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. 2019 Dec;10(6):2289-2304.
doi: 10.1007/s13300-019-00707-x. Epub 2019 Oct 28.

Impact of Carbohydrate on Glucose Variability in Patients with Type 1 Diabetes Assessed Through Professional Continuous Glucose Monitoring: A Retrospective Study

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Impact of Carbohydrate on Glucose Variability in Patients with Type 1 Diabetes Assessed Through Professional Continuous Glucose Monitoring: A Retrospective Study

Yi-Hsuan Lin et al. Diabetes Ther. 2019 Dec.

Abstract

Introduction: The aim of this study was to objectively analyze the correlation between dietary components and blood glucose variation by means of continuous glucose monitoring (CGM).

Methods: Patients with type 1 diabetes mellitus (T1DM) who received CGM to manage their blood glucose levels were enrolled into the study, and the components of their total caloric intake were analyzed. Glycemic variation parameters were calculated, and dietary components, including percentages of carbohydrate, protein and fat in the total dietary intake, were analyzed by a dietitian. The interaction between parameters of glycemic variability and dietary components was analyzed.

Results: Sixty-one patients with T1DM (33 females, 28 males) were enrolled. The mean age of the participants was 34.7 years, and the average duration of diabetes was 14 years. Glycated hemoglobin before CGM was 8.54%. Participants with a carbohydrate intake that accounted for < 50% of their total caloric intake had a longer DM duration and a higher protein and fat intake than did those with a carbohydrate intake that accounted for ≥ 50% of total caloric intake, but there was no between-group difference in total caloric intake per day. The group with a carbohydrate intake that accounted for < 50% of their total caloric intake also had lower nocturnal continuous overlapping net glycemic action (CONGA) 1, - 2 and - 4 values. The percentage of protein intake had a slightly negative correlation with mean amplitude of glycemic excursions (MAGE) (r = - 0.286, p < 0.05) and a moderately negative correlation with coefficient of variation (CV) (r = 0.289, p < 0.05). One additional percentage of protein calories of total calories per day decreased the MAGE to 4.25 mg/dL and CV to 0.012 (p < 0.05). The optimal dietary protein percentage for MAGE < 140 mg/dL was 15.13%. The performance of predictive models revealed the beneficial effect of adequate carbohydrate intake on glucose variation when combined with protein consumption.

Conclusions: Adequate carbohydrate consumption-but not more than half the daily total calories-combined with protein calories that amount to approximately 15% of the daily caloric intake is important for glucose stability and beneficial for patients with T1DM.

Keywords: Continuous glucose monitoring; Diet effect; Glucose variability; Nutrition; Risk factors; Type 1 diabetes.

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Figures

Fig. 1
Fig. 1
The receiver operator characteristic (ROC) curve analysis determines the best discrimination point of percentage of dietary protein and mean amplitude of glycemic excursions (MAGE) < 140 mg/dL. The best discrimination point of dietary protein percentage, as determined by the Younden  index was 15.13%, with a sensitivity of 55.6% and a specificity of 81.8%. Area under the ROC curve was 0.689 with a 95% confidence interval of 0.546–0.831; p = 0.019; standard error = 0.073
Fig. 2
Fig. 2
The ROC curve of MAGE ≥ 140 by model 1 (a), model 2 (b) and model 3 (c). Arrow indicates the optimal cutoff point

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