For all models, the random-effect patient was statistically significant (p < 0.001 by ANOVA).īeyond the meal characteristics (including glycemic index, fat and fiber content), individual traits significantly influence PGR. Among nutrients, dietary fiber was the only significant negative predictor of iAUC 0-3h (Estimate -550 p < 0.001) and iAUC 0-6h (Estimate -742 p = 0.01) and positive predictor of iAUC 3-6h minus 0-3h (Estimate 336 p = 0.043). A Low-Fat-HGI meal significantly predicted iAUC 3-6h minus 0-3h (Estimate 3268 p = 0.017). I am really happy that I have completed the SCIDA course.
High-glycemic-index (HGI) and low-glycemic-index meals were the best positive and negative predictors of glucose iAUC 0-3h, respectively. Predictors (type of meal or nutrient composition) of early (iAUC 0-3h), late (iAUC 3-6h), total (iAUC 0-6h), and time-course of postprandial blood glucose changes (iAUC 3-6h minus 0-3h) were evaluated using two mixed-effect linear regression models considering the patient's identification number as random-effect.
Reflective Course Design: An Interplay Between Pedagogy and Technology in a Language Teacher Education. To explore intraindividual (between-meals) and interindividual (between-subjects) variability of postprandial glucose response (PGR) in type 1 diabetes (T1DM).ĭata 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. PUPBLICATIONS Firdyiwek, Yitna & Emily Scida.
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