Effect of wheat bread with elevated amylose on postprandial glycaemic response: a randomised crossover trial delivered remotely using continuous glucose monitoring

Sucrose is commonly used for sweetening berry products. During processing and storage of berry products containing added sucrose, sucrose is inverted to glucose and fructose. We have previously shown that postprandial glycaemic response induced by intact sucrose is attenuated when sucrose is consumed with berries rich in polyphenols. It is not known how inversion of sucrose affects glycaemic response. We investigated postprandial glycaemic and insulinaemic responses to blackcurrant (Ribes nigrum) and lingonberry (Vaccinium vitis-idaea) nectars and a reference drink (water) sweetened with glucose and fructose, representing completely inverted sucrose. The nectars and reference drink (300 ml) contained 17·5 g glucose and 17·5 g fructose. Polyphenol composition of the nectars was analysed. A total of eighteen healthy volunteers participated in a randomised, controlled, cross-over study. Blood samples were collected at fasting and six times postprandially during 120 min. Inverted sucrose in the reference drink induced glycaemic and insulinaemic responses similar to those previously observed for intact sucrose. In comparison with the reference, the blackcurrant nectar attenuated the early glycaemic response and improved glycaemic profile, and the lingonberry nectar reduced the insulinaemic response. The responses induced by inverted sucrose in the berry nectars are similar to those previously observed for berry nectars containing intact sucrose, suggesting that inversion has no major impact on glycaemic response to sucrose-sweetened berry products. The attenuated glycaemic response after the blackcurrant nectar may be explained by inhibition of intestinal absorption of glucose by blackcurrant anthocyanins.

Glycemic and insulinemic responses as determinants of appetite in humans

BackgroundThere is a growing interest in the pathopysiological consequences of postprandial hyperglycemia. It is well known that in diabetic patients 2 h plasma glucose is a better risk predictor for coronary heart disease than fasting plasma glucose. Data on the glycemic response in healthy people are scarce.ObjectiveTo evaluate the effect of macronutrients (carbohydrates, fats, and proteins) and fiber on postprandial glycemic response in an observational study of a non-diabetic adult population.DesignCross-sectional study. 150 non-diabetic adults performed continuous glucose monitoring for 6 days. During this period they recorded food and beverage intake. The participants were instructed not to make changes in their usual diet and physical exercise.Variables analyzed included clinical parameters (age, sex, body weight, height, body mass index, blood pressure, and waist measurement), meal composition (calories, carbohydrates, fats, proteins, and fiber) and glycemic postprandial responses separated by sexes.The study period was defined from the start of dinner to 6 h later.ResultsA total of 148 (51% women) subjects completed all study procedures. Dinner intake was higher in males than in females (824 vs 531 kcal). Macronutrient distribution was similar in both sexes. No significant differences were found in fiber intake between men and women (5.5 g vs 4.5 g).In both sexes, the higher intake of carbohydrates corresponded to a significantly higher glycemic response (p = 0.0001 in women, p = 0.022 in men). Moreover, in women, as fat intake was higher, a flattening of the postprandial glycemic curve was observed (p = 0.003). With respect to fiber, a significantly lower glycemic response was observed in the group of women whose fiber intake at dinner was higher (p = 0.034).ConclusionsContinuous glucose monitoring provides important information about glucose levels after meals. In this study, the postprandial glycemic response in women was different from that of men, and carbohydrates were the main determinant of elevated postprandial glucose levels.

Background: Additional dietary fibers which can decrease the glycemic response by slowing down digestion whilst maintaining the available carbohydrate content is one approach of healthy diet. This study aimed to compare post-prandial glycemic and insulin response, hunger and satiety feeling after consuming fiber-rich milk compare with equi-carbohydrate food as morning snack in healthy adults.Methods: Cross-over study was conducted on 12 healthy subjects who fulfilled the criteria. Each test food was given after consuming standard breakfast. Venous blood samples for insulin and glucose level were taken before consuming test food, at 30, 60, 120, and 180 minutes after, and plotted against time to generate a curve. Hunger and satiety assessments were taken by visual analog scale (VAS) after each blood sampling.Results: In average, age was 30.8+4.3 years old, body mass index was 20.6±1.6 kg/m2. Seven of twelve subjects were females. There were significantly differences in postprandial glycemic response (p<0.001), insulin response (p=0.045) and hunger feeling (p=0.021) between the two foods. However, postprandial satiety feelings were not different significantly (p=0.357). The glycemic response area under the curve of fiber-rich milk was significantly lower than the equi-carbohydrate snack (p=0.010). Conclusion: Differences in glycemic and insulin response, and hunger feeling between two test foods, suggesting that fiber-rich milk can be used as an alternative snack for healthy adults. Further study is needed for the use of fiber-rich milk as an alternative snack for pre-diabetic patients.

Despite technological advances, results from various clinical trials have repeatedly shown 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 that will match the expected postprandial glycemic response (PPGR). The objective of this study was to develop a prediction model for PPGR in individuals with T1D. We recruited individuals with T1D who were using continuous glucose monitoring and continuous subcutaneous insulin infusion 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 900 healthy individuals to 41,371 meals were also integrated into the model. The performance of the models was evaluated with 10-fold cross validation. A total of 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 with emulation of standard of care (correlation of R = 0.59 compared with 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 min prior to meal, meal carbohydrate content, and meal's carbohydrate-to-fat ratio were the most influential features for the model. 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 on the basis of meals with expected low glycemic response.

A core challenge in managing diabetes is predicting glycemic responses to meals. Prior work identified significant interindividual variation in responses and developed personalized forecasts. However, intraindividual variation is still not well understood, and the most accurate approaches require invasive microbiome data. We aimed to investigate (1) whether postprandial glycemic responses (PPGRs) can be predicted with limited data and (2) sources of intraindividual variation. We used data collected from 397 people with Type 1 Diabetes (T1DEXI) and 100 people with Type 2 Diabetes (ShanghaiT2DM) who wore continuous glucose monitors (CGMs) and logged meals. Using dietary, demographic, and temporal features, we predicted 2 hours PPGR, and peak 2 hours postprandial glucose rise (Glumax). We evaluated the contribution of food features (eg, macronutrients, food category) and use of personal training data and investigated intraindividual variability in responses. We achieved comparable accuracy to prior work for PPGR (T1DEXI R = 0.61, ShanghaiT2DM R = 0.72) and Glumax (T1DEXI R = 0.64, ShanghaiT2DM R = 0.73), without using invasive data like microbiome. Including food category features led to higher accuracy than macronutrients alone. Analysis of glycemic responses to duplicate meals identified time of day (PPGR: T1DEXI P < .05 for lunch, ShanghaiT2DM P < .001 for lunch and dinner) and menstrual cycle (Glumax: P < .05 for perimenstrual) as sources of variability. We demonstrate that in individuals with T1D and T2D, glycemic responses to meals can be predicted without personalized training data or invasive physiological data.

Continuous Glucose Monitoring for Diagnosis and Treatment of Neonatal Hypoglycemia

While artificially sweetened beverages (ASBs) are widely reported to have minimal glycemic impact compared to sugar-sweetened beverages (SSBs), their effects in mixed meal conditions and individual variability in response remain poorly understood. This study aimed to evaluate postprandial glycemic response (PPGR) and individual variability in response to an SSB (regular cola) and an ASB (zero cola), both in single and mixed conditions, using continuous glucose monitoring (CGM). A total of 66 healthy young adults participated in this 14-day, non-randomized crossover intervention study. Test meals included 75g oral glucose load as a reference, muffin, regular cola, zero cola, muffin with regular cola (MRC), and muffin with zero cola (MZC). PPGR was evaluated using incremental area under the curve. The glucose dip was assessed as the minimum glucose reduction from baseline. Participants were classified as MZC-High (n = 17) if their glycemic response to MZC was higher than to MRC, and as MZC-Stable (n = 44) if MRC showed the higher response. The 75g oral glucose load reference exhibited a typical glycemic pattern, peaking at 45min before steadily declining. The muffin induced a moderate glycemic response, while regular cola led to a rapid glucose rise followed by a sharp decline. When combined with a muffin, MRC exhibited a slightly higher glycemic response (iAUC180:161.6 mmol∙min/L), whereas MZC showed a similar response to the muffin alone (113.3 and 111.1 mmol∙min /L, respectively). At 120min, the glucose dip was most pronounced for regular cola, whereas oral glucose load and muffin showed smaller reductions. These patterns persisted at 180min, with oral glucose load showing the largest drop. Mixed meals attenuated glucose dips, with MRC and MZC preventing excessive declines. Individual responses analysis revealed that while the overall iAUC was not significantly different between muffin alone and MZC, 26 participants (MZC-High Responders) exhibited a higher iAUC with MZC than with MRC, suggesting variability in glucose regulation. Comparisons between MZC-High Responders and MZC-Stable participants showed no significant differences in age or body composition. While zero cola alone or in combination with a muffin had a minimal overall glycemic impact, some individuals exhibited higher glycemic responses in mixed conditions. These findings suggest that individual variability and mixed condition should be considered when consuming artificially sweetened beverages. Clinical Research Information Service (CRIS, cris.nih.go.kr) No. KCT0009921.

Chickpeas are among the lowest glycaemic index carbohydrate foods eliciting protracted digestion and enhanced satiety responses. In vitro studies suggest that mechanical processing of chickpeas significantly increases starch digestion. However, there is little evidence regarding the impact of processing on postprandial glycaemic response in response to chickpea intake in vivo. Therefore, the aim of this study was to determine the effect of mechanical processing on postprandial interstitial glycaemic and satiety responses in humans. In a randomised crossover design, thirteen normoglycaemic adults attended 4 separate laboratory visits following an overnight fast. On each occasion, one of four test meals, matched for available carbohydrate content and consisting of different physical forms of chickpeas (whole, puree, and pasta) or control (mashed potato), was administered followed by a subsequent standardised lunch meal. Continuous glucose monitoring captured interstitial glucose responses, accompanied by periodic venous blood samples for retrospective analysis of C-peptide, glucagon like peptide-1 (GLP-1), ghrelin, leptin, resistin, and cortisol. Subjective appetite responses were measured by Visual Analogue Scale (VAS). Postprandial glycaemic responses were comparable between chickpea treatments albeit significantly lower than the control (p < 0.001). Similarly, all chickpea treatments elicited significantly lower C-peptide and GLP-1 responses compared to the control (p < 0.05), accompanied by enhanced subjective satiety responses (p < 0.05), whilst no significant differences in satiety hormones were detected among different intervention groups (p > 0.05). Chickpea consumption elicits low postprandial glycaemic responses and enhanced subjective satiety responses irrespective of processing methods.

Precision Nutrition and Reliability of Continuous Glucose Monitors: Insights From the PREDICT Study

Emerging evidence suggests that postprandial glycemic responses (PPGRs) to food may be influenced by and predicted according to characteristics unique to each individual, including anthropometric and microbiome variables. Interindividual diversity in PPGRs to food requires a personalized approach for the maintenance of healthy glycemic levels. To describe and predict the glycemic responses of individuals to a diverse array of foods using a model that considers the physiology and microbiome of the individual in addition to the characteristics of the foods consumed. This cohort study using a personalized predictive model enrolled 327 individuals without diabetes from October 11, 2016, to December 13, 2017, in Minnesota and Florida to be part of a study lasting 6 days. The study measured anthropometric variables, described the gut microbial composition, and assessed blood glucose levels every 5 minutes using a continuous glucose monitor. Participants logged their food and activity information for the duration of the study. A predictive model of individualized PPGRs to a diverse array of foods was trained and applied. Glycemic responses to food consumed over 6 days for each participant. The predictive model of personalized PPGRs considered individual features, including the microbiome, in addition to the features of the foods consumed. Postprandial response to the same foods varied across 327 individuals (mean [SD] age, 45 [12] years; 78.0% female). A model predicting each individual's responses to food that considers several individual factors in addition to food features had better overall performance (R = 0.62) than current standard-of-care approaches using nutritional content alone (R = 0.34 for calories and R = 0.40 for carbohydrates) to control postprandial glycemic levels. Across the cohort of adults without diabetes who were examined, a personalized predictive model that considers unique features of the individual, such as clinical characteristics, physiological variables, and the microbiome, in addition to nutrient content was more predictive than current dietary approaches that focus only on the calorie or carbohydrate content of foods. Providing individuals with tools to manage their glycemic responses to food based on personalized predictions of their PPGRs may allow them to maintain their blood glucose levels within limits associated with good health.

Objective: Postprandial glucose is a predictor of later diabetes and vascular complications. The study aims to identify determinants of inter-individual variability in postprandial responses in healthy and prediabetic individuals. Methods: A multi-centre study in the UK and the U.S. assessed the postprandial insulin, glucose and triglyceride responses to a dietary challenge (50g fat and 85g carbohydrate) in the clinic. The inter-individual variability in postprandial glycemic responses was then measured over 2 weeks for isocaloric meals of different macronutrient content using a continuous glucose monitor (CGM). Interim analyses of the 30- and 120-min insulin rises and meal content contributions to the incremental glucose area under the curve (iAUC) were performed in 573 individuals (age 47.5 (19-66) years, BMI 25.7 (17-53) kg/m2; F=76%; n=108 with HbA1c &amp;gt; 5.7% [prediabetic]). Results: A machine learning algorithm showed that 29% of variation in glucose iAUC of at home meals was explained by the meal’s macronutrient content. In clinic, the 30min insulin postprandial rise (strongly associated with same meal’s glucose iAUC r=0.39 p=1.25x10[-20]) was not correlated with at-home postprandial glucose iAUCs in healthy (r=-0.012 p=0.62) or prediabetic (r=-0.074 p=0.17) individuals after adjustment for age, sex and BMI. However, 2hr insulin rise was significantly associated in healthy (r=0.13 p=1.6 x 10[-7]) and prediabetic (r=0.22 p=3.2x10[-5]) individuals, explaining 15% of the variance in glucose iAUCs. This was the same for all meals regardless of macronutrient content. Conclusions: The 2hr postprandial insulin rise is a predictor of glycemic responses to different meal types. Ongoing exploration in PREDICT I using additional environmental, genetic and microbiome variables should advance our ability to predict an individual’s response to food. Disclosure P.W. Franks: Board Member; Self; Zoe Ltd. Research Support; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Lilly Diabetes, Novo Nordisk A/S, Novo Nordisk Foundation, Sanofi, Servier. S. Berry: Consultant; Self; Zoe Ltd. A.T. Chan: Consultant; Self; Bayer AG, Janssen Pharmaceuticals, Inc., Pfizer Inc. R.J. Davies: Employee; Self; Zoe Global Limited. D.A. Drew: Consultant; Spouse/Partner; PathAI. T.D. Spector: Stock/Shareholder; Self; Zoe Global Ltd. A.M. Valdes: Consultant; Self; Zoe Ltd Global. Research Support; Self; Pfizer Inc. Funding UK National Institute for Health Research; UK Wellcome Trust; Zoe Global Limited

Background: Diabetes in pregnancy portends increased maternal and fetal morbidity and mortality. Glucose testing is advised at 1 or 2 hours Pp. CGM during pregnancy may improve meal-time glycemic monitoring. Limited Pp CGM data exist for gestational diabetes (GDM), type 1 diabetes (T1D), or type 2 diabetes (T2D). Methods: We report prospective Pp glycemic data for 32 pregnant women collected during a 6-hour YSI clinic session for a 10-day Dexcom G6 CGM accuracy trial. Each subject wore 2 sensors. During the session, food consumption was recorded and analyzed if &amp;gt;5g. Meals were not standardized. CGM and YSI Pp responses within 2 hours of meals and carbohydrate content were evaluated. Results: Most women had T1D (n=20) (See table). Subjects consumed 1 (n=6), 2 (n=13) or 3 (n=13) meals for a total 71 meals. Mean carbohydrate intake per meal was 29 ± 19 g (range 6-101). Peak CGM values were recorded for 32 morning and 31 afternoon meals in 2 sensors/subject (n=139 values). Pp CGM values peaked at 136 ± 32 mg/dL (range 78-233) at median 60 minutes (interquartile range: 44, 99). Conclusions: Eating patterns are heterogeneous among pregnant women. These data support 1 hour Pp testing, and CGM use allows more detailed glycemic evaluations around meals. Our YSI results support CGM reliability. Timing of Pp peak glucose levels related to nutrient content in pregnancy needs further evaluation. Disclosure E.V. Nosova: None. K.N. Castorino: Research Support; Self; Abbott, Dexcom, Inc., Medtronic, Mylan, Novo Nordisk Inc. C.J. Levy: Consultant; Self; Dexcom, Inc. Employee; Spouse/Partner; Allergan plc. Research Support; Self; Abbott, Dexcom, Inc., Insulet Corporation. T. Johnson: Employee; Self; Dexcom, Inc. S. Shah: Employee; Self; Dexcom, Inc. G. Haroush: None. K. Nelson: None. G. O’Malley: Research Support; Self; Abbott, Dexcom, Inc. S.J. Ogyaadu: None. C. Levister: None. S.J. Brackett: None. S. Polsky: None.

We established a prospective birth cohort, the Westlake Precision Birth Cohort (WeBirth), based on 2000 pregnant women with gestational diabetes mellitus (GDM) in the second trimester and their offspring. The WeBirth provides a new framework for prospective birth cohort study with sophisticated integration of precision nutrition, wearable devices, and multiomics data collection among patients with GDM. [Image: see text]

This study aimed to investigate individual postprandial glycemic responses (PPGRs) to meal types with varying carbohydrate levels and examine their associations with 14-day glycemic variability using continuous glucose monitoring (CGM) in young adults. In a two-week intervention study with 34 participants connected to CGM, four meal types and glucose 75 g were tested. PPGRs were recorded for up to 2 h with a 15 min interval after meals. Data-driven cluster analysis was used to group individual PPGRs for each meal type, and correlation analysis was performed of 14-day glycemic variability and control with related factors. Participants had a mean age of 22.5 years, with 22.8% being male. Four meal types were chosen according to carbohydrate levels. The mean glucose excursion for all meal types, except the fruit bowl, exhibited a similar curve with attenuation. Individuals classified as high responders for each meal type exhibited sustained peak glucose levels for a longer duration compared to low responders, especially in meals with carbohydrate contents above 50%. A meal with 45% carbohydrate content showed no correlation with either 14-day glycemic variability or control. Understanding the glycemic response to carbohydrate-rich meals and adopting a meal-based approach when planning diets are crucial to improving glycemic variability and control.