Diabetes research and clinical practice · 2022
Postprandial glucose variability in type 1 diabetes: The individual matters beyond the meal.
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Bozzetto L, Pacella D, Cavagnuolo L, Capuano M, Corrado A, Scidà G, Costabile G, Rivellese A A, Annuzzi G
Аннотация
AIM: To explore intraindividual (between-meals) and interindividual (between-subjects) variability of postprandial glucose response (PGR) in type 1 diabetes (T1DM).
METHODS: Data were taken from five cross-over trials in 61 subjects with T1DM on insulin pump wherein the effects of different dietary components or the intraindividual-variability of PGR to the same meal were evaluated by CGM. Predictors (type of meal or nutrient composition) of early (iAUC0-3h), late (iAUC3-6h), total (iAUC0-6h), and time-course of postprandial blood glucose changes (iAUC3-6hminus0-3h) were evaluated using two mixed-effect linear regression models considering the patient's identification number as random-effect.
RESULTS: High-glycemic-index (HGI) and low-glycemic-index meals were the best positive and negative predictors of glucose iAUC0-3h, respectively. A Low-Fat-HGI meal significantly predicted iAUC3-6hminus0-3h (Estimate 3268; p = 0.017). Among nutrients, dietary fiber was the only significant negative predictor of iAUC0-3h (Estimate -550; p < 0.001) and iAUC0-6h (Estimate -742; p = 0.01) and positive predictor of iAUC3-6hminus0-3h (Estimate 336; p = 0.043). For all models, the random-effect patient was statistically significant (p < 0.001 by ANOVA).
CONCLUSION: Beyond the meal characteristics (including glycemic index, fat and fiber content), individual traits significantly influence PGR. Specific interindividual factors should be further identified to properly predict glucose response to meals with different composition in individuals with T1DM.
METHODS: Data were taken from five cross-over trials in 61 subjects with T1DM on insulin pump wherein the effects of different dietary components or the intraindividual-variability of PGR to the same meal were evaluated by CGM. Predictors (type of meal or nutrient composition) of early (iAUC0-3h), late (iAUC3-6h), total (iAUC0-6h), and time-course of postprandial blood glucose changes (iAUC3-6hminus0-3h) were evaluated using two mixed-effect linear regression models considering the patient's identification number as random-effect.
RESULTS: High-glycemic-index (HGI) and low-glycemic-index meals were the best positive and negative predictors of glucose iAUC0-3h, respectively. A Low-Fat-HGI meal significantly predicted iAUC3-6hminus0-3h (Estimate 3268; p = 0.017). Among nutrients, dietary fiber was the only significant negative predictor of iAUC0-3h (Estimate -550; p < 0.001) and iAUC0-6h (Estimate -742; p = 0.01) and positive predictor of iAUC3-6hminus0-3h (Estimate 336; p = 0.043). For all models, the random-effect patient was statistically significant (p < 0.001 by ANOVA).
CONCLUSION: Beyond the meal characteristics (including glycemic index, fat and fiber content), individual traits significantly influence PGR. Specific interindividual factors should be further identified to properly predict glucose response to meals with different composition in individuals with T1DM.