A glucose reference curve is the optimum method to determine the glycemic glucose equivalent values of foods in humans

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.

Perspective: Soy-based Meat and Dairy Alternatives, Despite Classification as Ultra-processed Foods, Deliver High-quality Nutrition on Par with Unprocessed or Minimally Processed Animal-based Counterparts

In many non-Asian countries, soy is consumed via soy-based meat and dairy alternatives, in addition to the traditional Asian soyfoods, such as tofu and miso. Meat alternatives are typically made using concentrated sources of soy protein, such as soy protein isolate (SPI) and soy protein concentrate (SPC). Therefore, these products are classified as ultra-processed foods (UPFs; group 4) according to NOVA, an increasingly widely used food-classification system that classifies all foods into 1 of 4 groups according to the processing they undergo. Furthermore, most soymilks, even those made from whole soybeans, are also classified as UPFs because of the addition of sugars and emulsifiers. Increasingly, recommendations are being made to restrict the consumption of UPFs because their intake is associated with a variety of adverse health outcomes. Critics of UPFs argue these foods are unhealthful for a wide assortment of reasons. Explanations for the proposed adverse effects of UPFs include their high energy density, high glycemic index (GI), hyper-palatability, and low satiety potential. Claims have also been made that UPFs are not sustainably produced. However, this perspective argues that none of the criticisms of UPFs apply to soy-based meat and dairy alternatives when compared with their animal-based counterparts, beef and cow milk, which are classified as unprocessed or minimally processed foods (group 1). Classifying soy-based meat and dairy alternatives as UPFs may hinder their public acceptance, which could detrimentally affect personal and planetary health. In conclusion, the NOVA classification system is simplistic and does not adequately evaluate the nutritional attributes of meat and dairy alternatives based on soy.

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.

Glycemic impact and health: new horizons in white bread formulations

The challenge of provision of a much wider range of foods of relatively low glycemic response than is currently available, especially in terms of cereal products, has been highlighted in recent years and this has particular relevance to bread consumption. Although there has been some transition to brown bread consumption, white bread remains a firm feature in the typical average western diet. This review first outlines the relationship between the glycemic impact of foods and health. What is important is that relatively small differences in glycemic potency of regularly consumed starch foods have been shown to have beneficial effects on health outcomes. Second, factors affecting glycemic response with particular application to white bread formulations are discussed. Novel ways of reformulating this highly favored carbohydrate staple, by using composite flours, with the aim of developing products of reduced glycemic response are highlighted in this review. Importantly, a new and significant focus on the role of unavailable carbohydrate in glycemic improvement is emerging. This has important application in increasing accessibility to health benefits by contributing to the prevention of and management of glucose intolerance, insulin resistance, and associated chronic disease to a wider range of consumers.

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.

Variability in measurements of blood glucose response to foods in human subjects is not reduced after a standard breakfast

Measurements of blood glucose response to food are highly variable. We determined whether within-individual variability in data for blood glucose responses were reduced if individuals consumed a standard meal 2 hours before testing and investigated the effect of serving size. Blood glucose responses to muesli and macaroni cheese were determined in 13 individuals by taking 2 fasting capillary blood samples. Food was then consumed, and capillary blood samples were taken every 15 minutes for the first hour and every 30 minutes for the second hour. The incremental area under the blood glucose response curve was determined, and glycemic glucose equivalents (GGEs) were calculated. The GGE values were not significantly different whether the muesli and macaroni cheese were fed fasting or after a standard breakfast (29.2 vs 34.5 g for muesli and 11.0 vs 14.6 g for macaroni cheese). Within-individual coefficients of variation were not significantly different whether the food was consumed fasting or after a standard breakfast (24.9% and 32.5% for muesli and 38.1% and 59.4% for macaroni cheese). Differences in GGE between measured and estimated half serving size for macaroni cheese were 0.8 g (P = .6) and for muesli, 3 g (P = .2); for the double serving size for macaroni cheese, 1.7 g (P = .7); and for muesli, 6.7 g (P = .06). The GGE values for foods and variability in blood glucose response within individuals were not significantly different whether individuals fasted or consumed a standard breakfast before testing. However, blood glucose levels tended to differ significantly after consumption of the double serving size of muesli compared with other serving sizes.