The glycemic load estimated from the glycemic index does not differ greatly from that measured using a standard curve in healthy volunteers

Glycemic index and glycemic load of brief sugary sweets: randomized controlled trials of eight Thai desserts

Background

Thai desserts, celebrated for their exquisite sweetness, are widely enjoyed for personal indulgence and as cherished souvenirs. However, their high sugar content raises concerns regarding health impacts. This study aimed to quantify the glycemic index (GI) and glycemic load (GL) in healthy volunteers following consumption of various Thai desserts, out of 10 renowned desserts from across Thailand, identified by the Tourism Authority of Thailand, characterized by differing sugar levels.

Method

Eight were selected based on the absence of preservatives and microbial or chemical contaminations. Each participant consumed a 50-g serving of available carbohydrate (50avCHO) from these desserts. Ninety-six healthy volunteers, with a mean age of 31.8 ± 5.7 years, a mean body weight of 57.2 ± 7.3 kg, and 63.5% women, were randomized into eight groups, with each group comprising 12 participants. Blood samples were collected pre-and post-consumption to assess GI and GL values following established protocols.

Results

The findings revealed that Phetchaburi's Custard Cake exhibited the lowest GI and GL values (53.4 and 26.7, respectively), with progressively higher values observed in Saraburi's Curry Puff (61.8 and 30.9), Nakhon Sawan's Mochi (68.9 and 34.4), Suphan Buri's Sponge Cake (75.9 and 38.0), Ayutthaya's Cotton Candy (81.4 and 40.7), Prachuap Khiri Khan's Pineapple Cheese Cake Biscuit (87.4 and 43.7), Chon Buri's Bamboo Sticky Rice (109.3 and 54.7), and Lampang's Crispy Rice Cracker (149.3 and 74.7), respectively.

Conclusion

The study demonstrates that while Thai desserts exhibit a range of GI values, their GL values are uniformly high. It underscores the importance of disseminating GI and GL information to consumers, enabling them to make informed dietary choices and moderate their intake of these sugary delicacies.

Dietary glycemic index and glycemic load predict longitudinal change in glycemic and cardio-metabolic biomarkers among old diabetic adults living in a resource-poor country

This study aims to investigate longitudinal associations between the dietary glycemic index (GI) and glycemic load (GL) and changes in glycemic and cardio-metabolic outcomes. A 28-month retrospective cohort study included 110 Vietnamese diabetic patients, collecting their dietary GI and GL values along with blood biochemical data from baseline 24-h dietary recall and medical records. Latent class growth modelling identified three distinct HbA1c trajectories during the follow-up period, with 51% of patients achieving good glycemic control. The adjusted linear mixed-effect model showed that 1 unit increase in logarithms in dietary GL was associated with a 0.14% increase in the log-HbA1c. Among poorly controlled diabetic patients, baseline GL values were positively correlated with increases in HbA1c; GI showed effects on changes in fasting plasma glucose and the triglyceride-glucose (TyG) index. No significant association was observed in patients with good glycemic control.

Influence of baking time and formulation of part-baked wheat sourdough bread on the physical characteristics, sensory quality, glycaemic index and appetite sensations

Raw materials and process parameters in bread production can modulate the glycemic index, which on itself has been linked with provision of better hunger satisfaction and maintaining better satiation. The objective of this research was to investigate if using unrefined wheat flour or the addition of intact cereals in formulation or alternating the baking time would have an effect on physical characteristics, sensory quality, glycaemic index and appetite sensations in wheat sourdough bread. In the study, three types of commercial part-baked frozen sourdough bread, baked to the final baking for two different times (long and short baking time) were used. A randomized controlled crossover trial was performed with 10 healthy adults who consumed sufficient quantity of bread to ingest 50 g available carbohydrates. Participants self-reported appetite sensations (desire to eat, hunger, fullness, satisfaction, appetite) on a 10 cm visual analog scale (VAS) scale in a course of 180 min. In addition, bread products were subjected to overall acceptability and different sensory attributes were examined on JAR "just about right" scale. Different bread formulations (refined flour, unrefined wheat flour, cereal flour or intact cereals) and different length of baking time significantly influenced (p < 0.005) physical, textural and sensory features of products. The alternation of aforementioned parameters decreased the glycemic index, but not significantly (p > 0.005). No correlation was found between lower GI, satiety and satiation. Liking score and incremental area under the curve (iAUC) of satiety and satiation were calculated as highest in sourdough bread with added cereals.

Accuracy in Determining the Glycaemic Impact of Meals by Adding Individual Food Values Is Affected by Food Number, Homeostasis and Glucose Reference Dose

Summing glycaemic glucose equivalent (GGE) values of foods in a meal would be a practical way to predict the relative glycaemic impact (RGI) of the meal, without measuring the whole meal postprandial effect. However, as glycaemic response is non-linear, and glycaemic responsiveness per gram of glucose decreases with dose, addition accumulates inaccuracy. This research described determined inaccuracies accruing during addition of GGE values of foods and identifies approaches to reduce inaccuracy. By combining five published glucose dose-glycaemic response curves, the relationship between GGE dose and response was shown to be nearly quadratic (R2 = 0.98). This curve allowed determination of the divergence between the theoretically true glycaemic glucose equivalence of food intakes and estimates obtained by extrapolating linearly from zero through responses to glucose reference doses of 10, 20, 30, 40, 50 and 60 g. For each reference, the disparity between the linearly determined sum of GGE values of foods in 20 realistic meals, and true homeostasis-adjusted glucose equivalence for each whole meal, was calculated. Summation of the GGE values of individual foods could lead to inaccurate (>5 g GGE) estimates of the RGI of meals, depending on the GGE total, the number of foods, and the size of the glucose reference. Inaccuracy that accumulates during linear addition of GGE values of foods limits the range in which they can be used linearly in dietary management, public health and epidemiology. However, the steps discussed herein may be taken to allow for non-linearity.

Glycemic index and glycemic load of common fruit juices in Thailand

Background

The glycemic index (GI) reflects body responses to different carbohydrate-rich foods. Generally, it cannot be simply predicted from the composition of the food but needs in vivo testing.

Methods

Healthy adult volunteers with normal body mass index were recruited. Each volunteer was asked to participate in the study center twice in the first week to consume the reference glucose (50 g) and once a week thereafter to consume the study fruit juices in a random order. The study fruit juices were Florida orange juice, Tangerine orange juice, Blackcurrant mixed juice, and Veggie V9 orange carrot juice which were already available on the market. The serving size of each fruit juice was calculated to provide 50 g of glycemic carbohydrate. The fasting and subsequent venous blood samplings were obtained through the indwelling venous catheters at 0, 15, 30, 45, 60, 90, and 120 min after the test drink consumption and immediately sent for plasma glucose and insulin. GI and insulin indices were calculated from the incremental area under the curve of postprandial glucose of the test drink divided by the reference drink. Glycemic load (GL) was calculated from the GI multiplied by carbohydrate content in the serving size.

Results

A total of 12 volunteers participated in the study. Plasma glucose and insulin peaked at 30 min after the drink was consumed, and then started to decline at 120 min. Tangerine orange juice had the lowest GI (34.1 ± 18.7) and GL (8.1 g). Veggie V9 had the highest GI (69.6 ± 43.3) but it was in the third GL rank (12.4 g). The insulin responses correlated well with the GI. Fructose to glucose ratio was inversely associated with GI and insulin responses for all study fruit juices. Fiber contents in the study juices did not correlate with glycemic and insulin indices.

Conclusions

The GIs of fruit juices were varied but consistently showed a positive correlation with insulin indices. Fruit juices with low GI are a healthier choice for people with diabetes as well as individuals who want to stay healthy since it produces more subtle postprandial glucose and insulin responses.

Egg and saturated fat containing breakfasts have no acute effect on acute glycemic control in healthy adults: a randomized partial crossover trial

BACKGROUND/OBJECTIVES

High egg consumption is associated with poor glycemic control. Considering the widespread consumption of eggs, it is crucial to determine causality in this association. We tested if egg consumption acutely alters glucose disposal in the absence or presence of saturated fat, which is frequently consumed with eggs.

SUBJECTS/METHODS

In a randomized partial crossover clinical trial, 48 subjects (consuming ≥ 1 egg/week) received two of four isocaloric, macronutrient-matched breakfasts. The groups were defined based on the main ingredient of the breakfasts offered: eggs (EB); saturated fat (SB); eggs and saturated fat (ES); and control, which included a cereal based breakfast (CB). The breakfasts were offered in two testing sessions spaced seven days apart. Six blood samples (pre breakfast (fasting); 30, 60, 90, 120, and 180 minutes post breakfast) were collected to measure glucose and insulin levels. Area under the curves (AUC) were analyzed controlling for the baseline concentrations using mixed-effects models accounting for within-subject dependencies to compare these across breakfast assignments.

RESULTS

Forty-eight patients (46% males, age 25.8 ± 7.7 years, BMI 25.7 ± 4.6 kg/m²) were included. Neither EB, SB nor ES was associated with a significant difference in AUC of glucose or insulin compared to CB (p > 0.1).

CONCLUSIONS

Acutely, consumption of egg breakfast with or without accompanying saturated fat does not adversely affect glucose disposal in healthy adults. While this is reassuring for continued egg consumption, a long-term evaluation of egg intake with or without saturated fat would be the next step.

Structural breakdown of starch-based foods during gastric digestion and its link to glycemic response: In vivo and in vitro considerations

The digestion of starch-based foods in the small intestine as well as factors affecting their digestibility have been previously investigated and reviewed in detail. Starch digestibility has been studied both in vivo and in vitro, with increasing interest in the use of in vitro models. Although previous in vivo studies have indicated the effect of mastication and gastric digestion on the digestibility of solid starch-based foods, the physical breakdown of starch-based foods prior to small intestinal digestion is often less considered. Moreover, gastric digestion has received little attention in the attempt to understand the digestion of solid starch-based foods in the digestive tract. In this review, the physical breakdown of starch-based foods in the mouth and stomach, the quantification of these breakdown processes, and their links to physiological outcomes, such as gastric emptying and glycemic response, are discussed. In addition, the physical breakdown aspects related to gastric digestion that need to be considered when developing in vitro-in vivo correlation in starch digestion studies are discussed. The discussion demonstrates that physical breakdown prior to small intestinal digestion, especially during gastric digestion, should not be neglected in understanding the digestion of solid starch-based foods.

Glycemic index of pulses and pulse-based products: a review

Pulses are a major source for plant-based proteins, with over 173 countries producing and exporting over 50 million tons annually. Pulses provide many of the essential nutrients and vitamins for a balanced and healthy diet, hence are health beneficial. Pulses have been known to lower glycemic index (GI), as they elicit lower post prandial glycemic responses, and can prevent insulin resistance, Type 2 diabetes and associated complications. This study reviews the GI values (determined by in vivo methodology) reported in 48 articles during the year 1992-2018 for various pulse type preparations consumed by humans. The GI ranges (glucose and bread as a reference respectively) for each pulse type were: broad bean (40 ± 5 to 94 ± 4, 75 to 93), chickpea (5 ± 1 to 45 ± 1, 14 ± 3 to 96 ± 21), common bean (9 ± 1 to 75 ± 8, 18 ± 2 to 99 ± 11), cowpea (6 ± 1 to 56 ± 0.2, 38 ± 19 to 66 ± 7), lentil (10 ± 3 to 66 ± 6, 37 to 87 ± 6), mung bean (11 ± 2 to 90 ± 9, 28 ± 1 to 44 ± 6), peas (9 ± 2 to 57 ± 2, 45 ± 8 to 93 ± 9), pigeon peas (7 ± 1 to 54 ± 1, 31 ± 4), and mixed pulses (35 ± 5 to 66 ± 23, 69 ± 42 to 98 ± 29). It was found that the method of preparation, processing and heat applications tended to affect the GI of pulses. In addition, removal of the hull, blending, grinding, milling and pureeing, reduced particle size, contributed to an increased surface area and exposure of starch granules to the amylolytic enzymes. This was subsequently associated with rapid digestion and absorption of pulse carbohydrates, resulting in a higher GI. High or increased heat applications to pulses were associated with extensive starch gelatinization, also leading to a higher GI. The type of reference food used (glucose or white bread) and the other nutrients present in the meal also affected the GI.

Kiwifruit Exchanges for Increased Nutrient Richness with Little Effect on Carbohydrate Intake, Glycaemic Impact, or Insulin Response

BACKGROUND

Kiwifruit are nutrient-rich and have properties which indicate a low glycaemic impact compared with many cooked cereal foods, suggesting that they may be used for dietary enrichment of vitamin C without glycaemic cost.

AIM

To develop tables for equi-carbohydrate and equi-glycaemic partial exchange of kiwifruit for glycaemic carbohydrate foods.

METHOD

The available carbohydrate content of Zespri^® Green and Zespri^® SunGold kiwifruit was determined as sugars released during in vitro digestive analysis. Glycaemic potency was determined as grams of glucose equivalents (GGEs) in a clinical trial using 200 g (a two-kiwifruit edible portion) of each cultivar, non-diabetic subjects (n = 20), and a glucose reference. GGE values were also estimated for a range of carbohydrate foods in the New Zealand Food Composition Database for which available carbohydrate and glycaemic index values were available. The values allowed exchange tables to be constructed for either equi-carbohydrate or equi-glycaemic partial exchange of kiwifruit for the foods.

RESULTS

GGE values of both kiwifruit cultivars were low ("Hayward", 6.6 glucose equivalents/100 g; "Zesy002", 6.7 glucose equivalents/100 g). Partial equi-carbohydrate substitution of foods in most carbohydrate food categories substantially increased vitamin C with little change in glycaemic impact, while equi-glycaemic partial substitution by kiwifruit could be achieved with little change in carbohydrate intake.

CONCLUSION

Equi-carbohydrate partial exchange of kiwifruit for starchy staple foods is a means of greatly increasing nutrient richness in a diet without the physiological costs of increased glycaemia and insulin responses or carbohydrate intake.

Effect of Cold Storage and Reheating of Parboiled Rice on Postprandial Glycaemic Response, Satiety, Palatability and Chewed Particle Size Distribution

Background: Globally, hot cooked refined rice is consumed in large quantities and is a major contributor to dietary glycaemic load. This study aimed to compare the glycaemic potency of hot- and cold-stored parboiled rice to widely available medium-grain white rice. Method: Twenty-eight healthy volunteers participated in a three-treatment experiment where postprandial blood glucose was measured over 120 min after consumption of 140 g of rice. The three rice samples were freshly cooked medium-grain white rice, freshly cooked parboiled rice, and parboiled rice stored overnight at 4 °C. All rice was served warm at 65 °C. Chewing time was recorded. Results: incremental area under the curve (iAUC) of the control rice, freshly cooked medium-grain white rice, was the highest: 1.7-fold higher (1.2, 2.6) than reheated parboiled rice (p < 0.001) and 1.5-fold higher (1.0, 2.2) than freshly cooked parboiled rice (p = 0.001). No significant difference in postprandial glycaemic response was observed between freshly cooked and reheated parboiled rice samples (p = 0.445). Chewing time for 10 g cold-stored parboiled rice was 6 s (25%) longer and was considered more palatable, visually appealing and better tasting than freshly cooked medium-grain (all p < 0.05). Conclusions: For regular consumers of rice, reheating cooked rice after cold storage would lower the dietary glycaemic load and, in the long term, may reduce the risk for type 2 and gestational diabetes. More trials are needed to identify the significance.

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 effect of dietary fibre on reducing the glycaemic index of bread

As bread is the most relevant source of available carbohydrates in the diet and as lowering dietary glycaemic index (GI) is considered favourable to health, many studies have been carried out in order to decrease the GI of bread. The most relevant strategy that has been applied so far is the addition of fibre-rich flours or pure dietary fibre. However, the effectiveness of dietary fibre in bread in reducing the GI is controversial. The purpose of the present review was to discuss critically the effects obtained by adding different kinds of fibre to bread in order to modulate its glycaemic response. The studies were selected because they analysed in vivo whether or not dietary fibre, naturally present or added during bread making, could improve the glucose response. The reviewed literature suggests that the presence of intact structures not accessible to human amylases, as well as a reduced pH that may delay gastric emptying or create a barrier to starch digestion, seems to be more effective than dietary fibre per se in improving glucose metabolism, irrespective of the type of cereal. Moreover, the incorporation of technologically extracted cereal fibre fractions, the addition of fractions from legumes or of specifically developed viscous or non-viscous fibres also constitute effective strategies. However, when fibres or wholemeal is included in bread making to affect the glycaemic response, the manufacturing protocol needs to reconsider several technological parameters in order to obtain high-quality and consumer-acceptable breads.

Relative glycaemic impact of customarily consumed portions of eighty-three foods measured by digesting in vitro and adjusting for food mass and apparent glucose disposal

Practical values to guide food choices for control of postprandial glycaemia need to refer to entire foods in amounts customarily consumed. We tested an in vitro method for determining the relative glycaemic impact (RGI) of customarily consumed portions of foods. Sugars released during in vitro pancreatic digestion of eighty-three foods were measured as glucose equivalents (GE) per gram of food, adjusted by the glycaemic indexes of the sugars to obtain glycaemic GE (GGE) per gram and multiplied by food portion weight to obtain the GGE contribution of the food portion, its RGI. The results were compared with clinical GGE values from subjects who consumed the same food amounts. In vitro and in vivo GGE values were significantly correlated, but the slope of the regression equation was significantly less than one, meaning in vitro GGE values overestimated in vivo GGE values. Bland-Altman method comparison showed the in vitro-in vivo disparity to increase as mean GGE increased, suggesting the need to allow for different rates of homeostatic blood glucose disposal (GD) due to different GGE doses in the customarily consumed food portions. After GD correction, Bland-Altman method comparison showed that the bias in predicting in vivo GGE values from in vitro GGE values was almost completely removed (y = 0.071x - 0.89; R2 0.01). We conclude that in vitro food values for use in managing the glycaemic impact of customarily consumed food quantities require correction for blood GD that is dependent on the GGE content of the food portions involved.

Baselines representing blood glucose clearance improve in vitro prediction of the glycaemic impact of customarily consumed food quantities

Glycaemic responses to foods reflect the balance between glucose loading into, and its clearance from, the blood. Current in vitro methods for glycaemic analysis do not take into account the key role of glucose disposal. The present study aimed to develop a food intake-sensitive method for measuring the glycaemic impact of food quantities usually consumed, as the difference between release of glucose equivalents (GGE) from food during in vitro digestion and a corresponding estimate of clearance of them from the blood. Five foods – white bread, fruit bread, muesli bar, mashed potato and chickpeas – were consumed on three occasions by twenty volunteers to provide blood glucose response (BGR) curves. GGE release during in vitro digestion of the foods was also plotted. Glucose disposal rates estimated from downward slopes of the BGR curves allowed GGE dose-dependent cumulative glucose disposal to be calculated. By subtracting cumulative glucose disposal from cumulative in vitro GGE release, accuracy in predicting the in vivo glycaemic effect from in vitro GGE values was greatly improved. GGEin vivo = 0·99GGEin vitro+0·75 (R2 0·88). Furthermore, the difference between the curves of cumulative GGE release and disposal closely mimicked in vivo incremental BGR curves. We conclude that valid measurement of the glycaemic impact of foods may be obtained in vitro, and expressed as grams of glucose equivalents per food quantity, by taking account not only of GGE release from food during in vitro digestion, but also of blood glucose clearance in response to the food quantity.

The influence of carbohydrate on cognitive performance: a critical evaluation from the perspective of glycaemic load

Links between nutrition and cognition are widely acknowledged. Within the context of short-term cognitive performance, carbohydrate has been the dietary component most commonly investigated. The majority of studies investigating the influence of carbohydrate on cognitive performance have employed oral glucose drink interventions followed by measures of performance on cognitive tests. More recently, studies have investigated the effect of different carbohydrates on cognitive performance rather than just pure glucose drinks. To date, studies have not been evaluated based on a standardised measure of glycaemic response, such as glycaemic load. The present review provides a critical evaluation of eight studies that have explored the relationships between food carbohydrate and cognitive performance and allow glycaemic load to be used as a basis for comparison. The key finding is that these provide insufficient evidence to support a consistent effect of glycaemic load on short-term cognitive performance. Future studies should employ consistent test methodologies and describe food interventions in more detail to facilitate meaningful comparisons and interpretations of results.

Considerable temporal variability in glucose reference curves in humans for a year period

Glycemic glucose equivalent (GGE) is a measure of the blood glucose response to a defined portion of food. Their calculation requires the measurement of a standard glucose-response curve, with beverages containing 0, 12.5, 25, 50, and 75 g of glucose measured twice each. This study was designed to determine the stability of an individual's glucose-response curve measured every 3 months for a year and of their GGE estimates for 10 foods for that period. The blood glucose response to beverages containing 0, 12.5, 25, 50, and 75 g glucose and to 10 foods was measured for 16 healthy individuals. Capillary blood samples were collected fasting, then every 15 minutes for 1 hour, and every 30 minutes for at least 2 hours. The slopes and intercepts of the 4 glucose curves and the GGE of the 10 foods calculated using the available curves for each food was compared. The results showed considerable temporal variability in the slope (intraindividual coefficient of variation (CV) = 30%) and intercept (intraindividual CV = 40%) of the glucose curves. However, if GGE values were categorized into 3 groups (low GGE, < or = 10; medium GGE, 10.01-19.99; and high GGE, > or = 20), all but one food was consistently classified in the same category across the 4 glucose curves. In conclusion, it appears that if the exact GGE value is required, glucose curves should be repeated at least once every 3 months, but if foods are classed into general GGE categories, it may be possible to use the same glucose curve for a longer period.

Another approach to estimating the reliability of glycaemic index

The usefulness of the glycaemic index (GI) of a food for practical advice for individuals with diabetes or the general population depends on its reliability, as estimated by intra-class coefficient (ICC), a measure having values between 0 and 1, with values closer to 1 indicating better reliability. We aimed to estimate the ICC of the postprandial blood glucose response to glucose and white bread, instant mashed potato and chickpeas using the incremental area under the curve (iAUC) and the GI of these foods. The iAUC values were determined in twenty healthy individuals on three and four occasions for white bread and glucose, respectively, and for potato and chickpeas on a single occasion. The ICC of the iAUC for white bread and glucose were 0·50 (95 % CI 0·27, 0·73) and 0·49 (95 % CI 0·22, 0·75), respectively. The mean GI of white bread was 81 (95 % CI 74, 90) with a reliability of 0·27 indicating substantial within-person variability. The GI of mashed potato and chickpeas were 87 (95 % CI 76, 101) and 28 (95 % CI 22, 37) respectively with ICC of 0·02 and 0·40.The ICC of the iAUC were moderate and those of the GI fair or poor, indicating the heterogeneous nature of individuals' responses. The unpredictability of individual responses even if they are the result of day-to-day variation places limitations on the clinical usefulness of GI. If the very different GI of potato and chickpeas are estimates of an individual's every-day response to different foods, then the GI of foods may provide an indication of the GI of a long-term diet.

Glycemic impact, glycemic glucose equivalents, glycemic index, and glycemic load: definitions, distinctions, and implications

Glycemic impact, defined as "the weight of glucose that would induce a glycemic response equivalent to that induced by a given amount of food" (American Association of Cereal Chemists Glycemic Carbohydrate Definition Committee, 2007), expresses relative glycemic potential in grams of glycemic glucose equivalents (GGEs) per specified amount of food. Therefore, GGE behaves as a food component, and (relative) glycemic impact (RGI) is the GGE intake responsible for a glycemic response. RGI differs from glycemic index (GI) because it refers to food and depends on food intake, whereas GI refers to carbohydrate and is a unitless index value unresponsive to food intake. Glycemic load (GL) is the theoretical cumulative exposure to glycemia over a period of time and is derived from GI as GI x carbohydrate intake. Contracted to a single intake of food, GL approximates RGI but cannot be accurately expressed in terms of glucose equivalents, because GI is measured by using equal carbohydrate intakes with usually unequal responses. RGI, on the other hand, is based on relative food and reference quantities required to give equal glycemic responses and so is accurately expressed as GGE. The properties of GGE allow it to be used as a virtual food component in food labeling and in food-composition databases linked to nutrition management systems to represent the glycemic impact of foods alongside nutrient intakes. GGE can also indicate carbohydrate quality when used to compare foods in equal carbohydrate food groupings.

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.