Source and amount of carbohydrate affect postprandial glucose and insulin in normal subjects

Circadian rhythms in cardiac metabolic flexibility

Numerous aspects of cardiovascular physiology (e.g., heart rate, blood pressure) and pathology (e.g., myocardial infarction and sudden cardiac death) exhibit time-of-day-dependency. In association with day-night differences in energetic demand and substrate availability, the healthy heart displays remarkable metabolic flexibility through temporal partitioning of the metabolic fate of common substrates (glucose, lipid, amino acids). The purpose of this review is to highlight the contribution that circadian clocks provide toward 24-hr fluctuations in cardiac metabolism and to discuss whether attenuation and/or augmentation of these metabolic rhythms through adjustment of nutrient intake timing impacts cardiovascular disease development.

The particle size of rice flour greatly affects the structural, textural and masticatory properties of steamed rice cake (Baekseolgi)

In this study, steamed rice cakes prepared with different particle sized flour were used to investigate how the structures of rice cakes affected masticatory properties and bolus starch hydrolysis. Decreasing the particle size increased the surface areas requiring hydration, resulting in a loose structure and fluffy starch network during gelatinization. Increasing the particle size led to a tight and firm network, but was easily melted in the oral cavity. The chewing cycle and time differed among the samples. The conditions of inter-individual salivary flow rate and salivary α-amylase were diverse. The oral carbohydrate hydrolysis in the bolus before swallowing showed no significant differences in reducing sugar levels among particle sizes. However, salivary concentration was related to initial starch hydrolysis in the oral cavity, indicating the food structures affected mastication factors and physiological conditions. This study provides food structure and physiological factor information that could help design customized foods in industry.

Dietary Glycaemic Index Labelling: A Global Perspective

The glycaemic index (GI) is a food metric that ranks the acute impact of available (digestible) carbohydrates on blood glucose. At present, few countries regulate the inclusion of GI on food labels even though the information may assist consumers to manage blood glucose levels. Australia and New Zealand regulate GI claims as nutrition content claims and also recognize the GI Foundation’s certified Low GI trademark as an endorsement. The GI Foundation of South Africa endorses foods with low, medium and high GI symbols. In Asia, Singapore’s Healthier Choice Symbol has specific provisions for low GI claims. Low GI claims are also permitted on food labels in India. In China, there are no national regulations specific to GI; however, voluntary claims are permitted. In the USA, GI claims are not specifically regulated but are permitted, as they are deemed to fall under general food-labelling provisions. In Canada and the European Union, GI claims are not legal under current food law. Inconsistences in food regulation around the world undermine consumer and health professional confidence and call for harmonization. Global provisions for GI claims/endorsements in food standard codes would be in the best interests of people with diabetes and those at risk.

Nutrition education on obesity and diabetes to medical students

It is important for medical students to understand the relationship between nutrition, obesity, and diabetes to educate their patients in the future. However, medical training does not always include nutritional education. An experiential learning project was incorporated into the medical school curriculum as an effort to implement nutrition in the physiology course. First-year medical students (n = 140) received lectures on the regulation of blood glucose levels and their relationship to carbohydrates with different glycemic indexes (GI), obesity, and diabetes. Lectures were followed by a laboratory exercise where students calculated their body mass index (BMI), percentage body fat, and percentage muscle using a Bioelectrical Impedance Commercial Scale. While 63% of students had normal BMI, 31% were overweight or obese and 5% were underweight. A subgroup of 54 students tested different types of breakfasts with varying GI and provided blood samples at 0, 30, 60, 90, and 120 min. Their glucose responses were plotted based on the breakfast GI. Pre- and posttests were conducted to assess the teaching intervention where the Wilcoxon signed ranks test indicated that posttest ranks were significantly higher than pretest ranks (Z = -6.6, P < 0.001), suggesting the intervention was beneficial to students.

Acute glycemic and insulin response of Fossence™ alone, or when substituted or added to a carbohydrate challenge: A three-phase, acute, randomized, cross-over, double blind clinical trial

Short chain fructo-oligosaccharides (scFOS) are well-recognized prebiotic fibers. Fossence™ (FOSS) is a scFOS that has been produced from sucrose via a proprietary fermentation process and has not been tested for its digestibility or glucose/insulin response (GR and IR, respectively). The present randomized, controlled, cross-over study was conducted in 3 phases to explore GR and IR to ingestion of FOSS, when replaced by/added to available-carbohydrates (avCHO) among 25 healthy adults (40 ± 14years). In each phase GR and IR elicited by 3-4 test-meals were measured among the fasted recruited subjects. The interventional test meals were as follows: Phase-1, water alone or 10g FOSS or 10g Dextrose in 250ml water; Phase-2, 250ml water containing Dextrose:FOSS (g:g) in the content as 50:0 or 50:15 or 35:0 or 35:15; Phase-3 portions of white-bread (WB) containing avCHO:FOSS (g:g) in the content as 50:0 or 50:15 or 35:0 or 35:15. Blood samples (finger prick method) were collected at fasting and 15, 30, 45, 60, 90 and 120 min after start of test meal ingestion. Plasma glucose and serum insulin were analyzed utilizing standard methods. The primary endpoint was differences in glucose IAUC. All subjects provided their written consent to participate in the study (ClinicalTrials.gov: NCT03755232). The results demonstrated that FOSS, when consumed alone, showed no raise in glycaemia or insulinemia and was statistically equivalent to response of water alone. GR and IR elicited by dextrose:FOSS and WB:FOSS test-meals of Phase 2 and Phase 3, were statistically equivalent to the respective test-meals without FOSS. Result of the 3 phases support the hypothesis that FOSS is resistant to breakdown and is indigestible in the human small-intestine, and therefore can be classified as an unavailable carbohydrate that does not raise post prandial blood glucose or insulin. FOSS, being sweet to taste, may be an acceptable sugar replacer in beverages without compromising their taste and sensory qualities.

The effect of oat β-glucan on postprandial blood glucose and insulin responses: a systematic review and meta-analysis

To determine the effect of oat β‑glucan (OBG) on acute glucose and insulin responses and identify significant effect modifiers we searched the MEDLINE, EMBASE, and Cochrane databases through October 27, 2020 for acute, crossover, controlled feeding trials investigating the effect of adding OBG (concentrate or oat-bran) to carbohydrate-containing test-meals compared to comparable or different carbohydrate-matched control-meals in humans regardless of health status. The primary outcome was glucose incremental area-under-the-curve (iAUC). Secondary outcomes were insulin iAUC, and glucose and insulin incremental peak-rise (iPeak). Two reviewers extracted the data and assessed risk-of-bias and certainty-of-evidence (GRADE). Data were pooled using generic inverse-variance with random-effects model and expressed as ratio-of-means with [95% CIs]. We included 103 trial comparisons (N = 538). OBG reduced glucose iAUC and iPeak by 23% (0.77 [0.74, 0.81]) and 28% (0.72 [0.64, 0.76]) and insulin by 22% (0.78 [0.72, 0.85]) and 24% (0.76 [0.65, 0.88]), respectively. Dose, molecular-weight, and comparator were significant effect modifiers of glucose iAUC and iPeak. Significant linear dose-response relationships were observed for all outcomes. OBG molecular-weight >300 kg/mol significantly reduced glucose iAUC and iPeak, whereas molecular-weight <300 kg/mol did not. Reductions in glucose iAUC (27 vs 20%, p = 0.03) and iPeak (39 vs 25%, p < 0.01) were significantly larger with different vs comparable control-meals. Outcomes were similar in participants with and without diabetes. All outcomes had high certainty-of-evidence. In conclusion, current evidence indicates that adding OBG to carbohydrate-containing meals reduces glycaemic and insulinaemic responses. However, the magnitude of glucose reduction depends on OBG dose, molecular-weight, and the comparator.

Potential contributions of the methodology to the variability of glycaemic index of foods

Glycaemic index (GI) testing provides a useful point of comparison between carbohydrate sources. For this comparison to be meaningful, the methods used to determine GI values need to be rigorous and consistent between testing events. This requirement has led to increasing standardization of the GI methodology, with an international standard developed in joint consultation with FAO/WHO (ISO 26642:2010) currently the most up to date document. The purpose of this review is to compare the international standard to methods of published studies claiming to have performed a GI test. This analysis revealed that the international standard permits a wide range of choices for researchers when designing a GI testing plan, rather than a single standardized protocol. It has also been revealed that the literature contains significant variation, both between studies and from the international standard for critical aspects of GI testing methodology. The primary areas of variation include; what glucose specification is used, which reference food is used, how much reference food is given, what drink is given during testing, the blood sampling site chosen and what assay and equipment is used to measure blood glucose concentration. For each of these aspects we have explored some of the methodological and physiological implications of these variations. These insights suggest that whilst the international standard has assisted with framing the general parameters of GI testing, further stan-dardization to testing procedures is still required to ensure the continued relevance of the GI to clinical nutrition.

Acute effects of hemp protein consumption on glycemic and satiety control: results of 2 randomized crossover trials

Research investigating hemp protein consumption on glycemic response is limited. The effects of hemp protein consumption on blood glucose (BG), insulin, and satiety compared with soybean protein and a carbohydrate control were examined. Two acute randomized repeated-measures crossover experiments were conducted. In both, participants consumed the following isocaloric treatments: 40 g of hemp protein (hemp40), 20 g of hemp protein (hemp20), 40 g of soybean protein (soy40), 20 g of soybean protein (soy20), and a carbohydrate control. In experiments 1 (n = 27) and 2 (n = 16), appetite and BG were measured before (0-60 min, pre-pizza) and after a pizza meal (80-200 min, post-pizza). In experiment 1, food intake was measured at 60 min by ad libitum meal; in experiment 2 a fixed meal was provided (based on body weight) and insulin was measured pre-pizza and post-pizza. In both experiments, BG response was affected by treatment (p < 0.01), time (p < 0.001) and time-by-treatment (p < 0.001) from 0-200 min. Protein treatments lowered 0-60-min BG overall mean and area under the curve compared with control (p < 0.05) dose-dependently. In experiment 2, hemp40 and soy40 lowered (p < 0.05) overall mean insulin concentrations compared with hemp20, soy20, and control pre-meal. Results suggest that hemp protein, like soybean, dose-dependently lowers postprandial BG and insulin concentrations compared with a carbohydrate control. Clinical trial registry: NCT02366598 (experiment 1) and NCT02458027 (experiment 2). Novelty: Hemp protein concentrate dose-dependently leads to lower postprandial BG response compared with a carbohydrate control. No differences were seen between hemp and soy protein.

Glycemic response in nonracing sled dogs fed single starch ingredients and commercial extruded dog foods with different carbohydrate sources

This study adapted the established glycemic index (GI) methodology used in human research to perform two studies in sled dogs in order to assess the blood glucose-raising potential of pulse-based dog foods. The first was a pilot study (n = 6 dogs) to determine the GI of single starch sources (white bread, cooked white rice, and cooked green lentils) using a glucose solution as control. Next, the effect on glycemic and insulinemic meal responses and GI of commercial extruded dog foods containing different categories of starch sources (traditional grain, whole grain, grain-free, and vegan) were investigated on 11 dogs using a glucose control. Results were compared using repeated measures analysis of variance (ANOVA). Consumption of 10 g of available carbohydrate (Av CHO) was insufficient to elicit a measurable response in blood glucose for GI determination, and as such, the amount was increased to 25 g for the second study. The GI (±SE) of the single starch sources and dog foods was: white bread: 47 ± 11, cooked white rice: 71 ± 14, cooked green lentils: 60 ± 20 (P = 0.569), traditional grain: 83 ± 17, whole grain: 56 ± 8, grain-free: 41 ± 6, and vegan: 65 ± 15 (P = 0.154). No statistical differences in glycemic response over time were observed between the single starch sources or the extruded diets tested (P = 0.1412; P = 0.2651). The insulinemic response elicited by the extruded diets was also not different (P = 0.079); however, the traditional grain diet did have the slowest time to peak for insulin (P = 0.0078). Among single starch sources and extruded dog foods, there were no differences in the glycemic indices measured in this study. The GI methodology has not been validated for use in canine species, and it is likely that our results were due to higher interindividual variation or inadequate study power. Regardless, this study will serve to better define future studies to investigate the potential physiological benefits of low GI foods for dogs.

Alpha-Cyclodextrin Attenuates the Glycemic and Insulinemic Impact of White Bread in Healthy Male Volunteers

The demonstration of a physiological benefit has recently become an indispensible element of the definition of dietary fibers. In the here-reported pilot study, the effect of alpha-cyclodextrin (alpha-CD) on the postprandial glycemic and insulinemic effect of starch was examined. Twelve fasted, healthy male volunteers received, on three subsequent days, a test breakfast consisting of (A) 100 g fresh white bread (providing 50 g starch) and 250 mL drinking water, (B) the same bread with a supplement of 10 g alpha-CD dissolved in the drinking water, and (C) 25 g alpha-CD dissolved in drinking water. Capillary and venous blood was sampled before the breakfast and in regular intervals for a three-hour period thereafter. Glucose was determined in capillary blood and insulin in the plasma of venous blood samples. Breakfast (A) led to a rapid rise in blood glucose and insulin. In breakfast (B), alpha-CD reduced the areas under the curve of blood glucose and insulin significantly by 59% and 57%, respectively, demonstrating that alpha-CD inhibits and thereby delays starch digestion. Treatment (C) was not associated with a rise of blood glucose. Hence, alpha-CD complies with the current definition of dietary fiber in every respect.

A Snack Formulated with Ingredients to Slow Carbohydrate Digestion and Absorption Reduces the Glycemic Response in Humans: A Randomized Controlled Trial

This study compared the effect of a snack with ingredients to slow carbohydrate digestion (Test-snack) on postprandial blood glucose and insulin concentrations and subjective appetite ratings. We hypothesized that Test-snack would lower glucose and insulin responses and reduce appetite compared with a Control-snack. Overweight or obese subjects (n = 17) completed a randomized crossover study. Glucose, insulin, and appetite ratings were measured before consuming each snack or white bread (Bread) and over a period of 4 h. Subjects received Test-snack, Control-snack, or Bread in random order at least a week apart. The a priori primary outcome was the glucose response, and the secondary outcomes were appetite ratings and insulin responses. Mixed effects statistical models were used to perform analysis of variance in terms of the area under curve (AUC) and at specific time points. The 2-h AUC for glucose was significantly lower with Test-snack compared to Control-snack and Bread (AUC and 95% confidence intervals: Test = 2186.43 [1783.36-2589.51]; Control = 3293.75 [2893.97-3693.54]; Bread = 2800.28 [2405.79-3194.77] mg/dL · min). Four-hour AUC for glucose, and insulin, followed a similar pattern except that Test-snack did not differ from Bread. The glucose concentrations peaked at 45 min under all three conditions, but Test-snack elicited a lower response than Control-snack and Bread (P < .01). Test increased fullness and satisfaction and reduced hunger and prospective intake compared to Bread (P < .02), but was not significantly different from Control-snack. Ingredients that slow carbohydrate digestion in a snack reduce the postprandial glucose and insulin responses compared to a product without these ingredients.

Comparative Effect of Low-Glycemic Index versus High-Glycemic Index Breakfasts on Cognitive Function: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis aims to compare the effect of High-Glycemic Index (GI) versus Low-GI breakfasts on cognitive functions, including memory and attention, of children and adolescents. We systematically searched the MEDLINE (via PubMed), EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science databases, from their inception until June 2019. Articles comparing the effect of Low-GI versus High-GI breakfasts on the cognitive function (i.e., immediate memory, delayed memory, and attention) of children and adolescents were included. The DerSimonian and Laird method was used to compute the pooled effect sizes (ESs) and their respective 95% confidence intervals (CIs). The pooled ESs were 0.13 (95% CI: −0.11, 0.37) for immediate memory and 0.07 (95% CI: −0.15, 0.28) for delayed memory. For attention, the pooled ES was −0.01 (95% CI: −0.27, 0.26). In summary, GI breakfasts do not affect cognitive domains in children and adolescents.

An Optimized, Slowly Digested Savory Cluster Reduced Postprandial Glucose and Insulin Responses in Healthy Human Subjects

BACKGROUND

Slowly digested carbohydrates are perceived as beneficial by some consumers, and various regulatory bodies have published specific criteria defining lower postprandial glycemic response. We developed an optimized savory cluster snack containing slowly digested starch.

OBJECTIVE

We compared the glucose and insulin responses elicited by the optimized (test-) cluster, a control-cluster, and an available-carbohydrate-matched portion of white bread in healthy individuals. The primary outcome was blood-glucose peak rise.We tested healthy individuals (n = 25) on 3 occasions using a randomized crossover design. On each occasion, the participants provided fasting blood samples and then consumed 1 serving of test-cluster, control-cluster, or white bread. We then measured the participants' blood-glucose and serum-insulin concentrations over the next 4 h.

RESULTS

The test-cluster elicited a significantly lower blood-glucose peak rise (mean ± SEM: 1.24 ± 0.09 mmol/L) and incremental area under the curve (iAUC; 67 ± 8 mmol × min/L) than the control-cluster (2.27 ± 0.13 mmol/L and 117 ± 10 mmol × min/L, respectively) and white bread (2.27 ± 0.16 mmol/L and 114 ± 9 mmol × min/L, respectively). The serum-insulin peak rise and iAUC elicited by the test-cluster (128 ± 13 pmol/L and 6.10 ± 0.73 nmol × min/L, respectively) and white bread (141 ± 20 pmol/L and 6.47 ± 1.11 nmol × min/L, respectively) were significantly lower than those elicited by the control-cluster (205 ± 26 pmol/L and 9.60 ± 1.31 nmol × min/L, respectively).

CONCLUSION

The test-cluster elicited lower glucose and insulin responses than the control-cluster. The results support the hypothesis that the carbohydrates in the test-cluster are digested and absorbed slowly in vivo.

Glycaemic and insulinaemic impact of oats soaked overnight in milk vs. cream of rice with and without sugar, nuts, and seeds: a randomized, controlled trial

Background/Objectives

Soaking oats overnight in milk renders them ready to eat the next morning, however, it is unknown whether oats prepared this way will retain its relatively low glycaemic and insulinaemic impact. Therefore, we compared the glycaemic, insulinaemic and subjective hunger responses elicited by oats soaked overnight in 110 g skim-milk (ONO) vs. cooked cream of rice cereal (CR), both with and without inclusions.

Subjects/Methods

The project was performed at two research centers (Toronto, Winnipeg) as two separate studies each using a randomized, cross-over design with similar methods. The glycaemic and insulinaemic responses of overnight-fasted participants without diabetes (males:females: Toronto, 24:16; Winnipeg, 20:20) were measured for 3 h after consuming CR and ONO fed alone (Toronto) or with added sugar, nuts, and seeds (CRsns and ONOsns) (Winnipeg). Participants rated subjective hunger using visual analog scales. Data were analyzed by paired t-test. The primary endpoint was 0–2 h incremental area under the curve (iAUC) for glucose.

Results

Mean glucose iAUC was 33% less, after ONO than CR (mean difference was 39 (51–27) mmol × min/l, p < 0.0001) and 24% less, after ONOsns than CRsns (mean difference was 43 (65–21) mmol × min/l, p = 0.0003). Serum-insulin iAUC was 33% less, after ONO than CR (mean difference 57 (81–40) pmol × hl, p < 0.0001) and 32% less, after ONOsns than CRsns (966 (1360–572) pmol × h/l, p < 0.0001). In both Toronto and Winnipeg, subjective hunger ratings were similar across the two treatments.

Conclusions

Oats prepared by soaking overnight in skimmed milk without and with inclusions retain their relatively low glycaemic and insulinaemic impact.

Predicting mixed-meal measured glycaemic index in healthy subjects

PURPOSE

To determine the influence of meal composition on the glycaemic impact of different carbohydrate staples, and the accuracy of "adjusted calculated meal GI" compared with "measured mixed-meal GI".

METHODS

In a non-blind randomized crossover trial fasted healthy subjects consumed four dinner-type mixed meals of realistic serving size comprising a carbohydrate staple of either mashed potato, pasta, rice or a glucose drink, combined with fixed portions of boiled carrots, poached salmon and herb sauce. Blood samples collected between 0 and 180 min were analysed for glucose and insulin concentrations. Adjusted calculated meal GI values were determined against a 50 g reference glucose drink, and compared to corresponding measured mixed-meal GIs, supplemented with data from four previous mixed-meal postprandial glycaemic response studies.

RESULTS

The common carbohydrate staples, and the glucose drink, ingested as part of the salmon mixed meal induced a significantly lower post-prandial relative glycaemic response (RGR) and concurrent higher relative insulin response than the same amount of staple eaten alone. Adjusted calculated mixed-meal GI closely predicted measured mixed-meal GI in healthy subjects for 15 out of 17 mixed meals examined, showing the need to account for effects of fat and protein when predicting measured mixed-meal GI. Further, we showed the validity of using customarily consumed food amounts in mixed-meal postprandial RGR study design.

CONCLUSIONS

Adjusted calculated mixed-meal GI appears a useful model to predict measured mixed-meal GI in healthy subjects and with further development and validation could aid nutrition research and rational design of healthy meals for personalized nutrition and particular consumer groups.

Intake of dietary carbohydrates in early adulthood and adolescence and breast density among young women

PURPOSE

Carbohydrate intake increases postprandial insulin secretion and may affect breast density, a strong risk factor for breast cancer, early in life. We examined associations of adolescent and early adulthood intakes of total carbohydrates, glycemic index/load, fiber, and simple sugars with breast density among 182 young women.

METHODS

Diet was assessed using three 24-h recalls at each of five Dietary Intervention Study in Children (DISC) clinic visits when participants were age 10-19 years and at the DISC06 Follow-Up Study clinic visit when participants were age 25-29 years. Associations between energy-adjusted carbohydrates and MRI-measured percent dense breast volume (%DBV) and absolute dense breast volume (ADBV) at 25-29 years were quantified using multivariable-adjusted mixed-effects linear models.

RESULTS

Adolescent sucrose intakes and premenarcheal total carbohydrates intakes were modestly associated with higher %DBV (mean %DBVQ1 vs Q4, 16.6 vs 23.5% for sucrose; and 17.2 vs 22.3% for premenarcheal total carbohydrates, all Ptrend ≤ 0.02), but not with ADBV. However, adolescent intakes of fiber and fructose were not associated with %DBV and ADBV. Early adulthood intakes of total carbohydrates, glycemic index/load, fiber, and simple sugars were not associated with %DBV and ADBV.

CONCLUSIONS

Insulinemic carbohydrate diet during puberty may be associated with adulthood breast density, but our findings need replication in larger studies. Clinical Trials Registration ClinicalTrials.gov Identifier, NCT00458588 April 9, 2007; NCT00000459 October 27, 1999.

Accurate Carbohydrate Counting Is an Important Determinant of Postprandial Glycemia in Children and Adolescents With Type 1 Diabetes on Insulin Pump Therapy

BACKGROUND

Carbohydrate (CHO) counting is a key nutritional intervention utilized in the management of diabetes to optimize postprandial glycemia. The aim of the study was to examine the impact of accuracy of CHO counting on the postprandial glucose in children and adolescents with type 1 diabetes on insulin pump therapy.

METHODS

Children/adolescents with type 1 diabetes who were on insulin pump therapy for a minimum of 6 months are enrolled in the study. Patients were instructed to record details of meals consumed, estimated CHO count per meal, and 2-hour postprandial glucose readings over 3-5 days. Meals' CHO contents were recounted by an experienced clinical dietician, and those within 20% of the dietician's counting were considered accurate.

RESULTS

A total of 30 patients (21 females) were enrolled. Age range (median) was 8-18 (SD 13) years. Data of 247 meals were analyzed. A total of 165 (67%) meals' CHO contents were accurately counted. Of those, 90 meals (55%) had in-target postprandial glucose ( P < .000). There was an inverse relationship between inaccurate CHO estimates and postprandial glucose. Of the 63 underestimated meals, 55 had above-target glucose, while 12 of the 19 overestimated meals were followed by low glucose. There was no association between accuracy and meal size (Spearman's rho = .019).

CONCLUSION

Accuracy of CHO counting is an important determining factor of postprandial glycemia. However, other factors should be considered when advising on prandial insulin calculation. Underestimation and overestimation of CHO result in postprandial hyperglycemia and hypoglycemia, respectively. Accuracy does not correlate with meal size.

Effect of macronutrients and fiber on postprandial glycemic responses and meal glycemic index and glycemic load value determinations

Background: The potential confounding effect of different amounts and proportions of macronutrients across eating patterns on meal or dietary glycemic index (GI) and glycemic load (GL) value determinations has remained partially unaddressed.Objective: The study aimed to determine the effects of different amounts of macronutrients and fiber on measured meal GI and GL values.Design: Four studies were conducted during which participants [n = 20-22; women: 50%; age: 50-80 y; body mass index (in kg/m²): 25-30)] received food challenges containing different amounts of the variable nutrient in a random order. Added to the standard 50 g available carbohydrate from white bread was 12.5, 25, or 50 g carbohydrate; 12.5, 25, or 50 g protein; and 5.6, 11.1, or 22.2 g fat from rice cereal, tuna, and unsalted butter, respectively, and 4.8 or 9.6 g fiber from oat cereal. Arterialized venous blood was sampled for 2 h, and measured meal GI and GL and insulin index (II) values were calculated by using the incremental area under the curve (AUC_i) method.Results: Adding carbohydrate to the standard white-bread challenge increased glucose AUC_i (P < 0.0001), measured meal GI (P = 0.0066), and mean GL (P < 0.0001). Adding protein (50 g only) decreased glucose AUC_i (P = 0.0026), measured meal GI (P = 0.0139), and meal GL (P = 0.0140). Adding fat or fiber had no significant effect on these variables. Adding carbohydrate (50 g), protein (50 g), and fat (11.1 g) increased the insulin AUC_i or II; fiber had no effect.Conclusions: These data indicate that uncertainty in the determination of meal GI and GL values is introduced when carbohydrate-containing foods are consumed concurrently with protein (equal amount of carbohydrate challenge) but not with carbohydrate-, fat-, or fiber-containing foods. Future studies are needed to evaluate whether this uncertainty also influences the prediction of average dietary GI and GL values for eating patterns. This trial was registered at clinicaltrials.gov as NCT01023646.

The concept of low glycemic index and glycemic load foods as panacea for type 2 diabetes mellitus; prospects, challenges and solutions

BACKGROUND

This article examines the concepts of low glycemic indices (GIs) and glycemic load (GL) foods as key drivers in the dietary management of type 2 diabetes as well as their shortcomings. The controversies arising from the analysis of glycemic index (GI) and GL of foods such as their reproducibility as well as their relevance to the dietary management of type 2 diabetes are also discussed.

METHODS

Search was conducted in relevant electronic databases such as: Pubmed, Google Scholar, HINARI, the Cochrane library, Popline, LILACS, CINAHL, EMBASE, etc to identify the current status of knowledge regarding the controversies surrounding management of diabetes with low GI and GL foods.

CONCLUSION

This article suggests that in view of discrepancies that surround the results of GI versus GL of foods, any assay on the GI and GL of a food with the aim of recommending the food for the dietary management of type 2 diabetes, could be balanced with glycated hemoglobin assays before they are adopted as useful antidiabetic foods.

Algorithms to Improve the Prediction of Postprandial Insulinaemia in Response to Common Foods

Dietary patterns that induce excessive insulin secretion may contribute to worsening insulin resistance and beta-cell dysfunction. Our aim was to generate mathematical algorithms to improve the prediction of postprandial glycaemia and insulinaemia for foods of known nutrient composition, glycemic index (GI) and glycemic load (GL). We used an expanded database of food insulin index (FII) values generated by testing 1000 kJ portions of 147 common foods relative to a reference food in lean, young, healthy volunteers. Simple and multiple linear regression analyses were applied to validate previously generated equations for predicting insulinaemia, and develop improved predictive models. Large differences in insulinaemic responses within and between food groups were evident. GL, GI and available carbohydrate content were the strongest predictors of the FII, explaining 55%, 51% and 47% of variation respectively. Fat, protein and sugar were significant but relatively weak predictors, accounting for only 31%, 7% and 13% of the variation respectively. Nutritional composition alone explained only 50% of variability. The best algorithm included a measure of glycemic response, sugar and protein content and explained 78% of variation. Knowledge of the GI or glycaemic response to 1000 kJ portions together with nutrient composition therefore provides a good approximation for ranking of foods according to their “insulin demand”.

Association between dietary carbohydrate intake, glycemic index and glycemic load, and risk of gastric cancer

PURPOSE

The association between dietary carbohydrate intake, glycemic index (GI) and glycemic load (GL), and risk of gastric cancer (GC) has been investigated by many studies. However, the results of these studies were controversial. The aim of our study was to systematically assess this issue.

METHODS

PUBMED and EMBASE were searched up to March 2015, and either a fixed- or a random-effects model was adopted to estimate overall relative risks (RRs). Dose-response, meta-regression, subgroup, and publication bias analyses were applied.

RESULTS

Twenty-six studies with approximately 540,000 participants were finally included in this meta-analysis. High level of dietary carbohydrate intake (pooled RR 1.17, 95 % CI 0.91-1.50), GI (pooled RR 1.17, 95 % CI 0.80-1.69), and GL (pooled RR 1.06, 95 % CI 0.90-1.26) were all nonsignificantly associated with incidence of GC. In addition, no significant dose-response relationship was observed between carbohydrate intake, GI and GL, and the risk of GC. However, further subgroup analyses based on gender and geographic region suggested a significant association between higher carbohydrate intake (pooled RR 1.52, 95 % CI 1.10-2.08), GL (pooled RR 1.41, 95 % CI 1.04-1.92), and GC risk in males subgroup, and between higher carbohydrate intake (pooled RR 1.69, 95 % CI 1.36-2.09) and GC risk in Asian studies.

CONCLUSIONS

No significant association was found between dietary carbohydrate intake, GI and GL, and risk of GC. However, significantly positive association was observed in the males subgroup and Asian studies.

Metabolic responses to high glycemic index and low glycemic index meals: a controlled crossover clinical trial

BACKGROUND

The consumption of low glycemic index (LGI) foods before exercise results in slower and more stable glycemic increases. Besides maintaining an adequate supply of energy during exercise, this response may favor an increase in fat oxidation in the postprandial period before the exercise compared to high glycemic index (HGI) foods. The majority of the studies that evaluated the effect of foods differing in glycemic index on substrate oxidation during the postprandial period before the exercise are acute studies in which a single meal is consumed right before the exercise. The purpose of this study was to investigate the effect of consuming two daily HGI or LGI meals for five consecutive days on substrate oxidation before the exercise and in the concentrations of glucose, insulin and free fatty acids before and during a high intensity exercise.

METHODS

Fifteen male cyclists, aged 24.4 ± 3.8 years, with body mass index of 21.9 ± 1.4 kg.m⁻² and a V(O2 max) of 70.0 ± 5.3 mL.kg⁻¹.min⁻¹, participated in this crossover study. All test meals were consumed in the laboratory. On days 1 and 5, substrate oxidation (30 minutes before and 90 minutes after breakfast (HGI or LGI)) and diet-induced thermogenesis (90 minutes postprandial) were assessed before the exercise. The levels of glucose, insulin, and free fatty acids were determined during 2 h after breakfast on these same days. Ninety minutes after breakfast, subjects completed a 30 min cycloergometric exercise at 85 to 95% of their maximum heart rate, during which lactate concentrations were assessed.

RESULTS

The consumption of HGI meals resulted in higher areas under the glycemic and insulinemic curves in the postprandial period. However, glycemia did not differ by study treatment during exercise. There were no differences in free fatty acids in the postprandial period or in lactate levels during exercise. LGI meals resulted in lower fat oxidation and higher carbohydrate oxidation than the HGI meal in the postprandial period.

CONCLUSIONS

The results do not support a differential glycemia according to glycemic index during exercise. The ingestion of LGI foods did not lead to higher fat oxidation relative to the ingestion of HGI foods.

Influence of oral antidiabetic drugs on hyperglycemic response to foods in persons with type 2 diabetes mellitus as assessed by continuous glucose monitoring system: a pilot study

BACKGROUND

The purpose of this prospective open-label trial was (1) to assess the influence of oral antidiabetic drugs (OAD) on the glycemic index (GI), glucose response curves (GRCs), daily mean plasma glucose (MPG) and (2) to compare the GI of foods in persons with OAD-treated type 2 diabetes mellitus (T2DM) with the respective GI in healthy persons (HP).

METHODS

Tested foods containing 50 g of carbohydrates were eaten for breakfast and dinner after 10 and 4 h of fasting, respectively. Glycemic index, GRC, and MPG were obtained using the CGMS System Gold (CGMS). In T2DM patients [n = 16; age (mean +/- standard error) 56.0 +/- 2.25 years], foods were tested four times: tests 1, 2, and 3 were performed within one week in which placebo was introduced on day 2, and test 4 was carried out five weeks after reintroduction of OAD. Glycemic indexes, GRC, and MPG from tests 1, 2, 3, and 4 were compared. In a control group of 20 HP (age 24.4 +/- 0.71 years), the mean GIs were calculated as the mean from 20 subject-related GIs.

RESULTS

In T2DM patients, subject-related assessment of GIs, GRC, and MPG distinguished persons with and without OAD effect. Nevertheless, the group-related GIs and the MPG on days 2, 8, and 39 showed no significant difference. There was no significant difference between the GIs in OAD-treated T2DM patients (test 4) versus HP (except in apple baby food). Glucose response curves were significantly larger in T2DM patients (test 4) versus HP.

CONCLUSIONS

Determination of GRC and subject-related GI using the CGMS appears to be a potential means for the evaluation of efficacy of OAD treatment. Further studies are underway.

Glycemic index and glycemic load of selected Chinese traditional foods

AIM: To determine the glycemic index (GI) and glycemic load (GL) values of Chinese traditional foods in Hong Kong.

METHODS: Fifteen healthy subjects (8 males and 7 females) volunteered to consume either glucose or one of 23 test foods after 10-14 h overnight fast. The blood glucose concentrations were analyzed immediately before, 15, 30, 45, 60, 90 and 120 min after food consumption using capillary blood samples. The GI value of each test food was calculated by expressing the incremental area under the blood glucose response curve (IAUC) value for the test food as a percentage of each subject’s average IAUC value for the glucose. The GL value of each test food was calculated as the GI value of the food multiplied by the amount of the available carbohydrate in a usual portion size, divided by 100.

RESULTS: Among all the 23 Chinese traditional foods tested, 6 of them belonged to low GI foods (Tuna Fish Bun, Egg Tart, Green Bean Dessert, Chinese Herbal Jelly, Fried Rice Vermicelli in Singapore-style, and Spring Roll), 10 of them belonged to moderate GI foods (Baked Barbecued Pork Puff, Fried Fritter, “Mai-Lai” Cake, “Pineapple” Bun, Fried Rice Noodles with Sliced Beef, Barbecue Pork Bun, Moon Cakes, Glutinous Rice Ball, Instant Sweet Milky Bun, and Salted Meat Rice Dumpling), the others belonged to high GI foods (Fried Rice in Yangzhou-Style, Sticky Rice Wrapped in Lotus Leaf, Steamed Glutinous Rice Roll, Jam and Peanut Butter Toast, Plain Steamed Vermicelli Roll, Red Bean Dessert, and Frozen Sweet Milky Bun).

CONCLUSION: The GI and GL values for these Chinese traditional foods will provide some valuable information to both researchers and public on their food preference.

Glycemic index, carbohydrates, glycemic load, and the risk of pancreatic cancer in a prospective cohort study

Diets with high glycemic index and glycemic load have been associated with insulin resistance. Insulin resistance has been implicated in the etiology of pancreatic cancer. We prospectively investigated the associations between glycemic index, carbohydrates, glycemic load, and available carbohydrates dietary constituents (starch and simple sugar) intake and the risk of pancreatic cancer. We followed the participants in the NIH-AARP Diet and Health Study from 1995/1996 through December 2003. A baseline self-administered food frequency questionnaire was used to assess the dietary intake and exposure information. A total of 1,151 exocrine pancreatic cancer cases were identified from 482,362 participants after excluding first-year of follow-up. We used multivariate Cox proportional hazards regression models to calculate relative risks (RR) and 95% confidence intervals (95% CI) for pancreatic cancer. There were no associations between glycemic index, total or available carbohydrates, gycemic load, and pancreatic cancer risk. Participants with high free fructose and glucose intake were at a greater risk of developing pancreatic cancer (highest compared with lowest quintile, RR, 1.29; 95% CI, 1.04-1.59; P trend = 0.004 and RR, 1.35; 95% CI, 1.10-1.67; P trend = 0.005, respectively). There were no statistically significant interactions by body mass index, physical activity, or smoking status. Our results do not support an association between glycemic index, total or available carbohydrate intake, and glycemic load and pancreatic cancer risk. The higher risk associated with high free fructose intake needs further confirmation and elucidation.

Development of a clinical type 1 diabetes metabolic system model and in silico simulation tool

OBJECTIVES

The goal of this study was to develop a system model of type 1 diabetes for the purpose of in silico simulation for the prediction of long-term glycemic control outcomes.

METHODS

The system model was created and identified on a physiological cohort of virtual type 1 diabetes patients (n = 40). Integral-based identification was used to develop (n = 40) insulin sensitivity profiles.

RESULTS

The n = 40 insulin sensitivity profiles provide a driving input for virtual patient trials using the models developed. The identified models have a median (90% range) absolute percentage error of 1.33% (0.08-7.20%). The median (90% range) absolute error was 0.12 mmol/liter (0.01-0.56 mmol/liter). The model and integral-based identification of SI captured all patient dynamics with low error, which would lead to more physiological behavior simulation.

CONCLUSIONS

A simulation tool incorporating n = 40 virtual patient data sets to predict long-term glycemic control outcomes from clinical interventions was developed based on a physiological type 1 diabetes metabolic system model. The overall goal is to utilize this model and insulin sensitivity profiles to develop and optimize self-monitoring blood glucose and multiple daily injection therapy.

Acute effect of meal glycemic index and glycemic load on blood glucose and insulin responses in humans

Objective

Foods with contrasting glycemic index when incorporated into a meal, are able to differentially modify glycemia and insulinemia. However, little is known about whether this is dependent on the size of the meal. The purposes of this study were: i) to determine if the differential impact on blood glucose and insulin responses induced by contrasting GI foods is similar when provided in meals of different sizes, and; ii) to determine the relationship between the total meal glycemic load and the observed serum glucose and insulin responses.

Methods

Twelve obese women (BMI 33.7 ± 2.4 kg/m²) were recruited. Subjects received 4 different meals in random order. Two meals had a low glycemic index (40–43%) and two had a high-glycemic index (86–91%). Both meal types were given as two meal sizes with energy supply corresponding to 23% and 49% of predicted basal metabolic rate. Thus, meals with three different glycemic loads (95, 45–48 and 22 g) were administered. Blood samples were taken before and after each meal to determine glucose, free-fatty acids, insulin and glucagon concentrations over a 5-h period.

Results

An almost 2-fold higher serum glucose and insulin incremental area under the curve (AUC) over 2 h for the high- versus low-glycemic index same sized meals was observed (p < 0.05), however, for the serum glucose response in small meals this was not significant (p = 0.38). Calculated meal glycemic load was associated with 2 and 5 h serum glucose (r = 0.58, p < 0.01) and insulin (r = 0.54, p < 0.01) incremental and total AUC. In fact, when comparing the two meals with similar glycemic load but differing carbohydrate amount and type, very similar serum glucose and insulin responses were found. No differences were observed for serum free-fatty acids and glucagon profile in response to meal glycemic index.

Conclusion

This study showed that foods of contrasting glycemic index induced a proportionally comparable difference in serum insulin response when provided in both small and large meals. The same was true for the serum glucose response but only in large meals. Glycemic load was useful in predicting the acute impact on blood glucose and insulin responses within the context of mixed meals.

Equivalent glycemic load (EGL): a method for quantifying the glycemic responses elicited by low carbohydrate foods

BACKGROUND

Glycemic load (GL) is used to quantify the glycemic impact of high-carbohydrate (CHO) foods, but cannot be used for low-CHO foods. Therefore, we evaluated the accuracy of equivalent-glycemic-load (EGL), a measure of the glycemic impact of low-CHO foods defined as the amount of CHO from white-bread (WB) with the same glycemic impact as one serving of food.

METHODS

Several randomized, cross-over trials were performed by a contract research organization using overnight-fasted healthy subjects drawn from a pool of 63 recruited from the general population by newspaper advertisement. Incremental blood-glucose response area-under-the-curve (AUC) elicited by 0, 5, 10, 20, 35 and 50 g CHO portions of WB (WB-CHO) and 3, 5, 10 and 20 g glucose were measured. EGL values of the different doses of glucose and WB and 4 low-CHO foods were determined as: EGL = (F-B)/M, where F is AUC after food and B is y-intercept and M slope of the regression of AUC on grams WB-CHO. The dose-response curves of WB and glucose were used to derive an equation to estimate GL from EGL, and the resulting values compared to GL calculated from the glucose dose-response curve. The accuracy of EGL was assessed by comparing the GL (estimated from EGL) values of the 4 doses of oral-glucose with the amounts actually consumed.

RESULTS

Over 0-50 g WB-CHO (n = 10), the dose-response curve was non-linear, but over the range 0-20 g the curve was indistinguishable from linear, with AUC after 0, 5, 10 and 20 g WB-CHO, 10 +/- 1, 28 +/- 2, 58 +/- 5 and 100 +/- 6 mmol x min/L, differing significantly from each other (n = 48). The difference between GL values estimated from EGL and those calculated from the dose-response curve was 0 g (95% confidence-interval, +/- 0.5 g). The difference between the GL values of the 4 doses of glucose estimated from EGL, and the amounts of glucose actually consumed was 0.2 g (95% confidence-interval, +/- 1 g).

CONCLUSION

EGL, a measure of the glycemic impact of low-carbohydrate foods, is valid across the range of 0-20 g CHO, accurate to within 1 g, and at least sensitive enough to detect a glycemic response equivalent to that produced by 3 g oral-glucose in 10 subjects.

Can the Glycemic Index (GI) be used as a tool in the prevention and management of Type 2 diabetes?

The large increase in type 2 diabetes (T2DM), the considerable lifetime risk of diabetes and the loss of lifetime call for concerted action to prevent T2DM and its complications. Since diabetes is characterized by abnormal glucose metabolism, the question arises of whether a high intake of carbohydrates that are rapidly absorbed as glucose may increase the risk and worsen the course of T2DM. To quantify the impact of carbohydrates on blood glucose the glycemic index (GI) and the glycemic load (GL) have been applied. The GI of a food is a method of ranking carbohydrate rich foods according to their glycemic responses. GI is defined as the incremental area under the blood glucose curve of 50g carbohydrate of a test food expressed as a percentage of the area of the response to an equivalent amount of a reference food (glucose or white bread). In relation to GI/GL and prevention of T2DM there is insufficient information from observational studies to determine whether a positive association exists or not. Only randomized controlled clinical intervention studies will be able to provide the final answer. From meta-analyses of randomised controlled clinical trials comparing low and high GI diets in the treatment of diabetes it has been found that low GI diets improve the glycemic control. Labeling of foods with GI would be helpful for persons with diabetes, but the usefulness for healthy subjects remains to be clarified. At present it seems premature to introduce GI labeling for the entire population.