The Glycemic Load Estimated from the Glycemic Index Does Not Differ Greatly from That Measured Using a Standard Curve in Healthy Volunteers

Venn, Bernard; Wallace, Alison J.; Monro, John; Perry, Tekla S.; Brown, Rachel; Frampton, Chris; Green, Timothy

doi:10.1093/jn/136.5.1377

Cited by 37 publications

(59 citation statements)

References 18 publications

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“…For example, eating six times the amount of bread results in an approximately threefold increase in AUC (Brand-Miller et al, 2003c). In other words, as the amount of food increased, the rate of increase in AUC declines, an effect shown in Figure 2 (Venn et al, 2006). Therefore, it is implicit in the calculation of GL that the AUC for both the test and the reference foods are attenuated to the same degree with increasing amounts consumed.…”

Section: Definition and Measurementmentioning

confidence: 99%

Glycemic index and glycemic load: measurement issues and their effect on diet–disease relationships

2007

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Glycemic index (GI) describes the blood glucose response after consumption of a carbohydrate containing test food relative to a carbohydrate containing reference food, typically glucose or white bread. GI was originally designed for people with diabetes as a guide to food selection, advice being given to select foods with a low GI. The amount of food consumed is a major determinant of postprandial hyperglycemia, and the concept of glycemic load (GL) takes account of the GI of a food and the amount eaten. More recent recommendations regarding the potential of low GI and GL diets to reduce the risk of chronic diseases and to treat conditions other than diabetes, should be interpreted in the light of the individual variation in blood glucose levels and other methodological issues relating to measurement of GI and GL. Several factors explain the large inter-and intra-individual variation in glycemic response to foods. More reliable measurements of GI and GL of individual foods than are currently available can be obtained by studying, under standard conditions, a larger number of subjects than has typically been the case in the past. Meta-analyses suggest that foods with a low GI or GL may confer benefit in terms of glycemic control in diabetes and lipid management. However, low GI and GL foods can be energy dense and contain substantial amounts of sugars or undesirable fats that contribute to a diminished glycemic response. Therefore, functionality in terms of a low glycemic response alone does not necessarily justify a health claim. Most studies, which have demonstrated health benefits of low GI or GL involved naturally occurring and minimally processed carbohydrate containing cereals, vegetables and fruit. These foods have qualities other than their immediate impact on postprandial glycemia as a basis to recommend their consumption. When the GI or GL concepts are used to guide food choice, this should be done in the context of other nutritional indicators and when values have been reliably measured in a large group of individuals.

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Section: Definition and Measurementmentioning

confidence: 99%

Glycemic index and glycemic load: measurement issues and their effect on diet–disease relationships

2007

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“…As the GGE quantity represented by the area under the whole curve had already been determined (8) , the GGE disposal rate could be calculated as:…”

Section: Establishing Glucose Disposal Baselinesmentioning

confidence: 99%

“…Modelling glycaemic response as the balance between glycaemic glucose equivalent loading and glucose disposal GGE released per g of food after 60 min in vitro digestion was multiplied by the food intake, and entered into the (8) . Line B (------): the maximum net glucose loading rate was achieved by about 20 min.…”

Section: Establishing Glucose Disposal Baselinesmentioning

confidence: 99%

“…For instance, customarily consumed food portions often have a much lower glycaemic impact than would be expected from a glycaemic load value calculated from the glycaemic index (6) , because glycaemic index is based on a 50 g glucose reference, which is much greater than the glucose equivalents that would be consumed in most servings of foods. With the need for in vitro methods that predict the relative glycaemic effect of realistic intakes of food (7) , methods that take account of the effects of homeostatic responses to dose are required.A recent well-replicated (n 20) clinical study of the glycaemic effects of five foods fed on three occasions, and glucose fed on four occasions, provided glucose response curves and clinical glycaemic glucose equivalent (GGE) values for the foods (8) .In vitro digestion of the same foods has given us curves of cumulative carbohydrate release. With detailed in vivo and in vitro datasets for the same foods we have an opportunity to measure improvements in the predictive validity of in vitro measures when glucose homeostasis is allowed for.…”

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confidence: 99%

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

2009

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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.). 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. Carbohydrate: Glycaemic impact: Glucose disposal: In vitro digestionFor practical consumer and dietetic use in glycaemia management, food values are required which will '. . . communicate glycaemic response in grams per serving of food' (1) , and which should be measured using '. . . validated in vitro methodology that accurately mimics in vivo behaviour' (2) . Current in vitro digestion methods for predicting relative glycaemic responses to foods measure carbohydrate that is rapidly available (for example, Englyst et al. ), or the area under the glucose release curve relative to a reference (5) . However, they cannot provide accurate or robust predictions of relative responses to whole foods varying in quantity, or accurately mimic the in vivo glycaemic response, because they do not allow for the effects of homeostasis. They do not take into account the fact that glycaemic responses to foods are a net effect of the balance between blood glucose loading and blood glucose disposal (GD), which depends dynamically on the rates of both. Not allowing for blood GD, and its dependence on the amount and rate of blood glucose loading, may lead to large inaccuracies when using glycaemic impact values from experimental portions to gauge the effect of the very different and varying food quantities that are consumed in a community setting. For instance, customarily consumed food portions often have a much lower glycaemic impact than would be expected from a glycaemic load value calculated from the glycaemic in...

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“…Secondary outcomes were chewing time, chewed particle size distributions, satiety and palatability responses. The design of the primary experiment was based on glycaemic index methodology but used a one cup serving of rice [19] to reflect the quantity of rice usually eaten rather than a standard dose of carbohydrate.…”

Section: Methodsmentioning

confidence: 99%

Effect of rice cooking methods on postprandial glycaemic response, satiety and palatability, and chewed particle size distribution

Lu¹,

Venn²,

Lu³

et al. 2017

Preprint

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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: Twentyeight healthy volunteers participated in a three treatment experiment where postprandial blood glucose was measured over 120 minutes after consumption of 140g of rice. Three rice samples were freshly cooked medium-grain white rice, freshly cooked parboiled rice, and parboiled rice stored overnight at 4 ºC. All rice was served warm at 65 ºC. Chewing time was recorded. Results: 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). Chewing time for 10g cold stored parboiled rice was 6 seconds (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 long term may reduce risk for type 2 and gestational diabetes. More trials are needed to identify the significance.

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The Glycemic Load Estimated from the Glycemic Index Does Not Differ Greatly from That Measured Using a Standard Curve in Healthy Volunteers

Cited by 37 publications

References 18 publications

Glycemic index and glycemic load: measurement issues and their effect on diet–disease relationships

Glycemic index and glycemic load: measurement issues and their effect on diet–disease relationships

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

Effect of rice cooking methods on postprandial glycaemic response, satiety and palatability, and chewed particle size distribution

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