Background. Glycemic index (GI) and glycemic load (GL) are tools to estimate the postprandial glycemic response (PPG) to carbohydrate-containing foods. Currently, the American Diabetes Association recommends matching insulin dose to the carbohydrate content of food in individuals with type 1 diabetes mellitus (T1DM) without considering GI or GL. Objectives. The objective of this study was to determine the following: (1) the relationship between the PPG and the carbohydrate content, GI, and GL of a meal in adolescents with T1DM and (2) whether mean GL per meal is related to long-term glycemic control as measured by hemoglobin A1c (HbA1c). Methods. A retrospective analysis of three 24-hour recall interviews was performed for 87 adolescents aged 12 to 17 years. Average GL was calculated for each meal and each day and compared with 2 weeks of blood glucose data and HbA1c data using linear regression analysis. Results. A significant correlation was seen between GL and PPG only in those who dosed prior to eating. Inverse correlations were found between HbA1c, average GL per meal, and average reported carbohydrate intake. However, when the analysis was repeated in only those with an HbA1c below 8%, a positive correlation was found between average GL and HbA1c. Conclusion. GI/GL may be clinically useful in managing PPG in those who dose before eating. The inverse correlation between GL and HbA1c may be explained by underreporting of carbohydrate intake in adolescents with poor glycemic control. For those with a HbA1c below 8%, GL may be considered an advanced tool to optimize management.
BackgroundNutrition screening is recommended to identify children at risk for malnutrition. A unique screening tool was developed based on American Society for Parenteral and Enteral Nutrition (ASPEN) recommendations and embedded in the electronic medical record to assess for nutrition risk.MethodsThe components of the tool included the Paediatric Nutrition Screening Tool (PNST) and other elements recommended by ASPEN. To evaluate the screening tool, retrospective data were analysed on all patients admitted to acute care units of Children's Wisconsin in 2019. Data collected included nutrition screen results, diagnosis and nutrition status. All patients who received at least one full nutrition assessment by a registered dietitian (RD) were included in analysis.ResultsOne thousand five hundred seventy‐five patients were included in analysis. The following screen elements were significantly associated with a diagnosis of malnutrition: any positive screen (p < 0.001), >2 food allergies (p = 0.009), intubation (p < 0.001), parenteral nutrition (p = 0.005), RD‐identified risk (p < 0.001), positive risk per the PNST (p < 0.001), BMI‐for‐age or weight‐for‐length z‐score (p < 0.001), intake <50% for 3 days (p = 0.012) and NPO > 3 days (p = 0.009). The current screen had a sensitivity of 93.9%, specificity of 20.3%, positive predictive value (PPV) of 30.9% and negative predictive value (NPV) of 89.8%. This is compared with the PNST which had a sensitivity of 32%, specificity of 94.2%, PPV of 71% and NPV of 75.8% in this study population.ConclusionThis unique screening tool is useful for predicting nutrition risk and has a greater sensitivity than the PNST alone.
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