Glycemic Variability: The Third Component of the Dysglycemia in Diabetes. Is it Important? How to Measure it?

Monnier, L.; Colette, C; Owens, David R.

doi:10.1177/193229680800200618

Cited by 241 publications

(214 citation statements)

References 33 publications

Supporting

Mentioning

204

Contrasting

Unclassified

Order By: Relevance

“…16, 21 1) The mean amplitude of glycemic excursions (MAGE), 2) standard deviation (SD), 3) the mean of the daily difference (MODD), which are calculated via continuous glucose monitoring (CGM).…”

Section: Tg and Uric Acidmentioning

confidence: 99%

Effect of Low Carbohydrate Diet on Type 2 Diabetic Patients and Usefulness of M-Value

Bando¹,

Ebe²,

Muneta³

et al. 2017

Diabetes Res Open J

View full text Add to dashboard Cite

Background: Debate between the Calorie Restriction (CR) and Low-Carbohydrate Diet (LCD) has been continued for several years. We have started LCD therapy for diabetic patients since 1999, and experienced good response to control hyperglycemia. Recently the risk of postprandial hyperglycemia for cardiovascular events is reported, and the range of glucose level is focused. Morbus (M) value is proposed to be a good index to show the range of glucose fluctuation. Usefulness of LCD and usefulness of M-value were shown by combined analysis. Methods: Twenty-six patients with Type 2 Diabetes Mellitus (T2DM), who admitted to the Takao Hospital, were the subjects. They were 12 males and 14 females, and their age ranged from 38 to 78 years old. A few patients were new patients, but mostly referred from other hospitals to receive LCD treatment. All patients started from CR therapy with 60% carbohydrate for 2 days, and then carried out LCD dietary therapy with 12% carbohydrates for 10-12 days. On day 2 and 12, blood glucose level was measured at 7 points a day to see the fluctuation from morning to night. Blood and urinary samples were corrected on the same day to get ordinary biochemical data and metabolic marker. Results: To know the effect of LCD on different level of HbA1c, the patients were grouped to low (L; 6.1±0.5%), middle (M; 7.9±0.4%) and high (H; 9.2±0.7%) by HbA1c value. The M-values after 2 day CR were 26.1 (L), 94.1 (M) and 343 (H), respectively. The LCD therapy decreased M-value to 10.4 (L), 18.8 (M) and 84.2 (H), respectively. The significant reduction of M-values was a reflection of the improved postprandial glucose level. LCD was effective even in the bad controlled diabetic patients. In addition to the decreased triacylglycerol and 24 hr urinary C-peptide, uric acid increased in all patients. Conclusion: Ten to 12 day-dietary therapy with LCD was quite effective to improve blood glucose profile. Patients with high HbA1c could be safely treated by LCD. Blood glucose improvement was well correlated to the M-value.

show abstract

Section: Tg and Uric Acidmentioning

confidence: 99%

Effect of Low Carbohydrate Diet on Type 2 Diabetic Patients and Usefulness of M-Value

Bando¹,

Ebe²,

Muneta³

et al. 2017

Diabetes Res Open J

View full text Add to dashboard Cite

show abstract

“…[1][2][3][4][5][6][7][8] The ability to obtain the quantity of data necessary for optimal calculation of such measures of glycemic variability has become easier with the development of continuous glucose monitoring (CGM) systems, which generally display a glucose value every 1-5 min. However, although an improvement over finger stick measures of glucose in terms of quantity, CGM data have problems; specifically, there may be missing data or gaps in measurement that exceed what is expected given the frequency at which the sensor is set to sample the wearer's interstitial fluid.…”

Section: Introductionmentioning

confidence: 99%

Minding the Gaps in Continuous Glucose Monitoring: A Method to Repair Gaps to Achieve More Accurate Glucometrics

Fonda

Lewis²,

Vigersky

2013

J Diabetes Sci Technol

View full text Add to dashboard Cite

Estimation of glycemic variability requires frequent measures of glucose and is greatly aided by continuous glucose monitoring (CGM); however, under real-world conditions, missing data or “gaps” of ≥ 10 minutes can occur in CGM data, affecting the reliability of certain estimates. Thus, we determined the magnitude of the gap problem as observed in a cohort of patients with type 2 diabetes and demonstrated an approach to fill the gaps. The approach takes the difference between readings before and after a gap and distributes the difference equally across the number of missing readings, as determined by the sensor's setting for reading frequency. The approach is easy to implement, conservative, and improves estimation of variability measures that reference time, namely, mean of daily differences and continuous overlapping net glycemic action.

show abstract

“…Indeed, postprandial glucose excursions are the major contributors to plasma glucose (PG) variability in subjects with T1DM, and the poor reproducibility of postprandial glucose response is burdensome for patients and healthcare professionals. 2 During the past 10-15 years, there has been an exponentially increasing use of technology in diabetes care with the expectation of making life easier for patients with diabetes. Some tools have been developed to facilitate the prandial bolus calculation, such as the ''bolus advisors.''…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Novel Continuous Glucose Monitoring-Based Method for Mealtime Insulin Dosing—the iBolus—in Subjects with Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion Therapy: A Randomized Controlled Trial

Rossetti

Ampudia‐Blasco

Laguna

et al. 2012

Diabetes Technology & Therapeutics

View full text Add to dashboard Cite

Objective: Prandial insulin dosing is an empirical practice associated frequently with poor reproducibility in postprandial glucose response. Based on continuous glucose monitoring (CGM), a method for prandial insulin administration (iBolus) is presented and evaluated for people with type 1 diabetes using CSII therapy. Subjects and Methods: An individual patient's model for a 5-h postprandial period was obtained from 6-day ambulatory CGM and used for iBolus calculation in 12 patients with type 1 diabetes. In a double-blind, crossover study each patient underwent four meal tests with 40 g or 100 g of carbohydrates (CHOs), both on two occasions. For each meal, the iBolus or the traditional bolus (tBolus) was given before mealtime (t 0 ) in a randomized order. We measured the postprandial glycemic response as the area under the curve of plasma glucose (AUC-PG 0-5h ) and variability as the individual coefficient of variation (CV) of AUC-PG 0-5h . The contribution of the insulin-to-CHO ratio, CHO, plasma glucose at t 0 (PG t0 ), and insulin dose to AUC-PG 0-5h and its CV was also investigated. Results: AUC-PG 0-5h was similar with either bolus for 40-g (iBolus vs. tBolus, 585.5 -127.5 vs. 689.2 -180.7 mg/dL$h) or 100-g (752.1 -237.7 vs. 760.0 -263.2 mg/dL$h) CHO meals. A multiple regression analysis revealed a significant model only for the tBolus, with PG t0 being the best predictor of AUC-PG 0-5h explaining approximately 50% of the glycemic response. Observed variability was greater with the iBolus (CV, 16.7 -15.3% vs. 10.1 -12.5%) but independent of the factors studied. Conclusions: A CGM-based algorithm for calculation of prandial insulin is feasible, although it does not reduce unpredictability of individual glycemic responses. Causes of variability need to be identified and analyzed for further optimization of postprandial glycemic control.

show abstract

Glycemic Variability: The Third Component of the Dysglycemia in Diabetes. Is it Important? How to Measure it?

Cited by 241 publications

References 33 publications

Effect of Low Carbohydrate Diet on Type 2 Diabetic Patients and Usefulness of M-Value

Effect of Low Carbohydrate Diet on Type 2 Diabetic Patients and Usefulness of M-Value

Minding the Gaps in Continuous Glucose Monitoring: A Method to Repair Gaps to Achieve More Accurate Glucometrics

Evaluation of a Novel Continuous Glucose Monitoring-Based Method for Mealtime Insulin Dosing—the iBolus—in Subjects with Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion Therapy: A Randomized Controlled Trial

Contact Info

Product

Resources

About