.7 mg/dL). Similar MAGE values could also be calculated from the descending limbs.

Mentions: MAGE was originally developed using hourly glucose samples taken from venous blood [6], and it has emerged as the preferred method for assessing CGM data. In the example presented in Fig. 2, 1 SD of the mean glucose level for each 24-hour period acts as an individualized standard; only one limb of the excursion >1 SD, either ascending or descending, is used to calculate subsequent excursions. The arithmetic mean of these glycemic excursions over the period of study (24 hours or longer) is then used to calculate the MAGE; an automated algorithm has been created for this calculation [7]. Using this method, the mean glucose value becomes the reference point for glycemic variability. However, because the MAGE represents only major excursions from the mean and ignores excursions of <1 SD, this approach disregards smaller excursions that may be important.


Clinical Implications of Glucose Variability: Chronic Complications of Diabetes.

Jung HS - Endocrinol Metab (Seoul) (2015)

Continuous glucose monitoring in a patient with type 1 diabetes mellitus. Qualifying excursions are shown as blue arrows (only the inflection components in this case). Each inflection incorporates several excursions smaller than 1 standard deviation (SD) within a given day (44 mg/dL for day 1 and 65 mg/dL for day 2). The averaged excursion (that is, mean amplitude of glycemic excursion [MAGE]) is (A) 85.0 mg/dL for day 1 and (B) 156.5 mg/dL for day 2. MAGE calculated from the entire 48-hour time course (SD, 56.5 mg/dL) was 131.5 mg/dL; this level was similar across each day of the study period (120.7 mg/dL). Similar MAGE values could also be calculated from the descending limbs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4508260&req=5

Figure 2: Continuous glucose monitoring in a patient with type 1 diabetes mellitus. Qualifying excursions are shown as blue arrows (only the inflection components in this case). Each inflection incorporates several excursions smaller than 1 standard deviation (SD) within a given day (44 mg/dL for day 1 and 65 mg/dL for day 2). The averaged excursion (that is, mean amplitude of glycemic excursion [MAGE]) is (A) 85.0 mg/dL for day 1 and (B) 156.5 mg/dL for day 2. MAGE calculated from the entire 48-hour time course (SD, 56.5 mg/dL) was 131.5 mg/dL; this level was similar across each day of the study period (120.7 mg/dL). Similar MAGE values could also be calculated from the descending limbs.
Mentions: MAGE was originally developed using hourly glucose samples taken from venous blood [6], and it has emerged as the preferred method for assessing CGM data. In the example presented in Fig. 2, 1 SD of the mean glucose level for each 24-hour period acts as an individualized standard; only one limb of the excursion >1 SD, either ascending or descending, is used to calculate subsequent excursions. The arithmetic mean of these glycemic excursions over the period of study (24 hours or longer) is then used to calculate the MAGE; an automated algorithm has been created for this calculation [7]. Using this method, the mean glucose value becomes the reference point for glycemic variability. However, because the MAGE represents only major excursions from the mean and ignores excursions of <1 SD, this approach disregards smaller excursions that may be important.

Bottom Line: However, there remains no generally accepted gold standard for assessing glucose variability.Representative indices for measuring intraday variability include calculation of the standard deviation along with the mean amplitude of glycemic excursions (MAGE).MAGE is used to measure major intraday excursions and is easily measured using continuous glucose monitoring systems.

View Article: PubMed Central - PubMed

Affiliation: Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea. junghs@snu.ac.kr.

ABSTRACT
Glucose variability has been identified as a potential risk factor for diabetic complications; oxidative stress is widely regarded as the mechanism by which glycemic variability induces diabetic complications. However, there remains no generally accepted gold standard for assessing glucose variability. Representative indices for measuring intraday variability include calculation of the standard deviation along with the mean amplitude of glycemic excursions (MAGE). MAGE is used to measure major intraday excursions and is easily measured using continuous glucose monitoring systems. Despite a lack of randomized controlled trials, recent clinical data suggest that long-term glycemic variability, as determined by variability in hemoglobin A1c, may contribute to the development of microvascular complications. Intraday glycemic variability is also suggested to accelerate coronary artery disease in high-risk patients.

No MeSH data available.


Related in: MedlinePlus