Riceberry rice beverage decreases postprandial glycemic response, inflammatory markers and antioxidant status induced by a high carbohydrate and moderate fat meal in overweight and obese men

Recent clinical studies support the beneficial role of riceberry rice and its food products on controlling glycemic response in healthy subjects. The aim of the current work was to determine...

E. coli Nissle 1917 modulates host glucose metabolism without directly acting on glucose

Managing postprandial glycemic response, or the increase in blood sugar following a meal, is a crucial component to maintaining healthy blood sugar in patients with diabetes. To test whether oral probiotics can impact postprandial glycemic response, E. coli Nissle 1917 (EcN) was evaluated in an oral glucose tolerance test. Oral gavage of EcN concurrent with a glucose bolus reduced the post-gavage glycemic response in mice. However, there was no difference in glycemic response when comparing EcN to a mutant deficient in glucose metabolism. This suggests that while EcN can alter glycemic response to a glucose bolus, this effect is not mediated by direct uptake of glucose. Of the possible indirect effects EcN could have, gastric emptying rate was highlighted as a likely cause, but EcN had no effect on gastric emptying rate in mice. This leaves many more possible indirect explanations for the interaction between EcN and host glucose metabolism to be explored in future work.

Assessment of Glycemic Response to Model Breakfasts Varying in Glycemic Index (GI) in 5–7-Year-Old School Children

Reduced Glycemic Index (GI) of breakfast has been linked to improved cognitive performance in both children and adult populations across the morning. However, few studies have profiled the post-prandial glycemic response (PPGR) in younger children. The aim of this study was to assess PPGR to breakfast interventions differing in GI in healthy children aged 5–7 years. Eleven subjects completed an open-label, randomized, cross-over trial, receiving three equicaloric test beverages (260 kcal) consisting of 125 mL semi-skimmed milk and 50 g sugar (either glucose, sucrose, or isomaltulose). On a fourth occasion, the sucrose beverage was delivered as intermittent supply. PPGR was measured over 180 min using Continuous Glucose Monitoring (CGM). The incremental area under the curve (3h-iAUC) was highest for the glucose beverage, followed by intermittent sucrose (−21%, p = 0.288), sucrose (−27%, p = 0.139), and isomaltulose (−48%, p = 0.018). The isomaltulose beverage induced the smallest Cmax (7.8 mmol/L vs. >9.2 mmol/L for others) and the longest duration with moderate glucose level, between baseline value and 7.8 mmol/L (150 vs. <115 min for others). These results confirm that substituting mid-high GI sugars (e.g., sucrose and glucose) with low GI sugars (e.g., isomaltulose) during breakfast are a viable strategy for sustained energy release and glycemic response during the morning even in younger children.

Development of a Prediction Model to Estimate the Glycemic Load of Ready-to-Eat Meals

The glycemic index (GI) and glycemic load (GL) of a single food item has been used to monitor blood glucose level. However, concerns regarding the clinical relevance of the GI or GL have been raised on their applicability to a combination of several foods consumed as meal. This study aimed to investigate the glycemic response after consuming commercially purchased ready-to-eat meal and to develop the GL prediction formula using the composition of nutrients in each meal. Glycemic responses were measured in healthy adults with various mixed meals comprising approximately 25 g, 50 g, and 75 g of carbohydrates. After fasting, participants consumed test meals, and the glycemic response was measured for a subsequent 120 min. The GI and GL values for mixed meals were calculated as area under curve for each participant. For the prediction formula, 70 mixed meals were analyzed, of which the GI and GL values of 64 participants were used. The prediction formula produced was as follows: GL = 19.27 + (0.39 × available carbohydrate) – (0.21 × fat) – (0.01 × protein2) – (0.01 × fiber2). We hope that this prediction formula can be used as a useful tool to estimate the GL after consuming ready-to-eat meals.

Prediction of Personal Glycemic Responses to Food for Individuals With Type 1 Diabetes Through Integration of Clinical and Microbial Data

<b><i>OBJECTIVE</i></b><i> </i>Despite technological advances, results from various clinical trials repeatedly showed that many individuals with type 1 diabetes (T1D) do not achieve their glycemic goals. One of the major challenges in disease management is the administration of an accurate amount of insulin for each meal which will match the expected postprandial glycemic response (PPGR). <p><b><i>RESEARCH DESIGN AND METHODS</i></b><i> </i>We recruited individuals with T1D using continuous glucose monitoring (CGM) and continuous subcutaneous insulin infusion (CSII) devices simultaneously to a prospective cohort and profiled them for 2 weeks. Participants were asked to report real-time dietary intake using a designated mobile app. We measured their PPGRs and devised machine-learning algorithms for PPGR prediction, which integrate glucose measurements, insulin dosages, dietary habits, blood parameters, anthropometrics, exercise, and gut microbiota. Data of the PPGR of 1,057 healthy individuals to 47,863 meals were also integrated into the model. The performance of the models was evaluated using 10-fold cross validation.</p> <p><b><i>RESULTS</i></b><i> </i>121 individuals with T1D, 75 adults and 46 children, were included in the study. PPGR to 6,377 meals was measured. Our PPGR prediction model substantially outperforms a baseline model emulating standard of care (correlation of R=0.59 compared to R=0.40 for predicted and observed PPGR respectively, p <10⁻¹⁰). The model was robust across different subpopulations. Feature attribution analysis revealed that glucose levels at meal initiation, glucose trend 30 minutes prior to meal, meal carbohydrate content and meal’s carbohydrate/fat ratio were the most influential features to the model. </p> <p><b><i>CONCLUSIONS</i></b><i> </i>Our model enables a more accurate prediction of PPGR and therefore may allow a better adjustment of the required insulin dosage for meals. It can be further implemented in closed-loop systems and may lead to rationally designed nutritional interventions personally tailored for individuals with T1D based on meals with expected low glycemic response. </p>